Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology (Plant Ecophysiology, Volume 8)

Organic Xenobiotics and Plants Plant Ecophysiology Volume 8 Series Editors: Luit J. De Kok University of Groningen,...

Author: Peter Schroder | Christopher D. Collins

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Organic Xenobiotics and Plants

Plant Ecophysiology Volume 8

Series Editors:

Luit J. De Kok University of Groningen, The Netherlands

Malcolm J. Hawkesford Rothamsted Research, United Kingdom

Ineke Stulen University of Groningen, The Netherlands

Aims & Scope: The Springer Series in Plant Ecophysiology comprises a series of volumes that deals with the impact of biotic and abiotic factors on plant functioning and physiological adaptation to the environment. The aim of the Plant Ecophysiology series is to review and integrate the present knowledge on the impact of the environment on plant functioning and adaptation at various levels: from the molecular, biochemical and physiological to a whole plant level. This series is of interest to scientists who like to be informed of new developments and insights in plant ecophysiology, and can be used as advanced textbooks for biology students.

The titles published in this series are listed at the end of this volume.

Organic Xenobiotics and Plants From Mode of Action to Ecophysiology

Edited by

Peter schröder Helmholtz Zentrum München Department of Microbe Plant Interactions Neuherberg Germany and

christopher d. collins University of Reading School of Human and Environmental Science Department of Soil Science, Reading, UK

Editors Peter Schröder Helmholtz Zentrum München Department of Microbe Plant Interactions Neuherberg Germany

Christopher D. Collins University of Reading School of Human and Environmental Science Department of Soil Science, Reading, UK

ISSN 1572-5561 e-ISSN 1572-5561 ISBN 978-90-481-9851-1 e-ISBN 978-90-481-9852-8 DOI 10.1007/978-90-481-9852-8 Springer Dordrecht Heidelberg London New York © Springer Science+Business Media B.V. 2011 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology is the eighth volume in the Plant Ecophysiology series. It presents an overview of the impact of organic xenobiotics on plants. Natural as well as agro-ecosystems are frequently exposed to a huge range of natural or synthetic substances. These, xenobiotics, may originate from both natural (fires, soil and microbes) and anthropogenic (air, water and soil pollution) sources. The latter, alone, represents an unmanageable plethora of compounds fed by the demands of our industry and daily life. In 2009 the 50 millionth synthetic organic compound was registered with Chemical Abstract Services. While many of these synthetic chemicals have no direct nutritional value or significance in metabolism, once released into the environment, they may negatively affect plant function, ecosystems and consequently human health. Divided into three sections; Principles of transport, deposition and uptake, Pollutant degradation and ecosystem remediation from enzymes to whole plants and Tools and novel applications, the volume has a broad coverage ranging from the cellular to the ecosystem and continental scale which has not been previously collated in this area of science. Principles of transport, deposition and uptake – Transport of organic xenobiotics is regularly described by the physico-chemical characteristics of the chemical of concern, however recent research indicates some of the current models may be incorrect, particularly for the more soluble compounds. Within a forest ecosystem there may be numerous anthropogenic and biogenic sources of chlorinated compounds which can induce negative impacts on the vegetation. Volatile organic compounds and semi-volatile organic compounds are both of interest because of their transport in the atmosphere away from point sources, and therefore the potential for negative impacts at significant distances from the origin of emission. The subsequent effects maybe either direct on the vegetation or indirect via accumulation of these pollutants in the food chain. Several case studies are provided to illustrate these processes and provide a deeper understanding of their underlying principles. Pollutant degradation and ecosystem remediation from enzymes to whole plants – Plant detoxification mechanisms include well developed enzymatic detoxification cascades, characterized by P450 monooxygenases, glutathione and glucosyl transferases, v

vi

Preface

peroxidases and ABC-transporters, plus a plethora of less known enzymatic reactions, the regulation of which may be altered in response to foreign chemicals. These enzymatic reactions may be characterised on the metabolic and the transcriptional level. Detoxification enzymes play a critical role in plant tolerance and degradation of a wide range of industrial pollutants and particular success has been observed in the degradation of industrial dyes in wastewaters. New remediation systems are also emerging where phytoremediation is part of an integrated approach to clean up contaminated soils. The emerging recognition of the roles of the rhizosphere as both a sink for xenobiotic metabolites and as an active zone for the degradation unwanted constituents of contaminated soils are evaluated in this volume. Tools and novel applications – When knowledge is accumulated it is vital that it is translated and used to develop new tools and applications relevant to the problems outlined above. This may be achieved by developing systems which use plants to determine the toxic loads in ecosystems or when developing computer programmes to determine the toxic potential of novel chemicals or those which have not been subject to detailed experimental analysis. Finally it is important that new technologies developed in other fields, such as high resolution metabolomic and proteomic analysis, are appied to the fields of environmental pollution, plant tolerance and metabolism of xenobiotics. Therefore we believe this volume will provide an excellent overview regarding the latest developments in research on plant performance under stress conditions induced by organic xenobiotics, from the cellular to the system level. The book will be of interest to a wide audience, from graduate students to senior researchers in a wide range of disciplines including plant ecology, plant biochemistry, agriculture and environmental assessment. It will also be of practical interest to environmentalists, policy makers and resource managers. Peter Schröder Chris Collins

Contents

Part I Principles of Transport, Deposition and Uptake Plant Uptake of Xenobiotics............................................................................ Chris D. Collins, Ian Martin, and William Doucette Haloorganics in Temperate Forest Ecosystems: Sources, Transport and Degradation............................................................. Nicholas Clarke, Milan Gryndler, Hans-Holger Liste, Reiner Schroll, Peter Schröder, and Miroslav Matucha Semivolatiles in the Forest Environment: The Case of PAHs...................... Claudio A. Belis, Ivo Offenthaler, and Peter Weiss

3

17

47

Part II Case Studies A Case Study: Uptake and Accumulation of Persistent Organic Pollutants in Cucurbitaceae Species................................................. András Bittsánszky, Gábor Gullner, Gábor Gyulai, and Tamas Komives Trichloroacetic Acid in the Forest Ecosystem............................................... Miroslav Matucha and Peter Schröder

77

87

Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transboundary Transport – Results of a Case Study with Special Emphasis on the Spatial Distribution of Polycyclic Aromatic and Chlorinated Air Borne Pollutants................... 105 Rolf Herzig, Christoph Bieri, Andreas Weber, and Peter Straehl

vii

viii

Contents

art III Pollutant Degradation and Ecosystem Remediation P from Enzymes to Whole Plants New Perspectives on the Metabolism and Detoxification of Synthetic Compounds in Plants................................................................. 125 Robert Edwards, David P. Dixon, Ian Cummins, Melissa Brazier-Hicks, and Mark Skipsey Using Plants to Remove Foreign Compounds from Contaminated Water and Soil.................................................................................................. 149 Jean-Paul Schwitzguébel, Valérie Page, Susete Martins-Dias, Luísa C. Davies, Galina Vasilyeva, and Elena Strijakova Biodegradation of Organic Xenobiotic Pollutants in the Rhizosphere....... 191 Hassan Azaizeh, Paula M.L. Castro, and Petra Kidd Bioindicators and Biomonitors: Use of Organisms to Observe the Influence of Chemicals on the Environment........................ 217 Bernd Markert and Simone Wünschmann SAR Based Computational Models as Decision Making Tools in Bioremediation................................................................................... 237 Nick Price and Qasim Chaudhry State-of-the-Art Chemical Analyses: Xenobiotics, Plant Proteomics, and Residues in Plant Based Products........................................................... 261 Touradj Solouki, Mohammad Ali Khalvati, Mahsan Miladi, and Behrooz Zekavat Index.................................................................................................................. 307

Part I

Principles of Transport, Deposition and Uptake

Plant Uptake of Xenobiotics Chris D. Collins, Ian Martin, and William Doucette

Abstract Plant uptake of organic chemicals is an important process when considering the risks associated with land contamination, the role of vegetation in the global cycling of persistent organic pollutants, the potential for contamination of the food chain and the design of pesticides. There have been some significant advances in our understanding of the processes of plant uptake of organic chemicals in recent years; most notably there is now a better understanding of the air to plant transfer pathway, which may be significant for a number of chemicals. This chapter identifies the key processes involved in the plant uptake of organic chemicals and also identifies other important factors in the uptake process e.g., plant lipid content, growth dilution and plant metabolism.

Introduction This chapter provides an introduction to the factors that influence plant uptake and accumulation of organic chemicals. There are four principal uptake pathways, as illustrated in Fig. 1. These pathways include passive and active uptake through the root system, gaseous and particulate deposition to above-ground shoots, and direct contact between soil and plant tissues. In addition to describing each pathway, this chapter also considers the controlling factors for uptake and the ways these can be used to model these processes.

C.D. Collins (*) Department of Soil Science, University of Reading, Reading RG6 6DW, United Kingdom e-mail: [email protected] I. Martin Human Health Division, Environment Agency, Olton Court, Solihull B92 7HX, United Kingdom W. Doucette Utah Water Research Laboratory, Utah State University, Logan, UT 84322-8200, USA

P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_1, © Springer Science+Business Media B.V. 2011

3

4

C.D. Collins et al. Evaporation from leaf Deposition of particles followed by desorption into leaf

Gaseous deposition to leaf Transport in the phloem

Transport in the transpiration stream Volatilisation from soil

Suspension of soil particles by wind and rain

Desorption from soil followed by root uptake from soil solution

Fig. 1 Principal pathways for plant uptake of organic chemicals

Root Uptake Soil-Root Interactions In general, plant roots are the most important site for uptake of chemicals from soil (Bell 1992). Root systems have been shown to take up organic chemicals from both water and air. In general, this uptake process has been shown to involve passive and diffusive transport, with chemicals carried into the plant during the natural transpiration cycle. Active uptake has been shown for a few organic chemicals including the phenoxy acid herbicides (Bromilow and Chamberlain 1995). Plants may actively transport biological organic compounds such amino acids (El-Naggar et al. 2009), but they cannot actively import more complex organic forms such as proteins from the soil (Rentsch et al. 2007). Experiments on the uptake of non-ionised chemicals from a hydroponic solution showed that uptake consisted of two stages (Briggs et al. 1983; Cousins and Mackay 2001). Firstly, equilibration between the chemical concentration in the aqueous phase within the plant root and the external solution, and secondly, chemical sorption on to lipophilic root solids. These solids include lipids in membranes and cell walls (Cousins and Mackay 2001). Uptake form the external solution is often expressed as a root concentration factor (RCF) which is the ratio of chemical

Plant Uptake of Xenobiotics

5

concentration in the root to the concentration found in an external solution (Briggs et al. 1983; Shone and Wood 1977). Lipophilic organic chemicals possess a greater tendency to partition into plant root lipids than hydrophilic chemicals. Polycyclic aromatic hydrocarbons (PAHs), chlorobenzenes, polychlorinated biphenyl (PCBs), and polychlorinated dibenzop-dioxins/dibenzofurans (PCDD/Fs) have all been found at elevated levels in plant roots (Duarte-Davidson and Jones 1996; Wild et al. 1992). Briggs et al. (1983) found a linear relationship between the octanol-water partition coefficient (KOW) of non-ionised chemicals and the observed root concentration factor, based on experiments using O-methylcarbamoyloximes and substituted phenylureas and their uptake by barley plants. Environmental scientists often use the octanol-water partition coefficient (KOW) as a surrogate for chemical lipophilic tendency (Schröder and Collins 2002). Wild et al. (1992) categorised non-ionised organic chemicals with log KOW > 4 as having a high potential for retention in plant roots. While, Cousins and Mackay (2001) suggested that for organic chemicals with log KOW 6 and log KAW > −6) in which adsorption from atmosphere is expected to occur (Mackay et al. 1992). Analogously, log KOA can be used to estimate the partition of an organic pollutant between the gaseous phase and the particles. Log KOA of many PAHs at 25°C ranges between 7 and 14, which means that in temperate areas heavier PAHs are predominantly in the particulate phase. The relative relevance of dry and wet deposition of a substance in the atmosphere depends partially on its air/water partition coefficient KAW. The most important environmental parameters influencing the deposition of SVOCs are temperature and wind speed. Temperature influences uptake processes in different ways. There is an inverse relationship between temperature and the concentration of POPs in plants; also the particle/gas partition of a given PAH increases when temperature decreases. Finally, diffusion coefficients increase by factors between 5 and 10 for every 10 K increase in temperature. Air masses circulating through large forested areas during the growing season may experience a significant reduction in SVOC concentrations due to the deposition of pollutants to the forest and consequently to the soil (Howsam et al. 2001a, b; Wania

Semivolatiles in the Forest Environment: The Case of PAHs

59

and McLachlan 2001). In particular, global scale modelling revealed that the filter effect of boreal deciduous forests may halve the long-range transport of SVOCs to the Arctic (Su and Wania 2005). Model calculations indicate that canopy interaction is stronger for SVOCs with octanol-air partition coefficient (logKOA) between 7 and 11 and an air-water partition coefficient (log KAW) above −6 (McLachlan and Horstmann 1998). Choi et al. (2008) report that concentrations of gaseous PAHs, such as phenanthrene, anthracene, and pyrene, within the canopy during the spring budburst are reduced compared to the air layers above and below the canopy. The authors found that concentrations of PAHs in the particulate phase do not present a vertical profile, and that the gas/particle partitioning behaviours above and within the canopy are statistically different. This implies a substantial canopy interaction for the gaseous PAHs. The median fluxes for the three PAHs are in the range of 35–71 pg·m −2·s−1 for phenanthrene, 3–7 pg·m−2·s−1 for anthracene, and 7–14 pg·m−2·s−1 for pyrene implying a net dry gaseous deposition of some micrograms of PAHs per square meter to the canopy during spring. Observed deposition velocities range between 3 and 12 cm s−1 (Fig. 6). Deposition values of 12 PAHs and BaP in rural areas in Europe and North America obtained with different sampling methods are reported in Table 4. Although the major PAH deposition process is particle-associated, it has been observed that spruce needles in the litterfall, present higher levels of semivolatile PAHs than those observed in open field deposition, indicating that gaseous dry deposition contributes significantly to the deposition in forest stands (Gocht et al. 2007). Wet deposition is the wash-out of both vapour phase and particulate-bound chemicals by rain, snow, dew formation, mist and fog and is dependent on the KAW and on the particle scavenging efficiency of the precipitation (Barber et al. 2004). The role of rain is also important in the removal of particles and adsorbed PAHs from the leaves of different species, in particular in the understorey (Howsam et al. 2001b) Considering that in temperate areas the highest emissions of PAHs coincide with the cold season and that many forested areas are located in mountain ranges, establishing whether snow or rain are more efficient in scavenging organic compounds from the atmosphere is crucial. To answer this question Lei and Wania (2004) devised a conceptual model of the equilibrium phase distribution of an organic chemical based on partitioning coefficients that makes it possible to estimate the behaviour of any compound for which these coefficients are known. As temperature decreases, chemicals tend to pass from the vapour phase to liquid water droplets, atmospheric particles or the snow surface. At 0°C rain is typically more effective in scavenging the vapors of small organic molecules than snow, because the capacity of the snow surface to sorb such chemicals is smaller than that of liquid water droplets. Snow is a more effective scavenger for vapors of larger, non-polar organic compounds like PAHs of more than four rings, which are less water soluble. For these substances temperature and snow

60

C.A. Belis et al.

Fig. 6 Temporal variation of the sum of 13 PAHs in the gaseous phase and in particles at different heights (A: above the canopy; B and C: within the canopy; D: below the canopy (Reproduced from Atmos. Chem. Phys. 8:4105–4113, Copyright Choi et al. 2008 under the Creative Commons Attribution 3.0 License)

properties (particle scavenging ratio and the specific snow surface area) determine the mode of scavenging (vapor vs particles) and the total scavenging efficiency (Lei and Wania 2004). The effect of temperature explains why snow is a more efficient scavenger than rain, especially below −10°C. It is also the reason why wet deposition processes become increasingly important with decreasing temperature, and even constitute the dominant deposition mechanism for some organic contaminants in cold environments (Lei and Wania 2004).


61

Table 4 Bulk deposition (wet + dry) of PAHs in rural areas using different sampling methods (mg m−2 year−1) (From Gocht et al. 2007) Sampling method Study area S 12PAH BaP Reference Bergerhoff Germany (forest) 2.2–11.2 Matzner 1984 Bergerhoff Germany (forest) 142 7.4 Horstmann and McLachlan 1998 Funnel–bottle France (suburban) 46 1.5 Motelay-Massei et al. 2003 Funnel–adsorber– Europe (high altitude) 12.6–16 0.4–0.7 Fernandez et al. cartridge 2003 Funnel–adsorber– Sweden (forest) 186 9.5 Brorström Lundén cartridge and Löfgren 1998 Funnel-adsorberGermany (forest) 137–347 4.2–11.6 Gocht et al. 2007 cartridge Funnel-adsorber Germany (open field) 108–201 4.9–10.5 Gocht et al. 2007 cartridge

Levels of PAHs in Leaves and Needles While plants have been widely used as passive samplers of organic compounds in the atmosphere, it is not possible to establish a linear quantitative relationship between PAH levels in the leaves or needles and air or soil PAH concentrations (Menichini 1992; Watts et al. 2006). It is difficult to compare literature accounts of the concentration of PAHs in needles, since authors often report the sum of different number and type of PAHs (Table 5). A comparable methodological approach was adopted in two studies conducted 10 years apart at remote Alpine sites. The median of the total PAH concentrations in needles at remote Austrian sites in 2004 (18 mg kg−1 d.w.) reported by Belis et al. (2007) was less than half that observed in 1993 (48 mg kg−1 d.w.) by Weiss et al. (2000). This decreasing trend in PAH levels in the needles coincides with the findings of another study on PAH concentration in needles at some German sites (Schroter-Kermani et al. 2006). The geographic patterns of most PAHs in needles in the Alpine area reported by Belis et al. (2007) displayed a clear longitudinal gradient, with light (three to four rings) PAHs peaking in the western and heavy (five to six rings) species cumulating in the eastern part of the investigated range. This is probably associated with differences in the sources between eastern and western European countries. Atmospheric deposition of 12 PAHs was observed also in the lichen Xantoria parietina (Srogi 2007). The total PAH concentration ranged between 25 and 40 mg kg−1, and the most abundant species were dibenzo(a,h)antracene and benzo(k)fluoranthene followed by benzo(a)anthracene, chrysene and fluorene.

Spruce forest n.a n.a. nursery 1 1997 Deciduous forest (oak, ash, hazel) 40 2004 Norway spruce background forest a Sum 17 PAHs b Sum 9 PAHs c Mean for oak, ash and hazel respectively d Sum 22 PAHs e w.w.

18–77b 39–264d

7–46

24 84, 69, 92 c;d

19

8.2–61a,e

2004

Spruce forest

7

24–38

Range 28–412

Table 5 Concentrations of PAHs in forest needles (mg kg−1) d.w. Sum 16 PAHs Type of site n Sites Year Median 24 1993 48 Norway spruce background forest Spruce forest 2 1995

0.25

BaP Median

0.04–1.05

0.19–0.86e

Range

Alps

Canada

Germany

Germany

Sweden

Country Austria

Belis et al. 2007

Howsam et al. 2001b

Brorström Lundén and Löfgren 1998 Schroter-Kermani et al. 2006 L.U.B.W. 1993

Reference Weiss et al. 2000

62 C.A. Belis et al.


63

Considering the susceptibility of PAHs to photodegradation it is likely that photolysis of PAHs deposited on vegetation influences the fate of these pollutants in forests. Degradation of particle-bound PAHs associated with exposure to light is highly dependent on the substrate to which they are adsorbed. The more polar the solvent, the faster the degradation process. Photodegradation has been found to be relatively fast in natural waters and in organic solvents (Zepp and Schlotzhauer 1979; Low et al. 1987). In contrast, PAHs adsorbed on fly ash and black carbon atmospheric particles can better resist photolysis and be transported with air masses from their sources to the receptor (Yokley et al. 1986). Photolysis of PAHs adsorbed on the waxy surface of spruce needles follows a first order kinetics and can be faster than on fly ash and black carbon (Wild et al. 2005). The half-lives of PAHs on needles under experimental conditions range from 15 h for dibenzo(a,h)anthracene to 75 h for phenanthrene (Niu et al. 2003); much faster photodegradation (half-lives under 30 min) was observed on maize leaves although the methodological comparability of studies is limited (Wild et al. 2005). Since photolysis is slower on the needle surface than in water a stabilizing effect of needle surface waxes has been hypothesized.

PAHs in the Litter and the Soil Deposition of PAHs Deposition of atmospheric pollutants, both in gaseous phase and particulate matter, to the soil may take place directly or with the vegetation as an intermediary sink before deposition by litterfall and subsequent decay of the organic material. Although PAHs are not permanently bound in leaves, their rate of loss is very low. The low desorption is probably controlled by diffusion through the cuticular skin (Barber et al. 2004). Therefore, no significant proportion of PAHs accumulated in vegetation will exchange directly back into the atmosphere, and PAHs in the vegetation will be incorporated to the soil when the leaves senesce or when the plant dies (and/or falls). Winterly PAH burdens in the litter layer are higher than those observed after litterfall by a factor between 4 and 11. This means that fluxes of PAHs associated with litterfall during the vegetative season are lower than throughfall fluxes to the litter layer after the fall of leaves. These differences are mainly ascribed to the low atmospheric concentrations of PAHs during the warm season, compared to winter, and to PAH losses from the litter by processes which are more intense in summer, like volatilization or degradation and assimilation by decomposers (Howsam et al. 2001a). The storage quotients (ratio of stock to input) of PAHs in the litter increases, as their degradation rate slows, with their molecular weight. In a Canadian forest, the amount of PAHs between 200 and 250 u stored in the soil is seven to ten times the annual deposition to the litter layer during winter, while the storage quotient of BghiP (276 u) in the soil is equivalent to 25 years’ worth of this deposition (Howsam et al. 2001a).

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C.A. Belis et al.

Soils are regarded as pollutant sink due to their great capacity for absorbing and retaining hydrophobic chemicals and then releasing them slowly back to the atmosphere (Wong et al. 2004). The uptake of PAHs by plant roots from the soil is not yet fully understood. Some authors consider the plant uptake of POPs from soil negligible, especially those with high lipophilicity and low water solubility (Bacci and Gaggi 1986; Kipopoulou et al. 1999). However, translocation of PAHs from contaminated soils and sediments with high concentrations of these pollutants to the roots of plants and trees has been observed (Fismes et al. 2002; Meudec et al. 2006). Due to their high hydrophobicity and high solid–water partitioning coefficients PAHs tend to interact with non-aqueous phases and organic matter in the soil, becoming virtually unavailable for microbial degradation. This is particularly true for heavier PAHs whose bioavailabilty decreases almost logarithmically with increasing molecular weight (Johnsen et al. 2005). In addition, the biodegradation rate of hydrophobic substrates is conditioned by two factors: the physical transfer rate of the chemical in the medium, and the metabolic activity of bacteria. In the soil, PAHs are heterogeneously distributed and may even be absorbed inside organic particles. Therefore PAH-degrading bacteria are physically separated from the PAH-sources and depend on diffusive transport. Since bacteria degrade PAHs with intracellular dioxygenases, PAHs have to be taken up by bacteria before degradation. One of the bacterial strategies to improve their access to PAHs is the release of biosurfactants, small detergent-like molecules, to increase water solubility of the PAHs. In addition, it was observed that the extracellular polymeric substances (EPS) secreted by bacteria in the formation of biofilms have PAH adsorbing properties (Johnsen et al. 2005). The bacterial strains able to degrade PAHs belong to a very limited number of taxonomic groups: Sphingomonas, Burkholderia, Pseudomonas and Mycobacterium (Ho et al. 2000; Bastiaens et al. 2000; Johnsen et al. 2002) Soil is considered to be a sink for organic pollutants, and its content of organic matter (OM) in particular is believed to be the principal factor controlling the sorption of these chemicals in soil and sediments (Burgess and Lohmann 2004; Cornelissen et al. 2005; Sweetman et al 2005). Understanding the influence of OM on the air/soil exchange in rural or remote areas is crucial for establishing the global budget of POPs. In recent years, one of the components of OM, namely the black carbon (BC), has received most attention due to its high sorption capacity for hydrophobic organic compounds (HOC) compared to that of amorphous organic carbon (AOC) (Nam et al. 2008). Both, organic pollutants and BC, are components of the atmospheric particulate matter that are emitted by incomplete combustion processes. However, it is not yet clear whether the association between HOC and BC in soils can be attributed to the common origin of these compounds or to processes occurring subsequently in the atmosphere or in the soils (Fig. 7). Krauss et al. (2000) found that PAHs in German forest soils are ten time less mobile than PCBs with similar Kow explaining these results as a consequence of the strong association of PAHs with soot or pyrolytic black carbon particles, which reduce PAH mobility and bioavailability compared to single PCBs with similar Kow (Gustafsson et al. 1997).


POP Gas

Air

65

2 Atmospheric transport and deposition

Particle BC

of POP and BC

Partitioning

either: A. together, following emission together B. together, following partitioning during transport C. separately POPs could be subject to dry gaseous or wet/dry deposition in non-BC form

1

Emission of POP and BC either: A. Together (combusition) B. Separately (non-combusion or volatilization)

BC

POP

3 Re-emission of POP BC

either : A. as vapour, separate form BC B. as re-entrained soil dust with BC

POP

Sources to atmosphere 4

Soil TOC/SOM stock

Dissolved Phase

Gas

formed from vegetation/ Microbial turnover

Soil Soil C forms

Historical & natural combustion sources (eg. forest fires, domestic coal/wood burning)

Soil BC Stock

Possible relation in soil either correlation of POP with:

A. BC, following 1A, 2A, or 2B. B. BC, following association formed in the soil, i.e. following deposition/leaching/bioturbation or other forms of soil mixing C. TOC or SOM, following association formed in the soil D. No correlation, Indication 1B and 2C occured, plus lack of time, mechanisms or affinities to combine with OM

Fig. 7 Possible scenarios for the relationship between POPs and SOM (with a focus on BC) and inferred sources, fate, and transport (Reprinted from Environ Pollut (156) Nam et al., Relationships between organic matter, black carbon and persistent organic pollutants in European background soils. 809–817. Copyright (2008), with permission from Elsevier)

However, Nam et al. (2008) report no relationship either between PAHs and TOC or between PAHs and BC in remote/rural forest and grassland soils from UK and Norway. In this study, TOC is strongly correlated with HCB and PCBs, while both TOC and BC are strongly correlated with PBDEs. These data suggest that the BC pool in the soil may be the result of the accumulation of emissions over centuries and that the association between non-pyrogenic pollutants like PBDEs and BC may be the consequence of their interaction in the soil or during deposition and re-emission cycles.

Levels of PAHs in Soils The concentration of PAHs in forest soils in different studies in Europe and North America are summarized in Table 6. Levels of the sum of 20 PAHs in the Alps collected in 2004 are comparable with those observed in Germany in the 1990s. On the other hand, the sum of 16 PAHs is lower than that observed in Austria in the 1990s but higher than that measured in Canadian deciduous forests. Average BaP concentrations in the Alpine area in 2004 are lower than those reported for Swiss forests but the range is comparable with the

77–501

0–5

Entire humus 189 layer

84–542

185

6.6

15

2–19

15–22

Canada (Toronto) Alps

CH

CH

Belis et al. 2007

36–214

0–20

From urban to forest – 105 all types Deciduous forest 3 (sugar maple) 30 Norway spruce background forest

221

0.5–22

32–8465

0–20

16

CH

Coniferous forest

0–20

12

10

Deciduous forest

176

DE

11–687

16

Spruce forest

15–20

Krauss et al. 2000 Krauss et al. 2000 Desaules et al. 2008 Desaules et al. 2008 Desaules et al. 2008 Wong et al. 2004

DE

Range

Source Weiss et al. 2000

BaP Median Country A

Range 68–1,342

Table 6 Concentration of PAHs in forest soils in different studies (mg kg−1) Sum 16 PAHs Sample depth Sum 20 PAHs Kind of site n (cm) Median Range Median 210 25 Entire Norway spruce humus background layer forest Spruce forest 16 0–5 60–2,606

66 C.A. Belis et al.


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one observed in the Canadian forests. These results are in agreement with the findings of the monitoring network for POPs in the Alpine region (project MONARPOP), which concludes that although the long-term trend is towards decreasing levels of PAHs, the Alps are a sink for these compounds, with the highest soil concentrations in their northern fringe and in the western part of the study area (Belis et al. 2007). These authors conclude that the stocks in the Alpine soils cannot be explained only by the accumulation of officially reported emissions within the area, suggesting that both transboundary transport and local emissions, like biomass burning, contribute significantly to the mass balance of these compounds. The existence on the global scale of two major source patterns for PAHs in soils, natural and anthropogenic, has been hypothesized by Wilcke (2007). The first of these, characterized by low molecular weight PAHs, is the result of biological activity or released by natural fires or volcanic emissions. This source of PAHs is represented by the sum of naphthalene, phenanthrene and perilene. The second source pattern is anthropogenic, and derives from the combustion of fossil fuels. Although anthropogenic emissions of light PAHs are not excluded, PAHs in this cluster are better represented by 11 of those with higher molecular weight ranging from benzo(a)anthracene (228 u) to dibenzo(a,h)anthracene (278 u) (Wilcke 2007). According to this hypothesis, local deviations from the general pattern may be associated with specific sources or changes in the relative abundance of PAHs due to transport processes. Average concentrations of the S 16 EPA PAHs in the soils of different sites of the world, including tropical and temperate areas, range from 3.6 to 170,000 mg kg−1. In the scatter plot of natural versus anthropogenic sources indicators samples from different areas of the globe arrange on a line (Fig. 8). As expected, the total amount of PAHs seems to be correlated to anthropogenic sources and decrease in areas where natural sources are predominant. However, the limitation of the study for generalization on the global scale is the small number of samples and the poor representation of the southern hemisphere and the polar regions.

Effects of PAHs on Ecosystems Once PAHs reach the forest ecosystem they can either (a) accumulate in one compartment, (b) exit the system by evaporation or leaching; (c) be degraded by physico-chemical agents (photolysis; oxidation etc.), or (d) interact with the biota. The interaction of a chemical with living organisms depends on the fraction of chemical that reaches the circulatory system or the physiologically active areas of the cells. An example is the relationship between water solubility of PAHs and their degradation by soil bacteria discussed above. The contact of the chemical with the organism does not necessarily imply it enters the active metabolising compartment of the cells. In leaves, the presence of a large volume of lipidic materials is likely to keep lipophilic PAH concentrations in the cytoplasm very low.

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Fig. 8 Regression of naphthalene, phenanthrene, and perylene (NAPH+PHEN+PERY) and the S11 high-molecular weight PAHs as percentages of S 20 PAHs at 12 locations around the world. Error bars indicate standard deviations (Reprinted from Geoderma (141) Wilke. Global patterns of polycyclic aromatic hydrocarbons in soil.157–166, Copyright (2007), with permission from Elsevier) ST = Stephanskirchen, Germany; BT = Bayreuth, Germany; CZ = Bohemia, Czech Republic; ZI = Ziar nad Hronom, Slovak Republic; UB = Uberlândia, Brazil; BK = Bangkok, Thailand; RU = Moscow Russia; GH = Accra, Ghana; PR = North American; BR = Brazil; CE = Cerrado Brazil; AM = Amazon rainforest

However, if PAHs deposited from the atmosphere are able to penetrate into the cells they are likely to be transformed into a conjugate. Nakajima et al. (1996) reported significant amounts of 1-hydroxypyrene conjugates in the leaves of four different woody species growing close to traffic congested roads. These authors found no conclusive evidence on whether the conjugates are produced in the leaves or taken up from the soil by the roots and transported to the leaves. Many PAH degradation products are more soluble and consequently more leachable and bioavailable than their precursors. For example, the mobility of nine to ten phenantrenodione was found to be higher than that of phenanthrene (Amellal et al. 2006). A biomonitoring study on background spruce forests in the Alps used the activity of the detoxifying enzyme glutathione transferase (GST) in the needles as a marker for exposure to xenobiotics (Offenthaler et al. 2008). These authors observed significant correlations between the GST activity and the concentration of PAHs (in particular benzo[g,h,i]perilene) in Norway spruce needles, and in the soil. Most of the damage observed in vegetation is associated with photo-oxidative stress. During photosynthesis the formation of active oxygen species is rapidly compensated by the anti-oxidative systems. The accumulation of active oxygen species triggers a part of the plant’s alarm-signal system that activates the scavenging


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mechanisms. However, when the plants are under the influence of stress factors such as chilling or toxic pollutants, their response may be insufficient to eliminate the active oxygen species that can damage the cell structures. Chlorosis and reddish-brown necrosis on the needles of the Japanese red pine (Pinus densiflora) have been reported following exposure to phenanthrene and fluoranthene (Oguntimehin et al. 2008). After 3 months fumigation with a solution of these pollutants, net photosynthesis, stomatal conductance, chlorophyll content and Rubisco concentrations decreased. The authors found that the effects of the studied PAHs were similar to those observed in previous studies using OH• radicals or OH• radical generating substances that are responsible for photo-oxidative damage to photosystem II. The authors concluded that the negative effects of PAHs on pine needles are similar to those obtained using a broad-spectrum herbicide and are associated with the generation of OH• radicals. Concern about the high levels of organic pollutants in the soil and vegetation arises from their possible introduction into the food web. It is commonly accepted that predators accumulate unmetabolized pollutants and thus have higher levels than their food supply (Cripps 1992). Food also appears to be the main source of PAH intake for humans not occupationally exposed to PAHs (Grova et al. 2006). In vivo studies suggest a transfer into the intestinal epithelium by diffusion even though other mechanisms like metabolism are not excluded. A study on the oral uptake of two PAHs by goats monitored the incorporation of these chemicals into the bloodstream (Laurent et al. 2002). These authors found that light PAHs like phenanthrene are absorbed at a higher rate than heavier ones like benzo(a)pyrene. Similar mechanisms can be hypothesized for wild fauna or cattle living in forested areas or grasslands. In nature, PAHs are present in complex mixtures, the ecotoxicology of which is hardly known for the single substances and much less for the whole mixture. It is possible, however, to predict the toxicity of a substance on the basis of its physical and chemical properties using the quantitative structure-activity relationships (QSAR) approach. PAHs, like PCDD/F, PCB and other aromatic compounds, bind to the animal aryl hydrocarbon receptor (AhR). This accelerates the breakdown of aromatic compounds, the metabolites of which have been linked to immunotoxic and carcinogenic effects. PAHs are also expected to exert narcotizing effects, a toxic action which does not involve binding to specific receptors, but modification of the fluidity and function of cell membranes. The lipophilicity of a chemical is considered a good indicator of its narcotic toxicity and can be estimated from the octanol–water partition coefficient (log KOW). The bioavailability of a chemical is useful in estimating its toxic effects. In the case of PAHs, bioavailability is a function of the pore-water concentrations and can be estimated from the organic carbon–water partitioning coefficient (log KOC). Sverdrup et al. (2002) applied a model based on log KOW and log KOC to predict the effects of the total concentration of 16 PAHs on survival and reproduction of the soil-dwelling springtail Folsomia fimetaria (Collembola). The authors concluded that only PAHs with log KOW £ 5.2 (i.e. naphthalene, acenaphthene, acenaphthylene, anthracene,

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phenantherene, fluorene, pyrene and fluoranthene) significantly affected the survival or reproduction of the springtails. The lack of toxicity of the heavier PAHs has been explained by their low water solubility and consequently their limited bioavailability. Another study on the effects of PAHs on soil-dwelling organisms was carried out by Bowmer et al. (1992), who found no effects of chrysene on the survival of the earthworm Eisenia fetida. Moreover, Van Brummelen et al. (1996) report effects of benzo(a)anthracene on the growth of the isopod Oniscus asellus while no effects were observed after exposure to benzo(a)pyrene. The approach followed by Sverdrup et al. (2002) seems to be useful in interpreting the results of these previous studies, since it predicts toxic effects for benzo(a) anthracene, but not for chrysene and benzo(a)pyrene.

References Amellal S, Boivin A, Perrin Ganier C, Schiavon M (2006) Effect of ageing on mobility and sequestration of phenanthrene in an agricultural soil. Agron Sustain Dev 26:269–275 ATSDR (1995) Toxicological profile for polycyclic aromatic hydrocarbons. US Department of Health and Human Services, Public Health Service, Atlanta Bacci E, Gaggi C (1986) Chlorinated pesticides and plant foliage: translocation experiments. Bull Environ Contam Toxicol 37:850–857 Baek SO, Field RA, Goldstone ME et al (1991) A review of atmospheric polycyclic aromatic hydrocarbons: sources, fate, and behaviour. Water Air Soil Pollut 60:279–300 Barber JL, Kurt PB, Thomas GO et al (2002) Investigation into the importance of the stomatal pathway in the exchange of PCBs between air and plants. Environ Sci Technol 36:4282–4287 Barber JL, Thomas GO, Kerstiens G, Jones KC (2004) Current issues and uncertainties in the measurement and modelling of air–vegetation exchange and within-plant processing of POPs. Environ Pollut 128:99–138 Bastiaens L, Springael D, Wattiau P et al (2000) Isolation of adherent polycyclic aromatic hydrocarbon (PAH)-degrading bacteria using PAH sorbing carriers. Appl Environ Microbiol 66:1834–1843 Belis CA, Bassan R, Iozza S et al (2007) PAHs in needles and humus of Alpine ecosystems (Project Monarpop). Organohalogen Compd 69:1689–1692 Belis CA, Offenthaler I, Uhl M et al (2009) A comparison of Alpine emissions to forest soil and spruce needle loads for persistent organic pollutants (POPs)- Environ Pollut 157:3185–3191 Boulter P (2005) A review of emission factors and models for road vehicle non-exhaust particulate matter. TRL Project Report for DEFRA, PPR065. Bowmer CT, Roza P, Henzen L, Degeling C (1992) The development of chronic toxicological tests for PAH contaminated soils using the earthworm Eisenia fetida and the springtail Folsomia candida; TNO report IMW-R 92/387, The Netherlands. Brorström Lundén E, Löfgren C (1998) Atmospheric fluxes of persistent semivolatile organic pollutants to a forest ecological system at the Swedish west coast and accumulation in spruce needles. Environ Pollut 102:139–149 Burgess RM, Lohmann R (2004) Role of black carbon in the partitioning and bioavailability of organic pollutants. Environ Toxicol Chem 23:2531–2533 Choi SD, Staebler RM, Li H et al (2008) Depletion of gaseous polycyclic aromatic hydrocarbons by a forest canopy. Atmos Chem Phys 8:4105–4113 Cornelissen G, Gustafsson O, Bucheli TD et al (2005) Extensive sorption of organic compounds to black carbon, coal, and kerogen in sediments and soils: mechanisms and consequences for distribution, bioaccumulation, and biodegradation. Environ Sci Technol 39:6881–6895 Cripps GC (1992) Baseline levels of hydrocarbons in seawater of the Southern Ocean; natural variability and regional patterns. Mar Pollut Bull 24:109–114


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Part II

Case Studies

A Case Study: Uptake and Accumulation of Persistent Organic Pollutants in Cucurbitaceae Species András Bittsánszky, Gábor Gullner, Gábor Gyulai, and Tamas Komives

Abstract Persistent organic pollutants (POPs) are a group of toxic compounds with global distribution and long persistence in the environment. POPs are hydrophobic with the potential to bioaccumulate in fatty bodies of tissues and biomagnified through food webs. Remediation of sites contaminated with POPs is highly desirable. Although phytoremediation of POPs is very difficult because of their low bioavailability, recent literature indicates that some plants, especially those belonging to the Cucurbitaceae family are capable of taking up and accumulate significant amounts of POPs. In this chapter we summarize the most important findings concerning POPs in relation to Cucurbitaceae.

Introduction The first chapter outlined the general principles of organic xenobiotic uptake and transport in plants. Like hyperaccumulators for heavy metals there are also certain plants that have been observed to hyperaccumulate persistant organic pollutants, POPs. POPs are a group of toxic compounds with global distribution and long persistence in the environment. POPs are hydrophobic with the potential to bioaccumulate in fatty bodies of tissues and biomagnified through food webs. Remediation of sites contaminated with POPs is highly desirable. Although phytoremediation of POPs is very difficult because of their low bioavailability, A. Bittsánszky (), G. Gullner, and T. Komives Hungariany Academy of Sciences, Plant Protection Institute, Herman Ottó 15, 1022 Budapest, Hungary e-mail: [email protected] G. Gyulai Szent István University, Institute of Genetics and Biotechnology, 2103 Gödöllő, Páter K. 1, Hungary P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_4, © Springer Science+Business Media B.V. 2011

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recent literature indicates that some plants, especially those belonging to the Cucurbitaceae family are capable of taking up and accumulate significant amounts of POPs. Historically, environmental research focused on contaminants that decompose in the environment, such as acids, oils, grease, and wastes from animal processing. The discovery, that there are pollutants that are highly toxic, chemically stable, non-biodegradable, and have the tendency to accumulate in living organisms opened an entirely new field in environmental sciences. For example, a group of synthetic organic chemicals characterized by multiple carbon–chlorine bonds, high chemical stability and high toxicity (persistent organic pollutants, POPs) have been clearly identified as an unusual and special class of hazard to human health and the environment. POPs are a variety of chemicals that have been produced for industrial and agricultural use or created as by-products or waste. They belong to three groups (Table 1, Fig. 1). Chlorinated insecticides (Group A) and polychlorinated biphenyls (Group B) have been developed in the first half of the twentieth century as pest-controlling agents and as additives to oils used in electrical transformers and hydraulic couplings, respectively, while polychlorinated dioxins and furans (Group C) are generated as by-products of industrial processes and incineration (Inui et al. 2008; White 2009). After decades of intensive production of POPs the damaging consequences became evident and showed that they are a global threat (Inui et al. 2008; White 2009; Oldal et al. 2006). Environmental and human health hazards demand the cleanup of sites polluted by POPs. There are various options available for remediating POPs, which may be divided into those that use either chemical or biological mechanisms to decompose them or remove them from the soils. There are abiotic and biological systems Table 1 Groups of persistent organic pollutants (POPs) Group POPs A. Chlorinated insecticides A family of seven active insecticide ingredients B. Polychlorinated biphenyls A family of 209 individual compounds used as industrial chemicals C. Polychlorinated dioxins and furans A family of 75 compounds formed as by-products of industrial processes (primarily combustion)

a Cl

Cl Cl

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Cl Cl Cl

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chlordecone

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Cl Cl

2,2’,3,4,5,6’-hexachlorobiphenyl

Cl

O

Cl

Cl

O

Cl

2,3,7,8-tetrachlorodiphenyl dioxin

Fig. 1 Representative chemicals belonging to POP groups (a), (b), and (c) as indicated in Table 1

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that may be classed as active (i.e., require continuous inputs of resources to sustain the process) or passive (i.e., require relatively little resource input once in operation). It is important to evaluate strategies that are currently used together with new and emerging technologies to mitigate POPs and compare their strengths and weaknesses. This paper will focus on the depollution method called phytoremediation, i.e. the use of plants for removing, sequestering, or chemically decomposing environmental pollutants. This method has become one of the most rapidly developing ones of environmental restoration. The efficiency of plants as detoxifiers, filters and traps has been proven in cleaning up soils polluted with crude oil, explosives, landfill leachates, metals, pesticides, and solvents (Kömives and Gullner 2000). Although information about the phytoremediation of POPs is rather scanty, very recent literature has indicated that some plants (primarily those belonging to the Cucurbitaceae family) are capable of taking up significant amounts of POPs and accumulate them in their tissues (Inui et al. 2008; White 2009).

The Cucurbitaceae Family The Cucurbitaceae family belongs to the order Cucurbitales, class Magnoliopsida (subclass Rosidae). Botanists have applied about 435 taxons at various taxonomic ranks to describe the extreme morphological diversity of cucurbits (Nee 1990). All of the cultivated species are found in subfamily Cucurbitoideae. The genus Cucurbita includes about 20–26 species (Cutler and Whitaker 1961). All species of Cucurbita genus contain 20 pairs of chromosomes (2n = 20). They are secondary polyploids with the base number of 10 (n = 2x = 10; Robinson and Decker-Walters 1997; Zraidi et al. 2007). The fruit shape, color and surface pattern is extremely variable. Fruits size ranges from 4–15 cm (wild species) up to 1–2 m in diameter (see Cucurbita maxima, the largest fruit known). The five cultivated species of the genus (C. pepo, C. maxima, C. moschata, C. argyrosperma and C. ficifolia) are insect-pollinatted and reproductively isolated. Other cucurbits produced for commercial fruit–vegetable production especially cucumber (Cucumis) and watermelon (Citrullus) are grafted on Cucurbita rootstocks characterized with vigorous growth and pest resistance (Doty 2008). Cucurbits are present in the archaeological records of the American continent from the earliest stages of agriculture (Whitaker 1981) and they have been a part of nearly all indigenous cultures of the New World from southernmost Canada to Argentina and Chile. Many cucurbits are important as foods and as medical plants. C. pepo was domesticated thousands of years ago in North America (Cutler and Whitaker 1961). There is evidence suggesting that several groups of edible cultivars (including zucchini) subsequently originated in Europe, perhaps by crossing of culinary forms with gourds, as early as the sixteenth century (Paris 1989). Today, C. pepo is among the economically most important fruit–vegetable crops worldwide and is grown in almost all temperate and subtropical regions (Paris 1996).

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Pumpkin seed oil and pumpkin seed extract are increasingly esteemed for their excellent nutritional quality and medicinal value (Paris 1996).

POPs and Cucurbita Species Ability of Cucurbita species to take up and accumulate unusually high levels of POPs in their tissues was first observed in 1994 when in fruits of zucchini (Cucurbita pepo L. convar. giromontiina) polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/PCDF) were detected with concentrations ca. two orders of magnitude higher than in other fruits and vegetables growing in the same polluted soil (Paris 1996). Findings on the uptake and accumulation of individual POPs in different plant species will be summarized below.

DDT/DDE/DDD DDT (2-bis(p-chlorophenyl)-1,1,1-trichloroethane) is the most infamous POP type chemical. It has been used as an inexpensive and effective insecticide worldwide. In the environment DDT can be converted microbiologically or abiotically to DDE (2,2-bis(p-chlorophenyl)-1,1-dichloroethylene) and DDD (2,2-bis(p-chlorophenyl)1,1-dichloroethane) that are also highly persistent and have similar chemical and physical properties. White (2002) assessed the bioavailability of DDE in soil to the Cucurbita (squash, pumpkin) and Cucumis (cucumber, melon) species. Bioconcentration factors were greater in Cucurbita species with ca. an order of magnitude. Based on these results a complete phytoremediation system was elaborated. The impacts of variable plant density, soil moisture content, plant age, interactions between earthworms and plants, nutrient amendments, intercropping, organic acids, etc. on the uptake of DDE were investigated. (White 2009; Wang et al 2004; Kelsey and White 2005; Kelsey et al 2006; Peters et al. 2007; White et al. 2003a, 2005a, 2006; White and Zeeb 2007). It was also shown that different subspecies of cucurbits take up different amounts of DDE (White et al. 2003b).

Dieldrin and Endrin Dieldrin and endrin are structurally similar organochlorine insecticides developed in 1940s as alternatives of DDT. Diedrin is stable in the soil, while aldrin is gradually converted to dieldrin. Otani et al. (2007) compared the uptake of dieldrin and endrin of 32 plant species belonging to 17 families and by 34 cultivars of Cucurbita


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sps. grown in contaminated soil. Uptake of cucurbits were the highest especially of zucchinis as the most efficient plants. As Cucurbita species are used as rootstock for grafted cucumbers and watermelons the selection of low uptake cucurbits is also desired for rootstock that reduce dieldrin concentration in cucumbers and watermelons (Otani and Seike 2007).

Heptachlor Heptachlor is a chlorocyclodiene insecticide that was used to control termites and crop insect pests. Campbell et al. (2009) indicated the use of Lagenaria siceraria (bottle gourd) for the uptake of heptachlor and heptachlor epoxide. Seven cultivars were screened and all the seven took up considerable amount of heptachlor epoxide into the vines. Cucumber (Cucumis sativus) was also found to be able to take up and accumulate considerable amounts of heptachlor in the fruits.

Chlordane Chlordane is another organochlorine insecticide developed for corn (Zea mays). Chlordane is efficiently taken up by Cucurbita species and here, too, zucchini was the most effective accumulator among 12 crop plants investigated (Lee et al. 2003; Mattina et al. 2002). Interestingly, structure-favorable translocation was also observed for this chemical, even between chiral components with the same physicochemical properties. Xenobiotic residues translocate enantioselectively from the soil into the plant with genera-specific patterns (Lee et al. 2003; Mattina et al 2002).

Polychlorinated Biphenyls Polychlorinated biphenyls (PCBs) are a group of synthetic halogenated aromatic hydrocarbons characterized with high chemical stability. PCBs were widely used for many industrial applications. Today, they can be found in soils, drinking water, and living organisms. Ability of nine plant species (including three varieties of C. pepo) to take up polychlorinated biphenyls (Aroclor 1260) was investigated in contaminated soils in a greenhouse experiment (Zeeb et al. 2006). The results indicated that C. pepo varieties were the most effective as regards to their PCB phytoextraction capacity. Phytoremediation efficacy depended on the soil composition and contaminant’s concentration in the soil. It was also shown that tetra- to hexachlorobiphenyls

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contribute the largest proportions to plant shoots, while heavier chlorinated congeners (especially hepta- and octa-chlorobiphenyls) were found mostly in the roots. Inui et al. (2008) also examined the correlation between chlorination level and bioconcentration factor (BCF: ratios of concentrations in the plant to those in the soil) and found that PCBs with five chlorine atoms were more bioconcentrated than tetraand hexachlorinated biphenyls. White et al. (2005b) examined the effect of citric acid and earthworm species on the uptake of PCBs in Cucurbita, Cucumis and Lupinus species. Citric acid amendment resulted higher PCB concentration in the stem and the leaf. Aslund et al. (2007, 2008) carried out a field experiment with Cucurbita pepo, greater straw sedge (Carex normalis) and tall fescue (Festuca arundinaceae) and observed that PCB concentrations in pumpkin shoots decreased as the distance above the root increased and was independent of the type of tissue. All these results confirmed that Cucurbita pepo has the highest PCB accumulation capacity of all the plants examined.

Dioxins and Furans The first data on the high uptake of dioxins into certain Cucurbita species was reported in 1994 (Hülster et al. 1994). Zucchini fruits were found to contain concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans two orders of magnitude higher than other plants examined. Further investigations showed that zucchini plants were able to take up and translocate these contaminants to the shoot. Cultivar specificity among three zucchini cultivars for uptake of 29 dioxinlike compounds was also detected, as phytoextraction capacity of cv. ‘Goldrush’ and ‘Black Beauty’ zucchini varieties were found to be greater than that of the cv. ‘Patty Green’ with at least two orders of magnitude (Inui et al. 2008). In addition, a negative correlation was shown between the extent of the chlorination of the dibenzo-p-dioxin and dibenzofuran molecule and its bioconcentration factor.

Conclusions The reviewed results raise the question why does this unique uptake and accumulation of POPs by some cucurbit plants take place? In spite of great efforts to answer this question the phenomenon is still unexplained. It is generally accepted that POPs enter the plants via pathways evolved for acquisition of soil-derived nutrients (Schröder et al. 2001): uptake by the roots, followed by translocation and sequestration along the transpiration stream (Lunney et al. 2004) (obviously, semivolatile POPs may also enter the plants via air, but this uptake mechanism is rather inefficient and nonspecific). Most plants are able to take up hydrophobic compounds from soil, e.g. significant amounts of DDE were detected in roots of zucchini, pumpkin, alfalfa (Medicago sativa), ryegrass (Lolium multiflorum) and tall fescue (Lunney et al. 2004). A possible explanation of this uptake process is that root exudates serve as


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mobilizing agents through impacting the physico-chemical structure of the soil and improving the release of contaminants into the soil solution. In regular, root exudates are produced to acquire nutrients (White et al. 2005a). Among the organic acids in root exudates citric acid content showed high correlation with phytoextraction capacity of several weathered organic contaminants (Gent et al. 2005). In addition to the efficient uptake of POPs from polluted soils some Cucurbita pepo cultivars showed significant amount of pollutants translocated into the aboveground parts of plant. Explanation for this unique process provides challenges for further research. Lunney et al. (2004) speculated that Cucurbitaceae plants might metabolize POPs with the help of endophytic bacteria living in the plant cells. Some plant species had the ability to recruit, or selectively augment, the necessary bacteria to remove pollutants, while other plants in the same area were unable to do so (Doty 2008). Grafting experiments revealed that translocation ability depends only on roots. Cucumber scion grafted on zucchini rootstock extracted considerable amount of dieldrin, endrin and chlordane in the shoot while zucchini scion on cucumber rootstock did not (Otani and Seike 2007; Mattina et al. 2007). Successful phytoremediation would include the conversion of the contaminants into less harmful derivatives. Most organics appear to undergo some degree of transformation in plant cells (Macek et al. 2000), but the metabolism of POPs is minimal and only a few enzymes are known to break these molecules down (White et al. 2006; Nash and Woolson 1967). Furthermore, microorganisms usually require longer times to degrade POPs because they are directly affected by the toxicity of these compounds. In order to develop an efficient and inexpensive phytoremediation system expression of such enzymes in Cucurbita species would be desirable, otherwise the POPs accumulated in the plant tissues needs to be degraded physicochemically (Campanella et al. 2002). High-uptake cucurbits are excellent candidates for phytoremediation of soils polluted by POPs. In addition, low-uptake cucurbits bred for substituting rootstocks used for grafting commercial Cucurbitaceae cultivars (i.e. cucumber, watermelon, melon, summer squash (Davis et al. 2008) are also desirable. In spite of continuous efforts mechanisms explaining the high POP accumulation capacity of Cucurbitaceae plants are not known. Clarification of the uptake, translocation, sequestration processes will help to select and breed new cucurbit cultivars with improved capacity for phytoremediation of POP-contaminated sites. Acknowledgement Financial support for this work came from OTKA grant PD-75169.

References Aslund MLW, Rutter A, Reimer KJ et al (2008) The effects of repeated planting, planting density, and specific transfer pathways on PCB uptake by Cucurbita pepo grown in field conditions. Sci Total Environ 405:14–25 Aslund MLW, Zeeb BA, Rutter A et al (2007) In situ phytoextraction of polychlorinated biphenyl- (PCB) contaminated soil. Sci Total Environ 374:1–12 Campanella B, Bock C, Schröder P (2002) Phytoremediation to increase the degradation of PCBs and PCDD/Fs. Environ Sci Pollut Res 9:73–85

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Campbell S, Arakaki AS, Li QX (2009) Phytoremediation of heptachlor and heptachlor epoxide in soil by Cucurbitaceae. Int J Phytoremediation 11:28–38 Cutler HC, Whitaker TW (1961) History and distribution of the cultivated cucurbits in the Americas. Am Antiquity 26:469–485 Davis AR, Perkins-Veazie P, Sakata Y et al (2008) Cucurbit grafting. Crit Rev Plant Sci 27:50–74 Doty SL (2008) Enhancing phytoremediation through the use of transgenics and endophytes. New Phytol 179:318–333 Gent MPN, Parrish ZD, White JC (2005) Nutrient uptake among subspecies of Cucurbita pepo L. is related to exudation of citric acid. J Am Soc Hort Sci 130:782–788 Hülster A, Müller JF, Marschner H (1994) Soil-plant transfer of polychlorinated dibenzop-dioxins and dibenzofurans to vegetables of the cucumber family (Cucurbitaceae). Environ Sci Technol 28:1110–1115 Inui H, Wakai T, Gion K et al (2008) Differential uptake for dioxin-like compounds by zucchini subspecies. Chemosphere 73:1602–1607 Kelsey JW, Colino A, Koberle M et al (2006) Growth conditions impact 2, 2-bis(p-chlorophenyl)-1, 1-dichloroethylene (p, p’-DDE) accumulation by Cucurbita pepo. Int J Phytoremediation 8:261–271 Kelsey JW, White JC (2005) Multi-species interactions impact the accumulation of weathered 2, 2-bis (p-chlorophenyl)-1, 1-dichloroethylene (p, p’-DDE) from soil. Environ Pollut 137:222–230 Kömives T, Gullner G (2000) Phytoremediation. In: Wilkinson RE (ed) Plant–environment interactions. Marcel Dekker, New York, pp 437–452 Lee WY, Iannucci-Berger WA, Eitzer BD et al (2003) Plant uptake and translocation of air-borne chlordane and comparison with the soil-to-plant route. Chemosphere 53:111–121 Lunney AI, Zeeb BA, Reimer KJ (2004) Uptake of weathered DDT in vascular plants: potential for phytoremediation. Environ Sci Technol 38:6147–6154 Macek T, Macková M, Kás J (2000) Exploitation of plants for the removal of organics in environmental remediation. Biotechnol Adv 18:23–34 Mattina MI, Berger WA, Eitzer BD (2007) Factors affecting the phytoaccumulation of weathered, soil-borne organic contaminants: analyses at the ex planta and in planta sides of the plant root. Plant Soil 291:143–154 Mattina MI, White J, Eitzer B et al (2002) Cycling of weathered chlordane residues in the environment: compositional and chiral profiles in contiguous soil, vegetation, and air compartments. Environ Toxicol Chem 21:281–288 Nash RG, Woolson EA (1967) Persistence of chlorinated hydrocarbon insecticides in soils. Science 157:924–927 Nee M (1990) The domestication of Cucurbita (Cucurbitaceae). Econ Bot 44:56–68 Oldal B, Maloschik E, Uzinger N et al (2006) Pesticide residues in Hungarian soils. Geoderma 135:163–178 Otani T, Seike N (2007) Rootstock control of fruit dieldrin concentration in grafted cucumber (Cucumis sativus). J Pestic Sci 32:235–242 Otani T, Seike N, Sakata Y (2007) Differential uptake of dieldrin and endrin from soil by several plant families and Cucurbita genera. Soil Sci Plant Nutr 53:86–94 Paris HS (1989) Historical records, origins, and development of the edible cultivar groups of Cucurbita pepo (Cucurbitaceae). Econ Bot 43:423–443 Paris HS (1996) Multiple allelism at the D locus in Squash. J Hered 87:391–395 Peters R, Kelsey JW, White JC (2007) Differences in p, p’-DDE bioaccumulation from compost and soil by the plants Cucurbita pepo and Cucurbita maxima and the earthworms Eisenia fetida and Lumbricus terrestris. Environ Pollut 148:539–545 Robinson RW, Decker-Walters DS (1997) Cucurbits. CAB International, New York Schröder P, Scheer C, Belford BJD (2001) Metabolism of organic xenobiotics in plants: conjugating enzymes and metabolic end points. Minerva Biotechnol 13:85–91


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Wang XP, White JC, Gent MPN et al (2004) Phytoextraction of weathered p, p’-DDE by zucchini (Cucurbita pepo) and cucumber (Cucumis sativus) under different cultivation conditions. Int J Phytoremediation 6:363–385 Whitaker T (1981) Archeological cucurbits. Econ Bot 35:460–466 White JC (2002) Differential bioavailability of field-weathered p, p’-DDE to plants of the Cucurbita and Cucumis genera. Chemosphere 49:143–152 White JC (2009) Optimizing planting density for p, p’-DDE phytoextraction by Cucurbita pepo. Environ Eng Sci 26:369–375 White JC, Mattina MI, Lee WY et al (2003a) Role of organic acids in enhancing the desorption and uptake of weathered p, p’-DDE by Cucurbita pepo. Environ Pollut 124:71–80 White JC, Parrish ZD, Gent MPN et al (2006) Soil amendments, plant age, and intercropping impact p, p’-DDE bioavailability to Cucurbita pepo. J Environ Qual 35:992–1000 White JC, Parrish ZD, Isleyen M et al (2005a) Influence of citric acid amendments on the availability of weathered PCBs to plant and earthworm species. Int J Phytoremediation 8:63–79 White JC, Parrish ZD, Isleyen M et al (2005b) Influence of nutrient amendments on the phytoextraction of weathered 2, 2-bis(p-chlorophenyl)-1, 1-dichloroethylene by cucurbits. Environ Toxicol Chem 24:987–994 White JC, Wang XP, Gent MPN et al (2003b) Subspecies-level variation in the phytoextraction of weathered p, p’-DDE by Cucurbita pepo. Environ Sci Technol 37:4368–4373 White JC, Zeeb BA (2007) Plant phylogeny and the remediation of persistent organic pollutants. In: Willey N (ed) Phytoremediaton. Humana Press, Totowa, NJ Zeeb BA, Amphlett JS, Rutter A et al (2006) Potential for phytoremediation of polychlorinated biphenyl-(PCB-) contaminated soil. Int J Phytoremediation 8:199–221 Zraidi A, Stift G, Pachner M et al (2007) A consensus map for Cucurbita pepo. Mol Breed 20:375–388

Trichloroacetic Acid in the Forest Ecosystem Miroslav Matucha and Peter Schröder

Abstract Trichloroacetic acid (TCA) is a ubiquitous phytotoxic substance that occurs at various levels in the environment. The last century, it was produced and used in agriculture as herbicide against perennial grasses for some time, before it was found as secondary atmospheric pollutant. It was considered a reason of coniferous forest decline. TCA was further found among products of disinfection of drinking water and of delignification of cellulose pulp by chlorine. In addition to these anthropogenic sources of TCA, is has been found to be formed in the forest ecosystem as a result of microbial chlorination of humic substances that subsequently yield TCA in the soil. TCA may be considered important intermediate of soil organic matter degradation and belongs thus to naturally-produced organohalogens and at the same time to relevant xenobiotics and stressors affecting plants in the forest ecosystem. Its role in the forest ecosystem is clearly shown.

Introduction Trichloroacetic acid (TCA) is a ubiquitous phytotoxic substance that presently occurs at various levels in all compartments of the environment. During the last century, it was produced and used in agriculture as herbicide against perennial grasses for some time, before it was also detected as secondary atmospheric pollutant formed by photooxidation of important solvents – methylchloroform and perchloroethylene emitted from anthropogenic sources (Frank 1984).

M. Matucha () Institute of Experimental Botany, Academy of Sciences of the Czech Republic, Vídeňská 1083, CZ-14220 Prague, Czech Republic e-mail: [email protected] P. Schröder Helmholtz-Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, FRG P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_5, © Springer Science+Business Media B.V. 2011

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TCA was further found among products of disinfection of drinking water and of delignification of cellulose pulp by chlorine. Astonishingly, and in addition to these anthropogenic sources of TCA, is has been found to be formed in the forest ecosystem as a result of microbial chlorination of humic substances followed by cleavage of aliphatic chlorinated residues that subsequently yield TCA in the soil. Natural TCA formation in soil from humic substances was shown several times (Haiber et al. 1996; Hoekstra et al. 1999; Fahimi et al. 2003; Matucha et al. 2003b) and together with the microbial degradation, TCA as well as DCA may be considered important intermediates of soil organic matter degradation (Matucha et al. 2007a,b). TCA thus belongs to naturally-produced organohalogens and at the same time to relevant xenobiotics and stressors affecting plants (especially conifers) in forest ecosystems (Schröder 1998). Its role (sometimes together with that of the other chloroacetic acids) in the environment has been reviewed several times (McCulloch 2002; Hoekstra 2003; Lewis et al. 2004; Laturnus et al. 2005; Cape et al. 2006; Clarke et al. 2009) documenting intensive research in this field, starting with its sources (natural and anthropogenic), continuing with its influence on coniferous trees and closing with its role and effects in the environment.

Properties of Trichloroacetic Acid and Its Occurrence in the Environment TCA is a simple chlorinated organic aliphatic acid with a molecular weight of 163.4, and a boiling point of 198°C. It is polar, has a hydrophilic character and low volatility (21 Pa at 25°C), is very well soluble in water (1,200 g/L) and at the same time a strong acid with a pKa of 0.26 (Weast et al. 1987; Suntio et al. 1988; Bowden et al. 1998). TCA is thermally degraded to produce chloroform and carbon dioxide. This decarboxylation proceeds only at elevated temperatures in presence of water, in aqueous solution, see Table 1, but in the environment abiotic cleavage proceeds only at low rates (Matucha et al. 2006). Its properties – high polarity and solubility in water – determine its physico-chemical behavior in air, soil and plants. Despite its low abiotic reactivity, TCA is easily biodegradable by soil microorganisms (Lignell et al. 1984; Matucha et al. 2003a) that possess dehalogenation enzymes as shown recently by PCR techniques (Leach et al. 2009). On the other hand, its stability in higher plants has been reported several times (Blanchard 1954; Ashton and Crafts 1973; Åberg 1982), whereas microbial degradation by endophytic bacteria in spruce needles has been detected (Forczek et al. 2004). An important turning point in the understanding of the fate of TCA in the environment was reached when Schöler et al. (2003) identified a significant TCA deficit in the balance of fluxes between atmosphere, biota, soil and groundwater. Hypothesizing that TCA formation would in fact need a biotic component, they were able to set up a comprehensive mass balance for the substance, and excellently explain its production in forest soils (Schöler et al. 2003). In fact, TCA formation from resorcinolic structures of humic acids by chlorination had earlier and convincingly

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Table 1 Decarboxylation of aqueous TCA solution. Results are means of at least three replicates and duplicate LS measurements; the calculated values for room temperature are from a 24 h experiment, 37 kBq [1,2-14C]TCA per experiment. (A) Influence of pH on a decarboxylation of [1,2-14C]TCA at 70 C/90 C for 2 h; (B) Influence of temperature on decarboxylation of [1,2-14C]TCA at pH 4.6 during 2 h exposure A pH TCA decomposition (%) 3.0 4.6 6.0 7.0 8.0 9.2

4.42/100 14.8/100 16.8/100 16.6/100 7.63/92.1 7.15/72.6

B Temperature (°C)

TCA decomposition (%)

23 ± 2 35 60 70 90

0.0024 0.011 1.8 14.8 100

been shown by Hoekstra et al. (1999), who used 13C-labelled resorcinol as a substrate. TCA turnover, including formation and degradation, in forest soil was then conclusively shown using chlorine 36. In these experiments, TCA formation was accompanied by large amounts of DCA (Matucha et al. 2007a,b). Yet another TCA source is atmospheric photooxidation of widely used chlorinated solvents like perchloroethene (PER) (Gay et al. 1976; Tuazon et al.1988; Itoh et al. 1994), and 1,1,1-trichloroethane (Frank et al. 1992, 1994). This mechanism of TCA formation from anthropogenic chlorinated C2-hydrocarbons was supposed by Weissflog et al. (1999, 2001, 2004, 2005, 2006). However, the yield of TCA from these reactions was found to be low and is still a matter of controversy (Franklin 1994; Sidebottom and Franklin 1996). Nevertheless, Weissflog et al. (1999 and 2004) suggested that these compounds could contribute to desertification of dry areas or in the vicinity of saline lakes where PER is produced and absorbed by plant leaves because of their detrimental and herbicidal effects.

Plants and TCA The effect of TCA on plants has been studied in the context of its use as a pre-emergence herbicide against perennial grasses and weeds since 1947 (Martin 1972). In spite of its commercial and widespread use, interaction of TCA in plants

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is not exactly understood. On the one hand, TCA is a known inhibitor of elongation reactions during the biosynthesis of very long chain fatty acids and thereby inhibits the formation of epicuticular waxes (Franich and Wells 1980; Gullvåg et al. 1996; Macey 1974; Böger et al. 2000). On the other hand, its capability to precipitate proteins indicates an interaction with soluble enzymes or with the photosynthetic apparatus, as many effects recorded under the influence of TCA concern especially the functioning of chloroplasts (Ashton and Crafts 1973; Weissflog et al. 2007; Forczek et al. 2008). The applications of TCA as herbicide were treated in several individual articles together with its behavior and degradation in soil and plants (Blanchard 1954; Lode 1967; Smith 1974; Chow 1976; McGrath 1976; Süss and Eben 1977; Lignell et al. 1984). A possible role of aerobic microorganisms in TCA decomposition was also investigated, and Arthrobacter sp. were found to be responsible for its degradation (Lode 1967; Süss and Eben 1977). Lower (microbial) degradation rates of TCA in sites where it had been used as herbicide was accounted for by its relatively high concentration in soil (120 kg/ha), which was recognized later (Forczek et al. 2001; Matucha et al. 2003a,b; Schröder et al. 2003). Indirectly, another branch of research focusing on aliphatic solvents as micropollutants discovered chloroacetates as active substances in plant decline. Monochloroacetate (MCA), di- (DCA) and trichloroacetate (TCA) may build up as a result of the atmospheric degradation of chlorinated solvents like tri- and tetrachloroethene and 1,1,1-trichloroethane. In the 1970s, it became clear that the world production rate of these compounds exceeds the atmospheric scavenging rate, leading to an increase of the concentration of aliphatic chlorocarbons in the atmosphere of regions void of any production sources. This finding did not attract public interest until a connection to the depletion of the stratospheric ozone layer by CFC was proven (Crutzen 1996). Today the predominant source of chloroacetic acids arises from aliphatic solvents as micropollutants. Hence, the Montreal protocol (1987) and follow up meetings forced industry to discontinue the production of CFC, halons, carbon tetrachloride, methyl chloroform, HCFC, HBFC and methyl bromide in favor of partially hydroxylated substitutes. Chlorinated C1 and C2 hydrocarbons, except CCl4 and 1,1,1-trichloroethane have, due to their considerably shorter lifetimes not been banned. In general these compounds have been regarded as “micropollutants” with concentrations in the pptv range and judged to have no considerable harmful potential. It was not before 1984 that researchers voiced suspect about possible effects of chlorinated aliphatics on plants (Frank 1984; Frank and Frank 1985; Grimmer and Schmidt 1986). Tetrachloroethene (PER) is of considerable importance as a degreasing and cleaning agent in the metal-processing and textile industries because of its physicochemical properties. Approximately 300 kt were industrially produced worldwide in 1996 (McCulloch and Midgley 1996), when industrial production ceased due to concerns about the involvement of chlorohydrocarbons in climate change. Still, extensive amounts of PER are emitted in the processes of bleaching raw celulose (Juuti et al. 1995; Juuti et al. 1993), incinerating chlorinated plastics (Weissflog et al. 2004) and the combustion of coal containing chloride in power stations (Garcia et al. 1992). Among natural sources, microbial production in forest soil (Hoekstra et al. 1998;


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Hoekstra et al. 1999) and formation in biomass fires (Rudolph et al. 2000; Weissflog et al. 2004;) are the predominant producers of PER. The microbial formation of PER and other C1/C2 chlorohydrocarbons in salt lakes has been shown by Weissflog et al. (2005). Forests are a strong sink for volatile chlorohydrocarbons. As a result of the large leaf area index and the cuticular properties of many plants, high scavenging rates for atmospheric organic xenobiotics (Gaggi et al. 1985; Plümacher and Schröder 1994; Plümacher et al. 1994) may be observed. The chloroacetates are globally distributed in rain and snow and may even be present in wet precipitation in concentrations higher than any other organochlorines (Laniewski et al. 1999). Especially trichloroacetic acid, and independent of its origin was supposed to exert phytotoxic effects on coniferous trees. Hence it became a topic for the intensive research of forest decline in the central and north Europe in the last 20 years. This was supported by the finding from laboratory experiments that not only aliphatic chlorocarbons, but also the much less lipophilic TCA can enter foliage directly from wet precipitation. When tree branches were treated with TCA containing mist significant uptake was shown (Cape et al. 2003; Dickey et al. 2004), and a possible uptake of TCA from fog or cloud droplets by stomata was assumed (Römpp et al. 2001). When available in the rhizosphere, TCA was also easily taken up by tree roots and transported by transpiration stream into leaves (Fig. 1). As third exposure pathway, perchloroethylene (PE) – well known TCA precursor – that was easily taken up across needle cuticules, could be bio oxidized in chloroplasts to yield TCA (Weissflog et al. 2006). Such an enzymatic production of TCA from aliphatic chlorocarbons has been shown in a study on trichloroethene (TCE) taken up from soil in the context of plant

radioactivity (kBq/g)

10

radioactivity of current year needles (spruce A)

wood radioactivity (spruce A)

radioactivity of current year needles (spruce B)

wood radioactivity (spruce B)

8 6 4 2 0

0

4

8

11

15

time (days)

Fig. 1 Radioactivity of current needles and of wood of potted spruce nurslings A and B (exposed to 360 and 720 kBq of [1,2-14C]TCA, resp., added to the roots); radioactivity in C + 1 and C + 2 needles at the end of the experiment was 6.82 and 3.26 kBq/g for spruce A and 1.63 and 1.28 kBq/g for spruce B, resp

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utilization in phytoremediation (Newman et al. 1997). In this study, poplar hybrids were found to take up and degrade TCE to several metabolites including TCA. The trees were also shown to transpire TCE in amounts sufficient to reclaim the contaminated site. Today, clear-cut evidence is available that TCA may enter plants through foliage or the roots. Co-transport in the transpiration stream leads to the accumulation of TCA in leaves or needles as the transported water is transpired, leaving TCA in the leaf. Attempts to correlate TCA concentrations in conifer needles with the degree of defoliation of trees or damage of the surface wax layer have been pursued (Frank et al. 1992, 1994; Plümacher et al. 1993; Norokorpi and Frank 1995; Gullvåg et al. 1996), however, they failed, because the conifers in the investigated stands were simultaneously affected by other stressors (e.g. Frank 1988; Frank et al. 1991, 1994; Norokorpi and Frank 1993; Sutinen et al. 1995; Weissflog et al. 1999, 2001) see also Table 2. However, the contribution of TCA was considered in several reviews (McCulloch 2002; Hoekstra 2003; Schöler et al. 2003; Lewis et al. 2004; Laturnus et al. 2005; Cape et al. 2006; Clarke et al. 2009).

Physiological Effects The investigation of physiological effects of TCA on plants almost ceased after it stopped being applied as a herbicide, so its behavior in soil has been described only for higher concentrations (Smith 1974; McGrath 1976; Süss and Eben 1977; Lignell et al. 1984). For those investigations 14C-labelled TCA and radioanalytical methods were succesfully used. Few exceptions were the investigations of possible role of glutathione S-transferase on detoxification of TCA and chlorinated solvents in spruce needles (Plümacher and Schröder 1994; Plümacher et al. 1994) and effects of TCA on pine-needle chloroplasts (Sutinen et al. 1995). High needle VCH concentrations were measured when the detoxification enzyme GST had its lowest activity, which might indicate a direct connection between the VCH detoxification potential and the actual concentrations of the pollutants. An array of papers have been devoted to adverse effects of TCA on conifers (from Germany, Finland and Scotland: Frank 1988 and 1994; Plümacher and Schröder 1994; Sutinen et al. 1995, 1997; Juuti 1997; Schröder et al. 1997; Cape et al. 2003; Dickey et al. 2004; Reeves et al. 2000; Heal et al. 2003a, b; Stidson et al. 2004a, b) and only rarely has the role of soil been mentioned (Frank 1988; Juuti 1997). The approach using labelled [1,2-14C]TCA of high specific activity (Bubner et al. 2001) has yielded some new aspects of the mode of action of TCA on conifers. After our preliminary investigation of uptake, translocation and fate of TCA in Norway spruce (Uhlířová et al. 1996; Matucha et al. 2001), the importance of processes in the plant/soil-system as a whole was recognized (Forczek et al. 2001; Matucha et al. 2001). In spite of the potentially important uptake of TCA from the air, mostly the uptake from soil via roots and subsequent transport upwards into needles, driven by the transpiration stream is to be considered here.

Scots pine Scots pine Norway spruce & Scots pine Norway spruce & Scots pine Norway spruce & Scots pine Scots pine

Frank et al. 1990

Matucha and Uhlířová 1999

Juuti et al. 1995 Juuti et al. 1996

Juuti et al. 1993a

Frank et al. 1994

Scots pine Norway spruce & Scots pine Norway spruce & Scots pine Norway spruce

Norway spruce Norway spruce

Frank 1991 Frank et al. 1989

Frank et al. 1992

Tree species Norway spruce

Reference Coufal et al. 2003

C C + 1 C + 2

130 2 2 11

Finland NW-Czech Republic

C + 2

130 10

C C + 2 C + 2 C

C + 2

C + 2

Needle age C C + 1 C + 2 C C + 2 C C + 2 C + 1 C, C + 2

NW-Finland W-Finland

>60

42

N-Finland E-Finland

246

n 29 29 19 15 n.d. 1 10 15 120

Black Forest

Finland Germany Finland

Black Forest S-Germany

Sample origin Czech Republic

Table 2 TCA content in conifer needles reported in literature

90–126 (108.0) 28–106 (47.0)

11–126 (68.5)

(continued)

1–180 (23) (5 > DCA)

2–50 5–135 6–276 3–14 (9)

3.3–90 (20.3) (19.7 > MCA)(DCA 0) 8–126

TCA content in needles (average) (ng/g FW) 3.4–110 (34.9) 1.5–127 (37.9) 4.2–144 (42.1) 4–67 10–300 30 33–180 (74), 20–73 (45) 4–96 3–126

Trichloroacetic Acid in the Forest Ecosystem 93

Scots pine Norway spruce Norway spruce Eur. silver fir Plümacher and Schröder Norway spruce 1994; Plümacher et al. Scots pine 1994 Reeves et al. 2000 Sitka spruce Schröder and Plümacher Norway spruce 1998 Scots pine Scots pine Sinkkonen et al. 1998 Scots pine Scots pine Weissflog et al. 1999 Scots pine

Germany Netherlands Italy Scandinavia UK Berlin, Achenkirch Hesse, Berlin

Spruce

Plümacher et al. 1993

N-Finland

Scots pine

Norokorpi and Frank 1995 Peters 2003

41 21 21 108 >150 50 55

Achenkirch, Austria Berlin Scotland Hessen Hessen Berlin S &W-Finland Caucasus Astrakhan,Volga,

10 10 10 20

n

3 3 3 3 3 56 18 1 1 48 6

NW-Czech Republic

Norway spruce

Matucha et al. 2001

Sample origin

Tree species

Reference

Table 2 (continued)

various C – C + 1 C – C + 1 C – C + 2 C – C + 2 C + 1 C + 1

n.d. n.d. n.d. n.d. n.d. C, C + 1 C, C + 2 n.d. n.d. C – C + 2 C – C + 1

C C + 1 C + 2 C + 1

Needle age

3–165 2–25 (11) 1–26 (6.6) 1–178 (2–56) n.d. (0–3.8 DCA) 3.5–5.3 3.2–68.9

(11.9) (39.2 DCA) (6.4) (4.0 DCA) (5.3) (4.9 DCA) (5.1) (2.1 DCA) (10.9) (8.7 DCA) 0.59–178 (55.6) 4–20 43 13 1–18 (7) (34)

28–110 (63.1) 34–127 (77.8) 28–144 (67.0) 8–65 (33)

TCA content in needles (average) (ng/g FW)

94 M. Matucha and P. Schröder


95

The TCA level in soil (given by the difference between influx plus formation and efflux plus degradation) determines the uptake by spruce roots. Formation of TCA in the forest soil was shown by Hoekstra et al. (1999). TCA decarboxylation in needles was also considered as a possible reason for the observed slow degradation in the tissue (Matucha et al. 2006). There has been no evidence for loss of other volatile degradation products (e.g. methane, carbon monoxide, trichloromethane) from foliage, except CO2 (Schröder et al. 2003). The plausible dehalogenation of TCA, which is confirmed in microorganisms (Weightman et al. 1992), has not yet been demonstrated in plants. A dehalogenase activity that has been described for aliphatics has not been demonstrated for TCA (Schröder 1997). The lack of metabolism of TCA has been also connected with forest decline (Frank et al. 1990, 1994; Schröder and Plümacher 1998).

The Role of the Rhizosphere Natural formation of TCA and of short chain halocarbons in soil has been reported (DeJong and Field 1997; Hoekstra et al. 1998; Hoekstra et al. 1999; Keppler et al. 2000) thus elucidating the origin of TCA levels found in nature. The five principle mechanisms used by microorganisms to dechlorinate chlorinated organic compounds include (i) reductive, (ii) oxidative, and (iii) hydrolytic dehalogenation, (iv) dehydrodehalogenation, and (v) dehalogenation after ring cleavage (Fritsche 1998; Alexander 1999). Other halogens such as bromine, iodine, and fluorine are also displaced by these processes. Of all the processes, reductive dehalogenation (halorespiration) by anaerobic microorganisms appears most important for the dechlorination of many chlorinated organics by replacing the chlorine through hydrogen. Aromatic and aliphatic chlorocarbons serve as terminal electron acceptors during oxidation of the electron rich hydrogen or organic acids (van Pée and Unversucht 2003). The driving force behind reductive dechlorination is the generation of energy for anaerobic microbes since the process is thermodynamically very favourable (Mohn 2004). In addition, there has been a significant natural background level of TCA in precipitation over the past 200 years (von Sydow et al. 2000), which could be caused by natural sources (Gribble 2003). Several genera of higher fungi have a capacity for biosynthesis of organohalogens, but they can also cause their reductive dechlorination (De Jong and Field 1997). Only aerobic microbial degradation of TCA was reported (e.g. Yu and Welander 1995; Olarinan et al. 2001). Microbial degradation of TCA to CO was supposed also by Weightman et al. (1992) and its decarboxylation leading to chloroform in soil was supposed in several other papers (Frank 1988; Frank et al. 1990; Plümacher et al. 1993; Uhlířová et al. 1995) or even reported (Haselmann et al. 2000). Further, enzymatic dechlorination of TCA to oxalic acid and related short chain aliphatic acids in pond waters was reported (Ellis et al. 2001). Shortchain chlorocarbons and chloroacetic acids are thus to be regarded as important minor contaminants and, because of their natural origin, also products of the environment, which undergo complex reactions.

96

M. Matucha and P. Schröder CO2 evolution

100% 90% 80% 70% 60% 50% 40%

A - O2 carrierless B - O2 isotopically diluted C - A1 carrierless D - A1 isotopically diluted

30% 20% 10% 0%

0

50

100

150

200

250

300

350

400

time [h]

Fig. 2 Microbial degradation of [1,2-14C]TCA in spruce-forest soil (horizon A and O2) amended with 396 kBq of [1,2-14C]TCA, i.e. with 17.5 (carrier-free) and 192 mg (11 times isotopically diluted) TCA, in 50 g soil

Also, dynamic equilibrium of the pollutant within the system has to be assumed. The uptake, translocation and effects of TCA on Norway spruce (Picea abies L. Karst.) has been recently examined using [1,2-14C]TCA of high specific activity (3.7 GBq. mmol−1, i.e. 22 Bq ng−1 TCA) thus enabling the following TCA levels appearing in nature, 5–200 ng g−1 TCA in needles, and 5–400 ng g−1 TCA in soil (Fig. 2). It was found that translocation of TCA occurs from the atmosphere into soil by precipitation water, followed by uptake by roots and then movement into needles via transpiration stream, where TCA interacts (at concentrations higher than 60 ng g/L) with the photosynthetic apparatus in an inhibitory manner (Uhlířová et al. 1996; Matucha et al. 2001). Our radioactivity balance studies could not fully explain the observed losses of [1,2-14C]TCA-derived radioactivity. Since the role of soil and plant in the TCA metabolism of the studied plant/soil-system was not fully understood, several studies were carried out to address these questions (Forczek et al. 2001; Matucha et al. 2001), see Fig. 3. The curves in Fig. 2 show the strong influence of soil character on the released 14 CO2 in the course of degradation at room temperature and 30% soil moisture; residual soil radioactivity corresponds mostly to microorganisms´ biomass. At the end of the experiment (after 17 days) the soil contained only 0.7 kBq [1,2-14C]-TCA from 76.4 kBq residual 14C-activity determined by combustion method (or 81.2 kBq estimated by substraction of the released 14CO2 from the sample). The major part of the residual 14C-activity of experiments is integrated into radioactive microbial biomass, less than 0.5% radioactivity (ca. 220 Bq) was found as oxalic acid, one of the major degradation products of TCA. Similarly, the available soil water has a huge impact on the degradation of TCA. Obviously, a high soil humidity inhibits aerobic processes which have a humidity


97

remaining radioactivity

100%

22% 25% 29% 39% 50% 79%

80% 60% 40% 20% 0% 0

50

100

150

200

250

300

350

400

time [h]

Fig. 3 Microbial degradation of [1,2-14C]TCA in forest soil: influence of soil humidity at room temperature

optimum (Fig. 3). These observations on microbial TCA biodegradation in the forest soil are contrary to TCA degradation in digester sludge that has been demonstrated by Chen et al. (1999). The actual rates are likely to be dependent on the degree to which soils are aerobic or anaerobic, determined by changes in temperature and precipitation amounts. Whereas the influence of temperature is low, the rate of TCA degradation in detached spruce branches [C1 & C2] may be highly dependent on the presence and activity of epiphyllic microorganisms. When the branches were immersed with cut end into TCA solution in the presence or absence of antibiotics and 14CO2 radioactivity release was measured in the atmosphere, a clear inhibition of TCA metabolism was observed in the branches treated with antibiotics.

Conclusions TCA is a ubiquitous phytotoxic substance, secondary air pollutant, photooxidation product of natural as well as of anthropogenic origin. Although more information has become available recently on the sources of chlorinated hydrocarbons, their reactions in the atmosphere are also important. The role of heterogeneous processes and Cl atoms in the oxidation of chlorinated hydrocarbons is not well known. Many atmospheric degradation processes favour the formation of chloroacetic acids. Dry deposition of gaseous or particulate TCA is expected to be only a small component, with wet deposition representing the most important pathway. As such they affect health state of forests, especially of conifers, because of the high scavenging rates of these canopies. The importance of trichloroacetic acid (TCA) and related compounds (mono- and di-chloroacetates

98


and other halogenoacetates) to terrestrial ecosystems has been clearly described in several recent reviews, and today, the sources of TCA in the environment are already known to a great extent. The largest source is probably forest soil containing humic acids with structures derived from degradation of lignin. TCA uptake from soil by roots and its translocation in spruce is governed by transpiration stream, the highest accumulation is then in current needles, however, the highest content in older ones. Measurements of TCA concentrations in conifer needles represent a balance between uptake rates or internal production, and decomposition or metabolic processing to less dangerous compounds. Consequently, single measurements of TCA concentrations will yield little quantitative information on the different processes. Long term investigations are needed to obtain sound data on the mode of action, detoxification and fate of TCA in plants. Chlorides have previously been considered to be chemically inert substances in the environment, and chlorinated organic compounds were assumed to be of solely anthropogenic origin. But the role of chlorine in the forest ecosystem is much more active and complex than previously thought. Adsorbable organic halogenes in the forest soil indicate their active role in a natural chlorine cycle, in which only a small fraction is absorbed by plants. Despite the large literature on the sources and fate of TCA in plants, there are still many unanswered questions worth of further research.

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Schröder P, Juuti S, Roy S, Sandermann H, Sutinen S (1997) Exposure to chlorinated acetic acids: responses of peroxidase and glutathione S-transferase activity in pine needles. ESPR 4:163–171 Schröder P, Matucha M, Forczek ST, Uhlířová H, Fuksová K, Albrechtová J (2003) Uptake, translocation and fate of trichloroacetic acid in Norway spruce/soil system. Chemosphere 52:437–442 Schröder P, Plümacher J (1998) Evaluation of the impact of volatile chlorinated hydrocarbons on forest trees based on air and needle measurement data and the detoxification capacity of spruce needles. ESPR S1:38–45 Sidebotton H, Franklin J (1996) The atmospheric fate and impact of hydrochloroflurocarbons and chlorinated solvents. Pure & Appl. Chen 68:1757–1769 Sinkkonen S, Rantio T, Paasivirta J, Alanko M, Lahtiperä M (1998) Chlorinated acetic and propionic acids in pine needles from industrial areas. Chemosphere 37:2653–2664 Smith AE (1974) Degradation of trichloroacetic acid in Saskatchewan soils. Soil Biol Biochem 6:201–202 Stidson RT, Dickey CA, Cape JN, Heal KV, Heal MR (2004a) Fluxes and reservoirs of trichloroacetic acid at a forest and moorland catchment. Environ Sci Technol 38:1639–1647 Stidson RT, Heal KV, Dickey CA, Cape JN, Heal MR (2004b) Fluxes and budgets of trichloroacetic acid through a conifer forest canopy. Environ Poll 132:73–84 Süss A, Eben C (1977) Abbau von Atrazin und Nata in verschiedenen Böden. Gesunde Pflanzen 29:188–192 Suntio LR, Shiu WY, Mackay D (1988) A review of the nature and properties of chemicals present in pulp mill effluents. Chemosphere 17:1249–1290 Sutinen S, Juuti S, Koivisto L, Turunen M, Ruuskanen J (1995) The uptake of and structural changes induced by trichloroacetic acid in the needles of Scots pine seedlings. J Exp Bot 46:223–1231 Sutinen S, Juuti S, Ryyppo A (1997) Long-term exposure of Scots pine seedlings to monochloroacetic and trichloroacetic acid: effects on the needles and growth. Ann Bot Fennici 34:265–273 von Sydow et al. (2000) Natural background levels of trifluoracetate in rain and snow. Environ Sci Technol 34:3115–3118 Tuazon EC, Atkinson R, Aschmann SM, Goodman MA, Winer AM (1988) Atmospheric reactions of chloroethenes with the OH radical. Int J Chem Kinet 20:241–265 Uhlířová H, Pasuthová J, Matucha M (1995) Znečištění ovzduší a lesy. II. Chlorované sloučeniny. Zprávy lesnického výzkumu 40:30–35 Uhlířová H, Matucha M, Kretzschmar M, Bubner M (1996) Aufnahme und Verteilung von Trichloressigsaeure in Trieben Norwegischer Fichte. Z Umweltchem Ökotox 8:138–142 Weast RC, Astle MJ, Beyer WH (1987) CRC handbook of chemistry and physics, 67th edn. CRC Press, Boca Raton, FL Weightman AL, Weightman AJ, Slater JH (1992) Microbial dehalogenation of trichloroacetic acid. World J Microbiol Biotechnol 8:512–518 Weissflog L, Manz M, Popp P, Elansky N, Arabov A, Putz E, Schuurmann G (1999) Airborne trichloroacetic acid and its deposition in the catchment area of the Caspian Sea. Environ Poll 104:359–364 Weissflog L, Pfennigsdorff A, Martinez-Pastur G, Puliafito E, Figueroa D, Elansky N, Nikonov V, Putz E, Kruger G, Kellner K (2001) Trichloroacetic acid in the vegetation of polluted and remote areas of both hemispheres – part I. Its formation, uptake and geographical distribution. Atmos Environ 35:4511–4521 Weissflog L, Elansky N, Putz E, Krueger G, Lange CA, Lisitzina L, Pfennigsdorff A (2004) Trichloroacetic acid in the vegetation of polluted and remote areas of both hemispheres – part II: salt lakes as novel sources of natural chlorohydrocarbons. Atmos Environ 38:4197–4204 Weissflog L, Lange CA, Pfennigsdorff A, Kotte K, Elansky N, Lisitzyna L, Putz E, Krueger G (2005) Sediments of salt lakes as a new source of volatile highly chlorinated C1/C2 hydrocarbons. Geophys Res Lett 32:L01401


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Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transboundary Transport – Results of a Case Study with Special Emphasis on the Spatial Distribution of Polycyclic Aromatic and Chlorinated Air Borne Pollutants Rolf Herzig, Christoph Bieri, Andreas Weber, and Peter Straehl Abstract Representative samples of the foliose lichen Parmelia sulcata (Taylor) were collected from 33 spatially distributed measuring sites in Switzerland, representing all relevant sources of air pollution, such as industrial sites, big and mid sized cities and agglomeration, motorized traffic sites, rural and background sites, from plateau to alpine regions and were analysed for a big number of 88 individual chlorinated air pollutants and other volatile POPs from eight different chemical classes: Short chain chlorinated paraffin’ (CFC), chlorobenzenes, polychlorinated biphenyls (PCBs), hexachlorocyclohexanes (HCH), organochlorine pesticides OCPs) polychlorinated dibenzo-p-dioxins and furans (PCDDs/PCDFs) and polycyclic aromatic hydrocarbons (PAHs). Measurements were performed in order to explore patterns of long-range transport of these volatile and persistent air pollutants across Switzerland. Comparison of the air pollution with POPs in areas with different types of landuse indicates that the burden emanating from within Switzerland, particularly in conurbations, is considerably greater than the amount transported over long-ranges and across national boundaries. However, the long-range transport certainly contributes to the background level of contamination.

R. Herzig (*) and C. Bieri AGB, Arbeitsgemeinschaft für Bioindikation, Umweltbeobachtung und ökologische Planung, Quartiergasse 12, CH3013 Bern, Schwitzerland e-mail: [email protected] A. Weber and P. Straehl Bundesamt für Umwelt, Bafu, Berne, Switzerland

P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_6, © Springer Science+Business Media B.V. 2011

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Introduction The primary objective of this first country-wide and spatially differentiated biomonitoring study in Switzerland was to gain an overview of the deposition pattern by as wide a range as possible of persistent organic pollutants (POPs) and by selected volatile organic compounds (Herzig 2002). The substances were selected largely on the basis of the POPs Protocol to the UN/ECE Convention on Long-range Transboundary Air Pollution and the UNEP Convention on Persistent Organic Pollutants. This broad form of screening determined which of the more than 100 evaluated organic and airborne trace substances could be determined quantitatively in lichen samples. POPs which are particularly critical in toxicological terms and which have since long been banned in Switzerland – i.e. polychlorinated biphenyls (PCBs), lindane, DDT and other organochlorine pesticides and additional substances from the chemical classes polycyclic aromatic hydrocarbons (PAHs), chlorofluorocarbons (CFCs), dioxins and furans (PCDD/PCDFs) which are also relevant in terms of air pollution or toxicological impact – were analysed quantitatively with high resolution GC-MS and GC-ECD-techniques, followed upon substance class specific cleanup-techniques.

Material and Methods The “passive biomonitoring of lichen” method using the foliose lichen species Parmelia sulcata (Taylor), was developed in the mid Eighties within the Swiss Priority Programme 14 (Herzig et al. 1989a,b, 1990, Herzig & Urech 1991, Herzig 1993a,b). The samples were collected in the late autumn of 1995. Since monitoring organisms generally have a delayed and cumulative response to environmental deposition, the data recorded can be said to reflect airborne contamination with POPs in the period 1990–1995. Representative measurement sites comprising 33 spatially differentiated locations throughout Switzerland (NABEL monitoring network and other measurement sites) produced a conclusive data base showing the geographical distribution and the load of these POPs. An attempt was also made to identify those POPs whose burden derives predominantly from their being transported over long ranges. The sampling site “Hagen” at 910m on the top of north-eastern Jurassic Mountains, at the border to Germany and the eastern European countries, and the nearby site “Hemmental”, a small village in the valley behind “Hagen”, reflects the main comparison sites to distinguish local from long-range transport (Figs 1 and 2). Since the study locations were assigned to six different land-use categories and comprised everything from towns, agglomerations, industrial and transport sites to rural locations and villages and locations with background contamination, special evaluation methods made it possible to identify the sources of relevant individual POPs.

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Fig. 1 Spatial distribution of PCB-Congeners PCB-28, 52, 101, 153, 138 and 180. Air Pollution load and differentiation of single PCBs among the six land-use categories in Switzerland in 1995. The loss of PCBs at the recycling plant at “Thörishaus/Industry” site could be stopped due to findings of the lichen biomonitoring analysis. Size of the circles is equivalent to concentrations; size of segments is contribution of individual contaminants

Fig. 2 A special analysis of age-dependent sub samples showed that the PCB burden at the most contaminated location, the “Thörishaus/Industry” site, had declined very considerably in the past 4–8 years since work on processing transformers was stopped there. The grey bars represent the initial PCB burden during the transformer recycling (>8 year old sub samples), and the white ones after that procedure have been stopped (4–8 year old sub samples). Obviously, the PCB load quickly declined after the stop of the transformer recycling by more than 50% for each PCB congener

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Results and Discussion Distinct use-dependent and, in many cases, even source-dependent differences in impact were found for all eight of the chemical classes observed and for most of the individual 82 and additional 23 sum parameters of POPs (Table 1):

Short Chain Chlorinated Paraffin’s (CFCs) Analysis of the CFC categories clearly identified industry and small businesses as the source of input into the environment. The method used (Headspace-GC) is not that sensitive, but certainly sensitive enough to pinpoint the loss of fluorotrichloromethane (R11; Freon) solvents from industrial recycling plant at “Thörishaus/ Industry” site.

Chlorobenzenes Similar findings were recorded for chlorobenzenes, with industry and small businesses again proving to be the main source of inputs. Motor vehicles were also identified as a potential source of individual chlorobenzenes (Table 2).

Hexachlorocyclohexanes (HCH) Lindane, one of the persistent organic pollutants whose use is largely banned nowadays, is present in the highest concentrations in the centres of towns and in industrial locations as a result of its former ubiquitous use as a broad-spectrum insecticide (Table 2).

Organochlorine Pesticides (OCPs) This is the first time that the largely banned category of organochlorine pesticides (OCPs) has been investigated, and the survey produced a number of unexpected findings. Strikingly high burden gradients with a factor of 9–11 were found for DDT, it’s degradation product DDD, heptachloroepoxide and endosulphane sulphate. The heaviest burdens of DDT and DDD were found at sampling sites in cities. No adequate explanation for this has been put forward so far. However, evaluations in terms of road traffic also show that concentrations of DDT and DDD

. 33 18 8 14 56 24 26 17 8

. 3842 771 246 2270 428 359 1319 1445 246

. . 756 829 . 564 801 737 120 564 829 . 13 8 4 39 11 26 17 13 4

. . 22 28 . 9 10 17 9 9 28 . 65 33 15 79 37 32 44 24 15

. . 68 56 . 49 31 51 15 31 68 . 96 49 37 162 44 101 82 48 37

. . 86 170 . 75 37 92 56 37 170

. 8.3 . . . . . 8.3 . 8.3

13.0 6.3 5.8 17.5

. 5.8 17.5 15.7 .

(continued)

2-Agglomeration and mid sized cities Allschwil93 4749 Dübendorf-NABEL 3730 Wil-SG 3009 Gossau-SG 1460 Rorschach 1441 Wallisellen-ATAL 1162 Basel-NABEL 1143 Mean 2385 SD 1448 Min 1143

. . 22 53 . 97 103 69 38 22 103

Table 1 Sum parameters of eight POP substance classes: air pollution load, and contamination patterns among the six land-use categories in Switzerland in 1995, obtained by Lichen Biomonitoring ITE (NATO/ SDi-HexacloroSOrgano-chloroCCMS) Sampling site PAH S20 SCFCs benzene SHCHs SPCBs Pestizides Unit ng/kg D.W mg/kg D.W mg/kg D.W mg/kg D.W mg/kg D.W mg/kg D.W mg/kg D.W

7667 7380 6839 3511 2667 2321 1649 4576 2613 1649 7667

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1-Urban sites Base-nord93 Bern-Brunnadern93 Zurich-NABEL St. Gallen-grabenhalle Chur 93 Luzern-löwenplatz Lugano-NABEL Mean SD Min Max


127 165 27 318

2828 1513 1487 5000

4-Motorized traffic outside cities Härkingen/nah-NABEL 2890 Schönbühl-N1 2643 Thörishaus-West 1797 Härkingen/fern1708 NABEL Piotta-N 2 1038 Erstfeld-N 2 946 Mean 1837 SD 801 Min 946 Max 2890

1459 . 2027 259 . . . .

318 . 27 37 . . . .

5000 2482 2343 1487 . . . .

2950 2934 508 340 273 219 1204 1350 219 2950

41 3 80 30 10 18 30 28 3 80

1248 903 259 2027

3842

56

4749

SDi-Hexaclorobenzene mg/kg D.W

Max 3-Industrial sites Thörishaus-industrie Jenaz93 KVA-Bern ZH-Nord-Hagenholz Buchs KVA Niederglatt Dornach-Industrie Rheinfelden-PCPIndustrie Mean SD Min Max

SCFCs mg/kg D.W

PAH S20 mg/kg D.W

Sampling site Unit

Table 1 (continued)

9 11 14 5 9 21

21 21 14 11

20 16 11 38

38 . 11 12 . . . .

39

SHCHs mg/kg D.W

15 22 28 18 8 57

57 38 26 8

93 99 29 207

207 . 42 29 . . . .

79

SPCBs mg/kg D.W

20 42 53 33 20 115

115 46 59 37

75 47 45 130

130 . 45 51 . . . .

162

SOrgano-chloroPestizides mg/kg D.W

6.1 1.3 4.4 7.1

4.4

7.1 7.0 5.0 7.1

20.7 12.8 5.3 42.4

5.3 . 21.3 10.3 32.2 18.2 15.0 42.4

8.3

ITE (NATO/ CCMS) ng/kg D.W

110 R. Herzig et al.

759 565 451 592 127 22 451 759

5-Rural sites Hemmental-SH Tänikon-NABEL Payerne-NABEL Mean SD CV Min Max 10 13 49 24 18 75 10 49

SCFCs 270 538 424 411 110 27 270 538

SDi-Hexaclorobenzene

6-Backgound sites Hagenturm 858 19 593 594 24 89 Forst-NeueneggReferenz Davos-NABEL 507 17 271 Mean 653 20 318 SD 183 4 256 Min 507 17 89 Max 858 24 593 Maximum load: bold; clearly enhanced: bold italics; minimum load: italics

PAH S20

Sampling site

17 4 24 15 10 4 24

8 10 3 8 13

9 31 10 17 10 63 9 31

SPCBs

13 8

11 23 14 16 5 32 11 23

SHCHs

26 25 12 13 36

36 13

23 75 48 49 21 44 23 75

SOrgano-chloroPestizides

3.3 0.1 3.2 3.3

3.2 3.3

3.0 0.4 13.3 2.6 3.4

2.6 3.4

ITE (NATO/ CCMS) Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transport 111

1.1 13.7 7.1 1.2 25.0 20.3 21.1 14.6 18.8 6.6

Hexachlorcyclohexane (HCHs) (mg/kg D.W) a-HCH 1.4 g-HCH (Lindane) 15.7 b-HCH 1.5 d-HCH 0.9

Polychlorineted Biphenyls (PCBs) (mg/kg D.W) PCB 28 4.7 PCB 52 3.6 PCB 101 9.7 PCB 153 13.0 PCB 138 12.7 PCB 180 8.6 3.0 4.3 12.7 10.3 9.2 5.1

1.6 11.5 3.0 1.3 6.2 4.4 7.4 3.7 6.4 4.0

1.4 11.2 1.9 0.6

3.5 2.1 8.5 3.1 1.9 1.2

2.1 12.8 2.7 0.3

3.3 2.6 3.6 3.0 2.5 2.0

0.9 9.3 2.2 0.4

8.2 9.9 5.8 4.8 9.7 7.4

2.3 1.7 4.8 4.8

Table 2 Air pollution load and differentiation of single POPs of chlorobenzenes, HCHs, PCBs and organochlorine pesticides among the six land-use categories in Switzerland in 1995, obtained by Lichen Biomonitoring Motorized Agglomeration traffic outside Background and mid sized Industrial Gradient of air cities Rural sites sites cities Urban sites sites pollution Site category S1 S3 S2 S4 S5 S6 Max/min Chlorobenzenes (CBs) (mg/kg D.W) 1,3-Dichlorobenzene 87 125 205 251 69 133 3.7 1,4-Dichlorobenzene 295 417 625 528 171 148 4.2 1,2-Dichlorobenzene 224 178 296 219 92 71 4.1 1,3,5 -Trichlorobenzene 34 235 154 100 29 33 8.0 1,2,4 -Trichlorobenzene 37 130 39 54 16 26 8.2 1,2,3 -Trichlorobenzene 35 128 44 101 11 37 11.6 1,2,4,5-Tetrachlorobenzene 10.0 13.0 33.3 26.9 11.0 6.2 5.4 1,2,3,5-Tetrachlorobenzene 9.5 8.8 2.8 14.6 2.0 7.2 1,2,3,4-Tetrachlorobenzene 36.8 87.2 52.4 32.3 28.1 17.8 4.9 Pentachlorobenzene (QCB) 8.8 9.9 9.5 8.6 2.7 2.4 4.1 Hexachlorobenzene (HCB) 1.3 2.9 2.7 2.5 2.3 2.0 2.2


1.7 6.9 8.1 8.0 4.9 13.3 5.3 2.9 3.0 7.5 4.9 13.2 12.2 2.8

1.9 2.0 3.6 4.6 5.7 7.1 5.7 10.5 4.5 21.9 4.5 8.3 6.1 2.9

Sum Parameters (mg/kg D.W)  Di- Hexachlorobenzenes 737 1269 1319  HCHs 17.1 22.2 16.8  PCBs 51.0 103.9 43.5  organochloro-Pestizides 92.0 80.7 81.5 Maximum load: bold; clearly enhanced: bold italics; minimum load: italics

Organochloro-Pestizides (OCPs) (mg/kg D.W) Heptachlor 0.7 Aldrin 2.1 Heptachlorepoxid 2.3 Endosulfan I 6.4 p,p-DDE 5.9 Dieldrin 5.3 Endrin 3.4 p,p-DDD 12.3 Endosulfan II 2.7 p,p-DDT 36.3 Endrin Aldehyd 4.3 Endosulfan Sulfat 1.4 Endrin Keton 9.8 Methoxychlor 4.7 1204 14.2 27.6 52.9

1.7 2.4 10.0 5.8 4.7 4.7 2.6 2.3 1.8 8.4 6.1 5.7 4.6 2.1 411 15.7 16.5 48.5

0.7 1.1 11.4 6.7 3.2 3.2 10.7 3.0 2.7 4.9 3.2 7.8 3.9 1.7 324 9.8 14.1 25.0

0.4 1.9 1.2 3.7 2.1 2.1 3.6 1.4 1.6 3.3 1.0 4.4 3.6 1.4 4.1 2.3 7.4 3.7

4.7 6.2 9.7 2.1 2.8 6.2 4.1 8.8 2.8 10.9 6.3 9.2 3.4 3.4

Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transport 113

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are similarly high at roadsides and in inner-city locations. It is difficult to explain this pattern of distribution in terms of the analyses that have been carried out. Verification analyses of selected organochlorine pesticides in all the archived samples using HR-GCMS, a technique that gives a very high resolution, have already shown very good agreement with the GC-ECD measurements recorded in the first round of analysis (Table 2).

Polychlorinated Biphenyls (PCBs) There are also very large differences in the burdens of PCBs which correlate closely with industrial locations (Table 2, Fig. 1). A special analysis of age- dependent subsamples showed that the PCB burden at the most contaminated location, the “Thörishaus/Industry” site, had declined very considerably in the past 4–8 years since recycling work on processing of old transformers was stopped there. This novel approach to “archiving” allows more detailed conclusions about the extent of the burden over time to be drawn from recent lichen samples. This technique can be used in future for a more detailed analysis of the burden of other POPs (Fig. 2).

Polychlorinated Dibenzo-p-Dioxins and Furans (PCDDs/PCDFs) Evaluations of the polychlorinated dioxins and furans (PCDD/PCDFs) as a function of land-use category show a clear relationship between the burden and industrial sources, including waste incineration facilities (Table 3). Figure 3 shows the air pollution load and spatial distribution of the Toxicity Equivalent TEQ, obtained for the dioxins and furans congeners at the sites of the six land-use categories in Switzerland in 1995. Obviously the map shows a clear relationship between the burden and industrial sources (Rheinfelden, Niederglatt), including domestic waste incineration facilities, without the highest efficient filter systems (Buchs, Berne), and the forbidden waste disposal by cheminee-fireing. The latter is represented by a very residential quarter of city of Berne (Brunnadern). It’s well known, that up to 50% of the actual PCDD-emissions comes from that forbidden waste disposal. Maps showing the geographical distribution of the burden point to only moderate levels of contamination in typical transport-related locations outside large settlements. However, a special evaluation of transport parameters as a function of the most conclusive criterion – distance from the road – paints a rather different picture. With very few exceptions, all the maximum values for dioxins and furans were recorded at the measurement points closest to roads (1–3 m), and the values were still distinctly elevated at points between 4 and 10 m from the road. The mean burdens for these roadside locations are in most cases as high as the maximum

Dibenzo-p-Dioxins (PCDD) (ng/kg D.W)  TetraCDD 32  PentaCDD 38  HexaCDD 84  HeptaCDD 200 OctaCDD 478  Tetra-OctaCDD 832 36 44 89 226 2739 3133 21 24 46 80 173 343

19 22 35 58 107 241

12 12 19 26 53 122

14 14 22 43 103 194

(continued)

3.0 3.7 4.7 8.5 51.5 25.7

Table 3 Air pollution load and differentiation of single Dibenzo-p-Dioxins and Furans and sum parameters among the 6 land-use categories in Switzerland in 1995. “Industrial sites”, clearly represents the strongest impact for most of the dioxins and furans Motorized Agglomeration traffic outside Background and mid sized Industrial Gradient of cities Rural sites sites cities Urban sites sites air pollution Site category S1 S3 S2 S4 S5 S6 Max/min Dibenzo-p-Furans (PCDF) (ng/kg D.W)  TetraCDF 143 163 84 97 56 72 2.9  PentaCDF 100 139 72 51 32 34 4.3  HexaCDF 86 122 55 29 17 18 7.3  HeptaCDF 114 89 40 18 11 11 10.8 OctaCDF 163 112 45 31 21 23 7.7  Tetra-OctaCDF 605 624 296 226 137 157 4.6 2378-TetraCDF 9.0 10.3 5.5 5.4 3.3 2.5 4.1 12378-/12348-PentaCDF a 7.2 15.1 5.3 3.9 2.4 2.3 6.5 23478-PentaCDF 6.8 10.2 5.1 3.5 1.9 1.9 5.3 123478-123479-HexaCDF a 8.9 26.6 6.3 3.6 1.7 2.2 15.3 123678-HexaCDF 6.6 11.4 4.9 2.9 1.4 1.7 8.0 123789-HexaCDF 0.8 1.1 0.6 0.6 0.2 0.2 7.3 234678-HexaCDF 8.3 12.6 6.0 3.5 1.7 2.2 7.3 1234678-HexaCDF 63.2 61.2 27.7 12.3 7.1 7.0 9.0 1234789-HeptaCDF 9.1 8.0 2.6 1.6 0.7 1.0 12.6

Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transport 115

0.7 2.0 3.3 6.9 5.4 111 205

13.9 13.0

Site category

2378-TetraCDD 12378-PentaCDD 123478-HexaCDD 123678-HexaCDD 123789-HexaCDD 12346789-HeptaCDD  Tetra-OctaCDF/D

TE (BGA) excl. Det. Lim. ITE (NATO/CCMS) excl. Det. Lim. 8.4 8.3

0.5 1.6 1.9 3.5 3.0 40 32

1.1 3.7 4.8 7.5 7.6 113 1879 21.5 21.4

Agglomeration and mid sized cities S2

Industrial sites S3

Maximum load: bold; clearly enhanced: bold italics; minimum load: italics

Urban sites S1

Table 3 (continued)

6.3 5.9

0.4 1.6 1.3 2.8 2.4 29 156

Motorized traffic outside cities S4

3.4 3.0

0.2 0.6 0.6 1.3 1.0 13 57

Rural sites S5

3.8 3.2

0.2 0.8 0.9 1.4 1.3 20 88

Background sites S6

6.2 7.1

5.4 5.9 7.5 6.0 7.8 8.8 58.8

Gradient of air pollution Max/min



117

Fig. 3 Air pollution load and spatial distribution of the Toxicity Equivalent TEQ obtained for the dioxins and furans congeners at the sites of the six land-use categories in Switzerland in 1995. Obviously the map shows a clear relationship between the burden and industrial sources (Rheinfelden, Niederglatt), including waste incineration facilities, without the highest efficient filter systems (Buchs, Berne), and the forbidden waste disposal by cheminee-fireing. The latter is represented by a very residential quarter of city of Berne (Brunnadern). Size of the circles is equivalent to concentrations; size of segments is contribution of individual contaminant

values recorded for industrial locations. It can be concluded from these findings that motor vehicles evidently have to be considered as a potential source of dioxins and furans, certainly up to 1999 when the leaded petrol that was still in use contained chlorine-based scavengers.

Polycyclic Aromatic Hydrocarbons (PAHs) There are strikingly high impact gradients between areas of maximum and minimum burden for PAHs bound to dust particles; these are of a factor of 10–20 for several PAHs, some of which have a carcinogenic and mutagenic effect. The highest PAH burdens are found at measurement points in the centres of large towns and, more particularly, in poorly ventilated sites (Fig. 4). However, more refined evaluations taking into account the distance between the sampling point and the nearest road showed that motor vehicles accounted for a significant proportion of this PAH burden (Fig. 5). A follow-up study in the direct vicinity of the transalpine highway A2 through Switzerland already showed a very clear relationship between the density of

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R. Herzig et al.

Fig. 4 Air pollution load and spatial distribution of three PAHs with both, carcinogenic and mutagenic activity: Benzo(a)anthracene, Benzo(b)fluoranthene, and Benzo(a)pyrene at the sites of the six land-use categories in Switzerland in 1995. Most elevated PAH concentrations were clearly found in the direct vicinity of intense motorized traffic. Size of the circles is equivalent to concentrations; size of segments is contribution of individual contaminants

otorized traffic, and specially of heavy duty vehicles and the PAH contamination m analysed in the lichen samples at 23 different locations all along the highway between Basel and Chiasso (Herzig 2007, 08).

Conclusion Comparison of the POP burden in areas with different types of land-use shows that the burden emanating from within Switzerland, particularly in conurbations, is considerably greater than the amount transported over long-ranges and across national boundaries. However, the latter category certainly contributes to the background level of contamination. Switzerland signed the Protocol on Persistent Organic Pollutants (POPs) to the UN/ECE Convention in November 2000, and in May 2001 also signed the global UNEP Convention on POPs in Stockholm. These documents place a ban on the production and use of 16 (UN/ECE) and 12 (UNEP) POPs of particular environmental relevance or subject them to limits on production, use and emissions. The first country-wide POP study established a representative and spatially differentiated biomonitoring network and a reference archive of samples in Switzerland.


119

Fig. 5 Overall influence of land use and traffic on the air born contamination of PAHs in lichen samples. The upper 3D-table shows with higher accumulated PAHs, whereas the lower shows the individual PAHs with smaller concentration. Urban sites, include intense traffic, directly followed “Street Distance 3.5 show high sorption to the roots, but slow or no translocation to stems and leaves. Nevertheless, plants readily take up organic xenobiotics with a log Kow between 0.5 and 3.5, as well as weak electrolytes (weak acids and bases or amphoteres as herbicides) (Barac et al. 2004). The metabolism of these pollutants may occur in the leaf and stem tissue; or they may be released into the atmosphere through leaf tissue or incorporated as bound residue inside the plant (Barac et al. 2004; Schröder 2007). When the pollutants make contact with the root surface, the plant uptake is diffusion driven for compounds with lipophilicity close to that of the respective plant root (which depends on the root epidermis). The root uptake and transport of organic xenobiotics can be evaluated by a root concentration factor (RCF) that is dependent on the log Kow; for example, for barley the following relationship has been proposed (Briggs et al. 1982; Schröder 2007):

Log (RCF − 0.82 ) = 0.77logK ow − 1.52 Phytoremediation of organic compounds can occur inside the plant or within the rhizosphere, as already mentioned. Many organic compounds such as solvents (trichloroethylene and ethylene dibromide) in groundwater, petroleum and aromatic compounds (pesticides, explosive compounds) in soils and volatile compounds in the air are removed by either process (Newman and Reynolds 2004).

Using Plants to Remove Foreign Compounds

153

The ability of plants to remove pollutants from wastewater has been thoroughly studied: a wide range of plants has this aptitude and is systematically selected for integration in CW systems. The vast majority of effluents treated by CW are domestic wastewaters, using hybrid systems in order to achieve both nitrification and denitrification processes; nevertheless, for secondary and tertiary treatment stages, horizontal (HF) or VF can be used. The more recent research aims to address the phytoremediation of trace organic compounds, such as pharmaceuticals and personal care products, also called micropollutants (Matamoros and Bayona 2006). CW are also increasingly applied in the treatment of industrial wastewaters such as dye-rich wastewaters (Mbuligwe 2005; Bulc and Ojstrsek 2008). For example, in Portugal there are two full scale systems in operation: one for aromatic and nitroaromatic effluents [4 VF (2625 m2 × 0.8 m)] (Haberl et al. 2003), and the other for effluents from a municipal solid waste transfer station and a closed landfill [1 VF (100 m2 × 0.7 m) + 2 HF (150 m2 × 0.7 m) + 1 lagoon (100 m3)] (Silva et al. 2003). Other applications include the treatment of acid mine drainage (full scale in USA), with the particularity of using mushroom compost and fermway as bed matrix (Mitsch and Wise 1998), BTEX and benzene (full scale, USA) (Wallace and Kadlec 2005) and phenol from papermill wastewaters (pilot scale in Kenya) (Abira et al. 2005). Other major applications are for runoff water and groundwater contamination (Scholes et al. 1999; Haberl et al. 2003). The most common bed matrix materials used in CW are sand, gravel, stones, and clay soils. Another approach that does not use soil for plant growth is hydroponic systems, which can be used to remove e.g. sulphonated anthraquinones and MTBE (Aubert and Schwitzguébel 2004; Ma et al. 2004). In the first case, plant metabolism is highlighted, whereas phytovolatilization is the main process for the removal of MTBE.

Sulphonated Aromatic Compounds in Wastewater The total world colourant production is estimated to be between 800,000 and 1 million tons per year (Heinfling et al. 1998; Mendez-Paz et al. 2005). More than 10% of this amount is released into the environment, mostly via industrial effluents. A huge variety of synthetic dye chemicals is used for textile dyeing and other industrial applications. Dye and textile industry effluents thus exhibit high colour, high suspended solids and dissolved organics. Except for colour, the other components can be removed using chemical and physical methods (Benkli et al. 2005). Synthetic sulphonated anthraquinones are very important starting material to produce a large palette of dyes (Fig. 1), and this family of compounds has a potential and actual impact on the environment. Not only sulphonated anthraquinones, but also many other sulphonated aromatic compounds are released into the environment, mainly via industrial wastewaters. For example, benzene sulphonate and p-toluene sulphonate are used as intermediates in the manufacture of optical brighteners, pickling agents, dyestuffs, tanning agents,

154

J.-P. Schwitzguébel et al. O 7 6

O

8

1

5

4

O

1

2 3

O AQ

O

SO3H

2

O AQ-1-S

SO3H 1

HO3S

O

O AQ-2-S

O

SO3H

8

1

2

SO3H

6

5

O HO3S AQ-1,5-SS

SO3H

HO3S O

AQ-1,8-SS

O

AQ-2,6-SS

Fig. 1 Chemical structure of anthraquinone and sulphonated derivatives; IUPAC names of the compounds: AQ-1-S: 9,10-dioxo-9,10-dihydro-1-anthracenesulphonic acid; AQ-2-S: 9,10-dioxo-9, 10-dihydro-2-anthracenesulphonic acid; AQ-1,8-SS: 10-dioxo-9,10-dihydro-1,8-anthracenedisulphonic acid; AQ-1,5-SS: 10-dioxo-9,10-dihydro-1,5-anthracenedisulphonic acid; AQ-2,6-SS: 10-dioxo-9, 10-dihydro-2,6-anthracenedisulphonic acid

insecticides, surfactants, antioxidants, wetting agents, and many other products. They are also used as acidic catalysts and standardizing agents in dyestuff manufacture. As with naphthalene sulphonic acids, they are important precursors for dye intermediates, wetting agents and dispersants.

Limits of Microbial Degradability Since they contain at least one sulphonate group and often also varying substitutions such as nitro groups, these foreign compounds are not uniformly susceptible to bacterial decolourisation and degradation in conventional wastewater treatment plants. The organosulphonate group plays an important role not only in altering the solubility and dispersion properties of the xenobiotic molecule, but also in increasing recalcitrance to microbial breakdown, because of the thermodynamically stable carbon-sulphur bond (Cook et al. 1999; Nigam et al. 2000). Effluents from detergent, dye and textile industry are thus often contaminated with sulphonated aromatic compounds. These loads are major sources of sulphur-organic pollutants to the environment, especially fresh water (Greim et al. 1994; Schwitzguébel et al. 2002). It has also been reported that benzene- and naphthalene-sulphonates are found in plumes and leachates from landfills (Riediker et al. 2000). Because dyes usually contain a wide variety of substituted sulphonated aromatic compounds, the substrate spectrum of many bacteria has thus been intensively investigated, for possible treatment of such industrial effluents. However, the microbial degradation of these pollutants often requires unusual catabolic activities rarely


155

found in a single species (Cook et al. 1999; McMullan et al. 2001). An important step in the biodegradation appears to be catalyzed by dioxygenases, adding oxygen across the double bond bearing the sulphonate group, leading to its elimination. Special attention has thus been paid to the substrate specificity of dioxygenases, usually the rate-limiting step in the biodegradation of sulphonated aromatic compounds. Unfortunately, a rather limited substrate range has been observed for bacterial isolates and the accumulation of dead-end products often occurs. The decolourisation of several synthetic dyes including azo and anthraquinone derivatives has also been examined in white-rot fungal cultures, known to produce powerful ligninase and other peroxidases (POD) (Claus et al. 2002; Nyanhongo et al. 2002; Wesenberg et al. 2003; Vanhulle et al. 2007). However an inhibition occurs at rather low concentrations, depending on the individual dye structure. Because of the limited ability of microorganisms to degrade sulphonoaromatic compounds, conventional wastewater treatment plants and biofilters are usually ineffective in managing this significant class of pollutants.

Potential of Phytotreatment In this context, the development of alternative biological treatments to efficiently eliminate these pollutants from industrial effluents is needed (Vandevivere et al. 1998; Robinson et al. 2001; Schwitzguébel et al. 2002). More precisely, selected plant species could remove xenobiotics from wastewater, by the use of CW (Biddlestone et al. 1991; Davies and Cottingham 1994; Haberl et al. 2003; Ojstrsek et al. 2007), or hydroponic type treatment plants (Furukawa and Fujita 1993). Both options offer a potentially low cost, low maintenance biological method for wastewater treatment. Most of the systems currently in use have been designed to treat domestic wastewater, but have a great potential to treat industrial effluents containing recalcitrant organics such as priority pollutants and dyes (Davies and Cottingham 1994; Haberl et al. 2003; Ojstrsek et al. 2007). Anthraquinones naturally occur in several plant genera like Rheum, Rumex, Cinchona, Galium, Morinda and Rubia (Van der Plaas et al. 1998; Demirezer et al. 2001; Matsuda et al. 2001; Han et al. 2002; Morimoto et al. 2002). Furthermore, the biosynthetic pathways of natural anthraquinones, often glycosylated, have been recently unravelled and several enzymes involved in the process characterized (Khouri and Ibrahim 1987; Han et al. 2002). It has therefore been assumed that the hardy rhubarb (Rheum palmatum) might possess enzymes capable of transforming sulphonated anthraquinones and could be harnessed to treat wastewater from the dye, textile and detergent industries. As a first step, cells have been isolated from rhubarb and grown in bioreactors in the presence of anthraquinones with sulphonate groups in different positions (Fig. 1): AQ-1-S totally disappears from the medium and a phytotransformation occurs, but not desulphonation; AQ-2-S is partially but rapidly taken up by rhubarb cells and desulphonated; AQ-1,5-SS and AQ-1,8-SS rapidly disappear from the medium, and no intermediates are released (Schwitzguébel et al. 2002; Schwitzguébel and Vanek 2003).

156

J.-P. Schwitzguébel et al.

The ability of rhubarb cells to accumulate, transform and/or degrade other sulphonated aromatic compounds, all being by-products and pollutants present in industrial effluents has also been investigated: 2-hydroxy-4-sulpho1-naphthalenediazonium is completely removed and transformed; 2-hydroxy-4 -sulpho-6-nitro-1-naphthalenediazonium is accumulated and desulphonated; 7-nitro-1,3-naphthalene-disulphonic acid, 7-amino-1,3-naphthalenedisulphonic acid and 2-chloro-5-nitro benzene sulphonic acid are taken up and transformed by rhubarb cells (Duc et al 1999; Schwitzguébel et al. 2002; Schwitzguébel and Vanek 2003). Even if plant cell cultivation in shake flasks or in bioreactors is a useful and valuable tool for a first assessment of the ability of a plant species to deal with a pollutant, such systems are too expensive and too fragile for large scale wastewater treatment. The use of whole plants cultivated under hydroponic conditions or in CW is thus required. Plant species or cultivars used in any phytoremediation or rhizofiltration process must be tolerant to the pollutants to be treated. For optimal applications, the capability of whole plants to germinate, grow, and develop in the presence of amounts comparable to those found in industrial effluents must be investigated. Therefore, different species have been screened against several sulphonated aromatic pollutants of concern. Not only rhubarb (Fig. 2), but also other plants producing anthraquinones have been grown under hydroponic conditions, like Rumex hydrolapatum and R. acetosa.

Fig. 2 Rheum palmatum and Rumex hydrolapatum cultivated under hydroponic conditions in a greenhouse


157

Table 1 Removal of various sulphonated anthraquinones by rhubarb (Valentine cultivar) and by maize cultivated under hydroponic conditions. The percentage of each sulphonated anthraquinone removed was measured and the transpiration stream concentration factor (TSCF) estimated 7 days after the simultaneous addition of 500 mmol l−1 of each sulphonated anthraquinone in the liquid medium Sulphonated Rhubarb Maize anthraquinone (% removal) Max. TSCF (% removal) Max. TSCF AQ-1-S 55 2.3 23 0.4 AQ-2-S 55 1.3 26 0.5 AQ-1,5-SS 45 0.9 19 0.4 AQ-1,8-SS 45 0.8 20 0.3 AQ-2,6-SS 48 2.4 21 0.4

As a comparison, plants not producing anthraquinones, like maize (Zea mays), rape (Brassica napus) and celery (Apium graveolens) have also been grown in the presence of different sulphonated anthraquinones. The most efficient plant to remove these xenobiotic compounds is the Valentine cultivar of rhubarb (Aubert and Schwitzguébel 2002, 2004; Schwitzguébel et al. 2008). Whereas rhubarb was able to remove 45–55% of all sulphonated anthraquinones added simultaneously to the cultivation medium within 7 days, maize could remove only 19–26% of these xenobiotics (Table 1). However, the disappearance of a pollutant from the medium does not mean automatically that it is accumulated by the plant. Therefore, the transpiration stream concentration factor (TSCF) was estimated for both plants and was found to be between 0.8 and 2.4 for rhubarb and 0.3–0.5 only for maize, suggesting a significant accumulation in the former plant species and cultivar (Table 1). The next step was to investigate any possible metabolism and degradation by the plant and its different organs. As measured by capillary electrophoresis, several sulphonated anthraquinones were found in leaves of rhubarb and R. hydrolapatum (Aubert and Schwitzguébel 2002). On the other hand, results obtained with different and complementary approaches suggest that apoplasmic storage plays a significant role in the phytoaccumulation of at least AQ-1,5-SS (Schwitzguébel et al. 2008). All these features indicate that these xenobiotics are taken up from the medium and translocated to the leaves. As compared to leaf extracts from plants cultivated in the absence of sulphonated anthraquinones, new metabolites appeared in leaf extracts from plants cultivated in the presence of these xenobiotics, suggesting that at least some of them were transformed by both plant species. Furthermore, the profile of metabolites produced depends on the plant used, highlighting the importance of a careful screening of plant species, ecotypes or cultivars before any application to phytoremediation. As shown in Fig. 3, the detoxification of xenobiotics in plant cells can follow three different pathways: vacuolar compartmentation without metabolism: conjugation to glutathione catalysed by glutathione-S-transferases, followed by accumulation of the conjugate in the vacuole (Coleman et al. 1997); transformation (hydroxylation) by microsomal cytochrome P450 monooxygenases (Gordeziani et al. 1999; Stiborova et al. 2000; Werck-Reichhart et al. 2000; Morant et al. 2003; Isin and Guengerich 2007),

158


Fig. 3 Classical detoxification pathways of xenobiotics in plant cells

followed by glycosylation catalysed by glycosyltransferases (Pflugmacher and Sandermann 1998; Jones and Vogt 2001; Lim et al. 2002; Messner et al. 2003: Taguchi et al. 2003), then by accumulation either in the vacuole or in the cell wall as bound residues (Coleman et al. 1997). All pathways have been explored for sulphonated anthraquinones. It appears that direct accumulation in the vacuole or conjugation to glutathione play only a minor role, if any, in the detoxification of sulphonated anthraquinones (Schwitzguébel et al. 2008). In contrast, cytochromes P450 monooxygenases are able to accept different sulphonated anthraquinones as substrates (Page and Schwitzguébel 2009a and b). It has been reported that the transformation of hydroxy-9,10-anthraquinones is mediated by POD isolated from horseradish and from Senna angustifolia (Arrieta-Baez et al. 2002); the possible involvement of POD in the metabolism of sulphonated anthraquinones by anthraquinone-producing plants remains however unknown. On the other hand, natural anthraquinones are usually glycosylated (Khouri and Ibrahim 1987; Van der Plaas et al. 1998). Anthraquinone-producing plants could also glycosylate transformed and desulphonated synthetic anthraquinones. In such a case, there would be cross talks between secondary metabolism and detoxification mechanisms of foreign compounds (Singer et al. 2003; Wink 2003). Finally, the activity of enzymes like cytochrome P450 or glutathione transferase should have some effects on the maintenance of the plant cell redox homeostasis. The regulation of the redox status is of utmost importance for the plant and is closely related to the mitochondrial processes, also involved in maintaining the energy status. Redox and energy balance are important regulatory parameters in determining the relative flux of metabolites through the anabolic and catabolic pathways. Environmental factors, like xenobiotic compounds and the need for the plant to detoxify them if present in different organs, will undoubtedly disturb the whole redox and energy balance. For the efficiency of any phytoremediation


159

process it is thus important to determine the critical threshold above which the efficiency of the process declines and the plant will suffer from stress conditions. In addition to cytochromes P450 in phase I are POD (Burken 2003), heme containing monomeric glycoproteins widely distributed in nature and easily extracted from most plant cells (Azevedo et al. 2003). Plant POD are mainly located in the cell walls and vacuoles and are associated with non-specific oxidative polymerisation of phenolic compounds in the cell wall to produce suberin and lignin, contributing to the improvement of the mechanical defence in plant tissues (Pflugmacher et al. 1999; Sandermann 2004). POD are involved in the general plant defence mechanism scavenging reactive oxygen species (ROS) formed during several stress conditions (wounding, exposure to sulphur compounds, xenobiotics or heavy metals) beyond those produced during normal plant metabolic activity (Passardi et al. 2005). They use H2O2 or O2 as the oxidant to catalyse a number of oxidative reactions, using a wide variety of organic and inorganic compounds (Veitch 2004). Plant contact with anthropogenic xenobiotics is directly related to plant endogenous capacity to produce ROS by the adjustments of photosystems I and II in chloroplast thylakoids, thus ROS such as superoxide ion (O2−), hydrogen peroxide (H2O2), hydroxyl radical (OH−) and singlet oxygen (1O2) are produced (Asada 2006). Under optimal growth conditions, the ROS production in cells is estimated at a constant rate of 240 mMs−1 O2−, and a steady state level of 0.5 mM H2O2. Under biotic and abiotic stress conditions cells enhance the production of ROS up to 720 mMs−1 O2− and 5–15 mM H2O2 (Mittler 2002). The imbalance between the plants capability of production and elimination of ROS leads to oxidative plant cell damage by inactivation of biomolecules (e.g. DNA mutation) or by initiation of chain reactions (lipid auto-oxidation), which can lead to tissue necrosis, senescence/ ageing processes and cell death (Kochhar and Kochhar 2005). ROS over-production signals to the plants the need to eliminate the xenobiotic from its tissues. Plant detoxification pathways comprise several metabolic functions, known as the “green-liver model” (Fig. 3) and the activation of antioxidant enzymes (Fig. 4). Superoxide dismutase (SOD) is a first line defence enzyme that converts ROS formed by the presence of the pollutant into H2O2 that will be converted into water and oxygen by the action of POD, catalase (CAT) and ascorbate peroxidase (APX). Thus, the balance between these enzymes is very important as H2O2 can easily cross biological membranes. If H2O2 enters the cell cytoplasm and reaches the nucleus it reacts with intracellular metal ions (e.g. Fenton reaction) to give OH− that is responsible for DNA site-specific attack (Mittler and Zilinskas 2003) and the initiation membrane self-perpetuating lipid peroxidation (Havaux 2003). The ascorbate-glutathione cycle (Fig. 4b) is important and has two main functions in plant cells: H2O2 detoxification and glutathione (GSH) regeneration (Asada 2006) allowing the conjugation with the pollutant and therefore its enzymatic degradation or transformation to less toxic compounds (Fig. 3), in order to sequestrate them in plant cell vacuoles or to bind them to insoluble cellular structures by covalent associations with macromolecules in the cell wall (lignin, hemicelluloses, protein, cellulose or pectin) or exudation of the conjugates to the rhizosphere (Coleman et al. 1997; Susarla et al. 2002; Harvey et al. 2002; Schröder et al. 2007).

160


Fig. 4 Main cellular pathways for elimination of ROS in plants. (a) Water-water cycle in chloroplasts. (b) Ascorbate-glutathione cycle in the stroma, cytosol, mitochondria and apoplast. (c) Glutathione peroxidase (GPX) and its glutathione (GSH) regenerating cycle in the cytosol. (d) Catalase (CAT) in the peroxisomes and POD in the cell wall, cytosol and vacuoles. SOD – Superoxide dismutase; tAPX – thylakoid-bound ascorbate peroxidase; ASC – Ascorbate; GSH and GSSG – Reduced and oxidized glutathione; DHA – Dehydroascorbate; DHAR – Dehydroascorbate reductase; MDA – Monodehydroascorbate; MDAR – Monodehydroascorbate reductase; GR – glutathione reductase; PSI – photosystem I; Fd – Ferredoxin; e− – electron

Azo Dyes in Industrial Effluents Azo dyes production is estimated to be 60–70% of the annual colourant world production and about 10% of these are lost to domestic and industrial wastewaters (Bandara et al. 1996; Kim and Shoda 1999; Coughlin et al. 2002; Scheeren et al. 2002; Ahlström et al. 2005; Franciscon et al. 2009). The most important azo dye precursors are phenol, anhydride phthalic, aniline, nitrobenzene, salicylic acid, p-nitroaniline, p-chloronitrobenzene, b-naphthol and dimethylaniline (Gregory 1994). Usually they are being produced far from dye application sites and so their contribution to dyestuff environmental pollution is not considered.


161

Azo dyes have been extensively used in textile (cotton dye), printing, leather and cosmetics industries. Textile effluents poses a threaten to environment wherever this industry is located due to its high salinity, dyes content, which have high water solubility and low degradability: high colour (3,000–4,500 ADMI units), high COD (800–1,600 mg l−1), alkaline pH (9–11) and high total solids content (6,000–7,000 mg l−1) (Manu and Chaudhari 2002). The presence of very small amounts of dyes in water (less than 1 ppm for some dyes) is highly visible and affects the aesthetic water transparency and gas solubility in lakes, rivers and other water bodies (Banat et al. 1996; Sanroman et al. 2004). Azo dyes pass through municipal wastewater treatment plants nearly unchanged due to their resistance to aerobic degradation (Méndez-Paz et al. 2005; Hailei et al. 2009). Azo dyes have one or more azo bonds (-N=N-) and the structural diversity of dyes derives from the use of different chromophoric groups (e.g. azo, anthraquinone, triarylmethane and phthalocyanine groups) and different application technologies (e.g. reactive, direct, disperse and vat dyeing). On the reactive dyes (azo dyes and anthraquinones), the reactive site reacts with the functional group on fibre to bind dye covalently under the influence of heat and alkaline pH to cotton, wool, silk and nylon; direct dyes (azo dyes) are applied using neutral or slightly alkaline baths containing additional electrolyte to cotton, rayon, paper, leather and nylon; disperse (azo dyes and anthraquinones) uses a fine aqueous dispersion that is often applied by high temperature-pressure or lower carrier methods to polyester, polyamide to acetate, acrylic and plastic; vat dyeing (anthraquinones) waterinsoluble dyes solubilised by reducing with sodium hydrosulphite applied to cotton, rayon and wool (O’Neill et al. 1999). Approximately 75% of the dyes discharged by textile processing industries belong to the classes of reactive (35%), acid (25%) and direct (15%) dyes (Franciscon et al. 2009). Textile factories use a high volume of water in each step of the process; cleaning, scouring, bleaching and dyeing. However dyeing, desizing and scouring processes are the major sources of water pollution (Manu and Chaudhari 2002). Around 1,000 mg l−1 of dye is present in a typical dye bath; however it is estimated that as much as 40% of the initial dyes remains unfixed and ends up in the textile wastewaters. In textile industry about 40–65 L of wastewater are produced per each kg of cloth produced (Manu and Chaudhari 2002). In India, an average textile mill produces 60 × 104 m of fabric and discharges approximately 1.5 × 106 l of effluent per day (Patil et al. 2009). A wide variety of dyes is used to fulfil the production orders request (Inthorn et al. 2004). Some examples are provided in Table 2. The log BCF, log Kow and Koc have been estimated for several azo dyes using EPI Suite programme developed by EPA office and are presented in Table 2. A log BCF of 0.5 indicates that in general they are biodegradable, and therefore, green technologies such as CW that exploit plant and microbial enzymatic interactions are suitable for their treatment. The major concern rely on biodegradation of Acid Dark Blue 5R and GR. Regarding Koc the higher values are also for the couple mentioned compounds, which indicates less mobile compounds, which adsorbs preferentially onto soil organic matter with estimated biotransformation half-lives of 6 up to 22 days (Reinhard and Drefahl 1999).

MW 534

452

551

327

350

Name CAS / C.I. Tartrazine / Acid Yellow 23 1934-21-0 / 19140

Sunset Yellow / Food Yellow 3 2783-94-0 / 15985

Fast Yellow/ Acid Yellow 17 6359-98-4 / 18965

Methyl Orange/ Acid Orange 52 547-58-0 / 13025

Orange I/ Acid Orange 20 523-44-4/14600

Structure

Log BCF 0.5

0.5

0.5

0.5

0.5

-1.18

-0.84

-0.66

-0.14

-6.74

Log Kow

1.45

0.93

0.87

0.87

-2.98

Koc

++

+++

++

+

h (%) +

Table 2 In-vitro colour removal efficiency by Phragmites sp. leaves crude extract, h ; + (0-25%), ++ (26-50%), ++++ (51-75%), ++++ (76-100%); C.I. - Colour Index; MW - Molecular Weight.

162 J.-P. Schwitzguébel et al.

MW

375

350

294

556

676

Name CAS / C.I.

Metanil Yellow/ Acid Yellow 36 587-98-4 / 13065

Orange II/ Acid Orange 7 633-96-5 / 15510

Chrysoin/ Acid Orange 6 547-57-9 / 14270

Ponceau BS/ Acid Red 66 4196-99-0 / 26905

Sirius Red 4B/ Direct Red 81 2610-11-9 / 28160

Structure

Log BCF

0.5

0.5

0.94

1.65

0.5

0.5

0.5

0.69

0.56

0.46

Log Kow

2.62

2.47

2.07

1.83

1.57

Koc

(continued)

+++

+++

++

+++

+++

h (%) Using Plants to Remove Foreign Compounds 163

MW

682

696

Name CAS / C.I.

Acid Dark Blue 5R/ Acid Blue 113 3351-05-1 / 26360

Acid Dark Blue GR/ Acid Blue 120 3529-01-9 / 26400

Structure 1.0

1.75

3.74

Log BCF

3.20

Log Kow

3.81

3.51

Koc

++

++++

h (%)

164 J.-P. Schwitzguébel et al.


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Fig. 5 Acid Orange 7 reductive degradation (a) and oxidative degradation (b) into the by-product aromatic amines (sulphanilic acid, 1-amino-2-naphtol, 1,2 naphthoquinone, 4- phenolsulphonic acid) (Adapted from Wojnárovits and Takács 2008)

Acid Orange 7 (AO7, is being used as a dye model molecule for treatment efficiency assessment in CW (Davies et al. 2006). Acid dyes, like AO7, are used on nylon, wool, silk, paper inks and leather. The dyeing process usually occurs via the use of neutral to acid dye baths. During textile processing, concentrations of AO7 in wastewaters of 10 up to 80 mg l−1 have been reported (Coughlin et al. 2002; Scheeren et al. 2002). AO7 is resistant to light degradation and does not undergo biological degradation in wastewater treatment plants (Lucarelli et al. 2000). Bacterial cleavage of the AO7 azo-bond gives rise to two aromatic amines, sulphanilic acid and 1-amino-2-naphtol (Fig. 5). It has been shown that the first step in the bacterial degradation is the cleavage of the azo bond by studying the mechanism of Pseudomonas aeruginosa degradation of Navitan Fast Blue S5R (Nachiyar and Rajakumar 2005). Coughlin et al. (2002) have also reported that the cleavage of the azo bond by the action of azo reductases is invariably the first step in the biotransformation of azo dyes and the second step is the mineralization of the intermediates. The toxicity of AO7 has been assessed using a bioluminescent bacterium, Vibrio fischeri. It has been found that the toxicity and genotoxicity of decolourised AO7 is due to the production of 1-amino-2-naphtol, as the toxicity and genotoxicity of SA is similar to that of the AO7 before azo bond cleavage (Gottlieb et al. 2003).

Conventional Dye Treatments Various methods have been suggested to handle the dye removal from water these include biodegradation, coagulation, adsorption, advanced oxidation process (AOP) and membrane process. Among these techniques the AOP appears to be a promising field of study (Donlagic and Levec 1998; Neamtu et al. 2004; Rauf and Ashraf 2009). In this process ROS that destroys the aromatic structures are produced. This evidence is very relevant to the application of green plants to the treatment of dyes, as plants

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produce ROS (OH• – hydroxyl radical) as a signalling pathway for the detoxification of xenobiotics and have POD isozymes, which are heme-containing proteins. Azo dyes microbial degradation in aerobic or anaerobic environment conditions are controversial (Tan et al. 1999; Padmavathy et al. 2003; Harmer and Bishop 1992; Coughlin et al. 2002). Anaerobic-aerobic treatment using sequencing reactors has been intensively studied (Melgoza et al. 2004). White-rot fungi are attractive organisms for bioremediation for several reasons; they are ubiquitous in natural environments and they have a powerful extracellular oxidative enzymatic system known as the lignin-degradative enzyme system (lignin POD and Mn-dependent POD) (Heinfling et al. 1998; Wesenberg et al. 2003). Despite the potential industrial use of fungal POD, the application in industrial processes is hampered by the limited protein availability and their rather low stability (Conesa et al. 2002).

Azo-Dyes Phytoremediation Log Kow has been estimated to vary from −10.17 (Acid Yellow 23) up to 3.74 (Acid Blue 120) for azo dyes that have aromatic amines such as sulphanilic acid and/or 1-amino-naphthol in its structure. For log Kow < 0.5 the azo dyes are so water soluble that they are readily lixiviated. Plants readily take up organic xenobiotics with a log Kow between 0.5 and 3.5, as well as weak electrolytes (weak acids and bases or amphoteres as herbicides) (Barac et al. 2004). Consequently, they can be sorbed into the soil or taken up by the plants. In Table 2, colour removal of several azo dyes using a crude saline extract of Phragmites australis leaves is presented. Despite the structural and hydrophobic differences, the azo-dyes colour removal is observed revealing that Kow is not restrictive for enzymatic biodegradation process if the compound can be translocated from rhizosphere to the leaves. When using CWs, a combination of aerobic, anaerobic and anoxic degradation occurs within the bed matrix, which is beneficial for dyes degradation. Plants use sunlight as an energy source and atmospheric CO2 as a source of carbon, however, as they also respire, they require catabolic enzymes to break down lignin, cellulose, coumarins, flavonoids and other complex molecules that are produced by the photoautotrophs. These molecules are similar to exogenous xenobiotics. So, plants tend to transform, conjugate and store endogenous and anthropogenic xenobiotics and get rid of them by delignification and degradation by enzymatic processes. Plant degradation process relies mostly on POD, enzymes typically activated as an oxidative stress response. Additionally, heterotrophic microorganisms use their enzymatic abilities to mineralize the xenobiotic compounds to obtain energy, carbon and nutrients. Any xenobiotic can find somewhere in the CW microbial and plant enzymatic tools that lead to its mineralization (McCutcheon and Schnoor 2003). Bioremediation by itself is not cost feasible unlike CWs, and also cannot compete with the plants ability to self-engineer and rapidly adapt to changes in pH, redox conditions, nutrient availability and to the presence of xenobiotics


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Rhizodegradation Ecological communities include bacteria, yeasts, fungi and protozoa. All are effective agents in the transformation of organic pollutants because of their extracellular enzymatic components, which are powerful catalysts able to extensively modify the structure and toxicological properties of contaminants, or to completely mineralize organic molecules into innocuous inorganic end-products. The extracellular enzymes include a large range of oxidoreductases and hydrolases, both having a degradation function transforming polymeric xenobiotics into partially degraded or oxidised products that can be easily taken up by the cells. Oxidoreductases have a protective function by oxidizing toxic soluble products to insoluble and thus protect the cell against it (Gianfreda and Rao 2004). An example of a possible bacterial enzymatic pathway for AO7 is given in Fig. 6. Phytodegradation The role of plants in the degradation of dyes has been addressed and among other research work, a study was performed using Origanum vulgare that is a high-phenolic containing plant on the ability of several oregano clonal lines to degrade polyanthraquinone dyes (Poly R-478 and Poly S-119). In this study POD activity increases and the total phenols decrease when compared with the controls (Zheng et al. 1998). A similar study has been conducted using thyme (Thymus vulgaris L.) and rosemary (Rosmarinus officinalis L.) clonal lines, where the increase in POD activity has been correlated with the activation of tolerance mechanisms in these plants to Poly S-119, enabling its survival in such environment (Zheng and Shetty 2000). These results show the possibility to develop new phytoremediation systems based on manipulated POD in plants tolerant to high polluted environments. Another study on degradation of textile dyes (Methyl Orange, Orange G, Azo Violet, Azocarmine, Methylene Blue, Bromophenol Blue) mediated by Ipomea palmate and Saccharum spontaneum POD has been carried out, using concentrations of dyes from 25 up to 200 mg l−1 (Shaffiqu et al. 2002). The Saccharum POD enzyme revealed to have high pH, temperature stability (30–80°C) and high specific activity degrading completely green textile dyes such as Procion Green and Supranol Green. The use of Saccharum POD immobilized on modified polyethylene matrix completely degraded Procion Navy Blue HER, Procion Green HE-4BD, Procion Blue H-7G and Supranol Green within 6–8 h at pH 3. The immobilized enzyme has been used in a reactor and found to be stable as the half-life was 60 h. Recently, plant polyphenol oxidases obtained by ammonium sulphate fractionation from potato (Solanum tuberosum) and brinjal (Solanum melongena) have been used to study the potential of decolourization and degradation of textile dyes such as Reactive Blue (4, 160, 171); Reactive Orange (4, 86); Reactive Red (11, 120); Reactive Yellow 84 and mixtures between them. Potato polyphenol oxidases are more effective in decolourization of individual dyes or mixture of dyes than the

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Fig. 6 A possible enzymatic pathway for the degradation of AO7 by bacterial strains (Blasco et al. 1995; Blumel et al. 1998). SA: sulphanilic acid (4-aminobenzenesulphonate); 4SC: 4-sulphocatechol; 3SM: 3-sulphomuconate; 4SL: 4-sulpholactone; MA: maleylacetate; 3OA: 3-oxoadipate; 2HMSA: 2-hydroxymuconic semialdehyde; CCM: cis,cis-muconate; ML: muconolactone; EL: enollactone ; I: 4-aminobenzenesulphonate 3,4-dioxygenase (deaminating); II: protocatechuate 3,4-dioxygenase type II; III: 3-carboxymuconate cycloisomerase type II; IV: sulpholactone hydrolase; V: maleylacetate reductase;VI: catechol 2,3-dioxygenase; VII: catechol 1,2-dioxygenase; VIII: muconate cycloisomerase; IX: muconolactone isomerase; X: enollactone hydrolase

brinjal polyphenol oxidases at pH 3, obtaining up to 99% decolourization for dyes concentrations between 50–100 mg l−1 (Khan and Husain 2007). Degradation of several dyes such as Golden yellow, Methyl orange, Orange M2RL, Navy blue HE2R, Reactive Red M5B and Reactive Red 198 has been verified


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using a cell culture of transgenic hairy roots, monitoring the enzyme activities of lignin POD, laccase, tyrosinase, Mn POD, DCIP reductase and azo reductase. It has been found that the hairy roots of Tagetes patula L. are able to decolourize the six structurally different textile dyes with an efficiency of more than 62%, and concentrations up to 110 mg l−1 are removed. Azo reductase, a key enzyme expressed in azo dye degrading bacteria that cleaves azo bonds reductively, is induced in Tagetes roots after decolourization, highlighting its role in the reduction of azo bonds. Reactive Red 198 decolourization has been monitored using analytical techniques such as UV-Vis spectroscopy, HPLC, GC-MS and FTIR spectroscopy, thus enabling the establishment of a possible degradation pathway. There are evidences that the metabolites produced by dye microbial biodegradation are different from the non toxic formed by hairy roots biodegrading activity (Patil et al. 2009). The technical advantage of using cell cultures instead of the whole plant rely on the fact that in vitro cultures are easily grown and maintained free from microbial contamination, therefore enabling the understanding of key enzyme pathways involved in the detoxification of pollutants allowing to distinguish among the metabolic capabilities of plant cells from those of microorganisms (Doran 2009). However, a question remains to be answered, how plant enzymes are activated when whole plants are used and integrated in a treatment system like a CW? Davies et al. (2006) have shown that using a pilot VFCW planted with Phragmites australis the decolourization and degradation of the azo dye model molecule, AO7 and the aromatic amines released is feasible (Fig. 7). AO7 has a log Kow of 0.56, so it is readily taken up by the plants, and as CW plants cannot move away from environmental stresses, they have to activate a signal transduction cascade in order to survive. Davies et al. (2005) have found using in vitro and in vivo studies that Phragmites australis POD, can play an active role in the degradation of AO7. For moderate stress conditions (130 mgAO7 l−1), Phragmites australis in contact with AO7 show POD activity increases in all tissues: 2.1-, 4.3- and 12.9-fold for leaves, stems and roots, respectively. The increase in POD activity is the result of Phragmites australis stress response, as the H2O2 is produced under oxidative stress conditions. For high oxidative conditions (700 mgAO7 l−1), POD activity inhibition occurs immediately after the toxic shock, which is related to the fact that POD is not able to degrade the high amounts of H2O2 produced immediately. However, a remarkable capacity of Phragmites australis to overcome the oxidative stress is observed, as after 2 days in the same feeding conditions, POD activity levels returns to previous levels, with no signs of phytotoxicity. In summary, plants do react to the presence of the model molecule (AO7) by increasing ROS concentration in order to signal plant defences. Carias et al. (2007) have shown that crude extracts of Phragmites leaves obtained by ammonium sulphate fractionation are successful in the decolourization of AO7. Activities of several enzymes involved in plant protection against stress have been assayed through the activity quantification of SOD, POD, CAT, APX, dehydroascorbate reductase (DHAR) and glutathione S-transferases (GST), obtained from Phragmites australis crude extracts of leaves, stems and roots. An activity increase has been detected for an AO7 concentration of 130 mg l−1 for most enzymes studied, especially in leaves, suggesting a response of the ROS scavenging enzymes to the chemical stress imposed. GST activity increases 6.6, 5.9 and 8.8-fold for leaves,

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Fig. 7 Vertical flow constructed wetland used on the treatment of Acid Orange 7 contaminated wastewater

stems and roots respectively, this situation can also be interpreted as an activation of the detoxification pathway and subsequent AO7 conjugation in order to minimize or eliminate the stressor agent effect. After the second shock (700 mgAO7 l−1), GST activity is inhibited and again the plants take only 2 days to restore GST activity in high oxidative stress conditions (Carias et al. 2008). Analysing Phragmites australis role at molecular level, the presence of AO7 in the plant root system leads to a subsequent cascade of biochemical reactions, which include mRNA accumulation for ROS production and ROS-scavenging enzymes as part of the integrated plant defence mechanism. This means that when a stress condition poses a threat to the plants, the DNA directs the transcription of several copies of mRNA, each of which leads to the production of hundreds of protein molecules (e.g. antioxidative enzymes and membrane repair enzymes). An example of a membrane repair enzyme is given by glutathione peroxidase (GPX), that repairs membranes by the reduction of lipid hydroperoxides to their corresponding alcohols and at the same time reducing free H2O2 to water (Edwards and Dixon 2003). Thus, the isolation of RNA from Phragmites australis coding for GPX revealed that gene expression is quickly reaching a maximum up to 1 day for the different


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Fig. 8 TAE 2% agarose gel electrophoresis of glutathione peroxidase gene expression in roots and leaves of plants (Phragmites sp.) submitted to high oxidative stress (700 mgAO7 l−1)

enzymes and fading after 8 days in contact with AO7 (Davies et al. 2009). This represents a very quick response to stress and a remarkable adaptation to different stress conditions, highlighting plants genetic plasticity (Fig. 8). This is an indirect methodology to measure the level of ROS produced after the azo dye is in contact with the plants in the pilot scale VFCW. Other studies also report short times after the exposure to a stress condition for instance, in RNA expression studies for sugarcane, gene overexpression reaches a maximum 2 days after exposure to low temperatures (Nogueira et al. 2003) and for rice the genes induced by cold, drought, high salinity and/or ABA are over expressed after 1 day exposure to those conditions (Rabbani et al. 2003). Plants survival in CWs that are in use for a long time in the treatment of wastewater containing xenobiotic compounds, is the proof that plants are able to biochemically selfengineer (Davies et al. 2009).

Hydrophobic Compounds: Phytoremediation of PCB-Contaminated Soils PCB are oily liquids consisting of a mixture of compounds, containing a total of 1–10 chlorine atoms on two C-C connected aromatic rings. There are 209 congeners in all. 3

2

2'

3'

4 Clm

4' 5

6

6'

5'

Cln m+n=10

Congeners of PCB homological groups with identical number of chlorine atoms (from 1 to 9) will be designated further as 1CB, 2CB, …, 9CB. From the 1940s until the 1980s, commercial PCBs have been widely used, mostly as hydraulic and dielectric fluids in electrical equipment. About 1.5 million tons of PCB are still in use today (mainly in closed systems) and about 0.5 million tons are contaminating

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the environment (Wiegel and Wu 2000). Contamination of soils and waters with PCB has often resulted from the manufacture, handling, use, and disposal of these chemicals. The PCB content in soils and sediments can reach hundreds or thousands milligrams per kg, while permissible concentrations range from 0.01 to 30 mg/kg, depending on country and land use (Bakker et al. 2000). One of the promising methods for cleaning up PCB-contaminated soils and sediments is bioremediation which differs from traditionally used methods (incineration or burial in secure landfills) by ecological compatibility, more economic, less energy consuming and saving soil fertility. PCB degradation in soil systems occurs basically under the influence of microorganisms. Microbial PCB degradation in soil depends on composition and concentration of the contaminants, presence and activity of PCB degraders and environmental conditions (Fig. 9). Highly chlorinated PCB congeners are more effectively transformed by anaerobic microorganisms through reductive dechlorination with formation of less chlorinated derivatives. However, full degradation of these pollutants in soil proceeds mainly by aerobic bacteria. Higher chlorinated PCB (with three and more chlorine atoms) are not capable to provide microorganisms with carbon or energy, therefore the majority of PCB are metabolized by microorganisms in co-oxidative conditions in the presence of other available substrates. The main role in PCB degradation belongs to bacteria capable to use biphenyl (BP) as a sole source of carbon and energy. BP-utilizing bacteria metabolize PCB primarily through bph-pathway to chlorobenzoic acids (CBA) which are usually mineralized by other bacteria. The rate and extent of aerobic degradation of PCB congeners negatively correlate with the number of chlorine atoms in the molecule. It strongly depends on their structure and on the specificity of enzymes in relation to various PCB congeners and varies for different bacterial strains. The bph-genes expression usually occurs during growth in the presence of BP or its utilized analogues. Microbial processes in the surface biofilms, which are formed at the interface between anaerobic and aerobic conditions, play a special role in PCB degradation (Furukawa 2006). High PCB persistence in soil is due to the absence and/or low activity of microorganisms, capable to degrade their numerous congeners and to transformation products which often possess high toxicity. Besides, high adsorptive ability of these extremely hydrophobic compounds limits their bioavailability in soil systems. The efficiency of bioremediation of PCB-contaminated matrices strongly depends on the character and degree of contamination. In the case of aerobic bioremediation, the best results are obtained with moderately contaminated soils and sediments (20–700 mg PCB/kg), in which the level of contamination decreased by 40–75%. These results could be achieved by repeated inoculation of a consortium of specific microorganisms (isolated or engineered) with concurrent addition of BP and biosurfactants. PCB concentration decreased mainly due to the degradation of congeners with one to three chlorine atoms. The content of higher-chlorinated PCB can be noticeably decreased only under sequential anaerobic/aerobic treatment; the best effect was achieved with anaerobic granules. However, only in individual cases, mainly in laboratory experiments with freshly spiked PCB at moderate concentrations, it is possible to reduce their content to a level permissible for technogenic soils (Vasilyeva and Strijakova 2007).

TCA cycle + COOH Cln O

Dioxigenase

HO

Cl-

(Cl-)

OH

OH

Cln (n-1)

COOH

Mineralization

OH

OHOH H

Cln

Mineralization

OH

Biphenyl dehydrogenase

H

Mineralization Cl-

Biphenyl dioxigenase

HO Cln

COOH

White rot fungies Plant cells

BPh-utilysing bacteria

COOH

Hydrolase

Cln

Aerobic conditions

Cln (n-1) (Cl-) HO

OH H2C

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Cln

Lignoperoxidase OH1-3 Mn-peroxidase Laccase Peroxidase

Methanotrophic bacteria

Biphenyl dioxigenase

Cl0-3

OH1,2

Conugation of intermediates with ce ll components and humic acids


Methane Cl monooxygenase

Cl

Biofilm Cl-

Cl2 ClCl3

Cl-

Cl4 Cl

-

Dehalogenase Cl5

Cl8

Cl-

.. .

Cl-

Anaerobic conditions

Methanogenic, sulfidogenic and other anaerobic bacteria

Fig. 9 Schematic representation of the main pathways of PCB biotransformation in solid natural and artificial media under aerobic and anaerobic conditions, as well as in aerobic biofilms under microaerophilic conditions (Vasilyeva and Strijakova 2007)

Although phytoremediation is less effective for highly hydrophobic compounds (PCB, PAHs and dioxins) than for BTEX and other moderately hydrophobic pollutants, this inexpensive method presents some possibilities of decreasing the risk linked to those pollutants. The potential of plants for the remediation of PCB contaminated soils has been discussed in reviews (Campanella et al. 2002; Mackova et al. 2006;

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Vasilyeva and Strijakova 2007; Mackova et al. 2009) and in a number of recent publications mentioned below. There are three different ways for plants to reduce the PCB concentration in soil: phytoextraction, phytodegradation and phytostimulation (or rhizoremediation).

Phytoextraction Phytoextraction is based on the use of plants for the depletion of pollutants in soil through their uptake by plants growing in the contaminated soil and removal together with the plants harvest. The appropriate phytoremediators should be tolerant to the toxicant, should have significant root and shoot biomass as well as high potential to accumulate the toxicant and translocate it from roots to shoots. Chekol et al. (2004) have demonstrated possibilities of all studied plants to influence positively PCB depletion in soil. The legumes (alfalfa, flatpea and sericea lespedeza) are more sensitive to freshly spiked Aroclor 1248 (100 mg/kg) than grass species (deertongue, reed, canary grass, switchgrass, and tall fescue). The weight of shoots and roots of beans grown in the polluted soil is 1.5–5 times less than in pure soil, whereas the decline in the growth of grasses does not exceed 1–10%. Grasses also reduce PCB content in the soil more effectively than legumes. After removal of grass roots, PCB concentration in the soil decreases by 62% and in unplanted soil by only 18%. Deeper depletion of PCB in the planted soil relates to higher fresh weight of roots and shoots, as well as with a higher number of bacteria and enzyme activity of rhizosphere soil in comparison to unplanted control (Chekol et al. 2004). Phytoextraction of weathered Aroclor 1260 from historically contaminated Canadian soils (from 90 till 4,200 mg PCB/kg) was much less effective. In those experiments, the phytotoxicity of PCB contaminated soils is also the lowest for grass tall fescue compared to other studied plant species. However three studied cultivars of Cucurbita pepo ssp. pepo (pumpkin, squash and zucchini) have been chosen as the most promising plants for PCB phytoextraction, because they have revealed high potential to accumulate PCB and are stressed in highly contaminated soil only. The best PCB extractor C. pepo cv Senator hybrid (a zucchini) accumulates in its roots and shoots up to 6,700 and 470 mg PCB/kg, respectively (Zeeb et al. 2006). This is mainly due to root uptake of PCB and translocation to the shoots, rather than volatilization of PCB from soil and deposition on the leaves. Tetra- to hexa-chlorobiphenyls represent the largest proportions in shoot tissues, but also hepta- to nona-chorobiphenyls are present in measurable amounts. The varieties of C. pepo ssp. pepo reveal the highest bioaccumulation factors (BAFs), calculated ratios of PCB concentrations in plant tissues and soil ([PCBplant]/[PCBsoil]) – up to 0.5–1.1 compared to 0.02–0.3 for others. Besides, C. pepo ssp. pepo plants have high translocation factors (TLFs), calculated ratios of PCB concentrations in shoot tissues compared to root tissues ([PCBshoot]/[PCBroot]) – 0.21–1.1 in all soil types compared to 0.01–0.19 for other plants. A distinctive nature of cucurbits root exudates (high protein content and low total sugar content, with a high proportion


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of monosaccharides) may have solubilised the PCB in the rhizosphere, thus allowing the plants to take up and translocate the contaminant to the shoots. Carex normalis species (sedge) has also demonstrated high potential for PCB phytoextraction; however those plants can be mostly used in wetlands. Nevertheless the decrease of PCB concentration in soil in the described experiments remains low. Although plants are taking up substantial quantities of PCB, maximum plant uptake in all plants within a growing period (2 months) deviates between 0.2% and 1.3% of the total PCB mass in the soil (Zeeb et al. 2006). Similar results have been obtained with other soils containing from 0.6 to 200 mg/kg PCB (Aroclor 1254/1260) (Whitfield Aslund et al. 2007). Citric acid and surfactant amendments increase to some extent the availability of weathered PCB to plant and earthworm species; however the up taken levels of PCB remain low (White et al. 2006). This confirms that phytoextraction is a slow technology and requires more than one growth cycle to reduce concentrations of PCBs in soil by detectable amounts.

Phytodegradation Phytodegradation is based on the possibility of many organic contaminants to be transformed by endo- and exoenzymes that has been demonstrated for PCB in several experiments in vitro. Some plants can transform absorbed PCB to mono- and dihydroxy derivatives with their following conjugation to cellular metabolites. It has been shown that all 22 congeners of Delor 103 (including 2CB, 3CB, 4CB, and 5CB) are transformed by a hairy root culture of Solanum nigrum L. (clone SNC-9O) in vitro: from 9% to 72% within 14 days, depending on the congener and Delor 103 concentration (25 or 50 mg PCB/l). This ability to metabolize PCB is higher for differentiated cells and positively correlates with the accumulation of endo- or exocellular POD in the presence of PCB. High phytotoxicity of 1CB and 2CB has been detected: these compounds at a concentration of 3 mg/l inhibit, and at 30 mg/l completely stop the growth of plant cells (Kucerova et al. 2000). This phytotoxic effect can be explained through higher ability of low chlorinated biphenyls (2CB and 3CB) to be transformed by POD to toxic hydroxy-derivatives (Rezek et al. 2007). The presence of POD in roots of some plants, participating in the degradation of low chlorinated PCB, has also been confirmed by Chu et al. (2006). The degradation of low chlorinated PCB and DDT in crude fresh extracts from roots of the wetland plant Phragmites australis is partly mediated by peroxidase and the plant cytochrome P-450 system, whereas in rice plants (Oryza sativa) such activity is low. Transgenic Nicotiana tabacum plants transiently expressing the biphenyl dioxygenase genes from Burkholderia xenovorans LB400 have been constructed. Biphenyl dioxygenases isolated from these transgenic plants oxidize 4-CB to 2,3-dihydro- or 2,3-dihydroxy-4¢-chlorobiphenyl (Mohammadi et al. 2007). Plant-microbial interactions within rhizosphere can evolve beneficial effect on PCB degradation. Mackova et al. (2007) have shown the possibility of additional metabolic interactions between bacteria and plants in PCB contaminated environment

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on level of their intermediates and their transformation. Some plants and microorganisms can metabolize products of microbial degradation of PCB and vice versa. For example, enzymes of bacterial bph-operon are able to metabolize some hydroxy-intermediates of plant PCB transformation. Similarly plant cells are able to metabolize, to a limited extent, bacterial products – chlorobenzoates. Thus, in vitro cultures of horseradish and black nightshade showed significant transformation abilities and metabolized more than 90% of 2-chlorobenzoate and 20–40% of 2,3-; 2,4-; 2,5- and 2,6-dichlorobenzoates in 2 weeks.

Rhizoremediation Rhizoremediation (i.e., the breakdown of an organic contaminant in soil through microbial activity that is enhanced by the presence of the root zone) is one of the emerging technologies in bioremediation of soils contaminated with PCB and other persistent pollutants in situ. This technique uses a dual plant-microorganism system, in which the plants provide nutrients, support, and a greater availability of the substrate, and the microorganisms drive the enzymatic remediation of the soil. Plants can stimulate indigenous microorganisms able to degrade PCB or provide conditions for bioaugmentation of specially isolated or gene-modified microbial strains (Mackova et al. 2009). Biostimulation through rhizoremediation is based on the postulate that rhizosphere microflora is usually more abundant, and the relative number of PCB- and especially CBA-degrading microorganisms is considerably higher among rhizosphere microorganisms in comparison to bulk soil. This has been confirmed for rhizosphere soil contaminated with Delor 103 (Chekol et al. 2004). Rhizostimulation of aromatic pollutant-degrading bacteria is supposed to occur via several different mechanisms, including analogue enrichment and/or induction of degradative genes by secondary plant metabolites or by less specific stimulation due to increased availability of simple substrates (sugars, amino acids, and organic acids), improved soil aeration, or other processes. It has been proved that plant compounds such as flavonoids, coumarins, terpenes, and resin acids can function as growth substrates and/or inducers of PCB-degrading bacteria. Plant secondary compounds may be released into soil via exudation from living roots or via lysis of dead fine roots as a result of seasonal root turnover (Leigh et al. 2006). Rhizosphere strains can metabolize many aromatic compounds, including PCB. It has been shown with the help of PCR that genomic DNA isolated from rhizosphere strains possess a genetic potential similar to that of PCB-degrading strain Comamonas testosteroni B-356 for the ability to decompose BP (Mackova et al. 2006). Phenylpropanoids (the most active bph-inducers) constitute 84% of the secondary metabolites exuded from roots of Arabidopsis thaliana, and BP-utilizing microorganisms grow on Arabidopsis roots 100 times better than the mutants which do not have bph-genes (Narasimhan et al. 2003). Alfalfa, black nightshade and especially tobacco, grown up in soil historically contaminated with PCB, positively


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influence the rate of soil remediation: up to 34% PCB is degraded in the soil planted by tobacco compared to unplanted control soil (Ryslava et al. 2003). Mature trees also raise the potential of microbial degradation of PCB in soil. Cultivable PCB degraders are associated with all studied species of mature trees growing naturally in a PCB contaminated site (8–500 mg PCB/kg), examined in both the rhizosphere (tightly adhered to the root) and root zone (bulk soil beneath the trees). Significantly higher numbers of PCB degraders are detected in the root zones of Austrian pine (Pinus nigra) and goat willow (Salix caprea) than in the root zones of other trees (ash, black locust and birch) or non-root-containing soil in certain seasons and at certain soil depths. The most spreaded cultivable PCB degraders associated with those trees belong to the genus Rhodococcus. One isolated Rhodococcus strain exhibits degradation abilities similar to those of B. xenovorans LB400, a well known PCB degrader (Leigh et al. 2006). Positive role of plant residues has been shown during composting a soil low contaminated with PCB. These plant additives raise the number of PCB-degraders and the rate of PCB transformation (Mackova et al. 2006). However no clear relation between the quantity of plant terpenes and the rate of PCB transformation in soil has been established. Besides, soil amendment with several plant secondary metabolites (carvone, isoprene, limonene, naringin, and coumarin) has also not revealed any positive influence on the degradation of 17 PCB congeners with two to six chlorine substitutes (170 mg PCB/kg) in aerobically incubated soil. At the same time amendment with BP in combination with the surfactant hydroxypropyl-cyclodextrin enhances PCB removal from a high organic matter soil, but not from a low organic matter soil (Luo et al. 2007). Low transpiring wetland plants, such as lake sedge (Carex aquatalis) and prairie cord grass (Spartina pectivata), accelerate the depletion of Aroclor 1260 in dredged sediments (20 mg PCB/kg) in a greenhouse study. In 1.5 years, after three cycles (3.5 months in the saturated soil amended with readily available carbon and mineral N followed by 1-month aerobic period) removal of PCB is 17–18%. The dominant role apparently belongs to anaerobic dechlorinators, because the disappearance rate increases in the row 5CB < 6CB < 7CB; carbon additives and high moisture content are critical, and hightranspiring wetland plants do not therefore promote this depletion (Smith et al. 2007).

Bioaugmentation by Rhizosphere Colonising Strains It has been proposed that rhizosphere is an ideal place for the introduction of PCBdegrading microorganisms, and the greatest success should be with PCB-degrading strains which can easily colonize rhizosphere. A strain of the nodulating bacteria Sinorhizobium meliloti has been genetically modified by transferring the PCB-degrading plasmid pE43. Its inoculation together with seeds of the symbiotic plant alfalfa promotes an accelerated degradation of 2,3¢,4-trichlorobiphenyl in soil. In a growth chamber, the concentration of this PCB congener has decreased from 0.33 to 0.11 mg/kg within 1.5 month, while planting with wild strain S. meliloti, not having bph-plasmid – to 0.23 mg/kg only. Meantime the concentration of the same congener is decreased even further

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(to 0.09 mg/kg) in the soil sowed by alfalfa seeds treated by a mixture of not identified indigenous PCB-degrading microorganisms (Chen et al. 2005). Other investigations have been performed with engineered strains of Pseudomonas fluorescens which are capable to colonize rhizosphere of various plants and simultaneously express bph-genes. They survive well in rhizosphere and intensively colonize roots of different plants: sugar beet (Brazil et al. 1995), alfalfa (Villacieros et al. 2005), and willows (De Carcer et al. 2007; Rein et al. 2007). A new strain P. fluorescens F113L::1180 has been constructed that cometabolizes PCB congeners present in Delor103 better than strain LB400, the donor of the bph genes used. Delor 103 is degraded faster in soil planted by alfalfa seeds by inoculating strain P. fluorescens F113L:1180 in comparison with uninoculated soil or with soil inoculated by strain LB400 not possessing the ability to colonize the rhizosphere (Villacieros et al. 2005). Inoculation of transgenic strains of P. fluorescens to the rhizosphere of Salix sp. has no influence on the function and structure of microbial community of the basic soil, but raises the biodegradation of spiked PCB (168–186 mg/kg), whereas no degradation of the xenobiotics is noticed for 186 days in control soil with a wild strain (De Carcer et al. 2007). Thus, certain positive results of phytoremediation for PCB-contaminated soils have been obtained. However the majority of the experiments have been performed with low and fresh contaminated soils and degradation rates of PCB are not so high. The possibility of phytoremediation of moderately and highly contaminated soils has not been proven yet.

Use of Activated Carbon One of the main reasons of low microbial degradation of PCB in moderately and highly contaminated soils is their toxicity to degrading microorganisms and plants. Recently we have demonstrated that activated carbon (AC) can help to overcome the toxicity of various organic pollutants and facilitate soil bioremediation. A comparatively degradable compound 3,4-dichloroaniline is almost totally degraded in highly contaminated soil amended with AC and inoculated with chloroanilinedegrading bacterial strains. The explosive 2,4,6-trinitrotoluene is transformed to strongly bound products in soil amended with AC (Vasilyeva et al. 2006). We have used a similar approach for PCB-contaminated soils sampled near a capacitor plant in Serpukhov (Moscow Region, Russia). Initial PCB concentrations were 1,585 or 4,190 mg/kg with tri-, tetra- and pentachlorinated congeners prevailing there. The influence of granular activated carbon (GAC) and powdered activated carbon (PAC) on PCB availability and persistence in the soils has been determined under outdoor conditions (Vasilyeva et al. 2010). Results have confirmed the extreme persistence of PCB in both soils; extractable PCB in unamended soils decreases by 26% and 35% within 39 months, respectively. This reduction is mostly due to a 75–80% decrease in tri-chlorinated congeners and a 10–20% decrease in tetrachlorinated congeners. Significant PCB degradation has started after a long lag-period and did occur during the second season of the experiment, but subsequent


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degradation did proceed rather slowly. Because vapour loss and leaching are low, the loss of PCB in both control soils is likely due primarily to microbial degradation. The T50 of 3CB is about 1 year in both soils, while the T50 of 4CB and that of total PCB congeners is more than 4 years. Amending the soils with ACs does result in a sharp decrease in extractable PCB, mainly due to strong binding to the adsorbent. Amending those soils with the AC substantially decreases the concentrations of less chlorinated extractable PCB (2CB, 3CB and 4CB) and to a lesser extent of higher chlorinated congeners. The introduced ACs substantially reduces the concentration of readily available PCB and other toxic compounds (dissolved and reversibly adsorbed to soil particles). This effect has been demonstrated by the greatly reduced phytotoxicity of both PCB-contaminated soils shortly after mixing with ACs as well as by reduction of Tenax extractable PCB. Slow PCB degradation is evidently related to high toxicity of the contaminated soils. PCB and their transformation products (such as hydroxy derivatives) or associated pollutants may exceed critical levels of toxicity, impeding natural attenuation processes. The AC maintains a low concentration of readily available toxicants in soil solution, creating more favourable conditions for plant growth and microbial activity than in the untreated soils. PCB binding to the AC is evidently one of the main mechanisms regulating toxicity reduction, especially in GAC-amended soils. The dispersive interactions should be more important for the PCB sorption by PAC. Two mechanisms of PCB binding to soil and activated carbon have been proposed. The most probable mechanism of PCB binding to AC is p-p-bonds with its graphene surface. These bonds can be especially strong when planar PCB molecules penetrate nanopores of GAC and form p-p-bonds with both walls of the slit-like pores. Nonplanar PCB molecules with two and more ortho-chlorines should form weaker p-p-bonds with the AC and these bulkier molecules have less potential to penetrate into narrow pores. Our results are consistent with the mechanism of sorption of planar PCB congeners and other highly hydrophobic, persistent organic pollutants with planar structures by soot and soot-like carbon particles such as activated carbon (Zhu and Pignatello 2005; Burgess et al. 2006). In natural soils PCBs are likely adsorbed primarily to soil organic matter through hydrophobic interactions, and the more hydrophobic higher chlorinated congeners should be adsorbed with greater intensity. Some fraction of the contaminants can penetrate into a condensed part of soil humus consisting of polyaromatic structures. Less chlorinated congeners with planar structures (especially 2CB) may bind to soot-like particles usually present in minor amounts in soil. Both of those fractions are poorly extracted by the hexane-acetone mixture but should be released by an aromatic solvent like toluene. Overall degradation of PCB in AC-amended soils is comparable to control soils, and the final Ct similar. This means adsorption to the AC does not inhibit PCB degradation. However, an additional mechanism of PCB depletion has been revealed in the AC-amended soils which may accompany microbial degradation. The AC surface may promote slow reductive degradation of highly chlorinated congeners (5CB, 6CB, and especially 7CB). Thus, degradation of total PCB in soil is neither accelerated nor decelerated after adding AC. However, the AC sharply reduces extractable and readily available PCB, mostly due to strong binding to the adsorbent and dispersive adsorption.

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The added AC maintains a low toxicant concentration in soil solution, increasing clover germination and growth, while plants die or are greatly inhibited in control soils. These observations indicate that sequestration of PCB by activated carbon can be used for phytoremediation of real highly contaminated soils. AC amendment in combination with mineral fertilizers may create favourable conditions for development of soil microorganisms and plants. The growing plants together with adsorbents will prevent movements of the contaminants to atmosphere and thus permit condition for long-year phytoremediation of PCB-contaminated site in situ. This approach can be used e.g. in the case for emergency, if remediation of the site through alternative methods is difficult or postponed.

Conclusions: Phytoremediation Trends for the Near Future Phytoremediation requires a thorough understanding of the underlying processes at genetic, molecular, biochemical, physiological and agronomic levels (Krämer 2005). Phytoremediation studies in the future may concern the elucidation of genetic, molecular and cellular mechanisms in order to clarify how phytoremediation can be enhanced. Therefore high throughput techniques such as antibody or enzyme assays, PCR amplification, and DNA fingerprint or microarray gene chip may give more insights on the process and may lead to choose a specific plant variety or microbial strain to be applied on a specific pollutant within a CW or a particular soil. The knowledge of genome from plants to be applied in phytoremediation should provide a useful tool for CW design or site clean up planning. On the other hand, since pollutants and their by-products can be toxic to humans and other living organisms, including plants, an in-depth monitoring of phytoremediation should be carried out, to know if metabolites produced or released are still toxic. Nowadays, plant defence inducers such as naphthyl-acetic acid (a plant hormone analogue) and BION® (inductor of plant resistance against diseases) are used to magnify the glutathione S-transferase activity, an enzyme involved in the detoxification of herbicides (Schröder et al. 2008). The development of DNA micro-arrays with complete plants genome will constitute an innovative monitoring tool, enabling the detection of mRNA transcripts e.g. for a plant enzyme activity as a function of the pollutant loads applied in CWs. An interesting example of monitoring plants health has been developed by the European Space Agency, with a complete lightweight sensing system for monitoring the ambient conditions for plant growth in space missions (Baratto et al. 2005). Another example is given by a plant tissue bioelectrode, sensitive to a variety of mono and polyphenols by coupling potato (Solanum tuberosum) tissue, which contains high concentrations of the enzyme polyphenoloxidase to a traditional O2 selective Clark electrode. Plant tissue based chemiluminescent biosensors have also been developed, for instance the molecular recognition device being spinach, soybean and coconut tissue with glycolate oxidase, POD, urease, oxalate oxidase and phenol oxidase (Schwitzguébel and Porta 2003).


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Nowadays we are applying molecular technique that were unthinkable 50 years ago, therefore we must expect further unthinkable developments. Among them, improving the ability of plants to remove and degrade foreign compounds present in contaminated water and soil by genetic engineering. However, before transgenic plants can be applied in the field for phytoremediation, the following issues must be addressed: performance under real conditions, safety of the modified plants, potential risk of transferring genes across natural and cultivated species, possible transfer of contaminants to the food chain, and environmental risk assessment. However, exploring and exploiting the biological diversity of the natural gene pools can be a more sustainable approach and transgenic plants might not be necessary. Consideration of plant taxonomy and phytochemistry should be the first steps in the appropriate use of the huge biochemical potential of plant species, since plants often produce natural chemicals whose structure is close to foreign compounds (Singer et al. 2003; Wink 2003). Finally, the success of phytoremediation depends also largely on the ability of plants to tolerate the pollutant(s) to be removed. It is thus of utmost importance to determine the maximal possible amount of the xenobiotic compounds that can be accumulated and detoxified without injury, critical stress and disruption of plant metabolism or redox processes in the species under consideration. Such information will help to maintain the plant wellness, a key factor to correctly design and size the CW to remove the foreign compounds present in a particular wastewater or to estimate the time required to clean up a contaminated soil. All these fundamental aspects are essential issues to determine the impact of chemical stress on the nutritional qualities of vegetables and crops, and thus on food safety and quality, and to make the best use of plants for the remediation of contaminated environment. One of the most important challenges is now to use this basic scientific knowledge to improve the efficiency of phytotechnologies in the field. The dissemination of results, risk assessment, public awareness and acceptance of this green technology, as well as the promotion of networking between scientists, industrials, stakeholders, end-users, non-governmental organizations and governmental authorities are major issues that must be tackled to ensure that phytoremediation programmes are implemented successfully. It is clear that phytotechnologies are not hype, but offer promising and sustainable approaches towards environmental remediation and human health for the twenty-first century (Schwitzguébel et al. 2009).

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Biodegradation of Organic Xenobiotic Pollutants in the Rhizosphere Hassan Azaizeh, Paula M.L. Castro, and Petra Kidd

Abstract Soil contamination by xenobiotic organic compounds is a serious problem in most industrialized countries, causing acute and diffuse contamination of soil and waters on a global scale. Microbial transformation plays a major role in contaminant degradation of many persistent organic pollutants (POPs). However, microbial degradation can be limited by factors such as contaminant bioavailability, - adsorption and mass transfer, while combined plant-microbial systems can overcome these drawbacks, leading to more efficient contaminant degradation at the soil-root interface or rhizosphere. Hypotheses that support improved degradation within the rhizosphere compared to nonvegetated soils include (i) increase in microbial density, diversity and/or metabolic activity, (ii) catabolic enzyme induction, (iii) cometabolism of contaminants with similar structures to rhizodeposits, (iv) improved contaminant bioavailability, and (v), selective increase in the number and activity of pollutant degraders. Root exudates or rhizodeposits not only provide a nutrientrich habitat for microorganisms but can potentially enhance biodegradation of xenobiotics in different ways: they may facilitate the co-metabolic transformation of pollutants with similar structures, induce genes encoding enzymes involved in the degradation process, increase contaminant bioavailability, and/or selectively increase the number and activity of pollutant degraders in the rhizosphere. The combination of microbial bioremediation and phytoremediation in this complementary manner is known as rhizoremediation, phytostimulation or rhizosphere bioremediation. Bacteria, fungi and mycorrhizal fungi are a major component of

H. Azaizeh (*) Institute of Applied Research (affiliated with University of Haifa), The Galilee Society, Shefa Amr, 20200 Israel e-mail: [email protected] P.M.L. Castro Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua Dr António Bernardino de Almeida, 4200-072 Porto, Portugal P. Kidd Consejo Superior de Investigaciones Científicas (CSIC), Instituto de Investigaciones Agrobiológicas de Galicia (IIAG), Avda. de Vigo s/n, 15706 Santiago de Compostela, Spain P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_9, © Springer Science+Business Media B.V. 2011

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the rhizosphere and form mutualistic associations with most plant species and their involvement in the biotransformation and biodegradation of various xenobiotic organic compounds is discussed. The diversity of bacterial and fungal genes and degradation pathways expressed in the rhizosphere is potentially huge, and the ways in which plants and associated symbionts enhance biodegradation remains much unexplored. Although a wide range of microbes able to degrade highly stable, toxic organic compounds such as polycyclic and aliphatic hydrocarbons have been discovered, the environmental pollution caused by these compounds remains an unsolved problem.

Introduction Soil contamination by xenobiotic organic compounds is a serious problem in most industrialized countries, causing acute and diffuse contamination of soil and waters on a global scale. Microbial transformation plays a major role in contaminant degradation of many persistent organic pollutants (POPs). However, microbial degradation can be limited by factors such as contaminant bioavailability, adsorption and mass transfer, while combined plant-microbial systems can overcome these drawbacks, leading to more efficient contaminant degradation at the soil-root interface or rhizosphere. Hypotheses that support improved degradation within the rhizosphere compared to nonvegetated soils include (i) increase in microbial density, diversity and/or metabolic activity, (ii) catabolic enzyme induction, (iii) co-metabolism of contaminants with similar structures to rhizodeposits, (iv) improved contaminant bioavailability, and (v), selective increase in the number and activity of pollutant degraders. Root exudates or rhizodeposits not only provide a nutrient-rich habitat for microorganisms but can potentially enhance biodegradation of xenobiotics in different ways: they may facilitate the co-metabolic transformation of pollutants with similar structures, induce genes encoding enzymes involved in the degradation process, increase contaminant bioavailability, and/or selectively increase the number and activity of pollutant degraders in the rhizosphere. The combination of microbial bioremediation and phytoremediation in this complementary manner is known as rhizoremediation, phytostimulation or rhizosphere bioremediation. Bacteria, fungi and mycorrhizal fungi are a major component of the rhizosphere and form mutualistic associations with most plant species and their involvement in the biotransformation and biodegradation of various xenobiotic organic compounds is discussed. The diversity of bacterial and fungal genes and degradation pathways expressed in the rhizosphere is potentially huge, and the ways in which plants and associated symbionts enhance biodegradation remains much unexplored. Although a wide range of microbes able to degrade highly stable, toxic organic compounds such as polycyclic and aliphatic hydrocarbons have been discovered, the environmental pollution caused by these compounds remains an unsolved problem.

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A highly industrialised society has led to the widespread introduction of organic pollutants into our environment, causing acute and diffuse contamination of soil and waters on a global scale. Soil contamination by xenobiotic organic compounds (such as chlordane, dioxins, 1,1,1-trichloro-2,2-bis(4-chlorophenyl) ethane (DDT), polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs), nitroaromatics) is a serious problem in most industrialized countries. Anthropogenic activities contributing to their accumulation in soils include industrial activities, fuel combustion, military activity, and use of pesticides, fertilisers and soil amendments in high-production agriculture (Alkorta et al. 2001; Samanta et al. 2002). Pesticides (herbicides, insecticides, fungicides, algaecides, bactericides, etc.) are chemicals used for crop protection and pest control, and are probably the most widely distributed contaminants in the environment over the last century: millions of tons of pesticides are produced and spread annually all over the world (Schwitzguébel et al. 2006). The persistence of these pollutants in the environment depends upon their physicochemical properties (volatility, reactivity, absorption and adsorption, solubility in water, partition between polar and non-polar phases (log Kow) and between soil and water (Kd)), degradability by microorganisms (MO), climatic conditions (influencing pesticide degradation through soil genesis), soil physicochemical properties (especially amount and nature of organic matter) and uptake by terrestrial and aquatic species including plants. Many persistent organic pollutants (POPs) are recognised as a potential health risk due to their intrinsic chemical stability, recalcitrance, and potential acute toxicity, mutagenicity or carcinogenity. There is also increasing concern in their transformation products because these can be present at higher levels in soil than the parent pesticide itself. In some cases these products are more toxic and more mobile, representing a greater risk to the environment than parent molecules (Schwitzguébel et al. 2006). Microorganisms are often considered to be the best indicators of soil pollution due to their intimate contact with the soil environment (large surface area). In general, they are very sensitive to low concentrations of contaminants and rapidly respond to soil disturbance. An alteration in their activity and diversity may result, and this in turn can lead to a reduced soil quality (Schloter et al. 2003). Soil enzyme activities are the driving force behind all the biochemical transformations occurring in soil. Their evaluation may provide useful information on soil microbial activity and be helpful in establishing the effects of soil specific environmental conditions on their biochemical biotransformation processes (Dick et al. 1996). The contribution of rhizospheric MO (bacteria, fungi and mycorrhizae) to the biodegradation and metabolism of xenobiotic compounds are discussed. Plant growth promoting rhizobacteria (PGPR) communities also exist naturally in the rhizosphere and can be an important tool in the decontamination of contaminated soils. Rhizo remediation of PAHs (e.g. naphthalene, phenanthrene, benzo(a)pyrene) is emphasised in this review as they represent widely spread organic pollution in our terrestrial environment.

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Biodegradation and Rhizoremediation of Xenobiotics Using Rhizospheric Bacteria Biodegradation in the Rhizosphere and Plant Growth Promoting Bacteria Plants may contribute to the dissipation and/or mineralization of organic contaminants through an increase in microbial density (more than 1–3 orders of magnitude than in non-vegetated or bulk soil), diversity and/or metabolic activity; improvement of physical and chemical soil properties; increased contact between root-associated MO and soil contaminants; increased humification; and adsorption on root surfaces of pollutants at the root-soil interface or rhizosphere, but the impact of each process has not been clearly elucidated (Binet et al. 2000; Boyle and Shann 1998). Plants (and plant species) vary widely with respect to root parameters such as morphology, root exudation (Grayston et al. 1996), fine root turnover (Gill and Jackson 2000), root decomposition (van der Krift et al. 2002), and associated microbial communities (Smalla et al. 2001). Biogeochemical processes are known to differ between the bulk or non-vegetated soil and this unique soil-root interface, leading to sharp gradients in elemental concentrations, pH, pCO2, pO2, redox potential and organic ligand concentrations (Hinsinger and Courchesne 2008). Several studies have investigated the effects of plant–microbe interactions on the degradation of organic contaminants; based on the hypothesis that root exudates, mucigel and root lysates (ectoenzymes, amino acids, carbohydrates, low-molecular-mass carboxylic acids, flavonones and phenolics) influence the rhizosphere microbial community (Adam and Duncan 2002; Siciliano et al. 2003; Kaimi et al. 2006). Compared to bulk soil, the rhizosphere may be modified due to the activity of the root system (Adam and Duncan 2002). A plant may secrete 10–20% of its photosynthate in root exudates, which support the growth and metabolic activities of diverse fungal and bacterial communities in the rhizosphere (Siciliano et al. 2003; Kaimi et al. 2006). Walton et al. (1994) speculated that when chemical stress occurs in soil a plant may respond by increasing its rate of exudation or exudates composition, which in turn modifies the rhizosphere microbial community composition or activity. A dynamic environment between plants and microbial communities exists in the rhizosphere, with the root surfaces of plants being continually subjected to a two-way traffic of solutes from plants to the soil and vice versa (Bais et al. 2006), and rhizoremediation occurs within such constraints. For many persistent organic pollutants (POPs) their low water solubility and high hydrophobicity impedes their uptake and translocation within plants (Burken and Schnoor 1998). In situations like these, microbial transformations play a major role in contaminant degradation. Enzymes catalysing the oxidation of pesticides (such as peroxidases) are not only more widely distributed among MO but are often more efficient than the same functional proteins in plants (Chaudhry et al. 2005). However, microbial degradation can be limited by factors such as contaminant bioavailability, adsorption and mass transfer, while the combined plant-MO system


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can overcome these drawbacks, leading to more efficient contaminant degradation at the soil-root interface or rhizosphere. Hypotheses supporting improved degradation within the rhizosphere compared to nonvegetated soils include (i) increase in microbial density, diversity and/or metabolic activity, (ii) catabolic enzyme induction, (iii) co-metabolism of contaminants with similar structures to rhizodeposits, (iv) improved contaminant bioavailability (surfactant activity), and (v), selective increase in the number and activity of pollutant degraders (Anderson and Coats 1995; Schnoor et al. 1995; Nichols et al. 1997; Burken and Schnoor 1998; Miya and Firestone 2001; Shaw and Burns 2003). Plants can also improve the physical and chemical properties of contaminated soil, and increase contact between the root-associated MO and the soil contaminants. The combination of microbial bioremediation and phytoremediation in this complementary manner is known as rhizoremediation, phytostimulation or rhizosphere bioremediation (Anderson et al. 1993). A study on the degradation of trichloroethylene (TCE) in the rhizosphere of wheat by bacteria expressing a stable, chromosomally encoded toluene ortho-monooxygenase (TOM) coined the term rhizoremediation (Yee et al. 1998). In this dual plant-microorganism system the plant provides nutrients, support, and possibly a greater availability of the substrate, while the MO drive the enzymatic remediation (Villacieros et al. 2005). Although the mechanisms involved are unclear, rhizosphere-enhanced biodegradation has been demonstrated for a wide range of organic pollutants, including chlorinated ethenes, polycyclic aromatic hydrocarbons and polychlorinated biphenyls (PCBs) (Kuiper et al. 2004). For that to happen it is important that the bacteria proliferate well in the root system and that the degradative pathways occur in the rhizosphere (Ramos et al. 2008). Enhanced mineralisation of 2,4-dichlorophenoxyacetic acid (2,4-D) was shown in soil collected from the rhizosphere of Trifolium pratense (Shaw and Burns 2003). Chekol et al. (2002) showed enhanced transformation of the explosive, trinitrotoluene (TNT) by the forage grasses Phalaris arundinacea and Panicum virgatum. Concentrations of the organochlorine p,p´-DDE (2,2-bis(pchlorophenyl)1,1-dichloroethylene), a metabolite of DDT, were significantly reduced in the rhizosphere of field-grown zucchini, pumpkin and spinach compared to bulk soil (White et al. 2006). An increased degradation of petroleum hydrocarbons (such as phenanthrene, benzo[a]pyrene, benzo[a]anthracene, chrysene, hexadecane, benzene, toluene, etc.) as a result of modified microbial activity was found in the rhizosphere of grasses and legumes (Nichols et al. 1997; Miya and Firestone 2001). Plant growth promoting rhizobacteria (PGPR) communities also exist naturally in the rhizosphere and can be an important tool in the decontamination of contaminated soils, playing a major role in the establishment of plants in polluted soils (Vivas et al. 2006). Plant growth promoting bacteria can be divided into two groups according to their relationship with the plants: symbiotic bacteria and free living rhizobacteria (Khan 2005). The enhancement of crop plant growth and nutrition in the presence of PGPR is well documented (Reed and Glick 2004; Fließbach et al. 2009) but more recently these microorganisms have been used to reduce plant stress associated with soil contaminants (Reed and Glick 2005). PGPR are able to enhance plant growth

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through various mechanisms, such as the production of phytohormones (such as indoleacetic acid (IAA), auxins, cytokinins, and ethylene; Glick et al. 1995). Ethylene is important for plant growth, but excessive ethylene promoted by stresses can depress growth. Levels of ethylene stress can be reduced by consuming 1-aminocyclopropane1-carboxylic acid (ACC), the immediate precursor of ethylene, through the synthesis of ACC deaminase (ACCD; Glick et al. 1998). Other known mechanisms to stimulate plant growth include enhancing nutrient and water uptake (e.g. N2 fixers, PO4 solubilisers, siderophore-producers), altering root morphology, producing antibiotics or cell wall lytic enzymes such as gluconases or chitinases to inhibit pathogens, and the induction of plant defence mechanisms (Lin et al. 1983; Kapulnik et al. 1996; Khan 2005). Khan and co-authors (2009) reviewed recent developments in the utilization of PGPR for direct application in soils contaminated with heavy metals. As examples, Zaidi et al. (2006) have shown that Brassica juncea plants growing in Ni contaminated soils exhibited lowest toxicity signs when inoculated with Bacillus subtilis strain SJ-101, as this rhizobacteria shared the Ni load by accumulating the metal and also enhanced the biomass formation of the plant. Liu and co-workers (2007) developed a Comamonas-alfalfa system for rhizoremediation of 4-chloronitrobenzene (4CNB), through the association of a 4CNB degrading strain with alfalfa and application to the rhizoremediation of 4CNB-polluted soil. It has also been demonstrated that endophytic bacteria can play a major role in the rhizosphere remediation. Mastretta and co-workers (2009) reported that endophytic bacteria from seeds of Nicotiana tabacum can reduce cadmium phytotoxicity, in which inoculation with endophytes resulted in improved biomass production under conditions of Cd stress. The latter study points to the use of inoculated seeds as a vector for plant beneficial bacteria. Endophyte-assisted rhizoremediation is regarded as a potential new field to improve remediation through the use of microorganisms that live within plants to improve plant growth, increase stress tolerance, and degrade pollutants.

Rhizoremediation of Organic Pollutants Polycyclic aromatic hydrocarbons (PAHs) (e.g. naphthalene, phenanthrene, benzo(a) pyrene) are organic pollutants widely spread in the solid-phases of the terrestrial environment. They are produced throughout the world as industrial by-products of fossil fuel combustion, asphalt production, wood preservation, and coal-processing, and are rarely encountered alone in the environment (Samanta et al. 2002). Since the aqueous solubility of PAHs decreases in an almost logarithmic fashion with increasing molecular mass, high molecular weight PAHs with five to seven rings are of particular environmental concern (Johnsen et al. 2005). Metabolism of PAHs occurs via the cytochrome P450-mediated mixed function oxidase system with oxidation or hydroxylation as the first step (Samanta et al. 2002; Stegeman et al. 2001). Three agricultural soils with contrasting physico-chemical properties and hydrocarbon-pollution history were studied for their phenanthrene-degrading potential (Andreoni et al. 2004). The three soils showed different levels of polycyclic


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aromatic hydrocarbons (PAHs) due to different pollution histories. Enzyme activity and bacterial diversity (assessed using denaturing gradient gel electrophresis (DGGE)) were highest in soils with lowest PAH levels. The enrichment of four mixed cultures capable of degrading solid phenanthrene in batch liquid systems was also studied. Phenanthrene degradation rates in batch systems were culturedependent, and simple (one-slope) and complex (two-slope) kinetic behaviours were observed. DGGE-profiles showed common bands in enrichment cultures and native soil bacterial DNA, indicating potential phenanthrene-degrading strains. In accordance, a decrease in PAH and phenanthrene contents were observed in corresponding cultures of one of the soils and phenanthrene-degrading bacteria were isolated (Andreoni et al. 2004). From those cultures showing the fastest rates of phenanthrene degradation, representative strains were identified as Achromobacter xylosoxidans (100% similarity), Methylobacterium sp. (99%), Rhizobium galegae (99%), Rhodococcus aetherovorans (100%), Stenotrophomonas acidaminiphila (100%), Alcaligenes sp. (99%) and Aquamicrobium defluvium (100%). The isolation of Rhodococcus aetherovorans and Methylobacterium sp. supports the hypothesis that different phenanthrene-degrading strategies, cell surface properties, or the presence of xenobiotic-specific membrane carriers could play a role in the uptake/ degradation of solid phenanthrene (Andreoni et al. 2004). The PAH degradation ability of four native Korean plant species (Panicum bisulcatum, Echinogalus crus-galli, Astragalus membranaceus, and Aeschynomene indica) revealed that dissipation of PAHs was higher in planted soil (i.e., with a rhizosphere) than in unplanted soil. Plant-induced effects were more pronounced in the case of pyrene dissipation compared to phenanthrene dissipation (Lee et al. 2008). After 80 days, >99 and 77–94% of phenanthrene and pyrene, respectively, had been degraded in planted soil, whereas 99% and 69% had been degraded in unplanted soil. This enhanced dissipation of PAHs in planted soils might be the result of increased microbial activity and/or plant-released enzymes. During the experimental period, a relatively large amount of phenolic compounds, a high microbial activity, and high peroxidase activity were found in planted soils (Lee et al. 2008). Chen and Aitken (1999) induced the PAH-degrading enzyme system through salicylate additions so as to stimulate high molecular weight (HMW)-PAH degradation. Other studies evaluating the phytoremediation of pyrene-contaminated soil using alfalfa (Medicago sativa L.) showed that pyrene had an inhibitive effect on alfalfa growth, and an increasing pyrene concentration seriously affected alfalfa growth (Fan et al. 2008). At concentrations of 492 mg kg−1 in the soil, shoot and root biomass was only 34% and 22% of that of alfalfa growing in non-spiked soil, respectively. The rhizospheric bacterial and fungi counts were 5.0–7.5 and 1.8–2.3 times higher than that in non-rhizosphere soil, respectively. After 60 days, 69–85% and 59–80% of spiked pyrene disappeared from the rhizosphere and non-rhizosphere soils, respectively (Fan et al. 2008). The removal percentage decreased with increasing pyrene concentration. However, the average removal of pyrene in the rhizosphere soil was 6% higher than that in the non-rhizosphere soil. The presence of alfalfa roots was therefore effective in promoting the biodegradation of freshly added pyrene into the soil (Fan et al. 2008). Yoshitomi and Shann (2001) found that

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the addition of root exudates stimulated the mineralization of 14C-pyrene in an unplanted soil to the same degree as that observed in actual rhizosphere soil. In rice (Oryza sativa) paddy soils, bacterial microbial communities have also been shown to be stimulated in the rhizosphere, leading to higher degradation of PAHs (pyrene (PYR) and phenanthrene (PHN)) in the soil root zone as compared to non- rhizospheric soils (Sue and Yang 2009). Enhanced decrease in the levels of other pollutants, such as hexachlorobenzene, has also been shown in rhizosphere of rice, and the authors suggested that this system showed promise for in situ degradation of this type of persistent organic pollutant (Hua et al. 2008). In contrast, phenanthrene-degrading activity of Pseudomonas putida ATCC 17484 was repressed after incubation with plant root extracts (Rentz et al. 2004). However, these authors suggested that the enhanced microbial growth on rhizodeposits is likely to compensate for this partial repression since a larger microbial population leads to a faster degradation rate. PAHs can easily accumulate in rice straw used as cattle feed, which can eventually be transferred to human beings through the food chain. The same is true for various other persistent pollutants (Sue and Zu 2007). The anaerobic and reducing environment during the rice growth period in paddy soils is similar to that of a wetland system, thus allowing PAHs to be effectively distributed in soil-plant-water systems. Compared to plants grown in uplands, such as ryegrass, wheat, and maize, the rhizosphere effect of the paddy soil would suggest an important environment for PAH removal. Degradation of PAHs in soils occurs mainly in the root zone (Wild et al. 2005), and rhizosphere soils present a higher degree of degradation of those compounds. The uptake of selected PAHs by Oryza sativa seedlings grown in soil spiked with naphthalene, phenanthrene, and pyrene exhibited volatilization loss of 98%, 95%, and 30%, respectively, with the remaining fraction being fixed by soil organic matter and/or degraded by soil microbes (Yu-Hong and Yong-Guan 2008). The relative contributions of plant uptake and plant-promoted rhizosphere microbial biodegradation to the total mass balance were 0.24% and 14%, respectively, based on PYR concentrations in rhizosphere and non-rhizosphere soils, the biomass of rice roots, and the dry soil weight (Yu-Hong and Yong-Guan 2008). The promotion of PAH degradation was also demonstrated in the rhizosphere of Festuca arundinacea using the bacteria Pseudomonas fluorescens. P. fluorescens 5RL interacted more significantly with salicylate and dextrose in agar cultures containing tall fescue compared to those without plant roots (Ho et al. 2007). Although the presence of tall fescue did not promote catabolic enzyme induction in the absence of salicylate, an increase in dioxygenase activity relative to non-planted controls implies that this plant may enhance the degradation of PAHs or facilitate the genotypes that are capable of transforming PAHs in the rhizosphere. Sipila et al. (2008) studied the genetic degradation potential of pristine and PAH-polluted soils using molecular tools (restriction fragment length polymorphism (RFLP) and terminal restriction fragment length polymorphism (T-RFLP)). A greenhouse microcosm experiment was carried out to elucidate structural and functional bacterial diversity in PAH-polluted soil and to test the suitability of Betula pendula for remediation. Bacterial 16S rRNA T-RFLP fingerprinting


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revealed a high structural bacterial diversity in soil where PAH amendment altered the general community structure as well as the rhizosphere community. Birch augmented extradiol dioxygenase diversity in rhizosphere showing a rhizosphere effect, and pyrene was more efficiently degraded in planted pots. Degraders of aromatic compounds upon PAH amendment were shown by the changed extradiol ring-cleavage community structure in soil. The RFLP analysis grouped extradiol dioxygenase marker genes into 17 distinct operational taxonomic units displaying novel phylogenetic clusters of ring-cleavage dioxygenases representing putative catabolic pathways, and the peptide sequences contained conserved amino-acid signatures of extradiol dioxygenases. A branch of major environmental TS cluster was identified as being related to Parvibaculum lavantivorans ring-cleavage dioxygenase. The described structural and functional diversity demonstrated a complex interplay of bacteria in PAH pollution. A hydrocarbon-degrading thermophilic bacterium, Nocardia otitidiscaviarum TSH1, an actinomycete was isolated from the soil, is able to grow on phenol, PAHs (pyrene, phenanthrene, anthracene and naphthalene) and straight chain aliphatic hydrocarbons with intermediate chain length (dodecane and hexadecane) as the sole sources of carbon and energy (Zeinali et al. 2007). N. otitidiscaviarum TSH1 was the first reported Nocardia strain able to grow on such a broad range of aliphatic and aromatic hydrocarbons under thermophilic conditions. This strain is known to possess extremely lipophilic cell surfaces, which may make it better suited to the direct uptake of highly hydrophobic hydrocarbons. The capacity of the PAH-utilizing N. otitidiscaviarum TSH1 isolate to produce bio-surfactants was also investigated. Fatty acids (C14–C18) were detected by GC-MS analysis during bacterial growth in PAH supplemented mineral media. High cell surface hydrophobicity and the ability of N. otitidiscaviarum TSH1 to degrade different hydrocarbons at 50°C may make it an ideal candidate to treat oil-contaminated desert soils (Zeinali et al. 2007). In a recent study, the transformation of naphthalene by strain TSH1, revealed different metabolites suggests that strain TSH1 initiates its attack on naphthalene by dioxygenation at its C-1 and C-2 positions to give 1,2-dihydro-1,2-dihydroxynaphthalene(Zeinali et al. 2008). The intermediate 2-hydroxycinnamic acid, characteristic of the meta-cleavage of the resulting diol was identified in the acidic extract. Apart from typical metabolites of naphthalene degradation known from mesophiles, benzoic acid was identified as an intermediate for the naphthalene pathway of this Nocardia strain. Neither phthalic acid nor salicylic acid metabolites were detected in culture extracts. Enzymatic experiments with cell extract showed the catechol 1,2-dioxygenase activity while transformation of phthalic acid and protocatechuic acid was not observed. The results of enzyme activity assays and identification of benzoic acid in culture extract provide strong indications that further degradation goes through benzoate and beta-ketoadipate pathway (Zeinali et al. 2008). The residual ecotoxicity of soils spiked with a combination of three PAHs at four levels (15, 75, 150, 300 mg PAHs kg−1 soil) after long-term bioremediation was evaluated using physico-chemical analyses, bioassays and soil microbial analyses (Hamdi et al. 2007). The pot-scale bioremediation process consisted of weekly moderate watering in the presence or absence of sewage sludge compost

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(SSC). After 15 months, anthracene and pyrene were almost completely degraded whereas benzo[a]pyrene persisted, especially in SSC-amended soil treatments. However, no apparent toxic effects of the residual PAHs could be detected. Despite the smaller number of culturable bacterial populations in SSC-amended soils, soil enzymatic activities were not affected by the organic amendment and residual PAHs; and the bioremediation efficiency was likely to be more limited by the bioavailability of PAHs rather than by the total number of PAH-degraders (Hamdi et al. 2007). Polychlorinated biphenyls (PCBs) have been detected in most ecosystems, and especially in soils and sediments of industrial areas (McFarland and Clarke 1989). Such ubiquitous presence is explained by their high chemical stability and low water solubility, which contribute to their persistence in the environment, but also by the fact that more than 300 million kg have been released in the environment before their use was abandoned in most countries (Holoubek 2001). PCBs accumulate in higher trophic levels of the food chain and cause numerous toxic effects on different organisms (Safe 1994). A number of plants have been shown to accumulate PCBs, e.g., Curcubita pepo grown in PCB contaminated soil has been shown to bio-accumulate Aroclors 1254 and 1260 through root uptake and translocation (Whitfield et al. 2008). Monoterpenes (such as cymene, a-pyrene and a-terpinene) and phenolics (such as salicylate) have been shown to induce biphenyl dioxygenase in PCB-degrading bacteria, enhancing total PCB degradation (Chen and Aitken 1999). Terpenes are also considered likely analogues for co-metabolism of PCBs due to a similarity in their molecular structure (Gilbert and Crowley 1997; Hernandez et al. 1997). Degradation of 2,4-dichlorophenol by indigenous soil MO was greater in soils amended and “aged” with monoterpenes (a-pinene, limonene and p-cymene) than in freshly spiked or control soils (Rhodes et al. 2007). Phenolic compounds such as naringen, coumarin or catechin, released by roots of certain plants have been shown to support the growth of rhizospheric PCB-degrading bacteria (Chaudhry et al. 2005). Narasimhan et al. (2003) showed that phenylpropanoids (such as flavonoids) constitute 84% of the secondary metabolites exuded from Arabidopsis roots, and that phenylpropanoid-utilizing microbes are more competitive and grow better than their autotrophic mutants on the roots of plants that are able to synthesize or overproduce phenylpropanoids. PCB removal by Pseudomonas putida PML2, a phenylpropanoid-utilizing and PCB-degrading rhizobacteria, was significantly lower in the rhizosphere of an Arabidopsis mutant exuding less flavonoids than in the rhizosphere of the wild-type strain. Salix sp. produce salicylic acid and related compounds that induce the degradation of many xenobiotic molecules (such as PAHs) and sustain bacterial growth (Leigh et al. 2006). Hence these trees are good candidates for rhizoremediation of PCBcontaminated soils. A 168-day microcosm experiment was used to assess the possible functional and structural shifts occurring in the bacterial community of a site with a history of PCB contamination, after the introduction of plants inoculated with genetically modified (GM) MO designed for rhizoremediation (Aguirre de Cárcer et al. 2007a). Salix viminalis x schwerinii var. Björn were inoculated with two different GM Pseudomonas fluorescens strains or with their parental wild-type


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strain. The introduced transgenes had no effect on the function and structure of the bacterial community in non-rhizospheric soil, although chemical analyses showed an enhanced biodegradation of PCBs. However, the transgenes affected the development of functionally and genetically distinct bacterial communities in the rhizosphere (Aguirre de Cárcer et al. 2007b). An enrichment of Betaproteobacteria (especially Burkholderiales) was suggested to represent selection due to the presence of PCBs. Leigh et al. (2007) used stable isotope probing (SIP) integrated with comprehensive functional gene analyses to monitor changes in biphenyl-degrading bacterial populations in the rhizosphere of Pinus nigra. SIP revealed 75 different genera that derived carbon from C-13-biphenyl, with Pseudonocardia, Kribella, Nocardiodes and Sphingomonas predominating carbon acquisition. These authors showed that results obtained using molecular tools do not always correspond with those from traditional cultivation methods. The organochlorine 1,2,3,4,5,6-hexachlorocyclohexane (HCH) is a broad- spectrum insecticide that was used on a large-scale worldwide since the 1940s, and is available in two formulations: technical-grade HCH (a mixture of different isomers, mainly a- (60–70%), b- (5–12%), g- (10–15%), and d-HCH (6–10%)) and lindane (almost pure g-HCH). All HCH isomers (HCHs) are acutely toxic to mammals, and residues of lindane and other HCH isomers can be found worldwide in air, water, sediments and soils (Willett et al. 1998; Schwitzguébel et al. 2006). Due to its physicochemical characteristics, HCH isomers tend to sorb organic material in the environment and have a low bioavailability (Rodríguez Garrido 2003). The low water solubility and high hydrophobicity (logKOW 3.7–4.1; Willett et al. 1998) of HCH isomers make their uptake and translocation within the plant unlikely. Various authors coincide in that the most likely mechanism of HCH accumulation in plants is the sorption of soil HCH on roots and sorption of volatilized HCH on aerial plant tissues (Calvelo Pereira et al. 2006, 2008; Abhilash et al. 2008). The b-HCH isomer is generally the main isomer in all plant tissues. Rhizoremediation is considered the most viable approach for the remediation of lindane and other HCH isomers (Schwitzguébel et al. 2006; Kidd et al. 2008). In the field, a lower level of HCH isomers (a-, b-, g-, d-HCH) was observed in the rhizosphere of Cytisus striatus and Avena sativa compared to nonvegetated soil (Calvelo Pereira et al. 2006). The same authors observed an increase in the bioavailability of a- and b-HCH in the rhizosphere of Avena sativa compared to nonvegetated soil. Exudation of surfactant-type compounds through the roots could increase isomer bioavailability in the rhizosphere. Plant root exudates have been shown to stimulate growth of lindane degrading bacterium Pseudomonas sp. augmenting lindane (g-HCH) mineralisation in the rhizosphere (Schwitzguébel et al. 2006). Kidd et al. (2008) evaluated HCH dissipation, and microbial densities (total heterotrophs, ammonifiers, amylolytics) and C substrate utilization patterns among rhizosphere and bulk soil of two contrasting plants, Cytisus striatus (Hill) Rothm and Holcus lanatus L. HCH degradation was isomer-specific: an enhanced degradation of a-HCH, but not b- or d-HCH, was observed in the rhizosphere. Significant changes in the microbial densities were observed between bulk and rhizosphere soils of Cytisus, and an increase in C source utilization indicated changes in community level physiological

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profiles (CLPP) in the rhizosphere of this species when grown in contaminated soils. HCH dissipation was also greater in soils planted with this species. The authors related changes in microbial physiological groups and CLPP in the rhizosphere of this species to a possible selective enrichment of HCH-degrading populations. In fact, the establishment of mutualistic interactions in the rhizosphere between plants and degrading strains is of great relevance, leading to the need of selecting appropriate strains promoted the degradation of lindane and the related compounds ∂-hexachlorocyclohexane and b-hexachlorocyclohexane through the use of a double enrichment approach that led to the isolation of four lindane degrading Sphingomonas strains that proliferate in the corn rhizosphere whereas the parent strains could not colonize the plant (Ramos et al. 2008).

Biodegradation of Xenobiotics with the Help of Mycorrhizal Fungi Mycorrhizal fungi are a major component of the rhizosphere and form mutualistic associations with most plant species (Azcón-Aguilar and Barea 1992). Ninety to 95% of all land plants form some type of mycorrhizal associations and the symbiotic association – mycorrhiza – seems to be the chief organ of nutrient uptake in the majority of plants (Bago et al. 2000; Entry et al. 2002). Of the known mycorrhizal associations (ectomycorrhizas, arbuscular mycorrhizas, ericaceous mycorrhizas, and orchid mycorrhizas) the arbuscular mycorrhizas (AM) are those occuring most frequently (Entry et al. 2002), and associations between arbuscular mycorrhizal fungi (AMF) and the roots of terrestrial plant species are by far the most widespread (Smith and Read 1997). AMF can benefit plants through improved nutrition (Clark and Zeto 2000), through extensive extraradical hyphal networks, which explore the soil, absorb nutrients, and translocate them to the roots (Giovannetti et al. 2002), and through root system modifications, generally resulting in a more extensive length and increased branching, and therefore in a more efficient nutrient – and contaminant – absorption (Berta et al. 2002). In addition, the AM symbiotic status also changes the chemical composition of root exudates (Laheurte et al. 1990) and influences soil pH (Li et al. 1991), thus affecting the microbial populations in the rhizosphere (Azcón-Aguilar and Barea 1992; Barea 1997), and improving soil structure (Rillig and Mummey 2006). Recently, exudates produced by AM extraradical mycelia have been shown to influence the growth and development of bacterial communities, increasing bacterial growth and vitality and changing soil bacterial community composition (Toljander et al. 2007). Bacterial communities can be markedly altered in the mycorrhizosphere compared to the rhizosphere of non-mycorrhizal roots (diagram 1). Larsen et al. (2009) have reported interactions between the AMF Glomus intraradices and the plant growth promoting rhizobacteria Paenibacillus polymyxa in the mycorrhizosphere of Cucumis sativus. The role of AMF in soils contaminated with heavy metals has been investigated for many years (Leyval et al. 1997; Leyval et al. 2002), while studies with organic pollutants are relatively scarce. Pre-inoculation of four plant species (Acacia melanoxylum, Cytisus


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striatus, Allium cepa and Trifolium pratense) with an isolate of Glomus deserticola obtained from a HCH-contaminated soil resulted in increased growth and fungal colonization of roots compared with plants pre-inoculated with the introduced fungus G. macrocarpum or colonized by a consortium of indigenous AM fungal species, when those plants were transplanted to an HCH-contaminated soil (Sainz et al. 2006). This suggests that the fungus increases plant tolerance to the toxic soil environment. This effect has been observed in studies with anthropogenic alkaline industrial sediment, for which the combined used of bacteria and AMF Glomus species has ameliorated the soil conditions and reduced plant stress associated with that specific pollution (Oliveira et al. 2005a, b). Mycorrhizal fungi can influence plant uptake or translocation of soil metals (Khan et al. 2000). The role of AMF in the plant response to metal stress is variable (Joner et al. 2004). Some studies report reduced metal bioaccumulation by plants due to mycorrhizal colonisation (Heggo et al. 1990; Jentschke et al. 1998; Huang et al. (2002), which may be related to exclusion strategies, whereas other reports indicate enhanced metal uptake and accumulation in plants due to AMF colonisation (Marques et al. 2006, 2007). A bulk of evidence seems to suggest a speciesspecific effect of AM associations on plant metal uptake and accumulation. As examples, Marques and co-workers (2008) have shown that inoculation with the AMF G. intraradices or G. claroideum protected the host plant Solanum nigrum of excessive Zn, with a decrease in metal accumulation in AMF inoculated plants, whereas at lower Zn levels in the growing matrix there was an increase in metal accumulation. It is possible that such a type of defence mechanism can also be triggered in the presence of organic contaminants. In fact, a recent study on the effect of mycorrhizal inoculation on the phytoextraction of weathered p,p-DDE from soil by Cucurbita pepo showed increases of up to 60% on the accumulation of the organic contaminant by the plant, however the response was dependent on the type of AMF inocula used (White et al. 2006). Increased accumulation was attributed to enhanced pollutant availability. Successful in vitro fungal degradation of organic contaminants has been demonstrated for a range of compounds or compound mixtures, such as aliphatic hydrocarbons, fuel oil and other mixed petroleum hydrocarbons, PAHs, explosives, pesticides and chlorinated organic compounds (Chang et al. 1998; Nicolotti and Egli 1998; Pradhan et al. 1998). Degradation of metsulfuron-methyl in simulated wheat (Triticum asetivum L.) rhizospheric soil was promoted by inoculation with Penicillium sp (He et al. 2007). Only recently has the degradation of organic compounds by ectomycorrhizal fungi (ECMF) been addressed, with major classes of environmentally important POPs, including PAHs, being potentially degraded (Meharg and Cairney 2000), although the role of ECMF in metal-polluted soils has been under investigation for more than 2 decades (Leyval et al. 1997). In one of such recent studies, Huang et al. (2002) reported the biodegradation of 1,1,1-trichloro-2,2-bis (4-chlorophenyl) ethane (DDT) by 4 ectomycorrhizal fungi (ECMF) species, Boletus edulis, Gomphidius viscidus, Laccaria bicolor, and Leccinum scabrum, using a pathway similar to that found in white rot fungi. Green et al. (1999) had described the mycorrhizal degradation of halogenated biphenyls by the ectomycorrhizal fungus Tylospora fibrilosa and

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several other mycorrhizal fungi. Ectomycorrhizae associations are in fact known to enhance root protection against adverse conditions such as a lack of water, extreme pH and temperatures, and presence of contaminants (Anderson and Cairney 2007). More importantly, ECMF play a vital role in nutrient cycling by degrading complex minerals or organic substances present in soil and making them available to the host plant. As for the plant host, it releases photosynthetic compounds and other exudates via roots into the immediate environment, which are used by the fungal partner (Alexander 2007). Inoculation of fungal-bacterial co-cultures (Penicillium janthine - Stenotrophomonas maltophilia) into PAH-contaminated soil resulted in significantly improved degradation of HMW PAHs, benzo[a]pyrene mineralization (53% of added [14C]benzo[a] pyrene was recovered as 14CO2 in 100 days), and reduction in the mutagenicity of organic soil extracts, compared with the indigenous microbes and soil amended with only axenic inocula (Boonchan et al. 2000). The bacteria S. maltophilia could use pyrene as their sole carbon and energy source in a basal salts medium (BSM) and mineralized significant amounts of benzo[a]pyrene cometabolically when pyrene was also present in BSM. P. janthinellum VUO 10,201 could not utilize any HMW PAH as sole carbon and energy source but could partially degrade these if cultured in a nutrient broth (Boonchan et al. 2000). It was clear from the work performed by Boonchan and coworkers (2000) that the combination of bacterial and fungal activity increased the mineralization of high molecular weight PAHs in liquid culture and in soil. How plants and associated mycorrhizal symbionts are involved in biodegradation of pollutants is however yet much unexplored. An improved understanding of species dynamic interactions and synergisms with plants will provide new tools to promote bioremediation of polluted soils.

Practical Implementation of Plant-Microbial Systems in PAHs Biodegradation Microbial transformation of POPs is important in nature as well as for various technological applications of MO, such as wastewater treatment, biodegradation, bioremediation, and biocatalysis. Although a wide range of MO have been discovered that are able to degrade highly stable, toxic organic compounds such as polycyclic and aliphatic hydrocarbons (Habe and Omori 2003; Kanaly and Harayama 2000; van Hamme et al. 2003), the environmental pollution caused by these compounds remains an unresolved problem. Poor bioavailability due to the low aqueous solubility of PAHs may also account for their overall low biodegradation extents in nature. The activity of degrading MO depends upon many factors, including contaminant uptake and toxicity (Cerniglia 1992). Very little is known, however, about how PAHs travel across bacterial membranes to reach the cytoplasmic metabolic enzymes. Toxic effects of PAHs on MO are not well documented. Despite the high partition of hydrophobic compounds into membranes (Sikkema et al. 1994, 1995), PAHs can be considered nontoxic to bacteria because of their


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very low water solubility (Neumann et al. 2005). Microbial cells are adapted to respond rapidly to environmental stress by regulating the fluidity of their membrane through a modification in membrane lipid composition. The influence of membrane-active organic compounds on membrane lipids in Gram-negative bacteria has been extensively studied (Heipieper et al. 2003; Mrozik et al. 2005; Segura et al. 1999). On the other hand, the effect on the membrane lipids of Gram-positive bacteria, particularly those that are able to utilize such compounds as a sole carbon source, has not yet been studied in detail, in spite of their biotechnological interest for biodegradation or bioremediation. More recently, Zhang and Zhu (2009) demonstrated that sorption of PAHs to ryegrass root cell membranes was actually regulated by carbohydrates and lipids rather than lipids individually. Conceivably, plants could be exploited to aid in the removal of PAHs and other contaminants from soils via two main mechanisms: (1) the accumulation by, and the subsequent metabolisation in plant tissues following the contaminant uptake by plant roots - phytodegradation (Wild et al. 2005) and (2) the enhanced microbial activity induced by root exudation of enzymes to transform and/or mineralize contaminants – rhizodegradation (Rentz et al. 2004; Kamath et al. 2005). Thus, the bioavailability of soil associated PAHs and plant uptake capacity would greatly influence the efficiency of PAH phytoremediation. If the dominant mechanism of PAH dissipation in planted soil is associated with rhizosphere microbial activity, then the remediation potential will significantly vary between plant species and ecotypes. In fact, a growing number of studies indicate that plant species do not all have the same potential for enhanced remediation (Frick et al. 1999; Hutchinson et al. 2003; Gaskin et al. 2008). This may be a result of differences in plant morphology (e.g. roots), physiology (e.g. root exudates), and microbial interactions in the rhizosphere (Pichtel and Liskanen 2001; Walker et al. 2003, Fig. 1). Some plants such as Cucurbita pepo (zucchini) accumulate high levels of hydrophobic chemicals (Campanella et al. 2002; Bittzanski et al., this volume), others, such as alfalfa possess extensive root systems that exhibit a high affinity toward hydrophobic chemicals (Schwab et al. 1998). Grasses in particular have often been proposed as potential candidates for the rhizoremediation of hydrocarbons due to their highly branched, fibrous root systems, which can harbour large microbial numbers and exert a greater influence on the soil environment (Anderson et al. 1993). In one soil type, PAH dissipation was highest in soils planted with a grass species and lowest in those with clover (Banks et al. 2000), whereas in a different soil type grass (Panicum bisulcatum) inhibited PAH mineralization (Watkins et al. 1994). Gaskin and co-workers (2008) identified three Australian native grass species (Brachiaria decumbens, Cymbopogon ambiguous, Microlaena stipoides var. Griffin) as suitable candidates for rhizoremediation applications of aliphatic hydrocarbon-contaminated soils. Hydrocarbon-degrading microbial numbers were increased in the rhizosphere. The number of culturable PCB-degrading rhizobacteria was also found to differ significantly depending on the plant species from where they were isolated (Leigh et al. 2006). Significantly higher numbers of PCB degraders (2.7- to 56.7-fold-higher means) were detected in the rhizosphere of Pinus nigra and Salix caprea than that of other plant species or in non-vegetated soil in certain seasons

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and at certain soil depths. Olson and co-workers (2008) studied the effects of different types of vegetation on PAH removal, and on the interaction between the plants and their associated MO. Aged PAH-polluted soil (753 mg PAHs kg−1) was planted with 18 plant species representing eight families. Planting significantly enhanced the dissipation rates of 17 PAHs within the first 7 months, but this effect was not significant after 14 months. The extent of removal of LMW PAHs was similar for planted and unplanted control soils after 14 months, but removal of fiveand six-ring PAHs was significantly greater in planted soils. Poaceae (grasses) were the most effective of the families tested, and perennial ryegrass was the most effective species; after 14 months, soils planted with perennial ryegrass contained 30% of the initial total PAH concentration (compared with 51% of the initial concentrations in unplanted control soil). There was no correlation across plant species between PAH dissipation and the size of the PAH-degrading population. Further research is needed to understand differences among plant families and species for stimulating PAH dissipation. To achieve maximum contaminant dissipation and successfully establish stable vegetation cover, various criteria must be considered. Plants should be chosen carefully so that they provide a maximum root surface area. They should be native to the area in which they are being used and should be tolerant to local soil conditions. Since cost is an important factor, plants that require little attention (e.g., plants that do not need fertilization) are preferable. Due to the usually poor nutrient availability in contaminated soils (Harris et al. 1996), much research has been conducted on the use of legumes capable of fixing nitrogen (Liste and Alexander 2000). Phenanthrene uptake as well as the effect of phenanthrene on the membrane phospholipid and fatty acid composition in a newly isolated bacterial strain, Sphe3, identified as Arthrobacter sp, was studied (Kallimanis et al. 2007). Strain Sphe3 is able to utilize phenanthrene as a carbon source at high rates and appears to internalize phenanthrene with two mechanisms: passive diffusion when cells are grown on glucose and an inducible active transport system when cells are grown on phenanthrene as a sole carbon source. Active transport followed Michaelis-Menten kinetics, and it was amenable to inhibition by 2,4-dinitrophenol and sodium azide. Evidence provided here indicates that apart from inducing an active PAH uptake, the presence of phenanthrene elicits significant changes in membrane fluidity (Kallimanis et al. 2007). The efficiency of contaminant dissipation will be greatly influenced by contaminant bioavailability (influenced by contaminant hydrophobicity, aqueous solubility and polarity). Rhizoremediation can suffer from a drawback due to the formation of bound-residues or sequestration of organic compounds during processes of decomposition and humification of organic matter (Alexander 2000). Contaminated soils often contain a separate non-aqueous phase liquid (NAPL) that may be present as droplets or films on soil surfaces. Many pollutants, especially those that are hydrophobic, are virtually insoluble in water and remain adsorbed in the NAPL (Chauhan et al. 2008). Biosurfactants can potentially increase the bioavailability of PAHs via mechanisms such as emulsification of non-aqueous phase liquid (NAPLs) in the soil, enhancement of the apparent solubility of the PAHs, or mobilization of PAHs adsorbed to the soil (Volkering et al. 1995). Biosurfactant additions were


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shown to increase the apparent solubilities of PAHs five to 20-fold and significantly increase their rate of biodegradation (Barkay et al. 1999; Rosenberg et al. 1999). Garcia-Junco and co-workers (2001) isolated a biosurfactant-producing strain, Pseudomonas aeruginosa 19SJ, which also had the capability to degrade phenanthrene. Root exudates or rhizodeposits not only provide a nutrient-rich habitat for MO but can potentially enhance biodegradation in different ways: they may facilitate the co-metabolic transformation of pollutants with similar structures, induce genes encoding enzymes involved in the degradation process, increase contaminant bioavailability (surfactant activity), and/or selectively increase the number and activity of pollutant degraders in the rhizosphere (Anderson and Coats 1995; Schnoor et al. 1995; Nichols et al. 1997; Burken and Schnoor 1998; Miya and Firestone 2001; Shaw and Burns 2003). As a result, the microbial community might increase the transformation rates of toxicants. Since the release of compounds or enzymes from roots is presumed to be associated with rhizosphere biodegradation and the nature and quantity of compounds released varies according to plant type, it follows that the plant species used will be a significant factor influencing the efficacy of accumulation/degradation of organic pollutants and associated risk. Plants (and plant types) vary widely with respect to root parameters such as morphology, root exudation (Grayston et al. 1996), fine root turnover (Gill and Jackson 2000), root decomposition (van der Krift et al. 2002), and associated microbial communities (Smalla et al. 2001). Interactions in the rhizosphere are very complex and they can be further unravelled through the use of sophisticated metabolic techniques, such as whole-transcriptome profiling and proteomics (Wood 2008). In general, there is a need to better understand the mechanisms involved in root surface colonization, which encompass complex regulatory mechanisms that modulate gene expression in both the plant and the associated bacteria (Rudrappa et al. 2008). Bioremediation and biodegradation of POPs can be facilitated by growing certain plant species that enhance the biodegradation activities of bacteria and fungi in the rhizosphere (Fig. 1). Combining bacteria with suitable degradation capacities

Plants/ Exudates

Rhizobacteria

AMF

PGPR

POPs Biodegradation/ Bioavailability to plants

Fig. 1 Effects of plants/rhizosphere interactions on the bioremediation of POPs

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with suitable plant hosts is of outmost importance. It is important to assess how microbial communities degrade POPs in order to understand their potential role for in situ bioremediation. The nature of plant microbial interactions in the rhizosphere, with a focus on those processes that are relevant to the breakdown and (or) removal of organic pollutants, has been overlooked so far. Key aspects are still unknown. Special attention goes to the degradation pathways expressed in the rhizosphere and how plants and associated symbionts are involved in biodegradation of pollutants. Our understanding of microbial dynamic interactions and synergisms with plants will provide new tools to promote bioremediation of polluted soils.

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Bioindicators and Biomonitors: Use of Organisms to Observe the Influence of Chemicals on the Environment* Bernd Markert and Simone Wünschmann

Abstract For a number of years “classical” programs for environmental monitoring are being supplemented by bioindication measures already. Here, investigations on living organisms or their remains (e.g. peat) are used to indicate the environmental situation in either qualitative (bioindication) or quantitative (biomonitoring) terms. This provides pieces of information on environmental burdens of a region at a given point of time or on its changes with time (trend analysis). Classical bioindication often deals with observation and measurements of chemical noxae (both inorganic and organic ones) in well-defined bioindicator plants or animals (including man). In terms of analytical procedures and results there are parallel developments between progresses in bioindication and innovation in analytical methods. After some 30 years of development in bioindication there are now following lines of further development: 1) more frequent inclusion of multi-element total analyses for a thorough investigation of mutual correlations in the sense of the Biological System of Elements, 2) more work on (analytical) speciation issues to proceed into real effect-oriented environmental sciences, and 3) there should and must be a focus on integrative bioindication methods because for a large number of environmental monitoring problems a single bioindicator will not provide any meaningful information: a single bioindicator is about as good as none at all. Integrative concepts

This article is in parts related to B. Markert, S. Wuenschmann, R. Herzig and Ph. Quevauviller, 2010: Bioindicateurs et biomoniteurs dans lénvironnment: Définitions, stratégies et applications, Editions Techniques de lÌngénieur, P 4 170, p. 1-16; Markert B.A., Breure A.M., and Zechmeister H.G., eds., 2003: Bioindicators and Biomonitors – Principles, Concepts and Applications, Elsevier, Amsterdam, New York, Tokyo; Markert B 1996: Instrumental Element and Multielement Analysis of Plant Samples, Wiley, Chichester, New York.

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B. Markert (*) Former director of the Internationales Hochschulinstitut Zittau, Lehrstuhl für Umweltverfahrenstechnik, Markt 23, 02763 Zittau, Germany e-mail: [email protected] B. Markert and S. Wünschmann Fliederweg 17, D-49733, Haren/Erika, Deutschland P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_10, © Springer Science+Business Media B.V. 2011

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such as the Multi-Markered Bioindication Concept (MMBC) provide basic means to get into precautionary environmental protection effects drawing upon such a second-generation bioindication methodology.

Introduction Investigations on living organisms are used to indicate the environmental situation in either qualitative (bioindication) or quantitative (biomonitoring) terms. Bioindication/biomonitoring often deals with observation and measurements of chemical noxae (both inorganic and organic ones) in well-defined bioindicator/ biomonitoring plants (including microbes) or animals (including man). According to the extracted conclusion, that the use of only one bioindicator/biomonitor is as good as none at all, an integrative approach (the Multi-Markered-Bioindication-Concept, MMBC) is presented and the final goal is focussed towards an absolutely essential need of international teaching campaigns. At all times, plants have served as particularly interesting and important objects of scientific investigation. This is seen even in Greek biology, in the works of Aristotle or Theophrastus, which were devoted to plant taxonomy. Other later examples are the studies of Linnaeus on taxonomy and evolution, and those on yield-based economy and agricultural chemistry for the nourishment of mankind, starting with “Gesetz des Minimums” by Justus von Liebig, as well as the general target of biological and chemical basic research at the molecular biological level as done by Calvin and Krebs, for example (Markert 1996). Interest in new scientific knowledge has increased exponentially in the last 50 years of plant research. There are essentially three reasons for this (Markert 1996). 1. Plants make up more than 99% of the total biomass of the earth. The protection and conservation of the species and of the diversity of species, particularly in tropical and subtropical areas, has been elevated to one of the most important ecological, economical and cultural demands in national and international politics. 2. Plants are responsible for the most important reaction on earth, photosynthesis, producing carbohydrates from water and carbon dioxide in a complicated, light driven reaction, and oxygen at the same time. Life on earth in its present form would not be possible without the production of carbohydrates and the oxygen which is necessary for the breakdown of carbohydrates in the respiratory chain. A system which diffuses into a state of imbalance, one that is fed constantly increasing CO2 emissions and the simultaneous indiscriminate destruction of tropical rain forests in particular, represents a great ecological and economical challenge for mankind today due to the resultant greenhouse effect. 3. With respect to their effect on the flow of matter and of energy in the food chain, plants represent an important link between the atmosphere and the soil on the one hand and between consumers from the first (microbes) to the highest order (animals and man) on the other. Frequently, pollutants are introduced into the food chain via plants which have taken them up, and these pollutants often cause

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irreversible damage of individual organisms to entire biological communities as a result of accumulation and exclusion processes. Point 3, in particular, places high demands on modern analytical chemistry. Through the development of increasingly sensitive analytical methods, today it is practically possible to quantify most of chemicals. In cooperation with associated disciplines such as geology, physics, and medicine, this has led to the situation where biological trace research has become very dynamic. Figure 1 gives the average concentration of 82 naturally occurring elements in plants and in the earth’s crust as a function of their atomic mass.

Fig. 1 Average concentration of 82 naturally occurring elements in earth´s crust and plants as a function of their atomic mass (Markert 1996)

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The largest portion of the fresh weight of living plants (showing active metabolism) consists of 85–90% water. The dried matter of the plant is made up mostly of the following elements: carbon (44.5%), oxygen (42.5%), hydrogen (6.5%), nitrogen (2.5%), phosphorus (0.2%), sulfur (0.3%), and the alkali or alkaline earth metals: potassium (1.9%), calcium (1.0%), and magnesium (0.2%). Thus, in contrast to the earth’s crust, the main mass of organic life consists largely of non-metals. There are also microelements, which are present in plants in reduced concentrations and which are vital for most plants. These microelements are chlorine (2,000 mg/kg dry material), silicon (1,000 mg/kg), manganese (200 mg/kg), sodium (150 mg/kg), iron (150 mg/kg), zinc (50 mg/kg), boron (40 mg/kg), copper (10 mg/kg), chromium (1.5 mg/kg), molybdenum (0.5 mg/kg), and cobalt (0.2 mg/kg). Macro- and microelements are plant nutrients necessary for the growth and normal development of the plant. Therefore they are essential (Fig. 2). With respect to inorganic environmental chemistry one can roughly envision that about every 2 years one of the chemical elements in the periodic table will change its former status. The developmental history of selenium is a classical example. In 1930, Se and its compounds were generally felt to be highly toxic. After 1943 they were also classed as carcinogenic. In 1957 it was recognized that selenium is required by some organisms, and in 1966 certain Se compounds were successfully used in

Fig. 2 The periodic table of the elements with indicators on elements that are essential and that have been quantitatively determined (Markert 1996). A so called Biological System of the Elements (BSE) is given in Markert (1994) and Fränzle & Markert (2002)


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c ancer therapy. In 1990 Prof. Braetter´s research group in Berlin characterized and isolated already a second Se protein and described its function.

General Information on the Environment Bioindication and biomonitoring must supply information on the extent of pollution or degradation of ecosystems. Two different forms of information are available from bioindication: firstly, general ones which oversimplify matters, e.g. plant damage, and secondly highly specific ones which latter is provided in a very detailed, objective, reproducible and precise manner. For example, a certain pollutant may be linked to one physiological reaction in a bioindicator organism in order to obtain some more general information on the environment. When data and information obtained by bioindication are extrapolated to provide some higher knowledge the subjectivity of interpretation increases with the complexity and dynamics of a system. This increase in subjectivity linked to an increase in knowledge is depicted by the “staircase of knowing” (Roots 1992). On the first step of this staircase (Fig. 3), observations and measurements become data when verified according to agreed standards.

Fig. 3 The staircase of “knowing” (modified after Roots 1992)

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When data are properly selected, tested and related to subject areas they can become (pieces of) information: In turn information, once being organized and interpreted or applied to areas of interest or concern, can become established knowledge. If assimilated and mentally assessed and backed by additional information, this knowledge may be comprehended and integrated into a basis of facts and notions assimilated before, eventually leading to understanding. And understanding combined with judgement according to certain values can become wisdom. In general, by moving up the staircase, the material and ideas become increasingly subjective, with increasing human value added (Roots 1996).

Specific Information on the Environment Specific and detailed information of systems are essential within bioindication to draw clear-cut conclusions e.g. in between a pollutant and an effect of an organism (bioindicator). Figure 4 gives a simplified representation of complex (eco-)system interrelations being influenced by some pollution, and of the consequences of changes as revealed by bioindication and biomonitoring (Markert 1996). As a rule, it is assumed that a pollutant affects an organism which latter is taken as bioindicator or biomonitor. Both the organism and the pollutant interact closely with other ecosystem compartments (Fig. 4).

Fig. 4 Simplified representation of complex (eco-)system interrelations with regard to a pollutant, and consequences for bioindication and biomonitoring (Markert 1996)


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The life activity of the organism is therefore influenced by a great number of abiotic and biotic factors and may often be subject to joint action of several pollutants, especially under “natural” field conditions (subjective selection of references of the large amount of literature by the authors of this article: Adriano 1992; Broadley et al. 2007, 2008; Cakmak 2008; Chaney et al. 2008; De Bruyn et al. 2009; Fargašová and Beinrohr 1998; Fraenzle 2009; Fraenzle and Markert 2002, 2007; Fraenzle et al. 2007, 2008; Franzering and van der Eerden 2000; Golan-Goldhirsh et al. 2004; Greger 2008; Herzig 1993, 2005; Hanikenne et al. 2008; Hartley and Lepp 2008; Herzig and Bieri 2002; Irtelli and Navari-Izzo 2008; Lepp and Madejon 2007; Li et al. 2008; Lux et al. 2004; Markert 1993, 1994, 1996, 2007; Markert and Weckert 1993; Markert et al. 2010; Marmiroli and Maestri 2008; Mench et al. 2006; Mohr 2007; Poschenrieder et al. 2008; Prasad 2008; Quartacci et al. 2007; Quevauviller and Maier 1999; Quevauviller et al. 2008; Rasemann and Markert 1998; Renella et al. 2004; Rezek et al. 2008; Schröder et al. 2007; Schröder et al. 2008a, b; Schwitzguébel et al. 2008; Smeets et al. 2008; Smodis 2003; Szárazová et al. 2008; Trapp et al. 2008; Verbruggen et al. 2008; Verkleij 2008; Wuenschmann et al. 2001, 2002, 2008). With regard to the interpretation of the “information” given by the bioindicator/ biomonitor, often the problem arises from where changes observed or measured by the bioindicator/biomonitor really originate. Even a combined multi-functional and multi-structural view of the various ecosystem compartments often left the specific operative mechanisms unaccounted for. What makes matters even more difficult is that the pollutant to be monitored is closely connected to all other environmental compartments. So it is by no means certain, although rather probable, that pollutant A does not interact synergistically or antagonistically with pollutant B (Fig. 4). Moreover, the absorption pathway, sites of actions and metabolisms of both A and B usually are not yet adequately described. Nevertheless pollutant A may also affect other biota which may react even more sensitively to A than the bioindicator itself. If this sensitivity alters the population density of a more sensitive organism the abundance of the very bioindicator may also be affected, at least if the former is in direct or indirect competition with the latter. It is an unsettled issue whether a statement about the current condition of an entire ecosystem can be obtained by examining a single bioindicator (Markert 1996). With respect to the age of “information technologies”, Lieth (1998) tries to render the “digitalized bit world” more efficient for ecosystem research. According to Lieth we have to ask: what is the crucial point of ecosystem research? What information does an ecosystem offer? Given the information content of all its parts an ecosystem readily compares to the level of an intelligent system. Toxicological implications often involve the flow of information as the cause of significant changes in material fluxes and energy fluxes in the system. Plants may produce chemicals to protect themselves against animal grazing. Animals may produce toxic chemicals as weapons; humans may produce toxic chemicals to kill each other. Each process is controlled by “bits of information” which flow from one point in the ecosystem to another, so-called biobits (Markert et al. 2002, 2003b). A detailed description of this straightforward concept for further study is given in Lieth (1998).

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Definitions It seemed clear from the start that bioindication and biomonitoring are promising (and possibly cheap) methods of observing the impact of external factors on ecosystems and their development over a long period, or of differentiating between one location (e.g. an unpolluted site) and another (polluted site) (Markert et al. 2002, 2003a, b). The overwhelming enthusiasm shown in developing these methods has resulted in a problem that is still unsolved: the definitions of bioindication and biomonitoring respectively, and therefore the expectations associated with these methods, have never led to a common approach by the international scientific community, so that different definitions (and expectations!) now exist simultaneously (Markert et al. 2002, 2003a, b). A fine overview of the various definitions is given by Wittig (1993). As a first starting point for the difficult use of bioindication methods following literature might be helpful (subjective selection of the large amount of literature by the authors: Altenburger and Schmitt 2003; Arndt 1992; Bacchi et al. 2000; Bargagli 1998; Bode et al. 2000; Breulmann et al. 1998; Carreras et al. 1998; Djingova and Kuleff 2000; Elias et al. 2006; Farago, 1994; Figueiredo et al. 2001; Fraenzle O 1993; Fraenzle and Markert 2002, 2007; França et al. 2005, 2007; Freitas et al. 2006, 1999; Garty 1998; Genßler et al. 2001; Herpin et al. 1997, 2001; Herzig 1993, 2005; Jeran et al., 1993; Klumpp et al. 2000; KostkaRick et al. 2001; Lieth 1998; Loppi and Bonini 2000; Markert 1993, 1994, 1996, 2007; Pacheco et al. 2003; Saiki et al. 1997; Schroeder et al. 2008a, b; Shtangeeva et al. 2005; Siewers and Herpin 1998; Siewers et al. 2000; Stoeppler et al. 1982; Suchara et al. 2007; Vtorova et al. 2001; Vutchkov 2001; Wolterbeek 2002; Wolterbeek et al. 1995; Zechmeister et al. 2007). In the following some definitions will be given that have been developed and used by us over the last 20 years (Markert et al. 1999, 2003b), since they differentiate clearly between bioindication and biomonitoring using the qualitative/quantitative approach to chemical substances in the environment. This makes bioindicators directly comparable to instrumental measuring systems (Markert et al. 2003a, b). From that angle it is possible to distinguish clearly between active and passive bioindication (biomonitoring). Especially where the bioindication of metals is concerned, the literature often makes a distinction between “accumulation indicators” and “effect indicators” in respect of the reaction of the indicator/monitor to changes in environmental conditions. Here we should bear in mind that this differentiation does not imply a pair of opposites; it merely reflects two aspects of analysis. As the accumulation of a substance by an organism already constitutes a reaction to exposure to this substance which – at least in the case of high accumulation factors – is measurably reflected in at least one of the parameters used in defining the term “effect indicator/monitor” (e.g. morphological changes at the cellular level; formation of metal-containing intracellular granules in many invertebrates after metal accumulation), we should discuss whether it is worthwhile distinguishing between accumulation and effect indicators or whether both terms fall under the more general expression “reaction indicator”. Often, too, it is not until a substance has been


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Fig. 5 Illustration of the terms reaction, accumulation and effect/impact indicator (Markert et al. 1997). Explanations are given in the text

accumulated in organisms that intercellular or intracellular concentrations are attained that produce effects which are then analyzed in the context of effect and impact monitoring (Fig. 5). From these preliminaries we come to the following definitions, firstly summarized in Markert et al. 1997 and 1999: A bioindicator is an organism (or part of an organism or a community of organisms) that contains information on the quality of the environment (or a part of the environment). A biomonitor, on the other hand, is an organism (or a part of an organism or a community of organisms) that contains information on the quantitative aspects of the quality of the environment. A biomonitor is always a bioindicator as well, but a bioindicator does not necessarily meet the requirements for a biomonitor. Both, bioindication and biomonitoring are referred to as “active”, when test organisms bred in laboratories are exposed in a standardized form in the field for a defined period of time. At the end of this exposure time the reactions provoked are recorded or the xenobiotics taken up by the organism are analyzed. In the case of passive biomonitoring, organisms already occurring naturally in the ecosystem are examined for their reactions. This classification of organisms (or communities of these) is according to their “origin”. A classification of organisms (or communities of these) according to their “mode of action” (Fig. 5) is as follows: Accumulation indicators/monitors are organisms that accumulate one or more elements and/or compounds from their environment. Effect or impact indicators/monitors are organisms that demonstrate specific or unspecific effects in response to exposure to a certain element or compound or a number of substances. Such effects may include changes in their morphological, histological or cellular structure, their metabolic-biochemical processes, their behavior or their population structure. In general the term “reaction indicator” also includes accumulation indicators/monitors and effect or impact indicators/monitors as described above.

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When studying accumulation processes it would seem useful to distinguish between the paths by which organisms take up elements/compounds. Various mechanisms contribute to overall accumulation (bioaccumulation), depending on the species-related interactions between the indicators / monitors and their biotic and abiotic environment. Biomagnification is the term used for absorption of the substances from nutrients via the epithelia of the intestines. It is therefore limited to heterotrophic organisms and is the most significant contamination pathway for many land animals except in the case of metals that form highly volatile compounds (e.g. Hg, As) and are taken up through the respiratory organs, (e.g. trachea, lungs). Bioconcentration means the direct uptake of the substances concerned from the surrounding media, i.e. the physical environment, through tissues or organs (including the respiratory organs). Besides plants, that can only take up substances in this way (mainly through roots or leaves), bioconcentration plays a major role in aquatic animals. The same may also apply to soil invertebrates with a low degree of solarization when they come into contact with the water in the soil. Besides the classic floristic, faunal and biocoenotic investigations that primarily record rather unspecific reactions to pollutant exposure at higher organizational levels of the biological system, various newer methods have been introduced as instruments of bioindication. Most of these are biomarkers and biosensors. Biomarkers are measurable biological parameters at the suborganismic (genetic, enzymatic, physiological, morphological) level in which structural or functional changes indicate environmental influences in general and the action of pollutants in particular in qualitative and sometimes also in quantitative terms. Examples: plant and animal enzyme or substrate induction of cytochrome P-450 and other Phase I enzymes by various halogenated hydrocarbons; the incidence of forms of industrial melanism as markers for air pollution; tanning of the human skin, but also protective colouring of plant leaves caused by UV radiation; changes in the morphological, histological or ultra-structure of organisms or monitor organs (flowers, chloroplasts, etc.) following exposure to pollutants. A biosensor is a measuring device that produces a signal in proportion to the concentration of a defined group of substances through a suitable combination of a selective biological system, e.g. enzyme, antibody, membrane, organelle, cell or tissue, and a physical transmission device (e.g. potentiometric or amperometric electrode, optical or optoelectronic receiver). Examples: toxiguard bacterial toximeter; EuCyano bacterial electrode. Biotest (bioassay): routine toxicological-pharmacological procedure for testing the effects of agents (environmental chemicals, pharmaceuticals) on organisms, usually in the laboratory but occasionally in the field, under standardized conditions (with respect to biotic or abiotic factors). In the broader sense this definition covers cell and tissue cultures when used for testing purposes, enzyme tests and tests using microorganisms, plants and animals in the form of single-species or multi-species procedures in model ecological systems (e.g. microcosms and mesocosms). In the narrower sense the term only covers single-species and model system tests, while the other procedures may be called suborganismic tests. Bioassays use certain biomarkers or – less often – specific biosensors and can be used in bioindication or biomonitoring.


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With regard to genetic and non-genetic adaptation of organisms and communities to environmental stress we have to differentiate between the terms tolerance, resistance and sensitivity. Tolerance (Oehlmann and Markert 1997): desired resistance of an organism or community to unfavorable abiotic (climate, radiation, pollutants) or biotic factors (parasites, pathogens), where adaptive physiological changes (e.g. enzyme induction, immune response) can be observed. Resistance, unlike tolerance, is a genetically derived ability to withstand stress (Oehlmann and Markert 1997).This means that all tolerant organisms are resistant, but not all resistant organisms are tolerant. However, in ecotoxicology the dividing line between tolerance and resistance is not always so clear. For example, the phenomenon of PICT (pollution induced community tolerance) is described as the phenomenon of community shifts towards more tolerant communities when contaminants are present. It can occur as a result of genetic or physiological adaptation within species or populations, or through the replacement of sensitive organisms by more resistant organisms (Blanck et al. 1988; Rutgers et al. 1998). Sensitivity of an organism or a community means its susceptibility to biotic or abiotic change. Sensitivity is low if the tolerance or resistance to an environmental stressor is high, and sensitivity is high if the tolerance or resistance is low. A fruitful example for a country-wide investigation by passive biomonitoring with lichen is the comparison of the POP burden in areas with different types of land use shows that the burden emanating from Switzerland, particularly in conurbations, is considerably greater than the amount transported over long-ranges and across national boundaries. However, the latter category certainly contributes to the background level of contamination. The first country-wide POP study established a representative and spatially differentiated biomonitoring network and a reference archive of samples in Switzerland. A monitoring instrument of this kind can also be used to document the success of efforts to reduce burden over time (Herzig and Bieri 2002; and chapter 3 in this book). Other stimulating examples of bioindication and biomonitoring studies for controlling organic pollutants are given amongst others in Schwarz and Jonas (1997).

Further Studies and Outlook: MMBC and Teaching Guidelines Bioindication and biomonitoring must supply information on the degree of pollution or degradation of ecosystems. For integrative approaches bioindication is not an “environmental monitoring machine” for a specific constellation of factors; ideally, it is an integrated consideration of various bioindicative test systems which attempts, in conjunction with other environmental parameters, to produce a definite picture of a pollution situation and its development in the interests of prophylactic care of health and the environment. Figure 6 is a diagram of a complete dynamic environmental monitoring system supported by bioindication which depicts how tightly bioindication is coupled to

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B. Markert and S. Wünschmann Public Health Diagnosis and prognosis for prophylactic health care

Singular use of TESTS

1 INTEGRATED APPROACH 1

2

TOOLBOX MED

TOOLBOX ECO

TOOLBOX HSB

TOOLBOX ESB

TRE

data/trends of SPECIMEN BANKING

2 2

DAT

basic data of system under investigation

1 1

HUMANTOXICOLOGY

Singular use of TESTS and MONITORS

2

ECOTOXICOLOGY

ECOSYSTEM

HUMAN

?

? POLLUTANT

TOOLBOX

SET OF TESTS, DATA or TRENDS

Fig. 6 Multi-Markered-Bioindication-Concept (MMBC): Possible hierarchical structure of a bioindicative toolbox model for integrative approaches in human- and ecotoxicology. The toolboxes MED and ECO contain single sets of tests that can be combined functionally to allow an integrated approach to any frame of reference or a specific scientific problem. The toolboxes HSB (human specimen banking) and ESB (environmental specimen banking) represent years of results from international environmental sample banks specializing in environmental and human toxicology. In addition to MED and ECO they provide important information on ecotoxicology and human toxicology of environmental chemicals. In the integrated approach, all results obtained are substantiated by existing basic data available from (eco-) systems research, toxicology and environmental sample banks. The parameter constellations necessary for this are taken from the toolboxes TRE and DAT (Markert et al. 2003b)

human toxicology. Hence biomonitoring is an important contribution to public health. It can re-combine its measurement parameters according to the particular system to be monitored or the scientific frame of reference. The two main subjects of investigation – man and the environment – and the disciplines human toxicology and ecotoxicology derived from them are associated with various “toolboxes” and sets of tests (“tools”, e.g. bioassays) for integrated environmental monitoring. The system shown in Fig. 6 consists of six toolboxes. The first two are derived mainly from environmental research: DAT (for data) and TRE (for trend). DAT contains, as a set, all the data available from the (eco-)system under investigation, i.e. including data acquired by purely instrumental means, for example from the meteorological sphere. DAT also contains maximum permissible concentrations of


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substances in drinking water, food or air at the workplace and the data for the relevant ADI (“acceptable daily intake”) and NO(A)EL (“no observed (adverse) effect level”). The toolbox TRE contains data on trends; these have been compiled mainly from years of investigations by national environmental sample banks, or information available from long-term national and international studies (e.g. Duvigneaud and Denayer-De Smet 1973; Ellenberg et al. 1986; Likens et al. 1977). Specific conclusions and trend forecasts can then be prepared using the subsequent toolboxes HSB (human specimen banking) and ESB (environmental specimen banking) (see also Kettrup 2003). The toolbox MED (medicine) contains all the usual methods employed in haematological and chemical clinical investigations of subchronic and chronic toxicity, whereas ECO is largely made up of all the bioindicative testing systems and monitors relevant to ecosystems which may be combined to suit the particular situation to be monitored. The data from all the toolboxes must interact with each other in such a way that it is possible to assess the average health risk for specific groups of the population or determine a future upper limit of risk from pollutants by forming networks. This risk assessment ultimately makes use of all the toxicological limits that take the nature of the effect and dose–effect relationships into account according to the current status of scientific knowledge. Since toxicological experiments cannot be carried out on human beings, recourse has to be made to experience at the workplace and cases of poisoning in order to permit an evaluation and risk assessment. Besides examining reports on individual cases, greater efforts must be made to reveal the effects of substances as a cause of disease by means of epidemiological surveys with exposed groups as compared to a control group. The development and use of simulation models supported by information technology, taking all the data collected into account, will play an important role here, since a large number of parameters that do not interact directly have to be combined. They include various data from the field of epidemiology, from mutagenicity studies, toxicokinetics, metabolism research and structure–effect relationships. The conclusions of such networking in between different tool boxes can be used for a whole concept of bioindication in general, outlined in the so called Multi-MarkeredBioindication-Concept (MMBC), which is outlined in Markert et al. (2002, 2003b). In conclusion there is very much interest on integrated monitoring which will require an interdisciplinary design and formation of research groups in future surveys, too. This would permit rapid and flexible adjustment of the working groups to the particular frame of reference and enable a quick exchange of information between the individual disciplines. To come closer to a prophylactic health care system between ecotoxicologists and medical doctors we should follow a common integrative way, and we should not work along parallel paths and thus separately as was most often done in the past 20 years. To this end, it could be worthwhile to have a look at the former ideas of combination of geoscientific ecology and medical sciences, which have some tradition in the German landscape ecology (Jusatz 1958; Jusatz and Flohn 1937; Mueller 1980; Schweinfurth 1974). Additionally, we should be aware of the interrelationships between culture, environmental quality and human health (Dansereau 1971; Warren and Harrison 1984).

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Table 1 Possible tracks to follow from environmental monitoring to human health (Markert et al. 2008). In the past, a gap of scientific collaboration between analytical scientists, ecotoxicologists and people concerned with environmental medicine (human toxicology) was obvious. This can be in the present overcome by developing a more intensive collaboration by communication and defining common goals in research and education. To reach a common scientific interest in the future, different specific research methods should be used for similar problems, which mean a common learning by doing research on the same interdisciplinary problems (symbolized by the middle column between present and future status). MMBC: Multi-MarkeredBioindication-Concept, a newly developed multidisciplinary system including integrated and functional “windows” of prophylactic healthcare Past (1980) Present (2000) Future (2020) Classical Interdiscipling Integrated International Interregional Intercultural Developing a Developing the link Goal Qualitative and prophylactic health between environmental quantitative care approach biomonitoring to human measurement health of environmental parameters MMBC-concept Defining common research Methods Comparison of own and some others interests between results with available on the eco- and human“others” scientific market toxicology Education, communication Quality Mental driving Knowledge forces Language cooperation Common science and Tools Instrumental and common goals education, success bioindicative and acceptance measurements

Table 1 tries to symbolise the “dilemma” of what blocks at the moment a fast development from present eco- and humantoxicological bioindication methods towards a more integrated understanding. Obviously, there exists a lack of intensive discussion and collaboration between ecologists and human medical people. Simply one example, which is obviously present in our day by day work. In the fine and most recent “Lehrbuch der Toxikologie [Treatise on Toxicology]” by Marquard and Schaefer (2004), an excellent content is given by around 100 scientists including most relevant topics in our common scientific field, but scientific findings of the ecotoxicologists (for example, nationally and internationally organized in SETAC) have more or less not been taken into account. To overcome this discrepancy there are two important issues that must be considered: 1. Common education of “toxicologists” at universities by integrative textbooks (for example, Fomin et al.’s, textbook on practical use of biotests published in 2003) 2. Common scientific projects as for example given in Table. 1 (Markert et al. 2008)


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Especially for topic two, an interdisciplinary language, common goals and methods have to be developed and finally successful research should be initiated. Acknowledgements We would like to thank all colleagues, friends, clients and students of numerous field studies worldwide for their critical and intensive discussions on our common topic (bioindication and biomonitoring) since a lot of years. A lot of their thoughts have influenced our MS.

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Siewers U, Herpin U, Strassburg S (2000) Schwermetalleinträge in Deutschland. Teil 2: MoosMonitoring 1995/1996Geol Jb, Sonderheft SD, Hannover, 3:1–121 Smeets K, Ruytinx J, Van Belleghem F, Semane B, Lin D, Vangronsveld J, Cuypers A (2008) Critical evaluation and statistical validation of a hydroponic culture system for Arabidopsis thaliana. Plant Physiol Biochem 46(2):212–218 Smodis B (2003) IAEA approaches to assessment of chemical elements in atmosphere. In: Markert BA, Breure AM, Zechmeister HG (eds) Bioindicators and biomonitors. Principles, concepts and applications. Elsevier, Amsterdam, The Netherlands, pp 875–902 Stoeppler M, Duerbeck HW, Nuernberg HW (1982) Environmental specimen banking. Talanta 29:963 Suchara I, Sucharova J, Hola M (2007) Bio-Monitoring of the atmospheric deposition of elements using moss analysis in the Czech Republic. Acta Pruhoniciana 87:186 Szárazová K, Fargašová A, Hiller E, Velická Z, Pastierová J (2008) Phytotoxic effects and translocation of Cr and Ni in washing wastewaters from cutlery production line to mustard (Sinapis alba L.) seedlings. Fresenius Environ Bull 17:58–65 Trapp S, Feificova D, Rasmussen NF, Bauer-Gottwein P (2008) Plant uptake of NaCl in relation to enzyme kinetics and toxic effects. Environ Exp Bot 64(1):1–7 Vtorova V, Kholopova L, Markert B, Leffler U (2001) Multi-elemental composition of tropical plants and bioindication of the environmental status. In: Biogeochemistry and geochemical ecology: selected presentations of the 2nd Russian School of Thought: Geochemical Ecology and the Biogeochemical Study of Taxons of the Biosphere, Moscow, 25–29 Jan 1999, pp 177–189 Vutchkov M (2001) Biomonitoring of air pollution in Jamaica through trace-element analysis of epiphytic plants using nuclear and related analytical techniques. In: Co-ordinated research project on validation and application of plants as biomonitors of trace element atmospheric pollution, analyzed by nuclear and related techniques, IAEA, NAHRES-63, Vienna Verbruggen N, Hermans Ch, Schat H (2008) Molecular mechanisms of metal hyperaccumulation and tolerance in plants. New Phytol. doi:10.1111/j.1469-8137.2998.02748x Verkleij JAC (2008) Mechanisms of metal hypertolerance and (hyper)accumulation in plants. Agrochimica 52(3):167–188 Warren A, Harrison CM (1984) People and the ecosystem: biogeography as a study of ecology and culture. Geoforum 15:365–381 Wittig R (1993) General aspects of biomonitoring heavy metals by plants. In: Markert B (ed) Plants as biomonitors – Indicators for heavy metals in the terrestrial environment. VCH, Weinheim, pp 3–27 Wolterbeek HT, Kuik P, Verburg TG, Herpin U, Markert B, Thöni L (1995) Moss interspecies comparisons in trace element concentrations. Enviro Moni Assess 35:263–286 Wolterbeek B (2002) Biomonitoring of trace element air pollution: principles, possibilities and perspectives. Environ Pollut London 120:11–21 Wuenschmann S, Oehlmann J, Delakowitz B, Markert B (2001) Untersuchungen zur Eignung wildlebender Wanderratten (Rattus norvegicus) als Indikatoren der Schwermetallbelastung, Teil 1. UWSF-Z Umweltchem Ökotox 13(5):259–265 Wuenschmann S, Oehlmann J, Delakowitz B, Markert B (2002) Untersuchungen zur Eignung wildlebender Wanderratten (Rattus norvegicus) als Indikatoren der Schwermetallbelastung, Teil 2. UWSF-Z Umweltchem Ökotox 14(2):96–103 Wuenschmann S, Fränzle S, Markert B, Zechmeister H (2008) Input and transfer of trace metals from food via mothermilk to the child: bioindicative aspects to human health, Chapter 22. In: Prasad MNV (ed) Trace elements – nutritional benefits, environmental contamination, and health implications. Wiley, New York, pp 555–592 Zechmeister HG, Dullinger S, Hohenwallner D, Riss A, Hanus-Illnar Scharf S (2007) Pilot study on road traffic emissions (PAHs, heavy metals) measured by using mosses in a tunnel experiment. Austria Embv Sci Poll Res 13:398–404

SAR Based Computational Models as Decision Making Tools in Bioremediation Nick Price and Qasim Chaudhry

Abstract Environmental pollution has been the focus of increasing concerns over potential harmful effects on human health and the environment. Amongst the available options for environmental cleanup, technologies based on biological remediation have emerged as low-cost, low-maintenance, environment-friendly, and renewable technologies for potential in situ remediation of organic and inorganic contaminants. However, both microbial and plant species used in these technologies have certain limitations, and it is desirable to know in the first instance whether a contaminant would need remedial action, and whether a biological process would be suitable to breakdown or remove it from the environment. This is where computational models based on structure-activity relationship can provide a quick assessment to support decision making. The (Q)SAR models and expert systems can help prioritise contaminants on the basis of potential toxicities, and inform on their likely behaviour and fate in the environment. This information is in turn helpful in the choice of appropriate remediation technologies, as well as in identifying the recalcitrant chemicals that can be monitored as markers for the success of remediation action. This chapter provides an overview of the rationale behind the development of structure-activity relationship models and provides an up-to-date list of the key relevant software tools that are currently available. However, the availability of a large number of software tools also requires a careful choice of appropriate models and/ or expert systems. The overview also shows that there is a need for development of more integrated systems that can cater specifically for biological remediation technologies.

N. Price () Technology for Growth, York, UK e-mail: [email protected] Q. Chaudhry The Food and Environment Research Agency, Sand Hutton, York, UK P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_11, © Springer Science+Business Media B.V. 2011

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Introduction Increasing public concerns over the presence of certain chemical pollutants in the environment have led to search for suitable technologies for clean up of the contaminated environments. A large proportion of the pollutants released to the environment are degraded by natural factors, such as sunlight, oxygen, microbial degradation etc. However, some pollutants, such as polychlorinated aromatics and dioxins, are known to be recalcitrant to such degradation processes and can linger on in the environment for long periods. In recent decades, bio- and phyto-remediation have emerged as lowcost, low-maintenance, environment-friendly and renewable technologies for in situ degradation and/or removal of organic and inorganic contaminants from the environment. Both technologies are considered to be more cost-effective than the other available ex-situ decontamination techniques. However, there are certain limitations to the removal and/or degradation of certain pollutants by microorganisms or by plants alone. Some pollutants are structurally very stable, i.e. they have chemical groups which normal microbial processes can not break down, or they may be toxic to microbial species in the environment, and hence not amenable to bioremediation. Similarly, whilst phyto-remediation is particularly suited for removing heavy metal ions from contaminated soils, plants have a limited ability to remove certain organic pollutants that are either highly hydrophobic or are soil-bound (Chaudhry et al. 2002). Some phytotoxic compounds may further limit root establishment by plants in contaminated soils. This is where a synergy between soil microflora and plants is known to overcome many of the limitations. The ability of rhizospheric microflora to transform recalcitrant and soil-bound organic compounds, in return for a favourable environment provided by plant roots for their propagation, and in many cases provision of ready energy in the form of root exudates, seems to work hand in hand for the survival of both microorganisms and plants in contaminated soils (Chaudhry et al. 2005). The effectiveness of the remediation technologies, however, requires sound decision-making at the outset. For example, whether there are harmful compounds in a given contaminated environment that warrant a remedial action, and whether such an action would need the use of one or more remediation technologies. In situations where chemical profile of contaminants has been established in a soil or water environment, the use of computational chemistry based models can provide tools for such a decision-making. For example, the use of Quantitative Structure Activity Relationship (QSAR) based mathematical models can help in rapidly assessing whether an environmental contaminant of concern would be biodegradable or persistent, bioavailable or in a bound form, and whether it would be amenable to a remedial action by microorganisms or plants. Other models can provide further pieces of crucial information that can help select appropriate microbial or plant-based processes to suit a particular contamination situation, and highlight whether additional measures may be needed, such as application of surfactants to increase bioavailability of certain contaminants. This chapter provides an overview of the methodology behind QSAR modelling, and discusses examples of relevant models and expert systems that can facilitate decision-making in biological remediation of contaminated environments.

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Computational Models Based on QSAR QSARs are mathematical descriptions of the biological activity of a group of chemical compounds, in terms of one or more of their physicochemical properties. The foundations of QSAR stemmed from the observation that, in a closely related series of chemicals, such as a congeneric series, the biological activity of the compounds varied according to steric, electronic and hydrophobic properties of the series, and that this could be expressed mathematically. The development of the concept from these early beginnings to the present day have been documented in a number of “histories”, notably Selassie (2003). Although the original principles of QSAR were elaborated on whole organism functions (Hansch et al. 1962), the most successful era for QSAR was in the study of relationships at the single receptor or enzyme activity level. It was found that as single chemical substituents in a homologous series were varied, so biological activity at receptor or enzyme level, changed, in a way which could be mathematically related to the change in properties resulting from the altered substituents. Simple steric, (molecular weight, molar refractivity), hydrophobic, (Log KOW) or electronic, (Hammet constant) properties of common substituent chemical groups could be measured and were collated in look-up tables, for use in QSAR studies. It was found that the effect of varying properties of substituents produced a hyperbolic response demonstrating an optimal value of, for example, molecular size on the receptor or enzyme being studied. Thus using a second-order linear regression analysis it was possible to reduce the phenomenon to a simple linear relationship:

LogMolarActivity = c + a1P1 − a1P12 + b1P2 − b 2 P2 2  + n1Pz − n 2 Pz 2 2 In practice, the variation in properties, (descriptors) rarely extended beyond a linear portion of the response surface, and so a more simple first order linear relationship could be used in many cases.

LogAct = c + aP1 + bP2  + nPz This process leads to the logical conclusion that, if such a relationship is known, then it is possible to predict the biological response of any compound in the series simply by knowing the magnitude of those properties that influence it. This is the basis of QSAR and from the 1960s it has been used to aid the design of new pharmaceutical molecules, and assessment of biological properties of chemical compounds.

Target Level It is easy to see that one or two simple properties can account for the activity of a series of compounds at the molecular level, (size of compound in relation to active

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site, hydrophobicity in terms of binding, charges in terms of reactivity to key parts of the receptor.) However more recently the potential of QSAR has been turned towards whole organism disciplines such as toxicology, in which the target response under investigation is a function of the whole organism rather than a single enzyme or receptor. For whole organisms the factors influencing activity are much more complex and possibly even conflicting, such as penetration through skin (dermal), entry into bloodstream, crossing blood/tissue barrier, effect of metabolic enzymes and the effect of internal environment. The increased complexity in the biological response at the whole organism level has lead to the search for more descriptors, (molecular properties) that might encode such complexities and this in turn has necessitated consideration of more complex mathematical models than simple nth order equations. The main steps in constructing a QSAR model are as follows: • • • •

Molecular modelling of the compounds in the dataset Calculation of descriptors to represent physiochemical parameters Mathematical correlation of one or more descriptors with biological activity Testing and validating model reliability and prediction limits

Chemical Structures In the early days, the simple descriptors used in QSAR modelling (such as Log KOW, molar refractivity, size/bulk) could be measured in the laboratory so details of the 2D or 3D structures of the compounds was not needed, over and above knowing the basics such as structural formula and connectivity. As computing power increased, the use of molecular mechanics and quantum mechanics algorithms allowed building virtual chemicals inside the computer, and calculate many properties from such models. Many molecular packages are available to draw and optimise chemical structure in silico. They range from very comprehensive, highly functionalised commercial suites such QUANTA CHARMM1 to freeware. The academic community has given rise to many good open source programs some of which rival top end commercial computational chemistry suites. One example is VEGA-ZZ (Pedretti et al. 2002), which utilises molecular mechanics, quantum mechanics and molecular dynamics suites to carry out all the processes necessary to build and optimise chemical structures. It can handle a wide range of chemical structure files including the chemical structural database format .sdf. In addition to building and optimising molecules, some molecular modelling suites can also generate a range of properties that can be used as QSAR descriptors.

http://accelrys.com/products/quanta/

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Molecular Descriptors The chemical structure of a compound carries a lot of embedded information on physicochemical features that ultimately determines biological activity. For (Q)SAR modelling, this information is ‘extracted’ from structural features into numerical data on physicochemical properties, termed as molecular descriptors. Many hundreds or even thousands of descriptors can now be calculated for each chemical structure to build datasets that are then used in QSAR modelling. Many of the complex molecular descriptors cannot be easily measured directly in the laboratory, or even at all, but can instead be calculated or estimated by computational means. Before the advent and rapid development of desktop computing, this was a time consuming affair, which could only be carried out at those laboratories that had expensive computer hardware and software. Pioneers of computational chemistry were able to use such theoretical concepts as molecular mechanics and quantum mechanics to calculate steric and electronic properties of molecules but such calculations were expensive and time consuming. The huge advances in computing in the recent decades have led to cheap, fast and versatile combinations of hardware and software for the modelling of chemicals and calculation of a wide range of properties. Standard desktop PCs can now carry out the complex computational tasks required and both proprietary and Open Source software is available to carry out such work. Many molecular descriptors can be calculated from molecular modelling software but other standalone programs also exist for calculation of descriptors. Some examples are DRAGON, and implementations of CDK and JOELib. –– DRAGON2 is a commercial program that calculates over 3,000 descriptors, (3,224 in v5 at time of writing). The descriptors fall into 22 categories, and include topological, geometric, connectivity, charge and functional group descriptors as well as molecular properties such as Log Kow. Full details of all the DRAGON descriptors can be found in Todeschini and Consonni (2000). The Chemistry Development Kit (CDK) is an Open Source Java library for structural chemo- and bioinformatics. The CDK library has evolved into a full functionality chemoinformatics package with code ranging from QSAR descriptor calculations to 2D and 3D model building. Although CDK can be used as a standalone application, it is implemented in a number of more user friendly applications such as; CDK-Taverna,3 KNIME,4 PADEL5 and more recently T.E.S.T,6 which calculates over 700 CDK descriptors. –– JOELib is a platform independent open source computational chemistry package written in Java. As with CDK, JOELib can be used standalone or incorporated into other packages such as Bioclipse.7 JOELib calculates over 100 descriptors. http://www.talete.mi.it/products/dragon_description.htm http://sourceforge.net/apps/mediawiki/cdk/index.php?title=CDK_Taverna 4 http://www.Knime.org/ 5 http://padel.nus.edu.sg/software/padeldescriptor/ 6 www.epa.gov/nrmrl/std/cppb/qsar/index.html 7 www.qsarworld.com/Temp_Fileupload/Shorthistoryofqsar.pdf 2 3

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A number of other programs are available to computational chemists for the calculation of other types of descriptor but essentially they still reflect the original grouping of steric, hydrophobic and electronic properties.

Data Analysis and Model Construction As the number of descriptors calculated in a QSAR study have burgeoned, so it has become necessary to apply more sophisticated mathematical means to investigate the relationship,(of any of these descriptors) to biological activity. In many cases the number of descriptors may indeed exceed the number of observations in the dataset thus making it very likely that any correlations observed will be chance correlations and not causative. It has become necessary therefore to carry out a number of steps to ensure robust handling of the data. These steps include: • • • • •

Variable (descriptor) reduction Variable normalisation Algorithm selection Model selection Model validation

In order to reduce variables to a meaningful number, those which are irrelevant need to be removed. This may be done by removing those that do not vary (constant or zero), or vary too widely, and those which are cross-correlated to other variables A range of techniques is available to accomplish this step including pairwise correlation, principal component analysis and filtering algorithms. Variable normalisation is usually performed to prevent some variables having an undue influence on a model. Normalisation or scaling can reduce the variation of all descriptors to the same range (for example 0–1). Algorithm selection is a critical step and can make the difference between a good model and no model at all. If the biological data is numerical and continuous, (for example LD50), then some form of multiple regression would be a likely choice. This might be a forward or reverse stepwise regression technique to develop the model with the smallest number of most significant variables. If however the biological response is discrete, (for example, active/inactive, or inactive/weak/moderate/strong), then likely choices might include, decision trees, neural networks, support vector classification, or clustering. The choice of different statistical algorithms for QSAR building has recently been reviewed by Chaudhry et al. (2007). Any one of the chosen methods may give a number of apparently acceptable models and the decision is then which model to accept. In general terms, however the models must be developed taking into account the principle of parsimony, often called Ockham’s Razor: “entities should not be multiplied beyond necessity” or “avoid complexity if not necessary”.8 In other words “The simplest solution is the best.” http://www.qsarworld.com/Temp_Fileupload/Shorthistoryofqsar.pdf

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Above all of course the chosen QSAR model should have good predictivity, so tests for predictive robustness must be carried out. This may be by cross validation techniques or the use of “confusion tables”, but ultimately the true test of a model will be external validation. In external validation, a stratified random subsample of the dataset is removed prior to modelling. The model is constructed using the larger “training set” and the removed subsample or “test set” is used to test the predictive ability of the model. All the above steps can be carried out using data mining or machine learning packages, such as WEKA,9 TANAGRA,10 KNIME11 or commercially available packages such as Knowledgeminer.12

Expert Systems Expert systems utilise a number of different approaches to predicting bioactivity, including decision trees based on rules, “structural alerts” in which chemical substructural features may be associated with particular biological activities, and nested QSARs. Examples include TOPKAT, Derek for Windows and MULTICASE. TOPKAT13 uses a range of robust, cross-validated QSAR models of a range of toxicological endpoints whilst Derek for Windows14 works by matching structural entities in a query compound with predetermined “structural alerts” that are known to be associated with a similar range of toxicological endpoints. The use of TOPKAT and Derek for Windows in rapid assessment of heat-derived toxicants in food is more fully described by Chaudhry et al (2006). MCASE15 combines both these approaches. Other relevant programmes include Ambit Database Tools16 that contains over 450,000 chemical compounds from a range of high quality databases, and can be used to search for properties as well as biological endpoint experimental data. External compounds can be used to “read across” from data in the internal databases. Ambit XT17 is a high quality chemical database with five modules for searching, compound profiling and fingerprinting. In addition it comes with a module for predicting PBT, (persistence, bioaccumulation and toxicity) using read

http://www.cs.waikato.ac.nz/ml/weka/ http://eric.univ-lyon2.fr/~ricco/tanagra/en/tanagra.html 11 http://www.knime.org/ 12 http://www.knowledgeminer.com/ 13 http://accelrys.com/products/discovery-studio/toxicology/ 14 http://lhasalimited.org/index.php?cat=2&sub_cat=64 15 http://multicase.com/ 16 http://ambit.acad.bg/ 17 http://ambit.sourceforge.net/intro.html 9

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across to compounds and PBT properties within the database. At present it does not support organometallics, polymers or mixtures. Toxtree18 estimates toxic hazard using a decision tree approach. Toxtree can be applied to datasets from a range of file types, or compounds can be entered by SMILES (Simplified Molecular Input Line Entry Specification) through the built-in 2D structure diagram editor. Another useful and comprehensive system is EPISuiteTM (US EPA 2009),19 which is a Windows®-based suite of physical/chemical property and environmental fate estimation programs developed by the US EPA’s Office of Pollution Prevention Toxics and Syracuse Research Corporation (SRC). EPI Suite™ uses a single input to run programs to estimate: • The log octanol–water partition coefficient, log KOW, of chemicals using an atom/fragment contribution method • The gas-phase reaction rate for the reaction between the most prevalent atmospheric oxidant, hydroxyl radicals, and a chemical • The Henry’s Law constant (air/water partition coefficient) using both the group contribution and the bond contribution methods. Melting point, boiling point, and vapor pressure of organic chemicals • Aerobic and anaerobic biodegradability of organic chemicals using seven different models • Biodegradation half-life for hydrocarbons.The organic carbon-normalized sorption coefficient for soil and sediment • Water solubility • Fish bioconcentration factor • Aqueous hydrolysis rate constants and half-lives for esters, carbamates, epoxides, halomethanes, selected alkyl halides, and phosphorus esters • The octanol/air partition coefficient. The fraction of airborne substance sorbed to airborne particulates • The rate of volatilization of a chemical from rivers and lakes • Predicts the removal of a chemical in a typical activated sludge-based sewage treatment plant • Partitioning of chemicals among air, soil, sediment, and water under steady state conditions for a default model environment • The toxicity of chemicals discharged to water. ECOSAR™ predicts toxicity to fish, aquatic invertebrates and algae using an extensive set of structure–activity relationships The Toxicity Estimation Software Tool (T.E.S.T.20 also developed by the US EPA) has been developed to allow users to easily estimate toxicity using a variety of QSAR methodologies. T.E.S.T. does not require molecular descriptors from

http://toxtree.sourceforge.net/ US EPA. (2009). Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.00. United States Environmental Protection Agency, Washington, DC, USA. 20 http://www.epa.gov/nrmrl/std/cppb/qsar/index.html#TEST 18

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e xternal software packages as the required descriptors are calculated within the programme, which estimates the value for several toxicity endpoints including 96h fathead minnow LC50, and 48h Tetrahymena pyriformis IGC50. The OECD QSAR ToolBox 21is another versatile suite of programs which can predict a range of endpoints for chemicals based on read-across, similarity or QSAR, using a substantial set of high quality databases. Additional databases with endpoint data can be imported into the toolbox to expand its usefulness. The Toolbox contains: –– Databases with results from experimental studies –– A library of QSAR models –– Tools to estimate missing experimental values by read-across, i.e. extrapolating results from tested chemicals to untested chemicals within a category –– Tools to estimate missing experimental values by trend analysis, i.e. interpolating or extrapolating from a trend (increasing, decreasing, or constant) in results for tested chemicals to untested chemicals within a category

QSAR and Expert Systems in Bioremediation Chemical Persistence, Bioaccumulation and Toxicity Chemical compounds which contaminate the environment are usually those characterised as “Persistent Bioaccumulative and Toxic”, (PBT). PBT compounds are of considerable interest to regulatory authorities as their use and possible release to the environment are key factors in the evolving chemical legislation in many countries. PBT compounds are also most likely to be those for which bio/ phyto-remediation solutions are sought and so it is relevant in this chapter to consider some methods available for the estimation or prediction of the PBT properties of chemicals. Under the EU REACH regulations, PBT compounds are defined in terms of half-life in water, sediments and soil, (persistence); bioconcentration factor (BCF), (bioaccumulation); and the No Observed Effect Concentration (NOEC), together with mammalian carcinogenicity, mutagenicity and reproductive toxicity (CMR). A number of SAR/QSAR/Expert system software tools are available to estimate each of these parameters and thus to make an overall estimate of PBT property of any given chemical. Persistence may be predicted from models using real half life data, or by an inverse property, biodegradability. The more rapid the inherent biodegradability of a compound, the shorter its persistence will be in soil or water. Bioaccumulation may be predicted from physical properties such as the octanol/water partition coefficient, (often called Log KOW), or from other physicochemical indicators such as molecular size (Koch 2008). Toxicity can be predicted

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from existing animal toxicity data, by read across from related compounds or by structural alerts or group contribution as previously described.

Prediction of Persistence/Biodegradation Persistence is the length of time that a compound remains in a particular environment, and is dependant on a number of factors including the chemical nature of the compound, the conditions in the environment, and the availability of biological organisms to perform biodegradation (Cronin and Livingstone 2004). Clearly an insight into the persistence of chemicals in any particular environmental situation is useful in determining any bioremediation strategy. For most compounds, direct data on persistence in the environment is not available and predictions of persistence must rely on estimates of biodegradability. Most of the methods for predicting persistence rely on biodegradation as an inverse predictor of persistence. A recent EU report compared 78 different SARs for biodegradation (Pavan and Worth 2006). The main conclusion was that only a few models provided an acceptable level of agreement between estimated and experimental data. Some of the main ones will be considered further here.

BIOWIN BIOWIN is part of the US EPA EPISUITETM package,22 and is freely downloadable. EPISUITETM is a set of routines for estimating a range of physicochemical properties and environmental fates of chemical compounds. BIOWIN estimates the probability of rapid biodegradation of an organic chemical in the presence of mixed populations of environmental microorganisms. The basic model is based on the use of 40 structural fragments from 295 organic compounds in the BIODEG database. The latest version of BIOWIN estimates aerobic and anaerobic biodegradability of organic chemicals using seven different models, one of which is an anaerobic model. A separate module of EPISUITETM, BioHCwin, is parameterised specifically for hydrocarbons. Also within EPISUITETM, HYDROWIN estimates aqueous hydrolysis rate constants and half-lives for esters, carbamates, epoxides, halomethanes, selected alkyl halides, and phosphorus esters whilst WVOLWIN estimates the rate of volatilization of a chemical from rivers and lakes and calculates the half-life for these two processes from their rates. Careful use of the BIOWIN program especially the use of the non-linear MITI model can give accurate prediction of biodegradability for up to 85% of compounds tested biodegradation (Pavan and Worth 2006).

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TOPKAT TOPKAT23 is a commercial suite of predictive toxicology programmes. TOPKAT is a QSAR-based system which generates and validates accurate, rapid assessments of chemical toxicity solely from a chemical’s molecular structure. TOPKAT uses patented Optimum Prediction Space technology to assure that the compounds under investigation are well represented in the models. TOPKAT includes an aerobic biodegradability module consisting of four submodels based on QSARs derived from MITI test results for 894 compounds. Within its domains, (acyclics, alicyclics, benzenes and heteroaromatics), it has been shown to be up to 96% accurate in prediction of biodegradability. MULTICASE MULTICASE24 is another commercial program which uses the MITI dataset of 894 compounds to derive its biodegradability model. MULTICASE uses all available molecular fragments to search for “biophores” responsible for biodegradability, and is accurate for up to 92% of compounds within the domain. Used in conjunction with the expert system META (Pavan and Worth 2006), MULTICASE can predict biodegradation products of a given organic chemical under realistic environmental conditions. META is an expert system linked to a dictionary consisting of metabolic rules, which can predict the metabolic transformations likely to occur when the chemical is disposed into the environment. CATABOL CATABOL25 is a commercial expert system for prediction of the biotransformation pathways linked to a probabilistic model that calculates probabilities of the individual transformations. The catabolic steps are derived from the set of most plausible metabolic pathways predicted by experts for each chemical from the training set. The MITI-I database is used, to provide a large structural diversity and consistent biodegradability assessments. Under the conditions tested32, CATABOL demonstrated about 85% accuracy of prediction. TOXTREE TOXTREE26 is a freely available decision tree tool for the prediction of toxic hazards of chemicals. In addition TOXTREE uses a system of structural alerts to predict biodegradability. http://accelrys.com/products/discovery-studio/toxicology/ http://www.multicase.com/ 25 http://oasis-lmc.org/?section=software&swid=1 26 http://ecb.jrc.ec.europa.eu/qsar/qsar-tools/index.php?c=TOXTREE 23

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The biodegradation and persistence module is based on a compilation of structural alerts for environmental persistence and biodegradability. These structural alerts are molecular functional groups or substructures that are known to be linked to the environmental persistence or biodegradability of chemicals. The rule base utilises the structural alerts in logical decision trees. If one or more of the structural alerts found in the molecular structure of the chemical are recognized, the system flags the potential persistence or biodegradability of the chemical. There are 32 structural alerts built into TOXTREE; 23 relate to the mechanisms of action of environmentally persistent chemicals, while nine relate to easily biodegradable chemicals. The structural alerts were derived from the Guidance Manual for the Categorization of Organic and Inorganic Substances on Canada’s Domestic Substances List (Guidance Manual 2003). According to the alerts detected chemicals are classified into one of the following three categories: –– Class 1 (easily biodegradable chemical) –– Class 2 (persistent chemical) –– Class 3 (unknown biodegradability) The performance of TOXTREE has not been extensively evaluated as yet. PBT Profiler The US EPA’s PBT Profiler27 is a free online tool for the estimation of PBT properties against the EPA criteria. The PBT Profiler first determines the amount of the chemical expected to be found in water, soil, and using a mass balance model. It then determines which of these three compartments the chemical is most likely to partition to. Using this predominant compartment, the half-life in that compartment is then compared to the EPA criteria to determine the persistence summary. If the half-life in the predominant compartment exceeds the EPA criteria, the chemical is designated as persistent or very persistent in the summary output. The PBT Profiler methodology was developed with the aid of a database of experimental biodegradation rates for 136 chemicals. AMBIT XT AmbitXT28 is a set of flexible user-friendly open source applications, which are able to store information about chemical compounds and efficiently search large databases of chemicals by various criteria. The application supports exact structure/ substructure searching, as well as fingerprint based similar structure searching, search by set of descriptors and search by experimental data. http://www.pbtprofiler.net/ http://ambit.acad.bg/

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AmbitXT includes a comprehensive set of modules for PBT assessment. The included database encompasses substances from the European INventory of Existing Commercial chemical Substances (EINECS),29 the BCF Gold standard Database,30 the ECETOC Aquatic Toxicity (EAT) Database Supplement (ECETOC 2003), historical skin sensitisation data (Gerberick et al. 2005), and the skin irritation and corrosion reference chemicals database (Skin irritation report 1995). Unlike the PBT Profiler which requires only a chemical identification to perform a PBT assessment based on QSARs within EPISUITETM, AMBIT XT uses measured, estimated or read across data for the PBT assessment and addresses the EU authorities’ position that some QSARs, e.g. the BCF and Biodegradation QSARs may be too optimistic in their estimates. This however does require that a considerable amount of data needs to be entered, (or available in the AMBIT2 database), to allow PBT assessment to be performed. It does on the other hand make a more accurate prediction of PBT more likely.

OECD QSAR Toolbox The OECD QSAR Toolbox31 is a multi-function application allowing the user to perform a number of operations, aimed at predicting a range of endpoints, (including PBT) for any given chemical or set of chemicals. The Toolbox can: –– Identify analogues for a chemical, retrieve experimental results available for those analogues and fill data gaps by read-across or trend analysis –– Categorise large inventories of chemicals according to mechanisms or modes of action –– Fill data gaps for any chemical by using the library of QSAR models –– Evaluate the robustness of a potential analogue for read-across –– Evaluate the appropriateness of a (Q)SAR model for filling a data gap for a particular target chemical –– Build QSAR models A comprehensive set of 18 chemical inventories comprising the OASIS Centralised Database of Existing Chemicals of over 200,000 compounds32 is built into the toolbox including a number of biodegradation databases. The principle behind the toolbox is that of categorising an unknown compound according to its physicochemical and toxicological properties, and then predicting the required properties based on such categorisation. Biodegradation and bioaccumulation are predicted from data for compounds in one or more of the datasets included in the full suite.

http://ecb.jrc.ec.europa.eu/esis/index.php?PGM=ein http://www.euras.be/eng/project.asp?ProjectId=92 31 http://www.oecd.org/document/23/0,3343,en_2649_34379_33957015_1_1_1_1,00.html 32 http://oasis-lmc.org/?section=software&swid=8 29 30

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Prediction of Bioaccumulation According to international guidelines “Bioaccumulation” is defined as the process where the chemical concentration in an aquatic organism achieves a level that exceeds that in the water as a result of chemical uptake through all routes of chemical exposure. The usual measure of bioaccumulation in the Biological Concentration Factor, (BCF) which is defined as, “the concentration of test substance in/on the fish or specified tissues thereof divided by the concentration of the chemical in the surrounding medium at steady state”. Pavan et al. (2006) have reviewed the QSAR methodologies used in the prediction of bioconcentration. The majority of QSARs for BCF are simple linear regression models using Log KOW, though the relationship breaks down with compounds of Log KOW > 6, (i.e. very hydrophobic compounds). A number of parabolic or bilinear models have been proposed to account for this. Although there have been many attempts to predict BCF using QSARs, most of these were before the OECD principles were formulated and are not necessarily acceptable as robust models under modern criteria. In addition they are dispersed amongst the scientific literature and not so widely available in software applications as the biodegradation models. Other QSAR models have been published which do not rely on measured or calculated values of Log KOW, but instead have been based on a range of theoretical molecular descriptors, such as, quantum chemical values, connectivity indices and descriptors derived from programs such as DRAGON, (see Section 1.3). Some of the applications discussed in Section 2.2 also allow the user to predict or get experimental values for, bioaccumulation, but in addition there are many published QSARs and other predictive applications which will give values for BCF31. Most models that perform well with a wide range of structural types are based on the relationship of BCF with Log KOW, (OW = octanol/water partition coefficient).

BCF Prediction Tools The BCFBAF model, (previously called BCFWIN), in the EPISUITETM application is according to Cronin and Livingstone (2004), one of the better predictors31 with 83% of Log.BCF predictions within one order of magnitude. The CATABOL commercial program computes BCF predictions based on a deterministic and probabilistic hybrid expert system. A hybrid model is also used in the online CAESAR model. CAESAR33 was an EU funded research project which developed QSAR models for five toxicological endpoints of regulatory importance, primarily for use in the REACH legislation process. All five models are freely available for use online (www.caesar-project.eu/software/index.htm), one of which is a BCF model. 33

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Both AMBIT XT and the USEPA PBT profiler have modules for BCF prediction, and as discussed earlier the AMBIT model uses, where possible experimental values input by the user to generate more accurate estimates, whilst the PBT profiler and the BCFBAF models are based on QSAR data. The OECD QSAR Toolbox also incorporates BCF and more general bioaccumulation estimations using a wide range of databases. Archived experimental BCF values will be extracted from the databases if the query chemical is found, otherwise the Toolbox allows read across and molecular similarity projections to be made prior to a prediction of BCF.

Accumulation/Concentration in Soil From a bioremediation point of view, although BCF is a useful indicator of the bioaccumulative properties of given pollutants, predictions of chemical accumulation for specific sites and conditions could be more useful in designing a bioremediation plan. QSAR can help in establishing the accumulative/ degradative profile of individual pollutants in order to establish the scope and magnitude of any specific problem and its likely response to bioremedial strategies. A compound which readily accumulates in soil, but is rapidly degraded, is unlikely to require bioremedial action but rapid accumulation with slow degradation may require remediation. Accumulation/concentration of chemicals in soils is usually referred to as sorption, and many QSARs have been derived for prediction of the soil sorption coefficient KOC. These have been reviewed by Dearden (2004). As with BCF, most QSARS for KOC rely heavily on the octanol–water partition coefficient Log KOW, though molecular connectivity descriptors have also been used successfully to predict soil sorption. Indeed a model based on such indices forms the basis of the only freely available application for prediction of soil sorption, KOCWIN, which is part of the EPISUITETM application previously discussed.27

Prediction of Toxicity The prediction of toxicity is a much wider field than persistence or bioaccumulation. This is mainly because of the large number of potential endpoints covered by the broad term “toxicity”. Toxicity can mean acute toxicity, for example an LD50 value for a particular species, or it can also mean chronic toxicity relating to a specific lesion or pathology, such as mutagenicity, carcinogenicity, or developmental toxicity. The field of predictive toxicology is fairly new and a good overview of most aspects has been provided by Cronin and Livingstone (2004). From an environmental, regulatory and bioremedial standpoints, the important aspects of toxicity which may need to be predicted are those which define the “T” in PBT. Whilst the actual details vary from country to country the definition of a

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Table 1 Toxicity prediction tools Application Status EPISUITE Free

Toxicological endpoints – Daphnid – Fish – Earthworm and green algae

Type of predication QSAR

DEREK

Commercial

– Carcinogenicity – Mutagenicity – Genotoxicity – Skin sensitization – Teratogenicity – Irritancy – Respiratory sensitization – Hepatotoxicity – Oculartoxicity

Structural alerts and QSARS

TOPKAT

Commercial

– Carcinogenicity – Mutagenicity – Daphnid – Fish – Skin sensitization – Skin irritancy – Inhalation toxicity

QSAR based

MULTICASE TOXTREE

Commercial Free

Over 180 endpoints

Biophor/QSAR Rules-based

OECD Toolbox

Free

QSAR, read-across, categorisation

T.E.S.T

Free

Many acute and chronic toxicity endpoints Fish, protozoa, rat oral

CAESAR

Free

– BCF – Mutagenicity – Carcinogenicity – Developmental toxicity – Skin sensitization

Hybrid QSARs

TOXMATCH1

Free

– Aquatic toxicity – BCF – Skin sensitivity

Read across

– Mutagenicity – Carcinogenicity – Skin sensitivity – Oral toxicity

QSARs

http://ecb.jrc.europa.eu/qsar/qsar-tools/index.php?c=TOXMATCH

1

PBT chemical relate to a chronic NOEC (No Effect Concentration), of one or more aquatic organisms and/or a specified level of carcinogenicity or mutagenicity. These so called “regulatory endpoints” have, in recent years been extensively modelled, and most of the applications referred to in this chapter have the capability to predict such toxicological endpoints. A list of the applications and their toxicological endpoint predictions is given in Table 1.


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Biotransformation Whilst the prediction of PBT properties can assist in assessing the challenge of a particular bioremediation situation, prediction of the biotransformation pathways of the offending compounds can aid the selection of a bioremediation strategy. Whilst phytoremediation may rely on the uptake and sequestration of pollutants, remediation by micro-organisms relies on the degradative metabolism or biotransformation of the pollutants.

Microbial Metabolism SAR or QSAR-based models on the microbial metabolism of compounds which are of interest in bioremediation are of three types; QSARs on specific chemical groups, (PCBs or hydrocarbons), rule-based predictive models based on metabolic pathways or molecular fragment approaches, and large (usually commercial) applications which predict metabolites for a range of medical and regulatory purposes. Polychlorinated biphenyls represent a group of compounds with significant environmental hazards on industrial sites. Microbial remediation is an attractive option for PCBs but susceptibility to microbial degradation varies widely amongst the PCBs. Cartwright (2002) used a self-organising map to analyse data on the degradation of a range of PCBs by Aspergillus niger. A QSAR based on the selforganising map was shown to predict biodegradability to within 25% of experimental values for 33 of a set of 44 PCBs, though it appeared that dichloro-PCBs appeared more difficult to predict. Other QSAR approaches to the microbial degradation of PCBs include Comparative molecular Field Analysis (Comfa) for biphenyl utilization by Pseudomonas stutzeri in aqueous media,34 and regression analysis for fungal laccase activity, (showing heat of formation and the quantum indices HOMO and LUMO as significant descriptors) (Jiang et al. 2008). Microbial degradation, in addition to being a useful predictor of bioremediation, is conventionally used as an indicator of environmental persistence, as previously discussed, and the QSAR-based approaches are reviewed in detail by Pavan and Worth (2006). Ideally a predictive tool should indicate biotransformation via a specific pathway. One way to achieve this is to use microbial biosensors linked to some kind of reporter gene such as fluorescence. Since hydrocarbon metabolism has long been a subject of study and hydrocarbons are frequently environmental problems, it’s not surprising that a number of biosensor systems have been developed for monitoring their biotransformation. Paton et al. (2004) have applied QSAR models to the data from a number of microbial biosensor systems, reporting on pathways in the catabolism of benzenes, PAHs, alkanes, naphthalenes and other 34 http://cfpub1.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/347/ report/F

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hydrocarbons occurring in petroleum and diesel oils. They concluded that QSARs can be applied to predict the specificity of regulatory proteins for hydrocarbon biotransformation pathways. Hydrophobicity appeared to be the most important descriptor, possibly because it determines uptake of the hydrocarbons into the cell systems used in the biosensor assay. However, ELUMO, the energy of the lowest unoccupied molecular orbital, (a quantum chemical descriptor), was also significant in a number of their models.

Software Tools Biotechnology has, in recent years generated vast amounts of data on genomics and metabolomics, which can be harnessed with modelling techniques to make predictions about biotransformations of interest in bioremediation projects. Pazos et al (2005) developed the expert system MetaRouter to mine biotransformation data from the number of sources, primarily the University of Minnesota Biocatalysis/Biodegradation Database.35 The system allowed the exploration and design of biodegradative strategies for chemical compounds depending on conditions such as environment and bacterial ecosystem. In addition to searching information in the contributing databases it incorporated the development of a system for predicting the biodegradative fate of chemical compounds based on their chemical structure using compounds in the database. http://pdg.cnb.uam.es/ MetaRouter as a training set. The application of these predictive tools to chemicals released into the environment can provide early indications that compounds are biodegradable or recalcitrant, and some indications of which microbial species may be most likely to degrade them. Gomez et al. (2007) further developed this approach using machine learning to establish a correlation between the frequency of 149 atomic triads (chemotopes) common in organic chemicals, and the capacity of microorganisms to metabolise them. Depending on the type of environmental fate defined, the system can correctly predict the biodegradative outcome for 73–87% of compounds. This system is available online.36 Also available from the home of the original database used in this work is the University of Minnesota Biocatalysis/Biodegradation Database Pathway Prediction System (PPS).37 The PPS predicts plausible pathways for microbial degradation of chemical compounds. Predictions use biotransformation rules, based on reactions found in the database or in the scientific literature. PPS predictions are most accurate for compounds that are: similar to compounds whose biodegradation pathways are reported in the scientific literature; in environments exposed to air, in moist soil http://umbbd.msi.umn.edu/index.html http://www.pdg.cnb.uam.es/BDPSERVER/ 37 http://umbbd.msi.umn.edu/predict/ 35 36


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or water, at moderate temperatures and pH, with no competing chemicals or toxins; and the sole source of energy, carbon, nitrogen, or other essential element for the microbes in these environments, rather than present in trace amounts. The OECD QSAR Toolbox has already been discussed, and in addition to its utility in predicting PBT parameters, it also has a module for the prediction of metabolism. The engine used is the same as that used in the CATABOL application, (see below). Single pathway catabolism is simulated using the abiotic and enzymemediated reactions extracted from documented hierarchical metabolic pathway databases. The simulation is based on a hierarchical searching, looking at the most likely transformations first and then working down the hierarchy. The procedure is repeated for the newly-formed products. The level of predictability based on documented experimental results of microbial catabolism of 200 chemicals is estimated at 83%. Recently under EU funding, CRAFT Explorer and CRAFT Editor have been released publicly under LGPL licensing terms.38 CRAFT Explorer generates all possible metabolites of the target chemicals and then uses the University of Minnesota Biocatalysis/Biodegradation Database likelihood model to derive the most probable metabolic route. CRAFT Editor allows the user to edit the knowledge base. CRAFT Explorer is a workflow-based program which has five steps: 1 . Import of target chemical by graphical editor from a file. 2. Preprocessing can be specified in terms of allowed reactions handling of isomers, structural validation checks. 3. User specifies the set of reaction rules that should be applied to the candidate chemicals. 4. All possible metabolites are generated. 5. Evaluation of most probable pathways. CRAFT appears to be a very comprehensive program for the generation of metabolic routes, though as yet there is little information on its performance.

Commercial Systems for Biotranformation CATABOL39 has been discussed in Section 2.2.4 with respect to estimation of persistence. The core of CATABOL is the biodegradability simulator including a library of hierarchically ordered individual transformations (catabolic steps) and matching substructure engine. The catabolic steps are derived from a set of most plausible metabolic pathways predicted by experts for each chemical from a training set extracted from the MITI-I. The model allows for identifying potentially persistent catabolic intermediates, their molar amounts, solubility and toxic properties.

http://www.molecular-networks.com/products/craft http://oasis-lmc.org/?section=software&swid=1

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METEOR40 is a knowledge-based expert system for predicting the metabolism of xenobiotics based on fragments in the parent compound. From a query structure input by the user Meteor generates results using reasoning rules within a knowledge base containing biotransformations, intermediates and reasoning rules. Predictions take into account lipophilicity, the prevalence of the biotransformation in the literature, species, and the relative likelihood of competing biotransformations. Meteor can also predict the likely chemical structures of, and metabolic pathways to, metabolites identified by mass spectrometry studies. META41 uses provided dictionaries to create metabolic paths of molecules submitted to it. All rules are based on reliable literature sources. Microbial metabolism is predicted on the basis of two separate models. Aerobic microbial biodegradation is based on observed rates of biodegradation of chemicals in sludge and a compendium of mechanistic studies of experimentally observed biodegradation products. This module contains information related to 505 metabolic transformation reactions. The Anaerobic Microbial Biodegradation (ANAR) Dictionary is based on observed rates of biodegradation of chemicals in anaerobic conditions. This module contains information related to 344 metabolic transformation reactions.

Phytoremediation Perhaps because of the inherent complexity of the modelling process for plant uptake and metabolism, there are only a few chemical structure-based models in the literature. Those models of uptake that have been reported have been reviewed by McKone and Maddalena (2007) who found that uncertainties in design, lack of data and lack of consistency in definition of terms, led to a wide discrepancy between models with no clear way of distinguishing the good from the poor. Gramatica et al. (2005) modelled the bioconcentration tendency of a range of plants towards 44 compounds of environmental concern, (pesticides, PAHs, PCBs). The approach was classical QSAR using theoretical molecular descriptors derived from the commercial programme DRAGON (Todeschini and Consonni 2000). Analysis of the data by principal component analysis and best model selection using a genetic algorithm led to a regression model based on the topological descriptor “Whete” (a Wiener-type index from electronegativity weighted distance matrix). The model enabled an estimation of those plants which were good root accumulators and those which were good leaf accumulators. The model showed a good predictivity. Collins et al. (2006) reviewed the basis for the standard model for predicting uptake of chemicals from the soil by plants, used in the UK’s Contaminated Land Exposure Assessment Model. The model is based on Log KOW. But had only been validated for compounds for Log KOW values between −1 and 5. The authors http://www.lhasalimited.org/index.php?cat=2&sub_cat=68 http://www.multicase.com/products/prod05.htm

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c oncluded that, model performance was highly variable, with predicted concentrations up to five orders of magnitude different from observed concentrations. None of the models tested were considered suitable as a general screening tool for all chemicals and vegetable types of relevance to contaminated land. An additional complication in a bioremediation context is the inability of many computational chemistry applications to handle metal or other inorganic elements, thus restricting modelling to organic compounds. The field of QSAR applied to metal ions is currently under review (Walker et al. 2010).

Conclusions The availability of reliable chemical property/effect databases, powerful data mining algorithms, and enormous computational power in the past decade have all led to the development of QSAR-based computational tools for decision making in situations where potential harmful effects of an environmental chemical are unknown, and chances of a successful remediation action are uncertain. Such chemical structure based modelling has already demonstrated clear benefits in predicting the properties of contaminants and pollutants that make them particularly troublesome environmentally. Whilst defining the problem is one half of the battle, identifying possible solutions is a more exciting prospect for QSAR and related approaches. In this respect, computational modelling approaches described in this chapter can provide answers to many of the uncertainties, and enable rapid and sound decisions on whether a remedial action would be needed and how best could it be accomplished in a prevailing contaminant situation in the field. For example, (Q)SAR based models and expert systems can rapidly assess potential toxicities of the main contaminants to indicate whether there are any compounds of concern in terms of adverse human health or environmental effects. Such systems can thus help in identifying priority compounds among the contaminants that are not likely to breakdown naturally in the environment, and for which additional remedial actions may be required. The models can also help assess whether a compound of interest will be persistent, (bio)accumulative, or toxic, how will it breakdown in the environment, and whether the breakdown products will still be harmful. The priority compounds thus identified can be used as indicators to monitor the success of a remediation action. The use of some of the expert systems described in this chapter can further help identify appropriate microbial species/strains that would suit best for the given contaminant profile of a soil/ water environment. The convergence of computational chemistry approaches with genomic and metabolomic databases, is also expected to lead to the discovery of novel natural biotransformation pathways, especially in micro-organisms, which could be exploited for bioremediation technologies. As discussed in this chapter, numerous computational tools are available to support decision making in bioremediation of contaminated environments. However,

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the shear large number of the available models/ systems may make it difficult for a novice to choose appropriate system(s), and to decide whether to rely on one or more of the computational tools. The reliability of prediction obtained from different computational models and expert systems is also likely to vary, as they will reflect the variability in the data that were used to build them, and the way models were developed, tested and validated. Although a few comprehensive software suites are available – such as EPA’s EPISUITETM – one may still need a number of different programmes to assess physicochemical properties, environmental fate, behaviour, and toxicity of the environmental chemicals. There is therefore a need for development of more focused and comprehensively integrated systems that specifically cater for bio- and phyto-remediation technologies.

References Cartwright HM (2002) Investigation of structure – biodegradability relationships in polychlorinated biphenyls using self-organising maps. Neural Comput Appl 11:30–36 Chaudhry Q, Blom-Zandstra M, Gupta S, Joner EJ (2005) Utilising the synergy between plants and rhizosphere microorganisms to enhance breakdown of organic pollutants in the environment. Environ Sci Pollut Res 12(1):34–48 Chaudhry Q, Chrétien J, Craciun, M, Guo, G, Lemke F, Müller JA, Neagu, D Piclin N, Pintore M, Trundle P (2007), Chapter 4. In: Benfenati E (ed) Algorithms for (Q)SAR model building; in quantitative structure-activity relationship (QSAR) for pesticide regulatory purposes. Elsevier, pp 111, ISBN 13: 978-0444-52710-3 Chaudhry Q, Cotterill J, Watkins R, Price NR (2006) A molecular modelling approach to predict the toxicity of compounds generated during heat treatment of foods. In: Skoog K, Alexander J (eds) Acrylamide and other hazardous compounds in heat treated foods. Woodhead Publishing Ltd, Cambridge, UK, pp 132–160 Chaudhry Q, Schroeder P, Werck-Reichhart D, Grajek W, Marecik R (2002) Prospects and limitations of phytoremediation for the removal of persistent pesticides in the environment. Environ Sci Pollut Res 9(1):4–17 Collins C, Martin I, Fryer M (2006) Evaluation of models for predicting plant uptake of chemicals from soil. UK Environment Agency. Science Report – SC050021/SR Cronin MTD, Livingstone DJ (2004) Predicting chemical toxicity and fate. CRC Press, Boca Raton, FL Dearden JC (2004) QSAR modelling of soil sorption. In: Cronin MTD, Livingstone DJ (eds) Predicting chemical toxicity and fate. CRC Press, Boca Raton, FL, pp 357–371 ECETOC (2003) Aquatic Hazard Assessment II. Technical Report No. 91. European Centre for Ecotoxicology and Toxicology of Chemicals, Brussels, Belgium Gerberick GF, Ryan CA, Kern PS, Schlatter H, Dearman RJ, Kimber I, Patlewicz G, Basketter DA (2005) Compilation of historical local lymph node assay data for the evaluation of skin sensitization alternatives. Dermatitis 16(4):157–202 Gomez MJ, Pazos F, Guijarro FJ, de Lorenzo V, Valencia A (2007) The environmental fate of organic pollutants through the global microbial metabolism. Mol Syst Biol 3(114):1–11 Gramatica P, Papa, E, Giani E, Cenci R, Preatoni D (2005). Organic pollutant uptake by vegetable probes and biomonitoring of moss-linked metals in regions of Northern Italy. In: 15th annual meeting SETAC-Europe, Lille, 22–26 May 2005 Guidance Manual for the Categorization of Organic and Inorganic Substances on Canada’s Domestic Substances List, Existing Substances Branch, Environment Canada, 2003, pp 89–90 Hansch C, Maloney PP, Fujita T, Muir RM (1962) Nature 194:178


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State-of-the-Art Chemical Analyses: Xenobiotics, Plant Proteomics, and Residues in Plant Based Products Touradj Solouki, Mohammad Ali Khalvati, Mahsan Miladi, and Behrooz Zekavat

Abstract Utilizing modern analytical tools, “x-omics” approaches (e.g., genomics, metabolomics, proteomics, etc.), and data mining techniques for comprehensive characterization of plant metabolism of xenobiotics can enhance our ability to assess environmental impacts. However, a solid understanding of metabolic pathways at the molecular level is required for targeted exploitation of species-specific detoxifying abilities of various plants. Characterization of phytotoxic pathways and dynamic molecular interactions in biological systems requires a systematic approach that can merge data from multiple analytical techniques. In this chapter, a brief review on recent advances in analytical instruments, particularly high performance mass spectrometers (MS) and allied techniques, and their impact on integrative biological studies in plant proteomics and botany are provided. Moreover, the importance of sample preparation, analyte separation, and standardization techniques are discussed. The significance of data correlation from high throughput and high resolution MS, multistage MS (MSn), “bottom-up” and “top-down” proteomics, determination of various stress responses, and identification of post-translational modifications in plants are also discussed. The conclusions provide a summary of the current instrumental limitations and anticipated future directions and challenges in plant system biology studies.

Complex Sample Analyses and System Biology Analyses of xenobiotics and complex biological/environmental sample mixtures require state-of-the-art instruments that can resolve different components of a mixture, provide individual molecular identities, yield relative abundance or concentration T. Solouki (), M. Miladi, and B. Zekavat Department of Chemistry, University of Maine, Orono ME 04469-5706 USA e-mail: [email protected] M.A. Khalvati Department of Microbe–Plant Interactions, German Research Centre for Environmental Health (Helmholtz-Zentrum München) Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_12, © Springer Science+Business Media B.V. 2011

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information, and unravel potential interactions among the various constituents of the sample at a high level of confidence. Performance characteristics of modern instruments are continuously improved to address analytical requirements in emerging areas of science, including “x-omics”. Here, we use “x-omics” to refer to comprehensive study of various complex systems such as the components of a living organism (e.g., genomics, metabolomics, proteomics, etc.) and/or a non- living system (e.g., petroleumics). Analogous to the use of the term “system biology” (Von Bertalanffy 1950; Mesarovic 1968; Founds 2009), “x-omics” refers to studies in genetics, protein biology, enzyme kinetics, lipids characterizations, cybernetics, neurophysiology, immunology, and others. System biology has recently emerged as a distinct discipline and several journals dedicated to this topic are now available (e.g., EURASIP Journal on Bioinformatics and Systems Biology published by the European Association for Signal Processing (EURASIP) and Molecular Systems Biology published by Nature Publishing Group (npg) and the European Molecular Biology Organization (EMBO)). Ideally, for comprehensive characterization of a complex ensemble and in system biology studies, three general questions regarding: (a) types, (b) concentrations, and (c) nature of the interactions of all individual components of the mixture under the investigation must be addressed (as a function of time). For example, as depicted pictorially in Fig. 1, a comprehensive “x-omics” study should provide information about the types (“what is there?”, Fig. 1a) and quantities (“how much is there?”, Fig. 1b) of all analytes. Moreover, beyond the detection and identification of all components of a biological system, it is desired to characterize all potential post-translational modifications as well as modulations of non-covalent interactions (“what are the relationships?”, Fig. 1c). For the specific case of mass spectrometry, the widths of lines in the m/z spectrum (in Fig. 1a) and y-axis (in Fig. 1b) represent the mass resolution and confidence in determining the relative abundance (RA) of each species, respectively. In part c of Fig. 1, more challenging questions about analyte interactions within the

Fig. 1 Demonstrates a general analytical approach to address questions on (a) what is the analyte?, (b) how much of the analyte is present in the sample, and (c) what are the interactions between different species present in the complex sample

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complex system are addressed which are represented by additional or multiple analysis dimensions (X). The X in Fig. 1c represents additional analysis dimensions and could include a number of complementary data analysis approaches such as the use of: ion-molecule reactions (e.g., Hydrogen/Deuterium (H/D) exchange reactions (Solouki et al. 1999; Kazazic et al. 2010), proton affinity (PA) measurements (Szulejko et al. 2006), etc.), multistage mass spectrometry (tandem MSn and ion fragmentation patterns), measurement of thermochemical properties (e.g., DG, DS), or others. The font size decrease in Fig. 1 is meant to suggest a progression in the degree of experimental difficulty by going from assigning the unknown’s identity (in Fig. 1a) to examination of its detailed interactions (in Fig. 1c). Characterization of the abovementioned parameters requires careful experimental designs and quantitative measurements (Oeljeklaus et al. 2009) of all species present in a sample. Currently and among the various analytical techniques available for proteomics studies, mass spectrometry offers a nearly “universal detection” capability that can provide meaningful quantification for complex mixtures; however, even mass spectrometry often provides a limited dynamic range, sensitivity, and precision for an ideal quantification approach. In other words, in high performance mass spectrometers (viz., ultrahigh resolution mass spectrometers such as FT-ICR MS and Orbitrap MS) precision and accuracy are far more superior in the x-axis (mass measurement accuracy (MMA) in sub part-per-million (ppm) (He et al. 2004; Heffner et al. 2007; Williams and Muddiman 2007) range is achievable for confident identification of the type, Fig. 1a) than in the y-scale (relative abundance or quantity of each species, Figure 1b). A common approach to improve quantification is to use internal or external standards (often using isotopically labeled species (Stewart et al. 2001; Previs et al. 2008; Winter et al. 2009) or immunoaffinity methods (Nicol et al. 2008)). Although there are numerous examples of successful quantification methods, confident quantitative analysis remains as a challenge for detecting minor variations of a few species in the presence of multitude of analytes that are present at much higher concentrations (a typical challenge in quantitative and/or comparative “x-omics” studies). For example, differential protein expressions or post-translational modifications may yield variations in protein concentrations in excess of orders of magnitude and such differences must be characterized for successful identification of biomarkers or panel of markers in proteomics studies. Successful characterization of the interactions between various components of a biological sample mixture (including protein–protein and non-covalent interactions, metal–protein interactions, etc.) can be more demanding than establishing their identities or concentrations. For example, metals may interact with biological molecules and change their conformations and functions. It is worth noting that interactions between proteins/macromolecules and potential xenobiotics may also yield conformational changes with significant consequences for altering biochemical interactions. Hence, a comprehensive characterization of the amounts of xenobiotics in the plant, its metabolites, and the interacting biomolecule partners at the molecular level is necessary for advancing retrospective data mining approaches to develop predictive models. Recent instrumental development efforts and research attempt to further our knowledge in these areas.

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Metabolic Profiling of Xenobiotics Heterotrophic plant cell suspension and in vitro assays are commonly used (Swisher 1987; Schmidt 2001, 2002) to acquire qualitative data on the metabolism of xenobiotics (viz., pesticides in plants). Detoxification of the hazardous foreign compounds (xenobiotics) is essential for survival of organisms. Numerous studies have investigated the uptake and metabolic fate of xenobiotics in plants and revealed a reaction sequence consisting of a first phase of chemical modification, a second phase of conjugation, and a third phase of storage (Swisher 1987; Lamoureux and Rusness 1989; Sandermann 1994). Advantage of in vitro assays is the elimination/reduction of interfering photochemicals and microbial transformations. Moreover, the absence of chlorophyll and other pigments facilitate extraction and identification of xenobiotics and their metabolites. Importance of the “earning” identification points through the use of high resolution mass spectrometry and multidimensional analyses for improving identification of xenobiotics has been highlighted (Szulejko et al. 2006; Thurman et al. 2006; Luo et al. 2009). Judicious utilization of the high MMA and additional analysis dimensions can improve selectivity and reduce the probability of error for identification of xenobiotics and microbial metabolites (Jackson et al. 2009). Recent instrumental improvements are allowing the analysis of macromolecules at a higher level of confidence and increase the likelihood for comprehensive studies of complex biological systems. Plant cell suspensions can be grown in scaled-up assays (up to 50 g fresh weight) or air-lift fermenters (~500 g fresh weight) (Knops et al. 1995; Schmidt 2002); cell cultures are thus convenient laboratory systems for monitoring the metabolic pathways of xenobiotics through plants and characterization of corresponding metabolites. Although xenobiotic turnover in plant cell cultures is usually higher than in plants, large-scale characterization of the pesticide or insecticide metabolites by plant cells (and systematic identifications, including conventional 1H-NMR analysis) is not possible with many compounds. This limitation is mainly due to the fact that often chemical modifications and reactions catalyzed by P450s exhibit slow kinetics. To date, details of many of these reactions and substrate specificities for xenobiotics metabolism are lacking (Durst et al. 1997; Siminszky et al. 1999). However, with recent advances in analytical instrumentation, including mass spectrometry, it is anticipated that significant parameters influencing reaction kinetics in metabolic pathways will be identified to resolve discrepancies between different plant studies.

Proteomics: An Overview Proteome is defined as the time- and cell-specific protein complement of the genome and consists of all proteins that are expressed in a cell at one time, including isoforms and post-translationally modified species (Rappsilber and


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Mann 2002a, b). The term proteome was initially used in 1994 at the first two dimensional electrophoresis (2-DE) meeting in Siena, Italy (Wilkins et al. 1996). The rapid growth in biological MS and addition of sequence databases have fueled the field of proteomics (Patterson and Aebersold 2003) and large scale protein studies (Anderson et al. 2000). Proteomics is the study of integrated function of all expressed proteins (Tyers and Mann 2003) and has been used for the mapping of complete proteomes, comparison of proteomes, and identification of differentially expressed proteins (Jacobs et al. 2000). Advancements in high resolution protein separation, introduction of more powerful MS software and hardware, and progress in bioinformatics technology have increased the applications of proteomics in all biological fields (Wang et al. 2008), including plant science. For instance, resistance-related proteins in plants can be identified by comparing resistant and susceptible plant tissues; just as the comparison between the proteomes of red and white flowers allows identification of the proteins that are involved in flower pigmentation (Jacobs et al. 2000). Recent improvements in biological mass spectrometry (Carpentier et al. 2008; Baginsky 2009), and in particular, ionization techniques such as matrix-assisted laser desorption ionization (MALDI) (Karas et al. 1987; Karas and Hillenkamp 1988; Tanaka et al. 1988), electrospray ionization (ESI) (Whitehouse et al. 1985; Fenn et al. 1989), desorption ESI (DESI), and data analysis methods are making it possible for scientists to explore and study real-world complex biological systems (Harris et al. 2008; Chen et al. 2009).

Proteomics in Plants In this chapter, a brief overview of various analytical techniques that are used in plant and soil research, with a particular emphasis on applications of plant proteomics and mass spectrometry, is provided. In general, analytical techniques can be classified into major categories such as: (a) extraction and separation techniques (e.g., supercritical fluid chromatography (SFC), liquid chromatography (LC), gas chromatography (GC), capillary zone electrophoresis (CZE), etc.), (b) spectroscopy and photon/electromagnetic wave based detection systems (e.g., fluorescent, nuclear magnetic resonance (NMR), infrared (IR), ultraviolet-visible (UV-Vis), X-ray, etc.), (c) surface analysis techniques (e.g., low energy electron diffraction (LEED), ultraviolet photoelectron spectroscopy (UPS), x-ray photoelectron spectroscopy (XPS), Raman, and Auger Spectroscopy, etc.), (d) electrochemical methods (e.g., potentiometry, coulometry, voltammetry, etc.), (e) spectrometry, and (f) others. Although unique contributions from each of the (a) to (f) techniques are quite valuable to the field, the major focus of this chapter is on the use of spectrometry techniques. The use of ion mobility spectrometry (IMS) (Kanu et al. 2008; Kanu and Hill 2008; Scarff et al. 2008; Trimpin and Clemmer 2008) is relatively new for classification of complex biological systems and mass spectrometry continues to play its historical role as a major contributor for characterization of small and macromolecules in complex mixtures. Depending on the ionization source types (e.g., electron impact

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(EI) (Mirsaleh-Kohan et al. 2008) and chemical ionization (CI) (Munson and Field 1966; Solouki and Szulejko 2007), MALDI (Karas et al. 1987; Karas and Hillenkamp 1988; Tanaka et al. 1988), ESI (Whitehouse et al. 1985; Fenn et al. 1989) , etc.), analyzer choices (e.g., electric sector, magnetic sector, time-of-flight (TOF)), and ion detection approaches (particle detectors, image detectors, etc.), mass spectrometers can be divided into different categories (e.g., MALDI-TOF, ESI FT-ICR, etc.). Another common classification approach for MS is based on the ion detection theory and the relationship between the ionization and detection events (e.g., ion image detectors (Comisarow and Marshall 1974)). For example, based on the partition of the ionization and detection processes, quadrupole, TOF, and magnetic and/or electric field sector instruments are classified as spatial (separated in space) whereas ion trapping instruments such as FT-ICR MS (Comisarow and Marshall 1974; Solouki and Russell 1992; Marshall et al. 1998; Kelleher 2004; Qian et al. 2004; Cooper et al. 2005; McLafferty et al. 2007) and Orbitraps (Makarov 1999b; Makarov et al. 2006; Perry et al. 2008) can be classified as temporal (separated in time) mass spectrometers. These spatial and temporal mass spectrometers offer application-specific advantages and each has its limitations. In addition to the enhanced mass resolving power (MRP), the performance characteristics of the ion-trapping mass spectrometers such as FT-ICR are often further exploited for acquiring multistage experiments and thermochemcial data via ionmolecule reactions (Szulejko et al. 2006). In this section, examples from different multidimensional approaches are presented. Experimental Design of Plant Developmental Proteome Analyses Plant proteome studies can be divided into two general categories (Rose et al. 2004) that (a) focus on establishing proteome reference maps of a defined organ at a certain developmental stage through identification of as many proteins of a particular proteome as possible and (b) address comparative proteome analyses. Both (a) and (b) types of proteomics studies utilize experimental designs (Schnable et al. 2004) centered around genotypic comparative approach (e.g., proteomes of two different genotypes such as mutant vs, wild type), temporal proteomic analysis (proteomes as a function of different developmental stages of a common genotype), or comparisons from a common genotype before and after application of an exogenous abiotic or biotic stimulus (Schnable et al. 2004). These experimental designs generally address questions related to “what” and “how much” (Figs. 1a and b). Other dimensions of analyses beyond addressing the “what” and “how much” are increasingly becoming more important in proteomics studies (Fig. 1c) and it is anticipated that future plant proteomics studies will provide crucial information about interactions between different ions and molecules. For example, we have utilized gas-phase hydrogen/deuterium (H/D) exchange reactions and ab initio calculations to show drastic peptide conformational changes upon alkali metal ion complexation (Solouki et al. 2001). Such conformational changes play significant role in biochemical reactions and are not limited to peptides or alkali metals. In the context of phytoremediation, transition


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metal ions contain dense and small atomic radii that can interact via both electromagnetic and electrostatic attraction to make such coordinating interactions one of the strongest of the metal–protein interactions (Freidberg 1974). The structural rearrangements induced by metal complexation may yield protein stabilities by restricting the mobility of domains via non-covalent cross-linking of charged amino acid side groups (Reed and Poyner 1997). These interactions can drastically change internal and/or external hydrogen bindings and stabilize specific conformation(s) of a protein and alter its enzymatic activities (Holm et al. 1996). Deciphering the details of chemical interactions between proteins and metals and understanding the biological implications of these interactions are of a broad interest. A wide range of biophysical techniques (e.g., absorbance spectroscopy, radiolabeled metal ion overlays on electrophoresis gels or blotted membranes (Maruyama et al. 1984), methods using metal affinity columns (Lopez et al. 2000), mass spectrometry (Lopez et al. 2000), and ion mobility (Ruotolo et al. 2002)) are being developed to probe molecular conformations. However, these approaches generally require a purified or semi-purified target of interest and do not facilitate identification of unknown targets from complex protein mixtures, or require complex multi-step processes and very specialized equipment. The metal affinity shift assay, developed by Kameshita and Fujisawa (1997), is similar to IMS in that binding of metal ions to proteins are presumed to change both the charge characteristics and the conformation(s) of proteins yielding mobility variations during the electrophoresis. Recent studies suggest that even under the harsh denaturing conditions of sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE), changes in mobility of metal-binding proteins could be detected. For example, divalent cation binding proteins were purified from complex mixtures of rat cerebral cortex (Kameshita and Fujisawa 1997) by using combinations of either divalent cation (Ca2+/Mg2+) treatment followed by or preceded by chelator (ethylenediamine tetra-acetic acid (EDTA)/ethylene glycolbis(2-aminoethyl-ether)-N,N,N’,N’-tetra-acetic acid (EGTA)) treatment in diagonal PAGE gels. An identical technique was used to characterize changes in expression of divalent cation-binding proteins during sperm maturation in mice (Gye et al. 2001). Gas-phase ion mobility and mass spectrometry techniques provide valuable complementary information; these techniques are quite sensitive and appropriate for investigating reactions occurring at physiological concentrations. A potential drawback with many of these gas-phase techniques is the possible convolution of the structural/ conformational changes caused by the analytical probe itself (e.g., ionization processes in MS and mobility experiments). Efforts on comparing gas- and solutionphase structures are ongoing and are expected to expand (Chung et al. 1997; Bogdanov and Smith 2005; Scarff et al. 2008). Other non-covalent binding interactions between proteins are also among the central physicochemical phenomena underlying biological signaling and functional control at the molecular level. For example, protein-protein interactions are intrinsic to virtually all cellular processes such as DNA replication, transcription, translation, signal transduction, and intermediary metabolism. In recent years, advances in analytical methods, and in particular molecular modeling and mass spectrometry, have aided the study of non-covalent interactions with unprecedented details (Barrera et al. 2008).

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Plant Proteome Analysis Availability of the nucleotide sequence information and sensitive analytical instruments (viz., high performance mass spectrometers) is opening up new perspectives for proteomic approaches and investigation of the complex functions of model plants and crop species. Although a true proteome analysis is still a distant objective and current studies focus on select portions of the total protein profiles, progress in proteomics has been rapid (Wilkins et al. 1996; Oeljeklaus et al. 2009; Pan et al. 2009; Sheoran et al. 2009; Sun et al. 2009). In other words, a true functional proteomics study which requires qualitative and quantitative characterization (e.g., temporal and spatial variations) of proteome as well as characterization of all chemical interactions is currently not possible. Nevertheless, interest in proteomics and plant proteomics (e.g., protein variations in different plant organs (Thiellement et al. 1999), variations in response to physiological events (Gallardo et al. 2003), identification of unknown plant viruses due to their proteome (Cooper et al. 2003), and the identification of microtubule binding proteins in plants (Chan et al. 2003) is increasing rapidly. Often, proteins that are involved in important dynamic cellular processes such as signal-transduction are also expressed at low physiological concentrations and exhibit rapid turnover. It is possible to employ metabolic 14N/15N labeling and MS-based proteomics techniques for identification of protein dynamics and turnover in plants. In most of the plant proteomics studies, model organisms such as Arabidopsis thaliana (that have a fully sequenced genome and a multitude of genetic mutants available for comparative experiments) are used (Kruger et al. 2007). Characterization of the minor components of a biological system at low-expression levels is a challenging analytical task and requires instruments with high sensitivity and wide dynamic range. Modern analytical approaches are being developed to address such complex biochemical questions. For example, modern “soft” ionization techniques such as MALDI (Karas et al. 1987; Karas and Hillenkamp 1988; Tanaka et al. 1988) and ESI (Whitehouse et al. 1985; Fenn et al. 1989) are allowing scientists to analyze larger proteins and more complex systems. However, these rapidly expanding fields are still in their infancy and further developments in sample preparation, analytical characterization, and advanced bioinformatics will play significant roles in “x-omics” studies of plants.

Protein Profiling in Plants Conventionally, proteomic analyses include four steps: (a) sample preparation, (b) protein separation and/or purification, (c) functional analysis, and (d) protein identification. Plants can grow in different culture types and each of the tissues or culture types may be associated with a specific set of proteins. There are some limitations in plant protein analysis compared to animal (or mammalian) protein characterization. The dynamic range challenges related to isolation of minor


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components of complex mixtures in the presence of other major components and “contaminants” are not limited to plant proteomics. However, comparisons between animal and plant cells indicate that plants’ protein contents and concentrations are lower. Moreover, plant cells are rich in other interfering compounds that can potentially hinder the sample preparation, separation, and characterization processes. In addition, plant cells generally have numerous vacuoles that contain additional interfering compounds such as salts, organic acids, phenolics, proteases, pigments, terpenes, and inhibitory ions. The presence of these seemingly superfluous compounds in plant cells has a negative impact on protein extraction and separation. Proteases and phenolic compounds can modify proteins and change their molecular weights or isoelectric points (PIs) (Jacobs et al. 2000). For example, phenolic compounds in banana plant combine with proteins reversibly by hydrogen binding and irreversibly by oxidation followed by covalent condensations. These bindings cause some issues in 2D electrophoresis and yield charge heterogeneity and streaks in the gel. Moreover, gel pores can be blocked by carbohydrates and cause precipitation and extend the focusing time. Other compounds like terpenoids, pigments, lipids, and wax-like polymers can also produce streaking and charge heterogeneity (Carpentier et al. 2008). Therefore, sample preparation in plant proteomics is a very important step and is associated with technical challenges (Wang et al. 2008). It is assumed that an ideal sample preparation for mass spectrometry characterization should result in disruption of all non-covalent bound protein complexes and removal of various interfering compounds such as salts, polysaccharides, and phenolic compounds to yield a solution of individual polypeptides. However, for more sophisticated analyses, where the preservation of the non-covalent bindings and other innate chemical interactions are desired, sample preparations must be less intrusive; this is an important current limitation. (a) Sample Preparation: Sample preparation in plant proteomics mass spectrometry may typically include three steps: (a-1) tissue disruption, (a-2) protein extraction from the source materials, and (a-3) solubilization of the available proteins prior to the final analysis (Jacobs et al. 2000; Wang et al. 2008). It is likely that future research on microfluidic devices (Ohno et al. 2008; Blow 2009; Kovarik and Jacobson 2009; Mukhopadhyay 2009) will focus on both reducing sample sizes and, more importantly, sample preparation steps to develop alternative direct analysis approaches in plant proteomics and allied fields of “x-omics”. Direct analysis of biological samples is an important consideration to preserve innate interactions between various components of the sample (i.e., chemical interactions can be lost during various stages of the conventional sample preparation approach in steps a-1 to a-3). Convenient sources of plant proteins for proteomic studies can be found from young seedlings which have higher protein contents and less interfering compounds (Jacobs et al. 2000). The amount and types of various proteins in seed depend on the species, genotype, and plant’s environment. Although, the concentration of proteins in the seed is higher, a major problem in seed’s protein extraction is the disturbance of lipids and carbohydrates (Jacobs et al. 2000).

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(b) Protein Separation and/or Purification: Among the various approaches for sample preparations in proteomics studies, the three commonly used/reported techniques for extraction of proteins from plant tissues include: (a) trichloroacetic acid (TCA)/acetone extraction (Catherine et al. 1986), (b) phenol extraction (Hurkman and Tanaka 1986), and (c) TCA/acetone/phenol extraction (Wang et al. 2003, 2006). Examples of the protein extraction methods have been reviewed recently (Wang et al. 2008; Jorrin-Novo et al. 2009). Technological advances in separation methodologies are expected to play a crucial role in advancing plant proteomics in several dimensions. Future improvements in separation resolving power will allow more comprehensive characterization of the proteome through the use of tandem separation techniques. Regardless of the projected progressions in performance characteristics of the analytical detectors in proteomics (e.g., mass spectrometers, ion mobility spectrometers/ devices, spectroscopic techniques), sample preparation will likely remain one of the most important and challenging steps in plant proteomics. Scientific appetite to continue combining mass spectrometers to other separation devices is fueled by the past remarkable successes in this area and tremendous future needs. Advances in small volume sampling (Song et al. 2006; Ohno et al. 2008; Blow 2009; Kovarik and Jacobson 2009; Mukhopadhyay 2009), CZE (Tagliaro et al. 2010; Viglio et al. 2010), IMS (Kanu et al. 2008; Trimpin and Clemmer 2008; Fernandez-Lima et al. 2009), and other relevant areas that can enhance sample handling for quantitative measurements (Jorrin-Novo et al. 2009) hold great promises and suggest a greater expansion of the plant proteomics. (c) Functional Analysis: Functional analysis of proteins (after identification) or “functional proteomics” is focused on characterizing protein-protein and other intermolecular interactions in biological systems. “Functional proteomics” facilitates the relationship between specific protein(s) to a particular biological pathway/process. Quantitative information regarding the identified proteins can also be obtained which is important in studying the variation of proteome in response to stimuli. “Functional proteomics” techniques can be classified as (i) affinitybased purification, (ii) pair-wise testing of the two partners, (iii) genetic-based techniques, and (iv) computational methods. The data obtained from these approaches may be combined to gain additional information about the function and evolution of biological systems. Recently, Orchard et al. have reported on a technique called “the minimum information required for reporting a molecular interaction experiment (MIMIx)” for improving the public access to the interacting protein data (Orchard et al. 2007). Mass spectrometry has played a major role in this area and interested readers are referred to a recent review by Köcher et al. (Köcher and Superti-Furga 2007). (d) Protein Identification: Several analytical approaches are available for identification of macromolecules. The following section provides a brief summary of some of the frequently used analytical techniques in plant studies. These techniques, such as chromatography, nuclear magnetic resonance (NMR), spectroscopy, and spectrometry, can be used as standalone or tandem systems for x-omic characterization.


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Analytical Techniques Chromatography Chromatographic techniques including high performance liquid chromatography (HPLC), 2D-HPLC, electrophoresis (Lei et al. 2005) (e.g., CZE, gel electrophoresis, micellar electrokinetic capillary chromatography (MEKC), isoelectric focusing), and 2D-electrophoresis are among the powerful separation techniques used in analytical chemistry and proteomics. These separation techniques offer excellent supplementary advantages when coupled to mass spectrometry (i.e., MS as detectors) in so called “hyphenated” instruments; these hyphenated instruments such as LC/ MS (Mitulovic and Mechtler 2006) and LC/FT-ICR MS (Smith et al. 2004) can be used for analyzing complex mixtures in proteomics and metabolomics. Additional information on hyphenated chromatographic techniques can be found in recent review articles (Mitulovic and Mechtler 2006; Deborah 2009).

Nuclear Magnetic Resonance (NMR) NMR is one of the most valuable analytical tools for structural determination of proteins/peptides (Chung et al. 1997; Shin and Lee 2008). In contrast to other types of spectroscopy, nuclei of atoms instead of their electrons (e.g., outer electrons for UV, IR, Vis, UV and inner electrons for x-ray techniques) are involved in the absorption of the electromagnetic energy. Moreover, NMR utilizes the magnetic component of the electromagnetic wave in the radiofrequency range (RF) of roughly 4 MHz–1 GHz (~23.5 T magnetic field) and requires a strong magnet for its operation. The use of Fourier transform and a strong superconducting magnet in most modern NMR instruments are common features between this and FT-ICR MS. As with the MS methods, and perhaps even more effectively, FT-NMR can provide a wealth of information for structural elucidation and about the chemical bonding/environment of a compound. However, NMR suffers from low sensitivity and slow data acquisition/collection rate; this is also true for multidimensional NMR which limits the use of separation system with NMR. For example, a simple 2D NMR protein characterization experiments may require orders of magnitude increased data acquisition time than the most sophisticated MS approaches (e.g., a single scan TOF experiment may only require tens of microseconds and even more sophisticated ultrahigh resolution FT-ICR MS experiments conducted under the ultrahigh vacuum (UHV) conditions can be completed in seconds). Moreover, experimental design and data interpretation in NMR can be very challenging for complex biological samples and large proteins. A significant advantage of the NMR approach is the non destructive nature of signal detection (as opposed to the required ionization step for all MS experiments); the non destructive nature of NMR approach makes it possible to study reaction kinetics, protein folding, and other chemical reactions in “real-time” and without significantly perturbing the chemical environment/system. However, serious instrumental

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limitations exist for using NMR to study fast reactions at physiologically relevant concentrations. Interested readers are referred to a recent review by Shin et al. (Shin and Lee 2008) on the topic. Spectroscopy Spectroscopy techniques cover a wide range of instruments that employ different wavelengths and/or photon energies to interrogate chemical bonds and structures. All spectroscopy methods, excluding NMR, utilize the electric component of the electromagnetic (light) wave. The light or energy spectrum may cover the range from high energy (e.g., x-ray) down to low energy ultraviolet (UV), infrared (IR), and rotational mode photons. For example, in the context of the plant studies, x-ray photoelectron spectroscopy (XPS) is an x-ray-based technique for elemental analyses and provides valuable information about distribution of various elements (e.g., metals) within a biological system, cell, and/or organ (Pothan et al. 2006; Fang and Wan 2008; Guo et al. 2008). Atomic absorption (AA) has also been historically a popular approach for elemental analysis in plants (Ogura 1970; Caldas et al. 2009; Wen et al. 2009 ) utilizing various types of sample extraction methods (Meeravali and Kumar 2000; Li et al. 2002b; Narin et al. 2004; Wei et al. 2007; Jiang et al. 2008; Malekpour et al. 2009). Fluorescence and IR spectroscopy (Berthomieu and Hienerwadel 2009; Cozzolino 2009) can provide structural and molecular fingerprints but most of these spectroscopic techniques have the disadvantage of low sensitivity (excluding fluorescence) and specificity for identification of individual components of a complex sample. Readers are referred to a review by Fournier et al. (2009) on the subject. Mass Spectrometry (MS) and Ion Mobility (IMS) Both MS and IMS techniques have been used for characterization of complex sample mixtures and are suitable for analysis of small and large molecules. These instruments can be used as standalone detectors but combination of the two instruments is a powerful arsenal for “x-omics” studies. Numerous types and configurations of mass spectrometers are used in biology, chemistry, environmental studies, forensics, physics, and other areas of science; it is therefore difficult to provide a comprehensive coverage of these diverse fields where MS has played a significant role to advance. In the following section, brief overviews of (a) MS and (b) IMS are provided. However, MS techniques are further explored in Section 10 of this chapter. Mass Spectrometry Ionization of large labile molecules (polar non-volatile compounds) such as proteins was initially reported by using plasma desorption (PD) (Macfarlane and Torgerson 1976). This invention was followed by introduction of various other ionization techniques (e.g., fast atom bombardment (FAB) (Barberr et al. 1981), secondary


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ionization MS (SIMS) (Benninghoven et al. 1976), including two of the very important ionization techniques in biological mass spectrometry, namely, MALDI (Karas et al. 1987; Karas and Hillenkamp 1988; Tanaka et al. 1988) and ESI (Whitehouse et al. 1985; Fenn et al. 1989)) that changed the field of modern mass spectrometry (each of the latter two ionization techniques resulted in the latest Nobel Prizes devoted to MS-related topics). Other important contributions and technological advances were related to the hardware and software improvements; for example, data mining has become a major field of its own in mass spectrometry (Matallana-Surget et al. 2010). Initially, the use of sector instruments, quadruple or TOF mass spectrometers were quite popular but their MRP (specially, in the case of TOF) was limited (e.g., M/DM50% 1 Hz) but offer other valuable advantages such as ultrahigh MMA, ultrahigh MRP, capabilities to perform ion–molecule reactions, and MSn under high mass resolving power conditions; these capabilities are necessary for “x-omics” analyses. Another consideration for instrument selection is the required routine maintenance and the ease of operation; all mass spectrometers operate under high vacuum or ultrahigh vacuum (UHV) conditions and require various degrees of routine maintenance (e.g., oil change for vacuum pumps, system cleanliness, etc.). Fortunately, nowadays, most commercial instruments are comparably friendly and require minimal training for routine analyses. However, for more complex experiments that necessitate high performance instruments, highly experienced workforce/scientists with professional training in mass spectrometry are often used so that instruments can be utilized near their full capacity. Ultimately, as with other analytical instruments, cost is an important determining factor.

Components of Mass Spectrometers There are several important factors that influence the performance characteristics in mass spectrometry. All mass spectrometers share the common feature of having (a) vacuum systems, (b) ionization sources, (c) analyzers, (d) detectors or signal transducers, and (e) software and computer system for data acquisitions, storage,

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processing, and interpretation. Following text provides a brief overview about different components of mass spectrometers and their influence on performance characteristics. (a) Vacuum Requirements: Ionization of the neutrals is required for all types of mass spectrometers. The generated negative and/or positive ions and potential charged radicals are generally unstable towards ion–molecule collisions and hence all MS experiments require high vacuum to avoid unwanted chemical reactions. In addition, because various ion optics and equations of motion are involved for unknown identification, ion–molecule collisions during the experiment should be minimized. In TOF experiments, ions travelling the distance between the ionization source and detector (~1 m for most typical TOF instruments and between ~1 and ~5 m for reflectron and high resolution TOF MS) may spend several hundreds of microseconds before hitting the detector; conversely, in FT-ICR MS or Orbitrap experiments ions may travel several kilometers during the course of an experiment. Hence, the UHV requirements for FT-ICR MS and Orbitrap instruments are more stringent (typically in the 10−10 torr range, where the room temperature mean free path for air molecules is about 500 km) than TOF and other mass spectrometers (e.g., ~ 10−7 torr for TOF and sector instruments). In order to take advantage of the state-of-the-art ultrahigh resolution mass spectrometers such as the FT-ICR MS or Orbitrap, UHV in the 10−10 torr range must be maintained which adds to the cost of these systems. (b) MS Ionization Sources: Selection of the “hard” (EI, LD, etc.) or “soft” (CI, MALDI, ESI, etc.) ionization types normally depends on the states (gas-, liquid-, or solid-phase) and classes of compounds under the study. In addition, ionization sources influence the selection of separation systems. For instance, both EI and CI ionization sources are more appropriately interfaced to GC systems whereas HPLC and CZE separation systems are easier to couple to ESI. Efficiency of the ionization and ion transport out of the source play a significant role on instrument sensitivity (detection limit). In case of TOF and pulsed experiments such as MALDI, the laser beam width can influence the resolving power; for example, commonly used nitrogen lasers with 3 ns beam width in the pulsed detection mode can limit the time resolution of such experiments to this value. (c) MS Analyzer Types: Different mass analyzer types include linear quadrupoles, various configurations of ion traps (e.g., ICR cell, Orbitrap, quadrupole/quistor, etc.), electrostatic (E) and magnetic (B) analyzers/sector instruments, TOF, and others. Generally, the analyzer type plays a key role in establishing achievable mass resolving power and the nature of the tandem MS (e.g., low and high energy CID, ECD, MPD, etc.). For example, the highest mass resolving power is achieved with FT-ICR systems, which incidentally are also suitable to perform multiple stages of mass spectrometry. TOF analyzers are simple to operate and provide reasonable mass resolving power once the unwanted ionization source related limitations (kinetic energy spread, laser pulse width in case of MALDI, post-source metastable decay, etc.) are addressed. Recent advances in


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data acquisition sampling rate (e.g., >GHz) and instrumental setup have improved the performance characteristics of high-end TOF instrument where MMA in the ppm range is achievable. The highest achievable MRP in biological MS is still routinely obtained using FT-ICR MS but the performance characteristics of other instruments are also continuously improving. (d) MS Detectors/Ion Transducers: Various types of image (e.g., in FT-ICR, Orbitrap) and particle detectors (e.g., discrete dynode electron multiplier, Faraday cup, charged-coupled devices) are used in mass spectrometry; detectors influence the dynamic range and sensitivity. (e) Data Acquisition: Most mass spectrometers use advanced electronics for signal detection and manipulation. For example, in an FT-ICR MS experiment (and in the absence of ion losses due to pressure dampening, Coulombic repulsions, ion-molecule reactions, etc.), the longer time-domain signals and higher sampling rates will yield better quality mass spectra (e.g., higher MRP, better MMA, higher S/N, and more accurate relative ion abundance). In TOF experiments, data acquisition rate influences mass resolution (e.g., faster oscilloscopes can record closely spaced ion arrival events); currently, fast acquisition oscilloscopes with sampling rates as high as several tens of GHz are commercially available.

Data Related Parameters (MRP, MMA, LOD) (a) Mass Resolving Power (MRP): MRP is generally defined as M/DM50%, and is the ratio of the experimentally measured mass (M) to the width of the peak at 50% or half height (DM50%). Other definitions of MRP include M/DM, for example at 10% valley, where DM refers to the distance between centroids of the two peaks when the overlap at their base is at 10% of the peak height. The reported MRPs from most high performance mass spectrometers (e.g., FT-ICR MS) are based on the M/DM50% definition; better mass separation leads to higher MRP. Mass resolution is the smallest mass difference between two adjacent peaks that can be resolved and it is the inverse of MRP. Higher MRPs allow separation of closely related peaks and can be utilized for direct detection of sulfur-containing amino acids and disulfide bridges in biomolecules (Solouki et al. 1997) and is valuable in “x-omics” studies (Shi et al. 1998). The need for a superconducting magnet and better UHV conditions contribute to the high cost of FT-ICR MS instruments. However, FT based mass spectrometers continue to provide the highest MRP currently achievable (e.g., for small molecule analysis using GC/FT-ICR MS (Solouki et al. 2004b) and GC/Orbitrap (Peterson et al. 2009) as well as macromolecules in biological mass spectrometry (Shi et al. 1998)) at high sensitivity. Other mass spectrometers also provide distinct advantages such as high scan rate (e.g., TOF MS), ease of operation (e.g., quadrupole MS), high throughput (e.g., TOF and linear ion trap), portability (e.g., quadrupole ion traps, TOF), (Domon and Aebersold 2006a, b) higher dynamic range (e.g., triple quadrupole and double quadrupole/linear ion traps), unique applications for

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fundamental studies of ion high and low energy collisions/fragmentation processes (e.g., sector instruments), etc. Comparisons of various commonly used biological mass spectrometers (Domon and Aebersold 2006b) reveal that the progress in the instrument development is closely related to the desired performance characteristics. Interested readers are referred to a recent review on this topic (Domon and Aebersold 2006b). (b) Mass Measurement Accuracy (MMA): MMA is defined as the relative difference between a measured mass and its theoretically calculated value in parts-permillion (ppm) {i.e., MMA (in ppm) = ((Mexperimental − Mtheoretical)/(Mtheoretical) × 106}. Smaller numbers for MMA indicate higher accuracy; FT based mass spectrometer can provide sub ppm (or ppb) range MMA values (Heffner et al. 2007; Williams and Muddiman 2007; Luo et al. 2009) and recent improvements in instrumentation are allowing MMA in the ppm range for the state-of-the-art TOF mass spectrometers (Kuzyk et al. 2009; Peters et al. 2009). (c) Limit of Detection (LOD) in Biological Mass Spectrometry: Introduction of the so called “soft” ionization techniques in mass spectrometry, especially MALDI (Karas et al. 1987; Karas and Hillenkamp 1988) and ESI (Whitehouse et al. 1985; Fenn et al. 1989) has enabled characterizations of a host of biological macromolecules and MS is becoming one of the major tools for understanding complex biological systems. One of the major goals in biological MS is characterization of single cell x-ome and biomarker discovery for disease diagnosis and prognosis. Therefore, improving MS sensitivity plays one the most important roles in biological mass spectrometry. Following the introduction of MALDI and ESI, efforts were focused on improving the sensitivity. Two of the initial attempts on femtomole peptide detection were reported using a MALDI/magnetic sector/ TOF instrument (Strobel et al. 1991) and a new MALDI matrix (i.e., 2, 5-dihydroxybenzoic acid (DHB)) (Strupat et al. 1991). Subsequent reports on improved detection limits using MALDI-type ionization (emphasizing the ionization or post-ionization steps) include: attomole peptide detection using picoliter vials (Jespersen et al. 1994) (in a TOF MS), small indentation (Solouki et al. 1995) (in FT-ICR MS), and multishot accumulation (in a high pressure MALDI/FT-ICR MS) (Moyer et al. 2003). Efforts to improve sensitivity were not limited to MALDI interface and improvements to ESI/MS and ESI/LC (or CZE)/MS played a major role in advancing the field. For example, using a dynamic ion funnel interfaced to ESI source, Belov et al. reported zeptomole sensitivity (~18,000 molecules) for analysis of proteins with molecular weights ranging from 8 to 20 kDa in an FT-ICR instrument (Belov et al. 2000). Ultrahigh sensitive protein identification (~75 zmol) of whole proteome extracts (~ 0.5 pg total sample size) was reported by online coupling of a capillary LC to solid-phase micro extraction (SPME) and nanoESI using an FT-ICR MS instrument (Shen et al. 2004). If ions are generated with sufficient number of charges, FT-ICR MS can be used for detecting multiply charged individual ions. Detection of multiply charged (~30 charges) single ions has been reported with FT-ICR MS (Bruce et al. 1994) to produce detectable image current (Bruce et al. 1994; Smith et al. 1994). Application of these


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single ion detection approaches for routine proteomic experiments will constitute a significant advance. Reduced sample consumption and the reported subattomole sensitivity in biological MS using accelerator MS (AMS) (Salehpour et al. 2008) hold promise for identification of isotopically labeled molecular markers in plant “x-omics”.

Future Directions and Challenges Although mass spectrometry continues to contribute significantly to the field of science, many future challenges remain. For example, current mass spectrometers are limited to analyzing molecules with masses that do not extend beyond several hundred thousand dalton and this limited range must be expanded. Moreover, instrumental limitations in terms of the required dynamic range for complex sample and “x-omics” studies must be addressed. Rapid characterization of biological samples (e.g., plants, microbes, etc.) outside the laboratory and in the field, using portable devices, is desirable and should contribute to statistical validity of data mining. Another significant current limitation is the lack of fast responding instruments that can be used to resolve temporal x-ome variations in biological systems (e.g., cell); although the use of temperature cooling and “reaction freezing” might be helpful (Ospelkaus et al. 2010), critical improvements are needed to be explored in the future. To understand the system biology, various multidimensional techniques and more precise/accurate quantification must be used and, in parallel, data from different techniques must be pooled and statistically analyzed; this requires extending interdisciplinary collaborations to beyond their current level where a team of analytical chemists, environmental scientists, instrumentalists, plant biologists, computer experts, physicists, and other professionals can work closely on a focused project. Such efforts should enhance our understanding of the plant biology and enable us to extent in vitro experiments to in vivo studies and ultimate field use. Ultimately, instrumental advances should increase the efficient use of plants for cleaning the environment and using plants as reliable sensors and indicators of environment and human health. Moreover, identifications of useful extractable biomarkers and natural products should enhance human health and pharmaceutical usage of these products. Because various statistical methods are used to handle complex and large data sizes for specific interpretations, the use of data repositories for sharing raw experimental data is strongly encouraged. A Philosophical Viewpoint: Discovering the intricate and dynamic interactions influencing signaling pathways in plants and other biosystems is the goal of many existing studies including mass spectrometry based “x-omics”, gene expression profiling and analyses, protein–protein interaction methods, protein microarray studies, and gene-disruption and engineering approaches (Zhu and Snyder 2002). Combing reliable experimental data from various analytical methods (with improved accuracy and precision in all measurement dimensions) and using progressive mathematical tools may someday allow scientists to define a

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multidimensional “phase space” and represent all possible states of the x-ome as a function of time. Achieving such ambitious goals will require the use of simpler biological models (Grigorov 2006) and characterization of their developmental x-ome, “pluralistic” research approach (Bruggeman et al. 2002), modern statistical tools and mathematical approaches including molecular dynamics-type simulations (Berne and Straub 1997), variety of analytical arsenal (e.g., including those methods presented in this chapter), massive computing power, and unprecedented interdisciplinary collaborations. Analytical instrumentation in general and mass spectrometry in particular is expected to continue its decisive role in decoding the concert of life by connecting seemingly chaotic relationships between complex molecular networks in biological systems and plant “x-omics”. Acknowledgements Partial Financial support from the Institute for Therapeutic Discovery and United States Civilian Research Development Foundation (US CRDF) is gratefully acknowledged. Authors would like to thank Sabina Solouki for her assistance with the editing.

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Index

A ABC proteins, 138, 143 ABC transporters, 127, 138, 139, 142 Abiotic stress, 138, 159, 279–281 Achromobacter xylosoxidans, 197 Acid orange, 162, 163, 165, 170 Agrochemicals, 133 Alachlor, 139 Alcaligenes, 197 Allium cepa, 203 Alopecurus myosuroides, 131, 142, 143 1-Aminocyclopropane-1-carboxylic acid (ACC), 196 Anthraquinones, 153, 155–158, 161 Arabidopsis thaliana, 127, 130, 131, 176, 268, 286 Aspergillus niger, 253 Astragalus membranaceus, 197 ATP-binding cassette (ABC), 138 ATP-dependent, 126, 128, 129 Avena sativa, 201 B Bacillus subtilis, 196 BADH acyltransferases, 137 Benzene, 12, 31, 48, 49, 130, 150, 153, 154, 195, 247, 253 Benzene sulphonate, 153, 154 Bioaccumulation, 58, 150, 174, 203, 226, 243, 245–246, 249, 250 Bioavailability, 30, 35, 64, 69, 70, 77, 80, 150, 152, 172, 192, 194, 195, 200, 201, 204–207, 238 Bioconcentration factor, 11, 80, 82, 152, 161, 244, 245 Biofilm, 172, 173 Bioindication, 218, 221, 222, 224–230 Bioinformatics, 241, 262, 265, 268

Biological concentration factor (BCF), 82, 152, 161–164, 245, 249–252 Biomonitoring, 68, 106, 107, 109, 112, 118, 218, 221, 222, 224–228, 230 Bioreactors, 155, 156 Biosensor, 180, 226, 253, 254 Biosurfactant, 64, 172, 206, 207 Biotic stress, 279, 281 Biotransformation, 126–128, 130, 131, 133, 135, 137, 161, 165, 173, 192, 193, 247, 253–257 Bisphenol A, 129 Boletus edulis, 203 Brachiaria decumbens, 205 Brassica juncea, 196 Brassica napus, 157 BTEX, 150, 151, 153, 173 Burkholderiales, 201 C Capillary chromatography, 271 Carboxypeptidase, 139, 141 Carrots, 11 Chlordane, 35, 81, 83, 193 Chloroacetanilide, 133, 134 Chloroform, 18, 28, 29, 37, 87, 88, 90, 95, 130 4-Chlorothiophenol, 136 Chlortetracycline, 133 Chlortoluron, 130, 131, 143 Cinchona, 155 Contamination, 10, 25, 28, 106, 109, 114, 118, 119, 150, 153, 169, 172, 192, 193, 200, 226, 227, 238 Cucurbita pepo, 80, 82, 83, 174, 203, 205 Cyanobacteria, 127 Cymbopogon ambiguous, 205 CYP, 129–131, 142, 143

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308 Cytisus striatus, 201, 202 Cytochromes P450, 158, 159 D 2,4-D, 36, 131, 132, 195 DDT, 18, 34, 80, 106, 108, 113, 175, 193, 195, 203 Degradation, 10, 17–38, 57, 63, 64, 67, 68, 88–90, 95–98, 108, 129, 134, 139–141, 150, 151, 154, 157, 159, 161, 165–169, 172, 175–179, 191–208, 221, 227, 238, 251, 253, 254, 276, 278, 282, 283 Denaturing gradient gel electrophresis (DGGE), 197 Detoxification pathway, 128, 158, 159, 170 Dibenzofurans, 5, 28, 34, 80, 82 Diesel oil, 254 2,4-Dinitrophenol, 206 Dioxins, 5, 34, 78, 80, 82, 106, 114, 115, 117, 152, 173, 193, 238 Dioxygenase, 32, 64, 129, 155, 168, 175, 198–200 Domestic wastewaters, 153, 155 Dyestuffs, 150, 153, 154, 160 E Earthworm, 70, 82, 175, 252 Echinogalus crus-galli, 197 Ecotoxicologist, 229, 230 Ectomycorrhiza, 202 Ectomycorrhizal fungi (ECMF), 203, 204 Effect indicator, 224 Electrophile response element (ERE), 128 Electrospray ionization (ESI), 265, 266, 268, 273, 276, 277, 280, 282, 290, 292 Eleusine indica, 151 Emulsification, 206 Endophyte, 196 Endophytic bacteria, 12, 83, 88, 150, 151, 196 Environmental monitoring, 227, 228, 230 ESI. See Electrospray ionization ERE. See Electrophile response element Ericaceous mycorrhiza, 202 Ethylene dibromide, 152 Extradiol dioxygenases, 199 Exudation, 159, 176, 194, 201, 205, 207 F Fenoxaprop, 139, 143 Festuca arundinacea, 82, 151, 198

Index Fingerprint, 180, 198, 243, 248, 272, 274, 287 FT-ICR, 266, 273, 276, 280, 282, 286, 290–292 FT-ICR MS, 263, 266, 271, 273, 274, 285, 286, 289–292 G Galium, 155 Genomics, 143, 254, 262, 275 Glomus mosseae, 284 Glucosylflavonoid, 157 g-Glu-Cys, 139 g-Glutamyl transpeptidase, 140, 141 Glutathione-S-transferases, 157 Glutathione transferase (GST), 68, 92, 126, 127, 132–136, 138, 142, 143, 158, 169, 170, 282, 283 Glycosyltransferase, 126, 135–137, 158 H Helianthus tuberosus, 131 Henry’s law constant, 152 Herbicides, 4, 8, 36, 69, 87, 89, 90, 92, 128, 130–139, 142, 143, 150, 152, 166, 180, 193, 279, 282, 283 Herbicide safeners, 136, 137, 142, 143 Hexachlorocyclohexane, 26, 108, 201, 202 Hexadecane, 199 Homoglutathione, 134 Human health, 6, 78, 150, 181, 229, 230, 257, 293 Hydrophobic, 27, 58, 64, 77, 82, 126, 128, 129, 135, 150–152, 166, 171–174, 179, 194, 199, 204–206, 238, 239, 242, 250 Hydrophobicity, 27, 31, 64, 152, 194, 199, 201, 206, 240, 254 Hydroxycinnamic acid, 199 I Imidazolinones, 130 Impact indicators, 225 Indoleacetic acid (IAA), 196 Ion mobility spectrometry (IMS), 265, 274–275 K KOA, 9, 10, 49, 56, 58, 59 Koc, 152, 161–164, 251 Kow, 5, 8, 9, 30, 64, 152, 166

Index L Laccaria bicolor, 203 Lipid hydroperoxides, 134, 170 Lipids, 4–6, 8, 9, 11, 58, 135, 141, 205, 262, 269, 284 Log Kow, 8, 166, 193 Lolium, 142 M Malonyltransferase, 126, 137–138 Mammals, 127 Matrix-assisted laser desorption ionization (MALDI), 265, 266, 268, 273, 278, 279, 282, 286, 289, 290, 292 Medicago sativa, 82, 197 Metabolomics, 254, 262, 271, 274, 284, 285 Metallothioneins, 279 Methylchloroform, 87, 90 Microlaena stipoides, 205 Microorganisms, 21–23, 31, 32, 35–37, 83, 90, 95–97, 133, 150, 151, 155, 166, 169, 172, 176–178, 180, 192, 193, 195, 196, 226, 238, 246, 254, 283 Mineralization, 32, 34, 35, 126, 165, 166, 194, 198, 204, 205 Morinda, 155 Multi-Markered-Bioindication-Concept (MMBC), 218, 228 Multistage MS, 273 Mycotoxin, 136 N N-malonylation, 138 Nocardia otitidiscaviarum, 199 Non-aqueous phase liquid (NAPL), 206 No observed effect concentration (NOEC), 245, 252 Nuclear magnetic resonance (NMR), 264, 265, 270–272 O Octanol-water partition coefficient, 5, 6, 9, 152 O-glucosides, 135 Orchid mycorrhiza, 202 Organosulphonate, 154 Oryza sativa, 175, 198 Oxidative polymerisation, 159 Oxidative stress, 68, 166, 169–171, 279, 281–282 5-Oxoprolinase1, 141

309 P Paenibacillus polymyxa, 202 PAGE, 267, 287 PAHs. See Polycyclic aromatic hydrocarbons Panicum bisulcatum, 205 Panicum virgatum, 195 Pannicum milliaceum, 151 Particulate deposition, 3, 9–10 PCDD. See Polychlorinated dibenzo-p-dioxins Periodic table, 220 Permissible concentrations, 172, 228 Peroxidases, 24, 32, 129, 137, 158, 159, 175, 194, 282 Persistent bioaccumulative and toxic (PBT), 243–245, 248, 249, 251–253, 255 Persistent organic pollutants (POP), 26, 37, 48, 52–54, 58, 64, 65, 67, 77–80, 82, 83, 105–120, 192–194, 203, 204, 207, 208, 227 Pesticides, 7, 8, 12, 25–27, 34, 35, 79, 106, 108–114, 127, 134, 139, 141, 150, 152, 193, 194, 203, 256, 264, 279, 282 Petroleum hydrocarbons, 151, 203 Phalaris arundinacea, 195 Phenanthrene, 48–51, 53, 59, 63, 67–69, 193, 195–199, 206, 207 Phenylpropanoid, 176, 200 Phospholipids, 284 Phragmites australis, 151, 162, 166, 169, 170, 175 Phytochelatin, 141 Phytoextraction, 81–83, 174–175, 203 Phytoremediation, 77, 79–81, 83, 92, 130, 133, 134, 136, 143, 150–153, 156–158, 166, 167, 171–174, 178, 180–181, 192, 195, 197, 205, 253, 256–257, 266 Phytostabilisation, 151 Phytostimulation, 192, 195 Phytotoxicity, 136, 169, 174, 175, 179, 196 Phytotransformation, 151, 155 Phytovolatilization, 153 Pinus nigra, 35, 177, 201, 205 Pisum sativum, 279 pKa, 7, 8 Plant growth promoting rhizobacteria (PGPR), 193, 195, 196 Poaceae, 206 Polar residues, 141 Polychlorinated biphenyls (PCBs), 5, 9, 26, 29, 30, 32, 34, 35, 64, 65, 81, 82, 106, 107, 109–114, 152, 171, 174, 175, 179, 193, 195, 200, 201, 253, 256 congeners, 107, 172, 177–179 transformation, 176, 177

310 Polychlorinated dibenzo-p-dioxins (PCDD), 5, 9–11, 18, 20, 21, 26, 30, 38, 69, 80, 106, 114–117 Polycyclic aromatic hydrocarbons (PAHs), 5, 9–11, 20, 29, 30, 38, 47–70, 106, 109–111, 117–119, 152, 173, 193, 196–200, 203–207, 253, 256 Polyphenoloxidase, 180 POP. See Persistent organic pollutants Post-translational modifications (PTMs), 262, 275, 276, 287, 289 Precipitation, 19, 26, 28, 59, 91, 95, 97, 269 Protein phosphorylation, 274, 277–278 Proteomics, 142, 207, 261–294 Pseudomonas fluorescens, 178, 198, 200 Pseudomonas putida, 198, 200 Q Quantitative structure-activity relationships (QSAR), 5, 69, 238–247, 249–257 R Radiolabel, 267, 284 RCF. See Root concentration factor Reaction indicator, 224, 225 Reactive oxygen species (ROS), 159, 160, 165, 166, 169–171 Residues, 27, 51, 52, 81, 88, 126, 128, 135, 137, 141, 152, 158, 177, 201, 206, 261–294 Resistance, 9, 31, 79, 126, 132, 134, 139, 141–143, 150, 161, 180, 227, 265, 278 Rheum, 155, 156 Rhizhoremediation, 174, 176–177, 192, 194–202, 205, 206 Rhizofiltration, 156 Rhizosphere, 35, 91, 95–97, 150–152, 159, 166, 175–178, 191–208 Rhodococcus aetherovorans, 197 Rhubarb, 155–157 Ring cleavage, 23, 32–34, 95, 199 Root concentration factor (RCF), 4, 152 Root exudates, 11, 82, 83, 151, 174, 192, 194, 198, 201, 202, 205, 207, 238 ROS scavenging, 169, 170 Rubia, 155 Rumex, 155 Rumex hydrolapatum, 156 S Salix caprea, 35, 177, 205 Salix viminalis, 200

Index Sample preparation, 268–270, 275, 285 S-bimane-cysteinylglycine, 140, 141 S-bimane-glutathione, 140 Senna angustifolia, 158 Sensitivity, 137, 223, 227, 252, 263, 268, 271, 272, 290–293 S-glutathionylation, 128 Soil horizon, 26, 28, 29, 38, 96 Solanum nigrum, 175, 203 Stenotrophomonas acidaminiphila, 197 Surfactants, 154, 175, 177, 195, 201, 207, 238 System biology, 261–263, 275, 293 T Tanning agents, 153 Tertiary treatment, 153 Tetrahymena pyriformis, 245 Textile industry, 153, 154, 161 Tobacco, 51, 129, 131, 132, 134–137, 176, 177 Tolerance, 129–134, 137, 139, 142, 143, 167, 196, 203, 227 Toxicity equivalent (TEQ), 114, 117 Translocation, 11, 27, 64, 81–83, 92, 96, 98, 152, 174, 194, 200, 201, 203 Transpiration, 5, 6, 9, 10, 12, 82, 91, 92, 157 Transpiration stream concentration factor (TSCF), 6, 7, 157 Trichloroethylene, 12, 32, 35, 130, 152, 195 Trifolium pratense, 195, 203 Trinitrotoluene (TNT), 129, 151, 195 Triticum asetivum, 203 Tylospora fibrilosa, 203 U UDP-glucose, 135 UGT, 126, 135–137, 142 UNEP Convention, 106, 118 V Vacuole, 126–129, 133, 137–141, 157–160, 269 Vetiveria, 151 Vibrio fischeri, 165 Volatilisation, 12, 28–30 Volatilization, 26, 27, 30, 37, 57, 151, 174, 198, 244, 246 W Weak acids, 7, 8, 152, 166 Weak bases, 7, 152, 166

Index Wetting agents, 154 Willow, 35, 151, 177, 178 Wood-degrading, 28 X Xenobiochemistry, 143 Xenobiotic response elements (XREs), 128

311 Xenobiotics, 3–13, 36, 68, 88, 91, 125–130, 132–141, 143, 150–152, 155, 157–159, 166, 167, 178, 192, 194–196, 202–204, 225, 256, 261–294 Xenome, 125–129, 138, 141–143 Z Zea mays, 81, 157

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