VIEWPOINT pubs.acs.org/est
Rainwater Management and Harvesting Strategies for Human Needs: An Indian Perspective Ravi Rangarajan and Prosenjit Ghosh* Centre for Earth Sciences, Indian Institute of Science, Bangalore, India
I
ndia is a growing economy with more than a billion people, and is dependent on monsoonal precipitation for its water needs. Recent data shows that only 1% of the existing water resources on the entire planet can be utilized by human population. The remaining 99% of the existing water resources consist of 97% saltwater and 2% ice caps and are nonexploitable.1 These numerical facts become even more alarming when it is estimated that the world’s population recently exceeded 6 billion people, and ∼25% of them stay in the Indian subcontinent. The annual need of 1800 cubicmeters of water per person is barely met and governmental agencies demand schemes for the better management of fresh water supply through river systems2 and other reservoirs of fresh water. A significant proportion of fresh water demand is met through precipitation received during the summer time through the South-West monsoon and partly during winter through the North-East monsoon. The observations on global warming and changing rainfall pattern, although uncertain, predicts a condition where driergets-drier and wetter-gets-wetter.3 The regions experiencing large scarcity of water due to excessive exploitation needs to adopt mechanisms to deal with the present scarcity situation. The idea and plan of rainwater harvesting to meet the water r 2011 American Chemical Society
need of the present population is highly attractive and it requires the tapping of water bodies like lakes and shallow aquifers. Within the Indian subcontinent, the regions covered by thick alluvium exhibit least water scarcity (Figure 1).4 The exploitation of water resources for human demand is largely based on available water through natural agencies like river and groundwater. The water scarcity index map for the region which captures the extent of exploitation of available water resources shows higher depletion for states in South Indian Peninsula. The regions in south and central India with bedrock made up of granite gneiss and Deccan basalt rock types are characterized by shallow aquifer having shorter residence time of groundwater. The rain fed groundwater reservoirs are over exploited due to high demand and shortage of supply through rainfall. A significant fraction of the rainfall flows into the ocean without being arrested by any aquifers or water bodies. The potential retrieval of this water to compensate for the scarcity of groundwater in the region can be addressed through rainwater harvesting program either through lakes or shallow aquifer. The economic exploitation of these resources, which carry the signature of monsoon-fed rainwater, has been well demonstrated in the study conducted on a brand of bottled water from the southern Indian city of Bangalore.5 A simultaneous monitoring of the stable isotopic ratios in regional rainwater and bottle water purchased at regular monthly time intervals showed isotopic imprints of rainwater in the bottle water. Based on the observations, it was concluded that commercially available bottled water is manufactured by tapping and treating of the monsoon-fed fresh water available in the region. The study provides a clear demonstration for the scope of harvesting monsoon rainfall to meet the need of portable water in this region. Further, the approach can be implemented in major scale through community based harvesting, augmentation of this scheme in municipality and a systematic distribution of conserved water through proper channelized network. Existing water bodies like lakes, ponds and wells, available in the urban locations can be efficiently utilized for this purpose. An approach of a similar kind in other region may allow circumventing the deficiency of drinking water at regional and community levels.
Received: September 14, 2011 Accepted: September 26, 2011 Published: October 20, 2011 9469
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Figure 1. (a) Showing the Water Scarcity Index (WSI)4 of Peninsular India along with the average net groundwater availability during the year 2010 (Data Source: Central Ground Water Board, Govt. of India, 2010 report) and the Population statistics of the year 2011(http://www.world-gazetteer.com). Major cities with high population density are marked. (b) Bangalore City which houses large inland water bodies, acts as an ideal ground for harvesting rainwater for human needs.
’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected].
(5) Rangarajan, R.; Ghosh, P. Tracing the source of bottled water using stable isotope techniques. Rapid Commun. Mass Spectrom. 2011, 25, 3323 3330.
’ REFERENCES (1) Rooy, T. B.V. Bottling up our natural resources: the fight over bottled water extraction in the United States. J. Land Use and Environ. Law 2003, 18, 267–298. (2) Fairless, D. Muddy waters. Nature 2008, 452, 278–281. (3) Science news. http://www.sciencedaily.com/releases/2010/02/ 100226093238.htm (accessed Aug 28, 2011). (4) Smakhtin, V.; Ravenga, C.; Doll, P. A pilot global assessment of environmental water requirements and scarcity. Water Int. 2004, 29 (3), 307–317. 9470
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VIEWPOINT pubs.acs.org/est
Anthrax Cleanup Decisions: Statistical Confidence or Confident Response Igor Linkov,*,† John B. Coles,† Paul Welle,† Matthew Bates,† and Jeffrey Keisler‡ †
Environmental Laboratory, US Army Engineer Research and Development Center, US Army Corps of Engineers, Vicksburg, Mississippi ‡ University of Massachusetts, Boston, Massachusetts
“C
an you guarantee this building is safe?” This question is difficult to answer in the wake of a biological attack. It is likewise difficult to decide when people may return to a place where dangerous agents were once dispersed. This decision becomes especially challenging when even minute quantities of the suspected contaminating agent could pose a significant threat (e.g., weaponized anthrax). Following the 2001 US anthrax attacks, the Government Accountability Office (GAO) directed the U.S. Department of Homeland Security (DHS) to develop a defensible strategy for making such decisions following biological incidents.1 Statistically based sampling and analysis has historically provided the information to guide remediation of contaminated sites. However, classical statistics approaches could require thousands of tests2 to conclude that the number of anthrax spores is below the level of 0.1 spores per square meter (a concentration which could still pose a significant risk). Supplemental experimental data (e.g., dispersion studies, preliminary sampling) may be difficult to obtain due to financial constraints and could be limited to specific experimental conditions. Moreover, classical statistics expresses results as confidence or tolerance intervals and does not actually state a probability of r 2011 American Chemical Society
contamination necessary for risk-based decision making, which poses a challenge of communicating test results in a meaningful way.3 For example, the confidence interval statement, “with 90% confidence the probability of contamination is less than 5%,” means “if the probability of contamination was really 5% or greater, there is only a 10% chance we would have observed no contamination.” Likewise, the tolerance interval statement “with 90% confidence, 95% of the room has no contamination” is not reassuring. Modern Bayesian statistical approaches (developed starting in the 1950s, building from a tradition going back to Bayes and Laplace in the 1700s-1800s) facilitate inferences about the probability of remaining contamination, effectively overcoming much of the aforementioned challenge. Bayesian methods can smoothly incorporate data from laboratory experiments (e.g., decontaminating different surfaces such as steel and concrete), which is useful when conditions are dynamic. Additionally, when relevant expert knowledge exists, Bayesian statistics can smoothly incorporate it into decision making, in place of further sampling. Finally, Bayesian methods can support direct statements about: probabilities, e.g., “there is a 95% probability that the room is not contaminated”; probability ranges, e.g., “are 95% confident that the probability of the room being clean is between 2% and 4%”; or probability distributions, e.g., “the probability distribution over the number of spores follows a beta distribution with these parameters” or even “there is a 1% chance that there are ten or more spores remaining” (which could be useful in less hazardous situations where some risk of exposure may be tolerable). An important caveat is that the probability statements are still fundamentally an assertion of the expert beliefs based on subjective inputs regarding properties of contaminants and cleanup methods under different conditions tempered by the logical implications of the observed data. They should not be viewed as a way to “launder” opinions into fact, and if experts do not know enough to provide strong judgments, new data will still be needed to gain adequate certainty about treatment success. The other key limitation of the Bayesian approach is that people— including subject matter experts—are known to have a variety of systematic biases in making subjective probability estimates. Scientific research is replete with examples of overconfidence, where experts were slow to update their beliefs in the face of new Received: September 29, 2011 Accepted: October 4, 2011 Published: October 26, 2011 9471
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assessment and remediation approach could more rapidly assess and respond to future biological threats.
’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected]. US Army Corps of Engineers, 696 Virginia Rd., Concord, MA 01742.
’ ACKNOWLEDGMENT Permission was granted by the Chief of Engineers to publish this information.
Figure 1. Proposed cleanup decision framework.
information. But there is also a rich literature on how to minimize these biases and validate results based on small (and thus, relatively inexpensive) sets of objective data. Correctly applied Bayesian methods fill an important gap when empirical data are limited. Figure 1 proposes a basic framework in which Bayesian methods could combine expert judgments about the threat with lab-data about decontamination efficacy to provide an estimate of remaining threat. Because it is able to integrate onsite judgment from first responders and experts in the field, such a model could help guide remediation and testing. The likelihood of remaining contamination is one input to a larger decision context for which experts from pertinent fields (e.g., counter-terrorism, law-enforcement, epidemiology, and policy), can provide other relevant information. Specifically, onsite observations (room characteristics, contaminated material composition, and wind/draft potential) are represented by the dark blue nodes. Onsite test data results (characterization, remediation test strips) are represented by light blue nodes. The red nodes are informed by historical laboratory data and offsite expert judgments about the probabilistic relations between these nodes and their predecessors, and Bayesian methods are then used to calculate probability distributions on each red node’s values, given those of its predecessors. The probability distribution on the number of surviving spores is derived directly from: (1) the initial number of spores (more generally, colony forming units), (2) the percentage that would be killed by remediation under laboratory conditions, and the (3) efficacy of onsite remediation compared to laboratory conditions. Issues 1 3 above are each informed by off or onsite data and judgment about their predecessors, as described. The distribution on the number (possibly zero) of remaining spores is an input to the overall threat calculation. With both strengths and limitations, the Bayesian approach is well-suited to guide remedial decisions in problems like anthrax remediation. Consistent procedures and inputs from both objective and subjective sources and procedures are important factors in making traceable and justifiable decisions based on estimates of probability given the available data. The better and more empirical the data, the less need there is to rely on judgment (and, in fact, Bayesian and classical statistical approaches converge to the same conclusions as sufficiently rich data become available). Through the continued development and integration of Bayesian and classical statistical method, a consistent, reliable, and scalable
’ REFERENCES (1) Agencies Need to Validate Sampling Activities in Order to Increase Confidence in Negative Results, GAO-05-251; Government Accountability Office: Washington, DC, 2005. (2) Price, P. N., Sohn, M. D.; Lacommare, K. S. H.; , McWilliams, J. A.; Framework for evaluating anthrax risk in buildings. Environ. Sci. Technol. 2009, 43, 1783 1787. (3) Benchmark Dose Analysis for Bacillus anthracis Inhalation Exposures in the Nonhuman Primate and Application to Risk-Based Decision Making, EPA 600/R-10/138; Environmental Protection Agency: Washington, DC, 2010.
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Technical, Economical, and Climate-Related Aspects of Biochar Production Technologies: A Literature Review Sebastian Meyer,*,† Bruno Glaser,‡ and Peter Quicker§ †
Bioenergy Unit, Ecofys, 81243 M€unchen, Germany Soil Biogeochemistry, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany § Unit of Technology of Fuels, RWTH Aachen, 52062 Aachen, Germany ‡
ABSTRACT: For the development of commercial biochar projects, reliable data on biochar production technologies is needed. For this purpose, peer-reviewed scientific articles on carbonization technologies (pyrolysis, gasification, hydrothermal carbonization, and flash carbonization) have been analyzed. Valuable information is provided by papers on pyrolysis processes, less information is available on gasification processes, and few papers about hydrothermal and flash carbonization technologies were identified. A wide range of data on the costs of char production (between 51 US$ per tonne pyrolysis biochar from yard waste and 386 US$ per tonne retort charcoal) and on the GHG balance of biochar systems (between 1054 kg CO2e and +123 kg CO2e per t dry biomass feedstock) have been published. More data from pilot projects are needed to improve the evaluation of biochar production technologies. Additional research on the influence of biochar application on surface albedo, atmospheric soot concentration, and yield responses is necessary to assess the entire climate impact of biochar systems. Above all, further field trials on the ability of different technologies to produce chars for agricultural soils and carbon sequestration are essential for future technology evaluation.
1. INTRODUCTION In recent years, biochar application to soil has been put forward as a tool to mitigate global warming and improve soil properties.1 3 In spite of considerable scientific work on the effects of biochar application to soil with respect to crop yields and stabilization of plant-derived carbon in agricultural soils, the commercial production of biochar for soil improvement and C sequestration is still very limited today. Parties interested in the development of commercial biochar need reliable and comprehensive data on the different technologies available for biochar production. For this reason, this paper summarizes the available peer-reviewed scientific literature (ISI Web of Knowledge) about the technological, economical, and climate-relevant aspects of carbonization technologies. Biochar is defined as “charred organic matter applied to soil in a deliberate manner, with the intent to improve soil properties” in Lehmann et al.4 Although biomass-derived char can be used as energy carrier, as adsorber, and for further applications, this paper focuses on the production of chars for the improvement of soil properties. Carbonized organic matter can have fundamentally different physical and chemical properties depending on the technology (e.g., torrefaction (a pyrolysis process at low temperature), slow pyrolysis, intermediate pyrolysis, fast pyrolysis, gasification, hydrothermal carbonization (htc), or flash carbonization) used for its production. Research on torrefied material as soil amendment has started only recently.5 In contrast to considerable r 2011 American Chemical Society
research which has already been carried out to assess the value of charcoal as soil amendment,6 10 no publication was identified which examines the use of chars from modern gasifiers as soil amendment. Charcoal can be produced both in traditional earthen charcoal kilns where pyrolysis, gasification, and combustion processes are carried out in parallel below the earthen kiln layer and in modern charcoal retorts where pyrolysis and combustion processes are physically separated by a metal barrier. Two papers have been published on the suitability of htc-char for the stabilization of organic carbon,11,12 and another on the suitability of htc-char for the improvement of soil properties.13 Only one publication is available today in the ISI Web of Knowledge on the suitability of the use of carbonized material from flash carbonization as a soil amendment.14 It is important to be aware that the results of the indicated publications with carbonized material from torrefaction, hydrothermal carbonization, and flash carbonization did not show an improvement of plant growth after the addition of carbonized material. As phytotoxic components have been found in torrefied material5 and torrefied material has hydrophobic properties, this technology is treated in less detail in this review. Apart from that, all main technology routes already mentioned have been fully Received: December 22, 2010 Accepted: September 30, 2011 Revised: September 13, 2011 Published: September 30, 2011 9473
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Table 1. Publications Identified and Reviewed Per Category technological
profitability
climate
maturity
analyses
impacts
pyrolysis
2
7
6
gasification
7
2
1
hydrothermal carbonization
2
0
0
flash carbonization
1
1
0
technology type
included in this literature review as today’s knowledge on the suitability of carbonized material from modern gasification, hydrothermal carbonization, and flash carbonization for the improvement of soil properties is still very limited. However, it is indispensable to further assess the ability of the different technologies to produce carbonized material suitable to increase the fertility of agricultural soils and to store carbon over a long period of time. In this context, special care has to be taken to avoid the use of chars contaminated with polycyclic aromatic compounds or dioxins for agricultural purposes. A detailed discussion of dioxin formation is presented by McKay,12 and limits for dioxin and polycyclic aromatic hydrocarbon levels in compost and sewage sludge in European countries can be found in Libra et al.12 It should be noted that in field trials, often mixtures of char and compost are used with the aim to produce a soil amendment similar to the fertile Terra Preta soils in the Amazon region.1 Though char makes up a minor weight component of this soil amendment, it is an essential part of the final product.
2. METHODOLOGY To identify the relevant literature for this review, the ISI Web of Knowledge was explored with the following method: By searching for articles containing the keywords “pyrolysis”, “gasification”, “hydrothermal carbonization”, and “flash carbonization” in connection with the keywords “reliability”, “availability”, “durability”, “development + hours”, and “scale up”, the technological maturity of carbonization technologies was assessed. To retrieve publications that analyze the economical profitability of carbonization technologies, the keywords “profitability”, “economics”, “production costs + char”, and “return + char” were used. Regarding the climate impact of carbonization technologies, the keywords “GHG balance”, “LCA”, “albedo”, and “atmospheric soot” were selected. The available peer-reviewed scientific literature about the technological, economical, and climate-relevant aspects of the different technologies varies considerably. This can be seen in the overview on the number of publications identified and reviewed per technology and assessment aspect (Table 1). In addition to that, information on carbonization technologies is often focused on the production of energy carriers only. This will be reflected in the following chapters. As this paper concentrates on publications in the ISI, it cannot be excluded that additional publications are available in other scientific databases. 3. OVERVIEW OF CARBONIZATION TECHNOLOGIES To produce carbonized organic matter, pyrolysis, gasification, hydrothermal carbonization, and flash carbonization technologies can be used. Pyrolysis can be differentiated from gasification
by the (nearly) complete absence of oxygen in the conversion process.16 Pyrolysis technologies can be further differentiated by the reaction time of the pyrolysis material (e.g., slow and fast pyrolysis processes) and the heating method (e.g., pyrolysis processes started by the burning of fuels, by electrical heating, or by microwaves). Bridgwater16 and IEA Bioenergy17 differentiate pyrolysis technologies according to the temperature and the residence time of the pyrolysis process (see Table 2). In gasification processes, the biomass is partially oxidized in the gasification chamber18 at a temperature of about 800 °C16 at atmospheric or elevated pressure. As already indicated by its name, the main product of this process is gas, only small amounts of char and liquids are formed. The hydrothermal carbonization of biomass is realized by applying elevated temperature (180 220 °C) to biomass in a suspension with water under elevated pressure for several hours. It yields solid, liquid, and gaseous products.19 Libra et al.12 refer to hydrothermal carbonization as “wet pyrolysis”. Because no oxygen is supplied to the reactor with the biomass water suspension, this classification is justified. For the flash carbonization of biomass, a flash fire is ignited at elevated pressure (at about 1 2 MPa) at the bottom of a packed bed of biomass. The fire moves upward through the carbonization bed against the downward flow of air added to the process. In total about 0.8 1.5 kg of air per kg of biomass are delivered to the process. The reaction time of the process is below 30 min, and the temperature in the reactor is in the range of 300 600 °C. The process results mainly in gaseous and solid products. In addition to that, a limited amount of condensate is formed. While the oxygen input into the carbonization process is a typical feature of gasification technologies, both process temperature and the product spectrum (distribution among solid, liquid, and gaseous outputs) of flash carbonization are uncommon for gasification processes. It should be noted that typical solid product yields obtained by gasification and fast pyrolysis processes are significantly lower as compared to the solid product yields of slow pyrolysis, flash carbonization, hydrothermal, carbonization and torrefaction (see Table 2). It is important to take into account that the development history of the different technologies reviewed varies considerably: The development of coal gasification started already a few centuries ago26 whereas the development of charcoal kilns has taken place over a time span of millennia.21
4. TECHNOLOGICAL MATURITY OF CARBONIZATION TECHNOLOGIES To understand the challenges that need to be solved to ensure a high annual availability of a biochar production system, Table 3 lists technical points that need special attention to ensure a longterm operation of the respective technologies. 4.1. Pyrolysis Technologies. Bridgwater et al.18 assumed an overall annual availability of 85% for an electricity production process based on liquids produced by fast pyrolysis. This assumption is used in a model to calculate the electricity production costs of the process. The assumption itself is based on the precondition that a buffer storage for pyrolysis liquids limits unplanned generation shutdowns. Thus, the pyrolysis process itself can have a considerably lower availability than 85%. 4.2. Gasification. Bridgwater et al.18 assumed a mean annual availability of 80% for an electricity production process based on 9474
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Table 2. Solid Product Yields, Solid Product Carbon Content, and Carbon Yield of Different Technologies
process type
typical process temperature
typical residence time
typical solid product yield
typical carbon content
typical carbon
on a dry wood feed-stock basis [in mass %]
of the solid product [in mass % ]
yield: (mass carbon, product/ mass carbon, feedstock)
61 84%
51 55%
0.67 0.85
16, 20
≈ 30%
95%
≈ 0.58
16, 21
12 26%
74%
0.2 0.26
reference
torrefaction
∼ 290 °C
10
slow pyrolysis
∼ 400 °C
minutes to days
fast pyrolysis
∼ 500 °C
∼1s
gasification
∼ 800 °C
∼10 to 20 s
≈ 10%
htc
∼ 180 250 °C
1 12 h
8.4) conditions, the retention of E. coli O157:H7 cells within quartz sand decreased
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Figure 3. (A) Equivalent radius of E. coli O157:H7 cells suspended in various electrolyte solutions. (B) Zeta potential of bacterial cells (left axis) and quartz sands (right axis) under different ionic strength and phosphate concentration conditions. Note that the zeta potential values of bacterial cells and quartz sands were plotted at different scales.
with increasing ionic strength.24 Consistent with findings reported in previous research, results from our study show that the deposition of E. coli O157:H7 cells decreased with increasing ionic strength under a pH of 7.2, regardless of phosphate concentrations (Figures 1 and 2). For instance, in the absence of phosphate, the deposition rate coefficients were 0.054 ((0.003) min1 and 0.025 ((0.0007) min1 for 10 and 100 mM of ionic strength, respectively. 3.2. XDLVO Interaction Energy Profiles. The measured contact angles of water, glycerol, and diiodomethane on E. coli O157:H7 lawns were 22.1° ((0.1°), 27.0° ((1.8°) and 63.0° ((0.7°), respectively. The values of γLW, γ+or γ for E. coli O157: H7 cells were calculated as 26.9, 47.6, and 4.82 mJ m2, respectively. Using the values previously determined for quartz in Morrow et al.,52 the Hamaker constant in eq 4 for the bacterium-waterquartz system was estimated as 1.522 1021 J. The estimated 2 value of ΔGAB ho was 24.94 mJ/m , suggesting a repulsive AB interaction between the E. coli O157:H7 cells and the quartz sand. The equivalent cell radius was around 0.85 μm and showed little variation with different ionic strength and phosphate concentrations (Figure 3A). The zeta potentials of both the E. coli O157:H7 cells and quartz sand were negative (Figure 3B). In general, the zeta potentials of sand were ∼30 mV less negative when ionic strength increased from 10 mM to 100 mM due to the compression of electric double layer. The zeta potentials of the E. coli O157:H7 cells were close to neutral and, in contrast to the trend observed for quartz sand, an increase in ionic strength 9569
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Figure 4. XDLVO energy interaction profiles between E. coli O157:H7 cells and surface of quartz sands. The energy interactions were expressed in kT, where k is Boltzmann constant and T is absolute temperature in Kelvin.
led to a slight decrease in the zeta potential of the bacterial cells (Figure 3B). For both quartz sand and bacterial cells, phosphate decreased zeta potential values. This could be related to adsorption of phosphate onto the surface of quartz sand (e.g., through the bonding between phosphorus and oxygen at the surface of quartz) and bacterial cells, which could increase the negative surface charge under the pH conditions employed in this research.60 The calculated XDLVO energy interaction profiles are shown in Figure 4. Under an ionic strength of 10 mM, there was no repulsive energy barrier for cell attachment to sand surface when phosphate was absent (Figure 4A). Energy barriers were present when phosphate concentrations were either 0.1 mM or 1 mM. The energy barrier values were 0.86 kT (0.1 mM phosphate) and 1.33 kT (1 mM phosphate), respectively, where k is the Boltzmann constant and T is the absolute temperature in Kelvin. Similarly, when ionic strength was 100 mM, the energy barrier changed from absent for no phosphate to ∼53 kT for both 0.1 mM and 1 mM phosphate (Figure 4B). The energy barriers, although small, could hinder the attachment of E. coli O157:H7 cells to the surface of quartz sand and thus change a system that would otherwise be favorable for deposition, and make it unfavorable for deposition. This trend is consistent with results from the column transport experiments, which suggest that phosphate increased the transport of E. coli O157:H7 cells. Additionally, the magnitude of the energy barriers was generally higher for the 100 mM ionic strength conditions than the 10 mM ionic strength conditions. This is consistent with the observation that the transport of E. coli O157:H7 cells within the sand columns increased with higher ionic strength (Figures 1 and 2).
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The total XDLVO energy interaction profiles reflect the summation of the LW, EDL, and AB interactions. The LW and AB components of the overall interaction energy are independent of water chemistry parameters and remain the same for all conditions. Ionic strength, however, had a significant impact on the zeta potential values of both the bacterial cells and the sands (Figure 3B). As the sand zeta potential became less negative when ionic strength increased from 10 to 100 mM, the zeta potential of E. coli O157:H7 cells became more negative. In response to the changes in the zeta potential values, the calculated EDL interactions between bacterial cells and quartz sand under 100 mM ionic strength conditions were more repulsive than the EDL interactions under 10 mM ionic strength conditions. 3.3. Release of Immobilized E. coli O157:H7 cells. Results from those column transport experiments using a solution that had an ionic strength of 100 mM and contained no phosphate, show that ∼33% of the bacterial cells were immobilized within the sand packs (Figure 1B). Upon the completion of the experiments, solutions with similar ionic strength but progressively higher phosphate concentrations (0.1 mM and then 1 mM) were sequentially injected into the columns to examine the release of the immobilized E. coli O157:H7 cells. The results show that the increase in phosphate concentration in the mobile aqueous phase led to the release of previously retained E. coli O157:H7 cells (Figure 1B). Maximum cell concentrations in the first and second release pulses reached ∼16% and ∼9% of influent concentration. Integration of the release pulses shows that the total quantity of released cells for the first and second pulses was equivalent to 2.5% ((0.5) and 1.4% ((0.5) of the total amount of bacterial cells injected into the columns, respectively. When combined, ∼12% of the immobilized bacterial cells were flushed out during the two-stage phosphate perturbation experiments. According to the XDLVO theory, when the repulsive barrier (and thus secondary energy minimum) is absent in the energy interaction profile of bacterial cells and sands, the primary energy minimum is primarily responsible for cell deposition, which is considered irreversible.50 The observed release of immobilized E. coli O157:H7 was thus contrary to the XDLVO energy interaction profiles, which suggests the absence of an energy barrier and secondary minimum (100 mM, no phosphate). The energy interaction profiles shown in Figure 4, however, were calculated using the average cell zeta potential values. Considering the variations in the measured cell zeta potential values (Figure 3B), repulsive interaction barrier and secondary energy minimum could be present in the interaction energy profiles of a fraction of the bacterial cells and sand surfaces, and cell deposition within the secondary energy minimum could thus occur. For these cells, perturbations in water chemistry (i.e., phosphate concentration) could lead to their release. This is consistent with the observation that only a fraction (∼12%) of the immobilized bacterial cells was released. For the fraction of E. coli O157:H7 cells that deposited into the primarily minimum, the increase in phosphate concentration actually made the release less likely because, as suggested in Hahn et al.,61 the energy barrier for cell release increased. Our results also highlight the vital importance of the energy interaction within 5 nm from the surface in cell deposition and release.61 According to the colloid filtration theory,62,63 the clean-bed deposition rate coefficient can be expressed as a function of the product of the collector efficiency (η) and collision efficiency (α). As the collector efficiency (η) can be estimated from the correlation equation reported in the literature,63 the collision 9570
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Figure 5. Steric interaction energy profile between E. coli O157:H7 cells and surface of quartz sands. The energy interaction was expressed in kT, where k is Boltzmann constant and T is absolute temperature in Kelvin.
efficiency (α) can then be estimated from the experimentally determined deposition rate coefficient and collector efficiency. The calculated values of α were all less than 1, suggesting that the deposition condition was not completely favorable (Supporting Information (SI) Table S1). This was also consistent with our observation that the retention of E. coli O157:H7 was reversible, even if the XDLVO theory predicted the absence of energy barrier. In addition, the values of α decreased with phosphate concentrations (SI Table S1). 3.4. Steric Interactions. As shown in the previous section, the XDLVO theory and our experimental observation did not always agree with each other (e.g., although XDLVO predicted no energy barrier, the deposition of E. coli O157:H7 was reversible under the 100 mM, no phosphate condition). Steric interactions due to the presence of extracellular macromolecules on bacterial surface were reported to be partly responsible for the discrepancies between model and experimental results.46 The model results (Figure 5) indicate that the steric interaction between E. coli O157:H7 surface and quartz sand was significantly higher than the XDLVO forces at comparable distances. This is qualitatively consistent with our observation that retention of E. coli O157:H7 is reversible when the XDLVO theory predicts the absence of energy barrier. Additionally, it has been hypothesized that the conformational changes caused by the deprotonation of bacterial surface lipopolysaccharides (LPS) carboxylic and phosphoric functional groups allowed for greater penetration of the counterions into the polymer layer, which in turn decreased the attachment of E. coli O157:H7 cells onto the surface of quartz sand.24 Elevated phosphate concentrations may have caused conformational changes of the bacterial extracellular polymers and consequently more repulsive steric interactions between the cell and quartz sand. More experimental investigations are needed to further examine the relationship between phosphate and the conformation of E. coli O157:H7 surface macromolecules, as well as the associated effects on the steric interactions. 3.5. Environmental Implications. Cattle manure represents a major source of E. coli O157:H7.4,79 Manure produced in cattle farms is commonly applied to agricultural fields as a fertilizer. The E. coli O157:H7 cells that are introduced into the soil through this process could leach out and contaminate the underlying groundwater. Findings from this research suggest that phosphate, a key ingredient used in numerous domestic and industrial applications, for example, detergents, metal surface coating,
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fertilizers, and drinking water distribution pipe corrosion control, could potentially change a system that would otherwise be favorable for E. coli O157:H7 cells deposition, and make it unfavorable for deposition. Since groundwater is the primary source of drinking water, particularly in rural areas, the enhanced transport of E. coli O157:H7 cells could translate into greater public health risks. Phosphate concentrations in the soil may also change as a result of fertilizer (including manure) application, plant uptake, rainfall, irrigation, and plant evatranspiration. Our results indicate that increase in phosphate concentration can lead to a pulse-type release of previously immobilized E. coli O157:H7 cells. This release represents another mechanism that can cause the wider spread of E. coli O157:H7 cells within the soilgroundwater system. Our results also provide insights into the mechanisms contributing to the reduced occurrence of coliform bacteria and biofilm inhibition in drinking water distribution systems through the addition of phosphate.
’ ASSOCIATED CONTENT
bS
Supporting Information. Additional material as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Phone: (414) 229-6891; fax: (414) 229-6958; e-mail: li@uwm. edu.
’ REFERENCES (1) Rangel, J. M.; et al. Epidemiology of Escherichia coli O157: H7 outbreaks, United States, 19822002. Emerging Infect. Dis. 2005, 11 (4), 603–609. (2) Karch, H.; Tarr, P. I.; Blelaszewska, M. Enterohaemorrhagic Escherichia coli in human medicine. Int. J. Med. Microbiol. 2005, 295 (67), 405–418. (3) Mead, P. S.; et al. Food-related illness and death in the United States. Emerging Infect. Dis. 1999, 5 (5), 607–625. (4) Smith, J. E.; Perdek, J. M. Assessment and management of watershed microbial contaminants. Crit. Rev. Environ. Sci. Technol. 2004, 34 (2), 109–139. (5) Centers for Disease Control and Prevention. Outbreak of Escherichia coli O157:H7 and campylobacter among attendees of the Washington County Fair - New York, 1999. Morbidity and Mortality Weekly Report, 1999. 43: p. 803805. (6) O’Connor, D., Report of the Walkerton Inquiry. 2002. (7) Caprioli, A.; et al. Enterohaemorrhagic Escherichia coli: Emerging issues on virulence and modes of transmission. Vet. Res. 2005, 36 (3), 289–311. (8) Chase-Topping, M. E.; et al. Risk factors for the presence of highlevel shedders of Escherichia coli O157 on Scottish farms. J. Clin. Microbiol. 2007, 45 (5), 1594–1603. (9) Valcour, J. E.; et al. Associations between indicators of livestock farming intensity and incidence of human Shiga toxin-producing Escherichia coli infection. Emerging Infect. Dis. 2002, 8 (3), 252–257. (10) Himathongkham, S.; et al. Survival of Escherichia coli O157: H7 and Salmonella typhimurium in cow manure and cow manure slurry. FEMS Microbiol. Lett. 1999, 178 (2), 251–257. (11) Zhao, T.; et al. Prevalence of enterohemorrhagic Escherichia coli O157-H7 in a survey of dairy herds. Appl. Environ. Microbiol. 1995, 61 (4), 1290–1293. (12) Cho, S.; et al. Prevalence and characterization of Escherichia coli O157 isolates from Minnesota dairy farms and county fairs. Journal of Food Prot. 2006, 69 (2), 252–259. 9571
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(35) Zhuang, J.; Jin, Y. Interactions between viruses and goethite during saturated flow: Effects of solution pH, carbonate, and phosphate. J. Contam. Hydrol. 2008, 98 (12), 15–21. (36) Blanford, W. J.; et al. Influence of water chemistry and travel distance on bacteriophage PRD-1 transport in a sandy aquifer. Water Res. 2005, 39 (11), 2345–2357. (37) Walczak, J. J.; et al. Influence of tetracycline resistance on the transport of manure-derived Escherichia coli in saturated porous media. Water Res. 2011, 45 (4), 1681–1690. (38) Brown, D. G.; Jaffe, P. R. Effects of nonionic surfactants on bacterial transport through porous media. Environ. Sci. Technol. 2001, 35 (19), 3877–3883. (39) Xu, S. P.; Gao, B.; Saiers, J. E. Straining of colloidal particles in saturated porous media. Water Resour. Res. 2006, 42, (12), W12S16, DOI: 10.1029/2006WR004948 (40) Walker, S. L.; Redman, J. A.; Elimelech, M. Influence of growth phase on bacterial deposition: Interaction mechanisms in packed-bed column and radial stagnation point flow systems. Environ. Sci. Technol. 2005, 39 (17), 6405–6411. (41) Kretzschmar, R.; et al. Experimental determination of colloid deposition rates and collision efficiencies in natural porous media. Water Resour. Res. 1997, 33 (5), 1129–1137. (42) Camesano, T. A.; Logan, B. E. Probing bacterial electrosteric interactions using atomic force microscopy. Environ. Sci. Technol. 2000, 34 (16), 3354–3362. (43) Tong, M. P.; et al. Detachment-influenced transport of an adhesion-deficient bacterial strain within water-reactive porous media. Environ. Sci. Technol. 2005, 39 (8), 2500–2508. (44) Tong, M. P.; Camesano, T. A.; Johnson, W. P. Spatial variation in deposition rate coefficients of an adhesion-deficient bacterial strain in quartz sand. Environ. Sci. Technol. 2005, 39 (10), 3679–3687. (45) Ong, Y. L.; et al. Adhesion forces between E-coli bacteria and biomaterial surfaces. Langmuir 1999, 15 (8), 2719–2725. (46) Bayoudh, S.; et al. Quantification of the adhesion free energy between bacteria and hydrophobic and hydrophilic substrata. Mater. Sci. Eng., C 2006, 26 (23), 300–305. (47) Bayoudh, S.; et al. Assessing bacterial adhesion using DLVO and XDLVO theories and the jet impingement technique. Colloids and Surfaces B-Biointerfaces 2009, 73 (1), 1–9. (48) Farahat, M.; et al. Adhesion of Escherichia coli onto quartz, hematite and corundum: Extended DLVO theory and flotation behavior. Colloids Surf., B 2009, 74 (1), 140–149. (49) Elimelech, M. Particle deposition on ideal collectors from dilute flowing suspensions—Mathematical formulation, numerical-solution, and simulations. Sep. Technol. 1994, 4 (4), 186–212. (50) Redman, J. A.; Walker, S. L.; Elimelech, M. Bacterial adhesion and transport in porous media: Role of the secondary energy minimum. Environ. Sci. Technol. 2004, 38 (6), 1777–1785. (51) Huang, X. F.; Bhattacharjee, S.; Hoek, E. M. V. Is surface roughness a “scapegoat” or a primary factor when defining particlesubstrate interactions? Langmuir 2010, 26 (4), 2528–2537. (52) Morrow, J. B.; et al. Macro- and nanoscale observations of adhesive behavior for several E coli strains (O157: H7 and environmental isolates) on mineral surfaces. Environ. Sci. Technol. 2005, 39 (17), 6395–6404. (53) van Oss, C. J., Acid-base interfacial interactions in aqueous media Colloids Surf., A 1993. 78: p. 1-49. (54) Butt, H. J.; Cappella, B.; Kappl, M. Force measurements with the atomic force microscope: Technique, interpretation and applications. Surf. Sci. Rep. 2005, 59 (16), 1–152. (55) Israelachvili, J. N., Intermolecular and Surface forces, 2nd ed.; Academic Press: San Diego, CA, 1991; Vol. xxi. (56) Strauss, J.; Burnham, N. A.; Camesano, T. A. Atomic force microscopy study of the role of LPS O-antigen on adhesion of E. coli. J. Mol. Recognit. 2009, 22 (5), 347–355. (57) Neidhardt, F. C., Curtiss, R., III, Ingraham, J. L., Lin, E. C. C., Low, K. B., Magasanik, B., Reznikoff, W. S., Riley, M., Schaechter, M., 9572
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Heavy Metal Sorption at the Muscovite (001)Fulvic Acid Interface Sang Soo Lee,*,† Kathryn L. Nagy,† Changyong Park,‡,§ and Paul Fenter‡ †
Department of Earth and Environmental Sciences, 845 West Taylor Street MC-186, University of Illinois at Chicago, Chicago, Illinois 60607, United States ‡ Chemical Sciences and Engineering Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States
bS Supporting Information ABSTRACT: The role of fulvic acid (FA) in modifying the adsorption mode and sorption capacity of divalent metal cations on the muscovite (001) surface was evaluated by measuring the uptake of Cu2+, Zn2+, and Pb2+ from 0.01 m solutions at pH 3.7 with FA using in situ resonant anomalous X-ray reflectivity. The molecular-scale distributions of these cations combined with those previously observed for Hg2+, Sr2+, and Ba2+ indicate metal uptake patterns controlled by cationFA binding strength and cation hydration enthalpy. For weakly hydrated cations the presence of FA increased metal uptake by approximately 60140%. Greater uptake corresponded with increasing cationFA affinity (Ba2+ ≈ Sr2+ < Pb2+ < Hg2+). This trend is associated with differences in the sorption mechanism: Ba2+ and Sr2+ sorbed in the outer portion of the FA film whereas Pb2+ and Hg2+ complexed with FA effectively throughout the film. The more strongly hydrated Cu2+ and Zn2+ adsorbed as two distinct outer-sphere complexes on the muscovite surface, with minimal change from their distribution without FA, indicating that their strong hydration impedes additional binding to the FA film despite their relatively strong affinity for FA.
’ INTRODUCTION Natural organic matter (NOM) in both dissolved and solid forms plays a significant role in controlling the disposition of toxic heavy metal elements in the environment. Dissolved organic matter (DOM) binds metal ions in solution, changing their speciation and mobility.13 Various thermodynamic models have been developed to predict metal binding by DOM,46 although they are mostly case-specific and still need to be refined for general applications.7 In many soils and sediments, NOM, including DOM, binds to mineral surfaces and can significantly alter the uptake of metals to both the organic matter and minerals.812 Unlike the case for DOM-metal binding in solution, there are no models that describe the systematics of metal binding at the DOMmineral interface. The development of such models requires observations at the molecular scale using surface-sensitive approaches to distinguish the various modes of ionmineralDOM interactions. Muscovite mica has often been chosen as a mineral substrate in experimental systems because its basal plane, the (001) surface, is similar to the dominant surfaces of many clay minerals, which, along with many micas, are the main constituents of argillaceous soils and sediments. The surface cleaves easily to provide a large atomically flat surface with a permanent negative charge (∼1e per unit cell area, AUC). The morphology of adsorbed DOM on the basal surface of muscovite has been characterized by atomic force microscopy (AFM).1316 The earlier AFM images were interpreted as showing the formation of aggregates of DOM on the r 2011 American Chemical Society
surface after reaction in 10200 mg DOM/L solutions.1315 The DOM aggregates were sparsely distributed leaving large areas of the surface exposed to solution. The coverage, size, and sorption stability of the aggregates increased with increasing cation concentration and decreasing pH, indicating that aggregate formation is related to the degree of cationorganic complexation and the hydrophobicity of the DOM. A more recent study showed that at acidic to near neutral pH DOM aggregates were located on top of a 536 Å thick organic film that covered the surface.16 The DOM film covered a larger area of the surface than the aggregates and therefore would be expected to affect the overall sorptive capacity of the underlying substrate. X-ray reflectivity (XR) is an in situ, nondestructive method suitable for probing the distribution of DOM and simultaneous uptake of metal cations at the mineralwater interface. Particularly, the approach measures surface signals averaged over a relatively large area (∼1 mm2), enhancing the sensitivity to an organic film with wide coverage and diminishing the sensitivity to sparsely distributed aggregates. Continuous organic films about 612 Å thick were modeled from XR data obtained on the muscovite (001) surface after reaction in 100 mg/kg H2O Elliott Soil Fulvic Acid II (ESFA) solutions at pH 26.17 At pH 3.7, Received: April 18, 2011 Accepted: October 5, 2011 Revised: September 15, 2011 Published: October 05, 2011 9574
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Table 1. Best-Fit Model Parameters of the RAXR Dataa sampleb
χ2 (R-factor)
inner-sphere peak c cIS
zIS
uIS
outer-sphere peak c cOS uOS
zOS
zbroad
broad peak c cbroad
ubroad
metal only muSr53.5
1.35 (0.006)
1.38(f)
0.014(5)
0.29(f)
4.52(6)
0.19(1)
0.66(7)
8.43(31)
0.06(2)
1.38(43)
muHg12.0 24
1.15 (0.005)
0.62(5)
0.06(1)
0.13(f)
3.58(4)
0.14(1)
0.68(6)
9.57(36)
0.06(2)
2.26(55)
muPb103.7 32
1.54 (0.012)
1.90(3)
0.29(4)
0.27(11)
4.39(42)
0.19(5)
1.60(43)
9.58(61)
0.05(1)
2.00(f)
muCu103.7 32 muZn103.7 32
1.32 (0.007) 1.23 (0.007)
0.14(19) 1.28(72)
0.03(1) 0.01(1)
0.29(f) 0.29(f)
3.97(4) 3.90(4)
0.28(4) 0.20(4)
0.92(11) 0.40(10)
7.69(106) 5.37(75)
0.19(7) 0.14(5)
3.45(88) 1.77(38)
muSr5ESFA3.5 21
1.18 (0.008)
1.38(f)
0.03(1)
0.25(f)
muHg1ESFA2.024
1.22 (0.004)
metal + FA 4.40(2)
0.20(1)
0.17(5)
6.66(9)
0.20(3)
1.28(11)
2.28(7)
0.15(2) e
1.28(11)
6.28(27)
0.47(5)
4.71(25)
inner-sphere peak c
outer-sphere peak c
diffuse profiled
zIS
cIS
uIS
zOS
cOS
uOS
zdiffuse
cdiffuse
k1diffuse 5.4(15)
muPb10SRFA3.7
1.28 (0.008)
1.91(2)
0.27(3)
0.20(9)
2.91(12)
0.42(4) e
1.46(7)
7.24(33)
0.21(5)
muCu10SRFA3.7
1.08 (0.009)
0.14(f)
0.05(1)
0.29(f)
3.71(10)
0.22(3)
0.67(12)
4.61(54)
0.20(8)
2.9(11)
muZn10SRFA3.7
1.17 (0.013)
1.28(f)
0.00(1)
0.29(f)
4.07(5)
0.15(1)
0.58(7)
4.97(39)
0.28(11)
4.9(20)
a The numbers in parentheses indicate 1σ uncertainties of the last digit(s) of the fitting parameters. f indicates parameter fixed during fitting. b mu: muscovite, SRFA: Suwannee River Fulvic Acid, ESFA: Elliott Soil Fulvic Acid II. The subscripted number after each metal name indicates the metal concentration in units of 103 m. All FA solutions contained 100 mg/kg of a dissolved FA. The number at the end of each sample name indicates the solution pH. c zj, cj, and uj: height (Å), occupancy (atom per unit cell area, AUC), and distribution width (Å) of a Gaussian peak j. d zdiffuse, cdiffuse, k1diffuse: height of the first peak (Å), total occupancy (atom/AUC), and debye length (Å) of the broad diffuse profile (see Supporting Information). e Modeled as a part of metalFA complexes.
Ba2+ adsorbed to the muscovite surface from a premixed BaCl2ESFA solution mostly as an apparent inner-sphere (IS) complex.18 The electron density of the fulvic acid (FA) film was higher with Ba2+ than without Ba2+, implying that some Ba2+ was located within the film. However, the amount and distribution of Ba2+ in the film could not be quantified because XR is not element-specific. Resonant anomalous X-ray reflectivity (RAXR)19 was applied to probe the distribution of Sr2+, a cation having affinity for organic matter similar to that of Ba2+,20 at the muscoviteESFA interface.21 The results showed that about 1040% of the adsorbed cation accumulated in the outer part of a FA film at pH 3.55.5 while the remaining Sr2+ adsorbed on the muscovite surface as an outer-sphere (OS) complex.21 Compared to these cations, Hg2+, a cation with a greater affinity for organic matter,20,22,23 did not form any discrete IS or OS complex on the muscovite surface reacted in a premixed Hg(NO3)2 and ESFA solution at pH 2. Instead, a large fraction of Hg was incorporated within the FA film, resulting in increases both in metal uptake (by ∼140% compared to that without FA) and in film thickness (by ∼20% compared to that without Hg).24 The way in which dissolved metals interact with the muscovite (001)solution interface in the presence of DOM must depend on specific properties of the cations, but those trends are unclear at present. Here we present new XR and RAXR data for Pb, Zn, and Cu uptake onto the muscovite (001) surface from premixed metalFA solutions at acidic pH, a condition typically observed in carbonate-depleted clayey organic-rich soil layers, e.g., in some humults ultisols or spodozols,25,26 or in many metal-contaminated environments, e.g., acid mine drainages27,28 and associated pit lakes.29 The results for total coverage and adsorbed cation distribution are combined with those observed previously for Ba, Sr, and Hg and characterized systematically as a function of the
relative cation affinity for organic matter and the cation hydration strength.
’ EXPERIMENTAL SECTION Sample Preparation. Experimental solutions were prepared by dissolving a high-purity (g99.99%) nitrate of Cu, Zn, Sr, or Pb (Aldrich Chemical Co., Inc.), Suwannee River Fulvic Acid (SRFA) from the International Humic Substances Society (IHSS), or both together in deionized water (DIW; ∼18.2 MΩ). Solutions containing SRFA were prepared with a relatively high FA content (100 mg/kg H2O; i.e., mg of dry FA in 1 kg of DIW) to ensure the formation of a FA film on the muscovite surface over the time of the reaction (>2 h).16,17 High concentrations of metal cation (510 103 m; molality) were used to minimize competitive effects of hydronium and other cations sourced from the muscovite and FA. The high concentrations also controlled the ionic strength of the solutions without addition of other electrolytes, which could increase the complexity of the system. Therefore, the observed structural changes are expected to derive purely from changing cationFA interactions. The pH of the SRFA solution without any adjustment was 3.7, close to the log K1 value (3.81) of the FA.30 The pH of all other solutions was also adjusted to 3.7 using high-purity 0.1 M HNO3 except that for muscovite reacted in a 5 103 m Sr(NO3)2 solution without FA (muSr53.5, Table 1) whose pH was 3.5 for comparison to results from the previous experiment conducted in a premixed 5 103 m Sr(NO3)2 and 100 mg/kg ESFA solution at the same pH (muSr5ESFA3.5, Table 1).21 Prepared solutions were stored in brown polypropylene bottles in a refrigerator until used. For each experiment, a gem-quality single crystal muscovite (Asheville Schoonmaker Mica Company) was cleaved to expose a fresh (001) surface and immersed vertically in a 50-mL centrifuge 9575
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Figure 2. Total electron-density profile derived from the best-fit model of XR for muscovite (001) in contact with a 100 mg/kg SRFA solution at pH 3.7 (muSRFA3.7). Those for muscovite (001) in a 100 mg/kg ESFA solution at pH 3.7 (muESFA3.7)18 and a 100 mg/kg PPFA solution at pH 3.6 (muPPFA3.6)18 are plotted for comparison. The electron density was normalized to that of bulk water, and plotted with a band indicating the 1σ uncertainty as a function of height from the surface. The profile below 0 Å (corresponding to the muscovite substrate beneath the top oxygen plane) is not shown. Figure 1. Normalized RAXR signal using the resonant amplitude normalization [(|Ftot(q,E)|2 |FNR(q)|2)/(2|FNR(q)|), where Ftot and FNR are total and nonresonant structure factors, respectively] for muscovite in solutions containing heavy metals (Pb, Cu, and Zn in comparison to Hg and Sr)21,24 in the absence (cyan circles) and presence (pink squares) of FA. Spectra are offset by 2 units, and open and filled symbols are used alternately for easier visual comparison. Each spectrum is labeled with the q value (Å1) where the spectrum was measured. The curves through data points are calculated intensities derived from the best-fit models. Solid (for solid symbol data) and dashed (for open symbol data) gray horizontal lines guide theoretical reflectivity when there is no resonant atom at the interface. The deviation of the RAXR signal from the reference lines is proportional to the ion coverage at small q.24
tube containing one of the solutions for at least 2 h,17 after which the wet muscovite was transferred to a thin-film sample cell for XR measurements as described previously.17,18,21,24 Specular X-ray Reflectivity. Measurements were made in situ at beamlines 6-ID-B (MU-CAT) and 11-ID-D (BESSRC), Advanced Photon Source, Argonne National Laboratory (Figure S1 in Supporting Information (SI)). X-ray experiments on samples containing FA were conducted in the dark. The stability of the experimental samples over the measurement time of approximately 1 h was confirmed by periodically measuring reflectivity at two reference points defined by momentum transfer values q = 0.85 and 1.83 Å1. Only experiment muPb10SRFA3.7 (muscovite in 10 103 m Pb and SRFA at pH 3.7; Table 1) showed a small but significant (∼6%) variation in reference point reflectivities. Measurements for muCu10SRFA3.7 and muZn10SRFA3.7 were duplicated with separately prepared samples. The XR data were fit with parameterized models (SI text) to obtain optimized structures for the total electron density profiles at the interfaces. Resonant Anomalous X-ray Reflectivity. Measurements were obtained by scanning the photon energy (E) near the X-ray absorption edge of the target metal at fixed q values (from 0.25 0.35 to 3.34.3 Å1) (Figure 1). One set of data typically included 1020 spectra measured over 36 h. The stability of the experimental system was monitored by periodically repeating measurements at low q values (0.250.57 Å1). The RAXR signals at the reference points varied less than 5% in amplitude, except for muPb10SRFA3.7, which showed a slow but continuous decrease
in signal amplitude (e.g., ∼20% decline after 3 h). This implies that adsorbed Pb was mobile during X-ray exposure. A similar result was observed after 2 h of X-ray measurements of adsorbed Hg on a pre-FA-coated muscovite (001) surface at pH 2.0.24 The RAXR data were fit by a model with two cation positions (represented as inner-sphere (IS) and outer-sphere (OS) positions in Table 1) near the muscovite surface, followed by a broad ion profile. Initial cation distributions were guided by a semiquantitative cation profile derived from model-independent analysis31 (Figures S2 and S3). For data collected in a pure metal-salt solution, the broad profile was modeled using a single Gaussian peak.24,32 With FA, a slightly better quality of fit could be obtained using a broad asymmetric peak simulated by overlapping a series of equally spaced Gaussian peaks whose occupancies decrease exponentially as a function of distance from the surface (SI text).
’ RESULTS AND DISCUSSION Fulvic Acid Sorption on the Muscovite (001) Surface. The total electron-density profile for muSRFA3.7 (Suwannee River FA adsorbed on muscovite at pH 3.7) has a broad peak near 2.5 Å followed by a broader profile with the maximum electron density located near 4.6 Å (Figure 2). This pattern is similar to those determined previously for FA sorbed on muscovite from a 100 mg/kg Elliott Soil FA II (ESFA) solution at pH 3.7 (muESFA3.7) and a 100 mg/kg Pahokee Peat FA (PPFA) solution at pH 3.6 (muPPFA3.6).18 The broad peak near the surface has a lower electron density in muSRFA3.7 than in muESFA3.7 and muPPFA3.6. This suggests that the fraction of SRFA adsorbed directly on the surface either has a lower electron density or covers less of the surface than the similar fraction of ESFA or PPFA. The higher electron density for the PPFA sample may be explained by its higher ash content (4.61 wt %) compared to that for SRFA (0.46 wt %) or ESFA (1.00 wt %).18,30 The next peak in the electron-density profile of muSRFA3.7 extends to about 7 Å from the surface and is narrower and closer to the surface compared to that of muESFA3.7. The muSRFA3.7 profile also has a third electron-dense region above ∼7 Å, indicating that a fraction of sorbed SRFA molecules extends farther from the surface. A similar pattern is observed in muPPFA3.6, but is relatively less prominent in muESFA3.7. 9576
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Figure 3. Fractional change in heavy metal uptake at the muscovite (001)solution interface in the presence of 100 mg/kg FA in relation to the calculated molar ratio between metalFA complexes and free metal cation in solution and the metal cation hydration enthalpy. The molar ratio [Me2+ FA]/[Me2+] was calculated based on the solution composition (Table S2). Fractional changes in cation coverage (cMeFA cMe)/cMe (%)) (see text) were calculated based on the RAXR results of five metals (marked in a pink dashed pentagon; except Ba). The color map with contours, generated by the best-fit empirical expression (eq 1), illustrates the trends in how the hydration enthalpy of the cation and affinity of the cation for FA under the experimental conditions control metal uptake at the interface. Total electron-density profiles derived from the best-fit models of the interfacial structure with adsorbed metal in the absence (blue dashed line) and presence (red solid line) of FA are also shown. Refer to Table 1 for sample codes, and Figure 2 for descriptions of the axes for profiles associated with individual elements. The electron-density profiles measured in FA solutions without metals (green dot-dashed line) are plotted for comparison. The element-specific profiles of adsorbed metals in the absence and presence of dissolved FA are shown in sky-blue and pink shaded areas, respectively. Note that the distribution of Ba is estimated based on the difference between total electron-density profiles (e.g., muBa5ESFA3.7 muESFA3.7) and was not determined by RAXR.
Table 2. Characteristics of Fulvic Acid Films Adsorbed on the Muscovite (001) Surface in the Absence and Presence of Heavy Metals average layer density (FWeq) samplea muSRFA3.7 muPb10SRFA3.7
layer thickness (Å) 6.0(5.96.3) 25.8(24.726.5)
with metals
without metals 1.02(0.971.07)
1.29(1.251.32) 1.11(1.071.14)
muCu10SRFA3.7
8.0(7.98.1)
1.09(1.041.14) 0.99(0.941.04)
muZn10SRFA3.7
8.1(8.08.2)
1.12(1.111.16) 1.02(0.961.07)
muESFA2.017
12.1(11.912.1)
1.12(1.071.16)
muHg1ESFA2.024
14.9(14.714.9)
1.34(1.291.39) 1.13(1.081.18)
muESFA3.717,18 muSr5ESFA3.521
7.2(7.17.2) 10.9(10.911.0)
1.02(0.961.07) 1.08(1.051.12) 0.99(0.961.03)
a
Refer to Table 1 for sample codes. The numbers in parentheses are the ranges of the values calculated from the lower (σ) and upper (+σ) limits of electron-density profiles derived from the best-fit models.
All three FA samples demonstrate the common feature of an approximately 10 Å thick film with a structure composed of a
directly adsorbed fraction of FA having a higher electron density and a remnant tail that has a lower electron density. Effect of Fulvic Acid on Pb Uptake on Muscovite. The amount of adsorbed Pb is enhanced in the presence of FA as shown by the RAXR data for muPb10SRFA3.7 vs muPb103.732 (Table 1). The total coverage of Pb [0.90(7) atom per unit cell area, AUC = 46.72 Å2]33 in the presence of FA is about twice as high as that (∼0.5 atom/AUC) needed to compensate the muscovite surface charge (∼1e/AUC), implying that some Pb is bonded to sorbed FA molecules. Comparing the electrondensity profiles of muPb103.7 and muPb10SRFA3.7 shows that the distribution of the additional Pb matches that of the FA film, indicating a direct association of Pb with the sorbed FA (Figure 3). The total electron density of the FA film in muPb10SRFA3.7 is also higher than that in muSRFA3.7 (Table 2) because of the presence of electron dense Pb in the layer. A large fraction of Pb is adsorbed as an apparent IS complex to the muscovite surface, while it is not possible to distinguish an OS complex because the modeled electron density of this species would be superimposed on that of PbFA complexes (Figure 3). The muPb10SRFA3.7 profile shows a small increase in the Pb distribution located immediately 9577
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Environmental Science & Technology adjacent to the surface (i.e., 106 M Pb in solution.34 The TER-XSW approach is better suited for investigating the distribution of an ion within a large-scale matrix (such as biofilms), but it has intrinsically a lower spatial resolution than RAXR (e.g., ∼25 Å vs ∼1 Å, respectively). Therefore, it is unknown if the fraction of Pb reported near the surface34 represents a true surface-adsorbed species. The fractional enhancement of cation uptake by FA (compared to that without FA) is larger for Pb than Sr (Table 1 and Figure 3). The dissimilar atomic-scale distributions of the cations in muPb10SRFA3.7 and muSr5ESFA3.5 indicate that the uptake is controlled by different sorption mechanisms. Whereas these two cations have similar hydration enthalpies, Pb has a stronger affinity for FA and forms organic complexes in the solution. Strontium does not complex significantly with FA in the solution. The enhanced sorption of Sr is, unlike Pb, localized in the outer part of the FA film, suggesting that, although Sr and FA were mixed in solution prior to adsorption, the Sr adsorbed independently after the FA.21 A similar result using only XR data had been observed for Ba, which is slightly less strongly hydrated than Sr but has a similar affinity for FA.18 The total electron-density profile above the muscovite surface for muBa5ESFA3.7 is higher than that of muESFA3.7, and can be attributed partly to an accumulation of Ba in the layer (Figure 3). The distribution of Pb throughout the FA film indicates that its enhanced uptake results mostly from sorption of metalorganic complexes, similar to results for Hg at pH 2.0 (muHg1ESFA2.0) (Figure 3).24 The total coverage of Hg [0.62(5) Hg/AUC], however, was smaller than that of Pb, in part because of greater competition by hydronium for sorption sites at pH 2.0 compared to pH 3.7 (Table 1). Also, little Hg occurred as distinct IS and OS species. The HgFA complexes would have been protonated at pH 2.0, resulting in decreased electrostatic repulsion at the surface and increased hydrophobicity of the complexes.2,17,35,36 Effect of Fulvic Acid on Copper and Zinc Uptake on Muscovite. The element-specific electron-density profiles of muCu10SRFA3.7 and muZn10SRFA3.7 show broad peaks at about 4 Å from the surface (Figure 3). The heights and occupancies of these peaks match those of the adsorbed OS (OSads) complex of Cu or Zn from solutions without FA (i.e., muCu103.7 and muZn103.7, respectively),32 indicating that the OSads species is not altered by the presence of FA. For each metal there is a second broader peak extending to ∼10 Å from the surface. This broader distribution is similar to that of an extended OS (OSext) complex observed in muCu103.7 and muZn103.7 (i.e., without FA).32 The position of the OSext species in the absence of FA is stabilized by multiple layers of water molecules, including those in higher-order hydration shells of the cation and the hydration layer at the muscovite surface.32 The lack of a significant increase in metal uptake in the presence of FA was unexpected especially for Cu2+ which has been reported to bind strongly to organic matter.4,37 Calculations based on the nonideal competitive adsorption-Donnan model show that the amount of Cu2+ bonded to SRFA in solution at
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pH 3.7 should be larger than that of Sr2+ at pH 3.5 (Table S3). Zn2+ has a slightly smaller affinity for organic matter compared to Cu2+,38 but still has a larger affinity than Ba2+ and Sr2+, especially for binding to phenolic or thiolic groups (Table S2). The cations Cu2+ and Zn2+ may occur mainly in nonsorbing metalorganic complexes that remain in solution during the experiments.39,40 Extended X-ray absorption fine structure (EXAFS) spectroscopy of Zn in organic-rich soils with a relatively high metal content (0.510 mg Zn/g of soil) at near neutral pH (5.67.3) showed that most Zn is bonded to organic matter with first-shell coordination ligands that are a mixture of oxygen (or nitrogen) and sulfur.41 Copper(II) complexed with multiple ligands (e.g., malate or malonate groups) and formed five- and six-membered chelate ring structures in solutions containing DOM (∼300500 g C/kg) and Cu (100 6500 mg/kg) at pH 4.5 and 5.5 as determined by EXAFS spectroscopy.42 If the positive charge of each metal cation is shielded by ligands, then sorption to the muscovite would be controlled by the net charge and hydrophobicity of the cationorganic complex. This phenomenon also helps to explain the RAXR results for Hg, in which the enhanced metal coverage observed at pH 2.0 declined at higher pH24 in part because of deprotonation of functional groups that did not bind Hg and in part because of increased hydrolysis of Hg to form neutral inorganic species. It is also possible that some Zn2+ and Cu2+ formed complexes within sorbed aggregates1315 which occurred at low surface coverage below the detection limits of XR and RAXR. A large fraction of Zn and Cu in the solutions should have been present in simple (hydrated) ionic form according to thermodynamic calculations (Table S2). Therefore, some aqueous Zn2+ and Cu2+ might have interacted with the sorbed FA film, similarly to Ba2+ and Sr2+.18,21 Part of the sorbed Zn and Cu identified as OSext species might be bonded within the outer region of FA; however, the locations of the peaks assigned to OSext species do not match well the location of the outer region of the film, supporting the interpretation that the majority of the cations are independently adsorbed species. It is not possible to determine why Zn2+ and Cu2+ sorbed to the FA film to a lesser extent than Sr2+ and Ba2+ based on the limited data in the current study. The reason may be related in part to the difference in hydration strength of the cations. The results show that the total amount of metals, especially within the FA layer, increases as the magnitude of the cation hydration enthalpy decreases (i.e., the hydration strengths become weaker) (Figure 3). This relationship indicates that strongly hydrated cations are less sorptive to the SRFA film than to the muscovite (001) surface at pH 3.7. Changes in the Internal Structure of Adsorbed FA by Adsorbed Metal Cations. The presence of metal cations leads to changes in the thickness, layer density, and detailed structure of the FA film on the muscovite (001) surface. In muCu10SRFA3.7 and muZn10SRFA3.7, the total electron density at 68 Å above the surface is greater than in muCu103.7 and muZn103.7 (Figure 3). This height range does not match the heights of any adsorbed Cu or Zn species, indicating that it is a region dominated by adsorbed FA. The position is 12 Å higher above the surface than the outer region of the film in muSRFA3.7, suggesting that the presence of the OS Cu and Zn complexes effectively increases the thickness of the organic film (Table 2). The overall average layer density increases mainly because of the added electron density from the cations. The layer densities corrected for the occupancy of the cations are 0.991.02 FWeq 9578
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(1 FWeq corresponds to the electron density of bulk water = 0.33 e/Å3)18 similar to that for muSRFA3.7 (1.02 FWeq) (Table 2). The electron density g10 Å above the surface is slightly higher than that when metal cations are absent, suggesting that the presence of small amounts of OSext species can induce some additional sorption of FA at the interface. Interfacial Hg and Pb, considered to be largely in the form of metalorganic complexes, also increased the electron density (g1.3 FWeq) of the FA films (Table 2). Similarly to the experiments for Cu and Zn, the densities adjusted by subtracting the contributions from the metals are comparable to those for the pure FA layers, indicating this increase results from the presence of the metals alone (Table 2). However, the greater layer thicknesses (g15 Å) must result from additional FA on the surface. For Sr and Ba, the prominent enhancement in the electron density observed in the outer part of the FA film confirmed the incorporation of these cations in this specific location. The overall film thickness increased slightly (Table 2), indicating that the adsorbed metals attracted more FA to the surface. Empirical Model of Metal Uptake at the MineralNOM Interface. At the muscoviteFA solution interface three effective ligands, the waters of hydration, the FA, and the mineral surface, compete to bind metal cations. Effects of this competition are characterized here for the first time for a group of divalent cations in terms of the metal coverage and its molecularscale distribution at the interface. The fractional changes in the amount of cation uptake relative to the system without FA [= (cMeFA cMe)/cMe, where cMeFA and cMe are the metal ion coverages (atom/AUC) with and without FA, respectively] for all metals (except Ba) were characterized by RAXR, and can be expressed empirically in terms of the dependence on cationFA binding strength and cation hydration enthalpy: ðcMeFA cMe Þ=cMe ¼ 3:2ðlog10
’ ASSOCIATED CONTENT
KFA 0:49Þ
1:011ðjΔHhyd j 2130Þ þ 0:62
DOM molecules, which could sorb differently to mineral surfaces than other fractions of DOM.22,23 For less strongly hydrated cations, the fractional coverage increases in the order Ba2+ ≈ Sr2+ < Pb2+ < Hg2+, in sequence of increasing cation affinity for FA. This trend is related, at the molecular-scale, to a transition from additional uptake of metal in the outer part of the FA film (presumably by electrostatic effects) to uptake effectively throughout the entire film via sorption of metalFA complexes. At the same time, sorbed FA does not appear to alter the binding of the strongly hydrated Cu and Zn to the mineral surface, in these cases demonstrating the importance of metal binding to the bare mineral surface. Weakly hydrated cations with a smaller affinity for organic matter tend to bind electrostatically to both the negatively charged muscovite surface and the negatively charged functional groups of the FA film formed on the muscovite surface. In soils or sediments where the concentrations of these cations are relatively small these cations may be readily displaced by background cations, such as Ca2+ or Na+. A moderately hydrated cation with a larger affinity for organic matter can also sorb as organic complexes, and may be less exchangeable and therefore less mobile, at least at low pH. As pH increases these metalorganic complexes may be released to solution owing to increased electrostatic repulsion between the muscovite surface and the sorbed NOM as a consequence of deprotonation reactions of the NOM. These results confirm that the complex interactions among ions, NOM, and mineral surfaces can be monitored systematically. This and similar types of molecular-scale characterization will be essential in the development of more robust predictive models for assessing the transport of toxic metals in nature.
bS ð1Þ
where KFA = [Me2+FA]/[Me2+] is the molar ratio of cation FA complexes to the free cation species in solution calculated based on the reported metalligand binding constants (Table S2) and ΔHhyd is the cation hydration enthalpy (kJ/mol). We note that KFA and ΔHhyd are not necessarily independent variables. The apparent high precision of some model parameters (SI) is mostly a result of the limited number of data points. This simple empirical equation may not explain fully the complex nature of metalFAmuscovite interactions. However, the equation effectively reproduced the observed data trends [with a good quality of fit (i.e., χ2 < 1 and R-factor 50 -132 > 595 > 5149 (Tables 1 and S4 of the SI). In addition to 591, three minor metabolites were formed in the incubation with PCB 91. We were able to identify these metabolites as 491, 3100 (NIH shift product), and 4,591. PCB 95 formed 595 and a second major monohydroxylated pentachlorobiphenyl X-95. The retention time of X-95 did not correspond to any putative PCB 95 metabolites with the
hydroxyl group in the 2,3,6-trichloro substituted ring system, which suggests that the hydroxy group of X-95 is present on the second, less chlorinated ring. This metabolite is most likely 30 -95, a metabolite that was reported in rats in vivo.35 In addition to 50 132, three minor metabolites were detected in incubations with PCB 132. We were able to unambiguously identify these metabolites as 40 -132, 30 -140 (NIH shift product) and 40 ,50 132 using authentic standards. Only a single metabolite, 5149, was found in incubations of PCB 149. Under the experimental conditions employed in this study, no appreciable amounts of NIH-shift products were detected, with only trace amounts of 3-100 and 30 -140 being detected in incubations with PCB 91 and PCB 132, respectively. In contrast, two in vivo studies reported the formation of NIH-shift metabolites of PCB 95 and 136 in rodent animal models.18,35 With exception of PCB 95, no metabolites with a hydroxyl group in the second, lower chlorinated ring were observed. This is consistent with the presence of a 4-chlorine substituent in these congeners, which essentially prevents metabolic attack in this ring system. Finally, only dihydroxylated PCB 91 and PCB 132 metabolites were detected in the microsomal incubations. Since both 4- and 5-136 are readily metabolized to 4,5-136 by recombinant enzymes 33 and human liver microsomes,34 the fact that only small quantities of dihydroxylated metabolites were formed for PCBs 91 and 132 is likely due to the relatively short incubation time (30 min) used in the present studies. Overall, the metabolite profiles observed for all four congeners are in agreement with previous in vitro and in vivo studies. Two unidentified monohydroxylated metabolites were reported by Warner et al. in an in vitro experiment where PCB 95 was metabolized by rat cytochrome P450 2B1 enzyme.19 Also, a single monohydroxylated metabolite was reported for PCB 91 in the same study. A comparative in vivo study by Sundstrom et al. 35 reported 40 -95 as the major metabolite in quail, whereas 5-95 was the major metabolite in the rat and mouse, with 3-103 (NIH shift product) and 40 -95 being only minor metabolites. In the rat, mouse, and guinea pig, 50 -132 was also the major metabolite of PCB 132. 40 - and 40 ,50 -132 were also formed, but in different ratios depending on the species.36 There are currently no studies reporting the formation of hydroxylated metabolites of PCB 149. Enantioselective Analysis of Microsomal Incubations of Chiral PCBs. Four chiral columns were used to determine the enantiomeric enrichment of the major OH-PCBs formed in the microsomal incubations discussed above (Tables 1 and S4; Figures 2 and S8S11 of the SI). All enantioselective analyses were performed isothermally at 160 °C unless otherwise stated (Table 1). The BDM column provided baseline separation of all major, 5-substituted metabolites of the PCB congeners investigated (Figure S2A of the SI). The EF values obtained with this column, as well as the CB and CD columns were comparable and revealed a congener-dependent enantiomeric enrichment for all OH-PCBs. The results from the BGB column showed good agreement with BDM, CB, and CD columns; however, the elution order of the atropisomers was reversed. Since the absolute configurations of neither the parent compounds nor the corresponding OH-PCBs shown in Figure 1 has been established, it is currently not possible to determine which of the atropisomers shown in Figure 1 is formed selectively and if there is a congener-specific difference in how R and S atropisomers are metabolized. The parent PCBs showed a congener specific enantiomeric enrichment (Table 1). In the case of PCBs 91, 132, and 149, the 9593
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enrichment of the second eluting atropisomer was also observed for 50 -132 (EF = 0.31). Due to the comparatively low levels of the minor metabolites and coelution problems, it was not possible to determine the EFs of the minor metabolites; however, it is likely that these metabolites also display some enantiomeric enrichment. Overall, this study demonstrates for the first time that chiral PCBs, such as PCB 91, 95, 132, and 149, are enantioselectively metabolized to OH-PCBs by cytochrome P450 enzymes. Considering the high cytochrome P450 2B activity in the microsomal preparation used in the incubations, it is likely that the enantioselective formation of 5-hydroxylated PCB metabolites is due to cytochrome P450 2B enzymes, which is consistent with PCB metabolism studies using recombinant enzymes. The enantioselective formation of OH-PCBs therefore in part explains the enantiomeric enrichment of the respective parent compounds in in vitro and in vivo studies.9 Further studies are needed to better understand the role of cytochrome P450 2B enzymes in the enantiomeric enrichment of both PCBs and OH-PCBs, and the toxicity of pure OH-PCB atropisomers. The later question is of particular interest from an environmental health perspective because of the enantiomeric enrichment of OH-PCBs reported in this study and the recently documented effect of OH-PCBs on Ryanodine receptor sensitization.8
’ ASSOCIATED CONTENT
bS
Figure 2. Enantiomeric enrichment of 5-hydroxylated metabolites of PCB 91 (A), PCB 95 (B), PCB 132 (C), and PCB 149 (D) in incubations with rat liver microsomes. Samples were analyzed on the DBM column at 160 °C.
second eluting atropisomers were enriched, whereas the first eluting atropisomer was enriched for PCB 95. In the case of PCB 132 and 149, this corresponds to an enrichment of (+)-PCB 132 and (+)-PCB 149.31,37 The most pronounced enantiomeric enrichment was observed for PCB 95 (EF = 0.64) and PCB 132 (EF = 0.39). These observations are in agreement with a previous study by Warner demonstrating an enantiomeric enrichment of PCBs due to metabolism by recombinant rat and human cytochrome P450 enzymes 19 as well as several in vivo studies reporting an enantiomeric enrichment of these congeners in rodents and humans. For example, in a study by Kania-Korwel et al.,38 the EFs of PCB 95 (EF = 0.63) and PCB 149 (EF = 0.45) in the liver of rats treated with Aroclor 1254 were comparable to the results obtained in this study. The most intriguing observation of the present study is that the major OH-PCB metabolites formed in the microsomal incubations displayed a clear, congener-specific enantiomeric enrichment. There was very little enrichment of the first eluting atropisomer observed for 5-91, with an EF of 0.54. In the case of 5-149, the first eluting congener was also enriched (EF = 0.65). Both 5-95 and the unknown monomethoxylated PCB 95 (X-95) showed an enrichment of the second eluting atropisomer, with comparable EFs of approximately 0.33 for both metabolites. An
Supporting Information. Description of microsomes preparation and cytochrome P450 enzyme activities; structures, nomenclature and resolution of all studied methoxylated PCBs on all columns investigated, both in temperature programmed and isothermal analysis; description of enantioselective columns; enantiomeric fractions of methoxylated PCBs in microsomal incubations; dependence of resolution on temperature in isothermal analysis for 5-91 and 50 -132; resolution of all methoxylated PCBs on BDM column and programmed temperature; resolution of 5-91 on all columns and programmed temperature; GC-MS and GC-ECD analysis of OH-PCBs formed in microsomal incubations of PCB 91, PCB 95, PCB 132, and PCB 149; comparison of the enantiomeric enrichment of 5-methoxylated derivatives of PCB 91, PCB 95, PCB 132, and PCB 149 isolated from microsomal incubations to the racemic standard. This material is available free of charge via the Internet at http:// pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Phone: (319) 335-4211; fax: (319) 335-4290; e-mail:
[email protected].
’ ACKNOWLEDGMENT The authors would like to thank Drs. Stelvio Bandiera and Eugene Hrycay (University of British Columbia) for the characterization of the microsomes, Drs. Yang Song, Sandhya M. Vyas, and Sudhir N. Joshi (University of Iowa) for the synthesis of the methoxylated PCB standards, and Ananya Pramanik (University of Iowa) for help with the microsomal metabolism studies. The OH- and MeO-PCB 136 metabolites were a generous gift from E.A. Mash and S.C. Waller of the Synthetic Chemistry Facility Core of the Southwest Environmental Health 9594
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Environmental Science & Technology Sciences Center, funded by NIH Grant ES06694. The project described was supported by Grant Nos. ES05605, ES013661, and ES017425 from the National Institute of Environmental Health Sciences.
’ REFERENCES (1) Fourth National Report on Human Exposure to Environmental Chemicals; Center for Disease Control and Prevention: Atlanta, GA, 2009; www.cdc.gov/exposurereport/pdf/FourthReport_ExecutiveSummary.pdf (2) Schecter, A.; Colacino, J.; Haffner, D.; Patel, K.; Opel, M.; Papke, O.; Birnbaum, L. Perfluorinated compounds, polychlorinated biphenyl, and organochlorine pesticide contamination in composite food samples from Dallas, Texas. Environ. Health Perspect. 2010, 118 (6), 796–802. (3) Harrad, S.; Ibarra, C.; Robson, M.; Melymuk, L.; Zhang, X.; Diamond, M.; Douwes, J. Polychlorinated biphenyls in domestic dust from Canada, New Zealand, United Kingdom, and United States: Implications for human exposure. Chemosphere 2009, 76 (2), 232–238. (4) Schantz, S. L.; Widholm, J. J.; Rice, D. C. Effects of PCB exposure on neuropsychological function in children. Environ. Health Perspect. 2003, 111 (3), 357–376. (5) Mariussen, E.; Fonnum, F. Neurochemical targets and behavioral effects of organohalogen compounds: An update. Crit. Rev. Toxicol. 2006, 36 (3), 253–289. (6) Pessah, I. N.; Lehmler, H. J.; Robertson, L. W.; Perez, C. F.; Cabrales, E.; Bose, D. D.; Feng, W. Enantiomeric specificity of ()-2, 20 ,3,30 ,6,60 -hexachlorobiphenyl toward ryanodine receptor types 1 and 2. Chem. Res. Toxicol. 2009, 22 (1), 201–207. (7) James, M. O., Polychlorinated biphenyls: Metabolism and metabolites. In PCBs: Recent Advances in Environmental Toxicology and Health Effects; Robertson, L. W.; Hansen, L. G., Eds.; The University Press of Kentucky: Lexington, KY, 2001; pp 3546. (8) Pessah, I. N.; Hansen, L. G.; Albertson, T. E.; Garner, C. E.; Ta, T. A.; Do, Z.; Kim, K. H.; Wong, P. W. Structure-activity relationship for noncoplanar polychlorinated biphenyl congeners toward the ryanodine receptor-Ca2+ channel complex type 1 (RyR1). Chem. Res. Toxicol. 2006, 19 (1), 92–101. (9) Lehmler, H. J.; Harrad, S. J.; H€uhnerfuss, H.; Kania-Korwel, I.; Lee, C. M.; Lu, Z.; Wong, C. S. Chiral polychlorinated biphenyl transport, metabolism and distribution: A review. Environ. Sci. Technol. 2009, 44 (8), 2757–2766. (10) Jorundsdottir, H.; Norstr€om, K.; Olsson, M.; Pham-Tuan, H.; H€uhnerfuss, H.; Bignert, A.; Bergman, A. Temporal trends of bis(4chlorophenyl) sulfone, methylsulfonyl-DDE, and -PCBs in Baltic guillemot (Uria aalge) egg 19712001—A comparison to 4,4’-DDE and PCB trends. Environ. Pollut. 2006, 141, 226–237. (11) Larsson, C.; Norstr€om, K.; Athanansidais, I.; Bignert, A.; K€onig, W. A.; Bergman, A. Enantiomeric specificity of methylsulfonyl-PCBs and distribution of bis(4-chlorophenyl) sulfone, PCB and DDE methyl sulfones in grey seal tissues. Environ. Sci. Technol. 2004, 38, 4950–4955. (12) Wiberg, K.; Letcher, R.; Sandau, C. D.; Duffe, J.; Norstrom, R.; Haglund, P.; Bidleman, T. F. Enantioselective gas chromatography/ mass spectrometry of methylsulfonyl PCBs with application of arctic marine mammals. Anal. Chem. 1998, 70, 3845–3852. (13) Chu, S.; Covaci, A.; Haraguchi, K.; Voorspoels, S.; van de Vijver, K.; Das, K.; Bouquegneau, J.-M.; de Coen, W.; Blust, R.; Schepens, P. Levels and enantiomeric signatures of methyl sulfonyl PCB and DDE metabolites in livers of harbor porpoises (Phocoena phocoena) from the Southern North Sea. Environ. Sci. Technol. 2003, 37, 4573–4578. (14) Karasek, L.; Hajslova, J.; Rosmus, J.; H€uhnerfuss, H. Methylsulfonyl PCB and DDE metabolites and their enentioselective gas chromatographuc separation in human adipose tissues, seal blubber and pelican muscle. Chemosphere 2007, 67, S22–227. (15) Ellerichmann, T.; Bergman, A.; Franke, S.; H€uhnerfuss, H.; Jakobsson, E.; K€onig, W. A.; Larsson, C. Gas chromatographic enantiomer separations of chiral PCB methyl sulfons and identification of
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selctively retained enantiomers in human liver. Fres. Environ. Bull. 1998, 7, 244–257. (16) Larsson, C.; Ellerichmann, T.; H€uhnerfuss, H.; Bergman, A. Chiral PCB methyl sulfones in rat tissues after exposure to technical PCBs. Environ. Sci. Technol. 2002, 36, 2833–2838. (17) Norstr€ om, K.; Eriksson, J.; Haglund, J.; Silvari, V.; Bergman, A. Enantioselective formation of methyl sulfone metabolites of 2,20 ,3,30 ,4,60 hexachlorobiphenyl in rat. Environ. Sci. Technol. 2006, 40 (24), 7649 –7655. (18) (a) Kania-Korwel, I.; Vyas, S.; Song, Y.; Lehmler, H. J. Gas chromatographic separation of methoxylated polychlorinated biphenyl atropisomer. J. Chromatogr. A 2008, 1207, 146–154. (b) Joshi, S. N.; Vyas, S. M.; Duffel, M. W.; Parkin, S.; Lehmler, H. J. Synthesis of Sterically Hindered Polychlorinated Biphenyl Derivatives. Synthesis 2011, 1045–1054. (19) Warner, N. A.; Martin, J. W.; Wong, C. S. Chiral polychlorinated biphenyls are biotransformed enantioselectively by mammalian cytochrome P-450 isozymes to form hydroxylated metabolites. Environ. Sci. Technol. 2009, 43, 114–121. (20) Ballschmiter, K.; Bacher, R.; Mennel, A.; Fischer, R.; Riehle, U.; Swarev, M. The determination of chlorinated biphenyls, chlorinated dibenzodioxins and chlorinated dibenzofurans by GC-MS. J. High Resol. Chromatogr. 1992, 15, 260–270. (21) Maervoet, J.; Covaci, A.; Schepens, P.; Sandau, C. D.; Letcher, R. A reassessment of the nomenclature of polychlorinated biphenyl (PCB) metabolites. Environ. Health Perspect. 2004, 112 (3), 291–294. (22) Wong, C. S.; Garrison, A. W. Enantiomer separation of polychlorinated biphenyl atropisomers and polychlorinated biphenyl retention behavior on modified cyclodextrin capillary gas chromatography columns. J. Chromatogr. A 2000, 866 (2), 213–220. (23) Vetter, W. Enantioselctive fate of chiral chlorinated hydrocarbons and their metabolites in environmental samples. Food Rev. Int. 2001, 17 (2), 113–182. (24) Milanowski, B.; Lulek, J.; Lehmler, H.-J.; Kania-Korwel, I. Assesment of disposition of chiral polychlorinated biphenyls in female mdr 1a/b knockout versus wild-type mice using multivariate analyses. Environ. Int. 2010, 36 (8), 884–892. (25) Kania-Korwel, I.; El-Komy, M. H. M. E.; Veng-Pedersen, P.; Lehmler, H. J. Clearance of polychlorinated biphenyl atropisomers is enantioselective in female C57Bl/6 mice. Environ. Sci. Technol. 2010, 44 (8), 2828–2835. (26) Kania-Korwel, I.; Zhao, H.; Norstrom, K.; Li, X.; Hornbuckle, K. C.; Lehmler, H. J. Simultaneous extraction and clean-up of polychlorinated biphenyls and their metabolites from small tissue samples using pressurized liquid extraction. J. Chromatogr. A 2008, 1214, 37–46. (27) Kania-Korwel, I.; Hornbuckle, K. C.; Peck, A.; Ludewig, G.; Robertson, L. W.; Sulkowski, W. W.; Espandiari, P.; Gairola, C. G.; Lehmler, H.-J. Congener specific tissue distribution of Aroclor 1254 and a highly chlorinated environmental PCB mixture in rats. Environ. Sci. Technol. 2005, 39, 3513–3520. (28) Kania-Korwel, I.; Hornbuckle, K. C.; Robertson, L. W.; Lehmler, H.-J. Influence of dietary fat on the enantioselective disposition of 2,20 ,3,30 ,6,60 -hexachlorobiphenyl (PCB 136) in female mice. Food Chem. Toxicol. 2008, 46 (2), 637–644. (29) Haglund, P. Enantioselective separation of polychlorinated biphenyl atropisomers using chiral high performance liquid chromatography. J. Chromatogr. 1996, 724, 219–228. (30) Pham-Tuan, H.; Larsson, C.; Hoffmann, F.; Bergman, A.; Fr€oba, M.; H€uhnerfuss, H. Enantioselective semipreparative HPLC separation of PCB metabolites and their absolute structure elucidation using electronic and vibrational circular dichroism. Chirality 2005, 17, 266–280. (31) Haglund, P.; Wiberg, K. Determination of the gas chromatographic elution sequences of the (+)- and ()-enantiomers of stable enantiomeric PCBs on Chirasil-Dex. J. High Resolut. Chromatogr. 1996, 19, 373–376. (32) Duignan, D.; Sipes, I.; Leonard, T.; Halpert, J. Purification and characterization of the dog hepatic cytochrome P-450 isozyme responsible for the metabolism of 2,20 ,4,40 ,5,50 -hexachlorobiphenyl. Arch. Biochem. Biophys. 1987, 255, 290–303. 9595
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(33) Waller, S. C.; He, Y. A.; Harlow, G. R.; He, Y. Q.; Mash, E. A.; Halpert, J. R. 2,20 ,3,30 ,6,60 -hexachlorobiphenyl hydroxylation by active site mutants of cytochrome P450 2B1 and 2B11. Chem. Res. Toxicol. 1999, 12 (8), 690–699. (34) Schnellmann, R.; Putnam, C.; Sipes, I. Metabolism of 2,20 ,3,30 ,6,60 -hexachlorobiphenyl and 2,20 ,4,40 ,5,50 -hexachlorobiphenyl by human hepatic microsomes. Biochem. Pharmacol. 1983, 32 (21), 3233–3239. (35) Sundstr€om, G.; Jansson, B. The metabolism of 2,20 ,3,50 ,6pentachlorobiphenyl in rats, mice and quails. Chemosphere 1975, 4 (6), 361–370. (36) Haraguchi, K.; Kato, Y.; Koga, N.; Degawa, M. Species differences in the tissue distribution of catechol and methylsulphonyl metabolites of 2,4,5,20 ,50 -penta and 2,3,4,20 ,30 ,60 -hexachlorobiphenyls in rats, mice, hamsters and guinea pigs. Xenobiotica 2005, 35 (1), 85–96. (37) Harju, M. T.; Haglund, P. Determination of the rotational energy barriers of atropisomeric polychlorinated biphenyls. Fres. J. Anal. Chem. 1999, 364, 219–223. (38) Kania-Korwel, I.; Garrison, A. W.; Avants, J. K.; Hornbuckle, K. C.; Robertson, L. W.; Sulkowski, W. W.; Lehmler, H.-J. Distribution of chiral PCBs in selected tissues in the laboratory rat. Environ. Sci. Technol. 2006, 40, 3704–3710.
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Binding of HgII to High-Affinity Sites on Bacteria Inhibits Reduction to Hg0 by Mixed FeII/III Phases Bhoopesh Mishra,* Edward J. O’Loughlin, Maxim I. Boyanov, and Kenneth M. Kemner Biosciences Division, Argonne National Laboratory, Argonne, Illinois 60439, United States
bS Supporting Information ABSTRACT: Magnetite and green rust have been shown to reduce aqueous HgII to Hg0. In this study, we tested the ability of magnetite and green rust to reduce HgII sorbed to 2 g 3 L 1 of biomass (Bacillus subtilis), at high (50 μM) and low (5 μM) Hg loadings and at pH 6.5 and 5.0. At high Hg:biomass loading, where HgII binding to biomass is predominantly through carboxyl functional groups, Hg LIII-edge X-ray absorption spectroscopy showed reduction of HgII to Hg0 by magnetite. Reduction occurred within 2 h and 2 d at pH 6.5 and 5.0, respectively. At low Hg:biomass loading, where HgII binds to biomass via sulfhydryl functional groups, HgII was not reduced by magnetite at pH 6.5 or 5.0 after 2 months of reaction. Green rust, which is generally a stronger reductant than magnetite, reduced about 20% of the total HgII bound to biomass via sulfhydryl groups to Hg0 in 2 d. These results suggest that HgII binding to carboxyl groups does not significantly inhibit the reduction of HgII by magnetite. However, the binding of HgII to biomass via sulfhydryl groups severely inhibits the ability of mixed FeII/III phases like magnetite and green rust to reduce HgII to Hg0. The mobility of heavy metal contaminants in aquatic and terrestrial environments is greatly influenced by their speciation, especially their oxidation state. In the case of Hg, reduction of HgII to Hg0 can increase Hg mobility because of the volatility of Hg0. Since Hg is typically present in aquatic and terrestrial systems at low concentrations, binding of HgII to high-affinity sites on bacteria could have important implications for the potential reduction of HgII to Hg0 and the overall mobility of Hg in biostimulated subsurface environments.
’ INTRODUCTION Mercury (Hg) is a contaminant of global concern, as bioaccumulation of methylmercury poses significant risk to aquatic ecosystems and human health.1 Although elemental mercury (Hg0) is far less reactive and toxic than the water-soluble ionic HgII species, the high mobility of Hg0 (due to low vapor pressure) and the relative ease of oxidization of Hg0 to HgII render Hg0 an environmental hazard. Historical records from lake sediments provide compelling evidence that long-range atmospheric transport of Hg0 results in significant inputs of Hg to remote areas.2 The reduction of HgII to Hg0 results from both abiotic and microbially mediated processes and is a key component of global Hg biogeochemical cycling.3 In soils and sediments, the reduction of HgII to Hg0 is generally attributed to direct microbial processes.4 However, abiotic reduction pathways,5 9 including photoreduction,10,11 can also contribute significantly to HgII reduction to Hg0. The mobility of heavy metal contaminants in aquatic and terrestrial environments is greatly influenced by their speciation, especially their oxidation state. For example, CrVI, UVI, and TcVII species tend to be more soluble and hence mobile than CrIII, UIV, and TcIV species. Thus, stimulating the in situ activity of native metal-reducing bacteria by the addition of organic substrates (e.g., acetate, ethanol) could potentially immobilize many heavy metals and radionuclides in contaminated environments. However, r 2011 American Chemical Society
the activity of metal-reducing bacteria (FeIII-reducing bacteria in particular) can lead to the reduction of FeIII oxides to FeIIbearing phases such as magnetite, vivianite, siderite, and green rust,12,13 some of which can be effective reductants for HgII to Hg0 reduction.10,11 Thus, promotion of metal-reducing conditions for immobilization of heavy metals and radionuclides can lead to increased mobility of Hg. Understanding the geochemical processes that mediate Hg transformations in aquatic and terrestrial environments is necessary to predict its fate and transport. O’Loughlin et al.10 showed the reduction of HgII to Hg0 by green rust and suggested that other FeII phases may also reduce HgII. Indeed, reduction of HgII by magnetite has recently been reported.11 Aqueous HgII reduction by magnetite occurs within minutes, and reaction rates increase with increasing magnetite surface area and solution pH.11 The same study showed that chloride, an environmentally important inorganic ligand with strong binding affinity for HgII, inhibits the rate and extent of HgII reduction by magnetite. Although the reduction of aqueous HgII to Hg0 by green rust and magnetite establishes the potential for abiotic HgII reduction Received: May 27, 2011 Accepted: September 13, 2011 Revised: September 13, 2011 Published: September 14, 2011 9597
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Environmental Science & Technology under FeIII-reducing conditions, the redox properties of Hg can be profoundly altered by the presence of organic ligands.14,15 Field observations indicate the effect of organic ligands by showing the coexistence of HgII with high levels of FeII in groundwater containing very low levels of chloride ions.16 It is possible that strong complexation of HgII with organic ligands significantly affects its availability for reduction by magnetite and other reactive FeII/III minerals resulting from the activity of FeIIIreducing bacteria. Hence, the effect of organic ligands on HgII reduction by magnetite and other FeII-bearing minerals must be evaluated to improve understanding of the geochemical processes that influence Hg transformations in the subsurface. Studies on the speciation of HgII indicate that complex organic ligands such as natural organic matter (NOM) form stable Hg complexes through their sulfhydryl, carboxyl, and amine groups.17 23 X-ray absorption spectroscopy (XAS) has shown that HgII interacts strongly with bacterial cell envelope through sulfhydryl and carboxyl functional groups.24 A systematic study of CdII binding to both gram-positive and gram-negative bacteria suggests that CdII (and HgII, which has similar coordination properties) binds to the high-affinity sulfhydryl groups (about 2% of total functional groups), followed by much higher extents of adsorption to the more abundant carboxyl and phosphoryl groups at higher metal:biomass ratios.25 Because typical Hg concentrations in contaminated environments are low and cell density in biostimulated environments may be high, preferential binding of HgII to sulfhydryl groups on bacterial cells could significantly impact the availability of HgII for reduction. This bacterial binding can affect the overall redox behavior of HgII, in both natural environments and bioremediation settings where Hg can be a cocontaminant with other metals and radionuclides that are being immobilized. Understanding the interplay between factors influencing the reduction of HgII by reactive FeII phases in the presence and absence of biomass will improve understanding of abiotic HgII reduction in contaminated environments. We have investigated the effects of Hg binding to bacteria on the reduction of HgII to Hg0 by magnetite and green rust. We hypothesized that sorption of HgII to biomass would inhibit abiotic reduction of HgII by mixed FeII/III phases. To test this hypothesis, after HgII adsorption to Bacillus subtilis (a common soil bacterium that is neither a methylator [i.e., cannot produce methylmercury] nor a dissimilatory metal reducer) at different metal:biomass ratios, we introduced a stoichiometric excess of magnetite or green rust, then used synchrotron XAS to determine the speciation and coordination environment of solidphase-associated Hg. Experiments were done as a function of pH (5.0 and 6.5), total Hg concentration (5 and 50 μM), and reaction time (2 h to 2 months).
’ METHODS AND MATERIALS Bacterial Growth Conditions. The procedures for growth and washing of B. subtilis 168 for use in this study were similar to those described earlier.25,26 Briefly, B. subtilis was cultured in tryptic soy broth with 0.5% yeast extract and incubated for 24 h at 32 °C on a shaker. The cells were collected by centrifugation (5800 g for 60 min) and rinsed five times with 0.1 M NaClO4 (the background electrolyte used in the HgII sorption experiments). The resulting cell density, reported on a wet mass basis, corresponds to approximately 8 times the dry mass of the cells. HgII Adsorption to Biomass. Washed bacteria were suspended in Teflon centrifuge tubes in 0.1 M NaClO4 electrolyte
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at 32 °C to form a suspension of 2 g 3 L 1 of bacteria (wet mass). HgII was added from a stock solution created from a commercially available (GFS Chemicals) reagent grade 5 mM HgII standard solution in 5% HNO3, which was titrated to pH 3.0 with 1 M NaOH. The pH of each system (pH 5 or 6.5) was adjusted with 1 M HNO3 or NaOH, and the systems were allowed to react for 3 h on a shaker. The pH ((0.3 pH units) was monitored every 15 min and adjusted as required with aliquots of 1 M HNO3 or NaOH. After 3 h of reaction, the suspensions were centrifuged, and the bacterial pellet was retained for analysis by X-ray absorption fine structure (XAFS) spectroscopy. The supernatant was filtered (0.45 μm) using nylon membrane (Millipore filter), acidified, and analyzed for dissolved HgII by inductively coupled plasmaoptical emission spectroscopy (ICP-OES; Perkin-Elmer) with matrix-matched standards. The amount of Hg adsorbed to bacteria was calculated by subtracting the concentration of Hg remaining in solution from the total Hg concentration in the experimental system. Reaction of Biomass-Bound HgII with Magnetite/Green Rust. Magnetite and hydroxysulfate green rust (GRSO4), a green rust containing SO42‑ as the interlayer anion, were synthesized as described by Cornell and Schwertmann.27 After 3 h of reaction time between HgII and the biomass, magnetite or green rust was added at a molar ratio of HgII:FeII = 1:50. The system was rotated end-over-end at 20 rpm. All reactions were carried out in an anoxic glovebox (Coy) containing an atmosphere of 5% H2 and 95% N2. After reaction for 2 h, 2 d, or 2 months, subsamples of the suspension were centrifuged under anoxic conditions. Pellets containing biomass and Fe oxides were retained for Hg XAFS analysis within 2 h. Hg XAS Measurements and Data Analysis. Hg LIII-edge X-ray absorption near edge structure (XANES) and extended X-ray absorption fine-structure (EXAFS) spectroscopy measurements were performed at the MRCAT sector 10-ID beamline,28 Advanced Photon Source, Argonne National Laboratory. Details of the XAS experiments, standards, and data analysis are in the Supporting Information.
’ RESULTS AND DISCUSSION HgII Complexation with Biomass. HgII concentrations in the
supernatants of samples without reductant were below the ICPOES detection limit (0.05 μM), indicating complete removal of Hg from solution by sorption to biomass. Figure 1a,b compares the XANES and k2-weighted χ(k) EXAFS data for Hg standards with HgII complexed to biomass at 5 μM (HgL-bio) and 50 μM (HgH-bio) HgII loadings, at pH 5.0. The spectra indicate that Hg is complexed via sulfhydryl groups in the HgL-bio sample. Spectral features supporting this conclusion are the small preedge peak and the slight dip at 12 300 eV in the XANES, as well as the large amplitude and the phase of oscillations in the k2weighted χ(k) data (which are similar for HgL-bio and Hgcysteine data). Similarly, a strong pre-edge peak at 12285 eV and a peak at 12300 eV in the XANES spectra, combined with smaller amplitudes of oscillation in the k2-weighted χ(k) data, suggest that Hg in the HgH-bio sample is predominantly complexed via carboxyl groups. The differences between the amplitudes and bond distances of HgL-bio and HgH-bio and their similarities with Hgcysteine and Hg-acetate solution standards, respectively, are further illustrated in the magnitude and real part of the Fourier transforms shown in Figure S1a,b (Supporting Information). 9598
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Figure 1. (a) Hg LIII-edge XANES spectra of HgII sorbed to biomass samples at high (HgH-bio) and low (HgL-bio) HgII loadings, with XANES spectra of Hg standards for comparison. (b) k2-weighted χ(k) spectra of Hg LIII-edge EXAFS for high and low loadings of HgII sorbed to biomass samples, with k2-weighted χ(k) spectra of Hg standards for comparison.
The first derivative of XANES data comapring HgH-bio and HgL-bio samples shown in Figure S1c of the SI also suggest that Hg in the HgH-bio and HgL-bio samples is predominantly complexed via carboxyl and sulfhydryl groups respectively.29 No differences in spectra are observed between pH 6.5 and 5.0 for the same metal to biomass ratio (Figure S2 in Supporting Information shows HgL-bio at pH 5 and 6.5), consistent with previous finding that the HgII binding mechanism to biomass does not change over this pH range.24 The EXAFS data from samples HgL-bio and HgH-bio and the Hg standards were modeled quantitatively as described in the SI, by using simultaneous multiple k-weight fits and multiple sample fits. Best-fit values are in Table 1, and the fits are shown in Figure S3 (SI). The best fit for HgL-bio was with 1.85 ((0.18) S atoms at 2.32 ((0.01) Å in the first shell. Inclusion of an O/N atom in the first shell or a C atom in the second shell did not significantly improve the fit (see SI). The best fit for HgH bio was with 1.65 ((0.24) O atoms at 2.06 ((0.01) Å in the first shell. Inclusion of a C atom (1.58 ( 0.36) in the second shell significantly improved the fit. However, the Hg C distance for the HgH bio sample was 3.05 ((0.02) Å— much longer than the Hg C distance determined for Hg acetate
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solution standard (2.83 ( 0.01 Å)—suggesting the formation of a carboxyl with α-hydroxy carboxylic acid or a malate-type coordination geometry, consistent with previous findings.22,24 In summary, the EXAFS results suggest an inner-sphere binding mechanism of HgII to biomass. At low metal:biomass, HgII binds to sulfhydryl groups, followed by carboxyl groups on bacterial biomass at higher metal:biomass ratios, consistent with previous findings.24 Preferential binding of HgII to sulfhydryl groups at low metal:biomass, followed by carboxyl groups at higher metal: biomass, has also been observed for HgII complexation with NOM.22,30 32 Reaction of Biomass-Bound HgII with Magnetite. Hg sorbed to biomass under the conditions described above was reacted with magnetite (HgH/L-bio-magnetite). At pH 6.5, over 90% of the added HgII in the HgH-biomagnetite sample was reduced to Hg0 by magnetite after only 2 h (see Figures 2a and S4 of the SI), indicating a rapid, possibly minute-scale reaction rate. This result is consistent with a previous study of HgII reduction by magnetite in the absence of biomass.11 In contrast to the nearly complete reduction of HgII at pH 6.5 within 2 h, only 60% of the added HgII was reduced to Hg0 at pH 5.0 over the same reaction period (data not shown), indicating slower reaction kinetics at pH 5 than at pH 6.5. However, almost complete reduction was observed at pH 5.0 after 2 d, and the sample remained reduced after 2 months (see Figures 2a, 2b, and S4 of the SI). Slower kinetics of reduction of HgII by magnetite at pH 5.0 than at pH 6.5 have also been observed with HgIIin aqueous solution.11 The similarity of reduction of aqueous HgII and HgII sorbed to biomass under HgH bio-magnetite conditions suggests that complexation to biomass via carboxyl groups does not significantly affect the susceptibility of HgII to reduction by magnetite. HgII in the HgL-bio-magnetite sample was not reduced to Hg0 by magnetite after 2 d or 2 months of reaction time at pH 6.5 and 5.0 (Figures 2c and 2d). XANES spectra of the HgL-bio sample with magnetite after 2 d and 2 months at pH 5.0 and 6.5 match well with the HgL-bio spectrum, suggesting that HgII bound to the sulfhydryl groups on biomass was not reduced by magnetite after 2 months (Figure 2c and 2d). This is also confirmed by the EXAFS data (Figures S2 and S4, Supporting Information). Effect of Magnetite Concentration. The stoichiometry of HgII:FeII was fixed at 1:50 for all experiments described above, while the concentration of biomass remained constant at 2 g 3 L 1 (wet mass). This resulted in a stoichiometric ratio of biomass: magnetite in the HgL bio-magnetite system ten times that of the HgH bio-magnetite system. To test the possibility that coating of the magnetite surface by biomass reduced reactivity in the HgL-bio-magnetite system, we increased the HgII:FeII stoichiometric ratio to 1:500 in the HgL-bio-magnetite system at pH 6.5, where magnetite effectively reduced HgII to Hg0 within 2 h. XANES spectra collected after 2 d reproduced the spectral features of the HgL bio or the HgL bio-magnetite (1:50) spectra, confirming that HgII reduction to Hg0 by magnetite is inhibited by binding of HgII to sulfhydryl groups on biomass, rather than by possible interaction between the bacteria and magnetite (Figure S5 of the SI). Reaction of Biomass-Bound HgII with Green Rust. In light of the inability of magnetite to reduce HgII bound to sulfhydryl groups on biomass, we repeated the experiment with green rust instead of magnetite as the reductant. Green rusts contain up to 75% FeII, are generally stronger reductants than magnetite,33 35 and readily reduce many heavy metals and radionuclides, 9599
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Table 1. Best-Fit Values for Solution Standards and Hg-bio Samples sample
c
R(Å)
N
σ2(10
3
Å2)
ΔE0(eV)
Hg2+
Hg O
6.12 ( 0.65
2.30 ( 0.01
15.1 ( 3.5
2.0 ( 1.2
HgAc
Hg O
1.78 ( 0.32
2.06 ( 0.01
10.9 ( 0.9
3.2 ( 1.8
Hg C
1.78 a
2.83 ( 0.01
12.8 ( 4.0
Hg O
1.65 ( 0.24
2.06 b
10.9 b
HgH bio
a
path
3.2 b
Hg C
1.58 ( 0.24
3.05 ( 0.02
Hg cysteine
Hg S
1.88 ( 0.21
2.32 ( 0.01
10.5 ( 1.2
1.7 ( 0.9
HgL bio
Hg S
1.85 ( 0.18
2.32 c
10.5 c
1.7 c
12.8
b
Fixed this value to be the same as O based on crystallographic data. b This variable was set to be equal to the HgAc standard during the simultaneous fit. This variable was set to be equal to the Hg-cysteine standard during the simultaneous fit.
Figure 2. Top: Hg LIII-edge XANES spectra at high Hg:biomass ratio (HgH bio) reacted with magnetite at pH 6.5 for (a) 2 h or (b) 2 d and 2 months at pH 5.0, with data for Hg0, Hg2+, and HgH bio samples. Bottom: Hg LIII-edge XANES spectra at low Hg:biomass ratio (HgL bio) reacted with magnetite for 2 d and 2 months at (c) pH 6.5 and (d) pH 5.0, with data for Hg0, Hg2+, and HgH bio samples. The 2-d spectrum is not clearly visible, because 2-d and 2-month spectra overlap.
including HgII.10,36 38 At pH 6.5, partial reduction of HgII to Hg0 by green rust was observed after 2 d (Figure 3), under the experimental conditions where no reduction was observed with magnetite (5 μM HgII, 2 g 3 L 1 biomass, and 250 μM FeII as green rust). A linear combination fit of the XANES spectrum revealed that while 80% of the HgII added to the system remained bound to biomass as a Hg cysteine complex, about 20% of the HgII was reduced to Hg0. First derivative of the Hg XANES spectrum, which is usually more senstive to changes in oxidation state of Hg than Hg XANES, also confirm this observation (Figure S6 of the SI). We did not conduct this experiment at pH 5.0, because green rust becomes unstable at pH 5.39
Summary of Reduction Results. Results of the reactions of magnetite and green rust with HgII complexed to biomass under different conditions are compiled in Table 2. The uncertainty in the XANES analyses is about 10%; therefore, although XANES data indicate 100% reduction in the HgH bio-magnetite system, up to 10% of the Hg in the solid phase might remain oxidized. The results for the HgL biomagnetite system (5 μM added HgII) indicate that the same amount of sulfhydryl-bound Hg probably remains oxidized in the HgH bio-magnetite system (50 μM added HgII). The specific mechanism by which binding to sulfhydryl groups inhibits HgII reduction is not clear. Specifically, this study did not distinguish whether the inihibition is 9600
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Figure 3. Linear combination fit of the XANES for the low Hg:biomass sample (HgL bio) reacted with green rust at pH 6.5 ((0.2) for 2 d. Of the total HgII sorbed to biomass, 20% was reduced to Hg0, while 80% of the sorbed HgII remained as Hg-cysteine complex after 2 d.
Table 2. Hg XANES and EXAFS Analysis Results for High And Low Loadings of Biomass-Sorbed Hg Sample Reacted with Magnetite and Green Rust at pH 6.5 and 5.0 ((0.2) for Different Reaction Timesa sample pH 5.0
50 (μMHg adsorbed to 2 g/L
5 μM Hg adsorbed to 2 g/L
Bacillus subtilis, and reacted with magnetite (2.5 mM FeII)
Bacillus subtilis and reacted with magnetite/GR(250 μM FeII)
2 h-60% reduced
2 days, not reduced
2 days, fully reduced
2 months, not reduced
2 months, fully reduced 6.5
2 h, fully reduced
2 days, not reduced )
2 months, not reduced 2 days (2.5 mM Fe ), not reduced 2 days (with Green Rust), 20% reduced a
The uncertainty in XANES analysis is about 10%.
because sulfhydryl-bound HgII cannot be reduced by magnetite or because the high binding constant of Hg cysteine complexes severely constrains the concentration of dissolved HgII. Additional studies are required to identify the exact mechanism of electron transfer for the reduction of HgII to Hg0. Implications for Subsurface Hg Biogeochemistry. The results of our study are relevant to the fate of HgII in the presence of FeII species in suboxic and anoxic environments. Reducing conditions are commonly encountered in natural aquatic environments. In addition, organic substrates have been injected into the subsurface and groundwater for the biostimulation of native metal-reducing bacteria, to promote in situ bioremediation by the reduction and potential immobilization of metals and radionuclides (e.g., CrVI, TcVII, UVI); however, when Hg is present as a cocontaminant, creation of reducing conditions may have undesired consequences for the speciation and mobility of Hg. Nonetheless, our results show that when conditions are favorable for Hg sorption to sulfhydryl groups on biomass, HgII is unlikely to be reduced to Hg0 by FeII species.
Our studies involved relatively high concentrations of Hg and biomass to enable spectroscopic analyses; however, concentrations of Hg in natural and contaminated geologic settings seldom exceed the nanomolar range. Biomass cell density in natural aquatic environments can also be orders of magnitude lower than those used in this study. However, since the ratio of Hg:biomass determines the nature of HgII complexation to biomass, the results of this study would be applicable under similar Hg:biomass ratios in the environment. For example, a natural environment with 5 nM HgII and 2 mg 3 L 1 biomass would likely exhibit same behavior as the 5 μM Hg and 2 g 3 L 1 biomass conditions in our study. At lower Hg concentrations or alternatively at higher biomass density, HgII forms more stable Hg(cysteine)2 and Hg(cysteine)3 complexes,24 which would likely further limit the availability of HgII complexed with biomass for reduction by mixed FeII/III phases. Hence, the biostimulation of a subsurface environment would likely inhibit the reduction of HgII to Hg0 by mixed FeII/III phases. The use of a model gram-positive aerobic bacterium in this study should not limit the applicability of our results to more complex systems. Sulfhydryl functional groups are ubiquitous in natural environments and have very high affinity for Hg. The relative abundances of functional groups corresponding to the deprotonation constant of cysteine (8.5 ( 1.0), obtained from potentiometric titration data of B. subtilis, Shewanella oneidensis MR-1, and Geobacter sulfurreducens, are 1.0:1.5:2.0, respectively.25,26,40 Moreover, complexation of HgII with sulfhydryl groups is not unique to bacterial biomass. Previous studies have shown HgII binding with natural and dissolved organic matter to be dominated by sulfhydryl groups.17,22,29,41 Recent work has shown that complexation of HgII with sulfhydryl groups is also prevalent in sulfide-rich environments.42 Reduction of HgII to Hg0 is a complex biogeochemical phenomenon, with competing microbial and abiotic redox pathways playing a role in surface and subsurface environments. Our results provide new insight into aspects of Hg biogeochemistry necessary for an effective assesment of HgII reduction and remobilization in surface and near-subsurface environments. Previous studies have shown a decline in the availability of HgII to mercuric reductase and in the rate of bacterial HgII reduction to Hg0 with increased cell density.43,44 Although both microbial and abiotic reductions of HgII are less likely under biostimulated conditions because of sorption of HgII to the high-affinity binding sites of biomass—given variations in cell structure, affinity, and biochemical processes—it might not be unreasonable to observe an increase in Hg0 budgets via enzymatic reduction under such conditions. Clearly, additional studies are required to assess the long-term stability of HgII bound to biomass in natural systems. Moreover, iron in the form of FeII sorbed to clays and other minerals is far more common in the environment than FeII mineral phases like magnetite and green rust; hence, it is also important to evaluate the potential for sorbed FeII to reduce HgII complexed with sulfhydryl ligands.
’ ASSOCIATED CONTENT
bS
Supporting Information. Details regarding the experimental procedures, EXAFS data collection and analysis; additional figures for XANES, derivative of XANES, and EXAFS data and their fits. This material is available free of charge via the Internet at http://pubs.acs.org.
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’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected].
’ ACKNOWLEDGMENT The authors thank Jeremy Fein and Jennifer Szymanowski for providing B. subtilis 168 strains and titration data for G. sulfurreducens. Help from Snow Rui (University of Notre Dame) and Tomohiro Shibata (MRCAT) with XAS data collection is also appreciated. This research is part of the Subsurface Science Scientific Focus Area at Argonne National Laboratory, supported by the Subsurface Biogeochemical Research Program, Office of the Biological and Environmental Research, Office of Science, U.S. Department of Energy (DOE), under contract DE-AC0206CH11357. MRCAT operations are supported by DOE and the MRCAT member institutions. Use of the Advanced Photon Source, an Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory, was supported by the DOE under contract DE-AC02-06CH11357. ’ REFERENCES (1) Mergler, D.; Anderson, H. A.; Chan, L. H. M.; Mahaffey, K. R.; Murray, M.; Sakamoto., M.; Stern, A. H. Methylmercury exposure and health effects in humans: A worldwide concern. Ambio: J. Human Environ. 2007, 36, 3–11. (2) Fitzgerald, W. F.; Engstrom, D. R.; Mason, R. P.; Nater, E. A. The case for atmospheric mercury contamination in remote areas. Environ. Sci. Technol. 1998, 32, 1–12. (3) Morel, F. M. M.; Kraepiel, A. M. L.; Amyot, M. The chemical cycle and bioaccumulation of mercury. Annu. Rev. Ecol. Syst. 1998, 29, 543–566. (4) Mason, R. P.; Morel, F. M. M.; Hemond, H. F. The role of microorganisms in elemental mercury formation in natural waters. Water, Air, Soil Pollut. 1995, 80, 775–787. (5) Alberts, J. J.; Schindler, J. E.; Miller, R. W.; Nutter, D. E. Elemental mercury evolution mediated by humic acid. Science 1974, 184, 895–897. (6) Skogerboe, R. K.; Wilson, S. A. Reduction of ionic species by fulvic acid. Anal. Chem. 1981, 53, 228–232. (7) Allard, B.; Arsenie, I. Abiotic reduction of mercury by humic substances in aquatic system—An important process for the mercury cycle. Water, Air, Soil Pollut. 1991, 56, 457–464. (8) O’Loughlin, E. J.; Kelly, S. D.; Kemner, K. M.; Csencsits, R.; Cook, R. E. Reduction of AgI, AuII, CuII, and HgII by FeII/FeIII hydrosulfate green rust. Chemosphere 2003, 53, 437–446. (9) Wiatrowski, H. A.; Das, S.; Kukkadapu, R.; Ilton, E. S.; Barkay, T.; Yee, N. Reduction of Hg(II) to Hg(0) by magnetite. Environ. Sci. Technol. 2009, 43, 5307–5313. (10) Amyot, M.; Mierle, G.; Lean, D.; McQueen, D. J. Effect of solar radiation on the formation of dissolved gaseous mercury in temperate lakes. Geochim. Cosmochim. Acta 1997, 61, 975–987. (11) Krabbenhoft, D. P.; Hurley, J. P.; Olson, M. L.; Cleckner, L. B. Diel variability of mercury phase and species distribution in the Florida Everglades. Biogeochem. 1998, 40, 311–325. (12) Fredrickson, J. K.; Zachara, J. M.; Kennedy, D. W.; Dong, H.; Onstott, T. C.; Hinman, N. W.; Li, S. Biogenic iron mineralization accompanying the dissimilatory reduction of hydrous ferric oxide by a groundwater bacterium. Geochim. Cosmochim. Acta 2002, 62, 3239– 3257. (13) Ona-Nguema, G.; Abdelmoula, M.; Jorand, F.; Benali, O.; Gehin, A.; Block, J.-C.; Genin, J-M R. Iron(II,III) Hydroxycarbonate green rust formation and stabilization from lepidocrocite bioreduction. Environ. Sci. Technol. 2002, 36, 16–20.
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(14) Jardim, W. F.; Bisinoti., M. C.; Fadini, P. S.; Silva, G. S. Mercury Redox Chemistry in the Negro River Basin, Amazon: The role of organic matter and solar light. Aqua. Geochem. 2010, 16, 267–278. (15) Gu, B.; Bian, Y.; Miller, C. L.; Dong, W.; Jiang, X.; Liang. L.; Mercury reduction and complexation by natural organic matter in anoxic environments, Proc. Natl. Acad. Sci., 2011, doi:10.1073/pnas.1008747108 (16) Barringer, J. L; Szabo, Z. Overview of investigations into mercury in ground, water, soils, and septage, New Jersey coastal plain. Water, Air, \Soil Pollut. 2006, 175, 193–221. (17) Xia, K.; Skyllberg, U. L.; Bleam, W. F.; Bloor, P. R.; Nater, E. A.; Helmke, P. A. X-ray absorption spectroscopic evidence for the complexation of Hg(II) by reduced sulfur in soil humic substances. Environ. Sci. Technol. 1999, 33, 257–261. (18) Qian, J.; Skyllberg, U.; Frech, W.; Bleam, W. F.; Bloom, P. R.; Petit, P. E. Bonding of methyl mercury to reduced sulfur groups in soil and stream organic matter as determined by x-ray absorption spectroscopy and binding affinity studies. Geochim. Cosmochim. Acta 2002, 66, 3873–3885. (19) Jay, J. A.; Morel, F. M. M.; Hemond, H. F. Mercury speciation in the presence of polysulfides. Environ. Sci. Technol. 2000, 34, 2196–2200. (20) Skyllberg, U.; Xia, K.; Bloom, P. R.; Nater, E. A.; Bleam, W. F. Binding of mercury(II) to reduced sulfur in soil organic matter along upland-peat soil transects. J. Environ. Qual. 2000, 29, 855–865. (21) Skyllberg, U.; Qian, J.; Frech, W. Bonding of methyl mercury to thiol groups in soil and aquatic organic matter. Phys. Scr. 2005, T115, 894–896. (22) Skyllberg, U.; Bloom, P. R.; Qian, J.; Lin, C. M.; Bleam, W. F. Complexation of mercury(II) in soil organic matter: EXAFS evidence for linear two-coordination with reduced sulfur groups. Environ. Sci. Technol. 2006, 40, 4174–4180. (23) Skyllberg, U., Competition among thiols and inorganic sulfides and polysulfides for Hg and MeHg in wetland soils and sediments under suboxic conditions: Illumination of controversies and implications for MeHg net production, J. Geophys. Res., 2008, 113, (24) Mishra, B., Fein, J. B., Yee, N., Beveridge, T. J., Myneni, S. C. B., Cell surface bound Hg complexes inhibits the rate and extent of Hgmethylation, Nat. Geosci., in review. (25) Mishra, B.; Boyanov, M.; Bunker, B.; Kelly, S. D.; Kemner, K. M.; Fein, J. B. High- and low-affinity binding sites for Cd on the bacterial cell walls of Bacillus subtilisand Shewanella oneidensis. Geochim. Cosmochim. Acta 2010, 74, 4219–4233. (26) Fein, J. B.; Boily, J. F.; Yee, N.; Gorman-Lewis, D.; Turner, B. F. Potentiometric titration of Bacillus subtilis cells to low pH and a comparison of modeling approaches. Geochim. Cosmochim. Acta 2005, 69, 1123–1132. (27) Cornell, R. M. Schwertmann, U. The Iron Oxides: Structure, Properties, Reactions, Occurrences, And Uses, 2nd ed., Willey-VCH: New York, 2003. (28) Segre, C. U., Leyarovsky, N. E., Chapman, L. D., Lavender, W. M., Plag, P. W., King, A. S., Kropf, A. J., Bunker, B. A., Kemner, K. M., Dutta, P., Duran, R. S., Kaduk, J., The MRCAT insertion device beamline at the Advanced Photon Source, CP521. In Synchrotron Radiation Instrumentation: Eleventh U.S. National Conference; Pianetta, P., Eds.; American Institute of Physics: NewYork, 2000, 419 422. (29) Rajan, M.; Darrow, J.; Hua, M.; Barnett, B.; Mendoza, M.; Greenfield, B. K.; Andrews, J. C. Hg L3 XANES Study of Mercury Methylation in Shredded Eichhornia crassipes” . Environ. Sci. Technol. 2008, 42, 5568–5573. (30) Hesterberg, D.; Chou, J. W.; Hutchison, K. J.; Sayers, D. E. Bonding of Hg(II) to reduced organic sulfur in humic acid as affected by S/Hg ratio. Environ. Sci. Technol. 2001, 35, 2741–2745. (31) Haitzer, M.; Aiken, G. R.; Ryan, J. N. Binding of Hg(II) to dissolved organic matter: The role of the mercury-to-DOM concentration ratio. Environ. Sci. Technol. 2002, 36, 3564–3570. (32) Drexel, R. T.; Haitzer, M.; Ryan, J. N.; Aiken, G. R.; Nagy, K. L. Mercury(II) sorption to two Florida Everglades peats: Evidence for strong and weak binding and competition by dissolved organic matter released from the peat. Environ. Sci. Technol. 2002, 36, 4058–4064. 9602
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(33) Elsner, M.; Schwarzenbach, R. P.; Haderlein, S. B. Reactivity of Fe(II)-bearing minerals toward reductive transformation of organic contaminants. Environ. Sci. Technol. 2004, 38, 799–807. (34) Lee, W.; Batchelor, B. Reductive capacity of natural reductants. Environ. Sci. Technol. 2003, 37, 535–541. (35) O’Loughlin, E. J.; Kelly, S. D.; Kemner., K. M. XAFS investigation of the interactions of UVI with secondary mineralization products from the bioreduction of FeIII oxides. Environ. Sci. Technol. 2010, 44, 1656–1661. (36) Loyaux-Lawniczak, S.; Refait, P.; Ehrhardt, J.-J.; Lecomte, P.; Genin, J.-M. R. Trapping of Cr by formation of ferrihydrite during the reduction of chromate ions by Fe(II) Fe(III) hydroxysalt green rusts. Environ. Sci. Technol. 2000, 34, 438–443. (37) Myneni, S. C. B.; Tokunaga, T. K.; Brown, G. E., Jr. Abiotic selenium redox transformations in the presence of Fe(II,III) oxides. Science 1997, 278, 1106–1109. (38) O’Loughlin, E. J.; Kelly, S. D.; Cook, R. E.; Csencsits, R.; Kemner, K. M. Reduction of uranium (VI) by mixed iron(II)/iron(III) hydroxide (green rust): Formation of UO2 nanoparticles. Environ. Sci. Technol. 2003, 37, 721–727. (39) Genin, J-M.R.; Refait, P.; Bourrie, G.; Abdelmoula, M.; Trolard, F. Structure and stability of the Fe (II)-Fe (III) green rust. Appl. Geochem. 2002, 16, 559–570. (40) Private communication with Dr. Jeremy Fein, University of Notre Dame, Notre Dame, (U.S.A.) (41) Ravichandran, M. Interactions between mercury and dissolved organic matter—A review. Chemosphere 2004, 55, 319–331. (42) Miller, C. L.; Mason, R. P.; Gilmour, C. C.; Heyes, A. Influence of dissolved organic matter on the complexation of mercury under sulfidic conditions. Environ. Toxicol. Chem. 2007, 26, 624–633. (43) Rasmussen, L. D.; Turner, R. R.; Barkay, T. Cell-density dependent sensitivity of a mer-lux bioassay. Appl. Environ. Microbiol. 1997, 63, 3291–3293. (44) Wiatrowski, H. A.; Ward, P. M.; Barkay, T. Novel reduction of mercury(II) by mercury-sensitive dissimilatory metal reducing bacteria. Environ. Sci. Technol. 2006, 40, 6690–6696.
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Effect of Radial Directional Dependences and Rainwater Influence on CVOC Concentrations in Tree Core and Birch Sap Samples Taken for Phytoscreening Using HS-SPME-GC/MS Olaf Holm† and Wolfgang Rotard*,† †
Department of Environmental Engineering, Technische Universit€at Berlin, Germany, Strasse des 17. Juni 135, D-10623 Berlin, Germany
bS Supporting Information ABSTRACT: Phytoscreening for chlorinated volatile organic compounds (CVOC) in tree core samples is influenced by many factors. For instance, greater fluctuations are observed for CVOC concentrations in samples taken around the trunk at a fixed height compared to samples taken directly next to each other. To avoid false negatives and inaccurate interpretation of the results, we investigated this radial directional dependence as well as the influence of rainwater on measured concentrations. CVOC analysis was performed by gas chromatography/mass spectrometry (GC/MS) following SolidPhase-Microextraction (SPME). Phytoscreening was successfully carried out at three sites using this method. In addition, sap samples were taken from white birches during their budding period as a novel phytoscreening approach. Birch sap sampling is shown to be a suitable means of characterizing contaminant distribution within the soil subsurface. Radial directional dependence of CVOC concentrations varies by almost 80% for tree core samples and 50% for birch sap samples. Variations in concentrations measured around the trunk do not, however, provide information on the inflow direction of contaminated groundwater. The weather conditions were shown to have a greater influence so that CVOC concentrations measured from samples taken during colder, rainier weather were, on average, a factor of 100 lower than those taken during a warm and dry period. Nevertheless phytoscreening is adequate for CVOC characterization in the soil subsurface if the campaign is carried out during a dry weather period, the results then can be taken as being semiquantitative.
’ INTRODUCTION The term phytoscreening is associated with the application of contaminant detection in plants as a means for extensive characterization of contaminant distribution in the subsoil.1 Work in the mid-1990s 2 4 established that volatile organic compounds (VOC) are incorporated by plants through their root uptake of water from the aquifer, soil or soil gas,5 and are transferred throughout the stems via the transpiration stream. Therefore, the use of xylem saps or plant tissues are suitable as sample sources for detection of contaminants. Taking drill cores from tree trunks is the most common sampling technique. Sampling of branches, leaves, and fruits as well as reeds and other types of plants is also possible.3,6,7 The first phytoscreening applications based on tree cores were published as early as 1999.8 Since then, several examples of applications have been added documenting not only the diverse utilization but also the limitations of this procedure.1,6,9 For example tree core samples were used to monitor natural attenuation.9 Phytoscreening to trace subsurface contaminations of volatile chlorinated hydrocarbons (CVOC) is widely applied and also by commercial users. Vroblesky10 summarized several aspects of this procedure for VOC in a user guide in 2008, presenting advantages and drawbacks of the technique and its applicability for particular contaminants as well r 2011 American Chemical Society
as a list of factors influencing the sampling and analysis of tree cores. Various aspects which have a significant influence on both the planning of sampling and the interpretation of results are considered in depth in this paper. These influential parameters are primarily the effects of rainwater and the sampling location on the tree. High fluctuations were observed for sampling at a given height around the tree trunk.1,5,8,11 The reasons for this radial directional dependence are headed by, among others, the inflow direction of contaminated groundwater,10 which should allow undetected points of contamination to be traced. Several of our investigations give additional support to the hypothesis that the contaminant inflow direction is decisive for analyte distribution within the tree. Therefore, more detailed examinations concerning radial directionality were performed. Because of the locally uniform groundwater flow at the examined site, it is expected that maximum concentrations in the trees should be measured in direction of the defined contaminant source. Received: June 14, 2011 Accepted: October 10, 2011 Revised: October 6, 2011 Published: October 10, 2011 9604
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Environmental Science & Technology Additional objectives of this work are to present the application of birch sap samples for phytoscreening and the fluctuations of rainwater influence and radial directional dependence of both, birch sap and tree core samples. Furthermore, we anticipate increasing concentrations in tree core samples with decreasing rainwater availability. The investigations presented in this paper make use of SolidPhase-Microextraction (SPME) for enrichment of CVOC. SPME combines sampling, analyte isolation, and enrichment and has been widely applied to the sampling and analysis of e.g. environmental samples.12 On the other hand the use of carboxen/ polydimethylsiloxane (PDMS) fibers are not that common. This fiber shows very high sensitivity for TCE and cDCE but also a poorer repeatability and prolongation of equilibrium time.13 Other fibers do not show these effects14 but are not as sensitive.
’ EXPERIMENTAL SECTION Site Description. A dry cleaning plant in the northwest area of the former military base Potsdam-Krampnitz (west of Berlin, Germany) has caused considerable groundwater contamination, which distributes subsequently into the neighboring (bordering) wetlands. The geology on the base is predominantly simple with a fixed top of the aquifuge. The groundwater level in the existing wells ranged from 0.85 to 2.40 m. In the northeast of the base the geology is more complex and suggested the situation in the wetlands is complex as well. The groundwater flow is directed to the north into the wetlands. The area of investigation is dominated by two tree species: in the wetland almost exclusively by white willow (Salix alba) and on the base by white birch (Betula alba). Based on direct-push groundwater screening, the concentrations of CVOC reached a maximum of 122 mg CVOC/L.15 Main components are trichloroethene (TCE) and cis-1,2-dichloroethene (cDCE). More details on the site are given in the Supporting Information. The main focus is placed on this contaminated site in Krampnitz. However, the data of two more sites (Hamburg and Neuruppin) are referred for the statistical information only. Sampling and Sample Handling. To avoid any contamination by surface-adsorbed components, the outer layer of the bark was removed from the sampling sites using hammer and chisel before either birch sap or tree core samples were taken. For this investigation, samples were taken at a height of 50 cm above ground level. In addition to the sampling documentation (see Table S3 in the Supporting Information), the coordinates of the trees were determined by GPS. Tree core samples were taken with increment borers (Suunto, Finland; length 15 cm, internal diameter 0.5 cm). The bark was discarded, and the first five centimeters of the xylem wood was collected. A second sample was obtained about two centimeters away from the first sample location. Tree core sampling for weather dependency investigations took place in July 2007 on one willow, one poplar, and one birch tree, respectively. In the same month, the radial direction dependence was determined for three birch trees by taking samples at the same height at eight points around the tree. At this stage the investigations were not yet standardized, hence the bark was not discarded and the core lengths varied between 3.8 and 4.3 cm. Further investigations to evaluate variability due to radial direction dependence were undertaken between 22nd of May and 10th of July 2008. Two parallel samples were taken from each of the four compass points for each tree. On two white willows, five samples were taken in an
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x-shaped configuration with a maximum distance of two centimeter between them. Altogether, 19 trees were sampled (eleven white willows, five birches plus one poplar, one oak, and one apple tree). Birch sap samples were taken by drilling an eight centimeter deep hole using a cordless drill (Bosch, model: PSR 14,4 Li-2). This was followed by pressing in a brass spreading dowel (eight mm with M6 internal thread) which had initially been covered with Teflon tape. Final insertion was completed with a hammer if necessary. A specially constructed aluminum tube with external thread, also covered with Teflon tape, was screwed in. To catch the birch sap, a vial was hung on a nut screwed onto the tube. The vial and the sampling syringe were first rinsed. In general it takes only a few seconds, round about 10 s, to fill the vial. Ten milliliters of birch sap was carefully transferred from the bottom of the vial into a second vial, while the sap is flooding. The vial was immediately sealed. Therefore the potential loss of CVOC is negligible. In general, the flow allowed the filling of at least two further vials. To close the wound after completion of sampling, the aluminum tube was unscrewed, and the hole was sealed with a threaded bolt covered with Teflon tape. To determine any radial direction dependence, six birches were sampled at eight points around the trunk, and six more birches were each sampled on sides facing and opposite to the contaminant source, all at the same height. To compare the results with those from tree cores, 31 holes were made with an increment borer as described above, and the respective tree cores and birch sap samples from each resulting core hole were analyzed separately. Data from all trees depicted in the graphics are compiled in Table S3, and their locations are shown in Figure S3 in the Supporting Information. All samples were transported and stored at room temperature. Analysis of the gas space in the sampling vial took place by means of SPME followed by GC/MS determination (see below) within 24 h of sampling. The fresh tree cores were subsequently weighed. Their water content was then determined by oven drying at 105 °C for at least 24 h and reweighing. Solid-Phase-Microextraction (SPME). The SPME system used consisted of a carboxen/PDMS coated fiber (Supelco) attached to a plunger within a protective needle, which directly pierced the septum of the vial thus being exposed to the headspace within the sample vial. The fibers showed slightly differences in the sensitivity to each other and a decrease in sensitivity when used repeatedly. Assuming a linear relation between the loss of fiber sensitivity and the number of measurements, it is possible to implement a drift factor based on aqueous standards measured at the beginning and end of a sample series. Applying the calculated drift, each peak area was corrected to the value that would have been obtained had the sample been on a fresh fiber. Using aqueous standards, it was likewise possible to calculate compensation factors for the differences between individual fibers within a measurement campaign. Each sample peak area value was multiplied by a factor relating to the ratio between the peak areas of standards using a chosen reference fiber and the peak areas of the standards for the actual fiber used. Data collected in 2007 relating to weather dependency and radial direction dependence were neither corrected for drift, nor was a fiber comparison carried out. The number of measurements within the relevant series was sufficiently low so that corrections were not required. The samples were conditioned and extracted each for 30 min at 35 °C. The fiber then is desorbed in the GC-injector for 1 min 9605
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Table 1. Semiquantitative Evaluation Scale Including Peak Area Relationships and Corresponding Concentrations for TCE and cDCE in Aqueous Samplesa scale
a
peak area
TCE [ng/L]
cDCE [ng/L]
1
nd
2
uncertain
3
500 10,000
3 67
2 49
4
10,000 25,000
67 169
49 122
5 6
25,000 50,000 50,000 100,000
169 337 337 674
122 243 243 487 487 4870
7
100,000 1,000,000
674 6740
8
1,000,000 10,000,000
6740 67,400
4870 48,700
9
10,000,000 100,000,000
67,400 674,000
48,700 487,000
10
>100,000,000
>674,000
>487,000
nd: not detected.
at 250 °C (see Table S2 in the Supporting Information). The calibrations are made by external standards with aqueous standards under the same conditions, which is widely accepted for SPME.16 The conditions of the subsequent GC/MS measurements and annotations to the calibration (Table 1) are summarized in the Supporting Information. Procedures of Data Processing. Graphical representations of contaminant distribution were based on the semiquantitative results, taking the main mass peak areas from the parallel samples. Gradation of the semiquantitative evaluation scale is given in Table 1. Variations in the results from a single tree are represented by normalizing the measured values to the maximum concentration (c/cmax) or to the maximum content of the applicable series of measurements, respectively. Assuming that a tree core length correlates to its weight, the 2007 samples, which were taken before the sampling technique had been standardized, were weight corrected. The results of the radial directionality tests are presented in three forms. In the first, a simple network diagram, the relative concentrations are shown in relation to compass points. For the second representation, the tree core sample results were evaluated using the semiquantitative scale and finally interpolated using the program Surfer (Version 8.05; Golden Software, Inc.) in kriging mode. This results in a two-dimensional representation of the isolines from all four compass points as well as of the previously averaged values. The third representation uses vectors to emphasize the direction for which the highest concentrations were measured. The vector representation uses the average from multiple tree core sampling on the south side, from which the value from the north is subtracted; similarly, the west from the east. These two vectors were then added. The resulting vector was then divided by the sum of all concentration values of the four compass points. The vector length thus becomes a relative value of the extent of concentration differences between the radially distributed sampling points on the tree. These radial concentration difference (RCD) vectors were recalculated into polar coordinates and described by the directional angle and length (see Figure 1). The maximum possible vector length value equal to one indicates that contaminants were only measured in one direction. The RCD vector is represented by an arrow, beginning at the point of the respective tree’s coordinates. Additionally, the RCD vectors were used to evaluate the correlation with respect to the four compass points as well as the direction of the contamination source. This required the
Figure 1. Schematic illustration for the determination of the radial concentration difference (RCD) vectors.
measurement of northings and eastings (positioning coordinates) of the trees and the angle with respect to the point of contamination, stated by the position of the storage tanks. Thus, the angular deviations relative to the contaminant source with respect to the compass points could be calculated for each tree. The smaller the resulting angle is, the greater was the agreement between the directions of the highest measured concentration to the reference direction. By using a factor which relates to the length of the RCD vector, these angles may be normalized so that the trees with especially high internal concentration differences may be more strongly weighted when determining a mean value of the angular deviation of all trees. From the reference-related averaging of these angles for all trees, it is possible to estimate the probability that the highest concentration will be measured in the reference direction.
’ RESULTS Radial Directionality for Samples Taken at the Same Height. Tree core samples (duplicate samples) of the phyto-
screening applications showed average variation coefficients for various CVOCs ranging from 15% to 22%, with the exception of PCE at the site in Hamburg with 40% (Table 2). Radial directionality examinations also support this variation range. In contrast, radial directionality values vary by 79% for cDCE and 63% for TCE. Variations in birch sap sample results show a similar trend. Variations from multiple single core samples show a much lower mean variation coefficient of 7% compared to radially distributed samples with 50%. Although they are somewhat lower than those for the tree cores, they nevertheless clearly show dependence on the radial direction. The network diagram shown in Figure 2 also supports the argument that this effect is not caused by statistical errors. Moreover, it can be seen that an area of higher concentrations is formed with pronounced directionality, as illustrated by the relatively similar shapes of the structures depicted. Statistical errors would have resulted in much more diffuse forms. The very similar mean variation coefficients for birch sap sampling from diametrically opposite sample sites and from the eight directions 9606
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Table 2. Summary of the Mean Relative Standard Deviations for Substances and Campaignsb mean relative standard deviation number of campaign
maximum
average
in %
in %
substance trees/samples Duplicate Samples PCE
9
104
40
1,2-DCA
13
36
15
cDCE
19
57
15
TCE
23
83
22
phytoscreening
tDCE
12
40
17
Krampnitz
cDCE
35
58
20
TCE TCE
120 59
130 31
21 7
tree coring
cDCE
51
116
21
(radial samples)
TCE
60
88
16
tree coring
cDCE
8
40
17
(radial samples)
TCE
8
36
20
birch sap (total)
TCE
phytoscreening Hamburga phytoscreening Neuruppina
birch sap
Quintuplicate Samples
Radial Distributed Samples 12
132
50
birch sap (two Sides) TCE birch sap (radial) TCE
6 6
91 132
46 54
tree coring
cDCE
14
136
79
TCE
15
122
63
Samples were taken at a height of 100 cm. b The “number of trees/ samples” represent the number of samples in which the substances are detected.
a
Figure 2. Radial concentration differences from birch sap samples; above: relative concentrations related to the maximal value for the particular tree; below: semiquantitative evaluation.
also argue against statistical errors. Therefore, a systematic radial direction dependence is indicated. All three trees sampled in 2007 show highest core concentrations from the side nearest to the contamination source (see Figure S8 in the Supporting Information). The birch sap samples from early 2008 do not,
however, support this. Core samples were taken from Tree 01 in 2007 and birch sap in 2008. The TCE concentration maximum shows a clear shift from a southwest direction (contamination source) toward the north (compare Figure 2 with Figure S8 in the Supporting Information). The RCD vectors for TCE and cDCE resulting from the 2008 radial tree core sampling campaign are presented in Figure 3. The direction as well as the magnitude of the radial variations as depicted by RCD vector length appears to be somewhat random. No dependence on tree species or concentration gradient of subsurface contaminants (compare Figure 3 with Figure 4 or Figure S2 in the Supporting Information) can be observed. Completely different vectors are seen for closely spaced single species trees of similar size or age. Angular variations with respect to the compass points and the contamination source are lower for the latter. However, the differences are small (Table 3). Figure 4 shows several isolines based on the semiquantitative evaluation (Table 1) from the radial tree core sampling campaign which enable interpolation in the case of a fixed sampling direction. It should be considered that samples were taken under varying weather conditions. This has, however, no relevance for the effects on semiquantitative evaluation. The differences for fixed sampling directions (in this case the four compass points) are only small when applying semiquantitative evaluation. Comparison of Birch Sap and Tree Cores. Parallel sampling of ten milliliter birch sap and five centimeter diameter birch tree cores resulted in almost identical results for TCE. The ratio of peak areas in birch sap to tree cores for 31 samples was around 1.0 with a variation coefficient of 20%. Birch sap was, however, clearly more sensitive for cDCE, which could be detected in 29 of the 31 samples but in only 22 of the respective tree cores. The ratio for these 22 values was around 5.0, with a variation coefficient of 66%. With respect to radial directionality, the birch sap and tree core samples show similar distribution patterns (compare Figure 2 with Figure S8 in the Supporting Information). The Influence of the Weather. Figure 5 shows the influence of an extreme change in weather conditions on TCE concentrations. On the first day of sampling (11th of July, 2007), just as during the days preceding the sampling campaign, the daily maximum temperature was around 15 °C accompanied by continuous medium to heavy rainfall. On the 14th of July, 2007, the temperature climbed to a maximum of over 30 °C with no rain. The following days remained dry with similar temperatures. The values measured during this campaign on the colder, rainier days were, on average, a factor of 100 lower, single values being up to 1000 times lower. This trend was confirmed for all three trees and also for cDCE (data not shown). The increase in concentrations occurred simultaneously for two of the trees, with the third following one day later.
’ DISCUSSION Radial directional dependence in sampling was investigated to assess possible fluctuations in results obtained in relation to the context of phytoscreening and to verify whether the inflow direction of contaminated groundwater contributes to this. The results of radial directionality correspond closely to previously published values. Various studies1,5,8,11 have shown that the variations in amounts of contaminants from tree core samples taken at the same height around the trunk are significantly greater than for sampling points placed directly next to each other (duplicate samples). 9607
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Figure 3. Radial concentration difference (RCD) vectors for trichloroethene (TCE, left), for cis-1,2-dichloroethene (cDCE, right) and tree location. The lengths of the RCD vectors do not reflect concentration levels but rather the extent of variation between the radially distributed tree core abstractions for each tree.
Figure 4. Isolines from semiquantitative evaluation of values related to the four compass points and the previously averaged values; left: for trichloroethene (TCE); right: for cis-1,2-dichloroethene (cDCE).
Table 3. Average Angular Deviation between the RCD Vector and the Contaminant Source for the Four Compass Points substance
contamination
north
east
south
west
source [deg]
[deg]
[deg]
[deg]
[deg]
c-DCE
69
76
82
104
98
TCE
69
94
73
86
107
The published variations in tree cores around the trunk are presented in different ways and show maxima of around 90% for TCE and cDCE8 and a factor of 5 for TCE1 and PCE,5 respectively. A single examination of birch sap samples from Tree 05 implies that the radial concentration differences converge with increasing sampling height, as discussed previously.8 The examinations presented here show that systematic radial dependence in tree cores at the same height exists. At this site this does not, however, allow any conclusions on the inflow direction of contaminated groundwater or the gradient of the contaminants in the subsurface. Concentration variations for radially distributed samples may involve a number of influencing factors, such as sorption,17 decomposition,4 and diffusion.18 The results
Figure 5. Relative concentrations (logarithmic scale) for Tree 04 related to the maximum of a set of measurements during a change in both temperatures from 15 °C to more than 30 °C and in daily rainfall.
of the birch sap sampling show that sap transport plays a substantial role. Exposure to the sun and heterogeneous soil and root structures are regarded to cause radial differences in sap flow.1,10 Contaminated sites are often subject to anthropogenic influences, so that surface sealing or root capping can contribute to heterogeneous sap flow. Also, each tree’s individual structure is especially relevant.19 In addition, it appears that the distribution characteristics of CVOC around the trunk are not constant over time. In Tree 01, the TCE concentration maximum shows a clear shift between tree core sampling in 2007 and birch sap sampling in 2008 (compare Figure 2 with Figure S8 in the Supporting Information). Both sampling campaigns were carried out at the same sampling height on the tree, although the tree cores from 2007 were not taken at exactly the four compass points. Any influence of spiral growth20 on the different concentration distributions can thus be excluded. Compared to variations due to dependency on radial difference (Figure 2), the weather (Figure 5) has a much greater 9608
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Environmental Science & Technology influence on overall variation and leads to a clear change of the categorization when semiquantitative evaluation is applied. In four of twelve samples, cDCE could not be detected attributable to the cold, wet weather. On dry days, the sampled trees could be allocated to categories 7 and 8 on the semiquantitative evaluation scale (Table 1). For TCE, the classification ranged from categories 3 to 9. Vroblesky11 previously illustrated the impact of precipitation on VOC concentration through an irrigation experiment, which resulted in a factor of less than two. This is below variations due to radial difference dependence.1,5,8,11 A rapid response to irrigation was seen within one day during the above experiment, namely at all sampled heights up to 3.5 m above ground level. Different influencing factors such as sorption,17 decomposition,4 and diffusion18 may well lead to changes in the spectrum of contaminants within the tree. In the period of active growth, these processes apparently play a minor role for CVOC. The concentration increase presented here shows the great dependence on weather conditions. Trees only take up groundwater from the saturated zone when there is insufficient water in the unsaturated zone. At sites where, due to climatic influences, trees mainly take up groundwater, this influence is probably not as serious. Doucette21 documented factors of 10 to 100 for variations in contaminant levels at climatically different sites with comparable groundwater concentrations. Volatile contaminants such as CVOC diffuse out of the tree trunk. Due to loss to air, concentrations usually decrease with height.5,8,18,22,23 The gradients shown by Vroblesky11 and Sorek et al.1 do not match this concept. Our own height profiles range from unclear to inverse gradients (data not shown). A change in contaminant concentration of the extracted water results in a likewise change of concentration in the tree. Depending on the retention, a step gradient may occur and migrate upward along the tree. In addition, a shift in contaminant spectrum with increased height may be expected. Ma and Burken18 assume that, based on losses by diffusion, the depth horizontical profile will show higher concentrations inside the trunk. Our own examinations show no consistent depth profiles (data not shown). Migration gradients caused by slow diffusive transport processes are likely responsible for these findings. Based on the many factors influencing water uptake, deposition, and behavior of CVOC,10 only a semiquantitative evaluation of the measured concentration is meaningful. Nevertheless, these evaluations lead to meaningful contaminant images at all three sites, which coincide closely with those from direct push groundwater samples.15 Sampling at different directions raises the probability of taking a sample from the side of highest concentration. As could be shown, additional effort in taking radially distributed samples is actually not required for semiquantitative evaluations. The limitation to sampling of one point is reasonable, however critical for trees, whose analyte concentrations are close to the detection limit. Sampling from one fixed direction, e.g. the sunny side 1 of a tree, is not decisive, considering the results presented here. Rainwater has a considerable influence on concentrations of TCE and cDCE measured in the tree. Sampling in the course of a phytoscreening should therefore be undertaken during dry weather. Implications for Phytoscreening. Birch sap sampling is a suitable means of characterizing contaminant distribution within the soil subsurface. The difference in sensitivity relative to cDCE and TCE from birch sap and tree cores from the same point on the tree confirms the dependence of the kind of substance. For better correlation factors to groundwater contents, birch sap
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sampling is an interesting option. However, the method is only applicable during a very narrow annual time window. The points of measurement cannot be used over periods of several years, since the tree reacts to such injuries by building various reaction or barrier zones24 thus sealing off this area. The sampling port already seals itself within a few days, so that it is not even usable for the roughly four week long budding period. The sap flow is certainly weather dependent. Birch sap sampling could be repeated at points which had already dried up.
’ ASSOCIATED CONTENT
bS
Supporting Information. Detailed site description. Data of tree location and sampling conditions. Conditions of SPME extraction, GC/MS measurements and annotations to the calibration with aqueous standards. Radial directional concentrations of tree cores in 2007. Height profiles. Geological data and groundwater levels. Interpolated groundwater concentrations. This material is available free of charge via the Internet at http:// pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Phone: +49 30 314-25220/-21978. Fax: +49 30 314-29319. E-mail:
[email protected].
’ ACKNOWLEDGMENT This work was funded by the German Federal Ministry of Education and Research, project SINBRA, contract no. 0330757D. The authors also thank A. Horn, F. Jaeger, S. Klemer, W. Seis, F. Zietzschmann, R. Hatton, and W. Frenzel for their assistance in preparing this manuscript. ’ REFERENCES (1) Sorek, A.; Atzmon, N.; Dahan, O.; Gerstl, Z.; Kushisin, L.; Laor, Y.; Mingelgrin, U.; Nasser, A.; Ronen, D.; Tsechansky, L.; Weisbrod, N.; Graber, E. R. ”Phytoscreening”: The Use of Trees for Discovering Subsurface Contamination by VOCs. Environ. Sci. Technol. 2008, 42 (2), 536–542. (2) Burken, J.; Dietz, A.; Jordahl, J.; Schnabel, W.; Thompson, P.; Licht, L.; Alvarez, P.; Schnoor, J., Phytoremediation at Hazardous Waste Sites. Proceedings - WEFTEC ’96, Annual Conference & Exposition, 69th, Dallas, Oct. 5 9, 1996 1996, 1, 327-332. (3) Schnabel, W. E.; Dietz, A. C.; Burken, J. G.; Schnoor, J. L.; Alvarez, P. J. Uptake and Transformation of Trichloroethylene by Edible Garden Plants. Water Res. 1997, 31 (4), 816–824. (4) Newman, L. A.; Strand, S. E.; Choe, N.; Duffy, J.; Ekuan, G.; Ruszaj, M.; Shurtleff, B. B.; Wilmoth, J.; Heilman, P.; Gordon, M. P. Uptake and Biotransformation of Trichloroethylene by Hybrid Poplars. Environ. Sci. Technol. 1997, 31 (4), 1062–1067. (5) Schumacher, J. G.; Struckhoff, G. C.; Burken, J. G. Contamination Using Tree Cores at the Front Street Site and a Former Dry Cleaning Facility at the Riverfront Superfund Site, New Haven, Missouri, 1999 2003; 2004 5049; Virginia, 2004; p 41. (6) Gopalakrishnan, G.; Negri, M. C.; Minsker, B. S.; Werth, C. J. Monitoring subsurface contamination using tree branches. Ground Water Monit. Rem. 2007, 27 (1), 65–74. (7) Chard, B. K.; Doucette, W. J.; Chard, J. K.; Bugbee, B.; Gorder, K. Trichloroethylene uptake by apple and peach trees and transfer to fruit. Environ. Sci. Technol. 2006, 40 (15), 4788–4793. 9609
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(8) Vroblesky, D. A.; Nietch, C. T.; Morris, J. T. Chlorinated Ethenes from Groundwater in Tree Trunks. Environ. Sci. Technol. 1999, 33 (3), 510–515. (9) Larsen, M.; Burken, J.; Machackova, J.; Gosewinkel Karlson, U.; Trapp, S. Using Tree Core Samples to Monitor Natural Attenuation and Plume Distribution After a PCE Spill. Environ. Sci. Technol. 2008, 42 (5), 1711–1717. (10) Vroblesky, D. A. User s Guide to the Collection and Analysis of Tree Cores to Assess the Distribution of Subsurface Volatile Organic Compounds; 2008 5088; Virginia, 2008; p 59. (11) Vroblesky, D. A.; Clinton, B. D.; Vose, J. M.; Casey, C. C.; Harvey, G.; Bradley, P. M. Ground Water Chlorinated Ethenes in Tree Trunks: Case Studies, Influence of Recharge, and Potential Degradation Mechanism. Ground Water Monit. Rem. 2004, 24 (3), 124–138. (12) Ouyang, G. F.; Vuckovic, D.; Pawliszyn, J. Nondestructive Sampling of Living Systems Using in Vivo Solid-Phase Microextraction. Chem. Rev. 2011, 111 (4), 2784–2814. (13) Popp, P.; Paschke, A. Solid phase microextraction of volatile organic compounds using carboxen-polydimethylsiloxane fibers. Chromatographia 1997, 46 (7 8), 419–424. (14) Avila, M. A. S.; Breiter, R.; Mott, H. Development of a Simple, Accurate SPME-based Method for Assay of VOCs in Column Breakthrough Experiments. Chemosphere 2007, 66 (1), 18–29. (15) Rein, A.; Popp, S.; Leven, C.; Bittens, M.; Dietrich, P., Comparison of approaches for the characterization of contamination at rural megasites. Environ. Earth Sci. 2010 (Online-First-Version), In Press. (16) Pawliszyn, J.; Ouyang, G. Recent developments in SPME for on-site analysis and monitoring. TrAC, Trends Anal. Chem. 2006, 25 (7), 692–703. (17) Trapp, S.; Miglioranza, K. S. B.; Mosbaek, H. Sorption of lipophilic organic compounds to wood and implications for their environmental fate. Environ. Sci. Technol. 2001, 35 (8), 1561–1566. (18) Ma, X.; Burken, J. G. TCE Diffusion to the Atmosphere in Phytoremediation Applications. Environ. Sci. Technol. 2003, 37 (11), 2534–2539. (19) Cohen, Y.; Cohen, S.; Cantuarias-Aviles, T.; Schiller, G. Variations in the Radial Gradient of Sap Velocity in Trunks of Forest and Fruit Trees. Plant Soil 2008, 305 (1 2), 49–59. (20) Vite, J. P.; Rudinsky, J. A. Water-conducting systems in conifers and their importance to the distribution of trunk-injected chemicals. Contrib. Boyce Thompson Inst. 1959, 20, 27–38. (21) Doucette, W. J.; Bugbee, B. G.; Smith, S. C.; Pajak, C. J.; Ginn, J. S., Uptake, metabolism, and phytovolatilization of trichloroethylene by indigenous vegetation: impact of precipitation. In Phytoremediation; McCutcheon, S. C.; J., S. J., Eds.; 2003; pp 561-588. (22) Baduru, K. K.; Trapp, S.; Burken, J. G. Direct Measurement of VOC Diffusivities in Tree Tissues: Impacts on Tree-Based Phytoremediation and Plant Contamination. Environ. Sci. Technol. 2008, 42 (4), 1268–1275. (23) Trapp, S. Fruit Tree Model for Uptake of Organic Compounds from Soil and Air. SAR QSAR Environ. Res. 2007, 18 (3 4), 367–387. (24) Shigo, A. L. Compartmentalization of Decay in Trees. Sci. Am. 1985, 252 (4), 96–103.
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Interactive Priming of Biochar and Labile Organic Matter Mineralization in a Smectite-Rich Soil Alexandra Keith,† Balwant Singh,*,† and Bhupinder Pal Singh‡ † ‡
Faculty of Agriculture, Food, and Natural Resources, The University of Sydney, NSW, 2006, Australia NSW Department of Primary Industries, P.O. Box 100, Beecroft, NSW 2119, Australia
bS Supporting Information ABSTRACT: Biochar is considered as an attractive tool for long-term carbon (C) storage in soil. However, there is limited knowledge about the effect of labile organic matter (LOM) on biochar-C mineralization in soil or the vice versa. An incubation experiment (20 °C) was conducted for 120 days to quantify the interactive priming effects of biochar-C and LOM-C mineralization in a smectitic clayey soil. Sugar cane residue (source of LOM) at a rate of 0, 1, 2, and 4% (w/w) in combination with two wood biochars (450 and 550 °C) at a rate of 2% (w/w) were applied to the soil. The use of biochars (∼ 36%) and LOM (12.7%) or soil (14.3%) with isotopically distinct δ13C values allowed the quantification of C mineralized from biochar and LOM/soil. A small fraction (0.41.1%) of the applied biochar-C was mineralized, and the mineralization of biochar-C increased significantly with increasing application rates of LOM, especially during the early stages of incubation. Concurrently, biochar application reduced the mineralization of LOM-C, and the magnitude of this effect increased with increasing rate of LOM addition. Over time, the interactive priming of biochar-C and LOM-C mineralization was stabilized. Biochar application possesses a considerable merit for long-term soil C-sequestration, and it has a stabilizing effect on LOM in soil.
’ INTRODUCTION Biochar production, the centuries old tradition of heating organic residues under oxygen limited conditions for application to soil, is now the focus of a rapidly expanding area of research. The interest in biochar is manifold and stems from the observations on old agricultural soils, called Terra Preta, in the Amazon Basin. These soils were treated with charcoal (or biochar) creating much higher soil fertility and carbon (C) content than the neighboring natural soils.1 Based on these observations, biochars produced by pyrolyzing organic waste materials in thermal reactors have been promoted as soil amendments. Biochar application to the soil can reduce greenhouse gas emissions,2 decrease the availability of heavy metals,3 and benefit soil fertility and plant productivity.1,4,5 Furthermore, the demonstrated large mean residence time (MRT) of natural char or biochar in soils and sediments68 has generated interest in the use of biochar for increasing C-sequestration in soil. Consequently, the production and application of biochar to soil is considered to possess considerable greenhouse gas emissions mitigation benefits compared to conventional management of biomass feedstocks.9,10 Despite the recalcitrant nature of biochars, research shows that biochar oxidizes both by abiotic and biotic mechanisms.8,11,12 The stability of biochar-C in soils is dependent on several factors, including the properties of biochar and soil, and environmental conditions.7,13 Furthermore, biochar application may affect the r 2011 American Chemical Society
mineralization rate of native soil organic matter (SOM), and similarly the addition labile organic matter (LOM), that is, organic material that mineralizes more rapidly than biochar or native SOM, may also impact biochar-C mineralization in soil.13,16 There are conflicting reports on the interactive priming effects on mineralization of biochar-C and LOM-C in the soil.1316 Here, we define the interactive, positive or negative, priming effect as the stimulation or suppression, respectively, of biochar-C or LOM-C mineralization above or below the respective control.17 Liang et al.13 studied the effects of black C, a form of biochar-type C, in Anthrosols (Terra Preta soils) on the C mineralization and cycling of relatively labile plant residues, that is, sugar cane leaves. They reported that the presence of black C in the soil caused rapid incorporation of added LOM into aggregates and organo-mineral fractions and thus stabilizing LOM in soil. Contrary to the study of Liang et al.,13 Wardle et al.14 reported that charcoal presence promoted the loss of humus-C from the forest floor in a 10-year old study at three contrasting boreal forest sites in northern Sweden. However, neither of these studies13,14 observed the effect of added or native organic matter on the mineralization of biochar-C. Black C in the Received: June 27, 2011 Accepted: September 28, 2011 Revised: September 13, 2011 Published: September 28, 2011 9611
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Table 1. Important Properties of the Soil, Biochars, and Sugar Cane Residue Used in the Incubation Experiment property
soil
450 °C biochar
550 °C biochar
sugar cane residue
total C (%)
0.45
67.8
74.9
total N (%)
0.04
0.5
0.6
39.6 0.4
δ13C
14.3
36.3
36.4
12.7
pH (1:5 H2O)
8.10
8.60
9.90
Electrical conductivity (1:5, dS m1)
0.11
0.90
1.10
CECa (mmolc kg1)
347
11.4
54.0
clay (%)
53
silt (%) sand (%)
14 33
specific surface areab (m2 g1)
191.0
228.3
pore volume (%)
57.2
67.5
clay minerals
S****, K*, I*
a Cation-exchange capacity measured by the silver thiorurea method. b CO2 adsorption method: S**** = smectite (>80%); K* = kaolinite (97%) and used without further purification. Sulfur hexafluoride (99.9%) was obtained from Messer Griesheim, Germany.
’ RESULTS AND DISCUSSION Photolysis Rate Coefficient. Photolysis of o-tolualdehyde follows first order kinetics: j
o-tolualdehyde þ hν f products
ðIÞ
where j is the photolysis rate coefficient. Assuming photolysis is the only loss process, j can be determined from a simple first order kinetic plot: ln½o-tolualdehydet =½o-tolualdehyde0 ¼ jt
ðIIÞ
where the subscripts 0 and t refer to the concentrations at initial time 0 and elapsed time t, respectively. Test experiments carried out in the presence and absence of excess amounts (1025 ppmV) of OH radical scavenger (isopropanol and cyclohexane in the indoor and outdoor chambers, respectively) produced very similar decay rates, indicating that loss of o-tolualdehyde due to reaction with OH in the chambers was negligible. However, 9650
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Figure 1. Concentrationtime profile and j(NO2) during the photolysis of o-tolualdehyde at EUPHORE on 3 July 2003. The vertical dotted lines indicate the time the chamber was opened (09:44) and closed (12:27).
Table 1. Experimental Details for the Photolysis of o-Tolualdehyde in the Outdoor (EUPHORE) and Indoor Simulation Chambersa EUPHORE 19 September 2002
indoor chamber
3 July 2003
4 July 2003
four experiments
initial concentration (ppbV)
525
508
256
364, 518, 712, 1354
irradiation period j(NO2)average (s1)
11.59 14:24 (6.41 ( 0.64) 103
09:4412:27 (8.13 ( 0.81) 103
11:3613:53 (8.44 ( 0.84) 103
13 h 1.56 103
kwall (s1)
(7.80 ( 0.47) 106
(1.03 ( 0.47) 105
(1.68 ( 0.50) 105
(7.03 ( 0.90) 106
6
(5.48 ( 0.90) 10
6
4
1
kSF6 (s )
(6.57 ( 0.72) 10
j(o-tolualdehyde)FTIR (s1)
(1.97 ( 0.05) 104
1
4
4
(4.16 ( 0.23) 105
j(o-tolualdehyde)GC‑PID (s )
(1.62 ( 0.05) 10
(2.12 ( 0.05) 10
(2.15 ( 0.06) 10
j(o-tolualdehyde)average (s1)
(1.62 ( 0.05) 104
(2.05 ( 0.05) 104
(2.15 ( 0.06) 104
(4.16 ( 0.23) 105
(2.53 ( 0.25) 10
2
2
(2.55 ( 0.26) 10
2
(2.66 ( 0.23) 102
(1.33 ( 0.13) 10 1.22 ( 0.03
4
(1.90 ( 0.19) 10 1.13 ( 0.05
4
j(o-tolualdehyde)/j(NO2) 1
maximum theoretical loss rate (s ) effective quantum yield
(2.52 ( 0.25) 10
4
(1.85 ( 0.19) 10 1.11 ( 0.03
molar yield of benzocyclobutenol
0.77 ( 0.04
0.71 ( 0.06
molar yield of o-phthalaldehyde
0.21 ( 0.02
0.22 ( 0.02
yield of aerosol
0.104
0.049, 0.059, 0.080, 0.133
Except for j(NO2), quoted errors are twice the standard deviation arising from the least squares fit of the data and include the uncertainty in calibration and response factors. For j(NO2) and the maximum theoretical loss rate, the estimated error is 10%. The molar yield of benzocyclobutenol is based on the use of 1-indanol as a surrogate compound. a
o-tolualdehyde was found to undergo a small amount of deposition to the walls of the reactor. The rate coefficient for this process (kwall) was determined by measuring the first order decay of the compound in the dark for about one hour prior to photolysis. This value was incorporated into the overall decay as follows: ln½o-tolualdehydet =½o-tolualdehyde0 kwall t ¼ jt
ðIIIÞ
Although dilution was also observed during the EUPHORE experiments, the rate determined by measuring the loss of SF6 from the chamber, kSF6, was lower than the wall loss and is therefore already incorporated into kwall. Thus a plot in the form of eq III should yield a straight line with gradient j.
Concentrationtime profiles and kinetic plots in the form of eq III were generated for all experiments. The concentration time profile for the EUPHORE experiment conducted on third July is presented in Figure 1 and clearly shows the rapid decay of o-tolualdehyde following exposure of the chamber to sunlight. The light intensity is represented by the photolysis rate coefficient for NO2, j(NO2), which was calculated from the solar flux measurements of the spectroradiometer and recommended values for the absorption cross-section and quantum yield.23 The corresponding kinetic plot used to calculate j from these FTIR spectroscopic measurements is shown in Figure S1 (Supporting Information) and exhibits good linearity and a near-zero intercept. The value for j(o-tolualdehyde) is listed in Table 1 along 9651
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Figure 2. Yield plot of the products detected by FTIR spectroscopy during the photolysis of o-tolualdehyde at EUPHORE on 3 July 2003.
with a summary of the reaction conditions and results obtained for all of the photolysis experiments. As shown in Table 1, there is very good agreement between the j values determined using FTIR spectroscopy and GC-PID in the experiment performed on third July 2003. In the experiment performed on fourth July 2003, o-tolualdehyde was among several aromatic aldehydes subjected to photolysis in the chamber and FTIR spectroscopy could not be used for analysis due to overlapping absorption bands. The value for j(o-tolualdehyde) obtained by GC-PID in this experiment is very similar to that obtained on third July 2003, indicating that the presence of the other compounds (2,3-dimethylbenzaldehyde and 2,6-dimethylbenzaldehyde) did not affect the photolysis rate. The average values for j(NO2) and hence j(o-tolualdehyde)/j(NO2) in these two EUPHORE experiments were also very similar, reflecting the fact that they were performed under almost identical, cloud-free conditions. As expected, the sunlight intensity was somewhat lower for the experiment performed on 19th September 2002 and although a slightly lower value was observed for j(o-tolualdehyde), the j(o-tolualdehyde)/j(NO2) ratio was virtually the same. This indicates that the average value of j(o-tolualdehyde)/j(NO2) = (2.53 ( 0.25) 102 is a useful parameter for calculating the photolysis rate of o-tolualdehyde under different light conditions in chambers or in the real atmosphere. The effective quantum yield for photolysis of o-tolualdehyde, jeff was determined using the following expression; jeff ¼ jexp =jmax
ðIVÞ
where jexp and jmax are the experimentally observed and maximum theoretical values of the photolysis rate coefficient respectively. The latter term was calculated using the solar flux intensity measurements of the spectroradiometer, the absorption cross section data reported by Thiault et al.,11 and assuming a quantum yield of unity over the atmospheric absorption range of the compound. The values obtained for jeff during the EUPHORE experiments are in reasonable agreement, yielding an average of jeff = (1.15 ( 0.05). However, the calculated value of jmax does not include the uncertainty in the reported absorption cross
sections, which is estimated to be 15% below 340 nm and 20% in the range 340363 nm.11 The results therefore suggest that, within experimental error, the effective quantum yield for photolysis of o-tolualdehyde by natural sunlight is unity. The results of this work can be compared to those obtained in the preliminary studies also performed at EUPHORE. A value of j(o-tolualdehyde) = (2.00 ( 0.10) 104 s1 was obtained by Volkamer et al.12 in one experiment during February, where the solar zenith angle was 50° and the UV flux reduced by around a factor of 2 compared to midsummer. In contrast, Thiault et al.13 obtained a value of j(o-tolualdehyde) = (1.10 ( 0.20) 104 s1 during an experiment performed in April. Values of j(o-tolualdehyde)/j(NO2) = 0.032 and jeff = 0.6 were also reported, but no information was provided on whether the wall loss of o-tolualdehyde was taken into account during data analysis. It is interesting note that in these preliminary studies,12,13 the photolysis of benzaldehyde, m- and p-tolualdehyde was found to be negligible, suggesting that the presence of the methyl group in the ortho position is a key factor in determining the photolysis efficiency of o-tolualdehyde. The photolysis rate of o-tolualdehyde in the indoor chamber was around a factor of 5 slower than in the outdoor chamber. This result was expected since the intensity and wavelength distribution of UV light produced from the TL05 lamps is quite different from natural sunlight. However, a useful comparison between the two chambers can be made by examining the values determined for j(o-tolualdehyde)/j(NO2), shown in Table 1. The values are in very close agreement, indicating that the TL05 lamps used in the indoor chamber studies provide reasonably realistic light conditions for studies of the atmospheric photolysis of the aromatic aldehydes. Photolysis Products. Gas-phase products arising from the photolysis of o-tolualdehyde were determined by FTIR spectroscopy and GCMS. The FTIR spectra obtained in both chambers enabled identification and quantification of o-phthalaldehyde and carbon monoxide as reaction products. Phthalide and formic acid were also detected during the later stages of the experiments, but below the limits of quantification. The FTIR product spectra also contain significant absorption features around 1050 cm1 9652
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Environmental Science & Technology and just below 1400 cm1 and 750 cm1 (Figure S2, Supporting Information). The peak at 1050 cm1 is characteristic of the OH bending vibration in an alcohol, and inspection of the literature reveals that UV photolysis of o-alkyl aromatic carbonyl compounds in solution and in the condensed phase leads to the efficient formation of the corresponding dienols and benzocyclobutenols.24,25 The dienol and benzocyclobutenol expected from photolysis of o-tolualdehyde are not commercially available, however, their IR absorption spectra (not quantified) in a low temperature matrix have been reported previously.26 The main absorption bands of benzocyclobutenol in the fingerprint region, located at 1055 cm1 (OH bend), 1212 cm1 and 1401 cm1, are all apparent in the product spectra obtained here, while there is no evidence for the presence of the dienol (at 1256 cm1 (OH bend) and 1102 cm1 (CO stretch)). It is therefore proposed that benzocyclobutenol is a product of the gas-phase photolysis of o-tolualdehyde. Since benzocyclobutenol is not commercially available, 1-indanol, the similarly structured aromatic cyclic alcohol, was used as a surrogate compound:
The main absorption features of 1-indanol are virtually identical to those observed in the product spectra (Supporting Information Figure S2) thus providing further evidence to support the formation of benzocyclobutenol as a reaction product from photolysis of o-tolualdehyde. Calibrated infrared spectra of 1-indanol were subsequently used for quantification of benzocyclobutenol and the concentrationtime profile in Figure 1 shows that the aromatic cyclic alcohol is in fact the major reaction product. The corresponding product yield plots for benzocyclobutenol and o-phthalaldehyde displayed in Figure 2 are linear, indicating that these compounds are primary products of the reaction and are not removed to any significant extent during the time scale of the experiments. Only trace amounts of carbon monoxide were observed and the yield plot in Figure 2 is curved, indicating that it is most likely a secondary product. Similar results were also obtained from analysis of the FTIR spectra obtained in the indoor chamber experiments, Table 1. A number of gas-phase photolysis products were also detected using GCMS. During the indoor chamber experiments, ophthalaldehyde, phthalide, phthalic anhydride, o-toluic acid, and o-cresol were identified by comparison of the retention times and mass spectra with those of standards (Figure S3 and Table S1, Supporting Information). The molar yields of o-phthalaldehyde and phthalide were (0.25 ( 0.04) and (0.02 ( 0.01), in good agreement with the results obtained by FTIR spectroscopy. Analysis of the denuder extracts showed the formation of ophthalaldehyde and very small amounts of glyoxal. Quantification of the other products identified by direct injection GCMS proved difficult due to losses during the sampling procedure. However, there was no evidence for the presence of benzocyclobutenol in the mass spectra of the products. Similar results were obtained at EUPHORE, where o-phthalaldehyde and phthalide were also detected as the major products, along with o-cresol and 2-hydroxymethylbenzaldehyde in trace amounts. A small peak, possibly due to benzocyclobutenol was also observed in the GCMS product spectra, but could not be confirmed due to the lack of a standard. The benzocyclobutenols are known to undergo thermal decomposition above 80 °C24 and it therefore
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Scheme 1
seems very likely that benzocyclobutenol decomposed during the GCMS analysis, either in the heated injector or on the column itself. In fact, degradation of the aromatic cyclic alcohol could be responsible for generating some of the aromatic products (phthalide, phthalic anhydride, o-cresol) which were detected by GCMS but not by in situ FTIR spectroscopy. The information obtained from these product studies can be used to propose a mechanism for the atmospheric photolysis of o-tolualdehyde, shown in Scheme 1. The initial step in the mechanism is photoexcitation of the aldehyde through the nfπ* electronic transition followed by intramolecular hydrogen abstraction (Norrish Type II process) to produce a 1,4-biradical species.24,25 Based on the distribution of identified reaction products, two possible reactions for the 1,4-biradical are proposed, cyclization to form benzocyclobutenol and reaction with O2 to produce o-phthalaldehyde. The cyclization reaction was initially proposed by Yang27 to explain the formation of cyclic alcohols from photolysis of aliphatic ketones in solution. However, more recent studies on o-alkyl aromatic carbonyl compounds, in solution and in the solid phase, indicate that direct cyclization of the 1,4-biradical is unlikely, and that formation of the dienol, in both (Z)- and (E)-configurations is preferred.24,25 The (Z)-dienol is very short-lived and reverts to the starting aldehyde via a rapid 1,5-H atom shift, whereas the (E)-dienol undergoes thermal conrotatory ring closure to form benzocyclobutenol. As indicated above, there is no evidence for the formation of the dienol in these experiments, although its lifetime in the gas-phase may be too short to be detected. Nevertheless, the possibility that formation of benzocyclobutenol proceeds via the (E)-dienol cannot be ruled out. The formation of o-phthalaldehyde is postulated to occur via reaction of the 1,4-biradical with O2. It is possible that this pathway may involve a number of concurrent or subsequent steps; H-atom abstraction from the OH group and addition of O2 to the methylene unit to form a peroxy radical. In the absence of NO, the peroxy radicals react together, with the major reaction pathway resulting in oxy radicals which also react with O2 to form o-phthalaldehyde. The minor reaction pathway involves combination of the peroxy radicals to produce two molecular products in one step; o-phthalaldehyde and 2-hydroxymethylbenzaldehyde. A very small amount of the latter species was detected by GCMS in the EUPHORE experiment. However, for the sake of simplicity, this mechanistic detail is omitted from Scheme 1. 9653
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Figure 3. Number and size distribution of particles formed during the photolysis of o-tolualdehyde at EUPHORE on 3 July 2003. The numbers in the legend refer to time during the experiment.
Additional reaction pathways are required to explain the formation of the minor products detected by GCMS and FTIR spectroscopy. Phthalide and phthalic anhydride are known to be generated from the UV photolysis of the primary product, ophthalaldehyde.28 The formation of the other aromatic compounds, o-cresol and o-toluic acid, is more difficult to explain and since these products were only detected in very small amounts, speculative mechanisms for their formation will not be considered here. As indicated above, carbon monoxide appears to be formed as a secondary product. Benzocylcobutenol is not expected to undergo rapid photolysis under the conditions employed in the chambers, suggesting that photolysis of ophthalaldehyde is the most likely source of carbon monoxide. However, preliminary experiments on the photolysis of o-phthalaldehyde in both chambers did not generate detectable levels of carbon monoxide. The origin of the secondary carbon monoxide therefore remains unknown. Secondary Organic Aerosol Formation. Secondary Organic Aerosol (SOA) formation was observed in all experiments performed in both chambers. The evolution of the aerosol produced from photolysis of o-tolualdehyde in the EUPHORE chamber is shown in Figure 3. A large number of particles with a mean diameter of around 20 nm were observed within 5 min of the start of photolysis. This initial “burst” of particles also corresponded to the greatest number present during the reaction. As photolysis continued, there was a gradual reduction in particle number and a corresponding increase in average particle diameter due to coagulation. Particles with a mean diameter of around 70 nm were present at the end of the experiment. Very similar results were obtained in the indoor chamber, although the greatest number of particles was typically observed around 10 min later when the mean diameter was 2535 nm. This slight difference in the particle-time profile is probably due to the fact that the photolysis process, and hence particle formation, was slower in the indoor chamber.
The mass and volume concentration of SOA generated in the EUPHORE experiment on third July 2003 is shown in Figure 4. The density of SOA formed in the experiment was determined to be (1.09 ( 0.01) g cm3 by plotting the measured mass (TEOM) versus the volume (SMPS), Figure 4 (inset). The calculated density is lower than the value of 1.35 g cm3 determined for SOA generated from photooxidation of benzene in the EUPHORE chamber using the same method,15 and also at the lower end of the range of effective densities (1.06 1.45 g cm3) for laboratory-generated SOA from anthropogenic precursors.29 The maximum aerosol concentration was observed at the end of the experiment when all of the o-tolualdehyde had reacted. After closing the chamber, the aerosol was found to undergo a first order decay, k = (1.46 ( 0.10) 105 s1, due to deposition at the walls of the reactor. This wall loss factor was used to provide a corrected value of 258 μg m3 for the maximum aerosol mass concentration, which when divided by the mass of o-tolualdehyde reacted (2491 μg m3) gives an aerosol yield of 0.104. SOA yields were also obtained from four different experiments performed in the indoor chamber by converting the measured volume concentrations to mass concentrations using the density of 1.09 g cm3. As shown in Table 1, the SOA yields were found to increase with starting concentration of o-tolualdehyde. This effect has been observed for many other SOA precursors and is consistent with gas/particle partitioning theory.29 The partitioning of semivolatile reaction products to the aerosol phase increases with the amount of available aerosol mass, resulting in higher yields of SOA when greater precursor concentrations are used. It is interesting to note that the yield obtained for the experiment with an initial concentration of 518 ppbV is considerably lower than that determined in the equivalent experiment at EUPHORE. The semivolatile reaction products would be expected to undergo a higher degree of wall loss in the smaller indoor chamber, resulting in less partitioning to the particle 9654
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Figure 4. Aerosol mass (TEOM) and volume (SMPS) concentrations measured during the photolysis of o-tolualdehyde at EUPHORE on 3 July 2003. The inset contains the plot of mass versus volume concentration used to determine the density of the aerosol.
phase and lower SOA yields.30 In addition, aerosol formation is slightly slower in the indoor chamber and this suggests that the photolysis rate may also influence SOA yield. A similar rate effect has also been observed in previous studies of the photooxidation of aromatic hydrocarbons, where various factors including OH precursor type and concentration,31,32 light intensity33 and relative humidity34 have been shown to affect OH levels, oxidation rate, and SOA mass yield. The results obtained here suggest that direct photolysis of otolualdehyde may contribute to SOA formation in chamber studies of o-xylene photooxidation35 and in the ambient atmosphere. Information on the chemical composition of the SOA can provide insights into the key species and processes involved in aerosol formation and also help to identify molecular markers for specific sources of ambient organic aerosol.29 The only carbonyl products identified in filter samples collected in the indoor chamber experiments were o-phthalaldehyde (approximate yield of 10%) and glyoxal (not quantified). The same carbonyl products were identified in particles collected at EUPHORE, along with o-toluic acid, a hydroxyl-containing compound with MW 120, tentatively attributed to benzocyclobutenol, phthalaldehydic acid, phthalide, and phthalic anhydride, Supporting Information Table S2. The latter three compounds were also detected in filter samples of SOA generated from the photooxidation of o-tolualdehyde, where direct photolysis and reaction with OH were both responsible for loss of the aromatic compound.36 Interestingly, a number of oxygenated polycyclic aromatic compounds were also identified in this study, indicating that intermolecular reactions could be involved in SOA formation and growth. There is evidence to suggest that biradical (Criegee) intermediates are involved in addition reactions with peroxy radicals leading to SOA formation during the ozonolysis of alkenes,29,37 and it is possible that the biradical species produced during the photolysis of o-tolualdehyde may also be involved in similar types of reactions leading to polycyclic compounds with low volatility and hence SOA formation.
Photolysis of aromatic carbonyls in the solid and liquid phases has been shown to generate a wide range of polycyclic aromatic products,24,25 and another intriguing possibility is that some of the photochemically active compounds present in the SOA, for example, o-phthalaldehyde, may undergo light-induced intermolecular reactions with other aromatic species to produce the oxygenated polycyclic aromatic compounds. This work is one of the first studies to demonstrate that direct photolysis of a volatile organic compound can produce SOA. The formation of aerosol from photolysis of 2,4-hexadienedial38 and ortho-nitrophenols39 has been noted, although not investigated in detail. More recently, Kessler et al.40 used the photolysis of alkyl iodides to generate single organic radical precursors and proposed that this technique could be used as a simplified experimental approach to investigate SOA formation from alkanes. Similarly, the photolysis of o-tolualdehyde could also be considered as a good model system to investigate SOA formation mechanisms. The possible range of reaction pathways is considerably less complex than in OH-initiated photooxidation systems and does not involve the use of OH precursors. Although beyond the scope of the present work, a systematic study of the various parameters affecting aerosol production from direct photolysis of o-tolualdehyde could prove to be beneficial in elucidating key processes responsible for SOA formation. Atmospheric Implications. The rate coefficient for the sunlight photolysis of o-tolualdehyde can be used to calculate the tropospheric lifetime with respect to photolysis (τp) from the relationship: τp = 1/j. Using the values of j(o-tolualdehyde) obtained from the EUPHORE experiments yields photolysis lifetimes in the range 1.3 1.7 h. The average value of j(o-tolualdehyde)/j(NO2) = 2.53 102 can be used to provide an estimate of the photolysis lifetime under a variety of other solar irradiation conditions. The other atmospheric loss processes for o-tolualdehyde are reaction with OH and NO3, which have lifetimes of 13.6 and 56.7 h respectively.10 Thus photolysis by sunlight is clearly the dominant atmospheric loss process for 9655
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’ ASSOCIATED CONTENT
bS
Supporting Information. Photolytic loss plot, FTIR spectra, GCMS data. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Phone: +353 21 4902454; fax: +353 21 4903014; e-mail:
[email protected].
’ ACKNOWLEDGMENT This work was supported by the Higher Education Authority in Ireland and the European Commission through the research project EUROCHAMP 2 (contract number 228335). The Instituto Universitario CEAM-UMH is partly supported by Generalitat Valenciana, Fundaci on Bancaja, and the projects GRACCIE (Consolider-Ingenio 2010) and FEEDBACKS (Prometeo - Generalitat Valenciana). ’ REFERENCES (1) Feng, Y.; Wen, S.; Chen, Y.; Wang, X.; L€u, H.; Bi, X.; Sheng, G.; Fu, J. Ambient levels of carbonyl compounds and their sources in Guangzhou, China. Atmos. Environ. 2005, 39, 1789–1695. (2) L€u, H.; Cai, Q.-Y.; Wen, S.; Chi, Y.; Guo, S.; Sheng, G.; Fu, J. Seasonal and diurnal variations of carbonyl compounds in the urban atmosphere of Guangzhou, China. Sci. Total Environ. 2010, 408, 3523–3529. (3) Obermeyer, G.; Aschmann, S. M.; Atkinson, R.; Arey, J. Carbonyl atmospheric reaction products of aromatic hydrocarbons in ambient air. Atmos. Environ. 2009, 43, 3736–3744. (4) Kean, A. J.; Grosjean, E; Grosjean, D.; Harley, R. On-road measurement of carbonyls in California light-duty vehicle emissions. Environ. Sci. Technol. 2001, 35, 4198–4204. (5) Jakober, C. A.; Robert, M. A.; Riddle, S. G.; Destaillats, H.; Charles, M. J.; Green, P. G.; Kleeman, M. J. Carbonyl emissions from gasoline and diesel motor vehicles. Environ. Sci. Technol. 2008, 42, 4697–4703. (6) Calvert, J. G. A.; Becker, K. H.; Kamens, R. M.; Seinfeld, J. H.; Wallington, T. J.; Yarwood, G., The Mechanisms of Atmospheric Oxidation of Aromatic Hydrocarbons; Oxford University Press: Oxford, UK, 2002. (7) Atkinson, R; Aschmann, S. M.; Arey, J. Formation of ringretaining products from the OH radical-initiated reactions of o-, m-, and p-xylene. Int. J. Chem. Kinet. 1991, 23, 77–97.
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(29) Hallquist, M.; Wenger, J. C.; Baltensperger, U.; Rudich, Y.; Simpson, D.; Claeys, M.; Dommen, J.; Donahue, N. M.; George, C.; Goldstein, A. H.; Hamilton, J. F.; Herrmann, H.; Hoffmann, T.; Iinuma, Y.; Jang, M.; Jenkin, M. E.; Jimenez, J. L.; Kiendler-Scharr, A.; Maenhaut, W.; McFiggans, G.; Mentel, T. F.; Monod, A.; Prev^ot, A. S. H.; Seinfeld, J. H.; Surratt, J. D.; Szmigielski, R.; Wildt, J. The formation, properties and impact of secondary organic aerosol: Current and emerging issues. Atmos. Chem. Phys. 2009, 9, 5155–5236. (30) Matsunaga, A; Ziemann, P. J. Gas-wall partitioning of organic compounds in a Teflon film chamber and potential effects on reaction product and aerosol yield measurements. Aerosol Sci. Technol. 2010, 44, 881–892. (31) Ng, N. L.; Kroll, J. H.; Chan, A. W. H.; Chhabra, P.; Flagan, R. C.; Seinfeld, J. H. Secondary organic aerosol formation from mxylene, toluene, and benzene. Atmos. Chem. Phys. 2007, 7, 3909–3922. (32) Song, C.; Na, K. S.; Warren, B.; Malloy, Q.; Cocker, D. R. Secondary organic aerosol formation from m-xylene in the absence of NOx. Environ. Sci. Technol. 2007, 41, 7409–7416. (33) Warren, B.; Song, C.; Cocker, D. R. Light intensity and light source influence on secondary organic aerosol formation for the mxylene/NOx photooxidation system. Environ. Sci. Technol. 2008, 42, 5461–5466. (34) Healy, R. M.; Temime, B.; Kuprovskyte, K.; Wenger, J. C. The effect of relative humidity on gas/particle partitioning and aerosol mass yield in the photooxidation of p-xylene. Environ. Sci. Technol. 2009, 43, 1884–1889. (35) Song, C.; Na, K.; Warren, B.; Malloy, Q.; Cocker, D. R. Secondary organic aerosol formation from the photooxidation of pand o-xylene. Environ. Sci. Technol. 2007, 41, 7403–7408. (36) Webb, P. J.; Hamilton, J. F.; Lewis, A. C.; Wirtz, K. Formation of oxygenated-polycyclic aromatic compounds in aerosol from the photooxidation of o-tolualdehyde. Polycyclic Aromat. Compd. 2006, 26, 236–252. (37) Sadezky, A.; Winterhalter, R.; Kanawati, B.; R€ompp, A.; Spengler, B.; Mellouki, A.; Le Bras, G.; Chaimbault, P.; Moortgat, G. K. Oligomer formation during gas-phase ozonolysis of small alkenes and enol ethers: New evidence for the central role of the Criegee Intermediate as oligomer chain unit. Atmos. Chem. Phys. 2008, 8, 2667–2699. (38) Klotz, B.; Barnes, I.; Becker, K. H. Kinetic study of the gas-phase photolysis and OH radical reaction of E,Z- and E,E-2,4-hexadienedial. Int. J. Chem. Kinet. 1999, 31, 689–697. (39) Bejan, I.; Abd El Aal, Y.; Barnes, I.; Benter, T.; Bohn, B.; Wiesen, P.; Kleffmann, J. The photolysis of ortho-nitrophenols: A new gas phase source of HONO. Phys. Chem. Chem. Phys. 2006, 8, 2028–2035. (40) Kessler, S. H.; Nah, T.; Carrasquillo, A. J.; Jayne, J. T.; Worsnop, D. R.; Wilson, K. R.; Kroll, J. H. Formation of secondary organic aerosol from the direct photolytic generation of organic radicals. J. Phys. Chem. Lett. 2011, 2, 1295–1300. (41) US Environmental Protection Agency. Estimation Programs Interface (EPI) Suite v.3.20, 2007, http://www.epa.gov/oppt/exposure/pubs/episuitedl.htm (accessed October 7, 2011). (42) Bloss, C.; Wagner, V.; Jenkin, M. E.; Volkamer, R.; Bloss, W. J.; Lee, J. D.; Heard, D. E.; Wirtz, K.; Martin-Reviejo, M.; Rea, G.; Wenger, J. C.; Pilling, M. J. Development of a detailed chemical mechanism (MCMv3.1) for the atmospheric oxidation of aromatic hydrocarbons. Atmos. Chem. Phys. 2005, 5, 641–664.
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An Automated Platform for Phytoplankton Ecology and Aquatic Ecosystem Monitoring Francesco Pomati,†,* Jukka Jokela,†,‡ Marco Simona,§ Mauro Veronesi,§ and Bas W. Ibelings†,|| †
)
Department of Aquatic Ecology, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Seestrasse 79, 6047 Kastanienbaum, Switzerland ‡ Department of Environmental Sciences, Aquatic Ecology, Institute of Integrative Biology (IBZ), ETH-Z€urich, € berlandstrasse 133, 8600 D€ubendorf, Switzerland U § Istituto Scienze della Terra, IST-SUPSI, 6952 Canobbio, Switzerland Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands
bS Supporting Information ABSTRACT: High quality monitoring data are vital for tracking and understanding the causes of ecosystem change. We present a potentially powerful approach for phytoplankton and aquatic ecosystem monitoring, based on integration of scanning flow-cytometry for the characterization and counting of algal cells with multiparametric vertical water profiling. This approach affords high-frequency data on phytoplankton abundance, functional traits and diversity, coupled with the characterization of environmental conditions for growth over the vertical structure of a deep water body. Data from a pilot study revealed effects of an environmental disturbance event on the phytoplankton community in Lake Lugano (Switzerland), characterized by a reduction in cytometry-based functional diversity and by a period of cyanobacterial dominance. These changes were missed by traditional limnological methods, employed in parallel to high-frequency monitoring. Modeling of phytoplankton functional diversity revealed the importance of integrated spatiotemporal data, including circadian time-lags and variability over the water column, to understand the drivers of diversity and dynamic processes. The approach described represents progress toward an automated and trait-based analysis of phytoplankton natural communities. Streamlining of high-frequency measurements may represent a resource for understanding, modeling and managing aquatic ecosystems under impact of environmental change, yielding insight into processes governing phytoplankton community resistance and resilience.
’ INTRODUCTION Freshwater ecosystems are characterized by high levels of biodiversity, and are among the most threatened ecosystems on earth 1,2 (Millennium assessment: http://www.maweb.org). Understanding and managing environmental change in aquatic ecosystems is complicated by co-occurring and interacting stressors like climate change, eutrophication, and pollution that, for example, can interact to favor harmful algal blooms.36 We suffer from a general lack of knowledge on the background rates and direction of change in pristine ecological systems, as well as in stressed ecological communities.7 These limits can hamper our ability to detect the signature of a range of anthropogenic impacts on ecosystems, or predict patterns of recovery. Phytoplankton communities are highly diverse and dynamic. They respond rapidly to climate change, eutrophication, and pollution, and play an important role in aquatic ecosystem biogeochemical processes.4,814 Phytoplankton density (algal blooms) and community composition (e.g., toxic cyanobacteria) are the prime agents impacting water quality, ecosystem and human r 2011 American Chemical Society
health,15 and have been suggested to be used as such for ecosystem assessment.1619 Monitoring, understanding, and predicting changes in structural (composition, diversity, evenness) and functional (phenotypic characteristics, growth rate, productivity) aspects of phytoplankton communities across space and over time represents however a challenge for aquatic ecology. The capturing of population dynamics, community succession and adaptation to environmental change requires: (1) high-frequency sampling to follow fast plankton fluctuations20 and potential chaotic dynamics;21 (2) vertical (depth) distribution of algal taxa and their physio-morphological characteristics (traits);22 (3) a functional, trait-based assessment of communities and ecosystems based on the characteristics of the organisms’ phenotypes
Received: June 7, 2011 Accepted: October 5, 2011 Revised: September 9, 2011 Published: October 07, 2011 9658
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Environmental Science & Technology that directly respond to environmental changes and determine effects on aggregated processes.13,23,24 The goal of this article is to present an integrated platform able to (1) provide automated high-frequency measurements of phytoplankton at different lake depths; (2) couple in situ biological monitoring with data about the physical environment; (3) provide a streamline of real-time data for modeling and forecasting phytoplankton dynamics. By integrating a Cytobuoy with an Idronaut vertical profiling system, we addressed the objective of increasing spatiotemporal resolution in field data collection. It has been proposed that scanning flow-cytometry, offered by instruments like the commercially available Cytobuoy, may offer advantages over microscopic methods for cell counting and classification of phytoplankton, including the possibility of automation and high frequency field measurements of phytoplankton physio-morphological characteristics.20,2527 A novel aspect of our monitoring approach, therefore, lays in the use of cytometrydata for a description of phytoplankton functional diversity and expressed phenotypic traits, which allow tracking phytoplankton responses at the functional group level. Trait-based approaches and functional groups are becoming increasingly important in understanding phytoplankton ecology.22,2830 In this study we tested our monitoring platform optimized for deep water bodies, designed to afford comprehensive data to study phytoplankton ecology and to improve water resource management. To support the validity of our approach we report the results form a monitoring campaign (spanning roughly one month in May 2010) during which automated measurements were coupled by fortnightly limnological data (physics, chemistry, and biology).31
’ MATERIALS AND METHODS Automated Monitoring Platform. Phytoplankton counting, characterization, and classification were performed using a scanning flow cytometer Cytobuoy (Woerden, The Netherlands), designed to analyze the full naturally occurring range from small (e.g., picoplankton) to large (e.g., colonial cyanobacteria) planktonic particles (1700 μm in diameter and a few mm in length) and relatively large water volumes (http://www.cytobuoy.com)25 (Supporting Information (SI) Figure S1-e). In our instrument, particles were intercepted by two laser beams (Coherent solidstate Sapphire, 488 and 635 nm, respectively, 15 mW) at the speed of 2 m s1. In this study, digital data acquisition was triggered by the sideward scatter (SWS) signal (908 nm). The light scattered at two angles, forward (FWS) and SWS, provided information on size and shape of the particles. The fluorescence (FL) emitted by photosynthetic pigments was detected as red (FLR), orange (FLO) and yellow (FLY) signals collected in the wavelength ranges of 668734 (chlorophyll-a, Chl-a), 601668 (phycocyanin and phycoerythrin), and 536601 nm (degraded pigments), respectively. Laser alignment and calibration processes were done before the monitoring campaign using yellow FL beads of 1 and 4 μm diameter. Our Cytobuoy allowed automatic acquisition of particles in time-intervals, time-specific measurement, and fixed-measurement on occurrence of a trigger signal (see below). This study was based on automated acquisition of 2 fixed-measurements for every trigger-signal received in order to optimize the detection and quantification of small and large particles in two separated analyzes, and on a scheduled time-specific background measurement per day with water being sampled at 25 m (no phytoplankton growth). Remote accessibility of the Cytobuoy via the
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Internet-UMTS network allowed unlimited data access and transmission rates along with increased location flexibility. Further technical details on our Cytobuoy, measuring settings and configurations are reported in the SI. In order to accomplish depth resolution, we employed a vertical profiling system made up of three integral parts: Controller Module (SI Figure S1-a,-b), Profiler Module (SI Figure S1-b), and OCEAN SEVEN 316Plus CTD (O7) multiparameter probe (SI Figure S1-c) (Idronaut, Brugherio, Italy, www.idronaut.it). The O7-probe was equipped with seven sensors: pressure, temperature (°C), conductivity (μS, absolute and at 20 °C), pH, oxygen (mg/L and % saturation), and NO3 (μg/L) (Idronaut). An external TriLux fluorimeter was interfaced with the O7 probe in order to quantify levels of Chl-a, phycoerythrin and phycocyanin (Chelsea Technologies Ltd., Surry, UK). More information on the Idronaut profiling system can be found in the SI. For automatic depth profiles, we allowed the Cytobuoy to accept an electric signal from the Idronaut Controller Module as a trigger to start the measurement cycle during O7 step-profiles. We ran two independent automatic monitoring programs, one with the Cytobuoy and one only with the O7-multiparameter probe, with separated profile settings and different monitoring frequencies. In this study we scheduled a step profile involving six depths—covering the entire photic zone—with the Cytobuoy (2, 4, 6, 8, 10, and 12 m) and a continuous profile with the O7-multiparameter probe from 1 to 20 m to be performed twice a day each, to catch diel variations in the temperature structure of the water column: the theoretical maximum and minimum daily stratification at 3 p.m. and 3 a.m. (12 h frequency), respectively. For step-profile phytoplankton measurements, we retrieved water from selected depths using an external pump (capacity 1 L min1), an antimicrobial silver-nanoparticle coated and shaded flexible polyethylene tubing (Flexelene, Eldon James Corp., Loveland, CO), and a surface plexiglass chamber (250 mL) from which the Cytobuoy subsamples through a needle injector (SI Figure S1-e). The pump was placed downstream from the chamber in order to avoid damaging algal cells or colonies prior to measurements. More information on structural components of the monitoring platform, how we integrated our instruments to achieve depth profiles, and an example of automated operation using the integrated system and maintenance details are reported in the SI. Sampling. The automated monitoring platform was moored in Lake Lugano, at a site protected from strong winds and currents and close to the location of the routine historic lake monitoring program (coordinates 45°570 33.4300 N, 8°520 53.4900 E) (SI Figure S2). This site is representative for the most eutrophic of the lake’s three distinct basins31 (SI Figure S2). Data presented in this article refer to the monitoring period from the 28th of April to the 31st of May 2010 (with six depths over the photic zone and a frequency of two profiles per day). Independent limnological data were collected at 300 m distance from the platform with a fortnightly frequency. They included physical characteristics of the whole water column, chemical analyses on algal nutrients and integrated phytoplankton samples (from 0 to 20 m). Additional information on these data can be found in the SI. For comparison between cytometry-based richness and phytoplankton species richness (Table 1, SI Figure S6) we used additional samples from Lake Lugano collected between June and December 2010 and data from a study conducted in Lake Zurich during spring 200932 (SI). 9659
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Table 1. Comparison of Selected Properties of Automated Measurements to Classical Phytoplankton Monitoring feature* number of samples year1 (n)
classical limnology 1218a
automated platform >700b
lag (Δ)
2 weeks 1 month
12 h
fundamental period (T0 = Δn)
12
>700
frequency (1/T0)
0.083
0.0014
nyquist frequency (1/2Δ), highest
12 months (612 cycles year1)
24 h (365 cycles year1)
possible frequency resolution of depth gradient
from 1 integrated to 10 samples over photic zone
from 6 to 12 samples over photic zone
phytoplankton density and physiomorphological traits
estimated from ca. 200500 counts/in 100200 mL
from ca. 30,000 counts/in 100400 μL volume
number of descriptors measured per individual
2 (size, volume)
54 (3D descriptors, pigment type, concentration etc.)
estimation of diversity
taxonomic, functional
Functional
number of taxa groups
14 to 61 per samplec
NA
number of functional groups
5 to 20 per samplec
4 to 53 per samplec
reproducibility/repeatability of data
lowd
high 27e
a
Considering one sample per month plus an extra fortnightly sample during productive seasons as in refs 14 and 31 (SI). b The automated system is currently producing data series across seasons. c Range in number of species and functional groups during intercalibration performed in Lake Zurich and Lake Lugano: Reynolds categories29 were utilized for functional grouping of microscopically identified species, for Cytobuoy-derived functional groups see the Materials and Methods, for a plot of Cytobuoy-derived versus taxonomic diversity see SI Figure S6, d Quality assessment trials highlighted that phytoplankton microscopic counts can be difficult to reproduce across laboratories since they rely on human subjective assessment, biased by the experience/ability/condition of the operator, and that they suffer from low repeatability (high differences between replicated samples) (http://www. planktonforum.eu)26,50 (SI); e Five consecutive-replicated sampling cycles were performed in this study at the same depth and data assessed by canonical discriminate function analysis (SI). * From ref 34.
Data Analysis. Data manipulation, analysis and graphics were performed in the R programming language (www.r-project.org). The Cytobuoy provided 54 descriptors of 3D structure and FL profile for each particle.25 Data sets also included original sampled volume, date, time, and depth at which particles were taken. We visually inspected the distribution of raw data with regards to FL signals and set database-specific threshold levels to divide fluorescent (FL) from non-FL particles. The overall FL and nonFL databases comprised 1 and 5 million particles, respectively. Cytobuoy particle descriptors were standardized to zero mean and unit variance and, by principal component analysis, reduced to 33 orthogonal vectors covering 99% of total variance in the data (data not shown). Principal components were utilized for grouping particles into functional categories using K-means clustering. We compared several K values and selected the optimal number of K based on the within groups sum of squares.33 Phytoplankton densities were calculated by inferring the number of cells from the number of humps in the SWS signal of each particle to account for colonial species.20,25 O7 sensor data were organized in a separated database. Cyanobacterial-like particles were identified based on FLO and FLR emissions after excitation by the 495 and 635 nm lasers, respectively, after visual inspection. These signals are expected as a response to the presence of the cyanobacterial-specific pigment phycocyanin.25 We modeled richness of Cytobuoy-derived functional groups of phytoplankton (response variables) in the upper 12 m of the water column based on high frequency environmental data (explanatory variables). Explanatory variables included: water parameters (mean of top 12 m), coefficient of variation (CV = SD/mean) of parameters over water-column and meteorological data at time-lag(0), -lag(1) (=24 h), and -lag(2) (=48 h). The response variables showed significant temporal autocorrelation only at time-lag(1) (data not shown). We therefore included for each model the response variable at time-lag(1) as explanatory, in order to account for temporal autocorrelation of data. All variables
were scaled in order to standardize effect sizes and let to compete in the same model. The best model was selected based on Akaike’s information criterion (AIC) with a stepwise procedure (alternation of forward selection and backward elimination of variables with p > 0.05).34 The relative importance of drivers was assessed by bootstrapping (999 times) the percentage contribution to the R2 of the model among the regressors, and extracting the relative 95% confidence intervals.
’ RESULTS AND DISCUSSION Phytoplankton Depth Heterogeneity. Our monitoring approach was able to reveal fine changes in the relative depth distribution of phytoplankton functional-group richness, Chl-a concentration and cell density with statistically significant differences between day and night profiles (SI, Figure S3S4). Similar data have been observed using flow-cytometry in oceanic profiles of phytoplankton communities.3537 We did not observe a significant difference in the vertical physical structure of the water column between day and night profiles (SI Figure S3S4), and limited changes between day and night airtemperatures during the study period (data not shown). Our data suggest that depth-specific day-night dynamics in phytoplankton community composition and abundance are driven by biological factors, rather than environmental changes (SI Results and Discussion). Temporal Phytoplankton Dynamics. The frequency and intensity of phytoplankton blooms are key elements for ecological status definition.16,17,19 Considering that most algal taxa can reach bloom conditions and disappear within a few days (implying a maximum oscillation frequency of 23 density peaks per week), a minimum sampling frequency of 46 times per week would be needed to follow algal dynamics (Nyquist frequency, Table 1) and quantify their intensity adequately.20 9660
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Figure 1. Automated measurements of phytoplankton density, diversity and associated changes in environmental heterogeneity. (A) Phytoplankton abundance (from Cytobuoy, solid line) compared to microscopic counts (black square), abundance of non-FL particles (dashed line, scaled to fit graph by dividing values by 250) and Chl-a concentration (from O7-probe, gray line); (B) Richness of Cytobuoy-based functional groups (black line) compared to microscopic species counts (black square), and Pielou’s evenness (Shannon-diversity/Log(species richness)) of groups (gray line) compared to the same index derived by microscopic counts (gray square); C) CV over the water column in temperature (black line) and conductivity at 20 °C (gray line). The CV can be used as a proxy of environmental (depth) heterogeneity.14 In (A) and (B), data represent the average of the top 12 m of the water column. The gray vertical line highlights the mixing event.
Our automated monitoring platform was able to perform 2 vertical profiles per day (at a fixed depth the maximum frequency could be of six samples per hr). Figure 1 reports results from daily monitoring samples (time is 3 pm, frequency = 1 day1) during the study. This frequency was capable of capturing fine fluctuations in FL particle density (phytoplankton) and total Chl-a concentration over the water column (Figure 1A). Our data were comparable to previous work using flow-cytometry in the field in terms of temporal resolution on algal dynamics (ref 27 and literature therein). Measured phytoplankton density was comparable with microscopic counts and correlated well with Chl-a concentration levels (Figure 1A) (R2-adjusted = 0.651, p = 4.32408), as also reported elsewhere.32 Our system was able to follow dynamics of non-FL particles (suspended solids, dead
cells, heterotrophic bacteria), which did not correlate with algal cell concentrations apart from a short period in the middle of the time-series (days 1518) (Figure 1A). Previous work using flow cytometry in phytoplankton aimed at identifying broad functional groups (such as picoeukaryotes, microalgae, cyanobacteria, etc.) and some phytoplankton species with clearly distinguished morphology or pigmentation (such as Pseudonitzschia, Cryptomonas, Synura, Dinobryon)20,25,27,38 (and literature therein). This type of analysis lacked a proper measure of diversity. We used the Cytobuoy to describe key phytoplankton traits like size, coloniality, pigment type, and content, which we used to create groups of functionally similar individuals.29,30 The possibility of monitoring individually measured phytoplankton physio-morphological descriptors may offer the best prospects in 9661
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Environmental Science & Technology terms of objectivity, reproducibility, functional properties and prediction of algal assemblages.22,23,30 The number of Cytobuoyderived functional groups was comparable with the total number of species detected in the photic zone of the water column (Figure 1B, SI Figure S6), as also reported elsewhere.32 Generally, the number of functional groups in a community is smaller than the number of species, since in current functional classification methods more than one species can be assigned to the same functional category.29,30 With our trait-based approach, however, it is also possible that individuals of the same species can be allocated to different functional groups based on their expressed morphology (for example, colonial species can be assigned to two different groups depending on whether they are present as single cells or colonies). The Cytobuoy description of the relative abundance of phytoplankton functional groups deviated from microscopically measured evenness (Figure 1B). This could be caused by superior precision of automated density measurements, and to the fact that the identity (and abundance) of Cytobuoy-derived functional groups does not fully reflect the identity (and abundance) of microscopically defined taxonomic groups as reported above (several species can map into one functional category and individuals of the same species can be assigned to different groups). We observed a strong decrease in phytoplankton functional richness and evenness in the middle of the time-series (Figure 1B), followed by a short recovery period that led to higher cell density (Figure 1A). These dynamics were completely missed by the fortnightly limnological sampling (Figure 1). Our approach offered the advantage of having automated measurements of environmental conditions for the observed algal dynamics (SI Figure S7). Six days of rainy and stormy weather (SI Table S1) were associated with a period of low phytoplankton diversity and productivity (with high levels of non-FL particles), and a decrease in CV in temperature and conductivity over the first 12 m of the water column. This eventually led to a mixing event on day 19 (Figure 1C, SI Figure S7). The phytoplankton community in the days preceding disturbance (started at day 5) showed a gradual decline, reaching the minimum of evenness and richness just before the mixing event (on days 17 and 18, respectively). The mixing event re-established evenness in the community that fully recovered functional diversity in 6 days (Figure 1BC). Functional diversity, as opposed to taxonomic diversity, appears to be a better predictor of ecosystem functioning across a range of communities and measures of functional diversity may afford a better description of the functionality of the ecosystem and its resilience to disturbance.12,13,23,24,39 Using Cytobuoy-Derived Phytoplankton Traits. Our approach allows tracking phytoplankton physio-morphological characteristics such as cell size and shape (which influence motility and nutrient uptake through surface/volume ratio), photosynthetic performance (driven by pigment type and concentration), active nutrient uptake and coloniality.22 Cell size and photosynthetic performance are key phytoplankton traits, affecting growth, metabolism, access to resources, susceptibility to grazing, and are extremely plastic responding to the environment and to species interactions.22,32 Analysis of dynamics and distributions of these focal phytoplankton traits could improve our forecasting capabilities of community structure and ecosystem functions.12,13,24,39 Pigment profiles can also be used to specifically target certain phytoplankton groups of interest in their spatiotemporal dynamics.20,25 We report temporal changes in mean and variance of phytoplankton size and suspended non-FL particles size (Figure 2A, SI
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Figure 2. Using phytoplankton traits such as size and pigment content to track community changes. A) Average size of FL (phytoplankton; black line) and non-FL (suspended solids, bacteria, dead cells; gray line) particles; B) Ratio between concentrations of phycocyanin and Chl-a (black line) and abundance of cyanobacterial-like cells (gray line) compared to microscopic counts of cyanobacteria (/). Phycocyanin is a cyanobacterial-specific pigment: the ratio between phycocyanin and Chl-a concentrations can be used as an indication of the dominance of cyanobacteria in the phytoplankton community. Data represent the average of the top 12 m of the water column. The gray vertical line highlights the mixing event.
Figure S8). In addition, we tracked the dynamics in abundance of cyanobacteria using Cytobuoy data and phycocyanin/Chl-a concentration ratios obtained with the O7-probe (Figure 2B). Shortly before “disturbance” (days 1517), a period characterized by low diversity and productivity (Figure 1), the study site was dominated by large cyanobacterial colonies (Figure 2A and B). Mean water column cyanobacterial density obtained by the Cytobuoy was almost identical to microscopic count levels (Figure 2B) and was likely associated with the presence of Planktothrix rubescens filaments (SI Table S2). The mixing event rapidly and dramatically reduced cyanobacterial abundance and the average size of the phytoplankton community (Figure 2).40 Variation in the dimensions of non-FL particles appeared to be very small compared to the dynamics in phytoplankton size (note the y-axis scales in Figure 2A). Compared to conditions before the disturbance, the final days of our time-series were characterized by smaller size phytoplankton cells (Figure 2A), probably eukaryotic nanoplankton of genera Stephanodiscus and Melosira (SI Table S2), dominating a more productive (Figure 1A) and diverse community (Figure 1B, SI Figure S8). Our approach introduces the possibility of monitoring a large number of phytoplankton individuals and their traits per population or through the entire community. Individuals and populations should be the basic units of investigation to assess the status of communities and ecosystems, since they respond phenotypically 9662
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Table 2. Multiple Linear Regression Model Describing Phytoplankton Richness (Cytobuoy-Derived Functional Groups) in Terms of Changes in Environmental Conditions over the Period of Study 95% confidencec drivera
coefficient p-value percentage of R2b lower upper
Air T-lag(1)
0.906
0.0000
22.7
0.113 0.277
Cond.-lag(1)
0.266
0.0282
16.3
0.067 0.230
Cond.-lag(2)
0.589
0.0000
15.7
0.096 0.193
CV-Cond.-lag(1)
0.751
0.0000
10.8
0.063 0.142
pH-lag(2)
0.709
0.0000
10.2
0.058 0.168
0.286 0.544
0.0000 0.0143
4.5 4.2
0.032 0.064 0.042 0.066
N-NO3 CV-pH-lag(1) N-NO3-lag(2)
2.246
0.0001
3.9
0.034 0.047
1.394
0.0000
3.8
0.025 0.073
Water T-lag(1)
0.932
0.0010
2.7
0.022 0.037
N-NO3-lag(1)
1.519
0.0035
2.6
0.031 0.037
CV-NO3-lag(1)
0.534
0.0097
1.7
0.016 0.040
Light -lag(1)
0.203
0.0012
0.9
0.015 0.079
CV-Water T-lag(1)
Drivers: T = temperature (°C); Cond. = conductivity at 20 °C; CV = coefficient of variation over the sampled depths; Light = maximum irradiance (W/m2); lag(1) and (2) = time-lag 24 and 48 h, respectively. b Drivers are ordered based on their relative contribution to the R2 of the model, expressed as percentage of total. c Confidence intervals refer to the bootstrapped relative contribution to the R2 of the model. a
(and genetically) to disturbance or stress and eventually evolve altering community processes and ecosystem functioning.41 Modeling High-Frequency Phytoplankton Dynamics. Our automated monitoring approach allows to better couple environmental forcing with phytoplankton community dynamics, in particular at the functional level (which may relate to crucial ecosystem services13,24,42). Using data from the period of study, we modeled the Cytobuoy-based phytoplankton functional richness in order to provide an example of how spatiotemporal measurements of environmental conditions, coupled with biological data, can provide insight into drivers of community responses and changes. Temperature (both atmospheric and water), conductivity (whose main contributors were carbonate and bicarbonate ions) and the heterogeneity of environmental conditions over the water column appeared to be the most important drivers of phytoplankton functional richness (Table 2). Most of the drivers appeared to influence the response variable with a time lag of 24 or 48 h (Table 2). Our modeling exercise highlights the importance of (i) time-lags between environmental change and response at the level of phytoplankton community, (ii) variability of parameters over the water column (depth heterogeneity), and (iii) in situ meteorological conditions for understanding and modeling phytoplankton community dynamics. Intensity of fluctuations and heterogeneity by depth in key environmental variables may represent fundamental factors to understand and predict changes in plankton diversity.14 The collection of the above type of high-resolution data would be intractable without the aid of an in situ automated monitoring station like the one presented in this study. A similar approach can be used to model and forecast cyanobacterial blooms.
Toward an Adaptive, Integrated Approach to Aquatic Ecosystem Monitoring. Monitoring frameworks that evolve
along with our improved knowledge of ecosystem processes would strongly benefit ecosystem health assessment and management by allowing to assess the impact of ongoing environmental change, to study recovery processes, and to built more reliable forecasting models.43 Sophisticated monitoring approaches like the one that we have developed can offer the spatiotemporal resolution and flexibility necessary to capture and model natural phytoplankton responses to disturbance or stress, or to test ecological and evolutionary hypotheses including the mechanisms that lead to stable coexistence of species. For example, high-frequency data afford the possibility of studying niche processes and environmental filters on diversity and trait distribution patterns,44,45 while tracking the vertical distribution of functional groups and their abundance allow testing for the importance of dispersal limitation among patches in the assembly of the phytoplankton community.46 Table 1 summarizes some of the properties of our automated data-series compared to traditional monitoring, including diel temporal resolution in phytoplankton community dynamics and water column structure over the photic zone of the lake (Figure 1, 2, SI Figures S3, S4, S7). We were not able to capture horizontal spatial heterogeneity of phytoplankton and the associated environment. The lack of spatial information across the water surface may be solved by integrating our platform data with remote sensing from satellites or from local devices that use spectral information reflected from the water surface47 (http:// www.waterinsight.nl). Depth represents however the most heterogeneous aspect of the phytoplankton spatial environment, and our vertical profiles may be crucial to understand and model the effects of disturbance, spatial heterogeneity and patch dynamics on phytoplankton community structure.48,49 Several phytoplankton groups are in fact capable of vertically migrating in the water column being motile (e.g., dinoflagellates) or able to regulate buoyancy (e.g., cyanobacteria).29 Depth resolution is therefore essential to track algal populations, which can be defined as groups of similar organisms (for example belonging to the same cytometry-derived cluster) that coexist at the same time in the same water layer. The bottleneck in monitoring natural systems is the development of automated technologies for the identification and counting of organisms.20,27,50,51 Our description of phytoplankton richness obtained by cluster analysis of automated flowcytometry data appeared to closely match the taxonomic richness derived by microscopic analysis (Table 1, SI Figure S6). Technical repeatability and across-lab reproducibility currently represent disadvantages of classical microscopic counts. An automated monitoring station like the one that we developed may offer the objectivity and reproducibility of a standardized measuring system that (1) reduces human error; (2) affords a detailed description of individual algal features; (3) provides high data complexity; and (4) increase spatiotemporal resolution compared to manmade monitoring campaigns (Table 1).20,50 The temporal and spatial monitoring scales of our analysis (Table 1) were roughly equivalent since both of them reflected processes operating over day-night cycles across the water column. The benefits of an integrated spatiotemporal approach to monitoring include52 (i) accounting for spatiotemporal coexistence mechanisms that purely spatial or temporal approaches would miss; (ii) generating new hypotheses and allowing rigorous testing of theoretical models; (iii) improving our descriptive 9663
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Environmental Science & Technology power for developing forecasting models; and (iv) optimizing monitoring strategies by choosing appropriate scales for sampling. A fine spatiotemporal resolution with regards to organisms and the environment may represent a critical resource for scientists and stakeholders challenged by understanding, modeling, and managing aquatic ecosystems.1719 The approach presented here can be applied to both freshwater and marine ecosystems, and to both natural and engineered environments such as drinking water reservoirs, water-treatment, and aquaculture plants.
’ ASSOCIATED CONTENT
bS
Supporting Information. Extended Materials and Methods and Results and Discussion Sections, Figures S1S8, and Tables S1S2. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Phone +41 58 765 2174; fax +41 58 765 2162; E-mail: francesco.
[email protected].
’ ACKNOWLEDGMENT This research was funded by the Swiss National Science Foundation (SNSF) R’Equip program (project no. 316030_121331 to B.W.I.), Eawag (to F.P.), and Schure-Beijerinck Popping Fonds (to B.W.I.). We are grateful to Referees for their constructive comments and to D. Steiner, M. Schurter, Idronaut, Cytobuoy, and Chavanne Bootswerft for technical support and advice. ’ REFERENCES (1) Abell, R.; Allan, J. D.; Lehner, B. Unlocking the potential of protected areas for freshwaters. Biol. Conserv. 2007, 134 (1), 48–63. (2) Williamson, C. E.; Saros, J. E.; Schindler, D. W. Climate change: Sentinels of change. Science 2009, 323 (5916), 887–888. (3) Mooij, W. M.; H€ulsmann, S.; De Senerpont Domis, L. N.; Nolet, B. A.; Bodelier, P. L. E.; Boers, P. C. M.; Dionisio Pires, L. M.; Gons, H. J.; Ibelings, B. W.; Noordhuis, R.; Portielje, R.; Wolfstein, K.; Lammens, E. H. R. R. The impact of climate change on lakes in the Netherlands: A review. Aquat. Ecol. 2005, 39 (4), 381–400. (4) Paerl, H. W.; Scott, J. T. Throwing fuel on the fire: Synergistic effects of excessive nitrogen inputs and global warming on harmful algal blooms. Environ. Sci. Technol. 2010, 44 (20), 7756–7758. (5) Jak, R. G.; Maas, J. L.; Scholten, M. C. T. H. Ecotoxicity of 3,4dichloroaniline in enclosed freshwater plankton communities at different nutrient levels. Ecotoxicol. 1998, 7 (1), 49–60. (6) Pomati, F.; Neilan, B. A.; Suzuki, T.; Manarolla, G.; Rossetti, C. Enhancement of intracellular saxitoxin accumulation by lidocaine hydrochloride in the cyanobacterium Cylindrospermopsis raciborskii T3 (Nostocales). J. Phycol. 2003, 39 (3), 535–542. (7) Magurran, A. E.; Baillie, S. R.; Buckland, S. T.; Dick, J. M.; Elston, D. A.; Scott, E. M.; Smith, R. I.; Somerfield, P. J.; Watt, A. D. Long-term datasets in biodiversity research and monitoring: Assessing change in ecological communities through time. Trends Ecol. Evol. 2010, 25 (10), 574–582. (8) Johnston, E. L.; Roberts, D. A. Contaminants reduce the richness and evenness of marine communities: A review and meta-analysis. Environ. Pollut. 2009, 157 (6), 1745–1752. (9) Downing, A. L.; DeVanna, K. M.; Rubeck-Schurtz, C. N.; Tuhela, L.; Grunkemeyer, H. Community and ecosystem responses to a pulsed
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Environmental Science & Technology (29) Reynolds, C. S.; Huszar, V.; Kruk, C.; Naselli-Flores, L.; Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 2002, 24 (5), 417–428. (30) Kruk, C.; Huszar, V. L. M.; Peeters, E. T. H. M.; Bonilla, S.; € Costa, L.; LURling, M.; Reynolds, C. S.; Scheffer, M. A morphological classification capturing functional variation in phytoplankton. Freshwater Biol. 2010, 55 (3), 614–627. (31) UPDA (Ufficio Protezione e Depurazione Acque). Campagna 2007 e Rapporto quinquennale 20032007. In Ricerche sull’evoluzione del Lago di Lugano. Aspetti limnologici; Commissione Internazionale per la Protezione delle Acque Italo-Svizzere, 2008. (32) Pomati, F.; Posch, T.; Kraft, N. J. B.; Eugster, B.; Jokela, J.; Ibelings, B. W. Trait-based analysis of natural phytoplankton communities: spring bloom dynamics in Lake Zurich (Switzerland). In preparation. (33) Kaufman, L.; Rousseeuw, P. J., Finding Groups in Data: An Introduction to Cluster Analysis; Wiley: New York, 1990. (34) Legendre, P.; Legendre, L. Ecological data series. In Numerical ecology; Legendre, P., Legendre, L., Eds.; Elsevier, 1998; pp 637705. (35) Durand, M. D.; Olson, R. J. Contributions of phytoplankton light scattering and cell concentration changes to diel variations in beam attenuation in the equatorial pacific from flow cytometric measurements of pico-, ultra and nanoplankton. Deep-Sea Res., Part II 1996, 43 (46), 891–906. (36) Vaulot, D.; Marie, D. Diel variability of photosynthetic picoplankton in the equatorial Pacific. J. Geophys. Res., C: Oceans Atmos. 1999, 104 (C2), 3297–3310. (37) Binder, B. J.; DuRand, M. D. Diel cycles in surface waters of the equatorial Pacific. Deep-Sea Res., Part II 2002, 49 (1314), 2601–2617. (38) Li, W. K. W. From cytometry to macroecology: A quarter century quest in microbial oceanography. Aquat. Microb. Ecol. 2009, 57 (3), 239–251. (39) Litchman, E.; de Tezanos Pinto, P.; Klausmeier, C. A.; Thomas, M. K.; Yoshiyama, K. Linking traits to species diversity and community structure in phytoplankton. Hydrobiologia 2010, 653 (1), 15–28. (40) Walsby, A. E.; Avery, A.; Schanz, F. The critical pressures of gas vesicles in Planktorhrix rubescens in relation tothe depth of winter mixing in Lake Z€urich, Switzerland. J. Plankton Res. 1998, 20 (7), 1357–1375. (41) Harmon, L. J.; Matthews, B.; Des Roches, S.; Chase, J. M.; Shurin, J. B.; Schluter, D. Evolutionary diversification in stickleback affects ecosystem functioning. Nature 2009, 458 (7242), 1167–1170. (42) de Bello, F.; Lavorel, S.; Díaz, S.; Harrington, R.; Cornelissen, J.; Bardgett, R.; Berg, M.; Cipriotti, P.; Feld, C.; Hering, D.; Martins da Silva, P.; Potts, S.; Sandin, L.; Sousa, J.; Storkey, J.; Wardle, D.; Harrison, P. Towards an assessment of multiple ecosystem processes and services via functional traits. Biodiversity Conserv. 2010, 19 (10), 2873–2893. (43) Lindenmayer, D. B.; Likens, G. E. Adaptive monitoring: a new paradigm for long-term research and monitoring. Trends Ecol. Evol. 2009, 24 (9), 482–486. (44) Chesson, P. Mechanisms of maintenance of species diversity. Ann. Rev. Ecol. Syst. 2000, 31, 343–366. (45) Kraft, N. J. B.; Valencia, R.; Ackerly, D. D. Functional traits and niche-based tree community assembly in an Amazonian forest. Science 2008, 322 (5901), 580–582. (46) Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography; Princeton University Press: Princeton, 2001. (47) Vos, R. J.; Hakvoort, J. H. M.; Jordans, R. W. J.; Ibelings, B. W. Multiplatform optical monitoring of eutrophication in temporally and spatially variable lakes. Sci. Total Environ. 2003, 312 (13), 221–243. (48) Fukami, T. Community assembly dynamics in space. In Community Ecology: Processes, Models and Applications; Verhoef, H. A., Morin, P. J., Eds.; Oxford University Press: Oxford, 2010; pp 4554. (49) Colwell, R. K.; Rangel, T. F. Hutchinson’s duality: The once and future niche. Proc. Natl. Acad. Sci. 2009, 106 (2), 19651–19658. (50) MacLeod, N.; Benfield, M.; Culverhouse, P. Time to automate identification. Nature 2010, 467 (7312), 154–155. (51) Preston, C. M.; Marin, R.; Jensen, S. D.; Feldman, J.; Birch, J. M.; Massion, E. I.; DeLong, E. F.; Suzuki, M.; Wheeler, K.; Scholin, C. A. Near real-time, autonomous detection of marine bacterioplankton
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on a coastal mooring in Monterey Bay, California, using rRNA-targeted DNA probes. Environ. Microbiol. 2009, 11 (5), 1168–1180. (52) White, E. P.; Ernest, S. K. M.; Adler, P. B.; Hurlbert, A. H.; Lyons, S. K. Integrating spatial and temporal approaches to understanding species richness. Philos. Trans. R. Soc., B 2010, 365 (1558), 3633–3643.
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Effect of Particle Size on Droplet Infiltration into Hydrophobic Porous Media As a Model of Water Repellent Soil Christopher A. E. Hamlett,† Neil J. Shirtcliffe,†,§ Glen McHale,† Sujung Ahn,‡ Robert Bryant,‡ Stefan H. Doerr,‡ and Michael I. Newton.*,† † ‡
School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, United Kingdom College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, United Kingdom
bS Supporting Information ABSTRACT: The wettability of soil is of great importance for plants and soil biota, and in determining the risk for preferential flow, surface runoff, flooding,and soil erosion. The molarity of ethanol droplet (MED) test is widely used for quantifying the severity of water repellency in soils that show reduced wettability and is assumed to be independent of soil particle size. The minimum ethanol concentration at which droplet penetration occurs within a short time (e10 s) provides an estimate of the initial advancing contact angle at which spontaneous wetting is expected. In this study, we test the assumption of particle size independence using a simple model of soil, represented by layers of small (∼0.22 mm) diameter beads that predict the effect of changing bead radius in the top layer on capillary driven imbibition. Experimental results using a three-layer bead system show broad agreement with the model and demonstrate a dependence of the MED test on particle size. The results show that the critical initial advancing contact angle for penetration can be considerably less than 90° and varies with particle size, demonstrating that a key assumption currently used in the MED testing of soil is not necessarily valid.
’ INTRODUCTION The wettability of soil is of great importance for plants and soil biota, and in determining the risk for preferential flow, surface runoff, flooding and soil erosion.13 There are a range of distinctive environmental conditions that can give rise to water repellent soil. It is well established that fires can volatilize hydrophobic compounds in the vegetation, litter or soil and these vapors can then condense on the sandy particles producing a hydrophobic granular texture that can exhibit high levels of water repellency. Under these circumstances vegetation recovery can be delayed, which further increases rates of surface runoff and erosion, and, on some slopes, the risk of debris flows.4 Where land with naturally high levels of water repellency, such as eucalyptus forest, is cleared for farming, productivity can be affected. This can be alleviated, but only at significant cost to farmers, by mixing in substantial amounts of clay.5 Where gray water is used for irrigation, or soil has been used as a natural filter for wastewater disposal, crop productivity can be significantly reduced due to the gradual increase in soilwater repellency.6 A less benign origin of soil or sediment water repellency is from hydrocarbon contamination. Such contaminated sites can show a long-term persistence in water repellency, which can sometimes re-establish itself after attempts to remediate the land.7 In these types of situations it is important to be able to monitor and classify water repellency. The molarity of ethanol droplet (MED) test,1 which is sometimes referred to as the critical surface tension2 and %ethanol3 r 2011 American Chemical Society
test is used widely to determine the severity of water repellency for soil and other porous or granular samples. It involves placing drops of aqueous ethanol solutions with decreasing surface tension on to different areas of the sample surface until a solution of sufficiently low surface tension is reached that just allows the drops to penetrate the soil within 310 s. The molarity (MED), concentration (%ethanol) of the solution, or the surface tension allowing the porous surface to be penetrated by the liquid, is then taken as being characteristic for that soil. This method has been shown to be quite reproducible and diagnostic of soilwater repellency, provided soil samples are reasonably dry, homogenized,and atmospheric conditions are controlled.3,810 The relationship between surface tension and the equilibrium contact angle, a concept that assumes there is contact angle hysteresis, is often described by Young’s equation. In the MED test it is assumed reducing the surface tension causes imbibition by reducing this contact angle to below 90° at which point a parallel walled capillary would spontaneously fill. The surface tension of the solution that just penetrates the soil, γc (i.e., the critical surface tension for penetration), has been used to estimate the average surface energy of the soil.2,11 It should be noted that the critical surface tension defined in this manner is not the same as Received: July 6, 2011 Accepted: October 19, 2011 Revised: October 18, 2011 Published: October 19, 2011 9666
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Environmental Science & Technology the critical surface tension often referred to within surface science and typically obtained from a Zisman plot by extrapolating the results of contact angle measurements using a range of liquids to give an estimated surface tension at which a smooth flat surface would be completely wetted.12,13 It has also been suggested14 that the (initial advancing) contact angle at the surface tension, which gives wetting into water repellent soil or other granular materials, is not 90° as often assumed, but is closer to 51° when the dominating forces are capillary,and a model of hexagonal close-packed spherical particles can be assumed. Experimental data suggested a contact angle of around 61°65° could describe the critical surface tension for penetration into water repellent sand. Given that the initial advancing contact angle for penetration can be considerably less than 90° according to these reports, it is important to assess whether the MED test is independent of particle size and whether there are consequences for the implied initial advancing contact angle. Here we extend this previous work by developing a model of the conditions under which a liquid will penetrate under capillary forces into a hexagonal symmetry pack of spherical particles (beads). This model uses a surface layer of beads that have a smaller radius than those upon which they rest, which themselves are in a close-packed arrangement. The model predicts that penetration will occur at a critical advancing contact angle for the liquid that depends on the ratio of the two sizes of beads. This implies that the advancing contact angle at the surface tension, which gives wetting into soil, can be above 51° when the surface layer of beads are smaller than subsequent layers. This implies that the MED test gives a critical advancing contact angle that is dependent on the arrangement of particles and their sizes. We then develop a systematic method of creating bead packs with the model geometry and use an MED test approach to assess the critical advancing contact angle at which ethanol solutions penetrate them. The experimental data is shown to follow the trend predicted by the model, but with penetration occurring at systematically lower concentrations corresponding to higher advancing contact angles. An implication of this work is that the MED test may give results that require subtle analysis to be able to classify the severity of water repellency for granular material such as soil.
’ GEOMETRIC MODEL To examine the effect of particle size on capillary driven droplet infiltration into hydrophobic soil and granular systems sandy soil particles can be modeled using packs of spherical particles (beads). For the transition from a system in which water does not penetrate to one which it does, one can imagine the top (surface) layer composed of beads of a radius r laying on top of close-packed beads having a larger radius R, thereby introducing a loose-packing element to the surface layer arrangement (Figure 1a and b). This allows the separation between surface particles and the distance from the top of particles in the surface layer to the top of particles in the layer below to be altered while retaining a hexagonal symmetry of the top layer arrangement. This symmetry can be visualized by imagining a pyramid (tetrahedral) arrangement defined by a bead in the top layer resting on the space defined by three close-packed beads of the layer below (Figure 1). The base-to-apex height of the pyramid can be found from the geometry of Figure 1a,b. From consideration of the surface free energy, the condition for capillary driven imbibition of a liquid into the beads is when the wetting front of
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Figure 1. Schematic representations of bead packing of a surface layer on top of a layer of close packed beads (side view (a) and top view (b)), equations relating the relevant lengths and configuration of bead beds investigated by MED tests (c).
an impinging liquid, touches the beads in the layer below; should this happen the liquid will then continue into the subsequent layers giving full penetration of the bead pack. This condition is met when the wetting front has an equilibrium position at a critical distance (dc) measured from the top of the beads in the top layer to the top of the beads in the layer below. Assuming that the liquid has a horizontal meniscus as it bridges between three adjacent beads defining a pore, this has an associated critical angle of contact (θc), at the liquid/solid/vapor phase boundary. The critical depth is determined by the comparative sizes of the beads in the top layer and subsequent layers of the bead pack provided only capillary forces are important and gravity can be ignored. From the geometry of Figure 1, the model predicts, 2sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3 2 R r 4 15 1 þ ð1Þ cos θc ¼ 4 r R 3 For eq 1 to be valid, capillary forces must dominate over gravity, and this requires the separations between beads to be significantly less than the capillary length of the liquid k1=(γ /Fg)1/2, where γ is the surface tension of the liquid, F is its density,and g = 9.81 ms2 is the acceleration due to gravity. In this model, any liquid with an advancing contact angle, θA, below that of the critical angle contact will penetrate into the bead pack. In the case of a uniform particle size throughout the particle bed (i.e., r/R = 1) the predicted critical angle is 50.73°, which is consistent with previous work reported in the literature.14,15
’ EXPERIMENTAL METHODS Glass beads of different sieve fractions in the size ranges 0.180.21 mm up to 1.82.0 mm (General Purpose Glass Microspheres, Whitehouse Scientific, a full list of sizes are included in the Supporting Information), comparable to sizes found in sandy soils, were immersed in HCl (30 vol. %) for 24 h and then rinsed with UHQ H2O (resistivity = 18 MΩ 3 cm1) and dried for 4 h at 110 °C. The hydrophilic glass beads were then immersed in chlorotrimethylsilane (CTMS) (2 vol.% in toluene; CTMS purchased from Aldrich) for 48 h at room temperature then rinsed with toluene and allowed to air-dry. CTMS was chosen because it provides a high contact angle to solutions of ethanol and persistent repellency on contact with the 9667
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angle (on CTMS modified glass surfaces). This can be achieved by creating interpolation equations through the data, θA ðcÞ ¼ 0:0000126c3 þ 0:0029998c2 0:090953c þ 87:022
ð2Þ
and 10 4 γ1 c þ 5:1594 108 c3 EtOH ðcÞ ¼ 1:2804 10
7:2619 106 c2 þ 6:5721 104 c þ 1:3954 102 ð3Þ Figure 2. Advancing contact angle (measured on CTMS modified glass slides) and surface tensions of ethanol solutions in water used for MED tests (Literature values shown are taken from ref 12).
liquid, thereby being suitable for MED type experiments seeking to determine the initial advancing contact angle. Bead packs were constructed by placing the glass beads into a triangular template etched into laser-cut acrylic sheet and agitated until the beads formed a close packed monolayer (Figure 1c). A layer (∼110 μm thick) of polyurethane adhesive (1A33, Humiseal) was applied to a glass microscope slide, which was then placed on top of the beads, removed and a thin triangular acetate frame placed around the beads. The adhesive was then cured at 80 °C for 16 h fixing this initial layer of beads in place and so providing a hexagonal packing symmetry for registration of subsequent layers of beads. A second acetate frame, with a slightly smaller triangular hole than the first, was then stuck to the first frame using double sided tape and a second layer of beads was poured into the frame and agitated until close packed. This layer of beads therefore registered with the first layer, but was loose and not fixed by any adhesive. This process was repeated for a third bead layer but this time using a frame of approximately half the bead thickness so the top of the third layer was exposed. This method of constructing a three-layer bead pack ensured a fixed base layer with a hexagonal close-packed structure acting as a template to ensure registration of subsequent layers of beads, which themselves did not include adhesive bonding that might interact with a penetrating liquid in the experiments (Figure 1). We also conducted tests with bead packs with more than three layers (top layer with beads of radius r and other layers with beads of radius R) and did not find any significant differences in the ethanol concentrations at which penetration began. The surface tensions, γetOH, of ethanol solutions, for use in the MED tests, were measured using a Du Nouy ring at 25 °C. The corresponding advancing contact angle, θA, on a CTMS treated flat glass microscope slide was measured using a Kr€uss DSA 10 contact angle meter by depositing a droplet of ethanol solution and increasing its volume to 20 μL at a rate of 20 μL 3 min1. The observed contact angle was measured using Kr€uss DSA software and the value just prior to the droplet’s contact line moving was taken as the advancing contact angle. The surface tension of the ethanol solutions were consistent with those reported in the literature16 and exhibit a range of advancing contact angles sufficient to investigate a range of r/R values of up to 1 (θc = 50.73°)14,15 (Figure 2). Since an MED test uses a range of concentrations of ethanol to estimate the advancing contact angle at which an ethanol solution just penetrates a porous system it is useful to be able to transform numerically from ethanol concentration, c, to surface tension or advancing contact
where c is the ethanol concentration by volume percentage, θA is the advancing contact angle in degrees and γEtOH1 is the inverse surface tension in units of m mN1. The accuracy of these interpolation formulas compared to the data is shown in the Supporting Information and the surface tension interpolation predicts the data of Vasquez et al.16 to within 0.5%. An important parameter for knowing whether capillary forces dominate over gravitational forces is the capillary length, k1. In the MED test, both the surface tension and the density vary with ethanol concentration and so we also measured the changes in density and have constructed an interpolation formula for the (inverse) capillary length, kEtOH ðcÞ ¼ 3:9315 100 c4 þ 1:1489 106 c3 1:2949 104 c2 þ 7:6214 103 c þ 0:37077 ð4Þ where eq 4 gives the inverse capillary length in units of mm1. Thus, for a 0% v/v concentration of ethanol (i.e., pure water), the capillary length is k1 = 2.70 mm and as ethanol concentration increases the capillary length reduces, e.g, at 20% v/v ethanol the capillary length is k1 = 2.08 mm. MED tests were carried out by placing a single droplet (8 18 μL) of aqueous ethanol solution at various concentrations onto a bead pack with the use of a syringe controlled by a stepper motor. The imbibition time of the droplet into the bead pack was measured by the video system of the goniometer (frame rate of up to 25 fps). The drop volume depended on the concentration of EtOH in solution and a single droplet was used per MED test. To estimate the lowest concentration at which a solution penetrated within 10 s, we constructed a plot of imbibition time versus concentration of ethanol (see example MED curves in the Supporting Information). Steps of 5% in concentration were used over the wider range and this was narrowed to steps of 3% around the concentrations at which a solution penetrated a bead pack. For each bead pack we observed a step-like transition curve in whether or not penetration occurred as the ethanol concentration was increased. The value of concentration at the transition to imbibition provides estimates of the initial advancing contact angle, θAc, and the surface tension, γetOHc, for penetration via the interpolation eqs 2 and 3. The plots together with the interpolation equations also allow the uncertainty of these values to be quantified. The measured value of θAc can then be compared to the critical angle of contact, θc, from eq 1 predicted by the model on the basis of the relative bead sizes.
’ RESULTS AND DISCUSSION Figure 3 shows the measured threshold ethanol concentrations for penetration as a function of bead size for bead packs with r/R = 1 (i.e., top layer beads having the same diameter as the 9668
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Figure 3. Ethanol concentration and advancing contact angles for penetration as determined by MED tests, of various 3 layer bead packs and loose packed beds with r/R = 1. Solid circles show where bead lifting was observed and open circles where it was not observed. Solid diamond symbols show data from thick random loose-packed beds.
lower two layers) thereby investigating whether absolute bead size matters; the secondary y-axis shows the equivalent values of advancing contact angle θAc as deduced from eq 2. This tests whether or not the assumption that capillary forces are dominant is valid for the various bead sizes used to obtain r/R = 1. As bead size reduces the capillary forces can be sufficiently strong that on contact with the lower surface of a droplet a bead is lifted up from the top layer of beads as the droplet surface is shaped by its surface tension. Bead lifting is caused by the strength of capillary forces relative to the force of gravity acting upon a loose bead and is related to the ability of droplets to encapsulate themselves with a shell of hydrophobic particles to create liquid marbles.17,18 The data for the three-layer close packed beads are shown as solid circles where bead lifting was observed and as open circles where it was not. In these experiments we also constructed and tested penetration of ethanol solutions into much thicker randomly packed beds of CTMS treated glass-beads from single size sieve fractions of beads, and these data points are shown as solid diamond symbols. From this figure we can see that the threshold ethanol concentration, and hence critical advancing contact angle θAc, increases as bead size increases. This is consistent with geometrical considerations for a hexagonal arrangement of spherical beads, which show that the radius of the meniscus, rgap, between three close-packed beads defining a pore at an angle of contact θc is rgap = 0.866R(1 rsinθc/0.866R) so that the assumption rgap/k 1,1 needed for gravity to be ignored fails as the bead size increases. At larger bead sizes the results show some scatter with contact angles between 65° and 85° and this probably arises from small imperfections in the shape and monodispersity of the beads creating defects in the hexagonal symmetry of the packing and, hence, larger gaps through which imbibition can commence. The results of the MED tests on close packed hydrophobic beads with a range of r/R values are shown in Figure 4 with a comparison to the theoretical model (dashed line) and fit to the data (solid line). Similar to Figure 3, eq 2 has been used to present that data with two y-axes thereby allowing a simple comparison between the percentage of ethanol and the equivalent advancing contact angle at which penetration occurs. This data contains data from two types of bead packs constructed using (i) only monodisperse beads and (ii) beads of sieved ranges (open circles where the beads showed no lifting and solid circles where bead lifting was observed). Details of the sieved ranges can
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Figure 4. Ethanol concentration and advancing contact angles for penetration into three-layer bead packs of varying r/R as determined by MED tests. Solid circles show where bead lifting was observed and open circles where it was not observed. The dashed line shows the theoretical curve and the solid line the fit to the experimental points.
be found in the Supporting Information. The trend of the data follows that of the theoretical curve with the value of the critical advancing contact angle θAc, decreasing significantly below 90° as the r/R ratio increases toward unity although the data tends to lie slightly above the theoretical curve. This may result from any defects in bead packing associated with variation in shape and/or size of adjacent beads within the packs producing defects and larger gaps between beads compared with model predictions and capillary length. The approach in this work uses a hexagonal symmetry packing model with hydrophobic spherical beads and is valid only under conditions where capillary forces are dominant. It does, however, provide a test of the prediction that under these conditions complete penetration occurs when the meniscus of the liquid advancing down the loose-packed surface layer comes into contact with particles from the layer below. This is a general principle for capillary driven penetration that should be applicable to other types of packing and to nonspherical particles. Should pores in the system approach in size to the capillary length or large defects with substantial pore size be present, penetration will occur at much lower ethanol concentrations. Similarly, the model does not include effects of a hydrostatic pressure. The model is intended to improve understanding of the MED test, which itself provides an estimate only of the initial advancing contact angle. In any system where the hydrophobicity of the particle coating changes over time on contact with water, the test will not provide an accurate indication of the length of time over which soil will remain repellent as indicated in previous work empirically8 and by direct observation of soil particle coatings.19 Similarly, prolonged exposure will lead to adsorbed vapors on particles and this may itself cause changes to the contact angle and hence lead to penetration of water. This work shows that the critical initial advancing contact angle for penetration into particle beds taken to represent sandy soil can be considerably less than 90° and varies with particle size. If soils of different particle size distributions are compared, the critical initial advancing contact angle θAc is likely to vary although some trends with both absolute size and packing of the layers can be expected. The results demonstrate that the widely held assumption11 that a liquid will just enter a porous substrate when it has a contact angle of 90° is not necessarily valid. This has important implications for evaluating the wettability 9669
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Environmental Science & Technology of soils and other granular materials. Some soils or granular materials previously classified as fully wettable, based on MED, surface tension or %ethanol tests, may in fact, exhibit a significant resistance to wetting, which in turn bear some of the environmental implications typically associated with the presence of water repellency.
’ ASSOCIATED CONTENT
bS
Supporting Information. (1) Table showing bead size and source, (2) example graph of MED data, (3) graph of data and interpolation for advancing contact angle as a function of ethanol concentration, (4) graph of data and interpolation for 1/surface tension as a function of ethanol concentration (5) graph of data and interpolation for 1/capillary length as a function of ethanol concentration. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Phone: +44 115 8483365; fax: +44 115 8486636; e-mail:
[email protected]. Present Addresses §
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(10) Roy, J. L.; McGill, W. B. Assessing soil water repellency using the molarity of ethanol droplet (MED) test. Soil Sci. 2002, 167 (2), 83–97. (11) Letey, J.; Carrillo, M. L. K.; Pang, X. P. Approaches to characterize the degree of water repellency. J. Hydrol. 2000, 231, 61–65. (12) Zisman, W. A. Relation of equilibrium contact angle to liquid and solid constitution. In Contact Angle Wettability and Adhesion. Advances in Chemistry Series; R. F.. Gould, ed.; American Chemical Society: Washington, DC, 1964; Vol. 43, pp 151. (13) Adamson, A. W. & Gast, A. Physical Chemistry of Surfaces; Wiley-Blackwell, 1997. (14) Shirtcliffe, N. J.; McHale, G.; Newton, M. I.; Pyatt, F. B. Critical conditions for the wetting of soils. Appl. Phys. Lett. 2006, 89 (9), 094101. (15) Ban, S.; Wolfram, E.; Rohrsetzer, S. The condition of starting of liquid imbibition in powders. Colloids Surf. 1987, 22 (24), 301–309. (16) Vazquez, G.; Alvarez, E.; Navaza, J. M. Surface tension of alcohol plus water from 20 degrees C to 50 degrees C. J. Chem. Eng. Data. 1995, 40 (3) 611614. (17) McHale, G.; Shirtcliffe, N.,J.; Newton, M.,I.; Pyatt, F.,B.; Doerr, S. H. Self-organization of hydrophobic soil and granular surfaces. Appl. Phys. Lett. 2007, 90 (5), 054110. (18) McHale, G.; Newton, M.,I. Liquid marbles: principles and applications. Soft Matter 2011, 7 (12), 5473–5481. (19) Cheng, S.; Bryant, R.; Doerr, S. H.; Wright, C. J.; Williams, R. Investigation of surface properties of soil particles and model materials with contrasting hydrophobicity using atomic force microscopy. Environ. Sci. Technol. 2009, 43 (17), 6500–6506.
Now at Rhine-Waal University of Applied Sciences, Germany.
’ ACKNOWLEDGMENT We thank the UK Engineering and Physical Sciences Research Council (EPSRC) for funding CAEH, SA and NJS under grants EP/H000704/1, EP/H000747/1 and EP/E063489/1 respectively. ’ REFERENCES (1) King, P. M. Comparison of methods for measuring severity of water repellence of sandy soils and assessment of some factors that affect its measurement. Aust. J. Soil Res. 1981, 19 (4), 275–285. (2) Watson, C. L.; Letey, J. Indices for characterizing soil-water repellency based upon contact angle-surface tension relationships. Soil Sci. Soc. Am. J. 1970, 34 (6), 841–844. (3) Dekker, L. W.; Ritsema, C. J. How water moves in a water repellent sandy soil. 1. Potential and actual water repellency. Water Resour. Res. 1994, 30 (9), 2507–2517. (4) DeBano, L. F. The role of fire and soil heating on water repellency in wildland environments: A review. J. Hydrol. 2000, 231, 195–206. (5) Cann, M. A. Clay spreading on water repellent sands in the South East of South Australia—Promoting sustainable agriculture. J. Hydrol. 2000, 231, 333–341. (6) Mataix-Solera, J.; García-Irles, L.; Morugan, A.; Doerr, S. H.; Garcia-Orenes, F.; Arcenegui, V.; Atanassova, I. Longevity of soil water repellency in a former wastewater disposal tree stand and potential amelioration. Geoderma 2011, 165 (1), 78–83. (7) Roy, J. L.; McGill, W. B. Investigation into mechanisms leading to the development, spread and persistence of soil water repellency following contamination by crude oil. Can. J. Soil Sci. 2000, 80 (4), 595–606. (8) Doerr, S. H. On standardizing the ’water drop penetration time’ and the ’molarity of an ethanol droplet’ techniques to classify soil hydrophobicity: A case study using medium textured soils. Earth Surf. Proc. Landforms 1998, 23 (7), 663–668. (9) Doerr, S. H.; Dekker, L.; W. Ritsema, C. J.; Shakesby, R. A.; Bryant., R. Water repellency of soils: The influence of ambient relative. Soil Sci. Soc. Am. J. 2002, 66 (2), 401–405. 9670
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Characterization of Aquatic Particles by Direct FTIR Analysis of Filters and Quantification of Elemental and Molecular Compositions Luc Tremblay* and Ghita Alaoui Department of Chemistry and Biochemistry, Universite de Moncton, Moncton, New Brunswick, Canada E1A 3E9
Marc N. Leger Department of Chemistry, St. Francis Xavier University, Antigonish, Nova Scotia, Canada B2G 2W5
bS Supporting Information ABSTRACT: This paper presents the first characterization of aquatic particles and particulate organic matter (POM) by attenuated total reflectance infrared spectroscopy (ATR-FTIR) using particles deposited on filters. Particles from 30 water samples from the St. Lawrence System (Canada) were analyzed. ATR-FTIR spectra revealed changes in numerous organic and inorganic functional group contents. Particles from marine waters contained POM enriched in amide, NH, and aliphatic groups, while terrigenous POM had more COO/COOH and aromatic groups. The spectra showed the selective degradation of amide, NH, aliphatic, and carbohydrate-like structures during the sinking of the particles. Partial least-squares (PLS) regression of the ATR-FTIR spectra was used to quantify 12 important elemental and molecular parameters, such as amino acids, bacterial biomarkers, and degradation indices. Most parameters were quantified with good accuracy compared to conventional methods (3350 cm1 for the other samples (Figure 1). It was also possible to detect other changes related to particle POM content (or %OC), %N, and amino acid yield (%CAA). The bands centered at 2923 and 2845 cm1 correspond to
asymmetric and symmetric CH2 stretching, respectively. These bands were more intense in POM-rich particles (Figure 1, Table 1). This correlation has been reported in a previous study with sediments.8 The particles from the surface waters of the gulf also exhibited a band at 2962 cm1 caused by the asymmetric stretching of CH3. These particles may not only contain more POM, but their POM appeared to be more aliphatic (and less aromatic, see below). The weak 1425 cm1 band can also be attributed to aliphatic groups (i.e., deformation vibrations). The amide I (CdO) and II (NH and CN) bands centered at ∼1645 and ∼1545 cm1, respectively, were more important in
Figure 1. ATR spectra of filtered particles from three stations. Spectra from particles collected at three different depths in the Gulf of St. Lawrence are presented. A description of these sites is in Table 1.
Table 1. Salinity, Particle Concentration, and Elementary and Amino Acid Compositions of the Particles Associated with the Spectra Shown in Figure 1 sample sitesa Chaudiere river St. Lawrence river
salinity (g kg1)
particles (mg L1)
100 μg P cm2 based on the response of the accumulation mass to the theoretical line, corresponding well to the authors’ previous report.26 This capacity is much greater than that of other binding gels used for DGT measurement of P, including conventional ferrihydrite gel (∼2 μg P cm2)23 and the recently 9683
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Environmental Science & Technology developed precipitated ferrihydrite gel (7 μg P cm2)24 and Metsorb gel (∼12 μg P cm2).29 Analytical Errors from High-Resolution, 2D DGT Measurements. Prior to application, the analytical errors inherent in the high-resolution, 2D DGT measurement were estimated. Errors may result from all steps of the measurement, including DGT uptake, 2D slicing, P elution, and microcolorimetric determination (Figure 1). The error from DGT uptake was considered negligible with the use of the homogeneous Zr-oxide binding gel. For the slicing step, the distance between each pair of adjacent cutting edges in the cutter was examined using a microscope (Olympus BX51) and it varied within 2% (0.45 ( 0.01 mm) (Figure S2D). The resulting area of each gel square was further examined by randomly measuring ∼100 squares using the microscope, and it varied within 5% (0.2025 ( 0.001 mm2) (Figure S2E). The error from elution of P was also considered negligible since the eluting solution (1 M NaOH) had been mixed thoroughly prior to use. The error caused by
Figure 3. Analytical errors for high-resolution, 2D DGT measurement of DRP (CDGT) in solutions (0) and homogeneous sediments (9) at different accumulated masses of P in the binding gel.
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microcolorimetric determination of P was investigated by detecting different concentrations of phosphate (0.010.6 mg L1) spiked in 1 M NaOH solutions, with 16 duplicates for each concentration. The results showed that the relative standard deviation (RSD) was 0.03 mg L1, corresponding to P accumulation masses of >0.6 μg cm2 in the binding gel. The total errors during the entire procedure were investigated through DGT deployments in phosphate-containing solutions of different concentrations as well as in microcosms with homogeneously mixed sediments. The results showed that the RSD values were very similar for the two types of deployments, both exhibiting a sharp decrease and then remaining steady as the accumulated masses of P in the binding gels increased. The analytical errors were estimated to be within 20% and 10% once the mass of P was >1.2 μg cm2 and >4.0 μg cm2, respectively (Figure 3). These errors should have predominantly resulted from the 2D slicing and microcolorimetric determination processes as explained earlier. High-Resolution, 2D DGT Measurements in Sediments. As a pilot study, the newly designed high-resolution, 2D DGT operation procedure was used to investigate the impact of tubificid worms on DRP distribution in sediments sampled from a eutrophic lake. Tubificid worms represent an important fraction of the benthic community in eutrophic lake sediments.30 A similar study has been carried out by Zhang et al.27 via 1D measurements of DRP concentrations at 1-cm resolution. The field experimental design in this study was similar to that of Zhang et al.,27 but the measurements for DRP were performed at a finer resolution and at the 2D level. Two 2D DRP distribution profiles (for the control and bioturbation treatments) with a resolution of 0.45 0.45 mm were obtained in the upper 3.0 cm of the sediment layers (Figure 4), where the majority of tubificid worms were found (>60%).27 The analytical errors were controlled within 20% throughout the 6-d deployments, with most of the accumulated masses of DRP in the gel squares greater than 1.2 μg cm2.
Figure 4. 2D distribution images of DRP concentrations (CDGT) at a spatial resolution of 0.45 mm in sediments without (left) and with (right) tubificid worm bioturbation. The location of the sediment-water interface is represented by zero. 9684
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Figure 5. The effects of tubificid worm bioturbation on DRP concentrations (CDGT) in sediments based on 1D (left) and 2D (right) analyses. The 1D data were the average concentrations of DRP at a given depth, with a 0.45 mm interval for the depths. “CK” and “Bio” point to the control and tubificid worm treatments, respectively, with the asterisks indicating their difference at significance levels of p < 0.05 (*) and p < 0.01 (**). The 2D data were obtained through comparisons of DRP concentration in one localized zone (1.8 1.8 mm) of the tubificid worm treatment with those in all the localized zones of the control at the same horizontal level. The numbers of 0 to 8 indicate an increase in the probability of an impact of tubificid worms. The location of the sediment-water interface is represented by zero.
For the control, the 2D distribution of DRP showed systematic changes in the vertical direction, with low concentrations (99%) in a Geobacter sp. phylogenetically related to Geobacter lovleyi strain SZ, a PCE-to-cDCE dechlorinator.17 KB-1/Geo was enriched by five successive 1% transfers in mineral medium containing 10 mM sodium acetate, 0.54 mM PCE, and a H2-free, N2/CO2 (80:20) headspace, a method similar to that used to isolate strain SZ. Cell Suspensions. Cultures were prepared for cell suspension assays by purging each culture free of chlorinated ethenes with N2/CO2 (80:20) for 15 20 min. Setup of cell suspension assays involved a dilution of the cultures into mineral salts medium as described below. Cell-Free Extracts. All cell-free extracts were prepared under anoxic conditions from cultures actively dechlorinating TCE, which had been fed within 48 72 h. For KB-1, OW, and BDI, cell-free extracts were prepared once ethene production began. Centrifuge tubes used in the procedure were left in the anaerobic chamber (Coy Laboratory Products Inc., MI) for 48 h prior to use in order to remove oxygen, and anaerobic conditions during centrifugation were monitored by observing the color of the resazurin-containing medium. To prepare cell-free extracts of KB-1,
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600 mL of culture was centrifuged in three 250-mL polypropylene bottles with cap assembly (Beckman Coulter, Brea, CA) at 9900g for 30 min at 4 C. In the anaerobic chamber the triplicate cell pellets were resuspended in a single final volume of 40 mL of suspension buffer (100 mM Tris-HCl, pH 7.4, 100 mM NaCl, 1 mM titanium(III) citrate, 5% (v/v) glycerol). Cells were broken by sonication (Sonics Vibra-Cell sonicator, Sonics & Materials, Inc., Newton, CT) on ice in the anaerobic chamber using two 5-min cycles of 1-s pulses at 40% amplitude (23 W), separated by a 2-min break. Crude extracts were aliquoted into smaller volumes in 2-mL tubes with screw-top O-ring caps, flashfrozen in liquid N2, and stored at 80 C. For each assay, an aliquot of the extract was thawed on ice and centrifuged for 10 min at 12 500g at 4 C. Inside the anaerobic chamber, the thawed extract was passed through a 0.2-μm filter to remove unbroken cells. Cell-free extracts of BDI, OW, KB-1/Dhc, and KB-1/Geo were prepared by concentrating 40 mL of culture (80 mL for KB-1/ Geo) by centrifugation in 50-mL polypropylene tubes (Fisher Scientific, Pittsburgh, PA) sealed with vinyl tape (3M, St. Paul, MN) at 5500g for 30 min at 4 C. Pellets were suspended in suspension buffer to a final volume of 5.5 mL. After sonication, the suspensions were centrifuged as described above for 15 min at 12 500g at 4 C to pellet unbroken cells. Inside the anaerobic chamber, the thawed extract was passed through a 0.2-μm filter to remove unbroken cells. The supernatant was aliquoted into smaller volumes, flash-frozen, and stored at 80 C. Crude extracts were thawed on ice prior to the start of each assay. Cell Suspension and Cell-Free Extract Dechlorination Assays. Assays were performed in the anaerobic chamber in 2-mL glass vials with PTFE-lined caps (Supelco, Bellefonte, PA). The vials were filled completely to eliminate headspace partitioning of the chlorinated compounds. Cell suspension assays were conducted in 1.87 mL of mineral salts medium16 supplemented with 5 mM acetate and purged with H2/CO2 (80%/20%) immediately prior to use. Cell-free extract assays were conducted in 1.90 mL of assay buffer containing 100 mM Tris-HCl, pH 7.4, 2 mM methyl viologen, and 2 mM titanium(III) citrate. 1,1,1-TCA or 1,1-DCA and cell-free extracts or cell suspensions were added directly to the assay buffer, whereas 10 50 μL of chlorinated ethenes (from saturated aqueous stocks) were added at different concentrations directly to the vial to start each assay. Saturated aqueous stocks were prepared in 2-mL vials by adding to anaerobic water 5-fold greater mass of the chlorinated solvent needed to obtain the solubility limit. Vials were shaken by hand for 2 min and the aqueous and solvent phases were allowed to partition overnight. For cell suspension assays, purged culture was added to the assay buffer to obtain, after resuspension in buffer, 75 μL of purged culture per assay vial, for a final total volume in the cell suspension assay vial of between 1.96 and 1.99 mL. For cell-free extract assays, cell-free extract was added to the assay buffer to obtain the equivalent of 10 30 μL of extract per assay vial, for a final total volume in the cell suspension assay vial of between 1.92 and 1.98 mL. These amounts of culture or extract were chosen such that accurate determination of daughter products could be made within a 1 3 h incubation period while maintaining less than 10% dechlorination of parent compound. This strategy provided an accurate determination of initial dechlorination rates without a significant change in initial chlorinated ethene concentration. Concentrations of TCE, cDCE, and VC tested (up to 10 concentrations per substrate/inhibitor combination) ranged from 2 to 600 μM, corresponding to 10 55 μL of aqueous stocks. Chlorinated ethane concentrations assayed ranged from 0 9694
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Table 1. Kinetic (Vmax, Km) and Inhibition (KI) Parameters for VC and cDCE and TCE Dechlorination in Either Cell Suspensions or Cell-Free Extracts in the Presence of Chlorinated Ethanesa substrate VC
culture KB-1
OW
BDI
cDCE
KB-1 OW BDI
TCE
KB-1 KB-1/Geo
inhibitor
assay
Vmax (nmol 3 min
1
1 3 mg protein )
Km (μM)
KI (μM)
inhibition model
1,1,1-TCA
suspension
50 ((3)
27 ((8)
0.7 ((0.2)
C
1,1,1-TCA 1,1,1-TCA
extract extract
47 ((3) 49 ((4)
74 ((14) 83 ((15)
0.8 ((0.2) 2.0 ((0.3)
C N
1,1-DCA
extract
67 ((5)
63 ((15)
110 ((56)
C
1,1-DCA
extract
73 ((6)
76 ((14)
300 ((111)
N C
1,1,1-TCA
suspension
5.8 ((0.5)
7 ((2)
0.2 ((0.1)
1,1,1-TCA
extract
2.8 ((0.6)
151 ((54)
1.1 ((0.3)
C
1,1,1-TCA
extract
2.9 ( 0.5
156 ( 51
2.0 ( 0.4
N
1,1-DCA
extract
2.3 ((0.2)
105 ((23)
58 ((24)
C
1,1-DCA 1,1,1-TCA
extract suspension
2.5 ( 0.2 13 ((1)
116 ( 16 17 ((4)
104 ( 24 0.4 ((0.1)
N C
1,1,1-TCA
extract
9.1 ((0.6)
81 ((11)
0.5 ((0.1)
C
1,1,1-TCA
extract
9.2 ( 0.7
84 ( 13
1.2 ( 0.2
N
1,1-DCA
extract
9.3 ((0.5)
95 ((11)
80 ((22)
C
1,1-DCA
extract
9.8 ( 0.5
105 ( 11
162 ( 39
N
1,1,1-TCA
extract
91 ((7)
86 ((22)
19 ((4)
N
1,1-DCA
extract
91 ((4)
92 ((13)
830 ((280)
N
1,1,1-TCA 1,1-DCA
extract extract
26 ((1) 25 ((1)
42 ((5) 40 ((6)
86 ((17) 130 ((57)
N N
1,1,1-TCA
extract
15 ((1)
43 ((8)
5.5 ((0.8)
N
1,1-DCA
extract
18 ((1)
45 ((4)
110 ((18)
N N
1,1,1-TCA
extract
82 ((13)
40 ((19)
1.5 ((0.6)
1,1-DCA
extract
82 ((16)
98 ((41)
No inhibition
N
1,1,1-TCA
extract
84 ((10)
1.4 ((0.9)
5.1 ((2)
N
KB-1/Dhc
1,1,1-TCA
extract
179 ((20)
180 ((40)
2.2 ((0.6)
N
OW BDI
1,1,1-TCA 1,1,1-TCA
extract extract
25 ((3) 53 ((5)
54 ((14) 3 ((1)
40 ((9) 43 ((17)
N N
For the substrate VC in cell suspensions, the competitive model fit the data best. For cell-free extracts, the noncompetitive model was usually the best fit for all substrates (see SI Table S3 for model fit data). Both competitive and noncompetitive model parameters are provided for VC extract data to enable comparison. Error values represent 95% confidence intervals. C = competitive inhibition model; N = noncompetitive inhibition model. a
to 38 μM for 1,1,1-TCA and 0 to 225 μM for 1,1-DCA. All of the substrate and inhibitor combinations tested in dechlorination assays are listed in Table 1. Inhibitor and initial substrate concentrations for each experiment are provided in Supporting Information (SI) Table S1. Analytical Procedures. Following the 1 3 h incubation period, sacrificial liquid samples (0.15 1.0 mL) from each dechlorination assay vial were transferred to 10-mL autosampler vials containing 5.0 5.85 mL of acidified water (12 mM HCl) to stop further enzymatic activity, to a total volume of 6 mL. Samples were analyzed with a HP 7694 headspace sampler connected to a HP 5890A gas chromatograph coupled to a flame ionization detector (GC-FID), with a GSQ column (30 m 0.53 mm i.d. PLOT column; J&W Scientific, Folsom, CA). The headspace autosampler settings were as follows: 75 C oven temperature, 80 C loop temperature, 90 C transfer line temperature, 12 min GC cycle time, 45 min vial equilibration time, 0 min pressurization time, 0.2 min loop fill time, 0 min loop equilibration time, 3 min injection time, vial pressure at 17.3 psi, and carrier pressure at 9.4 psi. The GC oven temperature program used was as follows: hold at 50 C for 2 min, increase to 100 at 50 C/min, increase to 185 at 25 C/min, and hold at 185 C for 3 min. When dechlorination assays involved testing TCE as a substrate and 1,1-DCA as an inhibitor, the following program was used to resolve coeluting
1,1-DCA and daughter product cDCE peaks: hold at 50 C for 2 min, increase to 100 at 50 C/min, increase to 150 at 25 C/min, hold for 2.5 min, increase to 185 at 15 C/min, and hold at 185 C for 0.5 min. Protein concentrations were determined in cell-free extracts and cell suspensions using the Bradford assay.18 Kinetic and Inhibition Models. For each experiment at a different initial substrate concentration [S], an initial dechlorination rate v, normalized to the amount of protein per vial, in units of nmol substrate dechlorinated per min per mg protein, was calculated. The moles of substrate dechlorinated were calculated as the sum of all daughter products measured by GC-FID. The maximum dechlorination rate (Vmax) and half-velocity constant (Km) for each culture/substrate combination were calculated using a nonlinear regression method for the Michaelis Menten single-substrate model in the Enzyme Kinetics 1.3 Module for SigmaPlot 10 (Systat Software Inc., Chicago, IL). Haldane substrate inhibition19 for chlorinated ethene dechlorination was not observed at the concentrations tested. Data sets from experiments testing the effect of a chlorinated ethane were modeled using the competitive, uncompetitive, and noncompetitive inhibition equations (SI Table S2) as described previously.18 The model deemed most appropriate was chosen primarily based on the statistical parameters determined using SigmaPlot. In some cases, these parameters (highest coefficient of determination 9695
dx.doi.org/10.1021/es201260n |Environ. Sci. Technol. 2011, 45, 9693–9702
Environmental Science & Technology
Figure 1. Kinetics of VC dechlorination in cell suspensions in the presence of increasing concentrations of 1,1,1-TCA. Three consortia are compared: (A) KB-1, (B) OW, and (C) BDI. The concentration of 1,1,1-TCA for each assay series is indicated on each graph (I = inhibitor concentration in μM). Solid lines represent the best fit to each data set based on nonlinear regression using a competitive inhibition model (eq 2 in SI Table S2).
(R2), the lowest corrected Akaike’s Information Criterion (AICc), and the lowest standard deviation of the residuals (Sy.x)) were not able to provide enough resolution to choose a prevailing model. In these cases, we selected a model to maintain consistency among substrate inhibitor sets in order to provide a meaningful comparison between the inhibition coefficients. All raw data (Table S1) and statistical parameters (Table S3) are provided as Supporting Information. For evaluation of the data from experiments involving KB-1 culture, KB-1/Geo, and KB-1/Dhc, an Eadie Hofstee transformation of the kinetic data was also generated. An Eadie Hofstee transformation plots the dechlorination rate as a function of the ratio of dechlorination rate to substrate concentration using a linearization of the Michaelis Menten equation, enabling easy visualization of model fit.20
’ RESULTS AND DISCUSSION Cell-Free Extract and Cell Suspension Control Experiments. No differences in dechlorination rates were observed
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between freshly prepared cell-free extracts and cell-free extracts that had been stored at 80 C (data not shown). Therefore, all experiments with the different substrates and inhibitors could be completed with extract of identical protein content and activity. No TCE dechlorination was observed when hydrogen or methanol was added as electron donors to cell-free extracts, indicating the electron transport pathway leading to the reductive dehalogenase (which acts as a terminal reductase in respiration) was no longer intact. Furthermore, dechlorination was only observed in the presence of the artificial electron donor methyl viologen reduced with titanium citrate. The addition of up to 1 μM of vitamin B12 (cyanocobalamin) (over 200-fold higher than the cyanocobalamin composition in the mineral medium) to heatdenatured (10 min at 85 C) cell-free extract did not result in any reductive dechlorination, indicating that observed TCE dechlorination resulted from the activity of reductive dehalogenases and not simply from any dechlorinating activity of reduced vitamin B12.21 Finally, no transformation of 1,1,1-TCA or 1,1-DCA was observed in any of the dechlorination assays. Initial Rates of Dechlorination versus Initial Substrate Concentration. Data for cell suspension assays amended with the substrate VC and increasing concentrations of 1,1,1-TCA are shown in Figure 1. Data for cell-free extract assays with the substrate VC in the presence of 1,1,1-TCA and 1,1-DCA are presented in Figure 2. These graphs reveal reasonable agreement with Michaelis Menten kinetics (i.e., rate increasing to a maximum). Similar patterns were observed for cell-free extract assays with the substrate cDCE in the presence of 1,1,1-TCA and 1,1-DCA (SI Figure S1), and for assays with the substrate TCE in the presence of 1,1,1-TCA (SI Figure S2). The data plotted in Figures 1, 2, and S1 also reveal that chlorinated ethene dechlorination rates decreased with increasing 1,1,1-TCA concentration, but were not significantly affected by similar concentrations of 1,1-DCA. Quantification of Kinetic Parameters. The raw data (initial degradation rate (v) vs substrate concentration [S], see SI Table S1) were fit to the competitive, noncompetitive, and uncompetitive inhibition equations to extract kinetic parameters for quantitative comparisons. For data with VC as a substrate, the competitive model fit best, particularly for cell suspension assays (SI Table S3). For cDCE and TCE as substrates in cell-free extract assays, the noncompetitive model fit best in most cases (SI Table S3). The solid lines in Figures 1, 2, S1, and S2 represent these model equations using a single best set of parameters (Vmax, Km, and KI) for each complete data set (i.e., all concentrations for each substrate/inhibitor combination tested). The resulting kinetic parameters (Tables 1 and 2) enabled quantitative comparisons among cultures, substrates, and inhibitors. Comparisons between cell suspensions and cell-free extracts with VC as substrate were made using parameters from the competitive model because the competitive model clearly fit the cell suspension data better, but comparisons between all cell-free extract experiments were made using parameters from the noncompetitive model to compare these data for the same model. The choice of model (competitive vs noncompetitive) only affected the determination of the inhibition constant, KI, and did not significantly change the values of Vmax or Km (Table 1). Because the cell suspension assays were conducted over a short time frame (