Environmental sampling for trace analysis

Environmental Sampling for Trace Analysis Edited by Bernd Markert

0 VCH Verlagsgesellschaft mbH, D-69451 Weinheim (Federal Republic of Germany), 1994

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Environmental Sampling for Trace Analysis Edited by Bernd Markert

4b

VCH

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Editor: Prof. Dr. Bernd Markert Internationales Hochschulinstitut Zittau Markt 23 D-02763 Zittau

This book was carefully produced. Nevertheless, authors, editor and publisher do not warrant the information contained therein to be free of errors. Readers areadvised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.

Published jointly by VCH Verlagsgesellschaft mbH, Weinheim (Federal Republic of Germany) VCH Publishers Inc., New York, NY (USA) Editorial Director: Dr. Hans-Joachim Krdus Production Manager: Dipl.-Wirt.-Ing. (FH) H.-J. Schmitt

Cover illustration: Heterogenous distribution of trace substances on the “xerothermic hills” near Wiirzburg, F. R.C..

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Die Deutsche Bibliothek - CIP-Einheitsaufnahmc: Environmental sampling for trace analysis / ed. by Bernd Markert. -Weinheim ; New York ; Basel : Cambridge ; Tokyo : VCH, 1YY4 ISBN 3-527-30051-1 NE: Markcrt, Bcrnd [Hrsg.]

0 VCH Verlagsgesellschaft mbH, D-69451 Weinheim (Federal Republic of Germany), 1994

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Printed in the Fcdcral Rcpublic of Germany.

Environmental analysis has entered a phase which is difficult to describe precisely because of its meteoric development. The advent of more and more modern systems of instrumental analysis now makes it possible to penetrate areas of ecology and ecotoxicology that would have been considered inaccessible a few years ago. Trace analysis and ultra-trace analysis of individual substances, in particular, have proved to be a useful dynamo for new scientific findings. One example is the development of possible concentration and action models for specific dioxin molecules which it would have been impossible to investigate without highly efficient analytical procedures. In addition to detecting lower and lower concentrations, such highly sophisticated measuring equipment has the important advantage of opening up totally new fields of work. Procedures such as multielement analysis, chemical fingerprinting and non-target screening are already familiar to many laboratories and will continue to pave the way for trendsetting research strategies. But orientation towards more and more efficient analytical methods also involves a risk that is all too easily overlooked. Too little attention is often given to analytical steps before and after actual instrumental measurement - sampling and the preparation of specimens, for example - because the measuring work itself is the focus of interest. Representative sampling, especially, has not kept pace with the development of increasingly sensitive analytical systems. Today it has to be said that the greatest error in the overall result of the analytical process is usually caused by improper sampling. This development becomes obvious when we compare the almost uncountable number of publications on the measurement of any arbitrary substance X in sample Y with the small number of scientific publications dealing with careful representative sampling suitable for trace and ultra-trace analysis. The ratio of publications is probably somewhere around 1000: 1. The reason for this development is clear. First of all, sampling is only a small step in the overall process of analysis and one that is unlikely to produce any “spectacular” data for a subsequent discussion of the overall result. This means that sampling is all too easily dismissed as uninteresting. Secondly, the equipment it requires is often less expensive than the apparatus needed for the instrumental measurement itself. In many cases a rigid PVC spade is all that is needed to acquire a soil specimen, so that manufacturers do not at once consider it lucrative to orient their product range towards sampling systems. Sampling as a field of work in its own right is therefore very quickly deemed unattractive from both the scientific and the commercial point of view. So it is not surprising that progress towards quality control in representative environmental sampling lags far behind that in actual instrumental mcasurcmcnt, where remarkable steps towards “true data” have

VI

Prc,fute

been made as a result of precise definition of the terms accuracy, reproducibility or concentration-dependence of the accuracy of the analytical results (“Horwitz Trumpet”), and the production of suitable reference materials. The results arc serious: the data from individual study groups cannot be compared with each other; harmonization of research in the various fields, so often demanded, is difficult to achieve even at the stage where the data are generated. A further problem is that the first principle of sampling, namely that “the sample taken from the system should have exactly the same chemical composition as the original material” cannot be followed even approximately in the field. It should never be forgotten that environmental processes are continuous in terms of space and time. Watercourses, for example, demonstrate most plainly that sampling has to be carried out in a time/space continuum if the findings are to be relevant. Many investigations, even recent ones, fail to take this into account sufficiently. In order to achieve meaningful and comparable results in respect of the current state of the environment it is necessary to develop standards for sampling similar to those that already exist in Germany for the water, sewage and sludge sector (German Standard Procedure for Water, Sewage and Sludge Analysis). By developing suitable guidelines at a national and, still more important, at an international level it must then be ensured that the src-ond and third principles of sampling can be implemented. These are: “The probability of being selected from a total population must be equal for each individual”, and “the amount of work required for sampling increases with the degree of dispersion of the individuals and the number of such individuals”. One must, however, bear in mind that practical difficulties during the actual sampling process make it virtually impossible to follow the first two principles. A sample taken out in the field can never have exactly the same chemical composition as the original material; at best it will be very similar. One of the reasons is that often only a tiny fraction of the original material is actually analyzed (e.g., 100mg of 100 kg of leaves from a forest ecosystem). The initial content of the material can be altered by contamination or volatilization of individual constituents while the sample is being taken and during transportation. Moreover, it is scarcely possible to give each individual the same likelihood of being selected when such individuals are diffusely distributed over the ecosystem. The objective must rather be to come as close as possible to the first two principles by means of a carefully prepared sampling strategy. Some practical, basic rules may be helpful here: Avoid contaminating the sample in any way with the equipment used, the containers, or by the person taking the sample. - Avoid any volatilization of chemical compounds as a result of microbial activity, absorption by the walls of the vessels in which the samples are kept or overheating of the samples during transportation and storage. - Take reasonably large samples, provided that there is enough material in the system and this is not subject to the nature conservation laws. - Take account of seasonal fluctuations in the composition of the original material and other parameters affecting its overall composition such as temperature, humidity, light etc.

-

Prefuce

VII

The aim of this book is to provide an overview of the techniques commonly used at present for taking many different kinds of environmental samples. Because of the host of different environmental samples a selection had to be made, which was painful in some cases but unavoidable in view of the limited printing capacity. The subject of human samples has been left out completely, for it is a domain of the medical profession and analysts with the relevant training. In water sampling emphasis has been put on fresh water; the sampling of “wet depositions” such as rain and snow has not been included, for excellent monographs already exist in this field. Most animals have also had to be excluded. Only ants are used as an example of how sampling might be conducted. The editor was not authorized by the Federal Office of the Environment to publish even an extract from the sampling guidelines of the “Environmental Specimen Programme” of the Federal Ministry for Research and Technology. This is a pity, especially with respect to long-term investigations, for the results are important and valuable. It remains to be hoped that these Standard Operating Procedures of the Federal Office of the Environment will soon be made accessible to a wide circle of readers in the form of a monograph. No attempt has been made to standardize the terminology used in sampling. The reason is that there is still a great need to establish unequivocal technical terms, especially in German and American English. In spite of the deficiencies of this book the editor and the authors agree that together they have been able to reflect the state of the art in a field which will have to undergo much more intensive development in the future. Again, this book would not have been come about if friends and associates had not created the scope necessary for its realization, from the initial idea to the finished product. The central figures were of course the authors, who once again succeeded in submitting, amending and improving the manuscripts within a year in spite of their numerous other commitments. The suggestions for improvements came from a multitude of associates in Germany and abroad and from my employees and associates at the GKSS Research Centre, Geesthacht. I was motivated not least by Mrs Vera Weckert of the study group on systems research at the University of Osnabriick, who missed no opportunity of urging me on and encouraging me in difficult phases. I also wish to thank VCH Publishers, especially Dr. Kraus, and Wirt.-Ing. Hans-Jochen Schmitt, for their generous and friendly assistance. The editor and the authors hope that the book will be disseminated widely and that the problems it discusses will be examined closely; they themselves will be pleased to listen to constructive criticism. Magdeburg, January 1994

Bernd Markert

Outline

List of Contributors XXVll

Part 1:

Historical Aspects

1

1 History of Sampling Demonstrated on the Ore Mining Industry Empirical and Theoretical Approaches 3 C. Kruff

Part 11: General Aspects 9 2 Gencral Aspects of Environmental Sampling 11 P . Hoffhiunn 3 Trace Elements Need Trace Analysis 73 I . Puis 4 Error Estimation in Environmental Sampling and Analysis M . H . Rumsey 5 Estimation of Varying Detection Limits 109 W. G. Wurren

Part 111: Examples for Sampling A. Air 123

123

6 Particle and Gas Measurements on Filters J . G. Wutson, C. C l i o ~ ~ 7 Organic Gas Sampling 163 B . Zielinsku, E. Fzrjita

B. Water

93

125

185 8 Sampling of Freshwaters for Estimation of all Detectable Elements 187 U. M . Cowgill 9 Guidelines for Sampling Freshwater for Eutrophication Management Programs 203 H . Klupper, W. Rust, D . Ulilmunn 10 The Sampling Strategy in the River Elbe - Experiences 223 H . Gtllir, E. Weher

X

Outline

1 1 Sampling Treated Wastewaters and Receiving Streams 249 J . E. Norris I2 Water and Wastewater Sampling for Environmental Analysis 255 E. M . Dick 13 Sampling of Groundwater for General Quality Monitoring 279 V. Schenk 14 Groundwater Sampling for Metals 287 R. Puls

C. Soils and Sediments 303 15 Representative Soil Sampling 305 0. Franzle 16 Problems and Results in the Development of International Standards for Sampling and Petreatment of Soils 321 A . Paeiz, G. Criflmann 17 Fixed and H ypothcsis-Guided Soil Sampling Methods - Principles, Strategies, and Examples 335 R. W. Scholz, N. Nothbaum, T. W. Muy 18 Sampling for Trace Analysis of Lake Sediments 347 U. M . Contgill 19 Sampling Design for Studying the Relationships between Heavy Metals in Soils, Sediments, and Discharged Wastewaters 365 Zueng-Sung Chen

D. Plants and Animals 379 20 Sampling of Plants for Environmental Trace Analysis in Terrestrial, Semiterrestrial and Aquatic Environments 38 1 W. H . 0. Ernst 21 On the Sampling of Vascular Plants for Monitoring of Heavy Metal Pollution 395 R. Djingova, I. Kuk# 22 Sampling of Terricolous Lichen and Moss Species for Trace Element Analysis with Special Reference to Bioindication of Air Pollution 41 5 Z . Tubu, Z . Csintalun, Z.Nugy, K. Szente, Z.Takacs 23 Comparative Investigation of the Distribution of Chemical Elements in an Aceri Tatarico-Quercetum Plant Community and in Stands of Cultivated Plants 435 M . Kozxics, K. Penksza, G . Turcsunyi, L. Kaszuh, S. Tbth, P. Szbke

Outline

XI

24 Sampling of Tropical Terrestrial Plants with Particular Reference to the Determination of Trace Elements 443 R. Jayasekeru 25 Sampling in the Stemflow and Throughfall Areas of Forests 449 G. Turcsanyi, K. Penksza, I . Siller, E. Fuhrer, S . Thth, M . Kovuc.~, S. Biittner 26 Sampling of Different Social Categories of Red Wood Ants (Formica s. str.) for Biomonitoring 465 V. Mauvara, A.-J. Martin, A . Oja, P. Nuorteva

Part IV: Literature Survey 491 27 Overview of References for Sampling and Related Topics 493 S. Hannappel

Index

511

Contents

List of Contributors XXVII Part I:

Historical Aspects 1

1

History of Sampling Demonstrated on the Ore Mining Industry - Empirical and Theoretical Approaches 3 G. Kruft

Part 11: General Aspects 9 2

2.1 2.2 2.3 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 2.3.6 2.3.7 2.3.8 2.4 2.4.1 2.4.2 2.5 2.6 3 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2

General Aspects of Environmental Sampling 1 1 P. Hoffkunn Introduction 1 1 Terms and Definitions 12 Aspects of Sampling 13 Location, Place, and Position of Sample Removal Size, Quantity, and Volume of the Sample 14 Number of Samples to be Taken 14 Time, Duration, and Frequency of Sampling 15 Homogeneity of the Sample 16 Contamination of the Sample 17 Losses in the Sample 18 Sample Storage and Conservation 19 Guidelines and Norms 19 Quality Assurance 20 Environmental Protection 2 1 References 22 Appendix 23 Trace Elements Need Trace Analysis 73 I . Pui.5 Problems of Trace Element Analysis 73 Introduction 73 Problems of Biological Analysis 74 Indicator Organs in Biological Evaluation 76 The Importance of Trace Elements 77 Introduction 77 Criteria of Essentiality and Beneficiality 80

13

3.2.3 3.2.4 3.2.5 3.2.6 3.2.7 3.3 4 4. I 4.2 4.3 4.4 4.4.1 4.4.2 4.4.3 4.4.4 4.4.5 4.5 4.6 4.7 5

5.1 5.2 5.2.1 5.2.2 5.3 5.4 5.5 5.6

Critcria of Toxicity 81 Changes in Element Concentrations 83 The Importance of Trace Elements in the Environment Interactions between Differcnt Elements 86 The Importancc of Interdisciplinary Trace Element Research 88 References 89

Error Estimation in Environmental Sampling and Analysis 93 M . H . RLirnsey Introduction 93 Basic Concepts and Terminology 93 Sampling Error in Contcxt 95 Methods for Estimating Quality of Measurements 96 Measuring Analytical Precision 96 Measuring Sampling Precision 99 Targets for Acceptable Levels of Prccision in Sampling and Analysis 102 Measuring Analytical Bias 103 Estimating Sampling Bias 105 Targets for Acceptable Levels of Bias in Sampling and Analysis 106 Conclusions 107 References 107 Estimation with Varying Detection Limits W. G. Warren Introduction 109 Methodology 110 The One-Dimensional Case 1 10 The Bivariate Case 112 Examples 113 Discussion 1 18 References 1 18 Appendix 118

Part 111 : Examples for Sampling A. Air 123 6 6.1 6.2

84

109

123

Particle and Gas Measurements on Filters J. G . Watson, J . C. Chow Introduction 125 Filter Analysis Methods 126

125

Outline

6.2.1 6.2.2 6.2.3 6.2.4 6.3 6.4 6.4.1 6.4.2 6.4.3 6.4.4 6.4.5 6.5 6.6 6.7

Mass 126 Elements 129 Water Soluble Ions 130 Organic and Elemental Carbon 132 Filter Media 134 Aerosol Sampling Systems 140 Size-Selective Inlets 142 Sampling Surfaces 146 Filter Holders 146 Pumps and Flow Controllers 147 Sampler Configurations 148 Sampling and Analysis Procedures 150 Summary 152 References 153

7

Organic Gas Sampling 163 B. Ziefinsku, E. Fujitu Introduction 163 Whole-Air Sampling 163 Sampling Media Selection and Preparation 164 Sampling 165 Storage and Transport 167 Preconcentration Methods 168 Preconcentration on Nonselective Solid Adsorbents 168 Sampling Media Selection and Preparation 168 Sampling 172 Storage and Transport 172 Selective Methods of Compound Preconcentration 172 Sampling Media Selection and Preparation 173 Sampling 175 Storage and Transport 175 Semi-Volatile Organic Compounds 175 Passive Sampling Techniques 177 Summary 178 References 18 1

7.1 7.2 7.2.1 7.2.2 7.2.3 7.3 7.3.1 7.3.1.1 7.3.1.2 7.3.1.3 7.3.2 7.3.2.1 7.3.2.2 7.3.2.3 7.4 7.5 7.6 7.7

B. Water 8

8. I 8.2

XV

185 Sampling of Freshwaters for Estimation of all Detectable Elements 187 U. M . Cowgill Introduction 187 Problems Associated with Sampling 187

8.2.1 8.2.2 x.2.3 8.2.4 8.2.5 8.3 8.3.1 8.3.2 8.4 8.4. I 8.4.2 8.5 8.6 9

9.1

9.2 9.3 9.3. I 9.3.2 9.3.3 9.4 9.5 9.6

Contamination from Sampling Devices and Laboratory Equipment 188 Sorption and Leaching of Pollutants by Sampling Tool Materials 189 Replication I90 Frequency of Sampling 192 Equipment, Field, and Sampling Blanks 193 Synple Fractions and Sample Preservation 194 Preservation of Samples 194 Pretreatment, Storage, and General Precautions 194 Sampling of Lakes. Rivers, and Groundwater 196 Stratified Bodies of Water 196 Unstratitied Bodies of Water 196 Sampling on Ice, Snow, Rain, Dew, and Fog 198 References 20 1 Guidelines for Sampling Freshwater for Eutrophication Management Programs 203 H . Klupper, W. Rast, D. Uhln2cinn Introduction 203 What to Sample 203 Necessary Temporal and Spatial Resolution for Data 206 Where to Sample 206 When to Sample 209 Sampling Strategies in Waterbodies with Longitudinal Water Quality Gradients 210 Calculating the Costs of Sample Collection 213 Compilation and Presentation of Data 21 8 References 220

The Sampling Strategy in the River Elbe - Experiences 2 2 3 H . Guhr, E. W(1ber 10.1 Characteristics of the River Elbe 223 10.1.1 Types of Use 223 10.1.2 Pollution Loads 223 10.1.2.1 Municipal Sewage Discharges 226 10.1.2.2 Discharges from Agriculture 227 10.1.3 Characteristics of Water Quality 227 10.2 Development of a Monitoring Strategy for the Rivcr Elbe Objectives 228 10.2.1 Selection of Sampling Points 229 10.2.2 Range of Measured Variables 230 10.2.3 Measuring Frequency 23 I 10

Outline

10.2.4 10.2.5 10.2.6 10.3

XVII

Use of Automatic Monitoring Stations 231 Data Flow, Data Processing, and Evaluation 232 Special Investigations 233 Experience Made in Implementing the Sampling Strategy Representativeness of Sampling Points 234 10.3.1 Frequency of Measurements 235 10.3.2 Data Collection 237 10.3.2.1 Sampling 237 10.3.2.2 Preparation and Preservation of Samples 240 10.3.2.3 Chemical Analysis 240 10.3.2.4 Measuring Errors 241 10.3.3 Consideration of Sewage Discharges 243 10.3.4 Data Processing and Evaluation 245 Conclusions 247 10.4 References 247 10.5

11 11.1 11.2 11.3 11.4 11.5 11.6

12

12.1 12.2 12.3 12.3.1 12.3.2 12.3.3 12.3.4 12.3.5 12.3.6 12.3.7 12.3.8 12.3.9 12.4 12.5 12.6

~

Sampling Treated Wastewaters and Receiving Streams 249 J . E. Norris Introduction 249 Sediment Sampling 249 Fish Sampling 250 Sampling of Industrial Wastewater Discharges 25 1 Sampling of Surface Waters: Receiving Streams 253 References 254 Water and Wastewater Sampling for Environmental Analysis 255 E. M . Dirk Introduction 255 Why Sample Water? 255 Elements of the Sampling Plan 256 Sampling Objective 256 Sampling Location 257 Sample Types and Collection Techniques 257 Sampling Equipment 257 Sample Containers and Sample Preservation 258 Sample Labeling and Shipping 258 Types of Analyses 258 Chain-of-Custody Documentation 258 Quality Assurance and Quality Control 259 Types of Samples 259 Sampling Programs 26 1 Sampling Equipment 262

XVIII

Outline

12.6.1 12.6.2 12.6.3 12.6.4 12.6.5 12.6.6 12.7 12.7.1 12.7.2 12.7.3 12.8 12.8.1 12.8.2 12.8.3 12.9 12.9.1 12.9.2 12.9.3 12.9.4 12.9.5 12.9.6 12.10

Power Source 264 Electronic Controller 264 Sample Intake 265 Sample Transport Line 265 Sample Storage 266 Sample Delivery System 268 Pumps and Representative Samples 269 Peristaltic Pumps 269 Vacuum Pumps 270 Bladder Pumps 271 Advancements in Sampling 272 Volatile Organic Sampling 272 Sample Volume Accuracy 272 Refrigeration 273 Preserving Sample Integrity 274 Sampling Equipment 274 Sampling Containers 275 Sample Handling 275 Sample Preservation 275 Sample Holding Time 275 On-Site Analysis 276 Conclusion 278

Sampling of Groundwater for General Quality Monitoring 279 V . Schmk Introduction 279 13.1 Sampling of Groundwater 279 13.2 13.2.1 Requirements for Sampling Sites 279 13.2.2 Sampling Equipment for Groundwater 28 1 Activities at the Sampling Site 282 13.3 13.3.1 Determinations and Preservations 282 13.3.2 Transport and Storage 283 Sampling Programs and Contents of Analyses 283 13.4 Interpretation 285 13.5 Conclusions 285 13.6

13

14 14.1 14.2 14.3 14.4

Groundwater Sampling for Metals 287 R. W. P U / S Introduction 287 Sampling Objectives 287 Sampling Point Design 288 Monitoring Well Development 289

Outline

14.5 14.6 14.7 14.8 14.8.1 14.8.2 14.8.3 14.8.4 14.9 14.10 14.1 1

XIX

Colloidal Transport 290 Well Turbidity 291 Sampling Preparation 293 Purging and Sampling 294 Low Flow Purging 294 Isolation of the Sampling Zone 295 Water Quality Indicator Parameters 296 Sampling Materials 298 Filtration and Analysis 298 Summary 299 References 300

C. Soils and Sediments 303 15

Representative Soil Sampling 305 0.Franzle 15.1 Selection of Representative Soil Samples 305 15.1.1 Methodology of Statistical Design 305 15.1.2 Small-Scale Variability of European Soils in the Light of Frequency Statistics 306 15.1.2.1 Determination of Regionally Representative Soils in Germany by Means of Crosstabulation and Neighborhood Analysis 306 15.1.2.2 Selection of Representative European Soils for SorptionTesting Purposes 309 15.1.3 Large-Scale Soil Variability in the Light of Variogram Analysis 310 15.1.3.1 Variogram Analysis 3 1 1 15.1.3.2 Model Applications of Variogram Analysis 3 15 15.2 Conclusions 3 19 15.3 Summary 319 15.4 References 320 16

16.1 16.2 16.3 16.4 16.5 16.6 16.7 16.8

Problems and Results in the Development of International Standards for Sampling and Pretreatment of Soils 321 A . Paetz, B. CroJmann Introduction 321 What is Soil? 322 Soil Sampling Objectives 323 Requirements on Sampling 323 Preliminary Investigation 325 Selection of Sampling Patterns 325 Sampling Depth 327 Sample Quantity 328

xx

0u t h e

16.9 16.10 16.11 16.12 16.13 16.14 16.15 16.16

Single Sample or Composite Samples 328 Preservation of Soil Samples 329 Use of Appropriate Sampling Tools and Containers 329 Sampling Report 330 Quality Control 330 Pretreatment of Soil Samples 33 1 Summary 333 References 333

17

Fixed and Hypothesis-Guided Soil Sampling Methods Principles, Strategies, and Examples 335 R . W . Scholz, N . Nothbuum, T. W. May Introduction 335 “Fixed” Sampling Plans/Grid Plans 338 Soil Sampling Plans Guided by Hypotheses 342 The IHEARU Schema 343 Conclusions 344 References 345

17.1 17.2 17.3 17.4 17.5 17.6 18 18.1 18.2 18.2.1 18.2.2 18.2.3 18.2.4 18.2.5 18.3 18.4 18.5 18.6

Sampling for Trace Analysis of Lake Sediments 347 U . M . Cowgill Introduction 347 Sampling Devices 348 Grab Samplers 349 Corers 352 Sampling Devices for the Collection of Suspended Sediments 355 Mud-Water Interface Samplers 355 Sediment Pore Water Sampling 356 Subsampling of Sediment Grabs and Cores 358 Quality Control 359 Statistical Considerations 36 1 References 362

Sampling Design for Studying the Relationships between Heavy Metals in Soils, Sediments, and Discharged Wastewaters 365 Zucng- Sung Clien Introduction 365 19.1 Case I : Studies of Rice-Growing Soils near Chemical 19.2 Plants 365 19.2.1 Principles of Sampling Design 365 19.2.2 Sampling Methods 367 19.2.2.1 Soil Sampling 367 19.2.2.2 Sampling Design for Water and Sediments 368

19

Outline

XXI

19.2.3 Analytical Methods 368 19.2.4 The Distribution of Heavy Metals in Soils, Discharged Water, and Sediments 368 19.2.5 The Relationship between Heavy Metals in Soils, Discharged Water, and Sediments 371 Case 11: Studies of Rice-Growing Soils near an Industrial 19.3 Park 372 19.3.1 Principles of Sampling Design 372 19.3.2 Sampling Methods 373 19.3.2.1 Soil Sampling 373 19.3.2.2 Sampling Design of Water and Sediments 373 19.3.3 Analytical Methods 373 19.3.4 The Distribution of Heavy Metals in Soils, Discharged Water, and Sediments 374 19.3.5 The Relationship between Heavy Metals in Soils, Discharged Water, and Sediments 376 19.4 Conclusions 377 19.5 References 377

D. Plants and Animals 379 20

20.1 20.2 20.3 20.4 20.4.1 20.4.2 20.4.3 20.5 20.6 20.7 21

21.1 21.2 21.3

Sampling of Plants for Environmental Trace Analysis in Terrestrial, Semiterrestrial and Aquatic Environments 38 1 W. H. 0. Ernst Introduction 38 1 What Do we Want to Know? 381 Are Trace Elements Relevant Objectives? 382 The Exploration of Environmental Compartments 384 The Hygro- and Hydrophytes 384 The Epiphytes 385 Soil Exploring Plants 386 Plant Parts and Life History 388 Conclusion 390 References 39 1 On the Sampling of Vascular Plants for Monitoring of Heavy Metal Pollution 395 R. Djingova, I. K u k f Introduction 395 Concentration Differences at Biological Levels 396 Seasonal Variations in the Heavy Metal Content of Plants 401

XXII

Outline

21.4 21.5 21.6

Sample Pretreatment Discussion 410 References 4 12

403

Sampling of Terricolous Lichen and Moss Species for Trace Element Analysis, with Special Reference to Bioindication of Air Pollution 415 Z . Tubu, Z . Csintulan, Z . Nagy, K . Szcnte, Z . Tak6cs Introduction 4 15 22.1 Materials and Methods 417 22.2 22.2.1 The Species of Investigation 417 22.2.2 The Original Habitat 417 22.2.3 The Cryptogam Transplantation Technique 417 22.2.4 Exposition 41 8 22.2.5 Sampling 418 22.2.6 Preparation of the Samples for Measuring Trace Element Contents 419 22.2.7 Element Analysis 419 22.2.8 Data Analysis 4 19 22.3 Results and Discussion 419 22.3.1 Vertical Distribution of Trace Elements within the Various Lichen and Moss Parts 419 22.3.2 Horizontal Distribution of Trace Elements within the Thalli of C. furcutu and the Cushions of T. rurulis 423 22.3.3 Distribution of Trace Element Contents in Parts of the Lichen Thalli and Moss Cushions Compared to Whole Thalli and Cushions, Respectively 424 22.3.4 Variability of the Trace Element Contents in the Lichen Thalli and Moss Cushions 426 22.3.5 Trace Element Contents in the Soil Substrate of the Lichen Thalli and Moss Cushions 426 22.3.6 The Influence of Sample Washing on Trace Element Contents 429 22.4 Summary 431 22.5 References 433 22

23

23.1 23.2

Comparative Investigation of the Distribution of Chemical Elements in an Aceri tatarico-Quercetum Plant Community and in Stands of Cultivated Plants 435 M . Kovucs, K . Penkszu, G . Turcsanyi, L. Kuszab, S . Toth, P . Szbke Introduction 435 Material and Methods 435

23.3 23.4 23.5

Results and Discussion 436 Summary 442 References 442

24

Sampling of Tropical Terrestrial Plants with Particular Reference to the Determination of Trace Elements 443 R. Juyasekera Introduction 443 Statistical Aspects 444 Plant Sampling 444 Concluding Remarks 447 References 447

24.1 24.2 24.3 24.4 24.5 25

25.1 25.2 25.2.1 25.2.2 25.2.3 25.2.4 25.2.5 25.2.5. I 25.2.5.2 25.2.5.3 25.2.5.4 25.2.6 25.2.7 25.2.8 25.3 25.4 25.5 25.6 26

26.1 26.2 26.3

Sampling in the Stemflow and Throughfall Areas of Forests 449 G. Turcshnyi, K. Penksza, I. Siller, E. Fuhrer, S . Toth, M . Kovucs, S. Buttner Introduction 449 Literature Data 449 The Amount of Stemflow and Throughfall in Stands of Different Tree Species 449 Chemical Composition of Stemflow and Throughfall 45 1 Physical Changes in Soils Due to Stemflow 452 Chemical Changes in Soils Due to Stemflow 452 Living Organisms Influenced by Stemflow 453 Microorganisms 453 Fungi 453 Mosses and Lichens 454 Other Plants 454 Impact of Stemflow on the Roots of Trees 454 Impact of Stemflow on Animals 454 Some Contradictions 455 Material and Methods 455 Results and Discussion 456 Conclusions 459 References 460 Sampling of Different Social Categories of Red Wood Ants (Formica s. str.) for Biomonitoring 465 V . Muavaru, A.-J. Martin, A . Oja, P. Nuorteva The Role of Ants in Nature 465 Pollutant Accumulation in Ants 466 Definition of the Red Wood Ants 468

XXIV

Outline

26.4 Structure of the Nests and Territories 469 26.4.1 The Nest Mound 469 26.4.2 The Base of the Nest 470 26.4.3 The Underground Part of the Nest 470 26.4.4 Thc Foraging Territory 471 26.4.5 Super- and Substructures 471 26.5 Social Structure of Ant Colonies 472 26.5.1 Sexuals 472 26.5.2 Workers 473 26.5.2.1 Reserve Workers 473 26.5.2.2 Nurses 474 26.5.2.3 Inside Workers 474 26.5.2.4 Outside Workers 47.5 26.6 Sampling 475 26.6.1 What Kind of Nest Mounds is Suitable for Sampling? 475 26.6.2 Sample Taking 476 26.6.3 Sampling Objects 477 26.6.3.1 Foragers Leaving the Nest 477 26.6.3.2 Foragers Traveling to the Nest 477 26.6.3.3 Surface Workers from the Top of the Nest 477 26.6.3.4 Workers and Brood from the Brood Chamber 478 26.6.3.5 Reserve Ants 478 26.6.3.6 Nest Material 478 26.6.4 Collection of Background Data and the Order of Sampling 479 26.6.4.1 Traffic Density of Foragers on Ant Roads 479 26.6.4.2 Ant Activity on the Nest Surface 479 26.6.4.3 Temperature of the Nest 479 26.6.4.4 Sample Taking from Ant Roads 479 26.6.4.5 Measuring the Size of thc Nest Mound 480 26.6.4.6 Sampling from the Nest 480 26.7 Feeding Experiments 480 26.7.1 Honey as a Feeding Substrate 480 26.7.2 Fish as a Feeding Substrate 481 Description of some Pilot Studies already Performed 48 1 26.8 26.8.1 Natural Cd Levels in Different Castes and Worker Groups 481 26.8.2 Cd Transfer to Ant Colonics under Pollution Stress 483 26.8.3 Artificial Cadmium Feeding Experiments 483 26.8.4 Effects of Metdl Pollution on the Enzyme Balance 486 References 486 26.9

Outline

XXV

Part IV: Literature Survey 491 27 27.1 27.2 27.2.1 27.2.2 27.2.3 27.2.4 27.2.5 27.2.6 27.2.7 27.3 27.3.1 27.3.2 27.3.3 27.3.4

Index

511

Overview of References for Sampling and Related Topics 493 S. Hannappet Introduction 493 Literature 493 General Aspects 493 Statistical Methodology 495 Air 498 Water 499 Soils, Sediments, Sludges, Rocks and Mining 503 Biota 504 Waste, Sewage, Sludge 507 Norms by the International Organization of Standardization in Geneva 507 Air 507 Water 508 Soil and Mining 508 Biota 509

List of Contributors

Dr. Sarolta Biittner Department of Botany and Plant Physiology Agricultural University PBter Karoly u. 1 H-2103 G6dii116 Hungary

Prof. Dr. W. H. 0. Ernst Department of Ecology and Ecotoxicology Faculty of Biology Vrije Universiteit De Boelelaan 1087 NL- 1081 HV Amsterdam The Netherlands

Prof. Dr. Judith C. Chow Desert Research Institutc University of Nevada System Energy and Environmental Engineering Center 5625 Fox Avenue P.O. Box 60220 Reno, NV 89506 U.S.A.

Prof. Dr. Otto Frlnzle Geographisches Institut der Christian-Albrechts-UniversitHt zu Kiel Ludewig-Mayn-Str. 14 D-24118 Kiel F.R.G.

Dr. Ursula M. Cowgill Department of Environmental, Population and Organismic Biology University of Colorado at Boulder P.O. Box 1327 Carbonale, CO 81623 U.S.A. Dr. Gerd CroRmann Landwirtschaftliche Untersuchungs- und Forschungsanstalt Postfach 5480 Nevinghoff 40 D-48 147 Miinster F.R.G. Dr. Zsolt Csintalan Plant Physiology Section Department of Botany and Plant Physiology Agricultural University H-2 103 God6116 Hungary Dr. Elie M. Dick ISCO Environmental Division 53 I Westgate Boulevard Lincoln NE 68528-1586 U.S.A. Dr. Rumiana Djingova Faculty of Chemistry University of Sofia I , J. Bouchier Blvd. BG-1126 Sofia Bulgaria

Dr. Ern6 Fuhrer Forest Research Institute Papret 17 H-9400 Sopron Hungary Dr. Eric Fujita University of Nevada System Energv and Environmental Engineering Center 5625-6ox Avenue P.O. Box 60220 Reno, NV 89506 U.S.A. Dr. Helmut Guhr Institute lor Inland Water Rescarch GKSS Research Centre Am Biederitzer Busch 12 D-39114 Magdeburg F.R.G. Mrs. Susanne Hannappel Department of Analytical Chemistry lnstitute for Inland Water Research GKSS Research Centre Gouvernementsberg 1 D-39104 Magdeburg F.R.G. Dr. Peter Hoflmann Fachgebiet Chemische Analytik Fachbereich Materialwissenschaft Technische Hochschule Darmstadt Hilpertstr. 31 D-64295 Darmstadt F.R.G.

XXVIll

Llrt of' Contribiciois

Dr. Ranjith Jayasekera Department of Botany University of Kelaniya Kelaniya Sri Lanka

Dr. Ants Martin Institute of Plant Protcction Estonian Agricultural University Riia 12 EE-2400 Tartu Estonia

Dr. Liszl6 Kaszab Department of Botany and Plant Physiology Agricultural University Pater Karoly u. 1 H-2103 Godollo Hungary

Dr. Thcodor W. May Gesellschaft I'ur Organisation und Ent. scheidung ApfelstraUe 119 D-33613 Bielefeld F.R.G.

Prof. Dr. Helmut Klapper Institute for Inland Water Research GKSS Research Centre Am Biederitzer Busch 12 D-39114 Magdeburg F.R.G.

Mr. Zoltan Nagy Plant Physiology Section Department of Botany and Plant Physiology Agricultural University of Godollo H-2103 Godollo Hungary

Prof. I h . Margit Kovacs Department of Botany and Plant Physiology Agricultural University Piter Karoly u. 1 H-2103 Godollo Hungary

Dr. Jamcs E. Norris BCM Engineers Inc. P.O. Box 1784 Mobile, AL 36633-1784 U.S.A.

Prof, Dr. Giinther Kraft Hans-Thoma-Str. 6 0-6'1476 Kron berg/TS I F.R.G. Dr. lvelin Kulcff Faculty of Chemistry University of Sofia 1, J. Bouchicr Blvd. BG- I 126 Sofia Bulgaria Dr. Vambola Maavara Institute of Plant Protection Estonian Agricultural University Riia 12 EE-2400 Tartu Estonia

Prof. Dr. Bernd Markert Lehrstuhl fur Umweltverfahrenstechnik Internationales Hochschulinstitut Zittau Markt 23 0-02763 Zittau F.R.G.

Dr. Norbert Nothbaum lnstitut fur Didaktik der Mathematik Universitit Bielefcld Universititsstrak D-33615 Biclefeld F.R.G. Prof. Dr. Pekka Nuortcva Department of Environmental Protection university of Helsinki Caloniuksenkatu 6 C 64 SF-00100 Helsinki Finland Mr. Ahto Oja Department of Environmental Protection University of Helsinki PB 21 SF-00014 Helsinki Finland Mr. Andrcas Pactz Deutsches Institut fur Normung Burggrafenstr. 6 D-10772 Berlin F.R.G.

List of’ Contributors

XXIX

Prof. Dr. Istvan Pais Department of Chemistry and Biochemistry University of Horticulture and Food Science Villanyi ut 29 - 3 1 H-1502 Budapest Hungary

Mr. Kalman Szente Plant Physiology Section Department of Botany and Plant Physiology Agricultural University H-2103 G6doll6 Hungary

Dr. Karoly Penksza Department of Botany and Plant Physiology Agricultural University Pater Karoly u. 1 H-2103 Godollo Hungary

Dr. Pal Sz6ke Department of Botany and Plant Physiology Agricultural University Piter Karoly u. 1 H-2 103 G6doll6 Hungary

Dr. Robert W. Puls Robert S. Kerr Environmental Research Laboratory U S . Environmental Protection Agency P.O. Box 1198 Ada, OK 74820 U.S.A.

Mr. Zoltan Takacs Plant Physiology Section Department of Botany and Plant Physiology Agricultural University H-2103 GSdijllo Hungary

Dr. Michael H. Ramsey Environmental Geology Research Department of Geology Imperial College London SW7 2AZ U.K. Prof. Dr. Walter Rast Water Resources Division US Geological Survey Austin, TX 18753 U.S.A. Dr. Volker Schenk Erftverband Pfaffendorfer Weg 42 D-50126 Bergheim F.R.G.

Dr. Sindor Toth Department of Botany and Plant Physiology Agricultural University Piter Karoly u. 1 H-2 103 God6116 Hungary Dr. Zoltin Tuba Plant Physiology Section Department of Botany and Plant Physiology Agricultural University H-2 103 God6116 Hungary Dr. Gabor Turcsanyi Department of Botany and Plant Physiology Agricultural University Piter Karoly u. I H-2103 Gijd611B Hungary

Dr. Roland W. Scholz ETH, Swiss Federal Institute of Technology Chair for Environmental Science Natural and Social Science Interface RamistraBe 101 CH-8092 Zurich Switzerland

Prof. Dr. Dieter Uhlmann Sektion Wasserwesen Technische Universitat Dresden Mommsenstr. 13 D-0 I069 Dresden F.R.G.

Dr. I r k Siller Department of Botany Veterinary University Rottenbiller u. 50 H- 1077 Budapest Hungary

Dr. William G. Warren Science Branch/CODE Department of Fisheries and Oceans P.O. Box 5667 St. Johns, N F AIC 5x1 Canada

XXX

List qj’ Contributors

Prof. Dr. John G. Watson Desert Research Institute University of Nevada System Energy and Environmental Engineering Center 5625 Fox Avenue P.O. Box 60220 Keno. NV 89506 U.S.A. Dr. Erich Wcbcr Institute for Inland Water Research GKSS Research Centre Heydeckstr. 9 D-39104 Magdchurg F.R.G.

Prof. Dr. Barbara Zielinska University o f Nevada System Energy and Environmental Engineering Ccnter 5625 Fox Avenue P.O. Box 60220 Reno, Nevada 89506 U.S.A. Prof. Dr. Zueng-Sang Chen Department of Agriculturitl Chemistry National Taiwan University Taipei, Taiwan 106 Republic of China

Part I Historical Aspects

Environmental Sampling for Trace Analysis

Edited by Bernd Markert 0 VCH VerlagsgesellschaftmbH, 1994

1 History of Sampling Demonstrated on the Ore Mining Industry Empirical and Theoretical Approaches * Gunther Kraft

The beginnings of sampling go back to the ore mining and metallurgy of the Middle Ages, i.e. the production of non ferrous metals, particularly precious metals and these beginnings were - inevitably - purely empirical. Georgius Agricola writes in Book VII of his famous work “De Re Metallica” in the year 1556: “Sampling of ores which serves to determine the metal content only can be distinguished from melting of the ores by the smaller amount of used material. By melting of smaller amounts we learn whether melting of bigger amounts will bring profit or not. If the metallurgist did not use these methods of investigation carefully, as said before, the melting of ores to metals would sometimes only cause loss or at least not be of benefit. We sample minted alloys, which we call coins, in the following way: smaller silver coins which are taken from the top, from below and from the sides of a heap are well cleaned firstly, then melted down in a crucible and the melt either granulated or poured to flat plates from which flakes are produced. Bigger coins weighing 1 drachm, 114, 1 /2 or even 1 ounce are hammered flat. One takes 1/2 pound of the granules, the same amount of the flakes and in the same way another 1/2 pound. Each amount is filled separately in small paper bags. The material obtained in this way is cupellated with lead.” The following quotation proves that even then double determinations were made : “Then you take the silver grains out of the cupel and free them from slags. If one of the scale pan in which the grains were put, is not pressed down more than the other one, but the weight of both grains is equal, then our sample has no mistake. But if one scale pan hangs down more than the other, then the sample is faulty and has to be repeated.” “Alloys of copper and silver are sampled in the following way: The sampler cuts pieces out of an ingot of copper, small pieces from small ones, medium-sized pieces from medium-sized ones and big pieces from big ones; the small pieces of the size of half a hazelnut, the big pieces not bigger than a chestnut, the medium-sized ones of a size in between. He takes these samples from the middle of the bottom of each ingot, puts them in a new, clean, three-cornered crucible and adds a piece of paper with the weight of each ingot of copper and how many ingots there have been.” This way Agricola. Then it still took a very long time, approximately to the end of the last century, until these purely empirical beginnings slowly became a kind of theoretical basis

* This article has been previously published in: Kraft, G. (ed.), 1993: Sampling in the Non-Ferrous Metals Industry, Trans Tech Publication, Clausthal-Zellerfeld.

G. Kraft

4

of sampling. It is virtually impossible to try to understand the development in detail. However, some stages can be well determined. First to be mentioned are the works of Reed (about 1880 to 1885) who established a connection between the size of a sample and the particle size of the material to be sampled in such a way that the amount of material to be taken as sample has to be proportional to the cube of the diameter of the biggest particle in the substance. As an example: if a sample amount of 3 g was right for a biggest particle of 0.1 mm, so a sample of about 1 t would be necessary for a particle size of 10 mm di amctcr . 3 Among others Richards (1903) did not agree to this v-rule, He took the view that just the composition of the biggest particles represents more the average of the 2 total material than the smaller particles. Therefore he pleated for a v-rule and additionally distinguished between types of ores. As an example: a sample weight of 2.25 g (similar to Reed's example) would only be sufficient for very poor or very uniform ores when the biggest particle which passes the mesh sizes of the sieve used is 0.1 mm, whereas for very rich or very inhomogeneous ores as much as 4.5 kg would be required. The values resulting from the derivations of Richards were compiled as a nomogram (Fig. 1) by Taggart (1948),which almost until today has been considered as the guide for ore sampling. Seeking a more exact, mathematical control of sampling Brunton (1895) proceeded on the assumption that the error relating to the amount of sample needed mainly originates from the presence of particles of richest content and biggest volume. The essential parameters are the following three quantities:

.-

16'

16'

I

10

10' mm lo3

Fig. 1. Dependence of sample weight on particle s i x and character of ore (from Taggart 1948). (a) Ores with very small or very uniformly distributed metal contents; (b) ores with small or uniformly distributed metal contents; (c, d) ores with medium contents and normal metal distribution; ( e ) rich ores or ores with irregularly distributed metal contents; (0 very rich ores or ores with very irregularly distributed metal contents.

History of Sampling

5

weight of the desired sample; obviously the finer the material is crushed, the smaller the sample should be the ratio of the contents of the richest pieces to the average content the specific gravity of the richest pieces; the higher the specific gravity the bigger its influence on the result. 3

The result of his examination is again a I/-correlation of the crushing degree with the sample weight.

D

=

2.1544

f . s . n(k

-

C)

D = mesh size of the sieve, cm (corresponds to the crushing degree) W = sample weight, kg k = percentage of the component of interest in the richest mineral c = average percentage s = specific gravity of the richest mineral n = number of biggest pieces of the richest ore f = ratio of the experimentally found weight of the biggest particle of richest material passing the sieve used and the weight of the biggest cube of richest material which could pass the same sieve (i.e. a kind of factor for the shape) p = allowed error, YO Of these quantities, W and p can be defined at will; k , s and c are either known for a given ore or can determined easily, f can only be determined empirically. In extensive mesurements the author found values between 1 and 6 for different ores of a milling degree of 0.05-0.1 mm. The reason for this big discrepancy in firstly the variability of the ores themselves and secondly the fact that many big pieces can be longish and thus pass through a sieve of a nominally smaller mesh size. Also n, which more or less is a measure for the quality of mixing, can only be determined experimentally; the values found are around 3. If the quantities W , p, s, f and n for a given material are summarized to the new constant R, the equation is simplified:

Gy (1955) obtains a very similar result from his derivation, which contains as a new factor the variance of the sampling error. His equation is: P.O2

~-

d3

-

const (c)

sample weight variance of the relative error which is made (or can be made) during sampling related to the contents of the component to be evaluated d = size of the biggest pieces ( = mesh size of the sieve which holds back 5 - 10% of the material) P

=

0’ =

6

G Kruft

c = a constant which characterizes the nature of the ore to be sampled. It includes: - a value a/a, the portion of usable ore, which is contained in thc lot to be

sampled the specific gravity 6, of the usable ore (if u / a < 70%) or the specific gravity 6, of gangue (if U/LY > 70%) - a parameter 1 which characterizes the crushing degree of the material but cannot be estimated easily a = content of sought element in the lot TX = content of the same element in the richest ore. -

Gy found the following correlations: I approaches one the purer the material is and/or the finer it is millcd. On the other hand this error becomes smaller the coarser the particles are compared to crushing degree. For example, if the biggest particles are 100 time the size of the medium crushing degree, I becomes approximately 0.05 only. But if all the material is milled to the crushing degree, I becomes 0.8. Of course, there was no shortage of efforts to solve this problem of sampling purely mathematically. Only the names Mika (1928) and Baule/Benedetti-Pilcher (1928) shall be mentioned here. In each case probability calculus was the basis; but finally only for two-component-systems could solutions be developed. Therefore these approaches shall not be further pursued here, although work still continues on their sophistication (i.e. Wilson, 1964). The most modern approach and perhaps even the solution to the sampling problem is the mathematical-statistical formulation (e.g. Kraft, 1978). It says vcry simply and pragmatically that

In words: take so many increments N , with any constant weight, of the material to be sampled, e.g. shovels of a weight of 1 kg each, so that this number is equal to the square of the quotient of the error of the complete sampling operation, including all the following dividing operations and the analytical determination s (s = standard deviation, t = Student factor, e.g. 2 for a statistical security of 95%) and the allowed uncertainty F U of the obtained result. The total weight G of the sample taken then is: G=N.g = weight of the increment). So we see: no data are needed on form factor, milling degrees, biggest particles, specific gravity or even values that can only determined empirically. Instead the standard deviation s, which indirectly includes all these factors, gains central importance. It must be repeatedly determined for each material to be sampled. This can be done, for example, by multiple samplings of at least 10, preferably 20 samples, which have to be assessed and analyzed separately. At a glance this seems to be excessive. But it is always the cheaper and above all the safer method whcn similar materials, for example concentrates from the same mine, have to be sampled over a longer period of time.

(g

References Baule, B., Benedetti-Pilcher, A . (1928). Z . Anal. Cketn. 74, 442. Brunton (1895), Trans. Am. Inst. Min. Eng. 25, 826. Gy. P. (1959, Firnc~tull8,B 199. Kraft, G. (1978). Erzmrtull31, 53. Kraft, G. (ed.) (199.3). Sumpling in the Non-t.i.rrons Metuls Industry. Trans Tech Publication, Clausthal-Zellerfeld. Mika, J. (1928). Z. Anal. Clrrm. 73, 257. Reed ( 1 881/82), School of' M i n ~ sQuurtedv 3, 253. Reed (1 884/85), Sclzool oj' Mines Qurrrierly 6 , 35 1. Richards, R. H. (1903), Ore Drcming ~ i n dConcentration, Vol. 2. London, New York, p. 843. Taggart, A. ( I 948), H(intlhook of Miricrrtl Drc,ssing, Ores ond Industrial Mineral,y. John Wiley, New York, p. 161.

Part I1 General Aspects

Environmental Sampling for Trace Analysis

Edited by Bernd Markert 0 VCH Verlagsgesellschaft mbH, 1994

2 Genera Aspects of Environmental Sampling *

2.1 Introduction The first and in many instances most important step in every determination in analytical chemistry, namely the sampling, unfortunately rather seldom receives due attention. This is, as already formulated by many authors, because mistakes made during sampling can no longer be corrected. With today’s possibilities of trace and ultra-trace analysis, sampling has gained more significance. Through the development and application of instrumental measurement methods analytical chemistry has developed capabilities that had to be considered Utopia several years ago. Mass-spectrometry with a11 inductively coupled plasma for its ion source (ICP-MS) serves as an example here: within about ten years this method of measurement has become a workhorse of the trace analyst, since with its help detection limits in the range of 10 to SOpg/g can be achieved, in ideal instances, for around 60 elements (pure standard solutions). Nevertheless, the chemical analysis of a material not only consists of the measurement of the material in one of the available analysis apparatus, but also of a multitude of single steps, which must be coordinated: definition of the analytical problem, sampling, storage of samples, preparation of samples, measurement, evaluation, samplc comparisons (standard samples, reference materials) and assessment of the results. While in many instances the measurement process is founded on physical principles (for example, absorption and emission of electromagnetic radiation, separation of masses in electrical and/or magnetic fields), a well-grounded knowledge of chemical and often of biological reactions is necessary for the other steps [I]. In the framcwork of this introduction, only the processes which play a role in sampling will be discussed. Here the term “sampling” is not to be equated with the term “sampling” as used in Anglo-Saxon scientific literature, since there the term includes all processes which must be carried out before the measurement, hence also storage and preparation of samples (solution, disintegration, separation, concentration, etc.). In the course of this discussion on sampling, the procedures of sample storage and conservation are treated, since correct and skillful sampling includes these matters. Nevertheless, it must be noted that each sampling procedure is strongly connected with storage, preparation, analytical method and assessment of the results. The connection between sampling and sample storage and conservation is illustrated with an example from water analysis: in ground water samples available under “anaerobic” conditions, iron is found predominantly in the oxidation state + 2. With incorrect sample taking, storage and conservation, oxygen infiltrates into the

*

This article has been prcviously published in Nudir. Chen7. Tr.r/i.Lob. ( I 992) 40, M 2 (in German).

12

I]. Hojynxinn

sample, the redox potential of the solution changes, and the iron is carried over into the oxidation state 3. Since a pH value in the range of 5 to 8 exists in natural water, iron precipitates as Fe(II1)hydroxide (oxidehydrate). As a result, not only too little iron is found in dissolved state, but also the concentration of other elements decreases in the solution through coprecipitation, or through adsorption to the precipitate. In such cases sampling can only be considered correct if the problems of sample storage and conservation are solved. As one can recognize from this, in sample taking, storage and conservation, grave mistakes are possible which cannot be compensated by any measurement, no matter how correct and reproducible [2, 3,4]. In this chapter, further aspects will be discussed which must be taken into consideration in sampling.

+

2.2 Terms and Definitions In the literature there exists a multitude of special expressions concerning the subject “sample”, which frequently are not explained, or receive specific interpretations by the various authors. In accordance with selected publications [5, 6, 71, a compilation of several important terms follows, with short explanations provided: -

-

-

-

-

Single or spot sample: Material taken from the bulk quantity in a single sampling procedure. Random sample: One or more samples taken successively in short time intervals. Short-time mixed sample: Composed of a mixture of at least live random samples gathered in intervals of not less than two minutes. Raw, mixed, or composite sample: Different designations of the sum of all single/spot samples processed collectively. Intermediate sample: Created through reduction of the collective sample, and ready for processing. Sample set: Parts of the sample that accumulate during sample preparation, and need to be kept separate (for example, metallic - oxidic, fine - coarse, gaseous - liquid - solid). Final sample: The sample amount obtained from the sum of all single/spot samples (not necessarily identical to raw, mixed, or collective sample, since every single sample may have been separately processed), from which the analysis sample is taken. Analysis sample: The end product of the sampling and sample preparation, which has been prepared for the intended investigations.

The technical process of sampling mostly is evident from the notation itself (drill sample, filter sample, impactor sample, saw sample, strike sample, scoop sample, prick sample, etc.) and does not need to be elaborated on here. On the other hand, the sampling types that arc above all relevant to quality control must be presented with appropriate commentary: -

Time-dependent sampling: Single samples of identical volume are taken in previously established time intervals (in case of continuous material flux this corresponds to mass- or volume-proportional sampling).

General Aspects of Sumpling

13

Volume-dependent sampling : Single samples of identical volume taken in variable time intervals which are corresponding to the flux (flow rate). - Flow-dependent sampling: Single samples taken in identical time intervals with volumes corresponding to variable flow rate. - Continuous sampling at fixed flow rates: Continuous sampling of a constant volume. - Continuous sampling a t variable flow rates: Sampling of variable volumes which correspond to the flow rate. - Surface samples: Sum of all spot samples taken at the surface of, for example, stagnant waters. - Depth-integrated samples: Sum of all spot samples taken a t the same place at different depths of, for example, stagnant waters. - “Isokinetic” sampling: Used in heterogeneous systems (chimneys, tubes, rivers) where the sampling is done through a suction tube and at a rate that the velocities in the tube and in the main stream are equal. -

2.3 Aspects of Sampling Upon examination of pertinent literature and detailed discussions with colleagues and coworkers, a considerable number of viewpoints arose in response to the question: “Which influences are to be considered in sampling?” It certainly depends upon the analyst’s line of work as to which influence he considers most important. In any event, it can be stated objectively that consideration of sampling conditions becomes more important as more demands are put on the quality of the analytical results. The lower the concentration of the element that is to be determined in the sample, the higher the desired precision of the result, and, the better the desired temporal or topochemical resolution of the analysis, the more thought has to be given to the sampling procedure. When purchasing a sampling system, it is wise to test it for a few weeks before actually buying and paying for it.

2.3.1 Location, Place, and Position of Sample Removal This parameter has two completely different aspects. When a massive material must be tested to determine its quality, it is necessary to carry out the analysis according to statistical viewpoints. The metal and ore manufacturing industry has investigated this area, and it is dealt with at length in the literature. G. Kraft has proposed sample taking schemes for a number of metals and materials, and for various problem statements [ 5 ] : - Arrangement of bore holes on pig iron slabs, or sections of slabs; - saw pattern for lead blocks (also for large numbers of blocks); - arrangement of bore holes on lead blocks and soft lead blocks;

P Hoffmcinn

14

taking sample cuttings from two diagonal surfaces of a casting format; drilling model for copper plates; - sector sample from a steel club; - drilling diagram and arrangemcnt of the saw cuts for a zinc alloy bar. -

-

Corresponding and expanded proposals are found in F. Pottkamp [8]. A completely different aspect results from the search for the most appropriate sampling location for problcms i n the area of cnvironmcntal protection, or when invcstigating causes of contamination. In such cases one must not proceed statistically, but sampling must be carried out systematically near the source (for example, in the direction of the wind, or downstream). In the analysis of different water samples the sampling location also requires manifold description. This can be seen in examples found in forms used as a sampling protocol, which are listed in Section A1 1 -A20 of the German Standardized Procedure for Water, Waste Water, and Mud Analysis [9]. ~

~

-

Waste water: samplc taking location; stagnant water: sample taking location, water level; drinking water: sample taking location, armature, hydrant; flowing water: bank (right, left), river center, sample taking depth, coordinates.

2.3.2 Size, Quantity, and Volume of the Sample The size of the single sample is dependent upon the expected concentration of the element to bc detcrmincd in the material, the analysis procedure that is to be used, the precision of the result that is to be evaluated, and the state of distribution of the material. Thc grcatcr thc volumc ofa liquid sample from which the element to be determined can be concentrated, the more prccisc the determination can be. Experience shows, however, that liter samples represent an optimal measure, while 5-liter samples are very difficult to manipulate, e.g., in waste water laboratories. In addition, with solid matter, the size of the sample is determined by the grain size of thc matcrial and the homogcncity of distribution of the element that is to be analyzed. A nomogram by Taggart, in which the minimum weight of an average sample can be determined as a function of the grain size of the largest grain, and of the content and distribution of the element to be determined, was developed for ores, but has also been applied to earth samples, rocks, salts, and grain [5, lo].

2.3.3 Number of Samples to be Taken The number of samples that need to be taken in most cases depends upon the problem, or the way the problem is posed. The necessary number N of single samples is determined according to the following equation: N

=

(ts/L)’

General Aspects of Sampling t =

15

Student-Factor ( = 1.98 for a statistical certainty of about 95%);

s = estimate of the standard deviation of the arithmetic mean value of all single

values (preliminary examination of at least 20 single samples using absolutely identical procedures: sample taking apparatus, sample quantities, component to be determined, measurement procedure); L = tolerable uncertainty (stated in YO)of the result. This equation, as well as its application, has been explained in detail by F. Holstein, using practical examples [I 11.

2.3.4 Time, Duration, and Frequency of Sampling These aspects have a significant influence when the material that is to be analyzed is exposed to a chronologically changeable influence, i.e., if a streaming system is under consideration, or if the way in which reactions proceed needs to be observed. The choice of these parameters is dependent upon - from an analytical standpoint - the speed of the observed changes only. Investigations by the Department of Water and Waste Management in Hagen, Germany (121 have shown that an official supervision of waste water is necessary “around the clock”, in order to register compliance with legal limits. In reality, however, the times of sample taking are determined by the working hours of the personnel in many instances. The duration of sampling in water and waste water supervision is officially regulated according to decisions regarding water laws. One distinguishes between the random sample, the short-time mixed sample, the two-hour mixed sample, and the 24-hour mixed sample, which corresponding to the problem at hand are established and defined in the form of norms by the water authorities on the basis of the relevant waste water management ordinances according to { 7a WHG [13]. The time requirement for taking a sample for gas analysis from liquid metals plays an important role in the metal manufacturing industry, whereby the diffusion coefficient for the gas in question (for example, hydrogen) at different temperatures is decisive. Diverse, and in part very costly, sampling apparatus are applied here: probes, suction guns, vacuum pipettes, suction pipettes, vacuum suction molds, and others. The frequency of sampling concerning waste water is officially regulated and depends on the following criteria: danger of harmful substances in waste water, special risk factors, effect on the pre-flooding system, utilization demand on the pre-flooding system. Four frequency levels of sampling result from this: twice per week, twice per month, four times per year, and six times per year. In many instances a continuous measurement of analytical quantities is desired. In one of the further steps, the necessity of automatic sampling and measuring may arise. Only a few analytical procedures, due to their character, are suitable for that purpose. X-ray fluorescence analysis has often been used for such purposes, especially its energy dispersive version, with radionuclides for excitation sources [ 15, 161. The efforts to develop Flow-Injection-Analysis (FIA) procedures and hyphenated methods such as combinations of chromatographic separation procedures with sensitive measuring systems (GC/MS, IC/ICP-AES, HPLC/AAS, IC/TXRF) have

16

P. Hofjtnunn

to be interpreted with regard to the desire to create continuously registering methods without any concrete sampling. Reports about automatic sampling are scarcely found in literature [17].

2.3.5 Homogeneity of the Sample In heterogeneous multi-phase systems such as aerosols, fogs, suspensions, emulsions, sediments, sludges, etc., the difficulties in sampling increase exponentially, and one has to accept the fact that for some of these mixtures no useful sampling devices are available. The homogeneity of the sample always plays a role when the material under investigation is not mixed thoroughly, when solid substances are suspended in the solutions, or when solid and liquid substances are distributed in gases. A thorough treatment of this complex of problems can be found in a paper by Ortner [18], where the determination of heterogeneously and homogeneously distributed trace impurities in refractory metals is discussed. Using several examples, it is shown in this chapter, that homogeneity is not ensured in all cases where thorough mixture of all components can be assumed. Inorganic as well as organic components exist in varied chemical forms and distributions, depending on their origin and history. For example, iron in a bivalent or trivalent state can exist in atmospheric water-based samples (clouds, fog, rain water). Thereby the distribution of the oxidation states depends on the pH-value and on the redox potential. These parameters are also responsible for the existence of Fe(J1 I) in dissolved, colloidal, or fine to coarsely dispersed form. So it is not irrelevant sampling is done without filter, with filter, or with ultrafilter [19]. In this example, one recognizes that the sampling type is directly related to the analytical problem statement. The sampling procedure is fundamentally different if the total iron concentration, the dissolvcd portion, or the portion of a definite valence is to be determined. This is similarly valid for mineral oil samples, in which trace elements can be present as solutions of organo-metallic compounds or as suspended and finely dispersed particles [20]. Sampling from natural gas can lead to an incorrect result if it is not observed that the removed sample and the area that is to be studied are geologically separate and therefore not in balance. In solid materials unexpected inhomogeneities can appear. Also glasses and alloys produced from smelting show that a homogeneous distribution is not given for all components. The production technique of antique glasses has not progressed so far that homogeneity can be presumed. In the study of such pieces, taking small samples for analysis can lead to results that are not representative of the whole object. The following observation made in modern materials rests on a different foundation. From a production charge of 200 t of steel, a random sample of 100 g is taken, 100 mg of which is examined by X-ray fluorescence analysis. The outcome of the measurement is representative for the complete amount, although only about 10-9th part was analyzed [21]. On the other hand, small eroded particles (pg-scale)

General Aspects of’ Sumpling

17

from high-grade steel tools (10 g-scale) do not show the composition of the original material, since here the single sample comes from an inhomogeneous structural region [22]. As is shown by this example no generally valid level of reduction for a sample can be given, and the decision must be made in each case. One of the most complicated sampling tasks to perform is the investigation of scrap material, waste dumps, and mining rubble, since in these instances the inhomogeneity can be especially striking. Among experts on the subject, the scrap material waste dump on which a container with 1 kg of mercury (80 mL) is found, is discussed as the archetypical example. In this area the widest spectrum of sampling methods is applied, from manual probes to heavy excavating equipment. Especially here, the expert and experienced “human” sampler is especially valued. Another example about sampling from inhomogeneous solid body accumulations shows a different human influence. With the help of a mobile X-ray fluorescence analysis apparatus, the composition of silver mining rubble in the Black Forest, Germany, had to be tested for its content of precious metals. Samples were taken from all over the dump area, then crushed, ground, mixed, separated and measured [23]. All results agreed within a margin of error with those that were obtained by other groups years earlier. An exception was the element uranium, for which distinctly lower concentrations were found in the newer measurement. The reason was a collection of radioactive samples that in the meantime had been taken by hobby mineralogists, who had scoured the dump with suitable detectors. The suspicion that the number of chunks containing uranium was depleted only on the surface, was confirmed by a new set of samples, where deeper layers and rocks that could not have been moved by human force were also included in the considerations. The values measured earlier were also confirmed for uranium by this second set of measurements. This example shows that for correct sampling, the history of the materials in question must also be known [l].

2.3.6 Contamination of the Sample This section, as well as the following concerning the topic of “losses in the sample” can, in spite of their enormous significance, be kept rather brief, since a survey article exists that deals with all these problems as extensively as necessary, with comprehensive tabulated material and a bibliography of no less than 577 quotations [24]. Since this article unfortunately is not yet generally accessible, and since it does not appear to make sense to limit oneself to singular quotations, the contents of the following paragraphs are tightly patterned after that text [24]. Contaminations can occur, for example, through the following influences: -

-

air: industrial area, street vicinity, forest area; premises: walls, ceilings, floors; furniture: tiles, wood, synthetics, history of the laboratory; human: clothing, cosmetics, medicines, smoke of cigarettes/cigars; reagents: acids, water, complex forming agents;

18

-

P.Hofrmunn

receptacle materials: containers, pipettes, glass, quartz, carbon fiber, platinum, and synthetics such as Polyethylene (PE), Polytetrafluorethylene (PTFE), and Polypropylene (PP).

The following set of problems puts the significance of contamination into the right light: reagent-grade water (repeatedly distilled, deionized, filtered, purified by sub-boiling method) is used in inorganic, as well as in organic, trace analysis. For inorganic trace analysis, containers made of PE or PTFE are preferred, while for organic analysis glass or quartz bottles are used. Nevertheless, it has been observed that organic substances, dissolved out of synthetic materials, lead to systematic errors in inorganic trace analysis through reactions (formation of complexes, oxidation) with the trace elements that need to be determined. contamination problems turn out to be the analyst's most serious problems at concentrations below pg/g. It is strongly advised to wear gloves while working with such samples, and if possible, to work in a clean environment (clean-room conditions, clean-benches, closed evaporation or steam vents). It is also important to mention that such work can only be performed by trained and qualified personnel. On the cleaning of receptacles, various suggestions are made corresponding to the various problems [18,24,251.

2.3.7 Losses in the Sample It is also necessary here to refer to the abovementioned survey article [24]. Losses can occur because of adsorption of elements or their compounds on rcceptable walls or on particular components (precipitation, suspended materials), as well as through evaporation. With sorption, the most significant effects are observed at the sides of glasses and in hydroxide precipitates, since there exchange reactions are decisive. The losses of trace elements are most important in solutions of low dielectric constants (e.g., organic solvents). Synthetics and quartz show noticeably lower adsorbing power and for this reason are most suitable for storage of aqueous solutions. It must be further mentioned that surfaces of receptacle materials change through time and contact with various chemicals, whereby the adsorbing power usually rises drastically. This observation can be made especially clearly when bases come into contact with glass walls, or if hydrofluoric acid comes into contact with quartz. Evaporation is observed with mercury in elementary form if conditions for reduction exist in the solution, while other elements evaporate as oxides (e.g., As, Sb, Re), halides (e.g., elements of groups IV, V, and VI of the periodic system), or hydrides ( e g , As, Sb, Se, Te), or they may diffuse through the sides of receptacles (synthetics). In the analysis of organic solvents (e.g., hydrocarbons, halogenized hydrocarbons), the effect of evaporation must be especially taken into account. Standard solutions stored in plastic bottles alter their concentrations by about I YOper year, since the solvent, water, escapes through the receptacle walls.

General Aspects of Sunipling

19

2.3.8 Sample Storage and Conservation Samples that cannot be processed immediately after sampling must be long-term protected against contamination, losses or other changes. So it makes sense to design the sampling in such a way that the sample can be stored and conserved without too much trouble. The sampling receptacles must be selected in order to reduce contamination of losses to a minimum also in long-term storage. When storing samples, biological activities, hydrolysis, and evaporation of parts of the sample must be suppressed, too. This can be accomplished with the following chemical and physical operations: acidifyingto pH 1.5; minimization of adsorption; prevention of metabolic processes of microorganisms; avoidance of hydrolysis and precipitation. Cooling and freezing: Reduction of bacterial activity, even this simple procedure has to be carried out very carefully. A paper was published describing an accelerated oxidation of nitrite to nitrate and of sulfide to sulfate by freezing an aqueous sample to about -20 "C [27]. - Addition of complex forming agents: Production of anionic complexes reduces the danger of losses through adsorption or evaporation. - Filtration: Prevention of reactions of particles with dissolved components. - UV-radiation: Destruction of biological and organic components, in order to avoid formation of larger complexes, frequently in combination with H 2 0 2 -additions. -

It must not remain unmentioned that samples conserved in a such manner can also be stored for a limited amount of time only before changes must be suspected. In general, a storage time of up to 28 days is recommended, but in some cases not more than six hours [6]. Further suggestions about sample storage and conservation can be taken from previously mentioned review articles [24]. Samples in which the chemical form ofelements or their state of distribution are to be investigated [26], can just not be stored and conserved at all, since in the above mentioned processes conditions change so that the original state can no longer be recognized.

2.4 Guidelines and Norms Analytical results have become the basis of planning, decisions, and legal proceedings [28]. Quality control of raw materials and products, the development and production of chemicals, pharmaceuticals, foodstuffs, pesticides, etc. as well as the monitoring of our environment, are founded on highly developed, integrated analytical procedures. These procedures must be painstakingly adhered to, for reasons of reproducibility and to guard against legal problems. This is why industry and authorities were forced to pass binding regulations, guidelines, norms and laws, according to which objective decisions can be achieved. These regulations include the complete analytical procedure and thereby also detailed specifications with regard to sampling. The following thoughts are the basis

20

P. Hojfmann

of all publications: no step is permitted to be left to chance, and the development of an analytical outcome must be completely retraceable. For this reason all instructions and information exist in writing. Subsequently, the structure and the contents of work instructions, standard operation procedures (SOPS),standard test procedures, test regulations, work notes and protocols are demonstrated in the form of some selected examples, and these may serve as models. It would be beyond the scope of this survey to attempt mentioning all publications of this type.

2.4.1 Quality Assurance A work guide on sampling for quality control must first of all define the objective and the scope. In an introduction it is established which analyses and tests on which materials are carried out. The description of the manner in which to proceed must be composed of thc following segments: -

-

-

-

Stipulation of the localities where the sampling has to take place. Containers whose contents have to be tested must be marked clearly, set up separately, visually examined and cleaned. Furthermore, waybills must be at hand. Sampling apparatus must be stipulated: type, area of use, cleaning. Preparation for sampling: description of clothing and personal cleaning and security precautions. Handling for sampling apparatus, preparation of labels/test report forms. Sampling: comparison of all statements on waybills and containers. In case of inconsistencies, an office to report to must be established. Contamination-free sampling (sometimes very specific instructions with dangerous, toxic or otherwise problematic materials). Prescribed number of samples, location of sampling (surface, under the surface, at the bottom of the receptacle). Homogenizing of material (melting, shaking, stirring). Observation of the article (smell, color, grain size, foreign substance). Closing and marking the container. Filling out the sampling record. Release of article, or rejection (documented by stickers). Enclosure: sample stickers, record forms, instructions.

The analysis of ores, cement, concrete, road construction materials, ceramic materials, bituminous coal, coke, liquid and gaseous fuels, lubricants, emissions, imissions, drinking water and waste water, raw iron, steel, metals alloys, painting materials, and filter paper is of such a large and general significance that numerous German (DIN) and international (ISO) norms were formulated for the analysis and therefore also for the sampling of these materials. A summary of these joint publications and guidelines is found in the “Handbuch fur das Eisenhuttenlaboratorium” [29]. The I S 0 norms for sampling of iron ores are used here as a model and cxamplc [30, 311.

General Aspects of Sumpling

21

2.4.2 Environmental Protection In order to obtain a justifiable or at all meaningful analytical result with regard to strains on the environment, sampling norms must likewise be observed which have already been cited in preceding sections on water, waste water and mud [9, 131. Therein the following matrices are included: waste water (A ll), stagnant water (A 12),ground water (A 13), raw water, drinking water (A 14), running water (A 15), ocean (A 16), falling wet precipitation in the liquid state (A 17), water from mineral and medicinal springs (A 18),swimming and bathing water (A 19), tidal water (A 20), and cooling water for industrial use (A 22). In spite of this, these norms must also be critically handled and constantly improved [32]. The norm in the subject of sample conservation is still being prepared [33]. Corresponding international norms that gain more and more significance with the integration of Europe are to be observed as well [34, 35, 361. For sampling in the atmosphere, the VDI guidelines above all are valid [37,38] in which the analysis - and therefore also the sampling - of components like sulfur dioxide, carbon monoxide, nitrogen dioxide, ozone, dust, or hydrocarbon is determined. Monographs treating this subject range can only be cited selectively [39,40,41,42]. The various collection techniques, like filter collection, multi-stage gas sampling tubes, adsorption on surface-active substances (Amberlit, Chromosorb, Silicagel, Tenax, etc.), are especially considered in [43]. In this context it has to be mentioned that at present bioindicators are also used officially in the investigation of air pollutants [44,45,46], whereby sampling takes on a new aspect. As already mentioned, sampling in solid materials of the environment (soil, waste, garbage, compost, sewage, cinders, ashes, flora, fauna), which are therefore especially afflicted by inhomogeneity, represents a particularly serious problem. Especially here the experienced human sampler, who can skillfully react to a given situation, is irreplaceable. He is aided by guidelines which have yet been cast in the form of norms or laws [46,47,48,49]. In summary, it must be stated that complaints are brought up frequently about the lack of proposals on sampling in various areas. Intensive studies of this matter, however, have shown, that many references can be found in the original literature and in monographs. Certainly not all problems have been solved, and many regulations or proposals require improvement. But one must also point out that sampling, in practice, must choose a middle course between scientifically unobjectionable procedure and economically justifiable expense. The importance of this object is shown by the fact that a “Sampling Club” is planned in the United Kingdom, which will assist its members with problems, give information, and encourage the interaction with other colleagues [50]. Acknowiedgement. In this sense I would like to thank all those who made it possible for me to deal with the complex field of sampling from a very diverse set of viewpoints - 1 would especially like to thank J. Bartl, J. Dahmen, G. Grubert, H. Gudernatsch, K. Hanewald, T. Hofmann, W. Kreisel, K. H. Lieser, B. Markert, K. Ohls, H. M. Ortner, G. Rasenberger, R. Voigt, H. Wunsch. The translation was carried out by M. and G. Kluckner.

22

P. Hojfmann

2.5 References [I] Baiulescu, G. E., Dumitrcscu, P., Zugravescu, P. G. (1991) Sampling. New York: Ellis Horwood, p. 12ff. [2] Kratochvil, B., Wallace, D., Taylor, J. K. (1984) Sampling for Chemical Analysis, Anal. Chem. 56. 113R. [3] Kratochvil, B., Taylor, J. K. (I98 1) Sampling for Chemical Analysis, A n d . Chem. 53,924A. [4]Klockow, D. (1987) Zum gegenwdrtigen Stand dcr Probcnahmc von Spurcnstoffen in der freien Atmosphiirc, Fresenius 2. Anal. Chem. 326, 5. [5] Kraft, G. (1980) Probenahme an festen Stoffen. Analytiker-Ta.schenhueh, Bd. I . Berlin, Heidelberg, New York: Springer-Verlag. [6] Gudernatsch. H. (1983) Probenahme und Probcaufarbcilung von W Taselzenhucti, Bd. 3. Berlin, Heidelberg, New York : Springer-Vcrlag. [7] Gassen, M.. Woffen, B. (1978) Zur Hiiufigkeit der Probenahme und der Bcurtcilung dcr Leistungsfiihigkeit von Klaranlagen, pf-Wasser/Ahwusser 119, 455. [XI Pottkamp, F. (1980) Probenahme von Blei und Zink, Prohenuhnze - Thwrie und Praxis 36, 155K der Schriftenreihe der Gesellschaft Deutscher Metallhutten- und Bergleute. Weinheim, Deerfield Beach, Basel: Verlag Chemie. [9] Deutschos Einheit.si)c,rfahrc.n zur Wasser-, Ahwassrr- und Sr.hlamrnunter.suchung, Bd. 1. Weinheim: Verlag Chemie. [ 101 Kraft, G. (1980) Theoretische Grundlagen der Probenahme, Prohenahme - Theorie wid Pruxis 36, I ff der Schriftenreihe der Gesellschaft Deutscher Metallhiitten- und Bergleute. Weinheim, Deerfield Beach, Basel: Verlag Chemie. [ I I] Holstein, F. (1980) Probenahme von NE-Konzentraten (Beispiel: Kupferkonzentrate), Probenalzme - Theorie und Praxis 36, 93ff der Schriftenreihe der Gesellschaft Deutscher Metallhutten- und Berglcute. Wcinhcim, Deerfield Bcach, Basel: Vcrlag Chcmic. [ I21 Selent, K. (1988) Die amtliche Abwasserprobcnahmc, Prohenahnesc/iulung Wasser und Abwasser. Landesamt fur Wasser und Abfall Nordrhein-Westfalei, Diisseldorf, 1988. [ 131 DIN 38402, Teil 1 I - Probenahme von Abwasser. [ 141 Feichtinger, H.-K. (1980) Probenahme aus fliissigen Metallen zum Zwecke der Gasanalyse. Probenahme - Tlzeorie und Praxis 36. 237 der Schriftenreihe der Gesellschaft Deutschcr Metallhiitten- und Bcrglcute. Weinhcim, Dccrfield Bcach, Basel: Verlag Chemie. [ I51 Autorenkollektiv (Federfuhrung: H. Ehrhardt) ( 1988) Ront~en/luorrs~enzona/yse - An wendung in Betriehslaboratorien, 2. Auflage. Leipzig: VEB Deutscher Verlag fur Grundstoffindustrie. [16] Pilz, N., Hoffmann, P., Lieser, K. H. (1988) In-line Determination of Heavy Elements by Gamma Ray-Induced Energy-Dispersive K-Line XRF, J. Rndioand. Nucl. Chem. 130, I4 1. [I71 Stock, H . D., Grubcrt, G., tc Hecscn, D., Sclcnt, K . (1990) Dcr selbstentleerende Ruckstellprobennehmer - ncuc Chanccn der Abwasscriiberwachung, Gewiisserschurz - Wusser - Abwusser 118, 285. [IK] Ortner. H. (1980) Uber die Problematik dcr Probcnahmc bci Sondcrmclallen, Prohenahme Tlzeorie und Pruxis 36. 18 1 ff der Schriftcnreihe der Gesellschaft Deutscher Metallhiitten- und Berglcutc. Wcinhcim, Decrficld Bcach, Basel: Vcrlag Chcmic. [IY] Hofmann, H., Hoffmann, P. Licser, K. H. (1991) Transition Metals in Atmospheric Aqueous Samples, Analytical Dctcrmination and Speciation, Fresenius J . Anal. Chem. 340, 59 I . [20] Karchmer, K. H., Gum, E. L. (1952) A n d Chem. 24,1733, in: Koch, 0.G., Koch-Dcdic, G. A., Handbircli deer Spurenunulyse, 2. Aufl. Bcrlin, Hcidclbcrg, Ncw York: Springer-Vcrlag, 1974. [21] Ohls, K., Hoesch Stahl AG, Dortmund, personal communication. [22] Hoffmann, P., Paller, G., Thybusch, B., Stingl, U . (1991) Dctcrmination of Stainlcss Steel Constituents in Plastics, Fresenius J. Anal. Chem 339, 230. [23] Hoffmann, P., Lieser, K. H., Hofmann, T., Sommer, R. (1983) A Mobile Installation for Energy-Dispersive Multielement X-Ray Fluorescence Analysis for Application in the Field, X-Kuy Sprctrom. 12 (4). 175. [24] Licht, K., Jank, B., Birkhahn, J, Scharf, H., Spieles, M., Koehler, P., Winnefeld, C., Kulick, I.. Konranzinntion und Verluste - Ursachen ,fir systematische Fehler in der anorganischrri Spurenrmulyse. Carl Zciss, Jcna.

General Aspec f s

of

Sampling

23

12.51 Mart, L. (1979) Prevention of Contamination and Other Accuracy Risks in Voltammetric Trace Metal Analysis of Natural Waters, Fresenius Z . Anal. Chem. 296, 3.50. 1261 Takenaka, N., Maeda, Y. (1992) Acceleration of the rate of nitrite oxidation by freezing in aqueous solution, Nuture 358, 736. [27] Lieser, K. H . (1992) Spezialion: Eine Herausforderung fur die Analytische Chemie, GIT Fachzeitschrft , f i r das Lahoratorium 36, 293. 1281 Vogel, H. (1990) Cute Analytische Praxis, in Anulytikw Taschenhuch, Bd. 9. Berlin, Heidelberg, New York, London, Paris, Tokyo, Hong Kong: Springer-Verlag, p. 3ff. [29] Hand6uch , f i r das Ei.senhurr~nlahoruto~ium (1987) Chemikerausschufi des Vereins Deutscher Eisenhuttcnleute (ed.), Bd. 3A - Probenahme. Dusseldorf: Verlag Stahleisen mbH. [30] I S 0 3081 (1987) Iron ores - Increment sampling - Manuul method. [3 I] I S 0 3082 (1987) Iron ores - Increment.samplingand,sampleprepuration- Mechunicalmethod. [32] Gortz, W.. Grubert, G. (1986) Probleme bei der Entnahme von Mischproben fur die Untersuchung von Wasser und Abwasser, Gewusserschutz - Wasser- Ahwusser 86, 2 1. [33] DIN 38409, Teil 21, Konservieren von Proben. [34] I S 0 5667/1 (1980) Water quality - Sampling - Part I ; Guidance on the Design of Sampling Programmes. 1351 I S 0 5667/2 (19x2) Water qualit), - Sampling - Purt 2: Guidance on Sampling Techniques. [36] I S 0 5667/3 (1985) Wutrr quality - Sampling - Purt 3: Guidance on the Preservation and Handling of Samples. [37] VDI-Handhuch - Reinhultung dcr L u f f , Beuth-Vertrieb GmbH, Berlin - Koln. 1381 VDI-Kommission Reinhaltung der Luft (1987) Aktuelle Aufgaben iker MeJtechnik in der Luftrcinhaltung. Dusseldorf: VDI-Verlag. [39] Birkle. H. (1979) M 1 week at 25 C

$

s9ro

$

-r

$

P b

T

p

P-pestizide, OSHA 62

no

no

no

no no no

-

60 -480 L 1 Limin

24 L 0.1 Llmin

Sample size (mass or volume) Output capacity Provision for separation of other phases (Particles, drops, gas) Homogenization of sample Reduction ofsample into small pieces Cooling of sample -

GC, FPD

HPLC, UV

Analysis method (AAS, XRF, MS. GC, LC)

no no no

3030 L 0.1 - 1 L/min

gdS

gdS

phenol, cresol. OSHA 32

research. industry, environmental protection, occupational safety gas amines and alcohols (NIOSH 5217,S233 andothers) GC, FID

research, industry, environmental protection, occupational safety

Which phases are investigated Which components are investigated

ca. l o g

ca. 25 g

ca. l o g

research, industry, environmental protection, occupational safety

6/4/70 mm 150/75 mg

13/11~50mm 270: 140 mg

6/4/70 mm 100:50 m_g

Area of application

Operation of apparatus Power source (voltage) Transportable Necessary accessories (e.g. pump, etc.)

Dimensions (width. height. depth) Sample space size (width, weight, depth) Weight (mass)

Orbo-52 (small) SUPELCO SUPELCO 50 pieces 166.44 DM

SUPELCO

Orbo-49 P SUPELCO SUPELCO 10 pieces 360 DM

-

Name of apparatus Model Manufacturer Distributor Price incl. tax

SUPELCO

Orbo-47 SUPELCO SUPELCO 50 pieces 191.52 D M

SUPELCO

(continued)

Company

Tab. 3.

3 s

3

3

Q

N

m

glass, glass fiber filter, polyurethane foam, SUPELPAK 20 P

glass, silanized glass wool, SUPELPAK 70

Materials in contact with sample

excessive flow rate and/or collection time -

Yes no breaking open at sampling location Orbo-tube-cutter

excessive flow rate and/or sample collection time -

Yes no opening of the closure cap at the sampling location elution with toluene yes (after elution)

excessive flow rate and/or sample collection time

-

Yes no

breaking open at sampling location

Orbo-tube-cutter

elution of both beds (separately) with methanol

yes (after elution)

Sample loss/time unit (e.g. via evaporation, diffusion) Apparatus is disposable reusable Cleaning procedure Maintenance procedure

Maintenance accessories

Method of sample taking

Possibility of taking an aliquot sample

elution of both beds (separately) with diluted H,SO, in 10% McOH yes (after elution)

-

high humidity

none known

-

glass, silanized glass wool, silica gel (20/40 mesh), urethane foam

anyplace 3 rnin - 50 h

none known

Sample space sealing material Contamination risks during sampling Danger of partial sample loss

anyplace 1-8h

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (2 collection beds)

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (2 collection beds)

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (2 collection beds)

anyplace 4h

-

17 days

15 days

Sampling location Length of sampling time

Redundancy of sampling procedure (Precaution against sample loss)

Possibility of sample storage in container Possibility of automatic sampling Sampling

G%

$ 9

2

2

h

%

2

9 3

-

ca. 15 g -

yes Pump

-

yes Pump

1-50L 0.05-0.2 L/min -

gas anorganic acid fumes HPLC, IC 3- I00 L (NIOSH 7903) 0.2-0.5 L/min -

no no no

gas amines GC, FID

3-30 L 0.01 - 1 L/min -

no no conly conditionally

Which phases are investigated Which components are investigated Analysis method (AAS, XRF, MS, GC, LC)

Sample size (mass or volume) Output capacity Provision for separation of other phases (Particles, drops, gas) Homogenization of sample Reduction of sample into small pieces Cooling of sample

no no no

research. industry, environmental protection, occupational safety gas PCBs. NIOSH 5503 GC, ECD

research, industry. environmental protection, occupational safety

research, industry, environmental protection, occupational safety

yes Pump

ca. l o g

Area of application

Operation of apparatus Power source (voltage) Transportable Necessary accessories (e.g. pump, etc.)

6/4/70 mm l00/50 mg

7/5/100 mm 400/200 mg

8/6/70 mm 150: 150 mg

Dimensions (width, height, depth) Sample space size (width, weight, depth) Weight (mass)

-

Orbo-60 SUPELCO SUPELCO 50 pieces 200.64 DM

Small sample tube Orbo-53 SUPELCO SUPELCO 50 pieces 237.12 DM

Orbo-52 (large) SUPELCO SUPELCO 50 pieces 196.08 DM

Name of apparatus Model Manufacturer Distributor Price incl. tax

SUPELCO

SUPELCO

SUPELCO

(continued)

Company

Tab. 3.

3

Q

3

P

a.

Yes no

Orbo- tube-cutter elution of both beds (separately) with 3 mM NaHCO,,,, 4 mM Na,CO, yes (after elution)

Yes no breaking open at sampling location

Orbo-tube-cutter

elution of both beds (separately) with diluted H,S04 in 10% MeOH

yes (after elution)

Sample lossitime unit (e.g. via evaporation, diffusion) Apparatus is disposable reusable Cleaning procedure Maintenance procedure

Maintenance accessories

Method of sample taking

Possibility of taking an aliquot sample

breaking open at sampling location

-

excessive flow rate and/or sample collection time -

excessive flow rate and/or collection time -

-

Sample space sealing material Contamination risks during sampling Danger of partial sample loss

glas, glass fiber filter, urethane foam, activated silica gel (20/40 mesh) none known

glass, sikdnized glass wool, silica gel (20/40 mesh), urethane foam

Materials in contact with sample

anyplace 6 - 500 min

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (2 collection beds)

Yes

high-humidit y

anyplace 3 min-50 h

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (2 collection beds)

Sampling location Length of sampling time

Redundancy of sampling procedure (Precaution against sample loss)

Possibility of sample storage in container Possibility of automatic sampling Sampling

yes (after elution)

elution o f both beds (separately) with hexane

breaking open at sampling location Orbo-tube-cutter

-

DDT, DDE, S-containing compounds excessive flow rate and/or sample collection time

glass, silanized glass wool, urethane foam

anyplace 5 min- 16 h 40 min

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (2 collection beds)

2 months

ss

3

2

91

2

$

5:

ze.

Q

m,

no no

no

no

no

no

I-12L 0.01 -0.2 Limin

1-24 L 0.05-0.15 L/min

50- 100 L 1 L/min

Sample size (mass or volume) Output capacity Provision for separation of other phases (Particles, drops, gas) Homogenization of sample Reduction of sample into small pieces

GC, ECD

Analysis method (AAS, XRF. MS. GC, LC)

gas ethyleneoxide

HPLC

gas acidic fumes, (HCI, NH,, Ac-acid, formic acid)

Which phases are investigated Which components are investigated

research, industry, environmental protection, occupational safety gas 2-butanone (methyl ethyl ketone) NIOSH 2500 GC. FID

research, industry, environmental protection, occupational safety

research, industry. environmental protection, occupational safety

Area of application

Operation of apparatus Power source (voltage) Transportable Necessary accessories (e.g. pump, etc.)

ca. l o g

ca. 20g

ca. l o g

6/4/70 mm 160iXO mg

6j4:lOO mm 400,'200 mg

8/6!100 mm 335 mg/165 mg

Dimensions (width, height, depth) Sample space size (width, weight, depth) Weight (mass)

Orbo-90 SUPELCO SUPELCO 25 pieces 184.68 DM

Orbo-78 SUPELCO SUPELCO 25 pieces 184.68 DM

Small sampling tube Orbo-70 SUPELCO SUPELCO 50 pieces 244 DM

Name of apparatus Model Manufdcturer Distributor Price incl. tax

SUPELCO

SUPELCO

SUPELCO

(continued)

Company

Tab. 3.

a

0 .

3\

glass, sikdnized glass wool, urethane foam, Carboxen 564 acetone, isopropanol

excessive water flow and/or collection time -

glass, silanized glass wool, HBr impregnated Carboxen 564 2-bromethdnol

excessive water flow and/or collection time -

Yes no breaking open at sampling location

glass, silanized glass wool, urethane foam, 5.0% Na,CO, on Chromosorb P (20/40 mesh)

excessive water flow and/or collection time -

Yes no

Materials in contact with sample

elution of both beds (separately) with D M F yes (after elution)

Orbo-tube-cutter

desorption of both beds (separately) with distilled water yes (after elution)

Maintenance accessories

Method of sample taking

Possibility of taking an aliquot sample

Orbo-tube-cutter

breaking open at sampling location

Sample loss/time unit (e.g. via evaporation, diffusion) Apparatus is disposable reusable Cleaning procedure Maintenance procedure

Sample space sealing material Contamination risks during sampling Danger of partial sample loss

none known

anyplace 5 min-20 h

anyplace 7min-8 h

50- 100 min

Sampling location Length of sampling time

Redundancy of sampling procedure (Precaution against sample loss)

elution of both beds (separately) with CS2 yes (after elution)

yes no breaking open at sampling location Orbo-tube-cutter

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (collection beds)

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (collection beds)

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (2 collection beds)

no 6 weeks at 25 “C

no 17 days in darkness

no yes, several days

Cooling of sample Possibility of sample storage in container Possibility of automatic sampling Sampling

B

$

%

k

fi

7

i

3 L (OSHA 69) SO mL/min (OSHA 69)

Sample size (mass or volume) Output capacity Provision for separation of other phases (Particles, drops, gas) Homogenization of sample Reduction of sample into small pieces Cooling of sample

no no no

gas acetone and other volatile hydrocarbons GC. FID

Which phases are investigated Which components are investigated

Analysis method (AAS, XRF, MS. GC, LC)

research, industry, environmental protection, occupational safety

Area of application

no

no no

-

no no no

varies varies -

24 L 0.1 Limin

GC. FID

vinylacetate, OSHA 51

gas

research, industry, environmental protection, occupational safety gas many types of organic compounds MS. GC, LC

research, industry. environmental protection. occupational safety

ca. 1Sg

ca. l o g

ca. l o g

Operation of apparatus Power source (voltage) Transportable Necessary accessories (e.g. pump, etc.)

7/S,'I 00 mm 350/175 g

6i4,'100 mm 160/80mg

6/4/70 mm 130/6S mg

Dimensions (width. height. depth) Sample space size (width, weight. depth) Weight (mass)

-

Orbo- 100 SUPELCO SUPELCO 2.5 pieces 29 1.84 DM

-

Orbo-92 SUPELCO SUPELCO 25 pieces 202.92 DM

Small sample tube Orbo-9 1 SUPELCO SUPELCO 25 pieces 196.10 DM

Name of apparatus Model Manufacturer Distributor Price incl. tax

SUPELCO

SUPELCO

SUPELCO

(continued)

Company

Tab. 3.

a

00

m

-

acids, bases, free radical react with vinyl acetate excessive water flow and/or collection time -

-

other adsorbable analytes

Sample space sealing material Contamination risks during sampling Danger of partial sample loss

desorption of both beds (separately) with 1% DMF in CS2 yes (after elution)

Method of sample taking

Possibility of taking an aliquot sample

Orbo-tube-cutter

Orbo-tube-cutter

Maintenance accessories

elution of both beds (separately) varies with 9/5 methylene chloride/methanol yes (after elution) yes (after elution)

breaking open at sampling location Orbo-tube-cutter

-

-

breaking open at sampling location

Yes no

Yes no

-

Yes no

breaking open at sampling location

-

Sample loss/time unit (e.g. via evaporation, diffusion) Apparatus is disposable reusable Cleaning procedure Maintenance procedure

excessive water flow and/or collection time -

glass, silanized glass wool, Carbotrap (20/40 mesh) various

glass, silanized glass wool, HBr urethane foam, Carboxen 564

glass, silanized glass wool, Carbonsiere S 111

Materials in contact with sample

excessive water flow and/or collection time

anyplace varies

anyplace 4h

anyplace 60 min (OSHA 69)

Sampling location Length of sampling time

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (2 collection beds)

yes (depends on pump used) continuous and discontinuous (depends on pump used) yes (2 collection beds)

yes (depends on pump used)

yes (through 2 separate packing beds)

varies

18 days at 17 C

24 h

Redundancy of sampling procedure (Precaution against sample loss)

Possibility of sample storage in container Possibility of automatic sampling Sampling

W m

P

gT

(continued)

Sample size (mass or volume) Output capacity

-

-

-

gas 2.3 dibromo propanol NIOSH (method not yet made public)

Which phases are investigated Which components are investigated

Analysis method (AAS. XRF, MS, GC, LC)

research, industry, environmental protection, occupational safety

Yes Pump

-

ca. 500 L 1 - 5 L,mh

GC, ECD

gas PCBs, pestizides, (ASTM D4 861-88)

research. industry, environmental protection, occupational safety

ca. l o g

ca. 10g -

6/25/1250 mm 76 x 22 mm diameter

6/4/70 mm 100,'50 mg

-

Orbo- 1000 SUPELCO SUPELCO 50 pieces 164.16 D M

-

SUPELCO

Orbo- I0 1 SUPELCO SUPELCO 25 pieces 207.48 DM

SUPELCO

Area of application

Operation of apparatus Power source (voltage) Transportable Necessary accessories (e.g. pump, etc.)

Dimensions (width. height, depth) Sample space size (width, weight, depth) Weight (mass)

Name of apparatus Model Manufacturer Distributor Price incl. tax

Company

Tab. 3.

~~

5 mL- 3 L:min 5 mL-3 Limin

research, industry, environmental protection, occupational safety gas hydrocarbons. CFC, HUW, PCB, acids PAM, amines, alcohols, acrolein, FA. pesticides, ethyleneoxide, phenols. cresols. aromatic substances MS. GC, HPLC

~

electrical battery (storage battery) Yes included in price: holder for small sample bottle, holder for filter cassettes, charging devices 220 V

ca. 1 kg

120/30,'70mm -

Sampling pump PAS 3000 SUPELCO S U PELCO 2844.30DM

SUPELCO

3

3

2

2

a

0

-4

-

Sample space sealing material Contamination risks during sampling Danger of partial sample loss

not known yes (after elution)

Method of sample taking

Possibility of taking an aliquot sample

~~

eluation with 5% diethyl ether in hexane yes (after elution)

Orbo-tube-cutter

Maintenance accessories

~

yes, depends on requirements yes, depends on requirements rinsing, Soxhlet extractable opening thecap at sampling location

Yes no breaking open at sampling location

Sample loss/time unit (e.g. via evaporation, diffusion) Apparatus is disposable reusable Cleaning procedure Maintenance procedure

-

excessive flow rate and/or sample collection time excessive flow rate and/or sample collection time

excessive flow rate and/or collection time

none known

glass, polyurethane foam

glass, silanized glass wool, carbotrap (20140 mesh)

Materials in contact with sample

none known

anyplace depends on the method

anyplace 100- 500 min

anyplace not known

Sampling location Length of sampling time

-

depends on method used

no Yes charging from electric outlet, calibrating charging device, flow rate measuring device

depends on method and adsorber excessive flow rate and/or sample collection time

-

only sampling test tube, sample does not enter the pump

yes (depends on collector used)

yes (2 collection beds)

yes (depends on type of analysis) yes (depends on pump used) discontinuous

30 days

-

Redundancy of sampling procedure (Precaution against sample loss)

no

no

no yes (depends on pump used) continuous and discontinuous (depends on pump used) no

no no

no no

no no

-

yes (depends on pump used) -

Provision for separation of other phases (Particles, drops, gas) Homogenization of sample Reduction of sample into small pieces Cooling of sample Possibility of sample storage in container Possibility of automatic sampling Sampling

Environmental Sampling for Trace Analysis

Edited by Bernd Markert 0 VCH Verlagsgesellschaft mbH, 1994

3 Trace Elements Neec Trace Analysis Istvan Pais

3.1 Problems of Trace Element Analysis 3.1.1 Introduction “Trace elements” are present in living organisms only in small trace amounts, i.e., their concentration is not exceeding 1 pg/g. According to the literature, the concentration of some trace elements is 2 1 ng/g in the tissues of plants, animals, and humans. This concentration ratio requires the use of sensitive analytical methods and accurate analytical applications. In the 20th century, the sensitivity and biological application of analytical methods have brought forth a few revolutionary changes (Jones, 1987). In the 1950s, e.g. the atomic absorption method utilizing flame-ionization was introduced and the accuracy of this method was just a few pg/g (Willis, 1970). The main disadvantage of this method was that it is time-consuming: in the analysis of different elements the lamp must be changed. The accuracy of this method for the most common trace elements, i.e., iron, copper, zinc, and manganese, was relatively sufficient but in spite of that more sensitive methods were required. In the late 1970s, the ICP method was available (Barnes, 1985) and offered several advantages. For most metals the analytical sensitivity was about 1 - 2 ng/g and, using a polychromator, it was possible to identify 20- 30 elements in the same sample with high accuracy. Today, this method is used in various laboratories, because of its speed and wideranging applications for biological analysis. The disadvantages of ICP are: The elements required can only be determined in clean solutions. The biological samples should pass a preliminary wet digestion with various digestive mixtures and techniques (Zunk, 1990).The preliminary ashing methods have some disadvantages too: At low temperatures (below +400 “C)carbon particles remain in the ash, while at higher temperatures (over +500 “C) some elements may disappear. In analytical chemistry, various methods with different sensitivities or detecting limits are used. From a review-paper by J. B. Jones (Jones, 1987) the values for 26 elements are presented in Tab. 1; these are the most important either due to physiological essentiality or to environmental contamination. No general rule exists for the selection of the most appropriate analytical method, because there are various advantages and disadvantages according to the elements to be identified and to the biological environment in which the given element exists (Behne, 1992; Cornelis, 1992a; Fardy and Warner, 1992; Kwiatek et al., 1992; Sanchez et al., 1988; Schramel, 1993; Tolg and Garten, 1985; Valkovic, 1989; Versieck et al., 1987).

74 Tab. 1.

1. Puis

Sensitivities of Different Analytical Methods in pg/g (after Joncs, 1987)

Symbol

AES

NAA

MS

AAS

Chem

XRF

Al As Ba Be Bi

0.5 10 0.1 0.05 0.2 2 0.1

0.004 0.005 0.02

0.002 0.0006 0.002 0.00008 0.002 0.007 0.0005 0.000 5 0.002 0.0002 0.000 5 0.007 0.00 1 0.00006 0.0004 0.003 0.002 0.003 0.002 0.002 0.002 0.004 0.02 0.004 0.0004 0.002

0.8 0.8 0.2 0.02 0.22 0.01 0.07 0.06 0.04

0.0005 0.0 1 0.1 0.008 0.6 0.003 0.003 0.007 0.002 0.05 0.2 0.005 0.0 1 0.003 0.005 0.01 0.004 0.006 0.004 0.2 0.1 0.06 0.4 0.02 0.01 0.1

17

Cd CO

Cr

cu F Fe

HI: I Li Mn Mo Ni Pb Sb Sc Si Sn Te Ti V Zn

1

0.1 I00 1 2

0.005 0.0 1 0.3 0.002 2 0.003 0.002

0.1

0. I 1 0.2 0.02 2 I00 0.5 0.5 50

2 0.5 5

0.00 I 0.1 0.7 0.5 0.007 0.01 0.03 0.03 0.002 0.1

0.06 2.2 0.02 0.02 0.33 0.07 0.1 0.3 0.5 2 1 0.26 0.03 0.9 0.009

0.4 1

2 2.5 0.2 0.7 0.4 1 0.3

0.2 1 0.2 1.5 0.2 2

0.15 0.2

The abovementioncd processcs may be the source of severe contamination and other analytical pitfalls (Caroli, 1992). As is shown in other chapters of this book, the method of sampling, the cleaning of natural biological samples, and the different contamination risks during the whole process cause great uncertainty in the final analytical results. For the ng/g ratio or in lower concentrations, the error rate can reach 100%. The terms utilized in analytical chemistry have also changed. In the 1970s, the term “trace” was equivalent to pg/g and “ultra-trace” was registered as ng/g. Now, and in the near future, “trace” will mean ng/g and “ultra-trace” will mean pg/g or less.

3.1.2 Problems of Biological Analysis The food chain or, better statcd, the “nutritional network” begins with geochemical sources and reaches the biosphere through the transmitting media, including the final station: man.

Truce Element5 Need Truce Anulysis

15

In the second part of this chapter, we will present the analytical constitution of sea water (see Tab. 6), which is the largest source of food for man, and therefore analytical data are important for trace-element science. The analysis of sea water is linked to various problems which must be solved in order to obtain correct results. According to a recent paper (McLaren et al., 1993), the use of the relatively easy method of ICP is problematic in the case of sea water with its extremely high salt concentration. Thus, a t least a ten-fold dilution is necessary to eliminate deposition at the torch and to avoid severe suppressions of salt. This unavoidable dilution results in extremely low concentrations of some element and thus accurate identification is impossible. Today’s analytical techniques provide the possibility of determining single elements in the pg/g scale, but the analytical result has no biological or physiological value. We should know, e.g., the compound form in which the given element exists, the oxidation state and the relative concentrations of 10- 12 other elements in the same system. With this information, we can begin to evaluate the role of the given element in the given biological system. Biological trace element research is probably the most multidisciplinary research field. As Fig. 4 indicates, the cooperation of 10- 12 different scientific fields is required. To illustrate this problem, we quote (after Iyengar, 1991) one of Mertz’s statements: “An analytical chemist should be more than a procurer of data and a life-scientist more than their interpreter.” Bioavailability is also largely a question of scientific evaluation. We can measure, e.g., a high amount of a given element in a food-sample (Cornelis, 1992b), but the digestive tract of an animal or human may only absorb a small percentage of it or, in other cases, the bioavailability may be near to 100%. This is very important for the determination of positive or toxic effects on human health. The resulting problems are contamination of laboratory instruments (Marshall et al., 1991), environment (e.g., laboratory air; Caroli, 1992), and materials used (Moyer et al., 1991). As will be shown, any “indicator organ” gives a relatively acceptable picture of the trace element supplementation of the whole organism, but in most cases - especially during illness - some organs contain higher or lower values than usual. In this context we should analyze various organs, which is possible in plant and animal experiments, but very limited in the case of humans. The so-called autopsy analysis (Lyon and Fell, 1992) can assist in some cases, but not in all. In these situations we must consider whether some kind of contamination is caused by sampling and whether the sample is really representative of the whole organ. As demonstrated by various authors we can find 2 - 4 or more times divergent results in different parts of the same organ (liver, kidney, brain, bones, etc.). In conclusion, we should in our approache always be like Boyle’s “sceptical chemist”, reviewing our analytical methods and the evaluation of analytical data.

16

1. Puis

3.1.3 Indicator Organs in Biological Evaluation Due to the fact that it is difficult to produce analytical samples from live human and (some) animal organs (excluding laboratory circumstances), analytical chemists have tried to identify certain “indicator organs” which can be collected without any damage to the living organism. These analytical data are well correlated with the supply of trace elements and represent a sound basis for the estimation of trace element supplementation. One of the oldest indicator organs is the deck hair of animals, which is accepted as a biological index of geographical region and of environmental pollution as well. In the case of humans, the same is valid for scalp hair (Moro et al., 1992; Robertson, 1987; Zhuang et al., 1991). Trace metal storage in hair has its own time-scale and biodynamics, not comparable to any other storage or excretory process known in human physiology. It was concluded (Gibson, 1984) that during sampling and washing of hair samples by standardized procedures, the effects of any adventitious trace element contamination are small and can be effectively ignored. Takagi et al. (1986) demonstrated that equally treated hair samples (washed and prepared with acetone, three times with deionized water, again with acetone and dried) from 5 countries showed great differences in their trace element concentrations. Copper was much higher in the USA and Canada than in Poland, India and Japan. Lead, arsenic and mercury were high in India; selenium was lowest in Poland, 10-50 times lower than in Japan, the USA, and Canada. According to recent literature (Sturaro et al., 1993a, b) dynamic ion-exchange chromatography is a convenient method for simultaneous identification of different metals in hair samples. Another important indicator organ is the toenail (Wilhelm et al., 1991). The authors concluded that in most cases toenail analysis is not as useful as hair analysis. As recently reported (Ovaskainen et al., 1993), the selenium concentration in toenails is proposed to be a long-term indicator of the human selenium status. The collection and storage of toenails is very easy and according to the cited author “selenium intake contributed significantly to tonail selenium concentrations”. In addition to the advantages mentioned, it should be noted that these results generally provide a picture of about 1 % of the total content of the given trace element in the living organism. In the literature we find the following data (Lyon and Fell, 1992): an adult (70 kg) contains about 80 mg of copper, the copper component in the serum is only about 3 mg. In the whole body there is an estimated 1.5 g of zinc, but the zinc component in the serum is only about 2.5 mg: this means that the serum contains only 0.2% of the total body content of zinc. For humans and domestic animals, the most important indicator organ is blood: usually serum is used, in some cases (e.g., in lead determination) whole-blood samples are analyzed. Heparin or another anti-coagulant compound is used for preservation of serum samples. As reflected in recent literature (Minoris et al., 1992; Caroli, 1992) heparin contains (pg/g) 2- 12 Ba, 0.6 Cu, 3.6 Mn, and 28 Zn. Conse-

Truce Elements Need Trace Analysis

77

Tab. 2. Trace-Element Contents of some Tissues Symbol

Blood-serum PglL

*

Toenails **

Scalp-hair *

w/g 0.1 -0.2 800-1100 800 - 1200 0.5- 1.0 75-120 800 - 1100

Cr Cu Fe Mn Se Zn

* **

= =

0.3 -0.8 15-25 30 - 60 0.5 - 1.5 0.5- 1.0 150-250

7.5 0.46 129

after Iyengar, 1991 after Wilhelm et al., 1991 and Oveskainen et al., 1993

quently, we should calculate with these values. Because the different heparins do not have the same purity, they have to be analyzed and corrections by evaluation are indispensable. Many values for blood sera, and other biological samples, have changed in the last 15 - 20 years. For example, the “normal” manganese value was considered to be 10 - 30 pg/L, but now recent research has demonstrated an average of 0.5 pg/L. Similarly, the chromium value used to be as high as 5 pg/L, today’s literature reports a lower value of 0.1 -0.3 pg Cr/L. The analytical survey of biological samples has to solve a quite new problem: the analytical research at cellular level. According to the most recent literature (Lindh, 1993) the most interesting problem is to screen the distribution of trace elements in individual cells. In immunological research this new knowledge is of outstanding importance. The investigation of the cellular mechanisms requires new techniques and broad cooperative, multidisciplinary coordination among different scientists. The isolation of some metal containing macromolecules from the given single cell is a very complicated task and we should use the finest and most accurate techniques available. We should also keep in mind that the chemical bonds between trace elements and organic molecules is highly correlated with bioavailability and with biochemical activity in different organs, because of the exchange of different ligands and other parameters.

3.2 The Importance of Trace Elements 3.2.1 Introduction The concentrations of trace elements in the dry matter of living organisms are not higher than a few pg/g. In spite of this fact they have an equal or even higher importance in physiological processes than elements with concentration ratios of 1 - 10% or more.

78

Tab. 3.

I . Puis

Elements without Significance in the Biosphere

Noble gases

Radioactive elemcnts

Argon Helium Krypton Neon Radon Xenon

Actinium Polonium Protactinium Radium Thorium Uranium

The earth, and according to our recent knowledge, the solar system contain 88 permanent chemical elements. The noble gases (which do not form compounds) and the strong radioactive elements may be excluded (Tab. 3) for they don’t have any positive physiological function, so that 76 elements remain. It is accepted worldwide that seven non-metals and four metals are called macroelements (Tab. 4), because in most organisms their concentrations are higher than 0.1% : usually the quantities of carbon, oxygen, hydrogen, nitrogen, phosphorus and sulfur exceed 96% in the dry matter of plants. The remaining 65 elements are called trace elements and some of them - due to their extremcly low concentrations - ultra-trace elements. I n Fig. 1, these elements are classified into four main groups. (1) and (2): The so-called generally and the partly essential trace elements (Tab. 5). To avoid any misunderstanding, we accentuate that this classification of elements represents the author’s opinion, and is strictly an arbitrary, free approach. (3): The physiologically beneficial elements, a term that was first defined by the author (Pais, 1990a). The beneficial character of some elements has also been demonstrated by the author and his research group. (4): Elements for which no positive physiological role has been demonstrated yet. Regarding the last sentence above, we recall a remark from Schwarz (Schwarz, 1970) as a basis for future development in trace-element science: to demonstrate Tab, 4. Essential Macroelcments Symbol

Nzme

H 0 C N P S

Hydrogen Oxygen Carbon Nitrogen Phosphorus Sulfur Chlorine Potassium Sodium Calcium M agnesiutn

C1

K Na Ca Mg

Truce Elements Need Truce Anulysis

I,

Ill

111,

IV,

v,

VI, VII,

VIII,

I,

11,

111,

Iv,

V,

vi,

19

VII,

vni,

Generally essential essential Physiologically beneficial Physiological role hardly known

I Partly

*

** Fig. 1. Physiological significance of trace elements.

that an element is essential is a very difficult task, but to exclude it categorically from being essential (or promotive) has no real basis. The nutritional network (theoretically and practically) contains all 88 permanent elements. 30 or more of them are the so-called little-known trace elements, and we should calculate with unknown physiological effects which may be the basis of different plant, animal and human diseases. Because of that it is most urgent to collect and accumulate as much knowledge as possible on these elements.

Tab. 5. Generally and Partly Essential Trace Elements Generally essential

Partly essential

Boron Cobalt Copper Iodine Iron Manganese Molybdenum Nickel Zinc

Arsenic Chromium Fluorine Lithium Lead Selenium Silicon Tin Vanadium

80

1. Puis

3.2.2 Criteria of Essentiality and Beneficiality The rigid rules of essentiality were originally provided by Arnon and Stout (Arnon and Stout, 1939).These rules have been reevaluated several times and the most recent version accepted woldwide is the following, according to Frieden (Frieden, 1985):

( I ) The organism can neither grow or complete its life cycle without the element. (2) The element should have a direct influence on the organism and be involved in its metabolism. (3) The effects of the essential element cannot wholly be replaced by those of any other element. (4) The element is present in tissues of different living organisms in comparable concentrations. ( 5 ) Its withdrawal produces similar physiological or structural abnormalities, regardless of species. (6) Its presence reverses or prevents these abnormalities. (7) These abnormalities are accompanied by specific biochemical changes that can be remedied or prevented when the deficiency is checked. In earlier trace element literature the author found that some scientists preferred the oversimplified classification of “essential” vs. “non-essential” groups. In order to establish a more multifaceted picture, in 1990 he created the following criteria of beneficiality: ( 1 ) By application of the given element, we can recognize promotive roles in plant growth, increase in weight of domesticated animals, or human health conditions. (2) After application of the given element, the activity of one or more enzymes with promotive physiological effects increases. (3) By application of the given element, plants, animals and humans demonstrate improved health conditions or better immunological responses to diseases. (4) By application of the given element, the toxic effects of other elements are decreased or otherwise diminished (“antidotum effect”). ( 5 ) By application of the given element, the intake and utilization of other essential elements can be increased. (6) By application of the given element, some physiological parameters show an advantageous change, c.g., the cholesterol level in blood will be lower.

According to these criteria, titanium, gallium and zirconium - previously regarded as physiologically useless elements - were demonstrated to be beneficial by the author and his research team (Kiss et al., 1985; Novak-Fodor et al., 1992; Novak et al., 1992; Pais, 1983, 1988; Pais et al., 1989; Simon et al., 1989). Horowitz (1988) demonstrated that scandium may have beneficial activity in some physiological processes. According to Liu and other scientists (Liu, 1988; Wu et al., 1985) some rare carth metals also demonstrate beneficial properties in the life of plants, and certain types of fertilizers containing them are widely used in Chinese agriculture.

Trace Elements Need Trace Analysis

81

3.2.3 Criteria of Toxicity The problem of toxicity is also a multifaceted question, and providing a correct answer is not an easy task. Some centuries ago, Paracelsus (Theophratus Bombastus von Hohenheim, 1493 - 1541) provided the main basis on which materials should be considered to be toxic. Based on this traditional opinion, we would like to make an apparently unusual and surprising statement: there is no material (elements and compounds) which is eo ips0 toxic, and yet all materials are toxic! The criterion is solely the concentration or quantity of the substance. We would like to illustrate this situation with Fig. 2, which is well-known in trace element literature. As has been clearly demonstrated, all substances can cause deficiences which appear as deficiency diseases (lethal in some cases), and, on the other hand, in higher concentrations the same element (or compound) causes severe toxicity or may be lethal. We will now try to classify the most important parameters which are meaningful in deciding what is toxic and when: (1) The first parameter is the compound form. Living organisms can rarely take up chemical elements in elemental status, but mostly as compounds. Some criteria for the toxicity of a form are the solubility in water or weakly acid media, or the formation of chelates that can be taken up into the digestive system. Some forms may be quite toxic, while the others usually are not harmful, as the following commonly accepted examples illustrate. Mercury chloride and even more methyl mercury is - between given concentration ratios - highly toxic, but HgS, because it is insoluble, does not represent any danger to living organisms. The soluble

L No growth

Mild Severe deficiency deficiency

Optimum range

Luxury Toxicity consumption

Fig. 2. Effects of trace element concentrations on living organisms.

Concentrat ion Lethality

82

1. Pais

barium compounds are usually very toxic, but BaSO, shows no real toxicity: this compound is used as a contrast-medium in X-ray diagnosis of the digestive tract. (2) Oxidation state. The international literature has provided many arguments that trivalent chromium demonstrates essentiality in most cases and has a very low toxicity, but the hexavalent form, i.e. chromates are usually highly toxic under common conditions. Nevertheless in daily praxis during uptake, the reduction from Cr(V1) to Cr(l1l) mostly proceeds very rapidly and, therefore, toxicity is not serious. The same situation is valid for As(II1) and As(V): the latter generally expresses greater toxic effects than the former. Here it is also important to consider the parameter of the organic compound form as the source of different uptake properties. ( 3 ) Presence or absence of other (synergetic or antagonistic) elements. A very illustrative example is the toxicity of cadmium in smoking. According to recent literature (Bell et al., 1992), one cigarette may contain 1.0 pg, of easily absorbable cadmium. This means that an average smoker has a cadmium uptake of 20- 30 pg/day which is the threshold of strong toxicity. Provided that the zinc and calcium levels in his organism are sufficient, this quantity of cadmium is not harmful. In case of insufficient zinc and calcium levels toxicity increases exponentially. Wilson’s disease is known from literature (Weisner et al., 1987) as an accumulation of copper in cells of the brain. With an increased zinc uptake in daily nutrition, the copper level decreases, but this way of treatment is not the best. Chelate therapy with a simple tripeptide is more appropriate. (4) Mode and manner of intake. Intravenous injection mostly is more dangerous than digestion via the intestinal system: it is known from literature that some elements demonstrate an excellent (nearly 100%) absorption rate from common foods, while others show a smaller percentage. These parameters vary from person to person, from food to food, etc. Therefore it is completely impossible to create a generally valid regulation of element uptake. These uptake processes, i.e., bioavailabilities are in clear correlation with the pH value and the exchange possibilities of ligands. ( 5 ) Duration of action. Usually a single (common) dose of the toxic compound is not very dangerous, but continuous uptake over an extended period of time, is much more harmful. It is quite understandable that the combination of timing and dose of the compound is so important. (6) Defense mechanisms of living systems. In the context of this chapter it is neither possible to deal in detail with metallothioneins (Kiigi and Schaffer, 1988) in human and animal organisms, nor with the role of newly found phytochelatins (Rauser, 1990) in plants, although these factors can also mitigate or diminish toxic effects. The activity of the immune system of different individuals under the influence of some microorganisms or viruses will not be discussed as well. The author does not contend that the given picture of toxicity is wholly complete, but to qualify a compound to be harmful requires the establishment of parameters under which the term toxicity may be judged as valid or not. Concluding our discussion of toxicity, we find it necessary to provide a general overview of what may constitute “too little” or “too much”, and when.

Trace Elements Need Truce Analysis

83

3.2.4 Changes in Element Concentrations In Tab. 6, the chemical composition of earth’s crust, sea water, and some land plants and animals (Pais, 1991) is provided. These data are taken from various sources (e.g., Adriano, 1986; Bowen, 1966) and therefore the figures presented are not absolutely exact. The most frequent and yet physiologically important elements were compiled. As can be seen from these data, the chemical composition of inorganic nature differs much from organic nature. The main differences are: (1) The quantity of hydrogen is 40-60 times higher in the organic than in the inorganic world. The ratio for nitrogen is about 700-2500 times higher in living organisms than in the earth’s crust. A similar difference can be seen in the quantity of carbon: the enrichment factor is > 1000. For most metals with concentrations in the earth’s crust 21% the ratio is decreased by a factor of 5 to 1000 in the organic world. On the other hand, some essential elements demonstrate enrichment in organic material. Tab. 6. Different Element Concentrations Name

Earth w. ratio

Sea water mg/L

Plants

Animals mg/kg

Oxygen Silicon Aluminium Iron Calcium Sodium Potassium Magnesium Titanium Hydrogen Phosphorus Manganese Sulfur Carbon Chlorine Fluorine Chromium Vanadium Zinc Nickel Copper Lithium Nitrogen Cobalt Molybdenum Boron Iodine Selenium

466000 277200 81 300 50000 36 300 28 300 25 900 20 900 4400 1400 1180 1000 520 320 314 300 200 150

132 80 70 65 40 23 15 3.0 0.3 0.09

857000 3.0 0.01 0.01 400 10 500 380 1350 0.001 108000 0.07 0.002 885 28 19000 1.3 0.00005 0.002 0.01 0.0054 0.003 0.18 0.5 0.0003 0.01 4.6 0.06 0.00009

410000 < 5000 < 500 140 I 8 000 1200 14000 3 200 < 1.0 55000 2 300 120 3 400 454000 < 2000 < 40 0.6 1.6 100

3.0 14.0 0.1 30000 500 pg . g Pb then, if the precision is sufficient to map the variation with confidence (i.e., measurement variance i,~siclierung hei P r o h l i ~ m s t ~ ~ ~ ~ t i i i r i ~ ~ n t i ~ n . [8] DIN Deutsches Institut fur Normung e.V. (19x3) D / N 18123 Subsoil; testing of soil samples; determiiiation of the particlc size distribution. [Y] International Organization for Standardization (1 992) I S 0 11464 Soil quality - Pretreatment of samples for physico-chemical analysis (at present at the stage of Draft International Standard). [ 101 Sprengart. J., Wagner, G. (1993) Unii~'I.lrp,ohenhanl~drs Bundim - Richilinie zur Prohi~nuhriii~ und Prohenrrirfbereiiurzg. lnstitut fur Biogeographie, Zentruin Uniweltforschung. Universitiit des Saarlandes. [ I I ] International Organization for Standardization (1985) I S 0 5667-3 Water quality - Sampling - Part 3: Guidance on the preservation and handling of samples. [ 121 EPA ( 1990) E P A ,spwifi'cutionsand guidance for ohtuining contaminant-jrre sumplc cnntainer.c. EPA Region 10, Seattle, WA. [ 131 Bodcnschutzzentrum des Landes Nordrhein-Westfalen (199 1) Mindestdutensutz Bodi~nuntivscichungen, AbschluBbericht des Arbeitskreises Mindcstdatensatz Bodcnuntersuchungen der Sonderarbeitsgruppe Informationsgrundlagen Bodenschutz. [I41 Kliirschlammverordnung (Abf'KliirV) vom 15. April 1992. - BGBI. 1992, Teil 1, 912-934. [I51 Houha, V. J. G., Chardon, W. J., Roelse, K. (1993) /nflztence ofgrinding qf'soil on apparent clieniii~uli~otnpo.sition. DLO-lnstituut voor Bodemvruchtbaarheid, Nola 263. [Ih] International Organization for Standardization (1992) /SO 11466 Soil quality - Extraction of [race metals using aqua regia (at present at the stage of Draft International Standard). [ 171 International Organization for Standardization (1992) I S 0 11465 Soil quality - Determination of dry matter and water content on a inass basis - Gravimetric method (at present at the stage of Draft International Standard). [ 181 lSO/working document: Soil quality - Sample pretreatment for determination of organic contaminants in soil. Sccond draft, Document ISOjTC I9O/SC 3/WG 9 N 45, May 1993.

Environmental Sampling for Trace Analysis

Edited by Bernd Markert 0 VCH Verlagsgesellschaft mbH, 1994

17 Fixed and Hypothesis-Guided Soil Sampling Methods - Principles, Strategies, and Examples Roland W . Scholz, Norbert Nothhuunz, and Theodor W. May

17.1 Introduction The development of an appropriate statistical soil sampling method is of prime importance for the quality of data and for the evaluation and the interpretation of the results. For the problem field of soil evaluation in the frame of strategies for the protection of the environment, several attempts have been made by work groups established by DIN (Deutsche Industrie Norm, German industrial norm) and I S 0 (the International Organization for Standardization)to develop guidelines for soil sampling. In the practice of environmental soil evaluation, however, potential sources of errors residing in taking, transporting, and processing samples and in analysis are as yet not taken into account seriously enough. Although sophisticated statistical sampling procedures are applied in geology, this method is in its infancy in environmental practice. One of the reasons is that environment analysts (in most cases chemists, biologists, geologists, or toxicologists) consider themselves as experts for the object level, e.g., for biochemical processes, while according to training and genesis, they are not ex~ planning of experiments and sampling protocols perts for the so-called sign I P V C The as well as the quality control of laboratory analysis, however, are mathematicalstatistical methods. Their conceptional and constructive bases require objectindependent operations at the sign level. If too little attention is given to planning the experiment, deficits in the economy of procedure, but also in the representativity of data, in the sufficiency and indicativity of results must be suffered. Conversely, environmental analyses are of course worthless if they are not related to previous knowledge and to the model of the object. In principle, planning of soil sampling must be understood as being dependent on previous information and objectives of the study. At this general level, there will certainly be no disagreement. Decisive, however, about planning a sampling and protocol is the interest of the assessment. The first column of the schema in Fig. 1 includes various types of previous knowledge and pollution hypotheses. If we consider the knowledge desired, different goals of knowledge having an influence on the soil sampling method can be distinguished independently of parameters and direction of the inquiry (i.e.,on the sign level). With regard to the knowledge desired, we distinguish four cases: (1) Determination of a mean value

The agricultural sector is frequently interested (as in the case of sewage sludge disposal) in the determination of mean values. This is often a low-cost procedure: by using mixed samples and an optimal distribution of the sampling points,

336

R . W. Sciiolz, N . Nothhaurn, und T. W . M a y

I

Vevious Knowledge I Wlution Hypotheses

I

I1

Knowledge Desired Values

Determining Natural Nutrient/ Agricultural Use Determining Polluting Agents Imrnission from Industrial Areas

‘Point’-ShapedSources (Balteries, Barrels)

v

W

W

0

J

The arrows indicate the direction of increasing cost

Fig. 1. Schema concerning the influence of starting point/previous knowledge o n the complexity of thc soil sampling plan to he choscn based on a specific cxample.

reliable statements concerning the mean contamination are possible even on the basis of a small number of soil samples. (2) Defernfination of nlaximurn values

Maximum values are of interest if an immediate hazard emanates from the soil system, e.g., in case of skin contact or ingestion. For such questions, the rcquirements for the soil sampling plan are higher. Using mixed samples usually is not justifiable, and a soil sampling pattern must be used which offers sufficient certainty of “discovering” and assessing maximum values, respectively. (3) Deternzinution of distributions

Determining value distributions is of significance for testing immission hypotheses. If knowledge about the distribution or profiles of contaminants is desired, a particularly comprehensive and careful sampling is necessary. (4) Verification of pollutionlcausal hypotheses Often, the objective is an assessment of the relative contribution of different sources of pollution. Before we deal with fixed and hypothesis-guided soil sampling methods, we would like to introduce a checklist of criteria which have to be met in every case of planning soil sampling (Tab. I). Some criteria have been taken from statistical experiment planning and from psychological test theory. They have been specified for ecological sampling.

Soil Sampling Methods

Tab. 1.

337

Checklist of Criteria for Ecological Sampling/Study Planning

A. Can the knowledge desired be obtained by the study planned? Representativity :

Does the sampling plan yield a good image of the real soil contamination, or is a systematical bias (overestimation or underestimation) of the data to be expected?

Sufficiency:

Are there sufficient data? (Is it necessary to calculate the sample size in order to obtain reliable data, given a specified error probability and an assumed critical effect size?) Is the sample big enough to allow for sufficiently precise statements about mean or variance of the actual data? Have all the relevant parameters been included? Is it possible to derive statements about spatial distributions or temporal changes?

Indicativity :

Do the soil sampling plan and the method of analysis chosen also permit the detection of pollution levels which are only slightly higher than the limit? Are the sum parameters selected indicative for specific contaminants? Are the selected contaminants or the analytical method indicative for the effect in question?

B. How reliable is the result of the study? Objectivity :

Reliability:

Validity:

Is the method of taking soil samples independent of the sampling person? Is the method of taking samples reconstructable (localization of mixed samples, localization of grid points)? Is the sampling error quantified, e.g., by a sufficiently large number of sampling repetitions? Is there an indication as to how much uncertainty in the data must be expected because of random effects on sampling, e.g., by sampling errors? Was the laboratory/analysis error quantified by repeated analysis of reference samples? Are there indications as to how much uncertainty in the data must be expected because of random effects of sample preparation and of the analytical method? Do special circumstances during sampling (rain, dryness), during sample transport or storage affect the data? Is the selection of the parameters determined sufficient to obtain the knowledge desired? (Can pollutant concentrations in vegetables, for instance, be predicted from known soil concentrations? Is the knowledge of additional soil parameters necessary? Relative importance of contaminant speciation for bioavailability and mobility?) Have all relevant intermitting variables (e.g., soil parameters) becn taken into account?

C. Can the knowledge desired be obtained at smaller cost? Economy:

Are more samples obtained than statistically necessary? (Calculating case number!) Is it possible to replace a complete sampling of several areas by an appropriate sampling plan? Is it possible to economize by early exclusion of certain hypotheses by n.eans of a sequential procedure?

338

R. W . Scliol;, N . Nothhuurn, und T. W . Miry

17.2 “Fixed” Sampling Plans/Grid Plans The principle of fixed sampling plans includes the application of a generalized grid plan to a specified situation in order to bc able to assess relevant characteristic data like mean, maximum, or distribution of a contamination. Criteria for selecting suitable fixed sampling plans can be derived from the previous information supplied and from the kind of knowledge desired. Fig. 2 shows how the selection of various types of grid plans is dependent on assumptions about the contamination distribution in the area to be sampled. As can be seen from the figure, there is no optimal grid plan, the plan must rather be chosen depending on the problem and the answers obtained according to the above checklist. The advantages and disadvantages of various fixed soil sampling plans arc illustrated by a real example: In the outskirts of a big city, building ground was offered on the site of a former ammunition factory. An environmental research institute subdivided the lot into about 100 plot areas. From each of these plot areas

Rectangular Grid / Bottle Rack Grid

I

Homogenous Contamination

Rectangular Grid / Bottle Rack Grid

Centered Contamination

Polar Grid / Rectangular Grid / Bottle Rack Grid

Contamination along a Line

7

Modification of the Rigid Grid Dependent on:

I

I

Particularitiesof the Area Type of Objective / Hypotheses Quality and Reliability of Previous Information

Fig. 2. Selecting and adapting a fixcd soil sampling plan according to previous information and to objcctives.

339

Soil Surnpling Methods

7a

KT-J

3b. Random grid

3a Grid plan used in the example

0

F - 7 0

0

0

a

0

a

L'1 3c. Limited random gnd

3d. Rectangular grid

3e. Bottle rack grid

Fig. 3a-f.

3f. Diagonal grid

Examples for various fixed grid plans

a sample of 10 holes was taken. The grid plan in Fig. 3a was considered to be favorable and applied. Now it was suspected that a residual stock of TNT (trinitotoluene) had been buried during demolition work in a former crater-shaped hole. The potential house builders wanted to know the probability of discovering such a crater by a given sampling procedure. As there was no information concerning the location of the crater, there was an equal probability for all areas of certain plots in the vicinity of the former factory (assumption of equal probability). In order to assess the probability of really hitting the crater with the sampling, (model)

340

R W . Sckok, N . Nothhuum, and T. W . May

assumptions had to be made. To simplify matters, we assumed that the crater was circular. Besides, we assumed that a mixed sample would be indicative if at least one sample hole hit the crater area. Thus we also neglected the depth of the contaminant location. For a crater of 40 square meters (approx. 160 square feet) and for a mixed sample taken from a total of 10 sampling holes, and a plot area of 2Ox20meters, this applied plan yields a hit probability of 87 percent! I). The hit probability was fiir determined by means of the simulation program “~e~~erwuhvscheinlic,hkeit AuJtrugungs-Flachen ( TRAF)“ developed by the “Gesellschaft fur Organisation und Entscheidung”. The house builders concerned also made their own calculations. Their assumptions (presumably developed by mathematics teachers) were that the sampler had selected the sampling holes purely at random and stochastically independently, as shown in Fig. 3b. Such a plan, for instance, leads to a hit probability which is 25 percent smaller, amounting to about 65 percent. Such a soil sampling plan, of course, is not only unrealistic, but also suboptimal. This can be easily made plausible by the fact that random sampling also will take samples at spots very close to each other, thus dismissing potential information. This effect could be avoided by subdividing the area, as shown in Fig. 3c, and then proceeding to random sampling of the individual plots. This plan again is suboptimal, as the lattice grid in the examples shows. The optimal grid plan under the assumptions made is the “bottle rack plan” of Fig. 3e. The mean hit probability of this grid plan is 97 percent, and thus 12 percent higher than according to the plan 3a actually used, and a full 49 percent higher than if the random plan 3 b had been used. When assessing the economy of the “bottle rack grid”, however, the time required to fix the sampling points must be taken into account as well. Occasionaly, a plan optimal under statistical aspects can be less economical than another, if the cost in terms of the time required for establishing the sampling points exceeds the cost for additional sampling holes and analyses. At least for cost-intensive sampling (e.g., deep-hole sampling) or expensive analytics, however, the ,,bottle rack grid” should be applied in any case. Fig. 4 shows the probabilitics for finding a contamination crater with a given radius as a function of the grid density for the “bottle rack grid” and the rectangular grid. It can be seen that the increase in hit probability by using the “bottle rack grid” is also dependent on the grid density. In the examplc quoted (area 10,000 square meters, crater radius 10 meters), a maximum is found at a grid density of about 40 sampling points (see Fig. 3 - 5). Increasing the number of sampling points in case of mixed samples creates the problem of diminuation of the contaminant content as the number of sampling points increases. In the worst case, the mixed sample is not even indicative when one of the sampling points is situated within the crater, as the contaminant content is reduced by the large number of samples taken from the uncontaminated area. ’) This term simplifies by assuming that each plot is surrounded by neighbor plots subjected lo

identical sampling.

Soil Sampling Methods

34 1

Rectan ular Grid 0,4

2

0,2

.......................................

.......................................

00

20

40

60

80

100

Number of Grid Points

Fig.4. The probability of detecting a contamination crater as a function of the grid density, illustrated for a rectangular and a “bottle rack” grid; crater radius 10meters on an area of 10,000 square meters.

Further questions and problems: (1) Determining the hit probabilities dependent on - the number of sampling points or the crater size (taking into account the indicativity of mixed samples), - the grid plan selected, - the shape of the contaminated area, - optimizing margin sampling. (2) Evaluating the efficiency by means of gain/loss functions for - costs of sample taking and analysis (including the cost of establishing the sampling points), - costs incurred if a contamination crater escapes discovery.

.....................................

.....................................

....................................

....................................

Number of Grid Points Fig. 5. Probability difference between “bottle rack” and rectangular grid for the detection of a crater as a function of the number of grid points; crater radius 10 meters on an area of 10,000 square meters.

342

R. W. Srholz, N . Nothhaum, and T. W. M ay

17.3 Soil Sampling Plans Guided by Hypotheses For the identification of possible sources of contamination or in order to clarfiy the variance shares of various interactions, fixed soil sampling plans are not sufficient. The testing of specific, determined hypotheses concerning initial pollution by contaminants and later spreading and reduction require soil sampling plans guided by hypotheses. It is necessary to find samples critical for a hypothesis decision, and to systematically exclude possible alternative explanations. The testing of specific hypotheses, however, does usually not occur at the beginning of studying certain ecocompartments, but is, as a rule, the result of a sequence of studies which have served to sophisticate the questions asked and the sampling plan. We should like to illustrate the quasi prototypical development of a hypothesis-guided soil sampling plan by another example, by the studies concerning fl-HCH (beta-hexachlorocyclohexane) contaminated parts of the East-German community of Bitterfeld during the years 1990- 1991 (see Scholz ct al., 1991). After the reunification of Germany, an ecological status investigation was started in Bitterfeld towards the end of 1989, since it has to be assumed that the evident changes in the environment possibly had had an effect on the population. The starting point was an indicator study in which at first a small number of samples of different foodstuffs were unsystematically drawn. These were foodstuffs which were available and consumed in critical areas. Within a general screening analysis for different groups of contaminants, only one single milk sample showed a critical load of 8-HCH. The goal of the second study was to investigate first whether the increased contaminant level obtained was a singular event, or not (quantification of load study). Besides, possible sources and paths of contamination had to be explored in order to find the responsible source and to be able to reduce the foodstuff contamination. Hence, the first two studies were mostly explorative. Their objective was to open .access to the problem field and to establish a basis for a study intended to verify hypotheses. In the second study, there was no systematical sampling plan either. In order to ascertain and to further differentiate the P-HCH load, milk and tissue samples were taken from several herds. To explore the contamination path, local authoritics took samples from river water, pasture soil, and stable wall paint. The attempt was to obtain at least one critical sample for each potential pollution path (hypotheses). The milk obtained from the several herds in the area under investigation was contaminated to different degrees, in some cases critically. Thus, the distribution of contaminants across the Bitterfeld area was not homogeneous, as it is typical for immissions. Suitable maps showed that all the herds with a high load grazed near the river (hypothesis 1) or near a fl-HCH waste disposal area (hypothesis 2). In a third study, hypothcscs of immission were tested by a statistical layman. The task was to test whether extent and sources of the contamination can be determined. Sampling unfortunately was not based on a sampling plan relying on methods of analysis of variance, and thus certain questions could not be tested because of

Soil Sampling Methods

343

incomplete sampling (see Nothbaum and Scholz, 1991b). Substantial contaminations were only found in the river’s flood zone. The fourth study was to classify the areas under consideration by using statistical experimental designs and to test the hypotheses of contamination, as several polluters were under suspicion. By means of a three-factor design of analysis of variance with the factors pollution A and pollution B (both dichotomous), as well as a 4-category soil level classification as indicator for flooding frequency, mixed soil samples were drawn from 33 of a total of 51 plots. The results reveal a significant influence of the “new” pollution and soil level on contamination. Except for one all plots could be classified as “suitable for grazing” or as “unsuitable for grazing”. Plots not sampled were assessed by using the median of all values from the same area.

17.4 The IHEARU Schema Each of the enquiries sketched in Fig. 6 is subdivided into six steps. We call this subdivision the IHEARU schema. Basically, this schema can be applied to any

1. Indicator Enquiry I General Screening

2. Quantificationof Pollution and Investigation of Sources

I: General concerns in the population / ‘obvious’changes of the environment

H Presence of contaminations / damage E: No explicit sampling . _ plan _ A: Unsystematical foodstuff samples

R: B-HCH contamination in milk

1 U: Further studies I

1L

I I

polluters H: Contamination dependent on pollution €5 Variance analytical design A: Sampling in flood area R: Classifying areas according to pollution and flooding U: Giving up certain areas as pastures .

I

I: Checkingtheresults of enquiry 1. / Exploration of sources of pollution H Milk pollution by refuse dump / sewage impact E No entirely systematical sampling plan A: Sources: soil, water, stable paint; acceptors: herd milk, tissue. R: Soil pollution; milk contaminations according to localitv U: Enquiry &to diverse river areas /polluters

on different pastures? H: Various contaminations by various polluters E Monovariableplan (pair samples) A Samples in the river and dump area R: Increased pollution in the highwater area of a

I

I

U: Assigment for systematical classifcation of areas

1

344

R . W . Srholz, N. Nothhaum, and T. W . M a y

I

H

E

A

R

U

Initial information /

Hypotheses

Enquiry

Adequate

Results

follow-Up

design

sampling plan

starting point / goal

ecological study, but it should be implemented particularly in case of sequential studies in order to record the results of the various studies, as these are the starting points for the next. The IHEARU schema describes the enquiry’s starting point/Initial information(1) and Hypothesis/goal (H). It shows which type of Enquiry (E) has been used and hence whether the sampling plan is Adequate (A), or not. By making Results (R) and follow-UP (U) explicit, is shows the status of knowledge attained and whether further enquiries will be necessary (Fig. 7).

17.5 Conclusions The case study “HCH-Contamination Bitterfeld” serves to illustrate the sequential character of the search for information in the analysis of cause-and-effect relationships in the environment in two ways. Firstly, various studies are based on each other in the sense that the results/ follow-up of the previous serve as the starting points for the next, showing that knowledge about statistical significance of relationships is required already for study two, and that the statistical effort increases from one study level to the next. The studies on the cause-and-effect relationships in the environment demonstrated that an environmental expert, working on the modeling and conceptualization of environmental effects, should not only be familiar with the theory of testing hypotheses, but also with multivariate schemata. These include completely crossed designs of analysis of variance and in particular the concept of interaction in order to be able to work competently, say, on the problem posed in the second example. These statements do not mean that the calculatory methods have to be mastered (these are a natural domain of the experts for the sign level, i.e., the mathematicians/statisticians and can be delegated to computersupported systems besides), but rather refer only to the qualitative schemata of the stochastical methods quoted. It is obvious that intuitive knowledge is not sufficient for a complex task like “hypothesis-guided search for information in the environment”. In addition, it was demonstrated that the choice of a suitable grid plan may also lead to complex statistical questions, as the example of fixed sampling plans has shown.

Sail Sampling Method~y

345

17.6 References Hortensius. D., Bosman, R., Harmsen. J., Wever, D. (1991) Entwicklung standardisiert. Probenahmestrategien fur Bodenuntersuchungen in den Niederlanden. NAW V12 02 -91. Landesanstalt fur Umweltschutz Baden-Wurttemberg. Inst. fur Altlastensanierung (1988) Proberiahme Boden, Abfall, Crundwasser, Sickerwasser .fur die chem.-physikal. Untersuchung. Vorlaufige Vercdhrensrichtlinie, LA f. Umweltschutz, Karlsruhe, 8 pp. Netherlands Normalisatie Instituut (1 991, September) Dutch Draft Standard-Soil: Investigation Strategy for Exploratory Suruey (1st. ed.), (UDC 628.5 16). Nothbaum, N., Scholz, R. W. (1991 a) Probenplanung und Datenanalyse bei kontaminierten Biiden - Projekthericht (erstellt im Auftrag des IWS-TU-Berlin). Bielefeld, Technical Report. Nothbaum, N., Scholz, R. W. (1991 b) Strategien hypothesengeleiteter Informationssuche im Umweltbereich, Technical Report. Scholz, R. W., Nothbaum, N. (1991) Beprobung und Flachenklasslfikation van Oberboden in den Uherschwemmungsgebie ten der Mulde beziiglich persistenter Clzlorkohlenu.assersto~fe.Bielefeld: GOE, Gesellschaft fur Organisation und Entscheidung. VDLUFA (1991) Die Untersuchung uon Boden. Methodenbuch VDLUFA. Darmstadt VDLUFAVerlag.

Environmental Sampling for Trace Analysis

Edited by Bernd Markert 0 VCH Verlagsgesellschaft mbH, 1994

18 Sampling for Trace Analysis of Lake Sediments Ursula M . Cowgill

18.1 Introduction Sampling and experimental design are of utmost importance in assuring the ultimate success of a study. It has been frequently stated (Keith et al., 1983, 1988; Hikanson, 1984) that more money and time have been wasted in environmental studies due to poor experimental design than for any other cause. It has also been pointed out by Gy (1 979) that improper experimental design and sample handling may introduce more error than is inherent in the analysis of the material. As yet no standard procedure has been established, such that two investigators, could unknown to each other, visit the same water body, take sediment samples in the same place, using similar equipment, and on chemical analysis arrive at concordant results. This is not particularly astonishing in view of the complexity of sediments. Hikanson and Jansson (1983) have noted 12 different factors that might influence the information it is possible to derive from sediment analysis. These factors include the size and depth of the water body, physical, chemical and biological characteristics of the sediment, anthropogenic factors, surface mud characteristics, currents, whether the samples are taken in the littoral or pelagic area of the water body under study, sampling devices, sample handling, number of samples collected and analyzed and finally the trustworthiness of the analytical results. The biggest single problem in the sampling of sediments to be used for trace analysis is the procurement of a representative sample. Under normal circumstances, in a lake, for example, the best place to take a sediment sample is in the deepest section where disturbance of the water column is minimal. Here, sedimentation proceeds in as orderly a fashion as is possible in nature. However, for the purpose of trace analysis, the intent of which is pollutant monitoring, the sample will normally be taken near the discharge area which usually is situated in or near the littoral zone, which often is the most disturbed and active portion of any body of water. In addition, continuous or near continuous turbulence is contributed by the discharge. Under such circumstances, a representative sample is almost impossible to obtain. Continuous sampling over time of such a situation will reveal trends. The ideal objective of any sampling program is to procure a representative undisturbed sample of the sediment to be investigated. Bottom sediments consist of a variety of particles varying in size, shape, and chemical composition which have fallen through the water column. The origin of these particles may be autochthonous such as dying algal blooms, fecal pellets, precipitated compounds, the composition of which exceeded the solubility product and thus a crystalline substance was formed, or an aged zooplankter that died. In

348

U . M . Cowgill

contrast, some particles, in fact, under most circumstances, the vast majority of particles are allochthonous in origin. That is to say, they have been transported by water, wind or ice action from their terrestrial environment. Such particles accumulate on the bottom of bodies of water at the rate of a few millimeters a year or in some areas as much as several centimeters per year. The “rule of thumb” for the world is a meter per millenium. Once these particles have sedimented to the bottom of the body of water under study, changes may occur that alter their structure as well as their chemical composition. Some of these changes are brought about by reduced oxygen concentrations or a lack of oxygen. Others, especially in the littoral zone, may be initiated by burrowing organisms. Some changes cause the release of chemical compounds from the particles with which they were associated resulting in their entrance back into the water column where they remain in the water column or enter the food chain. Thus, bottom sediments are viewed as a source of geochemical history of the basin in which the particular lake lies, as a contaminant sink as well as a source of contamination of the aquatic environment. This contribution will review sampling tools, though not exhaustively, some thoughts on experimental design and the number of samples required to obtain an adequate picture of the trace chemistry. In addition, adequate sampling of bottom sediments, surficial sampling as well as sampling at depth, mud/water sampling and pore water sampling will be discussed. The objective is to provide a basis for adequate sediment sampling for the purpose of trace analysis.

18.2 Sampling Devices This contribution is not intended to be an exhaustive review of sampling tools and techniques but rather an overview of the subject. Reviews of samplers and sampling techniques may be found in Murdoch and MacKnight (1991), Hopkins (1964), Wright et al. (1965), Bouma (1969), Sly (1969), Elliott and Tullett (1978, 1983), Robbe (1 98 I), and Hikanson and Jansson (1 983). The object of sampling is to extract a subsample of the sediment that resembles the original material as closely as possible. This subsample should be collected in such a way that in the process of extraction it should remain undisturbed. Mixing and compaction should be avoided as well as losses of water, particulate material and so forth. To attain these goals, the sampler should be lowered slowly to avoid setting up currents or causing shock waves that might disturb recently sedimented material. There are three types of sampler that are commercially available : dredges, grabs and corers. Dredges will not be discussed here since they are only used for qualitative studies as it is not possible to control the location or depth of the sampler. Thus, it is not possible to take, with any degree of certainty, samples of surface mud next to each other (Plumb, 1981). Grab samplers are used to collect surficial samplers. These samplers usually take only the surface 1 to 3 cm and are used for spatial

Sampling of' Luke Sediments

349

distribution of trace elements and surveys. Corers may be used to obtain both surficial as well as sediment column samples. These samplers extract mud with the least amount of disturbance. Such mud may on analysis provide historical information on trace element trends, may locally provide background values of trace substances and may establish trace element trends over time. Tab. 1 shows the types of samplers in relation to sediment depth sampled. Tab. 1. Sediment Depth Collected by Different Samplers under Optimal Conditions ( - 2 m of line-grained mud) Depth sampled

Sampling devices

0- 10 cm

Birge-Ekman, Ponar, mini-Ponar, mini-Shipek-light weight, small volume grabs Van Veen, Smith-McIntyre-heavy, large volume grabs Kajak-Brinkhurst, Phleger corers-single gravity corers box corers multiple corers Benthos, Alpine corers-single gravity corers Piston corers

0-30 cm 0-50cm

0-2 m Deeper than 2m

18.2.1 Grab Samplers Essential components of grab samplers are a messenger weight, a line and a sampler. Two main types exist: one type consists of a set of jaws which when lowered to the surface mud will shut as a result of the messenger weight acting as a trigger mechanism. The second type consists of a bucket which rotates into the sediment when it reaches the mud surface as a result of a trigger mechanism. There are a number of disadvantages to the use of grabs as sampling tools. The first is that the depth to which they penetrate is uncontrolable and unrealiable as this depth is determined by the physical nature of the sediments, the speed with which the sampler strikes the surface mud and the weight of the sampler. Secondly, depending on the nature and size of the particles that make up the surface mud, there is a loss of material as the sampler and the sample are drawn up through the water column. This is especially true of fine-grained sediments. In the process of lowering the sampler through the water column there is always the chance of creating currents which disturb the recently sedimented particles. There is always the danger that the sampling tool contaminates the sample. Finally, it is quite difficult to remove the sample from the grab without further disturbing it. The best way to approach these problems is to gather a large number of subsamples and amalgamate them into a single composite sample rather than to collect one sample and have it represent the whole site. The choice of sampler depends upon the desired depth, the volume of mud to be sampled, the nature of the mud to be sampled, the size of the water body to be

3 50

U . M. C'ou,gill

sampled, presence or absence of currents, the sampling platform (floating raft, boat, bridge, etc.), and the availability of lift equipment (Horowitz, 1991). Ponar and Van Veen samplers arc best for areas where large samples are desired, while Shipek, Ponar and Birge-Ekman are more commonly used in lakes and in slow-moving waters. The Peterson grab is most effective for bulk samples of mud, silt, sand and gravel (Sly, 1969). Shipek grab does poorly in cohesive sediments but is often used for soft clays, mud, silt, sand and gravel (Sly, 1969). The samples taken with this grab are often disturbed and arrive at the surface upside down from the way they were originally collected. Grabs should be closed after the sample has been collected to avoid loss of water and material and they should be stable. Generally, grabs d o well in weak currents and poorly in strong ones. Light weight samplers such as Birge-Ekman behave poorly in deep water with high currents and are much less stable than the heavier Van Veen grab. Tab. 2 shows the area sampled, the weight of the sampler sample, the volume the sampler sampled and the required lifting capacity for the Birge-Ekman, Ponar, Petersen and Van Veen grabs. Before discussing specific grab samplers, it is important to mention the comments of Hiikanson and Jannsson (1983) concerning the ideal sediment sampler. They state that an ideal sampler should permit water to pass through it as it descends through the water column so that pressure waves may be avoided when the sampler reaches the surface mud. I t should produce a minimal amount of friction, compaction and deformation of the sediment. To achieve this, the wall of the sampler must have a small thickness compared to the sample area and the inside surface of the sampler should be smooth. The cutting portion should be sharp and have a small edge angle. After sampling, thc top and the bottom of the sampler should close in situ. To record major features and any apparent stratification, it is most convenient if one side of the sampler is transparent. It should be able to take large samples, not be too heavy so that a winch is not necessary, be easy to operate so that lengthy instruction for use is unnecessary and finally this ideal sediment sampler should be adjustable in terms of weight so that it may be used on different substrata. Birgr-Ekman Grab. This grab is available in several sizes. It may be operated manually although the larger model requires a winch. It works wcll in sediments

+

~

Tab. 2.

Basic Information on VariousTypesofGrab Samplcrs (Murdochand MacKnight, 1991)

Grab

Birge-Ekman petite standard Ponar standard Petersen Van Vccii standard large

Sampled area (cm)

+ scdimciit

Wcight

Sample volumc

(kg)

(an3)

Needed lifting capacity (kg)

1 5 x 15 2 3 x 23

10- 15

3 400 13 300

I00

2 3 x 23 3 0 x 30

23

7250

35

9 450

150-250

3 5 x 70 50 x 100

30 65

18 L 15 L

150 -400 300 - 800

40

50

100

Sumpling of Luke Sediments

35 I

that are fine-grained, soft, and a mixture of silt and sand. Care must be taken to avoid sediments that contain or support high numbers of large objects such as gravel, shells, or pieces of wood as such objects may interfere with the closing of the jaws and thus result in the loss of some or all of the subsample. In very soft sediments, this sampler, due to its weight may penetrate the mud too deeply. This situation may be partly avoided by lowering the sampler slowly and releasing the messenger about the time the sampler has reached the mud surface. Once the sampler jaws have closed as a result of the messenger activating the trigger mechanism the sampler may be retreaved. If it is desirable to divide the sample into a number of subsamples, the sediment may be removed from the top of the sample. From a statistical viewpoint, it would be wiser to take a series of subsamples and amalgamate them into a composite. These may then be removed through the bottom of the sampler into a container. This then is a bulk surface sediment sample. The Ekman grab has been modified by Hikanson (1986) such that there is an automatic closing mechanism on a detachable trigger assembly, a n inside liner made of plexiglass that has a lid that closes and tight-fitting jaws. - Ponar Grub. Unlike the Birge-Ekman grab, which is unsuitable for use with coarse sediments, the Ponar is successful in extracting subsamples from such terrain as well as from muds rich in shells. This sampler is suitable for most sediment types ranging from soft, fine-grained to firm sandy muds. It does poorly in hard clays. This grab sampler is heavier than the Birge-Ekman and thus functions more efficiently in fast moving waters. The ponar is too heavy to use manually and thus it is lowered into the water by use of a winch. It consists of a pair of jaws weighted and tapered that are held open by a catch bar across the top of the sampler. This device is activated by the release of rope tension on the lifting mechanism once the sampler has reached the mud surface. During retrieval the tension on the rope keeps the jaws closed. Hikanson (1982) has modified the Ponar by lining the metal sampler with plexiglass and placing a closure mechanism on the top. - Petersen Grub. This sampler is ideal for collecting large bulk samples from hard bottomed materials such as sand, mud, gravel and hard clay. More weights may be added to increase penetration into hard clay sediments. It consists of a pair of weighted semicylindrical jaws held open by a catch bar. When the sampler has arrived a t the mud surface, the tension on the bar is released permitting the jaws to shut. For deeper penetration into hard sediment extra weights may be added to the jaws. On retrieval, the sampler may be removed and placed in an appropriate container by opening the sampler jaws. This sampler requires an electrical winch (cf. Tab. 2) as it is too heavy to operate manually. - Van Veen Grub. This sampler is among the largest of its kind and is quite heavy and funtions efficiently in deep water and strong currents. Its biggest advantage is that it has a strong closing mechanism which allows the jaws to collect relatively undisturbed sediments. This sampler can be lined with plexiglass or any other suitable material to avoid sample contamination. It is used to collect bulk samples of indurated or soft clay, mud, silt, sand, and gravel (Sly, 1969).

18.2.2 Corers Corers are samplers that take cross sections of sediment at varying depths. These instruments are used for the investigation of historical inputs of pollutants to aquatic systems, paleolimnological investigation as well as geological survey. Corers consist of a hollow metal tube, which when the object of the study is metal distribution, are lined with a plastic liner, that vary in length and diameter. As a rule, if more than 1 m of sediment is desired, a casing is necessary. The sample is extracted from the mud, and then the casing is pushed to the deepest level from which the sample was extracted. Tab. 3 shows the weight of sediment in relation to the diameter of the coring tube. The object of coring is to take an undisturbed sample of the sediment. At the end of the barrel a cutter is mounted which is used to penetrate the deeper sediment. Inside this cutter, a core catcher is placed to prevent the loss of sediment on retrieval. Once the sediment has been retrieved, the top and the bottom of the tube are identified and a clean stopper, usually rubber or neoprene, is placed in the top and in the bottom of the tube. Since the sediment will be around 4 "C when extracted, it should be kept cool but not cold until subsampling is possible. Freezing should be avoided. As a rule, core tubes should be about 0 . 3 m in length and 3.5 cm in diameter. If tubes are much longer than this, slumping of the sediment may occur and the stratigraphy of the sample will be destroyed. - H a i d Corers. These are corers that are suitable for the collection of sediment in the littoral zones of lakes. They may be purchased with metal or plastic tubes with a diameter of 3.5 to 7.5 cm (inside diameter) and extension handles on the top end for driving the corer into the mud. The weight of these devices varies from 5 to 17 kg and the extcnsion handles add another 4 to 12 kg. - Phleger Corer. This corer, which is a single gravity corer can be operated manually but the logistics of a raft or boat requires the presence of two people. Core tubes of this corer are 3.5 cm (inside diameter) and 50 cm in length. The Phleger corer does well in peat and soft to sandy sediments. Its biggest drawback Tab. 3. Estimated Weight of Dried Top 1 cm Mud Layer Subsampled from Core Liners of Different Diametcrs (Murdoch and MacKnight, 1991)

-

Weight of dry mud in top 1 cm sediment layer"

Soft fine-grained mudb (g)

3.5 5.1 6.6 10.0

0.71.52.65.9-

1.4 3.0 5.2 11.8

Firmer silty clay" (g)

1.1 - 2.2 2.3- 4.6 3.2- 7.9 8.R - 17.7

Based on 90-95%1 scdimcnt water content. Bascd on specific gravity of mud 1.5 (high organic matter content). Bascd on specific gravity of mud 2.3 (low organic matter content).

Sumpling

of

Luke Sediments

353

is that it tends to compact the sediment. If the material to be collected must be dated with lead, caesium or carbon fourteen then samples of the mud must be collected at relatively close intervals. Compaction will provide erroneous dating results. When this gravity corer arrives at the mud surface, the water pressure causes the stopper at the upper end of the coring tube to move up and sediment begins to fill the core tube. The pressure is maintained in the tube by the stopper as it slides back into position thus retaining the sediment sample for retrieval. This corer is not recommended if knowledge of stratigraphy is important and if dating of the mud is desired. For further discussion of this corer the reader is referred to Murdoch and MacKnight (1991). - Kujuk-Brinkhurst Corer. This gravity corer utilizes core tubes that are 5 cm (inside diameter) and 70cm in length. There is an automatic trigger mechanism which permits the user to select the time to close the valve which then creates a partial vacuum in the core tube. This partial vacuum assures the retention of the sediment in the core tube during retrieval. Although some authors (cf. Murdoch and MacKnight, 1991) suggest that this corer can be used manually, it is the experience of this writer that the corer + sediment is too heavy and the use of a winch is highly recommended. - Benthos Gravity Corer. The Benthos gravity corer is able to recover 3 m cores from soft fine-grained mud. It has stabilizing fins situated near the top of the instrument which assist vertical penetration into the mud. The auto-valve is open during free fall and sediment penetration. The plunger is pushed into its machined seat and thus a vacuum is developed which keeps the mud in the tube on retrieval. Corers may be purchased with inside diameters varying from 6.6 cm to 7.1 cm. The required lifting capacity varies from 750 kg to 1000 kg (Murdoch and MacKnight, 1991). - Alpine Gravity Corer. This corer is finless and thus tends to land on the sediment surface at an angle. It is winch operated. Its use is not recommended (Sly, 1969). For further and more detailed discussion on other corers, the reader is referred to reviews on the subject (Hopkins, 1964; Wright et al., 1965; Bouma, 1969; Sly, 1969; Mawhinney and Bisutti, 1987). - Box Corers. Box corers are gravity corers that collect large rectangular cores from the mud. Their main use is for geological and biological studies although such samples may be used in chemical surveys. They are versatile in that they can function in many sediment types and they can be made to extract subsamples at a variety of depths. The bottom of this corer can be designed as an Ekman type grab sampler or as a shovel-like device that slides under the box corer (Reineck sampler). These corers are quite heavy, weighing about 800 kg and require a lifting capacity of 2000 to 3000 kg. Their use requires a vessel capable of this lifting capacity as well as sufficient deck space to handle the samples. These corers can not be used in gravel-like muds or i n sediments that contain a lower layer of gravel since they can be damaged. Commercially available box corers can be 2 m by 2 m in size. For further discussion of this corer, see Mawhinney and Bisutti (1987). - Piston Corers. Piston corers, if carefully used, can extract cores of mud up to 30 m. They do the best job of removing sediment with a minimum amount of

354

I l . M . Cowgill

disturbance. Once the piston is at the mud surface, a partial vacuum is created and the sediment is slowly sucked into the core tube. Any sampling much greater than 1 in requires casing. The best description of the use of a piston corer may be found in Deevey (1965). Further discussion may be found in Wright et al. (1984). - Multiple Corers. The biggest single problem encountered with most coring is that the sample size is too small. One way around this problem is the use of multiple corers. This permits the extraction of several cores taken simultaneously. In this sampling tool (cf. Hamilton et al., 1970) usually four tubes, about 0.3 m in length each, are lowered to the mud surface and a messenger is released along a line from the surface which causes soft half balls to seal the top ends of each of the coring tubes. These tubes can be made transparent so that it is possible to view any stratigraphic changes and surface sediment is not lost during retrieval. It is possible to calculate the variance within a sampling station and all samples are extracted simultaneously. Since the tubes are open from both ends as they fall through the water column, the pressure waves set up are minimal. This instrument can not be used in gravel or organic debris. It is clear that there is no one sediment sampler that works effectively on all sediment types. Which sampler to select depends on the purpose of sampling. If it is for regulatory purposes, as a rule only the top few centimeters are of interest. It is also necessary to know how much materials is needed as this will govern the type of grab sampler needed. Tab. 4 provides an overview of the variation in surface sediments to be found in the USA. It is necessary to have some knowledge of the sediment terrain and if this terrain supports large shells, gravel of various sizes and sand or heavy clay. Somc attempt, therefore, should be made to gather all available information on the area to be sampled. It is mandatory to know the water depth of the area to be sampled and whether the current is fast or slow. Physical characteristics of the sediment may bc obtaincd by use of a sediment penetrometer (Hiikanson and Jansson, 1983). The penetrometer may have a variety of cores. The depth to which each core is able to penetrate reveals the nature of the sediments. A calibration table is supplied with each penetrometer purchased so that the depth each core has achieved may be converted to a variety of sediment types varying from very soft mud to very hard mud. Tab. 4. Range of Various Measured Variablcs of Freshwater Mud Samples Actual number of samplescollected was 102 (Suedel and Rodgers, 1991); N = number of replicates measured Minimum

Median

Maximum

N

11.9

+ 379

91.7

0.03

72.1 - 58 0.57

11.8

308 302 30x

'I/n sand

0.1 0 0 2.3

3.1 1.2 18.3 80.6

71 59.1 94.3 100

29 1 306 306 306

Particlc surface area cm'jg

45

96386

4.1 x 10"

306

Variable

' yn solids . Redox potential, mv% organic carbon Cation exchange capacity meq/ 100 g Yo clay 'Yn silt

- 40Y

Sampling of Luke Sediments

355

18.2.3 Sampling Devices for the Collection of Suspended Sediments Suspended sediment samplers may be divided into three general types: integrating samplers, pumping samplers and instantaneous samplers (Horowitz, 199I). The integrating samplers collect a water-mud mixture over time. Instantaneous samplers are usually a flow-through chamber which gather the whole water in the chamber when both ends are closed. Pumping samplers accumulate a whole water sample by pump action (Office of Water Data Coordination, 1982). The integrating samplers are usually the samplers of choice as these are thought to provide a more representative sample (Office of Water Data Coordination, 1982). However, it has been noted by some (Ongley and Blachford, 1982; Horowitz et al., 1989, 1990) that changes in suspended sediments can be large over time. In addition both horizontal and vertical changes in chemistry have been observed (Horowitz et al., 1989). No single sampling technique addresses directional as well as temporal variation. The investigator must decide what information is most crucial to his study and sample accordingly. Horowitz et al. (1989, 1990) have provided detailed discussions on the effect of sampling design, the type of sampler employed, temporal as well as directional variation and the grain size fractional contributions for suspended sediments on the subsequent trace chemical results.

18.2.4 Mud-Water Interface Samplers The best way to take a mud-water interface sampler in shallow water is to use a Naumann (1930) sampler, a device designed for this purpose. It consists of a plexiglass or glass or teflon tube that is beveled on one end. The length of the tube is at the discretion of the investigator. In the non-beveled end a neoprene stopper is fitted. The tube without the stopper is pushed slowly into the mud and when it has reached the desired depth the stopper is inserted and the sampler mud is slowly extracted from the mud. The liquid portion of the mud-water interface sample is removed with a syringe fitted with a glass needle, both fashioned from boron-free glass, if boron is one of the elements of interest. In this case a teflon sampler is desireable. Since the liquid portion of the mud-water interface is only a few millimeters thick, it is necessary to collect a large number of samples from different portions of the water body, not only to try to obtain a representative sample but also to have enough sample for analytical chemistry. These liquid subsamples are amalgamated into suitable bottles and kept cool until analysis is possible. The mud subsamples are extruded and amalgamated into suitable vessels and reserved in a cool environment until analysis is possible. This extrusion of both the liquid and solid portion of the sample may be accomplished in the field. For further discussion of this technique the reader is referred to Cowgill (1 988). The collection of mud-water interface samples from deep waters is best accomplished with a piston corer following the description of Deevey (1965).

+

356

I/. M . Coii~gill

There is a paucity of information of the chemical composition of the mud-water interface. However, it exerts a considerable influence in both shallow aerobic systems as well as deep eutrophic ones (Cowgill, 1988).

18.2.5 Sediment Pore Water Sampling To understand the physical and chemical mechanisms that are responsible for the actual chemical concentrations in mud, an accurate description of the physicochemical characteristics of both the liquid and solid portions of the mud-water interface must be provided (Forstner and Wittman, 1979). There are three methods presently in use to separate pore water or interstitial water or substances dissolved in the pore water from sediment particles (Hesslein, 1976; Mayer, 1976; Robbins and Gustinis, 1976; Brinkmann et al., 1982). It is important to realize that highly organic muds may contain in excess of 90% water but those that are basically minerogenic deposits may range from 30% to 50% in their water content. The three methods are centrifugation, pressure or vacuum filtration and dialysis. The amount of water extracted from the mud is usually no more than 25 to 50% of the total water content. The chemical composition of pore water is highly variable. The stability of its chemical composition is, for the most part, a function of proper handling of the mud prior to extraction and analysis. For example, Krom and Berner (1 980) found that the adsorption of phosphate by anoxic marine sediments is greatly increased when the samples are permitted to become aerobic. Centrifugation is the simplest way of obtaining pore water from muds. When the need for accuracy and reproducibility are low, as in the case of a survey, this is the procedure of choice. Centrifugation does a poor job of removing colloidal and fine particles from the supernatant liquid. It also encourages chemical changes such as oxidation or altered gas equilibria. Removal of colloidal particles is difficult although the larger ones can be separated by filtration. To minimize chemical changes of the mud sample, extrusion from the core tube or subsampling a large grab must be accomplished in a glove box under nitrogen gas. Samples intended for centrifugation should bc collected under nitrogen gas and their centrifuge tubes stoppered. Following such a procedure will certainly minimize chemical changes such as oxidation during centrifugation but i t will not eliminate it. It is important to realize that the speed of centrifugation will have an effect on the chemical composition of the pore water. For example, Adams et al. (1980) centrifuged muds at different speeds (7000 rpm to 19000 rpm) and found that the phosphate doubled but no change was noted in the concentration of Ca, Fe, Mn or Zn. If centrifugation is the method of choice for the extraction of pore water, then the centrifugation unit must be refrigcrated since the process of centrifugation heats the samples thus bringing about chemical changes. Filtration is the most frequently used process to obtain interstitial water. There are several dcvices in common use in laboratories as well as in situ sampling. Pressurc

Sampling of Lake Sediments

351

filtration equipment is usually operated with nitrogen gas, thus avoiding the oxidation of the mud during processing. A mud pore water squeezer (cf. Robbins and Gustinis, 1976) uses a 0.45 pm membrane filter. High pressure acts through a rubber membrane on the mud sample which is enclosed in a removable cassette. The pore water passes first through a coarse pre-filter and then through a fine membrane filter. When the system is made of stainless steel, the determination of anions, alkali metals or alkaline earths in pore water can be accomplished without contamination from the apparatus, but Fe and some transition metals cannot be (cf. Presley et al., 1967). Presley and his coworkers (1967) solved this problem by lining the gas-operated squeezer with teflon. Manheim (1972) used disposable plastic syringes fitted with screen discs and filter paper circles to extract pore water from muds. If organic compounds are of interest the plastic syringes can be replaced with stainless steel syringes. The main disadvantage of laboratory squeezers is the disturbances that occur in the mud during transport from the lake to the laboratory. Brinkman et al., 1982) described a squeezer that could be used in siru. It was designed primarily for use in shallow waters. It is difficult to use and functions poorly in depths of 5 m and greater. A number of difficulties have been noted by investigators who have obtained pore water by laboratory squeezers. For example, iron concentrations were initially high, presumably due to colloidal particles transporting iron as they passed through the filter (Klinkhammer 1980); other workers (Emerson et al., 1980) found higher amounts of ammonium ion in the initial liquid obtained from the squeezer. Higher sulfide values were detected in pore water obtained by in situ interstitial samplers than from laboratory squeezers (Hines et al., 1989). Seasonal variations have been noted (Klump and Martens, 1989) in pore water concentrations for sulfate, phosphate, total inorganic carbon and dissolved ammonium. Mayer (1976) describes an in situ procedure employing dialysis chambers or bags containing distilled water which are placed in the sediments. This system remains in the sediments until the concentration in the bag is the same as in the pore water of the sediments. The “extracted” pore water is free of colloidal particles and is very pure. In fact, this method provides the purest pore water sample of any of the methods yet described. However, there are disadvantages. To achieve equilibrium is time consuming. Most sediments in water as deep as 5 m or more remain close to 4 “C much of the year and thus attaining equilibrium takes time. In addition, a curious catfish may cause the dialysis unit some difficulties. In the centrifugation method, samples of sediment must be collected. As was described earlier, the nature of the material found at the bottom of most bodies of water is such that gathering replicate samples in the true statistical sense is not possible. Sampling with subsequent chemical analysis over time will hopefully show a trend if the object of the study is to monitor pollutants. Chemical concentrations in pore water vary as to where the sample is taken, that is to say in situ or transported to the laboratory, how the sample is taken, i.e., what device was employed, the speed at which the pore water was extracted as in the case of centrifugation, and what kinds of ions come out initially in contrast to those that come out at the end of the extraction process. Aside from all these problems it is not possible to collect replicate samples of pore water.

358

U . M . Cowgill

The amount of pore water needed to carry out chemical analysis will require the collection of large amounts of sediment. In the case of sampling for the purpose of establishing trends there may not be much recent sediment available after several years of collection.

18.3 Subsampling of Sediment Grabs and Cores The purpose of sampling will determine the frequency of subsampling. With grab samples, subsamples should be extracted as soon after collection as possible. The surface of the mud should be removed as soon after sampling as possible as it will contain contaminants from the sampler unless the metal sampler has been lined with a noncontaminating liner. Water content of 1 cm3 of mud should be determined immediately, so that all chemical determinations may be expressed on this basis. The simplest way to collect 1 cm3 of mud is to use a chemically clean porcelain spatula and push mud into the lower end of a 10 cm3 glass or plastic pipette (depending upon the chemical analysis of interest) until 1 cm3 has been measured. This 1 cm' of mud is then extruded with a clean glass or plastic rod into a preweighed porcelain crucible. The crucible samples is then weighed and placed in a 110 "C oven for 48 h and then weighed until constant weight is achieved. Sampling for water content should occur at each interval where samples are taken for chemical analysis. Sediment cores should be subsampled as soon as possible after retrieval of the cores. Should this not be possible, the cores must be stored at 4 "C in a humidity controlled room. This storage is possible for several months without any large change in sediment properties occurring. However, if it is the intent to analyze for chemical contaminants then cores should be extruded as soon as possible. The best way to extrude a core is as follows: (1) take a coring tube and cut it in half and clean it thoroughly; (2) remove the stopper from the end of the coring tube containing the mud to be sampled; (3) remove the stopper from the top of the coring tube containing the mud and replace it with a clean stopper the bottom of which is the same diameter as the mud; (4) using one of the coring rods from a piston corer, push slowly and gently, so as not to compact the sediment, against the stopper described in (3). Gradually extrude the core onto the clean halved coring tubc, pulling the core containing the mud down the halved coring tube so that finally the extruded core lies on the clean halved core tube described in (1).Once extruded, samples for water content and chemistry should be taken immediately. In the event that acid volatile sulfides and heavy metals are of interest, the complete procedure of extrusion just described must be carried out in a large glove box or a series of glove boxes evacuated with a very clean source of nitrogen. It should be pointed out that any sediment samples to be analyzed for their lead content should be extruded and subsampled in a nitrogen evacuated glove box. In the case of a grab sample, subsampling must be carried out in a nitrogen evacuated glove box. It has been found by the author that in some cities the laboratory air contained more lead than the samples being analyzed. Collected subsamples should be placed in clean glass vessels, sealed and stored at 4 "C.

+

Sumpling of Luke Sediments

359

18.4 Quality Control It is very difficult to measure sampling accuracy of sediments which are, as a rule, quite heterogeneous. It is possible to take two sets of samples in exactly the same way. The results will show variations due to sampling and subsampling techniques but the heterogeneity of the sediments will still affect the chemical results. Fig. 1 shows the distribution of some elemental concentrations in two cores taken about l 0 0 m apart in 4 m of water in Laguna de Petenxil, Guatemala (Cowgill et al., 1966). The only way that the two cores could be related to each other was through carbon fourteen dating techniques. Baudo et al. (1981) published some data form Lake Mezzola (Northern Italy) showing mean concentrations of elements for sediments and pore water. The 'YO sampling error is always greater for elemental quantities for littoral pore water where n = 32 pairs than for the littoral muds where n = 51 pairs. In some cases the 'YO sampling error is greater than the mean, as is the case for Cr in pore water (cf. Fig. 2). Williams and Pashley (1979) compared the carbon content of two cores, one taken with a piston borer and the other using a scuba diver operating a piston corer and found the results to be similar. Evans and Lasenby (1984) found similar results when they compared a Kajak-Brinkhurst corer with a diver driven one. Rutledge and Fleeger (1988) found that diver operated equipment resulted in a difference between the chemical composition of the outer portion of the core from the center portion because particles tended to concentrate in the center during core transport by the diver.

CI-core 2 S-core 3 S-core 2 K-core 3 ~

K-core 2

L

Na-core 3 c

Na-core 2

0

0.I

0.2

0.3

0.4

mg/cm*/a Fig. 1.

Chemical comparison of two cores from Laguna de Petenxil, Guatemala.

360

I/.

M . (‘owgill

Mn

Ni + t

-5 Zn w

CU

Cr

o

zoo

400

600

eoo

Fig. 2. Elcmental quantities and their YO sampling error (mud: n = 51 ; pore water: n = 32).

Concentration

Core shortening may occur when cores are collected with a gravity corer. Apparently this shortening is related to tube size. In addition when sampling soft sediments it is possible to obtain a core that is not representative of the mud (Blomqvist, 1985). Baudo et al. (1985) compared chemical results from cores taken with a gravity corer and a modified Ekman grab sampler in six locations in Lake Como (Northern Italy). They concluded that the variability in chemical results between cores extracted by the different sampling devices was no greater than the analytical error of the chemical analyses. Based on the work of these authors, it would seem that results from grab samplers and piston corers of the top 10 cm of mud give reasonably concordant results. Sample handling often affects the chemical results. Cores must never be frozen. Freezing and thawing not only destroy the stratigraphy but also tend to homogenize the chemical results. Rutledge and Fleeger (1988) found that fast freezing of cores distorts the sample, although the state that commercial freezing does not create distortion any more than the compaction due to the “drag of the corer wall upon insertion”. Some investigators (Ginsburg et a]., 1966; Bouma, 1969; Crevello et al., 1981) have suggested the use of epoxy or polyester resins as a means of preserving stratigraphy. However, this treatment will distory the chemical results. The best way to handle samples, whether they are grab samples or cores is to keep them at 4 ”C in the field and maintain them at this temperature until subsampling can occur. Finally, great effort must be exerted to avoid contaminating the core or grab sample. Samplers must be cleaned between sample sites. How they are cleaned is determined by the kind of chemical analysis intended. Most sampling devices can be fitted with liners that will avoid contamination. However, it should be borne in

Sampling of Lake Sediments

36 1

mind that water loss can occur through the walls of some liners. Under these circumstances, subsampling should occur on arrival at the laboratory. Blanks should be taken during sediment sampling. These are equipment, field and sampling blanks. The sampling blank is extremely important as the chemical composition of atmosphere can affect the chemical composition of the mud samples. The sampling blanks are merely a vessel of double distilled (glass) water exposed to the atmosphere during the entire sampling period. If sampling proceeds for days, then a new sampling blank should be exposed each day.

18.5 Statistical Considerations Sampling design is important to obtaining acceptable results. The design is governed by the purpose of sampling. If the purpose is paleolimnological, several cores taken in the deepest section of the lake will suffice. Hikanson and Jansson (1983) divided design stratigies into three general types: ( I ) A sampling design that is based on previous knowledge of the study site. This previous information should include geological conditions, hydrological conditions and some chemical and physical results. Hi'ikanson (1986) has suggested the use of a sediment penetrometer to provide information as to the character of surface sediments. (2) A design where sampling stations are randomly selected such that each sample has an equal chance of being chosen. This design is often used in areas where little is known of local conditions and background or baseline data are needed. Another kind of random design depends on a good deal of local information since the strategy depends on selecting heterogeneous areas and dividing these into local homogeneous sites wherein sampling sites are selected at random (Horowitz, 1991). (3) A sampling design based on a regular grid system. This is probably the best though most expensive approach. Many books have been written on planning and design (Watterson and Theobald, 1979; Green, 1979; Gy, 1979; Sanders et al., 1983; Keith, 1988). This contribution is not intended to provide a detailed discussion on planning and design, and therefore the reader is referred to the above noted citations. Green (1 979) has made some very useful suggestions concerning sampling. He recommends that some type of preliminary sampling be carried out to help evaluate a sampling design and develop statistical analysis. In addition, it should be verified that the sample size is sufficiently large to account for the type of particle distribution and physical aspects of the sediment. Then, based on the sample size selected, it will be possible to estimate how many replicates will be needed to obtain the kind of precision desired. For example, Baudo (1989) collected 57 samples of Lake Orta sediments and analyzed them for copper. The data were not distributed normally. They were found to be skewed and leptokuritic. Thus, the negative binomial distribution was used instead of the normal distribution. Hikanson and Jansson (1 983) have stated that six samples of surface mud taken at regular intervals over

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U . M . Cowgill

the lake surface should approximate the mean value for the muds. Kratochvil and Taylor (1981) state that six samples should be sufficient to estimate the mean with a coefficent of variation of 100% at the Student’s t-test level corresponding to 95% confidence limits. With Baudo’s 57 samples, the coefficient of variation was 25%. If a 10% coefficient of variation is desired, 412 samples would be necessary (cf. Baudo, 1989). Green (1979) points out that variation in the efficiency of sampling from place to place will ultimately bias the chemical results. Further, he suggests that the investigator ascertain that the desired population is in fact the population being sampled. He emphasizes that the data should be tested to ascertain whether the error variation is homogeneous, normally distributed and independent of the mean. For further discussion on statistically sound sampling techniques, the reader is referred to Cochran (1 963) and Green (1 979). An excellent discussion on how to estimate the number of samples required to obtain a particular coefficient of variation at a given confidence limit is given by Baudo et al. (1990). Some of the important points are given below. Frequency distribution of the concentrations of 13 elements were measured by Muntau et al. (1 986). The frequency distributions were calculated according to procedures described by Sokal and Rohlf (1969) and Davis (1971). Of the 13 elements, nine were best described by the negative binomial distribution, one element was best described by the Poisson distribution and the remaining three followed a normal distribution. The point of this study was to emphasize that all elemental concentrations do not exhibit the same frequency distribution. Presumably the explanation for different frequency distributions is due to the association of different elemental concentrations with particle size (cf. Horowitz et al., 1990). HBkanson and Jansson (1983) present an empirical formula from which the required number of samples may be calculated. This formula requires morphometric information, to wit, the shore development, a crude estimate of bottom roughness and the area of the lake. This formula is useful only for large lakes (Evans and Dillon, 1986). Sly (1975) sampled 843 stations following a nested grid pattern as a sampling strategy. Sodium, K, Hg and mean particle size were accurately estimated with a grid spacing of 100 In. He suggested that for shallow lakes a grid spacing of 10 mg/kg) of agricultural surface soils are shown in Fig. 4.

Heavy Metals in Soils, Sediments, and Wastewaters

369

Tab. 1. The Concentrations (mg/kg) of Heavy Metals in Soils (0- 15 cm) of Case I Area Sampling number"

n

=

118

n = 28

Element

Mean f Standard deviation

Cd Pb cu Zn Cr Ni

16.35 f 22.93 25.78 k 22.55 11.93 f 47.46 42.71 & 54.06 1.02 f 1.02 2.40 f 1.75

1.12 6.34 3.06 0.39 NDb 0.39

Cd Pb cu Zn Cr Ni

12.36 f 25.45 23.36 f 21.36 15.68 f 35.87 36.15 k 46.37 1.38 k 1.56 1.83 f 1.68

2.34 130 7.54 118 6.75 315 1.26 325 N D ~ 5.45 0.46 8.46

Rangc

-------

148 126 509 361 6.30 9.39

n = I 18 for sampling every field in the polluted region; n = 28 for random sampling of polluted soils. Non-detectable.

The concentrations of 0.1 N HCl extractable Cd in discharged water and sediments in case I region are shown in Tab. 3. The results indicate that the Cd concentrations in discharged water ranged from 2.7 to 13.5mg/L, and concentrations varied according to the season. Tab. 3 also indicates that the Cd concentrations in the sediments ranged from 120 to 560 mg/kg. The vertical distributions of Cd in polluted soils are shown in Fig. 5. The Cd concentrations in the surficial 20 cm of soil at the 6 m, 20 m, and 35 m distance from the outfall of the irrigation water were 26.18, 36.42, and 8.78 mg/kg, respectively. The Cd concentrations in soil layers below 20 cm depth were less than 0.5 mg/kg (Fig. 5). The results in Fig. 5 indicate that (1) the highest Cd concentraTab. 2. Proportions of Differing Levels of Metals in Soils (0- 15 cm) of Case I Area Percentage of total samplcs (n = 118)

Element Cd

10 30

Pb

20 40

Zn

Cu

--

30 50 > 50

-- 12040 > 120 30 - 80 80 - 120 21

12 6 1 14 6 1

> 120

12 12 4

100 > 100

8 1

-

t

t

Fig. 4. Distribution of thrcc lcvcls of cadmium conccntrations in the case I area. Very high concentrations of cadmium ;( 10 mg/kg) are mainly located alongside thc irrig,ation rivcr.

Tab. 3. The Concentrations of Cadmium and Lcad in Irrigation Water (mg/L) and Sediments (mg/kg) of Case I Area Location number

Sampling number

Mcan

STD"

Cd b

7 7

3.5 & 2.1 7.8 f 3.2

Scdiments 1- 6 7-13 14-20

6 I I

32.5 _+ 12.1 165 & 65 240 f 80

I-

STD: standard deviation.

Pb

Cd

Pb

**+ 0.81.2

3.5 - 13.5 2.7- 5.4 5.3 - 12.5

U. 1 - 3.6

650 _+ 350 210 & 150 325 250

175 - 560 I20 - 213 158-325

1 30 1 240 65- 370 85- 560

1.K

0

7- 13 14-20

Range

1.2 1.4

1.0

+

0 , 1 2.4 ~

0.1 - 2.7

~

Heavy Metals in Soils, Sediments, and Wastewaters

Fig.5a -d.

371

Three-dimensional distribution of HC1-extractable Cu, Zn, Cd, and P b in case 1 region.

tions were usually highest in the surficial20 cm of soil, and (2) that Cd in the water can be transported and accumulated in soil to about the 2 0 m distance from the irrigation outfall, and decreases to lower concentrations in the soil surface of the polluted soils.

19.2.5 The Relationship between Heavy Metals in Soils, Discharged Water, and Sediments In case 1 region, the distribution of cadmium in agricultural soils was significantly related to the Cd concentrations in the polluted river and sediments. If the farmers used the polluted water for irrigation, the rice-growing soil could be polluted by it.

312

Z . - S . Clien

In areas close to the irrigation river and as far out as 3 0 m from the river, soils could accumulate very high concentrations of Cd in the surficial 20 cm. From the sampling design, we can conclude that the concentrations of heavy metals in the soils were related to the flow pathway, direction, and distance of polluted irrigation water from the chemical plant.

19.3 Case 11: Studies of Rice-Growing Soils near an Industrial Park 19.3.1 Principles of Sampling Design As in case I region, it was presumed that the irrigation water was polluted by an industrial park. Thus, agricultural soils adjacent to it or receiving the discharged water for irrigation could be highly polluted by heavy metals. Fig. 6 shows the

Fig. 6. Distribution of sampling locations of rice growing soils, water, and sediments in case I1 arca, Shiang-Shang Industrial Park; (0) sampling site for soils (n = 28), ( 0 ) sampling site for irrigation watcr and sediments (n = Is), (+) flow direction of irrigation water, (A)sampling site for background samples (n = S ) , (m) chemical plants of the industrial park.

Heavy Metals in Soils, Sediments, and Wastewuter:y

373

distribution of sampling locations of rice-growing soils, irrigation water, and sediments in case I1 region contaminated by the discharged water from Shiang-Shang Industrial Park in northern Taiwan. This is also a famous industrial park in northern Taiwan comprising several chemical plants. Facilities include heavy metal coating, treatment of leather, production of glasses, dyeing of clothes, production of stabilizing materials for plastics, etc. The survey area covered about 112 hectares. The objectives of sampling design were: (1) to survey the distribution of heavy metals in agricultural soils, discharged water, and sediments, (2) to study the relationship between the concentration of heavy metals in soils, water, and sediments in case I1 region.

19.3.2 Sampling Methods 19.3.2.1 Soil Sampling Adjacent to the river polluted by the discharged water from the industrial park, a 4 ha area (20Ox200m) was proposed as a sampling site. In this sampling area, 10 soil samples were randomly collected and homogenized to one representative soil sample. The total number of representative soil samples was 28. There were only 5 samples for the control area (unpolluted region), as shown in Fig. 6 . The sampling depths were 0 to 15 cm and 15 to 30 cm, respectively. The soil was sampled with an 8 cm diameter auger.

19.3.2.2 Sampling Design of Water and Sediments Discharged water from the Shiang-Shang Industrial Park and sediments in the river were sampled at locations where the river just changes its flow direction (see Fig. 6). These are the sections of the river where heavy metals will be accumulated in the sediments.

19.3.3 Analytical Methods The concentrations of heavy metals in soil samples and sediments were determined by extraction with 0.1 N HC1 for one hour and this concentration was regarded as the quantity of plant bioavailable metals (EPA-ROC, 1991).For extraction, 100 mL 0.1 N HCl were added to 10 g of soil samples or sediments and shaken for one hour with 180 rpm, then filter with Whatman No. 42 filter paper, and concentrations of metals (Cd, Cr, Cu, Ni, Pb, and Zn) were determined with flame atomic absorption spectroscopy (Hitachi 180-30 type).

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The water samples of the discharged water were filtered with Whatman No. 42 filter paper, and concentrations of metals (Cd, Cr, Cu, Ni, Pb, and Zn) were determined as well with flame atomic absorption spectroscopy (Hitachi 180-30 type).

19.3.4 The Distribution of Heavy Metals in Soils, Discharged Water, and Sediments Tab. 4 shows the concentrations of heavy metals in agricultural soils in case I1 rcgion (Shiang-Shang Industrial Park). The results indicate that the 0.1 N HCI extractable metals in the surface soils (0- 15 cm) ranged from 11.3 to 60.3 mg/kg for Cr, 22.3 to 158 mg/kg for Cu, 11.1 to 122 mg/kg for Ni, and 35.0 to 214 mg/kg for Zn, respectively. Most of thc soils in the survey area showed much higher concentrations of Cr, Cu, Ni, and Zn in the surface soils than the background concentrations in these soils. Background concentrations of 0.1 N HCI extractable Cr, Cu, Ni, and Zn in uncontaminated soils were less than 5, 20, 10, and 25 mg/kg, respectively. Eleven sampling sites (44 ha) of agricultural soils in Fig. 6 adjacent to the discharged water show that the concentrations of heavy metals in agricultural soils were very high, with concentrations higher than 60, 125, 60, and 80 mg/kg, for Cr, Cu, Ni, and Zn, respectively. These also indicate that virtually the whole sampling area of 44 ha was considerably contaminated by the discharged water and sediments from the industrial park. Tab. 4. The Concentrations (mg/kg) of Heavy Metals in Soils of Case I1 Area Element"

Surface soil (0- 15 cm) (n = 26)' Conc. in high and very high conc. level =

Subsurface soil (1 5 - 30 cm) (n = 26)'

Sample (YO) Sample (Yo) in high in very conc. high levcl conc. level

Conc. in high and very high conc. level ~~

Cd Cr CU Ni Pb Zn As

Hg

0.4 1 11.3 60.3 22.3 158 11.1 122 24.1 42.8 35.0 214 0.46 0.66

----

4 15

50 69 19 15

0 12

~

0 4 8 4 0 42 0

14.6 21.2 31.2 12.1 63.4

0

-

--

34.1

Sample (YO) Sample (YO) in very in high conc. high level conc. level

-

140

~

0 4 19

46 0 35 0 0

~

0 0 0 0 0 4 0 0

As and Hg: total contents; Cd, Cr, Cu, Ni, Pb, Zn are 0.1 N HC1 extractable. Sample number: 26; every sample represents 4 ha (200 m x 200 m). High concentration level ofheavymetals:0.40-10, 11-16, 21-100, 11-100, 16-120,26-80, 16-60, and 0.4-20 mg/kg, for Cd, Cr, Cu, Ni, Pb, Zn, As and Hg, respectively. Very high concentration level of heavy metals: > 10, > 16, > 100, > 100, > 120, >80, > 60, and > 20 ing/kg, for Cd, Cr, Cu, Ni, Pb, Zn, As and Hg, respcctively.

Heavy Metals in Soils,Sediments, and Wastewaters

315

Tab. 5. The Concentrations (mg/L) of Heavy Metals in Irrigation Water of Case 11 Area

Element

Cd Cr

cu Ni Pb Zn As Hg

Range (n = 18);l

Sample (YO)of conc. in water higher than the criteria of irrigation water

---

0 16 6 44 0 0 6 0

ND ND 1.60 N D 0.20 0.13 2.89 ND 1.22 ND 0.30 1.95 ND

Sample number. Non-detectable.

The concentrations of 0.1 N HCl extractable metals in discharged water in case

I1 region are shown in Tab. 5. The results indicate that the concentrations of Cr, Cu, Ni, and Zn in discharged water ranged from N D (non-detectable) to 1.60 mg/L for Cr, N D to 0.20 mg/L for Cu, 0.13 to 2.89 mg/L for Ni, and N D to 1.22 mg/L for Zn, respectively. The concentrations of Cd, Hg, and Pb in the discharged water were considered within the normal range. Tab. 5 also indicates that the concentrations of Cr and Ni were significantly higher than those set for the irrigation water quality proposed by the Government of the Republic of China. The concentrations of 0.1 N HC1 extractable metals in sediments of the river in case I1 region are shown in Tab. 6. The results indicate that the concentrations of metals in the sediments ranged from 0.36 to 24.8 mg/kg for Cd, 40.7 to 787 mg/kg for Cr, 21.7 to 2250 mg/kg for Cu, 57.3 to 3590 mg/kg for Ni, 8.9 to 178 mg/kg for Pb, and 85 to 8710 mg/kg for Zn, respectively. Tab. 6 also indicates that the proportion of samples with very high concentrations of metals correspond to 16 to 50% of the total sediment samples. The critical concentrations of metals in the sediments were 15.4 mg/kg for Cd, 166 mg/kg for Cr, 1160 mg/kg for Cu, 482 mg/kg for Ni, 124 mg/kg for Pb, and 1090 mg/kg for Zn (see Tab. 6). Tab. 6. The Concentrations (mg/kg) of Heavy Metals in River Sediments of Case 11 Area Element

Cd Cr

cu

Ni Pb Zn As Hg

Range (n = 18)b 0.36 40.1 21.7 57.3 8.9 85 1.50 0.28

-----

Samples with very high concentrations Range

24.8 787 2250 3590 178 8710 14.3 1.84

15.4 166 1160 482 124 1090

----

24.8 787 2250 3590 178 8710

Sample (YO) 22 50 28 44 16 50 0 0

As and Hg: total contents; Cd, Cr, Cu, Ni, P b and Zn are 0.1 N HCI extractable. I, Sample number.

316

Z . - S . Chef1

19.3.5 The Relationship between Heavy Metals in Soils, Discharged Water, and Sediments Fig. 7 shows the distribution of heavy metals with high concentrations in agricultural soils and the course of the irrigation system in case I1 region. The concentrations of Cu, Cr, Ni, and Zn were significantly higher than those in the agricultural soils in the neighboring region, regarded as the background concentration. The concentrations of heavy metals in the agricultural soils along the irrigation river were higher than 125 mg/kg for Cu, higher than 122 mg/kg for Ni, and higher than 82.8 mg/kg for Zn, respectively. The proportion of samples with very high concentrations of Zn ( > 80 mg/kg) comprised more than 40%. The following relationships between heavy metals in soils, discharged water, and sediments were observed: ( I ) The high levels of Cr, Cu, Ni, and Zn in the contaminated discharged water were reflected also by their high levels of accumulation in soil and sediments.

Fig. 7. Distribution of heavy metal concentrations in the rice-growing soils in case I1 area. Very high concentrations of heavy metals in soils (@) are mainly located alongside of thc irrigation river, the concentrations of HCI-extractable Cr, Ni, Cu, and Zn in soils are higher than 16, 100, 100, and 80mg/kg, respectively. Medium high concentrations of heavy metals in soils (D) are mainly located at a long distance from the river. the concentration of HCI-extractable Cr, Ni, Cu, and Zn are 1 1 - 16, 11 - 100, 21 - 100, and 26-80 mg/kg, respectively; (A)sampling locations of background samples (n = 9, (u) chemical plants of the industrial park.

Heavy Metals in Soils, Sediments, and Wastewaters

377

(2) Soils adjacent to the irrigation river discharged high levels of metals, indicating the effect of the contaminated irrigation water by lateral movement. (3) The high concentrations of heavy metals, Cr, Cu, Ni, and Zn, in irrigation water were caused by the discharged water of the industrial park. (4) There were significant relationships between heavy metals (Cr, Cu, Ni, and Zn) in agricultural soils, irrigation water, and sediments.

19.4 Conclusions Two rice-growing regions where soils were suspected of being contaminated by heavy metals, were selected to study the relationships between heavy metal concentrations in soils, discharged water, and sediments. Sampling design and sampling methods in these two regions were described. In areas close to the irrigation river and as far out as 30 m from the river, soils could accumulate very high concentrations of Cd in the surficial 20 cm. From the sampling design, we can conclude that the concentrations of heavy metals in soils were related to the flow pathway, direction, and distance of polluted irrigation water from the chemical plant. Results also indicate that the concentration of Cr and Ni in case I1 study were significantly higher than those set for the irrigation water quality proposed by the Government of the Republic of China. The high levels of Cr, Cu, Ni, and Zn in the contaminated discharged water were reflected also by their high levels of accumulation in soil and sediments. Acknowledgements. The author thanks the Environmental Protection Administration of Republic of China (ROC) and the Council of Agriculture of ROC for their financial supports in 1988 and 1991. The author is thankful to Dr. Domy C. Adriano, Professor of Savannah River Ecology Laboratory, The University of Georgia, USA, for his comments and review of this manuscript. The author also thanks to Mr. T. C. Lin, Mr. J. C. Liu, Miss D. N. Huang, and Mr. H. H. Liau for their assistance in sampling of agricultural soils, discharged water, and sediments in the two case study regions and in chemical analyses, and to Mr. J. 1. Hsu and J. C. Liu for their preparation of the tables and figures.

19.5 References [I] Chen, Z. S. (1988) A survey of heavy metal concentration of soils and rice grain in northern Taiwan. Project report of Council of Agriculture. Executive Yuan, ROC. [2] Chen, Z. S. (1991) Cadmium and lead contamination of soils near plastic stabilizing materials producing plants in northern Taiwan. Water, Air, and Soil Pollution 57-58, 745-7754, [3] Chen, Z. S. (1992) Metal contamination of flood soils, rice plants, and surface waters in Asia, in: Biugeuchemistry of' Trace Metals, Adriano, D. C. (ed.). Lewis Publishers, pp. 85- 108.

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[4] Chen, Z. S., Lee, D. Y. (1993) The relation between pollution source and heavy metal concentrations in soils, in : Proceedings of the fourth Symposium of' Soil Pollution Preventioli andRernediution, pp. 163 - 176, Tai, K . P. (ed.), published by Energy & Resources Laboratories, Industrial Tcchnology, Hsinchu, Taiwan, ROC. [5] Chen, Z. S., Lec, D. Y., Huang, D. N., Liau, S. H. (1992) The relationships between heavy metals in soils, waters, and sediments: Case study in Shiang-Shun Industrial park. Project reports o f EPA-ROC in 1992. [6] Cheng, S. I+'., Lee, S. M . (1992) The relationships between heavy metals in soils, waters, and sediments: CUSP study in An-Nan Industrial purk. Project reports of EPA-ROC in 1992. [7] EPA-ROC ( 1 991) The standurd metliod,s,for determination of heavy metals in soils and plants. National Institute of Environmental Analysis of EPA-ROC. Taipei, Taiwan, ROC. [8] Lcc, C. D., Ling, H . T. (1983) The suroey of'heuvy metals in soils. Project report of Taiwan Water Pollution Protection Institute, Taichung, Taiwan, ROC. [9] Wang, M . C., Wang, Y. P. (1992) Effect of nitrogen and phosphorus contents in irrigation water from Ta-Chia River on soil properties and rice growth. J . Chinese Agric. Chem. Soc.. 30, 1-13. [lo] Wang, Y. P., Wang, M. K., Liu, C. L. (1992) The relationships between heavy metals in soils, watcrs, and scdimcnts: Cuse study in Chung-Mvu Industrial park. Project reports of EPA-ROC in 1992.

D. Plants and Animals

Environmental Sampling for Trace Analysis

Edited by Bernd Markert 0 VCH Verlagsgesellschaft mbH, 1994

20 Sampling of Plants for Environmental Trace Analysis in Terrestrial, Semiterrestrial and Aquatic Environments Wilfried H . 0. Ernst

20.1 Introduction When the atomic absorption spectrometry developed to a rapidly operating and sufficiently sensitive instrument nearly 30 years ago, an enormous amount of plant material was possible to be analyzed. The International Biological Program has stimulated inventories of chemical elements in ecosystems (cf. Duvigneaud and Denayer-De Smet, 1964; Bormann and Likens, 1976; Ellenberg et al., 1986). Obviously due to great gap on element uptake, accumulation and translocation in wild plants, this program was started without any directory, how, when and what to sample. In 1971, a survey of methods applied in the research of the German Solling Project was published, however, without any directory for sampling plant material for analytical purposes (Ellenberg, 1971). With increasing progress in instrumentation for multielement analysis (Lieth and Markert, 1988; Markert, 1992) standard reference material (Griepink et al., 1983) was established to achieve comparable data of chemical laboratories. Even for this reference material it is not known how it was sampled. In the meantime, various options have developed for appropriate sampling methodologies, i.e., twig sampling in biogeochemical prospecting (Warren, 1980), leaf sampling for seasonal dynamics of elements in plants (Ernst, 1975), leaf age gradients within a plant (Ernst, 1982), element allocation in plant organs (Ernst, 1974), leaf sampling of a few tree species for regional comparison of environmental conditions (Zimmermann and Plankenhorn, 1986; Wagner, 1990). The main question to be answered will be: Can we elaborate a few standardized sampling methods and strategies, which will enable a n interlaboratory comparison?

20.2 What Do we Want to Know? Plant species can explore all three environmental compartments, i.e., the atmosphere, the hydrosphere and the pedo(litho)sphere, or they can be restricted to only one sphere. The objectives of the analysis has to be clearly established because it will demand different sampling and preparation procedures. Plant samples which are intended to establish the concentration of a chemical element and its physiological

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and ecotoxicological consequences for a plant’s function and survival have to be sampled at random and cleaned from all remnants of the three environmental compartments (Otte et al., 1989). In the case of environmental monitoring and identification of an emission source, sampling has to be carried out in transects running from the suspected emission source to the background area (Ernst and Leloup, 1987). Atmospherically dispersed pollutants may be trapped by hairs or glutinous leaf surfaces so that species selection may be based on morphological characters. In food web studies sampling will depend on the type of the heterotrophic organism. Grazing mammals and caterpillars will ingest the plant material with all adherent soil particles and particulate fall-out (Joosse and van Vliet, 1982); sucking aphids will only take phloem sap (Ernst et al., 1990). Therefore, each herbivore typc demands another sampling procedure. Plants affected by parasites such as mildews and rusts may change the concentration of chemical elements (Ernst, 1983); therefore, a random sampling procedure will not be possible in ecosystems with a frequent occurrence of such parasites.

20.3 Are Trace Elements Relevant Objectives? Plants as other organisms demand a certain amount of elements of the periodic table for their metabolic processes, which enable growth, survival and reproduction, and for the support of biologic structures. The evolution of the uptake systems of chemical elements in plants has not resulted in a tailor-made uptake control, perhaps due to the metabolic costs or the chemical impossibility, e.g., the similarity of phosphate and arsenate (Meharg and Macnair, 1990). The moderate specificity of the element uptake systems (EUS) has as consequence that all plants can contain all chemical elements of the periodic table (Brooks, 1972) if these elements are present in a plant-available form. The lack of a high specificity of the EUS has advantages for the evolution of heterotrophic organisms. Jodium is an excellent example. Higher plants d o not use jodium in their metabolism ; mammalia inclusive humans, however, cannot live without jodium because this element is essential for the functioning of thc thyroidea. As a result of the evolution of metabolic processes, certain elements are demanded in high amounts, i.e., carbon (C), oxygen (0) and hydrogen (H) for all carbohydrates, and together with nitrogen (N) for all proteins; others are necessary in moderate amounts, i.e., phosphorous (P) for metabolic activations and energy conservation, potassium (K) for hydrature, sulfur (S) for some amino acids, peptides and sulfolipids, magnesium (Mg) for chlorophyll and calcium (Ca) for fine turning of the hydrature and biomembrane stability (cf. Marschner, 1986). All thcse elements (C, H, 0, N, P, K, S, Ca, Mg) are therefore categorized as mujor essentiul elements or mucronutrients. These macronutrients are out of the scope of this contribution and the topic of this book.

PLints f o r Trace Analysis

383

A group of other elements are only necessary in small to very small amounts in plants, sometimes only known for one process, i.e., nickel (Ni) for the activation of the enzyme urease. This group of elements, defined as minor essential elements or micronutrients,includes Fe, Mn, Zn, Cu, Mo, Ni, B, Na and C1. The sophistication of instruments for trace elements and of the purification procedure of chemicals, used in experimental approvals of the essentiality of an element will certainly let detect metabolic functions of elements beyond the above-mentioned groups of micronutrients. Due to the high diversity of biological organisms and the moderate specificity of the uptake system certain plant species or plant groups of a higher taxonomic unit have evolved a demand for some additional element. The amount demanded may range from high, i.e., equivalent to major essential elements as silicium (Si) in the structure of diatoms, Equisetaceae, Cyperaceae, and Poaceae, to low, as equivalent to minor essential elements in the stingle hairs of Urticaceae. Other elements are only specific for one group and for one process: cobalt in nitrogen fixating bacteria living in symbiosis with Fabaceae (Dilworth et al., 1979). Some of these and other elements are often defined as benejfi’cial(Marschner, 1986) because they can stimulate plant growth, i.e., titanium (Pais, 1983), although their role in the metabolism is not understood. All other elements of the periodic table are non-essential to plants. The are often called trace metals (Adriano, 1992), trace substances (Adriano, 1992) or trace elements (Stiles, 1946). Unfortunately, none of the authors gives a rational definition of “trace”. Often the inadequate use of the term “trace” provokes biologically awful misunderstanding. Adriano (1 992) looks to trace metals as “important environrnental contaminant that affect all the ecosystem components”. Either he excludes all micronutrients - what he does not - or he overlooks that each element - also N, P, K - injures biological systems when a certain concentration is surpassed. When the term “trace” should indicate the presence of a very small concentration of an element in biota, the high biological diversity of element accumulation will be contradictious. A lot of plant species can hyperaccumulate an element in one of its plant part, i.e., more than 1000 pg element g - ’ dry wt (Brooks et al., 1977). As an example, the concentration of lead in leaves is low in most plants (1 pg g-’ dry wt; Markert, 1992), but it can increase above 10000 pg g - I , as in leaves of Minuartia vernu growing on ore outcrops (Ernst, 1974). Taxon-specific accumulation is well known for several elements, such as zinc in the families of Betulaceae and Salicaceae or nickel in the taxon Alyssum. Certain taxa are able to incorporate an element into organic compounds, i t . , V in the amavadin of the Fly agaric Amanita muscariu (Kneiffel and Bayer, 1973) or F in fluoracetate of the F-accumulating Dichupetulum species (Meyer et al., 1992; O’Hagan et al., 1993). In other taxa an element can be bound in such a way to an organic compound that it will not affect the performance of adapted plant species. Examples are Se in seleno-amino acids of Se-accumulating plants (Peterson and Butler, 1967), and A1 in phenolic complexes of tea (Nagata et al., 1992). Therefore I will treat “trace” elements as the group of chemical elements being micronutrients and non-essential elements for plants. A good analysis of plant material, however, will also incorporate a part of the major nutrients, because

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the general knowledge about uptake, accumulation and retranslocation has such a good background (Baumeister and Ernst, 1978; Marschner, 1986), that ratios of N/P, P/K, K/Ca can give a realistic impression on a plant’s physiological status.

20.4 The Exploration of Environmental Compartments The diversity of plant species makes it possible that different portions of the environment in space and time can be exploited. Terrestrial higher plants are normally exposed to the hydrosphere, atmosphere and pedosphere; aquatic plants may only have access to elements present in the hydrosphere; epiphytic plants may be exposed only to elements of the atmosphere. Exposure to one environmental compartment may allow to restrict the sampling size due to a certain homogeneity of the exposed plants.

20.4.1 The Hygro-and Hydrophytes Plants which only explore the hydrosphere without contacts to the pedosphere and the atmosphere are planktonic algae, some aquatic mosses and some submersed aquatic angiosperms (hygrophytes). Although a lot of studies on the uptake of chemical elements are performed with unicellular algae like Chlorcdlu and Euglcwu (De Filippis et al., 1987;Weber, 1981; Kessler, 1986; Gekeler et al., 1988), it will be impossible to collect a monospecific, homogeneous sample of planktonic algae for chemical analysis. Nevertheless, some of these algae may be excellent reference material, because a periodic or permanent exposure of these algae to a surplus of a chemical element will result in the selection of specific resistant populations. Therefore, not the chemical analysis, but the biological resistance test will give excellent results of the environmental quality (Stokes et al., 1973; Butler et al., 1980; Ahlf and Weber, 1981; Foster, 1982). In the few possibilities of sufficient amounts it will be necessary for an adequate data interpretation of element concentrations in uni- to paucimulticellular algae to determine the surface/volume ratio, because many elements will not be taken up, but only adsorbed to the cell surface (Christlieb and Werner, 1980). A lot of other organisms in the aquatic environment makes also contact with the pedosphere enabling the fixation of the organism a t a certain site for a prolonged period of time. Element concentrations of these benthic organisms may be the result of the access to chemical elements of their substrates and the surrounding waters. Filamentous benthic algae such as Cladophora, Stigeoclonium and Vaucheria have only little contact with the substrate so that samples of these algae may give an integration of element exposure to the surrounding water over a half year to year period (Keeney et al., 1976). The drawback of these algae is the strong adhesion of fine soil particles from the surrounding water, which are difficult to be removed.

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A similar sampling problem will arise with the epiphytic algae and protozoa on these benthic algae. Although it has been stated that the element concentration of this algal “aufwuchs” is the same as its supporting host (Prosi, 198l), a generalization of this statement may fully underestimate the uptake and accumulation specifity of algae. The development of populations resistant to a surplus of zinc (Harding and Whitton, 1976), phosphorus and nitrogen (De Vries et al., 1985) may interfer with uptake and accumulation of chemical elements, as it is known from Cu-tolerant and non-tolerant populations of the marine fouling alga, Ectocurpus siliculous (Hall, 1981). In marine ecosystems analysis of macroalgae, e.g., Fucus serratus, F. spiralis and F. vesiculosus, Ascopliyllum nodosum and Ulva lactuca have widely been used to study the concentrations of chemical elements (for a review, see Phillips, 1977; Ernst, 1987). Gradient sampling can easily be performed, but particulate contamination is a significant problem in sample preparation (Barnett and Ashcroft, 1985). Aquatic plants with roots can take up chemical elements from the sediment and the surrounding water. Water sediments, however, are often characterized by steep oxygen gradients, which affects element speciation and bioavailability in a distance of a few millimeters. Due to strong polar element transport through the leaves of aquatic plants, photosynthetically active leaves can precipitate huge amounts of calcium carbonate on the upper leaf surface (Steemann Nielsen 1947; Baumeister and Ernst, 1978). These precipitates have to be removed during sampling, because they also contain coprecipitates of heavy metals. Another problem with aquatic plants is the colonization of leaves and stems by a lot of aquatic algae, the so-called “aufwuchs”. It is very difficult to remove part of this “aufwuchs”, especially diatoms and unicellular green and bluegreen algae. Sampling for a database of metal concentrations in aquatic plants demands a thorough cleaning procedure and a microscopial control of the material prior to analysis; especially leaves of Potamogeton species are difficult to handle. In addition, adsorbed elements have to be removed during sampling by short-term exchange processes. Plants floating on water surfaces like Lemna, Azolla, Nuphar and Nymphaea can take up chemical elements from the hydrosphere and the atmosphere. During sampling, two pollution sources have to be avoided: the “aufwuchs” and the adsorption of elements on the leaf surface surrounded by water, and the contamination by faeces on the leaf surface exposed to the atmosphere. In addition, wind can remove Lemna species from the sites where they have taken up the chemical elements, within a few hours so that sampling of floating water plants have carefully to register their site “stability”.

20.4.2 The Epiphytes Epiphytes on terrestrial higher plants have only access to chemical elements which are supplied to them by wet and dry deposition. Sampling of epiphytic lichens and

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epiphytic mosses for chemical analysis has a long history (Ferry et al., 1973). Due to the high cation exchange capacity (Clymo, 1963) most cations are adsorbed to the cell wall, so that washing procedures can remove at least a part of the adsorbed elements. Strong precipitation just before and during sampling, especially precipitation with a low pH, may affect the element concentrations (Brown and Brown, 1990). To my knowledge, there is no detailed study of the impact of the various factors, although the sampling procedure has to consider plant height, lichen exposition, etc. ; these details, however, are not mentioned in recent publications (Herzig et al., 1990). Without washing, the plant sample remains contaminated by particulate deposition. Another aspect of sampling epiphytic lichens and mosses is their exposition to precipitation. U s n w species hanging from a twig under an open canopy will collect chemical elements from throughfall more than from crown drip, whereas those under a closed canopy will rely on crown drip. Epiphytes near the lower part of a stem will be exposed to the chemical elements of the stem flow. Due to the great differences of element concentrations between throughfall, crown drip and stem flow (Ellenberg et al., 1986) sampling of these epiphytes has to register these conditions.

20.4.3 Soil Exploring Plants Exposure to chemical elements in the soil is conditioned by soil moisture. With the exception of some algae living a few millimeters below dune sand, all organisms being completely restricted to the pedosphere are heterotrophic. Sampling of these heterotrophs, mostly slime molds, bacteria and fungi, is very difficult, if not impossible. Their interaction with the substrate is so intense that they cannot be dissected without a high degree of sample contamination. There are only some exceptions like the hyphae of fungi condensed to rhizomorphs (Stark, 1972) and the sporophores of fungi (Allen and Steinnes, 1978; Mutsch et al., 1979; Hinneri, 1975; Tyler, 1982; Dietl, 1987; Gast et al., 1988). Due to the rapid uptake of chemical elements from the litter layer into the hyphae and the translocation to the sporophore, fruit-bodies can be used for the localization of particulate and wet deposition, as clearly demonstrated after the Chernobyl disaster (Ernst and van Rooij, 1987). Although sporophores of fungi are exposed to the atmosphere for some days, all chemical elements are derived from the pedosphere. Sampling can be carried out a t random, in relation to suspected host plants in the case of mycorrhizal fungi and as transects in the case of monitoring the impact of a point emission source on ecosystems. Litter and soil particles can be removed from the sporophores without essential loss (Allen and Steinnes, 1978; Hinneri, 1975); separation of stipes and pilei is often advised, but it will depend on the research scope (Hinneri, 1975). One aspect is unsufficiently recognized : The chemical composition of the pileus (cap) will depend on the amount of basidiospores already released. Therefore, during sampling the maturity of the cap is an essential factor to be considered.

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Roots of higher plants explore the pedosphere, often related to the various soil horizons. Sampling of roots is one of the most difficult tasks in plant analysis of chemical elements. First of all, most higher plant roots have a strong association with mycorrhizal fungi, either vesicular-arbuscular mycorrhizal (VAM) fungi and/or with ectomycorrhizal (EM) fungi (Cooke, 1979; Allen, 1991). Due to the seasonal variations of the infection degree with VAM fungi (Ietswaart et al., 1992; Veenendaal et al., 1992), the difficulty to collect sufficient hyphae for chemical analysis and the inability of a pure culture of VAM fungi, all samples of roots associated with VAM fungi will be a mixture of fungi and angiosperm tissue; the biomass of the fungal component is estimated between 1 and 10%. In the case of ectomycorrhizal roots, EM fungi can be grown in monoculture without host plants and have shown a very species-specific and population-specific uptake pattern of chemical elements (McCreight and Schroeder, 1982; Colpaert and Van Assche, 1992), resulting in great differences of the element concentration in the host tree (Colpaert and van Assche, 1993). During the life history of a tree there is an age-dependent succession of ectomycorrhizal fungi (Fleming, 1985). Therefore, ideal sampling procedures of roots will establish the degree of mycorrhizal infestation and the involved fungus species. However, I have to agree that such an ideal sampling is extremely time-consuming and it demands an intensive cooperation with fungi taxonomists. Independent of the mycorrhizal fungi, root sampling gives some more problems. Root hairs have a very intimate relationship with soil particles, so that it is impossible to remove soil particles without losing root hairs and fine roots. The root surface itself, i.e., the rhizoplane, is a well-known site for interactions between free-living microorganisms and plants. The number of cells of microorganisms in the rhizoplane are estimated up to 120 x lo9 cells cm-3 root (Paul and Clark, 1989). Also these organisms can only be removed with a loss of root material. Furtheron, removal of root tissue during sampling and sample preparation will have a strong impact on element concentrations, because the various root tissues accumulate the various elements to a different degree (Ernst, 1974). As mentioned earlier, many chemical elements are adsorbed to exchange sites of the root cell wall. If a difference will be made between adsorbed and cell-internal element concentrations, exchange of adsorbed elements has to be carried out immediately after sampling. Effective exchange procedures, however, will load the root cell wall with another ion. Therefore, the aim of the data collection will select the exchange solution applied, e g , Pb(NO,), for removal of adsorbed heavy metals others than lead (De Vos et al., 1992)or rinsing thoroughly with demineralized water for removal of adsorbed sodium and potassium. In wet ecosystems welladapted plants have aerenchyma which ensure not only oxygen supply to the root cells but also initiate an oxydation zone around the root building up an iron plaque (Otte et al., 1989). This iron plaque can be removed by treatments with dithionitecitrate-bicarbonate. In every case its presence has to be registered at sampling. All in all, sampling of roots will remain a difficult operation. Chemical analysis of root material will result in data which are inherently contaminated. As long as all sampling procedures are well described, “contaminated data” are better than no data.

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20.5 Plant Parts and Life History Representative sampling demands the recognition of the individual, which can be a difficult task in clonal plants. Individuals of these plants may cover surfaces from some to over hundred m2. e.g., Urticn dioica, Chamnenerion angustifolium, PhrugmitPs australis, Hedern helix and Hippophac rhamnoides (Ernst, 1990). When the stolons run over environmental gradients as in Agrostis stoloniferu and in Cure.u arennria, they may have access to completely different concentrations of chemical elements so that local uptake and transport through the stolon (Tietema, 1981) may be mixed. In plants with a well developed dioecy, it is necessary to sample both sexes. A combination of dioecy and clonal life form, e.g., Hippophue rhnmnoides, may give problems for representative sampling because only a few individuals make up a population. Plants are composed of various plant organs with different physiological functions and element demands. Therefore, each sampling procedure will depend on the research scope, so that only some general rules can be established. At a fixed time in the life of an individual each plant organ is characterized by concentrations of chemical elements, which are the result of uptake, translocation to and from other organs and accumulation potential in relation to the supply by the environmental compartments and to biological interactions, partially governed by its genetic make-up. Dynamics of elements is part of the physiological processes inherent to biodiversity. Therefore, accounts on mean concentrations of chemical elements of plants for a whole country are excellent examples of pseudoscience (Angelone and Bini, 1992) and demonstrate the misunderstanding of the principles of biodiversity, plant physiology, plant genetics and ecology. When the dynamics of chemical elements is the scope of the investigation, plant organs have to be collected during the growing season or a t certain developmental stages on a previously designed pattern. In the case of small plants, it may be necessary to sample a complete individual or a group of individuals, the amount of biomass to be sampled for at least three replicates of chemical analysis will be determined by the sensitivity of the analytical instrument for those chemical elements with an estimated low concentration. Samples of 300 mg dry mass may be sufficient. In the case of medium-sized plants, it may desirable to take plant organs at random. Plants with a high biomass of each or several plant organs and with a plant height exceeding that of the collector demand special designs and instruments for random sampling like the telescope knife as described by Muller and Wagner (see Lieth and Markert, 1988). Whereas in the case of small individuals or low biomass of sampled plant organs the whole sample can be used for analysis, samples with a biomass above the analytical demand will create problems of homogenization (see Markert, 1993). Critical examinations of damage by heterotrophs and the presence of fungi is necessary (see Sect. 20.4). In all sampling procedures, it should be obvious that the impact of sampling on the population has to be near zero. As soon as populations of the same species at various sites or ecosystems will be compared, sampling can be concentrated on one plant organ, and in the case

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of leaves, at one well-defined position of the leaves on the plant. Such a research scope makes it necessary, to enlarge the number of sampled individuals to ten or more per site, dependent on the visible genetic variation. If data sets show a high variability within sites, even other techniques such as isozyme analysis (Cuguen et al., 1985) can be applied. Relatively long-living plant organs such as the needles of coniferous trees and the annual increment of wood (cf. Ernst, 1990) can be sampled by age classes. Age gradients are often overlooked in shoots of rushes, where the top can be two years old and the shoot base only a few weeks, even if the shoot gives the impression of an evenly aged organ (Ernst, 1991). The developmental stage of the plant, however, has to be registered carefully . When the objectives of the research is the maximum of element load which will be recycled in the ecosystem, then deciduous plant parts just before shedding will be the sampling target. With a few exceptions like F and B, a surplus of an element taken up from the environment is mostly accumulated at highest concentration in a species-specific pattern in senescent plant organs (Ernst, 1990). Ageing may not occur simultaneously in all plant organs of the same plant; therefore, samples of the oldest leaf will give the most relevant information, as shown for leaf rosettes of Armeria nzaritima, Plantago lanceolatu and Thlaspi caluminave (Ernst, 1974). Loads of non-essential elements are generally very small in seeds, although the process hampering element translocation from the shoot to the seed is physiologically not well understood. Sampling of seeds has the advantage of a mostly clean material, especially seeds enclosed in capsules or in fleshy or dry fruits, but it has the disadvantage that the small seed mass of most plants demands the collection of a seed pool, sometimes of a great part of the population, thus endangering population recruitment. The preference of a plant species, a genus or even a family for the uptake and enrichment of specific elements, such as silicium by Equisetaceae and others (see Sect. 20.3) may give matrix problems in analysis. Therefore each sampling procedure should take into account the sampling of a “model” species. In each sampling protocol the following parameters have to be noted:

( I ) environmental conditions of soil, water table if high, temperature, last day of precipitation; (2) biological conditions of the plant such as developmental stage, presence of parasites, saprophytes and/or herbivores, health conditions of the plant or the plant part (visible necrosis, chlorosis, malformation, damage by herbivores) ; (3) obvious contaminations with soil particles, which is quite frequent in episodically or regularly flooded ecosystems like salt marshes and river banks. In the case of such contaminations analysis of Ti and A1 can later help to control the efficiency of the cleaning procedure. Chemical elements in plants are mostly derived from the pedosphere and/or hydrosphere. Therefore, a meaningful design of plant sampling has to be related to soil and/or water sampling. A general sampling strategy for plant materials is presented in Tab. 1.

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Tab. 1. Sampling Strategy for Plant Material Sampling

Definition of the objectives Ecosystem budgets

Community interaction (food web)

Population biology

Ecophysiology

Environmental monitoring and emission source identification

Sampling strategy

Random

Selected

Random/ selected

Random selected

I . Selected for emission source 2. Random for general changes

Sampled species

Dominant species in various ecosystem Strata

Interacting autotrophic plants and hctcrotrophs

Species of interest; genotype selection

Species of interest; genotypc selection

1. Same species along a trailsect 2. Sevcral species common in the area

Sampled organ

All organs

At least affected organ

Selection due to objectives

Selection due to objectives

Mostly leaves

Sampling time

Once a year

During the interacting period

Throughout the year

Throughout the growing season

Mostly a the end of the growing season (maximum accumulation)

General attention Special attention: None

Identification of individuals, dcvclopment (age) stage Impact on non-affected organ

Allocation changes during development

Allocation changes during development

Comparable age of the organ; comparable exposition; comparable environmental conditions except elements of thc monitoring or identification purpose

20.6 Conclusion A sampling strategy of plants has to be based on a well-formulated scope. The objective of the data analysis dcterniines the sampling period, the samplcd plant organ and the sampled biomass. The high dynamics of chemical elements in plants during growth and development demands a protocol with a lot of biological details. In the ideal situation the state of mycorrhization and the involved fungal species have to be determined, because finally all plant data will be related to data of thc

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39 1

surrounding environment. Except for food web studies, “aufwuchs” on leaves of aquatic plants and the phylloplane organisms on terrestrial plants will cause severe biological contamination of plant samples.

20.7 References Adriano, D. C. (ed.) (1992) Biogeochemistry of’ Truce Metuls. Boca Raton, Florida: Lewis Publ. Ahlf, W., Weber, A. A. (1981) A simple monitoring technique to determine the heavy metal load of algae in aquatic ecosystems. Environ. Technol. Letters 2, 3 17 - 322. Allen, R . 0..Steinnes, E. (1978) Concentrations of some potentially toxic metals and other trace elements in wild mushrooms from Norway. Chemospkere 4, 371 -378. Allen, M. F. (1991) The Ecology of Mycorrhizae. Cambridge: Cambridge University Press. Angelone, M., Bini, C. (1992) Trace elements concentrations in soils and plants of Western Europe, in: Biogeochemistry yj’Trace Metals; Adriano, D. C. (ed.). Boca Raton, Florida: Lewis Publishers, pp. 19-60. Barnett, B. E., Ashcroft, C. R. (1985) Heavy metals in Fucus uesiculosus in the Humber estuary. Environ. Pollut. B9, 193-213. Baumeister, W., Ernst. W. H. 0. ( I 978) Miniwdstoffe und Pflanzenwachstum. Stuttgart: Gustav Fischer Verlag. Hormann, F. H., Likens, G. E. (1979) Pattern and Process in a Forested Ecosystem. New York: Springer Verlag. Brooks, R. R. (1972) Ceohotany and Bioguochi>mistry in Mineral Exploration. New York: Harper & Row. Brooks, R. R., Lee, J., Reeves. R. D., Jaffre, T. (1977) Detection of nickeliferous rooks by analysis of herbarium specimens of indicator plants. J . Geochem. E-ypl. 7, 49. Brown, D. H., Brown, R. M . (1990) Reproducibility of sampling for element analysis using bryophytes, in: Elment Concentration Cudasters in Ecosystems, Lieth, H., Markert, B. (eds.). Weinheim: VCH Verlagsgesellschaft, pp. 5 5 - 62. , M . M. (1980), Copper tolerance in the green alga, Chlorella Butler, M., Haskew, A. E. .I.Young, vulguris. Plant Cell Emiron. 3, 1 I9 - 126. Christlieb, Th., Wcber, A. (1980) Dic Bedeutung der ZelloberflCche fur die Sorption von Blei durch cine coccale Grunalge. Emiron. Technol. Letters 1, 3 1 1 - 3 18. Clynio, R. S. (1963) Ion exchange in Sphagnum and its relation to bog ecology. Ann. Bat. 27, 309- 324. Cooke, W. B. (1979) The Ecology c?f’Fiingi. Roca Raton, Florida: CRC Press. Colpaert, J . V., Van Assche, J . A. (1992) The effects of cadmium and the cadmium-zinc interaction on the axenic growth of ectomycorrhizal fungi. Plant Soil 145, 237-243. Colpaert, J. V., Van Assche. J. A. ( I 993) The effects ofcadmium on ectomycorrhizal Pinus syluestris L. NCN’Plzytol. 133, 325 -333. Cuguen, J . , Thiebaut, B., N’Tsiba, F., Barrierc, G. (1985) Enzymatic variability of beechstands (Fagus sylvaticu L.) on three scales in Europe, in: Genetic Diflerentiation and Dispersal in Plants: Jacquard, P., Heim, G., Antonovics. J. (eds.). Berlin: Springer Verlag, pp. 17-39. De Filippis, L. F., Hampp, R.. Ziegler, H. (1981) The cffect of sublethal concentrations of zinc, cadmium and mercury on Euglena. Growth and pigments. Z . Pjlanzenphysiol. 101, 37 -47. De Vos, C. H. R., Vonk, M. J., Vooijs, R., Schat, H. (1992) Glutathione depletion due to copper-induced phytochelatin synthesis causes oxidative stress in Silene cucuhalus. Plant Physiol. 98, 853 - 858. De Vries, P. J . R., De Smet, S. J. M., van der Heide, J. (1985) Effccts of phosphorus and nitrogen enrichment on the yield of some strains of Stigeoclonium Kutz. (Chlorophyceae). Freshwater B i d . 15. 95- 103. Dietl, G. ( I 987) Abhingigkeit dcr Schwcrmetallaufnahme hoherer Pilze von der Substratzusammensetzung und von Standortsfaktorcn. Bihl. Mycol., Vol. 110, Cramer, Berlin.

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Dilworth, M. J., Robson, A. D., Chatel, D. L. (1979) Cobalt and nitrogen fixation in Lupinus ungustifolius L. 11. Nodule formation and functions. New Phytol. 83, 63 - 70. Duvigneaud, P., Denayer-De Smet (1964) Le cycle des elkments biogenes dans l’ecosysteme forEt (For?ts temperees caduesfoliees). Lqjeunia 28, 1 148. Ellenberg, 1 1 . (ed.) (197 1 ) Integroted Experimental Ec0/11g~~. MethoLis and Results of‘ G o s j ~ w t Research in the Gerniun So//ing Proyect. Ecoiogicdl Studies. Berlin: Springer Verlag. Vol. 2. Ellcnbcrg, H., Maycr, R., Schaucrmann, J. (1986) Okosystemjorschung. Ergehnisse des Sollingprojekts 1966- 1986. Stuttgart: Ulmer Verlag. Frrnst, W. H. 0. (1974) S c h ~ ~ ~ ~ r m r ~ f a l l o ~ ~der g ~ ~Erde. t r r t iStuttgart: on G. Fischer Verlag. Ernst, W. H. 0. Okophysiologic von Pflanzcn in Kiistenduncn Europas in cincm Gradientcn von dcr Nordsec zum Mittclmccr, in: Berichte der Rrinhold-Tuxen-Gt~sellschaft,Pott, R. (cd.). Gottingen: Goltze Verlag, Vol. 3, pp. 157- 172. Ernst, W. H. 0. (1975) Variation in thc mineral contents of lcavcs of trees in miombo woodland in South Central Africa. J . Ecol. 63, 801 -808. Ernst, W. H. 0 (1982) Schwermetallpflanzen, in: Pjlunzenijkokogie und Mineral.st~~~ivc,t.hsc.l, H . Kinzcl (cd.). Stuttgart: Ulmer Verlag, pp. 472-506. Ernst. W. H. 0. (1983) Population biology and mineral nutrition of Anemoiw nc~n7uru.s~with emphasis on its parasitic fungi. Flora 173, 335-348. Ernst, W. H. 0..Leloup, S. (1987) Perennial herbs as monitor for moderate levels of metal fall-out. Chemosphere 16, 233 - 238. Ernst, W. H. 0. (1987) Mctal fluxcs to coastal ccosystcms and the rcsponsc of coastal vegetation, in: Vqetation hetwec‘n 1,undand Sca, Huiskes, A. H. L., Blom, C. W. P. M., Rozema. J. (eds.). Dordrecht: Junk Publishers, pp. 302-310. Ernst, W. H. O., Van Rooij, L. F. (1987) 134’137Cs fall-out from Chcrnobyl in Dutch forest, in: H L ~ metals J in the Environment, Lindberg, S . E., Hutchinson, T. C. (cds.). Edinburgh: CEP Consultants, pp. 284-286. Ernst, W. H. 0. (1990) Element allocation and (re)translocation in plants and its impact on representative sampling, in: Elemenr Concentration Cadasters in Ecosystems: Lieth, H ,, Markerl. B. (eds.). Weinheim: VCH Verlagsgesellschaft, pp. 17-40. Ernst. W. H. O., Schat, H., Verkleij, J. A. C. (1990) Evolutionary biology of metal resistance in Silene oulgaris. Evol. trends plants. 4. 45 - 51. Ferry, B. W., Baddeley, M. S., Hawksworth, D. L. (1973) Air Pollution ond Licliens. London: Athlone Press. Fleming, L. V. (1985) Experimental study of sequences of cctomycorrhizal fungi on birch (Betulir sp.) seedling root systems. Soil Biol. Bioc/iem. 17, 591 -600. Foster, P. L. (1982) Metal resistance of Chlorophyta from rivers polluted by heavy metals. Freshwater B i d . 12, 41 -61. Cast, C. H., Jansen. E., Bierling, J., Haanstrd, L. (1988) Heavy metals in mushrooms and their relationship with soil characteristics. Chemosphere 17, 789 - 799. Gckclcr, W., Grill, E., Winnackcr, E. L., Zenk, M. H. (1988) Algae sequester heavy metals via synthesis of phytochelatin complexes. Arch. Microhiol. 150, 197-202. Griepink, B., Muntau, H., Colinet, E. (1963) Certifications of the contents of cadmium, copper. manganese, mercury, lead and zinc in two plant materials of aquatic origins and in olive leaves. Fresenius Z . Anal. Chem. 315, 193 - 196. Hall, A. (I98 I ) Copper accumulation in coppcr-tolcrant and non-tolcrant populations of the marinc fouling alga, Ectocarpus siliculosirs (Dillw.) Lynbyc. Bot. Mar. 24, 223 - 228. Harding, J. P. C., Whitton, B. A. (1976) Resistance to zinc of Stigeor.lonium tenuc’ in the field in the laboratory. Br. ph~vcol.J . 11, 417-426. Hcrzig, R., Urcch, M., Licbcndorfcr, L., Ammann, K., Guecheva, M., Landolt, W. (1990) Lichens as biological indicators of air pollution in Switzerland: passive biomonitoring as apart of an integrated measuring system for monitoring air pollution, in: Element Concenrrution Cadusters in Ecos,ystems, Lieth, H., Markcrt, B. (eds.). Weinheim: VCH Verlagsgesellschaft, pp. 55 - 62. I Iinneri, S. (1975) Mincral clements of macrofungi i n oak-rich forests on Lensholm Island, Inncr Archipelago of SW-Finland. Ann. Bot. Fenn. 12, 135- 140. ~

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Ietswaart, J. H., Griffioen, W. A. J., Ernst, W. H. 0. (1993) Seasonality of VAM infection in three populations of Agrostis cupillaris (Graminede) on soil with or without heavy metal enrichment. Plunr Soil 139, 67-73. Joosse, E. N. G., Van Vliet, L. H. H. (1982) Impact of blast-furnace plant emission on a dune ecosystem. Bull. Emiron. Cuntum. Toxicol. 29, 279 - 284. Keeney, W. L., Breck, W. G., Van Loon, G. W., Page, J. A. (1976) The determination of trace metals in Cludop//ora glomerirtu as a potential biological monitor. Watrr Research 10, 981 -984. Kessler, E. (1986) Limits of growth of tive Clzlurellu species in the presence of toxic heavy metals. Arch. Hydrohid. Suppl. 13 (I), 123- 128. Kneiffcl, H., Bayer, E. (1993) Strukturermittlung der Vanadiumverbindung des Fliegenpilzes, Amavadin. Angew. Chem. 85, 542- 543. Lieth, H. H. F., Markert, B. A. (1988) Aujstellung und Austwtung ijkosystrmnrer E k v w n f Kr)nzentrutions-Kataste~.Berlin. Springer Verlag. Markert, B. (1992) Multi-elemcnt analysis in plant materials. Analytical tools and biological questions, in: Biogrochemistry uf'Trucc1Metals. Boca Raton Florida, Lewis Publ., pp. 401 -428. Markert, B. (1993) Instrumental analysis of plants, in: Plants as Biomonitors. Indicatorsfor Henuy Metals in t l i ~Tcwt~striulEnuironment: Markert. B. (cd.). Weinheim: VCH Vcrlagsgesellschaft, pp. 65- 103. Markert, B. (1993) Instrumentelle Multielrrnc~ntunalpse uon Pflunzenprohen. Weinheim: VCH Verlagsgcsellschaft. Marschner, H . (1986) Minerd Nutrition 1 4 ' Higher Plants. London: Academic Press. McCreight, J. D., Schroeder, D. B. (1982) Inhibition of growth of nine ectomycorrhizal fungi by cadmium, lead and nickel in iiitro. Enuiron. Exp. But. 22, 1 - 7 . Mcharg, A. A,, Macnair, M. R. (1990) An altered phosphate uptake system in arsenate-tolerant Holclt.7 lutlutus L. neb^ Phytol. 116, 29 - 35. Meyer, J . J. M., Grobbelaar, N., Vleggaar, R., Louw, A. J. (1992) Fluoroacetylcoenzyme. A hydrolase-like activity in Dichupetahtm cymosum. J . P l m i Phpsiol. 139, 369- 372. Mutsch. F., Horak, O., Kinzel, H. (1979) Spurcnelemente in hoheren Pilzen. Z . Pflunzenpliysiol. 94, 1 10. Nagata, T., Hayatsu, M., Kosugc, N. (1992) Idcntification of aluminium forms in tea lcaves by "AI N M R . Phytochemistrp 31, 1215- 1218, O'Hagan, D., Perry, R., Loek, J. M., Meyer, J. J. M., Dasaradhi, L., Hamilton, J. T. G, Harper, D. B. ( I 993) High lcvels of monofluoroacctate in Diclzapetalum hraunii. Phytochernistry 33, 1043- 1045. Otte, M. L., Rozema, J., Koster, L., Haarsme, M. S., Broekman, R. A. (1989) Iron plague on roots of ,4ster tvipolium L.: interaction with zinc uptake. New Phytol. 111- 309-317. Pais. I. (1983) The biological importance of titanium. Plunt Nutrition 6, 3- 131. Paul, E. A., Clark, F. E. (1989) Soil microbiology and Biochemistry. San Diego: Academic Press. Peterson, P. J., Butler, G. W. (1967) Significance of selenocystathionine in an Australian selenium accumulating plant Neptuniu arnple.xicau1i.s. Nature 219, 599 - 600. Phillips, D. J. H. (1977) The use of biological indicator organisms to monitor trace metal pollution in marine and estuarine environments - a review. Enuiron. Pollut. 13, 282-317. Prosi, F. (1981) Heavy metals in aquatic organisms, in: Metal Pollution in rhe Aquacic Enuironment. Forstner, K., Wittmann, G. T. W. (eds.), Berlin: Springer Verlag, pp. 271 -323. Stark, N. (1977) Nutrient cycling pathways and litter fungi. Bio Science 22, 355-360. Stccmann Nielscn, E. (1947) Photosynthesis of aquatic plants with special reference to the carbon sources. Dunsk Bot. Ark. 12, 1-71. Stiles, W. (1964) Truce Elmnents in Plunts und Aniinuls. Cambridge: Cambridge Univ. Press. Stokes, P. M., Hutchinson, T. C., Krauter, K . (1973) Heavy-metal tolerance in algae isolated from contaminatcd lakes near Sudbury, Ontario. Cun. J . Bot. 51, 2155-2168. Tietema, T. (1991) Ecoykysiology uf'the Sund Sedge. Curex arennria L. Dissertation University of Utrecht. Tyler. G. (1980) Mctals in sporophores of basidiomycetes. Trans. Br. mycol. Suc. 74, 41 -49. ~

394

W . H . 0. Ernst

Veenendaal, E. M., Monnaapula, S. C . , Gilika, T., Magole, J. L. (1992) Vesiculardrbuscular mycorrhizal infection of grass seedlings in a degraded semiarid savanna in Botswana. Neic Phvtof. 121, 477-485. Wagner, G. (1990) Variability of element concentrations in tree leaves depending on sampling parameters, in: Element Concentration Cadasters in Ecosystems. Lieth, H., Markert, B. (eds.). Weinheim: VCH Verlagsgesellschaft, pp. 41 - 54. Warrcn. H. V. (1980) Biogeochemistry, trace elements, and mineral explorations, in: Applied Soil Truce Elements, B. E. Davies (ed.). Chichester: John Wiley. Wcber, A. (1981) An uncomplicated screening test to evaluate toxicity of environmental hazardous compounds in water. Environ. Tecknol. Letters 2, 323 -328. Zimmerman, R. D., Plankenhorn, W. E. (1986) Methodik der Blattprobenentnahme an der Rotbuche unter emissionsokologischem Aspekt. AlIg. Forstz. 41, 33 - 35.

Environmental Sampling for Trace Analysis

Edited by Bernd Markert 0 VCH Verlagsgesellschaft mbH, 1994

On the Samp ,,ig of Vascu ar Plants for Monitoring of Heavy Metal Pollution 3

Rumiunu Djingovu und Ivelin Kuleff

21.1 Introduction Sampling of environmental materials for chemical analysis has lately received serious attention since it was recognized that incorrect proceduces may introduce an error exceeding orders of magnitude the analytical error thus rendering the subsequent analysis meaningless (e.g., Sansoni, 1986; Markert and Steinbeck, 1988; Markert, 1988). While sampling procedures for soil and air pollution monitoring have been developed, discussed and standardized, special attention to plants has not been paid for a long time because it was considered that sampling strategies valid in forestry and agriculture may be utilized directly (Chapman, 1976). Nowadays it is already clear that due to the specificity of monitoring the respective sampling procedures for plants should fulfill special requirements (e.g., Ernst, 1990; Markert, 1993). A first step in any analytical strategy is the definition of the purpose for which the analysis is performed (Ku, 1979), and on this basis a sampling procedure is to be developed. If biomonitoring of heavy metal pollution is a final aim in plant analysis then some very specific points should be considered. According to Markert (1993) biomonitoring means quantification of the pollution which can be obtained by several means: -

comparison of the polluted areas; comparison of long-time series; comparison with normal values; comparison with direct measurements.

That is, in all cases the samples should be taken in a way that permits meaningful comparison of the analytical results according to time and/or place. This undoubtedly introduces except the general sampling requirements concerning: avoidance of contamination and losses; quantity of field and laboratory samples; - representative division of the sampling area. -

A number of additional factors are to be considered. Among them the most important to receive attention in the case of plant biomonitoring are: -

concentration differences at biological levels; sampling period; sampling pretreatment.

396

R . nlmgovtr uml 1. K u k f f

I n recent years, a number of excellent contributions have been published discussing different aspects of the above mentioned factors (e.g., Martin and Couphtrey, 1982; Fleming et al., 1986; Camerlynck and Kiekens, 1986; Ernst, 1990; Wagner, 1990; Markert, 1993) and proposing standardized sampling proccdures for some plant species for monitoring purposes (e.g., Wagner, 1987; Knabc, 1984). The present contribution aims to stress some problems of sampling for biomonitoring studies using vascular plants for the assessment of heavy metal pollution and to summarize some of the results obtained in this field.

21.2 Concentration Differences at Biological Levels Concentration fluctuations in the chemical composition of plants are due to genetic variability, different climatic and epidaphic conditions etc. which determine the differences among species, populations and stands (Markert, 1993). These factors are valid for both, higher and lower plants. Vascular plants, in comparison to bryophytes and lichens, have a well developed root and transport system. Therefore, they have a mixed mechanism of accepting nutrient and polluting elements from the surrounding environment which increases the importance of the processes of localization, transport and relocali7ation of substances in the plant. Therefore, the concentration differences at individual and compartment levels are much more important and should be considered with due attention. At equal microclimatic and edaphic conditions the most important factor for individual differences is the stage of development of the plant (namely its agc) which determines among other things the degree of exposure, the direction and the level of transportation and localimtion. Individual differences for some elements (mainly nutrients) have been reported to vary about 20% for Digifulispurpurea and Senecio syluaticus (Ernst, 1990), about 2 5 O h for Tiirusucutn officnalc (both, for nutrient and heavy metals) (Djingova and Kuleff, unpublished data), higher values have been also established in some bushes (Markert, 1987b, 1993). To overcome the influence of the individual variations two approaches have been in use. One is to define the number of individuals to be collected in order to mask the interindividual fluctuations and obtain a representative sample and the other is to define exactly the sampling parameters with respect lo age, height, position and exposure, the second approach being better developed in the case of trees. On the basis of typical individual elemental fluctuations between l0-50% it is recommended to collect samples from at least 10 but preferably 25 plants for chemical analysis (Markert, 1987b, 1993; Ernst, 1990). In the case of tree species excellent standardized sampling procedures have been developed for Popultis nigra (Wagner, 1987) and Pirca ahies (Knabe, 1984) specifying the height (4-6 m for Popufus nigra) and the age (7 year-old branches

Sumpling of Vusculur Plunts

397

for Picea abies), the age of the twigs (new-grown long sprouts from each branch for Populus nigra and 1 year old twigs for Piceu abies), and the position of the leaves/needles on the branch that are finally to be sampled. In this way the age and height factors are strongly reduced and comparative studies are possible. Additionally, to account for exposure and shading, sampling from solitary trees, clumps or lines of trees is standardized. Although it is considered that deposition of pollutants may raise with height (Wagner, 1990) the investigations of Zimmermann (1989) proved that in the case of Fugu,r siluutica L. the highest concentrations of heavy metals (Pb, Cd, Zn) are found in the lower inner parts of the trees. This difference in the observations may in our opinion, be due either to interspecies differences or to the fact that the investigations of Zimmermann (1989) were performed in areas not directly affected by pollution and that the presence of heavy metals in the trees (Fagus silvutica L.) is due only to the available quantities in the soil (not air deposition) and that the localization of the elements in the lower part is explained by the intensive water use. In two aspects, the inhomogeneous distribution of elements in the different organs of a single plant is very important for vascular plants. Firstly, the analysis of organs may give valuable information about the degree of exposure (Tyler et al., 1989) and the ways of pollutants entering the plant (Kabata-Pendias et al., 1991). Secondly, on this basis the most suitable “critical” organ of a plant may be defined for use in monitoring studies. Of course, in biomonitoring studies using tree leaves have been favored with the correct assumption that they can be obtained high above the ground, not subjected to soil contamination and splashing, and are exposed to direct atmospheric pollution (Martin and Couphtrey, 1982). But still there are investigations of the heavy metal distribution in other tree organs such as roots, twigs, seeds. Of course, roots of older trees are seldomly considered since in polluted areas the major part of the tree’s root system is likely to be in the less contaminated soil horizons (Martin and Couphtrey, 1982). Monitoring using tree bark will not be discussed here. An excellent review is to be found in (Walkenhorst et al., 1993). The results from the investigations indicate that there is certain difference in the behavior of deciduous and conifer tree species with respect to the distribution among twigs and leaves (needles) (Steubing and Haneke, 1993). Steubing and Haneke (1993) investigated the distribution of mainly U but also of Ce, Co and Au in Pinus syluestris, A h u s glutinosa, Carpinus betulus and Quercus robur, grown on uranium-rich soils and conclude that for conifers the elements are located in the twigs during the first two years. Sheppard et al. (1985) investigated the distribution of practically the same elements in Scots pine grown in waste-side soils and proved the accumulation of the elements in the roots. The study of Wotton et al. (1986) on Picea hunksiana and Piceu nzurinu in areas subjected to smelter air pollution with Ni, Cu, Fe, Pb and Cd established enrichment and localization of the elements also in the roots (seedling roots) and in the cones but not in seedling shoots, the concentrations decreasing with the distance from the smelter. Wagner (1990) reported higher concentrations of As i n spruce needles with respect to shoot axes, lower Pb, Co and Ni and similar Cd and Zn concentrations.

398

R. Djingorcr und I. K u k f f ’

Thus it seems that with conifers, no matter whether soil or air pollution is investigated, most of the heavy metals are enriched and localized in the roots and twigs and lower concenrations are detected in the needles. This assuniption is indirectly supported by the works of Krivan and Schaldach (1986) who demonstrated that the content of “washed” needles (after removal of all mechanically sticking particles) is not very high in polluted areas. On the other hand needles seem to be an excellent “passive” collector of air pollutants (to be discussed later). Rather contradictory to this assumption is the observation of Cousen (1990) who reports highest Cs-I37 contents i n young needles (grown a year after the Chernobyl accident) but as he remarks seemingly afterwards this effect of initial localization in young needles is to be repaired. For deciduous trees and bushes Steubing and Haneke ( I 993) report localization of U in the leaves. Martin and Couphtrey (1982) discuss that the concentration of Cd is higher in the leaves of Corylus uvellunu, but that of Pb is significantly highcr in small twigs while Zn and Cu are evenly distributed. Investigating organ distributions within different vascular plants from a birch forest area near a Cu smelter Lobersli and Steinnes (1988) prove that Betulnpuhescens is a good indicator of environmental pollution. Cu and Zn in the leaves and Cu. Zn, Cd and Pb in the twigs are decreasing with the distance from a smelter (displaying monitoring properties). Studying Betulu species as well Taha and Greim (1 979) demonstrated higher concentrations of Co, Ga and Au in the lcavcs and of As, Bi, Sb and La in the twigs and even distributions of Cd and Fe. Thus it seems that Pb, and probably As and Sb, are located in the twigs of deciduous trees and shrubs while there is strong evidence that U, Co and Cd are found in higher concentrations in the leaves, irrespective of the type of pollution. With grasses and herbage, the investigations of different organs concern the distributions of elements in roots, stems, leaves and seeds. Thornton (1983) studying the influence of soils heavily contaminated with Cd and Zn found highest concentrations in the roots of the grass Holcus lunutus and a decreasing concentration gradient from root to stem, leaf and head. At the same time he cstablished high Cd contents in the above-ground parts of Turuxacwn officinulr growing on the same soil. Kanias and Philianos (1 978, 1979) found highest conccntrations of Br, Th, Ba, Cs, Rb, Eu, C1 in petiols, of Sc, As, Zn, Cr, Fc, Hf, and La in the rhizomes and only of Au in the leaves of Hellehorus cyclophyllu,~ BIOSS. The petioles selectively accumulated Br, their content being 4 times higher than in the soil. In a detailed investigation of R1ziizanthu.s angustijulius Ernst (1 990) studied thc distribution of ninc nutrient elements in roots, stalk, leaves, calix, petals, capsule and head and established highest values of P, Na, Ca, Mg, Mn and Zn in the leaves, of P and N in the seeds and only of Fe in the roots. The investigations of herbage are mainly directed towards studying the distribution between roots and aerial parts aiming to discover the ways pollutants are entering the plant. In this respect the pot experiments of Aspiazu and Romero (1986) with Loliurn rnicltiflorum grown on perlite with different additions of heavy mctals are very intcresting. They proved that using smaller additions all elements (Zn, Co, Ni, Cd,

Sampling qf Vascular Plants

399

As, Pb) were accumulated in the roots, but with higher additions top uptake increased and, except for Pb and Cu, the elements were translocated and stored in the above-ground parts. Analyzing several weed species collected near the Metallochimia Factory (near Budapest) Kovacs et al. ( 1993) established highest concentrations of Zn in the leaves, of As and Pb in the roots of Ambrosia elutior, Atriplox tuturicu and Convolvulus urvense. The concentrations of Cd and Ni generally were similar in leaves and roots. In the case of LoZium perenne roots accumulated Cd, Cr, Cu, Fe, Pb and Zn. The presented accumulation capacities of the investigated weed species proved that the root systems of Lolium perenne, Pluntrrgo lunceolutu and Turuxucum offieinale are very effective accumulators of heavy metals. In several papers, Kabata-Pendias et al. (1 989, 1990, 1991) present results on the distribution of Cd, Zn, Cu, Cr, Fe, Mn, Ni and Pb in tops and roots of Turaxucum oflicinule that prove the enrichment of Cd, Zn and Pb in the tops. On this basis the authors come to the conclusion that increased levels are due to airborne contamination. Studying the elemental fluctuations among different organs of Turaxucum officinule we have investigated samples both from background areas (situated in the Rhodopes, the Balkan mountains and Rila mountains in Bulgaria and one polder region in the Netherlands), and from industrially polluted areas (in Bulgaria and in the Netherlands). The results obtained in the background regions are presented in Fig. 1. Higher concentrations of Br, Ca, Cd, Sb, Se are established in the leaves whereas the roots are enriched in Au, Co, Cr, Fe, Pb, Cu, Mn and Hg. Ba and K have highest concentrations in the stems, As in the blossoms, while Na, R b and Zn are evenly distributed. In polluted areas (Fig. 2) the comparison between the concentrations in the organs prove that the critical organ of Turaxucum qfficinule for As, Ba, Br, K, Hg, Sb, Se is the leaf while Au, Co, Cr, Cu, Fe, Mn and Pb are definitely higher in the roots. Zn and Cd have similar values in leaves and roots while in all cases the other organs show lower concentrations. The CR values (ratio between plant concentration and the available content in the soil) prove that As, Br, Pb, Cd and Cu are accumulated in the plant in quantities higher than available in the soil (Djingova and Kuleff, unpublished data). On the basis of these results we have made the assumption that Turuxucum oflicinale is reflecting not only soil but air pollution as well, the leaves accumulating more As, Br, Sb and Se and the roots more Co, Cr, Cu, Fe, Mn and Pb. Zn and Cd are evenly distributed in the roots and leaves. Although the conclusions are similar to those made by Kabata-Pendias et al. (1989, 1990, 1991) the observations concerning Pb, Zn and Cd are rather different since we have not established higher concentrations in the tops with respect to the roots of the plant. We confirm, however, the contribution of aerial pollution to the concentration in the plant, on the basis of the higher content in the plant than the available in the soil and on the basis of the investigations we performed earlier (Djingova et al., 1986) on the mathematical models describing the behavior of the element concentration in Turuxucum officinule in dependence on the distance from a pollution source. The results of the different authors concerning distributions in herbage lead to the more general conclusion that at normal levels, or in slightly polluted areas,

400

5 Root/Leaf

0Blossom/Leaf

Stem/Leaf Seed/Letlf

4 0 CI

I-

a CK

3

z 0 u

I-

a 0:

2

IZ

W

0

z 0 0

1

0

A s Au Ba B r Ca Cd Co C r Cu Fe Hg K MnNa Pb R b Sb Zn Fig. 1. Element distributions i n diffcrcnt organs of Thruxuciwi of7kinale from background regions.

most o f the heavy metals are accumulated in the roots, though the above-ground parts also react to the pollution. (In somc cases, e.g., in Turuxucum oJi:cinale (Kuleff and Djingova, 1984; Djingova et al., 1986; Kabata-Pendias et al., 1989, 1990, 1991) this reaction is very strong.) There are indications that under heavily pollutcd conditions (soil or air) the top uptake increases (Azpiazu and Romero, 1986) and most o f the elements are transferred to the above-ground parts. Of sourse, all these conclusions are valid in the case of washed samples.

5 Root/Leaf

Stem/Leaf

Blossom/Leaf

4 0 U

I-

4

c r 3 Z

0 U

I-

a 2

CK I-

z W

0

z 0 1 0

0 As Au Ba Br Ca Cd Co Cr Cu Fe Hg K MnNa Pb R b Sb Se Z n Fig. 2.

Element distributions i n diffcrent organs of Trrrusircum officinule from polluted regions.

21.3 Seasonal Variations in the Heavy Metal Content of Plants Most investigations on the seasonal changes of the heavy metal contents in vascular plants performed until 1980 are summarized by Martin and Couphtrey (1982). Starting from the earliest investigations of Hunter (1953) and Mitchell (1957) the results seem to be species and element dependent and obviously each case should

402

R. Djingoon mid I. Kukf]'

be studied separately. Nevertheless, some distinct trends can be detected for the different groups of plants. Seasonal changes in the elemental contents of deciduous trees and shrubs have been an object of investigation in a number of papers (e.g., Fidora, 1972; Ahrens, 1964; Smith, 1978; Guha and Mitchell, 1966; Martin and Couphtrey, 1982; Markerl, 1987a, b, 1993; Kovacs et al., 1982; Wagner, 1987; Deu and Kreeb, 1993). If the results are summarized according to Markert (1993) seasonal changes in the elcmcnt concentrations exceed the fluctuations due to site-by-site differences and to the analytical error. (Markert (1 989) established differences of up to 159% within 2 months for Mg, Ca, Sr, Ba and Zn in Vucciniun?myrtillus). In many cases, however, such drastic changes have not been observed. Capannesi et al. (1993) conclude that the seasonal changes of Pb in Quercus ilex as well as of Eu, Sb, As and Hg are far less important than site-by-site variations. Most of the results from the studies of the seasonal behavior of heavy metals (Pb, Cd, Cu, Fe, etc.) in Mulus purnilu, Pyrus comnzunis, Prunus cerusifkru (Deu and Krebs, 1993). Aesculus hippocastanurn (Guha and Mitchell, 1966), Celtis orientalis, Tilia tomentosa (Kovacs et al., 1982) and Populus nigru (Wagner, 1987), lead to the assumption that in the case of deciduous trees heavy metals increase with the season from May to October reaching maximum concentrations directly before leaf fall. There are some contradictory results for specific elements and/or species but generally maximum is detected in September - October and a possible minimum during the preceding summer months. Therefore, as Wagner ( 1 990) concluded, sampling of decidious plants is best to be done in early autumn shortly before scenescence. Studying seasonal changes in conifers Wyttenbach and Tobler (1988) divided the 20 determined elements in 4 groups depending on their behavior. Ahrens (1964) reports remobilization of trace elements in one-year old needles to ensure the growth of new twigs. Ernst (1990) mentions that plants with long-lived needles e.g., Picca ahirs and Pinus syluestris demonstrate a continuous age gradient. Anyhow, the changes of pollutants in washed needles are not so well expressed (Krivan et al., 1987b) as was established for decidious trees. The investigations of grasses prove a quite different behavior. Thornton (1983), Martin and Couphtrey (1982), Matthews and Thornton (1980), Mitchell and Reith (1966) Lindsay and Brokhout ( 1 9781, Wilkins ( 1978), Roberts and Goodman (1974), and Turney et al. (1972) established lowest concentrations of heavy metals in pasture herbage and grass species in spring and highest in winter. However, for non-grass species like Taruxucum qfjicinale (Matthews and Thronton, 1980; Djingova and Kuleff, unpublished data) another behavior was established which is similar to that of Loliumperenne (Crump, 1980),Krimyn grass (Beavington, 1976). Bermuda grass (Brown, 1983), rye grass and robur (Bacso et al., 1984) and rye grass (Fleming, 1986). In all these cases the seasonal changes of Zn, Cd, Ni, Sb, Cu, Pb, etc. show maximum contents in autumn and relatively high contents in spring. Thus in the general case of monitoring heavy metal pollution using vascular plants it seems that sampling of common pasture grass should be performed in winter (of course this may be possible in areas without a stable snow cover), of decidious trees in carly autumn, while for non-grass herbage spring is also possible. With conifers cumulative effects over the years may be expected.

Sumplrng of Vuscukur Piuntr

403

These final conclusions made on the basis of the reviewed literature, are absolutely correct but an additional question should be posed. Is it possible to standardize the exact time period of sampling in the case of comparative studies, including regions with various altitudes and latitudes? Due to climatic variations, differences in the stage of development of the plant are to be expected. This question is directly connected with the proper organization of global monitoring studies using vascular plants. Therefore, it is far more important to standardize the stage of development of the plant instead of the exact season. We applied this approach in the biomonitoring studies with Turuxucum officinule (Kuleff and Djingova, 1984; Djingova et al., 1986; Djingova and Kuleff, 1993). The samples were collected in 4 European countries (Bulgaria, Greece, Germany and The Netherlands) from regions situated from below sea level (The Netherlands) up to 2000 meters above sea level (Rila mountains, Bulgaria). Investigations proved that it is not possible to standardize an exact time period of sampling because when Turuxucum oJficinale was flowering in Thessaloniki (Greece) and Sofia (Bulgaria) it was not yet to be found in the mountains. Therefore flowering was standardized as a stage of growth and samples were collected in the period April - July.

21.4 Sample Pretreatment It is already accepted that in the case of food chain studies cleaning plant material prior to analytical procedures should be avoided while in physiological investigations it is an obligatory step. Cleaning may be performed in several ways (Markert, 1993), either dry or wet brushing or wiping or washing in different media. In biomonitoring studies final conclusions about the necessity of preliminary cleaning of plant inaterial have not been reached yet and a lot of investigations are being done with unwashed samples or after using various washing procedures which makes comparative studies rather difficult if not totally impossible. The cleaning of plant material in biomonitoring is usually discussed according to the following aspects: (1) Avoidance of soil contamination. This is the main aim of pretreatment of plant samples when analysis must provide information about heavy metal pollution, and grass and herbage are used as monitors. However, it is an important step in the treatment of tree leaves as well, especially when regions situated in the more southern parts of the Northern Hemisphere are monitored. The soil dust content in the atmosphere is very high during summer and autumn months and precautions should be taken to avoid an influence on the monitoring results. (2) Establishing a difference between aerial and soil pollution. (3) Reduction of the effect of drastic climatic changes.

Regarding the second aspect it is usually considered that atmospheric pollution is reflected by plants by foliar deposition, which more or less is physically sticking

404

R.Djiugovn mu’1. KulcIff’

of particles onto the leaf surface. Therefore, washing (or mechanical treatment) is used to assess (or discard) that part of pollution which is attributed to the atmosphere. It is, of course, not always true that atmospheric pollution is reflected by plants only as deposition. Due to climatic changes (draught periods and rainfalls) a lot of the deposited elements are transformcd to plant available forms and either enter the plants directly through the leaf stomata or are leached in available form to the soil and taken up by the plant via the roots. Additionally, there are a lot of processes with vascular plants including the allocation and transportation of the elements within the plant that further contradict this simplified assumption. Tab. 1 presents a brief overview of the washing procedures more often used in biomonitoring studies. It is clear that each treatment leads to different extents of removal of particulate matter. The situation is further complicated by the fact that for the various plant types different washing is proposed and even if one and the same procedure is applied it may lead to contradictory results. The most aggravating Tab. 1. Sample Pretreatment in Biomonitoring with Vascular Plants Type of treatment Mechanical treatment 1. Wiping with cloth

2. Blowing with nitrogen jet Washing I . Organic solvent and water rinse

2. Rinsing with distilled (tap) water

3. Tap (distilled) water and detergent 4. Acid solutions

5. Complexing agents

Effect

Reference

N o changes in Ca, Na, Cu, Mn; changes in Al, Fe, Si, Ti, Pb Cleaning off all particlcs

Buchauer, 1973; Camerlynck et al., 1980; Hall et al., 1975; Wallacc ct al., 1980 Capaunesi et al., 1993

Removal of Al, Pe, Sc, As, Sb; negligiblechanges ofCa, CI, Mg, Mn, Zn Removal 10- 80% of Pb, smaller changcs for othcr heavy mctals

Hartmann and Baechmann, 1988; Krivan et al., 1986; Wyttenbach et al., 1985 Aidid, 1988; Bacso et al., 1984; Camerlynck et al., 1986; Carlsoii et al., 1976; Deu and Kreeb, 1993; Ernst, 1972, 1978; Fidora, 1972; Fytianos et al., 1986; Hagcmcyciet al., 1985; Lerche and Brccklc. 1974; Martin and Couphtrcy, 1982; Schuck and Locke, 1970; Wagncr, 1987; Wissmath, 1982 Camerlynck et al., 1986; Kowalenko, 1984

Changes in macro- and micro-nutrients and in Sc, Cs, Zn, Fc Solubilizatioii and removal of small particles; reproducible removal ;leaching of P, Mg, K ; spccial conditions for Cd

Cainerlynck et al., 1986; Hagcmeyerct al., 1985; Kcller and Preis, 1967; Little, 1973; Lindberg and Harris, 1981 ; Wallacc et al., 1980; Wood and Bormann, 1975; Yainada ct al., 1964 Martiiicz et al., 1971 ; Pricbe et al.. 1981

Sumplinl: cf Vusculur Plunts

40 5

observation is that the same treatment applied to the same plant by different authors often gives different results. Nevertheless, we will try to present (as far as possible) the status of washing pretreatment of plants in biomonitoring systematically. With conifers these problems have been studied and clarified rather well. Investigating the major and trace element contents of Picea abies Wittenbach et al. (1985) used a cleaning procedure of an 1 min. treatment with chloroform and rinsing with water. A considerable amount of the waxy material was removed from the needles, one forth of which was attributed to mineral substances. The elements were divided in four groups according to the fraction in the removed material. The highest content was established for the dust and soil forming elements Al, Fe, V, Sc, As and Sb (about 80%). The second group included Br, Co and Na (with 45-50% in the removed surface fraction) followed by Ca, C1, Mg, Mn and Zn ( 1 - l0Y0). The smallest content (below 1%) was established for K. On this basis the authors conclude that washing with chloroform does not remove elements from the interior of the needles (negligible K) and that the values obtained without washing are often more representative for the dust than for the plant itself, and therefore are useless which is especially true for trace elements. For older needles the situation is even worse. In a series of papers Krivan et al. (1986, 1987a, b, 1989) devote their attention to the different washing procedures of needles. Comparison is performed between unwashed needles, and treated with water or chloroform or by the stripping technique. The result proved that for the elements B, Ba, Ca, Co, Cr, K, Mg, Mn, N, Rb, S and Zn the difference between treated and untreated needles is insignificant. Treatment with water removes Al, Pb, Sb, Th, Fe and Sc to a different extent. The effect is deepened by using chloroform and the results are more reproducible. Hartmann and Baechmann (1988) investigated the same problem using similar washing procedures (treatment of needles with chloroform for I min, and with water for 2.5, 10 and 20 min). The elements were divided in 3 groups: -

-

K, Rb and Mn were more efficiently removed by water; Ca and Zn were similarly removed by water and chloroform; Pu and Fe were efficiently removed only by chloroform.

The most interesting result of all these experiments is that needles react quantitatively to atmospheric pollution and that the deposited layer on the surface is representative. It was proved that the wax layer works as a collector of atmospheric deposits (Krivan, 1986) while after its removal the content of Cu, Fe, Pb, Sb, etc. in the tissue remains relatively constant in needles of different age, and even (for some of the elements) in needles from different sites and soils. Therefore, it is quite obligatory in the case of physiological and toxicological investigations with conifers to analyze separately the deposited and the tissue fraction. Since most of the experiments prove that water treatment of the needles is not very effective. (Even Krivan and Schafer (1 989) reported that heavy raining did not cause differences in the deposited matter.) Treatment with chloroform is the choice in these cases. As far as washing for biomonitoring is concerned on the basis of the results reported by Krivan and Schaldach (1 986), Krivan and Schifer (1989) and Wyttenbach et al. (1985) it seems that such severe treatment is not obligatory.

406

R. D J ~ I I ~ trnd OUU I. Kirleff

A number of cleaning techniques have been used i n the analysis of dcciduous tree leaves and bushes. Rinsing with distilled water (Schodeller, 1967; Schuck and Locke, 1970; Lerche and Breckle, 1974; Carlson et al., 1976; Ernst, 1972, 1978; Fidora, 1972; Hagemeyer et al., 1986; Wissmath, 1982; Wagncr, 1987; Deu and Kreeb, 1993; Aidid, 1988), mechanical treatment for removal of adhering particles (Buchauer, 1973; Hall et al., 1975; Wallace 1980), treatment with acid solutions (Keller and Preis, 1967; Little, 1973; Lindberg and Harris, 1981; Wallace, 1980; Yamada etal., 1964; Wood and Bormann, 1975) and with complexing agents (Priebe et al., 1981; Martinez et al., 1971) have been tested. The behavior of Pb during the different washing procedures has been intensely investigated and the majority of authors report a 50- 80% decrease in concentration even after simple rinsing with water (Martin and Couphtrey, 1982; Carlson et al., 1976; Ernst, 1972, 1978; Wissmath, 1982)but lower values also have been established in a number of cases (Goodman and Roberts, 1971 ; Leh, 1965, Pfeilsticker and Markard, 1975; Wagner, 1987; Deu and Kreeb, 1993). The changes of other clcments, heavy metals and nutrients have been shown to be not that drastic as of P b after cleaning. Kowalenko (1984) found differences in the analysis of washed and unwashed filbert orchard leaves for macro- and micronutrients, except for K and Ca. Since washing reduced leaf weight, contamination was attributed to particulate matter, washing including scrubbing with a soft brush using phosphate free soap, followed by two rinses and a final rinse with deionized water. Kowalenko (1 984) concluded that washing filbert leaves prior to analysis is essential for proper interpretation of Concentration data. Wagner (1987) analyzing Populus nigru leaves applied three 30 s rinses, the last with deionized water and established removal of Pb up to 40% and no significant changes for Zn and Cd. Finally Wagncr (1987) analyzed unwashed leaves. Hagemeyer et al. (1985) found only a minor effect in lowering the adsorbed fraction of Cd off the leaves after rinsing with demineralized water at pH 5.5. Cadmium was most efficiently removed from the leaves of Fugus silvutica L. eithcr at low pH (3.5) or high pH (9.0). Deu and Kreeb (1993) established a significant reduction of Fe in orchard tree leaves (Mulus purnilu, Pyrus communis and Prunus cerusiferu) after washing with tap water. Lead and copper were reduced only in Prunus cerasifkra while Mn and Zn were unaltered in all thrce specics. After comparison of washed and unwashed lcavcs from trccs in polluted and unpolluted areas the authors concluded that metals are easily deposited on the leaves of Prunus cerusiferu but adherence is weak and most probably metals are not adsorbed by the leaves, while the behavior of Mu1u.s puwiila is opposite. Aidid ( 1 988) investigated the difference between washed and unwashed leaves for 17 elements in two families of tropical plants. Washing included 3 rinses with doubly destilled water followed by soaking in doubly destilled water for 30 min. Thc cffcct of washing was more significant for Sb, Zn, Cs and Fe but not for toxic elements as As, Hg, Au, Br, etc. The cleaning was chosen as to resemble the way Malaysian housewives prepare vegetables for cooking.

Snmpling

of

Vnsculuv Plant.\

401

Obviously washing only with water is not very effective in removing heavy metals deposited on the surface of leaves of deciduous trees. Little (1973), however, demonstrated that washing with dilute nitric acid is advantageous in solubilization of small particles and that acidification leads to reproducible results. Yamada et al. (1964) presumed that heavy metals may be held on the ion-binding sites on the cuticle and therefore may be removed only by acidified agents. Wallace et al. (1980) compared the effects of: -

wipe with cloth; wipe plus detergent and acid (0.1 N HCI) wash; acid wash; wipe and acid wash, followed by washing with deionized water; a control untreated group of orange leaves.

Ca, Na and Cu and Mn were not changed by any procedure. P, Mg and K were leached by severe washing. Al, Fe, Si, Ti and Pb were removed to a different extent by all washing procedures but most reproducible results were obtained after wiping with acid. An example of quite a different technique of surface cleaning is the investigation of Capannesi et al. (1993) of 23 elements in Quercus ilex which proved (by microscopic determination) that blowing with a strong nitrogen jet is sufficient to clean off all adhering particles. This brief review demonstrates that cleaning deciduous tree leaves from adhering particles and deposited material is rather complicated and obviously dependent on the type and structure of the leaves, the type and form of the pollutants, etc. This is why the effect of one and the same procedure may be different when applied to various tree leaves. It seems that weak acid wiping is a promising cleaning technique in these cases. Attention may be paid to the proposed nitrogen jet blowing (Capannesi et al., 1993). The analysis of unwashed herbage material is further complicated not only by the foliar deposition problems already discussed, but mostly by the possibility of soil contamination. It is well known (though often disregarded) that due to rain splash or especially in areas with high soil concenrations of heavy metals soil contamination of herbage samples can be very problematic (Martin and Couphtrey, 1982). Usually, to assess the possible soil contamination, the determination of elements with a high soll/plant ratio is recommended (Mitchell, 1960; Fleming et al., 1986). As such elements Ti, Al, Fe, Cr and Co are being recognized (e.g., Berrow, 1988). In most investigations, however, only elements as Pb, Cd, Zn, Cu are determined and no assessment of the soil/plant ratio and of contamination is performed. According to Klocke and Riebartsch (1964) simple washing with water removes more than 80% of Pb from herbage. However, Garber (1970) established only 40% removal of Pb in this way. On the other hand Bacso et al. (1984) report a decrease of Pb and Br below 10% (with maximum 20% in single cases) after 5 min of shaking in deionized water of Lolium perenne, Trifolium, Taruxucum oficinale, Polygonurn Liviculure, Quercus rohur and Plutanus. Investigating the contamination of roadside vegetation with Pb, Cd and Zn, Fytianos et al. (1985) report a decrease of up to 60% in Zn and up to 40% in Pb

R. Qjingova and 1. KuIcfi

408

after simplc water rinsing of grass samples. N o significant changes in the Cd content were detected. The difference of washed and unwashed samples is strongest nearest to the road and diminishes with the distance from its edge. Cammerlynck et al. (1986) make the following recommendations for smaller leaves: washing with deionized water and detergent or only with destilled water; wiping with dry or damp cloth; - dipping for 30 s in 0.01 N HCI;

-

-

and for larger leaves: -

wiping with dry or damp cloth; dipping in 0.01 N HCl.

A comparison of the results from the analysis of Turaxacum offkinale leaves washed with tap and destilled water (for I min); dipped for 30 s in dilute nitric acid (pH = 3); - untreated;

-

-

is presented in Fig. 3.

r

50 ffl I-

-

unwa#hed

z 3

w

-

w a s h e d w i t h water

with d i l u t e d acid

t

40

> Id J

w

30

[2

z z

0

20

I-

d

rr: I-

z

10

w 0

z 0 0

Ca

Fig. 3.

Cd

Cu

Fe

K

Mn

Na

Pb

Effects of diffcrcnt washing procedures on Turcc.uucum officinule leaves.

Sampling of Vusculur Plunts

409

The samples were collected in the city of Sofia (Bulgaria). Washing with water brings reduction in the contents of Pb and Mn (about 25%) and practically no changes in the contents of Cu, Cd, K, Na and Ca. The acid treatment reduces strongly the contents of all determined elements with the exception of Cd. The significant decrease of K and Ca in this case means that most probably there is leaching from the interior of the leaves and not only removal of foliar deposition and contamination. The washing of samples is, in our opinion, closely related to a problem that has been mentioned several times in this contribution, namely the changes that may appear due to intensive and/or prolonged rainfalls. These changes have been acknowledged and discussed sparingly (Rentschler, 1977; Martin and Couphtrey, 1982). The effect of rain on the heavy metal concentrations of plants used in biomonitoring studies has two aspects. Firstly, it washes off part of the deposited metals from the surface of the leaves. Cawse (1980) reports mean water solubilities of Zn, Se and Br of 80% due to rain water, of A1 and Fe of 25%. On the other hand, as Lindberg and Harris (1981) point out, rain waters are enriched with heavy metals originating from deposited particles. Especially acid rain (Heinrichs and Mayer, 1980) plays an important role in the cycling of elements by transforming them into soluble forms and increasing their availability to plants. This, however, is rather a long-term effect. The immediate result from rainfalls in sampling may be generally detected as a decrease in the content of some elements due to washing off. As a result of this, comparison of the results for samples collected after draught and raining periods different in duration is not possible or very risky. In practice this has been investigated by Ho and Tai (1979) who determined the lead content in the leaves of Alocasia odora and Mikania guaco in dependence on rainfalls during a four months period. Two weeks of dry period preceded the first sampling, after only 13 mm of rain the content of Pb dropped 3 times and resumed its initial content after 6 days. At the end of spring after heavy rainfalls the lead content reached stable values which was assumed to be the lead content of the plants themselves or the so called “non-washable” fraction that corresponds to the portion of lead that cannot be washed with distilled water (Motto et al., 1970; Page, 1971; Little and Wiffen, 1977) or by simulated rain (Carlson et al., 1976). Thus Ho and Tai (1979) accept that the effect of rain is equal to the effect of water washing and simultaneously recommended sampling after at least one week of drought perioid (but not washing of the samples). The data from our experiments with Taraxacum qficinale collected in Sofia (Bulgaria) after 3 days of heavy rainfall are presented in Fig. 4, in comparison to samples collected the day before the rainfall after a 3 weeks dry period. Part of the latter are washed by tap and distilled water and the rest are analyzed without washing. It is obvious that the values for all elements are very similar between the water washed samples and those collected after the rainfalls (three days later). In both cases the values for Pb and to a certain extent Mn and Fe are lower than in the unwashed material, while Cd, Ca, K, Cu and Na are stable. Therefore, washing of herbage with tap and distilled water seems to reduce soil contamination and rain effects while acid treatment most probably leaches elements from the inside of the leaves or generally as Benton Jones (1971) pointed out “rinsing

R . Dlingoi:~utid I. Kulrff

410 50 v)

=

0after rain

washed with water

unwashed

kb.-

z 2 W

40

-

>

c a

-I W

30

[L

z M

z 0 20 3

F Q [L

t

ia W

0

z 0 0

0 Ca

Fig. 4.

Cd

Cu

Fe

K

Mn

Na

Pb

Cornparisoil of washing and rain cffccts on thc analytical rcsults or TU~USUCUIIZ leaves.

in running water will remove most attached substances. The washing procedure should not be prolonged or the plant material allowed to ‘stand’ in either washing or rinsing bath”.

21.5 Discussion The aim of this necessarily incomplete survey has been to stress the complexity and controversity of the problems concerning representative sampling of vascular plants for biomonitoring purposes. We have only discussed problems concerning differences (i.e., sources of error) due to individual and compartment biological levels, time of sampling and pretreatment of the samples (which in our opinions is definitely a sampling step since in most cases it should be done immediately after sampling in the fields and its being done or not changes the meaning of results), all of which are in a way connected to a very important problem in biomonitoring studies, namely what type ofhcavy metal pollution can be determined by using vascular plants - aerial or soil or both‘? As was mentioned already in the text the usual approach is to accept that deposition onto the leaf surface is representative of aerial pollution and the

Sumpling of' Vasculur Plunts

41 1

concentrations inside the plant of the soil conditions. If this assumption is accepted then sampling may turn out not to be such a difficult task after all. Generally, then it might be recommended to collect leaves (they are mostly subjected to deposition), just before scenescence (in most cases the deposited particulate matter is highest at that time) and to apply severe removal of all deposited particles to be analyzed for aerial pollution. However, such an approach may be contradicted by two arguments. Firstly, there is no case where continuous air pollution has not lead to simultaneously high soil pollution. (Elevated concentrations in soil without respective air pollution are well known but this is more in the line of geobotanical and geochemical studies.) Combining this fact with the mixed mechanism of accepting nutrient and polluting elements of vascular plants it becomes quite clear that in most cases the above mentioned division of aerial/soil pollution reflected by plants is too gross a simplification. Additionally, if rainfalls and other metereological conditions are considered the question becomes even more complicated. Therefore, we have tried to stress in the text that leaves are not always the organs collecting highest concentrations of pollutants and for biomonitoring other organs may be more useful in some cases. But if, for the sake of argument, it is accepted that the division into aerial and soil pollution is correctly represented by on/in plant concentrations (such cases have been reported) then ofcourse a question may be posed. Is it worth using plants as passive collectors of aerial pollution? Is it not much simpler and representative to use directly fallout sampling procedures and analysis? The main advantage of biomonitoring over direct methods for monitoring is that it gives an excellent, representative and relatively cheaper opportunity for assessment of the conditions of the ecosystem and prognostication for its future development, and for risk assessment. Additionally, it enables relatively cheap large-scale screening. All this means that data should be collected for what is in and fixed on the plant but these data should be reproducible, representative for the ecosystem and comparable both within the ecosystem and outside it, which leads to the big question of the pretreatment. While it is already possible to give some general recommendations concerning the minimum number of individuals to be sampled, the plant organs (for certain species), the season of sampling, etc. it is yet extremely difficult (if not impossible) to recommend a general procedure for cleaning. Though the purpose of cleaning in biomonitoring has been divided and subdivided into different points in fact, in our opinion, it is to remove contamination and loosly adhering particles so as to get reproducible, representative and comparable results. Therefore, cleaning should lead to the removal of direct soil contaminations in grasses and herbage and of soil-dust contaminations on tree leaves both of which are dependent on the type and state of soil, local metereological conditions and are easily changable by rainfalls. Evidence for such contaminations are higher and changable after water treatment concentrations of elements like Sc, Th, Al, Fe etc. Pretreatment should more or less copy the effect of metereological conditions leading to changes of the deposited fraction and to make results independent of the local factors. This does not simplify very much the question but it gives a direction in which to search for unification.

412

R. Djingoau and I . Kukff‘

The view we are trying to present here is that in order to prcscrvc the main advantages of biomonitoring over direct methods, namely a relatively quick, cheap and most of all representative and comparable evaluation of the state of ecosystems, pretreatincnt should be carried out as to remove contamination and losely adhering particles more or less modelling the action of meteorological factors. (This is not valid for physiological and food-chain investigations.) In many cases, this may turn out to be only water treatment or gas blowing but to come to a conclusion in this respect it is obligatory to investigate parallel to the usually studied heavy metals the behavior during washing of elements like La, Sc, Sm, Th, A1 etc. In practice this means that for a monitoring investigation i t is not sufficient to determine only the content of Pb, Cd, Hg, Zn etc. but also of “neutral” elements as the rare earths, Sc, etc. Their content in/on the plant is dependent on the type of soil, and the dust content of air. This undoubtedly implies a new approach for biomonitoring studies. Obviously the analytical methods routinely used up to now (e.g., AAS) do not provide such a possibility and combinations with other analytical techniques should be looked for. Only after that, after correct evaluation of what part of soil particles and dust are reliably removed by the different media off different plant types a more definite conclusion about the most appropriate pretreatment of plants may be achieved.

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Sampling .f' Vascular Plants

41 3

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Sunipling in StemfloM’ and Throughfall Areus

46 1

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