Reviews of Environmental Contamination and Toxicology 204 1441914390, 9781441914392

Citation preview

Volume 204

Reviews of Environmental Contamination and Toxicology David M. Whitacre Editor

Reviews of Environmental Contamination and Toxicology VOLUME 204

For further volumes: http://www.springer.com/series/398

Reviews of Environmental Contamination and Toxicology Editor

David M. Whitacre

Editorial Board Maria F. Cavieres, Valparaiso, Chile • Charles P. Gerba, Tucson, Arizona, USA John Giesy, Saskatoon, Saskatchewan, Canada • O. Hutzinger, Bayreuth, Germany James B. Knaak, Getzville, New York, USA James T. Stevens, Winston-Salem, North Carolina, USA Ronald S. Tjeerdema, Davis, California, USA • Pim de Voogt, Amsterdam, The Netherlands George W. Ware, Tucson, Arizona, USA

Founding Editor Francis A. Gunther

VOLUME 204

123

Coordinating Board of Editors D R . DAVID M. W HITACRE , Editor Reviews of Environmental Contamination and Toxicology 5115 Bunch Road Summerfield North, Carolina 27358, USA (336) 634-2131 (PHONE and FAX) E-mail: [email protected] D R . H ERBERT N. N IGG , Editor Bulletin of Environmental Contamination and Toxicology University of Florida 700 Experiment Station Road Lake Alfred, Florida 33850, USA (863) 956-1151; FAX (941) 956-4631 E-mail: [email protected] D R . DANIEL R. D OERGE , Editor Archives of Environmental Contamination and Toxicology 7719 12th Street Paron, Arkansas 72122, USA (501) 821-1147; FAX (501) 821-1146 E-mail: [email protected]

ISSN 0179-5953 ISBN 978-1-4419-1439-2 e-ISBN 978-1-4419-1440-8 DOI 10.1007/978-1-4419-1440-8 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2009911931 © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Foreword

International concern in scientific, industrial, and governmental communities over traces of xenobiotics in foods and in both abiotic and biotic environments has justified the present triumvirate of specialized publications in this field: comprehensive reviews, rapidly published research papers and progress reports, and archival documentations. These three international publications are integrated and scheduled to provide the coherency essential for nonduplicative and current progress in a field as dynamic and complex as environmental contamination and toxicology. This series is reserved exclusively for the diversified literature on “toxic” chemicals in our food, our feeds, our homes, recreational and working surroundings, our domestic animals, our wildlife, and ourselves. Tremendous efforts worldwide have been mobilized to evaluate the nature, presence, magnitude, fate, and toxicology of the chemicals loosed upon the Earth. Among the sequelae of this broad new emphasis is an undeniable need for an articulated set of authoritative publications, where one can find the latest important world literature produced by these emerging areas of science together with documentation of pertinent ancillary legislation. Research directors and legislative or administrative advisers do not have the time to scan the escalating number of technical publications that may contain articles important to current responsibility. Rather, these individuals need the background provided by detailed reviews and the assurance that the latest information is made available to them, all with minimal literature searching. Similarly, the scientist assigned or attracted to a new problem is required to glean all literature pertinent to the task, to publish new developments or important new experimental details quickly, to inform others of findings that might alter their own efforts, and eventually to publish all his/her supporting data and conclusions for archival purposes. In the fields of environmental contamination and toxicology, the sum of these concerns and responsibilities is decisively addressed by the uniform, encompassing, and timely publication format of the Springer triumvirate:

Reviews of Environmental Contamination and Toxicology [Vol. 1 through 97 (1962–1986) as Residue Reviews] for detailed review articles concerned with any aspects of chemical contaminants, including pesticides, in the total environment with toxicological considerations and consequences. v

vi

Foreword

Bulletin of Environmental Contamination and Toxicology (Vol. 1 in 1966) for rapid publication of short reports of significant advances and discoveries in the fields of air, soil, water, and food contamination and pollution as well as methodology and other disciplines concerned with the introduction, presence, and effects of toxicants in the total environment. Archives of Environmental Contamination and Toxicology (Vol. 1 in 1973) for important complete articles emphasizing and describing original experimental or theoretical research work pertaining to the scientific aspects of chemical contaminants in the environment. Manuscripts for Reviews and the Archives are in identical formats and are peer reviewed by scientists in the field for adequacy and value; manuscripts for the Bulletin are also reviewed, but are published by photo-offset from camera-ready copy to provide the latest results with minimum delay. The individual editors of these three publications comprise the joint Coordinating Board of Editors with referral within the board of manuscripts submitted to one publication but deemed by major emphasis or length more suitable for one of the others. Coordinating Board of Editors

Preface

The role of Reviews is to publish detailed scientific review articles on all aspects of environmental contamination and associated toxicological consequences. Such articles facilitate the often complex task of accessing and interpreting cogent scientific data within the confines of one or more closely related research fields. In the nearly 50 years since Reviews of Environmental Contamination and Toxicology (formerly Residue Reviews) was first published, the number, scope, and complexity of environmental pollution incidents have grown unabated. During this entire period, the emphasis has been on publishing articles that address the presence and toxicity of environmental contaminants. New research is published each year on a myriad of environmental pollution issues facing people worldwide. This fact, and the routine discovery and reporting of new environmental contamination cases, creates an increasingly important function for Reviews. The staggering volume of scientific literature demands remedy by which data can be synthesized and made available to readers in an abridged form. Reviews addresses this need and provides detailed reviews worldwide to key scientists and science or policy administrators, whether employed by government, universities, or the private sector. There is a panoply of environmental issues and concerns on which many scientists have focused their research in past years. The scope of this list is quite broad, encompassing environmental events globally that affect marine and terrestrial ecosystems; biotic and abiotic environments; impacts on plants, humans, and wildlife; and pollutants, both chemical and radioactive; as well as the ravages of environmental disease in virtually all environmental media (soil, water, air). New or enhanced safety and environmental concerns have emerged in the last decade to be added to incidents covered by the media, studied by scientists, and addressed by governmental and private institutions. Among these are events so striking that they are creating a paradigm shift. Two in particular are at the center of everincreasing media as well as scientific attention: bioterrorism and global warming. Unfortunately, these very worrisome issues are now superimposed on the already extensive list of ongoing environmental challenges. The ultimate role of publishing scientific research is to enhance understanding of the environment in ways that allow the public to be better informed. The term “informed public” as used by Thomas Jefferson in the age of enlightenment vii

viii

Preface

conveyed the thought of soundness and good judgment. In the modern sense, being “well informed” has the narrower meaning of having access to sufficient information. Because the public still gets most of its information on science and technology from TV news and reports, the role for scientists as interpreters and brokers of scientific information to the public will grow rather than diminish. Environmentalism is the newest global political force, resulting in the emergence of multinational consortia to control pollution and the evolution of the environmental ethic. Will the new politics of the 21st century involve a consortium of technologists and environmentalists, or a progressive confrontation? These matters are of genuine concern to governmental agencies and legislative bodies around the world. For those who make the decisions about how our planet is managed, there is an ongoing need for continual surveillance and intelligent controls to avoid endangering the environment, public health, and wildlife. Ensuring safety-in-use of the many chemicals involved in our highly industrialized culture is a dynamic challenge, for the old, established materials are continually being displaced by newly developed molecules more acceptable to federal and state regulatory agencies, public health officials, and environmentalists. Reviews publishes synoptic articles designed to treat the presence, fate, and, if possible, the safety of xenobiotics in any segment of the environment. These reviews can be either general or specific, but properly lie in the domains of analytical chemistry and its methodology, biochemistry, human and animal medicine, legislation, pharmacology, physiology, toxicology, and regulation. Certain affairs in food technology concerned specifically with pesticide and other food-additive problems may also be appropriate. Because manuscripts are published in the order in which they are received in final form, it may seem that some important aspects have been neglected at times. However, these apparent omissions are recognized, and pertinent manuscripts are likely in preparation or planned. The field is so very large and the interests in it are so varied that the editor and the editorial board earnestly solicit authors and suggestions of underrepresented topics to make this international book series yet more useful and worthwhile. Justification for the preparation of any review for this book series is that it deals with some aspect of the many real problems arising from the presence of foreign chemicals in our surroundings. Thus, manuscripts may encompass case studies from any country. Food additives, including pesticides, or their metabolites that may persist into human food and animal feeds are within this scope. Additionally, chemical contamination in any manner of air, water, soil, or plant or animal life is within these objectives and their purview. Manuscripts are often contributed by invitation. However, nominations for new topics or topics in areas that are rapidly advancing are welcome. Preliminary communication with the editor is recommended before volunteered review manuscripts are submitted. Summerfield, NC, USA

David M. Whitacre

Contents

Bioconcentration, Bioaccumulation, and Metabolism of Pesticides in Aquatic Organisms . . . . . . . . . . . . . . . . . . . . . Toshiyuki Katagi

1

Electron Transfer as a Potential Cause of Diacetyl Toxicity in Popcorn Lung Disease . . . . . . . . . . . . . . . . . . . . . . . . . . Peter Kovacic and Andrew L. Cooksy

133

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

149

ix

Contributors

Andrew L. Cooksy Department of Chemistry and Biochemistry, San Diego State University, CA 92182-1030, USA; Centro de Graduados e Investigacion del Instituto Tecnológico de Tijuana, Tijuana, B.C. México, [email protected] Toshiyuki Katagi Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., Takarazuka, Hyogo 665-8555, Japan, [email protected] Peter Kovacic Department of Chemistry and Biochemistry, San Diego State University, San Diego, CA 92182-1030, USA, [email protected]

xi

Bioconcentration, Bioaccumulation, and Metabolism of Pesticides in Aquatic Organisms Toshiyuki Katagi

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . Bioconcentration . . . . . . . . . . . . . . . . . . . . . 2.1 Controlling Factors . . . . . . . . . . . . . . . . . 2.2 Theoretical Approach . . . . . . . . . . . . . . . . 2.3 Pesticides and Other Chemicals . . . . . . . . . . . . 3 Bioaccumulation . . . . . . . . . . . . . . . . . . . . . 3.1 Controlling Factors . . . . . . . . . . . . . . . . . 3.2 Bioaccumulation of Pesticides and Theoretical Approach 4 Metabolism . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Metabolic Patterns . . . . . . . . . . . . . . . . . . 4.2 Enzymes . . . . . . . . . . . . . . . . . . . . . . 4.3 Metabolism of Pesticides and Other Chemicals . . . . . 5 Behavior of Pesticides in Larger-Scale Systems . . . . . . . . 5.1 Model Ecosystems . . . . . . . . . . . . . . . . . 5.2 Microcosms and Mesocosms . . . . . . . . . . . . . 6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2

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

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

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

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

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

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

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

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

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

1 3 3 19 23 42 42 49 51 51 52 79 91 92 96 97 103

1 Introduction From the viewpoint of protecting the natural environment, aquatic ecotoxicological assessment of new pesticides and many existing ones has increasingly become more important. To assess the impact of pesticides on aquatic organisms, international T. Katagi (B) Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., Takarazuka, Hyogo, 665-8555, Japan e-mail: [email protected] D.M. Whitacre (ed.), Reviews of Environmental Contamination and Toxicology Volume 204, Reviews of Environmental Contamination and Toxicology 204, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-4419-1440-8_1, 

1

2

T. Katagi

authorities (utilizing OECD and USEPA testing guidelines) require completion of many acute and chronic ecotoxicological studies. Among such studies is testing to measure the potential for bioconcentration. In addition, the authorities in these agencies insist that physico-chemical properties and environmental fate be determined for each registered pesticide. The rationale for such testing is based on the concept that, even if used in conformance with good agricultural practices, pesticides may enter surface waters by several routes such as spray drift, surface runoff, and field drainage, and they may be partitioned to bottom sediments (Katagi 2006). The endpoints of such ecotoxicological testing include mortality and effects on hatching, development, and reproduction. Such endpoints are usually expressed as median-lethal or median-effect concentrations (LC50 and EC50 ) and no-observed-effect-concentrations (NOEC); such values can be compared with predicted environmental concentrations in exposure media for purposes of risk assessment (Miyamoto et al. 2008). Because aquatic organisms interact with each other in the food web, knowledge of their tendency to bioconcentrate residues in water and from dietary exposure is important when evaluating real environmental pesticide effects. In general, bioconcentration is the most popular term for describing the process by which pesticides enter organisms directly from water through the gills or through epithelial tissues. In contrast, bioaccumulation includes the effect of dietary uptake through food consumption or intake of bottom sediments (Miyamoto et al. 1990). When the levels of a pesticide, accumulated by organisms, are concentrated through two or more trophic levels in a food web, the process is referred to as biomagnification (Connell 1988). Although information on ecotoxicity and bioconcentration is required and is useful from the regulatory standpoint, such information is also important for its use in evaluating complex processes and their outcomes. Such processes include interactions of pesticide molecules with dissolved and particulate components in water, uptake of pesticides from water into organisms, followed by elimination or metabolic transformation and storage as original or altered residues in tissues (Barron 1990; Connell 1988; Miyamoto et al. 1990). Although considerable amounts of relevant ecotoxicological data for pesticides are available from the ECOTOX database (USEPA 2007), information on pesticides in non-target aquatic organisms, other than fish, is rather limited. Furthermore, research on the metabolic profiles of pesticides in aquatic organisms, together with data on enzymes responsible for pesticide catabolism, has been mainly conducted on fish (James 1994; Schlenk 2005). It is my goal in this chapter to first address the bioconcentration and bioaccumulation profiles of pesticides and selected other chemicals known to exist as pollutants in aquatic organisms (other than in fish). I will do this by addressing the theoretical approach necessary to understand the mechanism by which uptake that results in bioconcentration or bioaccumulation occurs. The methods used to determine bioconcentration will also be discussed. Second, I will summarize the metabolic profiles for several pesticides and other chemicals in aquatic organisms and include a review of the enzymes involved in pesticide metabolism. In this chapter, I will only briefly address the process of biomagnification via food web uptake,

Bioconcentration, Bioaccumulation, and Metabolism

3

and, when I do, it will be in the context of either dietary exposure or higher-tier studies (e.g., model ecosystems). The structures of selected chemicals and pesticides, whose impact on aquatic organisms are discussed in this chapter, are presented in Appendices 1–6.

2 Bioconcentration 2.1 Controlling Factors The bioconcentration of pesticides and other chemicals into aquatic organisms mainly proceeds by passive diffusion through gills, epithelial tissues, or the gastrointestinal tract as shown in Fig. 1a. Bioconcentration is primarily

Fig. 1 Flow and transfer of chemicals in an aquatic system in relation to bioconcentration and bioaccumulation. (a) Conceptual flow diagram. DOM, dissolved organic matter; POM, particulate organic matter. (b) Kinetic model of bioconcentration: C, concentration; k, first-order rate constant; p, parent molecule; m, metabolite; w, water phase; b, biota phase; s, slowly exchangeable peripheral sites in biota; U, uptake; M, metabolism; E, elimination; G, growth

4

T. Katagi

controlled by the physico-chemical properties of the chemicals involved, the physiological disposition of each organism, and the surrounding environmental conditions (Barron 1990; Connell 1988; Landrum and Fisher 1998; Miyamoto et al. 1990). 2.1.1 Physico-chemical Properties Because chemicals first pass through a diffusion barrier, such as mucus and biological membranes, to reach circulating fluids, relative solubilities of such chemicals in water and n-octanol may act as a surrogate for lipids. Another factor, also important, is molecular size, which may simulate partitioning and diffusion processes in lipid-containing tissues. Lipid solubility and molecular size both affect the tendency to bioconcentrate. In general, the bioconcentration factor (BCF) is defined by Eq. (1). Cpb and Cpw constitute the concentrations of a chemical in biota and water, respectively. In the traditional hydrophobicity model, the good relationship that exists between BCF and the n-octanol/water partition coefficient (Kow ) or water solubility (WS) has been established for various combinations of chemical classes and fish. This relationship is described in Eq. (2), wherein a and b are constants (Ellgehausen et al. 1980; Mackay 1982; Neely et al. 1974; Veith et al. 1979): BCF = Cpb /Cpw .

(1)

log BCF = a log Kow (or WS) + b.

(2)

The correlation between BCF and physico-chemical properties has been reported for many aquatic organisms and is presented in Table 1. Rather than Kow and WS, Govers et al. (1984) successfully applied the molecular connectivity index, which is known to encode structural features that correlate with hydrophobicity to describe BCF value of polycyclic aromatic hydrocarbon (PAH) substances in Daphnia pulex. The coefficient of correlation (r2 ) between the two approaches is generally greater than 0.7–0.8, but lower values are observed when more than one chemical class or aquatic species are taken into account (Axelman et al. 1995; Hawker and Connell 1986; Mailhot 1987; Zaroogian et al. 1985). Manthey et al. (1993) reported a moderate correlation for a series of urea herbicides in Chlorella fusca; both hydrophobicity and other factors such as metabolism played a role in this study. The strong relationship between BCF and log Kow is shown clearly in Fig. 2. This plot shows the correlation between fish bioconcentration and log Kow for various pesticides registered in the past decade, and is based primarily on data available from the European Food Safety Authority (EFSA 2008) and the European Commission (EU 2008). In aquatic macrophytes, Turgut (2005) reported the good relationship of the concentration factor of a chemical in shoots of parrot feather with its Kow value; good relationships are also found for the concentration factors in transpiration stream and root in terrestrial plants.

Organochlorines and pesticides Herbicides Insecticides Pesticides Chlorinated aromatics

Organochlorines and pesticides Chlorobenzenes Organochlorines and PAH PAH PAH Pesticides, PAH, PCB PAH

Molluscs (four species)

Crassostrea virginica

10 4

PAH PAH

Pontoporeia hoyi

3.4–6.0 4.5–6.0

4.5–7.0

na 3.3–6.1 0.9–6.7 3.3–5.8

6 7 52 11 6

4.1–5.7 1.8–6.2

PAH

0.69

0.844 log Kow − 1.235, k

0.88

1.1 log Kow − 1.8, k

0.93 0.99

0.89 0.85 0.91 0.89

4.82 3 χc v + 1.276 0.752 log Kow − 0.4362 0.850 log Kow − 1.10 0.7207 log Kow − 0.334

0.38 log Kow + 3.78 0.65 log Kow + 1.8

0.97 0.93

1.76 log Kow − 6.33 0.898 log Kow − 1.315, k

0.29

0.92 0.96 0.91 0.87

r2d

Equation (log BCF =)c

2–6 −0.58 log WS (ppb) + 4.5 3.4–6.0 0.66 log Kow − 0.05 1.7–6.2 0.858 log Kow − 0.808 0.1–4 −0.843 log WS (ppb) + 5.15 Crassostrea virginica 3.9–6.5 0.72 log Kow + 0.41

4.0–7.8

Rangeb

4 22

17

4 6 16 17

34

na

Daphnia magna Daphnia pulex and magna Asellus aquaticus

Lymnaea stagnalis Daphnia pulex

Mytilus edulis

Mytilus edulis

Chemical class(s)

Species

van Hattum and Cid Montanes (1999) Curto et al. (1993) Landrum (1988)

Legierse et al. (1998) Hawker and Connell (1986) Govers et al. (1984) Southworth et al. (1978) Geyer et al. (1991) Axelman et al. (1995)

Zaroogian et al. (1985)

Hawker and Connell (1986) Watanabe et al. (1985) Zaroogian et al. (1985) Geyer et al. (1982) Ernst (1977)

References

Table 1 Correlation of bioconcentration factor (BCF) with physico-chemical properties for pesticides and other chemicals

Bioconcentration, Bioaccumulation, and Metabolism 5

Chlorobenzenes and PCB Pesticides, PCB Chlorobenzenes and PCB Pesticides and organics Urea herbicides Pesticides

Organochlorines and pesticides Aromatics Various chemicals

Oligochaetes (two species) Lemna minor Myriophyllum spicatum

Selenastrum capricornutum

4.1–7.1 2.1–5.2 1–7 >7

5 8 694

0.6–6.4 1.5–4.3 1.7–6.4

0.3–6.6 4.0–8.3

10 9 41 15 8

4.5–7.1

Rangeb

15

na

0.46 log Kow + 2.36 0.77 log Kow − 0.70 +  Fi − 1.37 log Kow + 14.4 +  Fi

0.83 0.73

0.64

0.81 0.56 0.93

0.681 log Kow + 0.164 0.53 log kw + 0.99 0.70 log Kow − 0.26 0.28 log Kow + 2.6

0.91 0.97

0.96

−0.75 (log Kow – 6.84)2 + 6.25 0.491 log Kow + 0.0562 0.98 log Kow – 2.24

r2d

Equation (log BCF =)c

Casserly et al. (1983) Meylan et al. (1999)

Geyer et al. (1984) Manthey et al. (1993) Ellgehausen et al. (1980) Mailhot (1987)

Lockhart et al. (1983) Gobas et al. (1991)

Connell et al. (1988)

References

b Range

of chemicals used to derive the equation. of physico-chemical properties (WS, Kow , kw ) in a logarithm unit. c BCF, bioconcentration factor on a wet weight basis, with k meaning the kinetic value; WS, water solubility with a unit in the parentheses; K , 1-octanol-water ow partition coefficient; kw , HPLC capacity factor; 3 χc v , third-order molecular connectivity index;  Fi, empirical correction factor. d Coefficient of correlation. na: not available. PAH = Polycyclic aromatic hydrocarbons; PCB = Polychlorinated benzenes

a Number

Fish

Scenedesmus acutus

Chlorella fusca

Chemical class(s)

Species

Table 1 (continued)

6 T. Katagi

Bioconcentration, Bioaccumulation, and Metabolism

7

Fig. 2 Correlation of log BCF (bioconcentration factor) and log Kow (octanol to water coefficient) in fish for pesticides developed in the most recent 10-year period

Mackay (1982) was the first to discuss the correlation of fish BCF with Kow . He used the fugacity model at thermodynamic equilibrium, as expressed in Eq. (3), wherein yL , γ , and v define the volume fraction of lipid in the organism, activity coefficient, and phase molar volume, with the subscripts of O (n-octanol phase) and L (lipid phase), respectively: log BCF = log Kow + log [yL (γO vO )/(γL vL )].

(3)

The ratio of γ O /γ L generally does not greatly change; vO is a constant and vL depends on the lipid composition of each organism (Connell 1988). A lower degree of correlation is observed when Eq. (3) is applied to all of the BCF data from many different species together, which may be partly accounted for by the span of the different vL values encountered. If n-octanol is a valid surrogate for lipid, when the (γ O vO )/(γ L vL ) term is at unity, “a” and “b” in Eq. (2) become 1 and log yL , respectively. However, the “a” value practically approaches unity (Table 1). The primary barrier for a chemical involved in passive uptake from water is the biological membrane, which consists basically of lipid bilayers, e.g., as in gill epithelium. Steric parameters such as molecular size and shape sometimes also become important factors in passive uptake (Barron 1990; Landrum and Fisher

8

T. Katagi

1998). Shaw and Connell (1984) did not demonstrate a correlation between BCFs of polychlorinated biphenyl (PCB) compounds normalized to that of 2 ,3,4,4 ,5 pentachlorobiphenyl, with their log Kow values, in mullet and the polychaete Capitella capitata. By introducing a steric effect coefficient (SEC), which effect varies with the substitution pattern of chlorine atoms, a better correlation between BCF and log (Kow × SEC) is produced. A clearer effect of molecular shape on BCF was demonstrated by Opperhulzen et al. (1985) for the bioconcentration of aromatic hydrocarbons in guppys. When the log Kow value exceeded 4, the uptake rate plateaued and the rate of elimination decreased. The BCF value was nearly constant at log Kow > 4–6, and the more hydrophobic compounds, such as hepta- and octa-chloronaphthalenes, did not bioconcentrate. By considering the average packing order in natural lipid membranes, these authors proposed a threshold cross section of 9.5 Å for molecules that can successfully pass through the membrane. A similar size effect was reported by Stange and Swackhamer (1994) for the bioconcentration of PCBs in three algae. The speciesspecific log BCF vs. log Kow plot showed a plateau near a log Kow value of 6, wherein the molecular cross section was estimated to be approximately 9 Å. The normalization of BCF with phospholipid content reduced this specificity; therefore, membrane permeability was considered to be important for uptake. Curto et al. (1993) reported a deviation from the log BCF vs. log Kow relationship for PAHs having five to six aromatic rings in the isopod Asellus aquaticus; these authors speculated that the reduced membrane permeability resulted from steric hindrance. The hydrophobicity of a chemical is well known to correlate with molecular surface area and volume (Katagi et al. 1995). Del Vento and Dachs (2002) reported the parabolic nature of the BCF values of PCB congeners in bacteria and phytoplankton, when data were plotted against the total surface area (TSA) of a molecule. The maximal BCF was observed at the TSA value of 250–270 Å2 , which corresponds to the threshold diameter of 8.9–9.3 Å, when a spherical shape of a molecule is assumed. This diameter is very close to the threshold cross section of 9.5 Å reported by Opperhulzen et al. (1985). By examining the parabolic nature of log BCF vs. log Kow plots for chemicals having various flexible structures, Dimitrov et al. (2002) proposed a maximal cross-sectional diameter (Dmax ) of a molecule that was based on molecular orbital calculations of energetically favorable conformers. Chemicals having a Dmax of less than 15 Å showed a high BCF value with an increase of log Kow , whereas those with Dmax of >15 Å were found to be accumulated up to a log BCF value of 3.3 at most. This threshold value of 15 Å was close to one half of the lipid-bilayer thickness in biological membranes. As with an increase of a molecular dimension, the solubility of a chemical in a lipid phase is considered to change the structural orientation of the molecule. The reason for this is because solvation of a chemical by lipid molecules requires more energy to structurally re-orient the molecule for fit than occurs with small molecules such as n-octanol. Florey-Higgins theory shows that the solubility of a chemical

Bioconcentration, Bioaccumulation, and Metabolism

9

is a function of molar volume and solvent, as well as solute–solvent interactions. By examining the solubilities (SL ) of 79 organic chemicals to triolein and dimyristoyl phosphatidylcholine in relation to log Kow , Chessells et al. (1992) obtained the parabolic relationship of log SL = − 0.08 + 0.52 log Kow − 0.09 (log Kow )2 (r = 0.78). When chemical concentration in fish was expressed by a polynomial function of log Kow (Connell and Hawker 1988), log BCF better correlated with log SL and n-octanol was found not to be a good surrogate for lipids over a wide range of log Kow . Isomerism of a chemical sometimes plays a role in bioconcentration. Hexachlorocyclohexane (HCH) has four isomers (α–δ), and its γ -isomer in known as lindane (30). BCFs in clam varied by isomer as follows: δ > α > β ≈ γ . However, the order for elimination was different: γ > α » δ ≈ β (Yamato et al. 1983). The corresponding values in the mussel Mytilus edulis (Ernst 1979) and bluegreen alga Anabaena sp. (Mathur and Saxena 1986) showed yet a different order; these differences indicate that species differences exist in the bioconcentration of these isomers. Species differences existed in the uptake of chlordane (24) isomers (Moore et al. 1977), and the enantio-selective metabolism of γ (or trans)-isomer accounted for the major difference in Mysis relicta between the isomers (Warner and Wong 2006). In oysters, the enantio-selective bioconcentration of flucythrinate (86) has also been addressed (Schimmel et al. 1983). The α (or I)- and β (or II)-isomers of endosulfan (27) differ in their conformation in the cycloheptyl moiety. The β-isomer has been reported to bioconcentrate more than the α-isomer in Daphnia magna (DeLorenzo et al. 2002) and in crayfish Procambarus clarkii (Naqvi and Newton 1990), probably because the β-isomer has lower metabolic transformation to the corresponding sulfate. However, the α-isomer was reported to be more highly bioconcentrated in algae (DeLorenzo et al. 2002; Rao and Lal 1987).

2.1.2 Aquatic Organisms Among the range of physiological conditions that exist in aquatic organisms, lipid content is considered to be the most important determinant for bioconcentration. The influence of lipid content on BCF is followed by extent of metabolism and excretion and developmental stage of the tested organism. Lipids are normally extracted from aquatic organisms using a chloroform–methanol mixture (Folch et al. 1957; Bligh and Dyer 1959). Chemical characterization and identification of each lipid component is usually performed using TLC (thin layer chromatography), HPLC (high-pressure liquid chromatography), GC (gas chromatography), and MS (mass spectrometry). Instead of chloroform, dichloromethane may be used to extract lipids (Booij and van den Berg 1994). Gardner et al. (1985) further developed microextraction techniques to deal with tissue samples weighing less than 10 mg. A summary of the total lipid content, on a dry or wet weight basis, of aquatic organisms is presented in Table 2.

10

T. Katagi Table 2 Lipid content of aquatic species

Species

Lipid content (%)a

References

Mussel

0.6–3.9 (w)

Oyster Clam Cockle Scallop Snail

1.5–2.8 (w) 0.8–2.3 (w) 0.6 (w) 0.5–1.1 (w) 0.4–4.4 (w)

Green alga

10–45 (d)

Blue-green alga

2.2–22 (d)

Golden alga Brown alga Diatom

7–41 (d) 1.2–5.3 (d) 7.6–24 (d)

Aquatic macrophyte Water flea

4.0–24 (d), 0.2 (w)

Bedford and Zabik (1973); Booij and van den Berg (1994) Copeman and Parrish (2004); Ernst (1979), King et al. (1990) Lin et al. (2003); Murphy et al. (2002); Pirini et al. (2000) Renberg et al. (1978); Serrano et al. (1997a) King et al. (1990) Copeman and Parrish (2004); King et al. (1990) Copeman and Parrish (2004) Copeman and Parrish (2004); King et al. (1990) Lalah et al. (2003); Legierse et al. (1998); Takimoto et al. (1987a) Thybaud and Caquet (1991) Borowitzka (1988); Canton et al. (1977); Choi et al. (1987) Halling-Sørensen et al. (2000); Kent and Currie (1995) Koelmans and Sánchez Jiménez (1994); Lu and Metcalf (1975) Rathore et al. (1993); Stange and Swackhamer (1994) Borowitzka (1988); Rathore et al. (1993) Stange and Swackhamer (1994); Sukhija et al. (1979) Borowitzka (1988) Dembitsky et al. (1990) Borowitzka (1988); Darley (1977) Stange and Swackhamer (1994) Dembitsky et al. (1992); Gobas et al. (1991)

Shrimp and crab Isopod Amphipod

Copepod Rotifer Midge Mayfly

5.5–23 (d), 0.3–2.0 (w)

Bychek and Gushchina (1999); Cowgill et al. (1984) Heisig-Gunkel and Gunkel (1982); Herbes and Allen (1983) Lu and Metcalf (1975) 10–40 (d), 1.0–2.0 (w) Cavaletto and Gardner (1988); King et al. (1990) Warner and Wong (2006) 0.3–0.7 (w) Gaskell et al. (2007); Van Hattum and Cid Montanes (1999) 2.4–43 (d), 2.3–5.3 (w) Arts et al. (1995); Cavaletto and Gardner (1988) Gardner et al. (1985); Herbes and Allen (1983) Meyer et al. (2000); Nebeker et al. (1989); Quigley et al. (1989) 8.3 (d) Herbes and Allen (1983) 1.1–1.5 (w) Guisande and Serrano (1989) 1.1 (w) Gaskell et al. (2007) 2–20 (d), 1.4–2.7 (w) Cavaletto and Gardner (1988); Drouillard et al. (1996) Meyer et al. (2000)

Bioconcentration, Bioaccumulation, and Metabolism

11

Table 2 (continued) Species

Lipid content

Caddisfly Polychaete

8.0–32.5 (d) 0.85–1.7 (w)

Oligochaete

aw

(%)a

References

Herbes and Allen (1983); Meyer et al. (2000) Booij and van den Berg (1994); Driscoll and McElroy (1996) 8–25.5 (d), 1.1–5.2 (w) Egeler et al. (1997); Cavaletto and Gardner (1988) Gardner et al. (1985); Gaskell et al. (2007) Nebeker et al. (1989); Whitten and Goodnight (1966) You et al. (2006)

and d mean the wet and dry weight basis in the whole body, respectively.

The lipid content of aquatic organisms greatly varies with species, but generally ranges from 1% to 5% on a wet weight basis. HPLC analysis of fresh muscle from eight fish species showed phospholipids (PL) and triacylglycerols (TAG) as main components, followed by sterols (ST) and free fatty acids (FA) (Ewald and Larsson 1994). Since the PL content explained only 31% of the variance in the tissue concentration of 2,2 ,4,4 -tetrachlorobiphenyl, not only the lipid content but also the chemical structure of the FA portion in lipids were considered to be important for bioconcentration. Lipids have been extensively characterized for shellfish in the mollusca phylum. Murphy et al. (2002) reported that PL (57–79%) and TAG (10–25%) comprise the main lipid components in mussels, followed by free FA and ST, with trace amounts existing of wax esters (6 for a short incubation period; this trend was more pronounced for algae with higher growth rates. Slower partition of more hydrophobic chemicals into algae, as compared with their growth, may result in dilution from the increased biomass. As previously mentioned, the difference in organismal lipid content sometimes accounts for age-dependent BCF. About fourfold more bioconcentration of pentachlorophenol (32) was reported for D. magna adults in a 24-hr static exposure than occurred for neonates (Kukkonen and Oikari 1988). Since the metabolic activity necessary to form the sulfate conjugate is very similar between adults and neonates, the difference in lipid content was regarded to account for the difference in BCF values. More trans-chlordane (24) was bioconcentrated in midge adults (Chironomus decorus) than in larvae; constant residues were reported in 2nd to 4th instars (Harkey and Klaine 1992). In contrast, the stepwise decrease in bioconcentration of chlorpyrifos (47) from the 2nd to 4th instars was observed in C. riparius (Buchwalter et al. 2004). The biotransformation rate was independent of instar stage; the reduced uptake through the body surface per body weight, in later-stage organisms with smaller surface area to volume ratios, may account for the difference. A similar size effect on uptake of PAHs was briefly reported for mayfly nymphs (Hexagenia limbata) by Stehly et al. (1990). Size effect also exists for bioconcentration in algae. As the size of green algae and diatoms decrease, their surface to volume ratio increases, which resulted in reports of higher uptake of atrazine (127) and fenitrothion (41) (Kent and Currie 1995; Tang et al. 1998a; Weiner et al. 2004). Growth is also an important factor when discussing metabolism in the context of bioconcentration. By comparing experimental BCF values of chlorinated anilines in the guppy with the predicted ones derived from BCF data on non-biotransformed chlorobenzenes, De Wolf et al. (1992) found that lower observed values are caused by biotransformation of anilines in the fish via N-acetylation. Serrano et al. (1997b) examined the bioconcentration of organophosphorus pesticides in Mytilus galloprovincialis for 35 days and found much lower BCF values than expected from the predictive equation (Connell 1988). This may have resulted from metabolic biotransformation of the pesticides during the experiment. More concrete evidence for an effect on bioconcentration from metabolism was demonstrated in Chironomus

Bioconcentration, Bioaccumulation, and Metabolism

15

R riparius and Lumbriculus variegatus for two polycyclic musk fragrances, Tonalid R  and Galaxolide (Artola-Garicano et al. 2003). The observed BCF values in the midge were much lower than ones calculated for non-metabolized chlorobenzenes (Roghair et al. 1992); the BCF values approached the predicted ones when the midges were exposed in the presence of piperonyl butoxide (PBO). Since PBO is known to be an inhibitor of cytochrome P450 enzymes, its effect on metabolic transformation is a key factor that influences bioconcentration rate. In contrast, observed and predicted values are almost the same in the oligochaete, and significant metabolism is unlikely to be involved. Because most chemicals that are bioconcentrated are deposited in the lipid phase of aquatic organisms, and tissues and organelles have different levels and types of lipids, one normally assumes that the absorbed chemical is heterogeneously distributed in organisms. However, detailed analysis of chemical residues in mollusca tissues, such as clam, mussel, and oysters, indicates that pesticides are generally distributed in visceral mass, including digestive glands and gonads, rather than in mantle and gill (Bedford and Zabik 1973; Brodtmann 1970; Rajendran and Venugopalan 1991; Sathe et al. 2005; Uno et al. 2001). Takimoto et al. (1987a) examined the distribution of radioactivity from uptake of 14 C-fenitrothion (41) in the snail Physa acuta by autoradiographic techniques and found that most 14 C resided in liver. Few investigations exist on the distribution of pesticides in algae. However, the algal surface structure seems to be an important factor that governs distribution. Vance and Maki (1976) have separately analyzed residues of dibrom (63) in the green alga Stigeoclonium pachydermum; these authors found that most of (63) was distributed in the cell wall fraction as a result of a thick mucilage coat that covers this organism’s filaments. In contrast, Chlamydomonas and Dunaliella sp. were found to take up the α-isomer of HCH into the cell interior, which is rich in lipids (Canton et al. 1977). Through the investigation of benzo[a]pyrene (7) uptake by periphyton communities, including desmids and diatoms collected from streams, Bruno et al. (1981) concluded, by using autoradiography, that most of benzo[a]pyrene (7) was distributed in the extensive mucilaginous sheaths that surround the desmid cells. The absorption and translocation of pesticides have been studied in aquatic macrophytes and terrestrial plants by using radiolabeled pesticides. The extent of acropetal and basipetal movement in plants seems to depend not only on pesticide type but also on plant species involved (Anderson et al. 1981; Frank and Hodgson 1964; Funderburk and Lawrence 1963; Hinman and Klaine 1992; Thomas and Seaman 1968). Only limited work has been reported on chemical distribution in other aquatic organisms. Crosby and Tucker (1971) reported some distribution of DDT (33) in the carapaces of D. magna. Derr and Zabik (1972) found that up to 30% of DDE (34) bioconcentrated by Chironomus tentans was distributed in egg mass, with much less distributed to the exuviae. Autoradiography has clearly shown that absorbed pesticides in amphipods and ostracods are concentrated in lipid material (Arts et al. 1995; Kawatski and Schmulbach 1970). Existing information strongly suggests that, in general, absorbed xenobiotic chemicals are usually distributed in lipid-rich tissues and organs, unless the organism has an impervious surface (skin, integument, etc.) that acts as a barrier to entry.

16

T. Katagi

2.1.3 Environment Temperature and water chemistry, such as salinity, pH, and content of dissolved or adsorbed particulate organic matter, also may affect bioconcentration. When the temperature increased from 5◦ C to 20◦ C, BCF values of hexachlorobenzene (31) increased by a factor of 7–10 in three fish species (Veith et al. 1979). Through the thermodynamic analysis of the bioconcentration of several chlorobenzenes in guppies at different temperatures, Opperhulzen et al. (1988) demonstrated that n-octanol is a poor surrogate for fish lipid. The transfer process from water to fish was governed by a positive entropy change from loss of structuring of water molecules that surrounded chlorobenzene, whereas the transfer process for n-octanol was accompanied by exothermic enthalpy changes and small entropy changes. A similar temperature dependency for bioconcentration was observed in algae. Several green algae species bioconcentrated higher amounts of ioxynil (145) and 2,4-D (75), when temperatures were higher (Neumann et al. 1987; Valentine and Bingham 1974). Koelmans and Sánchez Jiménez (1994) reported on the bioconcentration of chlorobenzenes in Scenedesmus sp. and its relationship to an entropy gain that dominated the transfer process from water to algae, and found it to be similar to what was observed in fish. A slight increase in the BCF for pentachlorophenol (32) was reported in zebra mussel when temperatures were higher; however, BCF changes for dieldrin (22), lindane (30), and pentachlorophenol (32) were insignificant in other mussels (Boryslawskyj et al. 1988; Ernst 1979; Fisher et al. 1999). Therefore, the temperature effect on BCF appears to be less important for bivalva. Ambient temperature affected the lipid content and respiration rate in water fleas, which was considered to partly influence rates of bioconcentration. At a higher temperature, a higher correlation was observed between BCF and the lipid content in D. magna for atrazine (127); in addition, there was a temperature-dependent increase in the relative amount of lipid vs. protein at a higher temperature (HeisigGunkel and Gunkel 1982). More DDE (34) was bioconcentrated in D. pulex at higher temperatures (Nawaz and Kirk 1995). The authors believed that at higher temperatures respiration increased, and the enhanced gas exchange at the tissue surface of the branchial chamber reduced the thickness of a diffusive boundary layer, which resulted in the facilitated uptake. Abiotic and metabolic transformation sometimes encumbers determining how much BCF values are affected by temperature. Carbaryl (101) is more susceptible to hydrolysis and metabolism at higher pH and temperature. Therefore, carbaryl BCF values were not significantly changed in Chironomus riparius (Lohner and Fisher 1990). Parathion (39) was metabolically transformed to the corresponding oxon and phenol in the midge, which resulted in reduced bioconcentration of (39) (Lydy et al. 1990). The effect of illumination levels on bioconcentration was reported in algae and aquatic macrophytes. Weinberger and Greenhalgh (1985) observed higher uptake of aminocarb (98) by axenic aquatic hornwort (Ceratophyllum demersum), and much reduced uptake by dead organisms, which indicated the presence of active uptake processes in macrophytes. Similarly, more glufosinate (73) was taken up under similar circumstances by Lemna gibba (Ullrich et al. 1990). In contrast, ioxynil (145) was absorbed fourfold less by the green alga Ankistrodesmus braunii under bright

Bioconcentration, Bioaccumulation, and Metabolism

17

light conditions; this may be related to the change of membrane potential caused by more intense lighting (Neumann et al. 1987). For chemicals having a dissociable functional group, bioconcentration may be affected by the pH level in the medium and also by the internal or cytoplasmic pH of an aquatic organism. The pKa value of pentachlorophenol (32) is approximately 5, and the dissociated chemical species predominates under ambient environmental conditions. Therefore, bioconcentration is generally independent of the pH of the medium, as has been observed for bioconcentration in zebra mussels (Fisher et al. 1999). In contrast, the bioconcentration of ioxynil (145) in the green alga (Ankistrodesmus sp.), which has a pKa value of 3.96, showed a clear pH-dependency at pH 5.5–7.3 (Neumann et al. 1987). There was insignificant uptake at pH 7.3; hence, pH-dependency under acidic conditions portends more favorable partitioning of the undissociated chemical species into this alga. Similar pH profiles were reported for the bioconcentration of 2,4,5-trichlorophenol in Lemna gibba (Tront and Saunders 2006). However, the degree of dissociation is not always the dominant factor that controls uptake of a chemical. Kenney-Wallace and Blackman (1972) examined uptake by Lemna minor of several benzoic acids, each with a different chlorine substitution; these authors reported a 90-fold difference in relative uptake, which could not be explained on the basis of proportion of undissociated species at the tested pH (1000-fold). It was concluded that hydrophobicity most likely governed the partition. Glufosinate (73) was taken up not only by passive diffusion but also by an active process that involved a proton co-transport mechanism in L. gibba (Ullrich et al. 1990). Furthermore, bioconcentration pH-dependency seems to differ among algae species. More uptake in Scenedesmus sp. was observed under acid conditions for 2,4-D (75), which has a pKa value of 3; however, no such dependency was noted in Chlorella and Chlamydomonas sp. (Valentine and Bingham 1974). Küsel et al. (1990) monitored the in vivo cytoplasmic pH value in Chlorella sp. by NMR; by using the 31 P signal of an inorganic phosphate, the pH was almost constant at 7.2–7.8 in air, even at an external pH of 3–10. The dissociation constant (pKa ) of sulfonylurea herbicides is approximately 3–5. Therefore, a higher uptake of undissociated chemical species through biological membranes is expected at a lower pH. Such a pH-dependent BCF profile was observed in Chlorella fusca with chlorsulfuron (122) and metsulfuron-methyl (123), and was determined by trapping the ionized sulfonylurea molecule at the cytoplasmic pH (Fahl et al. 1995). Similarly, less non-dissociative lindane (30) was taken up by midge larvae (Chironomus riparius) at a lower pH (Fisher 1985). Wildi et al. (1994) reported a similar profile in bioconcentration of pyrene (5) by midge larvae, and speculated that the mucus that covers the body surface at lower pH levels may reduce diffusion of a chemical into the larvae. Micro-sensors have been applied to study the gut environment of larvae of the midge, Chironomus plumosus (Stief and Eller 2006). In vivo measurements, using such sensors, showed lower oxygen concentration and redox potential and slightly higher pH in the food bolus of the larvae. Neither diffusion of O2 from hemolymph nor O2 uptake during ingestion of food was sufficient to oxygenate the food bolus; hence, aerobic metabolism in the gut was considered to be unlikely. Hardness of water as a medium was found to affect both uptake and elimination processes of

18

T. Katagi

3-trifluoromethyl-4-nitrophenol (148) in C. tentans larvae, which was explained by the difference in dissociation rendered by the pH change linked to water hardness (Kawatski and Bittner 1975). In natural waters, there are usually agents, e.g., organic carbonaceous matter or dissolved, colloidal, or suspended particulates, that alter the bioavailability of chemicals to aquatic organisms (Fig 1a; Farrington 1991; Haitzer et al. 1998; Katagi 2006). Humic substances including fulvic, humic, and hydrophilic acids are usually the predominant constituents of dissolved organic matter (DOM). When measuring or estimating the bioconcentration or bioaccumulation potential of a chemical in sediment-dwelling organisms, the type and content of colloidal organic matter that exists in interstitial water should be considered. If an adsorption/desorption equilibrium has been reached between a chemical and DOM, the apparent BCF in the presence of DOM (BCFd ) can be expressed as BCFd = BCF0 /(1 + m × foc × α × Kdoc ),

(4)

where BCF0 = the BCF value in the absence of DOM; M = concentration of DOM (kg L−1 ); foc = organic carbon fraction in DOM (kg DOM kg−1 organic C); α = a factor measuring the degree of bioavailability of a DOM-associated chemical to organism; α = 1 means no bioavailability; Kdoc = partition coefficient (L kg−1 organic C) between dissolved organic carbon and water. The presence of dissolved humic acids reduced BCF of benzo[a]pyrene (7) from water for D. magna (McCarthy 1983). Kukkonen and Oikari (1991) have examined the 24-hr bioconcentration of organic pollutants including (7) in D. magna, in 20 natural Finnish freshwater bodies, having different humic composition. BCF values gradually decreased with increasing concentration of dissolved organic carbon (DOC); Eq. (4), where α = 1 illustrates such a profile. Kukkonen and Oikari (1991) also reported good correlation between BCF and DOM content that had been enriched with aromatic constituents. Since the Kdoc value is larger for most hydrophobic chemicals, it is anticipated that DOM will retard uptake of pyrethroids. Day (1991) reported that the presence of DOC at 10–15 ppm caused about a fourfold reduction of BCF in D. magna for deltamethrin (83), lambda-cyhalothrin (84), and fenvalerate (85); approximately 20–40% of these pesticides were present as associated forms, based on Kdoc values of approximately 105 L kg−1 . In bioconcentration and 96-hr acute toxicity studies with D. magna and Ceriodaphnia dubia, a lack of bioavailability of associated forms was recently demonstrated by Yang et al. (2006). By analyzing the BCF and LC50 values with Eq. (4), the α values were statistically estimated to be equal to unity. Haitzer et al. (1998) reviewed the effects of DOM on bioconcentration rates in aquatic organisms and reported two possible mechanisms. Reduced

Bioconcentration, Bioaccumulation, and Metabolism

19

bioconcentration was considered to originate from less chemical dissolved in water, because the chemical was associated with DOM, whereas at lower DOM levels (6 are analyzed, the log kU vs. log Kow line showed a downward curvature as shown for the uptake of chlorinated hydrocarbons in fish (Connell and Hawker 1988), oligochaetes (Connell et al. 1988), and aquatic macrophytes (Gobas et al. 1991). Because the process of bioconcentration proceeds via adsorption, diffusion, and metabolism in organs with different biochemical composition, the two-compartment model is sometimes more suitable to describe uptake and elimination processes. The bioconcentration of lindane (30) in the snail Lymnaea palustris was analyzed by using a first-order two-compartment model, and assumed foot and visceral mass as being the central and peripheral compartments (Thybaud and Caquet 1991). BCF values, similar to the ones obtained with the one-compartment model, were obtained. The rapid elimination from foot, followed by the slower one from the visceral mass, could account for the biphasic profiles. Lydy et al. (1994) analyzed the uptake and elimination processes of pentachlorophenol (32) and 5,5 ,6trichlorobiphenyl (TCB) in Chironomus riparius using a two-compartment model. The authors speculated that the exoskeleton is a central compartment, judging from its apparent volume, which was almost the same for both compounds. In addition, internal tissues that have much more lipid were considered to be peripheral ones, because their apparent volumes retain more of the hydrophobic TCB. A twocompartment model can successfully describe the bioconcentration profiles of the parent and metabolite phases for 2-chlorobiphenyl (CB) in Chironumus tentans (Lydy et al. 2000). Rapid 14 C dissipation in the midge resulted from elimination of both CB and its metabolites. A similar approach was undertaken to analyze the bioconcentration profiles for tributyltin chloride (16), which was debutylated stepwise in the body of larval midges. Different BCF values between (16) and its triphenyl analog originated from differing degrees of metabolism (Looser et al. 2000). Much less metabolic activity existed in the oligochaete Lumbriculus variegatus than in larval midges for benzo[a]pyrene (7) in a study by Schuler et al. (2003). Nuutinen et al. (2003) scrutinized each estimated rate constant in Eq. (5) for effects of metabolism on bioconcentration values for fluoranthene (6), pentachlorophenol (32), and methyl parathion (39) in the amphipod Hyalella azteca. In this study, the larger value for kM and smaller kME showed the rapid metabolism of (39) through involvement of various enzymes and association of the oxon metabolite with acetylcholinesterase. The moderate but larger kM of (32) than for (6) implied that the former substance went through a phase-II conjugation reaction; the small kME of (6) portended less permeability of its oxidized metabolite through membranes.

22

T. Katagi

The elimination from aquatic organisms was kinetically examined for chlorinated hydrocarbons, PAHs, and PCBs that had a wide range of log Kows (van der Linde et al. 2001). The extent of metabolism was evaluated and results showed that the PAHs were metabolized at a moderate rate. Recently, Cowan-Ellsberry et al. (2008) successfully applied in vitro metabolism data, using fish liver hepatocytes or S9 fractions, to refine the BCF values of pesticides and surfactants in fish. When the growth rate of an organism becomes comparable to, or higher than, uptake and elimination rates, the kG value should be taken into account in any analysis of bioconcentration. Since the growth of algae is very rapid during exposure to a chemical, the exponential model for time-dependent increase of algal mass was introduced to examine the bioconcentration of organophosphorus pesticides in green algae (Nakamura and Mochida 1988; Jonsson et al. 2001). Instead of the exponential model, the Boltzman equation (Wang et al. 1996) and the allometric model (van der Linde et al. 2001) were successfully applied to bioconcentration in various aquatic organisms. Halling-Sørensen et al. (2000), by analyzing the bioconcentration of chlorinated hydrocarbons in Selenastrum sp., confirmed that algal growth was significantly retarded by nitrogen depletion in the growth medium. Approaches have been developed, other than using a simple kinetic model, for examining bioconcentration. For example, the fugacity model has been extensively applied to the theoretical investigation of bioconcentration (Mackay and Hughes 1984; Gobas and Mackay 1987). Gobas et al. (1986) utilized the diffusion rates through membrane-diffusion layer barriers to analyze uptake and elimination processes in fish. The uptake and elimination can be expressed by the following equations:   kU = (A/F) × Km × Dm × Dd / (δm × Dd + Km × δd × Dm ) ,   kPE = (A/F) × {(1 − a) + a × Km }   × Km × Dm × Dd / (δm × Dd + Km × δd × Dm ) ,

(11) (12)

where A = diffusion area; δ = diffusion layer thickness; D = diffusion coefficient; α = lipid fraction; F = fish weight; Km = a partition coefficient of a chemical between water and membrane; Subscripts m and d = membrane and diffusion layer, respectively. Membrane-controlled diffusion is considered to be dominant for chemicals of low hydrophobicity (log Kow < 3–4), and then the first term (δ m × Dd ) in the denominator of Eqs. (11) and (12) becomes much larger than the second one (Km × δ d × Dm ). In contrast, the diffusion-layer-controlled process becomes dominant for hydrophobic chemicals (log Kow > 3–4), meaning the opposite condition prevails

Bioconcentration, Bioaccumulation, and Metabolism

23

for δ m × Dd « Km × δ d × Dm . When log [(Dm × A)/(δ m × F)] and log [(Dd × A)/(δ d × F)] are defined as β m and β d , Eqs. (11) and (12) can be simplified as follows:   LogKow < 3 − 4; logkU = βm + logKm ; logkPE = βm − log (1 − α) /Km + α ; LogKow > 3 − 4; logkU = βd ; logkPE = βd − log[ (1 − α) + α × Km ]. Since Km can be approximated as Kow and β d is considered to increase with molecular size, the log kU and log kPE vs. log Kow plots would show parabolic curves. The log Kow dependency in the bioconcentration of non-metabolized chemicals with log Kow >3 could be well described by the above model. Legierse et al. (1998) applied this model to the bioconcentration of chlorthion (43) and several chlorobenzenes in snail Lymnaea stagnalis, by using the parameters from guppies; however, this approach failed to predict kU , KPE , and BCF values well, perhaps because of differences in the specific physiology of the snail. By using the diffusion theory, Wolf et al. (1991) successfully examined the partition of chlorobenzenes to aquatic macrophytes.

2.3 Pesticides and Other Chemicals The BCF and elimination clearance times (CL50 ) for pesticides and simple organic chemicals in aquatic organisms such as molluscs, algae, crustacean, and insecta (other than fish) are listed in Tables 3, 4, 5, and 6, together with brief descriptions of the study designs which yielded these values. For members of Insecta, data are included on the larvae and nymphs and on habitants in the aquatic environment. In these studies, algae were kept in a sterile medium under illumination and were mostly evaluated in the exponential growth phase. When pesticides and other chemicals are tested, exposures are conducted by using static or flow-through systems. Which system is used depends on the water solubility and hydrolytic stability of the chemical. The log BCF values determined for such xenobiotics mostly range between 0 and 6 for the 287 chemicals that have log Kow values between 3 and 7, as measured or estimated by the EPI-suite software and (USEPA 2008). Even for an individual chemical, BCF values significantly scatter among species tested. Values for the organochlorine pesticides, listed in Table 3, primarily show log BCF value of 3–5, with a variation within twofold, among species. A larger variance was observed for endosulfan (27), hexachlorobenzene (31), and DDT (33). The latter two insecticides have large log Kow values (5.73 and 6.91), and the difference in the lipid content of each organism is likely to result in these variations. By analogy to the metabolism of (27) in D. magna (DeLorenzo et al. 2002), the crayfish Procambarus clarkii may more significantly metabolize (27) xenobiotics to their sulfate forms, which results in much reduced BCF values. Aldrin (21) has a very high hydrophobicity (log Kow = 6.5), but its elimination is rapid in ostracods, which results from its oxidation to dieldrin (22) (Kawatski and Schmulbach 1972).

23

22

21

No.

Experimental conditionsc

Aldrin (6.5)

B: Anabaena cylindrica Aulosira fertilissima Anacystis nidulans Os: Chlamydotheca arcuata W: Daphnia magna I: Hexagenia bilineata Chironomus sp. C: Rangia cuneata Corbicula manilensis Mu: Lampsilis siliquoidea

n, st, 1 ppb, 23◦ C, 7 days/na n, st, 0.1–1 ppm, 29◦ C, 2 days/na n, st, 1 ppb, 23◦ C, 7 days/na n, st, 8.4 ppb, 22◦ C, 4 days/2 days n, fl, 0.02 ppb, 21◦ C, 3 days/na n, fl, 0.02 ppb, 21◦ C, 3 days/na n, fl, 0.02 ppb, 21◦ C, 3 days/na Dieldrin (5.2–5.4) n, fl, 0.55 ppb, 25◦ C, 72 hr/na y, fl, 0.89 ppb, na, 72 days/na n, fl, 0.57 ppb, 20◦ C, 3 weeks/3 weeks Sphaerium corneum n, st, 2.4–2.6 ppb, 10–19◦ C, 1 day/na Oy: Crassostrea virginica n, fl, 0.5 and 9 ppb, 23◦ C, 7 days/na G: Scenedesmus obliquus n, st, 1–20 ppb, 25◦ C, 36 hr/na B: Anabaena cylindrica n, st, 1 ppb, 23◦ C, 7 days/na Aulosira fertilissima n, st, 0.1–1 ppm, 27◦ C, 5 days/na Anacystis nidulans n, st, 1 ppb, 23◦ C, 7 days/na Nostoc muscorum n, st, 1 ppb, 23◦ C, 7 days/na W: Daphia magna n, st, 2–13 ppb, 21◦ C, 6 days/na Os: Chlamydotheca arcuata n, st, 6.6 ppb, 22◦ C, 4 days/2 days Photodieldrin (4.13) G: Ankistrodesmus amalloides n, st, 0.72 ppb, na, 2 days/na W: Daphnia pulex n, st, 3.3 ppb, na, 1 day/4 days

Pesticide (log Kow a ) Speciesb na na na 1 day na na na na na 4.7 days na na na na na na na na 1 day na 4 days

3.1∗ 2.3–2.6∗ 3.0∗ 3.9∗ 5.1 4.5 4.4 2.9–3.3 3.5 3.0–3.1 2.8 3.3–3.5 3.1∗ 2.3∗ 1.4–2.5∗ 2.7∗ 3.3∗ 4.1∗ 3.4∗ 1.6–1.7∗ 1.9∗

log BCFd CL50 e

Reinert (1972) Schauberger and Wildman (1977) Kumar and Lal (1988) Schauberger and Wildman (1977) Schauberger and Wildman (1977) Reinert (1972) Kawatski and Schmulbach (1972) Neudorf and Khan (1975) Khan et al. (1975)

Mason and Rowe (1976)

Boryslawskyj et al. (1988)

Schauberger and Wildman (1977) Dhanaraj et al. (1989) Schauberger and Wildman (1977) Kawatski and Schmulbach (1972) Johnson et al. (1971) Johnson et al. (1971) Johnson et al. (1971) Petrocelli et al. (1973) Hartley and Johnston (1983) Bedford and Zabik (1973)

References

Table 3 A summary of bioconcentration studies performed on organochlorine pesticides in aquatic organisms

24 T. Katagi

Endrin (5.2–5.4)

Endosulfan (3.83)

25

27

na na na na

n, fl, 1.5 ppb, 10◦ C, 20 days/na n, st, 0.1–1 ppm, 29◦ C, 2 days/na n, st, 0.1 ppm, 25◦ C, 16 hr/na n, st, 0.1 ppm, 8 weeks/8 weeks n, st, 0.1 ppm, 25◦ C, 24 hr/na

Mu: Anodonta piscinalis B: Anabaena sp. ARM310 G: Selenastrum capricornutum Cr: Procambarus clarkii

W: Daphnia magna

2.8 2.7–3.7∗ 3.4 – 0.7 (α), 0.3 (β) 3.5

na

n, fl, 0.1–1.4 ppb, 28◦ C, 10 days/na 1.0–1.8

C: Katelysia opima

na

na na na na na

Oy: Crassostrea virginica Mu: Mytilus edulis I: Pteronarcys dorsata

Ankistrodesmus amalloides Mc: Hydrilla verticillata W: Daphnia pulex I: Chironomus decorus 1 day na 1 day na

na na

na

G: Scenedesmus quadricauda Oy: Crassostrea madrasersis

n, fl, 1.5 ppb, 10◦ C, 20 days/na n, st, 0.1–100 ppb, na, 1 day/na

Mu: Anodonta piscinalis G: Scenedesmus quadricauda

3.7 (α), 3.6 (γ) 3.0 3.7–4.1 (α) 3.8–4.2 (γ) 3.7∗ 3.0 4.4∗ 2.3–2.5 (α) 3.2–3.4 3.4 2.9

log BCFd CL50 e

n, st, 0.72 ppb, 25◦ C, 1 day/na n, st, 5 ppb, 25◦ C, 6 days/na n, st, 0.5 ppb, 25◦ C, 1 day/3 days n, rn, 0.7–1.4 ppt, 20◦ C, 50 days/na n, fl, 0.1–50 ppb, 23◦ C, 7 days/na n, rn, na, 15◦ C, 7 days/na n, fl, 0.03–0.15 ppb, 15◦ C, 28 days/na n, st, 1 ppm, 25◦ C, 7 days/na 2.2∗ n, fl, 0.1–1.4 ppb, 28◦ C, 10 days/na 1.0–1.9

y, fl, 0.3–0.4 ppb, na, 72 days/na

Chlordane (6.1–6.22)

24

Experimental conditionsc

C: Corbicula manilensis

Pesticide (log Kow a ) Speciesb

No.

Table 3 (continued)

DeLorenzo et al. (2002)

Vance and Drummond (1969) Rajendran and Venugopalan (1991) Rajendran and Venugopalan (1991) Sabali¯unas et al. (1998) Narayana Rao and Lal (1987) DeLorenzo et al. (2002) Naqvi and Newton (1990)

Mason and Rowe (1976) Donkin et al. (1997) Anderson and DeFoe (1980)

Moore et al. (1977) Hinman and Klaine (1992) Moore et al. (1977) Harkey and Klaine (1992)

Sabali¯unas et al. (1998) Glooschenko et al. (1979)

Hartley and Johnston (1983)

References

Bioconcentration, Bioaccumulation, and Metabolism 25

Lindane (3.72–4.14) C: Corbicula manilensis Mu: Mytilus edulis

30

α-isomer

Hexachlorobenzene C: Corbicula manilensis (5.73) Mu: Mytilus edulis Sn: Lymnaea palustris O: Lumbriculus variegates

–

31

A: Hyalella azteca Gammarus lacustris

Mc: Hydrilla verticillata G: Chlorella pyrenoidosa Chlamydomonas sp. Dunaliella sp. W: Daphnia magna

Hexachlorohexane (4.52)

–

Is: Asellus aquaticus G: Chlorella pyrenoidosa C: Venerupis japonica

P: Lanice conchilega

Sn: Lymnaea palustris

Pesticide (log Kow

Speciesb

No.

a)

n, rn, 0.5 ppb, 10◦ C, 103 hr/na y, st, 0.5–5 ppb, na, 21 days/na y, fl, 0.7–4 ppb, 20◦ C, 28–44 days/na y, fl, 1.2–7.7 ppb, 20◦ C, 28 days/na n, fl, 0.3–5 ppb, 20◦ C, 28 days/na

4.1–4.4 4.3–4.5

>3.0 5.0–5.4 3.7–4.3

na na

na na na

na

na na na na na

1–2 days na na

4.7 days

na 0.7 hr

2.5 1.6–1.7 3.1

na na 22 hr

3.4 2.4–2.6 2.1

log BCFd CL50 e

1.7 2.6–3.1 2.2 (α), 2.1 (β) 2.1 (γ), 2.4 (δ) n, st, 0.12 ppm, 25◦ C, 6 days/na 1.6 n, st, 0.01–0.8 ppm, 28◦ C, 3 hr/na 2.2–2.4 n, st, 0.1–0.8 ppm, na, 2–3 hr/na 3.4∗ n, st, 0.1–0.8 ppm, na, 2–3 hr/na 3.2∗ n, st, 0.01–0.8 ppm, 20◦ C, 1.8–2.5 2 days/na y, fl, 0.59 ppb, na, 72 days/na 3.4

y, fl, 0.3 ppb, na, 72 days/na n, st, 0.9–1.8 ppb, 22◦ C, 8 days/na n, st, 2–5 ppb, 10◦ C, 8 days/18 days n, rn, na, 15◦ C, 7 days/na n, st, 6–600 ppb, 20◦ C, 10 days/7 days n, st, 2–5 ppb, 10◦ C, 8 days/18 days n, st, 2 ppb, 18◦ C, 5 days/3 days n,. st, 0.01–1 ppm, 20◦ C, 6 days/na n, fl, 1–2 ppb, na, 10 days/4 days

Experimental conditionsc

Table 3 (continued)

Schuytema et al. (1988) Nebeker et al. (1989)

Bauer et al. (1989) Baturo and Lagadic (1996) Schuytema et al. (1988)

Hartley and Johnston (1983)

Hinman and Klaine (1992) Canton et al. (1975) Canton et al. (1977) Canton et al. (1977) Canton et al. (1975)

Thybaud and Le Bras (1988) Hansen (1979) Yamato et al. (1983)

Ernst (1979)

Donkin et al. (1997) Thybaud and Caquet (1991)

Hartley and Johnston (1983) Renberg et al. (1978) Ernst (1979)

References

26 T. Katagi

32

A: Gammarus pulex Hyalella azteca Pontoporeia hoyi S: Mysis relicta I: Chironomus riparius W: Daphnia magna

Mc: Eichhornia crassipes

Anodonta anatina Oy: Crassostrea gigas Ab: Haliotis rufescens Haliotis fulgens P: Lanice conchilega

Dreissena polymorpha

Hexachlorobenzene G: Scenedesmus sp. (5.73) Mc: Myriophyllum spicatum I: Chironomus decoras Toxaphene (5.78) Oy: Crassostrea virginica S: Penaeus duorarum Palamonetes pugio Pentachlorophenol C: Corbicula fluminea (5.12) Mu: Mytilus edulis

31

28

Pesticide (log Kow a ) Speciesb

No.

2.2–2.5 1.4–1.7 1.2–1.3 1.6–1.8 3.6

0.4 day 3.6 hr 8.8 days 15 days 15 hr na

na

na