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Symbols and abbreviations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; + +, stron; induction (> 500% of control); *, bile MET, metabolites in bile; DNA add,DNA adducts [liver]; ALT, Alanine transaminase [serum]; AST Aspertate transaminase [serum]; LSI, liver sometic index [liver]; CF, condition factor [whole body].

Table 24 Symbols and abbreviations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; + +, stron; induction (> 500% of control); *, bile MET, metabolites in bile; DNA add,DNA adducts [liver]; ALT, Alanine transaminase [serum]; AST Aspertate transaminase [serum]; LSI, liver sometic index [liver]; CF, condition factor [whole body].

Related Figures (39)

Fig. 1. Schematic representation of the sequential order of responses to pollutant stress within a biological system. Modified from Bayne et al. (1985).
Fig. 1. Schematic representation of the sequential order of responses to pollutant stress within a biological system. Modified from Bayne et al. (1985).
Fig. 2. The principal scheme of responses in organisms to the detrimental effects of pollutant exposure. Modified from McCarthy et al. (1991). understanding of biomarker responses and gradually expand that understanding. In time the biomarkers will thus become a routine, well-characterized and scientifi- cally and legally defensible tool for monitoring and assessing environmental pollution. Based on the magni- tude and pattern of the biomarker responses, the environmental species offer the potential of serving as sentinels demonstrating the presence of bioavailable contaminants and the extent of exposure, surrogates indicating potential human exposure and effects and predictors of long-term effects on the health of popula- tions or the integrity of the ecosystem (McCarthy and Shugart, 1990).
Fig. 2. The principal scheme of responses in organisms to the detrimental effects of pollutant exposure. Modified from McCarthy et al. (1991). understanding of biomarker responses and gradually expand that understanding. In time the biomarkers will thus become a routine, well-characterized and scientifi- cally and legally defensible tool for monitoring and assessing environmental pollution. Based on the magni- tude and pattern of the biomarker responses, the environmental species offer the potential of serving as sentinels demonstrating the presence of bioavailable contaminants and the extent of exposure, surrogates indicating potential human exposure and effects and predictors of long-term effects on the health of popula- tions or the integrity of the ecosystem (McCarthy and Shugart, 1990).
Fig. 3. The relationship among the components of the risk characterization stage of retrospective assessments based on the process of ecological epidemiology, including their respective environmental monitoring methods. Based on Suter (1993), Henderson et al. (1989), De Zwart (1995).
Fig. 3. The relationship among the components of the risk characterization stage of retrospective assessments based on the process of ecological epidemiology, including their respective environmental monitoring methods. Based on Suter (1993), Henderson et al. (1989), De Zwart (1995).
Fig. 4. Bioaccumulation model for aquatic organisms. Koc: sorption coefficient; BCF: bioconcentration factor; BSAF: biota-sediment accumulation factor; BMF: biomagnification factor. C refers to a concentration and k to a rate constant. The subscripts S, W, F, B, EXC and MET refer to sediment, water, food, biota, excretion and metabolism, respectively. The digestible sediment fraction is consid- ered to be part of the food. Adapted from Van der Oost et al. (1996a).
Fig. 4. Bioaccumulation model for aquatic organisms. Koc: sorption coefficient; BCF: bioconcentration factor; BSAF: biota-sediment accumulation factor; BMF: biomagnification factor. C refers to a concentration and k to a rate constant. The subscripts S, W, F, B, EXC and MET refer to sediment, water, food, biota, excretion and metabolism, respectively. The digestible sediment fraction is consid- ered to be part of the food. Adapted from Van der Oost et al. (1996a).
Biota-sediment accumulation factors (BSAFs) of polychlorinated biphenyls (PCBs) in fish
Biota-sediment accumulation factors (BSAFs) of polychlorinated biphenyls (PCBs) in fish
Biota-sediment accumulation factors (BSAFs) of organochlorine pesticldes (OCPs) in fish
Biota-sediment accumulation factors (BSAFs) of organochlorine pesticldes (OCPs) in fish
Symbols and abbreviations *, DW, dry weight; FW, fresh weight; LW, lipid weight; OM, organic matter or organic carbon; **, suspended solids instead of sediments
Symbols and abbreviations *, DW, dry weight; FW, fresh weight; LW, lipid weight; OM, organic matter or organic carbon; **, suspended solids instead of sediments
Biota-sediment accumulation factors (BSAFs) of polychlorinated dibenzodioxins and dibenzofurans (PCDD/Fs) in fish Symbols and abbreviations *; DW, dry weight; FW, fresh weight; LW, lipid weight; OM, organic matter or organic carbon.
Biota-sediment accumulation factors (BSAFs) of polychlorinated dibenzodioxins and dibenzofurans (PCDD/Fs) in fish Symbols and abbreviations *; DW, dry weight; FW, fresh weight; LW, lipid weight; OM, organic matter or organic carbon.
Fig. 5. Possible toxication and detoxification pathways of xenobiotic compounds: (1) direct toxic effect (A); (2) metabolic activation; (3) formation of a stable metabolite which may cause a toxic effect (C); (4) detoxification. The reactive metabolite formed by bioactivation (2) may cause a toxic effect (B) through reaction with critical targets (5) or be detoxified through reaction with a protective agent (6). Adapted from Timbrell (1991), slightly modified.
Fig. 5. Possible toxication and detoxification pathways of xenobiotic compounds: (1) direct toxic effect (A); (2) metabolic activation; (3) formation of a stable metabolite which may cause a toxic effect (C); (4) detoxification. The reactive metabolite formed by bioactivation (2) may cause a toxic effect (B) through reaction with critical targets (5) or be detoxified through reaction with a protective agent (6). Adapted from Timbrell (1991), slightly modified.
Fig. 6. Simplified presentation of the fate of xenobiotic compounds in the liver cell. Route I, a possible mechanism for detoxification or toxication, and route II, a possible mechanism for enzyme induction. AhR, aryl hydrocarbon receptor; HSP90, 90 kDa heat shock protein; ARNT, Ah receptor nuclear translocator; DREs, dioxin responsive elements; cyt P450s, cytochrome P450 isozymes; GSTs, glutathione S-transferases; UDPGTs, UDP- glucuronyl transferases.
Fig. 6. Simplified presentation of the fate of xenobiotic compounds in the liver cell. Route I, a possible mechanism for detoxification or toxication, and route II, a possible mechanism for enzyme induction. AhR, aryl hydrocarbon receptor; HSP90, 90 kDa heat shock protein; ARNT, Ah receptor nuclear translocator; DREs, dioxin responsive elements; cyt P450s, cytochrome P450 isozymes; GSTs, glutathione S-transferases; UDPGTs, UDP- glucuronyl transferases.
Laboratory studies on responses of organic trace pollutants on fish hepatic phase l-related enzymes
Laboratory studies on responses of organic trace pollutants on fish hepatic phase l-related enzymes
Symbols and abbreviations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; ++, strong induction (> 500% of control); *, cyt P4 cytochrome P450; CYPIA, cytochrome P450 1A isozyme; AHH, aryl hydrocarbon hydroxylase; EROD, ethoxyresorufin O-deethylase; cyt b5, cytochrome b5; P450 RED, NAD(P)H cytochr« P450 reductase. **, mRNA, cytochrome P450 1A ‘messenger’ RNA; CND, caffeine V -demethylase; T6BH, testosterone 6B-hydroxlase; P6BH, progesterone 6B-hydroxylase; EH, epoxide hydroxyl. E2H, estradiol 2-hydroxylase; PROD, pentoxyresorufin O-dealkylase; APDM, aminopyrine N-demethylase; bSRED, cytochrome bS5 reductase; AE, aldrin epoxidase; ADH, aldehyde dehydrogen:
Symbols and abbreviations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; ++, strong induction (> 500% of control); *, cyt P4 cytochrome P450; CYPIA, cytochrome P450 1A isozyme; AHH, aryl hydrocarbon hydroxylase; EROD, ethoxyresorufin O-deethylase; cyt b5, cytochrome b5; P450 RED, NAD(P)H cytochr« P450 reductase. **, mRNA, cytochrome P450 1A ‘messenger’ RNA; CND, caffeine V -demethylase; T6BH, testosterone 6B-hydroxlase; P6BH, progesterone 6B-hydroxylase; EH, epoxide hydroxyl. E2H, estradiol 2-hydroxylase; PROD, pentoxyresorufin O-dealkylase; APDM, aminopyrine N-demethylase; bSRED, cytochrome bS5 reductase; AE, aldrin epoxidase; ADH, aldehyde dehydrogen:
Field studies on responses of organic trace pollutants on fish hepatic phase I-related enzymes
Field studies on responses of organic trace pollutants on fish hepatic phase I-related enzymes
Symbols and abbreviations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; ++, strong induction (> 500% of control); *, « ytochrome P450; CYP1A, cytochrome P450 1A isozyme; AHH, aryl hydrocarbon hydroxylase; EROD, ethoxyresorufin O-deethylase; cyt b5, cytochrome b5; P450 RED, NAD(P)H cyt 450 reductase. **, mRNA, cytochrome 450 1A ‘messenger’ RNA; AE, aldrin epoxidase; PROD, pentoxyresorufin O-dealkylase; ECOD, ethoxycoumerin O-deethylase; bSRED, cytoc! >ductase.
Symbols and abbreviations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; ++, strong induction (> 500% of control); *, « ytochrome P450; CYP1A, cytochrome P450 1A isozyme; AHH, aryl hydrocarbon hydroxylase; EROD, ethoxyresorufin O-deethylase; cyt b5, cytochrome b5; P450 RED, NAD(P)H cyt 450 reductase. **, mRNA, cytochrome 450 1A ‘messenger’ RNA; AE, aldrin epoxidase; PROD, pentoxyresorufin O-dealkylase; ECOD, ethoxycoumerin O-deethylase; bSRED, cytoc! >ductase.
Laboratory studies on responses of organic trace pollutants on fish hepatic phase II enzymes and cofactors
Laboratory studies on responses of organic trace pollutants on fish hepatic phase II enzymes and cofactors
Symbols and abbrevations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; ++, strong induction (> 500% of control) *, GSH, reduced glutathione; GSSG, oxidized glutathione; GST, glutathione-S-transferease, UDPGT, UDP
Symbols and abbrevations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; ++, strong induction (> 500% of control) *, GSH, reduced glutathione; GSSG, oxidized glutathione; GST, glutathione-S-transferease, UDPGT, UDP
Field studies on responses of organic trace pollutants on fish hepatic phase II enzymes and cofactors
Field studies on responses of organic trace pollutants on fish hepatic phase II enzymes and cofactors
Symbols and abbrevations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; + +, strong induction (> 500% of control);*, GSH, reduced glutathione; GSSG, oxidized glutathione; GST, glutathione S-transferease, UDPGT, UDP glucuronyl transferase; **, GSH/GSSG, thiol:disulfide ratio; DTD, DT diaphorase.
Symbols and abbrevations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; + +, strong induction (> 500% of control);*, GSH, reduced glutathione; GSSG, oxidized glutathione; GST, glutathione S-transferease, UDPGT, UDP glucuronyl transferase; **, GSH/GSSG, thiol:disulfide ratio; DTD, DT diaphorase.
Laboratory studies on responses of organic trace pollutants on fish hepatic antioxidant enzymes Symbols and abbrevations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; + +, strong induction (> 500% of control);*, SOD, superoxide dismutase; GPOX, glutathione peroxidase; GRED, glutathione reductase, CAT, catalase; **, LPOX, lipid peroxidation; G6PDH, glucose-6-phosphate dehydrogenase; TBARS, thiobarbitutric acid reactive substances.
Laboratory studies on responses of organic trace pollutants on fish hepatic antioxidant enzymes Symbols and abbrevations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; + +, strong induction (> 500% of control);*, SOD, superoxide dismutase; GPOX, glutathione peroxidase; GRED, glutathione reductase, CAT, catalase; **, LPOX, lipid peroxidation; G6PDH, glucose-6-phosphate dehydrogenase; TBARS, thiobarbitutric acid reactive substances.
Symbols and abbrevations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; + +, strong duction (> 500% of control); *, SOD, superoxide dismutase; GPOX, glutathione peroxidase; GRED, glutathione reductase; CAT, catalase; **, 36PDH, glucose-6-phosphate dehydrogenase; LPOX, lipid peroxidation; TBARS, thiobarbitutric acid reactive substances. Field studies on responses of organic trace pollutants on fish hepatic antioxidant enzymes Table 10
Symbols and abbrevations: — —, strong inhibition (< 20% of control); —, inhibition; =, no (significant) response; +, induction; + +, strong duction (> 500% of control); *, SOD, superoxide dismutase; GPOX, glutathione peroxidase; GRED, glutathione reductase; CAT, catalase; **, 36PDH, glucose-6-phosphate dehydrogenase; LPOX, lipid peroxidation; TBARS, thiobarbitutric acid reactive substances. Field studies on responses of organic trace pollutants on fish hepatic antioxidant enzymes Table 10
Laboratory studies on responses of organic trace pollutants on fish PAH related parameters, serum transaminases and gross indices
Laboratory studies on responses of organic trace pollutants on fish PAH related parameters, serum transaminases and gross indices
Field studies on responses of organic trace pollutants on fish PAH related parameters, serum transaminases and gross indices
Field studies on responses of organic trace pollutants on fish PAH related parameters, serum transaminases and gross indices
Fig. 7. Frequencies of pollutant-induced responses of phase I-related enzymes in fish: (A) cytochrome P450 (cyt P450); (B) cytochrome P450 1A (CYPIA); (C) aryl hydrocarbon hydroxylase (AHH); (D) ethoxyresorufin O-deethylase (EROD); (E) cytochrome bs (cyt bs); (F) cytochrome P450 (c) reductase (P450 RED). — —, strong decrease ( < 20% of control); —, decrease; =, no (significant) response; +, increase; + +, strong increase ( > 500% of control).
Fig. 7. Frequencies of pollutant-induced responses of phase I-related enzymes in fish: (A) cytochrome P450 (cyt P450); (B) cytochrome P450 1A (CYPIA); (C) aryl hydrocarbon hydroxylase (AHH); (D) ethoxyresorufin O-deethylase (EROD); (E) cytochrome bs (cyt bs); (F) cytochrome P450 (c) reductase (P450 RED). — —, strong decrease ( < 20% of control); —, decrease; =, no (significant) response; +, increase; + +, strong increase ( > 500% of control).
Fig. 8. Frequencies of pollutant-induced responses of phase II enzymes and cofactors in fish: (A) reduced glutathione (GSH); (B) oxidized glutathione (GSSG); (C) glutathione S-transferase (GST); (D) UDP-glucuronyl transferase (UDPGT); --: strong decrease ( < 20% of control); —: decrease; =: no (significant) response; +: increase; + +: strong increase (> 500% of control).
Fig. 8. Frequencies of pollutant-induced responses of phase II enzymes and cofactors in fish: (A) reduced glutathione (GSH); (B) oxidized glutathione (GSSG); (C) glutathione S-transferase (GST); (D) UDP-glucuronyl transferase (UDPGT); --: strong decrease ( < 20% of control); —: decrease; =: no (significant) response; +: increase; + +: strong increase (> 500% of control).
Fig. 9. Frequencies of pollutant-induced responses of antioxidant enzymes in fish: (A) superoxide dismutase (SOD); (B) catalase CAT; (C glutathione peroxidase (GPOX); (D) glutathione reductase (GRED). ——, strong decrease (< 20% of control); —, decrease; =, no (significant response; +, increase; + +, strong increase ( > 500% of control).
Fig. 9. Frequencies of pollutant-induced responses of antioxidant enzymes in fish: (A) superoxide dismutase (SOD); (B) catalase CAT; (C glutathione peroxidase (GPOX); (D) glutathione reductase (GRED). ——, strong decrease (< 20% of control); —, decrease; =, no (significant response; +, increase; + +, strong increase ( > 500% of control).
Fig. 10. Frequencies of pollutant-induced responses of fish biotransformation products, serum transaminases and morphological parameters: (A) fluorescent PAH metabolites in bile (bile FAC); (B) DNA adducts; (C) alanine transaminase (ALT) in plasma; (D) aspartate transaminase (AST) in plasma; (E) liver somatic index (LSI); (F) condition factor CF. — —, strong decrease ( < 20% of control); —, decrease; =, no (significant) response; +, increase; + +, strong increase ( > 500% of control).
Fig. 10. Frequencies of pollutant-induced responses of fish biotransformation products, serum transaminases and morphological parameters: (A) fluorescent PAH metabolites in bile (bile FAC); (B) DNA adducts; (C) alanine transaminase (ALT) in plasma; (D) aspartate transaminase (AST) in plasma; (E) liver somatic index (LSI); (F) condition factor CF. — —, strong decrease ( < 20% of control); —, decrease; =, no (significant) response; +, increase; + +, strong increase ( > 500% of control).
Fig. 11. A theoretical visualization of the relationships between ecological relevance and time-scales of pollutant-induced biomarker responses. Adapted from Adams et al. (1989).
Fig. 11. A theoretical visualization of the relationships between ecological relevance and time-scales of pollutant-induced biomarker responses. Adapted from Adams et al. (1989).
Fig. 12. Linkage between P450 and other biochemical systems. This figure illustrates the complex interactions that are known to occur between biochemical systems involved in responses to pollutant exposure. Further linkages remain to be discovered. AhR, Ah receptor; ALAS, 5-amino- levulinic acid synthase; ARE, antioxidant responsive element (electrophilic response element); ARNT, Ah receptor nuclear translocator; BR, bilirubin; BV, biliverdin; CO, carbon monoxide; DRE, dioxin responsive element; EH, epoxide hydrolase; GSH, glutathione; GST, glutathione S- transferase; HAH, halogenated aromatic hydrocarbon; HO, heme oxygenase; HQ, hydroquinone; HSF, heat shock factor; HSP90, 90 kDa heat shock protein; HSRE, heat shock response element; M, metal; MRE, metal responsive element; MRF, metal response factor; MT, metallothionein; NO, nitric oxide; NOS, nitric oxide synthase; cyt P450, cytochrome P450; PP, protoporphyrin; Q, quinone; QR, quinone reductase (a.k.a. DT- diaphorase); SOD, superoxide dismutase; SQ, semiquinone radical; XRE, xenobiotic response element. Adapted from Stegeman and Hahn (1994)
Fig. 12. Linkage between P450 and other biochemical systems. This figure illustrates the complex interactions that are known to occur between biochemical systems involved in responses to pollutant exposure. Further linkages remain to be discovered. AhR, Ah receptor; ALAS, 5-amino- levulinic acid synthase; ARE, antioxidant responsive element (electrophilic response element); ARNT, Ah receptor nuclear translocator; BR, bilirubin; BV, biliverdin; CO, carbon monoxide; DRE, dioxin responsive element; EH, epoxide hydrolase; GSH, glutathione; GST, glutathione S- transferase; HAH, halogenated aromatic hydrocarbon; HO, heme oxygenase; HQ, hydroquinone; HSF, heat shock factor; HSP90, 90 kDa heat shock protein; HSRE, heat shock response element; M, metal; MRE, metal responsive element; MRF, metal response factor; MT, metallothionein; NO, nitric oxide; NOS, nitric oxide synthase; cyt P450, cytochrome P450; PP, protoporphyrin; Q, quinone; QR, quinone reductase (a.k.a. DT- diaphorase); SOD, superoxide dismutase; SQ, semiquinone radical; XRE, xenobiotic response element. Adapted from Stegeman and Hahn (1994)
Fig. 13. The complexity of stress—response relationships. The dose— response paradigm, although necessarily simple for experimental practice, does not adequately account for the multiple, simultaneous stressors to which all species are subjected in natural environments. Adapted from Power and McCarty (1997).
Fig. 13. The complexity of stress—response relationships. The dose— response paradigm, although necessarily simple for experimental practice, does not adequately account for the multiple, simultaneous stressors to which all species are subjected in natural environments. Adapted from Power and McCarty (1997).
Fig. 14. Visualization of the evaluation of 12 groups of fish biomarkers by adding up the results of a judgment (good, 1; fair, 0.5 and poor, 0) using six biomarker criteria. phase I, phase I biotrans- formation enzymes; phase II, phase II biotransformation enzymes; antiox, antioxidant enzymes; biotrans, biotransformation products; proteins, stress proteins, metallothioneins & MXR-proteins; blood, serum transaminases; immune, immunological parameters; repro, reproductive parameters; genotox, genotoxic parameters; histopath, histopathological parameters; morpho, morphological parameters. purposes of environmental monitoring programs, such as first carrying out cost-effective measurements in a stepwise approach, obtaining insights into the cause of observed effects in the field, studying trends in time or spatial variation, or using biomarker responses as signals of negative effects on the ecosystem. The characteristics and specific research needs for the application of biomarkers to perform screening, diag- nosis, trend monitoring (both in time and space) or risk assessment are outlined in Table 14 (adapted from Den Besten, 1998). The use of biomarkers for risk assessment at the community and ecosystem level is still rather ambitious. In the assessment of site-specific risks,
Fig. 14. Visualization of the evaluation of 12 groups of fish biomarkers by adding up the results of a judgment (good, 1; fair, 0.5 and poor, 0) using six biomarker criteria. phase I, phase I biotrans- formation enzymes; phase II, phase II biotransformation enzymes; antiox, antioxidant enzymes; biotrans, biotransformation products; proteins, stress proteins, metallothioneins & MXR-proteins; blood, serum transaminases; immune, immunological parameters; repro, reproductive parameters; genotox, genotoxic parameters; histopath, histopathological parameters; morpho, morphological parameters. purposes of environmental monitoring programs, such as first carrying out cost-effective measurements in a stepwise approach, obtaining insights into the cause of observed effects in the field, studying trends in time or spatial variation, or using biomarker responses as signals of negative effects on the ecosystem. The characteristics and specific research needs for the application of biomarkers to perform screening, diag- nosis, trend monitoring (both in time and space) or risk assessment are outlined in Table 14 (adapted from Den Besten, 1998). The use of biomarkers for risk assessment at the community and ecosystem level is still rather ambitious. In the assessment of site-specific risks,
Symbols: +, good; +, fair; —, poor. Abbriviations: phase I, phase I biotransformation enzymes; phase II, phase II biotransformation enzymes; antiox, antioxidant enzymes; bi biotransformation products; proteins, stress proteins, metallothioneins & MXR-proteins; blood, serum transaminases; immune, immunological parameters; repro, reproductive parameters; ge gemotoxic parameters; histopath, histopathological parameters; morpho, morphological parameters (individual biomarker abbriviations in text of Section 6). Fish biomarker evaluation, using six biomarker criteria
Symbols: +, good; +, fair; —, poor. Abbriviations: phase I, phase I biotransformation enzymes; phase II, phase II biotransformation enzymes; antiox, antioxidant enzymes; bi biotransformation products; proteins, stress proteins, metallothioneins & MXR-proteins; blood, serum transaminases; immune, immunological parameters; repro, reproductive parameters; ge gemotoxic parameters; histopath, histopathological parameters; morpho, morphological parameters (individual biomarker abbriviations in text of Section 6). Fish biomarker evaluation, using six biomarker criteria
Table 14 Due to the interdisciplinary nature of biomarker studies and the need for integration of numerous research specialties, long-term progress will be acceler- ated by general agreement on a common research strategy. Future research should be focused on the possible implementation of biomarkers in environmen- tal monitoring programs. However, since monitoring information requirements and monitoring objectives are very situation-specific and are strongly dependent on national water management policies, it is unlikely that the near future will show a global trend towards unification of standard biomonitoring protocols (De Zwart, 1995). The ultimate objective for applied envir- information from biomarkers should be used in combi- nation with other biological data (e.g. species abun- dance) and chemical data (Den Besten, 1998). Ellis (2000) discussed the advantages and limitations of four different risk assessment approaches (chemical specific limits, biological assessment, direct toxicity assessment [DTA] and biomarker techniques) in urban receiving waters. The inability of DTA procedures to satisfacto- rily evaluate chronic, sub-lethal risks increased the interest in using in situ biomarkers for the fingerprinting of stress-response properties as a means of diagnosing risk assessment for integrated urban runoff management (Ellis, 2000).
Table 14 Due to the interdisciplinary nature of biomarker studies and the need for integration of numerous research specialties, long-term progress will be acceler- ated by general agreement on a common research strategy. Future research should be focused on the possible implementation of biomarkers in environmen- tal monitoring programs. However, since monitoring information requirements and monitoring objectives are very situation-specific and are strongly dependent on national water management policies, it is unlikely that the near future will show a global trend towards unification of standard biomonitoring protocols (De Zwart, 1995). The ultimate objective for applied envir- information from biomarkers should be used in combi- nation with other biological data (e.g. species abun- dance) and chemical data (Den Besten, 1998). Ellis (2000) discussed the advantages and limitations of four different risk assessment approaches (chemical specific limits, biological assessment, direct toxicity assessment [DTA] and biomarker techniques) in urban receiving waters. The inability of DTA procedures to satisfacto- rily evaluate chronic, sub-lethal risks increased the interest in using in situ biomarkers for the fingerprinting of stress-response properties as a means of diagnosing risk assessment for integrated urban runoff management (Ellis, 2000).