With my research I want to achieve a better understanding of toxic effects on aquatic organisms and create better tools for risk assessment of chemicals. For both purposes I develop ecotoxicological effect models, with a focus on toxicokinetic-toxicodynamic models. Hence my research consists of mathematical modelling and ecotoxicological experiments with organic xenobiotic compounds and freshwater invertebrates. Ultimately I hope to help find some of the general principles that determine the effects of chemicals in nature.

• What makes different chemicals more or less toxic?
• Why do biological species and individuals differ in their sensitivities?
• How do effects of chemicals propagate from lower to higher levels of biological organisation? 

Models are key tool to answer these questions.

The papers can be downloaded from this site, but that requires a password because the journals own the copyright for most papers. The password can be found in my email signature. You can also request reprints or the password from me and I'll send them to you.

28. Kretschmann A., Ashauer R., Hollender J., Escher B.I. (2012). Toxicokinetic and toxicodynamic model for diazinon toxicity—mechanistic explanation of differences in the sensitivity of Daphnia magna and Gammarus pulex. Environmental Toxicology and Chemistry. in press, (available online).

A mechanistic toxicokinetic and toxicodynamic model for acute toxic effects (immobilization, mortality) of the organothiophosphate insecticide diazinon in Daphnia magna is presented. The model was parameterized using measured external and internal (whole-body) concentrations of diazinon, its toxic metabolite diazoxon, and the inactive metabolite 2-isopropyl-6-methyl-4-pyrimidinol, plus acetylcholinesterase (AChE) activity measured during exposure to diazinon in vivo. The toxicokinetic and toxicodynamic model provides a coherent picture from exposure to the resulting toxic effect on an organism level through internally formed metabolites and the effect on a molecular scale. A very fast reaction of diazoxon with AChE (pseudo first-order inhibition rate constant k_i = 3.3 h⁻¹) compared with a slow formation of diazoxon (activation rate constant k_act = 0.014 h⁻¹) was responsible for the high sensitivity of D. magna toward diazinon. Recovery of AChE activity from inhibition was slow and rate-determining (99% recovery within 16 d), compared with a fast elimination of diazinon (99% elimination within 17 h). The obtained model parameters were compared with toxicokinetic and toxicodynamic parameters of Gammarus pulex exposed to diazinon from previous work. This comparison revealed that G. pulex is less sensitive because of a six times faster detoxification of diazinon and diazoxon and an approximately 400 times lower rate for damage accrual. These differences overcompensate the two times faster activation of diazinon to diazoxon in G. pulex compared to D. magna. The present study substantiates theoretical considerations that mechanistically based effect models are helpful to explain sensitivity differences among different aquatic invertebrates.

27. Nyman, A.-M.; Schirmer, K.; Ashauer, R. (2012). Toxicokinetic-toxicodynamic modelling of survival of Gammarus pulex in multiple pulse exposures to propiconazole: model assumptions, calibration data requirements and predictive power. Ecotoxicology, 21(7), p. 1828-1840. (link to paper, free access)

Toxicokinetic-toxicodynamic (TKTD) models quantify the time-course of internal concentration, which is defined by uptake, elimination and biotransformation (TK), and the processes which lead to the toxic effects (TD). TKTD models show potential in predicting pesticide effects in fluctuating concentrations, but the data requirements and validity of underlying model assumptions are not known. We calibrated TKTD models to predict survival of Gammarus pulex in propiconazole exposure and investigated the data requirements. In order to assess the need of TK in survival models, we included or excluded simulated internal concentrations based on pre-calibrated TK. Adding TK did not improve goodness of fits. Moreover, different types of calibration data could be used to model survival, which might affect model parameterization. We used two types of data for calibration: acute toxicity (standard LC50, 4 d) or pulsed toxicity data (total length 10 d). The calibration data set influenced how well the survival in the other exposure scenario was predicted (acute to pulsed scenario or vice versa). We also tested two contrasting assumptions in ecotoxicology: stochastic death and individual tolerance distribution. Neither assumption fitted to data better than the other. We observed in 10-d toxicity experiments that pulsed treatments killed more organisms than treatments with constant concentration. All treatments received the same dose, i.e. the time-weighted average concentration was equal. We studied mode of toxic action of propiconazole and it likely acts as a baseline toxicant in G. pulex during 10-days of exposure for the endpoint survival.

26. Stadnicka, J; Schirmer, K; Ashauer, R (2012). Predicting Concentrations of Organic Chemicals in Fish by Using Toxicokinetic Models. Environ. Sci. Technol. 46 (6), 3273-3280. (link to journal) or download here (free access)

Quantification of chemical toxicity continues to be generally based on measured external concentrations. Yet, internal chemical concentrations have been suggested to be a more suitable parameter. To better understand the relationship between the external and internal concentrations of chemicals in fish, and to quantify internal concentrations, we compared three toxicokinetic (TK) models with each other and with literature data of measured concentrations of 39 chemicals. Two one-compartment models, together with the physiologically based toxicokinetic (PBTK) model, in which we improved the treatment of lipids, were used to predict concentrations of organic chemicals in two fish species: rainbow trout (Oncorhynchus mykiss) and fathead minnow (Pimephales promelas). All models predicted the measured internal concentrations in fish within 1 order of magnitude for at least 68% of the chemicals. Furthermore, the PBTK model outperformed the one-compartment models with respect to simulating chemical concentrations in the whole body (at least 88% of internal concentrations were predicted within 1 order of magnitude using the PBTK model). All the models can be used to predict concentrations in different fish species without additional experiments. However, further development of TK models is required for polar, ionizable, and easily biotransformed compounds.

25. Ashauer, R; Hintermeister, A; O'Connor, I; Elumelu, M; Hollender, J; Escher, BI (2012). Significance of Xenobiotic Metabolism for Bioaccumulation Kinetics of Organic Chemicals in Gammarus pulex. Environ. Sci. Technol. 46 (6), 3498-3508. (link to journal) or download here (free access)

Bioaccumulation and biotransformation are key toxicokinetic processes that modify toxicity of chemicals and sensitivity of organisms. Bioaccumulation kinetics vary greatly among organisms and chemicals; thus, we investigated the influence of biotransformation kinetics on bioaccumulation in a model aquatic invertebrate using fifteen ¹⁴C-labeled organic xenobiotics from diverse chemical classes and physicochemical properties (1,2,3-trichlorobenzene, imidacloprid, 4,6-dinitro-o-cresol, ethylacrylate, malathion, chlorpyrifos, aldicarb, carbofuran, carbaryl, 2,4-dichlorophenol, 2,4,5-trichlorophenol, pentachlorophenol, 4-nitrobenzyl-chloride, 2,4-dichloroaniline, and sea-nine (4,5-dichloro-2-octyl-3-isothiazolone)). We detected and identified metabolites using HPLC with UV and radio-detection as well as high resolution mass spectrometry (LTQ-Orbitrap). Kinetics of uptake, biotransformation, and elimination of parent compounds and metabolites were modeled with a first-order one-compartment model. Bioaccumulation factors were calculated for parent compounds and metabolite enrichment factors for metabolites. Out of 19 detected metabolites, we identified seven by standards or accurate mass measurements and two via pathway analysis and analogies to other compounds. 1,2,3-Trichlorobenzene, imidacloprid, and 4,6-dinitro-o-cresol were not biotransformed. Dietary uptake contributed little to overall uptake. Differentiation between parent and metabolites increased accuracy of bioaccumulation parameters compared to total ¹⁴C measurements. Biotransformation dominated toxicokinetics and strongly affected internal concentrations of parent compounds and metabolites. Many metabolites reached higher internal concentrations than their parents, characterized by large metabolite enrichment factors.

24. Meredith‐Williams M, Carter LJ, Fussell R, Raffaelli D, Ashauer R, Boxall ABA (2012): Uptake and depuration of pharmaceuticals in aquatic invertebrates. Environmental Pollution, 165, 250-258. (link at journal)

The uptake and depuration of a range of pharmaceuticals in the freshwater shrimp (Gammarus pulex) and the water boatman (Notonecta glauca) was studied. For one compound, studies were also done using the freshwater snail Planobarius corneus. In G. pulex, bioconcentration factors (BCFs) ranged from 4.6 to 185,900 and increased in the order moclobemide < 5-fluoruracil < carbamazepine < diazepam < carvedilol < fluoxetine. In N. glauca BCFs ranged from 0.1 to 1.6 and increased in the order 5-fluorouracil < carbamazepine < moclobemide < diazepam < fluoxetine < carvedilol. For P. corneus, the BCF for carvedilol was 57.3. The differences in degree of uptake across the three organisms may be due to differences in mode of respiration, behaviour and the pH of the test system. BCFs of the pharmaceuticals for each organism were correlated to the pH-corrected liposome–water partition coefficient of the pharmaceuticals.

23. Ashauer R, Wittmer I, Stamm C and Escher BI (2011): Environmental Risk Assessment of Fluctuating Diazinon Concentrations in an Urban and Agricultural Catchment Using Toxicokinetic–Toxicodynamic Modeling. Environmental Science & Technology, 45 (22), 9783-9792. (link at journal) or (download below).

Temporally resolved environmental risk assessment of fluctuating concentrations of micropollutants is presented. We separated the prediction of toxicity over time from the extrapolation from one to many species and from acute to sublethal effects. A toxicokinetic–toxicodynamic (TKTD) model predicted toxicity caused by fluctuating concentrations of diazinon, measured by time-resolved sampling over 108 days from three locations in a stream network, representing urban, agricultural and mixed land use. We calculated extrapolation factors to quantify variation in toxicity among species and effect types based on available toxicity data, while correcting for different test durations with the TKTD model. Sampling from the distribution of extrapolation factors and prediction of time-resolved toxicity with the TKTD model facilitated subsequent calculation of the risk of undesired toxic events. Approximately one-fifth of aquatic organisms were at risk and fluctuating concentrations were more toxic than their averages. Contribution of urban and agricultural sources of diazinon to the overall risk varied. Thus using fixed concentrations as water quality criteria appears overly simplistic because it ignores the temporal dimension of toxicity. However, the improved prediction of toxicity for fluctuating concentrations may be small compared to uncertainty due to limited diversity of toxicity data to base the extrapolation factors on.

22. Ashauer R, Agatz A, Albert C, Ducrot V, Galic N, Hendriks AJ, Jager T, Kretschmann A, O’Connor I, Rubach MN, Nyman A-M, Schmitt W, Stadnicka J, van den Brink PJ, Preuss TG (2011): Toxicokinetic-toxicodynamic modelling of quantal and graded sub-lethal endpoints - a brief discussion of concepts. Environmental Toxicology and Chemistry, 30, (11), 2519-2524. (link at journal)

We report on the advantages and problems of using toxicokinetic-toxicodynamic (TKTD) models for the analysis, understanding, and simulation of sublethal effects. Only a few toxicodynamic approaches for sublethal effects are available. These differ in their effect mechanism and emphasis on linkages between endpoints. We discuss how the distinction between quantal and graded endpoints and the type of linkage between endpoints can guide model design and selection. Strengths and limitations of two main approaches and possible ways forward are outlined. 

21. Albert C, Ashauer R, Künsch HR, Reichert P (2012): Bayesian Experimental Design for a Toxicokinetic-Toxicodynamic Model. Journal of Statistical Planning and Inference, 142, 263-275. (link at journal)

The aim of this study is to apply the Bayesian method of identifying optimal experimental designs to a toxicokinetic–toxicodynamic model that describes the response of aquatic organisms to time dependent concentrations of toxicants. As for experimental designs, we restrict ourselves to pulses and constant concentrations. A design of an experiment is called optimal within this set of designs if it maximizes the expected gain of knowledge about the parameters. Focus is on parameters that are associated with the auxiliary damage variable of the model that can only be inferred indirectly from survival time series data. Gain of knowledge through an experiment is quantified both with the ratio of posterior to prior variances of individual parameters and with the entropy of the posterior distribution relative to the prior on the whole parameter space. The numerical methods developed to calculate expected gain of knowledge are expected to be useful beyond this case study, in particular for multinomially distributed data such as survival time series data.

20. Kretschmann A, Ashauer R, Hitzfeld K, Spaak P, Hollender J, Escher BI (2011): Mechanistic Toxicodynamic Model for Receptor-Mediated Toxicity of Diazoxon, the Active Metabolite of Diazinon, in Daphnia magna. Environmental Science and Technology, 45, (11), 4980-4987. (link at journal)

The organothiophosphate diazinon inhibits the target site acetylcholinesterase only after activation to its metabolite diazoxon. Commonly, the toxicity of xenobiotics toward aquatic organisms is expressed as a function of the external concentration and the resulting effect on the individual level after fixed exposure times. This approach does not account for the time dependency of internal processes such as uptake, metabolism, and interaction of the toxicant with the target site. Here, we develop a mechanistic toxicodynamic model for Daphnia magna and diazoxon, which accounts for the inhibition of the internal target site acetylcholinesterase and its link to the observable effect, immobilization, and mortality. The model was parametrized by experiments performed in vitro with the active metabolite diazoxon on enzyme extracts and in vivo with the parent compound diazinon. The mechanism of acetylcholinesterase inhibition was shown to occur irreversibly in two steps via formation of a reversible enzyme-inhibitor complex. The corresponding kinetic parameters revealed a very high sensitivity of acetylcholinesterase from D. magna toward diazoxon, which corresponds well with the high toxicity of diazinon toward this species. Recovery of enzyme activity but no recovery from immobilization was observed after in vivo exposure to diazinon. The toxicodynamic model combining all in vitro and in vivo parameters was successfully applied to describe the time course of immobilization in dependence of acetylcholinesterase activity during exposure to diazinon. The threshold value for enzyme activity below which immobilization set in amounted to 40% of the control activity. Furthermore, the model enabled the prediction of the time-dependent diazoxon concentration directly present at the target site.

19. Kretschmann A, Ashauer R, Preuss TG, Spaak P, Escher BI, Hollender J (2011): Toxicokinetic Model Describing Bioconcentration and Biotransformation of Diazinon in Daphnia magna. Environmental Science and Technology, 45, (11), 4995-5002. (link at journal)

A toxicokinetic model for Daphnia magna, which simulates the internal concentration of the insecticide diazinon, its detoxification product 2-isopropyl-6-methyl-4-pyrimidinol, and its active metabolite diazoxon, is presented. During in vivo exposure to diazinon with and without inhibition of cytochrome P450 by piperonyl butoxide, the parent compound as well as its metabolites were quantified with high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) in extracts of D. magna. Rate constants of all relevant toxicokinetic steps were obtained by modeling the time course of the internal concentrations with a multicomponent first-order kinetics model. When cytochrome P450 was inhibited, the kinetic bioconcentration factor (BCF) of diazinon increased from 17.8 to 51.0 mL·g_ww^-1. This clearly indicates that diazinon is biotransformed to a high degree by cytochrome P450 in D. magna. The dominant elimination step of diazinon was shown to be its oxidative dearylation to pyrimidinol (62% of total elimination) with a corresponding rate constant of 0.16 h^-1. In contrast, oxidative activation to diazoxon with a rate constant of 0.02 h^-1 amounted to only 8% of the total elimination. During exposure to diazinon, the active metabolite diazoxon could be detected only in very low concentrations (approximately 0.5% of the parent compound), presumably due to a very fast reaction with the target site acetylcholinesterase. During the exposure experiments (no feeding of daphnids), an exponential decline of the lipid content in D. magna with a first-order rate constant of 0.013 h^-1 was observed. For short exposure times (≥24 h), this had only a minor influence on the determined TK parameters. Such a TK model containing detailed biotransformation processes is an important tool for estimation of the toxic potential of chemicals, particularly, when active metabolites are formed inside an organism.

17. Rubach MN, Ashauer R, Buchwalter DB, de Lange HJ, Hamer M, Preuss TG, Töpke K, Maund SJ. (2011). Framework for traits-based assessment in ecotoxicology. Integrated environmental assessment and management, 7(2):172-186. (link at journal)

A key challenge in ecotoxicology is to assess the potential risks of chemicals to the wide range of species in the environment on the basis of laboratory toxicity data derived from a limited number of species. These species are then assumed to be suitable surrogates for a wider class of related taxa. For example, Daphnia spp. are used as the indicator species for freshwater aquatic invertebrates. Extrapolation from these datasets to natural communities poses a challenge because the extent to which test species are representative of their various taxonomic groups is often largely unknown, and different taxonomic groups and chemicals are variously represented in the available datasets. Moreover, it has been recognized that physiological and ecological factors can each be powerful determinants of vulnerability to chemical stress, thus differentially influencing toxicant effects at the population and community level. Recently it was proposed that detailed study of species traits might eventually permit better understanding, and thus prediction, of the potential for adverse effects of chemicals to a wider range of organisms than those amenable for study in the laboratory. This line of inquiry stems in part from the ecology literature, in which species traits are being used for improved understanding of how communities are constructed, as well as how communities might respond to, and recover from, disturbance (see other articles in this issue). In the present work, we develop a framework for the application of traits-based assessment. The framework is based on the population vulnerability conceptual model of Van Straalen in which vulnerability is determined by traits that can be grouped into 3 major categories, i.e., external exposure, intrinsic sensitivity, and population sustainability. Within each of these major categories, we evaluate specific traits as well as how they could contribute to the assessment of the potential effects of a toxicant on an organism. We then develop an example considering bioavailability to explore how traits could be used mechanistically to estimate vulnerability. A preliminary inventory of traits for use in ecotoxicology is included; this also identifies the availability of data to quantify those traits, in addition to an indication of the strength of linkage between the trait and the affected process. Finally, we propose a way forward for the further development of traits-based approaches in ecotoxicology.

14. Escher BI, Ashauer R, Dyer S, Hermens JLM, Lee J-H, Leslie HA, Mayer P, Meador JP, Warne MSJ (2010): Crucial role of mechanisms and modes of toxic action for understanding tissue residue toxicity and internal effect concentrations of organic chemicals. Integrated Environmental Assessment and Management, 7:28-49. (link at journal)

This article reviews the mechanistic basis of the tissue residue approach for toxicity assessment (TRA). The tissue residue approach implies that whole-body or organ concentrations (residues) are a better dose metric for describing toxicity to aquatic organisms than is the aqueous concentration typically used in the external medium. Although the benefit of internal concentrations as dose metrics in ecotoxicology has long been recognized, the application of the tissue residue approach remains limited. The main factor responsible for this is the difficulty of measuring internal concentrations. We propose that environmental toxicology can advance if mechanistic considerations are implemented and toxicokinetics and toxicodynamics are explicitly addressed. The variability in ecotoxicological outcomes and species sensitivity is due in part to differences in toxicokinetics, which consist of several processes, including absorption, distribution, metabolism, and excretion (ADME), that influence internal concentrations. Using internal concentrations or tissue residues as the dose metric substantially reduces the variability in toxicity metrics among species and individuals exposed under varying conditions. Total internal concentrations are useful as dose metrics only if they represent a surrogate of the biologically effective dose, the concentration or dose at the target site. If there is no direct proportionality, we advise the implementation of comprehensive toxicokinetic models that include deriving the target dose. Depending on the mechanism of toxicity, the concentration at the target site may or may not be a sufficient descriptor of toxicity. The steady-state concentration of a baseline toxicant associated with the biological membrane is a good descriptor of the toxicodynamics of baseline toxicity. When assessing specific-acting and reactive mechanisms, additional parameters (e.g., reaction rate with the target site and regeneration of the target site) are needed for characterization. For specifically acting compounds, intrinsic potency depends on 1) affinity for, and 2) type of interaction with, a receptor or a target enzyme. These 2 parameters determine the selectivity for the toxic mechanism and the sensitivity, respectively. Implementation of mechanistic information in toxicokinetic-toxicodynamic (TK-TD) models may help explain timedelayed effects, toxicity after pulsed or fluctuating exposure, carryover toxicity after sequential pulses, and mixture toxicity.We believe that this mechanistic understanding of tissue residue toxicity will lead to improved environmental risk assessment.

13. Rubach MR, Ashauer R, Maund SJ, Baird DJ, van den Brink PJ (2010): Toxicokinetic variation in 15 freshwater arthropod species exposed to the insecticide chlorpyrifos. Environmental Toxicology and Chemistry, 29(10):2225-2234. (link at journal) 

Recent advances in modeling the processes of the toxicity of chemicals-toxicokinetics (TK) and toxicodynamics (TD)-are improving environmental risk assessment (ERA) through prediction of effects from time-varying exposure. This has been achieved by linking chemical fate and toxicological effects mechanistically, based on internal concentrations, through the tissue residue approach. However, certain questions remain: for example, how do TK and TD differ among species and how does this relate to differences in species sensitivity? In a series of experiments, we studied the TK of [¹⁴C]chlorpyrifos in 15 freshwater arthropod species, two of which were studied in juvenile and adult life stages. Uptake (k_in) and elimination (k_out) rate constants were fitted using a one-compartment single first-order kinetic model. The application of two complementary parameter estimation methods facilitated the calculation of bioconcentration factors (BCF) with prediction intervals and 95% depuration times (t₉₅) for all tested species. Extremely slow elimination was observed in some species as well as high overall variation in k_in, k_out, BCF, and t₉₅ across the tested aquatic arthropod species. This variation has implications for the development of TKTD approaches in ERA, including assessing fluctuating exposure concentrations and the interpretation of observed toxicity responses in the laboratory and in the field.

9. Grimm G, Ashauer R, Forbes V, Hommen U, Preuss TG, Schmidt A, van den Brink PJ, Wogram J, Thorbek P (2009): CREAM: a European project on mechanistic effect models for ecological risk assessment of chemicals. Environmental Science and Pollution Research 16(6): 614-617. (link at journal)

8. Preuss TG, Hommen U, Alix A, Ashauer R, van den Brink P, Chapman P, Ducrot V, Forbes V, Grimm V, Schäfer D, Streissl, Thorbeck P (2009): Mechanistic effect models for ecological risk assessment of chemicals (MEMoRisk) - a new SETAC-Europe Advisory Group. Environmental Science and Pollution Research 16(3): 250-252. (link at journal)

7. Blackwell PA, Kay P, Ashauer R, Boxall ABA (2009): Effects of agricultural conditions on the leaching behaviour of veterinary antibiotics in soil. Chemosphere 75: 13-19. (link at journal) 

Antibiotics may be released to soils during the application of manure as fertiliser to land. The compounds may subsequently be transported to and contaminate groundwater and surface waters. This paper describes a series of lysimeter-based studies to explore the leaching behaviour of three veterinary antibiotics (sulfachloropyridazine, oxytetracycline and tylosin) under different conditions that could occur in the agricultural environment. The specific objectives were to: (1) explore the influence of slurry amendment and incorporation on leaching; (2) assess the effects of climate on leaching behaviour; and (3) evaluate the predictive capability of a leaching model used in the regulatory assessment of veterinary medicines. Sulfachloropyridazine was detected sporadically in leachate at concentrations up to 0.66 μg L⁻¹ under typical irrigation conditions and more frequently at concentrations up to 8.5 μg L⁻¹ under extreme irrigation conditions. Incorporation and timing of rainfall had no effect on leaching behaviour. Oxytetracycline and tylosin were not detected in any leachate samples. These differences in behaviour were explained by the sorption and persistence characteristics of the compounds. Comparison of the experimental measurements with simulations from the leaching model indicated that the model greatly underestimates the transport of antibiotics to groundwater which raises questions over the application of these models in the regulatory risk assessment process.

5. Ashauer R, Boxall ABA, Brown CD (2007c): Modelling combined effects of pulsed exposure to carbaryl and chlorpyrifos on Gammarus pulex. Environmental Science and Technology 41(15): 5535-5541. (link at journal) 

Aquatic risk assessment can be improved if we are able to quantitatively predict the effects resulting from sequential pulsed exposure to multiple compounds. We evaluate two modeling approaches, both extended to suit multiple compounds, the semi-mechanistic threshold damage model (TDM), and a model based on time-weighted averages (TWA). The TDM predicts that recovery of damage to Gammarus pulex from exposure to chlorpyrifos takes longer than that from exposure to carbaryl and consequently that the sequence of exposure matters. We measured survival of the freshwater invertebrate Gammarus pulex after sequential pulsed exposure to carbaryl and chlorpyrifos. Two groups of organisms were exposed to a first pulse of either carbaryl or chlorpyrifos for 1 day and then, after a recovery period of two weeks, to a second pulse with the other compound. The comparison of mortalities caused by each pulse, as well as combined mortalities in both treatments, show that the sequence of exposure to pulses of contaminants does indeed matter. Previous exposure to chlorpyrifos leads to significantly increased mortality from subsequent pulses of carbaryl, but not the other way round.The TDM facilitates a process-based ecotoxicological explanation by simulating the recovery dynamics and outperforms the TWA model.

4. Ashauer R, Boxall ABA, Brown CD (2007b): Simulating toxicity of carbaryl to Gammarus pulex after sequential pulsed exposure. Environmental Science and Technology 41(15): 5528-5534. (link at journal)

Aquatic nontarget organisms are typically exposed to sequential pulses of contaminants with fluctuating concentrations. We use the semimechanistic threshold damage model (TDM) to simulate survival of the aquatic invertebrate Gammarus pulex after sequential pulsed exposure to carbaryl and compare it to a simpler model based on time-weighted averages (TWA). The TDM is a process-based model and we demonstrate how to parametrize it with data from an uptake and elimination experiment together with data from a survival experiment with sequential pulses. The performance of the two models is compared by the fit to the first survival experiment and the simulation of another, independent survival experiment with different exposure patterns. Measured internal concentrations in the first survival experiment are used to evaluate the toxicokinetic submodel of the TDM. The TDM outperforms the TWA model, facilitates understanding of the underlying ecotoxicological processes, permits calculation of recovery times (3, 15, and 25 days for pentachlorophenol, carbaryl and chlorpyrifos respectively) and enables us to predict the effects of long-term exposure patterns with sequential pulses or fluctuating concentrations. We compare the parameters of the TDM for carbaryl, pentachlorophenol and chlorpyrifos and discuss implications for ecotoxicology and risk assessment.

3. Ashauer R, Boxall ABA, Brown CD (2007a): New ecotoxicological model to simulate survival of aquatic invertebrates after exposure to fluctuating and sequential pulses of pesticides. Environmental Science and Technology 41(4): 1480-1486. (link at journal)

Aquatic nontarget organisms are exposed to fluctuating concentrations or sequential pulses of contaminants, so we need to predict effects resulting from such patterns of exposure. We present a process-based model, the Threshold Damage Model (TDM), that links exposure with effects and demonstrate how to simulate the survival of the aquatic invertebrate Gammarus pulex, Based on survival experiments of up to 28 days duration with three patterns of repeated exposure pulses and fluctuating concentrations of two pesticides with contrasting modes of action (pentachlorophenol and chlorpyrifos) we evaluate the new model and compare it to two approaches based on time-weighted averages. Two models, the Threshold Damage Model and the time-weighted averages fitted to pulses, are able to simulate the observed survival (mean errors 15% or less, r² between 0.77 and 0.96). The models are discussed with respect to their theoretical base, data needs, and potential for extrapolation to different scenarios. The Threshold Damage Model is particularly useful because its parameters can be used to calculate recovery times, toxicokinetics are separated from toxicodynamics, and parameter values reflect the mode of action.

2. Ashauer R, Boxall ABA, Brown CD (2006): Uptake and elimination of chlorpyrifos and pentachlorophenol into the freshwater amphipod Gammarus pulex. Archives of Environmental Contamination and Toxicology. 51: 542-548.  (link at journal)

Uptake and elimination rates were determined for chlorpyrifos (CPF) and pentachlorophenol (PCP) in the freshwater amphipod Gammarus pulex. Internal concentrations of the two pesticides were measured over a three-day exposure phase and a subsequent three-day elimination phase. Rate constants were obtained by fitting measured internal concentrations to a one-compartment single first-order model. The uptake rate constants were 747 ± 61 [L kg⁻¹ day⁻¹] for CPF and 89 ± 7 [L kg⁻¹ day⁻¹] for PCP. The elimination rate constants were 0.45 ± 0.05 [day⁻¹] for CPF and 1.76 ± 0.14 [day⁻¹] for PCP. The resulting bioconcentration factors at steady state were 1660 and 51 for CPF and PCP, respectively. The parameter estimation method and possible variability due to varying lipid content are briefly discussed.

1. Ashauer R, Boxall ABA, Brown CD (2006): Predicting effects on aquatic organisms from fluctuating or pulsed exposure to pesticides. Environmental Toxicology and Chemistry 25(7): 1899-1912. (link at journal)

Exposure of aquatic nontarget organisms to pesticides almost always occurs as pulses or fluctuating concentrations. Extrapolation from laboratory to field thus depends on an understanding and ability to simulate effects resulting from these types of exposure. This paper reviews models that may be used to predict effects on aquatic organisms resulting from time-varying exposure to pesticides. We evaluate and compare the theoretical basis of these models and their applicability to the simulation of effects from fluctuating exposures. The many different models rest on only a few basic concepts with differing degrees of mechanistic character. Building on this critical review, we select the most appropriate models and propose modifications. Two process-based models, the threshold hazard model and the modified damage assessment model, represent the optimum descriptions that are available at present. They could facilitate a better understanding of the ecotoxicity of different compound and species combinations and even mixtures of noninteracting compounds. The possibility to model lethal and sublethal effects allows applications in risk assessment, standard setting, and ecological modeling.