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Integrated Environmental Assessment and Management
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How TK-TD and population models for aquatic macrophytes could support the risk assessment for plant protection products

Authors: Philip Manson; Gertie Arts; Walter Schmitt; Walter Schmitt; Peter van Vliet; Theo C.M. Brock; Hugo Ochoa-Acuña; +5 Authors

How TK-TD and population models for aquatic macrophytes could support the risk assessment for plant protection products

Abstract

Abstract This case study of the Society of Environmental Toxicology and Chemistry (SETAC) workshop MODELINK demonstrates the potential use of mechanistic effects models for macrophytes to extrapolate from effects of a plant protection product observed in laboratory tests to effects resulting from dynamic exposure on macrophyte populations in edge-of-field water bodies. A standard European Union (EU) risk assessment for an example herbicide based on macrophyte laboratory tests indicated risks for several exposure scenarios. Three of these scenarios are further analyzed using effect models for 2 aquatic macrophytes, the free-floating standard test species Lemna sp., and the sediment-rooted submerged additional standard test species Myriophyllum spicatum. Both models include a toxicokinetic (TK) part, describing uptake and elimination of the toxicant, a toxicodynamic (TD) part, describing the internal concentration-response function for growth inhibition, and a description of biomass growth as a function of environmental factors to allow simulating seasonal dynamics. The TK–TD models are calibrated and tested using laboratory tests, whereas the growth models were assumed to be fit for purpose based on comparisons of predictions with typical growth patterns observed in the field. For the risk assessment, biomass dynamics are predicted for the control situation and for several exposure levels. Based on specific protection goals for macrophytes, preliminary example decision criteria are suggested for evaluating the model outputs. The models refined the risk indicated by lower tier testing for 2 exposure scenarios, while confirming the risk associated for the third. Uncertainties related to the experimental and the modeling approaches and their application in the risk assessment are discussed. Based on this case study and the assumption that the models prove suitable for risk assessment once fully evaluated, we recommend that 1) ecological scenarios be developed that are also linked to the exposure scenarios, and 2) quantitative protection goals be set to facilitate the interpretation of model results for risk assessment. Integr Environ Assess Manag 2016;12:82–95. ©2015 SETAC Key Points We use an herbicide case study to demonstrate how toxicodynamics-toxicokinetics models coupled with macrophyte population models can link dynamic exposure patterns to expected biomass dynamics in the field. We introduce models for 2 macrophyte species, Lemna sp. and Myriophyllum spicatum to refine a risk assessment based on laboratory experiments. Based on specific protection goals, we propose example criteria for duration and magnitude of effects and show how the modeling results could be used as risk refinement option. We recommend further refining and testing the models to develop ecological scenarios linked to the exposure scenarios and to set quantitative protection goals to facilitate the interpretation of model results for risk assessment.

Country
Netherlands
Keywords

Environmental Risk Assessment, WIMEK, Dynamic exposure, Herbicides, Alterra - Environmental risk assessment, Ecotoxicology, Models, Biological, Plant Roots, Risk Assessment, Macrophytes, Magnoliopsida, Biomass, Mechanistic effect models, Water Pollutants, Chemical, Pesticide risk assessment, Environmental Monitoring

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    12
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Average
Average