Quantitative extrapolation in ecotoxicology

Project Information

QTOX

Grant agreement ID: 101072531

DOI

10.3030/101072531

EC signature date 6 July 2022

Start date 1 February 2023

End date 31 May 2027

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 2 727 056,97

Coordinated by

UNIVERSITEIT ANTWERPEN
Belgium

Periodic Reporting for period 1 - QTOX (Quantitative extrapolation in ecotoxicology)

Reporting period: 2023-02-01 to 2025-01-31

Summary of the context and overall objectives of the project

Reliable assessment of the ecological risks posed by chemicals is a fundamental component of European policies concerned with safe use of chemicals e.g. REACh, The Green Deal, and the Water Framework Directive. The assumption that adverse effects of chemicals can be extrapolated from laboratory test conditions (in-vitro and in-vivo) to the ecosystem level is inherently embedded in these policies. Also, the availability of extrapolation methods underpins efforts to reduce, replace, and refine the use of animals in toxicity testing. However, current methods for extrapolation of adverse effects across levels of biological organisation have limitations, due to the ecological processes that are disregarded by the models and the paucity of data for parameterisation and validation, with consequent limitations in their predictive capabilities. This lack of mechanistic underpinning raises questions about the robustness of environmental quality standards and confounds efforts to identify the cause of adverse effects and to design remediation strategies. QTOX addresses this situation via development of data efficient modelling tools based on mechanistic knowledge of the underlying processes in the chain from exposure to effects, across all levels of biological organisation, with close connection to regulatory endpoints, and under environmentally realistic conditions. The QTOX approach will bridge the gap between standard toxicity data (typically acute effects of single chemicals) and ecologically relevant end points arising from chronic, time variable exposures to mixtures. QTOX aims to develop predictive models for describing the adverse effects of chemicals under realistic long-term exposure scenarios based on systematic knowledge acquired under laboratory and semi-field (mesocosm) conditions.
The research objectives are:
1. To develop and validate quantitative mechanistic models for predicting the relationships between dynamic chemical exposure conditions and adverse effects on a range of aquatic organisms at the level of the individual under both laboratory and mesocosm conditions.
2. To elucidate the predictive capabilities of effect models from cellular to the individual and population levels, and validate the models with mesocosm data.
3. To link adverse effects on populations to those on communities (biodiversity, bioproductivity) and validate these with mesocosm data.
4. To identify the best predictors of adverse effects at the ecosystem level, and the potential ecotoxicological consequences of climate change stressors.
5. To develop an integrated data efficient open-access toolbox for quantitative extrapolations in ecotoxicology.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The chemical speciation, bioavailability, and bioaccumulation of metals and organic pollutants in aquatic macroinvertebrates under single compound and mixture conditions, also including climate change stressors is being investigated. To date, the acute effects of three stressors in various combinations (pH, temperature, and salinity) on the mortality of Daphnia magna and Brachionus calyciflorus have been determined.
The capabilities of effect-directed toxicokinetic-toxicodynamic and dynamic energy budget models to extrapolate from effects at the in-vitro to the population level are being assessed. To date, single-compound tests (growth rate) have been conducted with 13 algal species and tests with mixtures are underway using Cu, azoxystrobin, terbuthylazine. For macroinvertebrates, measurements of the effects of metal-organic mixtures on Daphnia magna populations show that independent action tends to underestimate the toxicity of binary metal-organic mixtures, while concentration addition tends to overestimate the toxicity.
For effects at the population and community levels, mechanistic relationships are being established between the potentially affected fraction of species (PAF) and biodiversity indicators, and models are being developed to assess the combined effects of chemicals and temperature. To date, a methodology for predicting the long-term impacts of chemicals has been developed based on the time-dependence of toxicity endpoints. The approach enables predictions of the PAF and the Mean Species Abundance Relationships, which evaluate biodiversity in relation to chemical concentration. It was demonstrated that the model can predict chronic effects based solely on acute data.
At the population and ecosystem levels, the best predictors of adverse effects are being identified and the interactive effects of temperature as an additional stressor are being characterised in the context of climate change scenarios. To date, using outdoor mesocosms, the combined effects of a herbicide and warming (heatwaves and elevated temperatures) on natural aquatic populations of phyto- and zoo-plankton, macrophytes, and macroinvertebrates have been evaluated. The macrophyte population growth exhibited a possible antagonistic effect in a low concentration treatment. Also, an investigation of the role of behavioural adaptation as a modulator of the negative impact of pollution on aquatic communities found that foraging switching plays a role in determining the stability of food webs that are undergoing pollution stress.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The QTOX approach will allow current ecotoxicological models to be placed in appropriate context. For example, (i) advances in mechanistic understanding of the dynamic nature of both chemical speciation in the exposure medium and biointerfacial processes, will avoid the need to establish empirical correlations for bioavailability for each chemical species and each organism, (ii) population and food models will allow the gap to be closed between the predictions made with simplistic risk assessment models and the biological responses observed in the field in a multiple stressor context, and (iii) information on the impact of climate change stressors on the adverse effects of chemicals will support predictions of the resilience capacity of ecosystems in a changing world.
In terms of potential economic/technological impact, the extrapolation methods to be developed by QTOX will support efforts to reduce, replace, and refine the use of animals in toxicity testing. Also, the mechanistic approach adopted by QTOX will inform design of environmental monitoring programs.
Regarding potential societal impact, the know-how generated by QTOX will enable stakeholders to perform robust environmental assessments of chemicals for the general benefit of society, e.g. by improving the environmental status of waterbodies thereby increasing their suitability for recreational use and as potential sources of drinking water, and by providing information on the bioavailability of chemicals to aquatic organisms that are part of the human food chain.

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