A method to evaluate the ecological risk of chemical mixtures in water bodies is here presented. In the first phase, the approach considered routine chemical monitoring data (MEC: measured environmental concentrations) obtained from the Italian National Institute for Environmental Protection and Research, which were georeferenced to a single coordinate system for each monitoring station. The overall mixture toxicity were then evaluated for three representative aquatic organisms (algae, Daphnia, fish) using the concentration addition model to combine exposure with ecotoxicological data (from different databases). A database management system was used to facilitate the creation, organisation, and management of the large datasets of this study. The outputs were obtained as GIS-based mixture risk maps and tables (listing the toxic unit of mixtures and individual substances) useful for further analysis. The method was applied to an Italian watershed (Adda River) as a case study. In the first phase, the mixture toxicity was calculated using two scenarios: best- and worst-case; wherein the former included only those compounds that were be detected, while the latter involved also substances with concentrations below the limit of quantification. The ratio between the two scenarios indicated the range within which mixture toxicity should ideally vary. The method demonstrates that these ratios were very small when the calculated toxicity using the best case indicated a potential risk and vice versa, indicating that the worst-case scenario could not be appropriate (extremely conservative). Consequently, in the successive phase, we focused exclusively on the best-case scenario. Finally, this approach allowed the priority mixture identification (those most likely occurring in the analysed water samples), algae as the organism at the highest risk, and the substances that contributed the most to the overall mixture toxicity (terbuthylazine and s-metolachlor for algae, and chlorpyrifos and chlorpyrifos-CH3 for Daphnia and fish).
Finizio, A., Grenni, P., Petrangeli, A., Barra Caracciolo, A., Santoro, S., Di Guardo, A. (2022). Use of large datasets of measured environmental concentrations for the ecological risk assessment of chemical mixtures in Italian streams: A case study. SCIENCE OF THE TOTAL ENVIRONMENT, 806(Part 2 (1 February 2022)) [10.1016/j.scitotenv.2021.150614].
Use of large datasets of measured environmental concentrations for the ecological risk assessment of chemical mixtures in Italian streams: A case study
Finizio A.;
2022
Abstract
A method to evaluate the ecological risk of chemical mixtures in water bodies is here presented. In the first phase, the approach considered routine chemical monitoring data (MEC: measured environmental concentrations) obtained from the Italian National Institute for Environmental Protection and Research, which were georeferenced to a single coordinate system for each monitoring station. The overall mixture toxicity were then evaluated for three representative aquatic organisms (algae, Daphnia, fish) using the concentration addition model to combine exposure with ecotoxicological data (from different databases). A database management system was used to facilitate the creation, organisation, and management of the large datasets of this study. The outputs were obtained as GIS-based mixture risk maps and tables (listing the toxic unit of mixtures and individual substances) useful for further analysis. The method was applied to an Italian watershed (Adda River) as a case study. In the first phase, the mixture toxicity was calculated using two scenarios: best- and worst-case; wherein the former included only those compounds that were be detected, while the latter involved also substances with concentrations below the limit of quantification. The ratio between the two scenarios indicated the range within which mixture toxicity should ideally vary. The method demonstrates that these ratios were very small when the calculated toxicity using the best case indicated a potential risk and vice versa, indicating that the worst-case scenario could not be appropriate (extremely conservative). Consequently, in the successive phase, we focused exclusively on the best-case scenario. Finally, this approach allowed the priority mixture identification (those most likely occurring in the analysed water samples), algae as the organism at the highest risk, and the substances that contributed the most to the overall mixture toxicity (terbuthylazine and s-metolachlor for algae, and chlorpyrifos and chlorpyrifos-CH3 for Daphnia and fish).
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