Department of Earth and Environmental Sciences DISAT, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy; Water Research Institute, National Research Council, via Salaria km 29,300, 00015 Monterotondo, Rome, Italy.
Water Research Institute, National Research Council, via Salaria km 29,300, 00015 Monterotondo, Rome, Italy.
Sci Total Environ. 2022 Feb 1;806(Pt 2):150614. doi: 10.1016/j.scitotenv.2021.150614. Epub 2021 Sep 29.
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-CH for Daphnia and fish).
本文提出了一种评估水体中化学混合物生态风险的方法。在第一阶段,该方法考虑了从意大利国家环境保护与研究研究所获得的常规化学监测数据(MEC:实测环境浓度),这些数据被地理参考到每个监测站的单一坐标系中。然后,使用浓度加和模型(将暴露与生态毒理学数据(来自不同数据库)相结合)来评估三种代表性水生生物(藻类、水蚤、鱼类)的整体混合物毒性。数据库管理系统用于方便地创建、组织和管理本研究的大型数据集。输出结果以基于 GIS 的混合物风险图和表格(列出混合物和单个物质的毒性单位)的形式获得,可用于进一步分析。该方法应用于意大利的一个流域(阿达河)作为案例研究。在第一阶段,使用两种情况计算混合物毒性:最佳情况和最坏情况;前者仅包括那些被检测到的化合物,而后者则包括浓度低于定量限的物质。这两种情况之间的比值表示混合物毒性理想变化范围。结果表明,当使用最佳情况计算出的毒性表明存在潜在风险时,这两个比值非常小,反之亦然,表明最坏情况的情况可能不适用(非常保守)。因此,在随后的阶段,我们只关注最佳情况。最后,这种方法允许优先识别混合物(最有可能出现在分析水样中的混合物),藻类是风险最高的生物,以及对整体混合物毒性贡献最大的物质(三丁基锡和 S-甲草氯对藻类,氯蜱和氯蜱-CH 对水蚤和鱼类)。