Quantitative ecotoxicity analysis for pesticide mixtures using benchmark dose methodology

文献类型： 外文期刊

第一作者： Yang, Guiling

作者： Yang, Guiling;Wang, Yanhua;Zhao, Huiyu;Li, Jian;Chen, Chen;Shao, Kan

作者机构：

关键词： Pesticide mixture; Ecotoxicity; Eisenia fetida; Benchmark dose

期刊名称：ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY （ 影响因子：6.291； 五年影响因子：6.393 ）

ISSN： 0147-6513

年卷期： 2018 年 159 卷

页码：

收录情况： SCI

摘要： Pesticide mixtures can often be found on crops and in the natural environment due to the usage of multiple pesticides in crop production. However, the toxicity of pesticides is mostly evaluated individually but not jointly. Many studies have pointed out that pesticide mixture may have elevated toxicity compared with its individual counterpart, therefore, it is important to quantify the change in toxicity. Such quantification can provide invaluable information for environmental and ecological risk assessment, and further support risk management to develop appropriate means to mitigate the risk. The objective of this study is to quantify the ecotoxicity of pesticide mixtures composed of different combinations of four pesticides (Le., Acetemiprid, Carbendazim, Chlorpyrifos, Cyhalothrin) to (1) understand if the co-presence of multiple pesticides will affect the toxicity and (2) to quantitatively approximate the change in toxicity. We first conducted acute toxicity testing and avoidance response testing using earthworms to obtain dose-response data for two different endpoints; then the benchmark dose (BMD) methodology was applied to estimate the toxicity values for the active ingredients of these four pesticides and their mixtures. The BMD analysis results suggest that the ecotoxicity of the active ingredients of the pesticides is very likely to increase when two or more pesticides are used simultaneously, highlighting the importance to consider toxicity of mixtures in the regulatory decision making process. This study demonstrates that the benchmark dose methodology can be a useful tool to quantify the toxicity of chemical mixtures and support cumulative risk assessment accordingly.

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