期刊
CHEMOSPHERE
卷 287, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chemosphere.2021.131910
关键词
Emerging contaminants; Ecological risk; Ecotoxicity; Uncertainty; And adverse outcome pathway
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- NSERC Drinking Water Chair at Laval University
Contaminants of Emerging Concern in natural water pose ecological risks, with uncertainties in risk estimates. Classification of ecotoxicity values based on environmental parameters can reduce uncertainty in risk estimates.
Contaminants of Emerging Concern (CECs) in natural water pose risks to ecosystems. The concentration of CECs varies spatially and temporally, and their estimated ecotoxicities differ widely by toxicological studies. This study extensively reviewed literature on ecological risk assessment and proposed a probabilistic framework for assessing ecological risk and its uncertainties (aleatory and epistemic). The framework integrated Adverse Outcome Pathway in risk assessment and was applied to a Canadian lake system for seven CECs: salicylic acid, acetaminophen, caffeine, carbamazepine, ibuprofen, drospirenone, and sulfamethoxazole. Altogether 264 water samples were collected and analyzed from 15 sites May 2016 to September 2017. Phytoplankton, zooplankton, and fish were also sampled and analyzed. The results show ecological risk estimates (Risk Quotient, RQ) varied considerably indicating a range of uncertainty. Based on the conservative estimate, the central tendency estimate of the ecological risk of mixture compounds was medium (RQ = 0.6) including drospirenone. However, the reasonably maximum estimate of the risk was high (RQ = 1.4) for mixture compounds including drospirenone. The high risk is primarily due to drospirenone as its individual risk was high (RQ = 1.1) to fish. The specific site and time of high drospirenone exposure was identified for implementing control measures. Classification of ecotoxicity values based on environmental parameters such as climate and water quality, can reduce uncertainty in the risk estimate.
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