Use of Interspecies Correlation Estimation (ICE) Models to Derive Water Quality Criteria of Microplastics for Protecting Aquatic Organisms

Microplastics (MPs) in the water environment pose a potential threat to aquatic organisms. The Species Sensitivity Distribution (SSD) method was used to assess the ecological risks of microplastics on aquatic organisms in this study. However, the limited toxicity data of aquatic organisms made it im...

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Veröffentlicht in:International journal of environmental research and public health 2022-08, Vol.19 (16), p.10307
Hauptverfasser: Wu, Jiangyue, Zhao, Xiaohui, Gao, Lin, Li, Yan, Wang, Dan
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Sprache:eng
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Zusammenfassung:Microplastics (MPs) in the water environment pose a potential threat to aquatic organisms. The Species Sensitivity Distribution (SSD) method was used to assess the ecological risks of microplastics on aquatic organisms in this study. However, the limited toxicity data of aquatic organisms made it impossible to derive water quality criteria (WQC) for MPs and difficult to implement an accurately ecological risk assessment. To solve the data gaps, the USEPA established the interspecies correlation estimation (ICE) model, which could predict toxicity data to a wider range of aquatic organisms and could also be utilized to develop SSD and HC (hazardous concentration, 5th percentile). Herein, we collected the acute toxicity data of 11 aquatic species from 10 families in 5 phyla to fit the metrical-based SSDs, meanwhile generating the ICE-based-SSDs using three surrogate species ( , , and ), and finally compared the above SSDs, as well as the corresponding HC . The results showed that the measured HC for acute MPs toxicity data was 112.3 μg/L, and ICE-based HC was 167.2 μg/L, which indicated there were no significant differences between HC derived from measured acute and ICE-based predicted values thus the ICE model was verified as a valid approach for generating SSDs with limited toxicity data and deriving WQC for MPs.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph191610307