Addressing data gaps in deriving aquatic life ambient water quality criteria for contaminants of emerging concern: Challenges and the potential of in silico methods

Abstract

The international community is becoming increasingly aware of the threats posed by contaminants of emerging concern (CECs) for ecological security. Aquatic life ambient water quality criteria (WQC) are essential for the formulation of risk prevention and control strategies for pollutants by regulatory agencies. Accordingly, we systematically evaluated the current status of WQC development for typical CECs through literature review. The results revealed substantial disparities in the WQC for the same chemical, with the coefficients of variation for all CECs exceeding 0.3. The reliance on low-quality data, high-uncertainty derivation methods, and limited species diversity highlights a substantial data gap. Newly developed in silico methods, with potential to predict the toxicity of untested chemicals, species, and conditions, were classified and integrated into a traditional WQC derivation framework to address the data gap for CECs. However, several challenges remain before such methods can achieve widespread acceptance. These include unstable model performance, the inability to predict chronic toxicity, undefined model applicability, difficulties in specifying toxicity effects and predicting toxicity for certain key species. Future research should prioritize: 1) improving model accuracy by developing specialized models trained with relevant, chemical-specific data or integrating chemical-related features into interspecies models; 2) enhancing species generalizability by developing multispecies models; 3) facilitating the derivation of environmentally relevant WQC by incorporating condition-related features into models; and 4) improving the regulatory acceptability of in silico methods by evaluating the reliability of “black-box” models.