Integrated risk assessment framework for transformation products of emerging contaminants: what we know and what we should know

Highlights

● A better risk assessment can combine the improved non-target analysis method.

● Multi-evidence is advised in molecular determination and risk-based prioritization.

● Combining omics, multi-endpoint EDA, and machine learning to assess product risks.

Abstract

The continuous input of various emerging contaminants (ECs) has inevitably introduced large amounts of transformation products (TPs) in natural and engineering water scenarios. Structurally similar to the precursor species, the TPs are expected to possess comparative, if not more serious, environmental properties and risks. This review summarizes the state-of-the-art knowledge regarding the integrated risk assessment frameworks of TPs of ECs, mainly involving the exposure- and effect-driven analysis. The inadequate information within existing frameworks that was essential and critical for developing a better risk assessment framework was discussed. The main strategic improvements include (1) non-targeted product analysis in both laboratory and field samples, (2) omics-based high-throughput toxicity assessment, (3) multichannel-driven mode of action in conjugation with effect-directed analysis, and (4) machine learning technology. Overall, this review provides a concise but comprehensive insight into the optimized strategy for evaluating the environmental risks and screening the key toxic products from the cocktail mixtures of ECs and their TPs in the global water cycle. This facilitates deciphering the mode of toxicity in complex chemical mixtures and prioritizing the regulated TPs among the unknown products, which have the potential to be considered a class of novel “ECs” of great concern.

Graphical abstract

Keywords

Risk assessment frameworks / Transformation products / Emerging contaminants / High-throughput toxicity screening / Water treatment

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Acknowledgements

This research was financially supported by the Natural Science Foundation of China-Joint Fund Project (No. U2005206), the Xiamen Municipal Bureau of Science and Technology (No. YDZX20203502000003). The authors want to thank the support of the President Research Funds from Xiamen University (No. 20720210081).

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