Due to limitations in large-scale toxicity assessment, the actual biological toxicity of wastewater effluents remains insufficiently characterized. The nematode Caenorhabditis elegans is a well-established model organism for evaluating whole effluent toxicity (WET). However, its standardized methods (e.g., ISO 10872) rely on time-consuming manual quantification, hindering large-scale toxicity assessment for decision-making in wastewater risk management. Herein, a model-driven high-throughput assay was developed that integrates spatiotemporal analysis of the nematode behavioral features with machine learning, reducing WET testing time by ∼77% compared to standard methods and enabling a comprehensive risk assessment of nationwide wastewater treatment plants (WWTPs) across China. The results showed that WWTP effluents consistently showed high toxicity (toxicity unit TU = 0.47-2.16), even when meeting permissible discharge limits for chemical indicators, substantially exceeding the toxicity of the corresponding receiving waters (p < 0.05, ANOVA). Interestingly, WWTP treatment capacity emerged as the predominant driver of WET variation, underscoring the need to prioritize large-size WWTPs in flexible wastewater risk control strategies. These findings expose a significant gap between wastewater risk management needs and current control practices, as WWTP effluents showed substantially higher toxicity than their receiving waters, advocating for the scale-prioritized toxicity-driven discharge standards to secure more safe and efficient water sustainability management in China.
Bai et al. (Fri,) studied this question.