流出物
环境科学
废水
废物管理
环境工程
污水处理
废物处理
水污染
排水
链接(几何体)
作者
Hongwei Bai,Kewei Liao,Hongqiang Ren,Haidong Hu
标识
DOI:10.1021/acs.est.6c00944
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI