Chen Ling, Wang Jiawei, Zhu Mengyuan, He Ruonan, Mu Hongxin, Ren Hongqiang, Wu Bing
State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, NO. 163 Xianlin Avenue, Nanjing 210023, China.
State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, NO. 163 Xianlin Avenue, Nanjing 210023, China.
Water Res. 2025 Jan 1;268(Pt B):122696. doi: 10.1016/j.watres.2024.122696. Epub 2024 Oct 24.
With the growing consensus of emerging pollutants and biological toxicity risks in wastewater treatment plant (WWTP) effluents, traditional water quality management based on general chemical parameters no longer meets the new challenges. Here, a first-hand dataset containing 9 conventional parameters, 22 mental and inorganic ions, 25 biotoxicity parameters, and 54 emerging pollutants from effluents of 176 municipal WWTPs across China were measured. Four clustering algorithms and five classification algorithms were applied to 65 well-performing models to determine a novel evaluation parameter system. A total of 14 parameters were selected by semi-supervised machine learning, including TN, TP, NH-N, NO-N, Se, SO, Caenorhabditis elegans body width, 72 hpf zebrafish embryo hatching rate, tetracycline, acetaminophen, gemfibrozil (Lopid), PFBA, PFHxA, and HFPO-DA. These parameters were then used to construct a Healthy Effluent Quality Index model (HEQi). The application efficiency of HEQi was compared with other common methods such as the Water Quality Index (WQI), Fuzzy Synthesized Evaluation (FSE), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in classifying 176 effluents. Results implicated that under the new evaluation criteria, the major task in North and Northeast China remains to reduce the conventional parameters, especially NO-N. However, it is necessary to strengthen the removal of biotoxicity and emerging pollutants in parts of Central and Eastern China. This study offers new methodological tools and scientific insights for improving water quality assessment and safe discharge of wastewater.
随着污水处理厂（WWTP）出水新兴污染物和生物毒性风险的共识日益增加，基于一般化学参数的传统水质管理已无法应对新挑战。在此，对来自中国176座城市污水处理厂出水的包含9个常规参数、22种金属和无机离子、25种生物毒性参数以及54种新兴污染物的第一手数据集进行了测量。将四种聚类算法和五种分类算法应用于65个性能良好的模型，以确定一个新的评估参数系统。通过半监督机器学习共选择了14个参数，包括总氮（TN）、总磷（TP）、氨氮（NH-N）、硝态氮（NO-N）、硒（Se）、硫酸根（SO）、秀丽隐杆线虫体宽、72小时斑马鱼胚胎孵化率、四环素、对乙酰氨基酚、吉非贝齐（Lopid）、全氟丁酸（PFBA）、全氟己酸（PFHxA）和全氟辛烷磺酸二聚体（HFPO-DA）。然后使用这些参数构建了健康出水水质指数模型（HEQi）。在对176份出水进行分类时，将HEQi的应用效率与其他常用方法进行了比较，如水质指数（WQI）、模糊综合评价（FSE）和逼近理想解排序法（TOPSIS）。结果表明，在新的评估标准下，中国北方和东北地区的主要任务仍然是降低常规参数，尤其是硝态氮（NO-N）。然而，有必要加强中国中部和东部部分地区生物毒性和新兴污染物的去除。本研究为改善水质评估和废水安全排放提供了新的方法工具和科学见解。
