State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
National Institute of Metrology, Beijing 100029, China.
Sci Total Environ. 2023 Jan 20;857(Pt 3):159638. doi: 10.1016/j.scitotenv.2022.159638. Epub 2022 Oct 21.
Municipal wastewater treatment plants (WWTPs) can reflect the pollution status of per- and polyfluoroalkyl substances (PFASs) pollution. Here, matched influent, effluent, and sludge samples were collected from 58 municipal WWTPs in China, South Sudan, Tanzania, and Kenya. Target and suspect screening of PFASs was performed to explore their profiles in WWTPs and assess removal efficiency and environmental emissions. In total, 155 and 58 PFASs were identified in WWTPs in China and Africa, respectively; 146 and 126 PFASs were identified in wastewater and sludge, respectively. Novel compounds belonging to per- and polyfluoroalkyl ether carboxylic acids (PFECAs) and sulfonic acids (PFESAs), hydrogen-substituted polyfluorocarboxylic acids (H-PFCAs), and perfluoroalkyl sulfonamides (PFSMs) accounted for a considerable proportion of total PFASs (ΣPFASs) in Chinese WWTPs and were also widely detected in African samples. In China, estimated national emissions of ΣPFASs in WWTPs exceeded 16.8 t in 2015, with >60 % originating from emerging PFASs. Notably, current treatment processes are not effective at removing PFASs, with 35 of the 54 WWTPs showing emissions higher than mass loads. PFAS removal was also structure dependent. Based on machine learning models, we found that molecular descriptors (e.g., LogP and molecular weight) may affect adsorption behavior by increasing hydrophobicity, while other factors (e.g., polar surface area and molar refractivity) may play critical roles in PFAS removal and provide novel insights into PFAS pollution control. In conclusion, this study comprehensively screened PFASs in municipal WWTPs and determined the drivers affecting PFAS behavior in WWTPs based on machine learning models.
城市污水处理厂（WWTP）可以反映出全氟和多氟烷基物质（PFAS）污染的污染状况。在这里，从中国、南苏丹、坦桑尼亚和肯尼亚的 58 个城市 WWTP 中收集了匹配的进水、出水和污泥样品。对 PFAS 进行了目标和可疑筛选，以探讨其在 WWTP 中的分布，并评估去除效率和环境排放。在中国和非洲的 WWTP 中分别鉴定出 155 种和 58 种 PFAS；在废水和污泥中分别鉴定出 146 种和 126 种 PFAS。属于全氟和多氟烷基醚羧酸（PFECAs）和磺酸（PFESAs）、氢取代多氟羧酸（H-PFCAs）和全氟烷基磺酰胺（PFSMs）的新型化合物在 WWTP 中占 PFASs 总量（ΣPFASs）的相当大比例，在非洲样本中也广泛检测到。在中国，2015 年 WWTP 中ΣPFASs 的估计全国排放量超过 16.8 吨，其中超过 60%来自新兴的 PFASs。值得注意的是，目前的处理工艺对去除 PFASs 效果不佳，54 个 WWTP 中有 35 个显示排放量高于质量负荷。PFASs 的去除也依赖于结构。基于机器学习模型，我们发现分子描述符（例如，LogP 和分子量）可能通过增加疏水性来影响吸附行为，而其他因素（例如，极性表面积和摩尔折射度）可能在 PFAS 去除中发挥关键作用，并为 PFAS 污染控制提供新的见解。总之，本研究全面筛选了城市 WWTP 中的 PFAS，并基于机器学习模型确定了影响 WWTP 中 PFAS 行为的驱动因素。
