Comprehensive profiles of per- and polyfluoroalkyl substances in Chinese and African municipal wastewater treatment plants: New implications for removal efficiency
Comprehensive profiles of per- and polyfluoroalkyl substances in Chinese and African municipal wastewater treatment plants: New implications for removal efficiency
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中国和非洲城市污水处理厂中全氟烷基物质和多氟烷基物质的综合概况：对去除效率的新影响
DOI：
10.1016/j.scitotenv.2022.159638
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发表时间：
2022
影响因子：
9.8
通讯作者：
Pan Yitao
中科院分区：
文献类型：
--
作者：
Jiang Lulin;Yao Jingzhi;Ren Ge;Sheng Nan;Guo Yong;Dai Jiayin;Pan Yitao
城市污水处理厂能够反映全氟和多氟烷基化合物（PFASs）的污染状况。在这里，从中国，南苏丹，坦桑尼亚和肯尼亚的58个城市污水处理厂收集了匹配的进水，出水和污泥样品。对PFASs进行了目标筛选和疑似筛选，以探索其在污水处理厂中的分布，并评估其去除效率和环境排放。在中国和非洲的污水处理厂中分别确定了155和58种PFAS;在废水和污泥中分别确定了146和126种PFAS。在中国污水处理厂和非洲样品中，全氟烷基醚羧酸（PFECAs）、多氟烷基醚磺酸（PFESAs）、氢取代多氟羧酸（H-PFCAs）和全氟烷基磺酰胺（PFSMs）等新化合物占PFASs总量的相当大比例。在中国，2015年污水处理厂中PFASs的估计全国排放量超过16.8吨，其中超过60%来自新兴PFASs。值得注意的是，目前的处理工艺在去除PFAS方面并不有效，54个污水处理厂中有35个的排放量高于质量负荷。PFAS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）可以通过增加疏水性来影响吸附行为，而其他因素（例如，极表面积和摩尔折射率）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，本研究综合筛选了城市污水处理厂中的PFAS，并基于机器学习模型确定了影响污水处理厂PFAS行为的驱动因素。
Municipal wastewater treatment plants (WWTPs) can reflect the pollution status ofper- 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 toper- 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.