Comprehensive profiles of per- and polyfluoroalkyl substances in Chinese and African municipal wastewater treatment plants: New implications for removal efficiency

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. • A total of 136 emerging PFASs were identified in 58 municipal WWTPs. • 22 and 67 emerging PFASs were detected for the first time in wastewater and sludge. • Different distribution patterns were observed between Chinese and African WWTPs. • Driving factors affecting the removal and adsorption of PFASs were explored by machine learning models. • Emerging PFAS environmental emissions exceeded that of legacy PFAS in China.