From carboxylates to chlorinated sulfonates: Contrasting fate and treatment prospects of GenX and F53B in WWTPs
By Huimin Zhang, and Samendra P. Sherchan
Science of The Total Environment
October 13, 2025
DOI: 10.1016/j.scitotenv.2025.180531
HFPO-DA (GenX) and Cl-PFAES (F
53B) are structurally distinct PFAS that behave differently in wastewater treatment plants (WWTPs). GenX, with a fully fluorinated ether backbone and high polarity, shows low sorption and persists in the aqueous phase. In contrast, F-53B's sulfonate group and non-fluorinated segments favor sludge partitioning, with potential desorption risks. Mechanistically, the CCl substituent in F53B can act as a microbial dehalogenation hotspot that precedes defluorination, whereas GenX shows limited biotransformation. In mixed matrices, competitive sorption and precursor transformation decrease removal efficiency, with short-chain species most affected. Non-destructive methods, including advanced adsorbents, membranes, and anion-exchange resins, have demonstrated variable removal efficiencies (10–99 %). Destructive approaches such as UV/sulfite photoreduction and electrochemical oxidation show promise for partial degradation (70–99 %), although their practical application is constrained by matrix complexity, material specificity, and risks of secondary pollution. In addition, both compounds generate persistent transformation products, such as trifluoroacetic acid (TFA) from GenX and PFBS-like species from F53B, which raise additional environmental and regulatory concerns. Their molecular structures critically influence transformation behavior, with GenX resisting biodegradation due to its fluorinated ether backbone, and F53B showing greater reactivity owing to aliphatic –CH2– groups and a chlorine substituent. Field-scale performance data and long-term monitoring remain limited, impeding accurate evaluation of treatment efficacy and environmental transformation. This review proposes a structure–fate–treatment framework and highlights the need for integrated byproduct monitoring, biotransformation research, and machine learning-assisted prediction to inform future remediation and policy efforts.
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