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PreprintCC BYChemRxiv

Mass Balance-based Computational Frameworks for the

  • Xiangui Huang1,2
  • Scott Kalev Hansen1
  • Sayan Sen1
  • Yuanyuan Liu2
  • Avner Ronen1
  1. 1Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede-Boqer, Israel, 8499000
  2. 2Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment of Ministry of 13 Education, Chongqing University
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Abstract

Per- and polyfluoroalkyl substances (PFAS) are persistent anthropogenic contaminants widely 20 detected in aquatic environments. Conventional wastewater treatment plants (WWTPs) are major 21 emission sources that discharge PFAS via effluent and the land application of biosolids, yet they 22 are ineffective in removing PFAS. Understanding PFAS distribution and transformation in 23 WWTPs is important for the development and integration of PFAS remediation technologies. 24 However, identifying the mechanisms governing each PFAS mass flow change across a treatment 25 compartment remains challenging, and parameters quantifying PFAS transformation and 26 distribution in the field are difficult to measure. To address these problems, we developed novel 27 mass balance-based computational frameworks for PFAS distribution and transformation in 28 WWTP. We demonstrated that they are robust tools for identifying the mechanisms driving 29 changes in PFAS mass flow and for estimating unobservable parameters describing PFAS 30 distribution and transformation. Also, based on these frameworks, we found that the impacts of 31 PFAS partitioning on PFAS concentrations in the aqueous phase were two to three orders of 32 magnitude higher than those of precursor transformation. These frameworks are extensible, and 33 remaining limitations in parameter uncertainty and transformation pathway completeness are 34 discussed. 35 36

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Cite

BibTeXRIS
BibTeX
@misc{huang2026mass,
  title = {{Mass Balance-based Computational Frameworks for the}},
  author = {Xiangui Huang and Scott Kalev Hansen and Sayan Sen and Yuanyuan Liu and Avner Ronen},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15010831/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010831/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Mass Balance-based Computational Frameworks for the
AU  - Xiangui Huang
AU  - Scott Kalev Hansen
AU  - Sayan Sen
AU  - Yuanyuan Liu
AU  - Avner Ronen
PY  - 2026
DA  - 2026/10/22
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15010831/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010831/v1
AB  - Per- and polyfluoroalkyl substances (PFAS) are persistent anthropogenic contaminants widely 20 detected in aquatic environments. Conventional wastewater treatment plants (WWTPs) are major 21 emission sources that discharge PFAS via effluent and the land application of biosolids, yet they 22 are ineffective in removing PFAS. Understanding PFAS distribution and transformation in 23 WWTPs is important for the development and integration of PFAS remediation technologies. 24 However, identifying the mechanisms governing each PFAS mass flow change across a treatment 25 compartment remains challenging, and parameters quantifying PFAS transformation and 26 distribution in the field are difficult to measure. To address these problems, we developed novel 27 mass balance-based computational frameworks for PFAS distribution and transformation in 28 WWTP. We demonstrated that they are robust tools for identifying the mechanisms driving 29 changes in PFAS mass flow and for estimating unobservable parameters describing PFAS 30 distribution and transformation. Also, based on these frameworks, we found that the impacts of 31 PFAS partitioning on PFAS concentrations in the aqueous phase were two to three orders of 32 magnitude higher than those of precursor transformation. These frameworks are extensible, and 33 remaining limitations in parameter uncertainty and transformation pathway completeness are 34 discussed. 35 36
ER  -