Comparative immobilization of 30 PFAS mixtures onto biochar, clay, nanoparticle, and polymer derived engineered adsorbents: Machine learning insights into carbon chain length and removal mechanism.

Journal: Journal of hazardous materials
Published Date: Feb 25, 2025

Abstract

Per- and polyfluoroalkyl substances (PFAS) are a group of fluorinated chemicals that cause potential risk in PFAS-impacted soil and water. The adsorption efficiency of 30 PFAS mixtures using different adsorbents in environmentally relevant concentrations was investigated. Different meso/microporous designed adsorbents (n = 7) were used for PFAS adsorption and their interfacial interactions. The adsorbents were tested for their ability to remove PFAS mixtures, including perfluoroalkyl sulfonic acids (PFSAs, n = 7, C4-C10), perfluoroalkyl carboxylic acids (PFCAs, n = 11, C4-C14), fluorotelomer sulfonic acids (FTSs, n = 4), perfluoroalkane sulfonamido acetic acids (FASAAs, n = 3, C8), perfluoroalkane sulfonamides (FASAs, n = 3, C8) and perfluoroalkane sulfonamidoethanols (FASEs, n = 2, C8). The overall removal rate of 30 PFAS was recorded as 86.20-89.29 %, 87.63-90.33 %, and 67.07-93.61 % for microporous biochar/modified biochar, halloysite nanoclays, and mesoporous polymer composites-based adsorbents, respectively. The presence of sugarcane bagasse-derived biochar, iron nanoparticles, and β-cyclodextrin in the composite adsorbents enhances the sorption of PFAS. Higher adsorption efficiency was observed for long-chain PFCAs, PFSAs, FTSs, FASAAs, FASAs, and FASEs, whereas, complete removal of short-chain PFCAs, PFSAs, and FTSs is still challenging by using all the studied adsorbents. The carbon chain length and head groups of PFAS play a vital role in removing PFAS. The correlation coefficient (R) values between removal rate and carbon chain length, for PFCAs (n = 11), and PFSAs (n = 7) were found as 0.73, and 0.31 respectively. Appropriate machine learning tools including efficient linear least squares, Gaussian process regression, and stepwise linear regression, were applied to fit experimental data and assess model accuracy.

Authors

  • Masud Hassan
    College of Resources and Environmental Engineering, Guizhou University, Guiyang, Guizhou 550025, China; Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW 2308, Australia; crc for Contamination Assessment and Remediation of the Environment (crcCARE), Callaghan, NSW 2308, Australia.
  • Ravi Naidu
    Global Centre for Environmental Remediation (GCER), Faculty of Science, The University of Newcastle, ATC Building, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment, P.O. Box 18, Callaghan NSW 2308, Australia.
  • Fangjie Qi
    Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW 2308, Australia; crc for Contamination Assessment and Remediation of the Environment (crcCARE), Callaghan, NSW 2308, Australia; Nanjing Institute of Soil Science, Chinese Academy of Sciences, 298 Chuangyou Road, Nanjing, Jiangsu Province 210008, China.
  • Bing Wang
    Computer Science & Engineering Department at the University of Connecticut.
  • Liang Wang
    Information Department, Dazhou Central Hospital, Dazhou 635000, China.
  • Srinivasulu Asadi
    Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW 2308, Australia; crc for Contamination Assessment and Remediation of the Environment (crcCARE), Callaghan, NSW 2308, Australia.
  • Amal Kanti Deb
    Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW 2308, Australia; crc for Contamination Assessment and Remediation of the Environment (crcCARE), Callaghan, NSW 2308, Australia; Institute of Leather Engineering and Technology, University of Dhaka, Dhaka 1000, Bangladesh.
  • Jianhua Du
    Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW 2308, Australia; crc for Contamination Assessment and Remediation of the Environment (crcCARE), Callaghan, NSW 2308, Australia; WSP Australia Pty Limited, Level 3, Mia Yellagonga Tower 2, 5 Spring Street, Perth 6000, Australia.
  • Yanju Liu
    Department of Astronautical Science and Mechanics, Harbin Institute of Technology (HIT), Harbin, People's Republic of China.

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