Grantee Research Project Results
2024 Progress Report: Improved Methods to Manage PFASs for Small Water and Wastewater Treatment Systems in Rural Areas
EPA Grant Number: R840080Title: Improved Methods to Manage PFASs for Small Water and Wastewater Treatment Systems in Rural Areas
Investigators: Huang, Qingguo , Chen, Yongsheng , Huang, Ching-Hua , Li, Ke , Hawkins, Gary
Institution: University of Georgia , Georgia Institute of Technology
EPA Project Officer: Packard, Benjamin H
Project Period: September 1, 2020 through August 31, 2023 (Extended to August 31, 2025)
Project Period Covered by this Report: September 1, 2023 through August 31,2024
Project Amount: $1,599,954
RFA: National Priorities: Research on PFAS Impacts in Rural Communities and Agricultural Operations (2020) RFA Text | Recipients Lists
Research Category: Aquatic Ecosystems , Endocrine Disruptors , Drinking Water , Water
Objective:
This project aims to develop improved, cost-effective treatment trains with advanced technologies for the removal of per- and polyfluoroalkyl substances (PFASs) from water, wastewater and biosolids to ensure safe water for drinking and agricultural applications in rural areas.
Progress Summary:
Additional data were collected on PFAS concentrations in wastewater and biosolids samples collected from nine WWTPs. The data provide more insights on the fate of PFASs, including known and unknown precursors, in WWTPs using different treatment technologies and the factors controlling their fate, and thus help to understand PFAS occurrences in rural areas. Additional samples were also collected from a pilot study to assess the effect of ozonation, and ozone/hydrogen peroxide advanced oxidation processes on the fate of PFASs in WWTPs. We continued in testing the performance of a few unit operations for treatment of PFASs in water. We have employed machine learning (ML) models to help predict, design and modify fit-for-purpose NF membranes. The resulting membranes achieved unprecedented high permeability without compromising selectivity. We have also tested treatment trains, such as NF-EO. All these operations are potentially useful for managing PFASs in rural areas. We have collected data to help understand PFAS degradation rates, pathways, and mechanisms in various unit operations as well as the factors that influence the treatment performances. We have conducted life cycle assessments (LCAs) for PFAS treatment, including unit operations and the combination of selected unit operations. We completed LCA for ion exchange resin (IXR)-EO and are working on other treatment trains. The data and models obtained will help water treatment facilities to select PFASs treatment technologies based on the raw water quality and treatment goals. We are in the process of testing NF-EO treatment train system with laboratory-spiked samples and environmental samples, to provide a modular system that have wide applications in rural areas for water treatment, reclamation, and reuse. We are continuing engagement with stakeholders at each level throughout the research.
Future Activities:
LCA will be performed on all unit operations and selected treatment trains. Task 4 pilot study will be completed. We will continue to engage stakeholders in our research to seek their inputs and disseminate the results of our studies.
References:
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Journal Articles on this Report : 20 Displayed | Download in RIS Format
| Other project views: | All 22 publications | 20 publications in selected types | All 20 journal articles |
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Dangayach R, Jeong N, Demirel E, Uzal N, Fung V, Chen Y. Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; |
R840080 (2023) R840080 (2024) |
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Gao H, Zhong S, Zhang W, Igou T, Berger E, Reid E, Zhao Y, Lanbeth D, Gan L, Afolabi M, Tong Z, Lan G, Chen Y. Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022;56(4):2572-2581. |
R840080 (2021) R840080 (2024) |
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Gao H, Zhong S, Dangayach R, Checn Y. Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023;Early Access |
R840080 (2021) R840080 (2024) |
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Helm W, Zhong S, Reid E, Igou T, Chen Y. Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction. FRONTIERS OF ENVIORNMENTAL SCIENCE & ENGINEERING 2024;18(2):17. |
R840080 (2022) R840080 (2024) |
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Huang Y, Afolabi M, Gan L, Liu S, Chen Y. MXene-Coated Ion-Selective Electrode Sensors for Highly Stable and Selective Lithium Dynamics Monitoring. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023;58(2):1359-1368. |
R840080 (2022) R840080 (2024) |
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Jeong N, Park S, Mahajan S, Zhou J, Blotevogel J, Li Y, Tong T, Chen Y. Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations. NATURE COMMUNICATIONS 2024;15(1) |
R840080 (2023) R840080 (2024) |
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Reid E, Igou T, Zhao Y, Crittenden J, Huang C, Westerhoff P, Rittmann B, Drewes J, Chen Y. The Minus Approach Can Redefine the Standard of Practice of Drinking Water Treatment. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023;9(57):7150-7161 |
R840080 (2021) R840080 (2024) |
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Shao Y, Tong X, Kim J, Tong T, Huang C, Chen Y. Capillary-Assisted Fabrication of Thin-Film Nanocomposite Membranes for Improved Solute-Solute Separation. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022;56(9):5849-5859. |
R840080 (2021) R840080 (2024) |
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Wang Y, Li L, Wang Y, Shi H, Wang L, Huang Q. Electrooxidation of perfluorooctanesulfonic acid on porous Magneli phase titanium suboxide Anodes:Impact of porous structure and composition. ECHEMICAL ENGINEERING JOURNAL 2022;144:49-56. |
R840080 (2021) R840080 (2024) |
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Wang Y, Warner M, Li K, Hawkins GL, Huang Q. Assessing explicit models of per-and polyfluoroalkyl substances adsorption on anion exchange resins by rapid small-scale column tests. Chemosphere 2022;300:134547. |
R840080 (2024) |
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Zhong S, Lambeth DR, Igou TK, Chen Y. Enlarging Applicability Domain of Quantitative Structure–Activity Relationship Models through Uncertainty-Based Active Learning. ACS ES&T Engineering2022;2(7):1211-20. |
R840080 (2024) |
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Li G, Dunlap J, Wang Y, Huang Q, Li K. Environmental life cycle assessment (LCA) of treating PFASs with ion exchange and electrochemical oxidation technology. ACS ES&T Water 2022;2(9):1555-64. |
R840080 (2024) |
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Gao H, Chen Y. Hybrid dimensional MXene/CNC framework-regulated nanofiltration membrane with high separation performance. ACS ES&T Water 2022;3(7):1767-1777. |
R840080 (2024) |
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Kim J, Xin X, Mamo BT, Hawkins GL, Li K, Chen Y, Huang Q, Huang CH. Occurrence and fate of ultrashort-chain and other per-and polyfluoroalkyl substances (PFAS) in wastewater treatment plants. ACS ES&T Water 2022;2(8):1380-90. |
R840080 (2024) |
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Wang Y, Kim J, Huang CH, Hawkins GL, Li K, Chen Y, Huang Q. Occurrence of per-and polyfluoroalkyl substances in water:a review. Environmental Science:Water Research & Technology 2022;8(6):1136-51. |
R840080 (2024) |
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Xin X, Kim J, Ashley DC, Huang CH. Degradation and defluorination of per-and polyfluoroalkyl substances by direct photolysis at 222 nm. ACS ES&T Water 2023;3(8):2776-2785. |
R840080 (2024) |
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Wang Y, Ji Y, Li K, Huang Q. Foam fractionation and electrochemical oxidation for the treatment of per-and polyfluoroalkyl substances (PFAS) in environmental water samples. Chemosphere 2023;339:139615. |
R840080 (2024) |
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Wang Y, Ji Y, Tishchenko V, Huang Q. Removing per-and polyfluoroalkyl substances (PFAS) in water by foam fractionation. Chemosphere 2023;311:137004. |
R840080 (2024) |
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Kim J, Xin X, Hawkins GL, Huang Q, Huang CH. Occurrence, Fate, and Removal of Per-and Polyfluoroalkyl Substances (PFAS) in Small-and Large-Scale Municipal Wastewater Treatment Facilities in the United States. ACS Es&t Water. 2024; Nov 15;4(12):5428-36. |
R840080 (2024) |
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Xin X, Kim J, Weng S, Huang CH. Pilot Assessment of Impacts of Ozone and Ozone/Hydrogen Peroxide Treatment on the Fate of Per-and Polyfluoroalkyl Substances and Precursors. ACS Es&t Water. 2024; Sep 25;4(10):4545-55. |
R840080 (2024) |
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Supplemental Keywords:
Persistent Organic Pollutants, Environmental Occurrence, Process Optimization, Feasibility AssessmentRelevant Websites:
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.