Constructed wetlands as nature-based solutions in managing per-and poly-fluoroalkyl substances (PFAS): evidence, mechanisms, and modelling

dc.contributor.authorSavvidou, Pinelopi
dc.contributor.authorDotro, Gabriela
dc.contributor.authorCampo, Pablo
dc.contributor.authorCoulon, Frederic
dc.contributor.authorLyu, Tao
dc.date.accessioned2024-05-23T14:21:55Z
dc.date.available2024-05-23T14:21:55Z
dc.date.issued2024-05-18
dc.description.abstractPer- and poly-fluoroalkyl substances (PFAS) have emerged as newly regulated micropollutants, characterised by extreme recalcitrance and environmental toxicity. Constructed wetlands (CWs), as a nature-based solution, have gained widespread application in sustainable water and wastewater treatment and offer multiple environmental and societal benefits. Despite CWs potential, knowledge gaps persist in their PFAS removal capacities, associated mechanisms, and modelling of PFAS fate. This study carried out a systematic literature review, supplemented by unpublished experimental data, demonstrating the promise of CWs for PFAS removal from the influents of varying sources and characteristics. Median removal performances of 64, 46, and 0 % were observed in five free water surface (FWS), four horizontal subsurface flow (HF), and 18 vertical flow (VF) wetlands, respectively. PFAS adsorption by the substrate or plant root/rhizosphere was deemed as a key removal mechanism. Nevertheless, the available dataset resulted unsuitable for a quantitative analysis. Data-driven models, including multiple regression models and machine learning-based Artificial Neural Networks (ANN), were employed to predict PFAS removal. These models showed better predictive performance compared to various mechanistic models, which include two adsorption isotherms. The results affirmed that artificial intelligence is an efficient tool for modelling the removal of emerging contaminants with limited knowledge of chemical properties. In summary, this study consolidated evidence supporting the use of CWs for mitigating new legacy PFAS contaminants. Further research, especially long-term monitoring of full-scale CWs treating real wastewater, is crucial to obtain additional data for model development and validation.en_UK
dc.description.sponsorshipThis research is gratefully supported by the project sponsor Anglian Water. P. Savvidou's PhD study is supported by Atkins Realis and the EPSRC Centre for Doctoral Training in Water Infrastructure and Resilience (EP/V519509/1). We also acknowledge support from the BBSRC/EPSRC Environmental Biotechnology Network (EBNet) NIBB (BB/S009795/1).en_UK
dc.identifier.citationSavvidou P, Dotro G, Campo P, et al., (2024) Constructed wetlands as nature-based solutions in managing per-and poly-fluoroalkyl substances (PFAS): evidence, mechanisms, and modelling. Science of The Total Environment. Volume 934, July 2024, Article number 173237en_UK
dc.identifier.eissn1879-1026
dc.identifier.issn0048-9697
dc.identifier.urihttps://doi.org/10.1016/j.scitotenv.2024.173237
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21662
dc.language.isoen_UKen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectEmerging contaminanten_UK
dc.subjectForever chemicalsen_UK
dc.subjectFate modelen_UK
dc.subjectMachine learningen_UK
dc.subjectMicropollutantsen_UK
dc.subjectTreatment wetlanden_UK
dc.titleConstructed wetlands as nature-based solutions in managing per-and poly-fluoroalkyl substances (PFAS): evidence, mechanisms, and modellingen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-05-12

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