Browsing by Author "Savvidou, Pinelopi"

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    Constructed wetlands as nature-based solutions in managing per-and poly-fluoroalkyl substances (PFAS): evidence, mechanisms, and modelling
    (Elsevier, 2024-07-15) Savvidou, Pinelopi; Dotro, Gabriela; Campo, Pablo; Coulon, Frederic; Lyu, Tao
    Per- 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 This study carried out a systematic literature review, supplemented by unpublished experimental data, demonstrating the promise of CWs for PFAS removal from the influents of varying sources and characteristics.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.