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Due to the implications of poly- and perfluoroalkyl substances (PFAS) on the environment and public health, great attention has been recently made to finding innovative materials and methods for PFAS removal. In this work, PFAS is considered universal contamination which can be found in many wastewater streams. Conventional materials and processes used to remove and degrade PFAS do not have enough competence to address the issue particularly when it comes to eliminating short-chain PFAS. This is mainly due to the large number of complex parameters that are involved in both material and process designs. Here, we took the advantage of artificial intelligence to introduce a model (XGBoost) in which material and process factors are considered simultaneously. This research applies a machine learning approach using data collected from reported articles to predict the PFAS removal factors. The XGBoost modeling provided accurate adsorption capacity, equilibrium, and removal estimates with the ability to predict the adsorption mechanisms. The performance comparison of adsorbents and the role of AI in one dominant are studied and reviewed for the first time, even though many studies have been carried out to develop PFAS removal through various adsorption methods such as ion exchange, nanofiltration, and activated carbon (AC). The model showed that pH is the most effective parameter to predict PFAS removal. The proposed model in this work can be extended for other micropollutants and can be used as a basic framework for future adsorbent design and process optimization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Soil contamination is increasingly a global problem with serious implications for human health. Among different soil decontamination approaches, electrokinetic (EK) remediation is a relatively new technology for treating organic and inorganic contaminants in soil. This research aims to develop an enhanced EK treatment method incorporating a compost-based reactive filter media (RFM) with the advantages of low-cost and strong affinity for heavy metals and test and improve the treatment efficiency for multiple heavy metals in natural soil. A series of EK operations were performed to investigate the performance of EK-RFM under different operating conditions such as the electric current and voltage, processing time, and the amount of RFM. The electric current and treatment time demonstrated a significant positive impact on removing Zn, Cd and Mn ions while changing the amount of RFM had an insignificant impact on the efficiency of heavy metals removal. Overall, 51.6%-72.1% removal of Zn, Cd, and Mn was achieved at 30.00 mA of electric current and 14 days of treatment duration. The energy consumption of the EK process was 0.17 kWh kg -1 . The soil organic matter adversely affected the mobilization and migration of heavy metals such as Cu and Pb during EK treatment. The results are valuable in optimizing the design of the EK-RFM system, which will extend its application to field-scale soil decontamination practices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)