Abstract

Free water surface constructed wetlands (FWSCWs) for the treatment of various wastewater types have evolved significantly over the last few decades. With an increasing need and interest in FWSCWs applications worldwide due to their cost-effectiveness and other benefits, this paper reviews recent literature on FWSCWs' ability to remove different types of pollutants such as nutrients (i.e., TN, TP, NH4-N), heavy metals (i.e., Fe, Zn, and Ni), antibiotics (i.e., oxytetracycline, ciprofloxacin, doxycycline, sulfamethazine, and ofloxacin), and pesticides (i.e., Atrazine, S-Metolachlor, imidacloprid, lambda-cyhalothrin, diuron 3,4-dichloroanilin, Simazine, and Atrazine) that may co-exist in wetland inflow, and discusses approaches for simulating hydraulic and pollutant removal processes. A bibliometric analysis of recent literature reveals that China has the highest number of publications, followed by the USA. The collected data show that FWSCWs can remove an average of 61.6%, 67.8%, 54.7%, and 72.85% of inflowing nutrients, heavy metals, antibiotics, and pesticides, respectively. Optimizing each pollutant removal process requires specific design parameters. Removing heavy metal requires the lowest hydraulic retention time (HRT) (average of 4.78days), removing pesticides requires the lowest water depth (average of 0.34m), and nutrient removal requires the largest system size. Vegetation, especially Typha spp. and Phragmites spp., play an important role in FWSCWs' system performance, making significant contributions to the removal process. Various modeling approaches (i.e., black-box and process-based) were comprehensively reviewed, revealing the need for including the internal process mechanisms related to the biological processes along with plants spp., that supported by a further research with field study validations. This work presents a state-of-the-art, systematic, and comparative discussion on the efficiency of FWSCWs in removing different pollutants, main design factors, the vegetation, and well-described models for performance prediction.

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