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Microplastics removal efficiency of drinking water treatment plant with pulse clarifier

Journal of Hazardous Materials 2021 190 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Dhruba Jyoti Sarkar, Soma Das Sarkar, Basanta Kumar Das, Jaya Krushna Praharaj, Dev Kumar Mahajan, Bidesh Purokait, Trupty Rani Mohanty, Debasmita Mohanty, Pranab Gogoi, Santhana Kumar, Bijay Kumar Behera, Ranjan Kumar Manna, Srikanta Samanta

Summary

Researchers evaluated how well a drinking water treatment plant using pulse clarification technology removes microplastics from river water sourced from the Ganges. Raw water contained about 18 microplastics per liter, and the treatment process removed 85% of them, with sand filtration being the most effective step. The study found that fibers and films were the most common microplastic shapes, and machine learning analysis revealed strong links between microplastic levels and water quality indicators like turbidity.

Polymers
Study Type Environmental

Microplastics are recognized as ubiquitous pollutants in aquatic environments; however, very little study is done on their occurrence and fate at drinking water treatment plants (DWTPs). Though, the toxic effect of microplastics on human health is not yet well established; there is global concern about their possible ill effect on the human. Hence, the present study evaluates the occurrence of microplastics at different treatment stages of a typical DWTP with pulse clarification and its removal efficiency. In the test DWTP, raw water, sourced from river Ganga, was found to contain microplastics 17.88 items/L. Cumulative microplastic removal at key treatment stages viz. pulse clarification and sand filtration was found to be 63% and 85%, respectively. The study also revealed higher microplastic abundance on the sand filter bed due to the screening effect. The most frequently occurring microplastics were fibers and films/fragments with polyethylene terephthalate and polyethylene as a major chemical type. The t-distributed stochastic neighbor embedding machine learning algorithm revealed a strong association between microplastic abundance with turbidity, phosphate and nitrate. The test DWTP with a pulse clarification system was having comparable microplastics removal efficiency with previously reported advanced DWTPs.

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