Effluent from conventional wastewater treatment plants (WWTPs) is a major source of micropollutants into the aquatic environment. The lack of fundamental insights on the fate of micropollutants in WWTPs presents one of the biggest challenges in optimizing their removal. To address this challenge, we designed a study to identify key drivers of micropollutants removal in WWTPs, including 529 independent observations for 84 micropollutants across the activated sludge process of full-scale WWTPs. We developed multivariable models to explain variability in removal efficiencies among micropollutants that undergo the same types of biotransformation. Eawag pathway prediction system was used to predict initial biotransformation for each of the 84 micropollutants and hierarchical clustering to group chemicals based on similarities in their predicted initial sets of biotransformation. We then applied stability selection to generate well-performing models that can be interpreted to uncover the key factors contributing to the range of removal efficiencies of micropollutants. The key factors considered in stability selection included six physicochemical properties of the micropollutants and six activated sludge process parameters. The sludge water partitioning coefficient, molecular weight, solids retention time, influent micropollutant concentration, and dissolved oxygen levels were consistently identified as key factors of micropollutant removal for clusters of micropollutants that undergo certain types of initial biotransformation. Our approach can be generalized and applied to other chemicals and biotransformation processes occurring in the natural or engineered systems to gain insight into chemical persistence and risks to human health.

Multivariate approach to identify key parameters influencing micropollutants biotransformation during conventional wastewater treatment

ACS Fall 2021

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