Header

UZH-Logo

Maintenance Infos

Clustering micropollutants based on initial biotransformations for improved prediction of micropollutant removal during conventional activated sludge treatment


Wang, Yuxin; Fenner, Kathrin; Helbling, Damian E (2020). Clustering micropollutants based on initial biotransformations for improved prediction of micropollutant removal during conventional activated sludge treatment. Environmental Science: Water Research and Technology, 6(3):554-565.

Abstract

The lack of fundamental insights on the fate of micropollutants during conventional activated sludge treatment in wastewater treatment plants (WWTPs) presents one of the biggest challenges in optimizing their removal. To address this challenge, we designed a study to identify the drivers of micropollutant removal in WWTPs. We calculated the removal efficiency of micropollutants across the activated sludge process of full-scale wastewater treatment plants from literature-reported data. Our final dataset consisted of 529 independent observations for 84 micropollutants along with a set of associated activated sludge process parameters. We used the Eawag pathway prediction system to predict initial biotransformations for each of the 84 micropollutants and hierarchical clustering to group chemicals based on similarities in their predicted initial sets of biotransformations. 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 calculated for each cluster 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 biotransformations. Our findings highlight the importance of considering initial biotransformations when evaluating micropollutant removal and identify important process parameters that determine the fate of micropollutants during activated sludge treatment.

Abstract

The lack of fundamental insights on the fate of micropollutants during conventional activated sludge treatment in wastewater treatment plants (WWTPs) presents one of the biggest challenges in optimizing their removal. To address this challenge, we designed a study to identify the drivers of micropollutant removal in WWTPs. We calculated the removal efficiency of micropollutants across the activated sludge process of full-scale wastewater treatment plants from literature-reported data. Our final dataset consisted of 529 independent observations for 84 micropollutants along with a set of associated activated sludge process parameters. We used the Eawag pathway prediction system to predict initial biotransformations for each of the 84 micropollutants and hierarchical clustering to group chemicals based on similarities in their predicted initial sets of biotransformations. 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 calculated for each cluster 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 biotransformations. Our findings highlight the importance of considering initial biotransformations when evaluating micropollutant removal and identify important process parameters that determine the fate of micropollutants during activated sludge treatment.

Statistics

Citations

Dimensions.ai Metrics
8 citations in Web of Science®
7 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

17 downloads since deposited on 16 Jan 2020
16 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Department of Chemistry
Dewey Decimal Classification:540 Chemistry
Scopus Subject Areas:Physical Sciences > Environmental Engineering
Physical Sciences > Water Science and Technology
Language:English
Date:1 January 2020
Deposited On:16 Jan 2020 09:30
Last Modified:13 Jun 2021 07:34
Publisher:Royal Society of Chemistry
ISSN:2053-1400
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1039/C9EW00838A

Download

Green Open Access