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Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches

Environmental Studies and Forestry

Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches

S. J. Lim, J. Seo, et al.

This study harnesses the power of machine learning to cluster micropollutants in wastewater, accurately estimating their sorption and biodegradation rate constants. Conducted by Seung Ji Lim and colleagues, this innovative approach improves monitoring of environmental contaminants, achieving significantly higher accuracy than past methods.

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Playback language: English
Abstract
This study used machine learning to cluster micropollutants (MPs) in wastewater effluent and estimate their sorption and biodegradation rate constants. A self-organizing map (SOM) clustered 29 of 42 MPs based on physicochemical properties, functional groups, and initial biotransformation rules, achieving 75% accuracy. Eleven marker constituents were identified for aerobic and anoxic conditions. A random forest classifier then estimated rate constants for the remaining 13 MPs using these markers, achieving 77% accuracy – significantly higher than previous methods. This approach simplifies MP monitoring in wastewater.
Publisher
npj Clean Water
Published On
Oct 28, 2023
Authors
Seung Ji Lim, Jangwon Seo, Mingizem Gashaw Seid, Jiho Lee, Wondesen Workneh Ejerssa, Doo-Hee Lee, Eunhoo Jeong, Sung Ho Chae, Yunho Lee, Moon Son, Seok Won Hong
Tags
micropollutants
wastewater
machine learning
sorption
biodegradation
monitoring
self-organizing map

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