Can AI Help Improve Water Quality? Towards the Prediction of Degradation of Micropollutants in Wastewater

Hiroko Satoh; Jasmin Hafner; Jürg Hutter; Kathrin Fenner

doi:10.2533/chimia.2023.48

Can AI Help Improve Water Quality? Towards the Prediction of Degradation of Micropollutants in Wastewater

Chimia (Aarau). 2023 Feb 22;77(1-2):48-55. doi: 10.2533/chimia.2023.48.

Authors

Hiroko Satoh¹, Jasmin Hafner², Jürg Hutter³, Kathrin Fenner^{4

5}

Affiliations

¹ University of Zurich, Research Organization of Information and Systems (ROIS). hiroko.satoh@chem.uzh.ch.
² Department of Environmental Chemsitry, Eawag. Jasmin.Hafner@eawag.ch.
³ Department of Chemistry, University of Zurich. hutter@chem.uzh.ch.
⁴ Department of Chemistry, University of Zurich. Kathrin.Fenner@eawag.ch.
⁵ Department of Environmental Chemistry, Eawag, Überlandstrasse 133, CH-8600 Dübendorf.

PMID: 38047853
DOI: 10.2533/chimia.2023.48

Abstract

Micropollutants have become a serious environmental problem by threatening ecosystems and the quality of drinking water. This account investigates if advanced AI can be used to find solutions for this problem. We review background, the challenges involved, and the current state-of-the-art of quantitative structure-biodegradation relationships (QSBR). We report on recent progress combining experiment, quantum chemistry (QC) and chemoinformatics, and provide a perspective on potential future uses of AI technology to help improve water quality.

Keywords: Cheminformatics; Degradation of micropollutants in wastewater; Quantitative structure-biodegradation relationships (QSBR); Quantum chemistry (QC).