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

Authors

  • Hiroko Satoh University of Zurich, Research Organization of Information and Systems (ROIS) https://orcid.org/0000-0003-4238-0286
  • Jasmin Hafner Department of Environmental Chemsitry, Eawag
  • Jürg Hutter Department of Chemistry, University of Zurich
  • Kathrin Fenner Department of Chemistry, University of Zurich; Department of Environmental Chemistry, Eawag, Überlandstrasse 133, CH-8600 Dübendorf

DOI:

https://doi.org/10.2533/chimia.2023.48

PMID:

38047853

Keywords:

Cheminformatics, Degradation of micropollutants in wastewater, Quantitative structure-biodegradation relationships (QSBR), Quantum chemistry (QC)

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.

CHIMIA Vol. 77 No. 1/2 (2023): Artificial Intelligence in Chemical Research

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Published

2023-02-22

Issue

Vol. 77 No. 1/2 (2023): Artificial Intelligence in Chemical Research

Section

Scientific Articles

License

Copyright (c) 2023 Hiroko Satoh, Jasmin Hafner, Jürg Hutter, Kathrin Fenner

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
H. Satoh, J. Hafner, J. Hutter, K. Fenner, Chimia 2023, 77, 48, DOI: 10.2533/chimia.2023.48.