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Summary

Machine learning accurately predicts iodinated trihalomethanes (I-THMs) in drinking water. This approach simplifies mitigation strategies, leading to safer water treatment by identifying key factors and optimizing disinfectant doses.

Keywords:
feature engineeringiodinated contrast media (ICM)iodinated disinfection byproducts (I-DBPs)iodinated trihalomethanes (I-THMs)machine learning (ML)

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Area of Science:

  • Environmental Chemistry
  • Water Treatment Technologies
  • Computational Chemistry

Background:

  • Disinfection byproducts (DBPs), specifically iodinated trihalomethanes (I-THMs), are a health concern in water treatment.
  • Predicting I-THM formation is complex due to interacting water quality parameters, disinfectants, and iodine sources.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting I-THM formation.
  • To identify key factors influencing I-THM generation and propose mitigation strategies.

Main Methods:

  • Utilized a dataset of 1534 samples from published studies.
  • Evaluated five ensemble machine learning models, with CatBoost Regression showing the best performance.
  • Incorporated domain-specific features and employed recursive feature elimination to simplify the model.

Main Results:

  • The CatBoost model achieved an R² of 0.67 on external validation data, demonstrating strong generalizability.
  • Identified key predictors and mitigation strategies, including minimizing iodine, bromide, iodine/DOC, UV254, and SUVA.
  • Optimized chlorine dose for I-THM minimization using incremental and Bayesian optimization.

Conclusions:

  • Machine learning is a powerful tool for predicting and mitigating I-THMs in water treatment.
  • The developed model offers actionable strategies for enhancing drinking water safety and reducing experimental efforts.