Prediction of Oxidant Exposures and Micropollutant Abatement during Ozonation Using a Machine Learning Method

Oxidation of micropollutants (MPs) by ozonation proceeds via the reactions with molecular ozone (O 3 ) and hydroxyl radicals ( • OH). To predict MP abatement during ozonation, a model that can accurately predict oxidant exposures (i.e., ) needs to be developed. This study demonstrates machine learning models based on the random forest (RF) algorithm to output oxidant exposures from water quality parameters (input variables) that include pH, alkalinity, dissolved organic carbon concentration, and fluorescence excitation–emission matrix (FEEM) data (to characterize organic matter). To develop the models, 60 different samples of natural waters and wastewater effluents were collected and characterized, and the oxidant exposures in each sample were determined at a specific O 3 dose (2.5 mg/L). Four RF models were developed depending on how FEEM data were utilized (i.e., one model free of FEEM…