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 (O3) and hydroxyl radicals (•OH). To predict MP abatement during ozonation, a model that can accurately predict oxidant exposures (i.e., ∫0t[O3]dt⁢ and⁢ ∫0t[O•H]dt) 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 O3 dose (2.5 mg/L). Four RF models were developed depending on how FEEM data were utilized (i.e., one model free of FEEM data, and three other models that used FEEM data of different resolutions). The regression performance and Akaike information criterion (AIC) were evaluated for each model. The models using high-resolution FEEM data generally exhibited high prediction accuracy with reasonable AIC values, implying that organic matter characteristics quantified by FEEM can be important factors to improve the accuracy of the prediction model. The developed models can be applied to predict the abatement of MPs in drinking water and wastewater ozonation processes and to optimize the O3 dose for the intended removal of target MPs. The machine learning models using higher-resolution FEEM data offer more accurate prediction by better calculating the complex nonlinear relationship between organic characteristics and oxidant exposures.

Prediction of hydroxyl radical exposure during ozonation using different machine learning methods with ozone decay kinetic parameters.

The abatement of micropollutants by ozonation can be accurately calculated by measuring the exposures of molecular ozone (O3) and hydroxyl radical (•OH) (i.e., ∫[O3]dt and ∫[•OH]dt). In the actual ozonation process, ∫[O3]dt values can be calculated by monitoring the O3 decay during the process. However, calculating ∫[•OH]dt is challenging in the field, which necessitates developing models to predict ∫[•OH]dt from measurable parameters. This study demonstrates the development of machine learning models to predict ∫[•OH]dt (the output variable) from five basic input variables (pH, dissolved organic carbon concentration, alkalinity, temperature, and O3 dose) and two optional ones (∫[O3]dt and instantaneous ozone demand, IOD). To develop the models, four different machine learning methods (random forest, support vector regression, artificial neural network, and Gaussian process regression) were employed using the input and output variables measured (or determined) in 130 different natural water samples. The results indicated that incorporating ∫[O3]dt as an input variable significantly improved the accuracy of prediction models, increasing overall R2 by 0.01-0.09, depending on the machine learning method. This suggests that ∫[O3]dt plays a crucial role as a key variable reflecting the •OH-yielding characteristics of dissolved organic matter. Conversely, IOD had a minimal impact on the accuracy of the prediction models. Generally, machine-learning-based prediction models outperformed those based on the response surface methodology developed as a control. Notably, models utilizing the Gaussian process regression algorithm demonstrated the highest coefficients of determination (overall R2 = 0.91-0.95) among the prediction models.

Modeling of ozone decomposition, oxidant exposures, and the abatement of micropollutants during ozonation processes.

This study demonstrates new empirical models to predict the decomposition of ozone (O3) and the exposures of oxidants (i.e., O3 and hydroxyl radical, OH) during the ozonation of natural waters. Four models were developed for the instantaneous O3 demand, first-order rate constant for the secondary O3 decay, O3 exposure (∫[O3]dt), and OH exposure ((∫[OH]dt)), as functions of five independent variables, namely the O3 dose, concentration of dissolved organic carbon (DOC), pH, alkalinity, and temperature. The models were derived by polynomial regression analysis of experimental data obtained by controlling variables in natural water samples from a single source water (Maegok water in Korea), and they exhibited high accuracies for regression (R2 = 0.99 for the three O3 models, and R2 = 0.96 for the OH exposure model). The three O3 models exhibited excellent internal validity for Maegok water samples of different conditions (that were not used for the model development). They also showed acceptable external validity for seven natural water samples collected from different sources (not Maegok water); the IOD model showed somewhat poor external validity. However, the OH exposure model showed relatively poor internal and external validity. The models for oxidant exposures were successfully used to predict the abatement of micropollutants by ozonation; the model predictions showed high accuracy for Maegok water, but not for the other natural waters.

Cupric ion in combination with hydrogen peroxide and hydroxylamine applied to inactivation of different microorganisms.

The microbial inactivation by cupric ion (Cu(II)) in combination with hydrogen peroxide (H2O2) and hydroxylamine (HA) was investigated for twelve different microorganisms (five Gram-negative bacteria, three Gram-positive bacteria, and four bacteriophages). The inactivation efficacy, protein oxidation, and RNA (or DNA) damage were monitored during and after treatment by Cu(II), Cu(II)/HA, Cu(II)/H2O2 and Cu(II)/HA/H2O2. The rate of microbial inactivation by the (combined) microbicides generally increased in the order of Cu(II) < Cu(II)/H2O2 < Cu(II)/HA < Cu(II)/HA/H2O2; Cu(II)/HA/H2O2 resulted in 0.18-0.31, 0.10-0.18, and 0.55-3.83 log inactivation/min for Gram-negative bacteria, Gram-positive bacteria, and bacteriophages, respectively. The degrees of protein oxidation and RNA (or DNA) damage increased in the order of Cu(II) < Cu(II)/HA < Cu(II)/H2O2 < Cu(II)/HA/H2O2. In particular, Cu(II)/HA/H2O2 led to exceptionally fast inactivation of the viruses. Gram-positive bacteria tended to show higher resistance to microbicides than other microbial species. The microbicidal effects of the combined microbicides on the target microorganisms were explained by the roles of Cu(I) and Cu(III) generated by the redox reactions of Cu(II) with H2O2, HA, and oxygen. Major findings of this study indicate that Cu(II)-based combined microbicides are promising disinfectants for different waters contaminated by pathogenic microorganisms.