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Apparent quantum yields (Φ) of photochemically produced reactive intermediates (PPRIs) formed by dissolved organic matter (DOM) are vital to element cycles and contaminant fates in surface water. Simultaneous determination of Φ PPRI values from numerous water samples through existing experimental methods is time consuming and ineffective. Herein, machine learning models were developed with a systematic data set including 1329 data points to predict the values of three Φ PPRIs (Φ 3DOM* , Φ 1O2 , and Φ •OH ) based on DOM spectral parameters, experimental conditions, and calculation parameters. The best predictive performances for Φ 3DOM* , Φ 1O2 , and Φ •OH were achieved using the CatBoost model, which outperformed the traditional linear regression models. The significances of the wavelength range and spectral parameters on the three Φ PPRI predictions were revealed, suggesting that DOM with lower molecular weight, lower aromatic content, and a more autochthonous portion possessed higher Φ PPRIs . Chain models were constructed by adding the predicted Φ 3DOM* as a new feature into the Φ 1O2 and Φ •OH models, which consequently improved the predictive performance of Φ 1O2 but worsened the Φ •OH prediction likely due to the complex formation pathways of •OH. Overall, this study offered robust Φ PPRI prediction across interlaboratory differences and provided new insights into the relationship between PPRIs formation and DOM properties.

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