Prediction of Photochemical Properties of Dissolved Organic Matter Using Machine Learning

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 ΦPPRI 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 ΦPPRIs (Φ3DOM*, Φ1O2, and Φ·OH) based on DOM spectral
parameters, experimental conditions, and calculation parameters. The
best predictive performances for Φ3DOM*, Φ1O2, 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 ΦPPRI predictions were revealed, suggesting
that DOM with lower molecular weight, lower aromatic content, and
a more autochthonous portion possessed higher ΦPPRIs. Chain models were constructed by adding the predicted Φ3DOM* as a new feature into the Φ1O2 and Φ·OH models, which consequently improved the predictive
performance of Φ1O2 but worsened the Φ·OH prediction likely due to the complex formation pathways
of ·OH. Overall, this study offered robust ΦPPRI prediction across interlaboratory differences and provided new insights
into the relationship between PPRIs formation and DOM properties.