化学
溶解有机碳
线性回归
预测建模
生物系统
训练集
预测值
线性关系
环境化学
人工智能
计算机科学
机器学习
统计
数学
医学
内科学
生物
作者
Zhiyang Liao,Jinrong Lu,Kunting Xie,Yi Wang,Yong Yuan
标识
DOI:10.1021/acs.est.2c07545
摘要
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.
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