title: Modeling the reactivities of hydroxyl radical and ozone towards atmospheric organic chemicals using quantitative structure-reactivity relationship approaches.
abstract: The persistence and the removal of organic chemicals from the atmosphere are largely determined by their reactions with the OH radical and O3. Experimental determinations of the kinetic rate constants of OH and O3 with a large number of chemicals are tedious and resource intensive and development of computational approaches has widely been advocated. Recently, ensemble machine learning (EML) methods have emerged as unbiased tools to establish relationship between independent and dependent variables having a nonlinear dependence. In this study, EML-based, temperature-dependent quantitative structure-reactivity relationship (QSRR) models have been developed for predicting the kinetic rate constants for OH (kOH) and O3 (kO3) reactions with diverse chemicals. Structural diversity of chemicals was evaluated using a Tanimoto similarity index. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation performed employing statistical checks. In test data, the EML QSRR models yielded correlation (R (2)) of ≥0.91 between the measured and the predicted reactivities. The applicability domains of the constructed models were determined using methods based on descriptors range, Euclidean distance, leverage, and standardization approaches. The prediction accuracies for the higher reactivity compounds were relatively better than those of the low reactivity compounds. Proposed EML QSRR models performed well and outperformed the previous reports. The proposed QSRR models can make predictions of rate constants at different temperatures. The proposed models can be useful tools in predicting the reactivities of chemicals towards OH radical and O3 in the atmosphere.
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow, 226 001, India.
authors.forename: Shikha
authors.initials: S
authors.lastname: Gupta
authors.affiliation: ETRC, Gomtinagar, Lucknow, 226010, India.
authors.forename: Nikita
authors.initials: N
authors.lastname: Basant
authors.affiliation: School of Environmental Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi, 110067, India.
authors.forename: Dinesh
authors.initials: D
authors.lastname: Mohan
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow, 226 001, India. kpsingh_52@yahoo.com.
authors.forename: Kunwar P
authors.initials: KP
authors.lastname: Singh
mesh_terms: D000393:Air Pollutants / Q000737:chemistry*; D001272:Atmosphere; D017665:Hydroxyl Radical / Q000737:chemistry*; D007700:Kinetics; D000069550:Machine Learning; D008962:Models, Theoretical; D009930:Organic Chemicals / Q000737:chemistry*; D010126:Ozone / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D013696:Temperature
chemical_list: D000393:Air Pollutants; D009930:Organic Chemicals; D017665:Hydroxyl Radical; D010126:Ozone
keywords: Chemical reactivities; Ensemble machine learning; Hydroxyl radical; Organic chemicals; Ozone