Evaluating and elucidating the reactivity of OH radicals with atmospheric organic pollutants: Reaction kinetics and mechanisms by machine learning. (15th April 2022)

• Record Type: Journal Article
• Authors: Sanches-Neto, Flávio O.; Dias-Silva, Jefferson R.; de Oliveira, Vitor M.; Aquilanti, Vincenzo; Carvalho-Silva, Valter H.
• Is Part Of: Atmospheric environment. Volume 275(2022)
• Publication Date: 2022-04-15
• Subjects: QSAR/QSPR -- OCDE guidelines -- Reaction rate constants -- SHAP -- Web application; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2022.119019
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: The rate constants of the reactions of OH radicals with atmospheric organic pollutants (AOPs) are crucial physicochemical parameters to guide in the elucidation of the kinetics and mechanisms of the reactive landscape. The experimental and theoretical difficulties in revealing the reactivity of these degradation processes motivated us to develop a protocol based on machine learning combining molecular fingerprints to estimate their rate constants. The present workflow is based on Organization for Economic Cooperation and Development (OECD) guidelines and state-of-the-art techniques involving (i) data collection including 903 AOPs cataloged in the literature, (ii) pre-processing and structuring of data, (iii) development of models based on three machine learning algorithms, (iv) the standard reference of validation, and (v) mechanistic interpretation. The results show that the built model has a high predictive capacity – R c v 2 > 0.959 and R M S E c v < 0.090 for the training set and R e x t 2 and Q e x t 2 > 0.889 and R M S E e x t < 0.084 for the test set. Additionally, through the SHapley Additive exPlanations (SHAP) method, it was possible to establish insight into the contribution of chemical classes to reaction kinetics and mechanism and to discuss it consistently with current experimental and theoretical observations. The availability of the evaluated reaction rate constants permitted to elucidate the role of AOPs in the photochemical ozone balance. Finally, to disseminate use of our results, we have presented them in a user-friendly web application that permits compilation of kinetic parameters, and that permits future implementations to account for the temperature dependence in the environmental relevant range, and the consideration of a wider class of chemicals and processes in the mechanistic networks of atmospheric reactivity. Graphical abstract: Image 1 Highlights: Six machine learning models were developed. The half-life time and POCP of the reaction is reported for OH radical. Workflow was fitted with experimental data to train ML models using of AOPs. The models development followed the OECD principles.