Identifying Driving Factors of Atmospheric N<sub>2</sub>O<sub>5</sub> with Machine Learning

Chen, Xin; Ma, Wei; Zheng, Feixue; Wang, Zongcheng; Hua, Chenjie; Li, Yiran; Wu, Jin; Li, Boda; Jiang, Jingkun; Yan, Chao; Petäjä, Tuukka; Bianchi, Federico; Kerminen, Veli-Matti; Worsnop, Douglas R.; Liu, Yongchun; Xia, Men; Kulmala, Markku

doi:10.1021/acs.est.4c00651

Environ. Sci. Technol.

2024

DOI: 10.1021/acs.est.4c00651

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Identifying Driving Factors of Atmospheric N₂O₅ with Machine Learning

Xin Chen,

Wei Ma,

Feixue Zheng

et al.

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Cited by 2 publications

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Refined source apportionment of nitrate aerosols based on isotopes and emission inventories in coastal city of northern China

Ni,

Luo,

Liu

et al. 2024

Science of The Total Environment

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Refined source apportionment of nitrate aerosols based on isotopes and emission inventories in coastal city of northern China

Ni,

Luo,

Liu

et al. 2024

Science of The Total Environment

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Explainable Machine Learning Reveals the Unknown Sources of Atmospheric HONO during COVID-19

Gao,

Wang,

Gligorovski

et al. 2024

ACS EST Air

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Nitrous acid (HONO) is a key precursor of the hydroxyl radical (•OH), playing an important role in atmospheric oxidation capacity. However, unknown sources of HONO (P unknown ) are frequently reported and the potential sources are controversial. Here, we explored P unknown during COVID-19 in different seasons and epidemic control phases in Shanghai by eXtreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP) for the first time. They demonstrated that the decrease of anthropogenic activity would inhibit secondary formation of HONO, as epidemic control policies turned strict. The explainable machine learning revealed that nitrogen dioxide (NO 2 ) had significant impacts on the P unknown during spring 2020 (P1), where P unknown could be fully explained by including light-induced heterogeneous conversion of NO 2 on ground, building, and aerosol surfaces. With the untightening of epidemic control in spring 2021 (P3), the HONO budget came to balance after further addition of the photolysis of particulate nitrate (NO 3 − ) and soil HONO emission. As for P2 (summer), P unknown decreased by 54% with all new sources added. These results provide new insights into HONO chemistry in response to reduced anthropogenic emissions, improving the predictions of atmospheric oxidation capacity.

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Identifying Driving Factors of Atmospheric N₂O₅ with Machine Learning

Cited by 2 publications

References 56 publications

Refined source apportionment of nitrate aerosols based on isotopes and emission inventories in coastal city of northern China

Refined source apportionment of nitrate aerosols based on isotopes and emission inventories in coastal city of northern China

Explainable Machine Learning Reveals the Unknown Sources of Atmospheric HONO during COVID-19

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Identifying Driving Factors of Atmospheric N2O5 with Machine Learning

Cited by 2 publications

References 56 publications

Refined source apportionment of nitrate aerosols based on isotopes and emission inventories in coastal city of northern China

Refined source apportionment of nitrate aerosols based on isotopes and emission inventories in coastal city of northern China

Explainable Machine Learning Reveals the Unknown Sources of Atmospheric HONO during COVID-19

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Identifying Driving Factors of Atmospheric N₂O₅ with Machine Learning