Contributions of various driving factors to air pollution events: Interpretability analysis from Machine learning perspective

• Published in: Environment international 2023-03, Vol.173, p.107861-107861, Article 107861
• Main Authors: Li, Tianshuai, Zhang, Qingzhu, Peng, Yanbo, Guan, Xu, Li, Lei, Mu, Jiangshan, Wang, Xinfeng, Yin, Xianwei, Wang, Qiao
• Format: Article
• Language: English
• Subjects: Aerosols - analysis; Air Pollutants - analysis; Air pollution; Air Pollution - analysis; China; Environmental Monitoring; Environmental Pollution - analysis; Machine learning; Particulate Matter - analysis; PDP; Permutation importance; PM2.5; Seasons; SHAP
• ISSN: 0160-4120; 1873-6750
• DOI: 10.1016/j.envint.2023.107861

[Display omitted] •Quantifying the contributions of various driving factors to air pollution events (APs).•From global understanding to local explanations with interpretable analysis.•The order of sensitivity of secondary inorganic aerosols (SIA) to PM2.5 was NH4+＞NO3-＞SO42-.•Lower temperature and higher humidity promoted the formation of NO3-. The air quality in China has been improved substantially, however fine particulate matter (PM2.5) still remain at a high level in many areas. PM2.5 pollution is a complex process that is attributed to gaseous precursors, chemical, and meteorological factors. Quantifying the contribution of each variable to air pollution can facilitate the formulation of effective policies to precisely eliminate air pollution. In this study, we first used decision plot to map out the decision process of the Random Forest (RF) model for a single hourly data set and constructed a framework for analyzing the causes of air pollution using multiple interpretable methods. Permutation importance was used to qualitatively analyze the effect of each variable on PM2.5 concentrations. The sensitivity of secondary inorganic aerosols (SIA): SO42-, NO3- and NH4+ to PM2.5 was verified by Partial dependence plot (PDP). Shapley Additive Explanation (Shapley) was used to quantify the contribution of drivers behind the ten air pollution events. The RF model can accurately predict PM2.5 concentrations, with determination coefficient (R2) of 0.94, root mean square error (RMSE) and mean absolute error (MAE) of 9.4 μg/m3 and 5.7 μg/m3, respectively. This study revealed that the order of sensitivity of SIA to PM2.5 was NH4+＞NO3-＞SO42-. Fossil fuel and biomass combustion may be contributing factors to air pollution events in Zibo in 2021 autumn–winter. NH4+ contributed 19.9–65.4 μg/m3 among ten air pollution events (APs). K, NO3-, EC and OC were the other main drivers, contributing 8.7 ± 2.7 μg/m3, 6.8 ± 7.5 μg/m3, 3.6 ± 5.8 μg/m3 and 2.5 ± 2.0 μg/m3, respectively. Lower temperature and higher humidity were vital factors that promoted the formation of NO3-. Our study may provide a methodological framework for precise air pollution management.