Spatiotemporal variations of air pollutants and ozone prediction using machine learning algorithms in the Beijing-Tianjin-Hebei region from 2014 to 2021. (1st August 2022)

• Record Type: Journal Article
• Authors: Lyu, Yan; Ju, Qinru; Lv, Fengmao; Feng, Jialiang; Pang, Xiaobing; Li, Xiang
• Is Part Of: Environmental pollution. Volume 306(2022)
• Publication Date: 2022-08-01
• Subjects: Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2022.119420
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: China was seriously affected by air pollution in the past decade, especially for particulate matter (PM) and emerging ozone pollution recently. In this study, we systematically examined the spatiotemporal variations of six air pollutants and conducted ozone prediction using machine learning (ML) algorithms in the Beijing-Tianjin-Hebei (BTH) region. The annual-average concentrations of CO, PM10, PM2.5 and SO2 decreased at a rate of 141, 11.0, 6.6 and 5.6 μg/m 3 /year, while a pattern of initial increase and later decrease was observed for NO2 and O3 _8 h. The concentration of SO2, CO and NO2 was higher in Tangshan and Xingtai, while northern BTH region has lower levels of CO, NO2 and PM. Spatial variations of ozone were relatively small in the BTH region. Monthly variations of PM10 displayed an increase in March probably due to wind-blown dusts from Northwest China. A seasonal and diurnal pattern with summer and afternoon peaks was found for ozone, which was contrast with other pollutants. Further ML algorithms such as Random Forest (RF) model and Decision tree (DT) regression showed good ozone prediction performance (daily: R 2 = 0.83 and 0.73, RMSE = 30.0 and 37.3 μg/m 3, respectively; monthly: R 2 = 0.93 and 0.88, RMSE = 12.1 and 15.8 μg/m 3, respectively) based on 10-fold cross-validation. Both RF model and DT regression relied more on the spatial trend as higher temporal prediction performance was achieved. Solar radiation- and temperature-related variables presented high importance at daily level, whereas sea level pressure dominated at monthly level. The spatiotemporal heterogeneity in variable importance was further confirmed using case studies based on RF model. In addition, variable importance was possibly influenced by the emission reductions due to COVID-19 pandemic. Despite its possible weakness to capture ozone extremes, RF model was beneficial and suggested for predicting spatiotemporal variations of ozone in future studies. Graphical abstract: Image 1 Highlights: Spatiotemporal variations of air pollutants are examined in BTH Region from 2014 to 2021. Random Forest outperforms Decision Tree regression in predicting ozone variations. Meteorological parameters display high variable importance in ozone prediction. Variable importance varies with cities and is influenced by COVID-19 pandemic.