Trends Analysis of Fine Particulate Matter and Ozone Pollution in Typical Cities in the Beijing–Tianjin–Hebei Region during 2015–2021

Atmospheric fine particulate matter (PM_2.5) and ozone (O₃) pollution are influenced by meteorological conditions and emissions from pollution sources. Based on the data from China’s national air quality monitoring stations and fifth-generation reanalysis data from the European Centre for Medium-Range Weather Forecasts, the relationships of atmospheric PM_2.5 and O₃ with major meteorological factors in typical cities in the Beijing–Tianjin–Hebei region, including Beijing, Tianjin, Baoding, and Shijiazhuang, during 2015–2021 were investigated in this study. Moreover, the contribution of emission reduction to the annual trend of PM_2.5 and O₃ was quantitatively evaluated using the random forest algorithm. Excluding summer, daily PM_2.5 was positively and negatively correlated with relative humidity and boundary layer height, respectively. Meanwhile, daily PM_2.5 was positively correlated with O₃ and temperature in summer. Combined with the analysis of the variation characteristics of meteorological conditions associated with different concentration levels of PM_2.5 and O₃, it was found that adverse atmospheric diffusion conditions primarily affected the individual pollution days in terms of PM_2.5 in winter and summer. On PM_2.5 and O₃ pollution days, strong atmospheric oxidation and high relative humidity conditions critically aggravated PM_2.5 pollution in winter, while high temperature and strong solar radiation were important meteorological conditions responsible for increased PM_2.5 and O₃ co-pollution in summer. Quantitative trend analysis revealed that emission reduction was the driving factor leading to the annual change in PM_2.5 and O₃, and the contribution to the annual decrease in PM_2.5 was 84%–95%. O₃ showed an increasing trend from 2015 to 2018 but decreased after 2018 (−3.4 to −6.4 µg m⁻³ a⁻¹). Furthermore, the trend of O₃ was overall consistent with the difference between the observed value and the prediction value obtained from the random forest algorithm. The contribution of emissions in Tianjin, Baoding, and Shijiazhuang to the trends was 18%–34%, indicating that the recent emission reduction measures may have had a certain effect on controlling O₃ pollution.