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Using the hourly monitoring data of pollutants from 16 automatic atmospheric monitoring stations in eastern Jilin Province from 2015 to 2020, this paper analyzed the temporal and spatial distribution laws of CO, SO2, NO2, PM10, PM2.5, and O3 in eastern Jilin Province. At the same time, the regional transport pathways of pollutants were analyzed using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model; the potential source contribution function (PSCF) analyzed the potential source area of PM2.5. Finally, the “weekend effect” of CO, NO2, PM2.5, and O3 was analyzed. The results showed that the six pollutants showed a downward trend year by year. The concentrations of O3, PM10, and PM2.5 were higher in northwest Jilin, and the concentrations of SO2 and CO were higher in southwest Jilin. Except for CO, the seasonal variation of pollutants was pronounced. Except for O3, most pollutants had the highest concentration in winter. Hourly variation analysis described that SO2 and O3 had only one peak in a day, and the other four pollutants showed “double peak” hourly variation characteristics. The study area was mainly affected by the airflow pathway from northwest and southwest. The weight potential source contribution function (WPSCF) high-value area of PM2.5 was northwest and southwest. O3 showed a “negative weekend effect”, and NO2 and CO showed a “positive weekend effect”.

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of “high–high” and “low–low”. The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective.

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This paper explored the changes of six significant pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Jilin City during the coronavirus disease 2019 (COVID-19) epidemic in 2022, and compared them with the same period of previous years to analyze the impact of anthropogenic emissions on the concentration of pollutants; The Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) model was used to evaluate the effect of meteorological factors on pollutant concentration. The results showed that except for O3, the concentrations of the other five pollutants decreased significantly, with a range of 21–47%, during the lockdown period caused by the government’s shutdown and travel restrictions. Compared with the same period in 2021, the decrease of PM2.5 was only 25% of PM10. That was because there was still a large amount of PM2.5 produced by coal-fired heating during the blockade period, which made the decrease of PM2.5 more minor. A heavy pollution event caused by adverse meteorological conditions was found during the lockdown period, indicating that only controlling artificial emissions cannot eliminate the occurrence of severe pollution events. The WRF–CMAQ results showed that the lower pollutant concentration in 2022 was not only caused by the reduction of anthropogenic emissions but also related to the influence of favorable meteorological factors (higher planetary boundary layer thickness, higher wind speed, and higher temperature).