Spatio-temporal association mining of intercity PM2.5 pollution: Hubei Province in China as an example

Inverse effects of aerosol radiative forcing on heavy PM2.5 pollution of local accumulation and regional transport over Central China

Regional transport of air pollutants is a crucial factor influencing atmospheric environment, and aerosol radiative forcing (ARF) feedback to atmospheric boundary layer (ABL) structure and ambient air pollution is yet to be comprehensively understood over the receptor region of regional transport. By simulating meteorology and air pollutants during a heavy PM2.5 pollution event with WRF-Chem model, we quantitatively investigated the ARF and ABL interaction for PM2.5 pollution over the Twain-Hu Basin (THB), a key receptor region of regional transport over central China. Driven by northerly winds, PM2.5 was transported from upstream north China to downstream THB accompanied by high PM2.5 levels in the free troposphere. The ARF exacerbated local PM2.5 accumulation by up to 20 μg m-3 and inhibited the impact of regional transport on PM2.5 levels in the ABL with reducing near-surface PM2.5 concentrations of 5 μg m-3 over the THB. The ARF-intensified air temperature inversion at the top of ABL was unfavorable for the transported air pollutants crossing the ABL top to the near-surface layer, thus weakening the impact of regional PM2.5 transport on air quality in the receptor region. Meanwhile, the ARF of transported PM2.5 induced updrafts in the free troposphere, promoting vertical mixing of air pollutants with positive feedback on increasing secondary PM2.5 concentrations in the free troposphere. The ARF induced more and less secondary PM2.5 formations respectively in the free troposphere and the near-surface layer during the regional transport period of air pollution. These results enhance our comprehension of aerosol-meteorology feedback in regional changes of atmospheric environment with inverse effects of ARF on PM2.5 pollution of local accumulation and regional transport.

Investigation of the spatial effects on PM2.5 in relation to land use and ecological restoration in urban agglomerations

Heavy pollution of particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) poses increasing threats to the living environment worldwide. Urban agglomerations often lead to regional rather than local air pollution problems. This study explored the underlying global and local spatial driving mechanisms of PM2.5 variations of the 195 county-level administrative units in the urban agglomeration in the middle reaches of the Yangtze River, China, in 2020, using the global spatial regression and geographically weighted regression methods. Results showed that (1) at the county level, there were spatial variations of PM2.5, fluctuating from 20.1263 μg/m3 to 44.8416 μg/m3. (2) The concentrations of PM2.5 presented a positive spatial autocorrelation with a remarkable direct spatial spillover effect. (3) Forestland, grassland, elevation and ecological restoration were negatively correlated with PM2.5 concentrations, the indirect spatial spillover effect of elevation was noticeable. (4) The indirect reduction effects of ecological restoration on PM2.5 concentrations were substantial in the Wuhan urban agglomeration. (5) The reduction effect of forestland, grassland, ecological restoration and elevation on PM2.5 showed a noticeable spatial heterogeneity. In the future, it is suggested regional variability and the spatial spillover effect of air pollution be taken into account in environmental governance. Simultaneously, utilization of the mitigation effect of ecological restoration on PM2.5 is anticipated for the concerted effort in air pollution governance.

Spatiotemporal characteristic analysis of PM2.5 in central China and modeling of driving factors based on MGWR: a case study of Henan Province

Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO2 emissions) on PM2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter "M", and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM2.5 concentrations shifted toward the north. (2) Distinct spatial clustering patterns of PM2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM2.5 concentration. To summarize the above conclusions, the management of the atmospheric environment in Henan Province still has a long way to go, and the formulation of relevant policies should be adapted to local conditions, taking into account the spatial scale effect of the impact of different influencing factors on PM2.5.