Spatial variations of PM2.5 in Chinese cities for the joint impacts of human activities and natural conditions: A global and local regression perspective

Abstract

Fine particulate matter (PM2.5) concentrations are mainly influenced by human activities and natural conditions, yet how these impacts are driven under these two circumstances is not well understood. Identifying the spatial characteristics and the potential determinants of PM2.5 variations from the joint perspectives, can provide insights into particulate pollution control. Due to the limited observations for PM2.5, here a timely structure adaptive modeling method was employed in order to estimate PM2.5 concentration levels in China in 2014. Then global and local regression approaches were combined in order to identify the joint impacts of natural conditions (elevation, vegetation, precipitation, and temperature) and eight anthropogenic factors (including urban sprawl and economic structure) on PM2.5 concentrations. Results indicated an annual mean concentration of 69.7 μg/m3 of PM2.5 in China, with significant differences being observed across space. More than 70% of Chinese cities were found to exceed Grade II of the Chinese National Ambient Air Quality Standard, with the highest levels in the Sichuan Basin and the North China Plain. Global regression results, showing the relative influence of the twelve factors on variations in PM2.5 levels, indicate that elevation, vegetation, precipitation, temperature, and per capita GDP all have potentially offsetting effects in relation to increasing PM2.5 levels that are being driven by industrial structure and energy-related carbon emissions. If China persists in its development pattern, industrialization and fossil energy consumption will inevitably increase a development which will lead to higher PM2.5 concentrations according to the results of the analysis. The geographically weighted regression results showed that the relationship between the factors studied and PM2.5 concentrations is spatially heterogeneous at the local geographic level. Elevation (77.3%) and urban sprawl (57.4%) had the most significant impacts on PM2.5 concentrations. Findings from this research can shed new light on the multiple mechanisms underlying spatial variations in PM2.5 throughout China and provide insights useful to particulate pollution control in China.