Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities

Ambient particulate pollution, especially PM₂.₅, has adverse impacts on health and welfare. To manage and control PM₂.₅ pollution, it is of great importance to determine the factors that affect PM₂.₅ levels. Previous studies commonly focused on a single or several cities. This study aims to analyze the impacts of meteorological and socio-economic factors on daily concentrations of PM₂.₅ in 109 Chinese cities from January 1, 2015 to December 31, 2015.To evaluate potential risk factors associated with the spatial and temporal variations in PM₂.₅ levels, we developed a Bayesian spatio-temporal model in which the potential temporal autocorrelation and spatial autocorrelation of PM₂.₅ levels were taken into account to ensure the independence of the error term of the model and hence the robustness of the estimated parameters.Daily concentrations of PM₂.₅ peaked in winter and troughed in summer. The annual average concentration reached its highest value (79 μg/m³) in the Beijing-Tianjin-Hebei area. The city-level PM₂.₅ was positively associated with the proportion of the secondary industry, the total consumption of liquefied petroleum gas and the total emissions of industrial sulfur dioxide (SO₂), but negatively associated with the proportion of the primary industry. A reverse U-shaped relationship between population density and PM₂.₅ was found. The city-level and daily-level of weather conditions within a city were both associated with PM₂.₅.PM₂.₅ levels had significant spatio-temporal variations which were associated with socioeconomic and meteorological factors. Particularly, economic structure was a determinant factor of PM₂.₅ pollution rather than per capita GDP. This finding will be helpful for the intervention planning of particulate pollution control when considering the environmental and social-economic factors as part of the strategies.