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  • Title: Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities.
    Author: Jin JQ, Du Y, Xu LJ, Chen ZY, Chen JJ, Wu Y, Ou CQ.
    Journal: Environ Pollut; 2019 Nov; 254(Pt A):113023. PubMed ID: 31404733.
    Abstract:
    OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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