Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities. (November 2019)

• Record Type: Journal Article
• Authors: Jin, Jie-Qi; Du, Yue; Xu, Li-Jun; Chen, Zhao-Yue; Chen, Jin-Jian; Wu, Ying; Ou, Chun-Quan
• Is Part Of: Environmental pollution. Volume 254(2019)Part A
• Publication Date: 2019-11
• Subjects: Bayesian spatio-temporal model -- PM2.5 -- Meteorological measures -- Socio-economic factors; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2019.113023
• Languages: English
• ISSNs: 0269-7491

Abstract

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/m 3 ) 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. Graphical abstract: The annual average PM2.5 and the proportion of days attaining the WHO guideline for 24-h PM2.5 in 109 cities in 2015. Image 1 Highlights: Bayesian spatio-temporal model is a robust method for assessing PM2.5 -related factor. The variations in PM2.5 were associated with socioeconomic and meteorological factors. Industrial structure instead of GDP is a determinant factor of PM2.5 levels. The findings will be helpful for integrated intervention plans for pollution control. Abstract : Factors associated with spatial and temporal variations in PM2.5 levels in China.