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Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China.

Lin G, Fu J, Jiang D, Hu W, Dong D, Huang Y, Zhao M - Int J Environ Res Public Health (2013)

Bottom Line: The distribution of PM2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic.The results indicated that the spatial pattern of PM2.5 concentrations in China remained stable during the period 2001-2010; high concentrations of PM2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province.Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM2.5 concentrations.

View Article: PubMed Central - PubMed

Affiliation: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Ding No.11 Xueyuan Road, Haidian District, Beijing 100083, China. jiangd@igsnrr.ac.cn.

ABSTRACT
The air quality in China, particularly the PM2.5 (particles less than 2.5 μm in aerodynamic diameter) level, has become an increasing public concern because of its relation to health risks. The distribution of PM2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic. Based on the newly published Annual Average PM2.5 gridded data, together with land use data, gridded population data and Gross Domestic Product (GDP) data, this paper explored the spatial-temporal characteristics of PM2.5 concentrations and the factors impacting those concentrations in China for the years of 2001-2010. The contributions of urban areas, high population and economic development to PM2.5 concentrations were analyzed using the Geographically Weighted Regression (GWR) model. The results indicated that the spatial pattern of PM2.5 concentrations in China remained stable during the period 2001-2010; high concentrations of PM2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province. Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM2.5 concentrations.

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Maps of standardized residuals from the GWR model in China for the years (a) 2001 and (b) 2010.
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ijerph-11-00173-f007: Maps of standardized residuals from the GWR model in China for the years (a) 2001 and (b) 2010.

Mentions: From the report created by the GWR tool, we can obtain the local R2 and the local R2 Adjusted (the adjusted R2). The local R2 values for 2001 and 2010 are 0.820 and 0.822, respectively. The local R2 adjusted values for 2001 and 2010 are 0.810 and 0.815, respectively. The values for the two years are very closewhich can denote that the overall performance of the model relatively high in both of the two models. The values of the standardized residual (StdResid) for 2001 and 2010 can be mapped; these are shown in Figure 7. Not surprisingly, some unusually high or low residuals can be observed. Those regions with some desert area have very large residuals (StdResid > 2). For example, the PM2.5 concentrations in the northwest region of Xinjiang are high because of the desert. Xinjiang has a high incidence zone of dust explosion. The concentrations of dust aerosol at altitude are closely related to the surface conditions below; the concentrations of particles above the desert areas will be greater than those in the vegetation-covered areas [24]. Therefore, the high PM2.5 concentrations in desert regions are mainly related to the dusty weather. The southern Hebei province, the northern Henan province and the northwest Shandong province also have much higher residuals because they are high pollution emission regions of northern China. For example, the Shijiazhuang Iron and Steel Company discharges an average of more than 2000 t/a of PM2.5. There are also many polluting enterprises in the urban areas [25]. The pollution in these regions is always more serious than the pollution in other regions. In addition, the Sichuan basin has high residuals because of its high aerosol optical depth values. The optical depth of the Sichuan Basin is higher than its surrounding areas due to its geographical climate characteristics; its annual average optical depth is approximately 0.7 [26]. PM2.5 has a strong positive correlation with AOD [27], so the Sichuan Basin has high PM2.5 concentrations. Regions that are rich in marine salt can also have high PM2.5 concentrations [28]. Those regions have a noticeable over-prediction of PM2.5 concentrations; this warrants closer inspection to discover the possible explanations. In those regions, the model under-predicts the levels of PM2.5 concentrations [18]. However, the regions with StdResid values in the range of −2 to 2 account for 94.8% and 94.6% of the whole country, which indicates that the relations between PM2.5 and that each of the three factors are stable. What’s more, we can also obtain from the results that the regions with the positive value of the local coefficients for urban areas, population, and GDP account for 92.72%, 90.52% and 95.62% respectively in 2001 and 92.01%, 95.29% and 90.50% respectively in 2010 of the whole country. There is agreement with our expectation on the direction of the influence of those variables.


Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China.

Lin G, Fu J, Jiang D, Hu W, Dong D, Huang Y, Zhao M - Int J Environ Res Public Health (2013)

Maps of standardized residuals from the GWR model in China for the years (a) 2001 and (b) 2010.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3924439&req=5

ijerph-11-00173-f007: Maps of standardized residuals from the GWR model in China for the years (a) 2001 and (b) 2010.
Mentions: From the report created by the GWR tool, we can obtain the local R2 and the local R2 Adjusted (the adjusted R2). The local R2 values for 2001 and 2010 are 0.820 and 0.822, respectively. The local R2 adjusted values for 2001 and 2010 are 0.810 and 0.815, respectively. The values for the two years are very closewhich can denote that the overall performance of the model relatively high in both of the two models. The values of the standardized residual (StdResid) for 2001 and 2010 can be mapped; these are shown in Figure 7. Not surprisingly, some unusually high or low residuals can be observed. Those regions with some desert area have very large residuals (StdResid > 2). For example, the PM2.5 concentrations in the northwest region of Xinjiang are high because of the desert. Xinjiang has a high incidence zone of dust explosion. The concentrations of dust aerosol at altitude are closely related to the surface conditions below; the concentrations of particles above the desert areas will be greater than those in the vegetation-covered areas [24]. Therefore, the high PM2.5 concentrations in desert regions are mainly related to the dusty weather. The southern Hebei province, the northern Henan province and the northwest Shandong province also have much higher residuals because they are high pollution emission regions of northern China. For example, the Shijiazhuang Iron and Steel Company discharges an average of more than 2000 t/a of PM2.5. There are also many polluting enterprises in the urban areas [25]. The pollution in these regions is always more serious than the pollution in other regions. In addition, the Sichuan basin has high residuals because of its high aerosol optical depth values. The optical depth of the Sichuan Basin is higher than its surrounding areas due to its geographical climate characteristics; its annual average optical depth is approximately 0.7 [26]. PM2.5 has a strong positive correlation with AOD [27], so the Sichuan Basin has high PM2.5 concentrations. Regions that are rich in marine salt can also have high PM2.5 concentrations [28]. Those regions have a noticeable over-prediction of PM2.5 concentrations; this warrants closer inspection to discover the possible explanations. In those regions, the model under-predicts the levels of PM2.5 concentrations [18]. However, the regions with StdResid values in the range of −2 to 2 account for 94.8% and 94.6% of the whole country, which indicates that the relations between PM2.5 and that each of the three factors are stable. What’s more, we can also obtain from the results that the regions with the positive value of the local coefficients for urban areas, population, and GDP account for 92.72%, 90.52% and 95.62% respectively in 2001 and 92.01%, 95.29% and 90.50% respectively in 2010 of the whole country. There is agreement with our expectation on the direction of the influence of those variables.

Bottom Line: The distribution of PM2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic.The results indicated that the spatial pattern of PM2.5 concentrations in China remained stable during the period 2001-2010; high concentrations of PM2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province.Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM2.5 concentrations.

View Article: PubMed Central - PubMed

Affiliation: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Ding No.11 Xueyuan Road, Haidian District, Beijing 100083, China. jiangd@igsnrr.ac.cn.

ABSTRACT
The air quality in China, particularly the PM2.5 (particles less than 2.5 μm in aerodynamic diameter) level, has become an increasing public concern because of its relation to health risks. The distribution of PM2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic. Based on the newly published Annual Average PM2.5 gridded data, together with land use data, gridded population data and Gross Domestic Product (GDP) data, this paper explored the spatial-temporal characteristics of PM2.5 concentrations and the factors impacting those concentrations in China for the years of 2001-2010. The contributions of urban areas, high population and economic development to PM2.5 concentrations were analyzed using the Geographically Weighted Regression (GWR) model. The results indicated that the spatial pattern of PM2.5 concentrations in China remained stable during the period 2001-2010; high concentrations of PM2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province. Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM2.5 concentrations.

Show MeSH
Related in: MedlinePlus