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  • Title: The spatial characteristics of ambient particulate matter and daily mortality in the urban area of Beijing, China.
    Author: Zhang Y, Guo Y, Li G, Zhou J, Jin X, Wang W, Pan X.
    Journal: Sci Total Environ; 2012 Oct 01; 435-436():14-20. PubMed ID: 22846759.
    Abstract:
    Few epidemiological studies have reported the spatial characteristics of the association between particulate matter <10μm in aerodynamic diameter (PM(10)) and mortality in China. This study explored the spatial characteristics of the association between ambient PM(10) and mortality in the urban area of Beijing, China. We collected daily data on air pollution, weather conditions and mortality in the eight urban districts of Beijing from Jan. 1st 2008 to Dec. 31st 2009. A Poisson Generalized Additive Model (GAM) was used to examine the district-specific effects of PM(10) on cause-specific mortality. A Poisson Generalized Additive Mixed Model (GAMM) was used to examine the urban-wide association between PM(10) and cause-specific mortality while controlling for the random effects of districts, compared with GAM which did not control for the random effects of districts. The inter-quartile ranges (IQRs) of annual PM(10) ranged from 83.5 μg/m(3) (Chaoyang district) to 96.0 μg/m(3) (Shijingshan district). A 96.0 μg/m(3) increase of PM(10) was associated with a 7.52% (95%CI: 1.78%-13.56%) increase of cardiovascular mortality in Shijingshan district while an 87.0 μg/m(3) increase of PM(10) was associated with a 7.68% (95%CI: 0.08%-15.86%) increase of respiratory deaths in Dongcheng district. The urban-wide effects derived from GAMM showed that an 88.0 μg/m(3) increase of PM(10) was associated with an increase of 1.30% (95%CI: 0.45%-2.16%), 2.60% (95%CI: 0.14%-5.11%) in non-accidental and respiratory mortality, illustrating, higher results than those from the GAM. In conclusion, there is spatial variation in ambient PM(10) concentration as well as in the effects of PM(10) on cause-specific mortality in the urban area of Beijing. Additionally, GAMM model may be more effective in estimating the spatial association between urban-wide PM(10) and cause-specific mortality.
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