北京
泊松回归
医学
薄雾
环境科学
亚洲尘埃
中国
人口学
微粒
环境卫生
毒理
人口
气象学
地理
气溶胶
生物
考古
社会学
生态学
作者
Fengchao Liang,Qingyang Xiao,Dongfeng Gu,Meimei Xu,Lin Tian,Qun Guo,Ziting Wu,Xiaochuan Pan,Yang Liu
标识
DOI:10.1016/j.envpol.2018.06.097
摘要
Severe and persistent haze accompanied by high concentrations of fine particulate matter (PM2.5) has become a great public health concern in urban China. However, research on the health effects of PM2.5 in China has been hindered by the lack of high-quality exposure estimates. In this study, we assessed the excess mortality associated with both short- and long-term exposure to ambient PM2.5 simultaneously using satellite-derived exposure data at a high spatiotemporal resolution. Adult registries of non-accidental, respiratory and cardiovascular deaths in urban Beijing in 2013 were collected. Exposure levels were estimated from daily satellite-based PM2.5 concentrations at 1 km spatial resolution from 2004 to 2013. Mixed Poisson regression models were fitted to estimate the cause-specific mortality in association with PM2.5 exposures. With the mutual adjustment of short- and long-term exposure of PM2.5, the percent increases associated with every 10 μg/m3 increase in short-term PM2.5 exposure were 0.09% (95% CI: -0.14%, 0.33%; lag 01), 1.02% (95% CI: 0.08%, 1.97%; lag 04) and 0.09% (95% CI: -0.23%, 0.42%; lag 01) for non-accidental, respiratory and cardiovascular mortality, respectively; those attributable to every 10 μg/m3 increase in long-term PM2.5 exposure (9-year moving average) were 16.78% (95% CI: 10.58%, 23.33%), 44.14% (95% CI: 20.73%, 72.10%) and 3.72% (95% CI: -3.75%, 11.77%), respectively. Both associations of short- and long-term exposure with the cause-specific mortality decreased after they were mutually adjusted. Associations between short-term exposure to satellite-based PM2.5 and cause-specific mortality were larger than those estimated using fixed measurements. Satellite-based PM2.5 predictions help to improve the spatiotemporal resolution of exposure assessments and the mutual adjustment model provide better estimation of PM2.5 associated health effects. Effects attributable to long-term exposure of PM2.5 were larger than those of short-term exposure, which should be more concerned for public health.
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