基于空间网格尺度的中国PM2.5污染健康效应空间分布

以全国城市空气质量实时发布平台的监测数据为基础,运用空间插值法模拟中国PM_2.5在10kmx10km空间网格尺度上的暴露水平,利用BenMap工具估计2017年中国PM_2.5污染的健康损失,在城市尺度上对PM_2.5污染的健康经济损失进行空间分析.结果表明,在统计意义层面上,2017年PM_2.5污染共计造成我国321435例早逝、746078例住院、14877551例患病,健康经济损失约为12625亿元,占当年全国GDP的1.53%.从城市尺度来看,健康效应呈现出一定的空间聚集效应,京津冀地区城市较为严重.在空间分布上,主要以“高-高”型和“低-低”型分布为主,即健康经济损失高值城市相互聚集、健康经济损失低值城市也相互聚集,并且存在高值城市之间相互影响并逐渐向四周扩散,进而影响周围低值城市的现象.

Health effects’ spatial distribution analysis of PM2.5 pollution in China based on spatial grid scale

Based on the monitoring data of the national urban air quality real-time release platform, this study used the spatial interpolation method to simulate the exposure level of China PM_2.5 on the spatial grid scale of 10kmx10km, and used the BenMap model to estimate the health loss of China's PM_2.5 pollution in 2017. Then this study conducted a spatial analysis of the health economic losses of PM_2.5 pollution on a city scale. In terms of statistical significance, the total PM_2.5 pollution in 2017 caused 321435 premature deaths, 746078 hospital admissions, and 14877551 morbidity in China. The health economic loss was about 126.25 billion yuan, accounting for 1.53% of the national GDP. From the perspective of urban scale, the health effect showed a certain spatial agglomeration effect, and the cities in Beijing-Tianjin-Hebei region were more serious. In terms of spatial distribution, the main distributions were "high-high" and "low-low", that was, high-value cities of health economic loss gathered with each other, and low-value cities of health economic loss gathered with each other, too. And there existed a phenomenon that the high-value cities influenced each other and gradually spread around, and then affected the low-value cities around.