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北京市区社会群体与臭氧空气污染暴露的空间关联。

Spatial associations between social groups and ozone air pollution exposure in the Beijing urban area.

机构信息

School of Environment, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China.

School of Environment, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China.

出版信息

Environ Res. 2018 Jul;164:173-183. doi: 10.1016/j.envres.2018.02.014. Epub 2018 Feb 28.

DOI:10.1016/j.envres.2018.02.014
PMID:29501004
Abstract

Few studies have linked social factors to air pollution exposure in China. Unlike the race or minority concepts in western countries, the Hukou system (residential registration system) is a fundamental reason for the existence of social deprivation in China. To assess the differences in ozone (O) exposure among social groups, especially groups divided by Hukou status, we assigned estimates of O exposure to the latest census data of the Beijing urban area using a kriging interpolation model. We developed simultaneous autoregressive (SAR) models that account for spatial autocorrelation to identify the associations between O exposure and social factors. Principal component regression was used to control the multicollinearity bias as well as explore the spatial structure of the social data. The census tracts (CTs) with higher proportions of persons living alone and migrants with non-local Hukou were characterized by greater exposure to ambient O. The areas with greater proportions of seniors had lower O exposure. The spatial distribution patterns were similar among variables including migrants, agricultural population and household separation (population status with separation between Hukou and actual residences), which fit the demographic characteristics of the majority of migrants. Migrants bore a double burden of social deprivation and O pollution exposure due to city development planning and the Hukou system.

摘要

在中国,很少有研究将社会因素与空气污染暴露联系起来。与西方国家的种族或少数民族概念不同,户籍制度(户籍制度)是中国社会剥夺存在的根本原因。为了评估不同社会群体(尤其是按户籍状况划分的群体)之间臭氧(O)暴露的差异,我们使用克里金插值模型将 O 暴露的估计值分配给北京市城区的最新人口普查数据。我们开发了同时自回归(SAR)模型,以考虑空间自相关,以确定 O 暴露与社会因素之间的关联。主成分回归用于控制多重共线性偏差,并探索社会数据的空间结构。独居者和非本地户籍移民比例较高的普查区(CT)的环境 O 暴露更大。老年人比例较高的地区 O 暴露较低。包括移民、农业人口和家庭分离(户籍和实际居住地之间的人口状况分离)在内的变量的空间分布模式相似,这些变量符合大多数移民的人口特征。由于城市发展规划和户籍制度,移民承受着社会剥夺和 O 污染暴露的双重负担。

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