对城市居民暴露于空气污染的风险进行了深入研究,但其中大多数是基于住宅单位人口的静态研究。忽略居民个人日常活动和流动过程中的真实环境动态,很难准确估计居民的空气污染暴露水平,并确定暴露风险较高的人群。本文以武汉都市圈为例,高精度空气污染,和人口时空动态分布数据,并应用地理加权回归模型,双变量LISA分析,和基尼系数。老年人接触空气污染的风险,低年龄,测量了武汉市的劳动年龄社区,并研究了老年人和儿童等弱势群体的健康公平性。我们发现忽略居民的时空行为活动低估了PM2.5对居民的实际暴露危害。老年人暴露于空气污染的风险高于其他年龄组。在老龄化群体中,少数老年人接触污染的风险较高。老年人的高暴露风险社区主要分布在城市的中心和副中心区域,具有连续分布特征。与其他人群相比,儿童的暴露风险没有显着差异,但是有几个孩子特别容易受到污染。儿童的高暴露社区主要位于郊区,具有离散分布。与传统的PM2.5静态暴露评估相比,本文提出的动态评估方法考虑了城市人口的高流动性和空气污染。因此,它可以准确揭示空气污染的实际风险,并确定空气污染高风险的地区和人群,为提出降低城市PM2.5、提高城市空间公平性的规划政策提供了科学依据。
In-depth studies have been conducted on the risk of exposure to air pollution in urban residents, but most of them are static studies based on the population of residential units. Ignoring the real environmental dynamics during daily activity and mobility of individual residents makes it difficult to accurately estimate the level of air pollution exposure among residents and determine populations at higher risk of exposure. This paper uses the example of the Wuhan metropolitan area, high-precision air pollution, and population spatio-temporal dynamic distribution data, and applies geographically weighted regression models, bivariate LISA analysis, and Gini coefficients. The risk of air pollution exposure in elderly, low-age, and working-age communities in Wuhan was measured and the health equity within vulnerable groups such as the elderly and children was studied. We found that ignoring the spatio-temporal behavioral activities of residents underestimated the actual exposure hazard of PM2.5 to residents. The risk of air pollution exposure was higher for the elderly than for other age groups. Within the aging group, a few elderly people had a higher risk of pollution exposure. The high exposure risk communities of the elderly were mainly located in the central and sub-center areas of the city, with a continuous distribution characteristic. No significant difference was found in the exposure risk of children compared to the other populations, but a few children were particularly exposed to pollution. Children\'s high-exposure communities were mainly located in suburban areas, with a discrete distribution. Compared with the traditional static PM2.5 exposure assessment, the dynamic assessment method proposed in this paper considers the high mobility of the urban population and air pollution. Thus, it can accurately reveal the actual risk of air pollution and identify areas and populations at high risk of air pollution, which in turn provides a scientific basis for proposing planning policies to reduce urban PM2.5 and improve urban spatial equity.