| 耦合土地利用回归与人口加权模型的PM2.5暴露风险评估 |
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| 引用本文： | 邹雨轩,吴志峰,曹峥.耦合土地利用回归与人口加权模型的PM2.5暴露风险评估[J].地球信息科学,2019,21(7):1018-1028. |
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| 作者姓名： | 邹雨轩 吴志峰 曹峥 |
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| 作者单位： | 1. 广州大学 地理科学学院,广州 5100062. 广东省地理国情监测与综合分析工程技术研究中心,广州 510006 |
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| 基金项目： | 国家自然科学基金项目（41671430） |
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| 摘 要： | PM2.5已成为人群健康的重要威胁之一,科学精准的暴露评估是PM2.5风险防控的前提,为提升PM2.5暴露精准评估,本文利用土地利用数据、道路数据、气象数据等构建PM2.5土地利用回归反演模型,实现了2013年12月1日-2014年2月8日（冬季）广佛都市区PM2.5时空动态演变监测,在此基础上将PM2.5反演结果与人口密度数据耦合,分别从PM2.5污染浓度与人口加权PM2.5浓度2个方面,评估广佛都市区PM2.5污染暴露风险。研究结果表明：① 土地利用回归模型能够较好的反映研究区域内PM2.5的空间分布特征,R2大于0.78;② 2013年12月1日-2014年2月8日,广佛都市区PM2.5浓度平均值呈现波动变化趋势,研究时段内,最高平均浓度为97.91 μg/m3 （12月29日-1月11日）,最低平均浓度为53.40 μg/m3 （1月26日-2月8日）,全时段PM2.5浓度超WHO健康标准的面积占比达99.8%;③ 广佛都市区PM2.5的空间分布具有异质性规律,其高值区分别位于广州市天河区、越秀区、番禺区北部、花都区北部及佛山市禅城区、南海区中部、三水区中部,低值区主要位于广州市白云区、番禺区东南部及佛山市顺德区南部。人口加权暴露风险存在2个高值中心,分别位于广州市和佛山市的主城区;④ 耦合人口加权模型前后,广佛都市区PM2.5暴露风险高风险区空间分布发生变化,未考虑人口加权模型时,广佛深高值区较为分散,主要位于南海区、天河区、越秀区、禅城区,考虑人口加权模型后,高值区更加集中于广州市和佛山市的主城区。
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| 关 键 词： | 土地利用回归模型 PM2.5 广佛都市区 人口加权模型 暴露风险评估 |
| 收稿时间： | 2018-12-28 |
Assessing PM2.5 Exposure Risk by Coupling Land Use Regression Model and Population Weighted Model |
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| Yuxuan ZOU,Zhifeng WU,Zheng CAO.Assessing PM2.5 Exposure Risk by Coupling Land Use Regression Model and Population Weighted Model[J].Geo-information Science,2019,21(7):1018-1028. |
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| Authors: | Yuxuan ZOU Zhifeng WU Zheng CAO |
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| Institution: | 1. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China2. Guangdong Province Engineering Technology Research Center for Geographical Conditions Monitoring and Comprehensive Analysis, Guangzhou, 510006, China |
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| Abstract: | With the rapid urbanization in the recent years, the deterioration of urban eco-environment and consequent impacts on human health have raised increasing concern. Air pollution, especially PM2.5, has become one of the most serious problems which threaten public health. As the key of air pollution health assessment, exposure risk assessment needs accurate data of air pollution concentration. However, it is impossible to get intra-urban PM2.5 concentration in random places based on existing monitoring data. Additionally, most PM2.5 risk exposure assessment studies take air pollution concentration as the evaluation index, without considering the spatial distribution of population. Coupling population-weighted assessment method is one of the feasible solutions to solve this problem. To this end, PM2.5 monitoring data, land use data, road data, and meteorological data were applied to developed the PM2.5 Land Use Regression (LUR) model in the Guangzhou-Foshan metropolitan area from December 1, 2013 to February 8, 2014. Then, the population density data were coupled to assess the population-weighted exposure risk of PM2.5. The results reveal that: (1) LUR predicted the spatial distribution of PM2.5 with good performance (R2 of 0.786-0.913). (2) From December 1, 2013 to February 8, 2014, the mean simulated PM2.5 concentration of the Guangzhou-Foshan metropolitan area changed fluctuatingly and the highest concentration was 97.91 μg/m3 (from December 29 to January 11) while the lowest was 53.40 μg/m3 (from January 26 to February 8). PM2.5 exposure in 99.8% of the study area was above the WHO require exposure standard. (3) The spatial distribution of PM2.5 concentration varied from place to place. High-concentration areas were located in Tianhe District, Yuexiu District, north of Panyu District, north of Huadu District, Chancheng District, middle of Nanhai District and middle of Sanshui District, while low-concentration areas included mainly Baiyun District, south-east of Panyu District and south of Shunde District. There were two high-level centers of population-weighted exposure risk located at the Guangzhou and Foshan downtowns. (4) After coupling the population weighted model, the high risk areas of PM2.5 in the Guangzhou-Foshan metropolitan area changed. The old high concentration areas focused on Nanhai District, Tianhe District, Yuexiu District, and Chancheng District, while coupling the population density data resulted in a more concentrated PM2.5 exposure centers, since the high risk areas tended to centralize around the downtowns of Guangzhou and Foshan. |
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| Keywords: | land use regression model PM2 5 Guangzhou-Foshan metropolitan area population weighted model exposure risk assessment |
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