耦合土地利用回归与人口加权模型的PM2.5暴露风险评估

doi:10.12082/dqxxkx.2019.180695

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (7): 1018-1028.doi: 10.12082/dqxxkx.2019.180695

• 地球信息科学理论与方法 • 上一篇下一篇

耦合土地利用回归与人口加权模型的PM_2.5暴露风险评估

邹雨轩^1,²(), 吴志峰^1,^2,^*(), 曹峥^1,²

1. 广州大学地理科学学院,广州 510006
2. 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

收稿日期:2018-12-28 修回日期:2019-03-24 出版日期:2019-07-25 发布日期:2019-07-25
通讯作者: 吴志峰 E-mail:1494192279@qq.com;gzuwzf@163.com
作者简介:
作者简介：邹雨轩（1995-）,女,广东惠州人,硕士生,研究方向为健康地理。E-mail: 1494192279@qq.com
基金资助:
国家自然科学基金项目（41671430）

Assessing PM_2.5 Exposure Risk by Coupling Land Use Regression Model and Population Weighted Model

Yuxuan ZOU^1,²(), Zhifeng WU^1,^2,^*(), Zheng CAO^1,²

1. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
2. Guangdong Province Engineering Technology Research Center for Geographical Conditions Monitoring and Comprehensive Analysis, Guangzhou, 510006, China

Received:2018-12-28 Revised:2019-03-24 Online:2019-07-25 Published:2019-07-25
Contact: Zhifeng WU E-mail:1494192279@qq.com;gzuwzf@163.com
Supported by:
National Natural Science Foundation of China, No.41671430

摘要/Abstract

摘要：

PM_2.5已成为人群健康的重要威胁之一,科学精准的暴露评估是PM_2.5风险防控的前提,为提升PM_2.5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PM_2.5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PM_2.5时空动态演变监测,在此基础上将PM_2.5反演结果与人口密度数据耦合,分别从PM_2.5污染浓度与人口加权PM_2.5浓度2个方面,评估广佛都市区PM_2.5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PM_2.5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PM_2.5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PM_2.5浓度超WHO健康标准的面积占比达99.8%;③ 广佛都市区PM_2.5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PM_2.5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。

关键词: 土地利用回归模型, PM_2.5, 广佛都市区, 人口加权模型, 暴露风险评估

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 PM_2.5, 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 PM_2.5 concentration in random places based on existing monitoring data. Additionally, most PM_2.5 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, PM_2.5 monitoring data, land use data, road data, and meteorological data were applied to developed the PM_2.5 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 PM_2.5. The results reveal that: (1) LUR predicted the spatial distribution of PM_2.5 with good performance (R² of 0.786-0.913). (2) From December 1, 2013 to February 8, 2014, the mean simulated PM_2.5 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). PM_2.5 exposure in 99.8% of the study area was above the WHO require exposure standard. (3) The spatial distribution of PM_2.5 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 PM_2.5 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 PM_2.5 exposure centers, since the high risk areas tended to centralize around the downtowns of Guangzhou and Foshan.

Key words: land use regression model, PM_2.5, Guangzhou-Foshan metropolitan area, population weighted model, exposure risk assessment

邹雨轩, 吴志峰, 曹峥. 耦合土地利用回归与人口加权模型的PM_2.5暴露风险评估[J]. 地球信息科学学报, 2019, 21(7): 1018-1028.DOI:10.12082/dqxxkx.2019.180695

Yuxuan ZOU, Zhifeng WU, Zheng CAO. Assessing PM_2.5 Exposure Risk by Coupling Land Use Regression Model and Population Weighted Model[J]. Journal of Geo-information Science, 2019, 21(7): 1018-1028.DOI:10.12082/dqxxkx.2019.180695

图/表 13

图1

表1

监测站点地理要素特征变量与PM2.5浓度的相关性"

自变量	Pearson相关系数	自变量	Pearson相关系数
植被_50 m(x₁)	-0.198	主干道长度_50 m(x₃₃)	-
植被_100 m(x₂)	-0.301	主干道长度_100 m(x₃₄)	-
植被_200 m(x₃)	-0.323	主干道长度_200 m(x₃₅)	0.183
植被_300 m(x₄)	-0.344	主干道长度_300 m(x₃₆)	0.175
植被_500 m(x₅)	-0.292	主干道长度_500 m(x₃₇)	0.221
植被_1000 m(x₆)	-0.379	主干道长度_1000 m(x₃₈)	0.247
植被_1200 m(x₇)	-0.392	主干道长度_1200 m(x₃₉)	0.297
植被_1500 m(x₈)	-0.416	主干道长度_1500 m(x₄₀)	0.292
水体_50 m(x₉)	-	一级公路长度_50 m(x₄₁)	-0.047
水体_100 m(x₁₀)	-	一级公路长度_100 m(x₄₂)	-0.047
水体_200 m(x₁₁)	-0.123	一级公路长度_200 m(x₄₃)	-0.032
水体_300 m(x₁₂)	-0.112	一级公路长度_300 m(x₄₄)	-0.023
水体_500 m(x₁₃)	-0.078	一级公路长度_500 m(x₄₅)	-0.054
水体_1000 m(x₁₄)	-0.181	一级公路长度_1000 m(x₄₆)	-0.033
水体_1200 m(x₁₅)	-0.388	一级公路长度_1200 m(x₄₇)	-0.036
水体_1500 m(x₁₆)	-0.482	一级公路长度_1500 m(x₄₈)	-0.002
不透水面_50 m(x₁₇)	0.329	二级公路长度_50 m(x₄₉)	0.164
不透水面_100 m(x₁₈)	0.356	二级公路长度_100 m(x₅₀)	0.164
不透水面_200 m(x₁₉)	0.337	二级公路长度_200 m(x₅₁)	0.644**
不透水面_300 m(x₂₀)	0.366	二级公路长度_300 m(x₅₂)	0.333
不透水面_500 m(x₂₁)	0.410	二级公路长度_500 m(x₅₃)	0.107
不透水面_1000 m(x₂₂)	0.492	二级公路长度_1000 m(x₅₄)	-0.053
不透水面_1200 m(x₂₃)	0.510	二级公路长度_1200 m(x₅₅)	-0.108
不透水面_1500 m(x₂₄)	0.597*	二级公路长度_1500 m(x₅₆)	0.038
道路总长度_50 m(x₂₅)	0.133	三级公路长度_50 m(x₅₇)	-0.132
道路总长度_100 m(x₂₆)	-0.127	三级公路长度_100 m(x₅₈)	-0.332
道路总长度_200 m(x₂₇)	0.076	三级公路长度_200 m(x₅₉)	-0.444
道路总长度_300 m(x₂₈)	0.076	三级公路长度_300 m(x₆₀)	-0.300
道路总长度_500 m(x₂₉)	0.278	三级公路长度_500 m(x₆₁)	-0.150
道路总长度_1000 m(x₃₀)	0.319	三级公路长度_1000 m(x₆₂)	-0.092
道路总长度_1200 m(x₃₁)	0.347	三级公路长度_1200 m(x₆₃)	-0.046
道路总长度_1500 m(x₃₂)	0.418	三级公路长度_1500 m(x₆₄)	-0.127

表1

表2

表3

表4

表5

图2

表6

表7

表8

图3

图4

图5

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时段	回归方程	R²
时段一	Y=3.876x₈+19.372x₁₆+29.967x₂₄+42.788x₂₆-34.143x₃₇-50.086x₄₄+93.066x₅₃-38.663x₅₉ +27.562x₆₅-11.11x₆₆-42.257x₆₇+89.081	0.825
时段二	Y=15.052x₈+48.009x₁₆+9.022x₂₄+22.677x₂₆-17.731x₃₇-26.441x₄₄+104.565x₅₃-31.973x₅₉ +5.38x₆₅-5.463x₆₆-21.818x₆₇+66.51	0.786
时段三	Y=48.153x₈+28.691x₁₆+37.224x₂₄+63.619x₂₆-27.226x₃₇+37.04x₄₄+22.141x₅₃-70.281x₅₉ -38.965x₆₅-38.5x₆₆-24.872x₆₇+87.678	0.892
时段四	Y=43.816x₈+25.451x₁₆+25.249x₂₄+30.497x₂₆-6.236x₃₇+15.35x₄₄+40.895x₅₃-47.251x₅₉ -7.594x₆₅-47.62x₆₆-52.629x₆₇+82.223	0.813
时段五	Y=20.882x₈+3.161x₁₆+17.805x₂₄-33.023x₂₆+7.659x₃₇+45.987x₄₄-33.884x₅₃-42.567x₅₉-15.06x₆₅+0.173x₆₆-74.48x₆₇+85.962	0.913

	时段一	时段二	时段三	时段四	时段五	平均
PM_2.5监测值	117.37	79.19	109.49	84.74	66.71	91.50
PM_2.5模拟值	112.68	77.54	111.67	90.37	66.66	91.78
误差率/%	4.00	2.10	2.00	6.60	0.70	3.10

	时段一	时段二	时段三	时段四		时段五	平均
中心监测点	11.2	9.0	9.6	11.9		6.6	9.7
外围监测点	13.4	18.2	7.8	8.4	13.0		12.2

	时段一	时段二	时段三	时段四	时段五	平均
PM_2.5平均值	95.13	71.19	97.91	80.69	53.40	79.67
面积比/%	100	100	100	100	99.10	99.80

耦合土地利用回归与人口加权模型的PM_2.5暴露风险评估

Assessing PM_2.5 Exposure Risk by Coupling Land Use Regression Model and Population Weighted Model

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子类	植被	水体	不透水面	道路总长	主干道	一级公路	二级公路	三级公路
缓冲区范围/m	1500	1500	1500	1500	1200	500	200	200
R	-0.416	-0.482	0.597	0.418	0.297	-0.054	0.644	-0.444

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耦合土地利用回归与人口加权模型的PM2.5暴露风险评估

Assessing PM2.5 Exposure Risk by Coupling Land Use Regression Model and Population Weighted Model

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耦合土地利用回归与人口加权模型的PM_2.5暴露风险评估

Assessing PM_2.5 Exposure Risk by Coupling Land Use Regression Model and Population Weighted Model