中国城市空气污染时空分布格局和人口暴露风险

doi:10.18306/dlkxjz.2021.10.004

地理科学进展 ›› 2021, Vol. 40 ›› Issue (10): 1650-1663.doi: 10.18306/dlkxjz.2021.10.004

中国城市空气污染时空分布格局和人口暴露风险

肖嘉玉¹(), 何超¹, 慕航¹, 杨璐¹, 黄佳伊², 辛艾萱¹, 涂佩玥³, 洪松¹^,^*()

1.武汉大学资源与环境科学学院地理信息系统教育部重点实验室,武汉 430079
2.悉尼大学商学院,澳大利亚悉尼 NSW 2006
3.湖北大学资源环境学院,武汉 430062

收稿日期:2020-10-05 修回日期:2021-05-14 出版日期:2021-10-28 发布日期:2021-12-28
通讯作者: *洪松(1973— ),男,湖北麻城人,博士,教授,博士生导师,研究方向为环境地学。E-mail: songhongpku@126.com
作者简介:肖嘉玉(1996— ),女,湖南娄底人,硕士生,研究方向为环境信息系统。E-mail: xiaojiayu1996@163.com
基金资助:
深圳市科技计划项目(JCYJ20150630153917252)

Spatiotemporal pattern and population exposure risks of air pollution in Chinese urban areas

XIAO Jiayu¹(), HE Chao¹, MU Hang¹, YANG Lu¹, HUANG Jiayi², XIN Aixuan¹, TU Peiyue³, HONG Song¹^,^*()

1. Key Laboratory of Geographic Information System, Ministry of Education, School of Resources and Environmental Sciences, Wuhan University, Wuhan 430079, China
2. School of Busniss, University of Sydney, Sydney NSW 2006, Australia
3. Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China

Received:2020-10-05 Revised:2021-05-14 Online:2021-10-28 Published:2021-12-28
Supported by:
Shenzhen Science and Technology Project(JCYJ20150630153917252)

摘要/Abstract

摘要：

近年来,中国空气污染及其对人口健康危害的时空分布呈现出新的特征。论文使用5 a(2015—2019年)间逐小时的空气质量监测数据,利用变化率计算、热点分析、趋势分析和超标频数统计等方法,分析了中国332个城市的空气质量及人口空气污染暴露风险的时空分布特征,结论如下：① 中国城市近年来空气质量有好转趋势,环境空气质量指数(AQI)下降的城市占所研究城市总数的91.3%;PM_2.5、PM₁₀、SO₂和CO等4种污染物浓度均有所下降,而NO₂和O₃浓度有所上升;② PM_2.5、PM₁₀、SO₂和CO浓度变化率的热点分布于新疆地区和云南—华南地区,NO₂浓度变化率的热点为新疆地区和河套平原,O₃浓度变化率的热点为华北平原至长江中下游流域;西北地区和华南地区空气质量变化幅度较小;③ 9个城市在PM_2.5、PM₁₀、SO₂、NO₂、O₃和CO等6种污染物中均有暴露,分布于山西、河北与山东。暴露风险均为0级的低风险城市共有12个,分别位于新疆、云南、贵州、四川、广东、福建和黑龙江。研究结论对于跨区域空气污染的协同治理以及制定差异化的空间人口流动管理政策具有重要参考价值。

关键词: 城市, 空气污染, 热点分析, 暴露风险, 中国

Abstract:

In recent years, the spatiotemporal distribution and its hazards to republic health of air pollution in China have shown new characteristics. Using hourly air quality monitoring data for five years (2015-2019) in 332 Chinese cities, this study analyzed the spatiotemporal distribution characteristics of air quality and urban population exposure risks by different methods. The results suggest that: 1) Air quality in Chinese urban areas has improved in recent years. Ambient Air Quality Index (AQI) decreased in 303 cities (91.3%). The concentrations of PM_2.5, PM₁₀, SO₂, and CO declined while the concentrations of NO₂ and O₃ increased. 2) The hotspots of PM_2.5, PM₁₀, SO₂, and CO concentration change rates were distributed in Xinjiang and Yunnan-South China. The hotspots of NO₂ concentration change rate were in the Xinjiang area and the Hetao Plain. The hotspots of O₃ concentration change rate were from the North China Plain to the middle and lower reaches of the Yangtze River. The trends of air quality change in the Northwest and South China were relatively slow. 3) Nine cities were exposed to PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO pollution, which were located in Shanxi, Hebei, and Shandong provinces; 12 cities had no exposure risks to these six pollutants, which were distributed in Xinjiang, Yunnan, Guizhou, Sichuan, Guangdong, Fujian, and Heilongjiang provinces. These conclusions are of important reference value for collaborative treatment of cross-regional air pollution and formulating spatially diffenrentiated population flow management policies in China.

Key words: cities, air pollution, hotspot analysis, exposure risks, China

肖嘉玉, 何超, 慕航, 杨璐, 黄佳伊, 辛艾萱, 涂佩玥, 洪松. 中国城市空气污染时空分布格局和人口暴露风险[J]. 地理科学进展, 2021, 40(10): 1650-1663.

XIAO Jiayu, HE Chao, MU Hang, YANG Lu, HUANG Jiayi, XIN Aixuan, TU Peiyue, HONG Song. Spatiotemporal pattern and population exposure risks of air pollution in Chinese urban areas[J]. PROGRESS IN GEOGRAPHY, 2021, 40(10): 1650-1663.

图/表 13

图1

表1

表2

图2

图3

图4

图5

图6

图7

图8

表3

表4

图9

参考文献 33

[1]	Chan C K, Yao X H. Air pollution in mega cities in China[J]. Atmospheric Environment, 2008, 42(1):1-42. doi: 10.1016/j.atmosenv.2007.09.003
[2]	Guo S, Hu M, Zamora M L, et al. Elucidating severe urban haze formation in China[J]. PNAS, 2014, 111(49):17373-17378. doi: 10.1073/pnas.1419604111
[3]	Shi X Q, Zhao C F, Jiang J H, et al. Spatial representativeness of PM_2.5 concentrations obtained using observations from network stations[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(6):3145-3158. doi: 10.1002/jgrd.v123.6
[4]	Guo H, Cheng T H, Gu X F, et al. Assessment of PM_2.5 concentrations and exposure throughout China using ground observations[J]. Science of the Total Environment, 2017, 601/602:1024-1030. doi: 10.1016/j.scitotenv.2017.05.263
[5]	Cao S S, Zhao W J, Guan H L, et al. Comparison of remotely sensed PM_2.5 concentrations between developed and developing countries: Results from the US, Europe, China, and India[J]. Journal of Cleaner Production, 2018, 182:672-681. doi: 10.1016/j.jclepro.2018.02.096
[6]	Han L J, Zhou W Q, Pickett S T A, et al. Risks and causes of population exposure to cumulative fine particulate (PM_2.5) pollution in China[J]. Earth's Future, 2019, 7(6):615-622. doi: 10.1029/2019EF001182
[7]	Song C B, Wu L, Xie Y C, et al. Air pollution in China: Status and spatiotemporal variations[J]. Environmental Pollution, 2017, 227:334-347. doi: 10.1016/j.envpol.2017.04.075
[8]	Zhao X L, Zhou W Q, Han L J, et al. Spatiotemporal variation in PM_2.5 concentrations and their relationship with socioeconomic factors in China's major cities[J]. Environment International, 2019, 133:105145. doi: 10.1016/j.envint.2019.105145. doi: 10.1016/j.envint.2019.105145
[9]	Cai S Y, Wang Y J, Zhao B, et al. The impact of the 'Air Pollution Prevention and Control Action Plan' on PM_2.5 concentrations in Jing-Jin-Ji region during 2012-2020[J]. Science of the Total Environment, 2017, 580:197-209. doi: 10.1016/j.scitotenv.2016.11.188
[10]	Fang X K, Park S, Saito T, et al. Rapid increase in ozone-depleting chloroform emissions from China[J]. Nature Geoscience, 2019, 12(2):89-93. doi: 10.1038/s41561-018-0278-2
[11]	Li K, Jacob D J, Liao H, et al. Anthropogenic drivers of 2013-2017 trends in summer surface ozone in China[J]. PNAS, 2019, 116(2):422-427. doi: 10.1073/pnas.1812168116
[12]	Zhang Y Z, Wang Y H. Climate-driven ground-level ozone extreme in the fall over the Southeast United States[J]. PNAS, 2016, 113(36):10025-10030. doi: 10.1073/pnas.1602563113
[13]	彭远新, 林振山. 能源消费产生的SO₂和工业烟尘排放量时空演变分析[J]. 自然资源学报, 2010, 25(1):52-59.
	[ Peng Yuanxin, Lin Zhenshan. Analysis of temporal and spatial evolution of SO₂ and industrial dust emissions of energy consumption. Journal of Natural Resources, 2010, 25(1):52-59. ]
[14]	周亮, 周成虎, 杨帆, 等. 2000—2011年中国PM_2.5时空演化特征及驱动因素解析[J]. 地理学报, 2017, 72(11):2079-2092. doi: 10.11821/dlxb201711012
	[ Zhou Liang, Zhou Chenghu, Yang Fan, et al. Spatio-temporal evolution and the influencing factors of PM_2.5 in China between 2000 and 2011. Acta Geographica Sinica, 2017, 72(11):2079-2092. ]
[15]	Wang Z B, Fang C L. Spatial-temporal characteristics and determinants of PM_2.5 in the Bohai rim urban agglomeration[J]. Chemosphere, 2016, 148:148-162. doi: 10.1016/j.chemosphere.2015.12.118
[16]	Zhang B L, Tsunekawa A, Tsubo M. Identification of dust hot spots from multi-resolution remotely sensed data in Eastern China and Mongolia[J]. Water, Air, & Soil Pollution, 2015, 226:117. doi: 10.1007/s11270-015-2300-2. doi: 10.1007/s11270-015-2300-2
[17]	Chen D S, Liu X X, Lang J L, et al. Estimating the contribution of regional transport to PM_2.5 air pollution in a rural area on the North China Plain[J]. Science of the Total Environment, 2017, 583:280-291. doi: 10.1016/j.scitotenv.2017.01.066
[18]	Chen Y, Ebenstein A, Greenstone M, et al. Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River policy[J]. PNAS, 2013, 110(32):12936-12941. doi: 10.1073/pnas.1300018110
[19]	Kuerban M, Waili Y, Fan F, et al. Spatio-temporal patterns of air pollution in China from 2015 to 2018 and implications for health risks[J]. Environmental Pollution, 2020, 258:113659. doi: 10.1016/j.envpol.2019.113659. doi: 10.1016/j.envpol.2019.113659
[20]	Shi Y S, Matsunaga T, Yamaguchi Y, et al. Long-term trends and spatial patterns of satellite-retrieved PM_2.5 concentrations in South and Southeast Asia from 1999 to 2014[J]. Science of the Total Environment, 2018, 615:177-186. doi: 10.1016/j.scitotenv.2017.09.241
[21]	韩立建. 城市化与PM_2.5时空格局演变及其影响因素的研究进展[J]. 地理科学进展, 2018, 37(8):1011-1021. doi: 10.18306/dlkxjz.2018.08.001
	[ Han Lijian. Relationship between urbanization and urban air quality: An insight on fine particulate dynamics in China. Progress in Geography, 2018, 37(8):1011-1021. ]
[22]	van Donkelaar A, Martin R V, Brauer M, et al. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors[J]. Environmental Science & Technology, 2016, 50(7):3762-3772. doi: 10.1021/acs.est.5b05833
[23]	He C Y, Han L J, Zhang R Q. More than 500 million Chinese urban residents (14% of the global urban population) are imperiled by fine particulate hazard[J]. Environmental Pollution, 2016, 218:558-562. doi: 10.1016/j.envpol.2016.07.038
[24]	环境保护部, 国家质量监督检验检疫总局. 环境空气质量标准(GB 3095—2012)[S]. 北京: 中国环境科学出版社, 2016.
	[Ministry of Environment Protection of the People's Republic of China, General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China. Ambient air quality standards, GB 3095-2012. Beijing, China: China Environment Science Press, 2016. ]
[25]	Environmental Protection Agency, USA. NAAQS table[EB/OL]. 2015 [2020-12-06]. https://www.epa.gov/criteria-air-pollutants/naaqs-table.
[26]	邹滨, 彭芬, 焦利民, 等. 高分辨率人口空气污染暴露GIS空间区划研究[J]. 武汉大学学报(信息科学版), 2013, 38(3):334-338.
	[ Zou Bin, Peng Fen, Jiao Limin, et al. GIS aided spatial zoning of high-resolution population exposure to air pollution. Geomatics and Information Science of Wuhan University, 2013, 38(3):334-338. ]
[27]	张亮林, 潘竟虎. 中国PM_2.5人口暴露风险时空格局[J]. 中国环境科学, 2020, 40(1):1-12.
	[ Zhang Lianglin, Pan Jinghu. Spatial-temporal pattern of population exposure risks to PM_2.5 in China. China Environmental Science, 2020, 40(1):1-12. ]
[28]	Guo H, Gu X F, Ma G X, et al. Spatial and temporal variations of air quality and six air pollutants in China during 2015-2017[J]. Scientific Reports, 2019, 9:15201. doi: 10.1038/s41598-019-50655-6. doi: 10.1038/s41598-019-50655-6
[29]	Wise E K, Comrie A C. Meteorologically adjusted urban air quality trends in the southwestern United States[J]. Atmospheric Environment, 2005, 39(16):2969-2980. doi: 10.1016/j.atmosenv.2005.01.024
[30]	程雪雁, 朱磊, 周艺萱. 2015—2018年京津冀城市群空气污染时空变化特征[J]. 北京师范大学学报(自然科学版), 2019, 55(4):523-531.
	[ Cheng Xueyan, Zhu Lei, Zhou Yixuan. Temporal and spatial characteristics of pollution in Beijing-Tianjin-Hebei urban agglomeration 2015-2018. Journal of Beijing Normal University (Natural Science), 2019, 55(4):523-531. ]
[31]	蔺雪芹, 王岱. 中国城市空气质量时空演化特征及社会经济驱动力[J]. 地理学报, 2016, 71(8):1357-1371. doi: 10.11821/dlxb201608006
	[ Lin Xueqin, Wang Dai. Spatio-temporal variations and socio-economic driving forces of air quality in Chinese cities. Acta Geographica Sinica, 2016, 71(8):1357-1371. ]
[32]	刘欢, 席鹏辉. 中国存在环境移民吗? 来自空气质量指数测算改革的自然实验[J]. 经济学动态, 2019(12):38-54.
	[ Liu Huan, Xi Penghui. Are there environmental migrants in China? Natural experiments from the reform of air quality index. Economic Perspectives, 2019 (12):38-54. ]
[33]	洪大用, 范叶超, 李佩繁. 地位差异、适应性与绩效期待: 空气污染诱致的居民迁出意向分异研究[J]. 社会学研究, 2016, 31(3):1-24, 242.
	[ Hong Dayong, Fan Yechao, Li Peifan. Socio-economic status, behavioral adaptation, and expectation for government performance: An analysis of the differentiation of residents' smog-related out-migration intetion. Sociological Studies, 2016, 31(3):1-24, 242. ]

指标	2015年	2016年	2017年	2018年	2019年	ROC/%
AQI	79.2	75.1	74.8	70.8	64.7	-18.4
PM_2.5/(μg·m^-3)	49.7	45.9	43.5	39.1	40.2	-19.1
PM₁₀/(μg·m^-3)	87.0	82.2	79.2	75.9	72.9	-16.3
SO₂/(μg·m^-3)	25.0	21.6	18.0	13.6	11.2	-55.2
NO₂/(μg·m^-3)	28.8	29.1	30.1	27.3	32.6	13.2
O₃/(μg·m^-3)	83.7	87.5	93.4	95.3	92.3	10.3
CO/(mg·m^-3)	1.08	1.04	0.97	0.85	0.81	-24.8

中国城市空气污染时空分布格局和人口暴露风险

Spatiotemporal pattern and population exposure risks of air pollution in Chinese urban areas

RichHTML

PDF (PC)

PDF (Mobile)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 13

参考文献 33

相关文章 15

Metrics

本文评价

推荐阅读 0

指标	单位	平均时间	浓度限值	备注
PM_2.5	μg·m^-3	年平均	35	—
PM_2.5	μg·m^-3	24 h平均	75	—
PM₁₀	μg·m^-3	年平均	70	—
PM₁₀	μg·m^-3	24 h平均	150	—
SO₂	μg·m^-3	年平均	60	—
SO₂	μg·m^-3	24 h平均	150	—
NO₂	μg·m^-3	年平均	40	—
NO₂	μg·m^-3	24 h平均	80	—
O₃	μg·m^-3	日最大8 h平均	160	每年第四高的O₃日最大8 h平均浓度达标,则该年O₃浓度达标
CO	mg·m^-3	24 h平均	4	每年超标天数≤1,则该年CO浓度达标

暴露风险等级	PM_2.5	PM₁₀	SO₂	NO₂	O₃	CO
0	62	99	317	224	28	253
1	27	27	9	40	27	36
2	17	21	3	13	29	7
3	30	24	3	14	29	22
4	32	29	0	11	46	10
5	164	132	0	30	173	4

污染物	超标天数全年占比/%	2015年	2016年	2017年	2018年	2019年
PM_2.5	0	11	12	20	24	38
	0~5	65	89	101	111	109
	5~10	48	49	77	82	66
	10~20	100	86	81	65	71
	20~30	56	58	48	44	46
	30~50	38	35	5	6	1
	>50	14	3	0	0	1
PM₁₀	0	30	40	44	48	68
	0~5	93	113	147	134	151
	5~10	66	67	63	55	49
	10~20	85	53	55	69	45
	20~30	27	29	16	17	13
	30~50	26	27	5	5	4
	>50	5	3	2	4	2
SO₂	0	257	294	307	320	328
	0~5	62	28	21	11	4
	5~10	8	3	2	1	0
	10~20	5	7	2	0	0
	>20	0	0	0	0	0
NO₂	0	170	189	195	210	225
	0~5	133	113	123	110	99
	5~10	19	21	11	11	8
	10~20	10	9	3	1	0
	>20	0	0	0	0	0
O₃	0	54	54	30	28	45
	0~5	163	143	134	130	122
	5~10	49	68	60	65	66
	10~20	60	62	81	77	66
	20~30	6	5	25	29	33
	30~50	0	0	2	3	0
	>50	0	0	0	0	0
CO	0	246	268	279	317	312
	0~5	75	57	51	14	20
	5~10	9	6	1	1	0
	10~20	2	1	1	0	0
	>20	0	0	0	0	0

[1]	方叶林, 苏雪晴, 黄震方, 程雪兰. 城市韧性对旅游经济的空间溢出效应研究——以长三角城市群为例[J]. 地理科学进展, 2022, 41(2): 214-223.
[2]	郭嘉颖, 魏也华, 陈雯, 肖伟烨. 空间重构背景下城市多中心研究进展与规划实践[J]. 地理科学进展, 2022, 41(2): 316-329.
[3]	薛冰, 赵冰玉, 李京忠. 地理学视角下城市复杂性研究综述——基于近20年文献回顾[J]. 地理科学进展, 2022, 41(1): 157-172.
[4]	王丰龙. 行为地理学的学科特色与拓展路径[J]. 地理科学进展, 2022, 41(1): 16-26.
[5]	陈曦亮, 李钢, 徐锋, 于悦, 张千禧. 基于无人机航拍照片的西安城市形象感知研究[J]. 地理科学进展, 2021, 40(9): 1600-1612.
[6]	郭丽佳, 李畅, 彭红松, 钟士恩, 章锦河, 虞虎. 节能减排约束下中国省域旅游生态效率评估及空间格局研究[J]. 地理科学进展, 2021, 40(8): 1284-1297.
[7]	彭珏, 何金廖. 电商粉丝经济的地理格局及其影响因子探析——以抖音直播带货主播为例[J]. 地理科学进展, 2021, 40(7): 1098-1112.
[8]	谢波, 王潇, 伍蕾. 基于自然实验的城市绿色空间对居民心理健康的影响研究——以武汉东湖绿道为例[J]. 地理科学进展, 2021, 40(7): 1141-1153.
[9]	骆晨, 郑伯红, 刘琳琳. 制度对城市都市区制造业空间演变的影响——以长沙市为例[J]. 地理科学进展, 2021, 40(7): 1167-1180.
[10]	刘诗喆, 谢苗苗, 武蓉蓉, 王亚男, 李新宇. 地理单元划分对城市热环境响应规律的影响——以北京为例[J]. 地理科学进展, 2021, 40(6): 1037-1047.
[11]	宋金彦, 李仙德, 徐宁. 中国新创通信设备制造企业空间格局及其影响因素研究[J]. 地理科学进展, 2021, 40(6): 911-924.
[12]	徐惠孝, 刘艳军. 收缩城市建设用地利用效率时空分异及影响机制——以黑龙江省伊春市为例[J]. 地理科学进展, 2021, 40(6): 937-947.
[13]	程雪兰, 方叶林, 苏雪晴, 李经龙. 中国东部沿海5大城市群旅游流网络结构空间分布特征研究[J]. 地理科学进展, 2021, 40(6): 948-957.
[14]	金泽润, 朱晟君. 基于关联度和复杂度的中国城市技术引入模式及其演化机制[J]. 地理科学进展, 2021, 40(6): 897-910.
[15]	唐昭沛, 吴威, 刘玮辰, 李晓丽. 高速铁路对生产性服务业空间集聚的影响——以长三角城市群为例[J]. 地理科学进展, 2021, 40(5): 746-758.

暴露风险等级	PM_2.5	PM₁₀	SO₂	NO₂	O₃	CO
0	62	99	317	224	28	253
1	27	27	9	40	27	36
2	17	21	3	13	29	7
3	30	24	3	14	29	22
4	32	29	0	11	46	10
5	164	132	0	30	173	4

暴露风险等级	PM_2.5	PM₁₀	SO₂	NO₂	O₃	CO
0	62	99	317	224	28	253
1	27	27	9	40	27	36
2	17	21	3	13	29	7
3	30	24	3	14	29	22
4	32	29	0	11	46	10
5	164	132	0	30	173	4