2000-2011年中国PM2.5时空演化特征及驱动因素解析

doi:10.11821/dlxb201711012

地理学报 ›› 2017, Vol. 72 ›› Issue (11): 2079-2092.doi: 10.11821/dlxb201711012

2000-2011年中国PM_2.5时空演化特征及驱动因素解析

周亮^1,²(), 周成虎¹, 杨帆³, 王波⁴, 孙东琪¹()

1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2. 兰州交通大学测绘与地理信息学院,兰州 730070
3.南京大学地理与海洋科学学院,南京 210093
4. 香港大学地理系,香港 999077

收稿日期:2016-10-08 修回日期:2017-07-28 出版日期:2017-11-20 发布日期:2017-11-16
作者简介:
作者简介：周亮(1983-), 男, 甘肃天水人, 博士后, 讲师, 中国地理学会会员(S110007587M),主要从事环境经济地理,城市与区域规划研究。E-mail: zhougeo@126.com
基金资助:
中国博士后科学基金项目(2016M600121);资源与环境信息系统国家重点实验室开放基金

Spatio-temporal evolution and the influencing factors of PM_2.5 in China between 2000 and 2011

Liang ZHOU^1,²(), Chenghu ZHOU¹, Fan YANG³, Bo WANG⁴, Dongqi SUN¹()

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. Facutly of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
3. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
4. Department of Geography, The University of Hong Kong, Hong Kong 999077, China

Received:2016-10-08 Revised:2017-07-28 Published:2017-11-20 Online:2017-11-16
Supported by:
China Postdoctoral Science Foundation, No.2016M600121;State Key Laboratory of Resources and Environmental Information System

摘要/Abstract

摘要：

高浓度PM_2.5是形成雾霾的主要原因之一,科学识别PM_2.5浓度的空间异质性与驱动因素对区域大气联动治理意义重大。研究采用2000-2011年NASA大气遥感影像反演的PM_2.5数据,结合地统计、地理探测器及GIS空间分析等方法,系统分析了中国2000-2011年PM_2.5浓度时空演化格局特征与其驱动因素。结果表明：① 2000-2011年中国PM_2.5污染平均浓度一直保持在22.47~28.26 μg/m³区间,总体呈现先快速增加后趋于稳定的演化态势,2006年是PM_2.5浓度值变化的拐点（峰值）。② 空间上PM_2.5浓度整体呈现北方高于南方,东部高于西部趋势,污染浓度高值区集中分布在黄淮海平原、长三角下游平原、四川盆地与塔克拉玛干沙漠四大区域,其中京津冀地区污染最为严重。③ 污染浓度重心研究表明PM_2.5重心总体呈现快速东移趋势,污染高值区持续向东移动,低值区向西移动,两者重心背向而行,表明东部雾霾污染程度在进一步加剧。④ 空间自相关分析表明PM_2.5年均浓度呈现强烈的局部空间正自相关特性,PM_2.5“高—高”集聚区连片分布在黄淮海平原、汾渭盆地、四川盆地及江汉平原地区,PM_2.5“低—低”集聚区分布在长城以北的内蒙古、黑龙江、青藏高原、以及台湾、海南与福建等东南沿海及岛屿地区。⑤ 地理探测分析表明气候等自然因素与人类活动共同对PM_2.5浓度空间变化产生巨大影响,其中自然地理区位、人口密度、汽车数量、工业烟尘、秸秆燃烧等因子是中国PM_2.5浓度空间变化的主要驱动因素。

关键词: PM2.5, 大气污染, 空间演化, 地理探测器, 中国

Abstract:

High concentration of PM_2.5 has been universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_2.5 concentration for the purpose of regional air quality control and management. Using PM_2.5 data from 2000 to 2011 that is inversed from NASA atmospheric remote sensing images, and employing the methods in geo-statistics, geographic detectors and geo-spatial analysis, this paper reveals the spatio-temporal evolution patterns and driving factors of PM_2.5 concentration in China. The main findings are as follows: (1) In general, the average concentration of PM_2.5 in China has increased quickly and reached its peak value in the year of 2006; after that, it has been maintained at around 22.47-28.26 μg/m³. (2) PM_2.5 is strikingly uneven in China, with a higher concentration in North and East than in South and West, respectively. In particular, the areas with a relatively high concentration of PM_2.5 are mainly the four regions including the Huang-Huai-Hai Plain, the Lower Yangtze River Delta Plain, the Sichuan Basin, and the Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_2.5. (3) The center of gravity of PM_2.5 has shown an overall eastward movement trend, which indicates an increasingly serious haze in eastern China. Particularly, the center of gravity of high-value PM_2.5 is kept on moving eastward, while that of the low-value PM_2.5 moves westward. (4) The spatial autocorrelation analysis indicates a significantly positive spatial correlation. The "High-High" PM_2.5 agglomeration areas include the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan plain regions. The "Low-Low" PM_2.5 agglomeration areas include Inner Mongolia and Heilongjiang to the north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan and Fujian and other southeast coastal and island areas. (5) Geographic detection analysis indicates that both natural and human factors account for the spatial variations of PM_2.5 concentration. In particular, factors such as natural geographical location, population density, automobile quantity, industrial discharge and straw burning are the main driving forces of PM_2.5 concentration in China.

Key words: PM2.5, air pollution, tempo-spatial evolution, geographical detector, China

周亮, 周成虎, 杨帆, 王波, 孙东琪. 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.

Liang ZHOU, Chenghu ZHOU, Fan YANG, Bo WANG, Dongqi SUN. Spatio-temporal evolution and the influencing factors of PM_2.5 in China between 2000 and 2011[J]. Acta Geographica Sinica, 2017, 72(11): 2079-2092.

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序号	变化类型	数量	占比(%)	序号	变化类型	数量	占比(%)
1	R—R—R—R	199	8.64	9	D—R—R—R	62	2.69
2	R—R—R—D	498	21.63	10	D—R—R—D	87	3.78
3	R—R—D—R	676	29.37	11	D—R—D—R	183	7.95
4	R—R—D—D	382	16.59	12	D—R—D—D	63	2.74
5	R—D—R—R	28	1.22	13	D—D—R—R	10	0.43
6	R—D—R—D	41	1.78	14	D—D—R—D	19	0.83
7	R—D—D—R	34	1.48	15	D—D—D—R	4	0.17
8	R—D—D—D	14	0.61	16	D—D—D—D	2	0.09

探测指标	2000年		2006年		2011年
探测指标	P	Q	P	Q	P	Q
自然地理区划(X₁)	0.7047	0.0000	0.7447	0.0000	0.7196	0.0000
人均GDP(X₂)	0.0077	0.9191	0.0062	0.9079	0.0068	0.8659
人口密度(X₃)	0.4320	0.0000	0.4372	0.0000	0.4120	0.0000
二产占比(X₄)	0.0984	0.0000	0.0665	0.0031	0.0917	0.0000
建成区面积占比(X₅)	0.0853	0.0030	0.0753	0.1033	0.1025	0.0282
城市绿化率(X₆)	0.0280	0.1503	0.0625	0.0319	0.0359	0.1083
居民汽车保有量(X₇)	0.0259	0.8637	0.0913	0.0226	0.1074	0.0080
农作物播种面积(X₈)	0.1396	0.0557	0.1487	0.0000	0.1046	0.0000
工业烟尘排放量(X₉)	0.0709	0.1537	0.0936	0.0000	0.0531	0.2766
地均能源消费强度(X₁₀)	0.3109	0.0000	0.4124	0.0000	0.4143	0.0000
地均钢铁产量(X₁₁)	0.2869	0.0000	0.3373	0.0000	0.3217	0.0000

2000-2011年中国PM_2.5时空演化特征及驱动因素解析

Spatio-temporal evolution and the influencing factors of PM_2.5 in China between 2000 and 2011

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2000-2011年中国PM2.5时空演化特征及驱动因素解析

Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2011

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2000-2011年中国PM_2.5时空演化特征及驱动因素解析

Spatio-temporal evolution and the influencing factors of PM_2.5 in China between 2000 and 2011