长江经济带PM_(2.5)分布格局演变及其影响因素
Evolution of the Distribution of PM_(2.5) Concentration in the Yangtze River Economic Belt and Its Influencing Factors
查看参考文献37篇
|
文摘
|
2000年以来,长江经济带高强度的人类社会经济活动引发了严峻的环境污染问题,灰霾污染尤为严重.研究该区域PM_(2.5)浓度的时空格局与影响因素是落实新发展理念、推进区域大气污染综合防治的迫切需要.本文基于遥感反演数据,研究了2000~ 2016年长江经济带PM_(2.5)浓度分布格局的演变过程,利用地理加权回归模型揭示了自然和社会经济因素对其影响的时空非平稳性.结果表明: ① PM_(2.5)浓度分布总体表现为东高西低,且城市群污染特征明显. ②以2007年为界,2000~ 2016年PM_(2.5)年均浓度经历了逐年上升和波动下降的过程,年均浓度由27.2 μg·m~(-3)上升至44.1 μg·m~(-3)后,2016年降至33.6 μg·m~(-3).污染范围则先由长三角城市群、长江中游城市群和成渝城市群核心区域向四周快速扩展,2007年后开始往回收缩. ③空间自相关分析表明,PM_(2.5)浓度分布有显著的正空间自相关性,热点持续稳定地分布在上海、江苏、安徽中北部、浙江北部和湖北中部,冷点分布在云南、四川西部和南部及贵州西部. ④自然因素与社会经济因素对PM_(2.5)浓度分布的影响具有时空差异性.其中社会经济因素主要呈正向影响;自然因素中,降水量主要呈负向影响,其余因子的影响大小和作用方向均随着时间和空间的变化而变化. |
|
其他语种文摘
|
Intensive social and economic activity has led to serious pollution in the Yangtze River economic belt since 2000. It is urgent to study the evolution of the distribution of PM_(2.5) concentration and its influencing factors in this area,to adopt new ways of development into practice and promote comprehensive regional air pollution prevention and control. Based on PM_(2.5) concentration estimated by remote sensing retrieval,this paper studied the evolution of the distribution of PM_(2.5) concentration in the Yangtze River economic belt from 2000 to 2016,and analyzed spatial non-stationarity of the influence of natural and socio-economic factors on this evolution via a geographically weighted regression model. The results showed that: ①The general law of PM_(2.5) concentration presented as higher in the east and lower in the west,with a significant trait of the pollution agglomerations corresponding to urban agglomerations. ②Taking the year 2007 as a divide,annual concentration of PM_(2.5) went through a pattern of annually increasing from 2000 to 2007. and then wavelike decreasing from 2007 to 2016. The annual average concentration increased to 44.1 μg·m~(-3) in 2007 from the record of 27.2 μg·m~(-3) in 2000,and then decreased to 33.6 μg·m~(-3) in 2016. In terms of regions polluted,before 2007,it covered areas including the Yangtze River Delta urban agglomerations,the Yangtze River Middle Reaches urban agglomerations,and the Chengdu-Chongqing urban agglomerations,before quickly stretching to their neighboring areas; after 2007,the extent of areas covered shrank. ③Based on spatial auto-correlation analysis,PM_(2.5) concentration had a significant spatial auto-correlation with hot spots spread over Shanghai,Jiangsu,north-central Anhui,northern Zhejiang,and the central part of Hubei,while cool spots were located in Yunnan,the western and southern parts of Sichuan,and the western part of Guizhou. ④There is a space-time discrepancy by socio-economic and natural factors in the distribution of PM_(2.5) concentration. The socio-economic factors mainly have a positive influence on the concentration,whereas precipitation,one of the natural factors,has a negative influence. The remaining natural factors not only varied in their degree of influence,but also triggered the influence either in a positive or negative manner from time to time and space to space. |
|
来源
|
环境科学
,2020,41(3):1013-1024 【核心库】
|
|
DOI
|
10.13227/j.hjkx.201906158
|
|
关键词
|
PM_(2.5)浓度
;
空间格局
;
演变
;
影响因素
;
长江经济带
;
地理加权回归模型(GWR)
|
|
地址
|
1.
山西师范大学地理科学学院, 临汾, 041004
2.
中国科学院地球环境研究所, 中国科学院气溶胶化学与物理重点实验室, 西安, 710061
3.
陕西师范大学地理科学与旅游学院, 西安, 710119
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
0250-3301 |
|
学科
|
环境污染及其防治 |
|
基金
|
中国科学院气溶胶化学与物理重点实验室项目
;
国家自然科学基金青年科学基金项目
|
|
文献收藏号
|
CSCD:6682763
|
参考文献 共
37
共2页
|
|
1.
严翔. 长江经济带城镇化对能源消费的经济门槛效应.
经济地理,2019,39(1):73-81
|
被引
8
次
|
|
|
|
|
2.
Sofowote U M. Constraining the factor analytical solutions obtained from multipleyear receptor modeling of ambient PM2.5 data from five speciation sites in Ontario,Canada.
Atmospheric Environment,2015,108:151-157
|
被引
5
次
|
|
|
|
|
3.
Li M N. Haze in China: current and future challenges.
Environmental Pollution,2014,189:85-86
|
被引
24
次
|
|
|
|
|
4.
段杰雄. 中国PM_(2.5)污染空间分布的社会经济影响因素分析.
环境科学,2018,39(5):2498-2504
|
被引
38
次
|
|
|
|
|
5.
Turpin B J. An in situ,time-resolved analyzer for aerosol organic and elemental carbon.
Aerosol Science and Technology,1990,12(1):161-171
|
被引
60
次
|
|
|
|
|
6.
刘可可. 合肥市PM_(2. 5)和PM_(10)中元素组成特征及重污染成因分析.
环境科学,2019,40(8):3415-3420
|
被引
15
次
|
|
|
|
|
7.
Zhao X J. Analysis of a winter regional haze event and its formation mechanism in the North China Plain.
Atmospheric Chemistry and Physics,2013,13(11):5685-5696
|
被引
79
次
|
|
|
|
|
8.
陈国磊. 承德市大气污染源排放清单及典型行业对PM_(2.5)的影响.
环境科学,2016,37(11):4069-4079
|
被引
21
次
|
|
|
|
|
9.
Jiang C. Modeling study of PM2.5 pollutant transport across cities in China's Jing-Jin-Ji region during a severe haze episode in December 2013.
Atmospheric Chemistry and Physics Discussions,2015,15(3):3745-3776
|
被引
6
次
|
|
|
|
|
10.
王晓琦. 京津冀及周边地区PM_(2.5)传输规律研究.
中国环境科学,2016,36(11):3211-3217
|
被引
31
次
|
|
|
|
|
11.
赵子菁. 南京市霾天气与主要气象条件的相关分析.
中国环境科学,2015,35(12):3570-3580
|
被引
23
次
|
|
|
|
|
12.
吴健生. 重庆市PM_(2.5)浓度空间分异模拟及影响因子.
环境科学,2015,36(3):759-767
|
被引
31
次
|
|
|
|
|
13.
贺祥. 基于GAM模型分析影响因素交互作用对PM_(2.5)浓度变化的影响.
环境科学,2017,38(1):22-32
|
被引
29
次
|
|
|
|
|
14.
吴浪. 基于遥感数据的PM2.5与城市化的时空关系研究———以成渝城市群为例.
长江流域资源与环境,2018,27(9):2142-2152
|
被引
8
次
|
|
|
|
|
15.
贺祥. 基于灰色关联模型对江苏省PM_(2.5)浓度影响因素的分析.
地理学报,2016,71(7):1119-1129
|
被引
42
次
|
|
|
|
|
16.
杨冕. 长江经济带PM_(2.5)时空特征及影响因素研究.
中国人口·资源与环境,2017,27(1):91-100
|
被引
45
次
|
|
|
|
|
17.
汪聪聪. 经济集聚对雾霾污染影响的空间计量研究———以长江三角洲地区为例.
长江流域资源与环境,2019,28(1):1-11
|
被引
7
次
|
|
|
|
|
18.
Van Donkelaar A. Global estimates of fine particulate matter using a combined geophysicalstatistical method with information from satellites,models,and monitors.
Environmental Science & Technology,2016,50(7):3762-3772
|
被引
49
次
|
|
|
|
|
19.
周亮. 2000-2011年中国PM_(2.5)时空演化特征及驱动因素解析.
地理学报,2017,72(11):2079-2092
|
被引
102
次
|
|
|
|
|
20.
Xie R. Long-term trend and spatial pattern of PM2.5 induced premature mortality in China.
Environment International,2016,97:180-186
|
被引
18
次
|
|
|
|
|
了解气象卫星更多信息，请访问