中文关键词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) 英文关键词PM_2.5 concentrationdistributionevolutioninfluencing factorsYangtze River economic beltgeographically weighted regression (GWR)

作者	单位	E-mail
黄小刚	山西师范大学地理科学学院, 临汾 041004 中国科学院地球环境研究所气溶胶化学与物理重点实验室, 西安 710061 陕西师范大学地理科学与旅游学院, 西安 710119	huangxg@sxnu.edu.cn
赵景波	中国科学院地球环境研究所气溶胶化学与物理重点实验室, 西安 710061 陕西师范大学地理科学与旅游学院, 西安 710119	zhaojb@snnu.edu.cn
曹军骥	中国科学院地球环境研究所气溶胶化学与物理重点实验室, 西安 710061
辛未冬	山西师范大学地理科学学院, 临汾 041004

中文摘要 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：①PM_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27.2μg·m^-3上升至44.1μg·m^-3后，2016年降至33.6μg·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. 英文摘要 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.

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