中文关键词细颗粒物区域联合治理遥感数据时空变异性地理时空加权回归模型 英文关键词PM_2.5regional linkage control and preventionremote sensing dataspatio-temporal variationgeographically and temporally weighted regression

作者	单位	E-mail
杨文涛	湖南科技大学地理信息系, 湘潭 411201 湖南科技大学地理空间信息技术国家地方联合工程实验室, 湘潭 411201	yangwentao8868@126.com
黄慧坤	湖南科技大学地理信息系, 湘潭 411201 中南大学地质资源与地质工程系, 长沙 410000
魏东升	中南林业科技大学测绘工程系, 长沙 410000
赵斌	中南大学地质资源与地质工程系, 长沙 410000
彭焕华	湖南科技大学地理空间信息技术国家地方联合工程实验室, 湘潭 411201

中文摘要 联合治理分区下PM_2.5关联关系时空变异特征识别对中国大气污染防治意义重大.本文主要基于2000~2016年遥感反演的中国大陆334个地级市PM_2.5浓度数据，利用空间单元聚合策略与地理时空加权回归技术，系统分析了大气污染联合治理分区视角下的中国PM_2.5关联关系时空变异特征.结果表明：①以PM_2.5为首要污染物，综合考虑污染程度、地理位置、气象、地形和经济等因素可将中国大陆地区划分为10个大气污染联合治理区.②地理时空加权回归能够有效刻画PM_2.5与关联因素间的时空非平稳关系.同时，人口规模、第二产业生产总值、SO₂排放量、年平均气温、年降水量以及年平均相对湿度被识别出对PM_2.5浓度的变化影响存在显著时空差异.③人口规模对PM_2.5浓度的影响程度各年最大的地区均为京津冀蒙区域；川渝滇黔区域中第二产业生产总值对PM_2.5浓度影响程度变异度最大，在黑吉辽区域之外，SO₂排放量回归系数值均先随时间逐渐减小再增大最后又减小；各治理区中年平均温度对PM_2.5影响程度的时间变异程度较小；而年降水量与年平均相对湿度对PM_2.5影响程度在各区域中呈现不同的变异特征. 英文摘要 Identification of spatio-temporal variation of PM_2.5 related relationships under joint management zones is of great significance for scientifically conducting joint control of air pollution in China. Based on the PM_2.5 concentration data of 334 prefecture-level cities in China from 2000 to 2016, from the perspective of air pollution regional linkage control and prevention, this paper systematically analyzes the spatio-temporal variation of PM_2.5 related relationships in China using a spatial unit aggregation strategy and geographically and temporally weighted regression. The results show that:① With PM_2.5 as the primary pollutant, ten air pollution joint management areas are obtained by considering the degree of pollution, geographical location, meteorology, topography, and economy. ② Geographically and temporally weighted regression can effectively reveal the spatio-temporal non-stationarity of the relationships between PM_2.5 concentration and related factors. Meanwhile, population size, secondary industry gross domestic product, SO₂ emissions, annual average temperature, annual precipitation, and annual relative humidity are identified as having a significant effect on changes in PM_2.5 concentration. ③ The population impacts on PM_2.5 concentration in the Beijing-Tianjin-Yunmeng region are the largest of all regions during the period. The influence of the secondary industry's gross domestic product on the PM_2.5 concentration in the Sichuan-Yunnan District is the most variable. Apart from these values in the northeast of China, the regression coefficient values of SO₂ emissions first decrease with time, then increase, and then decrease again. The time variability of the average annual temperature of each treatment area to PM_2.5 is small. The influences of annual precipitation and annual average relative humidity on PM_2.5 present different variability characteristics in each region.

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