摘要
研究成渝城市群PM_(2.5)浓度时空变化和驱动机制,对区域大气环境保护和国家经济可持续发展具有重要意义.基于PM_(2.5)遥感数据、DEM数据、基于站点的气象数据、MODIS NDVI数据、人口密度数据、夜间灯光数据、路网数据和土地利用类型数据,采用Theil-Sen Median趋势分析和Mann-Kendall显著性检验等方法,结合地理探测器,在多时空尺度上分析成渝城市群PM_(2.5)时空变化,并探测影响其变化的驱动机制.结果表明,2000~2021年成渝城市群PM_(2.5)浓度整体呈波动下降态势,冬季PM_(2.5)污染最为突出.PM_(2.5)浓度具有明显的空间异质性,呈现出“中间高,四周低”的空间分布特征,PM_(2.5)浓度高值区主要集中在自贡、内江、资阳和广安,PM_(2.5)浓度呈显著下降的区域主要集中在重庆西部等地.因子探测结果表明,成渝城市群PM_(2.5)浓度空间分异受气候、地形、植被和人文因子共同影响.高程、坡度和路网密度是影响成渝城市群PM_(2.5)浓度空间分异的主导因子.地形因子对成渝城市群PM_(2.5)浓度空间分异相对作用最强,而气候因子成渝城市群PM_(2.5)浓度空间分异相对作用最弱.2000~2021年地形因子和人文因子对成渝城市群PM_(2.5)浓度空间分异的相对作用呈递增趋势,气候因子和植被因子的相对作用呈递减趋势.交互作用探测结果表明,成渝城市群PM_(2.5)浓度空间分异较为显著的交互组合主要是高程与路网密度、坡度、降水、日照时数和土地利用类型.城市尺度上,交互作用探测结果表现出较大的地域差异,例如,成都、德阳和乐山PM_(2.5)浓度空间分异受不同类型因子间的交互作用十分显著,而达州、眉山、雅安、资阳、内江和自贡PM_(2.5)浓度空间分异受单一类别因子交互作用十分显著.
Studies on the spatio-temporal variation and driving mechanism of PM_(2.5)concentration in the Chengdu-Chongqing urban agglomeration are of great significance for regional atmospheric environment protection and national economic sustainable development.Based on PM_(2.5)remote sensing data,DEM data,in situ meteorological data,MODIS NDVI data,population density data,nighttime lighting data,road network data,and land use type data,a series of mathematical methods such as Theil-Sen Medium analysis and Mann-Kendall significance test,combined with the Geo-detector model were used to analyze the spatio-temporal variation and multi-dimensional detection of the driving mechanism of PM_(2.5)concentration in the Chengdu-Chongqing urban agglomeration.The results showed that the overall PM_(2.5)concentration showed a fluctuating downward trend in the Chengdu-Chongqing urban agglomeration from 2000 to 2021,and the PM_(2.5)pollution was the most prominent in winter.PM_(2.5)concentration exhibited obvious spatial heterogeneity with"high in the middle and low in the surrounding areas."The high-PM_(2.5)concentration areas were mainly concentrated in Zigong,Neijiang,Ziyang,and Guang'an,and the areas with a PM_(2.5)concentration decrease were mainly concentrated in the west of Chongqing.Influencing detection results showed that the spatial heterogeneity of PM_(2.5)concentration in the Chengdu-Chongqing urban agglomeration was influenced by the combined effects of climate factors,topographic factors,vegetation cover,and anthropogenic factors.Furthermore,elevation,slope,and road network density were regarded as the dominant factors influencing the spatial heterogeneity of PM_(2.5)concentration in the study area.Topographic factors and climate factors showed the highest and lowest contribution rate to the spatial heterogeneity of PM_(2.5)concentration,respectively.The contribution rate of topographic factors and anthropogenic factors had gradually increased,and the contribution rate of climate factors and vegetation cover had gradually decreased in the study area from 2000 to 2021.Interaction detection results showed that the spatial heterogeneity of PM_(2.5)concentration in the Chengdu-Chongqing urban agglomeration was mostly affected by the interaction effects of elevation and road network density,slope,precipitation,sunshine duration,and land use type.The interaction detection results exhibited obvious regional differences on the city level.For instance,the spatial heterogeneity of PM_(2.5)concentration in Chengdu,Deyang,and Leshan was mostly affected by the interaction between different influencing types,and the spatial heterogeneity of PM_(2.5)concentration in Dazhou,Meishan,Ya'an,Ziyang,Neijiang,and Zigong was mostly affected by the interaction within a single influencing type.
作者
徐勇
郭振东
郑志威
戴强玉
赵纯
黄雯婷
XU Yong;GUO Zhen-dong;ZHENG Zhi-wei;DAI Qiang-yu;ZHAO Chun;HUANG Wen-ting(College of Geomatics and Geoinformation,Guilin University of Technology,Guilin 541006,China)
出处
《环境科学》
EI
CAS
CSCD
北大核心
2023年第7期3724-3737,共14页
Environmental Science
基金
广西自然科学基金项目(2020GXNSFBA297160)
广西科技基地和人才专项(桂科AD21220133)
国家自然科学基金项目(42161028)
大学生创新创业训练计划项目(202210596388)。
关键词
成渝城市群
PM_(2.5)浓度
地理探测器
驱动机制
地形因子
气候因子
人文因子
Chengdu-Chongqing urban agglomeration
PM_(2.5)concentration
Geo-detector
driving mechanism
topographic factor
climate factor
anthropogenic factor
