| 2000~2021年成渝城市群PM2.5时空变化及驱动机制多维探测 |
| 摘要点击 1876 全文点击 450 投稿时间：2022-07-29 修订日期：2022-10-09 |
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| 中文关键词 成渝城市群 PM2.5浓度 地理探测器 驱动机制 地形因子 气候因子 人文因子 |
| 英文关键词 Chengdu-Chongqing urban agglomeration PM2.5 concentration Geo-detector driving mechanism topographic factor climate factor anthropogenic factor |
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| 中文摘要 |
| 研究成渝城市群PM2.5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PM2.5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PM2.5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PM2.5浓度整体呈波动下降态势，冬季PM2.5污染最为突出.PM2.5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，PM2.5浓度高值区主要集中在自贡、内江、资阳和广安，PM2.5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PM2.5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PM2.5浓度空间分异的主导因子.地形因子对成渝城市群PM2.5浓度空间分异相对作用最强，而气候因子成渝城市群PM2.5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PM2.5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PM2.5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PM2.5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PM2.5浓度空间分异受单一类别因子交互作用十分显著. |
| 英文摘要 |
| Studies on the spatio-temporal variation and driving mechanism of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration are of great significance for regional atmospheric environment protection and national economic sustainable development. Based on PM2.5 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 PM2.5 concentration in the Chengdu-Chongqing urban agglomeration. The results showed that the overall PM2.5 concentration showed a fluctuating downward trend in the Chengdu-Chongqing urban agglomeration from 2000 to 2021, and the PM2.5 pollution was the most prominent in winter. PM2.5 concentration exhibited obvious spatial heterogeneity with "high in the middle and low in the surrounding areas." The high-PM2.5 concentration areas were mainly concentrated in Zigong, Neijiang, Ziyang, and Guang'an, and the areas with a PM2.5 concentration decrease were mainly concentrated in the west of Chongqing. Influencing detection results showed that the spatial heterogeneity of PM2.5 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 PM2.5 concentration in the study area. Topographic factors and climate factors showed the highest and lowest contribution rate to the spatial heterogeneity of PM2.5 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 PM2.5 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 PM2.5 concentration in Chengdu, Deyang, and Leshan was mostly affected by the interaction between different influencing types, and the spatial heterogeneity of PM2.5 concentration in Dazhou, Meishan, Ya'an, Ziyang, Neijiang, and Zigong was mostly affected by the interaction within a single influencing type. |
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