运用地理探测器研究京津冀城市群PM2.5浓度变化及影响因素
Study of the PM2.5 Concentration Variation and its Influencing Factors in the Beijing-Tianjin-Hebei Urban Agglomeration Using Geo-detector
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摘要: 研究京津冀城市群PM2.5浓度时空格局变化和影响因素,对区域大气环境保护和经济可持续发展具有十分重要的意义. 基于PM2.5遥感数据、地面站点气象数据、DEM数据、MODIS NDVI数据、夜间灯光数据、人口密度数据、土地利用类型数据和路网数据,利用Theil-Sen Median趋势分析、Mann-Kendall显著性检验和Getis-Ord Gi*分析,运用地理探测器,分析京津冀城市群PM2.5浓度时空变化和空间聚集特征,并探究影响其空间分异的影响因素. 结果表明:①2000—2021年京津冀城市群PM2.5污染严重,全年平均PM2.5浓度为59.94 μg/m3,冬季是京津冀城市群PM2.5污染的高发季,但京津冀城市群PM2.5浓度总体呈下降趋势,变化斜率为–0.85 μg/(m3·a). ②PM2.5浓度在空间上呈现出东南高、西北低的分布格局,且PM2.5浓度呈显著下降的区域占比76.52%,主要集中在京津冀城市群中南部.③ PM2.5浓度变化的聚集性呈西北高、东南低的空间分布格局,PM2.5浓度变化热点区域占比为50.95%. ④因子探测结果表明,气温(0.91)、高程(0.81)和路网密度(0.63)是影响京津冀城市群PM2.5浓度空间分异最主要的因子,研究时段内,降水对京津冀城市群PM2.5空间分异的影响力呈上升趋势,相对湿度、日照时数、夜间灯光和路网密度对京津冀城市群PM2.5空间分异的影响力均呈下降趋势. 交互探测结果表明,气温在因子交互中发挥十分重要的作用,气温与降水、高程和路网密度的交互作用是影响京津冀城市群PM2.5空间分异的主要因子组合. 研究显示,2000—2021年京津冀城市群PM2.5浓度整体呈下降态势,气温、高程和路网密度对京津冀城市群PM2.5浓度的空间分异有着显著的作用.
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关键词:
- 京津冀城市群 /
- PM2.5浓度 /
- 空间自相关分析 /
- Getis-Ord Gi*分析 /
- 地理探测器
Abstract: It is of great significance for regional atmospheric environmental protection and sustainable economic development to study the spatial and temporal pattern changes and influencing factors of PM2.5 concentration in Beijing-Tianjin-Hebei Urban Agglomeration Based on PM2.5 remote sensing data, ground station meteorological data, DEM data, MODIS NDVI data, night light data, population density data, land use type data and road network data, Theil Sen Median trend analysis, Mann Kendall significance test and Getis Ord Gi * analysis are used to analyze the spatial and temporal changes and spatial aggregation characteristics of PM2.5 concentration in Beijing-Tianjin-Hebei Urban Agglomeration, and use Geo-detector to explore the influencing factors of its spatial differentiation.The results show that: (1) PM2.5 pollution is serious in Beijing-Tianjin-Hebei Urban Agglomeration from 2000 to 2021, and the annual average PM2.5 concentration is 59.94 μg·m-3, winter is the high incidence season of PM2.5 pollution in Beijing-Tianjin-Hebei Urban Agglomeration, but the PM2.5 concentration in Beijing-Tianjin-Hebei Urban Agglomeration generally shows a downward trend, with a change slope of -0.85 μg/(m3·a). (2) The spatial distribution pattern of PM2.5 concentration is high in the southeast and low in the northwest, and the area where PM2.5 concentration decreased significantly accounted for 76.52%, mainly concentrated in the middle and south of Beijing-Tianjin-Hebei Urban Agglomeration. (3) The aggregation of PM2.5 concentration changes is high in the northwest and low in the southeast. The hot spot area of PM2.5 concentration changes accounts for 50.95% (4) The factor detection results show that temperature (0.91), elevation (0.81) and road network density (0.63) are the main factors affecting the spatial differentiation of PM2.5 concentration in the Beijing-Tianjin-Hebei Urban Agglomeration. During the study period, the influence of precipitation on the spatial differentiation of PM2.5 in the Beijing-Tianjin-Hebei Urban Agglomeration shows an upward trend, while the influence of humidity, sunshine hours, night light and road network density on the spatial differentiation of PM2.5 in the Beijing-Tianjin-Hebei Urban Agglomeration shows a downward trend The interactive detection results show that temperature plays a very important role in the factor interaction, and the interaction between temperature and precipitation, elevation and road network density is the main factor combination affecting the spatial differentiation of PM2.5 in Beijing-Tianjin-Hebei Urban Agglomeration The research results showed that the PM2.5 concentration in the Beijing-Tianjin-Hebei Urban Agglomeration showed a decreasing trend from 2000 to 2021, and temperature, elevation, and road network density played a significant role in the spatial differentiation of PM2.5 concentration in the Beijing-Tianjin-Hebei Urban Agglomeration.
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