| 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 |
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| 引用本文： | 徐勇,郭振东,郑志威,戴强玉,赵纯,黄雯婷.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[J].环境科学研究,2023,36(4):649-659. |
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| 作者姓名： | 徐勇 郭振东 郑志威 戴强玉 赵纯 黄雯婷 |
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| 作者单位： | 桂林理工大学测绘地理信息学院，广西 桂林 541006 |
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| 基金项目： | 广西自然科学基金项目(No.2020GXNSFBA297160)；广西科技基地和人才专项(No.桂科AD21220133)；桂林理工大学大学生创新创业训练计划项目(No.202210596388) |
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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：(1)2000—2021年京津冀城市群PM2.5污染严重，全年平均PM2.5浓度为59.94μg/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–0.85μg/(m3·a).(2)PM2.5浓度在空间上呈东南高、西北低的分布格局，且P...
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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 |
| 收稿时间： | 2022-09-09 |
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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| Affiliation: | College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China |
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| Abstract: | Studying the spatial and temporal pattern changes and influencing factors of PM2.5 concentration in the Beijing-Tianjin-Hebei Urban Agglomeration is of great significance to regional atmospheric environmental protection and sustainable economic development. 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 were used to analyze the spatial and temporal changes and spatial aggregation characteristics of PM2.5 concentration in the Beijing-Tianjin-Hebei Urban Agglomeration, and Geo-detector was used to explore the influencing factors of its spatial differentiation. The results showed that: (1) From 2000 to 2021, PM2.5 pollution in Beijing-Tianjin-Hebei Urban Agglomeration was serious, the annual average PM2.5 concentration was 59.94 μg/m3, winter was the high incidence season of PM2.5 pollution, but the PM2.5 concentration generally showed a downward trend, with a change slope of ?0.85 μg/(m3·a). (2) The spatial distribution pattern of PM2.5 concentration was high in the southeast and low in the northwest, and the area where PM2.5 concentration decreased significantly accounted for 9.92%, mainly concentrated in Zhangjiakou City. (3) The aggregation of PM2.5 concentration changes was high in the northwest and low in the southeast. The hot spot area of PM2.5 concentration changes accounted for 50.95%. (4) The factor detection results showed that temperature, elevation and road network density were 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 showed an increasing trend, while the influence of humidity, sunshine hours, night light and road network density on the spatial differentiation of PM2.5 showed a downward trend. The interactive detection results showed that temperature played a very important role in the factor interaction, and the interaction between temperature and precipitation, elevation and road network density was 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 downward 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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