运用地理探测器研究京津冀城市群PM<sub>2.5</sub>浓度变化及影响因素

徐勇; 郭振东; 郑志威; 戴强玉; 赵纯; 黄雯婷

doi:10.13198/j.issn.1001-6929.2023.01.01

运用地理探测器研究京津冀城市群PM_2.5浓度变化及影响因素

doi: 10.13198/j.issn.1001-6929.2023.01.01

桂林理工大学测绘地理信息学院，广西桂林　541006

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)

详细信息

作者简介:
徐勇（1988-），男，湖南益阳人，副教授，博士，主要从事环境遥感、气候变化和植被覆盖动态变化研究，yongxu@glut.edu.cn

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出版历程
- 收稿日期: 2022-09-09
- 修回日期: 2022-12-09

Study of the PM_2.5 Concentration Variation and its Influencing Factors in the Beijing-Tianjin-Hebei Urban Agglomeration Using Geo-Detector

College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

Funds: Natural Science Foundation of Guangxi, China (No.2020GXNSFBA297160); Guangxi Science & Technology Program, China (No.GuikeAD21220133)；College Students' Innovation and Entrepreneurship Training Program of Guilin University of Technology, China (No.202210596388)

摘要

摘要: 研究京津冀城市群PM_2.5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PM_2.5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 G_i^*分析，运用地理探测器分析京津冀城市群PM_2.5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PM_2.5污染严重，全年平均PM_2.5浓度为59.94 μg/m³，冬季是京津冀城市群PM_2.5污染的高发季，但京津冀城市群PM_2.5浓度总体呈下降趋势，变化斜率为–0.85 μg/(m³·a). ②PM_2.5浓度在空间上呈东南高、西北低的分布格局，且PM_2.5浓度呈显著下降的区域占比为9.92%，主要集中在张家口市. ③PM_2.5浓度变化的聚集性呈西北高、东南低的空间分布格局，PM_2.5浓度变化热点区域占比为50.95%. ④因子探测结果表明，气温、高程和路网密度是影响京津冀城市群PM_2.5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PM_2.5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PM_2.5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均是影响京津冀城市群PM_2.5空间分异的主要因子组合. 研究显示，2000—2021年京津冀城市群PM_2.5浓度整体呈下降趋势，气温、高程和路网密度对京津冀城市群PM_2.5浓度的空间分异有显著影响.
- 京津冀城市群 /
- PM_2.5浓度 /
- 空间自相关分析 /
- Getis-Ord G_i^*分析 /
- 地理探测器
Abstract: Studying the spatial and temporal pattern changes and influencing factors of PM_2.5 concentration in the Beijing-Tianjin-Hebei Urban Agglomeration is of great significance to regional atmospheric environmental protection and sustainable economic development. Based on PM_2.5 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 G_i^* analysis were used to analyze the spatial and temporal changes and spatial aggregation characteristics of PM_2.5 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, PM_2.5 pollution in Beijing-Tianjin-Hebei Urban Agglomeration was serious, the annual average PM_2.5 concentration was 59.94 μg/m³, winter was the high incidence season of PM_2.5 pollution, but the PM_2.5 concentration generally showed a downward trend, with a change slope of −0.85 μg/(m³·a). (2) The spatial distribution pattern of PM_2.5 concentration was high in the southeast and low in the northwest, and the area where PM_2.5 concentration decreased significantly accounted for 9.92%, mainly concentrated in Zhangjiakou City. (3) The aggregation of PM_2.5 concentration changes was high in the northwest and low in the southeast. The hot spot area of PM_2.5 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 PM_2.5 concentration in the Beijing-Tianjin-Hebei Urban Agglomeration. During the study period, the influence of precipitation on the spatial differentiation of PM_2.5 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 PM_2.5 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 PM_2.5 in Beijing-Tianjin-Hebei Urban Agglomeration. The research results showed that the PM_2.5 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 PM_2.5 concentration in the Beijing-Tianjin-Hebei Urban Agglomeration.
- Beijing-Tianjin-Hebei Urban Agglomeration /
- PM_2.5 concentration /
- spatial autocorrelation analysis /
- Getis-Ord G_i^* analysis /
- Geo-detector

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图 1 研究区示意

Figure 1. Location of the study area

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图 2 2000—2021年京津冀城市群PM_2.5浓度时间变化趋势

Figure 2. Temporal variation of PM_2.5concentration in the Beijing-Tianjin-Hebei Urban Agglomeration from 2000 to 2021

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图 3 2000—2021年京津冀城市群PM_2.5浓度平均值、变化斜率和显著性检验的空间分布情况

Figure 3. Spatial distribution of multi-year average, changing trend, and significance test of PM_2.5 concentration in the Beijing-Tianjin-Hebei Urban Agglomeration from 2000 to 2021

下载: 全尺寸图片幻灯片

图 4 2000—2021年京津冀城市群PM_2.5浓度变化冷热点区域空间分布情况

Figure 4. Spatial distribution of the cold and hot spots of PM_2.5 concentration variation in the Beijing-Tianjin-Hebei Urban Agglomeration from 2000 to 2021

下载: 全尺寸图片幻灯片

表 1 京津冀城市群PM_2.5浓度变化显著性统计

Table 1. Statistics result of the significance test of PM_2.5 concentrtion in the Beijing-Tianjin-Hebei Urban Agglomeration

时间尺度	面积占比/%
时间尺度	PM_2.5浓度不显著下降	PM_2.5浓度显著下降
春季	75.34	24.66
夏季	48.81	51.19
秋季	92.87	7.13
冬季	71.28	28.72
全年	90.08	9.92

下载: 导出CSV

表 2 京津冀城市群PM_2.5浓度变化冷热点区域统计

Table 2. Statistics result of the cold and hot spots of PM_2.5 concentration variation in the Beijing-Tianjin-Hebei Urban Agglomeration

时间尺度	占比/%
时间尺度	冷点区	次冷点区	不显著区	次热点区	热点区
春季	42.97	0.25	4.24	0.86	51.69
夏季	44.77	0.22	3.85	0.64	50.53
秋季	43.97	0.25	4.54	1.02	50.23
冬季	43.49	0.42	6.63	1.56	47.91
全年	44.04	0.26	4.10	0.65	50.95

下载: 导出CSV

表 3 2000年、2010年和2021年京津冀城市群因子探测q值统计

Table 3. Detection Statistics of q value in the Beijing-Tianjin-Hebei Urban Agglomeration factors in 2000, 2010 and 2021

影响因子	q值
影响因子	2000年	2010年	2021年	平均值
X₁	0.83	0.84	0.76	0.81
X₂	0.03	0.02	0.02	0.02
X₃	0.50	0.47	0.41	0.46
X₄	0.92	0.91	0.90	0.91
X₅	0.17	0.34	0.41	0.31
X₆	0.62	0.43	0.30	0.45
X₇	0.67	0.23	0.00	0.30
X₈	0.06	0.04	0.05	0.05
X₉	0.67	0.66	0.57	0.63
X₁₀	0.03	0.09	0.12	0.08
X₁₁	0.54	0.52	0.43	0.50
X₁₂	0.35	0.34	0.29	0.33

下载: 导出CSV

表 4 2000—2021年影响因子交互作用的q平均值

Table 4. Statistic result of the mean q value of interaction detection from 2000 to 2021

项目	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂
X₁	0.81
X₂	0.82	0.02
X₃	0.83	0.48	0.46
X₄	0.93	0.91	0.93	0.91
X₅	0.87	0.33	0.62	0.93	0.31
X₆	0.87	0.46	0.77	0.92	0.69	0.45
X₇	0.83	0.31	0.55	0.92	0.55	0.60	0.30
X₈	0.83	0.08	0.51	0.91	0.40	0.49	0.36	0.05
X₉	0.85	0.64	0.70	0.93	0.75	0.81	0.74	0.66	0.63
X₁₀	0.81	0.10	0.47	0.91	0.35	0.50	0.37	0.13	0.64	0.08
X₁₁	0.82	0.51	0.61	0.92	0.64	0.70	0.64	0.53	0.69	0.51	0.50
X₁₂	0.83	0.36	0.53	0.92	0.53	0.68	0.49	0.40	0.68	0.35	0.57	0.33

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