黄河流域五大城市群PM2.5时空演变与影响因素探讨

doi:10.12118/j.issn.1000－6060.2023.088

Abstract

Abstract:

This study focuses on 82 cities in five major urban agglomerations in the Yellow River Basin and uses PM_2.5 data published by the China National Environmental Monitoring Centre from 2016 to 2020. Spatial autocorrelation, geographic detectors, and geographically weighted regression methods were employed to investigate the spatiotemporal distribution characteristics and main driving factors of spatial heterogeneity of PM_2.5. This provides a reference for relevant departments to improve atmospheric pollution prevention and control policies. The results are as follows: (1) The change in the annual mean of PM_2.5 roughly follows an inverted “N” shape, and the seasonal mean changes in a “U” shape with a periodicity of first decreasing and then increasing. (2) In terms of spatial distribution, a gradient-decreasing pattern is formed from downstream to midstream to upstream of the Yellow River, exhibiting a gradually decreasing trend. (3) PM_2.5 exhibits positive spatial autocorrelation and overall aggregation distribution over the five years, with high-high, low-low, and low-high agglomeration types. (4) Natural geographical factors have a stronger driving force on PM_2.5 spatial differentiation than socio-economic factors in 2016 and 2020. The interaction results show two types: bi-factor and nonlinear strengthening. (5) The geographically weighted regression model is used to fit the five factors with the largest explanatory power for differentiation. The negative effects of each factor on PM_2.5 pollution in the five urban agglomerations have increased, whereas the positive effects have decreased over the past 5 years. Significant differences in spatial direction and strength were observed.

Key words: the Yellow River Basin, PM_2.5, urban agglomeration, ecological protection, high-quality development

MU Shilei, YANG Yuhuan, Wuritaoketaohu . Spatiotemporal evolution and influencing factors of PM_2.5 in the five urban agglomerations in the Yellow River Basin[J].Arid Land Geography, 2024, 47(4): 707-719.

Figures/Tables 8

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[58]	杨春雪, 阚海东, 陈仁杰. 我国大气细颗粒物水平、成分、来源及污染特征[J]. 环境与健康杂志, 2011, 28(8): 735-738, 753.
	[Yang Chunxue, Kan Haidong, Chen Renjie. Research on level, composition, source and pollution characteristics of ambient fine particles in China[J]. Journal of Environment and Health, 2011, 28(8): 735-738, 753. ]

年份	兰州-西宁城市群	黄河“几”字弯都市圈	关中平原城市群	中原城市群	山东半岛城市群	黄河流域城市群
2016年	-0.416^***	0.078^***	0.263^***	0.144^***	0.529^***	0.481^***
2020年	-0.034^***	0.040^***	0.187^***	0.316^***	0.537^***	0.435^***

影响因素	因子	评价指标	单位	q		差距
影响因素	因子	评价指标	单位	2016年	2020年	差距
自然地理	地形起伏度（X₁）	WAVI	m	0.466^***	0.443^***	-0.023
	气温（X₂）	年平均气温	℃	0.417^***	0.462^***	0.045
	降水量（X₃）	年平均降水量	mm	0.085^***	0.068^***	-0.017
	植被覆盖指数（X₄）	NDVI	-	0.256^***	0.301^***	0.045
	净第一性生产力（X₅）	NPP	g·m^-2·a^-1	0.500^***	0.546^***	0.046
社会经济	经济增长（X₆）	人均GDP	元	0.154^***	0.136^***	-0.018
	城市化（X₇）	城市人口/总人口	%	0.056^***	0.086^***	0.030
	工业化（X₈）	第二产业占地区生产总值的比重	%	0.100^***	0.101^***	0.001
	人口规模（X₉）	常住人口数量	10⁴人	0.249^***	0.209^***	-0.040
	工业规模（X₁₀）	规模以上工业总产值	10⁴元	0.203^***	0.103^***	-0.100
	科技创新（X₁₁）	专利授权数	件	0.151^***	0.159^***	0.008
	能源效率（X₁₂）	单位GDP工业用电量倒数=GDP/工业用电量	%	0.036^***	0.221^***	0.185
	烟尘和粉尘排放量（X₁₃）	工业SO₂、工业氮氧化物、工业颗粒物排放量	10³kg	0.023^***	0.098^***	0.075
	公众环境关注度（X₁₄）	百度雾霾搜索指数	-	0.154^***	0.198^***	0.044
	城市绿化率（X₁₅）	建成区绿化覆盖率	%	0.014^***	0.185^***	0.171

Spatiotemporal evolution and influencing factors of PM_2.5 in the five urban agglomerations in the Yellow River Basin

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Spatiotemporal evolution and influencing factors of PM2.5 in the five urban agglomerations in the Yellow River Basin

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Spatiotemporal evolution and influencing factors of PM_2.5 in the five urban agglomerations in the Yellow River Basin