Journal of Geo-information Science

Figure/Table detail

Spatio-Temporal Evolution of PM_2.5 and Heterogeneity Analysis of Influencing Factors in Three Urban Agglomerations of the Yangtze River Economic Belt

ZHANG Keyi, XIAO Jia, FANG Jian

Journal of Geo-information Science, 2025, 27(6): 1478-1498. DOI: 10.12082/dqxxkx.2025.240617

影响因素	VIF	影响因素	VIF
年均降水量	1.745	第二产业占比	1.309
年均气温	2.481	人均GDP	3.800
NDVI	4.190	人口密度	3.743
年均风速	1.447	夜间灯光指数	12.447
坡度	4.006	规模以上工业数量	3.041
道路密度	11.391	—	—

Tab. 3 VIF of influencing factors

Other figure/table from this article

Fig. 1 Location and elevation of the study area
Tab. 1 Sources and introduction of influencing factors
Fig. 2 Analysis process of spatiotemporal evolution and influencing factors of PM_2.5 spatiotemporal heterogeneity
Fig. 3 Annual variation of PM_2.5 concentrations in the three urban agglomerations of the Yangtze River Economic Belt from 2012 to 2022
Fig. 4 Spatial distribution of annual average PM_2.5 concentrations in the three urban agglomerations of the Yangtze River Economic Belt from 2012 to 2022
Fig. 5 Seasonal variation of PM_2.5 concentrations in the three urban agglomerations of the Yangtze River Economic Belt from 2012 to 2022
Fig. 6 Seasonal spatial distribution of PM_2.5 concentrations in the three urban agglomerations of the Yangtze River Economic Belt in 2012, 2017 and 2022
Fig. 7 Monthly variations of PM₂.₅ concentrations in the three urban agglomerations of the Yangtze River Economic Belt in 2012, 2017, and 2022
Fig. 8 Spatial distribution of monthly average PM_2.5 concentrations in the three urban agglomerations of the Yangtze River Economic Belt in 2012
Fig. 9 Spatial distribution of monthly average PM_2.5 concentrations in the three urban agglomerations of the Yangtze River Economic Belt in 2022
Tab.2 Moran Index of PM_2.5 about three urban agglomerations of the Yangtze River Economic Belt and its test
Fig. 10 Local spatial autocorrelation analysis results of PM_2.5 in the three urban agglomerations of the Yangtze River Economic Belt in 2012, 2017 and 2022
Tab. 4 Factor detector analysis results
Tab. 5 Fitting results of the spatial regression model
Tab. 6 Temporal bandwidth and spatial bandwidth of the impact factor
Tab. 7 Regression coefficient results of the impact factor
Fig. 11 Spatial distribution of regression coefficients of impact factors in 2012
Fig. 12 Spatial distribution of regression coefficients of impact factors in 2017
Fig. 13 Spatial distribution of regression coefficients of impact factors in 2022

模态框（Modal）标题

Figure/Table detail