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. 2023 Feb 5;20(4):2814.
doi: 10.3390/ijerph20042814.

Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China's Cities Based on Spatial Autocorrelation Analysis and MGWR Model

Yanzhao Wang  1   2 , Jianfei Cao  1   2
Affiliations

Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China's Cities Based on Spatial Autocorrelation Analysis and MGWR Model

Yanzhao Wang et al. Int J Environ Res Public Health. .

Abstract

Understanding the characteristics of PM2.5 and its socioeconomic factors is crucial for managing air pollution. Research on the socioeconomic influences of PM2.5 has yielded several results. However, the spatial heterogeneity of the effect of various socioeconomic factors on PM2.5 at different scales has yet to be studied. This paper collated PM2.5 data for 359 cities in China from 2005 to 2020, as well as socioeconomic data: GDP per capita (GDPP), secondary industry proportion (SIP), number of industrial enterprise units above the scale (NOIE), general public budget revenue as a proportion of GDP (PBR), and population density (PD). The spatial autocorrelation and multiscale geographically weighted regression (MGWR) model was used to analyze the spatiotemporal heterogeneity of PM2.5 and explore the impact of different scales of economic factors. Results show that the overall economic level was developing well, with a spatial distribution trend of high in the east and low in the west. With a large positive spatial correlation and a highly concentrated clustering pattern, the PM2.5 concentration declined in 2020. Secondly, the OLS model's statistical results were skewed and unable to shed light on the association between economic factors and PM2.5. Predictions from the GWR and MGWR models may be more precise than those from the OLS model. The scales of the effect were produced by the MGWR model's variable bandwidth and regression coefficient. In particular, the MGWR model's regression coefficient and variable bandwidth allowed it to account for the scale influence of economic factors; it had the highest adjusted R2 values, smallest AICc values, and residual sums of squares. Lastly, the PBR had a clear negative impact on PM2.5, whereas the negative impact of GDPP was weak and positively correlated in some western regions, such as Gansu and Qinghai provinces. The SIP, NOIE, and PD were positively correlated with PM2.5 in most regions. Our findings can serve as a theoretical foundation for researching the associations between PM2.5 and socioeconomic variables, and for encouraging the coequal growth of the economy and the environment.

Keywords: PM2.5 concentration; multiscale geo-weighted regression; scale effect; spatial autocorrelation; spatial heterogeneity.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Spatial and temporal distribution of socioeconomic influences in China in 2005 and 2020: (a) spatial and temporal distribution of GDP per capita in 2005; (b) spatial and temporal distribution of GDP per capita in 2020; (c) spatial and temporal distribution of the proportion of secondary industry in 2005; (d) spatial and temporal distribution of the proportion of secondary industry in 2020; (e) spatial and temporal distribution of the number of industrial enterprise units above the scale in 2005; (f) spatial and temporal distribution of the number of industrial enterprise units above the scale in 2020; (g) spatial and temporal distribution of the proportion of general public budget revenues to GDP in 2005; (h) spatial and temporal distribution of the proportion of general public budget revenues to GDP in 2020; (i) spatial and temporal distribution of population density in 2005; (j) spatial and temporal distribution of population density in 2020.
Figure 1
Figure 1
Spatial and temporal distribution of socioeconomic influences in China in 2005 and 2020: (a) spatial and temporal distribution of GDP per capita in 2005; (b) spatial and temporal distribution of GDP per capita in 2020; (c) spatial and temporal distribution of the proportion of secondary industry in 2005; (d) spatial and temporal distribution of the proportion of secondary industry in 2020; (e) spatial and temporal distribution of the number of industrial enterprise units above the scale in 2005; (f) spatial and temporal distribution of the number of industrial enterprise units above the scale in 2020; (g) spatial and temporal distribution of the proportion of general public budget revenues to GDP in 2005; (h) spatial and temporal distribution of the proportion of general public budget revenues to GDP in 2020; (i) spatial and temporal distribution of population density in 2005; (j) spatial and temporal distribution of population density in 2020.
Figure 2
Figure 2
Spatial and temporal distribution of national PM2.5 concentrations from 2005 to 2020: (a) spatial and temporal distribution of PM2.5 concentrations in 2005; (b) spatial and temporal distribution of PM2.5 concentrations in 2010; (c) spatial and temporal distribution of PM2.5 concentrations in 2015; (d) spatial and temporal distribution of PM2.5 concentrations in 2020.
Figure 3
Figure 3
Spatial clustering of annual average PM2.5 concentration values in China from 2005 to 2020: (a) spatial clustering of annual average PM2.5 concentration values in 2005; (b) spatial clustering of annual average PM2.5 concentration values in 2010; (c) spatial clustering of annual average PM2.5 concentration values in 2015; (d) spatial clustering of annual average PM2.5 concentration values in 2020.
Figure 4
Figure 4
Spatial distribution of local R2 values for the GWR model from 2005−2020: (a) spatial distribution of the local R2 values of the GWR model for 2005; (b) spatial distribution of the local R2 values of the GWR model for 2010; (c) spatial distribution of the local R2 values of the GWR model for 2015; (d) spatial distribution of the local R2 values of the GWR model for 2020.
Figure 5
Figure 5
Spatial distribution of local R2 values for the MGWR model from 2005−2020: (a) spatial distribution of the local R2 values of the MGWR model for 2005; (b) spatial distribution of the local R2 values of the MGWR model for 2010; (c) spatial distribution of the local R2 values of the MGWR model for 2015; (d) spatial distribution of the local R2 values of the MGWR model for 2020.
Figure 6
Figure 6
Spatial and temporal distribution of regression coefficients of GDP per capita in the MGWR model, 2005−2020: (a) spatial and temporal distribution of regression coefficients for GDP per capita in 2005; (b) spatial and temporal distribution of regression coefficients for GDP per capita in 2010; (c) spatial and temporal distribution of regression coefficients for GDP per capita in 2015; (d) spatial and temporal distribution of regression coefficients for GDP per capita in 2020.
Figure 6
Figure 6
Spatial and temporal distribution of regression coefficients of GDP per capita in the MGWR model, 2005−2020: (a) spatial and temporal distribution of regression coefficients for GDP per capita in 2005; (b) spatial and temporal distribution of regression coefficients for GDP per capita in 2010; (c) spatial and temporal distribution of regression coefficients for GDP per capita in 2015; (d) spatial and temporal distribution of regression coefficients for GDP per capita in 2020.
Figure 7
Figure 7
Spatial and temporal distribution of regression coefficients of the proportion of the secondary industry in the MGWR model, 2005−2020: (a) spatial and temporal distribution of regression coefficients for the proportion of the secondary industry in 2005; (b) spatial and temporal distribution of regression coefficients for the proportion of the secondary industry in 2010; (c) spatial and temporal distribution of regression coefficients for the proportion of the secondary industry in 2015; (d) spatial and temporal distribution of regression coefficients for the proportion of the secondary industry in 2020.
Figure 8
Figure 8
Spatial and temporal distribution of regression coefficients of the number of industrial enterprises above the scale in the MGWR model, 2005−2020: (a) spatial and temporal distribution of regression coefficients for the number of industrial enterprises above the scale in 2005; (b) spatial and temporal distribution of regression coefficients for the number of industrial enterprises above the scale in 2010; (c) spatial and temporal distribution of regression coefficients for the number of industrial enterprises above the scale in 2015; (d) spatial and temporal distribution of regression coefficients for the number of industrial enterprises above the scale in 2020.
Figure 9
Figure 9
Spatial and temporal distribution of regression coefficients of the general public budget revenue as a proportion of GDP in the MGWR model, 2005−2020: (a) spatial and temporal distribution of regression coefficients for the general public budget revenue as a proportion of GDP in 2005; (b) spatial and temporal distribution of regression coefficients for the general public budget revenue as a proportion of GDP in 2010; (c) spatial and temporal distribution of regression coefficients for the general public budget revenue as a proportion of GDP in 2015; (d) spatial and temporal distribution of regression coefficients for the general public budget revenue as a proportion of GDP in 2020.
Figure 10
Figure 10
Spatial and temporal distribution of regression coefficients of population density in the MGWR model, 2005−2020: (a) spatial and temporal distribution of regression coefficients for population density in 2005; (b) spatial and temporal distribution of regression coefficients for population density in 2010; (c) spatial and temporal distribution of regression coefficients for population density in 2015; (d) spatial and temporal distribution of regression coefficients for population density in 2020.
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