## 3 Results and Discussions
### Spatial Distribution Characteristics of PM\({}_{2.5}\) in Chenzhou City  
Utilizing the PM\({}_{2.5}\) monitoring data collected from various districts and counties in Chenzhou City for the year 2022, we undertook a spatial interpolation analysis. This analysis employed the Kriging interpolation technique to delineate both the annual mean and seasonal averages of PM\({}_{2.5}\), as depicted in Figure 4.  
This approach provides superior interpolation accuracy, adeptly delineating the spatial distribution patterns of pollutants [62; 63]. In contrast to the inverse distance weighting (IDW) method, the Kriging approach considers spatial correlation, rendering it more reliable and precise in the processing of multi-data points [64]. Furthermore, integrating prior analytical findings and considering the analogous nature of pollutants, spatial distribution maps depicting the annual mean values of CO, NO\({}_{2}\), SO\({}_{2}\), and PM\({}_{2.5}\)/PM\({}_{10}\) were meticulously delineated (Figure 5). In light of the spatial distribution patterns of annual mean PM\({}_{2.5}\) concentrations in Chenzhou City, a distinctive pollution gradient emerges, characterized by a shift \"from the northern to the southern regions in the west and central zones, tapering to lower concentrations in the east.\" In particular, heightened concentrations of PM\({}_{2.5}\) pollutants were detected in Suvian District, Beihu District, Yongxing County, and the northeastern region of Guiyang. The pinnacle of pollution intensity was documented in the northwest of Zixing, with Zixing itself reaching a peak concentration of 52.03 \(\mu\)g\(\cdot\)m\({}^{-3}\). In contrast, the eastern parts of Anren and Guidong boasted the most immaculate air quality, with pollution levels plummeting to a mere 17.41 \(\mu\)g\(\cdot\)m\({}^{-3}\). Using SPSS, we executed a Pearson correlation analysis to explore the relationships between PM\({}_{2.5}\) and PM\({}_{10}\), CO, NO\({}_{2}\), SO\({}_{2}\), and O\({}_{3}\)_8h across the five principal districts of Chenzhou City, as delineated in Table 1.  
Figure 4: Spatial distribution patterns of annual mean PM\({}_{2.5}\) in Chenzhou City (note: the illustration was crafted utilizing ArcGIS software, specifically version 10.2. For further reference, the software can be accessed at the following URL: [[https://www.arcgis.com/index.html](https://www.arcgis.com/index.html)]([https://www.arcgis.com/index.html](https://www.arcgis.com/index.html)) (accessed on 16 October 2023)). Evidently, PM\({}_{2.5}\) levels in Chenzhou City manifest a positive correlation with PM\({}_{10}\), CO, NO\({}_{2}\), and SO\({}_{2}\). Notably, PM\({}_{10}\) and NO\({}_{2}\) exhibit a pronounced association with PM\({}_{2.5}\) \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**District**} & \multicolumn{3}{c}{**Correlation Coefficient Between PM\({}_{2.5}\) and Various Pollutants**} \\ \cline{2-6}  & **PM\({}_{10}\)** & **CO** & **NO\({}_{2}\)** & **SO\({}_{2}\)** & **O\({}_{3\_8}\)**h** \\ \hline
**Northern region** & 0.949 **\({}^{**}\)** & 0.748 **\({}^{**}\)** & 0.877 **\({}^{**}\)** & 0.472 **\({}^{**}\)** & \(-\)0.822 **\({}^{**}\)** \\
**Southern region** & 0.897 **\({}^{**}\)** & 0.758 **\({}^{**}\)** & 0.824 **\({}^{**}\)** & 0.290 **\({}^{**}\)** & \(-\)0.782 **\({}^{**}\)** \\
**Central region** & 0.931 **\({}^{**}\)** & 0.406 **\({}^{**}\)** & 0.792 **\({}^{**}\)** & 0.232 **\({}^{**}\)** & \(-\)0.810 **\({}^{**}\)** \\
**Western region** & 0.881 **\({}^{**}\)** & 0.507 **\({}^{**}\)** & 0.776 **\({}^{**}\)** & 0.411 **\({}^{**}\)** & \(-\)0.814 **\({}^{**}\)** \\
**East region** & 0.954 **\({}^{**}\)** & 0.900 **\({}^{**}\)** & 0.860 **\({}^{**}\)** & 0.309 **\({}^{**}\)** & \(-\)0.815 **\({}^{**}\)** \\ \hline \hline \end{tabular} Note: \({}^{*}\) means \(p<0.05\); \({}^{**}\) means \(p<0.01\).  
\end{table} 
Table 1: The Pearson correlation coefficients between PM\({}_{2.5}\) and PM\({}_{10}\), CO, NO\({}_{2}\), SO\({}_{2}\), and O\({}_{3\_8}\)h in Chenzhou City were meticulously calculated and analyzed.