## 3 Results and Discussions  
### Temporal Variation Characteristics of Atmospheric Particulate Matter Concentration  
This study selected hourly concentration data of PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), O\({}_{3}\), and CO in Chenzhou City from 1 March 2022 to 28 February 2023, which were obtained from the China National Environmental Monitoring Center. In order to fully reflect the spatiotemporal distribution characteristics of PM\({}_{2.5}\) in the ambient air of Chenzhou City, we adopted the Kriging interpolation analysis method. Kriging interpolation, as a spatial local interpolation technique, can make unbiased optimization estimates of pollutant concentrations in the entire study area based on limited monitoring station data by considering spatial correlation. By using the daily concentration data of PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), O\({}_{3}\), and CO, we calculated their annual and seasonal means, respectively, and used the Kriging interpolation method to draw spatial distribution maps, thereby analyzing the spatial distribution characteristics of pollutants mainly based on PM\({}_{2.5}\) and O\({}_{3}\) 8h. The time series and seasonal variation characteristics of PM\({}_{2.5}\) concentrations shown in Figures 2 and 3 are the average results based on the data of multiple monitoring stations, rather than the observation data of a single station. This comprehensive analysis method can more accurately simulate the PM\({}_{2.5}\) concentration distribution in the entire Chenzhou city, providing more comprehensive data support for the study of air pollution prevention and control. In 2022, Chenzhou City recorded an annual average atmospheric PM\({}_{2.5}\) concentration of 50.22 \(\upmu\)g\(\cdot\)m\({}^{-3}\) and an annual average PM\({}_{10}\) concentration of 85.34 \(\upmu\)g\(\cdot\)m\({}^{-3}\). These values surpass the respective annual average concentrations of PM\({}_{2.5}\) and PM\({}_{10}\) in Hunan Province by factors of 1.32 and 1.22. Moreover, they exceed the national standard secondary concentration limits (35 \(\upmu\)g\(\cdot\)m\({}^{-3}\) for PM\({}_{2.5}\) and 70 \(\upmu\)g\(\cdot\)m\({}^{-3}\) for PM\({}_{10}\)) stipulated in the \"Ambient Air Quality Standards\" (GB 3095-2012) by 43.49% and 21.91%, respectively. Notably, compared to PM\({}_{10}\), the pollution from PM\({}_{2.5}\) in Chenzhou City appears to be more severe. Figure 3: Seasonal fluctuations in PM\({}_{2.5}\) and PM\({}_{10}\) concentrations in Chenzhou City (Note: In a box plot, (1) the colored balls represent outliers in the data set, (2) the triangles are used to represent the mean, because the mean is an important reference point in the box plot, indicating the average trend of the data set. (3) The black line is used to represent the distribution range of the data, especially to connect the upper and lower bounds of the box, i.e., the third quartile (Q3).). Figure 2: The diurnal variations in concentrations of PM\({}_{2.5}\), PM\({}_{10}\), and the ratio of PM\({}_{2.5}\) to PM\({}_{10}\) in Chenzhou City spanning from March 2022 to February 2023. (Note: The dashed lines in (**a**,**b**) delineate the standard daily average secondary concentration thresholds, while the dashed lines in (**c**) serve as dividing markers at 0.45 and 0.55, respectively). Upon examining individual months (refer to Figure 2), November exhibited the peak PM\({}_{2.5}\) concentration at 80.23 \(\mathrm{\SIUnitSymbolMicro g}\).m\({}^{-3}\), while July recorded the nadir at 23.61 \(\mathrm{\SIUnitSymbolMicro g}\).m\({}^{-3}\). For PM\({}_{10}\), the zenith concentration of 131.94 \(\mathrm{\SIUnitSymbolMicro g}\).m\({}^{-3}\) was observed in January, whereas the trough, 47.55 \(\mathrm{\SIUnitSymbolMicro g}\).m\({}^{-3}\), occurred in July. Remarkably, the pinnacle of PM\({}_{2.5}\) concentration for the year was documented on 9 December, soaring to 220 \(\mathrm{\SIUnitSymbolMicro g}\).m\({}^{-3}\).