## 4 Experimental Results and Analysis  
### Comparison of Original Data and Model Prediction Results  
Our study uses Figure 5 to analyze the spatial distribution of PM2.5 concentration in Nanning urban area in 2023 and the prediction performance of LightGBM, Prophet and SARIMA models. Figure 6 selects some months (January, April, July, and September) to display the prediction results, presenting the spatial prediction distribution results at different time resolutions from year to month. Figure 5: Comparison of Observed and Predicted PM2.5 for 2023. From the annual distribution of PM\({}_{2.5}\) in 2023 shown in Figure 5, it can be deduced that the city center has dense industrial activities, heavy traffic flow, and large exhaust emissions. In parts of Jiangnan District and Xixiangtang District, PM\({}_{2.5}\) concentrations are high due to the combined effects of industrial activities and traffic emissions, while in parts of Qingxiu District and Yongning District, due to the high green coverage rate, vegetation has a significant adsorption and filtration effect on particulate matter. At the same time, there are relatively few industrial activities in this area and relatively few pollution sources, so PM\({}_{2.5}\) concentrations are low. From the monthly distribution in Figure 6, the concentration distribution in different months is significantly affected by factors such as meteorological conditions. In January, high-concentration areas are concentrated in urban centers and industrial areas. This is because winter heating activities increase energy consumption, which in turn leads to  
Figure 6: Comparison Chart of Observed versus Predicted PM\({}_{2.5}\) for Selected Months in 2023.  
increased pollutant emissions. At the same time, the atmospheric diffusion conditions in winter are poor. In April, the overall concentration decreased and the scope of the high-concentration area decreased. This is mainly attributed to the strong wind in spring, which is conducive to the diffusion of pollutants. In July, the concentration further decreased, and the high-concentration areas were mainly concentrated in busy traffic sections and local industrial areas. This is because summer precipitation has a scouring effect on particulate matter. In September, the concentration distribution is similar to that in July, but it can be clearly observed that the concentration in some areas has rebounded. This may be because the meteorological conditions have changed after the summer, resulting in worse pollutant diffusion conditions. From the annual prediction results, although the SARIMA model can present a general concentration distribution trend, its spatial distribution prediction accuracy is insufficient in some streets in Xiaxiangtang District and some industrial parks in Jiangnan District. This shows that the model does not capture the spatial variation characteristics of these areas, such as the local airflow changes caused by building shading that affect pollutant diffusion. The Prophet model is not accurate enough in predicting spatial location distribution. In areas with large concentration gradient changes such as the border between Xingning District and Qingxiu District, the predicted values in some areas are low, which shows that the model has limitations in dealing with concentration gradient changes. Relatively speaking, the LightGBM model has a better ability to capture spatial distribution, but within the high-concentration industrial area of Jiangnan District, its prediction of details such as emission differences between different factories is not accurate enough, resulting in relatively high predicted values. In the comparison of monthly prediction results, the LightGBM model has a good range prediction effect for high-concentration areas such as April and September, but there is still a large deviation in the prediction of concentration values.