## 5 Discussion
### Analysis of Influencing Factors on PM\({}_{2.5}\) Prediction  
Meteorological conditions: Meteorological factors have a significant impact on PM\({}_{2.5}\) concentration. Changes in temperature will change the stability of the atmosphere, thereby affecting the diffusion capacity of pollutants. Humidity can not only affect the hygroscopic growth of particulate matter, but also participate in some chemical reactions. Wind speed and direction determine the direction and speed of pollutant transmission. Precipitation is like nature's \"cleaner\", which can effectively wash PM\({}_{2.5}\) in the air. For example, in winter, the temperature is low, the atmosphere is stable, and the diffusion conditions of pollutants are poor. PM\({}_{2.5}\) is easy to accumulate and the concentration increases. While in summer, there is abundant precipitation, which can significantly reduce the concentration of PM\({}_{2.5}\). Although our research uses monthly raster data to capture certain seasonal characteristics, it does not track changes in meteorological elements in real time. In months with changeable weather, such as the intersection of cold and warm air, the meteorological conditions are complex. Due to the lack of real-time meteorological data, the model is difficult to accurately simulate the diffusion, transmission and transformation process of pollutants, resulting in increased prediction errors. Anthropogenic emission sources: Anthropogenic emissions are an important source of PM\({}_{2.5}\), covering industrial activities, traffic exhaust, coal-fired heating and many other aspects. Taking the Nanning urban area as an example, with the rapid development of urbanization, the scale of industry continues to expand, the number of motor vehicles has increased significantly, and the emission sources have become increasingly diversified. However, the models in our study failed to fully consider the dynamic changes in emission sources. Factories will adjust production according to orders and production plans, and traffic flow will fluctuate with time, weather and special events. These changes will directly lead to changes in PM\({}_{2.5}\) emissions. However, the model does not take these real-time changes into account, and cannot accurately capture the changes in PM\({}_{2.5}\) concentration caused by fluctuations in anthropogenic emissions, reducing the accuracy of the prediction. Topographic and geomorphic features: The topography and geomorphic features of the Nanning urban area have a significant impact on PM\({}_{2.5}\) concentration. The basin-shaped area with the Yongjiang River Valley as the center and the mountains on three sides hinder the diffusion of atmospheric pollutants, making PM\({}_{2.5}\) easily accumulate in the urban area. Unfortunately, the model of our study was not optimized in combination with local topographic and geomorphic information during the construction process. Since the model cannot accurately reflect the hindering effect of the terrain on the diffusion of pollutants, deviations occur when simulating the transmission and distribution of pollutants, which in turn affects the prediction accuracy of PM\({}_{2.5}\) concentration. Data uncertainty: Data quality is crucial to the accuracy of model predictions, but there are some problems with the data in our study. Although monthly raster data are used to reflect the concentration changes in time and space to a certain extent, there are still limitations. On the one hand, the data has a limited time span and it is difficult to cover various scenarios of environmental changes, which limits the model's ability to learn the laws of long-term changes. On the other hand, there is a lag in data updates, and it is impossible to timely reflect the latest changes such as new pollution sources and traffic control. In addition, the granularity of monthly raster data is coarse, and it is difficult to accurately present the details of concentration changes in local areas in the short term.