## 5 Discussion
### Comparative Analysis and Innovation of Research Findings  
Compared with other related studies, our study is unique in several ways, which have different impacts on the results:  
Model selection: Our research shows unique innovation in model selection. Unlike most studies that focus only on a single type of model, our research innovatively compares statistical models (SARIMA), models that handle seasonal and holiday effects (Prophet), and Machine Learning models (LightGBM). When traditional studies simply use Machine Learning models, although they can mine complex relationships, they often ignore the precise analysis of data seasonality and trends. Our research breaks this limitation and systematically compares different types of models to comprehensively evaluate their performance in PM\({}_{2.5}\) concentration prediction. This not only allows us to clearly see the advantages of each model, such as SARIMA's ability to capture time series trends, Prophet's advantages in handling seasonality and special events, and LightGBM's expertise in mining complex nonlinear relationships. it also clarifies the disadvantages of each model, providing a strong basis for subsequent studies to select the most appropriate model according to different needs. This multi-model comparison method has opened up new ideas for PM\({}_{2.5}\) concentration prediction research. Data application: Our research is also innovative in data application. We selected the China High-Resolution and High-Quality PM\({}_{2.5}\) Dataset (CHAP), which has a 1 km resolution raster data format and combines two time resolutions, year and month, to bring a new perspective to the research. The innovative use of dual-time resolution data, year-resolution data can present the long-term trend of PM\({}_{2.5}\) concentration from a macro perspective, and help analyze the impact of long-term factors such as urban development and industrial structure adjustment on concentration. Monthly resolution data focuses on short-term changes and accurately captures concentration fluctuations caused by seasonal factors, such as the difference in the impact of changes in meteorological conditions in different seasons on concentration. This innovative data application method provides a comprehensive and detailed time dimension perspective for the research, which is conducive to in-depth analysis of the changing characteristics and mechanisms of PM\({}_{2.5}\) concentration at different time scales. Compared with the use of data with a single time resolution, it can more comprehensively reveal the law of PM\({}_{2.5}\) concentration changes. Research area and time scale: Our study selected Nanning urban area as the research area, which is significantly innovative and targeted. Nanning urban area has unique geographical, climatic and pollution source characteristics. It belongs to the subtropical monsoon climate, with distinct dry and wet seasons and diversified pollution sources, providing rich and special samples for research. Compared with other regional studies, the environmental differences in different regions are large. For example, the winter coal-fired heating in northern cities has a significant impact on PM\({}_{2.5}\) concentration, which is very different from the pollution source composition in Nanning urban area, which makes our research more unique. Our research was carried out in Nanning urban area, which not only provides an in-depth understanding of the variation law of PM\({}_{2.5}\) concentration in the region, but also provides a practical case for the application of the model in special environments. In terms of time scale, data from 2012 to 2023 were selected. This period covers changes in many aspects such as urban development and implementation of environmental protection policies.