## 5 Discussion
### Model Application and Practical Significance  
In the PM\({}_{2.5}\) concentration prediction work, different models are suitable for different scenarios, and there are complementary characteristics between each other. In terms of short-term prediction, although the LightGBM model has errors, it performs well in quickly locating the scope of high-concentration areas and can provide preliminary pollution area distribution information for emergency response. The Prophet model has high prediction stability in some months and is good at dealing with seasonality and special events. When special activities are clearly arranged, it can effectively predict short-term concentration changes. Combining the two, LightGBM first determines the scope of high-concentration areas, and Prophet then accurately predicts the concentration fluctuations in these areas during special periods, which can improve the accuracy and completeness of short-term predictions. In long-term trend analysis, the SARIMA model can effectively identify the long-term change law of PM\({}_{2.5}\) concentration by virtue of its ability to accurately capture time series trends. However, it is insufficient in dealing with complex nonlinear relationships and special events. At this time, combined with LightGBM's ability to mine complex relationships and Prophet's ability to deal with fluctuations in special events, long-term trend analysis can be improved to provide a more reliable basis for long-term monitoring and policy making. In the scenario of regional pollution warning, a single model is difficult to meet the needs. By comprehensively utilizing the spatial capture capability of LightGBM, the special event processing capability of Prophet, and the trend analysis capability of SARIMA, a composite early warning system can be constructed, which can significantly improve the accuracy of early warnings and provide strong support for timely prevention and control of pollution.  
In practical applications, appropriate models should be flexibly selected or multiple model results should be integrated according to different scenarios. In daily monitoring work, the SARIMA model is used as the basis for analyzing long-term trends, the Prophet model is used to focus on concentration changes in special periods, and the LightGBM model is used to supplement spatial distribution information to achieve comprehensive and continuous monitoring of air quality. At the same time, model fusion techniques such as weighted averaging and stacking generalization are used to combine the advantages of each model to further improve the reliability and accuracy of the prediction.  
Our research results provide a solid scientific basis for the formulation of environmental protection policies. Based on the model predictions, differentiated prevention and control strategies can be formulated for different regions and seasons [80]. For example, in industrial areas, supervision can be strengthened, emission standards can be strictly enforced, and clean production technologies can be promoted. in traffic-intensive areas, traffic management can be optimized, public transportation can be encouraged, and new energy vehicles can be promoted. By accurately predicting changes in air quality during special periods, traffic control, enterprise production restrictions and other measures can be formulated in advance to effectively reduce pollution emissions. In addition, accurate prediction results can help the public understand the air quality status in a timely manner so that they can take corresponding protective measures, such as wearing masks and reducing outdoor activities, to effectively protect public health.