## 1 Introduction
In addition, [PERSON] et al., based on the Informer architecture and the residual transformer, developed a PM\({}_{2}\) artificial time-series prediction model named ResInformer to predict the PM\({}_{2.5}\) concentration in three major cities in China (Beijing, Shijiazhuang, and Wuhan) [60]. At the same time, there are also many explorations in the fusion of Deep Learning models. [PERSON] et al. proposed a deep learning-based method, AC-LSTM, which includes one-dimensional convolutional neural network (CNN), long and short-term memory (LSTM) network, and attention-based network for urban PM\({}_{2.5}\) concentration prediction [61]. [PERSON] et al. had developed a hybrid attention converter (HAT) to accurately obtain daily PM\({}_{2.5}\) Seoul predictions. The performance of HAT was evaluated by comparing its predictions with ground observations and the 3 D chemical transport model (3-DCTM). The experimental results show that this fusion model can effectively reduce the model training time while improving the prediction accuracy [62]. Through a comprehensive analysis of the above literature, we can find that models such as neural networks and decision trees in machine learning models have made great progress in dealing with nonlinear problems, but the parameter selection and training process of the model are often complex and require a lot of computing resources and data. For example, the LSTM model may encounter the problem of gradient vanishing or gradient explosion when processing long sequence data, which requires special techniques to alleviate. Although the ensemble learning model can improve the prediction performance, how to select the appropriate base model and combination method still needs further research. At the same time, Deep Learning models such as Transformer have shown strong feature learning capabilities, but when applied to PM\({}_{2.5}\) concentration prediction, they may face problems such as overfitting and computational efficiency. Moreover, current research pays less attention to the interpretability of the model, and it is difficult to understand the prediction results of the model in a physical sense. In addition, most studies have certain limitations in data selection and processing. Some studies only consider a few influencing factors and do not fully explore the multi-source driving factors of PM\({}_{2.5}\) concentration changes. At the same time, the quality and spatiotemporal resolution of the data will also affect the prediction accuracy of the model, but research in this regard is not deep enough. In view of the above situation, our research aims to address the shortcomings of existing research. Taking Nanning urban area in China as an example, we conducted a prediction study on the PM\({}_{2.5}\) concentration in the area based on the Machine Learning method. We selected the SARIMA, Prophet, and LightGBM models in our research. Among them, SARIMA, as a classic time series model, is good at capturing the seasonality and trend of data [63]. The Prophet model is designed for processing data with seasonal and holiday effects and has strong flexibility and interpretability [64]. LightGBM is an efficient gradient boosting framework with significant advantages in processing large-scale data and complex nonlinear relationships [65]. We selected the annual and monthly PM\({}_{2.5}\) raster data from 2012 to 2023 nationwide in ChinaHighAirPollutants (CHAP), cropped it with the Nanning urban area mask, used the 2012-2022 data as the training set and the 2023 data as the test set, and performed model training and prediction according to the standard process. By predicting and saving the 2023 results raster by raster, we provided data support for subsequent analysis, compared the performance of different models, explored the prediction method suitable for the region, and provided a scientific basis for air quality management. It is worth mentioning that our research has several notable innovations that distinguish it from existing studies.