## 1 Introduction
Internationally, PM\({}_{2.5}\) concentration prediction has become a critical area of research due to its significant impact on public health and environmental quality. In Europe, studies have utilized high-resolution chemical transport models (CTMs) combined with machine learning techniques to improve PM\({}_{2.5}\) predictions [49]. These hybrid models leverage the strengths of both approaches, using CTMs to simulate atmospheric chemistry and machine learning algorithms to capture complex spatial and temporal patterns in PM\({}_{2.5}\) data. For example, a study in Germany demonstrated that integrating machine learning with CTMs improved prediction accuracy by 20% compared to traditional methods alone [50]. In North America, researchers have focused on developing advanced machine learning models that incorporate satellite data and ground-based measurements to enhance spatial coverage and prediction accuracy [51]. For instance, a study in the United States used a combination of Random Forest and Gradient Boosting algorithms to predict PM\({}_{2.5}\) concentrations across different regions, highlighting the importance of meteorological factors and land use in model performance [52]. These models have shown significant potential in capturing regional variations in PM\({}_{2.5}\) concentrations, which is crucial for effective air quality management. In Asia, particularly in countries like India and South Korea, research has focused on developing localized prediction models tailored to specific urban and industrial environments. A study in South Korea used a novel spectral clustering algorithm combined with machine learning techniques to analyze PM\({}_{2.5}\) data and identify key pollution sources [53]. In India, researchers have developed ensemble machine learning models to predict PM\({}_{2.5}\) concentrations in highly polluted urban areas, such as Bengaluru and Delhi [54]. These studies emphasize the need for region-specific approaches due to variations in pollution sources and meteorological conditions. In recent years, numerous innovative approaches have been developed to improve PM\({}_{2.5}\) concentration prediction. For example, the integration of Convolutional LSTM (ConvLSTM) and Graph Convolutional Network (GCN) architectures has been proposed to leverage both spatial and temporal features in multi-source data [55]. Another study introduced a hybrid model combining LSTM with a Deep Auto-Encoder (DAE) to enhance the model's ability to capture complex patterns in PM\({}_{2.5}\) data [56]. Additionally, the application of ensemble learning techniques, such as stacking, has shown promise in improving prediction accuracy by combining the strengths of multiple machine learning models [57]. These advancements highlight the ongoing efforts to develop more accurate and robust PM\({}_{2.5}\) prediction models, which are crucial for effective air quality management and public health protection. The integration of high-resolution data from satellites, ground-based sensors, and meteorological models, combined with advanced machine learning techniques, is expected to further enhance the accuracy and reliability of PM\({}_{2.5}\) predictions in diverse geographical regions. After achieving remarkable results in fields such as natural language processing, the Transformer model has also emerged in the field of environmental science, especially in PM\({}_{2.5}\) concentration prediction. Many scholars have conducted in-depth research on it. For example, [PERSON] et al. pointed out in their study that the unique architecture of the Transformer model enables it to effectively handle complex dependencies in time series data, which provides new ideas for PM\({}_{2.5}\) concentration prediction [58]. [PERSON] et al. used a large-scale air quality monitoring data set to train and verify the Transformer-based prediction model. The results showed that the model performed well in capturing the long-term trend and seasonal changes of PM\({}_{2.5}\) concentration [59].