## 1 Introduction
These studies provide important references for in-depth understanding of pollutant characteristics, distribution patterns, and related research from different dimensions, and also provide ideas and methods for our research focusing on PM\({}_{2.5}\). Early studies on PM\({}_{2.5}\) concentration primarily focused on the development of monitoring technologies and traditional statistical models. Initially, researchers relied on basic air sampling techniques and gravimetric methods to measure PM\({}_{2.5}\) levels, which were time-consuming and less precise compared to modern methods [25; 26; 27]. With the advancement of technology, automated monitoring systems using beta attenuation and light scattering techniques were introduced, significantly improving the accuracy and efficiency of PM\({}_{2.5}\) monitoring [28]. In addition to ARIMA and SARIMA models, the multivariate linear regression (MLR) model was also widely used to predict PM\({}_{2.5}\) concentration by establishing a linear relationship between PM\({}_{2.5}\) concentration and multiple influencing factors such as meteorological factors and pollution source emissions [29]. However, due to the complexity of the air pollution process, these traditional models often had limitations in complex environments and struggled to effectively capture the nonlinear and multi-factor influencing characteristics of PM\({}_{2.5}\) concentration changes [30]. With the rapid development of science and technology, Machine Learning (ML) and Deep Learning (DL) have emerged as powerful tools for predicting PM\({}_{2.5}\) concentration. These techniques offer significant advantages over traditional statistical methods, especially in handling nonlinear relationships and large datasets [31]. For instance, Support Vector Machines (SVMs) have been widely used due to their ability to handle high-dimensional data and provide accurate predictions [32; 33]. Random Forests have also proven effective in capturing the complex interactions between various factors influencing PM\({}_{2.5}\) levels [34; 35]. More recently, deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been employed to leverage spatial and temporal patterns in PM\({}_{2.5}\) data [36]. For example, the Long Short-Term Memory (LSTM) network has shown superior performance in capturing long-term dependencies in time series data, making it particularly suitable for PM\({}_{2.5}\) concentration prediction [37]. Hybrid models combining LSTM with other techniques, such as wavelet transform and decomposition methods, have further enhanced prediction accuracy [38; 39]. Research on PM\({}_{2.5}\) concentration has been conducted across various geographical regions, including urban, rural, and industrial areas, to better understand its spatial and temporal variations. In urban areas, studies have shown that local meteorological conditions, traffic emissions, and industrial activities significantly influence PM\({}_{2.5}\) levels [40; 41]. For example, in the Beijing-Tianjin-Hebei region of China, PM\({}_{2.5}\) concentrations exhibit pronounced seasonal and diurnal patterns due to the combined effects of meteorology and anthropogenic emissions [42]. In contrast, rural areas are more influenced by agricultural activities and biomass burning, which can lead to significant PM\({}_{2.5}\) spikes during specific periods [43; 44]. Industrial areas, particularly those with heavy manufacturing and chemical plants, often experience higher PM\({}_{2.5}\) concentrations due to direct emissions and secondary pollution formation [45; 46]. Recent studies have also highlighted the importance of considering regional differences in PM\({}_{2.5}\) prediction models. For instance, a study in the United States found that machine learning models trained on data from one region may not perform well in another due to variations in pollution sources and meteorological conditions [47; 48].