## 1 Introduction  
Against the backdrop of the continued advancement of global industrialization and urbanization, air pollution has become increasingly serious and has become one of the key factors threatening human health and the ecological environment. Pollutants in the atmosphere are rich and diverse, including sulfur dioxide (SO\({}_{2}\)), nitrogen oxides (NO\({}_{\chi}\)), particulate matter (PM), etc. Among them, PM\({}_{2.5}\), as the main air pollutant, can penetrate into the human respiratory system due to its tiny particle size (\(\leq\)2.5 \(\upmu\)m), causing serious harm to human health, such as causing respiratory diseases, cardiovascular diseases, and even increasing the risk of death [1; 2; 3]. In high-density urban areas, traffic pollutant emissions have a significant impact on air quality and residents' health [4]. Many studies have shown that PM\({}_{2.5}\) is closely related to a variety of health problems [5; 6]. In terms of respiratory system effects, some studies have pointed out that it can cause damage to the human respiratory system [7; 8], Additionally, epidemiological studies associate PM\({}_{2.5}\) with allergic rhinitis [9]. Furthermore, environmental exposure to PM\({}_{2.5}\) elevates influenza-like illness risks through airborne transmission mechanisms [10; 11]. Heavy metals in PM\({}_{2.5}\) also have an impact on lung health [12; 13; 14]. In addition, there are also studies summarizing the impact of ambient PM\({}_{2.5}\) on human health in China, especially the updated summary of the adverse health effects of PM\({}_{2.5}\) exposure [15; 16]. In view of this, accurate prediction of PM\({}_{2.5}\) concentration is of vital importance for formulating pollution prevention and control strategies in advance, protecting public health, and maintaining ecological balance [17]. Meteorological properties and satellite datasets can play an important role in PM\({}_{2.5}\) inversion, especially in model building and training. Many studies have shown that meteorological conditions have a significant impact on the changes in atmospheric pollutant concentrations, not only in the inversion of PM\({}_{2.5}\), but also in the inversion of other pollutants (such as NO\({}_{2}\), O\({}_{3}\), and nitrogen oxides). For example, [PERSON] et al. evaluated the impact of meteorological and emission changes on O\({}_{3}\) concentrations in different regions of China using machine learning methods [18]. In addition, [PERSON] et al. reversed the concentration of tropospheric NO\({}_{2}\) column in southern China by improving the aerosol quality factor (AMF) [19]. [PERSON] et al. studied the influence of meteorological conditions on the effectiveness of traffic measures in the control of NOx concentration [20]. These studies provide an important reference for the inversion of atmospheric pollutant concentrations using meteorological properties and satellite data. At the level of meteorological attribute application, many studies have pointed out that meteorological factors have a significant impact on the changes in atmospheric pollutant concentrations [21]. In terms of satellite data application and its combination with ground monitoring, relevant explorations have also emerged. Some studies have developed methods that combine satellite remote sensing technology and low-cost sensor networks to estimate the long-term PM\({}_{2.5}\) concentration in specific areas [22; 23]. In the study of China, some studies have proposed using three-dimensional variational data fusion methods to improve the modeling of spatiotemporal changes in fine particulate matter (PM\({}_{2.5}\)). By fusing multi-source data, including satellite remote sensing data and ground monitoring data, the spatiotemporal distribution characteristics of PM\({}_{2.5}\) can be more accurately portrayed, and the simulation and prediction capabilities of PM\({}_{2.5}\) concentrations have been improved [24].