Abstract: With the rapid development of China's economy, environmental issues, particularly air pollution, have become increasingly severe, posing significant threats to human health and ecosystems. This study employed statistical learning methods to analyze air pollution composition data from thirty major Chinese cities throughout 2019-2022. The study used algorithms like regression, classification, fitting, and filtering to find changes in multi-source heterogeneous data patterns and predict how air pollutant concentrations would change over time and space. At the same time,the results indicate that the primary factors influencing the Air Quality Index (AQI) are fine particulate matter (PM2.5) and inhalable particulate matter (PM10). Additionally, this research is significant for improving public health, advancing environmental protection, fostering economic growth, strengthening laws and regulations, ensuring social stability, and promoting scientific and cultural advancements. Through this research, scientific bases can be provided for formulating policies that reduce disease, enhance government credibility, drive innovation in atmospheric research, and preserve cultural heritage.
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