Spatial Patterns and Determinants of PM 2.5 Concentrations: A Land Use Regression Analysis in Shenyang Metropolitan Area, China

Identifying impact factors and spatial variability of pollutants is essential for understanding environmental exposure and devising solutions. This research focused on PM 2.5 as the target pollutant and developed land use regression models specific to the Shenyang metropolitan area in 2020. Utilizing the Least Absolute Shrinkage and Selection Operator approach, models were developed for all seasons and for the annual average, explaining 62–70% of the variability in PM 2.5 concentrations. Among the predictors, surface pressure exhibited a positive correlation with PM 2.5 concentrations throughout most of the year. Conversely, both elevation and tree cover had negative effects on PM 2.5 levels. At a 2000 m scale, landscape aggregation decreased PM 2.5 levels, while at a larger scale (5000 m), landscape splitting facilitated PM 2.5 dispersion. According to the partial R 2 results, vegetation-related land use types were significant, with the shrubland proportion positively correlated with local-scale PM 2.5 concentrations in spring. Bare vegetation areas were the primary positive factor in autumn, whereas the mitigating effect of tree cover contrasted with this trend, even in winter. The NDVI, an index used to assess vegetation growth, was not determined to be a primary influencing factor. The findings reaffirm the function of vegetation cover in reducing PM 2.5 . Based on the research, actionable strategies for PM 2.5 pollution control were outlined to promote sustainable development in the region.