Spatial Patterns and Determinants of PM2.5 Concentrations: A Land Use Regression Analysis in Shenyang Metropolitan Area, China

Abstract

Identifying impact factors and spatial variability of pollutants is essential for understanding environmental exposure and devising solutions. This research focused on PM2.5 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 PM2.5 concentrations. Among the predictors, surface pressure exhibited a positive correlation with PM2.5 concentrations throughout most of the year. Conversely, both elevation and tree cover had negative effects on PM2.5 levels. At a 2000 m scale, landscape aggregation decreased PM2.5 levels, while at a larger scale (5000 m), landscape splitting facilitated PM2.5 dispersion. According to the partial R2 results, vegetation-related land use types were significant, with the shrubland proportion positively correlated with local-scale PM2.5 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 PM2.5. Based on the research, actionable strategies for PM2.5 pollution control were outlined to promote sustainable development in the region.