Prediction of PM2.5 Concentrations in the Pearl River Delta by Integrating the PLUS and LUR Models

Since land use considerably affects the spatial variation of PM2.5 levels, it is crucial to predict PM2.5 concentrations under future land use changes. However, prior research has primarily concentrated on meteorological factors influencing PM2.5 predictions, while neglecting the effect of land use configurations. Consequently, in our study, a novel Patch-generating Land Use Simulation–Land Use Regression (PLUS-LUR) method was developed by integrating the PLUS model’s dynamic prediction capability with the LUR model’s spatial interpretation strength. The incorporation of landscape indices as key variables was essential for predicting PM2.5 concentrations. First, the random forest-optimized LUR method was trained with PM2.5 datasets from the Pearl River Delta (PRD) monitoring stations and multi-source spatial datasets. We assessed the modeling accuracy with and without considering landscape indices using the test dataset. Subsequently, the PLUS approach was applied to forecast land use as well as associated landscape indices in 2028. Based on these projections, grid-scale influencing factors were input into the previously constructed LUR model to forecast future PM2.5 distributions at a grid scale. The results reveal a spatial pattern with higher PM2.5 levels in central areas and lower levels in peripheral regions. Furthermore, the PM2.5 concentrations in the PRD are all below the Grade II threshold of the China Ambient Air Quality Benchmark in 2028. Notably, the predictions incorporating landscape indices demonstrate higher accuracy and reliability compared to those excluding them. These results provide methodological support for future PM2.5 assessment and land use management.