Although fine particulate matter with a diameter of <2.5 μm (PM2.5) has a greater negative impact on human health than particulate matter with a diameter of <10 μm (PM10), measurements of PM2.5 have only recently been performed, and the spatial coverage of these measurements is limited. Comprehensively assessing PM2.5 pollution levels and the cumulative health effects is difficult because PM2.5 monitoring data for prior time periods and certain regions are not available. In this paper, we propose a promising approach for robustly predicting PM2.5 concentrations. In our approach, a generalized additive model is first used to quantify the non-linear associations between predictors and PM2.5, the bagging method is used to sample the dataset and train different models to reduce the bias in prediction, and the variogram for the daily residuals of the ensemble predictions is then simulated to improve our predictions. Shandong Province, China, is the study region, and data from 96 monitoring stations were included. To train and validate the models, we used PM2.5 measurement data from 2014 with other predictors, including PM10 data, meteorological parameters, remote sensing data, and land-use data. The validation results revealed that the R² value was improved and reached 0.89 when PM10 was used as a predictor and a kriging interpolation was performed for the residuals. However, when PM10 was not used as a predictor, our method still achieved a CV R² value of up to 0.86. The ensemble of spatial characteristics of relevant factors explained approximately 32% of the variance and improved the PM2.5 predictions. The spatiotemporal modeling approach to estimating PM2.5 concentrations presented in this paper has important implications for assessing PM2.5 exposure and its cumulative health effects.
Keywords: PM10 predictor; PM2.5; ensemble model; exposure estimation; kriging.