Spatiotemporal modeling of PM_2.5 concentrations at the national scale combining land use regression and Bayesian...

Concentrations of particulate matter with aerodynamic diameter <2.5 μm (PM_2.5) are relatively high in China. Estimation of PM_2.5 exposure is complex because PM_2.5 exhibits complex spatiotemporal patterns. To improve the validity of exposure predictions, several methods have been developed and applied worldwide. A hybrid approach combining a land use regression (LUR) model and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals were developed to estimate the PM_2.5 concentrations on a national scale in China. This hybrid model could potentially provide more valid predictions than a commonly-used LUR model. The LUR/BME model had good performance characteristics, with R² = 0.82 and root mean square error (RMSE) of 4.6 μg/m³. Prediction errors of the LUR/BME model were reduced by incorporating soft data accounting for data uncertainty, with the R² increasing by 6%. The performance of LUR/BME is better than OK/BME. The LUR/BME model is the most accurate fine spatial scale PM_2.5 model developed to date for China.