Evaluation of a data fusion approach to estimate daily PM_2.5 levels in North China

PM_2.5 air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM_2.5 exposure spatiotemporally in China, but all these have limitations. This study was to develop a data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM_2.5 in grid cells with a resolution of 10 km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF-Chem). A data fusion technique was developed by fusing PM_2.5 concentration predicted by KED and WRF-Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF-Chem and observations. Model performances were evaluated by comparing them with ground observations and the spatial prediction errors. KED and data fusion performed better at monitoring sites with a daily model R² of 0.95 and 0.94, respectively and PM_2.5 was overestimated by WRF-Chem (R²=0.51). KED and data fusion performed better around the ground monitors, WRF-Chem performed relative worse with high prediction errors in the central of study domain. In our study, both KED and data fusion technique provided highly accurate PM_2.5. Current monitoring network in North China was dense enough to provide a reliable PM_2.5 prediction by interpolation technique. - Highlights: • KED and data fusion model predicted daily PM_2.5 with high accuracy. • WRF-Chem performed worse in PM_2.5 prediction compared with KED and data fusion. • The PM_2.5 monitoring network in North China was able to support reliable PM_2.5 interpolation.