Evaluation of a Data Fusion Approach to Estimate Daily PM2.5 Levels in North China

Abstract

Background/Aim: PM2.5 air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM2.5 exposure spatiotemporally in China, but all these have limitations. This study was to develop a new spatiotemporal model with data fusion approach based on Kriging and Chemistry Module models. Methods: Two techniques were applied to create daily spatial cover of PM2.5 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 PM2.5 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 performance was evaluated by comparing them with ground observations and the spatial prediction errors. Results: KED and data fusion performed better with a daily model R2 of 0.95 and 0.94, respectively and the PM2.5 was overestimated by WRF-Chem (R2=0.51). The annual mean PM2.5 concentration of the 365 ground monitors was 90.9 μg/m3, similar with the PM2.5 fields modeled by KED and data fusion at monitoring sites, but lower than that simulated by WRF-Chem. KED and the data fusion model performed better around the ground monitors (15-35 μg/m3), WRF-Chem performed relative worse with high prediction errors in northern Henan, southeastern Hebei and southern Shanxi (65-75 μg/m3). Conclusions: Both KED and data fusion technique provided highly accurate PM2.5. Current monitoring network in North China was dense enough to provide a reliable PM2.5 prediction by interpolation technique. Data fusion is an effective approach to improve the accuracy of WRF-Chem simulation spatiotemporally.