Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China

• Type of Material: Article
• Authors: Fengchao Liang, Emory University; Meng Gao, University of Iowa; Qingyang Xiao, Emory University; Gregory R. Carmichael, University of Iowa; Xiaochuan Pan, Peking University; Yang Liu, Emory University
• Language: English
• Date: 2017-10-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 Elsevier Inc. All rights reserved.
• Final Published Version (URL): http://dx.doi.org/10.1016/j.envres.2017.06.001
• Title of Journal or Parent Work: Environmental Research
• ISSN: 0013-9351
• Volume: 158
• Start Page: 54
• End Page: 60
• Grant/Funding Information: The work of F. Liang at Emory University as a visiting student was supported by the China Scholarship Council (CSC).; The work of Y. Liu was partially supported by the NASA Applied Sciences Program (grant NNX14AG01G) and Jet Propulsion Laboratory (contract # 1558091).; The work of M. Gao and G. R. Carmichael were supported by the NASA Applied Sciences Program (grant NNX11AI52G) and EPA STAR program (RD-83503701).; The work of X. Pan was supported by the Natural Sciences Foundations of China (No. 81273033 and No. 81372950).
• Keywords: Science & Technology; Environmental Sciences & Ecology; EXPOSURE; Public, Environmental & Occupational Health; EXTERNAL DRIFT; Data fusion; Life Sciences & Biomedicine; WRF MODEL; WRF-Chem; SIMULATION; HAZE EVENT; MODELING SYSTEM; AIR-POLLUTION; SENSITIVITY; Spatiotemporal model; Environmental Sciences; KED; PM2.5; FINE PARTICULATE MATTER; VARIABILITY
• Research Categories: Health Sciences, Public Health; Health Sciences, Occupational Health and Safety; Environmental Sciences

Abstract

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 R 2 of 0.95 and 0.94, respectively and PM 2.5 was overestimated by WRF-Chem (R 2 =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.