Evaluation of a data fusion approach to estimate daily PM{sub 2.5} levels in North China

Journal Article · Sun Oct 15 04:00:00 EDT 2017 · Environmental Research

DOI:https://doi.org/10.1016/J.ENVRES.2017.06.001· OSTI ID:22708025

Liang, Fengchao ^[1]; Gao, Meng ^[2]; Xiao, Qingyang ^[3]; Pan, Xiaochuan ^[1]; Liu, Yang ^[3]

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1. Department of Occupational and Environmental Health, School of Public Health, Peking University, Beijing 100191 (China)
2. Center for Global and Regional Environmental Research, the University of Iowa, Iowa City, IA 52242 (United States)
3. Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322 (United States)

PM{sub 2.5} air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM{sub 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{sub 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{sub 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{sup 2} of 0.95 and 0.94, respectively and PM{sub 2.5} was overestimated by WRF-Chem (R{sup 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{sub 2.5}. Current monitoring network in North China was dense enough to provide a reliable PM{sub 2.5} prediction by interpolation technique. - Highlights: • KED and data fusion model predicted daily PM{sub 2.5} with high accuracy. • WRF-Chem performed worse in PM{sub 2.5} prediction compared with KED and data fusion. • The PM{sub 2.5} monitoring network in North China was able to support reliable PM{sub 2.5} interpolation.

• OSTI ID: 22708025
• Journal Information: Environmental Research, Journal Name: Environmental Research Vol. 158; ISSN ENVRAL; ISSN 0013-9351
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
AIR POLLUTION MONITORING
CHINA
COMPARATIVE EVALUATIONS
FORECASTING
MONITORS
PERFORMANCE