Journal article
Evaluation of a data fusion approach to estimate daily PM 2.5 levels in North China
Environmental research, Vol.158, p.54
10/2017
DOI: 10.1016/j.envres.2017.06.001
PMCID: PMC5612782
PMID: 28599195
Abstract
PM
air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM
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
in grid cells with a resolution of 10km 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
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
of 0.95 and 0.94, respectively and PM
was overestimated by WRF-Chem (R
=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
. Current monitoring network in North China was dense enough to provide a reliable PM
prediction by interpolation technique.
Details
- Title: Subtitle
- Evaluation of a data fusion approach to estimate daily PM 2.5 levels in North China
- Creators
- Fengchao Liang - Emory UniversityMeng Gao - University of IowaQingyang Xiao - Emory UniversityGregory R Carmichael - University of IowaXiaochuan Pan - Peking UniversityYang Liu - Emory University
- Resource Type
- Journal article
- Publication Details
- Environmental research, Vol.158, p.54
- DOI
- 10.1016/j.envres.2017.06.001
- PMID
- 28599195
- PMCID
- PMC5612782
- NLM abbreviation
- Environ Res
- eISSN
- 1096-0953
- Grant note
- NNX14AG01G / NASA
- Language
- English
- Date published
- 10/2017
- Academic Unit
- Civil and Environmental Engineering; Nursing; Chemical and Biochemical Engineering
- Record Identifier
- 9984185368102771
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