Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China.
Environ Res
; 158: 54-60, 2017 Oct.
Article
en En
| MEDLINE
| ID: mdl-28599195
ABSTRACT
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 data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM2.5 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 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 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 PM2.5 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 PM2.5. Current monitoring network in North China was dense enough to provide a reliable PM2.5 prediction by interpolation technique.
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1
Bases de datos:
MEDLINE
Asunto principal:
Monitoreo del Ambiente
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Contaminantes Atmosféricos
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Contaminación del Aire
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Material Particulado
Tipo de estudio:
Prognostic_studies
País/Región como asunto:
Asia
Idioma:
En
Revista:
Environ Res
Año:
2017
Tipo del documento:
Article