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• W2940897106 abstract "Abstract Studies of adverse human health effects due to exposures to particles with an aerodynamic diameter smaller than 2.5 μm (PM2.5) are often limited by sparse ground in situ measurements. Satellite remote sensing technique provides an effective tool for PM2.5 assessment in areas where surface PM2.5 monitoring network is not available. The MODIS aerosol optical depth (AOD) with spatial resolution of 10 km has been widely used in retrieving PM2.5 concentrations in a large scale, but it is insufficient for city-scale PM2.5 studies. In this study, we used the newly released AOD product with higher resolution of 3 km incorporating meteorological fields from Goddard Earth Observing System-Forward Processing (GEOS-FP) and road density for ground-level PM2.5 estimation in Fuzhou, i.e. a coastal city of China. A two-stage statistical model combing linear mixed effects model (LME) and support vector regression model (SVR) was proposed in this study, and a 10-fold cross validation approach was employed for model validation. We obtained an overall R2 of 0.81, root mean square error (RMSE) of 8.83 μg/m3 in model fitting, and R2 of 0.77, RMSE of 9.51 μg/m3 in model validation. The retrieved PM2.5 presented a spatial pattern with high concentrations in urban areas and low values in suburban or mountainous areas. We also found that the spatial distribution of PM2.5 concentrations showed a very strong correlation with the terrain features, forest cover, road density, and industrial pollution sources. The results revealed that the combined LME-SVR model using 3 km AOD along with GEOS-FP field and road density could achieve high accuracy in PM2.5 estimation, and would be helpful for air quality monitoring in Fuzhou." @default.
• W2940897106 created "2019-05-03" @default.
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• W2940897106 date "2019-08-01" @default.
• W2940897106 modified "2023-09-25" @default.
• W2940897106 title "Estimating ground-level PM2.5 over a coastal region of China using satellite AOD and a combined model" @default.
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• W2940897106 doi "https://doi.org/10.1016/j.jclepro.2019.04.231" @default.
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