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Tian, J, Chen, DM (2010). A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements. REMOTE SENSING OF ENVIRONMENT, 114(2), 221-229.

A semi-empirical model is developed to predict the hourly concentration of ground-level fine particulate matter (PM2.5) coincident to satellite overpass, at a regional scale. The model corrects the aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) by the assimilated parameters characterizing the boundary layer and further adjusts the corrected value according to meteorological conditions near the ground. The model was built and validated using the data collected for southern Ontario, Canada for 2004. Overall, the model is able to explain 65% of the variability in ground-level PM2.5 concentration. The model-predicted values of PM2.5 mass concentration are highly correlated with the actual observations. The root-mean-square error of the model is 6.1 mu g/m(3). The incorporation of ground-level temperature and relative humidity is found to be significant in improving the model predictability. The coarse resolution of the assimilated meteorological fields limits their value in the AOD correction. Although MODIS AOD data is acquired on a daily basis and the valid data coverage can sometimes be very limited due to unfavourable weather conditions, the model provides a cost-effective approach for obtaining supplemental PM2.5 concentration information in addition to the ground-based monitoring station measurement (C) 2009 Elsevier Inc. All rights reserved.



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