Satellite Remote Sensing for Developing Time and Space Resolved Estimates of Ambient Particulate in Cleveland, OH

Authors

Naresh Kumar, University of IowaFollow
Allen D. Chu, Goddard Space Flight Center, National Aeronautics and Space Administration (NASA), Washington, District of Columbia, USA
Andrew D. Foster, Brown University
Thomas Peters, University of Iowa
Robert Willis, Environmental Protection Agency, Research Triangle Park, North Carolina, USA

Date of this Version

2011

Citation

Aerosol Science and Technology, 45:1090–1108, 2011

Comments

U.S. Government Work

Abstract

This article empirically demonstrates the use of fine resolution satellite-based aerosol optical depth (AOD) to develop time and space resolved estimates of ambient particulate matter (PM) ≤2.5 μm and ≤10 μm in aerodynamic diameters (PM_2.5 and PM₁₀, respectively). AOD was computed at three different spatial resolutions, i.e., 2 km (means 2 km × 2 km area at nadir), 5 km, and 10 km, by using the data from MODerate Resolution Imaging Spectroradiometer (MODIS), aboard the Terra and Aqua satellites. Multiresolution AOD from MODIS (AODMODIS) was compared with the in situ measurements of AOD by NASA’s AErosol RObotic NETwork (AERONET) sunphotometer (AOD_AERONET) at Bondville, IL, to demonstrate the advantages of the fine resolution AOD_MODIS over the 10-km AOD_MODIS, especially for air quality prediction. An instrumental regression that corrects AOD_MODIS for meteorological conditions was used for developing a PM predictive model.

The 2-km AOD_MODIS aggregated within 0.025◦ and 15-min intervals shows the best association with the in situ measurements of AOD_AERONET.

The 2-km AOD_MODIS seems more promising to estimate time and space resolved estimates of ambient PM than the 10-km AOD_MODIS, because of better location precision and a significantly greater number of data points across geographic space and time. Utilizing the collocated AOD_MODIS and PM data in Cleveland, OH, a regression model was developed for predicting PM for all AOD_MODIS data points. Our analysis suggests that the slope of the 2-km AOD_MODIS (instrumented on meteorological conditions) is close to unity with the PM monitored on the ground. These results should be interpreted with caution, because the slope of AOD_MODIS ranges from 0.52 to 1.72 in the site-specific models. In the cross validation of the overall model, the root mean square error (RMSE) of PM₁₀ was smaller (2.04 μg/m³ in overall model) than that of PM_2.5 (2.5 μg/m³). The predicted PM in the AOD_MODIS data (~2.34 million data points) was utilized to develop a systematic grid of daily PM at 5-km spatial resolution with the aid of spatiotemporal Kriging.