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 PM
10
, 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 (AOD
MODIS
) 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 PM
10
was smaller (2.04 μg/m
3
in overall model) than that of PM
2.5
(2.5 μg/m
3
). 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.
