Satellite remote sensing for developing time and space resolved estimates of ambient particulate in Cleveland, OH

Naresh Kumar, Allen D. Chu, Andrew D. Foster, Thomas Peters, Robert Willis

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


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 (PM2.5 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 (AODAERONET) at Bondville, IL, to demonstrate the advantages of the fine resolution AODMODIS over the 10-km AOD MODIS, especially for air quality prediction. An instrumental regression that corrects AODMODIS for meteorological conditions was used for developing a PM predictive model. The 2-km AODMODIS aggregated within 0.025° and 15-min intervals shows the best association with the in situ measurements of AODAERONET. The 2-km AOD MODIS seems more promising to estimate time and space resolved estimates of ambient PM than the 10-km AODMODIS, because of better location precision and a significantly greater number of data points across geographic space and time. Utilizing the collocated AODMODIS and PM data in Cleveland, OH, a regression model was developed for predicting PM for all AODMODIS data points. Our analysis suggests that the slope of the 2-km AODMODIS (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 AODMODIS 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/m 3 in overall model) than that of PM2.5 (2.5 μg/m 3). The predicted PM in the AODMODIS 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.

Original languageEnglish (US)
Pages (from-to)1090-1108
Number of pages19
JournalAerosol Science and Technology
Issue number9
StatePublished - Sep 2011
Externally publishedYes

ASJC Scopus subject areas

  • Environmental Chemistry
  • Materials Science(all)
  • Pollution


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