In this paper, we present a method to detect atmospheric pollutants (i.e., SO2) using Ozone Monitoring Instrument (OMI) and MODeratae Resolution Imaging Spectroradiometer (MODIS) data over La Oroya city in Peru. SO2 loads measured in the planetary boundary layer (PBL) are extracted from the OMI data, and these pollutants are characterized according to their particle size using atmospheric optical depth (AOD) and Ångström coefficient derived from MODIS imagery. The OMI level 2 sulfur dioxide data collected over the test site for the period of 467 days from July 27th, 2007 to November 4th, 2008 are scanned to select candidate datasets that meet the requirements of optimal viewing geometry and cloud conditions. Total of 42 days of satellite measurements that complies with these conditions are used to measure anthropogenic loads, and further validated using field measurements. Results show that there is significant logarithmic correlation between satellite estimated and field measured SO2, and this correlation can be substantially increased when Ångström exponents are between 0.7 and 1. It is concluded in this contribution that introducing aerosol size distributions may improve SO2 estimation from satellite data, and there is a greater chance of success for detecting atmospheric pollution when smaller sized aerosols associated with anthropogenic pollutions are dominant.