The Ozone Monitoring Instrument (OMI) on EOS/Aura offers unprecedented spatial and spectral resolution, coupled with global coverage, for space-based UV measurements of sulfur dioxide (SO/sub 2/). This paper describes an OMI SO/sub 2/ algorithm (the band residual difference) that uses calibrated residuals at SO/sub 2/ absorption band centers produced by the NASA operational ozone algorithm (OMTO3). By using optimum wavelengths for retrieval of SO/sub 2/, the retrieval sensitivity is improved over NASA predecessor Total Ozone Mapping Spectrometer (TOMS) by factors of 10 to 20, depending on location. The ground footprint of OMI is eight times smaller than TOMS. These factors produce two orders of magnitude improvement in the minimum detectable mass of SO/sub 2/. Thus, the diffuse boundaries of volcanic clouds can be imaged better and the clouds can be tracked longer. More significantly, the improved sensitivity now permits daily global measurement of passive volcanic degassing of SO/sub 2/ and of heavy anthropogenic SO/sub 2/ pollution to provide new information on the relative importance of these sources for climate studies.

[1] Satellite sulfur dioxide (SO₂) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor, averaged over a period of several years, were compared with emissions inventories for major US sources. Low‐ and high‐ spatial frequency filtration was applied to OMI data to reduce the noise and bias to enhance and reveal weak SO₂ signals that are otherwise not readily apparent. Averaging a large number of individual observations enables the study of SO₂ spatial distributions near larger SO₂ emissions sources with an effective resolution superior to that of an individual OMI observation and even to obtain rough estimates of the emissions level from those sources. It is demonstrated that individual sources (or multiple sources within 50 km) with annual SO₂ emissions greater than about 70 kT y⁻¹ produce a statistically significant signal in 3‐year averaged OMI data. A correlation of 0.93 was found between OMI SO₂ integrated around the source and the annual SO₂ emission rate for the sources greater than 70 kT y⁻¹. OMI SO₂ data also indicate a 40% decline in SO₂ values over the largest US coal power plants between 2005–2007 and 2008–2010, a value that is consistent with the reported 46% reduction in annual emissions due to the implementation of new SO₂ pollution control measures over this period.

Satellite remote sensing is increasingly being used to monitor air quality over localized sources such as the Canadian oil sands. Following an initial study, significantly low biases have been identified in current NO₂ and SO₂ retrieval products from the Ozone Monitoring Instrument (OMI) satellite sensor over this location resulting from a combination of its rapid development and small spatial scale. Air mass factors (AMFs) used to convert line-of-sight "slant" columns to vertical columns were re-calculated for this region based on updated and higher resolution input information including absorber profiles from a regional-scale (15 km × 15 km resolution) air quality model, higher spatial and temporal resolution surface reflectivity, and an improved treatment of snow. The overall impact of these new Environment Canada (EC) AMFs led to substantial increases in the peak NO₂ and SO₂ average vertical column density (VCD), occurring over an area of intensive surface mining, by factors of 2 and 1.4, respectively, relative to estimates made with previous AMFs. Comparisons are made with long-term averages of NO₂ and SO₂ (2005–2011) from in situ surface monitors by using the air quality model to map the OMI VCDs to surface concentrations. This new OMI-EC product is able to capture the spatial distribution of the in situ instruments (slopes of 0.65 to 1.0, correlation coefficients of >0.9). The concentration absolute values from surface network observations were in reasonable agreement, with OMI-EC NO₂ and SO₂ biased low by roughly 30%. Several complications were addressed including correction for the interference effect in the surface NO₂ instruments and smoothing and clear-sky biases in the OMI measurements. Overall these results highlight the importance of using input information that accounts for the spatial and temporal variability of the location of interest when performing retrievals.