So 2 and no 2 mapping and emission estimations from satellite retrievals
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SO 2 and NO 2 mapping and emission estimations from satellite retrievals. C. McLinden and V. Fioletov. Outline. Spatial smoothing, hi-res mapping Air Mass Factor Emission inventories vs. OMI signals Detection of sources, global picture Summary. Satellites measure

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SO 2 and NO 2 mapping and emission estimations from satellite retrievals

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SO2 and NO2 mapping and emission estimations from satellite retrievals

C. McLinden and V. Fioletov


  • Spatial smoothing, hi-res mapping

  • Air Mass Factor

  • Emission inventories vs. OMI signals

  • Detection of sources, global picture

  • Summary

Satellites measure

vertical column density

or number of molecules per cm2


Mean OMI total column SO2 for Bowen power station with annual emissions of about 170 kT y-1and Belews Creek power station (88 kT y-1)as a function of the distance between the station and the pixel centre.

The secondary maximum on the Bowen curve is caused by the contribution of two power plants located about 80 km to the south.

Challenges: Small spatial scales


  • Methodologies to better resolve small-scale features in satellite data:

  • need to use a large amount of data

  • employ a pixel averaging technique

  • e.g.:

  • x=  y=4 km, r=20 km

  • or

  •  x=  y=1 km, r=5 km

  • The value assigned to a gridbox is the average of all data within radius r

40 km


320 km

Mildred Lake


Pixel Averaging Technique

SO2 from OMI, average for 2005-2010

For each grid point of a 2x2 km grid, all overpasses centered within a 12 km from that point were averaged

60 km

OMI smallest

pixel size

60 km

John Amos power plant, 2900 MW, ~110 kT/year of SO2

Air Mass Factors

VCDtrop = (SCD – VCDstrat AMFstrat) / AMFtrop

  • Air mass factor (AMF) describe the sensitivity of the satellite sensor to absorbing layer. They are computed using a multiple-scattering radiative transfer models and their accuracy relies in large part on the validity of input parameters, including:

  • Shape of the absorbing profile

  • Surface reflectivity or albedo



Removal of


Convert Slant

to vertical column


Vertical Column

Density (Level 2)



(Level 0)



Spectra (L1)

UV/vis Processing Sequence



AMFs and profile shape

Altitude-resolved AMF, SZA=60, albedo=0.04


High probability of reaching surface


Low probability of reaching surface

More difficult for UV light to penetrate down to these altitudes due to increased scattering and absorption

AMF =  AMF’(z) n(z) dz /  n(z) dz

Profile Shape




VCD [1015 cm-2]



AMF = 1.23

VCD [1015 cm-2]

GMI global model NO2 - 2 x 2.5

Satellites measure

vertical column density

In Situ


or number of molecules per cm2

AQHI research: Innovations using satellite data



MODIS Aerosol Optical Depth

Daily OMI Tropospheric Column

it can be converted

into surface mixing ratio

Coincident Model Profile

Ground-Level NO2 inferred Lamsal et al., JGR, 2008

Ground-Level PM2.5

van Donkelaar et al., JGR, 2006

OMI NO2 summertime

Tropospheric VCD


MODIS aerosol

optical depth


Optical Depth

Estimate of emissions

  • These analyses are used to estimate annual emission rates over local sources

  • Concept: use accurate, reported emissions from several locations to “calibrate” the satellite observations

    mass = constant  emission rate

    [tonnes] = [days]  [tonnes/day]

  • Done initially using OMI SO2; will be extended with other species

  • Constant not universal, additional factors such as windspeed can modify it

Interpreted as

a “lifetime”

Annual SO2 emissions vs. estimates from a fit of mean OMI SO2 by 2D Gaussian function (2005-2007 data)




Since , a is the total number of molecules.

If is in DU, i.e. in 2.69·1026 mol·km-2 , and σx,σy are in km, then a is in 2.69·1026 mol.

Mean OMI SO2 Fit Residuals

A scatter plot of annual SO2 emission from the largest US sources in 2005 vs. mean OMI SO2 for 2005-2007 integrated around the source estimated using the best fits by 2D Gaussian function. Emissions are given in kT y-1 and molec h-1 units calculated assuming a constant emission rate.

Global SO2 emission source catalogue (~200 sources)

Example: Volcanoes in Japan







The largest SO2 source in the Arctic: Norilsk, Russia, 70N.


-1.0 0.0 1.0 2.0 3.0 DU

1% of Russia’s GDP

2% of Russia’s industrial production

3% of Russia’s export

… and 2,000,000 T of SO2 per year

Satellite data can be used to track emission changes over time

Cantarell and Ku-Maloob-Zaap Oil Fields, Mexico



Oil production:

800,000+500,000 bpd


OMI estimated SO2 emissions:

about 200 kT/y in 2005-2007

about 330 kT/y in 2008-2011

OMI data show a 40% decline in mean SO2 values over Eastern US



The sum of SO2 values from the top 40 emission sources as a function of distance from the source for 2005-2007 (red) and 2008-2010 (blue)

Mean OMI SO2 values over the Eastern US for 2005-2007 and 2008-2010. The dots indicate emission sources from the top 40 sources

Oil Sands: OMI monitoring of NO2 emission trends


  • Fit constant + 2D Gaussian to seasonal NO2 VCD data (DJF, MMA, JJA, SON)

  • Derive trend in total mass (above background) of the enhancement


Total mass [t(NO2)]

of enhancement

Background VCD

Maximum VCD

Widths of distribution [km]

“Background” VCD

In-situ NO2

Production [millions of barrels per day]


There is a good correlation between measured OMI SO2 values averaged over a long period and reported emission values. OMI data can be used to monitor sources that emit more than ~70 kT per year.

High spatial resolution (a few km) of both measurements and retrieval algorithms is required for emission monitoring

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