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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So 2 and no 2 mapping and emission estimations from satellite retrievals

SO2 and NO2 mapping and emission estimations from satellite retrievals

C. McLinden and V. Fioletov


Outline

Outline

  • Spatial smoothing, hi-res mapping

  • Air Mass Factor

  • Emission inventories vs. OMI signals

  • Detection of sources, global picture

  • Summary


So 2 and no 2 mapping and emission estimations from satellite retrievals

Satellites measure

vertical column density

or number of molecules per cm2

OMI SO2 VCD

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

Challenges: Small spatial scales

x

  • 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

y

320 km

Mildred Lake

r


So 2 and no 2 mapping and emission estimations from satellite retrievals

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


So 2 and no 2 mapping and emission estimations from satellite retrievals

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


Air mass factors

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

Spectral

Fit

Removal of

stratosphere

Convert Slant

to vertical column

Tropospheric

Vertical Column

Density (Level 2)

Raw

Spectra

(Level 0)

Calibrated,

geolocated

Spectra (L1)

UV/vis Processing Sequence

measured

modelled


Amfs and profile shape

AMFs and profile shape

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

Visible

High probability of reaching surface

UV

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

Profile Shape

June

AMF=0.93

OMI NO2

VCD [1015 cm-2]

June

June

AMF = 1.23

VCD [1015 cm-2]

GMI global model NO2 - 2 x 2.5


Aqhi research innovations using satellite data

Satellites measure

vertical column density

In Situ

GEOS-Chem

or number of molecules per cm2

AQHI research: Innovations using satellite data

PM2.5

NO2

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


So 2 and no 2 mapping and emission estimations from satellite retrievals

OMI NO2 summertime

Tropospheric VCD

2005-2010


So 2 and no 2 mapping and emission estimations from satellite retrievals

MODIS aerosol

optical depth

2000-2005

Optical Depth


Estimate of emissions

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”


So 2 and no 2 mapping and emission estimations from satellite retrievals

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

Fit

where

R=0.93

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.


So 2 and no 2 mapping and emission estimations from satellite retrievals

Global SO2 emission source catalogue (~200 sources)

Example: Volcanoes in Japan

Asama

Suwanose-jima

Kikai

Sakura-jima

Aso

Miyake-jima


So 2 and no 2 mapping and emission estimations from satellite retrievals

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

Norilsk

-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


So 2 and no 2 mapping and emission estimations from satellite retrievals

Satellite data can be used to track emission changes over time


Cantarell and ku maloob zaap oil fields mexico

Cantarell and Ku-Maloob-Zaap Oil Fields, Mexico

SO2

2005-2007

Oil production:

800,000+500,000 bpd

2008-2011

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 so 2 values over eastern us

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

2005-2007

2008-2010

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 no 2 emission trends

Oil Sands: OMI monitoring of NO2 emission trends

Max VCD

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

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

Widths

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]


Summary

Summary

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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