# Presentation: 670115

## Global SO2 sources detected in OMI operational data and improvements with a new iterative spectral fit SO2/O3 algorithm

- Goddard Earth Sciences and Technology Center (GEST), UMBC, Baltimore, MD
- Laboratory for Atmospheres, NASA Goddard Flight Center, MD
- Michigan Technological University , Houghton, MI
- Joint Center for Earth Systems Technology (JCET), UMBC, Baltimore, MD
- NOAA/NESDIS
- Department of Atmospheric and Oceanic  Physics, University of Maryland College Park, MD
- SSAI, Lanham, MD
- KNMI, Netherlands

- N. Krotkov 1,  K. Yang 1, P. Bhartia 2,  S. Carn 3, A. Krueger 4, K. Pickering 2, G. Vincente 5, G. Serafino 5, R. Dickerson 6,  C. Li 6,
-  J. Witte 7, P. Levelt 8

**Notes:**

Aura/OMI offers the highest-available spatial resolution coupled with contiguous, daily global coverage
of atmospheric column SO2 amounts. Currently, operational SO2 data are produced with an
intermediate algorithm using soft calibrated residuals from a TOMS-like total ozone algorithm at
discrete UV wavelengths. The OMI SO2 volcanic data set continues the 25 year TOMS SO2 record, but
the improved sensitivity and smaller footprint of OMI extends the range of detection to smaller eruptions
and older clouds, and to degassing volcanoes. These measurements highlight the deficiencies of
previous compilations of volcanic SO2 emissions, which were biased towards accessible, frequently
monitored volcanoes. Emissions from strong anthropogenic sources of SO2 (such as smelters and coal
burning power plants) and from heavy regional pollution have been detected in the operational pixel
data. Time averaging of operational data allows detection of weaker sources such as power plant
emissions in Greece, Bulgaria, Turkey, South Africa, India and the US Ohio River valley, as well as
emissions from Persian Gulf refineries and pollution plumes from industry near Mexico City. Although,
such long-term averages are useful to determine the global distribution of anthropogenic SO2
emissions, a better algorithm is required for observing daily anthropogenic emissions in the boundary
layer taking full advantage of the OMI hyperspectral capabilities. In this talk we describe next
generation iterative spectral fit (ISF) combo SO2/O3 algorithm and demonstrate its enhanced
performance for both pollution and volcanic plumes. Using a full number of OMI spectral measurements
information contents about both ozone and SO2 can be more precisely retrieved including
simultaneous estimation of volcanic plume altitude. We present results for the 2005 eruption of Sierra
Negra volcano and most recent eruptions of Okmok and Kasatochi volcanoes on Aleutian Islands. We
demonstrate significant improvements in the determination of both SO2 and ozone column amounts
inside volcanic plumes as well as in background areas. We also present new Chinese SO2 pollution
results to demonstrate significant signal to noise improvements over operational data. These algorithm
improvements allow even weaker SO2 sources to be monitored routinely.

## OMI operational SO2 data are produced with provisional algorithms using discrete UV2 wavelengths

- OMSO2  algorithm [Krotkov et al  2006; Yang et al 2007]  uses calibrated residuals at SO2 absorption band centers produced by the NASA operational ozone algorithm (OMTO3 V8.5 [P.K. Bhartia] ) and radiative cloud pressures from Rotational Raman algorithm [J. Joiner and A. Vasilkov]

**Four SO****2**** retrievals based on **
**    ****a-priori SO****2**** vertical profiles or **_**Center of Mass Altitude (CMA):**_** **
**Planetary Boundary Layer (PBL): **_**CMA = 0.9 km**_
**Lower tropospheric column (TRL): **_**CMA = 2.5 km**_
**Mid-tropospheric column (TRM): **_**CMA = 7.5 km**_
**UTLS column (STL):          ****	****	****           **_**CMA = 17 km**_

**Notes:**
Currently, operational SO2 data are produced with an intermediate algorithm using soft calibrated residuals from a TOMS-like total ozone algorithm at discrete UV wavelengths. 

We currently use linked ozone, cloud pressure and SO2 algorithms for Aura/OMI SO2 retrievals

## Linear Fit (LF) SO2 algorithm [Kai Yang, JGR 2007] operationally used with NRT OMI data. LF is accurate up  to SO2 <100DU  with 100% plume detection capability (daylight only).
Cloud noise corrected by using OMI Rotational Raman cloud heights [J. Joiner and A. Vasilkov]
Volcanic ash detected using UV Aerosol Index (AI). Interference from absorbing aerosols (dust, smoke) and sun glint.
NOAA/NESDIS NRT volcanic web site pre-operational

- _Linear Fit (LF) SO__2__ algorithm_ [Kai Yang, JGR 2007] operationally used with NRT OMI data. LF is accurate up  to SO2 <100DU  with 100% plume detection capability (daylight only).
- Cloud noise corrected by using OMI Rotational Raman cloud heights [J. Joiner and A. Vasilkov]
- Volcanic ash detected using UV Aerosol Index (AI). Interference from absorbing aerosols (dust, smoke) and sun glint.
- NOAA/NESDIS NRT volcanic web site pre-operational

- Volcanic aviation hazards alerts

**Aug 10**

**Aug 11**

**Aug 9**

**Aug 8**

**Ash (AI)**

**SO****2**

**Aug 10**

**Aug 11**

- Kasatochi volcanic eruption on August 7 2008

**SO****2**

**SO****2**

**SO****2**

**Notes:**
NRT Volcanic data are used by NOAA for volcanic aviation hazards alerts  

## NOAA/NESDIS NRT Volcanic Website http://satepsanone.nesdis.noaa.gov/pub/OMI/OMISO2/index.html

- NOAA/NESDIS NRT Volcanic Website http://satepsanone.nesdis.noaa.gov/pub/OMI/OMISO2/index.html

- Kasatochi volcanic eruption (August 7’08) SO2 plumes

- High noise near terminator: SZA> 750

- latest OMI orbit (~ 3 hours)

## Chinese SO2 pollution lofting above the PBL and long-range transport over Pacific ocean

- Chinese SO2 pollution lofting above the PBL and long-range transport over Pacific ocean

- OMI data were confirmed with *in-situ* aircraft SO2 profiles measured in the lower troposphere over China during the EAST AIRE campaign in April 2005.  The satellite-derived measurements of SO2 confirm the *in situ* observations of high concentrations of SO2 ahead of the front and lower concentrations behind it and provide evidence for a large-scale impact of pollutant emissions
-  [Krotkov et al JGR, 2008]

**Notes:**
Quantifying satellite measurements is challenging, because Rayleigh scattering reduces sensitivity to pollutants in the lower atmosphere.  

These high noise levels limit operational pixel data to detection of strong anthropogenic SO2 sources such as smelters.  In-situ aircraft profiles measured in the troposphere over China during the EAST-AIRE campaign in April 2005 offer an opportunity to evaluate and enhance SO2 detection from space.  

With the improved OMI retrievals, we were able to characterize individual daily pollution events on spatial scales of ~100 km. 

The satellite-derived measurements of SO2 confirm the in situ observations of high concentrations of SO2 (ca 4 DU) ahead of the front and lower concentrations behind it and provide evidence for a large-scale impact of pollutant emissions. 

This comparison demonstrates that OMI can distinguish between background SO2 conditions and heavy pollutions on a daily basis, suggesting potential of using OMI SO2 data for the regional pollution monitoring.

Lofting of substantial amounts of SO2 above the PBL of Northeastern China was observed, showing that convection and frontal activity can lead to long-range transport. 

## Pollution SO2 data are used to monitor efficiency of emission controls in China before and during Olympic games [Ken Pickering, J. Witte, D. Streets, G. Carmichael ] 

- http://aura.gsfc.nasa.gov [J. Witte,
- K. Pickering, M. Schoeberl]

- http://SO2.umbc.edu/omi

**Notes:**
OMI data helps monitoring AQ effects of pollution controls in China before and during Olympic games.

## Compare long-term SO2 burdens in different regions of the world 

- Compare long-term SO2 burdens in different regions of the world

- 25.5 million tons of SO2 was emitted by Chinese factories in 2005 up 27% from 2000

- Popocatepetl volcano

- Tula Industrial complex

**Notes:**
IN the US Ohio River valley was identified as major SO2 pollutatnt. 

In Mexico:  Tula industrial complex near Mexico City rivals Popocatepetl volcano.  

Previously unknown major emitters have been identified.  

Long-term averages can be used to determine the global distribution of anthropogenic SO2 burdens, and the efficacy of abatement strategies. 

Spatial smoothing and/or time averaging allow significant signal to noise enhancements. Applying these techniques, power plant emissions in Greece, Bulgaria, Turkey, and the US Ohio river valley can be seen in OMI data as well emissions from Persian Gulf refineries, and pollution plumes in a Mexico city area industrial complex. Unknown sources are also being identified. Using long-term averages anthropogenic SO2 burdens can be compared directly in different parts of the world.

OMI average operational PBL SO2 retrievals showing stationary anthropogenic sources over:
USA, Ohio valley
Popocatepetl volcano with Tula industrial complex, North of Mexco city
 Eastern China industrial region 
Emission inventories indicate that:
 Largest increases in tropospheric SO2 emissions have occurred in Asia during the last 20 years (35% per decade over China between 1979-2000 [Massie et al 2004] )
2) This result in positive trends (17% per decade) in sulfate aerosol loading over China  in winter between 1979 and 2000.

**SO****2 ****enhancements in the Balkan region first observed by GOME  **
**in February 1998  [Eisinger  and Burrows, GRL 1998].**** **

**3 year average SO****2 ****enhancements detected by OMI over Eastern Europe and Turkey  coincide with locations of power plants **

- Pinpoint pollution effects from individual power plants and smelters

- Etna volcano

- GOME: 40km by 320km

- OMI: 13km by 24km

**Notes:**
Anthropogenic SO2 emissions in the PBL present challenges, because these typically weak signals need to be separated from the noise in the radiances. Given relatively large noise only plumes from strong anthropogenic sources of SO2 (such as smelters and coal burning power plants) and from strong regional pollution have been detected in operational pixel data. 

Time averaging allow significant signal to noise enhancements. Applying these techniques, power plant emissions in Greece, Bulgaria, Turkey.

2) Left: Enhanced SO  observed by GOME over the Balkans on 8 February 1998. The small dots indicate the locations of the largest thermal power plants burning lignite in Romania (Turceni, Rovinari, and Craiova, total 5065 MWe) and Bulgaria (Gulubovo and Radnevo, forming the Maritsa-Iztok complex, total 3485 MWe).
 Together, these plants represent 27 % of the electric power capacity installed in these countries [US Department of Energy].Low GOME ground resolution does not allow locate pollution regions.

3) Right: 3 year average OMI SO2 vertical column over south-Eastern Europe and Turkey, 2005 – June 2007 . 
Image is scaled from low (white) to high (red) values of average SO2 vertical column amount.
OMI small footprint and high ground resolution allow precise location of the pollution regions.

4) 2 main Average SO2 enhancements detected by OMI in Eastern Europe correspond in both location and approximate column amount to GOME observations from February 1998 reported in Eisinger and Burrows [1998]. These SO2 emissions are sourced from lignite-burning power plants in the Balkan region.

5)  Strong SO2 plume continuing off Sicily  is due to Etna volcanic emissions.

4) Evidence of long-range pollution transport can be seen from Bulgarian sources over Greece and from Greece and Turkey over Mediterranean.

5) Strength of OMI SO2 enhancements do not correspond directly to power production of the plants.
  For example power plants in Northern Greece near Thessaloniki do not produce as much pollution as smaller Megalopulus plant in the South of Greece.
This could be due to different technology employed.

6) 

## OMI SO2 burdens over India

- Smallest emissions detected  so far < 0.1DU !

**Notes:**
Note really small scale up to 0.1DU !

Not all power plants can be seen;

Possible artifacts in central West India

## Systematic errors in operational PBL SO2 data:(PBL SO2 = [Slant Column Density] /0.36)

- SAA

- A-priori ozone profile effect

**Notes:**
Time averaging reduces noise, but emphasizes small systematic errors:
1) Ozone profile artifacts: +/-30lat, +60N=> need better ozone profile climatology [McPeters, Labo, Logan]
2) Negative SO2  N and S of real So2 sources:  sliding median residual correction ?
3) North Atlantic positive SO2:  low signal artifact?
4) Antarctic belt:  similar artifact to N Atlantic?

3) Negative correlation between biomass burning (smoke) aerosols and SO2 ?

## New combo O3 and SO2 ISF algorithm uses OMI hyperspectral measurements

**Improved profile shapes: O****3**** (****Ω****) uses V8 climatology, SO****2  ****(****Ξ****) uses explicitly specified profile shape (from models)**
**Initial guess: latitude based O****3 ****guess or an adjacent pixel O****3**** value, no SO****2****, derive MLER cloud fraction (*****F******c*****) at λ=333 nm, using OMCLDRR cloud pressure.**
**Iterative Spectral Fitting (ISF) Method [Kai Yang et al submitted GRL’08]:**

- New combo O3 and SO2 ISF algorithm uses OMI hyperspectral measurements

**Notes:**
Profile shapes: O3 (Ω) uses V8 climatology, SO2  (Ξ) uses Gaussian specified by a central height and a width.
Initial guess: latitude based O3 guess or an adjacent pixel O3 value, no SO2, derive MLER cloud fraction (Fc) at λ=333 nm, using OMCLDRR cloud pressure.
Iterative Spectral Fitting (ISF) Method:

## Iterative Spectral Fit (ISF) combo O3/SO2 algorithm improves high SO2 and O3 retrievals (~1000 DU).

- _Iterative Spectral Fit (ISF)_ combo O3/SO2 algorithm improves high SO2 and O3 retrievals (~1000 DU).

- SO2 **plume height** retrieved with ISF
- [K. Yang, submitted, GRL’08.]

**Operational  SO****2**** Mass    **
**950 kilotons (-45%)**

**ISF SO****2**** Mass**
**1740 kilotons**

- ISF SO2

- LF SO2

- ISF O3

- LF O3

- Sierra Negra Oct 2005 eruption, Aqua/MODIS, RGB

## OMI volcanic SO2 dataset is being improved using new ISF algorithm [K. Yang submitted GRL’08]

- OMI volcanic SO2 dataset is being improved using new ISF algorithm [K. Yang submitted GRL’08]

- Sierra Negra

- Soufriere Hills

- Nyamulagira

- Rabaul

- P. Fournaise

- Manam

- Simon Carn, 2008

**Notes:**
OMI volcanic So2 dataset will be updated using new 

## Background noise is reduced with new ISF algorithm

## Future work

- Operational implementation of  combo Ozone SO2 Iterative Spectral Fit algorithm
- Better stray light and Ring corrections
- Using SO2 profile shapes from CTM model

- Better aerosol and cloud correction using A-train data and improvements based on comparison with models and measurements.

- algorithm improvements to account for changes in instrument performance
- Transition to future instruments (GOME-2, NPP)