Ozone Monitoring Instrument (OMI) & AQS NO2 The comparisons just keep getting better*

Bryan Duncan, Lok Lamsal, Yasuko Yoshida NASA Goddard Space Flight Center

AQAST8, December 2-4, 2014; Georgia Tech, Atlanta, GA

*This work was done as part of David Streets’ Tiger Team: “Relationships and trends among satellite NO2 columns, NOx emissions, and air quality in North America”

Aura Ozone Monitoring Instrument (OMI)

How do OMI NO2 data compare to surface observations?

OMI detects pollution in the free troposphere and

boundary layer.

The AQS surface sites only detect “nose-level”

concentrations.

Plume rise

OMI & AQS/CEMS NO2

→Lok, Yasuko, and I have been working to: 1) tailor the OMI NO2 retrieval algorithm for AQ applications (see Lok’s poster). 2) show the correspondence of OMI NO2 data to quantities familiar to the AQ community. OMI NO2 & CEMS data Duncan, B., Y. Yoshida, B. de Foy, L. Lamsal, D. Streets, Z. Lu, K. Pickering, and N. Krotkov, The observed response of Ozone Monitoring Instrument (OMI) NO2 columns to NOx emission controls on power plants in the United States: 2005-2011, Atmos. Environ., 81, p. 102-111, doi:10.1016/jatmosenv.2013.08.068, 2013.

OMI NO2 & AQS data Lamsal, L., B. Duncan, Y. Yoshida et al., U.S. NO2 variations and trends (2005-2013) estimated from an improved Ozone Monitoring Instrument (OMI) tropospheric column data product mirror those estimated from AQS surface observations, will be submitted to Atmos. Environ.

Model Simulation: Correspondence of a Tropospheric Column to Surface Data

% Reduction of NO2 (2005-2010) Tr

opos

pher

ic C

olum

n

Surface

Column reductions higher.

Observed Correspondence of a Tropospheric Column to Surface Data

% Reduction of NO2 (2005-1013)

OM

I Tro

posp

heric

Col

umn

AQS Surface (ppbv)

Reduction of NO2

AQS Surface

OM

I Tro

posp

heric

Col

umn

AQS 1) Molybdenum converter 2) Sparse network/siting OMI 1) Coarse spatial resolution 2) Free tropospheric NO2 3) Retrieval algorithm assumptions

Why is the Observed Correspondence Weaker?

Measures some fraction of NOz. Yorkville, GA

Absolute Column Absolute Change (2005-2013) Relative Change (%)

o = AQS sites

AQS % reduction lower.

But, don’t despair! Region Domain Number of sites

NO2 reduction (%) 2005-2013 AQS OMI

Mid-Atlantic 41-45 N, 70-75 W 13 38.3 37.9

New England 36-41 N, 72-81 W 19 41.4 43.1

S. California 31-36 N, 116-122 W 50 42.8 47.2

Central Valley 36-41 N, 118-124 W 30 37.2 41.2

Land type Number of sites NO2 reduction (%) 2005-2013

AQS OMI

Residential 88 37.9 40.3

Commercial 74 39.5 37.0

Agriculture 19 35.7 38.7

Industrial 15 37.2 34.7 Mobile 6 34.9 43.1

Land use Number of sites NO2 reduction (%) 2005-1013

AQS OMI

Urban and center city

88 37.6 37.2

Suburban 89 39.0 40.1

Rural 30 35.1 35.5

The linear trends and monthly anomalies agree pretty well.

Monthly Anomalies (Meteorological Variations + Error)

Atlanta

Chicago

Houston

Dallas

OMI AQS

→ Test the sensitivity of the OMI NO2 data product to the elevation of wildfire aerosol plumes using DISCOVER-AQ data, including ACAM, collected when agricultural fire smoke impacted Houston.

AQAST IP Work for the Coming Year

Effort to Tailor OMI Retrieval Algorithm for AQ Applications

While the versions of the OMI NO2 data have improved substantially over the years, there is still room for improvement.

OMI Team’s next steps to improve the NO2 algorithm: (1) Improved spectral fitting for NO2 - is being developed by our group (KNMI's spectral fitting has problem). (2) High resolution surface reflectivity data base (MODIS) (3) High resolution year-specific a-priori NO2 profile shape (4) Inclusion of aerosols in the retrieval of NO2 (5) Development of independent cloud product for use in NO2 retrievals.

Improve for high aerosol conditions, such as wildfires – i.e., “aerosol shielding effect”.

SP: version 3 (early 2015) – include (1) & (3) version 4 (early 2016) – include (1) – (5)

Russ Dickerson

Bryan Duncan

• The vast majority of Americans believe their air quality is worsening and their tax dollars that go to improving air quality are for naught. • Air quality managers often complain of this erroneous perception by the general public. • On June 27th, NASA AQAST members did over 20 live interviews (e.g., Fox News, The Weather Channel), several taped interviews (e.g., CNN), and numerous phone and email interviews. David Streets was interviewed by NPR. • The story was reported in numerous online news outlets (e.g., Smithsonian, Science World Report).

Air Quality Media Campaign NASA AQAST Members Get the Word Out on Improving Air Quality

June 27th, 2014

• Media Campaign a Success I received quite a bit of feedback from the AQ community, such as a few requests for specific analyses (e.g., Dallas SIP, Pennsylvania power plants). • AQAST Spotlight at NASA HQ Mike Freilich (Director, Earth Sciences Division at NASA HQ) showed all 3 slides that I sent for the Monthly Status Review (MSR) – he never shows 3 slides from any one person or any one topic!

A Simple Message: Air Quality is improving, but we’re not done yet!

Air Quality “User’s Guide”

Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-User Resources, Answers to FAQs, and Common Mistakes to Avoid Bryan N. Duncan, Ana I. Prados, Lok Lamsal, Yang Liu, David G. Streets, Pawan Gupta, Ernest Hilsenrath, Ralph Kahn, J. Eric Nielsen, Andreas Beyersdorf, Sharon Burton, Arlene M. Fiore, Jack Fishman, Daven Henze, Chris Hostetler, Nickolay A. Krotkov, Pius Lee, Meiyun Lin, Steven Pawson, Gabriele Pfister, Kenneth E. Pickering, Brad Pierce, Yasuko Yoshida, Luke Ziemba Atmospheric Environment, doi:10.1016/j.atmosenv.2014.05.061 → The article is “open access” so it’s free to download! → See Ana Prados’ (Pawan Gupta) presentation tomorrow at 9 am on NASA’s ARSET (Applied Remote Sensing Training) program.

Slides for David Streets

Duncan, Lamsal, Yoshida

Provided analysis to Tad Aburn (MDE) upon request

Provided analysis to Mark Estes (TCEQ) upon request for Dallas SIP

Duncan, Lamsal, Yoshida

Duncan, Lamsal, Yoshida

Will deliver similar analyses for 25 major US cities to ARSET website Data in Excel spreadsheets & downloadable images. AQ folks can provide feedback.

Met

ropo

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Are

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Backup Slides

OMI & AQS NO2: 30-40% decrease

“Which OMI NO2 data product should I use?”

→ We proposed to compare the differences and commonalities of the main retrieval algorithms, evaluate the trends and monthly anomalies calculated from the data products with EPA AQS surface data, better quantify uncertainties, and highlight the implications of our findings for AQ applications. → We proposed to investigate the roles of the differing input parameters and assumptions used to develop the data products, and assess the strengths and weaknesses of those implementations using EPA AQS data.

Work for the Coming Year???

Main Products SP: Standard Product from NASA Goddard DP: DOMINO Product from KNMI (Netherlands) SP-HR: NASA’s Standard Product - High Resolution BEHR: Berkeley’s Product - High Resolution

“Raw” Satellite Radiances

HR Regional Products (North America)

NO2 SCD

SP NO2 VCD (NASA) DP NO2 VCD (KNMI)

SP-HR (NASA)

BEHR (Berkeley)

“Family Tree” of OMI NO2 Data Products

Acronyms SCD: Slant Column Density from DOAS spectral fit VCD: Vertical Column Density = SCD/AMF AMF: Air Mass Factor from radiative transfer model to convert SCD to VCD

Various “Levels” of data indicate the degree of processing. L0/L1 = raw radiance data obtained by OMI L2 = original geolocated observations (i.e., not spatially gridded) for each daily satellite overpass L3 = data mapped to a regular spatial grid and averaged over time (e.g., month)

L0/L1

L2

L2/L3

L2/L3

Levels

**The differences between the four products’ retrieval methods are too many to indicate here.

Ω 𝒕𝒕 = 𝜶𝜶(𝒕𝒕) + 𝜷𝜷(𝒕𝒕) + 𝑵𝑵 𝒕𝒕

Ω 𝒕𝒕

𝜷𝜷(𝒕𝒕)

𝑵𝑵 𝒕𝒕

𝜶𝜶𝟏𝟏 𝒕𝒕

𝜶𝜶𝟐𝟐 𝒕𝒕

OMI NO2 tropospheric column

Seasonal Cycle

Seasonal Amplitude

Linear Trend

“Noise” (not captured by regression model (e.g., weather))

NOx lifetime is shorter in summer. Greater accumulation in winter.

NOx emissions decreasing by 30-40% so less accumulation in winter.

Impact can be significant (e.g., 10-15%).