Towards improved emissions inventories of soil NOx via model/satellite measurement intercomparisons
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Towards improved emissions inventories of soil NOx via model/satellite measurement intercomparisons. Heidy Plata 1 , Ezinne Achinivu 1 , Szu-Ting Chou 1 , Sheryl Ehrman 1 , Dale Allen 2 , Kenneth Pickering 2♦ , Thomas Pierce 3 , James Gleason 3

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Towards improved emissions inventories of soil NOx via model/satellite measurement intercomparisons

Heidy Plata1, Ezinne Achinivu1, Szu-Ting Chou1, Sheryl Ehrman1, Dale Allen2, Kenneth Pickering2♦, Thomas Pierce3, James Gleason3

1Department of Chemical and Biomolecular Engineering

2Department of Atmospheric and Oceanic Science

University of Maryland, College Park, Maryland

♦Laboratory for Atmospheres, NASA Goddard Space Flight Center

3 Atmospheric Modeling and Analysis Division, NERL, US EPA, Research Triangle Park, North Carolina

UNIVERSITY OF MARYLAND


Outline
Outline model/satellite measurement intercomparisons

  • Current Problem

  • Objectives

  • Brief introduction to BEIS3 and satellite products

    • Biogenic Emissions Inventory System

    • OMI-standard and OMI-DOMINO tropospheric NO2 products

  • Effect of precipitation on NO emissions

  • Approach

  • Discussion and Future Work


Current problem
Current Problem model/satellite measurement intercomparisons

Production of Tropospheric Ozone

Nitrogen Deposition

NOx contributes

Formation of Particulate matter

Stratospheric Ozone depletion


Sources of nox
Sources of NOx model/satellite measurement intercomparisons

Soil

Biogenic

Lightning

Sources of Nitrogen Oxides

Motor Vehicles

Anthropogenic

Point Combustion Sources/Power Plants

  • Modeling NOx emissions from biogenic sources poses a challenge as the frequency and magnitude of their emissions are uncertain.


Objectives
Objectives model/satellite measurement intercomparisons

  • Develop a better understanding of soil based sources of nitrogen oxides

  • Evaluate whether satellite observations of NO2 can be used to improve emissions estimates for soil derived NOx

  • Use this understanding and satellite observations to improve model estimates of NOx emissions in BEIS3, which is the biogenic emission module used in CMAQ


Details about beis3
Details about BEIS3 model/satellite measurement intercomparisons

Soil NO emissions in BEIS3 are a function of:

  • Land use and temperature

  • Precipitation: Emissions can increase by up to a factor of 12 with heavy rain.

  • Fertilizer: It doesn’t vary with region. Emissions are constant for first month of growing season (April) and then decrease

  • Canopy:the canopy adjustment factor is 1 for the first 30 days of the growing season then goes down linearly until it is 0.5 and then remains constant.  


Details about beis31
Details about BEIS3 model/satellite measurement intercomparisons

Land use (crop) and temperature


Details about ozone monitoring instrument omi tropospheric no2 column
Details about model/satellite measurement intercomparisons Ozone Monitoring Instrument (OMI) Tropospheric NO2 column

NASA Standard Product

Estimate stratospheric NO2 column using data from areas without significant tropospheric pollution.

Interpolate globally using a wave-2

pattern.

AMF assumes

annual mean vertical profiles from GEOS-Chem global model

OMI

Start with same slant column densities from

spectral fit of OMI observed radiances

Use stratospheric Column NO2 from TM4 global chemical transport model.

KNMI DOMINO Product

AMF assumes daily vertical profiles from TM4 model


Effect of precipitation on no emissions
Effect of precipitation on NO emissions model/satellite measurement intercomparisons

  • If dry soil is wetted, a large burst, or pulse occurs and then decays rapidly over a period of time following the wetting event.

Nutrient Accumulation

Wetting

Drying

NO


Effect of precipitation on no emissions1
Effect of precipitation on NO emissions model/satellite measurement intercomparisons

  • <0.1 cm/day no pulse

  • 0.1<rain<0.5 sprinkle (3 day pulse)

  • 0.5<rain<1.5 shower (1-week pulse)

  • 1.5<rain heavy rain (2 week pulse)


Approach
Approach model/satellite measurement intercomparisons

Choose

dates with likely NOx soil emissions due to precipitation

Spring 2005

(April6-May15)

Remove days with lightning or days with aerosol index>1

Select regions in which Biogenic emissions are substantial compared to anthropogenic


North and south dakota
North and South Dakota model/satellite measurement intercomparisons


Missouri and arkansas
Missouri and Arkansas model/satellite measurement intercomparisons


Approach1
Approach model/satellite measurement intercomparisons

Choose

dates with likely NOx soil emissions due to precipitation

Spring 2005

(April6-May15)

Remove days with lightning or days with aerosol index>1

Evaluate response of CMAQ tropospheric NO2 columns using OMI-retrieved columns

Evaluate response of BEIS3 emissions and CMAQ tropospheric NO2 columns to precipitation events

Select regions in which Biogenic emissions are substantial compared to anthropogenic


Episode of April 11 model/satellite measurement intercomparisons

Time


Episode of April 11 model/satellite measurement intercomparisons

Biogenic(mol/s)


Episode of April 11 model/satellite measurement intercomparisons

cm/day

1015 molecules /cm2

Time


Episode of April 12 model/satellite measurement intercomparisons

Precipitation ( cm/day)

Biogenic(mol/s)

Time


Episode of April 12 model/satellite measurement intercomparisons

Biogenic(mol/s)

Precipitation ( cm/day ) and CMAQ(10^15 molecules /cm^2)


Episode of April 12 model/satellite measurement intercomparisons

cm/day

1015 molecules /cm2

Time


Episode of May 9 model/satellite measurement intercomparisons

Biogenic(mol/s)

Time


Episode of May 9 model/satellite measurement intercomparisons

Biogenic(mol/s)

Precipitation ( cm/day ) and CMAQ(1015 molecules /cm2)

Time


Episode of May 9 model/satellite measurement intercomparisons

cm/day

1015 molecules /cm2

Time


Discussion
Discussion model/satellite measurement intercomparisons

Analysis is hampered by lack of OMI data on days during and sometimes following rainfall events due to clouds.

For cases in which CMAQ tropospheric NO2 columns show the clearest response to increases in biogenic emissions:

  • CMAQ high-bias relative to OMI increases after precipitation events implyingthat the sensitivity of BEIS3 soil emissions to precipitation events is overestimated at least for these cases

    Firm conclusions must await analysis of additional cases.

    Can additional cases be found in regions where the magnitudes of anthropogenic and biogenic emissions are comparable?


Episode of May 08 model/satellite measurement intercomparisons

Biogenic(mol/s)

Precipitation ( cm/day )


Episode of May 08 model/satellite measurement intercomparisons

Biogenic(mol/s)

Precipitation ( cm/day ) and CMAQ(10^15 molecules /cm^2)

Time


Episode of May 08 model/satellite measurement intercomparisons

cm/day

1015 molecules /cm2

Time


cm/day model/satellite measurement intercomparisons

1015 molecules /cm2

Time


Discussion1
Discussion model/satellite measurement intercomparisons

  • For regions with a greater fraction of anthropogenic emissions, NO2 pulses reflected in BEIS3 output but response of CMAQ tropospheric NO2 columns is controlled by other factors

  • Suggests utility of our approach limited to rural regions


Future work
Future Work model/satellite measurement intercomparisons

  • Continue focus on Northern Great Plains and Upper Midwest Region

  • Expand analysis to include spring 2006 precipitation events

  • Re-run analysis with reprocessed OMI data

  • Refine screening algorithms


Future work1
Future Work model/satellite measurement intercomparisons

  • Refine method used to determine if tropospheric NO2 column response to changes in biogenic emissions is more than expected from normal day-to-day variations

  • Use satellite-derived adjustments to improve BEIS-3 emissions

  • Consider modifying BEIS3 to better resolve the magnitude and duration of soil NOx pulses associated with precipitation


Acknowledgments
Acknowledgments model/satellite measurement intercomparisons

  • Financial Support: NASA Applied Sciences Air Quality Decision Support System Program.


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