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Using the CMAQ Model to Simulate a Dust Storm in the Southwestern United States

The 6 th CMAS Workshop. Using the CMAQ Model to Simulate a Dust Storm in the Southwestern United States. Daniel Tong $ , George Bowker * , Rohit Mathur + , Tom Pierce + , Shaocai Yu $ , Dale Gillette +.

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Using the CMAQ Model to Simulate a Dust Storm in the Southwestern United States

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  1. The 6th CMAS Workshop Using the CMAQ Model to Simulate a Dust Storm in the Southwestern United States Daniel Tong$, George Bowker*, Rohit Mathur+, Tom Pierce+, Shaocai Yu$, Dale Gillette+ Atmospheric Sciences Modeling Division, ARL/NOAA, RTP, NC 27711 * Atmospheric Modeling Division, US EPA, RTP, NC $ On assignment from STC + on assignment to NERL/ORD/EPA October 2, 2007 Chapel Hill, NC

  2. Environmental Impacts of Dust Particles Climate : • Direct: absorbing & scattering; • Indirect: CCN; • Bio-available iron  phytoplankton  CO2 sink; Atmospheric Chemistry: • Reduce photolysis rates by over 50%; • Reacting platform for O3, HO2 and N2O5; • Buffering acid rain; Air Quality: • Reduce visibility; • PM air quality standards; Human Health: • Sources for toxic metals; • Ubiquitous constituents of inhalable PM; (Source: IPCC, 2007)

  3. Agricultural sources (Dust from crop land) Natural desert sources (Chihuahua desert) Sources of Dust Emissions: 3 major types Anthropogenic sources EPA’s National Emission Inventory (NEI) includes anthropogenic dust emissions (Dust from unpaved road) The other two major sources are not accounted for

  4. Impact on CMAQ Modeling CMAQ simulation of PM2.5 on April 15, 2003

  5. MODIS Image on April 15, 2003 – Wind-blown dust storm Comparing CMAQ with IMPROVE

  6. Box Model CMAQ Simulation (w/ dust emissions) • Size distribution • Chemical speciation • Merge with SMOKE • Other • Model Evaluation • IMPROVE, AQS, etc • Satellite Met-Driven Dust Emission Model How to Simulate Dust Storm with CMAQ? Post-processing Dust Emission

  7. Modeling Wind-blown Dust Emissions Important parameters • Open barren areas (land use data); • Dry soil (precipitation, soil moisture, and snow cover); • Soil components – sand, silt and clay (soil type data); • Vegetation coverage (seasonal crop data, roughness) • High wind to mobilize particles (surface wind speed); • Threshold wind speed for each soil type (threshold wspd)

  8. Building a Box Model Purpose:Sensitivity test and compare various parameters Dust emission modeling fundamentals • Dust Emission Flux = Kvh * [Horizontal Flux] • Horizontal flux Equations were derived from wind tunnel and field experiments over different soils. Dust emission is more sensitive to threshold friction velocity than to formulation of flux equation

  9. The Stand-alone Dust Emission Model Purpose:put everything together to cook some dust Dust emission model: • Driven by MM5 with Pleim-Xiu scheme; • Owen’s flux equation; • USGS land use and soil data; • Model dust emissions from desert and agricultural lands; • Threshold friction velocities taken from field and wind tunnel measurements; • MM5-predicted U* adjusted for local conditions; • Crop map subroutine by Shan He;

  10. The Dust Emission Model (Continued) Owen’s Equation (source: Marticorena et al, 1997): Threshold Friction Velocity (source: Gillette 1980, 1988): Convert MM5-predicted U* into surface U* (U*s) (source: Marticorena et al, 1995):

  11. Monthly average rate of dust emissions (DTOT) We got some dust here!

  12. 15-m Met. Tower Sensit Sediment collector Accurate capture of the occurrence and frequency of dust emissions. Nice! Comparing Dust Emissions with SENSIT Measurement

  13. Make it Ready for CMAQ Splitting b/w Fine and Coarse Modes (Cheng et al, 1997): (We put 45% into fine mode and 55% into coarse) Chemical Speciation (Pelt & Zobek, 2007; Ansley et al., 2006): • FAC_PSO4 = 0.0000773 ! Sulfate • FAC_PNO3 = 0.0000154 ! Nitrate • FAC_PEC = 0.0000953 ! EC • FAC_POA = 0.000638 ! OC • FAC_PMF = 0.45 - FAC_PSO4 - FAC_PNO3 … • FAC_PMC = 0.55

  14. Toxic Metal Emissions with Dust Chemical speciation for toxic metal (Pelt & Zobek, 2007): • FAC_PHG = 0.0000000638 ! Hg • FAC_PPB = 0.00000106 ! Pb • FAC_PFE = 0.000879 ! Fe • FAC_PCR = 0.00000309 ! Cr • FAC_PCD = 0.000000019 ! Cd • FAC_PAG = 0.000000007 ! Ag • FAC_PAS = 0.00000113 ! As • FAC_PCU = 0.00000119 ! Cu …… (factors may vary with soil type and land use) If not running CMAQ with toxic pollutants, these are put into the “other” portion in PM2.5

  15. CMAQ Simulation with Wind-Blown Dust CMAQ modeling details: • SAPRC gas chemistry, ae4 with recent updates (v4.6.1?) • 36 km resolution, vertical 14 layers • Domain over continental U.S., northern Mexico and southern Canada Dust Impact on O3 Concentrations (max difference) Missing in this version of CMAQ: online calculation of photolysis; dust as reaction platform for O3, N2O5, HO2 etc.

  16. Dust Impacts on PM2.5 Concentrations Mean difference (dust – base) Maximum hourly difference Huge impact on Peak PM2.5!

  17. Comparison with IMPROVE (Now with dust emissions) Improved, but not enough. Why?

  18. (Source: Rivera et al., 2006) Missing Dust Sources in Northern Mexico So we have missed at GUMO1 site! Comparison Dust Sources with MODIS

  19. Conclusion • A stand-alone emission model for wind-blown dust • CMAQ modeling of a dust storm • Missing PM emissions from desert and crop lands • Captured occurrence and frequency of dust emissions • Potentially useful for both Criteria and Toxics air pollutants • Dust impact on O3 is small – maybe too small! • Improved CMAQ performance for PM at some sites; • Missing dust source outside US;

  20. Future Work • Finer and more complete land use and soil data • Vegetation cover • Chemical speciation and size distribution • More calibration and evaluation • Sources outside US; • Resolution of barren areas; • Temporal variation; • Canopy scavenging; • Surface sheltering;

  21. Acknowledgement We thank Daiwen Kang for comments, Lucille Bender for providing CMAQ input, Steve Howard, David Wong, Rob Pinder for help with data processing, and Shan He for an earlier version of the dust emission algorithm. Disclaimer:The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce's National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect their policies or views.

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