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.

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Presentation transcript:

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

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)

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

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

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

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

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)

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

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;

The Dust Emission Model (Continued) Owen’s Equation (source: Marticorena et al, 1997): Threshold Friction Velocity (source: Gillette 1980, 1988): Soil type Sand Loamy Sand Sandy Loam Silt Loam Loam Sandy Clay Loam Silty Clay Loam Clay Loam Sandy Clay Silty Clay Clay Desert Land 0.42 0.51 0.66 0.34 0.49 0.78 0.33 0.71 0.56 Agricultural 0.28 0.29 1.08 0.64 0.54 Convert MM5-predicted U* into surface U* (U*s) (source: Marticorena et al, 1995):

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

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

(We put 45% into fine mode and 55% into coarse) Make it Ready for CMAQ Splitting b/w Fine and Coarse Modes (Cheng et al, 1997): Sites Sanggen Dalai Beijing Species PM2.5 PM10 Non-dusty day 36 53 81 142 Heavy dusty day 2815 4128 200 444 Difference 2779 4073 119 302 PM2.5 / PM10 68.2% 39.2% (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

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

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.

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

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

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

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;

Future Work Chemical speciation and size distribution 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;

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.