Transcript Document

The 7th CMAS Workshop
CMAQ Dust Module:
Development and Initial Applications
Daniel Tong$, Rohit Mathur+, David Mobley+, David-C Wong+,
Shaocai Yu$, Hsinmu Lin$, Tianfeng Chai+
$ ARL/NOAA, on assignment from Science & Technology Corp.
+ Atmospheric Modeling and Analysis Division, US EPA, RTP, NC
Acknowledgement: We thank Jon Pleim for comments, Marc Houyoux and
Alice Gilliland for CMAQ input, Steve Howard for help with data processing.
Disclaimer: This work constitutes a contribution to the NOAA Air Quality Program.
Although it has been reviewed by EPA and approved for presentation, it does not
necessarily reflect their policies or views.
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;
(Source: IPCC, 2007)
 Ubiquitous constituents of inhalable PM;
PM2.5 Emissions in the U.S.
Anthropogenic
?
(Source: US EPA, NEI Air Pollutant Emissions Trends, 2006)
Calculating Natural Dust Emissions in U.S.
Key factors:
Dessert and agricultural land;
Soil moisture (rain & snow cover);
Soil components (sand, silt and clay);
Vegetation coverage
and roughness
Surface wind speed);
Threshold wind speeds
(Source: blog.maricopanewhomes.net)
Dust Emission Algorithm
Dust production using Owen’s Equation
(Marticorena et al, 1997):
Flux  K vh

g
u* (u*2  u*2t )
Threshold Friction Velocity (source: Gillette 1980, 1988):
Sandy
Loam
Silt
Loam
Loam
Sandy
Clay
Loam
Silty
Clay
Loam
Clay
Loam
Sandy
Clay
Silty
Clay
Clay
Soil type
Sand
Loamy
Sand
Desert Land
0.42
0.51
0.66
0.34
0.49
0.78
0.33
0.71
0.71
0.56
0.78
Agricultural
0.28
0.34
0.29
1.08
0.78
0.78
0.64
0.71
0.71
0.56
0.54
Size Distribution & Chemical Speciation
Size Distribution -- Bins (source: Marticorena et al, 1995; Ginoux
et al., 2001; Draxler et al., 2001):
 Threshold wind speeds from wind tunnel experiments
 Threshold values dependent on average particle sizes for each bin
Chemical Speciation (Pelt & Zobek, 2007; Ansley et al., 2006):
 99% of PM2.5 emissions into PMFINE (eventually A25J);
 1% of PM2.5 emissions into PSO4, PNO3, PEC & POA;
 100% of PM2.5-10 emissions into PMC (eventually ASOIL);
CMAQ Configuration
Emissions
 2002 NEI for anthropogenic emissions;
 BEIS 3 for biogenic emissions;
 Year-specific wildfires and prescribed burning;
 Natural dust emissions turning on and off.
Meteorology
 MM5 with Pleim-Xiu Land Surface module on;
CMAQ (version 4.6)
 Jan. 1 – Dec. 31, 2002;
 Domain: Continental US, N Mexico and S. Canada;
 MOZART-2 for lateral boundary conditions;
 CB05 gas chemistry and AE4 aerosol modules;
 Online calculation of photolysis rates;
Natural Dust (PM2.5) Emissions in 2002
Dust Emissions over Western Canadian
Topography Filter
Impact of topography on dust availability
(Prospero et al., 2001)
Monthly Profile of U.S. Dust Emissions
Dust emissions most active in Spring
PM2.5 Emissions by Sector (2002)
Top PM2.5 emission sectors (and uncertainties!):
Miscellaneous (anthropogenic) and Natural Emissions
Dust Impact on PM2.5 and O3
(a Major Dust Episode in late May 2002)
PM2.5 Change
O3 Change
Dust impact on O3 through reduced photolysis rates only;
Direct interactions of O3 with dust not implemented.
Dust Impact on PM2.5 Performance
(dust contribution < 1 mg/m3)
Without Dust
NMB = -26.2%
With Dust
NMB = -25.2%
Outside dust plumes, the addition of natural PM emissions
improves CMAQ performance to predict PM2.5
Dust Impact on PM2.5 Performance
(dust contribution 1 ~ 2 mg/m3)
w/o dust NMB = -11%
w/ dust NMB = 9%
Improved performance in areas of moderate impact
Dust Impact on PM2.5 Performance
(dust contribution: > 3 mg/m3)
Inside plume, resolved under-prediction,
but working too hard!
Conclusion
Annual natural PM2.5 emissions in the U.S.
 Contribute to 30% of primary PM2.5 emissions (preliminary)
 Most active in Spring
 Mostly from Southwest and Rocky Mountain regions
Impact of natural emissions on air quality simulation
 Dust emissions affect both PM2.5 and O3 concurrently
 The additional emissions improve CMAQ performance
for PM2.5, but cause over-prediction inside the plume;
Future Work
Data verification
 Comparison with more ground observations;
 AOD calculation and comparison with satellite data;
Implementation for air quality forecast
 Change PREMAQ to extract soil type and moisture
from MET model;
 Update CMAQ codes to include dust module;