Fugitive Dust Research at DRI Portable Wind Tunnel Unpaved Road Dust Emission Factors TRAKER measurements at Lake Tahoe Near Field Deposition

Research by:

Hampden Kuhns Vicken Etyemezian Jack Gillies Alan Gertler Djordje Nikolic Sean Ahonen Cliff Denney John Skotnik Nicholas Nussbaum Dave Dubois Jin Xu 1. Portable Wind Tunnel

USDA-ARS Wind Erosion Research Unit http://www.weru.ksu.edu/vids LWT at Ft. Bliss, TX

•J. Gillies and B. Nickling testing emission flux potential •LWT is closest measurement to a “standard” •SWT - e.g. D. James (UNLV), D. Gillette (NOAA) •Concerns with boundary layer development, maximum wind speeds, and accounting for saltation PI-SWIRL Schematic

Computer Controller/ Data System PM Monitor

Blower for clean air injection Sample Tube 60 cm Variable Speed Motor Open-bottomed Cylindrical Chamber 40 cm Annular Ring

Side View Bottom View PI-SWIRL v.2 The PI-SWIRL-ogram

SWIRLER RPM and PM10 Dust Concentration

5000 500 Test 1: Test 2: Stable Soil Disturbed Soil ) 3 400 4000

PM10 3000 300 RPM RPM 200 2000 Concentration (mg/m 10 100 1000 PM

0 0 14:52 15:21 15:50 16:19 16:48 Time of Day PI-SWIRL Status

• Version 3 is currently being tested – Lower weight and smaller size – Faster measurement – Low cost custom circuitry • Patent application filed • PI-SWIRL has been collocated with LWT to draw empirical relationship – Data still being analyzed

• Contact Vic Etyemezian ([email protected]) for more information 2. Unpaved Road Dust Emission Factors -DT -SA -DT Top View

Legend DT_3 DT_2 DT_1

1220 DT: DustTrak 1220 Trailer with visibility GR: GRIMM equipment SA: Sonic Anemometer LIDAR 3,000 Generator meters : Wind Vane 200 meters : Cup Anemometer : Laptop computer

Emission -DT -GR -DT -GR -DT 570 570 517

-DT -GR -DT -DT-GR

factor 260 260 266 125 125

-DT -GR -DT -GR 128 -DT -DT calculated as 40 -DT 76 700 5,000 10,000 horizontal flux 20 of PM 10 15 passing instrumented 10

5 Vehicle passes by DT_1

towers DustTrak(mg/m3) Reading

0

Baseline DT_1DT_2 DT_3 -5 18:02:10 18:02:27 18:02:44 18:03:01 18:03:19 18:03:36 18:03:53 18:04:11 Time Unpaved Emissions Measured on Flux Towers in Ft. Bliss TX (April 2002)

Vehicle Weight (kg) # Wheels Dodge Neon 1,176 4 Ford Taurus 1,516 4 Dodge Caravan 1,759 4 HUMVEE 2,445 4 TRAKER (Chevy Van) 3,100 4 26’ UHAUL Truck 5,227 6 LMTV 8,060 4 Freightliner (Tractor) 8,982 22 HEMMET 17,727 8 5-ton Truck 14,318 6 EFPM10 = b W S

4500

EFPM10 [g/VKT] = 10.3 (W [Mg]) (S [m/s]) 4000 R2 = 0.89

3500

3000

2500

2000

1500 Emission Factor (g/VKT) 10

PM 1000

500

0 0 50 100 150 200 250 300 350 Vehicle Mass*Speed (Mg*m/s) Unpaved Road Dust Emission Factor Status • Emission factors are dependent on vehicle speed and weight • Emission potentials of unpaved road soils were relatively constant in Ft. Bliss TX based on TRAKER. • Need to determine how emission potential varies in other regions. • Time since last rainfall is correlated with unpaved road emission factors

John A. GILLIES, Vicken ETYEMEZIAN, Hampden KUHNS, Djordge NIKOLIC & Dale A. Gillette (2004) Effect of Vehicle Characteristics on Unpaved Road Dust Emissions. Accepted in Atmospheric Environment

Kuhns H., V. Etyemezian, J. Gillies, S. Ahonen, C. Durham, D. Nikolic (2003) Spatial Variability of Unpaved Road Dust Emissions Factors near El Paso, Texas. Accepted in J. Air & Waste Manage. Assoc.

Kuhns H., V. Etyemezian, M. Green, Karin Hendrickson, Michael McGown, Kevin Barton, Marc Pitchford (2004) Vehicle- based road dust emissions mesasurement (II): Effect of precipitation, winter time road sanding, and street sweepers on PM10 fugitive dust emissions from paved and unpaved roads. Atmospheric Environment. 3. Testing Re- entrained Aerosol Kinetic Emissions from Roads (TRAKER) Measurements in Lake Tahoe • Particle Sensors – TSI DustTrak 5830 – Grimm Particle Size Analyzer 1.108 •GPS – Ashtech/Magellan Promark X Data Acquisition and Processing

•Lab View program displays and logs data from •6 DustTraks •3 Grimms •1 GPS •Uniform time stamp applied to all data for synchronization •Data tables are loaded into MS Access for processing and analysis 2.5

Ft. Bliss Regression TRAKER Signal 2.75 T=0.00012*speed 2 R2 = 0.923 ) vs Vehicle Speed 3 1.5

•T = Ctire –Cbkgrnd 1 3 (mg/m TRAKER Signal •T = a S 0.5

0 0 5 10 15 20 25 30 35 40 •On the same paved road Speed (m/s) the TRAKER signal 4.5 Treasure Valley Regression increases with the speed 4 T=0.00017*speed2.96 R2 = 0.972 3.5 ) cubed 3 3

2.5 •Factoring out speed leaves 2 1.5 TRAKER Signal (mg/m Signal TRAKER a signal proportional to the 1 emission potential of the 0.5 0 0 5 10 15 20 25 30 35 road. Speed (m/s) Flux Ladder in Lake Tahoe 450

400

350 Roadside PM Flux 300 250 Measurements 200 150

Height Above Ground (cm) Ground Above Height 100 PM concentration profile 50 0 drops off with height 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 3 PM10 Conc Profile 1 m Downwind of Paved Road (mg/m )

Real time instruments 1.5 360 Flux PM10 Wind Direction help when wind doesn’t 1.25 270 cooperate 1

. 0.75 180

0.5

90 Wind Direction (degrees) Flux Perpindicular to Road (mg/m s)

10 0.25 PM

0 0 16:09:07 16:10:34 16:12:00 16:13:26 16:14:53 16:16:19 Time on 2003/03/31 TRAKER vs Horizontal PM Flux

1000

Unpaved EF = 8.36 T1/3

100

10

Emission Factor (g/vkt) Emission Factor 1

Lake Tahoe Paved EF = 0.33 T1/3 0.1 0.1 1 10 100 1000 10000 Traker Signal (mg/m3) Comparison of EF’s with Snow Precip

3.5 0.6 Heavenly Valley Snotel Rubicon Snotel 3 Marlette Lake Snotel 0.5 Average EF CA_Loop 2.5 Average EF NV Loop 0.4

2 0.3 1.5

0.2 Emission Factor (g/vkt)

Snow Precipitation (cm) 1

0.1 0.5

0 0 3/1/2003 3/8/2003 4/5/2003 5/3/2003 6/7/2003 7/5/2003 3/15/2003 3/22/2003 3/29/2003 4/12/2003 4/19/2003 4/26/2003 5/10/2003 5/17/2003 5/24/2003 5/31/2003 6/14/2003 6/21/2003 6/28/2003 7/12/2003 Date Spatial/Temporal Variability of Road Dust Tahoe TRAKER Status • Road Dust EF’s drop by 70-80% from Spring to Summer • Previous TRAKER Calibration based on unpaved roads was way off – Maybe due to whole fleet vs just TRAKER? • Cities roads are dirtier than high speed rural highways • Something is different b/w CA and NV roads that create less dust

Draft report completed for CARB in June. Final expected by Sept. Transportable Fraction of Dust

• Basic Problem Statement: Inventory of dust sources appears to be too high compared with what we find in the air • Possible Causes – Our inventory as measured at the source is inaccurate – We are not accounting for removal of dust near the source Evolution of Plume Downwind Approaches

• Modeling – Advantages: Inexpensive, easy to simulate countless environments – Disadvantages: Who knows if its right! • Measurement – Disadvantage: Expensive and labor intensive (e.g. Gillies SERDP), unclear if possible to measure – Advantage: Results based on a “Real” data Measurements of TF: >95% at 100 m at Ft. Bliss (Etyemezian et al., 2004)

<20% at 100 m at Dugway Proving Grounds Mock Urban Environment (Veranth et al., 2004)

USDA Proposal Submitted to measure TF in cornfield over growing season (Gillies et al., 2004)

-DT -SA -DT Top View

Legend DT_3 DT_2 DT_1

1220 DT: DustTrak 1220 Trailer with visibility GR: GRIMM equipment SA: Sonic Anemometer LIDAR 3,000 Generator meters : Wind Vane 200 meters : Cup Anemometer : Laptop computer

-DT -GR -DT -GR -DT 570 570 517

-DT -GR -DT -DT-GR 260 260 266 125 125

-DT -GR -DT -GR 128 -DT -DT 40 -DT 76

700 5,000 10,000 Change in Integrated Horizontal Flux at Ft. Bliss Comparison of Model and Measurements Change is Particle Size Distibution Downwind Transportable Fraction Research: Status • Initial attempt completed (WESTAR report) • Next round of research should target – Additional field studies – Model improvement – Consideration of vegetation, landscape

Etyemezian V., J. Gillies, H. Kuhns, D. Gillette, S. Ahonen, D. Nikolic, and J. Veranth (2004) Deposition and removal of fugitive dust in the arid southwest United States: Measurements and model results. Acceptd in J. Air & Waste Manage. Assoc.