Fugitive Dust Research at DRIPortable Wind Tunnel

Unpaved Road Dust Emission FactorsTRAKER measurements at Lake Tahoe

Near Field Deposition

Research by:

Hampden KuhnsVicken Etyemezian

Jack GilliesAlan Gertler

Djordje NikolicSean AhonenCliff DenneyJohn Skotnik

Nicholas NussbaumDave Dubois

Jin Xu

USDA-ARS Wind Erosion Research Unit http://www.weru.ksu.edu/vids

1. Portable Wind Tunnel

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

Side View Bottom View

Computer Controller/ Data System PM Monitor

Sample Tube

Open-bottomed Cylindrical Chamber

Variable Speed Motor

Annular Ring

60 cm

40 cm

Side View Bottom View

Computer Controller/ Data System PM Monitor

Sample Tube

Open-bottomed Cylindrical Chamber

Variable Speed Motor

Annular Ring

Side View Bottom View

Computer Controller/ Data System PM Monitor

Sample Tube

Open-bottomed Cylindrical Chamber

Variable Speed Motor

Annular Ring

60 cm

40 cm

Blower for clean air injection

PI-SWIRL v.2

The PI-SWIRL-ogramSWIRLER RPM and PM10 Dust Concentration

0

100

200

300

400

500

14:52 15:21 15:50 16:19 16:48

Time of Day

PM

10 C

on

cen

trat

ion

(m

g/m

3)

0

1000

2000

3000

4000

5000

RP

M

PM10

RPM

Test 1: Stable Soil

Test 2: Disturbed Soil

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 (vic@dri.edu) for more information

2. Unpaved Road Dust Emission Factors

Emission factor

calculated as horizontal flux

of PM10 passing

instrumented towers

-DT-DT

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76

517266

128

125

1220570

26040

125

1220570

260

-GR

-GR

-GR

-GR

-GR

-GR

-SA

7005,000

10,000

DT_1DT_2DT_3

Trailer with visibility

equipment

Generator 200 meters

LIDAR 3,000 meters

Top View

DT_1DT_2DT_3

Trailer with visibility

equipment

Generator 200 meters

LIDAR 3,000 meters

Top View

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

Legend

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

Legend

-DT-DT

-DT

-DT

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-DT

-DT

-DT

-DT

-DT

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76

517266

128

125

1220570

26040

125

1220570

260

-GR

-GR

-GR

-GR

-GR

-GR

-SA

7005,000

10,000

DT_1DT_2DT_3

Trailer with visibility

equipment

Generator 200 meters

LIDAR 3,000 meters

Top View

DT_1DT_2DT_3

Trailer with visibility

equipment

Generator 200 meters

LIDAR 3,000 meters

Top View

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

Legend

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

Legend

-5

0

5

10

15

20

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

Du

stT

rak

Rea

din

g (

mg

/m3)

DT_1 DT_3DT_2Baseline

Vehicle passes by DT_1

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

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

R2 = 0.89

0

500

1000

1500

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2500

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3500

4000

4500

0 50 100 150 200 250 300 350

Vehicle Mass*Speed (Mg*m/s)

PM

10

Em

issi

on F

acto

r (g

/VK

T)

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

TRAKER Signal vs Vehicle Speed

0

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1

1.5

2

2.5

3

3.5

4

4.5

0 5 10 15 20 25 30 35

Speed (m/s)

TR

AK

ER

Sig

na

l (m

g/m

3 )

Treasure Valley Regression

T=0.00017*speed2.96

R2 = 0.972

•T = Ctire – Cbkgrnd

•T = a S3

•On the same paved road the TRAKER signal increases with the speed cubed

•Factoring out speed leaves a signal proportional to the emission potential of the road.

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30 35 40

Speed (m/s)

TR

AK

ER

Sig

na

l (m

g/m

3 )

Ft. Bliss Regression

T=0.00012*speed2.75

R2 = 0.923

Flux Ladder in Lake Tahoe

Roadside PM Flux Measurements

PM concentration profile drops off with height

Real time instruments help when wind doesn’t cooperate

0

0.25

0.5

0.75

1

1.25

1.5

16:09:07 16:10:34 16:12:00 16:13:26 16:14:53 16:16:19Time on 2003/03/31

PM

10

Flu

x P

erpi

ndic

ular

to R

oad

(mg/

m s

) .

0

90

180

270

360

Win

d D

irect

ion

(deg

rees

)

Flux PM10Wind Direction

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450

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009

PM10 Conc Profile 1 m Downwind of Paved Road (mg/m3)

Hei

ght A

bove

Gro

und

(cm

)

TRAKER vs Horizontal PM Flux

Unpaved EF = 8.36 T1/3

0.1

1

10

100

1000

0.1 1 10 100 1000 10000

Traker Signal (mg/m3)

Em

issi

on

Fac

tor

(g/v

kt)

Lake Tahoe Paved

EF = 0.33 T1/3

Comparison of EF’s with Snow Precip

0

0.5

1

1.5

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2.5

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3.53/

1/20

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3/8/

2003

3/15

/200

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/200

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/200

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2003

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/200

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2003

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/200

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/200

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2003

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/200

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2003

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/200

3

Date

Sn

ow

Pre

cip

itat

ion

(cm

)

0

0.1

0.2

0.3

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0.6

Em

issi

on

Fac

tor

(g/v

kt)

Heavenly Valley Snotel

Rubicon Snotel

Marlette Lake Snotel

Average EF CA_Loop

Average EF NV Loop

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-DT

-DT

-DT

-DT

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-DT

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76

517266

128

125

1220570

26040

125

1220570

260-GR

-GR

-GR

-GR

-GR

-GR

-SA

7005,000

10,000

DT_1DT_2DT_3

Trailer with visibility

equipment

Generator 200 meters

LIDAR 3,000 meters

Top View

DT_1DT_2DT_3

Trailer with visibility

equipment

Generator 200 meters

LIDAR 3,000 meters

Top View

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

Legend

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

Legend

-DT-DT

-DT

-DT

-DT

-DT

-DT

-DT

-DT

-DT

-DT

-DT

-DT

76

517266

128

125

1220570

26040

125

1220570

260-GR

-GR

-GR

-GR

-GR

-GR

-SA

7005,000

10,000

DT_1DT_2DT_3

Trailer with visibility

equipment

Generator 200 meters

LIDAR 3,000 meters

Top View

DT_1DT_2DT_3

Trailer with visibility

equipment

Generator 200 meters

LIDAR 3,000 meters

Top View

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

Legend

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

DT: DustTrak

GR: GRIMM

SA: Sonic Anemometer

: Wind Vane

: Cup Anemometer

: Laptop computer

Legend

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.