Presented by Gerard E. Mansell ENVIRON International Corporation Novato, California February 25,...

Presented by

Gerard E. MansellENVIRON International CorporationNovato, California

February 25, 2004

DETERMINING FUGITIVE DUST EMISSIONS FROM WIND EROSION

Project Team

• Gerard Mansell; ENVIRON

• Martinus Wolf, Paula Fields; ERG

• Jack Gillies; DRI

• Mohammad Omary; CE-CERT, UCR

• Bill Barnard; MACTEC Engr. & Consulting

• Michael Uhl; DAQM, Clark County, NV

Outline

• Project Background & Overview

• Literature Review

• Estimation Methodology

• Agricultural Considerations

• Data Sources

• Summary of Assumptions

• Program Implementation

• Results

• Sensitivity Simulations

• Recommendations

Background and Overview of Project

• Develop General Methodology to Facilitate Future Revisions and Control Strategy Development

• Develop Integrated SMOKE Processing Modules for PM10 and PM2.5 Emissions Modeling

• Develop PM10 and PM2.5 Emission Inventory Applicable to the Western Region

Overview of Technical Approach

• Categorize Vacant Land Types

• Identify Wind Tunnel Emission Factors

• Develop Meteorological Data

• Develop Threshold Wind Velocities, Wind Events, Precipitation Events

• Apply Emission Factors to Vacant Land Categories

Literature Review

• Portable field wind tunnels have been used to investigate particle entrainment thresholds, emission potentials, and transport of sediment by wind.

• Major contributions of information on: – thresholds from Gillette et al. (1980), Gillette et al.

(1982), Gillette (1988), Nickling and Gillies (1989);– emission fluxes from Nickling and Gillies (1989),

James et al. (2001), Columbia Plateau PM10 Program (CP3), Houser and Nickling (2001).

• Key information has also come from dust emission modeling (e.g., Alfaro et al., 2003) and desert soil characterization studies (e.g., Chatenet et al., 1996).

Wind Tunnel Study Results: Thresholds

u*t = 0.31e7.44x(Zo)

R2 = 0.60

u*t = 0.30e7.22x(Zo)

0

0.5

1

1.5

2

2.5

3

0.00001 0.0001 0.001 0.01 0.1 1

zo (cm)

u *t (

m s

-1)

wind tunnel data Marticorena et al. 1997Expon. (wind tunnel data) Expon. (Marticorena et al. 1997)

Comparison between modeled relationship of threshold friction velocity and aerodynamic roughness length and wind tunnel data.

*

*(Gillette et al., 1980; Gillette et al., 1982; Gillette, 1988; Nickling & Gillies, 1989)

Wind Tunnel Study Results: Emissions

FFS

F = 2.45x10-6 (u*)3.97

FS

F = 9.33x10-7 (u*)2.44

MS

F = 1.243x10-7(u*)2.64

CS

F = 1.24x10-7 (u*)3.44

0.000000001

0.00000001

0.0000001

0.000001

0.00001

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Friction Velocity (m s-1)

Em

issi

on F

lux

(F, g

cm

-2 s

-1)

FSS

FS

MSCS

Power (FSS)

Power (FS)

Power (MS)Power (CS)

The emission flux as a function of friction velocity predicted by the Alfaro and Gomes (2001) model constrained by the four soil geometric mean diameter classes of Alfaro et al. (2003).

Wind Tunnel Study Results: Emissions as a function of texture

Sand(CS) Silty sand (MS) Sandy silt (FS) Silt (FSS)

Clayey silt

Silty clay

sand silt

clay

Clay

Sandy siltyclay

Clayey sandysilt

Clayey siltysand

Sand

y cl

ay

Cla

yey

sand

50%

7525

7525

10

10

10

Sand(CS) Silty sand (MS) Sandy silt (FS) Silt (FSS)

Clayey silt

Silty clay

sand silt

clay

Clay

Sandy siltyclay

Clayey sandysilt

Clayey siltysand

Sand

y cl

ay

Cla

yey

sand

50%

7525

7525

10

10

10

Sand(CS) Silty sand (MS) Sandy silt (FS) Silt (FSS)

Clayey silt

Silty clay

sand silt

clay

Clay

Sandy siltyclay

Clayey sandysilt

Clayey siltysand

Sand

y cl

ay

Cla

yey

sand

50%

7525

7525

10

10

10

Relations between the soil types deduced from aggregate size distributions of various desert soils and soil textural categories (Chatenet et al. 1996). The “gray” highlighted textural classes indicate the 4 sediment types; the arrows indicate the pathways linking these types to the other textures. These can be linked to the North American soil texture triangle.

Wind Tunnel Study Results: Emissions

1E-10

1E-09

1E-08

1E-07

1E-06

10 100u* (cm/s)

Fv

(g

/cm

2/s

)

FS

Casa Grande

Mesa (Salt River)

Hayden

Ajo

Yuma (ag)

Glendale

Tucson (Sta Cruz)

Tucson (const.site)Mesa (ag)

CS

Yuma (scrubdeser t)Yuma (distdeser t)

Comparison between model relationship for FS and CS sizes and the wind tunnel data of Nickling and Gillies (1989). Ten (out of 13) sites have a dust production potential similar to the FS model and one site (Mesa agricultural) is closely aligned with the CS model (after Alfaro et al., 2003).

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

20 - 24.9 25 - 29.9 30 - 34.9 35 - 39.9 40 - 44.9 45 - 49.9 50 - 54.9

Emission Rates by Soil Group for Stable Soils E

mis

sio

n F

ac

tor

(to

n/a

cre

/ho

ur)

Soil Group 1

Soil Group 2

Soil Group 3

Soil Group 4

Soil Group 5

10-m Wind Speed (mph)

Emission Rates by Soil Group for Unstable Soils

0

0.005

0.01

0.015

0.02

0.025

0.03

20 - 24.9 25 - 29.9 30 - 34.9 35 - 39.9 40 - 44.9 45 - 49.9 50 - 54.9

10-m Wind Speed (mph)

Em

iss

ion

Fa

cto

r (t

on

/ac

re/h

ou

r)

Soil Group 1

Soil Group 2

Soil Group 3

Soil Group 4

Soil Group 5

Agricultural Considerations

• Non-climatic factors significantly decrease soil loss from agricultural lands

• Similar approach to CARB, 1997

• Five “adjustment” factors simulate these effects:

– Bare soil within fields

– Bare borders surrounding fields

– Long-term irrigation

– Crop canopy cover

– Post-harvest vegetative cover (residue)

Agricultural Adjustment Factor Development

• New regional data collected for WRAP project:

– Crop calendars with growth curves from Revised Universal Soil Loss Equation (RUSLE2) model

– Residues remaining after harvest due to conservation tillage practices from Purdue’s Conservation Technology Information Center (CTIC)

– Irrigation events from crop budget databases

• Factors applied by county/crop type, crop management zones (CMZs)

Data Sources

• Land Use/Land Cover (LULC)

– Biogenic Emission Landcover Database (BELD3)

– North American Land Cover Characteristics

– National Land Cover Database (NLCD)

• Soils Characteristics

– State Soil Geographic Database (STATSGO)

– Soil Landscape of Canada (SLC_V2)

– International Soil Reference and Information Centre

• Meteorological Data

– 1996 MM5 36-km (Wind Velocity, Precipitation, Snow/Ice, Soil Temperature)

Land Use/Land Cover Data

• BELD3 LULC Data

Summary Total Area (Acres) % % excluding waterUrban 6,781,771 0.26% 0.34%Agriculture 531,231,552 20.54% 26.35%Shrub/grassland 720,022,464 27.84% 35.71%Forest 741,902,639 28.69% 36.80%Barren 5,801,931 0.22% 0.29%Wetlands 681,383 0.03% 0.03%Tundra 9,096,875 0.35% 0.45%Snow&Ice 603,210 0.02% 0.03%Water 569,829,853 22.04%Total 2,585,951,680 100.00%

Total excluding water 2,016,121,827 100.00%

Meteorological Data

• 1996 MM5

– 1996 Annual, hourly, gridded meteorology

– 36-km horizontal resolution

– 10-m wind speeds

– Precipitation rates

– Snow/ice cover flag

– Soil temperature

Data Compilation for Land Use and Soil Types

• Land use and soil texture aggregated to 12-km resolution

• Major land use categories

– Urban

– Agricultural

– Shrub and grasslands

– Forest

– Barren and Desert

• Land use fractions from 1-km data retained as percentages

• Dominate soil texture at 12-km resolution

Soil Texture Categorization

• Standard soil types mapped to 5 major types for dust calculations

– Silty Sand and Clay

– Sandy Silt

– Loam

– Sand

– Silt

STATSGO Soil Texture

Soil Texture Code

Soil Group Code

No Data 0 0Sand 1 4Loamy Sand 2 4Sandy Loam 3 2Silt Loam 4 1Silt 5 5Loam 6 3Sandy Clay Loam 7 2

Silty Clay Loam 8 5Clay Loam 9 3Sandy Clay 10 2

Silty Clay 11 5Clay 12 1

Vacant Land Stability

• Windblown dust emissions affected by soil stability• Stability determination based on land types• Urban lands may be stable or unstable

LULC Category Stability Urban Stable/Unstable (see below) Agricultural -- Shrubland Stable Grassland Stable Mixed Shrub/Grassland Stable Forest Stable Barren Unstable Desert Unstable

Reservoir Characteristics

• Reservoirs characteristics based on stability

– Stable = limited

– Unstable = unlimited

• Stable reservoirs are depleted within 1 hour

• Unstable reservoirs are depleted within 10 hours

• Reservoirs require 24 hours to recharge

Precipitation and Freeze Events

• No dust emissions during rain events

• Rainfall from MM5 at 36-km resolution

• No dust emissions if snow/ice cover present

• Dust emissions re-initiated:

– 72 hours after rain

– 72 hours after snow/ice meltdown

– 12 hours after thaw

Vegetative Cover Adjustments

• Vegetation cover reduces dust emissions

• Methodology developed for bare soil

• Emissions reduction factors developed from White (2000)

• Vegetation density based on land use types

Vegetative Cover Adjustments

LULC Category Vegetation Cover % Reduction Factor

Urban 55(stable)/0(unstable) 0.07/1.0

Shrubland 11 0.70

Grassland 23 0.19

Mixed Shrub/Grassland

17 0.45

Forest 55 0.07

Barren 0 1.0

Desert 0 1.0

Summary of Assumptions

• Threshold velocity = 20 mph

• Vacant land stability

• Urban lands

• Dust reservoirs

• Reservoir depletion and recharge times

• Precipitation, snow and freeze events

• Vegetation density

Program Implementation

• Daily/Hourly Meteorological Data

• State/County, Crop Management Zone, and Soil Type, For Each 12km Cell.

• Area fractions For Each 12km Cell, and Land Use For Each Area Fraction.

• Agricultural Adjustment Data

• Emission Rates by Soil and Wind Speed Categories

Summary of Annual PM10 Emissions

Annual PM10 by Landuse

urbanAg_nonadjustAgdesert,barren landforestshrubgrasslandshrub/grassland

PM10 Dust Emissions by Month

Total Dust Emission

00

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1 2 3 4 5 6 7 8 9 10 11 12

Month

(ton

s)

Monthly PM10 Emissions by Landuse Type

Total PM10 (tons)

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Month

tons

water/weland

shrub/grassland

grassland

shrub

forest

desert,barren land

Ag

Ag_nonadjust

urban

Monthly PM10 Emissions by Crop Type

Monthly PM10 by crops

0

50000

100000

150000

200000

250000

300000

350000

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Month

PM

10(t

ons)

Wheat

Tobacco

Soybeans

Sorghum

Rye

Rice

Potatoes

Peanuts

Pasture

Oats

Misc

Hay

Grass

Cotton

Corn

Barley

Alfal

Annual PM10 and PM2.5

Sensitivity Simulation

• Evaluate impact of threshold velocity and reservoir assumptions

• Extend emissions factor relations to lower wind speeds

– 15 mph threshold velocity

• Relax reservoir recharge assumptions:

– 12 hours between wind events

– 36 hours after rain events

– 36 hours after snow/ice meltdown

– 6 hours after thaw

Comparison of PM10 Dust Emissions by Month

Monthly PM10 Fugitive Dust Emissions

0

2000000

4000000

6000000

8000000

10000000

12000000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Ton

s

PM10 (Original)

PM10 (Sensitivity)

Annual PM10

Application to Imperial Valley, CA

• Applied to Imperial County, CA

• 2-km modeling domain

• CALMET Meteorology – 15 mph threshold

• BELD3 and Dept. of Water Resources (DWR) LULC

• Reservoir recharge assumptions:

– 12 hours between wind events

– 36 hours after rain events

– 36 hours after snow/ice meltdown

– 6 hours after thaw

Monthly PM10 Emissions by DWR Landuse Type

Monthly Dust Summary

0.0

10000.0

20000.0

30000.0

40000.0

50000.0

60000.0

70000.0

80000.0

90000.0

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

Month

PM

10

(to

ns)

URBAN GRASS FOREST

BARREN NonAdj_Ag Ag w/Adj

Monthly PM10 Emissions by BELD3 Landuse Type

Monthly Dust Summary

0.0

2000.0

4000.0

6000.0

8000.0

10000.0

12000.0

14000.0

Jan. Feb Mar Apr May June July Aug Sept Oct Nov Dec

Month

PM

10 (

tons

)

URBAN GRASS FOREST BARREN NonAdj_AG AGw/adj

Annual PM10

Air Quality Modeling

WRAP 96 Fugitive Dust vs. Basei(IMPROVE evaluation)

• 1st half of 1996: date 1-19 and 90-109

• 2nd half of 1996: date 180-199 and 270-289

CM: Fugitive Dust vs. Basei

1st half of 1996 2nd half of 1996

SOIL: Fugitive Dust vs. Basei

1st half of 1996 2nd half of 1996

SO4: Fugitive Dust vs. Basei

1st half of 1996 2nd half of 1996

NO3: Fugitive Dust vs. Basei

1st half of 1996 2nd half of 1996

OC: Fugitive Dust vs. Basei

1st half of 1996 2nd half of 1996

EC: Fugitive Dust vs. Basei

1st half of 1996 2nd half of 1996

PM25: Fugitive Dust vs. Basei

1st half of 1996 2nd half of 1996

PM10: Fugitive Dust vs. Basei

1st half of 1996 2nd half of 1996

BEXT: Fugitive Dust vs. Basei

1st half of 1996 2nd half of 1996

Recommendations

• Methodology review and refinement

• Current, detailed data to characterize vacant lands

• Methodology validation with small-scale, high resolution domain

• Identification and evaluation of additional wind tunnel studies

• Application to other domains, years