characterizing and quantifying local and regional particulate matter ...

CHARACTERIZING AND QUANTIFYING LOCAL AND REGIONAL PARTICULATE MATTER EMISSIONS FROM DEPARTMENT OF DEFENSE INSTALLATIONS

Dr. J.A. Gillies, Dr. P. Arnott, Dr. V. Etyemezian, Dr. H. Kuhns, Dr. E. McDonald, Dr. H. Moosmüller

Mr. G. Schwemmer

Dr. W.G. Nickling

Dr. D.A. Gillette

Dr. T. Wilkerson

Annual Report for SERDP Project CP-1191 Prepared for: Mr. Bradley Smith and Dr. Robert Holst SERDP

December 21, 2001

TABLE OF CONTENTS 1.

PROJECT BACKGROUND.......................................................................................... 1

2.

PROJECT OBJECTIVES.............................................................................................. 1 2.1

3.

TECHNICAL APPROACH........................................................................................... 3 3.1

4.

Goals in FY 01 ...................................................................................................... 1

Technical Approach for Each Task....................................................................... 4 3.3.1 Air Quality Monitoring/CMB Receptor Modeling ................................... 4 3.3.2 Vehicle Generated Emission Factor Measurements, Horizontal/ Vertical Flux Relationships....................................................................... 4 3.3.3 TRAKER................................................................................................... 5 3.3.4 Wind Tunnel Testing to Assess Surface Disturbance Effects on Dust Emissions.......................................................................................... 5 3.3.5 Contributions to Regional Visibility Degradation .................................... 5

PROJECT ACCOMPLISHMENTS ............................................................................. 6 4.1 4.2

4.3

4.4

Air Quality Monitoring/CMB Receptor Modeling ............................................... 6 Vehicle Generated Emission Factor Measurements, Horizontal/ Flux Relationships .............................................................................................. 11 4.2.1 Vehicle Flux Measurements ................................................................... 12 4.2.2 Flux Calculation from Meteorological and Particulate Concentrations ........................................................................................ 13 4.2.3 The Gillette Emission Model .................................................................. 18 TRAKER............................................................................................................. 20 4.3.1 Description of TRAKER......................................................................... 20 4.3.1.1 Inlets...................................................................................... 22 4.3.1.2 Inlet Dilution System ............................................................ 24 4.3.1.3 Instruments............................................................................ 24 4.3.2 TRAKER Quality Assurance.................................................................. 26 4.3.2.1 Measurement Range, Precision, and Out of Range Data............................................................................ 27 4.3.3.2 TRAKER Signal and Vehicle Speed Relationships.............. 33 4.3.3.3 Comparison of TRAKER Measurements with Tower-Derived Fluxes .......................................................... 36 Wind Tunnel Testing to Assess Surface Disturbance Effects on Dust Emissions............................................................................................... 40 4.4.1 Site Description and Vehicle Impact Strategy ........................................ 40 4.4.2 The Portable Wind Tunnel...................................................................... 42 4.4.2.1 Wind Velocity Measurements............................................... 42 4.4.2.2 PM10 Dust Concentration Measurements.............................. 43

i

4.5

4.4.2.3 Measurement of Horizontal Mass Transport Rate ................ 44 4.4.2.4 Soil and Surface Characteristics of the Test Plots ................ 44 4.4.2.5 Wind Tunnel Testing Procedures.......................................... 45 4.4.5 Test Results............................................................................................. 47 Contributions to Regional Visibility Degradation .............................................. 52 4.5.1 In Situ Measurements.............................................................................. 52 4.5.2 Optical Remote Sensing.......................................................................... 53 4.5.2.1 Holographic Airborne Rotating Lidar Instruments (HARLIE) ............................................................................. 53 4.5.2.2 Sun Photometer..................................................................... 56

Acknowledgements ................................................................................................................... 57 5.

REFERENCES.............................................................................................................. 58

Appendix I ................................................................................................................................. 60

ii

LIST OF FIGURES

Page No.

Figure 1.

Schematic diagram of the structure and links between components of the empirically-based field study......................................................................................3

Figure 2.

Map of upwind-downwind flux measurement towers at Ft. Bliss. The black dots indicate the position of the upwind and downwind towers. The open dots are the GPS points denoting the path of the TRAKER vehicle through the test sections ....12

Figure 3.

Time series plots of vertical concentrations measured on downwind tower #1 (9 m from unpaved road) and downwind tower #2 (50 m from unpaved road) .......14

Figure 4.

Diagram of sampler positions and relative plume concentrations downwind of the unpaved road .......................................................................................................15

Figure 5.

Comparison of emissions factors measured on towers 9 m and 50 m downwind of an unpaved road. Error bars on the figure represent the standard error of the flux measurements ....................................................................................................17

Figure 6.

Unpaved road dust emissions factors versus vehicle speed for two weight classes of vehicles. The Ford Ranger weighs ~1.5 Mg while the TRAKER and HUMVEE weigh ~3.5 to 4 Mg ..........................................................................18

Figure 7.

Box model for obtaining estimates of regional scale vertical dust flux (Gillette, 2001)..........................................................................................................20

Figure 8.

Principle of operation of the TRAKER. Influence monitors measure the concentration of particles behind the tires. The background monitor is used to establish a baseline....................................................................................................21

Figure 9.

The TRAKER vehicle (1979 Chevrolet Van) during the testing period ..................21

Figure 10. Photographs depicting TRAKER vehicle and instrumentation used in the Ft. Bliss study. a. Location of inlets (right side and background shown) on the TRAKER; b. Generator and pumps are mounted on a platform on the back of the van; c. Photo showing two sampling plenums (bottom), a suite of DustTrak and GRIMM particle monitors (top right), and three rotameters used for ensuring proper flows through plenum; d. a dashboard-mounted computer screen is used to view the data stream (top) and a GPS logs the TRAKER’s position every 1 second.............................................................................................23 Figure 11. Photograph showing inlet configuration of the TRAKER dilution system ..............24

iii

LIST OF FIGURES (cont.)

Page No.

Figure 12. TRAKER Control Panel. Real-time displays show the magnitude of the response of DTs. Additional displays show measurements from 3 GPSAs and the GPS receiver. The 10 lights in the top left of the screen serve as indicators of the health of onboard instruments (green = OK; red = not functioning). .......................26 Figure 13. TRAKER coefficient of variation expressed as a percentage for Left and Right PM10 DT signals as a function of speed. The data represent Left and Right PM10 DT signals over a 1.6-km stretch of road in Treasure Valley, ID. The coefficient of variation is equal to the standard deviation of a measurement divided by the average and gives an estimate of the precision of the measurement .............................................................................................................27 Figure 14. TRAKER coefficient of Variation expressed as a percentage for Left and Right PM2.5 DT signals as a function of speed. These data represent Left and Right PM2.5 DT signals over a 1.6-km stretch of road in Treasure Valley, ID. Note that data for speeds less than 50 km/hr are below detection limits for the PM2.5 DTs..................................................................................................................28 Figure 15. PM10 and PM2.5 DT signals when they are collocated in the same sampling plenum within the TRAKER. An R2 of 0.89 shows that the two measures are strongly correlated and that on average, the PM2.5 value is 39% of the PM10 value ..............29 Figure 16. Photograph showing two GPSAs and two DTs as part of the configuration of line loss test for TRAKER center line. Though DTs are shown above, the directional nature of the inlets for those instruments did not allow for comparison with DTs connected to the manifold inside the TRAKER .......................................................31 Figure 17. Photograph showing dust generation as part of line loss characterization test.........32 Figure 18. Fractional line losses associated with each of the particle sizes measured by the GPSA and collocated instrument precision for TRAKER right, middle, and left inlets....................................................................................................................33 Figure 19. Average fractional line loss for left, middle, and right TRAKER inlet lines ...........34 Figure 20. Combined dilution and line losses associated with each of the particle sizes measured by the GRIMM OPC and collocated instrument precision for diluted TRAKER right and left inlets ...................................................................................35 Figure 21. Average fractional dilution and line loss for left and right TRAKER inlet lines when dilution system is in use ..................................................................................35

iv

LIST OF FIGURES (cont.)

Page No.

Figure 22. Relationship between differential DT measurements and vehicle speed for tests conducted on a road in Treasure Valley, ID .....................................................37 Figure 23. Relationship between unpaved road dust PM1o emissions factors and the TRAKER signal from Ft. Bliss. The error bars on the figure are the standard errors of the mean of each measurement ..................................................................39 Figure 24. The military vehicle used in the disturbance testing. Top to bottom: 5-ton truck, HUMVEE, and Bradley Fighting Vehicle ................................................................41 Figure 25. The University of Guelph portable field wind tunnel deployed on the BFV plot ....43 Figure 26. Instrumentation set-up in the wind tunnel working section......................................44 Figure 27. Example of the dust concentration as a function of height above the surface and through time for a test with no added saltation component (top figure) and when a simulated upwind source is introduced at the front of the tunnel (bottom figure). These examples are from the BFV tests ...................................................................48 Figure 28. Relationships observed between saltation and dust emission flux on the test plots...........................................................................................................................51 Figure 29. Layout of the optical instrumentation .......................................................................53 Figure 30. Location of HARLIE measurements for vertical and 45° HARLIE axis. Figs. 2c, d:Location of HARLIE measurements for horizontal HARLIE axis and location A..................................................................................................................55

v

LIST OF TABLES

Page No.

Table 1.

Mass concentrations of PM10 observed at the four ambient sampling sites during the Spring-Summer 2001 monitoring period.................................................. 7

Table 2.

Average emission factors calculated from unpaved road travel at Fort Bliss........... 16

Table 3.

Validity criteria applied to each TRAKER data point .............................................. 30

Table 4.

Regression exponents for differential TRAKER signal and vehicle speed from speed tests performed at Ft. Bliss, TX, and Treasure Valley, ID ............................. 37

Table 5.

Average TRAKER signals measured on unpaved road test sections at Ft. Bliss ..... 39

Table 6.

Soil characteristics of the four test plots ................................................................... 46

vi

1.

PROJECT BACKGROUND

Military activities on Department of Defense (DoD) installations in the southwest U.S. are potentially large contributors of mineral dust to the atmosphere. These contributions can arise from wind erosion processes acting upon the large expanses of fragile desert soils found within military installations and via testing and training activities. Testing and training activities inject dust particles into the air through various mechanisms including vehicle traffic and troop movements. Particulate matter (PM) emitted by these activities threatens the health and safety of military personnel due to inhalation of PM, impacts vehicle performance, and through loss of visibility. It impacts the performance of electro-optical systems and compromises flight operations. Potential off-post effects include regional visibility and air quality degradation that may result in DoD facing pressures from regulatory agencies. Developing cost-effective strategies to mitigate these problems requires identification of the main on-post PM source contributions and quantifying the absolute and relative amounts of the different sources. It is also important to understand the environmental conditions that control emissions and their subsequent transport. By accurately identifying the major sources of PM, resources can be targeted to reduce contributions more effectively. 2.

PROJECT OBJECTIVES The objectives of this project can be stated as follows: • • • • • •

2.1

Determine contributions from dust and other sources at Ft. Bliss, TX, to assess the regional impacts of these emissions on ambient particulate matter levels. Develop a dust emission factor database for military vehicles traveling on unpaved surfaces that reflects the influence of the surface over which the travel takes place and the speed of the vehicles. Develop and test a dust emission transport model that can effectively determine the potential of these emissions for long-range transport. Evaluate military vehicle disturbance effects on soil and surface properties and quantify the effects of disturbance on dust emission potential from impacted surfaces. Assess the potential visibility degradation off-post by the emitted PM. Develop emission components that will be integrated with a GIS-based emission model to estimate dust emission contributions from testing and training activities.

Goals in FY 01

To reach the overall objectives as stated above a series of specific objectives for the first year of the project were defined. Task 1: Air Quality Monitoring/CMB Receptor Modeling. 1) Establish the laboratory protocols for filter processing, shipping, and receiving to and from Ft. Bliss, 2) establish the four monitoring sites, collect three months of every six day monitoring samples, and 14 days of intensive monitoring, 3) carry out gravimetric analysis of the collected filters.

1

Task 2: Vehicle Generated Emission Factor Measurements, Horizontal/Vertical Flux Relationships. 1) Coordinate with Ft. Bliss personnel to schedule the use of three vehicle types for testing (Bradley fighting vehicle, Five-ton truck and HUMVEE) for testing, 2) select an unpaved or off-road site for testing through consultation with Ft. Bliss personnel, 3) deploy the instrumented towers to measure dust fluxes to calculate vehicle emission factors and the downwind relationship between horizontal and vertical flux, 4) collect soil samples for sedimentological analyses, 5) evaluate and analyze the collected data determine emission factors for the specific vehicles tested, 6) assemble the emission factor and soils data into a database, 7) assemble the meteorological, dust concentration profile, and dust deposition data into a database for analyses of the horizontal/vertical flux relationships in year 2. Task 3: TRAKER. 1) Simultaneously with the upwind-downwind measurements, have the instrumented TRAKER vehicle measure the plume concentrations and particle size distributions directly in the emission zone behind the tires, 2) evaluate these data and put them in a database, 3) analyze the relationship between plume characteristics and the estimated vehicle emission factors. Task 4: Wind Tunnel Testing to Assess Surface Disturbance Effects on Dust Emissions. 1) Select a site for testing through consultation with Ft. Bliss personnel, 2) establish the sites for the individual test plots, 3) have the selected vehicles (Bradley Fighting Vehicle, Fiveton truck, and HUMVEE) disturb the plots, 4) carry out wind tunnel testing and soil sampling for estimating vertical dust flux and the relationship of emissions with disturbance levels and soil properties. Task 5: Contributions to Regional Visibility Degradation. Prepare the in situ visibility instruments (photoacoustic instrument, nephelometer, and extinction meter) and HARLIE lidar system for deployment in year 2. The visibility instrument preparation is on schedule and the experimental design for the in situ and lidar measurements is being refined to insure the quality of the collected data is high and that it can be used to support the upwind-downwind measurements to maximum effectiveness. Task 6: Develop GIS-Based Model Components. As the data analyses are completed for Task 1 through 4 a database in GIS format will be assembled. Task 7: Technical and Annual Reporting. Quarterly Progress Reports were completed and submitted by the due dates. Submission of First Annual Report December 2001.

2

3.

TECHNICAL APPROACH

The general approach of this project is an empirically based field study with multiple components. The components of this program and their links are shown in Figure 1. The specifics of the technical approach for the Tasks that were focused on in FY 01 are detailed below.

Air Quality Near-Field Monitoring at Tower Monitoring Post Boundaries of Emissions

TRAKER Wind Tunnel Testing

CMB Modeling

Horizontal and Vertical Emission Factors

On-vehicle Emissions Measurements

Source Attribution

Relationship between Horizontal and Vertical Flux and effects of Meteorology

Monitoring of Visibility Degrading Aerosols

Lidar & in situ Instruments Quantify Visibility Degrading Characteristics of the Emissions Disturbance Effects on Wind-Generated Emissions

GIS-based Model Components Figure 1. Schematic diagram of the structure and links between components of the empiricallybased field study.

3

3.1

Technical Approach for Each Task

3.3.1

Air Quality Monitoring/CMB Receptor Modeling

Particulate matter samples are collected on EPA-approved PM10 medium-volume (MedVol) samplers following established measurement protocols (Gertler et al., 1993). Particles less than 10 µm aerodynamic diameter are collected simultaneously on two types of filters. Teflon-membrane filters are used to collect samples for gravimetric and chemical speciation by x-ray fluorescence (XRF). Quartz-fiber filters are used for determination of the amount of organic and elemental carbon by thermal optical reflectance (TOR), and ionic species (chloride, sulfate, and nitrate anions, ammonium, soluble potassium and sodium) by ion chromatography (IC), automated colorimetry (AC) and atomic absorption spectroscopy (AAS). Dynamic field blanks (10% of the total number of ambient samples) are taken and used to evaluate measurement precision. To define sources (e.g., wind-blown dust, mobile sources, forest fires, industry, secondary species, and others) and their relative contributions to ambient PM levels the Chemical Mass Balance (CMB) receptor model (Watson et al. 1990) will be applied to the speciated PM10 database. The CMB estimates the relative contributions of different emission sources with known precision and accuracy to the loading of atmospheric PM. In order to ensure reasonable model estimates the CMB applications and validation protocols described by Watson et al. (1991) will be applied. Individual CMB model runs will be carried out for selected pairs (upwind and downwind on the same sampling date) of the valid 24-hour samples for which the full chemical analyses have been carried out. The differences in the downwind versus upwind apportionment for similar sources will define the in-post contributions of the identified source types. 3.3.2

Vehicle Generated Emission Factor Measurements, Horizontal/Vertical Flux Relationships.

Theoretical predictions and empirical observations indicate that a large percentage of the initially entrained dust is deposited within a short distance of its origin, and is therefore unavailable for transport over long distances. Most emission measurements for vehicle-entrained dust characterize the horizontal flux of particles although downwind dispersion models require vertical flux emissions as input. To assess the potential long-range transport of the generated dust particles, it will be necessary to define the relationship between the horizontal flux of particles and its transition to a vertical flux. To define this relationship will require collecting data to characterize the change in plume characteristics as it moves downwind and becomes mixed into the atmospheric boundary layer, the particle size distribution of the emissions, and the near field deposition process. The dispersion process and the near-field deposition flux will be affected by the mass concentration and particle size distribution of the particles in the emitted plume and the meteorological conditions (e.g., wind speed, relative humidity, atmospheric stability, etc.) into which the particles are being emitted. To measure the transition from a horizontal to a vertical flux a series of three towers instrumented to measure vertical mass concentration and particle size distributions of the suspended sediment between 0 and 12 m above ground level over a distance of 50 to 100 will be

4

employed. The towers will be instrumented with TSI Model 8520 DustTraks (DT) and GRIMM 1.08 particle size analyzers (GPSA) (at the same sampling height) to observe changes in the particle size distribution in the plumes. Dust fallout traps (Vallack 1995) and will be placed between these two towers spaced 5 meters apart. The accumulated fallout material will be weighed to determine dust deposition rates (µg m-2 s-1) and also analyzed to determine the particle size distribution. Changes in the particle size distribution of the suspended sediments and the deposition flux as the dust plume moves downwind will influence the ratio of the vertical to horizontal flux. The dust deposition and particle size distribution data will aid in resolving the effect of these plume properties on this ratio. 3.3.3 TRAKER In order to develop a greater understanding of how emissions of dust change as a function of surface type a complimentary measurement procedure that utilizes the TRAKER (Testing Reentrained Aerosol Kinetic Emissions from Roads) (Kuhns and Etyemezian 1999) approach is also being utilized in this project. The TRAKER is a vehicle designed to measure the dust emissions potential of a roadway (paved and unpaved) or any off-road surface over which vehicles traverse. The principle of operation of the TRAKER is fairly simple. The TRAKER instrumentation is designed to measure the concentration of airborne particles in a specific size range in the dust plume generated behind the front tires of a vehicle. A background measurement of particle concentrations is obtained simultaneously at a location on the vehicle far away from the tires. The difference in the signals between the influence monitors and the background monitor is related to the amount of dust generated from the surface by the vehicle moving over it. The TRAKER measurement is related to the emission of dust from the surface over which the vehicle is traveling. The strength of the signal is influenced by such factors as vehicle speed, weight, and also the surface characteristics. 3.3.4

Wind Tunnel Testing to Assess Surface Disturbance Effects on Dust Emissions

Portable wind tunnel tests are being conducted on a test surface (within the M88 Driver Training Area, Ft. Bliss) with different levels of disturbance by three different military vehicle types (HUMVEE, 5-ton truck, and Bradley Fighting Vehicle). A control surface with no disturbance is used for comparison purposes. Horizontal saltation, and vertical dust fluxes are measured in the wind tunnel and surface characteristics (e.g., soil texture) monitored. The effect of disturbance levels on the absolute and relative amount of dust emissions will be subsequently used to improve emission estimates. The post-disturbance recovery process is also being evaluated by taking repeated measurements of the surface characteristics (e.g., degree of crusting, plant cover, and emission flux) in conjunction with the wind tunnel tests. 3.3.5 Contributions to Regional Visibility Degradation Visibility and its degradation due to aerosol can be quantified by aerosol light extinction (NRC, 1993). To quantify the flux of visibility degrading aerosol (VDA) originating from emissions measured as a function of vehicle activity, light extinction and wind velocity vectors need to be measured over a three-dimensional surface enclosing the emission zone. To characterize the visibility degrading potential of the emitted particulates and estimate the particle

5

flux, in situ light extinction and its scattering and absorption components will be measured at one location in the downwind flux plane. Semi-quantitative lidar (light detection and ranging) measurements of light extinction covering the whole downwind flux plane will be calibrated with the in situ measurements resulting in a quantitative determination of VDA concentrations for the downwind flux plane. The lidar system will also determine spatially resolved wind velocities, thereby yielding VDA fluxes for the downwind plane. The spatial and temporal resolution of these measurements makes it feasible to correlate VDA structure in space and time with particulate matter emission processes (i.e., vehicle activity). A sun photometer that measures the slant-path integrated light extinction in this flux plane will also be deployed to assess possibility of using these much simpler instruments for the routine monitoring of VDA flux from DoD installations. 4.

PROJECT ACCOMPLISHMENTS This section provides details on the accomplishments for the Tasks identified above.

4.1

Air Quality Monitoring/CMB Receptor Modeling

Four sites were selected as the locations for the air quality monitoring equipment. Two sites (upwind) are located on the west side of the installation and two on the eastern (downwind side). The first western location is on El Paso Water property on the western edge of the El Paso suburb of North Hills, adjacent to the Castner Range of Ft. Bliss. The second western site (northwest) is at the Dona Ana Range Camp. The eastern locations are the SHORAD installation (northeast) near Orogrande and at McGregor Camp an installation to the south of Orogrande. These four sites were established in May 2001. The filters from the first three months of every-sixth-day air quality monitoring covering the period from May through August 2001, and including the 14 day intensive monitoring period in June 2001, during Roving Sands, have been weighed to determine mass concentration for each sampling day at the four sampling site (Table 1). The filters are now in storage in the Environmental Analysis Facility (EAF) of the Desert Research Institute. The next sampling period will begin on January 02, 2001, and continue for three months on the U.S. EPA everysixth-day sampling schedule. Upon completion of the next period of sampling those filters will be weighed and based on the selection criteria, approximately 30% of the total number will be submitted for full chemical analyses. Meteorological data records from Ft. Bliss and monitoring sites in the El Paso, TX, area are being acquired for the dates on which ambient sampling occurred. The meteorological data will be used to ascertain the wind speed and wind direction of the regional airflow on the sampling days. Sampling dates that coincide with predominantly westerly or easterly flow will be flagged as potential days for which the full suite of chemical analysis will be undertaken. Samples of soils from the wind tunnel and vehicle emission test sites were delivered to Dr. John Veranth (CP-1190) for the subsequent development of mineral aerosol source profiles. These source profiles will be used in Chemical Mass Balance receptor model analysis to apportion the collected particulate matter to its respective sources.

6

Table 1. Mass concentrations of PM10 observed at the four ambient sampling sites during the Spring-Summer 2001 monitoring period. Site

Location

Date

Mass

Associated

Site

Location

Date

Concentration Uncertainty (µg/m3)

(µg/m3)

Mass

Associated

Concentration Uncertainty (µg/m3)

(µg/m3)

SHORAD

NE

5/25/01

16.1

0.8

Dona Ana

NW

5/25/01

38.3

1.9

SHORAD

NE

6/6/01

13.7

0.7

Dona Ana

NW

6/12/01

47.7

2.4

SHORAD

NE

6/6/01

0.2

0.3

SHORAD

NE

6/6/01

9.0

0.5

SHORAD

NE

6/11/01

41.7

2.1 Dona Ana

NW

6/12/01

16.1

0.8

SHORAD

NE

6/13/01

54.6

2.7

Dona Ana

NW

6/13/01

34.2

1.7

SHORAD

NE

6/14/01

22.4

1.1

Dona Ana

NW

6/14/01

39.5

2.0

SHORAD

NE

6/15/01

12.4

0.7

Dona Ana

NW

6/15/01

23.7

1.2

SHORAD

NE

6/16/01

24.2

1.2

Dona Ana

NW

6/16/01

34.1

1.7

SHORAD

NE

6/17/01

16.6

0.9

Dona Ana

NW

6/17/01

17.2

0.9

SHORAD

NE

6/18/01

43.6

2.2

Dona Ana

NW

6/18/01

41.7

2.1

SHORAD

NE

6/19/01

16.8

0.9

Dona Ana

NW

6/19/01

33.4

1.7

SHORAD

NE

6/20/01

13.0

0.7

Dona Ana

NW

6/20/01

16.8

0.9

SHORAD

NE

6/21/01

15.4

0.8

Dona Ana

NW

6/21/01

15.7

0.9

SHORAD

NE

6/22/01

14.9

0.8

Dona Ana

NW

6/22/01

23.9

1.2

SHORAD

NE

6/23/01

14.2

0.8

Dona Ana

NW

6/23/01

26.5

1.4

SHORAD

NE

6/24/01

14.8

0.8

Dona Ana

NW

6/24/01

46.0

2.3

SHORAD

NE

6/25/01

19.3

1.0

Dona Ana

NW

6/25/01

59.5

3.0

SHORAD

NE

6/30/01

18.8

1.0

7

Table 1 (cont.). Mass concentrations of PM10 observed at the four ambient sampling sites during the Spring-Summer 2001 monitoring period. Site

Location

Date

Mass

Associated

Concentration

Uncertainty

3

Site

Location

Date

3

(µg/m )

(µg/m )

Mass

Associated

Concentration

Uncertainty

3

(µg/m )

(µg/m3)

SHORAD

NE

7/6/01

23.8

1.2

Dona Ana

NW

7/6/01

25.3

1.3

SHORAD

NE

7/12/01

19.0

1.0

Dona Ana

NW

7/12/01

18.0

0.9

SHORAD

NE

7/18/01

10.7

0.6

Dona Ana

NW

7/18/01

28.6

1.5

SHORAD

NE

7/24/01

9.1

0.5

Dona Ana

NW

7/24/01

9.0

0.5

SHORAD

NE

7/30/01

7.7

0.5

Dona Ana

NW

7/30/01

22.3

1.1

SHORAD

NE

8/5/01

13.3

0.7

Dona Ana

NW

8/5/01

23.2

1.2

SHORAD

NE

8/12/01

6.5

0.4

SHORAD

NE

8/17/01

9.9

0.6

SHORAD

NE

8/23/01

14.2

0.8

SHORAD

NE

8/29/01

8.3

0.5

Dona Ana

NW

8/29/01

51.2

2.6

Dona Ana

NW

9/4/01

23.9

1.2

Dona Ana

NW

9/4/01

22.6

1.2

Dona Ana

NW

9/10/01

25.4

1.3

Castner

SW

5/25/01

22.5

1.2

Castner

SW

6/11/01

27.3

1.4

Castner

SW

6/12/01

37.7

1.9

McGregor

SE

6/13/01

124.0

1.1

Castner

SW

6/13/01

124.4

6.2

McGregor

SE

6/14/01

78.3

1.2

Castner

SW

6/14/01

45.1

2.3

8

Table 1 (cont.). Mass concentrations of PM10 observed at the four ambient sampling sites during the Spring-Summer 2001 monitoring period. Site

Location

Date

Mass

Associated

Concentration

Uncertainty

3

Site

Location

Date

3

(µg/m )

(µg/m )

Mass

Associated

Concentration

Uncertainty

3

(µg/m )

(µg/m3)

McGregor

SE

6/15/01

13.3

1.1

Castner

SW

6/15/01

17.8

0.9

McGregor

SE

6/16/01

16.5

1.7

Castner

SW

6/16/01

28.2

1.4

McGregor

SE

6/17/01

14.2

1.1

Castner

SW

6/17/01

18.8

1.0

McGregor

SE

6/18/01

57.3

1.6

Castner

SW

6/18/01

64.2

3.2

McGregor

SE

6/19/01

27.8

1.1

Castner

SW

6/19/01

46.4

2.3

McGregor

SE

6/20/01

11.5

1.1

Castner

SW

6/20/01

15.7

0.8

McGregor

SE

6/21/01

14.9

1.1

Castner

SW

6/21/01

22.6

1.2

McGregor

SE

6/22/01

16.8

1.1

Castner

SW

6/22/01

21.2

1.1

McGregor

SE

6/23/01

19.7

1.1

Castner

SW

6/23/01

30.6

1.5

McGregor

SE

6/24/01

25.6

1.1

Castner

SW

6/24/01

30.5

1.5

McGregor

SE

6/25/01

23.6

1.1

Castner

SW

6/25/01

30.4

1.5

McGregor

SE

7/6/01

30.7

1.6

Castner

SW

7/6/01

23.3

1.2

McGregor

SE

7/12/01

10.2

0.6

McGregor

SE

7/18/01

17.1

0.9

Castner

SW

7/18/01

12.6

0.7

McGregor

SE

7/24/01

42.3

2.1

Castner

SW

7/24/01

9.1

0.5

McGregor

SE

7/30/01

6.8

0.4

Castner

SW

7/30/01

7.2

0.5

McGregor

SE

8/5/01

14.5

0.8

Castner

SW

8/5/01

15.8

0.8

Castner

SW

8/6/01

11.6

0.6

McGregor

SE

8/12/01

7.7

0.5

9

Table 1 (cont.). Mass concentrations of PM10 observed at the four ambient sampling sites during the Spring-Summer 2001 monitoring period. Site

Location

Date

Mass

Associated

Concentration

Uncertainty

3

Site

Location

Date

3

(µg/m )

(µg/m )

Mass

Associated

Concentration

Uncertainty

3

(µg/m )

(µg/m3)

McGregor

SE

8/17/01

9.8

0.6

Castner

SW

8/17/01

9.5

0.6

McGregor

SE

8/23/01

19.2

1.0

Castner

SW

8/23/01

24.1

1.2

McGregor

SE

8/29/01

13.5

0.7

Castner

SW

8/29/01

14.1

0.8

McGregor

SE

9/4/01

29.5

1.5

Castner

SW

9/4/01

28.9

1.5

Castner

SW

9/10/01

29.7

1.5

10

4.2

Vehicle Generated Emission Factor Measurements, Horizontal/Vertical Flux Relationships.

A methodology commonly used to define the contributions from different sources of PM to observed ambient levels is the emission inventory approach to estimate relative contributions to ambient levels based upon estimated emission rates, and activity levels of emission sources. The accuracy of the inventory approach is directly related to the accuracy of the emission factors ascribed to the emission sources, and the ability of the dispersion model to mimic the transport of the pollutant. A second approach is the use of receptor models that utilize the information contained within the chemical composition of particulate or gaseous phase pollutants in ambient air samples to attribute the relative amounts of pollutants to their respective sources. The relative contributions from each source are estimated based on knowledge of the characteristic chemical species emitted from the sources and the quantities measured in ambient samples. The U.S. EPA currently sanctions the Chemical Mass Balance (CMB) receptor model (Watson et al. 1990) for determining source apportionment of ambient samples. Reconciliation between inventory and receptor model estimates of source attribution remains problematic (e.g., Watson and Chow 1999). Of key importance in the arid southwest U.S. are the contributions to the fugitive dust component of PM. Fugitive dust consists of geological material injected into the atmosphere by wind blowing across disturbed and undisturbed land and by vehicle related activities on paved, unpaved, and off-road surfaces. These are typical conditions and activities associated with DoD installations such as Ft. Bliss and many other major installations. Vehicle movement over soils and unpaved roads is an important source of dust. The forces created by the rolling wheels or tracks of vehicles remove fine particles from the surface and also pulverize soil crusts and aggregates lying on the surface. Dust is ejected into the air by the shearing force of the tires or tracks and by the turbulent vehicle wakes (Nicholson et al. 1989). Dust emission rates for unpaved roads have been reported to depend on the fine particle content of the road (Cowherd et al. 1990), soil moisture content, road bed load capacity (Rosbury and Zimmer 1983), and vehicle speed (Nicholson et al. 1989). Empirically derived particulate emission factors contain exponential relationships of dust emissions to vehicle weight and number of wheels (U.S. EPA 1996). These relationships are based on data with considerable scatter. The DoD currently uses U.S. EPA models to calculate annual emission inventories for vehicle-generated dust. These estimates contain a high degree of uncertainty due to the nonrepresentativeness of the emission factors for military vehicles and off-road conditions. Surface properties that influence dust emissions by vehicles need to be better identified and quantified. The role of vehicle type, size, weight, shape, and speed in affecting the magnitude of emissions from different surfaces also requires further investigation. Theoretical predictions and empirical observations indicate that a large percentage of the initially entrained dust can be deposited within a short distance of its origin, and is therefore unavailable for transport over long distances. Most emission measurements for vehicle-entrained dust characterize the horizontal flux of particles although downwind dispersion models require vertical flux emissions as input. To assess the potential long-range transport of the generated dust particles, it will be necessary to define the relationship between the horizontal flux of particles and its transition to a vertical flux. Gillette (2000) has proposed a theoretical model to characterize this transition, but validation of the model requires testing with empirical data. Necessary data include the changes in plume characteristics as it moves downwind and becomes

11

mixed into the atmospheric boundary layer, the particle size distribution of the emissions, and the near field deposition process. The dispersion process and the near-field deposition flux will be affected by the mass concentration and particle size distribution of the particles in the emitted plume and the meteorological conditions (e.g., wind speed, relative humidity, atmospheric stability, etc.) into which the particles are being emitted. To fully test the model will require the data collected in FY 01 and FY 02 to be combined in a complete database. In this report the model development is outlined. 4.2.1

Vehicle Flux Measurements

The test site at Ft Bliss was the M88 Driver Training Site. Three towers were set up at the site, one 9 m upwind tower and 2 downwind towers, one 9 m and the other 12.2 m in height. A detailed map of the orientation of the flux towers is shown in Figure 2. Historical meteorological data indicated that winds at this time of year in this area were predominantly from the west. The towers were aligned so that the upwind tower was 30 m west of the unpaved road and the downwind towers were positioned 9 m and 50 m downwind of the road. For most days of sampling wind was from the west to northwest. On May 24, 2001, winds originated from the north. On this day, vehicles were driven on the East-West Test Section (Figure 2) so that emissions from the road would pass by the two downwind flux towers. In this configuration the both downwind towers were approximately 30 m from the East-West Test Section.

Figure 2. Map of upwind-downwind flux measurement towers at Ft. Bliss. The black dots indicate the position of the upwind and downwind towers. The open dots are the GPS points denoting the path of the TRAKER vehicle through the test sections.

12

The instrumentation on each tower consisted of: four DTs spaced logarithmically between 1.25 and 9 m (or 12.2 m) to measure vertical profiles of particulate matter concentration. The DTs were fitted with a 10 µm aerodynamic size cut inlet and measured PM10 at 1 Hz. Data on the DTs were logged on an internal data logger and downloaded to a portable computer at the end of the sampling day. GPSAs were mounted at various positions on the towers to measure changes in the particle size distribution both vertically and with downwind transport. For the majority of the sampling, one GPSA was mounted at 1.25 m above ground level (AGL) on each of the three towers. A fourth GPSA was moved to different positions on the towers to measure changes in the particle size distribution at different points in the plume. Data from the GPSAs were reported in 6-second intervals and were logged onto a laptop computer at the base of each tower. Five anemometers, one wind vane, and one temperature probes were mounted on the upwind tower to characterize the local meteorological conditions. The meteorological data was recorded on a Campbell 10X data logger and downloaded to a laptop computer at the end of each day. Meteorological data were averaged and stored at 5-minute intervals. 4.2.2 Flux Calculation from Meteorological and Particulate Concentrations Horizontal flux emissions were calculated from the 1-second DT data and the vertical wind profiles measured on the towers. A typical time series of the PM10 concentrations measured by the DTs at each tower is shown in Figure 3. The concentration spikes in Figure 3 represent individual vehicle passes by the towers. The time series are labeled based on the position of the sampler. For example, DW1_2 is the second from the bottom DT on downwind tower 1. The results of downwind tower 1 are shown in the upper panel of Figure 3 while the results from downwind tower 2 are shown in the lower panel. The typical vertical concentration profile at the first downwind tower is a decreasing mass concentration with height (Figure 3). At the second down tower, the second highest sampler (2.4 m) typically measures the highest concentrations. Moreover, the duration of the plume impact on the first downwind tower is typically 3-7 seconds while the duration at the second downwind tower is 10-15 seconds. These results indicate that the road dust emissions plume is dispersing vertically as the plume moves down wind of the road. The relative concentrations of the plume at 9 m and 50 m downwind of the road are shown in Figure 4. Concentrations in Figure 4 are normalized so that the sum of concentrations on the towers adds up to one. The profile of the plume at the far down tower clearly shows that the plume is dispersing vertically (Figure 4). Analysis of the DT data from the three towers indicated that baseline drift over the course of a day did not affect all instruments equally. In order to calculate emissions fluxes from the PM10 concentrations, a baseline for each instrument was determined from the peaks shown in Figure 3. This baseline concentration was subtracted from the PM10 time series so that the resulting concentration time series was due only to the emissions from the unpaved road surface. PM10 emissions fluxes were calculated for each downwind tower using the assumption that each DT represented a uniform concentration over an interval half way between the monitor and the next highest monitor on the tower. For example, the lowest monitor on downwind tower 1 (DW1_1) was representative of the PM10 concentration from the ground surface to 1.83 m AGL. The second monitor represented the PM10 concentrations from 1.83 m AGL to 3.56 m AGL, and so on. The flux of particles at each tower was calculated using the following equation:

13

30 DW1_1 DW1_2 DW1_3 DW1_4

Downwind Tower #1 (9 m from Road) 25

DustTrak PM 10 (mg/m3)

20

15

10

5

0 16:50:53

16:51:36

16:52:19

16:53:02

16:53:46

16:54:29

16:55:12

Time

30 Downwind Tower #2 (50 m from Road)

DW2_1 DW2_2 DW2_3 DW2_4

25

DustTrak PM 10 (mg/m3)

20

15

10

5

0 16:50:53

16:51:36

16:52:19

16:53:02

16:53:46

16:54:29

16:55:12

Time

Figure 3. Time series plots of vertical concentrations measured on downwind tower #1 (9 m from unpaved road) and downwind tower #2 (50 m from unpaved road).

14

4

EF = cos(θ ) ∑ ui Ci ∆z i ∆t

(1)

i =1

where:

EF = emission factor of PM10 (g/VKT) θ = angle between the wind direction and a line perpendicular to the road (degrees) i = one of the four positions of the monitors on the tower ui = average wind speed over the interval represented by the ith monitor (m/s) Ci = average PM10 concentration as measured by the ith monitor (mg/m3) ∆z = vertical interval represented by the ith monitor (m) ∆t = duration that the plume impacts the tower (s)

12.2 m

9.0 m

0.16

0.05

5.7 m 4.7 m

0.12

2.41 m

2.67 m

0.27

1.25 m

Road

0.22

0.55

DownWind 9 m

1.25 m

0.33

0.27

DownWind 50 m

Figure 4. Diagram of sampler positions and relative plume concentrations downwind of the unpaved road.

15

Using this formulation emission factors were calculated for each vehicle pass by both towers. A database of emission factors was assembled for the parameters: vehicle speed, vehicle type (i.e., Ford Ranger, TRAKER, HUMVEE), date, and road orientation (i.e., East-West or North-South). The database is presented in Table 2. For the North South test section, average emission factors were calculated from passes of the Ford Ranger and TRAKER for a variety of speeds ranging from 5 to 50 km/hr (1.4 to 14 m/s). Passes were repeated at least 10 times at each speed and the standard error of the emissions flux for each tower was calculated. The comparison of the emissions fluxes calculated at the two towers is shown in Figure 5. The regression line relating the two sets of emissions factors has a slope of 1.03 ±0.05 and an intercept of –2 ± 8 g/VKT. This result suggests that the difference of PM10 mass flux of particles between the two towers is less than 5% of the total PM10 mass flux. In addition, particles less than 10 µm are not depositing immediately after emissions and are available for longer-range transport. Table 2. Average emission factors calculated from unpaved road travel at Fort Bliss. Test Date

5/18/01 5/18/01 5/18/01 5/18/01 5/18/01 5/18/01 5/18/01 5/18/01 5/20/01 5/20/01 5/20/01 5/20/01 5/24/01 5/24/01 5/24/01 5/24/01 5/24/01 5/24/01 5/24/01

Road Test Section NS NS NS NS NS NS NS NS NS NS NS NS EW EW EW EW EW EW EW

Vehicle

Ranger Ranger Ranger Ranger TRAKER TRAKER TRAKER TRAKER Ranger Ranger Ranger Ranger HUMVEE HUMVEE HUMVEE TRAKER TRAKER TRAKER TRAKER

Target Speed (km/hr) 20 30 40 50 5 7 10 15 20 30 40 50 10 20 30 5 10 15 20

Number Emissions EMFAC Emissions EMFAC of Factor on DW1 Factor on DW2 Vehicle DW1 Standard DW2 (g/ Standard Passes (g/VKT) Error VKT) Error (g/VKT) (g/VKT) 16 49 15 53 13 16 172 47 147 52 16 308 57 331 66 15 239 43 242 41 10 101 26 41 11 15 12 3 17 5 18 26 4 28 5 3 45 22 36 5 20 52 19 20 154 30 20 234 49 20 232 38 19 34 9 37 13 20 167 51 161 51 20 242 48 237 44 10 30 15 40 14 10 56 12 82 24 10 134 51 109 30 10 45 15 186 110

16

Figure 5. Comparison of emissions factors measured on towers 9 m and 50 m downwind of an unpaved road. Error bars on the figure represent the standard error of the flux measurements. Analysis of emissions factors from the tower experiments indicated that unpaved road dust emissions factors increase with vehicle speed as well as vehicle weight. The Ford Ranger pickup truck has a gross vehicle weight of 1.5 Mg while both the TRAKER vehicle and HUMVEE have gross vehicle weights of ~3.5 Mg. The relationships between emissions factors calculated from towers DW1 and DW2 versus vehicle speed are shown in Figure 6. The Ford Ranger emissions factors have been grouped separately from the TRAKER and HUMVEE emissions factors because of the weight discrepancies. The slope of the regression line for the TRAKER and HUMVEE vehicles is on average 35% higher than the slope of the Ford Ranger. The linear relationship between emissions factor and vehicle speed is consistent with the equation used to model unpaved road dust emissions in the U.S. E.P.A.’s AP-42 emissions factor documentation. The AP-42 annual emissions factors for unpaved roads are calculated based on the following equation:  365 − p  EF = 0.161⋅ s ⋅ S ⋅ W 0.7 w 0.5 ⋅  (2)   365  where: EF = emission factor (g/VKT) s = silt percent of the road material S = vehicle speed (m/s) W = weight of the vehicle (Mg) w = number of wheels on the vehicle p = number of days per year with measurable (i.e., >0.25 mm) precipitation. 17

400

400 Ford Ranger on NS Road by DW1

TRAKER and HUMVEE on EW Road by DW1 EF = 20 s R2 = 0.65

350

350 300 Emissions Factor (g/vkt)

Emissions Factor (g/vkt)

300 250 200 150

200 150

100

100

50

50

0

EF = 24 s R2 = 0.68

250

0 0

2

4

6

8

10

12

14

16

0

2

4

6

Vehicle Speed (mps) 400

10

12

400 Ford Ranger on NS Road by DW2

14

16

EF = 29.0 s R2 = 0.92

TRAKER and HUMVEE on EW Road by DW2

EF = 19 s R2 = 0.67

350

350

300

300 Emissions Factor (g/vkt)

Emissions Factor (g/vkt)

8 Vehicle Speed (mps)

250 200 150

250 200 150

100

100

50

50

0

0 0

2

4

6

8

10

12

14

16

0

Vehicle Speed (mps)

2

4

6

8

10

12

14

16

Vehicle Speed (mps)

Figure 6. Unpaved road dust emissions factors versus vehicle speed for two weight classes of vehicles. The Ford Ranger weighs ~1.5 Mg while the TRAKER and HUMVEE weigh ~3.5 to 4 Mg. FY 01 measurements of vehicle emissions from vehicles traveling on unpaved roads have provided clear indication that the instrument system (upwind-down wind towers) used to measure the characteristics of the emitted dust plumes can be utilized to calculate vehicle emission fluxes. The measurements show that these estimates are reproducible and that this methodology allows for the calculation of the uncertainty associated with the emission factor estimates. In FY 01 the emission factor for a HUMVEE was reasonably defined and it is expected that the emission factor database for military vehicles will be expanded considerably in FY 02. 4.2.3

The Gillette Emission Model

Gillette (2000) developed a theoretical model that expresses the relationship between the horizontal flux of dust close to a source area, such as an unpaved road, and the regional-scale vertical dust flux in terms of two empirical constants that represent turbulent diffusion characteristics of the atmosphere and the deposition velocity for dust particles. The Gillette model (Figure 7) has the following assumptions:

18

1) The system is at steady state with respect to the total mass of particles in the control volume – practically, this means that there is no net flux through the side boundaries of the rectangular control volume. 2) The background ambient concentration of dust is very small compared to the contribution from the road surface – i.e., there are no other nearby significant sources of dust emissions. 3) No dust “escapes” through the ceiling boundary directly above the road. 4) The box is long enough so that the concentration of dust at the downwind boundary is approximately equal to the concentration at the boundary upwind of the road. 5) The dust emitted from the road is well-mixed vertically within the box at all points downwind. The model expresses the relationship between the horizontal flux of dust close to an emission source such as an unpaved road and the regional-scale vertical dust flux in terms of two empirical constants that represent turbulent diffusion characteristics of the atmosphere and the deposition velocity for dust particles. Gillette proposed a value for the diffusion coefficient that is based on the friction velocity that is determined from the vertical wind speed profile. The deposition velocity of particles will, in general, depend on particle size, wind speed and profiles, and availability of deposition surfaces such as vegetation. Note that the presence of vegetation also affects the local wind characteristics that in turn affect the diffusive properties of the atmosphere. Assumption 5 may be relaxed under some conditions and Gillette (2001) has proposed a preliminary framework for including conditions where dust particles are not completely vertically mixed within the control volume. In general, the assumptions made in the Gillette box model of uniform, or near-uniform particle concentration with height are not physically valid. However, those assumptions may be adequate in the context of a correction to horizontal dust flux measured with the upwind downwind technique. An example of how the dust plume develops with distance downwind of an unpaved road is shown in Figure 4. The height of the center of mass of the plume, and indeed, the height of maximum concentration appear to increase with distance from the source. The concentration profile of dust particles is regulated both by the rate of deposition and by the degree of turbulent diffusion. Measurements of turbulent diffusion must be compared with the formulation proposed by Gillette which relies on a variant of “K-theory” which in turn employs the friction velocity u* as deduced from the vertical profile of the average wind speed. A thorough testing of the Gillette (2001) model will require the completion of the database from measurements taken in FY 01 and FY 02.

19

Wind

dmup/dt

dmceil/dt

dmambin/dt dmambout/dt dmroad/dt X

X=Xo dmdepos/dt dmceil/dt

dmambin/dt dmout/dt

∆Z

dmroad/dt

∆L=1

Z=0

Figure 7. Box model for obtaining estimates of regional scale vertical dust flux (Gillette, 2001). 4.3

TRAKER

The TRAKER is a vehicle designed to measure the dust emissions potential of a roadway (paved and unpaved) or an off-road surface over which vehicles traverse. The principle of operation of the TRAKER is fairly simple (Figure 8). The concentration of airborne particles in a specific size range is monitored with particle sensors that are mounted near the front tires of a vehicle. The dust generated from the spinning of the tire influences these particle sensors. A background measurement of particle concentrations is obtained simultaneously at a location on the vehicle far away from the tires. The difference in the signals between the influence monitors and the background monitor is related to the amount of dust generated from the surface by the vehicle moving over it. Use of the TRAKER system in this project is intended to serve three purposes. First, the TRAKER is used in conjunction with the upwind/downwind technique on an unpaved road to establish a calibration curve. This allows for estimation of dust emissions directly from the TRAKER signal. Second, the TRAKER vehicle is used to survey roads (paved and unpaved) on Ft. Bliss and its testing and training range. Off-road surfaces will be tested as well. These TRAKER surveys allow for estimation of dust emissions potential on a much larger scale than can be achieved by other techniques such as upwind/downwind and silt content measurement. 4.3.1

Description of TRAKER

For the present study, several improvements have been made over the original TRAKER test vehicle (Kuhns et al. 2001). The current TRAKER vehicle is a 1979 Chevrolet van equipped with three exterior steel pipes that act as inlets for the onboard instruments (Figure 9). 20

Front

Background Monitor

Top View Top View Influence Monitor

Front

Background Monitor

Side View Influence Monitor

Figure 8. Principle of operation of the TRAKER. Influence monitors measure the concentration of particles behind the tires. The background monitor is used to establish a baseline.

Figure 9. The TRAKER vehicle (1979 Chevrolet Van) during the testing period.

21

Two of the pipes are located behind the front tires and are used to measure the emissions from the tires; previous experience indicates that there may be differences between the dust loadings on the left and right sides of the travel lanes. The third pipe runs along the centerline of the van underneath the body and extends through the front bumper. This pipe is the inlet for “background” air. Dust and exhaust emissions from other vehicles on the road can cause an elevation in the particle concentration above the road surface. The “background” measurement is used to correct the measurements behind the tires. The three exterior pipes enter the cargo compartment of the van through the underbody. Each pipe then goes into a plenum/manifold; the plenum is used to distribute the air sampled to up to five instruments (Figure 10). Two types of instruments have been used over the course of this study: TSI DustTrak monitors and GRIMM optical particle size analyzers. A central computer collects the data generated by the onboard instruments (Figure 10). Data from TRAKER measurements are imported into a Microsoft Access database for subsequent data processing and analysis. 4.3.1.1

Inlets

The TRAKER employs three inlet pipes, one behind each of the two front tires, and one that protrudes through the front bumper. The shapes, lengths and flow rates of the inlet lines have been optimized to minimize particle loss. Unlike gases, particles have inertia; as a result, sampling of particles through an inlet necessarily results in some particle loss to inlet surfaces. These losses could be due to diffusion of particles towards inlet walls or impaction/settling of particles upon inlet walls. Diffusion is a phenomenon that is inherent to very small particles (less than 0.1 µm). Since dust is comprised primarily of larger particles (greater than 0.3 µm), diffusion is not an important consideration for TRAKER. Impaction and gravitational settling can be important processes for particles with aerodynamic diameters greater than 1 µm. In general, gravitational settling can be minimized by reducing the amount of time a particle spends in an inlet line (e.g., by increasing the speed of the flow). On the other hand, reducing the speed of the flow and avoiding sharp turns within the inlet lines can minimize particle impaction. The inlet lines, visible in (Figure 10a) are 19 mm (3/4”) in diameter and 2.3 m (7.5’) in length for the tire lines and 3.7 m (12’) in length for the background line. The influence inlets on the right and left are in slightly different positions with respect to the tires. On the right, the inlet is 165 mm (6.5”) above the ground, 50 mm (2”) behind the tire, and 63 mm (2.5”) in (towards the center of the vehicle) from the outside edge of the tire. On the left, the inlet is 165 mm (6.5”) above the ground, 63 mm (2.5”) behind the tire, and 63 mm (2.5”) in from the outside edge of the tire. Because of the vehicle configuration, it is not possible to avoid bends in the inlet lines. However, wherever necessary, bends have been kept as shallow as possible in order to minimize loss of particles to the inlet walls. Flow rates through the inlets are 75 liters per minute, corresponding to an average face velocity of 4 m/s at the inlet opening. Each of the inlet lines feeds into a torpedo-shaped plenum (Figure 11c). All particle sampling instruments are connected through the plenum via short Tygon tubes that are in turn attached to 200 mm (8”) long steel tubes that extend into the body of the plenum.

22

a.

b.

c.

d.

Figure 10. Photographs depicting TRAKER vehicle and instrumentation used in the FT. Bliss study. a. Location of inlets (right side and background shown) on the TRAKER; b. Generator and pumps are mounted on a platform on the back of the van; c. Photo showing two sampling plenums (bottom), a suite of DustTrak and GRIMM particle monitors (top right), and three rotameters used for ensuring proper flows through plenum; d. a dashboard-mounted computer screen is used to view the data stream (top) and a GPS logs the TRAKER’s position every 1 second.

23

4.3.1.2

Inlet Dilution System

An optional dilution system was developed for the TRAKER for conditions where dust loadings were too high for the operating range of the onboard instruments such as on unpaved roads. The dilution system utilizes a second on-board carbon vane pump to provide clean air to the left and right TRAKER inlets. The pump draws in ambient air from the roof of the TRAKER. The compressed air is then forced through a high efficiency filter in order to remove ambient particles and particles introduced by the carbon vanes inside the pump. A manual valve allows for independent flow control for the left and right dilution lines. The dilution air lines are attached to the inlet pipe via a U-shaped tube. A ¼” barbed elbow fitting is inserted 1.5 cm into the inlet in order to ensure that the mixing ratios for the dilution air are not influenced by small changes in the wind regime near the tire. A photograph of the inlet line with the dilution attachment is shown (Figure 11). 4.3.1.3

Instruments

DustTraks The TSI DustTrak (DT) particle monitor (model # 8520) is a rugged portable monitor that uses particle light scattering to infer PM mass concentration. The instrument is calibrated using NIST Arizona Dust to relate light scattering intensity to aerosol mass concentrations in mg/m3. The DT measures aerosol mass over a range of concentrations from 0.001 mg/m3 to 150 mg/m3 at a maximum frequency of 1 Hz.

Figure 11. Photograph showing inlet configuration of the TRAKER dilution system.

24

(Note: 1 mg/m3 = 1000 µg/m3). The instrument is shipped from the manufacturer with 3 impactor inlets that can be used to configure the DT to remove particles greater than 10 µm, 2.5 µm, and 1 µm. The flow rate of the instrument is 1.6 lpm. In this study six DTs were used as part of the TRAKER measurements. Each of the three inlets (left, right, and background) was equipped with two DTs, one with a 10µm inlet and the other with a 2.5 µm inlet. Grimm 1.108 Particle Size Analyzer The Grimm 1.108 Particle Size Analyzer (GPSA) provides real-time size-segregated particle counts for particles in 15 different size bins that span the range from 0.3 - 20 µm. The PSA uses the light scattering properties of a particle to estimate its physical size. A backup Teflon filter captures all the particles that have passed through the instrument’s measurement chamber. The PSA measured particle size number distribution can be converted to a mass distribution using an assumed or inferred particle density. The flow rate of the instrument is 1.8 lpm. In this study three GPSAs were used to supplement the measurements obtained by the DTs. Ashtech Promark GPS The global positioning system (GPS), as the name implies, provides a highly accurate method of obtaining real-time position information. A mobile GPS unit consists of a portable device that receives signals broadcast by at least four different satellites. The satellites broadcast time signals that are used by the mobile unit to triangulate a location on the surface of the earth. Due to correctable interferences such as clock and ephemeris errors and ionospheric and tropospheric delays, the accuracy of the raw GPS signal is approximately 15 m. This accuracy can be improved to within 3-4 m by a post-processing scheme known as differential processing. In this scheme, a GPS is situated at a station with a known position that is in the approximate vicinity of the mobile unit. By comparing the known coordinates of the station with the coordinates reported by the GPS at the station, it is possible to achieve an accuracy of a few meters with a handheld mobile device. Data for stationary GPS are readily available on the Internet for the purpose of applying a differencing scheme like the one described. Data Capture and Measurement Documentation The TRAKER uses a central onboard computer to log all real-time data and to provide a means for documenting environmental conditions. The TRAKER may utilize up to 10 instruments (6 DTs, 3 GPSAs, and a GPS), each generating data at a rate of up to 60 readings per minute. A central onboard computer is used to capture the data in real-time as it is generated. Data from individual instruments are transferred via RS-232 serial interfaces to a multiplexing unit that is in turn connected to the computer. Specialized software has been written to capture the data, use the computer clock to provide a common time stamp, write to a database in real-time, and provide the operator(s) with feedback regarding the status of instruments. An example of the TRAKER display panel is shown in (Figure 12).

25

4.3.2

TRAKER Quality Assurance

In this section the procedures used to characterize the TRAKER measurement, accuracy, and response to various conditions are discussed. These include: the measurement range and precision of TRAKER, the loss of particles in the inlet sample line, the effect of diluting the sample stream in the inlets, and the effect of vehicle speed on the TRAKER signal. The TRAKER measures surface dust emissions potential over a wide range of dust loadings. Under normal operation, the TRAKER is well suited for measurements conducted on paved roads. For using TRAKER on unpaved roads or off road surfaces, an inherently much dustier environment, a special dilution system has been designed in order to keep particle concentrations low enough to be in the operating range of the onboard instruments.

Figure 12. TRAKER Control Panel. Real-time displays show the magnitude of the response of DTs. Additional displays show measurements from 3 GPSAs and the GPS receiver. The 10 lights in the top left of the screen serve as indicators of the health of onboard instruments (green = OK; red = not functioning). The TRAKER inlets consist of steel tubes that have an opening either behind a tire, or in the front bumper. The steel tubes feed into a plenum that acts as a manifold for multiple instruments. Particles drawn in by the inlet have the potential to deposit within either the tubes or the plenum. In the following report section (4.3.3.1) the magnitude of the particle loss for normal TRAKER measurements and in line lossws when the dilution system is employed are discussed. In the initial TRAKER study in Las Vegas, NV, Kuhns and Etyemezian (1999) clearly showed that the TRAKER signal depends on vehicle speed. That is, for the same amount of dust on the surface, higher speeds give higher TRAKER readings. We discuss the dependence of the TRAKER signal on vehicle speed in Section 4.3.3.2.

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4.3.2.1

Measurement Range, Precision, and Out of Range Data

The DT instrumentation on board the TRAKER vehicle has a lower detectable limit of 1 µg/m . Thus, the smallest measurable difference in concentration between the tire and the background monitors is 1 µg/m3. This corresponds approximately to a single-point minimum detection limit equivalent to an emissions factor of 0.9 grams per vehicle kilometer traveled (g/VKT), meaning that any 1 s measurement can only be resolved to within this value. Note that substantially smaller emissions factors can be measured with the TRAKER if multiple points are used to calculate an average. At the other end of the range, DT measurements above 200 mg/m3 are not reliable. This corresponds to an emissions factor for PM10 of approximately 50 g/VKT. Note that the upper end of the range can be increased to 110 g/VKT if the optional dilution system is in use. This ceiling can be further increased to 160 g/VKT if the relationship between the PM2.5 and the PM10 DT measurements is exploited for cases where the PM10 measurement is out of range, but the PM2.5 measurement is within the acceptable limits. The precision of the TRAKER has been measured as a function of speed. Figure 13 shows the TRAKER coefficient of variation for the left and right PM10 DT signals as a function of the vehicle speed. The coefficient of variation is a measure of the precision and is equal to the standard deviation of the measurement divided by the average of the measurement. In Figure 13 the measurement corresponds to multiple passes on the same 1.6-km stretch of road in Treasure Valley, ID. 3

90 80

Coefficeient of Variation %

70 60 50 40 30 20 10 0 0

20

40

60

80

100

120

Vehicle Speed (km/hr)

Figure 13. TRAKER coefficient of variation expressed as a percentage for Left and Right PM10 DT signals as a function of speed. The data represent Left and Right PM10 DT signals over a 1.6-km stretch of road in Treasure Valley, ID. The coefficient of variation is equal to the standard deviation of a measurement divided by the average and gives an estimate of the precision of the measurement.

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100 90

Coefficeient of Variation %

80 70 60 50 40 30 20 10 0 0

20

40

60

80

100

120

Vehicle Speed (km/hr)

Figure 14. TRAKER coefficient of Variation expressed as a percentage for Left and Right PM2.5 DT signals as a function of speed. These data represent Left and Right PM2.5 DT signals over a 1.6-km stretch of road in Treasure Valley, ID. Note that data for speeds less than 50 km/hr are below detection limits for the PM2.5 DTs. These data (Figure 13) clearly show that the precision of the measurement improves dramatically between the low speeds (16 km/hr) and higher speeds (>30 km/hr). Since most TRAKER measurements are conducted at speeds greater than 33 km/hr (approximately 20 mph), the precision of the TRAKER over a 1.6-km stretch of road ranges between 10% and 30%. The poor precision at low speeds is probably due to the influence of ambient winds and gusts on the flow regime behind the front tires. As the vehicle speed increases, such small variations become much less important compared to the speed of the vehicle (see section 4.3.3.2 for discussion of the relationship between the TRAKER signal and vehicle speed). The equivalent information for PM2.5 DTs is shown in Figure 14. Note that for PM2.5, DT data less than 50 km/hr are below the detection limits of the instruments. We expect that a surface with higher dust loadings than the one sampled for this test would result in a curve similar to the PM10 precision curve shown in Figure 13. Inferring PM10 from PM2.5 Unpaved roads or off road surface are by nature much dustier than paved roads. A dilution system was designed for the TRAKER in order to reduce the particle concentration in the TRAKER inlets and allow the DT measurements to remain within the manufacturer’s specified operating range. In some cases, the PM10 DT upper limit (200 mg/m3) was exceeded even with the dilution system in operation. In this study PM2.5 DTs were collocated with PM10 DTs in the sampling plenum within the TRAKER. Figure 15 shows the relationship between a PM2.5 and a PM10 DT collocated within the TRAKER sampling plenum. The relationship between the PM10 and PM2.5 (PM2.5 is 39% of PM10) DTs was used to estimate the PM10 values

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250

PM2.5 signal (mg/m3)

200

150

y = 0.3913x R2 = 0.8852 100

50

0 0

50

100

150

200

250

3

PM10 signal (mg/m )

Figure 15. PM10 and PM2.5 DT signals when they are collocated in the same sampling plenum within the TRAKER. An R2 of 0.89 shows that the two measures are strongly correlated and that on average, the PM2.5 value is 39% of the PM10 value. when the PM10 DTs were out of range. That is, when the PM10 DT was out of range and the PM2.5 DT was in range, the PM2.5 value was divided by 0.39 in order to estimate the PM10 value. TRAKER Validity Criteria A TRAKER data point is only considered valid if it meets all of the criteria outlined in Table 3. Criteria are applied to the speed, acceleration, deceleration, and the wheel angle of the TRAKER vehicle. If a TRAKER data point does not meet any one of the criteria, then that data point is flagged as “Invalid” and is not used in any data processing activities. Note that the TRAKER measurement uses the difference between the particle concentration measured behind the front tire and the concentration measured through the front bumper (see equation 3 in section 4.3.3.2). Under certain conditions, the concentration at the front bumper may be higher than it is behind the front tire resulting in a negative value for the TRAKER measurement. Negative values are NOT considered invalid and are retained in the database (unless of course the data point in question fails one of the tests in Table 3). It is important to retain negative values so that a systematic bias is not introduced into the dataset. The vehicle speed can become important under conditions of moderate to high winds. If the TRAKER is not moving fast enough, crosswinds and fluctuations in the ambient winds can lead to unsteady flow conditions between the front tire and the TRAKER inlet. To avoid this possibility a minimum speed of 5 m/s is required to consider a TRAKER data point valid. This criterion is relaxed for unpaved roads (1.5 m/s) where traveling at high speeds can cause the DTs and GPSAs on board the TRAKER to be overwhelmed with high dust concentrations. 29

Table 3. Validity criteria applied to each TRAKER data point. Parameter

Criterion

Threshold

Speed

>

5 m/s – paved roads (~11 miles/hr) 1.5 m/s – unpaved Roads (~3.5 miles/hr)

Acceleration