Wind erosion potential for fugitive dust sources in the Athabasca Oil Sands Region

Aeolian Research 18 (2015) 121–134

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Wind erosion potential for fugitive dust sources in the Athabasca Oil Sands Region Xiaoliang Wang a,c,⇑, Judith C. Chow a,b,c, Steven D. Kohl a, Laxmi Narasimha R. Yatavelli a,c, Kevin E. Percy d, Allan H. Legge e, John G. Watson a,b,c a

Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA The State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, Shaanxi 710075, China Graduate Faculty, University of Nevada, Reno, NV 89503, USA d Wood Buffalo Environmental Association, Fort McMurray, Alberta, Canada e Biosphere Solutions, Calgary, Alberta, Canada b c

a r t i c l e

i n f o

Article history: Received 24 March 2015 Revised 13 July 2015 Accepted 16 July 2015

Keywords: Fugitive dust Windblown dust Particulate matter PI-SWERL Oil sands Emission potential

a b s t r a c t This study characterized the generation of windblown dust from various sources in the Athabasca Oil Sands Region (AOSR) in Alberta, Canada. The Portable In-Situ Wind Erosion Laboratory (PI-SWERL) equipped with two real-time dust monitors and nine-channel filter packs was used to simulate wind-driven erosion and measure emissions. Sixty four sites were measured, including oil sands mining facilities, quarry operations, and roadways in the vicinity of Ft. McMurray and Ft. McKay. Key parameters related to windblown dust generation were characterized including: threshold friction velocity, reservoir type, and particle size-segregated emission potential. The threshold wind speed for particle suspension varies from 11 to 21.5 km/h (u+10; measured at 10 m above ground level), and saltation occurs at higher speeds of u+10 >32 km/h. All surfaces have limited dust supplies at lower wind speeds of <27 km/h, but have unlimited dust supplies at the highest wind speed tested (56 km/h). Unpaved roads, parking lots, or bare land with high abundances of loose clay and silt materials along with frequent mechanical disturbances are the highest dust emitting surfaces. Paved roads, stabilized or treated (e.g., watered) surfaces with limited loose dust materials are the lowest emitting surfaces. Surface watering proved effective in reducing dust emissions, with potential emission reductions of 50–99%. Surface disturbances by traffic or other activities were found to increase PM10 emission potentials 9–160 times. These data will improve the accuracy of emission inventories, dust dispersion, transport, and source apportionment models, and help design and evaluate dust control strategies. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction Fugitive dust (Cowherd, 2001; Watson et al., 2000; Ziskind et al., 1995) is an important source of suspended particulate matter (PM) in ambient air near mining operations. Mechanicallygenerated dusts are caused by pulverization and abrasion of surface materials through application of mechanical forces from vehicular traffic, mining and mineral processing, rock crushing, and farming (Etyemezian et al., 2003a,b; Kuhns et al., 2001). Windblown dusts are caused by the action of turbulent air currents on erodible surfaces when the wind speed exceeds suspension threshold velocities (Chepil, 1945a,b; Gillette et al., 1972; ⇑ Corresponding author at: Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA. E-mail address: [email protected] (X. Wang). http://dx.doi.org/10.1016/j.aeolia.2015.07.004 1875-9637/Ó 2015 Elsevier B.V. All rights reserved.

Gillette, 1974). Although dust storms generated by high-magnitude, but low-frequency, wind gusts are more visible, fugitive dust generated by more frequent, but lower wind speed, events is a potentially larger contributor to annual and 24-hour average PM concentrations (Lee and Tchakerian, 1995). Fugitive dust emissions are poorly characterized, particularly for the fraction of transportable dust that can travel more than a hundred meters from the emitter (Cowherd, 2001; Merritt et al., 2003; Sehmel, 1980a; Simon et al., 2008; Watson et al., 2000). Dust contributions to PM emission inventories and ambient concentrations are often overestimated because emission factors derived from a small number of tests are extrapolated to unrelated situations. When emission factors measured close to a source are applied to large area-source emission grids (typically 1  1 km to 10  10 km), there is no accommodation for the deposition of larger particles within the grid. Dust suspension mechanisms are

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(a)

5 L/min PM2.5 Teflon: 5 L/min

Blower Excess Flow

Mass, light absorption, elements, rare-earth elements, and Pb isotopes

PM2.5 Teflon: for Lichen Study -

-

-

=

+

+

++

5 L/min

ions (Cl , NO2 , NO3 , PO4 , SO4 , NH4 ,Na , Mg PM2.5 Quartz: carbohydrates, organic acids, and WSOC

Carbon Filter

5 L/min

EC, carbon fractions, carbonates, PM2.5 Quartz: OC, alkanes, alkene, hopanes, steranes, and PAHs

HEPA Filter

5 L/min

100 L/min

5 L/min

PM10 Teflon:

, K+, Ca++),

Mass, light absorption, elements, rare-earth elements, and Pb isotopes

PM10 Teflon: for Lichen Study

5 L/min

PM10 Quartz:

ions (Cl-, NO2-, NO3-, PO4 , SO4=, NH4+,Na+, Mg++, K+, Ca++), carbohydrates, organic acids, and WSOC

5 L/min

PM10 Quartz:

OC, EC, carbon fractions, carbonates, alkanes, alkene, hopanes, steranes, and PAHs

5 L/min 1 L/min

PM10 Nucleopore:

Microscopy Analysis

DustTrak DRX: PM1, PM2.5, PM4, PM10, and PM15

3 L/min

Optical Particle Sizer: 0.3-10 µm in 16 channels

PI-SWERL Conical Sampling Manifold

(b)

Conical Sampling Manifold Computer

Filter Packs

DustTrak DRX PI-SWERL

Blower

Pump

OPS Fig. 1. The PI-SWERL configuration including: (a) system schematic and (b) system set-up. The filters collected dust deposits through PM2.5 and PM10 size-selective inlets at 5 L/min for later chemical characterization in the laboratory. The DustTrak DRX and Optical Particle Sizer measured particle mass and number concentrations in different size fractions at 1 s intervals. Weighted sums of these values are used to estimate instantaneous PM mass emissions, which are normalized to the mass of the Teflon filter. See the Supplemental information for greater detail.

intermittent and vary by location, yet they are reported as annual averages within large political boundaries (from counties to countries). These limitations are verified by PM2.5 and PM10 (particles with aerodynamic diameters <2.5 and <10 lm aerodynamic diameter, respectively) source apportionment studies showing that, on average, fugitive dust contributes 5% to 20% of PM2.5 and 40% to 60% of PM10 measured in the atmosphere (Watson and Chow, 2000). In contrast, national emission inventories estimate that fugitive dust accounts for 34% of PM2.5 and 74% of PM10 in the U.S. and 77% of PM2.5 and 92% of PM10 in Canada (Environment Canada, 2013; U.S.EPA, 2013a).

Emission rates of windblown dust from erodible surfaces depend on wind friction speed and soil characteristics (Watson and Chow, 2000). Measureable factors related to windblown dust emissions and transport are: (1) the amount of dust available for suspension (dust reservoir); (2) threshold suspension velocities; and (3) PM size distributions. Reservoirs are classified as limited for stable surfaces and unlimited for unstable surfaces. If not recharged, dust from a limited reservoir is depleted after the loose top soil is eroded, while an unlimited reservoir constantly emits dust. Reservoir characteristics depend on soil type, soil layer depth, soil moisture content, soil disturbance, and meteorological parameters. Threshold friction

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55.8

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(a)

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PM2.5 3

1000 0

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0.91

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62.4

Friction Speed (m/s)

Wind Speed at 10 m agl (km/h)

(c)

Time (s) Fig. 2. Examples of: (a) ramp test; (b) multiple-step test; and (c) single-step test with PM10 (solid line) and PM2.5 (dashed line) instantaneous fluxes measured by the DustTrak DRX. The thick black line indicates the rotational speed in revolutions per minute (RPM), which is related to wind friction speed at the surface (u⁄) and wind speed on a 10 m tower (u+10) through Eqs. (3)–(6). For each test, the PI-SWERL chamber was flushed with clean air to remove residues from prior tests. The PI-SWERL was moved to several nearby locations with similar surface characteristics for each test. (See Supplemental Tables S-1 and S-2 for motor speed settings and relation of RPM to friction speed and wind speed).

speed is the wind speed at the surface, usually extrapolated from that measured with a standard above-ground wind speed sensor, at which soils begin to erode. The particle size distribution dictates how far PM will rise vertically and travel horizontally, with larger particles (typically J 20 lm aerodynamic diameter) depositing near the source (Sehmel, 1980b; Slinn, 1982). The PM2.5 and PM10 fractions can transport further, and these are regulated by ambient

air quality standards in many countries (Cao et al., 2013; Environment Canada, 2015; U.S.EPA, 2013b). National inventories apply the following approach to estimate windblown dust emissions (U.S.EPA, 2006):

EF wfd ¼ k

N X Pi i¼1

ð1Þ

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Fig. 3. Location of 64 surfaces samples in the AOSR. Pushpin labels indicate sites sampled in 2012 and balloon labels indicate sites sampled in 2013. Detailed descriptions are associated with each site number in Supplemental Table S-3. This map derives from Google Earth Image Landstat.

where EFwfd is the emission factor (EF) in g/m2/y, k is the particle size multiplier for different aerodynamic size range: 1.0 for TSP (Total Suspended Particulate matter, typically <30 lm aerodynamic diameter), 0.5 for PM10, and 0.075 for PM2.5, N is number of surface disturbances per year, and Pi is the erosion potential (g/m2) corresponding to the fastest kilometer of wind for the ith period between disturbances. The erosion potential for a dry, exposed surface with a limited reservoir is calculated as:

P ¼ 58ðu  ut Þ2 þ 25ðu  ut Þ for u > ut ; and P¼0

for u 6 ut

ð2Þ

where u* is the friction velocity (m/s), and ut is the threshold friction speed (m/s). The wind speed u+h from a reference anemometer at height of h (m) is often available from nearby towers with h = 10 m (u+10) above ground level (agl). Short-duration wind gusts affecting these towers can be related to the friction velocity u* by a logarithmic distribution of wind speed profile:

uþh ¼

  u h Ln R0 0:4

ð3Þ

where 0.4 is the dimensionless von Karman’s constant, and R0 is the surface roughness in m. Assuming a typical roughness R0 of 0.005 m for open terrain, and h = 10 m agl, Eq. (3) becomes:

u ¼ 0:053uþ10

ð4Þ

u* and ut have been quantified for different surfaces with laboratory- and field-operated wind tunnels (Gillette, 1978; Nickling et al., 1997; Viner et al., 1982; Zingg, 1951). These systems are large (L  W  H: 10 m  1 m  1 m) with high power requirements, which makes transportation and field operation costly. The Portable In-Situ Wind Erosion Laboratory (PI-SWERL) (Etyemezian et al., 2007), as described in the Supplemental material (Section S.2 and Figs. S-1 and S-2) and applied in this study, is more amenable to sampling in remote or confined areas typical of many mining operations. PI-SWERL measurements have shown

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41.0

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Wind Speed at 10 m agl (km/h)

54.7

PM Threshold (PM10>6.4x10-4 mg/m2/s, and increasing for >4 s)

0.8

1.E+04

Saltation Threshold

1.E+03

(both OGS flux >400 particle/m2/s or increasing for >4 s)

1.E+02 1.E+01

0.6

OGS1

PM10 0.4

OGS2

1.E-01 1.E-02

0.2

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1.E+00

Friction/Wind Speed 0

200

400

600

1.E-03 800

PM10 Flux (mg/m2/s) OGS Flux Rate (particle/m2/s)

1.0

68.4

1.E-04 1000

Time (s) Fig. 4. Smoothed PM10 (blue line, 10 s moving average) and optical gate sensor (OGS) fluxes for a multiple-step test on a section of Hwy 63 with visible trackout from unpaved areas (surface 39; located in Fig. 3 and pictured in Fig. S-3). Although there is an initial spike in PM emissions at the first wind speed step (black line), consistent emissions are not seen until the second step, and emissions increase with additional wind speed. Large particle saltation is only observed by the two OGS (red and green lines) near the rotator at the highest wind speeds, with a concomitant increase in the PM10 flux.

comparability with larger wind tunnels (Sweeney et al., 2008) and are being used in a growing number of fugitive dust evaluation and control applications (Bacon et al., 2011; Buck et al., 2011; China and James, 2012; Etyemezian et al., 2014; Goossens and Buck, 2009; Kavouras et al., 2009; King et al., 2011; Kuhns et al., 2010; Neuman et al., 2009; Sweeney et al., 2011). This study examines fugitive dust suspension potential and emission rates (fluxes) within and around mining operations in the Athabasca Oil Sands Region (AOSR) of northeastern Alberta, Canada. Specific objectives are to: (1) characterize windblown dust reservoir types, threshold suspension velocities, and PM2.5 and PM10 emission potentials from a variety of surfaces; and (2) evaluate the effectiveness of fugitive dust control methods. Although the surfaces are specific to the study region, the methodology and results are applicable to similar mining activities conducted elsewhere.

2. Experimental methods Fig. 1 illustrates the PI-SWERL configuration used for these tests. The PI-SWERL was enhanced with respect to previous tests for the AOSR measurements. A five-channel DustTrak DRX (Model 8534, TSI, Shoreview, MN) (Wang et al., 2009) and a 16-channel Optical Particle Sizer (OPS; Model 3330, TSI, Shoreview, MN) (Castellini et al., 2014) measured PM emission fluxes in different size fractions (PM1, PM2.5, PM4, PM10, and PM15) at one second intervals. These instruments are based on light scattering, which is sensitive to the size distribution of the suspended dust. Four Optical Gate Sensors (OGS) were located inside the PI-SWERL chamber to detect dislodgement of very large (100 lm) particles that may bounce along the ground and enhance suspension of smaller particles when they re-deposit to the surface. Two of these were located 1 cm above the annular blade, and two more were located 5 cm higher. The factory DRX calibration uses standard Arizona Road Dust, which may have different size distribution, optical properties, and density from those of the tested AOSR surfaces (Wang et al., 2009). DRX readings were normalized to the PM2.5 and PM10 gravimetric mass concentrations acquired from the concurrent sampling of Teflon-membrane filters, as detailed in the Supplemental

information (Fig. S-9). Since the OPS measures particle number distributions with optical equivalent diameters ranging from 0.3 to 10 lm in 16 channels, mass concentrations are approximated by assuming spherical particles and assuming an appropriate density (typically 2–3 g/cm3 for most minerals). As the OPS is inaccurate for concentrations >3000 particle/cm3, it is sometimes overwhelmed for very dusty surfaces (Whitby and Willeke, 1979). Only the DRX and Teflon-membrane filter concentrations are used here. As shown in Fig. 1, integrated samples were acquired for chemical characterization, which will be reported in a companion paper. Spinning the annular blade (see Fig. S-1) at different revolutions per minute (RPM) simulates the wind-shear force above the surface (Etyemezian et al., 2014). RPMs can be increased at a constant rate (ramp test), stepped through several set-points (multiple-step or hybrid test), or held constant for specified periods (single-step test), as illustrated in Fig. 2. For each surface, the ramp test was performed first to determine the size of the reservoir and the range of RPMs needed to suspend dust while not exceeding the DRX upper concentration limit (400 mg/m3). The PI-SWERL was then moved to an adjacent location on the same surface for the multiple-step test, which determined the incremental amounts of dust suspended with increasing wind speeds. Most of the results reported here are derived from three multiple-step tests of nearby locations for each surface. The single-step test was applied to obtain sufficient sample loadings on the filters (1 mg) for PM2.5 and PM10 chemical detection, as indicated by the integrated DRX readings; flows through the PM10 filters were turned off to minimize overloading ( J 5 mg) if necessary. Dust emission potential is calculated based on the measured air flow rate, dust concentration, and the effective area covered by the annular blade. The relationship between shear stress (s), friction speed (u⁄), and RPM (Etyemezian, 2011) for the PI-SWERL used in this study is:

s ¼ 4:05  1012 RPM3 þ 5:35  108 RPM2  2:20  105 RPM þ 0:0351

ð5Þ

u ¼ 1:49  1012 RPM3 þ 8:20  109 RPM2 þ 1:42  104 RPM þ 0:0872

ð6Þ

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1.0

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SaltationThreshold Friction Speed (m/s)

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Site Fig. 5. Threshold friction (u⁄) and wind speeds (u+10) for: (a) suspension and (b) saltation for each of the tested surfaces. Error bars represent the standard deviation of three multiple-step tests for each surface.

The cumulative PM emission potential (Pi,cum, in g/m2) is:

Pi;cum ðg=m2 Þ ¼

Pend;i

3

3

begin;1 Cðmg=m Þ  Qðm =sÞ  Aeff ðm2 Þ  1000ðmg=gÞ

1ðsÞ

ð7Þ

where Pi,cum is the cumulative PM emission potential from the beginning of the test (tbegin,1) to the end of step i (tend,i); C is the mass concentration of a specific size fraction measured every second; Q is the blower flow rate (nominally 0.0017 m3/s); and Aeff is the effective erosion area (0.026 m2). For a surface with an unlimited reservoir, the potential emission flux is:

F i;cum ðg=m2 =sÞ ¼

Pi;cum ðg=m2 Þ teff ðsÞ

ð8Þ

where Fi,cum is the cumulative PM emission flux from the beginning of the test (tbegin,1) to the end of step i (tend,i), and teff is the effective averaging period (the residence time at each step). For a limited

reservoir, total emissions can be calculated using Eq. (1) with P from Eqs. (2) or (7) for each event. For unlimited reservoirs, a time-averaged emission potential, i.e., emission flux, can be used with wind speed data to calculate total emissions. A purpose of this study was to identify which surfaces had the highest dust suspension potential, in order to focus emission reduction priorities, so a wide range of surfaces were sampled. More intensive study of emission variability for specific surface types, especially the high-emitters, and evaluation of control effectiveness, are deferred to future measurements. Fig. 3 locates the sampled surfaces, which are detailed in Supplementary Table S-3. These include paved and unpaved public and haul roads, parking lots, storage and waste piles, and tailings ponds. Several surfaces were subject to dust suppression measures. Materials were collected from most surfaces for soil texture and chemical composition analysis. For paved and unpaved roads, loose

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PM10 Flux (mg/m2/s)

20.5

Fit Ln(PM10)=--0.077t+57.9

0.4

Friction/Wind Speed

Fit Ln(PM10)=--0.039t+17.2

27.4

Fit Ln(PM10)=--0.036t+7.0

0.5 Fit Ln(PM10)=--0.105t+14.7

34.2

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X. Wang et al. / Aeolian Research 18 (2015) 121–134

0.001

800

0.0001

Time (s) Fig. 6. PM10 flux change at different friction speed as an illustration of the dust reservoir type (surface 1; located in Fig. 3 and pictured in Fig. S-4). The solid straight lines and equations indicate the fit of exponential decay equation (Eq. (9)) to the measured flux decay.

surface material was swept with a whisk broom into a dustpan. For sand and soil surfaces, grab samples were collected with a small garden spade to a depth of 10–15 cm from the top. The samples were sealed in double air-tight polyethylene bags before analysis. They were weighed before and after oven drying at 105 °C for 24 h to obtain moisture content. The gravel fraction (2–62.5 mm geometric diameter) was removed by dry sieving and weighed. The sand (2 mm–62.5 lm geometric diameter), silt (62.5–2 lm geometric diameter), and clay (<2 lm geometric diameter) mass fractions of the fine earth fraction (<2 mm) were determined using a combination of standard dry sieving and laser particle-size analysis (LPSA) (Gee and Or, 2002) using a Saturn DigiSizer 5200 (Micromeretics Corp, Norcross, GA). These surface soil characteristics are tabulated in Table S-4. 3. Results and discussion The data reduction procedure and detailed results intended for emission calculations are presented in the Supplemental material. PM10 examples are presented below that describe how threshold suspension speeds, emission potential, and emission fluxes were derived and how they can be used to better estimate fugitive dust emissions. 3.1. Threshold friction speeds for initial suspension and saltation suspension Threshold friction speeds (ut) affect both direct suspension, in which the wind force is sufficient to overcome the inertia of small particles (such as those in the PM2.5 and PM10 fractions), and saltation suspension (Bisal and Nielsen, 1962; Gillies and Lancaster, 2013), in which larger particles (>10 lm) are levitated, then impact the surface, thereby deagglomerating or liberating additional small particles. The sheltering effect of the larger particles is diminished during saltation. The onset of saltation is determined by signals from the OGS described above. Fig. 4 illustrates the method for determining suspension and saltation thresholds. PM10 fluxes were first smoothed by a 10 s moving average of the 1-s values. The suspension threshold friction speed was defined when the instantaneous PM10 flux exceeded 6.4  104 mg/m2/s for >4 s. The saltation threshold friction speed was defined when the two lower level OGS registered >400 particles/m2/s or when the OGS flux increased over four

consecutive seconds. These criteria correspond to DRX PM10 concentration of >0.01 mg/m3 and OGS count rate of >10 particles/s, respectively, which can be reliably distinguished from background and noise levels. Fig. 5 compares suspension and saltation thresholds for the u⁄ and u+10 for each tested surface (Table S-5 provides a numerical catalogue). Most of the u+10 suspension thresholds clustered around 12–15 km/h. As shown in Table S-17, these wind speeds are close to the monthly average wind speeds in the AOSR, implying that windblown dust is being generated on many days. The lowest suspension u+10 were 8 km/h for some unpaved roads, tailings dikes, and disturbed storage piles. The highest suspension u+10 of 20 km/h were found for an unpaved shoulder near paved Hwy 63, an undisturbed tailings pond dike, and an overburden pit. No saltation was observed for 25 of the 64 tested surfaces, and wind speeds of 40–50 km/h were needed for saltation. The lowest consistent saltation threshold was 34 ± 8 km/h for a tailings pond sand beach. These are very high gusts that occur only for short periods during frontal passages, probably accompanied by precipitation, so it is unlikely that saltation is a major cause of AOSR dust emissions except under extreme conditions. Standard deviations from the three tests are reported in Table S-5 and demonstrate variabilities that are ±20% for a given surface. No consistent relationships are found between threshold speeds and soil texture. For example, the u+10 suspension thresholds varied 7.8–18.7 km/h with an average of 13.6 km/h for the finer loam texture, while they varied 8.6–20.2 km/h with an average of 14.7 km/h for the coarser sand texture.

3.2. Dust reservoirs Eq. (2) only requires the suspension threshold friction speed, which is insufficient to determine emissions over a given time period. While the threshold may be low, the amount of dust available for suspension may also be low for some surfaces. Dust reservoir depletion is represented as a negative exponential (Anspaugh et al., 1975; Linsley, 1978) or inverse function of time (Garland, 1983; Nicholson, 1993; Reeks et al., 1985). Depletion of smaller particles exposes larger non-erodible agglomerates that can shield spendable PM from the wind (Marshall, 1971; Raupauch, 1992), thereby decreasing the effective reservoir until saltation commences. Fig. 6 illustrates reservoir depletion at different friction speeds using the exponential decay model for PM10 flux:

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Threshold Wind Speed at 10 m agl to Generate Specific Emission Potentials (km/h)

Threshold Friction Speed to Generate Specific Emission Potentials (m/s)

10

PM2.5, 0.002 g/m2

0.0

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25

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Surface Fig. 7. Threshold friction speeds for generating 0.002, 0.02, and 0.2 g/m2 emission potential of PM2.5 (top three panels) and PM10 (bottom three panels). Surfaces without a bar except surface 3 indicate that the specified emission potential was not reached at the maximum friction speed tested for that site. Surface 3 data were not available, but it is considered similar to surface 2. (See Fig. 3 for surface locations and Table S-3 for surface descriptions.)

LnðPM10;t Þ ¼ LnðPM 10;0 Þ 

t

sc

ð9Þ

where PM10,0 is the PM10 concentration at time 0 (full reservoir), PM10,t is the PM10 concentration at time t, and sc is the decay constant fitted by maximum likelihood least squares. The surface in Fig. 6 (pictured in Fig. S-4) represents a limited reservoir at the lower friction speeds (u+10 627 km/h), as it follows the exponential

decay. However, this model breaks down at high friction speeds (u+10 P32 km/h) and the reservoir becomes unlimited. This demonstrates why the multiple-step test is most useful, as it provides the amount of dust available for suspension under different weather conditions. sc was calculated for each surface and friction speed, and reservoirs for each combination are classified as limited for sc < 100 s and unlimited for sc P 100 s, as summarized in Table S-6.

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Instantaneous Emissions

D. Cumulative Emissions at end of 47.3 km/h

Instantaneous (mg/m2/s) or Cumulative Emissions (g/m2)

1.E+00 Friction/Wind Speed

0.7

47.9

0.6

41.0

0.5

1.E-01 C. Cumulative Emissions at end of 37.4 km/h

0.4

B. Cumulative Emissions at end of 26.8 km/h

0.3

1.E-02

1.E-03

A. Cumulative Emissions at end of 16.1 km/h

Cumulative Emissions

1.E-04

1.E-05

0

150

300

450

600

750

Friction Speed (m/s)

1.E+01

34.2

27.4 20.5

0.2

13.7

0.1

6.8

0

Wind Speed at 10 m agl (km/h)

X. Wang et al. / Aeolian Research 18 (2015) 121–134

0

Test Duration (s) 2

Fig. 8. Example of the PM10 emission potential (g/m ) estimates for different wind speeds for an unpaved road (surface 15 in Fig. 3 and Table S-3).

All of the surfaces tested were supply-limited at u+10  11–16 km/h, and all but two surfaces (tailings ponds at Facility C) were supply-limited at u+10 627 km/h. Most surfaces were supply unlimited at the highest wind speeds of u+10 = 56 km/h, with the exceptions being some of the storage piles, moist unpaved road surfaces, unpaved roads with minimal traffic, and paved roads with no visible trackout. Many surfaces with unlimited dust reservoirs are those with saltation. Paved surfaces with visible trackout could be unlimited reservoirs for high winds. Some of the active and dormant storage piles were not unlimited reservoirs, probably owing to the presence of larger particles (e.g., the surface 31 quarry storage pile contains 58% of gravel as shown in Table S-4) that shielded the smaller ones from wind forces and the shape of the pile (Diego et al., 2009; Furieri et al., 2013). 3.3. Emission potential and flux From an air quality management standpoint, it is important to know which surfaces have the potential to contribute most to PM2.5 and PM10 emissions in the region. This can be approached from several viewpoints. One approach is meteorological, which estimates the wind speeds needed to generate fluxes of various values, for example 0.002, 0.02, and 0.2 g/m2, and assumes that wind speed is maintained until the reservoir is exhausted. These results are shown in Fig. 7. Twenty of the 64 surfaces did not reach 0.2 g/m2 for PM2.5 at the highest wind speed tested on those surfaces. An alternative method is to estimate the emission potential corresponding to the wind speeds simulated by the multiple-step tests using Eq. (7). Emission potential is the maximum that might be expected under sustained winds. As illustrated in Fig. 8, points A, B, C, and D identify the cumulative emissions at the end of each PI-SWERL step, corresponding to wind speeds of 16, 27, 37, and 47 km/h at 10 m agl, respectively. Tables S-7 through S-11 summarize the PM1, PM2.5, PM4, PM10, and PM15 cumulative emission potentials for the tested surfaces for different wind speeds. The emission potentials at 47 km/h are one to four orders of magnitude higher than those at 11 km/h. Therefore, dust generated by wind erosion is much higher under high wind conditions. On the other hand, gusty winds usually last for a few minutes, and they do

not often occur. As shown in Fig. 5, wind erosion occurs on many surfaces for AOSR wind speeds. Although lower-magnitude winds generate less dust per event, their contribution to ambient PM cannot be ignored due to their more frequent occurrence (Lee and Tchakerian, 1995; Macpherson et al., 2008). The cumulative emission flux (g/m2/s) is calculated using Eq. (8) by dividing the emission potential by the effective averaging period. This is a more useful parameter for unlimited dust reservoirs as it can be multiplied by the duration of a wind gust when these data are available. Cumulative emission flux values are detailed in Tables S-12 to S-16. Fig. 9 compares PM10 emission fluxes for different tested surfaces. A wide range of emission fluxes are found within the same geographical area, between surface types, for different wind speeds, and among different surfaces within the same type. The surfaces with the highest PM10 emission fluxes at u+10 = 47 km/h are unpaved roads and parking lots of various types. Emissions also commence at lower wind speeds for these surfaces. Although detailed traffic counts were not obtained as part of this study, it was evident that these highest emitters were heavily travelled or recently disturbed, as evidenced by tire tracks, thereby continually replenishing the reservoirs through comminution of the surface aggregates. Eight of the ten highest PM10 emitting surfaces (ranging 0.028–0.11 g/m2/s; except for surfaces 8 in Fig. 9a and 23 in Fig. 9b) are also among the top ten surfaces with highest PM2.5 emission fluxes (ranging 0.016–0.058 g/m2/s). Surfaces 39 and 49 in Fig. 9e are among the ten highest PM2.5 emitting surfaces. Several surfaces (i.e., 57 and 59 in Fig. 9c, surfaces 31 and 33 in Fig. 9d, surfaces 14 and 41 in Fig. 9e, and surface 18 in Fig. 9f) exhibit the lowest emissions PM2.5 fluxes (ranging 5.9  106–2.3  104 g/m2/s) and PM10 (ranging 1.5  105–5.7  104 g/m2/s). These include clean paved roads and stabilized or treated (e.g., moist) surfaces. Surface 59 (Fig. 9c) is a lightly-travelled unpaved road, and it appears that without continuous disturbances by traffic its dust reservoir is depleted. The nature of the underlying soils also has an effect, as illustrated in Fig. S-5 which contrasts unpaved road surfaces 27 (high potential) and 59 (low potential); Surface 27 contains finely divided clay and silt, while surface 59 consists of coarser sand

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a)

Facility C

b) Facility B 0.14

0.10 0.08 0.06

8. Tailings dike road 1 11. Tailings dike road 2 9. Light vehicle unpaved road-dry 6. Tailings sand beach 12. Tailings dike drifting sand 7. Overburden 5. Tailings flat sand beach 4. Tailings sand strip 13. Tailings dike overburden 2. Road with sulfur deposit 10. Light vehicle unpaved road-wet

0.12

2

PM10 Emission Flux (g/m /s)

0.12

2

PM10 Emission Flux (g/m /s)

0.14

0.04 0.02

0.10 0.08

27. Unpaved road, tire track 23. Tailings beach, truck track 22. Tailings beach, tractor track 25. Main haul road 28. Overburden berm 21. Tailings dike 20. Tailings dike 24. Tailings dike 26. Main haul road, undisturbed 19. Tailings dike, undisturbed

0.06 0.04 0.02 0.00

0.00 20

30

40

20

50

c)

Facility E

0.08

2

0.12

0.06 0.04 0.02

0.10 0.08

37. Road near exit scale 29. Conveyor area 38. Parking lot for haul trucks 30. Processing ground, tire tracks 35. Waste dump, truck track 36. Waste pile 32. Dry road in processing ground 34. Unpaved road in Pit 31. Waste storage pile hill foot 33. Wet road in processing ground

0.06 0.04 0.02 0.00

0.00 20

30

40

20

50

Ft. McMurray and Ft. McKay

f)

0.08 0.06

51. Ft. McMurray unpaved parking lot 16. Ft. McMurray unpaved road near Wilson 49. Ft McMurray Thickwood construction 1. Ft. McKay unpaved road 39. Ft. MacKay Industrial Park track-out 42. Hwy 63 construction zone near BornCo 43. Hwy 63 rest area s. of Ft. McMurray 52. WBEA Ft. McKay AMS 1 unpaved road 40. Ft. McKay gravel road 50. Ft. McMurray Thickwood land clearance 17. Ft. McKay Community Center parking lot 14. Ft. McMurray paved road near WBEA AMS 7 41. Ft. McKay paved road after turn to CNRL

0.12

2

PM10 Emission Flux (g/m /s)

2

PM10 Emission Flux (g/m /s)

0.10

40

50

Other locations

0.14

0.14 0.12

30

Wind Speed at 10 m agl (km/h)

Wind Speed at 10 m agl (km/h)

e)

50

0.14 55. Haul road 58. Tailings pond beach 60. Unpaved road near sulfur pile 54. Disturbed coke pile 56. Tailings pond dike 53. Undisturbed coke pile 59. Unpaved road near sulfur pile 57. Overburden pit

PM10 Emission Flux (g/m

2

PM10 Emission Flux (g/m /s)

0.10

40

d) Quarry

0.14 0.12

30

Wind Speed at 10 m agl (km/h)

Wind Speed at 10 m agl (km/h)

0.04 0.02

0.10 0.08 0.06

48. Hwy 63 unpaved north of Aurora 62. Bare land near N Hwy 63 ice road 47. Sandy road near WBEA Station R2 64. Athabasca Hwy shoulder near firebag 44. Sandy surface near Hwy 63 between tailings 15. WBEA Shell AMS 16 unpaved road 46. Athabasca Hwy, shoulder 45. Athabasca Hwy, below shoulder 61. Forest fire site near north Hwy 6 63. Dirt road across Hwy 63 18. Highway 63 shoulder near Facility M

0.04 0.02 0.00

0.00 20

30

40

50

Wind Speed at 10 m agl (km/h)

20

30

40

50

Wind Speed at 10 m agl (km/h)

Fig. 9. PM10 emission fluxes as a function of wind speed for each of the tested surfaces, grouped by test areas for: (a) Facility C; (b) Facility B; (c) Facility E; (d) Quarry; (e) Ft. McMurray and Ft. McKay; and (f) Other locations. (The numerical number in each panel represents surface locations in Fig. 3 and Table S-3).

and gravel. In Fig. 9d, the waste limestone storage piles (e.g., surface 31) had 50–60% gravel (Table S-4) and were well stabilized with limited dust supplies; and watering the road (surface 33) increased the soil moisture content and reduced emissions potential. For public surfaces shown in Fig. 9e and f, unpaved roads and parking lots with high vehicle traffic and loose dust materials show the highest dust emissions.

4. Effectiveness of dust control measures Fig. 10 contrasts unpaved road emissions before and after water-spray suppression. At Facility C (Fig. 10a), PM10 emission

potential decreased by 57%, 98%, and 99% at 27, 37, and 47 km/h wind speeds, respectively. The dry surface turned from supply-limited at lower wind speeds to an unlimited reservoir at 47 km/h, while the moist surface required winds exceeding 56 km/h to reach the unlimited reservoir (not shown). Efficiencies are probably underestimated, as the fairly long multi-step residence times exceed the durations of most wind gusts, and dry soil was exposed as the moist surface coating was removed during the test. The quarry comparison (Fig. 10b) shows similar results, with reductions of 48% at 27 km/h and 86–94% at 37–56 km/h. Road watering is an effective dust suppression measure (Evans et al., 1983; Thompson and Visser, 2002) when: (1) it is sprayed

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1.0

54.7

0.8

41.0

27.4

1.E+01

1.E+00 PM10, Dry Road

0.6

1.E-01

Friction/Wind Speed

0.4

1.E-02

0.2

13.7

1.E-03 PM10, Moist Road

0.0

0.0

1.E-04 0

200

400

600

Cumulative PM10 Emission Potential (g/m2)

68.4

Friction Speed (m/s)

Wind Speed at 10 m agl (km/h)

(a)

800

Time (s) 1.0

54.7

0.8

41.0

27.4

1.E+00

1.E-01 PM10, Dry Road

1.E-02

0.6 Friction/Wind Speed

1.E-03

0.4 PM10, Moist Road

13.7

0.2

0.0

0.0

1.E-04

0

200

400

600

800

1.E-05 1000

Cumulative PM10 Emission Potential (g/m2)

68.4

Friction Speed (m/s)

Wind Speed at 10 m agl (km/h)

(b)

Time (s) Fig. 10. PM10 emission potentials for different dry and moist unpaved road surfaces at: (a) Facility C (surfaces 9 and 10); and (b) Quarry (surfaces 32 and 33).

frequently, approximately every 1–2 h, to counteract evaporation and adhesion to vehicles; and (2) the amount of water sprayed is optimized, as too little water reduces effectiveness, while too much water creates mud that is tracked out onto roadways. The second limitation is minimized in many mining operations by vibrators and wheel washers. On the other hand, fresh water is a precious resource and each oil sands facility has a limited quota to draw fresh water from the Athasbasca river to protect its flow level. Watering is not used in winter because snow cover naturally suppresses dust suspension and frozen water would pose safety risks. Longer lasting chemical suppressants are also available for unpaved surfaces (Gillies et al., 1999; Watson et al., 1996), but there are many effectiveness claims that have not been confirmed by measurements such as those reported here. Another method to control emissions is to reduce the area exposed to soil-disrupting activities. Fig. 11 compares nearby sections of the same surfaces that were disturbed and undisturbed. Figs. S-7 and S-8 contrasts the appearances of these surfaces, demonstrating the reservoir replenishment by continuous

activities. For the Facility B haul road (Fig. 11a), cumulative PM10 emission potentials for the disturbed portion (tire track) were 160, 99, 44, and 14 times of those on the undisturbed surface at 27, 37, 47, and 57 km/h wind speeds, respectively. A 5 m2 section of the coke pile surface was artificially disturbed by walking on it, a common practice during inspections, to break the thin crust surface. Care was taken to gently place the PI-SWERL over a nearby undisturbed portion, a feat that would be impractical with the large wind tunnels normally used for these tests. In this case, the simple act of a person walking on the surface increased emissions by factors of 12, 35, 22, and 9 at 27, 37, 47, and 57 km/h wind speeds, respectively.

5. Using test methods and results for air quality management The methods applied and the results achieved by the tests described here have several applications to air quality management.

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1.0

54.7

0.8

41.0

27.4

1.E+01

1.E+00 Friction/Wind Speed

PM10, Disturbed Surface 0.6

1.E-01

0.4

1.E-02

13.7

0.2

0.0

0.0

PM10, Undisturbed Surface

0

200

400

600

1.E-03

800

1.E-04 1000

Cumulative PM10 Emission Potential (g/m2)

68.4

Friction Speed (m/s)

Wind Speed at 10 m agl (km/h)

(a)

Time (s) 1.0

54.7

0.8

41.0

27.4

1.E+01

1.E+00 PM10, Disturbed Surface 1.E-01

0.6

1.E-02

0.4 Friction/Wind Speed

13.7

0.2

0.0

0.0 0

200

PM10, Undisturbed Surface

400

600

800

1.E-03

1.E-04 1000

Cumulative PM10 Emission Potential (g/m2)

68.4

Friction Speed (m/s)

Wind Speed at 10 m agl (km/h)

(b)

Time (s) Fig. 11. PM10 emission potentials for: (a) Facility B mine haul road and adjacent undisturbed area (surfaces 26 and 27; Fig. S-7); and (b) Facility E undisturbed and disturbed sections of a coke pile (surfaces 53 and 54; Fig. S-8).

Table 1 Estimation of PM2.5 and PM10 emissions from several surfaces near Ft. McKay, Alberta, Canada.

a

Tested surface

PM2.5 (g/m2/y)

PM10 (g/m2/y)

Heavily-travelled public paved road (surface 41)a Lightly-travelled public unpaved road (surface 52) Typical stabilized tailings dike (surface 24) Heavily-travelled public unpaved road (surface 48)

0.6 10.1 19.5 335.2

1.5 44.2 56.0 515.2

See surface locations in Fig. 3.

 Improving windblown fugitive dust emission inventories: Using wind data from nearby towers or from wind fields used for air quality modeling, it is possible to develop more accurate episodic and annual emission rates for the National Pollutant Release Inventory (NPRI). As an example, Table 1 illustrates the annual PM2.5 and PM10 emissions from several surfaces near

Ft. McKay using the wind speed and frequency data from Table S-17 and emission flux data in Fig. 9. Only June through September are included in the calculation, as there is no snow on the ground and conditions are dry. With appropriate association of threshold friction speeds and reservoir capacities with different surface types in Geographical Information System (GIS) layers, it would be possible to make maps of the highest emission areas that could be aggregated for annual emissions, or used episodically to better understand high pollution events.  Assigning priorities to and testing the effectiveness of surface stabilization efforts: These tests demonstrate that not all surfaces are large emitters, and that activities and soil types can be used to screen surfaces that need amelioration. A simplified version of the PI-SWERL (without detailed size and filter measurements) could be used to further narrow the choices, and before and after tests would validate the effectiveness of the suppressants.

X. Wang et al. / Aeolian Research 18 (2015) 121–134

 Reducing surface disturbances: A small section of worker training programs could make them aware that even small disturbances can have a great effect on emissions. Creating access pathways on crusted surfaces and minimizing unpaved roads as well as swerving and speeds would allow the reservoirs to be depleted, with subsequent emission reductions. 6. Summary and conclusions All surfaces were supply limited at u+10 = 11–16 km/h, as well as at u+10 = 27 km/h except two tailings beach surfaces. Most surfaces were supply unlimited at u+10 = 56 km/h, with exceptions of several surfaces at the lime stone quarry, the coke pile, paved surfaces, and stabilized land clearances. The average PM threshold wind speed varied from u+10 = 11–21.5 km/h, while the saltation occurred at higher speeds of u+10 > 32 km/h. Saltation is often related to unlimited reservoirs. Threshold wind speeds, emission potentials, and fluxes varied among surfaces, even among those in the same category (e.g., unpaved roads). Twenty of the 64 surfaces did not reach 0.2 g/m2 PM2.5 emission potential at the maximum tested speed (u+10 = 56 km/h). A high emitting unpaved mine haul road can emit 0.025 and 0.13 g/m2/s PM10 under wind speeds u+10 of 37 and 56 km/h, respectively. In contrast, a low emitting highway shoulder emits 2–4 orders of magnitude lower PM10 under these wind speeds. Unpaved roads, parking lots, and bare land with high abundances of loose clay and silt materials along with frequent mechanical disturbances are the highest dust emitting surfaces. Paved roads, stabilized or treated (e.g., watering) surfaces with limited loose dust materials are the lowest emitting surfaces. Watering reduced windblown dust emission potentials of the tested unpaved roads by 50–99% at different wind speeds. Watering is effective in reducing fugitive dust emissions from dry surfaces when applied at the right places and at the right quantities. The effectiveness of other dust suppressants that may last longer or be more cost effective, such as polymer stabilizers and surfactants, can be evaluated with methods employed in this study. Surface disturbances by traffic or other activities increased PM10 emission potentials by 9–160 times. Minimizing surface disturbances is effective in reducing windblown dust. Acknowledgement This work was sponsored by the Wood Buffalo Environmental Association (WBEA), Alberta, Canada (www.wbea.org). The content and opinions expressed by the authors in this paper do not necessarily reflect the views of WBEA or of the WBEA membership. The authors wish to acknowledge Dr. Vic R. Etyemezian and Mr. George Nikolich of the Desert Research Institute for assistance in PI-SWERL operation and insightful discussions on data analysis. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.aeolia.2015.07. 004. These data include Google maps of the sampling sites and study area described in this article. References Anspaugh, L.R., Shinn, J.H., Phelps, P.L., Kennedy, N.C., 1975. Resuspension and redistribution of plutonium in soils. Health Phys. 29, 571–582. Bacon, S.N. McDonald, E.V. Amit, R.; Enzel, Y. Crouvi, O. (2011). Total suspended particulate matter emissions at high friction velocities from desert landforms. J. Geophys. Res.-Earth Surf., 116. Bisal, F., Nielsen, K.F., 1962. Movement of soil particles in saltation. Can. J. Soil Sci. 42, 81–86.

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Buck, B.J., King, J., Etyemezian, V.R., 2011. Effects of salt mineralogy on dust emissions, Salton Sea, California. Soil Sci. Soc. Am. J. 75 (5), 1971–1985. Cao, J.J., Chow, J.C., Lee, S.C., Watson, J.G. (2013). Evolution of PM2.5 measurements and standards in the U.S. and future perspectives for China. AAQR, 13(4) 1197– 1121. Castellini, S., Moroni, B., Cappelletti, D. (2014). PMetro: Measurement of urban aerosols on a mobile platform. Measurement, 49(0):99–106. Chepil, W.S., 1945a. Dynamics of wind erosion II. Initiation of soil movement. Soil Sci. 60, 397–411. Chepil, W.S., 1945b. Dynamics of wind erosion. Soil Sci. 60, 305–320. China, S., James, D.E., 2012. Influence of pavement macrotexture on PM10 emissions from paved roads: A controlled study. Atmos. Environ. 63, 313–326. Cowherd, C., 2001. Fugitive dust emissions. In: Baron, P., Willeke, K. (Eds.), Aerosol Measurement: Principles, Techniques, and Applications, 2nd ed. John Wiley & Sons, New York, NY, pp. 845–857. Diego, I., Pelegry, A., Torno, S., Torano, J., Menendez, M., 2009. Simultaneous CFD evaluation of wind flow and dust emission in open storage piles. Appl. Math. Model. 33 (7), 3197–3207. Environment Canada (2013). CAC Emission Summaries. prepared by Environment Canada, Ottawa, Ontario, http://www.ec.gc.ca/pdb/cac/Emissions1990-2015/ emissions_e.cfm. Environment Canada (2015). Canadian ambient air quality standards. prepared by Environment Canada, Ottawa, Ontario, http://www.ec.gc.ca/default.asp?lang= En&n=56D4043B-1&news=A4B2C28A-2DFB-4BF4-8777-ADF29B4360BD. Etyemezian, V.R., Kuhns, H.D., Gillies, J.A., Green, M.C., Pitchford, M.L., Watson, J.G., 2003a. Vehicle-based road dust emission measurement I. Methods and calibration. Atmos. Environ. 37 (32), 4559–4571. Etyemezian, V.R., Kuhns, H.D., Gillies, J.A., Chow, J.C., Hendrickson, K., McGown, M., Pitchford, M.L., 2003b. Vehicle-based road dust emissions measurement (III): effect of speed, traffic volume, location, and season on PM10 road dust emissions in the Treasure Valley. ID. Atmos. Environ. 37 (32), 4583–4593. Etyemezian, V.R., Nikolich, G., Ahonen, S., Pitchford, M.L., Sweeney, M., Purcell, R., Gillies, J.A., Kuhns, H.D., 2007. The Portable In Situ Wind Erosion Laboratory (PISWERL): A new method to measure PM10 potential for windblown dust properties and emissions. Atmos. Environ. 41 (18), 3789–3796. Etyemezian, V.R. (2011). User’s Guide for the Miniature Portable In-Situ Wind ERosion Lab (PI-SWERL). prepared by DUST-QUANT LLC, Las Vegas, NV. Etyemezian, V.R., Gillies, J.A., Shinoda, M., Nikolich, G., King, J., Bardis, A.R., 2014. Accounting for surface roughness on measurements conducted with PI-SWERL: evaluation of a subjective visual approach and a photogrammetric technique. Aeolian Res. 13, 35–50. Evans, J.S., Cooper, D.W., Harrington, J.J., 1983. Strategies for the optimal control of dust emissions from unpaved roads. J. Air Poll. Control Assoc. 33 (4), 312–317. Furieri, B., Russeil, S., Santos, J.M., Harion, J.L., 2013. Effects of non-erodible particles on aeolian erosion: wind-tunnel simulations of a sand oblong storage pile. Atmos. Environ. 79, 672–680. Garland, J.A., 1983. Precipitation, scavenging, dry deposition and resuspension. In: Pruppacher, H.R., Semonin, R.G., Slinn, W.G.N. (Eds.), 2, pp. 1087–1097. Gee, G.W.; Or, D. (2002). Particle-size analysis. In Method of Soil Physical Analysis: Part 4 - Physical Methods, Dane, J. H., Topp, G. C., Eds.; Soil Science Society of America: Madison, WI, 255–293. Gillette, D.A., Blifford Jr., I.H., Fenster, C.R., 1972. Measurements of aerosol-size distributions and vertical fluxes of aerosols on land subject to wind erosion. J. Appl. Meteorol. 11, 977–987. Gillette, D.A., 1974. On the production of soil wind erosion aerosols having the potential for long-range transport. J. Rech. Atmos. 8, 735–744. Gillette, D.A., 1978. A wind tunnel simulation of the erosion of soil: effect of soil texture, sandblasting, wind speed, and soil consolidation on dust production. Atmos. Environ. 12, 1735–1743. Gillies, J.A., Watson, J.G., Rogers, C.F., DuBois, D.W., Chow, J.C., Langston, R., Sweet, J. (1999). Long term efficiencies of dust suppressants to reduce PM10 emissions from unpaved roads. J. Air Waste Manage. Assoc., 49(1):3–16. Gillies, J.A., Lancaster, N., 2013. Large roughness element effects on sand transport, Oceano Dunes, California. Earth Surf. Process. Landform. 38 (8), 785–792. Goossens, D., Buck, B., 2009. Dust dynamics in off-road vehicle trails: measurements on 16 arid soil types, Nevada, USA. J. Environ. Manage. 90 (11), 3458–3469. Kavouras, I.G., Etyemezian, V.R., Nikolich, G., Gillies, J.A., Sweeney, M., Young, M., Shafer, D., 2009. A new technique for characterizing the efficacy of fugitive dust suppressants. J. Air Waste Manage. Assoc. 59 (5), 603–612. King, J., Etyemezian, V.R., Sweeney, M., Buck, B.J., Nikolich, G., 2011. Dust emission variability at the Salton Sea, California, USA. Aeolian Res. 3 (1), 67–79. Kuhns, H.D., Etyemezian, V.R., Landwehr, D., Macdougall, C.S., Pitchford, M.L., Green, M.C., 2001. Testing Re-entrained Aerosol Kinetic Emissions from Roads (TRAKER): A new approach to infer silt loading on roadways. Atmos. Environ. 35 (16), 2815–2825. Kuhns, H.D., Gillies, J.A., Etyemezian, V.R., Nikolich, G., King, J., Zhu, D.Z., Uppapalli, S., Engelbrecht, J.P., Kohl, S.D., 2010. Effect of soil type and momentum on unpaved road particulate matter emissions from wheeled and tracked vehicles. Aerosol Sci. Technol. 44 (3), 187–196. Lee, J.A., Tchakerian, V.P., 1995. Magnitude and Frequency of Blowing Dust on the Southern High Plains of the United States, 1947–1989. Ann. Assoc. Am. Geogr. 85 (4), 684–693. http://dx.doi.org/10.1111/j.1467-8306.1995.tb01820.x. Linsley, G.S. (1978). Resuspension of the trans-uranium elements – a review of existing data. Report Number NRPB-R75; prepared by HSMO, London.

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X. Wang et al. / Aeolian Research 18 (2015) 121–134

Macpherson, T., Nickling, W.G., Gillies, J.A., Etyemezian, V.R., 2008. Dust emissions from undisturbed and disturbed supply-limited desert surfaces. J. Geophys. Res. – Atmosph. 113 (F2). Marshall, J.K., 1971. Drag measurements in roughness arrays of varying density and distribution. Agric. Meteorol. 8, 269–292. Merritt, W.S., Letcher, R.A., Jakeman, A.J., 2003. A review of erosion and sediment transport models. Environ. Model. Softw. 18 (8–9), 761–799. Neuman, C.M., Boulton, J.W., Sanderson, S., 2009. Wind tunnel simulation of environmental controls on fugitive dust emissions from mine tailings. Atmos. Environ. 43 (3), 520–529. Nicholson, K.W., 1993. Wind tunnel experiments on the resuspension of particulate material. Atmos. Environ. 27A (2), 181–188. Nickling, W.G., Lancaster, N., Gillies, J.A. (1997). Field wind tunnel studies of relations between vegetation cover and dust emissions at Owens Lake. prepared by University of Guelph, Ontario, Canada and Desert Research Institute, Reno, NV, for Great Basin Unified Air Pollution Control District. Raupauch, M.R., 1992. Drag and drag partition on rough surfaces. Bound. Lay. Meteorol. 60, 375–395. Reeks, M.W., Reed, J., Hall, D. (1985). The long-term suspension of small particles by a turbulent flow – Part III: Resuspension for rough surfaces. Report Number TPRD/B/0640/N85; prepared by CEGB Berkeley Nuclear Labs, Gloucestershire, U.K. Sehmel, G.A., 1980a. Particle resuspension: A review. Environ. Int. 4, 107–127. Sehmel, G.A., 1980b. Particle and gas dry deposition: A review. Atmos. Environ. 14, 983–1011. Simon, H., Allen, D.T., Wittig, A.E., 2008. Fine particulate matter emissions inventories: comparison’s of emissions estimates with observations from recent field programs. J. Air Waste Manage. Assoc. 58 (2), 320–343. Slinn, W.G.N., 1982. Predictions for particle deposition to vegetative canopies. Atmos. Environ. 16 (7), 1785–1794. Sweeney, M., Etyemezian, V.R., Macpherson, T., Nickling, W.G., Gillies, J.A., Nikolich, G., McDonald, E., 2008. Comparison of PI-SWERL with dust emission measurements from a straight-line field wind tunnel. J. Geophys. Res. – Atmosph. 113 (F1). Sweeney, M.R., McDonald, E.V., Etyemezian, V.R., 2011. Quantifying dust emissions from desert landforms, eastern Mojave Desert, USA. Geomorphology 135 (1–2), 21–34. Thompson, R.J., Visser, A.T., 2002. Benchmarking and management of fugitive dust emissions from surface-mine haul roads. Trans. Inst. Min. Metall. Sect. A 111, A28–A34.

U.S.EPA (2006). 13.2.5 Industrial wind erosion. In Compilation of air pollutant emission factors. Volume I: Stationary point and area sources, U. S. Environmental Protection Agency: Research Triangle Park, NC, 13.2.5-1-13.2.5-15. http://www. epa.gov/ttn/chief/ap42/ch13/final/c13s0205.pdf. U.S.EPA (2013a). 2011 National Emissions Inventory Data. prepared by U.S. Environmental Protection Agency, Research Triangle Park, NC, http://www. epa.gov/ttn/chief/net/2011inventory.html. U.S.EPA (2013b). 40 CFR Parts 50, 51, 52, 53, and 58-National ambient air quality standards for particulate matter: Final rule. Federal Register, 78(10):3086-3286. http://www.gpo.gov/fdsys/pkg/FR-2013-01-15/pdf/2012-30946.pdf. Viner, A.S., Ranade, M.B., Shoughnessy, E.J., Drehmel, D.C., Daniels, B.E., 1982. A Wind Tunnel for Dust Entrainment Studies. U.S. Environmental Protection Agency, Research Triangle Park, NC. Wang, X.L., Chancellor, G., Evenstad, J., Farnsworth, J.E., Hase, A., Olson, G.M., Sreenath, A., Agarwal, J.K., 2009. A novel optical instrument for estimating size segregated aerosol mass concentration in real time. Aerosol Sci. Technol. 43, 939–950. Watson, J.G., Chow, J.C., Gillies, J.A., Moosmüller, H., Rogers, C.F., DuBois, D.W., Derby, J.C. (1996). Effectiveness demonstration of fugitive dust control methods for public unpaved roads and unpaved shoulders on paved roads. Report Number 685-5200.1F; prepared by Desert Research Institute, Reno, NV, for San Joaquin Valley Unified Air Pollution Control District, Fresno, CA; http://www. arb.ca.gov/airways/Documents/reports/dri_dustcontrol.pdf. Watson, J.G.; Chow, J.C. (2000). Reconciling urban fugitive dust emissions inventory and ambient source contribution estimates: Summary of current knowledge and needed research. Report Number 6110.4D2; prepared by Desert Research Institute, Reno, NV, for U.S. Environmental Protection Agency, Research Triangle Park, NC; http://www.epa.gov/ttn/chief/efdocs/fugitivedust.pdf. Watson, J.G.; Chow, J.C.; Pace, T.G. (2000). Fugitive dust emissions. In Air Pollution Engineering Manual, Second Edition, 2nd; Davis, W. T., Ed.; John Wiley & Sons Inc: New York, 117–135. Whitby, K.T.; Willeke, K. (1979). Single particle optical counters: Principles and field use. In Aerosol Measurement, Lundgren, D. A., Lippmann, M., Harris, F. S., Jr., Clark, W. E., Marlow, W. H., Eds.; University Presses of Florida: University of FL, Gainesville, 145–182. Zingg, A.W. (1951). A portable wind tunnel and dust collector developed to evaluate the erodibility of field surfaces. Agron. J., 43:189-191. Ziskind, G., Fichman, M., Gutfinger, C., 1995. Resuspension of particulates from surfaces to turbulent flows – review and analysis. Aerosol Sci. Technol. 26 (4), 613–644.