Windblown Fugitive Dust Characterization in the Athabasca Oil Sands
Region
WBEA-DRI Agreement Number: T108-13
Report submitted to:
Kevin E. Percy and Jean-Guy Zakrevsky
Wood Buffalo Environmental Association
#100 – 300 Thickwood Boulevard
Ft. McMurray, AB, Canada T9K 1Y1
Report prepared for:
Wood Buffalo Environmental Association
Report prepared by:
John G. Watson, Ph.D.
Judith C. Chow, Sc.D.
Xiaoliang Wang, Ph.D.
Steven D. Kohl, M.S.
Laxmi Narasimha R. Yatavelli, Ph.D.
Desert Research Institute
Nevada System of Higher Education
2215 Raggio Parkway
Reno, NV 89512
March 31, 2014
Table of Contents
Page
List of Abbreviations ..................................................................................................................... iii List of Tables ................................................................................................................................. iv List of Figures ..................................................................................................................................v Executive Summary ....................................................................................................................... ix 1 Introduction ................................................................................................................... 1-1 1.1 Background ............................................................................................................. 1-1 1.2 Study Objectives ..................................................................................................... 1-3 1.3 Report Overview ..................................................................................................... 1-3 2 Experimental Methods .................................................................................................. 2-1 2.1 Windblown Dust Emission Calculation .................................................................. 2-1 2.2 Fugitive Dust Sampling System.............................................................................. 2-1 2.3 Test Procedure ........................................................................................................ 2-5 2.4 Sampling Sites ........................................................................................................ 2-8 2.5 Laboratory Analysis .............................................................................................. 2-13 3 Data Validation ............................................................................................................. 3-1 3.1 Mass Closure........................................................................................................... 3-1 3.2 Anion and Cation Balance ...................................................................................... 3-3 3.3 SO4= versus Total S ................................................................................................. 3-3 3.4 Concentration Uniformity ....................................................................................... 3-4 3.5 DRX and OPS Calibrations .................................................................................... 3-5 4 Windblown Fugitive Dust Emission Characteristics .................................................... 4-1 4.1 Data Reduction........................................................................................................ 4-1 4.2 Dust Reservoir Type ............................................................................................... 4-1 4.3 Threshold Friction Velocity .................................................................................... 4-6 4.4 Emission Potential and Flux ................................................................................. 4-12 4.5 Effectiveness of Dust Control Measures............................................................... 4-19 5 Source Profiles .............................................................................................................. 5-1 5.1 Water-soluble Ions .................................................................................................. 5-1 5.2 Major and Rare-earth Elements .............................................................................. 5-7 5.3 Lead Isotopes ........................................................................................................ 5-18 5.4 Carbon Fractions ................................................................................................... 5-21 5.5 Organic Compound Profiles ................................................................................. 5-25 5.6 Profile Similarities, Differences, and Composite Source Profile ......................... 5-29 6 Summary and Recommendations for Future Studies.................................................... 6-1 6.1 Summary of Key Results ........................................................................................ 6-1 6.2 Recommendations for Future Studies ..................................................................... 6-3 7 References ..................................................................................................................... 7-1 Appendix A Analytical Detection Limits for Mass, Elements, Lead Isotopes, Ions,
Carbon, and Organic Compounds ....................................................................... A-1
Appendix B Cumulative PM1, PM2.5, PM4, PM10, and PM15 Emission Potential (g/m2)
at Different PI-SWERL RPMs for the 64 Fugitive Dust Sampling Sites ............B-1
Appendix C Cumulative PM1, PM2.5, PM4, PM10, and PM15 Emission Flux (g/m2/s) at
Different PI-SWERL RPMs for the 64 Fugitive Dust Sampling Sites ................C-1
i
Table of Contents, Continued
Appendix D
Appendix E
Appendix F
Appendix G
Appendix H
Appendix I
Page
Source profile tables of elements from Na to U by XRF, water-soluble
ions, and carbon fractions ................................................................................... D-1
Source profile tables of elements measured by ICP-MS including Cs, Be,
and 14 rare-earth elements ................................................................................... E-1
Source profile tables for non-polar organics ........................................................ F-1
Source profile tables of carbohydrates, organic acids, and total WSOC ............ G-1
Tables of comparison of statistical measured for PM2.5 geological samples
from facility and non-facility sites ...................................................................... H-1
Tables of composite source profiles ..................................................................... I-1
ii
List of Abbreviations
σ: uncertainty
τ: shear stress
τc: time constant for exponential concentration decay
AAS: atomic absorption spectroscopy
AC: automated colorimetry
ADT: average daily traffic
Aeff: effective area of the PI-SWERL blade
agl: above ground level
AMS: WBEA air monitoring station
AOSR: Athabasca Oil Sands Region
AP-42: U.S. EPA Compilation of Air Pollution Emission
Factors
ARD: Arizona road dust
babs: light absorption coefficient
Ba: barium
C: PM mass concentration (mg/m3)
Ca++: calcium ion
Cl-: chloride
CMB: Chemical Mass Balance receptor models
CO3=: carbonate
Cs: cesium
DDW: distilled deionized water
DRI: Desert Research Institute
DRX: DustTrak DRX
EAF: DRI’s Environmental Analysis Facility
EC: elemental carbon
EC1, EC2, and EC3: elemental carbon evolved at 580,
740, and 840 °C, respectively, in a 98% He / 2% O2
atmosphere
Fi,cum: cumulative emission flux (g/m2/s) till ith
period/step
g-PM/VKT: grams of particulate matter produced per
kilometer of travel
h: height (m) above ground level
H: height
HEPA: high efficiency particulate air
i: ith period between disturbance or step in the PISWERL cycle
ICP/MS: inductively coupled plasma/mass spectrometry
IC: Ion chromatography
IMPROVE: Interagency Monitoring of Protected Visual
Environments
k: particle size multiplier in AP-42 emission estimate
K+: potassium ion
L: length
Mg++: magnesium ion
MDL: Minimum detection limit
N: number of disturbance per year
Na+: sodium ion
NH4+: ammonium
NO2-: nitrite
NO3-: nitrate
OC: organic carbon
OC1, OC2, OC3, and OC4: organic carbon evolved at
140, 280, 480, and 580 °C, respectively, in a 100%
He atmosphere
OGS: optical gate sensors
OP: pyrolyzed carbon
OPS: optical particle sizer
P: emission potential (g/m2)
Pi: non-cumulative emission potential (g/m2) for ith
period/step
Pi,cum: cumulative emission potential (g/m2) till ith
period/step
PAH: polycyclic aromatic hydrocarbon
Pb: lead
PCF: DRX photometric calibration factor
PI-SWERL: Portable In-Situ Wind Erosion Laboratory
PM: particulate matter
PM1: particles with aerodynamic diameter < 1 µm
PM2.5: particles with aerodynamic diameter < 2.5 µm
PM4: particles with aerodynamic diameter < 4 µm
PM10: particles with aerodynamic diameter < 10 µm
PM15: particles with optical diameter < 15 µm
PMF: Positive Matrix Factorization receptor models
PO4≡: phosphate
Q: flow rate (m3/s)
R0: surface roughness (m)
RH: relative humidity
RPM: revolutions per minute
SCF: DRX size calibration factor
SO4=: sulfate
t: time
tbegin,1: beginning time of a test
tend,i: ending time of step i in a test
teff: effective averaging time (s)
T: temperature
TC: total carbon
TD-GC/MS: thermal desorption-gas
chromatography/mass spectrometry
TOC: total organic carbon
TOR: thermal-optical reflectance
TOT: thermal/optical transmittance
TPM: particles with aerodynamic diameter < ~100 µm
TRAKER: Testing Re-entrained Aerosol Kinetic
Emissions from Roads
u*: wind friction velocity (m/s)
uh+: fastest mile of wind at h m above ground level (m/s)
ut: threshold friction velocity (m/s)
U.S. EPA: United States Environmental Protection
Agency
W: width
WBEA: Wood Buffalo Environmental Association
WSOC: water-soluble organic carbon
XRF: X-ray fluorescence
iii
List of Tables
Page
Table 2-1. PI-SWERL motor speed settings for ramp (R5000), hybrid (H5000), and step
(S5000) test protocols. ......................................................................................... 2-6 Table 2-2. List of 64 fugitive dust sampling sites characterized in 2012 and 2013. ................... 2-9 Table 2-3. Wind statistics of Ft. McMurray, Alberta (http://www.weatherbase.com/). ............ 2-12 Table 2-4. Friction velocity (u*) and fastest mile of wind measured at 10 m above ground
level (u10+) in units of m/s and km/h corresponding the PI-SWERL blade
rotating speed. .................................................................................................... 2-13 Table 2-5. Laboratory analysis of filter samples. ...................................................................... 2-14 Table 4-1. Summary of dust reservoir type of each tested site. ................................................... 4-4 Table 4-2. Threshold RPM, friction speed, and corresponding wind speed at 10 m above
the ground level for PM10 emissions and saltation to occur. Values are
expressed as average ± standard deviation of multiple runs. NA indicates
that saltation was not observed for that surface. .................................................. 4-8 Table 4-3. The ten sites with highest and lowest PM10 emission fluxes.................................... 4-14 Table 5-1. Elemental weight percent (%) of oil sands feed and scroll centrifuge tailing in
one oil sands facility (Ciu et al., 2003). ............................................................. 5-12 Table 5-2. Comparison of OC, EC, and CO3=-C in PM10 between this and other studies......... 5-23 Table 5-3. Source profile-compositing scheme. ........................................................................ 5-32 Table 5-4. Comparison of statistical measures of the variability in Level II and III
composite PM2.5 profiles. Yellow highlights indicate P values < 0.05,
indicating dissimilarities between the composite profiles. ................................ 5-32 Table 5-5. Abundance ratios of profile groups for PM2.5. Level II facility dusts are
normalized to overburden, non-facility dusts are normalized to bare land,
and Level III is normalized to non-facility dust. Some species with low
abundances in all groups are not listed. Cells with yellow highlight
indicate ratios > 2 and cells with blue highlight indicate ratios < 0.5. .............. 5-37 iv
List of Figures
Page
Figure 2-1. Schematic diagram of the fugitive dust sampling system. During ramp and
hybrid tests, the PI-SWERL was only connected to the Optical Particle
Sizer and DustTrak DRX, and filter packs were connected only during
step tests. See sampling protocol for detailed description. .................................. 2-2 Figure 2-2. Photograph of the fugitive dust sampling system under operation. .......................... 2-3 Figure 2-3. Components of the PI-SWERL. Left-top view; right: Bottom view
(Etyemezian, 2011). ............................................................................................. 2-3 Figure 2-4. Example of a) ramp, b) hybrid, and c) step tests. Only PM2.5 and PM10 of the
five size fractions measured by the DRX are illustrated...................................... 2-7 Figure 2-5. Photograph of rings created after PI-SWERL runs. Each ring represents one
of the ramp, hybrid, or step test. .......................................................................... 2-8 Figure 2-6. Location of the 64 sampling sites. Yellow labels indicate sites sampled in
2012 and red labels indicate sites sampled in 2013. .......................................... 2-11 Figure 3-1. Sum of measured species in PM2.5 and PM10. The sum of species includes TC
(including CO3=), Na+, Mg++, K, Cl, Ca, PO4≡, and SO4= and excludes OC
and EC fractions, OC, EC, Na, Mg, P, S, K+, Cl- , and Ca++. .............................. 3-2 Figure 3-2. Sum of major constituents in PM2.5 and PM10 after assuming mineral oxides
forms (Al2O3=2.2[Al]; SiO2=2.49[Si]; CaO=1.63[Ca];
FexOy+K2O=2.42[Fe], and TiO2=1.94[Ti]) and organics (1.4OC)
following the IMPROVE mass reconstruction equation (Malm et al.,
1994) except that CO3= was added. (See site description in Table 2-2 and
site location in Figure 2-6) ................................................................................... 3-2 Figure 3-3. Cation versus anion balance for PM2.5 and PM10 geological samples (based on
Eqs 3-1 and 3-2). .................................................................................................. 3-3 Figure 3-4. Sulfate versus sulfur in a) PM2.5 and b) PM10 geological samples. .......................... 3-4 Figure 3-5. Comparison PM mass collected on a) two PM2.5 and b) two PM10 Teflonmembrane filter channels for all 64 tests. ............................................................ 3-4 Figure 3-6. Comparison of PM2.5 and PM10 mass concentration measured by the Teflonmembrane filters and the DustTrak DRX in 2012 (a and b) and 2013 (c
and d). Because different internal calibration factors were used in 2012
and 2013, the regression slopes are different for 2012 and 2013 tests. Test
at three sites (8, 30, and 53) were not plotted because the DustTrak DRX
was saturated by the high dust concentrations. .................................................... 3-6 Figure 3-7. Comparison of a) PM2.5 and b) PM10 mass concentration measured by the
Teflon-membrane filters and the OPS for tests in 2012 and 2013. ...................... 3-6 Figure 4-1. PM10 concentration as a function of the PI-SWERL blade rotating speed
during a hybrid test at Site 1 as an illustration of the dust reservoir type.
The red lines and equations indicate the fit of exponential decay equations
to the concentration drop. .................................................................................... 4-3 Figure 4-2. Pictures of Site 1: a) an area view of the unpaved road near Ft. McKay that
was constantly disturbed by traffic; and b) a ring after the PI-SWERL test
indicating sand movement. .................................................................................. 4-3 Figure 4-3. PM10 concentration and optical gate sensors (OGS) count rate as a function of
rotating speed during a hybrid test at Site 39 as an illustration of
v
determining the threshold friction speed (RPM) for PM emission and
saltation (as indicated by the orange and purple dash lines, respectively). ......... 4-7 Figure 4-4. Pictures of Site 39: a) an area view of the track-out accumulation along Hwy
63 near the Ft. McKay Industrial Park; and b) a ring after the PI-SWERL
test indicating sand movement. ............................................................................ 4-7 Figure 4-5. Threshold RPM for a) PM emission and b) saltation. ............................................. 4-10 Figure 4-6. Threshold RPM for generating 0.002, 0.02, and 0.2 g/m2 emission potential of
PM2.5 (first three red panels) and PM10 (last three green panels). Sites
without a bar except Site 3 indicate that the specified emission potential
was not reached at the maximum RPM tested for that site. Site 3 was not
measured but is similar to Site 2. ....................................................................... 4-11 Figure 4-7. Example of cumulative PM10 emission potential (g/m2) calculation at
different points during the PI-SWERL hybrid test cycle at Site 15. .................. 4-12 Figure 4-8. Cumulative emission flux (g/m2/s) of a) PM2.5 and b) PM10 of each site at the
end of each PI-SWERL hybrid test cycle steps. ................................................ 4-13 Figure 4-9. Pictures of the rings after PI-SWERL tests at a) Site 27 and b) site 59. Site 27
has more loose clay and silt materials than Site 59. .......................................... 4-14 Figure 4-10. Potential emission fluxes at different sites in a) Facility C, b) Facility B, c)
Facility E, d) Quarry, e) Ft. McMurray and Ft. McKay, and f) other
locations. The number in the legend indicates the site ID. Sites in each
graph are sorted by the order of decreasing emission flux at 4000 RPM. ......... 4-16 Figure 4-11. Pictures of unpaved roads with high vehicle traffic at a) Site 16 and b) Site
48........................................................................................................................ 4-19 Figure 4-12. PM10 concentration (C) and emission potential (P) before and after watering
at two unpaved roads: a) Sites 9 and 10, and b) Sites 32 and 33. ...................... 4-21 Figure 4-13. Picture of a haul road with stabilized and disturbed (tire track) surfaces
(Sites 26 and 27). ............................................................................................... 4-22 Figure 4-14. PM10 concentration (C) and emission potential (P) of stabilized and
disturbed (tire track) surfaces (Sites 26 and 27) on a haul road......................... 4-22 Figure 4-15. Picture of a coke pile (Sites 53 and 54) with and without disturbances. .............. 4-23 Figure 4-16. PM10 concentration (C) and emission potential (P) of a coke pile (Sites 53
and 54) before and after disturbance. ................................................................. 4-23 Figure 5-1. Abundance of anions in PM2.5 and PM10 of the 64 dust samples.............................. 5-2 Figure 5-2. Abundance of cations in PM2.5 and PM10 of the 64 dust samples............................. 5-3 Figure 5-3. Abundance of individual anions in PM2.5 and PM10 of the 64 dust samples. ........... 5-4 Figure 5-4. Abundance of individual cations in PM2.5 and PM10 of the 64 dust samples. .......... 5-5 Figure 5-5. Comparison of abundances between a) Ca++ and Ca, and b) Ca++ and CO3= in
PM2.5 and PM10 of the 64 dust samples. .............................................................. 5-6 Figure 5-6. Correlations between Ca++ and Mg++ in PM2.5 and PM10 of the 64 dust
samples. ................................................................................................................ 5-7 Figure 5-7. Elements with average abundance >1% in PM2.5 and PM10 of the 64 dust
samples. .............................................................................................................. 5-10 Figure 5-8. Individual major elements (Al, Si, K, Ca, and Fe) with average abundance
>1% in PM2.5 and PM10 of the 64 dust samples. ................................................ 5-11 Figure 5-9. Elements with average abundance 0.02‒1% (S, Cl, Ti, Cr, Mn, Ni, and Zr) in
PM2.5 and PM10 of the 64 dust samples. ............................................................ 5-13 vi
Figure 5-10. Elements with average abundance <0.05% but greater than XRF or ICP-MS
minimum detection limit in at least one site in PM2.5 or PM10. ......................... 5-14 Figure 5-11. Abundance of rare earth elements in PM2.5 and PM10. ......................................... 5-16 Figure 5-12. Lead isotope ratios in geological samples: a) 204Pb/206Pb vs. 206Pb/207Pb
in PM2.5; b) 208Pb/207Pb vs. 206Pb/207Pb in PM2.5; c) 204Pb/206Pb vs.
206Pb/207Pb in PM10; and d) 208Pb/207Pb vs. 206Pb/207Pb in PM10.
Numbers in these figures denote the sampling sites as detailed in Table 2-2
and Figure 2-6. ................................................................................................... 5-20 Figure 5-13. Lead isotope ratios 208Pb/207Pb vs. 206Pb/207Pb for various samples: 1)
This study from all sites (open circles); 2) Soil Group 1 covering most
lichen sites from 2008 study (red triangle); 3) Soil Group 2 covering most
oil sands sites from 2008 study (blue inverse triangle); 4) stack emissions
collected from AOSR in summer 2008 (red star) (Watson et al., 2010a); 5)
stack emissions collected from AOSR in winter 2011 (pink star) (Watson
et al., 2011a); 6) engine exhaust from mining trucks collected from AOSR
in 2009 (cyan squares) (Watson et al., 2010b); 7) engine exhaust from
mining trucks collected from AOSR in 2010 (green circle) (Watson et al.,
2011b); 8) lichen samples collected from western Canada from Yukon to
the Canada–USA border (Simonetti et al., 2003) and from northeastern
America from Hudson Bay to Maryland (purple plus) (Carignan et al.,
2002; Carignan and Gariépy, 1995); 9) lichen samples from AOSR
(circular hourglass) (Graney et al., 2011); 10) Pb-bearing minerals from
northwest Alberta (Paulen et al., 2011) and New Brunswick (Cumming
and Richards, 1975; Sturges and Barrie, 1987) (dark yellow cross); 11)
Pb-bearing ores from British Colombia, Ontario, and Quebec (Brown,
1962; Cumming and Richards, 1975; Sturges and Barrie, 1987) (blue
square); and 12) Ambient aerosols from 7 Canadian cities (Burnaby,
Chicoutimi, Victoria, Calgary, Winnipeg, Toronto, and Newfoundland)
collected from 1994 to 1999 (dark green triangle)(Bollhöfer and Rosman,
2001). ................................................................................................................. 5-21 Figure 5-14. Abundances of OC, EC, and CO3=-C in PM2.5 and PM10 of the 64 dust
samples. .............................................................................................................. 5-22 Figure 5-15. Abundances of carbon fractions in PM2.5 and PM10 of the 64 dust samples.
OC1 to OC4 are organic carbon fractions evolved in a 100% helium (He)
atmosphere at 140, 280, 480, and 580 °C, respectively. OP is pyrolyzed
carbon. EC1 to EC3 are elemental carbon fractions evolved in a 98%
He/2% O2 atmosphere at 580, 740, and 840 °C, respectively. Thermal
analysis followed the IMPROVE_A thermal/optical reflectance analysis
(TOR) protocol (Chow et al., 2007a). ................................................................ 5-24 Figure 5-16. Abundances of non-polar organic compounds grouped into PAHs, lower
molecular weight n-alkanes (nC15-nC24), higher molecular weight nalkanes (nC25-nC40), iso/anteiso-alkane, hopanes, steranes, and others
(including methyl-alkane, branched-alkane, cycloalkane, and 1octadecene) in PM2.5 and PM10 of the 64 geological samples. .......................... 5-27 Figure 5-17. Abundances of mono and di-acids, and water soluble organic carbon
(WSOC) normalized to PM2.5 and PM10 mass. .................................................. 5-28 vii
Figure 5-18. Level II PM2.5 composite profiles for subgroups in facility facilities. .................. 5-34 Figure 5-19. Level II PM2.5 composite profiles for subgroups in non-facility sites. ................. 5-35 Figure 5-20. Level III PM2.5 composite profiles. ....................................................................... 5-36 Figure 6-1. Map of dust suspension “hotspots” for Las Vegas, NV determined with the
TRAKER. Most of the high surface loadings were found near
construction sites where vehicles tracked out dust from unpaved surfaces
onto the pavement. The paved road traffic then ground up and suspended
the carryout along the roadway surface, thereby creating larger
contributions to ambient PM10 and PM2.5. Extending pavement into the
entrance to construction sites and wheel washing largely eliminated this
carryout. ............................................................................................................... 6-5 viii
Executive Summary
Fugitive dust refers to small particles that become airborne from open sources (e.g.,
unpaved and paved roads, mining pits, tailings ponds, unenclosed storage piles, quarry
operations, construction sites, agricultural fields, and dry lakes). Fugitive dust is an important
source of ambient particulate matter (PM) in Alberta, Canada. According to the National
Pollutant Release Inventory (Environment Canada, 2013), fugitive dust accounted for 88 and
95% of total PM2.5 and PM10 (particles with aerodynamic diameter less than 2.5 µm and 10 µm,
respectively) primary emissions in Alberta in 2011. Dust plumes are often seen over the tailings
ponds during high wind conditions and behind vehicles that are driving on some unpaved roads
in the Athabasca Oil Sands Region (AOSR).
Several concerns are related to fugitive dust emissions. Extended exposure to elevated
levels of dust can cause adverse health effects, particularly if the dust contains crystalline silica,
asbestos fibers, heavy metals, disease spores, and other toxins. First Nations communities in the
AOSR have implied that dust depositions on their traditional food sources, such as blueberries,
have reduced the product yields and made the food more difficult to clean. Excessive dust
deposits are found on surfaces inside residences near mining facilities, causing health concerns.
Dust plumes can also reduce visibility, possibly leading to lower productivity, more mechanical
wear on machinery, and traffic accidents.
Knowledge about fugitive dust is limited. The processes of fugitive dust emission,
transport, and deposition are poorly characterized. The contributions of fugitive dust to ambient
PM concentrations are often overestimated by dispersion models. The chemical composition of
dust is not well characterized, and usually limited to routinely analyzed elements and watersoluble ions. Therefore, the impacts of dust on human and ecosystem health are not well
understood.
In a pilot study supported by the Wood Buffalo Environmental Association (WBEA), 27
geological samples were collected from dust-generating surfaces inside oil sands mining
facilities and in forests near the AOSR in 2008 and 2009. These samples underwent laboratory
resuspension at the Desert Research Institute (DRI). The PM2.5 and PM10 fractions were collected
on filters and analyzed for both conventional and unconventional chemical species. This study
differentiates dust sources in the AOSR. Distinct differences were observed between the facility
and forest sites, particularly in the abundances of sulfur, sulfate, lead isotopes, and organic
compounds.
This study extended the pilot study to obtain a comprehensive understanding of
windblown dust sources and chemical compositions of the dust from various sources in the
AOSR. A fugitive dust sampling system consisting of a novel Portable In-Situ Wind Erosion
Laboratory (PI-SWERL), a conical sampling manifold, nine-channel filter packs, and two realtime dust monitors was deployed to conduct measurements at 64 sites in 2012 and 2013. These
sites covered a wide range of fugitive dust-generating sources in the AOSR, including three oil
sands mining operations, one quarry operation, and main dust sources in the vicinity of Ft.
McMurray and Ft. McKay. This study characterized the three key parameters related to
windblown dust generation: reservoir type, threshold friction velocity, and size-segregated dust
emission potential and flux. The effectiveness of fugitive dust control methods (e.g., surface
watering and minimizing disturbance) was evaluated. Detailed chemical compositions of fugitive
dust from different sources were analyzed, and comprehensive source profiles were derived.
ix
All test sites have limited dust supplies at low wind speeds of 11-16 km/h (u10+, measured
at 10 m above ground level), as well as at higher u10+ of 27 km/h. Most sites have unlimited dust
supplies at the highest wind speed measured in this study (u10+ of 56 km/h), except for sites at the
lime stone quarry, the coke pile, paved surfaces, and stabilized land clearances. The threshold
wind velocity to produce particulate entrainment varied from 11-21.5 km/h, while saltation
occurred at higher speeds (u10+ > 32 km/h). Saltation is often related to unlimited reservoirs.
Dust emission flux (i.e., the amount of dust emitted from a unit area and within a unit
time (g/m2/s)) varied significantly with wind speed and location. For example, a high emitting
unpaved mine haul road can emit 2.38E-05, 8.05E-05, 7.92E-03, 0.025, 0.11, and 0.13 g/m2/s
PM10 under wind speeds u10+ of 11, 16, 27, 37, 47 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, 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. Therefore, minimizing surface disturbance is effective in
reducing windblown dust.
To find the variance of dust compositions from different sources and to establish
composite source profiles, three levels of compositing source profiles are applied based on the
similarities of source sub-types and their close vicinity in sample locations. Level I is the
individual source profile. These Level I profiles are composited into Level II subgroups: road
near sulfur pile, coke pile, tailings pond-dike sand, overburden-bare land, unpaved road in mine
facilities, quarry, unpaved road outside mine facilities, paved road outside mine facilities and
bare land outside mine facilities. The Level II profiles are further composited into two Level III
groups: facility and non-facility dust.
Geological-related element abundances (i.e., Al, Si, K, Ca, and Fe) are >1% of PM from
all sites and account for 5‒43% of PM mass, and the summation of their normal oxides accounts
for 13‒87% of PM mass. Si is the most abundant element, accounting for 2.2–28.8% of PM mass
with no significant difference between facility and non-facility soils. Organic matter
(OM=OC×1.4) is the second most abundant species, with average abundance ranging from 1449% of PM2.5 mass and 12-75% of PM10 mass. Similar proportions were found for water-soluble
ions but at ~1/3 the level (average abundance of ~4.5%). Other measured elements (excluding C,
Al, Si, K, Ca, and Fe) and those in ions account for 1.5‒1.9% of PM. EC abundance is low,
accounting for <2% of PM.
SO4= is on average 45% and 68% more abundant in PM2.5 and PM10, respectively, in the
facility sites than the non-facility sites. CO3= and Ca++ abundances are highest at quarry sites with
CO3= contributing as much as 46% of PM2.5. Cl- abundance varies among sites, with average
abundances (of both PM2.5 and PM10) higher at non-facility sites compared to facility sites. The
average OC abundances in facility sites is 17% higher than non-facility sites in PM2.5 samples
and 19% lower in PM10 samples. The highest EC abundances (34‒101% and 35‒39% in PM2.5
and PM10, respectively) were measured in samples collected at the coke pile. Al is 26–33% more
abundant in facility sites than non-facility sites. Several sites close to the tailings ponds (i.e.,
Sites 4, 5, 6) have the highest Al abundance of 7.67-9.35% and 6.05-6.71% in PM2.5 and PM10,
respectively. Fe content varies from 1‒16% of PM mass, with several unpaved road sites
x
showing higher Fe abundances than paved road sites. No clear enrichment of V is observed in
the tailings sands, although it is highest in samples from coke pile. Cu and Zn are highest in
paved road dusts collected outside facilities.
Among the Level II facility soil profiles, the coke pile profile has the highest abundances
of EC, V, and Ni. The road near the sulfur pile has higher Cl-, Ca++, carbonate carbon (CO3=-C),
Sc, Tb, organic acids, and WSOC. Tailings pond-dike sand has higher CO3=-C, Sc, Pb, and U.
Unpaved road has higher abundances CO3=-C, Ca, Fe, Sc, Pb, and U. Quarry has the highest
abundance of NO3-, Ca++, CO3=-C, Ca, Sc, formic and acetic acids. In Level II non-facility soil
profiles, compared to bare land profile, unpaved road has higher NO3-, CO3=-C, Sc, Br, Nb, Pb,
U, and acetic acid, but it has lower SO4=. Paved road has Ca++, CO3=-C, Sc, Cr, Cu, rare earth
elements, and formic and acetic acids. In the Level III profiles, compared to non-facility soil
profile, the facility dust profile has 2‒5 times higher EC, S, V, Ni, and Tl. On the other hand,
abundances of Cl-, NO3-, Mg++, Mn, Zn, Ba, and Cs in facility dusts were 20‒60% of non-facility
dust.
The dust reservoir type, threshold friction velocity, emission potential and flux, and
speciated chemical composition obtained from this study can be used as input in dust dispersion
and transport models to estimate windblown dust emissions from various dust sources. Dust
sources with lower threshold velocities and higher emission potentials and fluxes require higher
priorities for dust controls. The effectiveness of other fugitive dust control methods, such as
polymer stabilizers, can be evaluated with methods employed in this study. The source profiles
can be also used as inputs to receptor models for apportioning ambient PM contributions from
fugitive dust, and dust contributions from different sources. The impacts of dust on human and
ecosystem health can also be evaluated.
xi
1
1.1
Introduction
Background
Fugitive dust is an important source of ambient particulate matter (PM) in Alberta,
Canada, including the Alberta Oil Sand Region (AOSR). Large dust plumes are often visible
when wind speeds are high or when vehicles are moving on unpaved roads. The National
Pollutant Release Inventory published by Environment Canada shows that fugitive dust
generated from paved and unpaved roads, construction operation, agriculture tilling and wind
erosion, landfills, and mine tailings contributed 97%, 95%, and 88%, respectively, to total
particulate matter (TPM), PM2.5, and PM10 (particles with aerodynamic diameter less than ~100
µm, 2.5 µm and 10 µm, respectively) primary emissions in Alberta in 2011 (Environment
Canada, 2013).
Fugitive dust is more than just a nuisance. Extended exposure to elevated levels of dust
can cause adverse health effects, particularly if the dust contains crystalline silica, asbestos
fibers, heavy metals, disease spores, and other toxins. Wind erosion can remove topsoil from
farm lands and deposit the dust on foliage; both processes reduce agricultural yields. There have
been complaints from First Nations communities in the AOSR implying that dust deposition on
their traditional food sources, such as blueberries, has reduced the product yields and made the
food more difficult to clean. Dust plumes can also reduce visibility that would reduce
productivity, cause more mechanical wear of machinery, and lead to traffic accidents.
Fugitive dust emissions are poorly characterized, particularly for the fraction of
transportable dust that can travel more than a few hundred meters from the emitter. Contributions
of fugitive dust to emission inventories and ambient concentrations are often overestimated by
dispersion models that simulate contributions to receptor concentrations (Watson et al., 2012a).
PM2.5 and PM10 source apportionment studies show 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). Resolving the discrepancies between emission estimates and ambient source
contributions is an important consideration in designing, applying, and evaluating control
strategies intended to reduce fugitive dust emissions. Fugitive dust emissions estimates contain a
high amount of variability owing to lack of knowledge about the meteorological, physical, and
chemical factors on which they are based. These factors can vary widely on a national, regional,
or local basis.
Fugitive dust can be separated into two broad categories based on their generation
mechanisms: windblown generated dust and mechanically generated dust. Windblown dusts are
caused by the action of turbulent air current on erodible surfaces when the wind speed exceeds
certain threshold velocities. Mechanically generated dusts are caused by pulverization and
abrasion of surface materials by application of mechanical force through disturbances such as
vehicle traffic, mining and mineral processing, rock crushing, and farming operations.
In an effort to understand fugitive dust source types and their contribution to local PM2.5
and PM10 concentration levels as well as the potential health and environmental impacts, the
Desert Research Institute (DRI) conducted two fugitive dust emission characterization studies in
the AOSR. In the first study, geological materials were collected from 27 AOSR sites during
2008 and 2009, including 16 paved and unpaved mine haul road sites, tailings dikes and ponds,
and overburden in four oil sands facilities (A, B, C, and D), one site on the shoulder of Hwy 63,
and 10 sites from the forest where lichen samples were collected. The geological material
samples were resuspended in the laboratory and analyzed for chemical compositions to generate
1-1
source profiles (Watson et al., 2014). The second study was conducted in 2012 and 2013 to
characterize windblown dust sources as well as the chemical composition of dust from various
sources. The number of sampling sites was expanded to 64 covering a wide range of potential
fugitive dust-generating sources in AOSR, including oil sands mining operations in Facilities B,
C, and E, quarry operation, and main dust sources in the vicinity of Ft. McMurray and Ft.
McKay. Besides dust chemical compositions, main source characteristics for windblown dust
generation were characterized. This report focuses on this second dust characterization study.
The emission rate of windblown dust from an erodible surface depends on the wind
friction velocity and soil characteristics (Watson and Chow, 2000). Major soil characteristics
related to windblown dust emissions are: 1) dust reservoir characteristics, 2) threshold friction
velocity, and 3) emission potential. Reservoirs are classified as limited for stable surfaces and
unlimited for unstable surfaces. If not recharged, dust supply from a limited reservoir is depleted
after the loose top soil is eroded, while an unlimited reservoir can constantly supply dust. The
reservoir characteristics depend on soil type, soil layer depth, soil moisture content, soil
disturbance, and meteorological parameters. Threshold friction velocity is the wind velocity
above which erosion starts and is dependent on surface characteristics, particularly land use and
land cover. Emission potentials are the amount of PM that can be generated after exposing to
different wind speed.
Laboratory- or field-operated wind tunnels are conventionally used to characterize
windblown dust (Gillette et al., 1982; Neuman et al., 2009; Nickling and Gillies, 1989; Shao and
Raupauch, 1993). These tunnels are generally quite large (L × W × H: ~10 m × 1 m × 1 m)
which makes transportation and field operation cumbersome and labor-intensive. This study used
a Portable In-Situ Wind Erosion Laboratory (PI-SWERL) (Etyemezian et al., 2007) to measure
threshold friction velocity and emission potential for major wind erodible surfaces in the AOSR.
Results from this study can be used to improve the accuracy of windblown dust emission
inventories and to evaluate efficacy of dust control measures. Additional information regarding
particle size distribution and chemical composition of the windblown dust can be used to
evaluate particle transport distance, and human and ecosystem health effects, as well as for
source apportionment (Chow et al., 1992).
Chemical composition analysis on Epiphytic lichens shows an exponential decrease in
inorganic elemental concentrations, including the crustal material marker element aluminum
(Al), between 0 and 50 km from the oil sands facilities (Graney et al., 2012). Receptor modeling
using lichen data shows that fugitive dust has the largest impact on elemental concentrations for
lichen tissue in the AOSR (Landis et al., 2012). This study also found that the similarity of
source profiles when including only conventional chemical species limited the performance of
source apportionment receptor modeling. Nevertheless, there appear to be non-elemental source
markers that can differentiate between different types of fugitive dust emissions, including those
from different roadways (Watson et al., 2012b; Watson et al., 2014).
Detailed size-specific chemical compositions and source profiles of fugitive dust are
useful for multiple purposes including: 1) evaluation of the impacts of anthropogenic activities,
including mining processes, on dust composition and identifying chemical fingerprints of
different dust sources; 2) improvement of speciated emission inventories; 3) inputs to transport
and dispersion models to estimate current and future ambient concentrations, deposition, and
ecosystem effects; 4) receptor models input to evaluate ambient PM contributions from fugitive
dust, and dust contributions from different sources; 5) evaluation of health effects from dust
exposure; and 6) development of control strategies.
1-2
1.2
Study Objectives
The objectives of this project are to:
1) Characterize windblown dust reservoir type, threshold friction velocity, and sizesegregated dust emission potential and flux from fugitive dust sources in the
AOSR;
2) Evaluate the effectiveness of fugitive dust control methods;
3) Measure the chemical composition of fugitive dust from various sources and
generate comprehensive source profiles.
1.3
Report Overview
This report is organized in seven sections. Section 1 summarizes the background and
states the study objectives. Section 2 documents the experimental methods, including the dust
sampling system, test procedure, sampling sites, and laboratory chemical analysis methods.
Section 3 describes consistency checks and validation of laboratory and field data. Section 4
details the windblown fugitive dust emission characteristics. Section 5 presents source profiles of
different chemical species. Section 6 summarizes study results and discusses recommendations
for future studies. Section 7 is the bibliography and references. This report also contains 9
appendices.
1-3
2
2.1
Experimental Methods
Windblown Dust Emission Calculation
The U.S. EPA’s Compilation of Air Pollutant Emission Factors (AP-42) calculates windgenerated PM emissions (in g/m2/y) from mixtures of erodible and non-erodible surfaces subject
to disturbance as (U.S.EPA, 2006):
∑
(2-1)
where k is the particle size multiplier for different aerodynamic size range: 1.0 for PM30, 0.6 for
PM15, 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 mile of wind for the ith period between
disturbances. The erosion potential for a dry, exposed surface with limited erosion potential is
calculated as:
58
0
where
∗
∗
25
∗
is the friction velocity (m/s), and
for
for
∗
∗
, and
(2-2)
is the threshold friction velocity (m/s).
For surfaces with unlimited erosion potential, the U.S. EPA suggest using the dry
aggregate structure of the soil obtained by sieving tests to estimate erosion potentials. However,
these values vary considerably even for a given land type (Countess Environmental, 2006).
The fastest kilometer of wind ( ) from a reference anemometer at height of h (m) is a
routinely measured meteorological variable that best reflects the magnitude of wind gusts. It
relates to the friction velocity ∗ by the logarithmic distribution of wind speed profile in the
surface boundary layer:
∗
(2-3)
.
where 0.4 is the dimensionless von Karmon’s constant, and R0 is the surface roughness in m.
Assuming a typical roughness R0 of 0.005 m for open terrain, and an anemometer height of 10 m
above the ground level (agl), Eq. 2-3 can be converted to the following:
∗
2.2
0.053
(2-4)
Fugitive Dust Sampling System
Figure 2-1 shows a schematic diagram of the fugitive dust sampling system, and Figure
2-2 shows a photograph of the dust sampling system when sampling on the shoulder of Hwy 63
north of the Aurora mining site. The system consists of four core components: a PI-SWERL, a
conical sampling manifold, nine-channel filter packs, and two real-time dust monitors (i.e.,
DustTrak DRX [DRX] and Optical Particle Sizer [OPS]).
2-1
Figure 2-1. Schematic diagram of the fugitive dust sampling system. During ramp and hybrid tests, the PI-SWERL
was only connected to the Optical Particle Sizer and DustTrak DRX, and filter packs were connected only during
step tests. See sampling protocol for detailed description.
The PI-SWERL is a novel device developed by researchers at the Desert Research
Institute (DRI) for measuring the potential for wind erosion and dust suspension (Etyemezian et
al., 2007). Direct comparison of the PI-SWERL measurements with the University of Guelph
straight-line field wind tunnel at 17 sites in the Mojave Desert showed good correspondence
between these two methods (Etyemezian et al., 2007; Sweeney et al., 2008). Compared to field
wind tunnels, the PI-SWERL is much smaller and easier to operate. As shown in Figure 2-3, the
PI-SWERL consists of an open-bottomed cylindrical chamber (diameter = 30 cm and height = 20
cm) with a rotating annular ring blade (inner diameter = 16 cm and outer diameter = 25 cm) that
hangs parallel to and ~5 cm above the soil surface to be tested (Etyemezian, 2011). Soft foam
along the circumference of the open end forms a seal with the test surface. When the annular
blade revolves about its center axis, a velocity gradient is created between the flat bottom of the
blade and the ground, creating a shear stress on the surface that simulates wind effects. Dust
particles are removed from the surface by the shear stress, and are mixed with and carried out for
measurement by filtered air blown into the chamber. The filtered air are generated by a DC
blower and the flow rate is controlled at 100 L/min, which flushes the chamber 7 times a minute
to provide sufficient ventilation while avoiding particle suspension by the filtered air. Dust
concentrations are typically monitored by a DustTrak aerosol monitor every second with a
concentration range of 0.001-400 mg/m3. The instrument is powered by two 12 V sealed leadacid batteries connected in series. Four optical gate sensors (OGS) were installed on the PISWERL used in this study that count particles larger than ~100 µm based on light extinction.
The OGS are not sensitive to smaller particles, but can provide information about sand grain
movement to infer the threshold friction velocity for saltation.
2-2
Figure 2-2. Photograph of the fugitive dust sampling system under operation.
Figure 2-3. Components of the PI-SWERL. Left-top view; right: Bottom view (Etyemezian, 2011).
A typical PI-SWERL measurement begins with running the clean air blower to purge out
any dust in the chamber. After flushing with clean air, a computer directs the motor to spin the
annular blade to achieve a target rate of rotation specified in revolutions per minute (RPM). The
target RPM may be held for some period (step test) or varied continuously to achieve a specified
rate of change (ramp test). The vertical dust flux is calculated based on the measured air flow
rate, dust concentration, and the effective area of influence from the annular blade. Earlier tests
(Etyemezian, 2011) have shown that the rotating blade in PI-SWERL used in this study has an
effective influence area Aeff of 0.026 m2, and the following polynomial equations fit the relation
between shear stress (τ), friction speed (u*), and RPM:
2-3
τ
∗
4.05 10 RPM
1.49 10 RPM
5.35 10 RPM
8.20 10 RPM
The cumulative PM emissions potential (
,
⁄
∑
∑
⁄
,
,
,
2.20 10 RPM 0.0351 (2-5)
1.42 10 RPM 0.0872 (2-6)
, in g/m2) can be calculated as:
⁄
⁄
/
(2-7)
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), Pi is the non-cumulative emission flux at step i, C is the mass concentration
of a specific size fraction measured every second, and Q is the blower flow rate. For a surface
with unlimited dust supply, a potential emission flux can be calculated:
,
⁄
⁄
,
⁄
(2-8)
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. Strictly, the concept of emission flux
does not apply to dust supply-limited surface since after a certain period above the threshold
friction velocity, the dust supply will be completely consumed until it is replenished by next
disturbance. In that case, Eq. 2-1 with P calculated from 2-2 or 2-7 should be used. For supplyunlimited reservoirs, a time-averaged emission potential, i.e., emission flux, can be calculated
and used in dust emission models.
The PI-SWERL has been successfully deployed in many studies, including comparison
with a straight-line field wind tunnel for dust flux from multiple soil surfaces (Sweeney et al.,
2008), dust dynamics study in off-road trails (Goossens and Buck, 2009), fugitive dust
suppressant efficacy evaluation (Kavouras et al., 2009), unpaved road emission study (Kuhns et
al., 2010), and dust emission variability study with seasons and landforms (King et al., 2011).
Several modifications were made to the standard PI-SWERL for this study. Instead of
using the DustTrak that only provides one size fraction, a five-channel DRX and a 16-channel
OPS were used to measure size-segregated dust concentrations in real time. The DRX measures
mass concentrations of PM1, PM2.5, PM4, PM10, and PM15 every second based on light scattering
(Wang et al., 2009). The PM15 channel can measure maximum concentrations of 600 µg/m3. By
default, the DRX uses calibration factors generated with Arizona Road Dust. Since the dust in
this study had different optical, physical, and chemical properties, the DRX readings need to be
calibrated with PM2.5 and PM10 gravimetric mass concentrations from the Teflon-membrane
filters. The OPS measures particle number distributions with optical equivalent diameter range of
0.3-10 µm in 16 channels every second. The number distribution is further converted to mass
distribution assuming particles are spherical and have density of 1 g/cm3. However, the OPS is
limited for low concentration measurement (<3000 particle/cm3). It will suffer concentration and
sizing errors due to coincidence at higher concentrations (Whitby and Willeke, 1979). The OPS
was used in this study to test its feasibility for fugitive dust measurements coupled with the PISWERL. Similar to the DRX, the OPS mass needs to be calibrated with gravimetric mass.
In addition to real-time dust concentration measurements by the DRX and OPS, sizesegregated PM2.5 and PM10 are collected on filter media for physical and chemical analyses. For
particle collection, the suspended dust was carried through a conductive tube to the cone
sampling manifold. As shown in Figure 2-1, nine filter packs were installed at the bottom of the
manifold, together with two other ports for the DRX and OPS. The conical shape of the manifold
2-4
allowed particles to be uniformly distributed and collected on the filters as demonstrated by
similar masses on the two sets of Teflon-membrane filters for PM2.5 and PM10 discussed in
Section 3.2. For each run, a total of nine filter packs (four PM2.5 and five PM10) with different
filter media were collected for different analyses (Figure 2-1). A PM2.5 or PM10 impactor was
installed at the inlet of the filter pack to achieve the size cut. The flow rate through each filter
pack was set at 5 L/min at the beginning of sampling and verified after the run. The average of
the two flow rates was used for concentration calculation. The vacuum created by the high total
flow (48 L/min) drawn by the filters, DRX, and OPS during particle collection caused difficulties
for the DC blower to maintain 100 L/min, an external pump was used to supply the 100 L/min
dilution air after filtration by a carbon capsule and a HEPA filter.
2.3
Test Procedure
The field test started with a site survey. The sampling site was selected by finding a
relatively flat surface with uniform roughness and surface properties and an area of
approximately 2 m × 5 m. Traffic cones were placed around the sampling location to ensure safe
operations.
After the PI-SWERL was set up, a ramp test was conducted first. The DRX and OPS
were connected directly to two sampling ports on the PI-SWERL, while the cone manifold and
filters were not connected. The PI-SWERL ramp protocol R5000 is shown in Table 2-1 and
Figure 2-4a. The PI-SWERL chamber was first flushed with filtered air for 120 s, then the motor
speed was linearly ramped from 0 to 5000 RPM in 360 s, and finally the motor speed was set to
0 RPM and purged for 60 s to allow the ring blade to come to a stop and the dust in the chamber
to be cleaned. The sampling spots (rings) after the PI-SWERL runs are illustrated in Figure 2-5.
The ramp test served three purposes: 1) It helped determine the maximum motor speed for the
subsequent hybrid tests. Some surfaces generate high dust concentrations at high speeds that
would exceed the maximum concentration that the DRX can measure (600 µg/m3) and put high
dust loading to the instruments. In those cases, the maximum motor speed was set to lower
values in the hybrid tests so that the DRX range would not be exceeded. 2) It helped clean the
residual particles from the previous run. Although the PI-SWERL was cleaned by blowing
compressed air, vacuuming, and wiping after each run, there were always some residual particles
stuck on the instrument surface that would re-entrain at high speeds. The ramp test would
effectively remove most of those particles. If significant re-entrainment was observed, a second
ramp test was run to remove residual particles; and 3) It provided information about threshold
shear stress and friction velocity when dust suspended from the surface or small sand particles
started moving causing saltation. However, this information was derived from the hybrid test in
this study, and the ramp data was only used for backup and confirmation.
2-5
Table 2-1. PI-SWERL motor speed settings for ramp (R5000), hybrid (H5000), and step (S5000) test protocols.
Ramp (R5000)
Hybrid (H5000)
Step (S5000)
Duration (s)
RPM
Duration (s)
RPM
Duration (s)
RPM
120
0
150
0
60
0
360
0-5000
1
0-500
1
0-5000
60
5000-0
60
500
120
5000
30
500-1000
45
5000-0
60
1000
60
1000-2000
60
2000
60
2000-3000
90
3000
60
3000-4000
90
4000
60
4000-5000
90
5000
60
5000-0
After the ramp test, the PI-SWERL was moved to the next spot to conduct the first hybrid
ramp-step test. The basic hybrid test protocol H5000 is shown in Table 2-1 and Figure 2-4b. The
cylindrical chamber was first flushed with clean air, and then the computer directed the motor to
ramp quickly to 500 RPM and stay at that speed for 60 s. Next the blade speed ramped from 500
to 1000 RPM linearly in 30 s, and maintain at 1000 RPM for 60 s before ramping to the next
speeds. Similar to the ramp test, only the DRX and OPS were connected to the PI-SWERL and
the filters were not connected during the hybrid test. The maximum optimum speed was
determined from the ramp test to not overwhelm the DRX concentration range. The hybrid test
was typically repeated twice or three times, each at a different sampling spot. The hybrid tests
were used to answers the following four questions:
 Q1: Does the surface have limited or unlimited dust supply at specific wind speed (or
friction velocity)? Check if the PM concentration decreases after certain time in the
constant RPM step.
 Q2: What is the threshold friction velocity for PM emission and saltation to occur?
Examine the PM10 concentration and OGS count rate increase pattern.
 Q3: How hard would the wind have to blow in order for PM2.5 or PM10 emission
potential to exceed 0.002, 0.02, and 0.2 g/m2 [for example]? Find the friction velocity
corresponding to a specific cumulative PM potential.
 Q4: How much PM is available for emissions after exposing to different wind speed??
Integrate the dust flux to the end of a specific RPM step.
 Q5: How effective is surface watering at reducing dust emissions? Compare dust
emissions before and after watering of the same surface.
 Q6: What are the effects of surface disturbances on dust emissions? Compare dust
emissions from stabilized and nearby disturbed surfaces.
2-6
a)
200
Rotating
Speed
4000
160
3000
120
80
2000
PM10
1000
40
PM2.5
PM Concentration (mg/m3)
Rotating Speed (RPM)
5000
0
0
0
100
200
300
400
500
600
Test Duration (s)
b)
120
Rotating
Speed
100
4000
80
3000
60
2000
40
PM10
1000
PM2.5
0
0
200
400
600
800
20
PM Concentration (mg/m3)
Rotating Speed (RPM)
5000
0
1000
Test Duration (s)
c)
Rotating Speed (RPM)
240
4000
Rotating
Speed
PM10
3000
180
120
2000
PM2.5
60
1000
0
PM Concentration (mg/m3)
300
5000
0
0
50
100
150
200
250
Test Duration (s)
Figure 2-4. Example of a) ramp, b) hybrid, and c) step tests. Only PM2.5 and PM10 of the five size fractions measured
by the DRX are illustrated.
2-7
Figure 2-5. Photograph of rings created after PI-SWERL runs. Each ring represents one of the ramp, hybrid, or step
test.
During the ramp and hybrid tests, the filter packs were installed on the sampling
manifold, and flow rates were set to 5 L/min while sampling filtered air. The water content and
surface hardness were measured at multiple spots of the sampling location. A grab sample of
~500 g surface soil was collected for possible resuspension in the laboratory and chemical
analysis in the future.
Step tests were conducted after the ramp tests. The dust output from the PI-SWERL was
connected to the conical sampling manifold, and the DRX and OPS were moved to sample from
the manifold. An example of step protocol S5000 is illustrated in Table 2-1 and Figure 2-4c.
Different step speeds were used for each sampling site based on the dust emission potential with
the target of collecting ~1 mg PM2.5 per filter for laboratory analysis. To obtain a representative
composite of the dust composition of the sampling site, the step test was typically run at a
minimum of three different spots, resulting in a total sample volume of 30 L per filter (5 L/min ×
2 min × 3 runs). Therefore, it would require ~33 mg/m3 PM2.5 concentration to collect 1 mg
PM2.5 per filter over three runs. The required motor speed was determined from the
corresponding concentrations observed during the hybrid test. After the step tests, the flow rates
through the filters were measured, and filters were unloaded and stored in air-tight bags in an
ice-cooled cooler. The PI-SWERL and impactors were cleaned. The step test served two
purposes: 1) collect PM2.5 and PM10 on filters for chemical analysis; and 2) obtain DustTrak
DRX and OPS calibration factors by comparing their concentrations to the gravimetric PM2.5 and
PM10 concentrations.
2.4
Sampling Sites
A total of 64 sites were sampled, with 48 sites in 2012 and 16 sites in 2013. As shown in
Table 2-2 and Figure 2-6, these sites cover a wide range of fugitive dust sources in AOSR,
including paved and unpaved roads, parking lots, industrial park, construction sites around Ft.
McMurray and Ft. McKay, shoulders of Hwy 63 and Athabasca Highway, tailings ponds and
2-8
tailings dikes, mine haul roads, near sulfur and coke piles, quarry operations, and forest fire and
land clearance sites. Some sites were selected to study the effectiveness of dust emission control
measures. For example, Sites 9 and 10 were the same light vehicle unpaved road before and after
watering, and Sites 32 and 33 were another unpaved road before and after watering. Some sites
were selected to study the effects of surface disturbance on dust emissions. For example, Sites 53
and 54 represent stabilized and disturbed surfaces.
Table 2-2. List of 64 fugitive dust sampling sites characterized in 2012 and 2013.
Sample
Date
Site
ID
8/7/2012
1
8/8/2012
Waypoint
Description
Elevation
N
W
(m)
Ft. McKay unpaved road
57.18840
-111.65849
288
2
Facility C unpaved road with sulfur deposit
57.03905
-111.66093
313
8/8/2012
3
Facility C unpaved road near sulfur pile
57.03903
-111.66097
313
8/10/2012
4
Facility C tailings sand strip
57.02193
-111.57673
316
8/10/2012
5
Facility C tailings flat sand beach
57.02153
-111.57759
317
8/10/2012
6
Facility C tailings sand beach wind gate
57.02055
-111.57814
317
8/10/2012
7
Facility C overburden
56.94141
-111.74614
386
8/10/2012
8
Facility C unpaved road on tailings dike
56.94172
-111.74630
386
8/11/2012
9
Facility C light vehicle unpaved road-dry
57.02913
-111.67284
307
8/11/2012
10
Facility C light vehicle unpaved road-wet
57.02926
-111.67225
299
8/11/2012
11
56.98863
-111.73785
392
8/11/2012
12
56.98859
-111.73786
393
8/11/2012
13
Facility C tailings dike unpaved road
Facility C tailings dike drifting sand, below pipeline
facing wind
Facility C tailings dike overburden between pipelines
56.98872
-111.73798
389
8/12/2012
14
Ft. McMurray paved road near WBEA AMS 7
56.73340
-111.39014
253
8/12/2012
15
WBEA Shell AMS 16 unpaved road
57.19769
-111.60127
279
8/12/2012
16
Ft. McMurray unpaved road outside Wilson
56.77465
-111.42702
252
8/13/2012
17
Ft. McKay Community Center paved parking lot
57.17941
-111.63581
252
8/13/2012
18
Highway 63 paved shoulder near Facility C
57.03767
-111.55663
302
8/15/2012
19
57.23893
-111.54681
335
8/15/2012
20
57.23895
-111.54620
332
8/15/2012
21
Facility B tailings dike 1, flat undisturbed
Facility B tailings dike 2, near a slope of windblown
dust accumulation
Facility B tailings dike 3
57.23896
-111.54684
333
8/15/2012
22
Facility B tailings beach 1 tractor track
57.23922
-111.56397
329
8/15/2012
23
Facility B tailings beach 2 truck track
57.23923
-111.56385
325
8/16/2012
24
Facility B tailings dike 4, near a pumping station
57.22677
-111.54743
332
8/16/2012
25
57.23433
-111.53659
289
8/16/2012
26
57.23423
-111.53653
283
8/16/2012
27
Facility B T-section by main haul road
Facility B T-section by main haul road, undisturbed,
crusted
Facility B unpaved road, tire track
57.23420
-111.53659
281
8/16/2012
28
Facility B overburden berm
57.23380
-111.53449
281
8/17/2012
29
Quarry, conveyor area
57.19288
-111.55604
281
2-9
Table 2-2 continued.
Sample
Site
Description
Date
ID
8/17/2012 30
Quarry, processing ground, tire tracks
57.19296
-111.55647
273
8/17/2012
31
Quarry, waste storage pile hill foot
57.19358
-111.55915
277
8/17/2012
32
Quarry, dry unpaved road in processing ground
57.19353
-111.55883
271
8/17/2012
33
Quarry, wet unpaved road in processing ground
57.19354
-111.55879
279
Waypoint
Elevation
8/18/2012
34
Quarry, unpaved road in Pit 1
57.18909
-111.55154
248
8/18/2012
35
Quarry, waste dump, truck track
57.19166
-111.54979
273
8/18/2012
36
Quarry, waste pile
57.19166
-111.54992
276
8/18/2012
37
Quarry, road near exit scale
57.19959
-111.54722
271
8/18/2012
38
57.19962
-111.54693
283
8/19/2012
39
57.17389
-111.60368
270
8/19/2012
40
57.17853
-111.63645
259
8/19/2012
41
Quarry, parking lot for haul trucks
Ft. MacKay Industrial Park track-out Hwy 63 paved
road
Ft. McKay gravel road at an intersection, watered not
long ago
Ft. McKay paved road after turn to CNRL
57.15880
-111.64403
252
8/19/2012
42
Hwy 63 construction zone near BURNCO
56.76690
-111.42254
250
8/19/2012
43
56.55874
-111.31184
405
8/20/2012
44
57.01791
-111.55832
305
8/20/2012
45
Hwy 63 rest area south of Ft. McMurray
Sandy surface near Hwy 63 between Facility C
ponds
Athabasca Hwy, unpaved, below shoulder slope
57.11384
-111.42962
331
8/20/2012
46
Athabasca Hwy, unpaved shoulder
57.11380
-111.42962
337
8/20/2012
47
Sandy road near WBEA Site R2
57.11939
-111.42387
336
8/20/2012
48
Hwy 63 unpaved north of Aurora
57.25078
-111.61219
283
8/11/2013
49
Ft. McMurray Thickwood BLVD new construction
56.73342
-111.47567
362
8/11/2013
50
Ft. McMurray Thickwood BLVD land clearance
56.73306
-111.47672
361
8/11/2013
51
Ft. McMurray unpaved parking
56.71555
-111.34820
235
8/12/2013
52
WBEA Ft. McKay AMS 1 unpaved road
57.18950
-111.63941
257
8/13/2013
53
Facility E undisturbed coke pile
57.35661
-111.71744
283
8/13/2013
54
Facility E disturbed coke pile
57.35665
-111.71751
285
8/13/2013
55
Facility E haul road
57.35749
-111.70969
272
8/13/2013
56
Facility E tailings pond dike
57.35931
-111.86965
378
8/13/2013
57
Facility E overburden pit
57.32488
-111.80890
277
8/13/2013
58
Facility E tailings pond beach
57.35062
-111.87298
347
8/13/2013
59
Facility E unpaved road near sulfur pile
57.34635
-111.71634
289
8/13/2013
60
Facility E unpaved road near sulfur pile
57.34689
-111.72028
299
8/14/2013
61
Forest fire site near north Hwy 63
57.45164
-111.55064
305
8/14/2013
62
57.54018
-111.36118
299
8/15/2013
63
57.20641
-111.59849
275
8/15/2013
64
Bare land near north Hwy 63 ice road gate
Unpaved road across Hwy 63 near Facility B tailings
pond dike
Athabasca Hwy shoulder near Firebag
57.21364
-111.03337
519
2-10
Figure 2-6. Location of the 64 sampling sites. Yellow labels indicate sites sampled in 2012 and red labels indicate
sites sampled in 2013.
2-11
Table 2-3 shows the historical wind statistics for Ft. McMurray, Alberta. Note that the
maximum hourly wind speed ranged 48-72 km/h, while the highest reported wind gust reached
113 km/h. Table 2-4 lists the friction velocities and wind speeds 10 m agl corresponding to
different PI-SWERL rotating speeds, assuming a surface roughness of 0.005 m. Note that 5000
RPM corresponds to ~56 km/h which in the range of maximum hourly wind speed observed in
Ft. McMurray. Due to the limitation of the PI-SWERL and dust monitors, higher speeds were not
measured in this study.
Table 2-3. Wind statistics of Ft. McMurray, Alberta (http://www.weatherbase.com/).
Average Wind Speed (km/h)
ANNUAL
JAN
FEB
MAR
APR
MAY
JUN
JUL
10.3
11.3
10.9
9.8
9.2
Highest Reported Hourly Wind Speed (km/h)
ANNUAL
JAN
FEB
MAR
APR
MAY
JUN
JUL
62.9
47.9
71.9
9.7
71.9
8.5
66.9
9.2
56.0
54.1
Highest Reported Wind Gust (km/h)
ANNUAL
JAN
FEB
MAR
113.0
89.0
94.0
74.0
61.0
APR
MAY
JUN
JUL
79.0
80.0
97.0
113.0
Avg. # of Days w/Wind Above 52 km/h (Days)
ANNUAL
JAN
FEB
MAR
APR
0.6
0.1
0.1
0.1
0.1
Avg. # of Days w/Wind Above 63 km/h (Days)
ANNUAL
JAN
FEB
MAR
APR
0.1
0.1
---
---
---
Average Primary Wind Direction (Degree)
ANNUAL
JAN
FEB
MAR
APR
90
90
90
90
90
MAY
JUN
JUL
---
0.1
0.1
MAY
JUN
JUL
---
---
---
MAY
JUN
JUL
90
90
270
2-12
Years on Record: 28
AUG SEP OCT NOV
9.0
9.8
10.1
9.0
Years on Record: 56
AUG SEP OCT NOV
50.0
DEC
52.0
Years on Record: 41
AUG SEP OCT NOV
DEC
97.0
85.0
Years on Record: 16
AUG SEP OCT NOV
DEC
---
96.1
62.9
8.7
60.0
80.0
51.0
DEC
0.1
97.0
---
---
Years on Record: 16
AUG SEP OCT NOV
---
---
---
---
Years on Record: 28
AUG SEP OCT NOV
270
270
90
90
---
DEC
---
DEC
90
Table 2-4. Friction velocity (u*) and fastest mile of wind measured at 10 m above ground level (u10+) in units of m/s
and km/h corresponding the PI-SWERL blade rotating speed.
PI-SWERL Speed
u* (m/s) u10+ (m/s) u10+ (km/h)
(RPM)
500
0.16
3.0
10.9
1000
0.24
4.5
16.1
1500
0.31
6.0
21.5
2000
0.39
7.5
26.8
2500
0.47
8.9
32.2
3000
0.55
10.4
37.4
3500
0.62
11.8
42.5
4000
0.69
13.1
47.3
4500
0.76
14.4
51.7
5000
0.82
15.5
55.8
2.5
Laboratory Analysis
The PM2.5 and PM10 filter samples were analyzed for mass, light absorption, elements,
lead (Pb) isotopes, ions, carbon, and organic compounds (Chow and Watson, 2012) to establish
source profiles of fugitive dust source types. Figure 2-1 and Table 2-5 illustrate the analyses on
each filter. The detection limits of analyses are listed in Appendix A.
Teflon-membrane filters were equilibrated in a clean room with controlled temperature
(T; 21.5 ± 1.5 °C) and relative humidity (RH; 35 ± 5%) before gravimetric analysis (Chow,
1995). Filters were weighted before and after sampling using a XP6 microbalance (Mettler
Toledo Inc., Columbus, OH) with a sensitivity of ±1 µg. Light absorption was measured by an
optical densitometer (Tobias Model TBX-10). Teflon-membrane filters were then analyzed for
51 elements (from sodium to uranium) by high sensitivity X-ray fluorescence (XRF;Watson et
al., 1999). The same filters were then analyzed for cesium (Cs), barium (Ba), 14 rare-earth
elements, and four Pb isotopes (i.e., 204Pb, 206Pb, 207Pb, and 208Pb) by inductively coupled
plasma/mass spectrometry (ICP/MS).
Half of the first PM2.5 and PM10 quartz-fiber filters were extracted in distilled deionized
water (DDW) and analyzed for chloride (Cl-), nitrite (NO2-), nitrate (NO3-), phosphate (PO4≡) and
sulfate (SO4=) by Ion Chromatography (IC; Chow and Watson, 1999). Water-soluble sodium
(Na+), magnesium (Mg++), potassium (K+), and calcium (Ca++) were determined by Atomic
Absorption Spectroscopy (AAS), and ammonium (NH4+) was measured by Automated
Colorimetry (AC). The second half of the first quartz-fiber filters were analyzed for total watersoluble organic carbon (WSOC) from the water extract by total organic carbon (TOC) analyzer.
Sixteen carbohydrates (i.e., glycerol, inositol, erythritol, xylitol, levoglucosan, arabitol, sorbitol,
mannosan, malitol, arabinose, glucose, xylose, galactose, fructose, trehalose, and mannitol) and
nine organic acids (i.e., oxalic acid, malonic acid, succinic acid, glutaric acid, lactic acid, acetic
acid, formic acid, maleic acid, and methanesulfonic acid) were measured by IC.
Punches of ~0.5 cm2 were removed from the second PM2.5 and PM10 quartz-fiber filters
to quantify OC, EC, and eight thermal fractions (OC1–OC4, pyrolyzed carbon [OP], EC1–EC3)
by the IMPROVE_A thermal/optical protocol (Chow et al., 1993; 2001; 2004; 2005; 2007a;
2-13
2011). Carbonate (CO3=) carbon was acquired by acidification with 15 µl of hydrochloride
solution prior to carbon analyses. Approximately 1–2 cm2 of the quartz-fiber filters were
analyzed for 113 non-polar speciated organic carbon compounds including n-alkanes,
iso/anteiso-alkanes, hopanes, steranes, other alkanes, one alkene, cyclohexanes, and polycyclic
aromatic hydrocarbon (PAHs) by thermal desorption-gas chromatography/mass spectrometry
(TD-GC/MS; Chow et al., 2007b; Ho and Yu, 2004).
Table 2-5. Laboratory analysis of filter samples.
Sampling Method
Teflon®-membrane filter for both PM2.5
and PM10 channels (2 µm pore size; Teflo
PTFE-membrane with
polymethylpropylene support ring; Pall
Sciences, Port Washington, NY, USA)
Quartz-fiber filter (1) for both PM2.5 and
PM10 channels (Tissuquartz 2500 QATUP; (Pall Sciences, Port Washington, NY,
USA)
Quartz-fiber filter (2) for both PM2.5 and
PM10 channels (Tissuquartz 2500 QATUP; Pall Sciences, Port Washington, NY,
USA)
Teflon®-membrane filter for both PM2.5
and PM10 channels (2 µm pore size; Teflo
PTFE-membrane with
polymethylpropylene support ring; Pall
Sciences, Port Washington, NY, USA
Nuclepore Track-etch polycarbonate filter
(0.4 µm pore size; Whatman, Inc.,
Fairfield, CT, USA)
Gases and Chemical Species
PM2.5 and PM10 mass concentration
Filter light transmission
Elements
Cs, Ba, Rare-earth elements, Pb
isotopes
Ions (Cl-, NO2-, NO3-, PO4≡, SO4=,
NH4+, Na+, Mg++, K+, Ca++)
Total WSOC
WSOC classes, carbohydrate,
organic acids
Analysis Method/ Instruments
Gravimetry
Tobias TBX-10 Densitometer
XRF
ICP/MS
IC, AC, AAS
TOC
IC
OC/EC, carbon fractions, carbonate
TOR/TOT Carbon Analyzer
Alkanes, alkenes, PAH, hopanes,
steranes
TD-GC/MS
Elements affecting lichen
ICP/MS
For future microscopic particle
morphology analysis
Optical/Electron Microscopes
AAS
Atomic Absorption Spectrophotometry by Varian Model Spectro880 (Varian, Walnut Creek, CA, USA)
AC
Automated Colorimetry by Astoria Model 302A (Astoria, Astoria OR, USA)
EC
Elemental Carbon by DRI Model 2001 thermal/optical carbon analyzer (DRI, Reno, NV, USA)
TD-GC/MS
Thermal Desorption Gas Chromatography/Mass Spectrometry by Agilent Model 6890N/5973 (Agilent
Technology, Foster City, CA, USA)
IC
Ion Chromatography by Dionex Model ICS-3000 (Dionex, Sunnyvale, CA, USA)
ICP/MS Inductively Coupled Plasma Mass Spectrometry by Thermo X Series (Thermo Scientific, Madison, WI, USA)
OC
Organic Carbon by DRI Model 2001 thermal/optical carbon analyzer (DRI, Reno, NV, USA)
TOC
Total Organic Carbon by Shimadzu TOC Analyzer Model VCSH (Shimadzu, Columbia, MD, USA)
TOR
Thermal/Optical Reflectance by DRI Model 2001 thermal/optical carbon analyzer (DRI, Reno, NV, USA)
TOT
Thermal/Optical Transmittance by DRI Model 2001 thermal/optical carbon analyzer (DRI, Reno, NV, USA)
WSOC Water Soluble Organic Carbon by TOC Analyzer Model VCSH (Shimadzu, Columbia, MD, USA)
XRF
X-Ray Fluorescence by PANalytical Model Epsilon 5 (PANalytical, Almelo, the Netherlands)
2-14
3
Data Validation
Laboratory and field data are gone through quality control and quality assurance
procedures to ensure data quality. Laboratory data validation evaluates the internal consistency
of PM2.5 mass and chemical composition (Chow et al., 1994a). Physical consistency is tested for:
1) mass closure, 2) anion and cation balance, and 3) SO4= versus total sulfur (S). Field data
validation includes checking particle distribution uniformity in the sampling manifold, and
calibrating the DRX and OPS readings with gravimetric mass concentrations. The data presented
here has completed Level I data validation which includes excluding data from instrument
maintenance and calibrations, investigating extreme values, blank subtraction, precision
estimation, and assigning data quality flags (Watson et al., 2001a). However, sample reanalysis
may be required after Level II data validation (e.g., investigating outliers, comparison of
collocated PM2.5 and PM10 measurements for mass and chemical constituents).
3.1
Mass Closure
The sum of measured species should be less than or equal to the corresponding
gravimetric PM2.5 and PM10 mass loading, since unmeasured species such as oxygen (O) and
hydrogen (H) are not included. Figure 3-1 shows that the sum of species accounts for 42‒44% of
PM mass, with PM2.5 sum of species ~4% higher than PM10 on average. The low mass
percentage is mainly because the O in minerals and other elements (e.g., O, H, and N) in OC
were not accounted for in the sum of species. Sites 15, 29, 53 and 54 are significantly different in
the sum of the species from the average.Figure 3-2 shows reconstructed PM2.5 and PM10 mass
after assuming major oxide forms (Al2O3, SiO2, CaO, K2O, FeO, Fe2O3, and TiO2, with an
additional 1.16 multiply factor for other oxides) and an organic matter (OM) to OC ratio of 1.4
following the IMPROVE equation (Malm et al., 1994). The reconstructed mass, on average,
accounts for 98% (ranging 62.3%‒163.6%) of PM2.5 and 93.7% (ranging 68%‒193.6%) of PM10
mass.
Crust mineral oxides are the major compositions of the samples, accounting for 13-94%
of PM mass. Si is the most abundant element, accounting for 2-29% of PM mass. Organic matter
is the second most abundant species, with average abundance ranging from 14-49% of PM2.5
mass and 12-75% of PM10 mass. Similar proportions were found for water-soluble ions but at
~one third the level with average abundance of 4.3% and 4.4% for PM2.5 and PM10, respectively.
Other measured elements excluding C, Al, Si, K, Ca, and Fe and those in ions account for 1.5‒
1.9% of PM. Abundance of EC is low, accounting for 1.85% and 1.65% of PM2.5 and PM10,
respectively.
3-1
PM2.5
100
PM10
80
60
40
20
0
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
Sum of species (% of PM mass)
120
Site ID
Figure 3-1. Sum of measured species in PM2.5 and PM10. The sum of species includes TC (including CO3=), Na+,
Mg++, K, Cl, Ca, PO4≡, and SO4= and excludes OC and EC fractions, OC, EC, Na, Mg, P, S, K+, Cl- , and Ca++.
200
PM2.5
Al2O3
SiO2
160
CaO
FexOy
120
TiO2
Ions
Elements
Organics
EC
CO3
80
40
0
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
Constituents (% of PM2.5 mass)
a)
Site ID
200
PM10
Al2O3
SiO2
150
CaO
FexOy
TiO2
100
Ions
Elements
Organics
EC
CO3
50
0
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
Constituents (% of PM10 mass)
b)
Site ID
Figure 3-2. Sum of major constituents in PM2.5 and PM10 after assuming mineral oxides forms (Al2O3=2.2[Al];
SiO2=2.49[Si]; CaO=1.63[Ca]; FexOy+K2O=2.42[Fe], and TiO2=1.94[Ti]) and organics (1.4OC) following the
IMPROVE mass reconstruction equation (Malm et al., 1994) except that CO3= was added. (See site description in
Table 2-2 and site location in Figure 2-6)
3-2
3.2
Anion and Cation Balance
The anion and cation balance compares the sum of anions (i.e., Cl-, CO3=, NO2-, NO3-,
PO4≡, and SO4=) to the sum of cations (NH4+, Na+, Mg++, K+, and Ca++) in microequivalent mole
concentrations (µeq/m3). Carbonate ion (CO3=) is not measured by IC (since carbonate is used as
an eluent, especially for PM10 samples), but is expected to be an important anion for soil
samples. Since the ion analysis extract is slightly acidic due to absorption of CO2 (pH as low as
5), the weakly-soluble carbonate is present as water-soluble CO3= ion in the DDW extract. For
this study, carbonate carbon (CO3=) is measured by acidification prior to carbon analysis. Species
concentrations (in µg/m3) are divided by the atomic weight of the chemical species times the
species’ charge:





[Cl  ] [CO3 ] [ NO2 ] [ NO3 ] [ PO4 ] [ SO4 ]
µeq / m for anions 





35.5
60 / 2
46
62
95 / 3
96 / 2
3
(3-1)

[ NH 4 ] [ Na  ] [ Mg   ] [ K  ] [Ca   ]




µeq / m for cations 
18
23
24.3 / 2 39.1 40.1 / 2
3
(3-2)
Figure 3-3 shows the anion and cation balance for PM2.5 and PM10 samples. Most
samples have fair ion balances, although cations are 5‒55% higher than anions, especially for
PM2.5 samples. If CO3= were not included, the ions were not balanced, with average cations 1112 times more abundant than anions.
2.5
S29
1:1
S33
3
Cations (eq/m )
2.0
1.5
S32
1.0
0.5
PM2.5
S30
PM10
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3
Anions (eq/m )
Figure 3-3. Cation versus anion balance for PM2.5 and PM10 geological samples (based on Eqs 3-1 and 3-2).
SO4= versus Total S
SO4= is measured by IC on quartz-fiber filter extracts while total S is measured by XRF
on Teflon®-membrane filters. The ratio of SO4= to S is expected to be equal to three if all S is
present as SO4=. Due to the possible existence of water-insoluble S minerals in the sample,
water-soluble SO4= should not exceed three times the S concentration within precision estimates.
3.3
3-3
Figure 3-4 shows that SO4= is <three times S for most soil samples, and there is significant
insoluble S present in the geological samples from sites near the sulfur and coke piles.
a)
b)
18
16
16
14
PM2.5
3:1
PM10
12
Sulfate (% of PM)
Sulfate (% of PM)
14
12
10
8
6
S44
4
S53
8
6
4
S2
2
10
S2
2
S54
S53
S54
0
0
0
1
2
3
4
5
0
6
2
4
6
8
10
12
14
16
Sulfur (% of PM)
Sulfur (% of PM)
Figure 3-4. Sulfate versus sulfur in a) PM2.5 and b) PM10 geological samples.
3.4
Concentration Uniformity
As shown in Figure 2-1 and Figure 2-2, particles were collected on nine filter packs at the
bottom of the conical sampling manifold. To ensure the comparability among filters and the
validity of source profiles, particles need to be distributed uniformly at the filter inlets. The
particle uniformity can be inferred from the comparability of the PM2.5 and PM10 mass collected
on two separate filter channels as shown in Figure 3-5 for all tests. Both PM2.5 and PM10 linear
regressions show slopes close to one with high correlation coefficients (R2 ≈ 1). The relative
difference between the two filter channels were 3.8±3.5% (ranging 0-15.6%) for PM2.5 and
20000
Mass on PM10 Filter Channel 2 (µg/filter)
Mass on PM2.5 Filter Channel 2(µg/filter)
7000
(a) PM2.5
6000
5000
y = 0.996x
R² = 0.975
4000
3000
2000
1000
0
0
1000
2000
3000
4000
5000
6000
7000
Mass on PM2.5 Filter Channel 1 (µg/filter)
(b) PM10
15000
y = 1.045x
R² = 0.992
10000
5000
Outliers
0
0
5000
10000
15000
20000
Mass on PM10 Filter Channel 1 (µg)
Figure 3-5. Comparison PM mass collected on a) two PM2.5 and b) two PM10 Teflon-membrane filter channels for
all 64 tests.
6.9±5.6% (ranging 0-21.7%) for PM10. Paired student t-tests show that the two filter channels
were not statistically different, with p values of 0.45 and 0.16 for PM2.5 and PM10 channels,
respectively. Two outliers were identified in the PM10 comparison as indicated by the open
3-4
symbols in Figure 3-5b. Some particles were found to dislodge from the filters and deposit on the
filter holder for two of those filters, probably due to vibration during sampling shipping and
handling. The outlier samples were excluded from chemical and data analyses.
3.5
DRX and OPS Calibrations
Both the DRX and OPS are based on light scattering, and calibration with gravimetric
mass is needed to convert the instrument reported mass to gravimetric mass. The DRX had an
internal custom photometric calibration factor (PCF) of 0.48 and size calibration factor (SCF) of
1.48 in the 2012 tests, and the factory default calibration factors of PCF = SCF = 1 were used in
the 2013 tests. Figure 3-6 compares the PM2.5 and PM10 mass concentrations measured by the
Teflon-membrane filters and DRX for 2012 (a and b) and 2013 (c and d) tests. Note that there is
significant scatter in the data, however, fair correlations (R2 = 0.62-0.82) between the two
measurements are observed. Although the main composition of particles sampled in this study is
geological dust, particles at different sites have quite different optical property, density, and size
distributions. Therefore, better correlations between the gravimetric mass and DRX were not
expected, and the DRX reading were corrected based on the gravimetric PM2.5 and PM10 for
individual site. It is also interesting to note that in the 2013 tests with the DRX using default
calibration factors, the PM2.5 and PM10 regression slopes are close to one, indicating that the dust
particles sampled in this study have similar properties to the Arizona Road Dust (ARD) that was
used to calibrate the DRX by its manufacturer (Wang et al., 2009). The calibration factors used
in 2012 (PCF=0.48 and SCF=1.48) were established based on ambient aerosol measurement in
Sparks, NV. The urban aerosol properties are quite different from the fugitive dust, and
recalibrating the 2012 DRX dust data based on gravimetric mass would result in PCF and SCF
close to one. A new set of PCF and SCF were calculated based on the DRX and filter
concentrations for each site, and all five DRX size channels were corrected based on the new
PCF and SCF.
Figure 3-7 compares the gravimetric PM2.5 and PM10 mass concentrations with those
measured by the OPS. Note that while PM10 had reasonable correlation, the PM2.5 correlation
was not as strong as the DRX. This is probably because of the sizing and concentration errors
caused by coincidence at high concentrations of smaller particles. The larger particles are
influenced less by coincidence errors because of their relatively lower concentrations and larger
scattering pulses. Since the OPS calculates mass concentration based on the number size
distribution based on optical-equivalent diameter, the very different regression slopes (3.7 for
PM2.5 and 0.3 for PM10) indicate that different correction factors are needed for different size
fractions. Significant efforts are required to develop a correction algorithm. For this report, the
OPS will be used for qualitative size distribution information. Quantitative emission flux
information will be based on the DRX.
3-5
120
Filter PM10 Concentration (mg/m3)
Filter PM2.5 Concentration (mg/m3)
40
(a) PM2.5 -2012 Tests
y = 2.318x
R² = 0.802
30
20
10
0
0
5
10
15
(b) PM10 -2012 Tests
100
y = 0.531x
R² = 0.615
80
60
40
20
0
20
0
DustTrak DRX PM2.5 Concentration (mg/m3)
40
60
80
100
120
160
70
(c) PM2.5 -2013 Tests
Filter PM2.5 Concentration (mg/m3)
Filter PM2.5 Concentration (mg/m3)
20
DustTrak DRX PM10 Concentration (mg/m3)
60
y = 1.054x
R² = 0.808
50
40
30
20
10
0
0
10
20
30
40
50
60
(d) PM10-2013 Tests
y = 1.128x
R² = 0.821
120
80
40
0
70
0
DustTrak DRX PM2.5 Concentration (mg/m3)
40
80
120
160
DustTrak DRX PM2.5 Concentration (mg/m3)
Figure 3-6. Comparison of PM2.5 and PM10 mass concentration measured by the Teflon-membrane filters and the
DustTrak DRX in 2012 (a and b) and 2013 (c and d). Because different internal calibration factors were used in
2012 and 2013, the regression slopes are different for 2012 and 2013 tests. Test at three sites (8, 30, and 53) were
not plotted because the DustTrak DRX was saturated by the high dust concentrations.
200
Filter PM10 Concentration (mg/m3)
Filter PM2.5 Concentration (mg/m3)
70
(a) PM2.5
60
y = 3.748x
R² = 0.364
50
40
30
20
10
0
0
2
4
6
8
10
OPS PM2.5 Concentration (mg/m3)
(b) PM10
y = 0.305x
R² = 0.802
150
100
50
0
0
100
200
300
400
500
600
OPS PM10 Concentration (mg/m3)
Figure 3-7. Comparison of a) PM2.5 and b) PM10 mass concentration measured by the Teflon-membrane filters and
the OPS for tests in 2012 and 2013.
3-6
4
Windblown Fugitive Dust Emission Characteristics
4.1
Data Reduction
The following steps were taken to process and analyze the real-time data:
1) Raw data files produce by the PI-SWERL were separated into ramp, hybrid, and step
tests.
2) The average DRX PM2.5 and PM10 concentrations during step tests at each site were
compared to the gravimetric masses to calculate PCF and SCF calibration factors.
3) The new PCF and SCF were used to correct the DRX concentrations in hybrid tests. The
background concentration before the PI-SWERL blade was turned on was subtracted
from the measured concentrations. The PM1, PM2.5, PM4, PM10, and PM15 instantaneous
emission rate (µg/s), step mass emissions potential (µg/m2), and cumulative mass
emission potential (µg/m2) were recalculated.
4) An exponential decay curve was fit to the constant RPM steps of the hybrid test, and the
reservoir types were estimated from the PM10 concentration decay rate.
5) Running averages and derivatives of the PM10 and OGS data were calculated, and the
threshold RPM and corresponding threshold friction velocity and wind speed for PM
emission and saltation to occur were calculated.
6) Cumulative emission potentials (g/m2) at the end of each constant RPM step and the
corresponding step duration were determined, and the cumulative emission fluxes
(g/m2/s) were calculated for each RPM step at each run, which was further averaged to
obtain cumulative emission potentials and fluxes at each site.
7) Cumulative emission potentials for surfaces before and after watering, as well as
stabilized and disturbed surfaces were compared to evaluate the effects of watering and
disturbance.
4.2
Dust Reservoir Type
Dust emissions from surfaces are limited by the amount of erodible material available for
suspension into the atmosphere. In addition to the amount of erodible material present, the
condition of the surface, including textural and stability, as well as climatological factors
influence the total windblown dust emission potential of a given parcel of exposed surface. The
amount of particles available for a given surface is referred to as the dust reservoir and can be
classified as dust supply-limited or unlimited. Most soil surfaces are limited reservoirs, i.e.,
suspendable dust is depleted after a short time in the absence of direct abrasion. This depletion is
represented as a negative exponential (Anspaugh et al., 1975; Linsley, 1978) or inverse (Garland,
1983; Nicholson, 1993; Reeks et al., 1985) function of time. On exposed land, depletion of fine
particles often results in the exposure of larger non-erodible sediments that shield the
suspendable particles from the wind. The larger non-erodible elements also absorb momentum,
thereby decreasing the wind’s ability to erode the surface (Marshall, 1971; Raupauch, 1992).
When surfaces are continually disturbed by very intense winds, by vehicular movement, or by
other human activities, unlimited reservoirs are created that emit dust whenever winds exceed
threshold suspension velocities. Suspendable dust loadings may vary substantially, even over
periods of a few minutes, when there are no mechanisms to replenish the reservoir. Classification
of reservoirs as limited or unlimited has implications with respect to the duration of time over
which the dust emissions are generated, and therefore need to be parameterized differently in
fugitive dust emission models.
4-1
To answer Q1: “Does the surface have limited or unlimited dust supply at a specific wind
speed (or friction velocity)”, the PM concentration change patterns of the PI-SWERL hybrid
tests under different RPMs were examined. An example of a hybrid PI-SWERL test at Site 1 is
illustrated in Figure 4-1. As the rotating speed was ramped up, the PM10 concentration increased.
However, at the constant speed steps of 1000, 1500, and 2000 RPM, the PM10 concentration
decreased with time. An exponential decay equation was fitted to the PM10 concentration–time
(t) relation as follows:
,
(4-1)
,
where
, is the PM10 concentration at time t,
, is the PM10 concentration at time 0
chosen as the fit starting point, and τc is the time constant indicating the concentration decay rate.
The red lines and fit equations in Figure 4-1 indicate the negative exponential equation fits well
with the concentration decreases. Therefore, this surface has limited dust supplies at these lower
friction velocities. The PM10 at 2500 RPM remained at high concentration levels without clear
indication of exponential decay. Therefore, the reservoir is unlimited at this high friction
velocity. The concentration decayed exponentially after the PI-SWERL blade was turned off as
the clean air purged the chamber. The decay constant (13 s, i.e., 1/0.077) is about twice of the
value if the PI-SWERL is assumed as an ideal stirred cylindrical reactor. Figure 4-2 depicts an
area view of Site 1 and the ring created after the PI-SWERL test. The swirl shape of sand grains
indicates that the larger sand grains were moving at high speeds, causing more particles to be
suspended from the surface, which created an unlimited dust supply reservior at higher speeds.
An exponential decay curve was fitted to each constant RPM step of all hybrid tests, and
a decay constant of 100 s (a slope of -0.01 in Eq. 4-1) was used to differentiate supply limited or
unlimited reservoir types. Note that most reservoirs would ultimately be supply limited if the
wind is above the threshold friction speed for a long time. The choice of 100 s decay constant for
separating limited or unlimited reservoir is somewhat arbitrary. Table 4-1 summarizes the dust
reservoir types as a function of PI-SWERL RPM for all sites measured in this study. All sites are
supply limited at lower speeds (i.e., 500 and 1000 RPM, corresponding to u10+ = 11-16 km/h). At
2000 RPM (u10+ = 27 km/h), all sites are also supply limited except two sites (Sites 5 and 6) at a
tailings beach. Most sites are supply unlimited at higher speeds (i.e., 5000 RPM; u10+ = 56 km/h).
Note that several quarry sites have limited supply even at 5000 RPM, because the stabilized lime
stone has a very limited dust supply. It’s also interesting to note that the undisturbed and
disturbed coke pile also have limited dust supply at 5000 RPM, probably because of the larger
coke particles. Paved surfaces are expected to have limited supplies since after the top dust is
removed, the pavement is not suspendable. Some of the paved surfaces in Table 4-1 have
unlimited supply because there was a thick layer of dust deposited on these surfaces that
provided a near constant PM output within the duration of a hybrid test.
4-2
PM10
500
100
10
1
0.1
0.01
PM10 Concentration (mg/m3)
1000
Fit
Ln(PM10)=--0.077t+60.3
1500
Rotating
Speed
Fit
Ln(PM10)=--0.039t+19.9
2000
Fit
Ln(PM10)=--0.038t+10.4
Fit
Ln(PM10)=--0.122t+21.2
Rotating Speed (RPM)
2500
0.001
0
0
200
400
600
800
Test Duration (s)
Figure 4-1. PM10 concentration as a function of the PI-SWERL blade rotating speed during a hybrid test at Site 1 as
an illustration of the dust reservoir type. The red lines and equations indicate the fit of exponential decay equations
to the concentration drop.
a)
b)
Figure 4-2. Pictures of Site 1: a) an area view of the unpaved road near Ft. McKay that was constantly disturbed by
traffic; and b) a ring after the PI-SWERL test indicating sand movement.
4-3
Table 4-1. Summary of dust reservoir type of each tested site.
Site
1
RPM
Description
500
1000
2000
3000
4000
5000
L
L
L
UL*
NA
NA
L
NA
L
L
UL
UL
2
Ft. McKay unpaved road
Facility C unpaved road with sulfur deposit
3
Facility C unpaved road near sulfur pile
4
Facility C tailings sand strip
L
L
L
UL
UL
UL
5
Facility C tailings flat sand beach
L
L
UL
UL
UL
UL
6
Facility C tailings sand beach wind gate
L
L
UL
UL
UL
UL
7
Facility C overburden
Facility C unpaved road on tailings dike
L
L
L
UL
UL
UL
8
L
L
L
UL
UL
NA
9
Facility C light vehicle unpaved road-dry
L
L
L
L
UL
NA
10
Facility C light vehicle unpaved road-wet
L
L
L
L
UL
UL
11
L
L
L
L
UL
UL
L
L
L
L
UL
UL
13
Facility C tailings dike unpaved road
Facility C tailings dike drifting sand, below pipeline
facing wind
Facility C tailings dike overburden between pipelines
L
L
L
L
UL
UL
14
Ft. McMurray paved road near WBEA AMS 7
L
L
L
L
UL
UL
15
WBEA Shell AMS 16 unpaved road
Ft. McMurray unpaved road outside Wilson
L
L
L
L
L
NA
16
L
L
L
L
UL
NA
17
Ft. McKay Community Center paved parking lot
L
L
L
L
L
UL
18
Highway 63 paved shoulder near Facility C
L
L
L
L
L
UL
19
L
L
L
UL
UL
UL
L
L
L
UL
UL
UL
21
Facility B tailings dike 1, flat undisturbed
Facility B tailings dike 2, near a slope of windblown
dust accumulation
Facility B tailings dike 3
L
L
L
UL
UL
UL
22
Facility B tailings beach 1 tractor track
L
L
L
UL
UL
UL
23
Facility B tailings beach 2 truck track
L
L
L
UL
UL
NA
24
Facility B tailings dike 4, near a pumping station
L
L
L
UL
UL
UL
25
L
L
L
L
UL
UL
L
L
L
L
UL
UL
27
Facility B T-section by main haul road
Facility B T-section by main haul road, undisturbed,
crusted
Facility B unpaved road, tire track
L
L
L
L
UL
UL
28
Facility B overburden berm
L
L
L
L
UL
UL
29
Quarry, conveyor area
Quarry, processing ground, tire tracks
L
L
L
L
UL
UL
L
L
L
L
UL
UL
12
20
26
30
31
Quarry, waste storage pile hill foot
L
L
L
L
L
L
32
Quarry, dry unpaved road in processing ground
L
L
L
L
L
UL
33
Quarry, wet unpaved road in processing ground
L
L
L
L
L
L
4-4
Table 4-1 continued.
Site
RPM
Description
500
1000
2000
3000
4000
5000
34
Quarry, unpaved road in Pit 1
L
L
L
L
L
L
35
Quarry, waste dump, truck track
L
L
L
L
L
UL
36
Quarry, waste pile
L
L
L
UL
UL
UL
37
Quarry, road near exit scale
L
L
L
UL
UL
UL
38
L
L
L
L
UL
UL
L
L
L
L
UL
UL
L
L
L
L
L
UL
41
Quarry, parking lot for haul trucks
Ft. MacKay Industrial Park track-out Hwy 63 paved
road
Ft. McKay gravel road at an intersection, watered not
long ago
Ft. McKay paved road after turn to CNRL
L
L
L
L
L
L
42
Hwy 63 construction zone near BURNCO
L
L
L
L
UL
UL
43
Hwy 63 rest area south of Ft. McMurray
L
L
L
L
UL
UL
39
40
44
Sandy surface near Hwy 63 between Facility C ponds
L
L
L
UL
UL
UL
45
Athabasca Hwy, unpaved, below shoulder slope
L
L
L
L
L
L
46
Athabasca Hwy, unpaved shoulder
L
L
L
L
UL
UL
47
Sandy road near WBEA Site R2
L
L
L
UL
UL
UL
48
Hwy 63 unpaved north of Aurora
L
L
L
UL
UL
NA
49
Ft. McMurray Thickwood BLVD new construction
L
L
L
UL
UL
UL
50
Ft. McMurray Thickwood BLVD land clearance
L
L
L
L
L
L
51
Ft. McMurray unpaved parking
L
L
L
UL
UL
UL
52
WBEA Ft. McKay AMS 1 unpaved road
L
L
L
UL
UL
UL
53
Facility E undisturbed coke pile
L
L
L
L
L
L
54
Facility E disturbed coke pile
L
L
L
L
L
L
55
Facility E haul road
L
L
L
UL
UL
UL
56
Facility E tailings pond dike
L
L
L
L
UL
UL
57
Facility E overburden pit
L
L
L
UL
UL
UL
58
Facility E tailings pond beach
L
L
L
L
UL
UL
59
Facility E unpaved road near sulfur pile
L
L
L
L
L
UL
60
Facility E unpaved road near sulfur pile
L
L
L
L
UL
UL
61
Forest fire site near north Hwy 63
L
L
L
L
UL
UL
62
Bare land near north Hwy 63 ice road gate
Unpaved road across Hwy 63 near Facility B tailings
pond dike
Athabasca Hwy shoulder near Firebag
L
L
L
UL
UL
UL
L
L
L
L
UL
UL
L
L
L
L
UL
UL
63
64
Note: L-limited; UL-unlimited; NA-not measured.
* The maximum speed measured at Site 1 was 2500 RPM.
4-5
4.3
Threshold Friction Velocity
The threshold friction velocity is the wind velocity above which particle emission occurs,
and is a key parameter for modeling wind erosion as shown in Eq. 2-2. The threshold friction
velocity depends on particle size. For this study, we determine the threshold friction velocities
for PM emissions and for saltation. The process for determining these threshold friction
velocities (from RPM) are illustrated in Figure 4-3 for a hybrid run at Site 39. Figure 4-4 shows
pictures of Site 39.
For estimating the PM threshold RPM, a 10 s moving average was calculated to smooth
the PM10 concentration curve, and then the derivative of the PM10 concentration (ΔPM10/Δt) was
calculated for every second. The sign of the derivative indicates if the PM10 concentration is
increasing or decreasing with time. The criteria for the PM emission threshold RPM is chosen as
when the PM10 concentration was >0.01 mg/m3 and increasing for at least four consecutive
seconds. The >0.01 mg/m3 concentration criterion was used to reduce interference from the
background PM. The orange dash line in Figure 4-3 indicates the moment when PM emission
was trigged (at 617 RPM) on this surface.
The determination for saltation threshold RPM is similar to PM. The two lower OGS (L1
and L2) count were used. Since the OGS are only sensitive to particles >100 µm, the increase of
OGS count rate indicates sand grain movement. The criteria for estimating the saltation threshold
RPM is that both OGS counted >10 particles/s, or the OGS counts rate were increasing for at
least four consecutive seconds. The threshold speed (4019 RPM) for triggering saltation is
indicated by the purple dash line in Figure 4-3. Table 4-2 lists the threshold RPM, threshold
friction velocity, and corresponding wind speed at 10 m agl for PM emissions and saltation to
occur. The threshold RPM are also plotted in Figure 4-5. The average PM threshold RPM varied
from ~100 to 1500 RPM. The sites with <500 RPM threshold speeds indicate that there was a
layer of top soil that would suspend at low wind speeds. Note that while all sites emitted PM,
saltation did not occur for several sites. Saltation occurred at speeds >2500 RPM, significantly
higher than the PM emission threshold RPM. Comparing Table 4-1 and Table 4-2 indicates that
saltation is very often related to unlimited reservoirs. This is expected since sand grain moment
will disturb the surface and induce more particle emissions.
Another way to examine the threshold wind velocity is to find the RPM corresponding to
specific dust emission potentials. Figure 4-6 plots the threshold RPM for PM2.5 or PM10 emission
potential to reach 0.002, 0.02, and 0.2 g/m2. This figure answers the question “How hard would
the wind have to blow in order for PM2.5 or PM10 emission potential to exceed 0.002, 0.02, and
0.2 g/m2”. The threshold RPM varied among sites. Twenty of the 64 sites did not reach 0.2 g/m2
PM2.5 emission potential at the highest measured speeds (mostly 5000 RPM) as indicated by no
bar for those sites in Figure 4-6.
4-6
4000
1000
Saltation
Threshold
100
PM
Threshold
10
3000
OGS
L1
PM10
2000
OGS
L2
1000
Rotating
Speed
200
400
0.1
0.01
0
0
1
600
800
PM10 Concentration (mg/m3)
OGS Count Rate (s-1)
Rotating Speed (RPM)
5000
0.001
1000
Test Duration (s)
Figure 4-3. PM10 concentration and optical gate sensors (OGS) count rate as a function of rotating speed during a
hybrid test at Site 39 as an illustration of determining the threshold friction speed (RPM) for PM emission and
saltation (as indicated by the orange and purple dash lines, respectively).
a)
b)
Figure 4-4. Pictures of Site 39: a) an area view of the track-out accumulation along Hwy 63 near the Ft. McKay
Industrial Park; and b) a ring after the PI-SWERL test indicating sand movement.
4-7
Table 4-2. Threshold RPM, friction speed, and corresponding wind speed at 10 m above the ground level for PM10
emissions and saltation to occur. Values are expressed as average ± standard deviation of multiple runs. NA
indicates that saltation was not observed for that surface.
PM10 Threshold
Site
Saltation Threshold
RPM
u* (m/s)
u10 (m/s)
u10 (km/h)
RPM
u* (m/s)
u10+ (m/s)
u10+ (km/h)
959±384
0.23±0.14
4.36±2.71
15.71±9.77
NA
NA
NA
NA
968±471
0.23±0.16
4.39±2.96
15.80±10.65
4231±382
0.72±0.14
13.72±2.71
49.39±9.75
4
1362±310
0.29±0.13
5.55±2.51
19.98±9.02
3913±335
0.68±0.14
12.91±2.58
46.46±9.28
5
1013±867
0.24±0.22
4.52±4.09
16.28±14.74
2709±230
0.50±0.12
9.55±2.29
34.38±8.23
6
930±718
0.23±0.19
4.28±3.66
15.40±13.19
NA
NA
NA
NA
7
1038±47
0.24±0.09
4.59±1.79
16.54±6.43
4627±150
0.77±0.11
14.67±2.06
52.83±7.43
8
191
0.11
2.18
7.84
3558
0.63
11.95
43.04
9
1042±473
0.24±0.16
4.61±2.97
16.58±10.68
4004
0.69
13.14
47.31
10
783±345
0.20±0.14
3.85±2.61
13.87±9.38
NA
NA
NA
NA
11
1269±12
0.28±0.09
5.27±1.69
18.99±6.08
3899±13
0.68±0.09
12.87±1.69
46.33±6.09
12
841±904
0.21±0.22
4.02±4.20
14.47±15.13
4378±314
0.74±0.13
14.08±2.52
50.69±9.07
13
200±2
0.12±0.09
2.20±1.66
7.93±5.99
NA
NA
NA
NA
14
1232±401
0.27±0.15
5.16±2.76
18.59±9.95
4018
0.69
13.18
47.44
15
921±233
0.22±0.12
4.25±2.29
15.31±8.26
NA
NA
NA
NA
16
632±41
0.18±0.09
3.42±1.77
12.30±6.36
NA
NA
NA
NA
17
1285±51
0.28±0.09
5.32±1.80
19.16±6.46
NA
NA
NA
NA
18
1386±332
0.30±0.14
5.62±2.57
20.23±9.24
NA
NA
NA
NA
19
1438±105
0.30±0.10
5.78±1.94
20.79±7.00
4758±179
0.79±0.11
14.97±2.15
53.90±7.72
20
264±91
0.13±0.10
2.38±1.90
8.57±6.85
4025
0.69
13.19
47.50
21
694±179
0.19±0.11
3.60±2.14
12.95±7.72
4151±196
0.71±0.12
13.52±2.19
48.66±7.89
22
688±318
0.19±0.13
3.58±2.53
12.88±9.11
4008
0.69
13.15
47.35
23
485±214
0.16±0.12
3.00±2.24
10.80±8.07
3965±41
0.69±0.09
13.04±1.77
46.95±6.37
24
829±114
0.21±0.10
3.99±1.97
14.35±7.08
4012±0
0.69±0.09
13.16±1.66
47.39±5.97
25
753±197
0.20±0.12
3.77±2.20
13.56±7.90
4935±88
0.81±0.10
15.37±1.90
55.31±6.82
26
718±168
0.19±0.11
3.67±2.12
13.20±7.62
4717±154
0.78±0.11
14.88±2.08
53.57±7.48
27
684±61
0.19±0.10
3.57±1.82
12.83±6.56
4193±261
0.72±0.12
13.62±2.37
49.04±8.54
28
750±148
0.20±0.11
3.76±2.06
13.52±7.42
4670±572
0.78±0.17
14.77±3.25
53.18±11.69
29
656±41
0.18±0.09
3.49±1.77
12.55±6.36
NA
NA
NA
NA
30
774±72
0.20±0.10
3.83±1.85
13.77±6.67
NA
NA
NA
NA
31
1244±17
0.27±0.09
5.20±1.70
18.72±6.13
NA
NA
NA
NA
32
1109±216
0.25±0.12
4.80±2.25
17.29±8.09
NA
NA
NA
NA
33
1295
0.28
5.35
19.26
NA
NA
NA
NA
1
2
3
+
+
4-8
Table 4-2 continued.
PM10 Threshold
Site
+
Saltation Threshold
+
RPM
u* (m/s)
u10 (m/s)
u10 (km/h)
RPM
u* (m/s)
u10+ (m/s)
u10+ (km/h)
34
1009±56
0.24±0.10
4.51±1.81
16.23±6.51
NA
NA
NA
NA
35
1113±303
0.25±0.13
4.81±2.49
17.33±8.96
4585±62
0.77±0.10
14.58±1.82
52.47±6.57
36
577±16
0.17±0.09
3.26±1.70
11.74±6.12
4911
0.81
15.31
55.12
37
655±12
0.18±0.09
3.48±1.69
12.54±6.09
3799±284
0.66±0.13
12.61±2.44
45.38±8.77
38
703±129
0.19±0.11
3.62±2.01
13.03±7.23
3896±162
0.68±0.11
12.86±2.10
46.30±7.55
39
617±552
0.18±0.17
3.37±3.19
12.15±11.49
3932±122
0.68±0.10
12.96±1.99
46.64±7.16
40
558±480
0.17±0.16
3.21±2.99
11.54±10.75
NA
NA
NA
NA
41
1131±158
0.26±0.11
4.87±2.09
17.52±7.52
NA
NA
NA
NA
42
707±145
0.19±0.11
3.63±2.05
13.08±7.38
3332±24
0.60±0.09
11.33±1.72
40.79±6.20
43
518±250
0.16±0.12
3.09±2.34
11.13±8.43
4499±16
0.76±0.09
14.37±1.70
51.74±6.12
44
650±70
0.18±0.10
3.47±1.85
12.49±6.65
5004
0.82
15.51
55.85
45
851±517
0.21±0.16
4.05±3.09
14.58±11.12
NA
NA
NA
NA
46
611±75
0.18±0.10
3.36±1.86
12.09±6.70
4667±432
0.78±0.15
14.77±2.85
53.16±10.26
47
673±38
0.19±0.09
3.54±1.76
12.73±6.33
NA
NA
NA
NA
48
724±317
0.19±0.13
3.68±2.53
13.26±9.10
NA
NA
NA
NA
49
489±221
0.16±0.12
3.01±2.26
10.83±8.14
4987
0.81
15.48
55.72
50
405±314
0.15±0.13
2.77±2.52
9.99±9.07
NA
NA
NA
NA
51
606±42
0.18±0.09
3.34±1.77
12.03±6.38
4815±21
0.79±0.09
15.10±1.71
54.37±6.16
52
915±393
0.22±0.14
4.24±2.74
15.25±9.86
4622±514
0.77±0.16
14.66±3.08
52.79±11.10
53
693±719
0.19±0.19
3.59±3.67
12.94±13.21
NA
NA
NA
NA
54
199±1
0.12±0.09
2.20±1.66
7.92±5.98
NA
NA
NA
NA
55
196±1
0.12±0.09
2.19±1.66
7.89±5.98
5004
0.82
15.51
55.85
56
508±445
0.16±0.15
3.07±2.89
11.04±10.39
4861±183
0.80±0.11
15.20±2.16
54.74±7.76
57
1468±9
0.31±0.09
5.86±1.68
21.11±6.05
NA
NA
NA
NA
58
632±144
0.18±0.11
3.42±2.05
12.30±7.37
2135±2308
0.41±0.44
7.85±8.37
28.27±30.12
59
706±283
0.19±0.13
3.63±2.43
13.07±8.76
4386±152
0.74±0.11
14.10±2.07
50.76±7.46
60
539±474
0.17±0.16
3.15±2.97
11.35±10.68
NA
NA
NA
NA
61
848±658
0.21±0.18
4.04±3.49
14.55±12.57
4418
0.75
14.18
51.04
62
808±146
0.21±0.11
3.92±2.05
14.12±7.39
2966±307
0.54±0.13
10.29±2.50
37.05±9.00
63
1268±602
0.28±0.18
5.27±3.33
18.98±12.00
3777±347
0.66±0.14
12.55±2.61
45.16±9.40
64
677±20
0.19±0.09
3.55±1.71
12.76±6.16
3890±170
0.68±0.11
12.84±2.12
46.24±7.63
4-9
a)
PM Emission Threshold Speed (RPM)
6000
5000
4000
3000
2000
1000
0
5
10
15
20
25
30
35
40
45
50
55
60
40
45
50
55
60
Site
b)
Saltation Threshold Speed (RPM)
6000
5000
4000
3000
2000
1000
0
5
10
15
20
25
30
35
Site
Figure 4-5. Threshold RPM for a) PM emission and b) saltation.
4-10
6000
5
10
15
20
25
30
35
40
45
50
55
60
20
25
30
35
40
45
50
55
60
2
PM2.5, 0.002 g/m
5000
4000
3000
2000
1000
Threshold RPM to Generate Specific Emission Potentials (RPM)
0
6000
2
PM2.5, 0.02 g/m
5000
4000
3000
2000
1000
0
6000
PM2.5, 0.2 g/m2
5000
4000
3000
2000
1000
0
6000
PM10, 0.002 g/m2
5000
4000
3000
2000
1000
0
6000
PM10, 0.02 g/m2
5000
4000
3000
2000
1000
0
6000
PM10, 0.2 g/m2
5000
4000
3000
2000
1000
0
5
10
15
Site
Figure 4-6. Threshold RPM for generating 0.002, 0.02, and 0.2 g/m2 emission potential of PM2.5 (first three red
panels) and PM10 (last three green panels). Sites without a bar except Site 3 indicate that the specified emission potential was
not reached at the maximum RPM tested for that site. Site 3 was not measured but is similar to Site 2.
4-11
4.4
Emission Potential and Flux
The cumulative emission potential (g/m2) was calculated using Eq. 2-7 to answer the
question: “How much PM is available for emissions after exposing to different wind speed”. An
example is illustrated in Figure 4-7. Points A, B, C, and D illustrate the cumulative emission
potentials at the end of each PI-SWERL steps of 1000, 2000, 3000, and 4000 RPM,
corresponding to wind speeds of 16, 27, 37, and 47 km/h at 10 m agl. The cumulative emission
flux (g/m2/s) is calculated using Eq. 2-8 by dividing the emission potential by the effective
averaging period. For simplicity, the effective averaging period is chosen as the step duration
with constant RPM at each step in the hybrid protocol. This approach assumes that all dust
emitted at the lower RPMs (e.g., <3000 RPM) before the starting of the constant RPM (e.g.,
3000 RPM) will be emitted during the step of constant speed (3000 RPM) if the PI-SWERL
speed were stepped from 0 to 3000 RPM and maintained at 3000 RPM for 90 s.
4000
1.E+00
1.E-01
3000
RPM
C. Cumulative Emissions
at end of 3000 RPM
1.E-02
2000
B. Cumulative Emissions
at end of 2000 RPM
1.E-03
A. Cumulative Emissions
at end of 1000 RPM
Instaneous
Emissions
Rotating Speed (RPM)
Instaneous (mg/s) or
Cumulative Emissions (g/m2)
D. Cumulative Emissions
at end of 4000 RPM
1000
1.E-04
Cumulative
Emissions
0
1.E-05
0
150
300
450
600
750
Test Duration (s)
Figure 4-7. Example of cumulative PM10 emission potential (g/m2) calculation at different points during the PISWERL hybrid test cycle at Site 15.
Figure 4-8a and b show the cumulative PM2.5 and PM10 emission flux of the 64 sampling
sites at different RPMs, respectively. Detailed data of emission potentials and fluxes for all size
fractions are listed in Appendices B and C.
4-12
a)
500 RPM
1000 RPM
2000 RPM
3000 RPM
4000 RPM
5000 RPM
0.25
2
PM2.5 Emission Flux (g/m /s)
0.30
0.20
0.15
0.10
0.05
0.00
5
10
15
20
25
30
35
40
45
50
55
60
40
45
50
55
60
Site
b)
0.30
500 RPM
1000 RPM
2000 RPM
3000 RPM
4000 RPM
5000 RPM
PM10 Emission Flux (g/m2/s)
0.25
0.20
0.15
0.10
0.05
0.00
5
10
15
20
25
30
35
Site
Figure 4-8. Cumulative emission flux (g/m2/s) of a) PM2.5 and b) PM10 of each site at the end of each PI-SWERL
hybrid test cycle steps.
4-13
The ten sites with the highest and lowest PM10 emission fluxes at 4000 RPM (not all
surfaces were measured at 5000 RPM) are listed in Table 4-3. Eight of the ten highest PM10
emitting sites (except Sites 8 and 23) are also among the top ten surfaces with highest PM2.5
emission fluxes. Sites 39 and 49 are the other two sites among the ten highest PM2.5 emitting
surfaces. Note that most of these high emitting surfaces are related to unpaved roads, parking
lots, or bare land with frequent disturbances. Sites 18, 33, 41, 57, 59, and 14 have the lowest
emissions fluxes of both PM2.5 and PM10 at 4000 RPM. Most of the low emitting surfaces are
paved road or stabilized or treated (e.g., watering) surfaces. Sites 59 and 63 are unpaved road but
with low PM emissions at 4000 RPM. Figure 4-9 shows the surfaces of unpaved road sites 27
and 59 after the PI-SWERL runs. Note that Site 27 has significant more loose clay and silt
materials, while Site 59 has more coarse sands. The differences in soil texture at these two sites
caused the significantly different potential emission fluxes.
Table 4-3. The ten sites with highest and lowest PM10 emission fluxes.
Rank
Highest PM10 Emissions Sites
Lowest PM10 Emissions Sites
1
Site 27 - Facility B unpaved road, tire track
Site 18 - Highway 63 shoulder near Facility C
2
Site 55 - Facility E haul road
Site 33 - Quarry, wet road in processing ground
3
Site 51 - Fort McMurray dirt parking lot
Site 41 - Ft. McKay paved road after turn to CNRL
4
Site 48 - Hwy 63 unpaved north of Aurora
Site 57 - Facility E overburden pit
5
Site 37- Quarry, road near exit scale
Site 59 - Facility E unpaved road near sulfur pile
6
Site 8 - Facility C dirt road on tailings dike
Site 14 - Ft. McMurray paved road near WBEA AMS 7
7
Site 29 - Quarry, conveyor area
Site 53 - Facility E undisturbed coke pile
8
Site 38 - Quarry, parking lot for haul trucks
Site 31 - Quarry, waste storage pile hill foot
Site 63 - Dirt road across Hwy 63 near facility A
9
Site 23 -Facility B tailings beach 2 truck track
tailings pond dike
Site 49 - Fort McMurray Thickwood Blvd new
10
Site 61 - Forest fire site near north Hwy 63
construction
a)
b)
Figure 4-9. Pictures of the rings after PI-SWERL tests at a) Site 27 and b) site 59. Site 27 has more loose clay and
silt materials than Site 59.
4-14
Figure 4-10 plots potential PM10 emission fluxes grouped by facilities and locations (i.e.,
Facilities M, A, and C, Quarry, Ft. McMurray and Ft. McKay, and other locations). The sites in
each graph are in the order of decreasing emissions at 4000 RPM. For all three oil sands facilities
(Figure 4-10a, b, and c), the high dust emitting surfaces are the unpaved roads with high
abundances of loose clay and silt materials along with frequent mechanical disturbance by
vehicle traffic. On the other hand, the stabilized or undisturbed surfaces with limited dust
supplies have the lowest dust emissions. In the quarry operation (Figure 4-10d), the highest
emitting surfaces are also those with vehicle or other mechanical disturbances (i.e., unpaved
roads with high truck traffic, near the conveyor belts, and haul truck parking lot). The waste lime
stone storage piles were very well stabilized with limited dust supply, and watering the road
significantly reduced potential dust emissions. For sites in the vicinity of Ft. McMurray and Ft.
McKay, and other locations outside mining facilities (Figure 4-10e and f), the unpaved roads and
parking lots with high vehicle traffic and loose dust materials have the highest dust emissions.
Pictures of high emitting unpaved roads near Ft. McMurray (Site 16) and after the Hwy 63 turns
unpaved (Site 48) are shown in Figure 4-11.
4-15
a) Facility C
2
PM10 Emission Flux (mg/m s)
0.14
0.12
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
0.04
0.02
0.00
20
30
40
50
Wind Speed at 10 m agl (km/h)
b) Facility B
2
PM10 Emission Flux (mg/m s)
0.14
0.12
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
20
30
40
50
Wind Speed at 10 m agl (km/h)
Figure 4-10. Potential emission fluxes at different sites in a) Facility C, b) Facility B, c) Facility E, d) Quarry, e) Ft.
McMurray and Ft. McKay, and f) other locations. The number in the legend indicates the site ID. Sites in each graph
are sorted by the order of decreasing emission flux at 4000 RPM.
4-16
c) Facility E
2
PM10 Emission Flux (mg/m s)
0.14
0.12
0.10
0.08
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
0.06
0.04
0.02
0.00
20
30
40
50
Wind Speed at 10 m agl (km/h)
d) Quarry
2
PM10 Emission Flux (mg/m s)
0.14
0.12
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
20
30
40
Wind Speed at 10 m agl (km/h)
Figure 4-10 continued.
4-17
50
e) Ft. McMurray and Ft. McKay
2
PM10 Emission Flux (mg/m s)
0.14
0.12
0.10
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
0.04
0.02
0.00
20
30
40
50
Wind Speed at 10 m agl (km/h)
f) Other locations
2
PM10 Emission Flux (mg/m s)
0.14
0.12
0.10
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
0.04
0.02
0.00
20
30
40
Wind Speed at 10 m agl (km/h)
Figure 4-10 continued.
4-18
50
a)
b)
Figure 4-11. Pictures of unpaved roads with high vehicle traffic at a) Site 16 and b) Site 48.
4.5
Effectiveness of Dust Control Measures
Fugitive dust mitigation measures include some combination of reducing suspendable
dust reservoirs, preventing its deposit, stabilizing it, enclosing it, and reducing the activities that
suspend it. These methods are applied with various degrees of effectiveness and diligence.
Control effectiveness estimates vary considerably and there is no single value appropriate for all
situations.
The application of chemical suppressants on unpaved surfaces can reduce fugitive dust
emissions. Watson et al. (1996) enumerate commercially available dust suppressants. These
products are classified into six categories according to their chemical composition and the
suppressant mechanism they employ:
 Surfactants: Chemicals that reduce water surface tension and allow available moisture
to more effectively wet the particles and aggregates in the surface layer.
 Salts: Hygroscopic compounds such as magnesium chloride or calcium chloride that
adsorb water as ambient RH exceeds 50%. Since salts are water soluble, precipitation
tends to wash them away.
 Polymers: Long-chain molecular compounds that act as adhesives to bond soil particles
together. Polymers may be able to stick to more particles than ordinary resins.
 Resin or petroleum emulsions: Non-water-soluble organic compounds that are
emulsified or suspended in water. When these emulsions are sprayed onto soil, they stick
the soil particles together, and eventually harden to form a solid mass. Several emulsion
products are based on tree resin, petroleum, or asphalt compounds.
 Bitumen: Materials such as asphalt or road oil that act as adhesives to bond soil particles
together.
 Lignin sulfonate: A wood by-product from paper manufacture that forms a sticky but
water-soluble layer on unpaved surfaces.
Most suppressants require repeated application at frequencies on the order of weeks or
months. The effectiveness of chemical suppressants depends on road surface conditions, soil
composition, application intensity, traffic volume, vehicle weight, and environmental factors
4-19
such as precipitation and temperature. Prior to suppressant application, the road surface often
needs to be graded or wetted. Most products can be dispensed as liquids by a truck equipped
with a tank and spray bar. The spraying process injects the suppressant into the road material.
Solid materials can be spread and mixed into the soil or road bed with a grader.
Surface watering is the most widely used method in AOSR to control dust emissions
from disturbed land, such as unpaved roads and mining sites, to reduce particle resuspension by
vehicles or mechanical disturbances. Flocchini et al. (1994) found that the addition of sufficient
water to increase the surface moisture content from 0.56% to 2% can achieve greater than 86%
reduction in PM10 emissions. Kinsey and Cowherd (1992) found immediate dust reductions at
construction sites as a result of surface watering; however, the effectiveness of this measure did
not increase as more water was applied to the site.
Figure 4-12 plots the PM10 concentration and emission potential from two unpaved roads
before and after watering. For Sites 9 and 10 (Figure 4-12a), the watering reduced PM10 emission
potential by 57%, 98%, and 99% at 2000, 3000, and 4000 RPM, respectively. The dry surface
turned from supply limited to unlimited at 4000 RPM, while after watering it was still supply
limited at 4000 RPM, but turned to unlimited at 5000 RPM, partially because the moisture was
removed by the PI-SWERL during the test. Similarly, the watering reduced PM10 emission
potential of Sites 32 and 33 (Figure 4-12b) by 48% at 2000 RPM, and 86-94% at 3000-5000
RPM. These tests confirm that watering is an effective method of reducing dust emissions. The
drawbacks of surface watering for dust control are: 1) water needs to be sprayed frequently,
approximately every 1-2 hours, thus consuming significant water and truck resources; 2) the
amount of water sprayed need to be well controlled; too little water would reduce effectiveness,
while too much water would cause more track-out and make the road slippery. Several AOSR
facilities have started experimenting with other dust suppressant chemicals for road treatment,
with the main goal of reducing water usage. Their effectiveness needs to be systematically
evaluated.
Another method to reduce fugitive dust emission is to reduce surface disturbances.
Figure 4-13 shows a haul road with surfaces that were stabilized and that were disturbed by haul
trucks. The PM10 concentration and emission potential from these surfaces are plotted in Figure
4-14. The cumulative PM10 emission potentials on the tire track were 160, 99, 44, and 14 times of
those on the stabilized surface at 2000, 3000, 4000, and 5000 RPM, respectively. Figure 4-15
shows a picture of coke pile before and after disturbance. The disturbance was intentionally
created by walking on the surface to study the effects of disturbances on dust emissions. Figure
4-16 plots the PM10 concentration and emission potential from the undisturbed and disturbed
coke pile. Disturbance caused PM10 emission potentials to increase by factors of 12, 35, 22, and
9 at 2000, 3000, 4000, and 5000 RPM, respectively. Therefore, reducing surface disturbances
and allowing them to stabilize would significantly reduce windblown dust emissions from some
surfaces.
4-20
a)
6000
1000
RPM
5000
100
C(PM10)-Dry Road
4000
10
3000
1
2000
0.1
P(PM10)
P(PM10) Dry Road
Wet Road
1000
RPM
Dry Road
0
0
200
400
0.01
RPM
Wet Road
600
0.001
PM10 Concentration (mg/m3) or
Cumulative PM10 Emission Potential (g/m2)
C(PM10)-Wet Road
800
Test Duration (s)
b)
100
RPM
C(PM10)-Wet Road
5000
10
C(PM10)-Dry Road
RPM
4000
1
3000
0.1
2000
P(PM10)
Dry Road
0.01
1000
P(PM10)
Wet Road
0
0
200
400
600
0.001
PM10 Concentration (mg/m3) or
Cumulative PM10 Emission Potential (g/m2)
6000
800
Test Duration (s)
Figure 4-12. PM10 concentration (C) and emission potential (P) before and after watering at two unpaved roads: a)
Sites 9 and 10, and b) Sites 32 and 33.
4-21
Figure 4-13. Picture of a haul road with stabilized and disturbed (tire track) surfaces (Sites 26 and 27).
C(PM10)
Disturbed
100
5000
C(PM10)
Undisturbed
4000
RPM
RPM
10
1
3000
P(PM10)
Disturbed
0.1
2000
0.01
1000
P(PM10)
Stabilized
0.001
0
0
200
400
600
PM10 Concentration (mg/m3) or
Cumulative PM10 Emission Potential (g/m2)
1000
6000
800
Test Duration (s)
Figure 4-14. PM10 concentration (C) and emission potential (P) of stabilized and disturbed (tire track) surfaces (Sites
26 and 27) on a haul road.
4-22
Without disturbance
After disturbance
Figure 4-15. Picture of a coke pile (Sites 53 and 54) with and without disturbances.
RPM
C(PM10)
Disturbed
100
5000
10
RPM
4000
C(PM10)
Undisturbed
1
3000
2000
0.1
P(PM10)
Disturbed
0.01
1000
P(PM10)
Undisturbed
0
0
200
400
600
0.001
PM10 Concentration (mg/m3) or
Cumulative PM10 Emission Potential (g/m2)
1000
6000
800
Test Duration (s)
Figure 4-16. PM10 concentration (C) and emission potential (P) of a coke pile (Sites 53 and 54) before and after
disturbance.
4-23
5
Source Profiles
Source profiles are assembled as Tables and Figures in the following Appendices:
 Appendix D: Source profile tables of elements from Na to U by XRF, watersoluble ions, and carbon fractions;
 Appendix E: Source profile tables of elements measured by ICP-MS including Cs,
Be, and 14 rare-earth elements;
 Appendix F: Source profile tables for non-polar organics;
 Appendix G: Source profile tables of carbohydrates, organic acids, and total
WSOC;
 Appendix H: Tables of comparison of statistical measures for PM2.5 fugitive dust
samples from facility and non-facility sites;
 Appendix I: Tables of composite source profiles;
Key observations are discussed in the following Sections.
5.1
Water-soluble Ions
Figure 5-1 and Figure 5-2 exhibit the sum of water-soluble anions and cations in the PM2.5
and PM10 size fractions for the 64 geological soil samples, respectively. Abundances of
individual anions and cations are shown in Figure 5-3 and Figure 5-4, respectively. The main
observations are:
 SO4= is on average 45% and 68% more abundant in PM2.5 and PM10, respectively, in the
facility sites than the non-facility sites. For the facility sites, abundance of SO4= ranged
from 0.04–3.3% with an average of 1.4±0.9% and 0.03-4.8% with an average of
1.5±1.1% of PM2.5 and PM10 mass, respectively. For the non-facility sites, the abundance
of SO4= ranged from 0.1-5.1% with an average of 0.98±1.07% and 0.08–4.96% with an
average of 0.89±1.02% of PM2.5 and PM10 mass, respectively. The difference in SO4=
abundance observed between the facility and non-facility soils is lower than that observed
in 2008 for oil sands sites and lichen sites (average ratio of 7). The sample from Site 44
(sandy surface near Hwy 63 between facility C ponds) has the highest SO4= abundance
(5.1% of PM2.5 and 4.9% of PM10). The higher abundances of SO4= in facility sites
indicates that the surface soils at these sites are probably contaminated by SO4=
deposition from mining and upgrading activities, e.g., from deposition of stack emissions.
 CO3= is abundant in many sites, but is highly variable. CO3= abundances are highest at
Sites 29-38 with highest contribution of 46% of PM2.5 at Site 29 (Quarry conveyor area)
and 54% of PM10 at Site 33 (Quarry, wet unpaved road in processing ground). This is
expected since these sites corresponding to limestone (CaCO3) quarry activities.
 Cl- abundance is variable among the sites with average abundance (of both PM2.5 and
PM10) higher at non-facility sites compared to facility sites. Cl- abundance is <0.5% at
most facility sites, except that it is 2.47% and 1.09% of PM2.5 and PM10 at the Site 50 (Ft.
McMurray Thickwood Blvd land clearance), respectively, and 2.22 and 1.73% of PM2.5
and PM10 at the S46 site (Athabasca Hwy, unpaved shoulder).
 Abundances of other anions are <0.5% of PM mass at most sites except at the Site 51
(Ft. McMurray unpaved parking lot) with 0.52% NO3- in PM2.5.
5-1
Anion (% of PM2.5 mass)
60
50
PM2.5
NO2ClNO3PO43SO4=
CO3=
40
30
20
10
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Anion (% of PM10 mass)
Site ID
60
PM10
NO2ClNO3PO43SO4=
CO3=
40
20
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
Figure 5-1. Abundance of anions in PM2.5 and PM10 of the 64 dust samples.


Ca++ is the most abundant cation at most sites, with large variations ranging from 0.12–
42.2% with an average of 8.4±9.9% of PM2.5 mass and 0.1-36.4% with an average of
7.15±7.85% of PM10 mass as shown in Figure 5-2. Figure 5-5a compares the abundances
between water-soluble Ca++ and total Ca and shows that that the Ca++/Ca ratio is nearly
one, indicating most Ca is in water-soluble form. Figure 5-5b compares Ca++ and CO3=
and shows that the Ca++/CO3= abundance ratio (0.63) is close to that of CaCO3 (0.67),
indicating the mineralogical form of calcite (CaCO3). Good correlations between Ca++
and CO3= (r2=0.6) also confirm the presence of calcite. As seen in Figure 5-2, the highest
Ca++ abundances are observed at Sites 29-38 (limestone quarry activities).
NH4+ abundance is <0.4% of PM mass at most sites, and the distribution is relatively
uniform among sites. Facility soils (0.12%) and non-facility sites (0.13%) have the same
abundance of NH4+ in PM2.5 samples. Sites 22 (Facility B tailings beach 1 tractor track)
and 23 (Facility B tailings beach 2 truck track) have the highest abundances (0.21% and
0.37%, respectively) in PM10 and PM2.5, while site 30 (Quarry processing ground, tire
tracks) has NH4+ below MDL.
5-2
Na+ is ~34% more abundant at facility sites (average: 0.34%; range: 0.01‒2.3%) than
non-facility sites (average: 0.26%; range: 0.03‒0.74%) in PM10 samples while it is
comparable at both site types in PM2.5 samples. Sites 32 (Quarry, dry unpaved road in
processing ground) and 33 (Quarry, wet unpaved road in processing ground) have high
Na+ abundance in both PM2.5 and PM10 (1.01 ‒2.27%).
Mg++ is variable both in facility and non-facility sites. There is no clear evidence that the
facility dusts have systematically more Mg++ abundance than the non-facility dusts.
Figure 5-6 shows that except for several outliers (Sites 29-41 related to limestone quarry
activities), Ca++ and Mg++ have good correlation, with Ca++ abundance ~7.5 times of
Mg++. This indicates the coexistence of Ca and Mg in some minerals, e.g., dolomite
CaMg(CO3)2.
K+ is distributed relatively uniformly among all sites, except that it is more abundant in
PM2.5 and PM10 for dust collected on the truck tracks near quarry waste dump (i.e., Site
35).
Among all sites, Sites 7, 8, 53 and 54 have the lowest abundances of anions and cations.
These sites correspond to overburden and unpaved road soils from Facility C tailings dike
and from undisturbed and disturbed coke pile in Facility E.





50
Cation (% of PM2.5 mass)
PM2.5
NH4+
40
Na+
Mg++
30
K+
Ca++
20
10
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
50
Cation (% of PM10 mass)
PM10
+
NH4
40
Na+
Mg++
30
K
+
Ca
++
20
10
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
Figure 5-2. Abundance of cations in PM2.5 and PM10 of the 64 dust samples.
5-3
SO4
=
6
5
PM2.5
4
PM10
3
2
1
CO3
=
0
100
80
PM2.5
60
PM10
40
0
3.0
PM2.5
2.0
PM10
Cl
-
2.5
1.5
1.0
0.5
0.0
0.4
PM2.5
0.3
NO2
-
PM10
0.2
0.1
0.0
1.0
NO3
Anion abundance (% of PM mass)
20
0.8
PM2.5
0.6
PM10
0.4
0.2
0.0
PO4
3-
0.6
PM2.5
PM10
0.4
0.2
Site ID
Figure 5-3. Abundance of individual anions in PM2.5 and PM10 of the 64 dust samples.
5-4
64
62
60
58
56
54
52
50
48
46
44
42
40
38
36
34
32
30
28
26
24
22
20
18
16
14
12
8
10
6
4
2
0
0.0
Ca
++
50
40
PM2.5
30
PM10
20
10
0
1.0
PM2.5
NH4
PM10
0.6
0.4
0.2
0.0
3.0
PM2.5
2.0
PM10
Na
+
2.5
1.5
1.0
0.5
0.0
2.5
PM2.5
2.0
PM10
++
1.5
Mg
Cation abundance (% of PM mass)
+
0.8
1.0
0.5
0.0
0.4
PM2.5
K+
0.3
PM10
0.2
0.1
Site ID
Figure 5-4. Abundance of individual cations in PM2.5 and PM10 of the 64 dust samples.
5-5
64
62
60
58
56
54
52
50
48
46
44
42
40
38
36
34
32
30
28
26
24
22
20
18
16
14
12
8
10
6
4
2
0
0.0
a)
Ca++ = 1.07 x Ca
r2 = 0.69
50
40
30
20
PM2.5
10
PM10
Ca
2+
abundance (% of PM mass)
60
0
0
10
20
30
40
50
Ca abundance (% of PM mass)
b)
Ca++ = 0.68 x CO3=
PM2.5
50
r2 = 0.6
PM10
40
30
20
10
Ca
++
abundance (% of PM mass)
60
0
0
10
20
30
40
50
60
=
CO3 abundance (% of PM mass)
Figure 5-5. Comparison of abundances between a) Ca++ and Ca, and b) Ca++ and CO3= in PM2.5 and PM10 of the 64
dust samples.
5-6
S29-S41
PM2.5
40
30
Ca++ = 7.5 x Mg++
r2 = 0.6
20
10
Ca
2+
abundance (% of PM mass)
50
0
0.0
0.5
1.0
1.5
2.0
2.5
Mg2+ abundance (% of PM mass)
PM10
40
S29-S41
30
20
++
Ca
++
= 7.4 x Mg
10
Ca
++
abundance (% of PM mass)
50
0
0.0
0.2
0.4
++
Mg
0.6
0.8
1.0
1.2
abundance (% of PM mass)
Figure 5-6. Correlations between Ca++ and Mg++ in PM2.5 and PM10 of the 64 dust samples.
5.2
Major and Rare-earth Elements
Geological-related elements (i.e., Al, Si, K, Ca, and Fe) are abundant, present at >1% of
PM from all sites. Figure 5-7 shows the sum of these five elements accounts for 5‒43% of PM
mass, and the summation of their normal oxides accounts for 13‒87% of PM mass as shown in
Figure 3-2. Individual abundances of these geological elements are shown in Figure 5-8. The
main features are:
 Si is the most abundant element, accounting for 2.2-28.8% of PM mass. The Si
abundances in the facility (averaging 13.9% and 13.4% in PM2.5 and PM10,
respectively) and non-facility dust (averaging 12.9% and 11.6% in PM2.5 and PM10,
5-7




respectively) is similar. This observation agrees with sampling conducted in 2008
which showed Si abundances of 11-35% of PM mass. In addition, these
measurements generally agree with an earlier study that showed lower Si in tailings
(25%) than in oil sands feed (44%) from samples acquired in 1996, as shown in Table
5-1 (Ciu et al., 2003). Among all the sites, Sites 7 (Facility C tailing dike overburden)
and 8 (Facility C unpaved road on tailing dike) have the highest Si abundances.
Al is ~26-33% more abundant in facility sites (averaging 4% and 3.7% of PM2.5 and
PM10, respectively) than non-facility sites (averaging 3.17% and 2.7% of PM2.5 and
PM10, respectively). Several sites close to the tailings pond (i.e., Sites 4, 5, 6) have the
highest Al abundance, although the tailings sands from Sites11 and 12 only show
average Al abundance.
K content distributed uniformly among sites, varying 0.5‒2% in facility sites and 0.6‒
1.3% in non-facility sites. The non-facility site (61) that was impacted by forest fire
do not show elevated K abundance. Total K is 13‒16 times higher than water-soluble
K+, in agreement with earlier observations for dust samples (Chow et al., 1994b).
Ca content has larger variations among sites (0.4‒26% of PM mass), similar to the
Ca++ variation in Figure 5-4. Among the facility sites, the several sites in and around
limestone quarrying facility (e.g., sites S29-38) have the highest Ca abundances than
the sites related to tailings pond (e.g., sites S4-S8 and S20–S24).
Fe content varies 1‒16% of PM mass, with several unpaved road sites (e.g., sites S15,
S48, S59, S60) showing higher Fe abundances than paved road sites (e.g., sites S45,
S46, S64), probably due to deposition from vehicle rust.
Other detected elements were low, varying from 0.0001% to <1% on average. Elements
with average abundance between 0.02% and 1% include S (0.02‒12.6%), Cl (0‒1.32%), Ti
(0.09‒0.66%), Mn (0.01‒0.55%), Sr (0.004‒0.05%), and Ba (0-0.3%). Abundances of these
seven elements are shown in Figure 5-9. It is found that:
 Consistent with the SO4= distribution (Figure 5-3), S is ~38% more abundant at
facility sites (average 0.83% of PM2.5) than in non-facility sites (average 0.6% of
PM2.5), while it is twice in PM10 samples from facility sites (1.04%) compared to nonfacility sites (0.465%). Unpaved road with sulfur deposit in Facility M has the highest
S of all sites (5.4% and 12.6% of PM2.5 and PM10, respectively). Sites S53 and S54
(Facility C coke pile) and site 44 (sandy surface near Hwy 63 between Facility M
ponds also have relative high S. It is interesting to note that site S44 has the highest
SO4= abundance of all sites.
 Cl is more abundant at unpaved and Hwy 63 shoulder sites (e.g., sites S16, S44, S46
and S63).
 Ti is distributed relatively uniform among all sites. The facility sites have ~20-27%
higher abundances (average 0.36% and 0.37% of PM2.5 and PM10, respectively) than
non-facility sites (average 0.3% and 0.29% of PM2.5 and PM10, respectively). Among
the facility sites, sites closer to tailings pond sites (e.g., sites S4-S6, S19–S24) have
higher abundance than other sites. However, the Ti abundances are much lower than
the values reported by Ciu et al. (2003) as listed in Table 5-1, and the difference
between facility and non-facility sites are also much lower than those in Table 5-1. It
is possible some of the Ti in the tailings were recovered during the waste treatment
process (Ciu et al., 2003).
5-8


Mn is distributed uniformly among all sites except that Sites 47 (sandy road near
WBEA site R2) and 61 (forest fire site near North Hwy 63) have the highest Mn
abundances.
Sr is higher in Hwy 63 unpaved shoulder samples (Sites 44-46) compared to other
sites. Ba has higher uncertainty at all sites except for Site 18 (Hwy 63 paved
shoulder).
Other elements with average abundances <0.02% but greater than XRF or ICP-MS
measurement uncertainty at one or more sites include: V, Ni, Cu, Zn, Rb, Y, Cs, and Pb. Figure
5-10 shows the abundances of these elements. It is observed that:
 V is most abundant in coke pile samples (Sites 53 and 54) with average abundance of
0.08% of PM2.5 and PM10. No clear enrichment of V in the tailings sands is observed.
 Ni is highest at Site 49 (Ft. McMurray Thickwood Blvd new construction) and Sites
53 and 54 (coke pile) while Cu is highest at Site 18 (Hwy 63 paved shoulder near
Facility C).
 Zn is highest at Site 43 (Hwy 63 rest area south of Ft. McMurray) with no clear
differences in facility and non-facility soils.
 Most of these elements are distributed uniformly across all sites, with no obvious
differences between facility and non-facility sites.
Abundances of 14 rare earth elements are plotted in Figure 5-11. These elements are
uniformly distributed among all sites, except for sites in and around limestone quarrying
activities (Sites 29-38).
5-9
Elements (% of PM2.5 mass)
50
PM2.5 elements > 1%
40
Al
Si
K
Ca
Fe
30
20
10
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
Elements (% of PM10 mass)
50
PM10 elements > 1%
40
Al
Si
K
Ca
Fe
30
20
10
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
Figure 5-7. Elements with average abundance >1% in PM2.5 and PM10 of the 64 dust samples.
5-10
PM2.5
PM10
8
6
4
2
0
40
PM2.5
Si
30
PM10
20
10
0
2.5
PM2.5
2.0
PM10
K
1.5
1.0
0.5
0.0
30
PM2.5
PM10
Ca
20
10
0
20
15
Fe
Major elements (Al, Si, K, Ca, Fe) abundance (% of PM mass)
Al
12
10
PM2.5
PM10
10
5
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
Figure 5-8. Individual major elements (Al, Si, K, Ca, and Fe) with average abundance >1% in PM2.5 and PM10 of the
64 dust samples.
5-11
Table 5-1. Elemental weight percent (%) of oil sands feed and scroll centrifuge tailing in one oil sands facility (Ciu
et al., 2003).
Element
Al
Ca
Fe
Mg Si
Ti
Zr
Oil sands feed 0.83 0.05 0.27 0.02 43.52 0.18 0.04
Tailings
5.83 0.75 6.07 0.74 24.99 6.54 2.7
5-12
16
PM2.5
S
12
PM10
8
4
Cl
1.2
PM2.5
PM10
0.8
0.4
0.0
0.8
PM2.5
Ti
0.6
PM10
0.4
0.2
0.0
0.8
Mn
0.6
PM2.5
PM10
0.4
0.2
0.0
0.08
0.06
Sr
Elements (S, Cl, Ti, Mn, Sr, Ba) abundance (% of PM mass)
0
1.6
PM2.5
PM10
0.04
0.00
0.6
0.5
0.4
0.3
0.2
0.1
0.0
PM2.5
PM10
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
Ba
0.02
Site ID
Figure 5-9. Elements with average abundance 0.02‒1% (S, Cl, Ti, Cr, Mn, Ni, and Zr) in PM2.5 and PM10 of the 64
dust samples.
5-13
0.10
PM2.5
0.06
PM10
V
0.08
0.04
0.02
Ni
0.04
PM2.5
0.03
PM10
0.02
0.01
0.00
Cu
0.05
0.04
PM2.5
0.03
PM10
0.02
0.01
Zn
0.00
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
PM2.5
PM10
0.04
PM2.5
0.03
Rb
Elements (V, Ni, Cu. Zn, Rb, Y) abundance (% of PM mass)
0.00
0.05
PM10
0.02
0.01
PM2.5
PM10
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
Y
0.00
0.030
0.025
0.020
0.015
0.010
0.005
0.000
Site ID
Figure 5-10. Elements with average abundance <0.05% but greater than XRF or ICP-MS minimum detection limit
in at least one site in PM2.5 or PM10.
5-14
Cs
PM2.5
PM10
PM2.5
0.03
PM10
0.02
0.01
0.00
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
Pb
Elements (Cs, Pb) abundance (% of PM mass)
0.25
0.20
0.15
0.10
0.05
0.00
0.04
Site ID
Figure 5-10 continued. Elements with average abundance <0.05% but greater than XRF or ICP-MS minimum
detection limit in at least one site in PM2.5 or PM10.
5-15
4.0e-3
PM2.5
La
3.0e-3
PM10
2.0e-3
0.0
1.4e-2
1.2e-2
1.0e-2
8.0e-3
6.0e-3
4.0e-3
2.0e-3
0.0
1.0e-3
PM2.5
PM10
Pr
8.0e-4
PM2.5
PM10
6.0e-4
4.0e-4
2.0e-4
0.0
4.0e-3
PM2.5
3.0e-3
Nd
PM10
2.0e-3
1.0e-3
0.0
8.0e-4
PM2.5
Sm
6.0e-4
PM10
4.0e-4
2.0e-4
0.0
2.0e-4
PM2.5
1.5e-4
PM10
Eu
Elements (La, Ce, Pr, Nd, Sm, Eu, Gd) abundance (% of PM mass)
Ce
1.0e-3
1.0e-4
5.0e-5
0.0
8.0e-4
PM2.5
Gd
6.0e-4
PM10
4.0e-4
2.0e-4
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0.0
Site ID
Figure 5-11. Abundance of rare earth elements in PM2.5 and PM10.
5-16
1.0e-4
PM2.5
Tb
8.0e-5
PM10
6.0e-5
4.0e-5
2.0e-5
Dy
5.0e-4
PM2.5
4.0e-4
PM10
3.0e-4
2.0e-4
1.0e-4
0.0
1.0e-4
Ho
8.0e-5
PM2.5
PM10
6.0e-5
4.0e-5
Er
2.0e-5
0.0
3.0e-4
2.5e-4
2.0e-4
1.5e-4
1.0e-4
5.0e-5
0.0
5e-5
PM2.5
PM10
PM2.5
Tm
4e-5
PM10
3e-5
2e-5
1e-5
0
3.0e-4
2.5e-4
PM2.5
2.0e-4
Yb
Elements (La, Ce, Pr, Nd, Sm, Eu, Gd) abundance (% of PM mass)
0.0
6.0e-4
PM10
1.5e-4
1.0e-4
5.0e-5
0.0
5e-5
PM2.5
Lu
4e-5
PM10
3e-5
2e-5
1e-5
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
Figure 5-11 continued. Abundance of rare earth elements in PM2.5 and PM10.
5-17
5.3
Lead Isotopes
Four Pb isotopes (204Pb, 206Pb, 207Pb, and 208Pb) were quantified by ICP-MS. Isotope
204Pb is the only primordial stable isotope with a constant abundance on the Earth in time, while
206Pb, 207Pb and 208Pb are radiogenic as products of radioactive decay of 238U, 235U and
232Th, respectively (Komárek et al., 2008). Pb sources can have distinct (sometimes
overlapping) isotopic ratio ranges. The isotopic composition is not significantly affected by
industrial or biological processes and does not fractionate during transport and deposition
processes. Therefore, the source composition is retained, and these isotope ratios can be used to
study sources and pathways of Pb pollution (Bollhöfer and Rosman, 2000; Bollhöfer and
Rosman, 2001; Bollhöfer and Rosman, 2002; Carignan et al., 2002; Carignan and Gariépy, 1995;
Dolgopolova et al., 2006; Erel et al., 2006; Komárek et al., 2008; Miller et al., 2007; Notten et
al., 2008; Patterson, 1965; Saint-Laurent et al., 2010; Simonetti et al., 2003; Sturges and Barrie,
1989). The abundance of 207Pb has changed very little with time compared to 206Pb because
most 235U has already decayed while 238U still has a relatively high abundance on the Earth.
Therefore, the Pb isotopic composition is commonly expressed as ratios of 206Pb/204Pb,
206Pb/207Pb, 208Pb/207Pb (Bollhöfer and Rosman, 2001; Carignan et al., 2002; Carignan and
Gariépy, 1995; Komárek et al., 2008; Miller et al., 2007; Notten et al., 2008; Simonetti et al.,
2003; Sturges and Barrie, 1989).
Figure 5-12 plots the Pb isotope ratios of 204Pb/206Pb vs. 206Pb/207Pb and
208Pb/207Pb vs. 206Pb/207Pb in PM2.5 and PM10, respectively. All sites form a single cluster
with no obvious difference between the facility and non-facility sites, except for some outliers.
Average ratios of 204Pb/206Pb, 206Pb/207Pb, and 208Pb/207Pb for PM2.5 samples ranged from
0.046‒0.057, 1.120‒1.603, and 2.392‒2.861, and for PM10 these ratios are 0.048-0.054, 1.1651.259, and 2.451-2.548, respectively. There are several outlier sites: Site 29 (PM2.5; Quarry,
conveyor area), Site 33 (PM2.5; Quarry, wet unpaved road in processing area), Site 38 (PM2.5;
Quarry, parking lot for haul trucks) and Site 64 (PM10; Athabasca Hwy shoulder near Firebag).
The Quarry sites have higher 206Pb/207Pb and 208Pb/207Pb ratio in PM2.5 samples with the
highest ratios (1.603 and 2.861, respectively) at Site 38. This could be because of the engine
exhaust from the haul trucks waiting in the parking lot of the Quarry. The ratio for 206Pb/207Pb
is higher than that reported in other similar studies but is within the measurements from dust
sample in northern Australia close to a uranium mine (Bollhöfer, 2006). The 208Pb/207Pb ratio
is higher than any published result. Site 64 had the highest ratio of 206Pb/207Pb and
208Pb/207Pb (1.211 and 2.491, respectively) in PM10 samples. It is interesting to note that this
site is farther away from other mining facilities but the higher Pb ratios are not explainable
readily.
Figure 5-13 depicts lead isotope ratios (208Pb/207Pb vs. 206Pb/207Pb) from various
sites in north America, including the geological materials collected in this study (from all sites;
same as those in Figure 5-12b), study from 2008 (assigned as two groups with Soil Group 1 from
lichen sites and Soil Group 2 from oil sands sites), engine exhaust from mining trucks and stack
emissions from AOSR in companion source characterization studies (Watson et al., 2013a;
Watson et al., 2013b; Watson et al., 2013c; Watson et al., 2013d), lichen studies in western and
northeastern Canada (Carignan et al., 2002; Carignan and Gariépy, 1995; Simonetti et al., 2003)
and in the AOSR (Graney et al., 2011), and Pb-bearing ores from northwest Alberta (AB; Paulen
et al., 2011), New Brunswick, British Columbia, Ontario, and Quebec (Brown, 1962; Cumming
and Richards, 1975; Sturges and Barrie, 1987), and ambient aerosols from 7 Canadian cities
(Burnaby, Chicoutimi, Victoria, Calgary, Winnipeg, Toronto, and Newfoundland) collected from
5-18
1994 to 1999 (Bollhöfer and Rosman, 2001). The Pb isotope ratios from this study are largely
overlapped by data from 2008 for Soil Group 2 (all oil sands sites) and stack emissions measured
in 2008. On the other hand, the stack samples collected in 2011 had higher 208Pb/207Pb ratios
than other samples, probably due to different fuel and processes from those in 2008. Pb isotope
ratios in truck exhaust also overall with the ratios observed in this study as are the lichen samples
from western and northeastern Canada and ambient aerosols. Lichen samples collected from sites
>50 km away from the mining sites (collocated with soil group 1 samples during 2008) showed
208Pb/207Pb and 206Pb/207Pb ratios of ~2.434 and ~1.156, respectively, which is in the same
range as other lichen samples, and in the intermediate range between the Soil group 1 and 2
samples (Graney et al., 2011). These ratios increased as sampling sites moved closer to the
mining operation, which is consistent with the higher ratios near mining observed in Figure 5-12
and Figure 5-13. Pb isotope ratios in Zn–Pb minerals (galena and sphalerite) collected from
northwestern Alberta (Fort Nelson Lowland region) have isotope ratios close to truck exhaust,
and are similar to those found in lead-bearing ores from New Brunswick. On the other hand, Pbbearing ores from British Colombia, Ontario, and Quebec typically have much lower
208Pb/207Pb and 206Pb/207Pb ratios (2.27‒2.33 and 0.92‒1.07, respectively).
Since stacks are among the largest emitters in AOSR, the overlap of Pb isotopes between
the facility sites and the stack emissions sampled in 2008 indicates that deposition on surface
soils from stack emissions probably is the major source of the observed Pb isotope ratios.
Lichens obtain their nutrients from ambient air (Carignan and Gariépy, 1995), which come from
a mixture of emission sources and local soil resuspension, and therefore showing isotopes in the
middle of two soil groups and close to truck exhaust and ambient aerosols. The truck exhaust
isotope ratios are probably dominated by the trace amount of Pb in the fuel and lubrication oil.
5-19
a)
b)
3.0
0.060
PM2.5
PM2.5
2.8
208Pb/207Pb
204Pb/206Pb
0.055
S29
0.050
S33
S38
S38
0.045
1.2
1.4
1.6
S29
2.6
2.4
0.040
1.0
2.2
1.8
1.0
1.2
206Pb/207Pb
1.4
1.6
1.8
206Pb/207Pb
c)
d)
0.058
2.65
PM10
0.056
2.60
0.052
0.050
0.048
1.16
1.20
1.24
2.55
2.50
2.45
2.40
S64
0.046
1.12
PM10
S64
0.054
208Pb/207Pb
204Pb/206Pb
S38
S33
1.28
2.35
1.12
1.32
206Pb/207Pb
1.16
1.20
1.24
1.28
1.32
206Pb/207Pb
Figure 5-12. Lead isotope ratios in geological samples: a) 204Pb/206Pb vs. 206Pb/207Pb in PM2.5; b) 208Pb/207Pb
vs. 206Pb/207Pb in PM2.5; c) 204Pb/206Pb vs. 206Pb/207Pb in PM10; and d) 208Pb/207Pb vs. 206Pb/207Pb in PM10.
Numbers in these figures denote the sampling sites as detailed in Table 2-2 and Figure 2-6.
5-20
2.9
208Pb/207Pb
2.8
This study
Soil group 1 (2008 study)
Soil group 2 (2008 study)
Stach emissions 2008
Stack emissions 2011
Truck exhaust 2009
Truck exhaust 2010
Lichen W/NE Canada
2.7
2.6
2.5
Lichen AOSR
Pb ore AB/NB
Pb ore BC/ON/QC
Ambient aerosol
2.4
2.3
2.2
0.8
1.0
1.2
1.4
1.6
1.8
206Pb/207Pb
Figure 5-13. Lead isotope ratios 208Pb/207Pb vs. 206Pb/207Pb for various samples: 1) This study from all sites
(open circles); 2) Soil Group 1 covering most lichen sites from 2008 study (red triangle); 3) Soil Group 2 covering
most oil sands sites from 2008 study (blue inverse triangle); 4) stack emissions collected from AOSR in summer
2008 (red star) (Watson et al., 2010a); 5) stack emissions collected from AOSR in winter 2011 (pink star) (Watson
et al., 2011a); 6) engine exhaust from mining trucks collected from AOSR in 2009 (cyan squares) (Watson et al.,
2010b); 7) engine exhaust from mining trucks collected from AOSR in 2010 (green circle) (Watson et al., 2011b); 8)
lichen samples collected from western Canada from Yukon to the Canada–USA border (Simonetti et al., 2003) and
from northeastern America from Hudson Bay to Maryland (purple plus) (Carignan et al., 2002; Carignan and
Gariépy, 1995); 9) lichen samples from AOSR (circular hourglass) (Graney et al., 2011); 10) Pb-bearing minerals
from northwest Alberta (Paulen et al., 2011) and New Brunswick (Cumming and Richards, 1975; Sturges and Barrie,
1987) (dark yellow cross); 11) Pb-bearing ores from British Colombia, Ontario, and Quebec (Brown, 1962;
Cumming and Richards, 1975; Sturges and Barrie, 1987) (blue square); and 12) Ambient aerosols from 7 Canadian
cities (Burnaby, Chicoutimi, Victoria, Calgary, Winnipeg, Toronto, and Newfoundland) collected from 1994 to 1999
(dark green triangle)(Bollhöfer and Rosman, 2001).
5.4
Carbon Fractions
Figure 5-14 shows abundances of OC, EC, and CO3=-C in the 64 dust samples. The
average OC abundances in facility sites is 17% higher than non-facility sites in PM2.5 samples
while it is 19% lower in PM10 samples. Sites 53 and 54 (Coke pile undisturbed and disturbed,
respectively) has the highest EC abundance (34 and 101% and 35 and 39% in PM2.5 and PM10,
respectively) among all sites, clearly indicating the elemental carbon content of the coke fuel.
Even with the high EC content of Sites 53 and 54, average EC abundances 44 and 33% higher in
PM2.5 and PM10, respectively of non-facility sites than facility sites. EC is also higher at Sites 56
(Facility E tailing pond dike), 57 (Facility E overburden pit), and 61 (Forest fire site near North
Hwy 63) in both PM2.5 and PM10. Among the low EC containing sites, Site 29 (Quarry, conveyor
area) has the highest OC abundance in PM2.5 and Site 15 (WBEA AMS 16 unpaved road) has the
highest OC abundance in PM10, while Site 8 (Facility C dirt road on tailings dike) and 30
(Quarry, processing ground tire tracks) have low OC abundance. Carbonate carbon (CO3=-C) is
highly variable (0‒10% of PM) among sites with the highest abundance at the limestone quarry
5-21
Carbon (% of PM2.5 mass)
operation (Sites 29-38). Figure 5-5b shows that Ca++ has similar variation to CO3=, with a
regression slope of 0.68 and squared correlation coefficient (r2) of 0.6.
Table 5-2 compares the OC, EC, CO3=-C, and TC between the geological samples in
AOSR collected in study with other samples collected at oil sand sites and lichen sites in 2008
and reported in the literature (Cao et al., 2008; Chow et al., 2003; Ho et al., 2003; Watson and
Chow, 2001). It is found that AOSR oil sands sites have OC abundance closer to paved road
dust, while EC is higher than other studies due to the high abundance of EC in the coke pile
samples and CO3=-C is higher due to samples near the limestone quarry. OC is comparable to the
AOSR highway road dust sampled in 2008 and has OC close to other paved road dust.
Figure 5-15 plots the abundances of carbon fractions from thermal/optical analysis
(Chow et al., 1993; 2001; 2004; 2005; 2007a; 2011). Note that high-temperature OC (i.e., OC3
and OC4 at 480 and 580 °C in a pure helium atmosphere, respectively), OP, and low-temperature
EC (i.e., EC1 at 280 °C in an oxidized atmosphere) are the most abundant carbon fractions. OP
abundance comparable to EC1 at most of the sites except Sites 29, 53, 54, 56, 57, and 61
indicating significant charring occurs for the OC components in these geological materials along
with low temperature elemental carbon fraction.
120
100
80
PM2.5 carbon fractions
OC
EC
CO3=-C
60
40
20
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
Carbon (% of PM10 mass)
100
PM10 carbon fractions
80
60
OC
EC
CO3=-C
40
20
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
=
Figure 5-14. Abundances of OC, EC, and CO3 -C in PM2.5 and PM10 of the 64 dust samples.
5-22
Table 5-2. Comparison of OC, EC, and CO3=-C in PM10 between this and other studies.
OC
Ave
Range
a
Facility and non-facility sites
8.58
0-53.6
AOSR-Oil sands sites
9.9 3.5‒16.5
AOSR-Lichen sites
5.2 1.0‒13.8
AOSR-Highway
8.5±0.9
Chinese Loess
3.1±1.5
SJV paved road, California, USA
6.9±3.7
SJVb unpaved road, California,
3.2±1.4
USA
SJVb agriculture soil, California,
3.0±1.7
USA
SJVb animal husbandry,
18.4±7.3
California, USA
SJVb lake drainage, California,
2.6±1.0
USA
SJVb construction, California,
2.9±1.5
USA
Hong Kong country park soil
4.2±0.4
Hong Kong urban soil
6.5±2.8
HK paved road
13.9±2.6
Mexicali road dust, Mexico
8.2±1.9
Imperial County dirt, California,
3.6±1.7
USA
Imperial County road dust,
11.4±4.2
California, USA
See Table 2-2 for site descriptions.
SJV: San Joaquin Valley, central California
Sample
a
b
EC
Ave
Range
2.13
0-38.7
1.1 0.1‒10.1
0.2
0‒0.8
0.12±0.08
0.03±0.03
1.0±1.0
CO3=-C
Ave Range
2.06 0-10.9
0.8 0‒3.0
0.6 0‒2.7
0.57±0.36
3.4±1.6
1.3±0.6
0.3±0.3
1.2±0.3
0.2±0.5
0.9±0.7
0.6±0.7
0.3±0.5
0.1±0.4
0.9±0.3
0.4±0.4
0.5±0.6
0.04±0.05
0.4±0.8
1.3±0.8
0.09±0.13
NA
NA
NA
NA
0.01±0.07
NA
0.38±0.21
NA
5-23
Reference
This study
2008 study
(Cao et al., 2008)
(Chow et al., 2003)
(Ho et al., 2003)
(Watson and Chow, 2001)
Carbon (% of PM2.5 mass)
120
100
80
60
40
OC1
OC2
OC3
OC4
OP
EC1
EC2
EC3
PM2.5 carbon fractions
20
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
Carbon (% of PM10 mass)
100
80
60
40
OC1
OC2
OC3
OC4
OP
EC1
EC2
EC3
PM10 carbon fractions
20
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
Figure 5-15. Abundances of carbon fractions in PM2.5 and PM10 of the 64 dust samples. OC1 to OC4 are organic
carbon fractions evolved in a 100% helium (He) atmosphere at 140, 280, 480, and 580 °C, respectively. OP is
pyrolyzed carbon. EC1 to EC3 are elemental carbon fractions evolved in a 98% He/2% O2 atmosphere at 580, 740,
and 840 °C, respectively. Thermal analysis followed the IMPROVE_A thermal/optical reflectance analysis (TOR)
protocol (Chow et al., 2007a).
5-24
5.5
Organic Compound Profiles
Figure 5-16 plots preliminary non-polar organic compounds in PM2.5 and PM10 grouped
into different classes. The sum of non-polar organic compounds contributed <0.012% of total
mass and <0.17% of OC in PM2.5 and PM10. Detailed non-polar organic compounds profiles are
listed in Appendix F. Abundances of iso/anteiso-alkanes were below detection limits, therefore
they are excluded from the analysis presented below.
The most striking finding in Figure 5-16 is that Site 49-64 have higher abundances of
PAHs and lower and higher molecular weight (MW) n-alkanes (nC15-nC24 and nC25-nC40,
respectively), especially in PM2.5, while hopanes, steranes and sum of other non-polar organics
(including methyl-alkane, branched-alkane, cycloalkane, and 1-octadecene) are higher in Sites 148. Sum of other non-polar organic compounds are more abundant in PM2.5 than in PM10 at most
sites.
Sum of PAH abundances have highest abundances at Site 54 (0.037% and 0.02% of
PM2.5 and PM10, respectively), Site 53 (0.028% and 0.012%), and Site 49 (0.02% and 0.002%).
However, abundances in individual species are different at different sites. Facility C light vehicle
unpaved road-wet (Site 10) has the highest concentration of retene in PM10 while Forest fire site
near north Hwy 63 (Site 61) has the highest concentration in PM2.5. Sites 53 and 54 (Facility E
coke piles) have the highest concentration of most of the PAHs including chrysene,
phenanthrene, pyrene, etc. in PM2.5 and PM10.
n-Alkanes are abundant in samples from Sites 49-64, with average of the sum of all nalkanes being 0.001% in PM2.5 and PM10, respectively, while the highest abundances in PM2.5
are measured at Site 60 (Facility E unpaved road near sulfur pile) and in PM10 at Site 56 (Facility
E tailings pond dike). Figure 5-16 shows that while the lower MW n-alkanes (nC15-nC24) are
higher among samples collected from sites related to construction and land clearance in Ft.
McMurray (Sites 49-52), higher MW n-alkanes (nC25-nC40) are distributed among different
types of sites from coke pile (Site 53) to bare land (Site 62).
Hopanes and steranes are widely used as sedimentary fingerprints for bacterial and
eukaryotic source inputs (Siljeström et al., 2010) and as markers for engine lubricant oil (Fraser
et al., 1998). Hopanes originate almost exclusively from hopane polyols present in the cell
membranes of many bacteria, whereas steranes mainly originate from sterols that modify the cell
membranes of Eukaryota (Peters et al., 2005). These compounds are abundant in the Alberta oil
sands (Brooks et al., 1988; Ram et al., 1990; Yang et al., 2011). The sums of hopanes in facility
sites are 76% and 92% more abundant than those in non-facility sites for PM2.5 and PM10,
respectively, while the sums of steranes in facility sites are 14% and 18% less abundant. These
differences are not significant enough to serve as indicators for oil sands operation influences as
was evident in 2008 where oil sands sites had 26 and 15 times more abundant hopanes and
Steranes in PM2.5 compared to lichen sites. Highest abundance for sum of Hopanes were
measured at Facility B tailings dike (Sites 19-21) while highest abundance for sum of steranes
was measured at Site 16 (Ft. McMurray unpaved road outside Wilson). Dust from several sites
along Hwy 63 (Sites 42-46) have somewhat elevated hopanes and steranes abundances among
the non-facility sites.
Other non-polar compounds (methyl-alkane, branched-alkane, cycloalkane, and 1octadecene) have an average abundance of 0.002% and 0.0003% in PM2.5 and PM10,
respectively, with highest abundances at several sites in the quarry area (Site 29, Sites 37-39).
Also interesting is the higher abundance in these non-polar organics in PM2.5 compared to PM10.
Source profiles for carbohydrates, organic acids, and total WSOC are listed in Appendix
G. Almost all carbohydrates are near or below detection limit, except that glycerol was 0.13% of
PM2.5 at Site 39 (Ft. MacKay Industrial Park track-out Hwy 63 paved road) and 0.1% of PM10 at
Site 57 (Facility E overburden pit). Figure 5-17 show abundances of lactic, acetic acid, formic
5-25
acid, succinic, glutaric, malonic, maleic, oxalic acids and WSOC normalized to PM2.5 and PM10
total mass, respectively. Lactic, acetic, and formic acids are variable at all sites with the highest
contribution (2.75% of PM2.5 mass) at Site 3 (Facility C road near sulfur pile) and lowest
abundance at Site 23 (Facility B tailings beach 2 truck track). Acetic and formic acids
abundances at other sites are <1% of PM mass, respectively. Formic acid is significant at most of
the sites with no difference between facility and non-facility sites. In 2008 samples, formic acid
was below detection limit at several oil sands sites, but it was above detection limit at almost all
lichen sites. This is thought to be because formic acid is produced in the forest by photochemical
reactions and accumulated in soil by deposition (Andreae et al., 1988; Comerford, 1990; Jacob
and Wofsy, 1988).
Diacids are lower than <0.5% of PM mass at most sites, except for Site 3 (Facility C road
near sulfur pile) where oxalic and glutaric acids contribute significantly. Along with oxalic and
glutaric acids, maleic acid is also above the detection limit in PM10 samples. Total WSOC
accounts for 0‒2% of PM mass, except for Site 3 where it is as high as 10% of PM2.5 mass.
5-26
0.04
PM2.5
PAHs
0.03
PM10
0.02
0.01
Non-polar Organic Compound Abundance (% of PM mass)
nC15-nC24
Steranes
Hopanes
nC25-nC40
0.00
0.014
0.012
0.010
0.008
0.006
0.004
0.002
0.000
0.006
PM2.5
PM10
0.005
PM2.5
0.004
PM10
0.003
0.002
0.001
0.000
0.030
0.025
0.020
0.015
0.010
0.005
0.000
0.004
PM2.5
PM10
PM2.5
0.003
PM10
0.002
0.001
0.014
0.012
0.010
0.008
0.006
0.004
0.002
0.000
X Data
PM2.5
64
62
60
58
56
54
52
50
48
46
44
42
40
38
36
34
32
30
28
26
24
22
20
18
16
14
12
8
10
6
4
2
PM10
0
Others
0.000
Site ID
Figure 5-16. Abundances of non-polar organic compounds grouped into PAHs, lower molecular weight n-alkanes
(nC15-nC24), higher molecular weight n-alkanes (nC25-nC40), iso/anteiso-alkane, hopanes, steranes, and others
(including methyl-alkane, branched-alkane, cycloalkane, and 1-octadecene) in PM2.5 and PM10 of the 64 geological
samples.
5-27
3.0
2.5
2.0
Lactic acid
Acetic acid
Formic acid
PM2.5 monoacid fractions
Monoacids (% of PM mass)
1.5
1.0
0.5
0.0
2.0
1.6
1.2
Lactic acid
Acetic acid
Formic acid
PM10 monoacid fractions
0.8
0.4
0.0
2.5
PM2.5 diacid fractions
2.0
Succinic
Glutric
Malonic
Maleic
Oxalic
Diacids (% of PM mass)
1.5
1.0
0.5
0.0
0.30
0.25
PM10 diacid fractions
0.20
0.15
0.10
Succinic
Glutaric
Malonic
Maleic
Oxalic
0.05
WSOC
0.00
12
10
PM2.5
8
PM10
6
4
2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
0
Site ID
Figure 5-17. Abundances of mono and di-acids, and water soluble organic carbon (WSOC) normalized to PM2.5 and
PM10 mass.
5-28
5.6
Profile Similarities, Differences, and Composite Source Profile
Previous sections have shown that there are some differences in source profiles between
samples from facility and non-facility sites. The differences are less apparent between PM2.5 and
PM10 size fractions within each source sub-type. This section aims to use statistical method to
elucidate the similarity and differences among the profiles and group similar sites to generate
composite profiles as appropriate.
Similar to the method used by Chow et al. (2003), three performance measures are used
to quantify similarities and differences among profile pairs. First, the distribution of weighted
differences [residual(R)/uncertainty(U)] as defined below:
|
|
⁄
.
(4-1)
where Fi1 and Fi2 are the mass abundances for species i in source profiles 1 and 2, respectively,
and σi1 and σi2 are the corresponding abundance uncertainties. R/U shows how many of the
chemical abundances differ by multiples of the uncertainty of the difference. Second, the
correlation coefficient (r) between the abundance (Fij) for species i from source j divided by
uncertainty (σij) quantifies the strength of association between profiles. Third, the Student t-test
estimates the statistical significance of differences between chemical abundances.
The R/U ratio indicates how many of the 115 reported chemical abundances differ by
more than an expected number of uncertainty intervals. The normal probability density function
of 68%, 95.5%, and 99.7% for ±1σ, ±2σ, and ±3σ, is used to evaluate the R/U ratios. For the
correlation coefficients, r > 0.8 indicates similar profiles, 0.5 ≤ r ≤ 0.8 indicates a moderate
similarity, and r < 0.5 indicates little or no similarity. For the t-test, a probability level (P) of 5%
is selected as a similarity cutoff criterion: if P<0.05, there is a 95% probability that the profiles
are different. When 80% of the R/U ratios are within ±3σ; with r > 0.8 and P > 0.05, the two
profiles are considered to be similar, within the uncertainties of the chemical abundances (Chow
et al., 2003).
Table 5-4 in Appendix H lists the statistical measures of the variability in PM2.5
geological samples collected from Facilities B, C, E, and the Quarry. The ten samples from
Facility B (Sites 19-28) showed R/U ratios within ±3σ for 82‒98% of the species, with r in the
range of 0.65‒0.98. However, there are 2‒18% species with R/U >3σ, and fifteen out of forty
five pairs have P values less than or close to 0.05. It indicates that these sites are similar in many
species, but are different in some species. Sites 25 and 26 (Facility B T-section by main haul
road) show the best similarity with only 2% of species having R/U > ±3σ, but Sites 21 (Facility
B tailings dike 3) and 27 (Facility B unpaved road, tire track) show large differences in all three
statistical measures. Compared to tailings dike 3 sample from Site 21, the unpaved road tire track
sample from Site 27 contains 22 times higher Na+, 4.6 times higher Mg++, 3.8 time higher Ca++,
36% lower Al and S, thirteen times higher Cl, six times higher Ca, eighteen times higher Sc, 3.5
times higher Cu, 79% higher Zn, up to 16% lower rare earth elements, but nine times higher Pb,
thirty times higher lactic acid, and six times higher WSOC. Site21 has the lowest correlation and
P values with other pairs, indicating that tailings dike 3 sample is different from other samples.
Among the eight samples from Facility E, best similarities are observed among Site 57
(Facility E overburden pit) and Site 60 (Facility E unpaved road near sulfur pile), and Site 58
(Facility E tailings pond beach) and Site 59 (Facility E unpaved road near sulfur pile). Tailing
pond beach Site 58 is different from overburden pit Site 57 as indicated by the 28% R/U > ±3σ
and lower r (0.85) and P value (0.021). Compared to the overburden pit Site 57, the tailings pond
beach Site 58 showed 78% higher K+, but 72% and 53% lower Ca++ and Mg++, respectively, 76%
5-29
and 97% lower OC and EC, respectively, 2.2 times higher Al, 59% lower S, 50-60% higher Ti,
V, Cr, Mn, 40% lower Ni and Cu, two times higher rare earth elements, and 40-50% lower mono
and diacids and WSOC. Sites 53 (Facility E undisturbed coke pile) and 54 (Facility E disturbed
coke pile) have higher > ±3σ percentage and smaller correlation coefficient values (0.54-0.82)
with other sites. These sites are found to have significantly lower ions, rare earth elements, Al,
K, mono and diacids and WSOC, but significantly higher EC and S compared to other sites. The
Facility E haul road Site 55 and unpaved road near sulfur pile Site 60 have the lowest P value
(0.006) and lower correlation coefficients (0.75) among the sites. Compared to Site 55, Site 60
has eighteen times higher Cl-, 2.9 times higher NO3-, seven time higher SO4=, three times higher
NH4+ and Mg++, Thirteen times higher Na+, two times higher OC, 28% higher S, 80% more Cr, 4
times higher Ni and Cu, 3 times higher Zn, 30% lower rare earth elements, 6 times more lactic
and oxalic acids and three time more WSOC.
The twelve sites in Facility C show 75‒100% species are within ±3σ with correlations
coefficients of 0.5‒0.94, and P value ranging between 0.001-0.994. However, there are 0‒25%
species with R/U >3σ, and eighteen out of sixty six pairs have P values less than or close to 0.05.
Therefore, there is significant difference among some species while there are also similarities.
Compared to tailings sand strip Site 4, unpaved road on tailings dike (Site 8), light vehicle
unpaved road-wet (Site 9), and light vehicle unpaved road-dry (Site 10) show the largest
differences in R/U >3σ and P value, although they are moderately correlated (0.75-0.78).
Compared to tailings sand, unpaved road on tailings dike (Site 8) and light vehicle unpaved roadwet (Site 10) have lower anions and cations abundance, 65% and 35% lower OC, 65% and 44%
lower Al, 86% and 54% lower Ca, 76% and 57% lower Cu, and 79% and 64% lower Pb. On the
other hand, compared to the tailings dike sand (Site 4) light vehicle unpaved road-dry (Site 9)
has twenty nine times higher SO4= and Na+, 5.5 times higher Cl-, 6.7, 4, and 17.7 times higher
Mg++, K+, and Ca++, respectively, 40% higher OC, two times higher Al, 4.8 times higher Ca,
70% higher Fe and Ni, 2.3 time higher Cu, thirty one times higher Ce, 80% higher rare earth
elements, 7.4 times higher formic acid and 4 times higher WSOC. Although, Site 3 (Facility C
unpaved road near sulfur pile) has the lowest correlations with Sites 4-13, it has relatively high
P-values (0.152-0.18) indicating some similarities.
Among the ten samples collected in the quarry, best similarities are observed among Site
31(Quarry, waste storage pile hill foot) and Site 36(Quarry, waste pile) while the least
similarities are between Site29 (Quarry, conveyor area) and all the other sites (Sites 30-38) and
Site 34 (Quarry, unpaved road in Pit 1) and Sites 35-38. Compared to the conveyor area Site 29
and other sites, waste storage pile hill foot Site 31 has the largest differences with 2-4 times
higher anions, three times higher OC, 32% lower Al, 75% higher S, 35-45% lower Ti, V, Cr,
Mn, and Fe, 57% higher Cu but 48% lower Pb, five times higher lactic acid and 2.3 times higher
WSOC.
Although there are large variability’s among samples from the same sites, some
differences are noticeable among different source sub-groups, such as overburden-bare land,
tailings sands, unpaved road, road near sulfur pile, coke pile, and quarry dust.
Table H-2 in Appendix H compares PM2.5 samples of the same subgroup collected from
different facilities. Among the tailings sands samples from the same facility, largest differences
were observed among Facility C Site 4 and Site 12, Facility B Site 21 and Sites 22-24, and
Facility E Site 56 and 58. The other pairs show high similarities indicating that those tailings
pond sands can be grouped into one subgroup. Among the unpaved road samples inside the
facilities, large differences are observed between Facility C unpaved road samples from Site 8
5-30
(Facility C unpaved road on tailings dike) and 10 (Facility C light vehicle unpaved road-wet) and
Facility B unpaved road Sites 25-27 along with Facility E Site 55 (Facility E haul road). These
sites are still included in the facility unpaved road subgroup since they represent the profile
variations on the unpaved road dust. Even among this sub-group, Site 10 and Site 25 have the
largest variation with Site 25 having higher ions, OC, elements, and WSOC abundance compared
to Site 10. Sites with overburden-bare land and coke pile have differences within the sub group
with P values <0.05 and correlation coefficient <0.8. For the overburden-bare land soils (Sites 7,
28, and 57), these differences are indicating to different sources affects the soils since all three
sites are from different facilities.
Table H-3 in Appendix H compares samples from non-facility sites with sub-groups such
as unpaved roads, paved roads and bare land. There are significant difference within unpaved
road sub-group with 3-25% R/U >3σ, r = 0.55-0.95 and P = 0 – 0.975. Paved road sub-group has
more similarity among samples indicating that these sites can be grouped in the same subgroup.
Bare land subgroup had high P values (0.126-0.918) but higher R/U >3σ (19-31%) and lower r
(0.66-0.85) indicating that there are considerable differences in soils.
Based on the similarities of source sub-types and their close vicinity in sample locations,
three levels of compositing source profiles are applied as listed in Table 5-3. Level I is the
individual source profile. These Level I profiles are composited into Level II subgroups: road
near sulfur pile, coke pile, tailings pond-dike sand, overburden-bare land, unpaved road in mine
facilities, quarry, unpaved road outside mine facilities, paved road outside mine facilities and
bare land outside mine facilities. The Level II profiles are further composited into two Level III
groups: facility and non-facility soil. Composite source profiles for Level II and Level III groups
are listed in Appendix I. Table 5-4 lists the statistical measures of the variability in Level II and
III composite profiles. Due to the grouping of several profiles, the uncertainties (the larger of
standard deviation of profiles in the average and the analytical uncertainty) in the composite
profiles are usually larger, which causes >98% species with R/U<±3σ. The correlation
coefficients range 0.26‒0.93, significantly lower than those in Level I comparison, indicating
greater dissimilarities among profiles. Student t-tests show the largest dissimilarities exist
between the following Level II pairs: road near sulfur pile and tailings pond-dike sand (P =
0.005), coke pile and tailings pond-dike sand (P = 0.034), overburden-bare land and unpaved
road in mine facilities (P = 0.037), non-facility paved road (P=0.025) and non-facility bare land
(P=0.039). The two Level III profiles also show significant dissimilarities with P = 0.028.
Figure 5-18 and Figure 5-19 shows the Level II PM2.5 composite profiles for samples in
facility and non-facility sites, respectively, and Figure 5-20 shows the two Level III composite
profiles. It is difficult to discern the differences among profiles due to similarities in many
species, wide range of abundances, and log-scale y-axis.
5-31
Table 5-3. Source profile-compositing scheme.
Level III
Facility soil
Non-facility
soil
Level II
Road near sulfur pile
Coke pile
Tailing pond-dike
sand
Overburden-bare
land
Unpaved road
Quarry
Level I
S2, S3, S59, S60
S53, S54
S4, S5, S6, S11, S12, S13, S19, S20, S21, S22, S23, S24,
S56, S58
S7, S28, S57
S8, S9, S10, S25, S26, S27, S55
S29, S30, S31, S32, S33, S34, S35, S36, S37, S38
Unpaved road
S1, S40, S52, S15, S47, S16, S49, S51, S63, S48, S45, S46,
S64
Paved road
Bare land
S41, S39, S17, S14, S18, S42, S43
S50, S61, S62, S44
Table 5-4. Comparison of statistical measures of the variability in Level II and III composite PM2.5 profiles. Yellow
highlights indicate P values < 0.05, indicating dissimilarities between the composite profiles.
Profile #1
Level II
Road near
sulfur pile
Coke pile
Tailing ponddike sand
Percent Distribution
<1σ
1σ-2σ
2σ-3σ
>3σ
Correlation
coefficient (r)
t-statistic
P value
Coke pile
82%
16%
3%
0%
0.26
0.073
Tailing pond-dike sand
Overburden-bare land
Unpaved road
Quarry
Non-facility unpaved
road
Non-facility paved road
Non-facility bare land
Tailing pond-dike sand
Overburden-bare land
Unpaved road
Quarry
Non-facility unpaved
road
Non-facility paved road
Non-facility bare land
99%
99%
100%
85%
1%
1%
0%
10%
0%
0%
0%
4%
0%
0%
0%
0%
0.90
0.71
0.73
0.91
0.005
0.485
0.501
0.671
100%
0%
0%
0%
0.75
0.914
98%
100%
70%
78%
64%
81%
2%
0%
11%
14%
20%
7%
0%
0%
15%
3%
10%
8%
0%
0%
4%
4%
6%
4%
0.79
0.51
0.32
0.53
0.37
0.37
0.181
0.400
0.034
0.083
0.067
0.080
65%
25%
8%
3%
0.40
0.074
59%
93%
22%
3%
11%
2%
7%
2%
0.36
0.41
0.057
0.066
Overburden-bare land
100%
0%
0%
0%
0.77
0.505
Unpaved road
100%
0%
0%
0%
0.86
0.078
Profile #2
5-32
Table 5-4 continued
Profile #1
Overburden-bare
land
Unpaved road
Quarry
Non-facility
unpaved road
Non-facility
paved road
Level III
Facility soil
Profile #2
Percent Distribution
<1σ
1σ-2σ
2σ-3σ
>3σ
Correlation
coefficient (r)
t-statistic
P value
79%
8%
9%
3%
0.93
0.851
91%
9%
0%
0%
0.87
0.591
Quarry
Non-facility unpaved
road
Non-facility paved
road
Non-facility bare land
91%
9%
0%
0%
0.89
0.048
100%
0%
0%
0%
0.67
0.082
Unpaved road
99%
1%
0%
0%
0.79
0.037
Quarry
Non-facility unpaved
road
Non-facility paved
road
Non-facility bare land
Quarry
Non-facility unpaved
road
Non-facility paved
road
Non-facility bare land
Non-facility unpaved
road
Non-facility paved
road
Non-facility bare land
Non-facility paved
road
Non-facility bare land
84%
13%
3%
1%
0.80
0.109
93%
7%
0%
0%
0.77
0.081
89%
11%
0%
0%
0.75
0.025
99%
79%
1%
9%
0%
12%
0%
0%
0.62
0.83
0.039
0.157
96%
4%
0%
0%
0.87
0.690
93%
7%
0%
0%
0.81
0.115
100%
0%
0%
0%
0.66
0.461
80%
20%
0%
0%
0.82
0.367
78%
7%
13%
2%
0.85
0.123
97%
3%
0%
0%
0.63
0.190
98%
2%
0%
0%
0.91
0.080
100%
0%
0%
0%
0.72
0.153
Non-facility bare land
99%
1%
0%
0%
0.63
0.062
Non-facility soil
99%
1%
0%
0%
0.90
0.028
5-33
1e+3
Road near sulfur pile PM2.5
1e+2
1e+1
1e+0
1e-1
1e-2
1e-3
1e-4
1e+3
Coke pile PM2.5
1e+2
1e+1
1e+0
1e-1
1e-2
1e-3
1e-4
1e+2
Tailing pond-dike sand PM2.5
Abundance (% of PM2.5 mass)
1e+1
1e+0
1e-1
1e-2
1e-3
1e-4
1e+2
Overburden bare land PM2.5
1e+1
1e+0
1e-1
1e-2
1e-3
1e-4
1e+2
Unpaved road PM2.5
1e+1
1e+0
1e-1
1e-2
1e-3
1e-4
1e+2
Quarry PM2.5
1e+1
1e+0
1e-1
1e-2
1e-3
NO2ClNO3PO43SO42NH4+
Na+
Mg2+
K+
Ca2+
OC1
OC2
OC3
OC4
OPT
OPR
OC
EC1
EC2
EC3
EC
CO32TC
Na
Al
Si
P
S
Cl
K
Ca
Sc
Ti
V
Cr
Mn
Fe
Co
Ni
Cu
Zn
Ga
As
Se
Br
Rb
Sr
Y
Zr
Nb
Mo
Pd
Ag
Cd
In
Sn
Sb
Cs
Ba
La
Ce
Sm
Eu
Tb
Wo
Au
Hg
Tl
Pb
U
133Cs
137Ba
139La
140Ce
141Pr
146Nd
147Sm
153Eu
157Gd
159Tb
163Dy
165Ho
166Er
169Tm
172Yb
175Lu
208Pb
Glycerol
Lactic acid
Acetic acid
Formic acid
Glutaric acid
Maleic acid
Oxalic acid
WSOC
1e-4
Species
Figure 5-18. Level II PM2.5 composite profiles for subgroups in facility facilities.
5-34
1e+2
Non-facility unpaved road PM2.5
1e+1
1e+0
1e-1
Abundance (% of PM2.5 mass)
1e-2
1e-3
1e-4
1e+2
Non-facility paved road PM2.5
1e+1
1e+0
1e-1
1e-2
1e-3
1e-4
1e+2
Non-facility bare land PM2.5
1e+1
1e+0
1e-1
1e-2
1e-3
NO2ClNO3PO43SO42NH4+
Na+
Mg2+
K+
Ca2+
OC1
OC2
OC3
OC4
OPT
OPR
OC
EC1
EC2
EC3
EC
CO32TC
Na
Al
Si
P
S
Cl
K
Ca
Sc
Ti
V
Cr
Mn
Fe
Co
Ni
Cu
Zn
Ga
As
Se
Br
Rb
Sr
Y
Zr
Nb
Mo
Pd
Ag
Cd
In
Sn
Sb
Cs
Ba
La
Ce
Sm
Eu
Tb
Wo
Au
Hg
Tl
Pb
U
133Cs
137Ba
139La
140Ce
141Pr
146Nd
147Sm
153Eu
157Gd
159Tb
163Dy
165Ho
166Er
169Tm
172Yb
175Lu
208Pb
Glycerol
Lactic acid
Acetic acid
Formic acid
Glutaric acid
Maleic acid
Oxalic acid
WSOC
1e-4
Species
Figure 5-19. Level II PM2.5 composite profiles for subgroups in non-facility sites.
To elucidate the differences among the composite profiles, Table 5-5 lists the ratios
among subgroups: overburden and bare land are used as references for Level II facility dust and
non-facility dust, respectively. For Level III, non-facility dust is used as a reference to normalize
facility dust. Average of PM2.5 abundances of all individual profiles in each group are used. If the
reference profile has abundances of zero, their uncertainties are used to prevent “divided by
zero” error.
Among the Level II facility dust profiles, the facility coke pile profile has the highest
abundances in EC, V, and Ni. Road near sulfur pile has higher Cl-, Ca++, carbonate carbon
(CO3=-C), Sc, Tb, organic acids, and WSOC. Tailings pond-dike sand has higher CO3=-C, Sc, Pb,
and U. Unpaved road has higher abundances CO3=-C, Ca, Fe, Sc, Pb, and U. Quarry sites had the
highest abundance of NO3-, Ca++, CO3=-C, Ca, Sc, formic and acitic acids.
In Level II non-facility dust profiles, compared to bare land profile, unpaved road has
higher NO3-, CO3=-C, Sc, Br, Nb, Pb, U, and acetic acid, but it has lower SO4=. Paved road has
Ca++, CO3=-C, Sc, Cr, Cu, rare earth elements, and formic and acetic acids.
5-35
In the Level III profiles, compared to non-facility dust profile, the facility dust profile has
higher EC (5.31), S (2.4), V (2.3), Ni (2), Tl (2.8). On the other hand, facility dusts have lower
Cl- (0.2), NO3- (0.6), Mg++ (0.4), Mn (0.4), Zn (0.4), Ba (0.5), and 133Cs (0.2).
1e+2
Facility soil PM2.5
Abundance (% of PM2.5 mass)
1e+1
1e+0
1e-1
1e-2
1e-3
1e-4
1e+2
Non-facility soil PM2.5
1e+1
1e+0
1e-1
1e-2
1e-3
NO2ClNO3PO43SO42NH4+
Na+
Mg2+
K+
Ca2+
OC1
OC2
OC3
OC4
OPT
OPR
OC
EC1
EC2
EC3
EC
CO32TC
Na
Al
Si
P
S
Cl
K
Ca
Sc
Ti
V
Cr
Mn
Fe
Co
Ni
Cu
Zn
Ga
As
Se
Br
Rb
Sr
Y
Zr
Nb
Mo
Pd
Ag
Cd
In
Sn
Sb
Cs
Ba
La
Ce
Sm
Eu
Tb
Wo
Au
Hg
Tl
Pb
U
133Cs
137Ba
139La
140Ce
141Pr
146Nd
147Sm
153Eu
157Gd
159Tb
163Dy
165Ho
166Er
169Tm
172Yb
175Lu
208Pb
Glycerol
Lactic acid
Acetic acid
Formic acid
Glutaric acid
Maleic acid
Oxalic acid
WSOC
1e-4
Species
Figure 5-20. Level III PM2.5 composite profiles.
1
Ratio between facility and non-facility soils.
5-36
Table 5-5. Abundance ratios of profile groups for PM2.5. Level II facility dusts are normalized to overburden, nonfacility dusts are normalized to bare land, and Level III is normalized to non-facility dust. Some species with low
abundances in all groups are not listed. Cells with yellow highlight indicate ratios > 2 and cells with blue highlight
indicate ratios < 0.5.
Level II
Species
Facility dust
Tailing pond- Overburdendike sand
bare land
1.3
1.0
1.3
1.0
1.5
1.0
0.7
1.0
0.8
1.0
1.5
1.0
1.2
1.0
1.4
1.0
Unpaved
road
1.6
1.5
0.7
0.4
0.8
1.7
1.7
1.6
Non-facility dust
Unpaved
Paved
road
road
0.8
0.4
6.4
1.1
0.6
0.5
0.9
1.1
0.4
0.4
1.3
1.6
0.9
0.8
1.6
2.2
ClNO3SO42NH4+
Na+
Mg++
K+
Ca++
Road near
sulfur pile
2.9
1.1
0.9
0.8
0.6
1.9
1.2
2.2
Coke
pile
1.1
1.3
0.2
0.5
0.2
0.5
0.5
0.7
OC2a
OC3a
OC4a
OPTa
OPRa
OCa
EC1a
EC2a
EC3a
ECa
CO3=TC
0.6
0.8
0.6
0.4
0.5
0.6
0.4
0.8
0.1
0.0
33.1
0.6
1.2
1.2
1.3
1.7
1.5
1.4
7.0
1.6
0.4
24.7
0.2
4.9
0.7
0.8
0.8
0.5
0.5
0.7
0.5
1.5
0.1
0.5
8.7
0.6
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
0.6
0.6
0.6
0.3
0.4
0.5
0.3
1.0
0.1
0.0
9.5
0.4
0.7
0.7
0.9
0.5
0.6
0.6
0.4
1.7
0.0
0.0
114.7
0.8
1.7
1.2
0.8
0.8
1.6
1.2
0.5
1.0
0.1
0.0
2.5
0.8
2.5
1.4
1.0
0.9
1.7
1.4
0.6
1.6
0.0
0.1
3.9
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.7
1.0
0.9
2.1
2.1
1.4
3.6
1.5
2.9
5.3
0.8
2.1
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Al
Si
S
Cl
K
Ca
Sc
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
Br
Rb
Sr
Y
Zr
Nb
Pd
In
Sn
Sb
Ba
Eu
Tb
Wo
Tl
Pb
U
133Cs
137Ba
139La
140Ce
141Pr
0.7
0.5
1.7
2.3
0.7
2.3
5.9
0.6
0.4
1.0
1.5
2.0
0.6
1.5
1.9
1.5
0.5
0.9
0.6
0.5
0.6
3.0
3.4
4.0
1.1
0.7
2.3
13.3
2.8
0.4
0.2
0.0
1.0
1.3
1.1
1.3
1.1
0.2
0.2
4.5
0.7
0.2
0.6
0.9
0.4
10.8
0.2
0.7
0.6
4.6
0.9
1.1
0.3
0.3
0.4
0.3
0.4
0.1
0.6
0.0
0.1
0.2
0.3
0.0
0.7
0.0
0.0
0.0
1.6
0.0
0.4
0.4
0.4
0.4
1.5
0.9
0.9
0.7
1.1
1.2
2.6
1.0
0.8
1.2
1.3
1.3
0.7
0.9
1.8
0.8
1.3
0.9
0.5
0.6
0.7
2.7
0.4
1.7
0.2
0.9
1.1
0.0
0.6
0.3
0.9
5.6
1.3
1.0
1.3
1.3
1.3
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
0.9
0.6
1.3
1.1
2.2
2.4
0.9
0.9
0.8
1.5
2.1
0.3
1.0
1.7
0.9
1.1
1.2
1.1
0.7
0.8
2.8
1.0
0.7
0.2
1.0
0.0
0.6
0.7
0.4
0.9
4.5
1.2
1.2
1.2
1.1
1.2
0.6
0.4
0.5
0.9
0.9
12.0
3.6
0.5
0.3
0.6
0.4
0.5
0.2
1.2
1.1
0.7
1.1
1.5
0.7
0.4
0.6
0.6
0.9
0.8
0.8
1.0
0.9
0.2
0.7
0.1
1.1
0.3
1.0
0.5
0.6
0.6
0.5
1.0
0.9
0.8
1.4
1.2
2.0
30.0
1.0
0.8
1.3
0.6
1.5
1.9
1.0
0.6
7.0
1.1
1.4
1.3
1.1
2.4
0.4
0.7
3.7
13.6
1.5
6.9
1.2
0.4
0.2
0.5
4.0
2.1
1.1
1.2
1.2
1.2
1.2
0.9
0.8
0.7
1.2
3.0
93.2
1.0
1.9
2.3
0.5
1.5
0.8
2.5
1.2
3.9
1.2
1.4
3.5
1.3
5.1
1.5
3.7
9.1
11.1
3.2
3.2
0.5
0.7
3.3
0.2
4.2
35.2
1.5
1.5
1.5
1.5
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
0.9
2.4
0.2
0.9
0.6
0.9
1.1
2.3
1.4
0.4
0.7
2.0
0.6
0.4
0.8
0.9
0.7
0.8
1.0
0.6
1.1
1.6
1.5
1.3
0.5
0.7
0.8
0.5
2.8
0.7
0.7
0.2
0.8
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
5-37
Quarry
2.7
6.1
1.1
0.6
2.0
2.2
3.5
19.1
Bare
land
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Level III
Nonfacility
dust
0.2
1.0
0.6
1.0
1.0
1.0
0.9
1.0
0.7
1.0
0.4
1.0
0.8
1.0
0.8
1.0
Facility
dust
Table 5-5 Continued.
Level II
Species
146Nd
147Sm
153Eu
157Gd
159Tb
163Dy
165Ho
166Er
169Tm
172Yb
175Lu
208Pb
Lactic acid
Acetic acid
Formic acid
Oxalic acid
WSOC
a
Road near
sulfur pile
1.2
1.2
1.2
1.3
1.3
1.4
1.4
1.5
1.2
1.6
1.2
1.6
3.9
4.3
2.5
8.6
4.7
Coke
pile
0.4
0.5
0.5
0.6
0.6
0.6
0.6
0.6
0.4
0.5
0.3
0.8
0.5
0.0
0.6
0.8
0.5
Facility dust
Tailing pond- Overburdendike sand
bare land
1.3
1.0
1.4
1.0
1.4
1.0
1.3
1.0
1.4
1.0
1.4
1.0
1.4
1.0
1.3
1.0
1.4
1.0
1.3
1.0
1.4
1.0
1.2
1.0
0.5
1.0
1.1
1.0
1.1
1.0
0.5
1.0
0.8
1.0
Unpaved
road
1.1
1.2
1.2
1.2
1.3
1.3
1.4
1.4
1.5
1.4
1.5
1.0
0.8
1.5
1.5
1.5
0.6
Quarry
0.5
0.5
0.5
0.5
0.5
0.5
0.6
0.6
0.7
0.7
0.8
0.8
1.3
3.4
2.0
0.6
0.9
Non-facility dust
Unpaved
Paved
road
road
1.3
1.4
1.2
1.5
1.3
1.6
1.2
1.4
1.3
1.6
1.3
1.6
1.4
1.9
1.4
1.8
1.7
2.7
1.5
1.9
2.0
3.0
0.9
0.9
1.5
1.5
2.0
5.4
1.8
2.0
1.5
1.4
0.9
1.4
Bare
land
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Level III
NonFacility
facility
dust
dust
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
0.8
1.0
1.0
1.0
0.6
1.0
0.5
1.0
1.3
1.0
0.9
1.0
OC1 (not shown), OC2, OC3 and OC4 are organic carbon evolved at 140, 280, 480, and 580 °C, respectively,
in a 100% helium (He) atmosphere. EC1, EC2, and EC3 are elemental carbon evolved at 580, 740, and 840 °C,
respectively, in a 98% He/2% oxygen atmosphere. OPT and OPR are pyrolysis by transmittance and
reflectance, respectively.
5-38
6 Summary and Recommendations for Future Studies
6.1
Summary of Key Results
This study characterizes the windblown dust generation characteristics of major dust
sources in the AOSR and measures the chemical composition of the fugitive dust. The 64
sampling sites covered a wide range of potential fugitive dust sources, including oil sands mining
operations in three facilities, a limestone quarry operation, unpaved and paved roads, parking
lots, and bare lands in the vicinity of Ft. McMurray and Ft. McKay. The three key parameters
related to windblown dust generation were quantified by: dust reservoir type, threshold friction
velocity, and emission potential and flux. Detailed chemical compositions of PM2.5 and PM10
were analyzed to seek chemical signatures of different dust sources. The following questions are
answered.
Q1: Does the surface have limited or unlimited dust supply at specific wind speed (or friction
velocity)?
A1: Table 4-1 summarizes the dust reservoir types as a function of PI-SWERL RPM for all
sites measured in this study. All sites were supply limited at 500 and 1000 RPM (u10+ = 1116 km/h), as well as at 2000 RPM (u10+ = 27 km/h) except two tailings beach sites. Most
sites were supply unlimited at 5000 RPM (u10+ = 56 km/h), with exceptions of several sites
at the lime stone quarry, the coke pile, paved surfaces, and stabilized land clearances.
Q2: What is the threshold friction velocity for PM emission and saltation to occur?
A2: The threshold RPM, friction velocity, and corresponding wind speed at 10 m gal are
summarized in Table 4-2. The average PM threshold RPM varied from ~100 to 1500 RPM
(u10+ = 11-21.5 km/h), while the saltation occurred at higher speeds of >2500 RPM (u10+ >
32 km/h). Saltation is often related to unlimited reservoirs.
Q3: How hard would the wind have to blow in order for PM2.5 or PM10 emission potential to
exceed 0.002, 0.02, and 0.2 g/m2 [for example]?
A3: The threshold RPM for PM2.5 or PM10 emission potential to exceed 0.002, 0.02, and 0.2
g/m2 are plotted in Figure 4-6. The threshold RPM varied among sites. Twenty of the 64
sites did not reach 0.2 g/m2 PM2.5 emission potential at the maximum tested speed (mostly
5000 RPM; u10+ = 56 km/h).
Q4: How much PM is available for emissions after exposing to different wind speed?
A4: The emission potential and flux for PM1, PM2.5, PM4, PM10, and PM15 under different PISWERL RPMs are listed in Appendices B and C, and the emission fluxes for PM2.5 and
PM10 are plotted in Figure 4-8. Emission potentials and fluxes varied significantly with
wind speed and locations. For example, a high emitting unpaved mine haul road can emit
2.38E-05, 8.05E-05, 7.92E-03, 0.025, 0.11, and 0.13 g/m2/s PM10 under wind speeds u10+ of
11, 16, 27, 37, 47 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, 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., watering) surfaces with limited loose dust materials are the
lowest emitting surfaces.
6-1
Q5: How effective is surface watering at reducing dust emissions?
A5: Figure 4-12 compares the PM10 concentration and emission potential for surfaces before
and after watering. Watering reduced emission potentials by 50-99% at different wind
speeds (PI-SWERL RPMs). Therefore, watering is effective in reducing dust emissions
when applied at the right quantity.
Q6: What are the effects of surface disturbances on dust emissions?
A6: Figure 4-14 and Figure 4-16 compare PM10 emissions between stabilized and disturbed
surfaces. Surface disturbances by traffic or other activities increased PM10 emission
potentials by 9‒160 times. Therefore, minimizing surface disturbances is effective in
reducing windblown dust.
Q7: What are the major chemical constituents of fugitive dust?
A7: Figures 3-2 a, b show the reconstruction of PM2.5 and PM10 with the major constituents
and Appendix D, E, F, and G list the contribution of measured species to PM2.5 and PM10
mass. Minerals (Al, Si, Ca, K, Fe, Ti) in their oxide form account for the majority of the
PM mass with average contribution of 13-94%. Si is the most abundant element accounting
for 2-29% of PM mass. Organic matter is the second most abundant species with average
abundance of 14-49% of PM2.5 and 12-75% of PM10 mass. Water soluble ions, trace and
rare earth elements and EC account for 4.3% and 44%, 1.5% and 1.9%, and 1.85% and
1.65% of PM2.5 and PM10, respectively.
Q8: What are the chemical signatures for different types of fugitive dust?
A8: Specific chemical components of PM2.5 and PM10 vary significant among fugitive dusts
sampled. SO4= is higher among facility sites compared to non-facility sites. Ca++ and CO3=
are highest near the limestone quarry while EC, V, and Ni are highest in samples collected
at the coke pile. Most of the trace elements and rare earth elements are similar between
facility and non-facility sites. Tailings pond sands are similar to soils from overburden-bare
land except for higher abundances in Sc, Pd, and U.
Q9: Are certain types of fugitive dusts enriched with toxic components?
A9: There are certain sites with higher abundances of toxic metals compared to a background
Site 27 sampled in 2008. Tailings pond – dikes sand and paved roads have enrichment of
Fe and Cu; Cu and Zn are enriched in shoulder dust of Hwy 63; U is higher in quarry
samples. Other toxic metals such as Pb and As are comparable between forest background
site and facility sites.
Q10: Are there significant difference between fugitive dusts from mining facilities and
surrounding areas?
A10: A comparison of composite profiles between facility and non-facility dusts is provided
in Appendix I. There are several significant differences in chemical constituents of dusts
between facility and non-facility sites. Facility sites have a higher abundance of OC, EC, S,
V, Ni, and Tl while these sites are depleted in Cl-, Mg++, Mn, Zn, Ba, and Cs. Apart from
these species, other source profiles are comparable between facility and non-facility sites,
6-2
and profiles from this study show more similarity compared to oil sands and forest soils
collected in 2008.
Q11: How different is Pb and its isotope ratios between facility and non-facility fugitive
dusts?
A11: Figure 5-13 shows the ratio of isotope abundances of Pb in PM2.5 and PM10 fugitive
dusts. Generally, it is not significantly different between facility and non-facility sites and
is also similar to the background forest site dust from 2008. PM2.5 samples from quarry
have higher 206Pb/207Pb and 208Pb/207Pb ratios indicating enrichment of 206Pb and
208Pb compared to 207Pb in these dusts compared to other facility and non-facility sites.
Unlike PM2.5, these ratios are only higher at Site 64 (Athabasca Hwy shoulder near
Firebag) in PM10 samples. Interestingly, Pb isotope ratios in PM2.5 samples are similar to
soils group 1 (oil sands facilities) and stack emissions samples from 2008, truck emission
in 2009, lichen from west/northeast Canada and from the Alberta oil sands region,
indicating to the influence of various mining activities on facility and non-facility soils
sampled in this study.
Q12: Where and how can the data from this study be used?
A12: This study quantified dust reservoir type, threshold friction velocity, and particle sizesegregated emission potential and flux, the three key parameters for calculating windblown
dust emissions. This information can be used as input in dust dispersion and transport
models to estimate windblown dust emissions from various dust-generating surfaces. Dust
sources with lower threshold velocities and higher emission potentials and fluxes require
higher priorities for dust controls. This study clearly shows that surface watering and
reducing disturbance effectively reduces dust emissions. The effectiveness of other fugitive
dust control methods, such as polymer stabilizers, can be evaluated with methods employed
in this study. The detailed chemical composition data and source profiles can be used as
inputs to transport and dispersion models to estimate concentrations at receptors, or as
input to receptor models for apportioning ambient PM contributions from fugitive dust, and
dust contributions from different sources. The impacts of dust on human and ecosystem
health can also be evaluated.
6.2
Recommendations for Future Studies
1) Characterize vehicle-induced road dust emissions from unpaved and paved roads
The current study focuses on windblown dust characterization. The other major dust
source is mechanically generated dust, particularly those induced by vehicle traffic from unpaved
and paved roads. The Canadian National Pollutant Release Inventory shows that road dust from
unpaved and paved roads contributed to >50% of PM2.5 and PM10 in Alberta in 2011
(Environment Canada, 2013). Contributions of road dust could be higher in the AOSR than the
Alberta average since many roads are unpaved, and heavy vehicles are routinely moving on these
roads. Section 4 shows that unpaved roads with frequent traffic and paved roads with loose dust
layer or track-out usually have unlimited dust supplies, low threshold friction velocities, and high
emission fluxes. Dust plumes are often seen behind trucks or heavy haulers driving on unpaved
roads (e.g. Figures 4-2, 4-11, and 4-13).
Several remediation measures are currently used to suppress road dust in the AOSR.
These include: 1) road watering; 2) application of suppressant chemicals; 3) truck vibration and
6-3
wheel washing; 4) street sweeping; and 5) vehicle speed controls. The effectiveness of these
measures, both in terms of emission reduction and cost/benefit, have not been sufficiently
quantified for the AOSR, or for that matter, at any other large industrial site.
Dust emissions caused by vehicle entrainment differ from those caused by wind erosion.
Road dust is generated by the interaction of a vehicle tire or track with a road surface. Depending
on the surface type, the tire or tread may penetrate through or deform the road surface and
suspend material from deeper layers than are available to wind erosion (Kuhns et al., 2010). A
measurement system called the Testing Re-entrained Aerosol Kinetic Emissions from Roads
(TRAKER) was developed at DRI that uses tires to physically disturb the soil for more accurate
measurement of road dust emissions (Etyemezian et al., 2003a; 2003b; 2006; Kuhns et al., 2001;
2003; 2005; Kuhns and Etyemezian, 1999; Zhu et al., 2009) The measurements relate vehicle
speed and flux of PM generated by the vehicle to infer a PM road dust emissions potential for all
areas where the vehicle travels and express the data as grams of PM produced per kilometer of
travel (g-PM/VKT).
Figure 6-1 shows an example of road dust measurement with the TRAKER system in Las
Vegas, NV. Over 500 km of roads in Las Vegas were sampled by the TRAKER vehicle. Silt
loading results indicated that roads with high average daily traffic (ADT) such as interstates were
cleaner than lower ADT roads such as residential streets. The TRAKER system can be deployed
to AOSR for rapid survey of a wide range of roads to characterize dust loadings, relationships of
suspension to vehicle speed, size distributions, and source chemical fingerprints. By sampling at
the same velocities over remediated and unremediated surfaces, the effectiveness of different
dust control strategies is evaluated.
6-4
Figure 6-1. Map of dust suspension “hotspots” for Las Vegas, NV determined with the TRAKER. Most of the high
surface loadings were found near construction sites where vehicles tracked out dust from unpaved surfaces onto the
pavement. The paved road traffic then ground up and suspended the carryout along the roadway surface, thereby
creating larger contributions to ambient PM10 and PM2.5. Extending pavement into the entrance to construction sites
and wheel washing largely eliminated this carryout.
2) Dust source apportionment study
Since dust plumes and dust particle deposits on surfaces are easily visible, there has been
great interest in quantifying how much PM2.5 and PM10 in the AOSR are contributed by fugitive
dust. It is also of interest to further apportion dust concentrations at receptor sites to different
dust sources. For example, it will be informative to quantify the fractions of dust particles in Ft.
McKay originated from mining operations and from road dust. Through extensive source
characterization activities, the DRI team has assembled sources profiles for major sources in
AOSR, including diesel vehicle emissions, stack emission, and various dust sources (e.g.,
unpaved and paved roads inside and outside mining facilities, tailings ponds and dikes, sulfur
and coke piles, quarry operations, overburden and bare lands). WBEA has been collecting PM2.5
and PM10 filter samples at the Ft. McKay AMS 1 every 6th day which are analyzed for elements,
ions, OC, and EC. These samples could be further analyzed for more detailed organic species,
which have shown significant differences between sites inside and outside mining facilities. Both
the Positive Matrix Factorization (PMF) and Effective Variance solutions to the Chemical Mass
Balance (CMB) receptor models can be applied to quantify different dust source contributions to
the PM at receptor sites (Chen et al., 2007; 2011; Chow et al., 1992; 2007d; Watson et al., 1994;
2001b; 2002; 2008; Watson and Chow, 2007; 2013).
6-5
7
References
Andreae, M.O.; Talbot, R.W.; Andreae, T.W.; Harriss, R.C. (1988). Formic and Acetic Acid Over the Central
Amazon
Region,
Brazil
1.
Dry
Season.
J.
Geophys.
Res.,
93(D2):1616-1624.
http://dx.doi.org/10.1029/JD093iD02p01616.
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.
Bollhöfer, A.; Rosman, K.J.R. (2000). Isotopic source signatures for atmospheric lead: the Southern Hemisphere.
Geochimica
et
Cosmochimica
Acta,
64(19):3251-3262.
http://www.sciencedirect.com/science/article/pii/S0016703700004361.
Bollhöfer, A.; Rosman, K.J.R. (2001). Isotopic source signatures for atmospheric lead: the Northern Hemisphere.
Geochimica
et
Cosmochimica
Acta,
65(11):1727-1740.
http://www.sciencedirect.com/science/article/pii/S001670370000630X.
Bollhöfer, A.; Rosman, K.J.R. (2002). The temporal stability in lead isotopic signatures at selected sites in the
Southern and Northern Hemispheres. Geochimica et Cosmochimica Acta, 66(8):1375-1386.
http://www.sciencedirect.com/science/article/pii/S0016703701008626.
Brooks, P.W.; Fowler, M.G.; Macqueen, R.W. (1988). Biological marker and conventional organic geochemistry of
oil
sands/heavy
oils,
Western
Canada
basin.
Organic
Geochemistry,
12(6):519-538.
http://www.sciencedirect.com/science/article/pii/0146638088901441.
Brown,
J.S.
(1962).
Ore
leads
and
isotopes.
Economic
Geology,
57(5):673-720.
http://economicgeology.org/content/57/5/673.abstract.
Cao, J.J.; Chow, J.C.; Watson, J.G.; Wu, F.; Han, Y.M.; Jin, Z.D.; Shen, Z.X.; An, Z.S. (2008). Size-differentiated
source profiles for fugitive dust in the Chinese Loess Plateau. Atmos. Environ., 42(10):2261-2275.
Carignan, J.; Simonetti, A.; Gariépy, C. (2002). Dispersal of atmospheric lead in northeastern North America as
recorded
by
epiphytic
lichens.
Atmospheric
Environment,
36(23):3759-3766.
http://www.sciencedirect.com/science/article/pii/S1352231002002947.
Carignan, J.; Gariépy, C. (1995). Isotopic composition of epiphytic lichens as a tracer of the sources of atmospheric
lead emissions in southern Québec, Canada. Geochimica et Cosmochimica Acta, 59(21):4427-4433.
http://www.sciencedirect.com/science/article/pii/001670379500302G.
Chen, L.-W.A.; Watson, J.G.; Chow, J.C.; Magliano, K.L. (2007). Quantifying PM2.5 source contributions for the
San Joaquin Valley with multivariate receptor models. Environ. Sci. Technol., 41(8):2818-2826.
Chen, L.-W.A.; Watson, J.G.; Chow, J.C.; DuBois, D.W.; Herschberger, L. (2011). PM2.5 source apportionment:
Reconciling receptor models for U.S. non-urban and urban long-term networks. J. Air Waste Manage. Assoc.,
61(11):1204-1217. http://www.tandfonline.com/doi/pdf/10.1080/10473289.2011.619082.
Chow, J.C.; Watson, J.G.; Lowenthal, D.H.; Solomon, P.A.; Magliano, K.L.; Ziman, S.D.; Richards, L.W. (1992).
PM10 source apportionment in California's San Joaquin Valley. Atmos. Environ., 26A(18):3335-3354.
Chow, J.C.; Watson, J.G.; Pritchett, L.C.; Pierson, W.R.; Frazier, C.A.; Purcell, R.G. (1993). The DRI
Thermal/Optical Reflectance carbon analysis system: Description, evaluation and applications in U.S. air quality
studies. Atmos. Environ., 27A(8):1185-1201.
Chow, J.C.; Fujita, E.M.; Watson, J.G.; Lu, Z.; Lawson, D.R.; Ashbaugh, L.L. (1994a). Evaluation of filter-based
aerosol measurements during the 1987 Southern California Air Quality Study. Environ. Mon. Assess., 30(1):4980.
Chow, J.C.; Watson, J.G.; Houck, J.E.; Pritchett, L.C.; Rogers, C.F.; Frazier, C.A.; Egami, R.T.; Ball, B.M. (1994b).
A laboratory resuspension chamber to measure fugitive dust size distributions and chemical compositions. Atmos.
Environ., 28(21):3463-3481.
Chow, J.C. (1995). Critical review: Measurement methods to determine compliance with ambient air quality
standards for suspended particles. JAWMA, 45(5):320-382.
Chow, J.C.; Watson, J.G. (1999). Ion chromatography in elemental analysis of airborne particles. In Elemental
Analysis of Airborne Particles, Vol. 1, Landsberger, S., Creatchman, M., Eds.; Gordon and Breach Science:
Amsterdam, 97-137.
7-1
Chow, J.C.; Watson, J.G.; Crow, D.; Lowenthal, D.H.; Merrifield, T.M. (2001). Comparison of IMPROVE and
NIOSH
carbon
measurements.
Aerosol
Sci.
Technol.,
34(1):23-34.
http://www.tandfonline.com/doi/pdf/10.1080/02786820600623711.
Chow, J.C.; Watson, J.G.; Ashbaugh, L.L.; Magliano, K.L. (2003). Similarities and differences in PM10 chemical
source profiles for geological dust from the San Joaquin Valley, California. Atmos. Environ., 37(9-10):13171340. doi: 10.1016/S1352-2310(02)01021-X.
Chow, J.C.; Watson, J.G.; Chen, L.-W.A.; Arnott, W.P.; Moosmüller, H.; Fung, K.K. (2004). Equivalence of
elemental carbon by Thermal/Optical Reflectance and Transmittance with different temperature protocols.
Environ. Sci. Technol., 38(16):4414-4422.
Chow, J.C.; Watson, J.G.; Chen, L.-W.A.; Paredes-Miranda, G.; Chang, M.-C.O.; Trimble, D.L.; Fung, K.K.; Zhang,
H.; Yu, J.Z. (2005). Refining temperature measures in thermal/optical carbon analysis. Atmos. Chem. Phys.,
5(4):2961-2972.
1680-7324/acp/2005-5-2961.
http://www.atmos-chem-phys.net/5/2961/2005/acp-5-29612005.pdf.
Chow, J.C.; Watson, J.G.; Chen, L.-W.A.; Chang, M.-C.O.; Robinson, N.F.; Trimble, D.L.; Kohl, S.D. (2007a). The
IMPROVE_A temperature protocol for thermal/optical carbon analysis: Maintaining consistency with a longterm
database.
J.
Air
Waste
Manage.
Assoc.,
57(9):1014-1023.
http://www.tandfonline.com/doi/pdf/10.3155/1047-3289.57.9.1014.
Chow, J.C.; Yu, J.Z.; Watson, J.G.; Ho, S.S.H.; Bohannan, T.L.; Hays, M.D.; Fung, K.K. (2007b). The application
of thermal methods for determining chemical composition of carbonaceous aerosols: A Review. Journal of
Environmental Science and Health-Part A, 42(11):1521-1541.
Chow, J.C.; Watson, J.G.; Lowenthal, D.H.; Chen, L.-W.A.; Zielinska, B.; Mazzoleni, L.R.; Magliano, K.L. (2007d).
Evaluation of organic markers for chemical mass balance source apportionment at the Fresno supersite. Atmos.
Chem. Phys., 7(7):1741-1754. http://www.atmos-chem-phys.net/7/1741/2007/acp-7-1741-2007.pdf.
Chow, J.C.; Watson, J.G.; Robles, J.; Wang, X.L.; Chen, L.-W.A.; Trimble, D.L.; Kohl, S.D.; Tropp, R.J.; Fung,
K.K. (2011). Quality assurance and quality control for thermal/optical analysis of aerosol samples for organic
and elemental carbon. Anal. Bioanal. Chem., 401(10):3141-3152. DOI 10.1007/s00216-011-5103-3.
Chow, J.C.; Watson, J.G. (2012). Chemical analyses of particle filter deposits. In Aerosols Handbook :
Measurement, Dosimetry, and Health Effects, 2; Ruzer, L., Harley, N. H., Eds.; CRC Press/Taylor & Francis:
New York, NY, 179-204.
Ciu, Z.; Liu, Q.; Etsell, T.H.; Oxenford, J.; Coward, J. (2003). Heavy minerals in the athabasca oil sands tailings Potential and recovery processes. Canadian Metallurgical Quarterly, 42(4):383-392.
Comerford, N.B. (1990). Low-Molecular-Weight Organic Acids in Selected Forest Soils of the Southeastern USA.
Soil Sci. Soc. Am. J., 54(4):1139-1144. https://www.crops.org/publications/sssaj/abstracts/54/4/1139.
Countess Environmental (2006). WRAP Fugitive Dust Handbook. Western Regional Air Partnership: Denver,
Colorado.
Cumming, G.L.; Richards, J.R. (1975). Ore lead isotope ratios in a continuously changing earth. Earth and
Planetary Science Letters, 28(2):155-171. http://www.sciencedirect.com/science/article/pii/0012821X7590223X.
Dolgopolova, A.; Weiss, D.J.; Seltmann, R.; Kober, B.; Mason, T.F.D.; Coles, B.; Stanley, C.J. (2006). Use of
isotope ratios to assess sources of Pb and Zn dispersed in the environment during mining and ore processing
within the Orlovka–Spokoinoe mining site (Russia). Applied Geochemistry, 21(4):563-579.
http://www.sciencedirect.com/science/article/pii/S0883292706000229.
Environment Canada (2013). 2011 Air Pollutant Emissions for Alberta, National Pollutant Release Inventory.
prepared by Environment Canada, Ottawa, Ontario,
http://www.ec.gc.ca/inrp-npri/donneesdata/ap/index.cfm?lang=En.
Erel, Y.; Dayan, U.; Rabi, R.; Rudich, Y.; Stein, M. (2006). Environmental Science & Technology 40(9):2996-3005.
Etyemezian, V.; 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.; 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.
7-2
Etyemezian, V.; Kuhns, H.D.; Nikolich, G. (2006). Precision and repeatability of the TRAKER vehicle-based paved
road dust emission measurement. Atmos. Environ., 40(16):2953-2958.
Etyemezian, V.; 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 (PI-SWERL): A new method to measure PM10 potential
for windblown dust properties and emissions. Atmos. Environ., 41(18):3789-3796.
Etyemezian, V. (2011). User's Guide for the Miniature Portable In-Situ Wind ERosion Lab (PI-SWERL). prepared
by DUST-QUANT LLC, Las Vegas, NV.
Flocchini, R.G.; Cahill, T.A.; Matsumura, R.T.; Carvacho, O.F.; Lu, Z. (1994). Study of fugitive PM10 emissions
from selected agricultural practices on selected agricultural soils. prepared by University of California, Davis,
CA, for California Air Resources Board, Sacramento, CA.
Garland, J.A. (1983). Precipitation, Scavenging, Dry Deposition and Resuspension. 2; Pruppacher, H. R., Semonin,
R. G., Slinn, W. G. N., Eds.; 1087-1097.
Gillette, D.A.; Adams, J.B.; Muhs, D.R.; Kihl, R. (1982). Threshold friction velocities and rupture moduli for
crusted desert soils for the input of soil particles into the air. J. Geophys. Res., 87(C11):9003-9015.
Goossens, D.; Buck, B. (2009). Dust dynamics in off-road vehicle trails: Measurements on 16 arid soil types,
Nevada, USA. Journal of Environmental Management, 90(11):3458-3469.
Graney, J.R.; Landis, M.S.; Krupa, S.V. (2011). Using Stable Pb Isotopes in Epiphytic Lichens as Tracers of Source
Oriented Air Pollution . In International Symposium - Alberta Oil Sands: Energy, Industry and the Environment,
Proceedings of the International Symposium - Alberta Oil Sands: Energy, Industry and the Environment, Fort
McMurray, Alberta, Canada .
Graney, J.R.; Landis, M.S.; Krupa, S. (2012). Coupling lead isotopes and element concentrations in epiphytic
lichens to track sources of air emissions in the Athabasca Oil Sands Region. In Alberta Oil Sands: Energy,
Industry, and the Environment, Percy, K. E., Ed.; Elsevier: Amsterdam, The Netherlands, 343-372.
Ho, K.F.; Lee, S.C.; Chow, J.C.; Watson, J.G. (2003). Characterization of PM10 and PM2.5 source profiles for
fugitive dust in Hong Kong. Atmos. Environ., 37(8):1023-1032.
Ho, S.S.H.; Yu, J.Z. (2004). In-injection port thermal desorption and subsequent gas chromatography-mass
spectrometric analysis of polycyclic aromatic hydrocarbons and n-alkanes in atmospheric aerosol samples. J.
Chromatogr. A, 1059(1-2):121-129.
Jacob, D.J.; Wofsy, S.C. (1988). Photochemistry of Biogenic Emissions Over the Amazon Forest. J. Geophys. Res.,
93(D2):1477-1486. http://dx.doi.org/10.1029/JD093iD02p01477.
Kavouras, I.G.; Etyemezian, V.; Nikolich, G.; Gillies, J.; Sweeney, M.; Young, M.; Shafer, D. (2009). A new
technique for characterizing the efficacy of fugitive dust suppressants. JAWMA, 59(5):603-612.
King, J.; Etyemezian, V.; Sweeney, M.; Buck, B.J.; Nikolich, G. (2011). Aeolian Research 3(1):67-79.
Kinsey, J.S.; Cowherd, C. (1992). Fugitive emissions. In Air Pollution Engineering Manual, Buonicore, A. J., Davis,
W. T., Eds.; Van Nostrand Reinhold: New York, 133-146.
Komárek, M.; Ettler, V.; Chrastný, V.; Mihaljevic, M. (2008). Lead isotopes in environmental sciences: A review.
Environment
International,
34(4):562-577.
http://www.sciencedirect.com/science/article/pii/S0160412007001985.
Kuhns, H.D.; Etyemezian, V. (1999). Testing re-entrained aerosol kinetic emissions from roads (TRAKER): A new
approach to inver silt loadings on roads in Clark County, Nevada. prepared by Desert Research Institute, Las
Vegas, NV, for Clark County Regional Transportation Commission, Las Vegas, NV.
Kuhns, H.D.; Etyemezian, V.; Landwehr, D.; Macdougall, C.S.; Pitchford, M.L.; Green, M.C. (2001). Testing Reentrained Aerosol Kinetic Emissions from Roads (TRAKER): A new approach to infer silt loading on roadways.
Atmos. Environ., 35(16):2815-2825.
Kuhns, H.D.; Etyemezian, V.; Green, M.C.; Hendrickson, K.; McGown, M.; Barton, K.; Pitchford, M.L. (2003).
Vehicle-based road dust emission measurement Part II: Effect of precipitation, wintertime road sanding, and
street sweepers on inferred PM10 emission potentials from paved and unpaved roads. Atmos. Environ.,
37(32):4573-4582.
Kuhns, H.D.; Gillies, J.A.; Etyemezian, V.; DuBois, D.W.; Ahonen, S.; Nikolic, D.; Durham, C. (2005). Spatial
variability of unpaved road dust PM10 emission factors near El Paso, Texas. J. Air Waste Manage. Assoc.,
55(1):3-12.
7-3
Kuhns, H.D.; Gillies, J.A.; Etyemezian, V.; 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.
Landis, M.S.; Pancras, J.P.; Graney, J.R.; Stevens, R.K.; Percy, K.E.; Krupa, S. (2012). Receptor modeling of
epiphytic lichens to elucidate the sources and spatial distribution of inorganic air pollution in the Athabasca Oil
Sands Region. In Alberta Oil Sands: Energy, Industry, and the Environment, Percy, K. E., Ed.; Elsevier:
Amsterdam, The Netherlands, 427-467.
Linsley, G.S. (1978). Resuspension of the trans-uranium elements - a review of existing data. Report Number
NRPB-R75; prepared by HSMO, London.
Malm, W.C.; Sisler, J.F.; Huffman, D.; Eldred, R.A.; Cahill, T.A. (1994). Spatial and seasonal trends in particle
concentration and optical extinction in the United States. J. Geophys. Res., 99(D1):1347-1370.
Marshall, J.K. (1971). Drag measurements in roughness arrays of varying density and distribution. Agricultural
Meteorology, 8:269-292.
Miller, J.R.; Lechler, P.J.; Mackin, G.; Germanoski, D.; Villarroel, L.F. (2007). Evaluation of particle dispersal from
mining and milling operations using lead isotopic fingerprinting techniques, Rio Pilcomayo Basin, Bolivia.
Science
of
The
Total
Environment,
384(1–3):355-373.
http://www.sciencedirect.com/science/article/pii/S0048969707005992.
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.; Gillies, J.A. (1989). Emission of fine-grained particulates from desert soils. In Paleoclimatology
and Paleometeorology: Modern and Past Patterns of Global Atmospheric Transport, Leinen, M., Sarnthein, M.,
Eds.; Kluwer Academic Publishers: Dordrecht, 133-165.
Notten, M.J.M.; Walraven, N.; Beets, C.J.; Vroon, P.; Rozema, J.; Aerts, R. (2008). Investigating the origin of Pb
pollution in a terrestrial soil-plant-snail food chain by means of Pb isotope ratios. Applied Geochemistry,
23(6):1581-1593. http://www.sciencedirect.com/science/article/pii/S0883292708000620.
Patterson, C.C. (1965). Contaminated and natural lead environments of man. Archives of environmental health,
11:344-360.
Paulen, R.C.; Paradis, S.; Plouffe, A.; Smith, I.R. (2011). Pb and S isotopic composition of indicator minerals in
glacial sediments from NW Alberta, Canada: implications for Zn-Pb base metal exploration. Geochemistry:
Exploration, Environment, Analysis, 11(4):309-320. http://geea.lyellcollection.org/content/11/4/309.abstract.
Peters, K.E.; Walters, C.C.; Moldowan, J.M. (2005). The Biomarker Guide: II. Biomarkers and Isotopes in
Petroleum Systems and Earth History. Cambridge University Press.
Ram, S.; Saraswat, D.K.; Narayan, K.A. (1990). Spectroscopic studies of Athabasca oil sands. Fuel, 69(4):512-515.
http://www.sciencedirect.com/science/article/pii/001623619090324J.
Raupauch, M.R. (1992). Drag and drag partition on rough surfaces. Boundary Layer Meteorology, 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.
Saint-Laurent, D.; St-Laurent, J.; Hehni, M.; Ghaleb, B.; Chapados, C. (2010). Using Lead Concentrations and
Stable Lead Isotope Ratios to Identify Contamination Events in Alluvial Soils. Applied and Environmental Soil
Science, 2010
Shao, Y.; Raupauch, M.R. (1993). Effect of saltation bombardment on the entrainment of dust by wind. J. Geophys.
Res., 98(D7):12719-12726.
Siljeström, S.; LAUSMAA, J.; Sjövall, P.; BROMAN, C.; THIEL, V.; HODE, T. (2010). Analysis of hopanes and
steranes in single oil-bearing fluid inclusions using time-of-flight secondary ion mass spectrometry (ToF-SIMS).
Geobiology, 8(1):37-44. http://dx.doi.org/10.1111/j.1472-4669.2009.00223.x.
Simonetti, A.; Gariépy, C.; Carignan, J. (2003). Tracing sources of atmospheric pollution in Western Canada using
the Pb isotopic composition and heavy metal abundances of epiphytic lichens. Atmos. Environ., 37(20):28532865. http://www.sciencedirect.com/science/article/pii/S1352231003002103.
7-4
Sturges, W.T.; Barrie, L.A. (1987). Lead 206/207 isotope ratios in the atmosphere of North America as tracers of US
and Canadian emissions. Nature, 329(6135):144-146. http://dx.doi.org/10.1038/329144a0.
Sturges, W.T.; Barrie, L.A. (1989). The use of stable lead 206/207 isotope ratios and elemental composition to
discriminate the origins of lead in aerosols at a rural site in eastern Canada. Atmospheric Environment (1967),
23(8):1645-1657. http://www.sciencedirect.com/science/article/pii/0004698189900498.
Sweeney, M.; Etyemezian, V.; 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. -Atmospheres, 113(F1)
U.S. EPA (2006). AP-42, Volume I: Compilation of air pollution emission factors. Report Number Fifth Edition;
prepared by U.S. Environmental Protection Agency, Washington, D.C.
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.; Lu, Z.; Fujita, E.M.; Lowenthal, D.H.; Lawson, D.R. (1994). Chemical mass balance
source apportionment of PM10 during the Southern California Air Quality Study. Aerosol Sci. Technol., 21(1):136. http://www.tandfonline.com/doi/pdf/10.1080/02786829408959693.
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.
Watson, J.G.; Chow, J.C.; Frazier, C.A. (1999). X-ray fluorescence analysis of ambient air samples. In Elemental
Analysis of Airborne Particles, Vol. 1, Landsberger, S., Creatchman, M., Eds.; Gordon and Breach Science:
Amsterdam, 67-96.
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. (2001). Source characterization of major emission sources in the Imperial and Mexicali
valleys along the U.S./Mexico border. Sci. Total Environ., 276(1-3):33-47.
Watson, J.G.; Turpin, B.J.; Chow, J.C. (2001a). The measurement process: Precision, accuracy, and validity. In Air
Sampling Instruments for Evaluation of Atmospheric Contaminants, Ninth Edition, 9th; Cohen, B. S.,
McCammon, C. S. J., Eds.; American Conference of Governmental Industrial Hygienists: Cincinnati, OH, 201216.
Watson, J.G.; Chow, J.C.; Fujita, E.M. (2001b). Review of volatile organic compound source apportionment by
chemical mass balance. Atmos. Environ., 35(9):1567-1584.
Watson, J.G.; Zhu, T.; Chow, J.C.; Engelbrecht, J.P.; Fujita, E.M.; Wilson, W.E. (2002). Receptor modeling
application framework for particle source apportionment. Chemosphere, 49(9):1093-1136.
Watson, J.G.; Chow, J.C. (2007). Receptor models for source apportionment of suspended particles. In Introduction
to Environmental Forensics, 2nd Edition, 2; Murphy, B., Morrison, R., Eds.; Academic Press: New York, NY,
279-316.
Watson, J.G.; Chen, L.-W.A.; Chow, J.C.; Lowenthal, D.H.; Doraiswamy, P. (2008). Source apportionment:
Findings from the U.S. Supersite Program. J. Air Waste Manage. Assoc., 58(2):265-288.
http://www.tandfonline.com/doi/pdf/10.3155/1047-3289.58.2.265.
Watson, J.G.; Chow, J.C.; Wang, X.; Kohl, S.D.; Sodeman, D.A. (2010a). Measurement of real-world stack
emissions with a dilution sampling system. Report Number 010109-123109; prepared by Desert Research
Institute, Reno, NV, for Ft. McMurray, AB, Canada, Wood Buffalo Environmental Association.
Watson, J.G.; Chow, J.C.; Wang, X.; Kohl, S.D.; Gronstahl, S. (2010b). Measurement of in-use emissions from nonroad diesel trucks. Report Number 010109-123109; prepared by Desert Research Institute, Reno, NV, for Ft.
McMurray, AB, Canada, Wood Buffalo Environmental Association.
7-5
Watson, J.G.; Chow, J.C.; Wang, X.L.; Kohl, S.D.; Gronstal, S. (2011a). Winter stack emissions measured with a
dilution sampling system. prepared by Desert Research Institute, Reno, NV, for Ft. McMurray, AB, Canada,
Wood Buffalo Environmental Association.
Watson, J.G.; Chow, J.C.; Wang, X.L.; Lowenthal, D.H.; Kohl, S.D.; Gronstal, S. (2011b). Real-world emissions
from non-road mining trucks. Report Number 010109-123109; prepared by Desert Research Institute, Reno, NV,
for Ft. McMurray, AB, Canada, Wood Buffalo Environmental Association.
Watson, J.G.; Chow, J.C.; Wang, X.L.; Kohl, S.D.; Chen, L.-W.A.; Etyemezian, V. (2012a). Overview of real-world
emission characterization methods. In Alberta Oil Sands: Energy, Industry, and the Environment, Percy, K. E.,
Ed.; Elsevier Press: Amsterdam, The Netherlands, 145-170.
Watson, J.G.; Chow, J.C.; Wang, X.L.; Kohl, S.D.; Chen, L.-W.A.; Etyemezian, V. (2012b). Overview of real-world
emission characterization methods. In Alberta Oil Sands: Energy, Industry, and the Environment, Percy, K. E.,
Ed.; Elsevier Press: Amsterdam, The Netherlands, 145-170.
Watson, J.G.; Chow, J.C. (2013). Source apportionment. In Encyclopedia of Environmetrics, El-Shaarwi, A. H.,
Piegorsch, W. W., Eds.; John Wiley & Sons, Ltd.: Chichester, UK.
Watson, J.G.; Chow, J.C.; Wang, X.L.; Kohl, S.D.; Sodeman, D.A. (2013a). Measurement of real-world stack
emissions in the Athabasca Oil Sands Region with a dilution sampling system during August, 2008. prepared by
Desert Research Institute, Reno, NV USA.
Watson, J.G.; Chow, J.C.; Wang, X.L.; Kohl, S.D.; Gronstal, S.; Zielinska, B. (2013b). Measurement of real-world
stack emissions in the Athabasca Oil Sands Region with a dilution sampling system during March, 2011.
prepared by Desert Research Institute, Reno, NV USA.
Watson, J.G.; Chow, J.C.; Wang, X.L.; Lowenthal, D.H.; Kohl, S.D.; Gronstal, S. (2013c). Characterization of realworld emissions from nonroad mining trucks in the Athabasca Oil Sands Region during October, 2010. prepared
by Desert Research Institute, Reno, NV, for Ft. McMurray, AB, Canada, Wood Buffalo Environmental
Association.
Watson, J.G.; Chow, J.C.; Wang, X.L.; Zielinska, B.; Kohl, S.D.; Gronstal, S. (2013d). Characterization of realworld emissions from nonroad mining trucks in the Athabasca Oil Sands Region during September, 2009.
prepared by Desert Research Institute, Reno, NV, for Ft. McMurray, AB, Canada, Wood Buffalo Environmental
Association.
Watson, J.G.; Chow, J.C.; Wang, X.L.; Kohl, S.D. (2014). Chemical Source Profiles for Geological Dust Samples
from the Athabasca Oil Sand Region. prepared by Desert Research Institute, Reno, NV, for Ft. McMurray, AB,
Canada, Wood Buffalo Environmental Association.
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.
Yang, C.; Wang, Z.; Yang, Z.; Hollebone, B.; Brown, C.E.; Landriault, M.; Fieldhouse, B. (2011). Chemical
Fingerprints of Alberta Oil Sands and Related Petroleum Products. Environmental Forensics, 12(2):173-188.
http://dx.doi.org/10.1080/15275922.2011.574312.
Zhu, D.Z.; Kuhns, H.D.; Brown, S.; Gillies, J.A.; Etyemezian, V.; Gertler, A.W. (2009). Fugitive dust emissions
from paved road travel in the Lake Tahoe Basin. J. Air Waste Manage. Assoc., 59(10):1219-1229.
7-6