Windblown Fugitive 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 2 Appendix B Cumulative PM1, PM2.5, PM4, PM10, and PM15 Emission Potential (g/m ) at Different PI-SWERL RPMs for the 64 Fugitive Dust Sampling Sites ...... B-1 2 Appendix C Cumulative PM1, PM2.5, PM4, PM10, and PM15 Emission Flux (g/m /s) at Different PI-SWERL RPMs for the 64 Fugitive Dust Sampling Sites ...... C-1

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Table of Contents, Continued Page Appendix D Source profile tables of elements from Na to U by XRF, water-soluble ions, and carbon fractions ...... D-1 Appendix E Source profile tables of elements measured by ICP-MS including Cs, Be, and 14 rare-earth elements ...... E-1 Appendix F Source profile tables for non-polar organics ...... F-1 Appendix G Source profile tables of carbohydrates, organic acids, and total WSOC ...... G-1 Appendix H Tables of comparison of statistical measured for PM2.5 geological samples from facility and non-facility sites ...... H-1 Appendix I Tables of composite source profiles ...... I-1

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List of Abbreviations σ: uncertainty OC1, OC2, OC3, and OC4: organic carbon evolved at τ: shear stress 140, 280, 480, and 580 °C, respectively, in a 100% τc: time constant for exponential concentration decay He atmosphere AAS: atomic absorption spectroscopy OGS: optical gate sensors AC: automated colorimetry OP: pyrolyzed carbon ADT: average daily traffic OPS: optical particle sizer 2 A : effective area of the PI-SWERL blade P: emission potential (g/m ) eff 2 th agl: above ground level Pi: non-cumulative emission potential (g/m ) for i AMS: WBEA air monitoring station period/step 2 th AOSR: Athabasca Oil Sands Region Pi,cum: cumulative emission potential (g/m ) till i AP-42: U.S. EPA Compilation of Emission period/step Factors PAH: polycyclic aromatic hydrocarbon ARD: road dust Pb: lead babs: light absorption coefficient PCF: DRX photometric calibration factor Ba: barium PI-SWERL: Portable In-Situ Wind Erosion Laboratory C: PM mass concentration (mg/m3) PM: particulate matter ++ Ca : calcium ion PM1: particles with aerodynamic diameter < 1 µm - Cl : chloride PM2.5: particles with aerodynamic diameter < 2.5 µm CMB: Chemical Mass Balance receptor models PM4: particles with aerodynamic diameter < 4 µm = CO3 : carbonate PM10: particles with aerodynamic diameter < 10 µm Cs: cesium PM15: particles with optical diameter < 15 µm DDW: distilled deionized water PMF: Positive Matrix Factorization receptor models ≡ DRI: Desert Research Institute PO4 : phosphate 3 DRX: DustTrak DRX Q: flow rate (m /s) EAF: DRI’s Environmental Analysis Facility R0: surface roughness (m) EC: elemental carbon RH: relative humidity EC1, EC2, and EC3: elemental carbon evolved at 580, RPM: revolutions per minute 740, and 840 °C, respectively, in a 98% He / 2% O2 SCF: DRX size calibration factor = atmosphere SO4 : sulfate 2 th Fi,cum: cumulative emission flux (g/m /s) till i t: time period/step tbegin,1: beginning time of a test g-PM/VKT: grams of particulate matter produced per tend,i: ending time of step i in a test kilometer of travel teff: effective averaging time (s) h: height (m) above ground level T: temperature H: height TC: total carbon HEPA: high efficiency particulate air th TD-GC/MS: thermal desorption-gas i: i period between disturbance or step in the PI- chromatography/mass spectrometry SWERL cycle TOC: total organic carbon ICP/MS: inductively coupled plasma/mass spectrometry TOR: thermal-optical reflectance IC: Ion chromatography TOT: thermal/optical transmittance IMPROVE: Interagency Monitoring of Protected Visual TPM: particles with aerodynamic diameter < ~100 µm Environments TRAKER: Testing Re-entrained Aerosol Kinetic k: particle size multiplier in AP-42 emission estimate + Emissions from Roads K : potassium ion u*: wind friction velocity (m/s) L: length + uh : fastest mile of wind at h m above ground level (m/s) Mg++: magnesium ion ut: threshold friction velocity (m/s) MDL: Minimum detection limit U.S. EPA: United States Environmental Protection N: number of disturbance per year Agency Na+: sodium ion + W: width NH4 : ammonium - WBEA: Wood Buffalo Environmental Association NO2 : nitrite - WSOC: water-soluble organic carbon NO3 : nitrate XRF: X-ray fluorescence OC: organic carbon

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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 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

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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 Teflon- membrane filter channels for all 64 tests...... 3-4 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...... 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

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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 2 Figure 4-7. Example of cumulative PM10 emission potential (g/m ) calculation at different points during the PI-SWERL hybrid test cycle at Site 15...... 4-12 2 Figure 4-8. Cumulative emission flux (g/m /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

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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) 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 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 Figure 5-17. Abundances of mono and di-acids, and water soluble organic carbon (WSOC) normalized to PM2.5 and PM10 mass...... 5-28

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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

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Executive Summary Fugitive dust refers to small particles that become airborne from open sources (e.g., unpaved and paved roads, mining pits, 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 water- soluble 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 real- time 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.

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+ 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 . Organic matter (OM=OC×1.4) 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 1 ions but at ~ /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

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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.

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1 Introduction 1.1 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

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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.

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1.2 Study Objectives The objectives of this project are to: 1) Characterize windblown dust reservoir type, threshold friction velocity, and size- segregated 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.

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2 Experimental Methods 2.1 Windblown Dust Emission Calculation The U.S. EPA’s Compilation of Air Pollutant Emission Factors (AP-42) calculates wind- generated 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 25 for , and (2-2) ∗ 0 for

∗ where is the friction velocity (m/s), and 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:

∗ 0.053 (2-4)

2.2 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]).

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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 lead- acid batteries connected in series. Four optical gate sensors (OGS) were installed on the PI- SWERL 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.

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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 2 effective influence area Aeff of 0.026 m , and the following polynomial equations fit the relation between shear stress (τ), friction speed (u*), and RPM:

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τ 4.05 10RPM 5.3510RPM 2.2010RPM 0.0351 (2-5) ∗ 1.4910RPM 8.2010RPM 1.4210RPM 0.0872 (2-6)

2 The cumulative PM emissions potential (,, in g/m ) 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 supply- unlimited 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 3 (Wang et al., 2009). The PM15 channel can measure maximum concentrations of 600 µg/m . 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 PI- SWERL. 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, size- segregated 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

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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.

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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.

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a) 5000 200 Rotating Speed )

4000 160 3

3000 120

2000 80 PM10

1000 40 Rotating Speed (RPM) Speed Rotating PM2.5 PM Concentration (mg/m

0 0 0 100 200 300 400 500 600 Test Duration (s) b) 5000 120 Rotating Speed

100 ) 4000 3

80 3000 60 2000 40 PM10

1000 20 Rotating Speed (RPM) Speed Rotating PM2.5 PM Concentration (mg/m 0 0 0 200 400 600 800 1000 Test Duration (s) c) 5000 300 ) 4000 240 3 Rotating PM Speed 3000 10 180

2000 120

PM2.5 1000 60 Rotating Speed (RPM) PM Concentration (mg/m

0 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.

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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 × 3 2 min × 3 runs). Therefore, it would require ~33 mg/m 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

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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 Site Waypoint Elevation Description Date ID N W (m) 8/7/2012 1 Ft. McKay unpaved road 57.18840 -111.65849 288 8/8/2012 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 Facility C tailings dike unpaved road 56.98863 -111.73785 392 Facility C tailings dike drifting sand, below pipeline 8/11/2012 12 56.98859 -111.73786 393 facing wind 8/11/2012 13 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 Facility B tailings dike 1, flat undisturbed 57.23893 -111.54681 335 Facility B tailings dike 2, near a slope of windblown 8/15/2012 20 57.23895 -111.54620 332 dust accumulation 8/15/2012 21 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 Facility B T-section by main haul road 57.23433 -111.53659 289 Facility B T-section by main haul road, undisturbed, 8/16/2012 26 57.23423 -111.53653 283 crusted 8/16/2012 27 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

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Table 2-2 continued. Sample Site Description Waypoint Elevation 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 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 Quarry, parking lot for haul trucks 57.19962 -111.54693 283 Ft. MacKay Industrial Park track-out Hwy 63 paved 8/19/2012 39 57.17389 -111.60368 270 road Ft. McKay gravel road at an intersection, watered not 8/19/2012 40 57.17853 -111.63645 259 long ago 8/19/2012 41 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 Hwy 63 rest area south of Ft. McMurray 56.55874 -111.31184 405 Sandy surface near Hwy 63 between Facility C 8/20/2012 44 57.01791 -111.55832 305 ponds 8/20/2012 45 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 Bare land near north Hwy 63 ice road gate 57.54018 -111.36118 299 Unpaved road across Hwy 63 near Facility B tailings 8/15/2013 63 57.20641 -111.59849 275 pond dike 8/15/2013 64 Athabasca Hwy shoulder near Firebag 57.21364 -111.03337 519

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Figure 2-6. Location of the 64 sampling sites. Yellow labels indicate sites sampled in 2012 and red labels indicate sites sampled in 2013.

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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) Years on Record: 28 ANNUAL JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 9.7 8.5 9.2 10.3 11.3 10.9 9.8 9.2 9.0 9.8 10.1 9.0 8.7

Highest Reported Hourly Wind Speed (km/h) Years on Record: 56 ANNUAL JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 71.9 66.9 56.0 54.1 61.0 62.9 47.9 71.9 50.0 51.0 62.9 60.0 52.0

Highest Reported Wind Gust (km/h) Years on Record: 41 ANNUAL JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 113.0 89.0 94.0 74.0 79.0 80.0 97.0 113.0 80.0 96.1 97.0 97.0 85.0

Avg. # of Days w/Wind Above 52 km/h (Days) Years on Record: 16 ANNUAL JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 0.6 0.1 0.1 0.1 0.1 --- 0.1 0.1 --- 0.1 ------

Avg. # of Days w/Wind Above 63 km/h (Days) Years on Record: 16 ANNUAL JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 0.1 0.1 ------

Average Primary Wind Direction (Degree) Years on Record: 28 ANNUAL JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 90 90 90 90 90 90 90 270 270 270 90 90 90

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* + 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) u + (m/s) u + (km/h) (RPM) 10 10 500 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 water- soluble 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. 2 Punches of ~0.5 cm 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 Gases and Chemical Species Analysis Method/ Instruments

Teflon®-membrane filter for both PM2.5 PM2.5 and PM10 mass concentration Gravimetry and PM10 channels (2 µm pore size; Teflo Filter light transmission Tobias TBX-10 Densitometer PTFE-membrane with Elements XRF polymethylpropylene support ring; Pall Cs, Ba, Rare-earth elements, Pb ICP/MS Sciences, Port Washington, NY, USA) isotopes - - - ≡ = Ions (Cl , NO2 , NO3 , PO4 , SO4 , Quartz-fiber filter (1) for both PM and + + ++ + ++ IC, AC, AAS 2.5 NH , Na , Mg , K , Ca ) PM channels (Tissuquartz 2500 QAT- 4 10 Total WSOC TOC UP; (Pall Sciences, Port Washington, NY, WSOC classes, carbohydrate, USA) IC organic acids Quartz-fiber filter (2) for both PM2.5 and OC/EC, carbon fractions, carbonate TOR/TOT Carbon Analyzer PM10 channels (Tissuquartz 2500 QAT- Alkanes, alkenes, PAH, hopanes, UP; Pall Sciences, Port Washington, NY, TD-GC/MS USA) steranes Teflon®-membrane filter for both PM2.5 and PM10 channels (2 µm pore size; Teflo PTFE-membrane with Elements affecting lichen ICP/MS polymethylpropylene support ring; Pall Sciences, Port Washington, NY, USA Nuclepore Track-etch polycarbonate filter For future microscopic particle (0.4 µm pore size; Whatman, Inc., Optical/Electron Microscopes morphology analysis Fairfield, CT, USA)

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)

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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

120

PM 100 2.5 PM10 80

60

40

20

0 Sum of species (% of PM mass) species (% of Sum 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 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 .

a) 200 PM 2.5 Al2O3

mass) 160 SiO2 2.5 CaO 120 FexOy TiO2 80 Ions Elements Organics 40 EC CO3

Constituents (% of PM 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 Site ID b) 200

PM10 Al2O3 mass) SiO 10 150 2 CaO FexOy 100 TiO2 Ions Elements 50 Organics EC CO3

Constituents (% of PM 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 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)

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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 3 = concentrations (µeq/m ). 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  ] [CO  ] [NO  ] [NO  ] [PO  ] [SO  ] µeq / m3 for anions   3  2  3  4  4 (3-1) 35.5 60 / 2 46 62 95/ 3 96 / 2

[NH  ] [Na  ] [Mg  ] [K  ] [Ca  ] µeq / m3 for cations  4     (3-2) 18 23 24.3 / 2 39.1 40.1/ 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 11- 12 times more abundant than anions.

2.5 S29 1:1 2.0 S33 ) 3 1.5 eq/m

 S32

1.0 Cations (

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).

= 3.3 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.

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= Figure 3-4 shows that SO4 is

16 PM 14 3:1 2.5 PM10 14 12 12 10 10 8 8 6 6 Sulfate (% of PM) Sulfate Sulfate (% of PM) (% Sulfate S44 4 4 S2 S2 2 2 S53 S54 S53 S54 0 0 0123456 0 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

7000 20000

(a) PM2.5 (b) PM10 6000

15000 5000 y = 0.996x y = 1.045x R² = 0.975 R² = 0.992 4000 10000 3000 Filter ChannelFilter 2(µg/filter) Filter Channel 2 (µg/filter) 10 2.5 2000 5000

1000

Outliers Mass on Mass on PM Mass onPM 0 0 0 1000 2000 3000 4000 5000 6000 7000 0 5000 10000 15000 20000 Mass on PM Filter Channel 1 (µg/filter) Mass on PM Filter Channel 1 (µg) 2.5 10 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

40 120 ) ) 3 (a) PM2.5 -2012 Tests 3 (b) PM10 -2012 Tests 100 y = 2.318x y = 0.531x 30 R² = 0.802 R² = 0.615 80

20 60

Concentration (mg/m Concentration (mg/m 40 10 2.5 10 20 Filter PM Filter PM Filter 0 0 0 5 10 15 20 0 20406080100120

3 3 DustTrak DRX PM2.5 Concentration (mg/m ) DustTrak DRX PM10 Concentration (mg/m ) 70 160 ) ) 3 (c) PM2.5 -2013 Tests 3 (d) PM10-2013 Tests 60 y = 1.054x y = 1.128x 120 50 R² = 0.808 R² = 0.821

40 80 30 Concentration (mg/m Concentration Concentration (mg/m Concentration 20 2.5 2.5 2.5 40

10

Filter PM Filter 0 Filter PM 0 0 10203040506070 0 40 80 120 160

3 3 DustTrak DRX PM2.5 Concentration (mg/m ) DustTrak DRX PM2.5 Concentration (mg/m )

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.

70 200 ) ) 3 (a) PM2.5 3 (b) PM10 60 y = 3.748x y = 0.305x 150 50 R² = 0.364 R² = 0.802

40 100 30 Concentration (mg/m Concentration (mg/m Concentration

20 10 2.5 50

10 Filter PM Filter Filter PM 0 0 0246810 0 100 200 300 400 500 600 OPS PM Concentration (mg/m3) OPS PM Concentration (mg/m3) 2.5 10 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.

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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

2500 Rotating 100 Speed ) 2000 10 3 )=--0.077t+60.3 10 )=--0.038t+10.4 10 1500 1 )=--0.122t+21.2 Fit Ln(PM 10 Fit Ln(PM

1000 )=--0.039t+19.9 0.1 Fit Ln(PM 10 Fit Ln(PM 500 PM10 0.01 Concentration (mg/m Rotating Speed (RPM) 10 PM 0 0.001 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.

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Table 4-1. Summary of dust reservoir type of each tested site.

RPM Site Description 500 1000 2000 3000 4000 5000 1 Ft. McKay unpaved road L L L UL* NA NA 2 Facility C unpaved road with sulfur deposit L NA L L UL UL 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 L L L UL UL UL 8 Facility C unpaved road on tailings dike 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 Facility C tailings dike unpaved road L L L L UL UL Facility C tailings dike drifting sand, below pipeline 12 L L L L UL UL facing wind 13 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 L L L L L NA 16 Ft. McMurray unpaved road outside Wilson 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 Facility B tailings dike 1, flat undisturbed L L L UL UL UL Facility B tailings dike 2, near a slope of windblown 20 L L L UL UL UL dust accumulation 21 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 Facility B T-section by main haul road L L L L UL UL Facility B T-section by main haul road, undisturbed, 26 L L L L UL UL crusted 27 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 L L L L UL UL 30 Quarry, processing ground, tire tracks L L L L UL UL 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

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Table 4-1 continued.

RPM Site 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 Quarry, parking lot for haul trucks L L L L UL UL Ft. MacKay Industrial Park track-out Hwy 63 paved 39 L L L L UL UL road Ft. McKay gravel road at an intersection, watered not 40 L L L L L UL long ago 41 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 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 L L L UL UL UL Unpaved road across Hwy 63 near Facility B tailings 63 L L L L UL UL pond dike 64 Athabasca Hwy shoulder near Firebag L L L L UL UL

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 3 when the PM10 concentration was >0.01 mg/m 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

5000 Saltation 1000 Threshold ) 100 3 4000 PM ) Threshold -1 10 3000 OGS L1 1 PM 2000 10 OGS 0.1

L2 (mg/m Concentration Rotating Speed (RPM)Rotating OGS Count (s Rate OGS Count 1000 10 Rotating 0.01 Speed PM 0 0.001 0 200 400 600 800 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 Saltation Threshold Site + + + + RPM u* (m/s) u10 (m/s) u10 (km/h) RPM u* (m/s) u10 (m/s) u10 (km/h) 1 959±384 0.23±0.14 4.36±2.71 15.71±9.77 NA NA NA NA 2 968±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 3 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

4-8

Table 4-2 continued.

PM10 Threshold Saltation Threshold Site + + + + 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) 6000

5000

4000

3000

2000

1000 PM Emission Threshold Speed (RPM) Speed Threshold Emission PM

0 5 1015202530354045505560 Site b) 6000

5000

4000

3000

2000

1000 Saltation Threshold Speed (RPM) Saltation Threshold

0 5 1015202530354045505560 Site

Figure 4-5. Threshold RPM for a) PM emission and b) saltation.

4-10

5 1015202530354045505560 6000 PM , 0.002 g/m2 5000 2.5 4000 3000 2000 1000 0 6000 PM , 0.02 g/m2 5000 2.5 4000 3000 2000 1000 0 6000 PM , 0.2 g/m2 5000 2.5 4000 3000 2000 1000 0 6000 PM , 0.002 g/m2 5000 10 4000 3000 2000 1000 0 6000 PM , 0.02 g/m2 5000 10 4000 3000 Threshold RPM to Generate (RPM) RPM Potentials Emission Specific Threshold 2000 1000 0 6000 PM , 0.2 g/m2 5000 10 4000 3000 2000 1000 0 5 1015202530354045505560 Site 2 Figure 4-6. Threshold RPM for generating 0.002, 0.02, and 0.2 g/m 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.

1.E+00 4000

D. Cumulative Emissions at end of 4000 RPM

) 1.E-01 2 3000 RPM C. Cumulative Emissions at end of 3000 RPM 1.E-02

B. Cumulative Emissions 2000 at end of 2000 RPM 1.E-03 Rotating Speed (RPM) A. Cumulative Emissions Instaneous Instaneous (mg/s) or at end of 1000 RPM Emissions 1000

Cumulative Emissions (g/m Emissions Cumulative 1.E-04 Cumulative Emissions

1.E-05 0 0 150 300 450 600 750 Test Duration (s)

2 Figure 4-7. Example of cumulative PM10 emission potential (g/m ) calculation at different points during the PI- SWERL 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) 0.30 500 RPM 1000 RPM 0.25 2000 RPM

/s) 3000 RPM 2 4000 RPM 0.20 5000 RPM

0.15

0.10 Emission Flux (g/m Emission 2.5 PM 0.05

0.00 5 1015202530354045505560 Site b) 0.30 500 RPM 1000 RPM 0.25 2000 RPM

/s) 3000 RPM 2 4000 RPM 0.20 5000 RPM

0.15

0.10 Emission Flux (g/m 10 PM 0.05

0.00 5 1015202530354045505560 Site

2 Figure 4-8. Cumulative emission flux (g/m /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 0.14 8. Tailings dike road 1

s) s) 11. Tailings dike road 2 2 0.12 9. Light vehicle unpaved road-dry 6. Tailings sand beach 0.10 12. Tailings dike drifting sand 7. Overburden 5. Tailings flat sand beach 0.08 4. Tailings sand strip 13. Tailings dike overburden 2. Road with sulfur deposit 0.06 10. Light vehicle unpaved road-wet

0.04 Emission Flux (mg/m

10 0.02 PM 0.00 20 30 40 50 Wind Speed at 10 m agl (km/h) b) Facility B 0.14 27. Unpaved road, tire track

s) s) 23. Tailings beach, truck track 2 0.12 22. Tailings beach, tractor track 25. Main haul road 0.10 28. Overburden berm 21. Tailings dike 20. Tailings dike 0.08 24. Tailings dike 26. Main haul road, undisturbed 19. Tailings dike, undisturbed 0.06

0.04 Emission Flux (mg/m

10 0.02 PM 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 0.14

55. Haul road s)

2 0.12 58. Tailings pond beach 60. Unpaved road near sulfur pile 54. Disturbed coke pile 0.10 56. Tailings pond dike 53. Undisturbed coke pile 59. Unpaved road near sulfur pile 0.08 57. Overburden pit

0.06

0.04 Emission Flux (mg/m

10 0.02 PM 0.00 20 30 40 50 Wind Speed at 10 m agl (km/h) d) Quarry 0.14 37. Road near exit scale

s) s) 29. Conveyor area 2 0.12 38. Parking lot for haul trucks 30. Processing ground, tire tracks 0.10 35. Waste dump, truck track 36. Waste pile 32. Dry road in processing ground 0.08 34. Unpaved road in Pit 31. Waste storage pile hill foot 33. Wet road in processing ground 0.06

0.04 Emission Flux (mg/m

10 0.02 PM 0.00 20 30 40 50 Wind Speed at 10 m agl (km/h)

Figure 4-10 continued.

4-17

e) Ft. McMurray and Ft. McKay 0.14 51. Ft. McMurray unpaved parking lot

s) s) 16. Ft. McMurray unpaved road near Wilson 2 0.12 49. Ft McMurray Thickwood construction 1. Ft. McKay unpaved road 39. Ft. MacKay Industrial Park track-out 0.10 42. Hwy 63 construction zone near BornCo 43. Hwy 63 rest area s. of Ft. McMurray 0.08 52. WBEA Ft. McKay AMS 1 unpaved road 40. Ft. McKay gravel road 50. Ft. McMurray Thickwood land clearance 0.06 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 Emission Flux (mg/m

10 0.02 PM 0.00 20 30 40 50 Wind Speed at 10 m agl (km/h) f) Other locations 0.14 51. Ft. McMurray unpaved parking lot

s) s) 16. Ft. McMurray unpaved road near Wilson 2 0.12 49. Ft McMurray Thickwood construction 1. Ft. McKay unpaved road 39. Ft. MacKay Industrial Park track-out 0.10 42. Hwy 63 construction zone near BornCo 43. Hwy 63 rest area s. of Ft. McMurray 0.08 52. WBEA Ft. McKay AMS 1 unpaved road 40. Ft. McKay gravel road 50. Ft. McMurray Thickwood land clearance 0.06 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 Emission Flux (mg/m

10 0.02 PM 0.00 20 30 40 50 Wind Speed at 10 m agl (km/h)

Figure 4-10 continued.

4-18

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 C(PM10)-Wet Road ) 2

5000 100 C(PM10)-Dry Road ) or 3 4000 10

RPM 3000 1 Emission Potential (g/m Emission Potential

2000 0.1 10 P(PM10)

Dry Road (mg/m Concentration P(PM10) Wet Road 10 1000 0.01 PM RPM RPM Dry Road Wet Road

0 0.001 PM Cumulative 0 200 400 600 800 Test Duration (s) b) 6000 100 RPM ) 2 C(PM10)-Wet Road 5000 10 C(PM )-Dry Road

10 ) or 3 4000 1

RPM 3000

0.1 Emission Potential (g/m

2000 10

P(PM10) Dry Road Concentration (mg/m

0.01 10 1000 PM

P(PM10) Wet Road

0 0.001 PM Cumulative 0 200 400 600 800 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.

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Figure 4-13. Picture of a haul road with stabilized and disturbed (tire track) surfaces (Sites 26 and 27).

6000 1000

C(PM10) ) Disturbed 2 5000 100 RPM C(PM10) ) or Undisturbed 3 4000 10

RPM 3000 1

P(PM10) Disturbed Emission Potential (g/m

2000 0.1 10 Concentration (mg/m Concentration 10 1000 0.01 PM P(PM10) Stabilized

0 0.001 PM Cumulative 0 200 400 600 800

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.

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Without disturbance

After disturbance

Figure 4-15. Picture of a coke pile (Sites 53 and 54) with and without disturbances.

6000 1000

C(PM ) RPM ) 10 2 Disturbed 5000 100 ) or 3 4000 10

C(PM10) Undisturbed

RPM 3000 1 Emission Potential (g/m

2000 0.1 10 P(PM10)

Disturbed Concentration (mg/m 10 1000 0.01 PM

P(PM10) Undisturbed

0 0.001 PM Cumulative 0 200 400 600 800

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.

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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, water- soluble 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

60 - PM2.5 NO 50 2 Cl- NO - mass) 40 3

2.5 3- PO4 30 = SO4 = CO3 20

Anion (% of PM of (% Anion 10

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 Site ID

PM - 60 10 NO2 Cl- NO - mass) 3

10 3- 40 PO4 = SO4 = CO3 20 Anion (% of PM of (% Anion

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 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 = 2 and CO3 (r =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.

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 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

PM2.5 NH + 40 4 Na+

mass) Mg++ 2.5 30 K+ Ca++ 20

10 Cation (% of PM (% Cation

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

Site ID 50

PM10 + NH4 40 Na+ ++ mass) Mg 10 + 30 K ++ Ca 20

10 Cation (% of PM of (% Cation

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

Site ID

Figure 5-2. Abundance of cations in PM2.5 and PM10 of the 64 dust samples.

5-3

6

5 PM2.5

4 PM10 = 4 3 SO 2 1 0 100

80 PM2.5

PM10 = 60 3

CO 40

20

0 3.0 2.5 PM2.5

2.0 PM10 - 1.5 Cl 1.0 0.5 0.0 0.4

PM 0.3 2.5

- PM10 2 0.2 NO 0.1

0.0 1.0 Anion abundance (% of PM mass)

0.8 PM2.5 PM

- 10

3 0.6

NO 0.4

0.2

0.0

0.6 PM2.5

PM10 3-

4 0.4 PO 0.2

0.0 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 Site ID Figure 5-3. Abundance of individual anions in PM2.5 and PM10 of the 64 dust samples.

5-4

50

40 PM2.5 PM 30 10 ++

Ca 20

10

0 1.0

0.8 PM2.5

PM10

+ 0.6 4

NH 0.4

0.2

0.0 3.0 2.5 PM2.5

2.0 PM10 + 1.5 Na 1.0 0.5 0.0 2.5

2.0 PM2.5 PM 1.5 10 ++ Cation abundance (% of PM mass)

Mg 1.0

0.5

0.0 0.4

PM2.5 0.3 PM10

0.2 K+

0.1

0.0 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

Site ID Figure 5-4. Abundance of individual cations in PM2.5 and PM10 of the 64 dust samples.

5-5

a) 60

Ca++ = 1.07 x Ca 50 r2 = 0.69

40

30

20

PM abundance (% of PM mass) 10 2.5 2+ PM10 Ca 0 0 1020304050

Ca abundance (% of PM mass) b) 60

Ca++ = 0.68 x CO = PM2.5 3 50 r2 = 0.6 PM10 40

30

20

abundance (% of PM mass) 10 ++ Ca 0 0 102030405060 CO = abundance (% of PM mass) 3

++ ++ = 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

50 S29-S41 PM2.5 40

30

Ca++ = 7.5 x Mg++ 20 r2 = 0.6

10 abundance (% of PM mass) (% abundance 2+ Ca 0 0.00.51.01.52.02.5 2+ Mg abundance (% of PM mass)

50

PM10 40 S29-S41

30

20 Ca++ = 7.4 x Mg++

abundance (% abundance of PM mass) 10 ++ Ca 0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 ++ Mg 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 non- facility 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

50 PM2.5 elements > 1% 40 mass)

2.5 Al 30 Si K 20 Ca Fe

10 Elements (% of PM of (% Elements

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 Site ID

50

PM10 elements > 1% 40 mass) 10 30 Al Si K 20 Ca Fe 10 Elements (% of PM Elements (%

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 Site ID

Figure 5-7. Elements with average abundance >1% in PM2.5 and PM10 of the 64 dust samples.

5-10

12

10 PM2.5 PM 8 10

Al 6 4 2 0 40

PM2.5 30 PM10

Si 20

10

0 2.5 PM 2.0 2.5 PM10 1.5 K 1.0 0.5

, Fe) abundance (% of PM mass) 0.0 30

PM2.5

20 PM10 Ca 10

0 20 PM 15 2.5 PM10

Major elements (Al, Si, K, Ca Major elements 10 Fe

5

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 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 PM 12 2.5 PM10

S 8

4

0 1.6 PM 1.2 2.5 PM10

Cl 0.8

0.4

0.0 0.8 PM 0.6 2.5 PM10

Ti 0.4

0.2

0.0 0.8

0.6 PM2.5

PM10 0.4 Mn

0.2

0.0 0.08 PM 0.06 2.5 PM10

Sr 0.04 Elements (S, Cl, Ti, Mn, Sr, Ba) abundance (% of PM mass) of (% abundance Cl, Ti, Mn, Sr, Ba) (S, Elements 0.02

0.00 0.6 0.5 PM2.5

0.4 PM10 0.3 Ba 0.2 0.1 0.0 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

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

0.08 PM2.5

0.06 PM10 V 0.04 0.02 0.00 0.05

0.04 PM2.5

0.03 PM10 Ni 0.02 0.01 0.00 0.05

0.04 PM2.5 PM 0.03 10

Cu 0.02 0.01 0.00 0.07 0.06 PM 0.05 2.5 PM 0.04 10

Zn 0.03 0.02 0.01 0.00 0.04 PM 0.03 2.5 PM

Elements (V,Elements Ni, Cu. Zn, Rb, Y) mass) (% of abundance PM 10 0.02 Rb 0.01

0.00 0.030 0.025 PM2.5 0.020 PM10

Y 0.015 0.010 0.005 0.000 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 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

0.25

0.20 PM2.5

0.15 PM10

Cs 0.10 0.05 0.00 0.04 PM 0.03 2.5 PM10 0.02 Pb 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 Elements (Cs, Pb) abundance (% of PM mass) PM of (% abundance Pb) (Cs, Elements 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

PM 3.0e-3 2.5

PM10 2.0e-3 La

1.0e-3

0.0 1.4e-2 1.2e-2 PM 1.0e-2 2.5 PM 8.0e-3 10

Ce 6.0e-3 4.0e-3 2.0e-3 0.0 1.0e-3

8.0e-4 PM2.5 PM 6.0e-4 10

Pr 4.0e-4 2.0e-4 0.0 4.0e-3

PM 3.0e-3 2.5 PM10 2.0e-3 Nd

1.0e-3

0.0 8.0e-4

PM 6.0e-4 2.5

PM10 4.0e-4 Sm 2.0e-4

0.0 2.0e-4

PM 1.5e-4 2.5 PM10

Elements Gd) abundance (La, Ce, Pr, Nd, Sm, Eu, mass) (% of PM 1.0e-4 Eu

5.0e-5

0.0 8.0e-4 PM 6.0e-4 2.5 PM10 4.0e-4 Gd

2.0e-4

0.0 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

Site ID Figure 5-11. Abundance of rare earth elements in PM2.5 and PM10.

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1.0e-4

8.0e-5 PM2.5 PM 6.0e-5 10

Tb 4.0e-5 2.0e-5 0.0 6.0e-4 5.0e-4 PM2.5

4.0e-4 PM10 3.0e-4 Dy 2.0e-4 1.0e-4 0.0 1.0e-4

8.0e-5 PM2.5 PM 6.0e-5 10

Ho 4.0e-5 2.0e-5 0.0 3.0e-4 2.5e-4 PM2.5 2.0e-4 PM10 1.5e-4 Er 1.0e-4 5.0e-5 0.0 5e-5

4e-5 PM2.5 PM 3e-5 10

Tm 2e-5 1e-5 0 3.0e-4 2.5e-4 PM2.5

2.0e-4 PM10

Elements (La, Ce, Pr, Nd, Sm, Eu, Gd) abundance (%mass) of PM 1.5e-4 Yb 1.0e-4 5.0e-5 0.0 5e-5

4e-5 PM2.5

PM10 3e-5

Lu 2e-5

1e-5

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

Site ID Figure 5-11 continued. Abundance of rare earth elements in PM2.5 and PM10.

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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, 1.165- 1.259, 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

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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, Pb- bearing 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.

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a) b) 0.060 3.0

PM2.5 PM2.5

0.055 2.8 S33 S38

S29 0.050 2.6 S29

S33 204Pb/206Pb 208Pb/207Pb S38 0.045 S38 2.4

0.040 2.2 1.0 1.2 1.4 1.6 1.8 1.0 1.2 1.4 1.6 1.8 206Pb/207Pb 206Pb/207Pb c) d) 0.058 2.65

PM10 PM 0.056 2.60 10 S64 0.054 2.55

0.052 2.50

204Pb/206Pb 0.050

208Pb/207Pb 2.45

0.048 S64 2.40 0.046 2.35 1.12 1.16 1.20 1.24 1.28 1.32 1.12 1.16 1.20 1.24 1.28 1.32 206Pb/207Pb 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.

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2.9

2.8 This study Soil group 1 (2008 study) 2.7 Soil group 2 (2008 study) Stach emissions 2008 2.6 Stack emissions 2011 Truck exhaust 2009 Truck exhaust 2010 2.5 Lichen W/NE Canada

208Pb/207Pb Lichen AOSR 2.4 Pb ore AB/NB Pb ore BC/ON/QC 2.3 Ambient aerosol

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

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++ = 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 PM2.5 carbon fractions 100 OC mass) 80 EC 2.5 = CO3 -C 60

40

20 Carbon (% of PM of (% Carbon 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 Site ID 100 PM10 carbon fractions 80 OC mass) EC

10 = 60 CO3 -C

40

20 Carbon (% of PM of (% Carbon 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

Site ID = Figure 5-14. Abundances of OC, EC, and CO3 -C in PM2.5 and PM10 of the 64 dust samples.

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= Table 5-2. Comparison of OC, EC, and CO3 -C in PM10 between this and other studies.

OC EC CO =-C Sample 3 Reference Ave Range Ave Range Ave Range Facility and non-facility sitesa 8.58 0-53.6 2.13 0-38.7 2.06 0-10.9 This study AOSR-Oil sands sites 9.9 3.5‒16.5 1.1 0.1‒10.1 0.8 0‒3.0 AOSR-Lichen sites 5.2 1.0‒13.8 0.2 0‒0.8 0.6 0‒2.7 2008 study AOSR-Highway 8.5±0.9 0.12±0.08 0.57±0.36 Chinese Loess 3.1±1.5 0.03±0.03 3.4±1.6 (Cao et al., 2008) SJV paved road, California, USA 6.9±3.7 1.0±1.0 1.3±0.6 SJVb unpaved road, California, 3.2±1.4 0.3±0.3 1.2±0.3 USA SJVb agriculture soil, California, 3.0±1.7 0.2±0.5 0.9±0.7 USA SJVb animal husbandry, (Chow et al., 2003) 18.4±7.3 0.6±0.7 0.3±0.5 California, USA SJVb lake drainage, California, 2.6±1.0 0.1±0.4 0.9±0.3 USA SJVb construction, California, 2.9±1.5 0.4±0.4 0.5±0.6 USA Hong Kong country park soil 4.2±0.4 0.04±0.05 NA Hong Kong urban soil 6.5±2.8 0.4±0.8 NA (Ho et al., 2003) HK paved road 13.9±2.6 1.3±0.8 NA Mexicali road dust, Mexico 8.2±1.9 0.09±0.13 NA Imperial County dirt, California, 3.6±1.7 0.01±0.07 NA USA (Watson and Chow, 2001) Imperial County road dust, 11.4±4.2 0.38±0.21 NA California, USA a See Table 2-2 for site descriptions. b SJV: San Joaquin Valley, central California

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120

OC1 PM2.5 carbon fractions 100 OC2

mass) OC3 80 2.5 OC4 OP 60 EC1 EC2 40 EC3

20 Carbon(% of PM 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

Site ID 100

OC1 PM10 carbon fractions 80 OC2

mass) OC3

10 OC4 60 OP EC1 40 EC2 EC3 20 Carbon (% of PM (% of Carbon 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

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).

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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 1- 48. 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 n- alkanes 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 1- octadecene) 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.

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0.04

0.03 PM2.5

PM10 0.02 PAHs 0.01

0.00 0.014 0.012 PM2.5 0.010 PM 0.008 10 0.006 0.004 nC15-nC24 0.002 0.000 0.006 0.005 PM 0.004 2.5 PM 0.003 10 0.002

nC25-nC40 0.001 0.000 0.030 0.025 PM 0.020 2.5 PM 0.015 10 0.010 Hopanes 0.005 0.000 0.004

0.003 PM2.5

PM10 0.002 Non-polar Organic Compound Abundance (% of PM mass) of PM (% Abundance Compound Organic Non-polar

Steranes 0.001

0.000 0.014 0.012 X Data PM2.5 0.010 PM 0.008 10 0.006 Others 0.004 0.002 0.000 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 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.

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3.0

2.5 Lactic acid PM2.5 monoacid fractions 2.0 Acetic acid Formic acid 1.5 1.0 0.5 0.0 2.0 PM monoacid fractions Lactic acid 10 1.6 Acetic acid 1.2 Formic acid

Monoacids (% of (% PM mass) Monoacids 0.8

0.4

0.0 2.5

PM2.5 diacid fractions Succinic 2.0 Glutric Malonic 1.5 Maleic 1.0 Oxalic

0.5

0.0 0.30

0.25 PM10 diacid fractions Succinic Glutaric 0.20 Malonic Diacids (% ofmass) PM (% Diacids 0.15 Maleic Oxalic 0.10 0.05 0.00 12 10 PM2.5

8 PM10 6

WSOC 4 2 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 Site ID

Figure 5-17. Abundances of mono and di-acids, and water soluble organic carbon (WSOC) normalized to PM2.5 and PM10 mass.

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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%

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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 road- wet (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

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(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.

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Table 5-3. Source profile-compositing scheme.

Level III Level II Level I Road near sulfur pile S2, S3, S59, S60 Coke pile S53, S54 Tailing pond-dike S4, S5, S6, S11, S12, S13, S19, S20, S21, S22, S23, S24, sand S56, S58 Facility soil Overburden-bare S7, S28, S57 land Unpaved road S8, S9, S10, S25, S26, S27, S55 Quarry S29, S30, S31, S32, S33, S34, S35, S36, S37, S38 S1, S40, S52, S15, S47, S16, S49, S51, S63, S48, S45, S46, Unpaved road Non-facility S64 soil Paved road S41, S39, S17, S14, S18, S42, S43 Bare land 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.

Percent Distribution Correlation t-statistic Profile #1 Profile #2 <1σ 1σ-2σ 2σ-3σ >3σ coefficient (r) P value Level II Road near Coke pile 82% 16% 3% 0% 0.26 0.073 sulfur pile Tailing pond-dike sand 99% 1% 0% 0% 0.90 0.005 Overburden-bare land 99% 1% 0% 0% 0.71 0.485 Unpaved road 100% 0% 0% 0% 0.73 0.501 Quarry 85% 10% 4% 0% 0.91 0.671 Non-facility unpaved 100% 0% 0% 0% 0.75 0.914 road Non-facility paved road 98% 2% 0% 0% 0.79 0.181 Non-facility bare land 100% 0% 0% 0% 0.51 0.400 Coke pile Tailing pond-dike sand 70% 11% 15% 4% 0.32 0.034 Overburden-bare land 78% 14% 3% 4% 0.53 0.083 Unpaved road 64% 20% 10% 6% 0.37 0.067 Quarry 81% 7% 8% 4% 0.37 0.080 Non-facility unpaved 65% 25% 8% 3% 0.40 0.074 road Non-facility paved road 59% 22% 11% 7% 0.36 0.057 Non-facility bare land 93% 3% 2% 2% 0.41 0.066 Tailing pond- Overburden-bare land 100% 0% 0% 0% 0.77 0.505 dike sand Unpaved road 100% 0% 0% 0% 0.86 0.078

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Table 5-4 continued Percent Distribution Correlation t-statistic Profile #1 Profile #2 <1σ 1σ-2σ 2σ-3σ >3σ coefficient (r) P value Quarry 79% 8% 9% 3% 0.93 0.851 Non-facility unpaved 91% 9% 0% 0% 0.87 0.591 road Non-facility paved 91% 9% 0% 0% 0.89 0.048 road Non-facility bare land 100% 0% 0% 0% 0.67 0.082 Overburden-bare Unpaved road 99% 1% 0% 0% 0.79 0.037 land Quarry 84% 13% 3% 1% 0.80 0.109 Non-facility unpaved 93% 7% 0% 0% 0.77 0.081 road Non-facility paved 89% 11% 0% 0% 0.75 0.025 road Non-facility bare land 99% 1% 0% 0% 0.62 0.039 Unpaved road Quarry 79% 9% 12% 0% 0.83 0.157 Non-facility unpaved 96% 4% 0% 0% 0.87 0.690 road Non-facility paved 93% 7% 0% 0% 0.81 0.115 road Non-facility bare land 100% 0% 0% 0% 0.66 0.461 Non-facility unpaved Quarry 80% 20% 0% 0% 0.82 0.367 road Non-facility paved 78% 7% 13% 2% 0.85 0.123 road Non-facility bare land 97% 3% 0% 0% 0.63 0.190 Non-facility Non-facility paved 98% 2% 0% 0% 0.91 0.080 unpaved road road Non-facility bare land 100% 0% 0% 0% 0.72 0.153 Non-facility Non-facility bare land 99% 1% 0% 0% 0.63 0.062 paved road Level III Facility soil Non-facility soil 99% 1% 0% 0% 0.90 0.028

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1e+3 1e+2 Road near sulfur pile PM2.5 1e+1 1e+0 1e-1 1e-2 1e-3 1e-4 1e+3 1e+2 Coke pile PM2.5 1e+1 1e+0 1e-1 1e-2 1e-3 1e-4 1e+2

1e+1 Tailing pond-dike sand PM2.5 1e+0 1e-1

mass) 1e-2 2.5 1e-3 1e-4 1e+2

1e+1 Overburden bare land PM2.5 1e+0 1e-1

Abundance (% of PM 1e-2 1e-3 1e-4 1e+2

1e+1 Unpaved road PM2.5 1e+0 1e-1 1e-2 1e-3 1e-4 1e+2

1e+1 Quarry PM2.5 1e+0 1e-1 1e-2 1e-3 1e-4 Y P S K V U In Tl Ti Al Si Ni Zr Cl Br Sr Cr La As Sc Fe Zn Tb Se Eu Au Pb Pd Ag Sn Sb Cs Ba K+ Co Cu Ce Hg Rb Nb Cd Na Ca Cl- Ga TC Mn Mo EC Sm OC Wo Na+ EC1 EC2 EC3 OC1 OC2 OC3 OC4 OPT OPR NO3- NO2- Ca2+ Mg2+ NH4+ 141Pr 166Er 139La 175Lu 159Tb PO43- SO42- 133Cs 137Ba 153Eu 163Dy 172Yb 208Pb CO32- 140Ce 146Nd 165Ho 157Gd 169Tm WSOC 147Sm Glycerol Lactic acid Acetic acid Oxalic acid Oxalic Maleic acid Maleic Formic acid Formic Species Glutaric acid

Figure 5-18. Level II PM2.5 composite profiles for subgroups in facility facilities.

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1e+2

1e+1 Non-facility unpaved road PM2.5 1e+0 1e-1 1e-2 1e-3 1e-4 1e+2 mass)

2.5 1e+1 Non-facility paved road PM2.5 1e+0 1e-1 1e-2 1e-3 1e-4 1e+2 Abundance (% of PM 1e+1 Non-facility bare land PM2.5 1e+0 1e-1 1e-2 1e-3 1e-4 P S K V Y U In Ti Tl Al Si Cl Ni Zr Br Sr Cr La Sc As Fe Zn Tb Eu Au Pb Se Pd Ag Sn Sb Cs Ba K+ Hg Co Cu Na Ca Cd Ce Cl- Rb Nb Ga TC Mn EC Mo Sm OC Wo Na+ EC1 EC2 EC3 OC1 OC2 OC3 OC4 OPT OPR NO2- NO3- Ca2+ Mg2+ NH4+ 166Er 141Pr 175Lu 139La 159Tb PO43- SO42- 172Yb 208Pb 153Eu 163Dy 133Cs 137Ba CO32- 165Ho 140Ce 146Nd 157Gd 169Tm 147Sm WSOC Glycerol Lactic acid Lactic Acetic acid Acetic Oxalic acidOxalic Maleic acid Maleic Formic acid Formic Species acid Glutaric 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.

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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

1e+1 Facility soil PM2.5 1e+0 1e-1

mass) 1e-2 2.5 1e-3 1e-4 1e+2

1e+1 Non-facility soil PM2.5 1e+0 1e-1

Abundanceof PM (% 1e-2 1e-3 1e-4 P S K V Y U In Ti Tl Al Si Cl Ni Zr Br Sr Cr La Sc As Fe Zn Tb Se Pd Ag Sn Sb Cs Ba Eu Au Pb K+ Na Rb Ca Co Cu Cd Nb Ce Hg Cl- TC Ga EC Mn Mo Sm OC Wo Na+ EC1 EC2 EC3 OC1 OC2 OC3 OC4 OPT OPR NO2- NO3- Ca2+ Mg2+ NH4+ 166Er 141Pr 175Lu 139La 159Tb PO43- SO42- 153Eu 163Dy 172Yb 208Pb 133Cs 137Ba CO32- 165Ho 140Ce 146Nd 157Gd 169Tm WSOC 147Sm Glycerol Lactic acid Lactic Acetic acid Oxalic acidOxalic Maleic acid Maleic Formic acid Formic Glutaric acid

Species

Figure 5-20. Level III PM2.5 composite profiles.

1 Ratio between facility and non-facility soils.

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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.

Level II Level III Facility dust Non-facility dust Non- Species Facility Road near Coke Tailing pond- Overburden- Unpaved Unpaved Paved Bare facility Quarry dust sulfur pile pile dike sand bare land road road road land dust Cl- 2.9 1.1 1.3 1.0 1.6 2.7 0.8 0.4 1.0 0.2 1.0 - NO3 1.1 1.3 1.3 1.0 1.5 6.1 6.4 1.1 1.0 0.6 1.0 2- SO4 0.9 0.2 1.5 1.0 0.7 1.1 0.6 0.5 1.0 1.0 1.0 + NH4 0.8 0.5 0.7 1.0 0.4 0.6 0.9 1.1 1.0 0.9 1.0 Na+ 0.6 0.2 0.8 1.0 0.8 2.0 0.4 0.4 1.0 0.7 1.0 Mg++ 1.9 0.5 1.5 1.0 1.7 2.2 1.3 1.6 1.0 0.4 1.0 K+ 1.2 0.5 1.2 1.0 1.7 3.5 0.9 0.8 1.0 0.8 1.0 Ca++ 2.2 0.7 1.4 1.0 1.6 19.1 1.6 2.2 1.0 0.8 1.0

OC2a 0.6 1.2 0.7 1.0 0.6 0.7 1.7 2.5 1.0 1.7 1.0 OC3a 0.8 1.2 0.8 1.0 0.6 0.7 1.2 1.4 1.0 1.0 1.0 OC4a 0.6 1.3 0.8 1.0 0.6 0.9 0.8 1.0 1.0 0.9 1.0 OPTa 0.4 1.7 0.5 1.0 0.3 0.5 0.8 0.9 1.0 2.1 1.0 OPRa 0.5 1.5 0.5 1.0 0.4 0.6 1.6 1.7 1.0 2.1 1.0 OCa 0.6 1.4 0.7 1.0 0.5 0.6 1.2 1.4 1.0 1.4 1.0 EC1a 0.4 7.0 0.5 1.0 0.3 0.4 0.5 0.6 1.0 3.6 1.0 EC2a 0.8 1.6 1.5 1.0 1.0 1.7 1.0 1.6 1.0 1.5 1.0 EC3a 0.1 0.4 0.1 1.0 0.1 0.0 0.1 0.0 1.0 2.9 1.0 ECa 0.0 24.7 0.5 1.0 0.0 0.0 0.0 0.1 1.0 5.3 1.0 =- CO3 33.1 0.2 8.7 1.0 9.5 114.7 2.5 3.9 1.0 0.8 1.0 TC 0.6 4.9 0.6 1.0 0.4 0.8 0.8 1.0 1.0 2.1 1.0

Al 0.7 0.2 1.5 1.0 1.0 0.6 1.0 1.2 1.0 1.0 1.0 Si 0.5 0.2 0.9 1.0 0.9 0.4 0.9 0.9 1.0 0.9 1.0 S 1.7 4.5 0.9 1.0 0.6 0.5 0.8 0.8 1.0 2.4 1.0 Cl 2.3 0.7 0.7 1.0 1.3 0.9 1.4 0.7 1.0 0.2 1.0 K 0.7 0.2 1.1 1.0 1.1 0.9 1.2 1.2 1.0 0.9 1.0 Ca 2.3 0.6 1.2 1.0 2.2 12.0 2.0 3.0 1.0 0.6 1.0 Sc 5.9 0.9 2.6 1.0 2.4 3.6 30.0 93.2 1.0 0.9 1.0 Ti 0.6 0.4 1.0 1.0 0.9 0.5 1.0 1.0 1.0 1.1 1.0 V 0.4 10.8 0.8 1.0 0.9 0.3 0.8 1.9 1.0 2.3 1.0 Cr 1.0 0.2 1.2 1.0 0.8 0.6 1.3 2.3 1.0 1.4 1.0 Mn 1.5 0.7 1.3 1.0 1.5 0.4 0.6 0.5 1.0 0.4 1.0 Fe 2.0 0.6 1.3 1.0 2.1 0.5 1.5 1.5 1.0 0.7 1.0 Ni 0.6 4.6 0.7 1.0 0.3 0.2 1.9 0.8 1.0 2.0 1.0 Cu 1.5 0.9 0.9 1.0 1.0 1.2 1.0 2.5 1.0 0.6 1.0 Zn 1.9 1.1 1.8 1.0 1.7 1.1 0.6 1.2 1.0 0.4 1.0 Br 1.5 0.3 0.8 1.0 0.9 0.7 7.0 3.9 1.0 0.8 1.0 Rb 0.5 0.3 1.3 1.0 1.1 1.1 1.1 1.2 1.0 0.9 1.0 Sr 0.9 0.4 0.9 1.0 1.2 1.5 1.4 1.4 1.0 0.7 1.0 Y 0.6 0.3 0.5 1.0 1.1 0.7 1.3 3.5 1.0 0.8 1.0 Zr 0.5 0.4 0.6 1.0 0.7 0.4 1.1 1.3 1.0 1.0 1.0 Nb 0.6 0.1 0.7 1.0 0.8 0.6 2.4 5.1 1.0 0.6 1.0 Pd 3.0 0.6 2.7 1.0 2.8 0.6 0.4 1.5 1.0 1.1 1.0 In 3.4 0.0 0.4 1.0 1.0 0.9 0.7 3.7 1.0 1.6 1.0 Sn 4.0 0.1 1.7 1.0 0.7 0.8 3.7 9.1 1.0 1.5 1.0 Sb 1.1 0.2 0.2 1.0 0.2 0.8 13.6 11.1 1.0 1.3 1.0 Ba 0.7 0.3 0.9 1.0 1.0 1.0 1.5 3.2 1.0 0.5 1.0 Eu 2.3 0.0 1.1 1.0 0.0 0.9 6.9 3.2 1.0 0.7 1.0 Tb 13.3 0.7 0.0 1.0 0.6 0.2 1.2 0.5 1.0 0.8 1.0 Wo 2.8 0.0 0.6 1.0 0.7 0.7 0.4 0.7 1.0 0.5 1.0 Tl 0.4 0.0 0.3 1.0 0.4 0.1 0.2 3.3 1.0 2.8 1.0 Pb 0.2 0.0 0.9 1.0 0.9 1.1 0.5 0.2 1.0 0.7 1.0 U 0.0 1.6 5.6 1.0 4.5 0.3 4.0 4.2 1.0 0.7 1.0 133Cs 1.0 0.0 1.3 1.0 1.2 1.0 2.1 35.2 1.0 0.2 1.0 137Ba 1.3 0.4 1.0 1.0 1.2 0.5 1.1 1.5 1.0 0.8 1.0 139La 1.1 0.4 1.3 1.0 1.2 0.6 1.2 1.5 1.0 1.0 1.0 140Ce 1.3 0.4 1.3 1.0 1.1 0.6 1.2 1.5 1.0 1.0 1.0 141Pr 1.1 0.4 1.3 1.0 1.2 0.5 1.2 1.5 1.0 1.0 1.0

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Table 5-5 Continued. Level II Level III Facility dust Non-facility dust Non- Species Facility Road near Coke Tailing pond- Overburden- Unpaved Unpaved Paved Bare facility Quarry dust sulfur pile pile dike sand bare land road road road land dust 146Nd 1.2 0.4 1.3 1.0 1.1 0.5 1.3 1.4 1.0 1.0 1.0 147Sm 1.2 0.5 1.4 1.0 1.2 0.5 1.2 1.5 1.0 1.0 1.0 153Eu 1.2 0.5 1.4 1.0 1.2 0.5 1.3 1.6 1.0 1.0 1.0 157Gd 1.3 0.6 1.3 1.0 1.2 0.5 1.2 1.4 1.0 1.0 1.0 159Tb 1.3 0.6 1.4 1.0 1.3 0.5 1.3 1.6 1.0 1.0 1.0 163Dy 1.4 0.6 1.4 1.0 1.3 0.5 1.3 1.6 1.0 1.0 1.0 165Ho 1.4 0.6 1.4 1.0 1.4 0.6 1.4 1.9 1.0 1.0 1.0 166Er 1.5 0.6 1.3 1.0 1.4 0.6 1.4 1.8 1.0 1.0 1.0 169Tm 1.2 0.4 1.4 1.0 1.5 0.7 1.7 2.7 1.0 1.0 1.0 172Yb 1.6 0.5 1.3 1.0 1.4 0.7 1.5 1.9 1.0 1.0 1.0 175Lu 1.2 0.3 1.4 1.0 1.5 0.8 2.0 3.0 1.0 1.0 1.0 208Pb 1.6 0.8 1.2 1.0 1.0 0.8 0.9 0.9 1.0 0.8 1.0 Lactic acid 3.9 0.5 0.5 1.0 0.8 1.3 1.5 1.5 1.0 1.0 1.0 Acetic acid 4.3 0.0 1.1 1.0 1.5 3.4 2.0 5.4 1.0 0.6 1.0 Formic acid 2.5 0.6 1.1 1.0 1.5 2.0 1.8 2.0 1.0 0.5 1.0 Oxalic acid 8.6 0.8 0.5 1.0 1.5 0.6 1.5 1.4 1.0 1.3 1.0 WSOC 4.7 0.5 0.8 1.0 0.6 0.9 0.9 1.4 1.0 0.9 1.0 a 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.

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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 = 11- + 16 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 2 sites did not reach 0.2 g/m 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 PI- SWERL 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 + 2.38E-05, 8.05E-05, 7.92E-03, 0.025, 0.11, and 0.13 g/m /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.

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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,

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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 size- segregated 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

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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.

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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 th and PM10 filter samples at the Ft. McKay AMS 1 every 6 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).

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