Windblown Fugitive Dust Characterization in the Athabasca Oil Sands Region
WBEA-DRI Agreement Number: T108-13
Report submitted to:
Kevin E. Percy and Jean-Guy Zakrevsky
Wood Buffalo Environmental Association #100 – 300 Thickwood Boulevard Ft. McMurray, AB, Canada T9K 1Y1
Report prepared for: Wood Buffalo Environmental Association
Report prepared by:
John G. Watson, Ph.D. Judith C. Chow, Sc.D. Xiaoliang Wang, Ph.D. Steven D. Kohl, M.S. Laxmi Narasimha R. Yatavelli, Ph.D.
Desert Research Institute Nevada System of Higher Education 2215 Raggio Parkway Reno, NV 89512
March 31, 2014
Table of Contents Page List of Abbreviations ...... iii List of Tables ...... iv List of Figures ...... v Executive Summary ...... ix 1 Introduction ...... 1-1 1.1 Background ...... 1-1 1.2 Study Objectives ...... 1-3 1.3 Report Overview ...... 1-3 2 Experimental Methods ...... 2-1 2.1 Windblown Dust Emission Calculation ...... 2-1 2.2 Fugitive Dust Sampling System...... 2-1 2.3 Test Procedure ...... 2-5 2.4 Sampling Sites ...... 2-8 2.5 Laboratory Analysis ...... 2-13 3 Data Validation ...... 3-1 3.1 Mass Closure ...... 3-1 3.2 Anion and Cation Balance ...... 3-3 = 3.3 SO4 versus Total S ...... 3-3 3.4 Concentration Uniformity ...... 3-4 3.5 DRX and OPS Calibrations ...... 3-5 4 Windblown Fugitive Dust Emission Characteristics ...... 4-1 4.1 Data Reduction...... 4-1 4.2 Dust Reservoir Type ...... 4-1 4.3 Threshold Friction Velocity ...... 4-6 4.4 Emission Potential and Flux ...... 4-12 4.5 Effectiveness of Dust Control Measures ...... 4-19 5 Source Profiles ...... 5-1 5.1 Water-soluble Ions ...... 5-1 5.2 Major and Rare-earth Elements ...... 5-7 5.3 Lead Isotopes ...... 5-18 5.4 Carbon Fractions ...... 5-21 5.5 Organic Compound Profiles ...... 5-25 5.6 Profile Similarities, Differences, and Composite Source Profile ...... 5-29 6 Summary and Recommendations for Future Studies ...... 6-1 6.1 Summary of Key Results ...... 6-1 6.2 Recommendations for Future Studies ...... 6-3 7 References ...... 7-1 Appendix A Analytical Detection Limits for Mass, Elements, Lead Isotopes, Ions, Carbon, and Organic Compounds ...... A-1 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 Air Pollution Emission period/step Factors PAH: polycyclic aromatic hydrocarbon ARD: Arizona 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 dusts are normalized to overburden, non-facility dusts are normalized to bare land, and Level III is normalized to non-facility dust. Some species with low abundances in all groups are not listed. Cells with yellow highlight indicate ratios > 2 and cells with blue highlight indicate ratios < 0.5...... 5-37
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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 (1.4OC) 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) Soil Group 1 covering most lichen sites from 2008 study (red triangle); 3) Soil Group 2 covering most oil sands sites from 2008 study (blue inverse triangle); 4) stack emissions collected from AOSR in summer 2008 (red star) (Watson et al., 2010a); 5) stack emissions collected from AOSR in winter 2011 (pink star) (Watson et al., 2011a); 6) engine exhaust from mining trucks collected from AOSR in 2009 (cyan squares) (Watson et al., 2010b); 7) engine exhaust from mining trucks collected from AOSR in 2010 (green circle) (Watson et al., 2011b); 8) lichen samples collected from western Canada from Yukon to the Canada–USA border (Simonetti et al., 2003) and from northeastern America from Hudson Bay to Maryland (purple plus) (Carignan et al., 2002; Carignan and Gariépy, 1995); 9) lichen samples from AOSR (circular hourglass) (Graney et al., 2011); 10) Pb-bearing minerals from northwest Alberta (Paulen et al., 2011) and New Brunswick (Cumming and Richards, 1975; Sturges and Barrie, 1987) (dark yellow cross); 11) Pb-bearing ores from British Colombia, Ontario, and Quebec (Brown, 1962; Cumming and Richards, 1975; Sturges and Barrie, 1987) (blue square); and 12) Ambient aerosols from 7 Canadian cities (Burnaby, Chicoutimi, Victoria, Calgary, Winnipeg, Toronto, and Newfoundland) collected from 1994 to 1999 (dark green triangle)(Bollhöfer and Rosman, 2001)...... 5-21 = Figure 5-14. Abundances of OC, EC, and CO3 -C in PM2.5 and PM10 of the 64 dust samples...... 5-22 Figure 5-15. Abundances of carbon fractions in PM2.5 and PM10 of the 64 dust samples. OC1 to OC4 are organic carbon fractions evolved in a 100% helium (He) atmosphere at 140, 280, 480, and 580 °C, respectively. OP is pyrolyzed carbon. EC1 to EC3 are elemental carbon fractions evolved in a 98% He/2% O2 atmosphere at 580, 740, and 840 °C, respectively. Thermal analysis followed the IMPROVE_A thermal/optical reflectance analysis (TOR) protocol (Chow et al., 2007a)...... 5-24 Figure 5-16. Abundances of non-polar organic compounds grouped into PAHs, lower molecular weight n-alkanes (nC15-nC24), higher molecular weight 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, tailings ponds, unenclosed storage piles, quarry operations, construction sites, agricultural fields, and dry lakes). Fugitive dust is an important source of ambient particulate matter (PM) in Alberta, Canada. According to the National Pollutant Release Inventory (Environment Canada, 2013), fugitive dust accounted for 88 and 95% of total PM2.5 and PM10 (particles with aerodynamic diameter less than 2.5 µm and 10 µm, respectively) primary emissions in Alberta in 2011. Dust plumes are often seen over the tailings ponds during high wind conditions and behind vehicles that are driving on some unpaved roads in the Athabasca Oil Sands Region (AOSR). Several concerns are related to fugitive dust emissions. Extended exposure to elevated levels of dust can cause adverse health effects, particularly if the dust contains crystalline silica, asbestos fibers, heavy metals, disease spores, and other toxins. First Nations communities in the AOSR have implied that dust depositions on their traditional food sources, such as blueberries, have reduced the product yields and made the food more difficult to clean. Excessive dust deposits are found on surfaces inside residences near mining facilities, causing health concerns. Dust plumes can also reduce visibility, possibly leading to lower productivity, more mechanical wear on machinery, and traffic accidents. Knowledge about fugitive dust is limited. The processes of fugitive dust emission, transport, and deposition are poorly characterized. The contributions of fugitive dust to ambient PM concentrations are often overestimated by dispersion models. The chemical composition of dust is not well characterized, and usually limited to routinely analyzed elements and 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 soils. 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):