Characterization of PM<sub>2.5</sub> and PM<sub>10</sub> fugitive dust source profiles in the Athabasca Oil Sands Region

Wang, Xiaoliang; Chow, Judith C.; Kohl, Steven D.; Percy, Kevin E.; Legge, A.H.; Watson, John G.

doi:10.1080/10962247.2015.1100693

Cited by 64 publications

(55 citation statements)

References 63 publications

(72 reference statements)

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“…EC abundances were lowest in NS (0.004% ± 0.009%) while Inorganic ions fraction were most abundant in URD (1.49% ± 1.06%). These results agreed well with studies in other regions of the world [15][16][17]19], and indicated that human activity-related factors, such as vehicle exhaust, can greatly modify the chemical characteristics of entrained crustal material from sources affected by these activities.…”

Section: Contributions Of the Major Componentssupporting

confidence: 81%

Characteristics of PM10 Chemical Source Profiles for Geological Dust from the South-West Region of China

Liu

Zhang

et al. 2016

Atmosphere

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Ninety-six particulate matter (PM 10 ) chemical source profiles for geological sources in typical cities of southwest China were acquired from Source Profile Shared Service in China. Twenty-six elements (Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Sr, Cd, Sn, Sb, Ba, Be, Tl and Pb), nine ions (F − , Cl − , SO 4 2− , NO 3 − , Na + , NH 4 + , K + , Mg 2+ and Ca 2+ ), and carbon-containing species (organic carbon and elemental carbon) were determined to construct these profiles. Individual source profiles were averaged and compared to quantify similarities and differences in chemical abundances using the profile-compositing method. Overall, the major components of PM 10 in geological sources were crustal minerals and undefined fraction. Different chemical species could be used as tracers for various types of geological dust in the region that resulted from different anthropogenic influence. For example, elemental carbon, V and Zn could be used as tracers for urban paved road dust; Al, Si, K + and NH 4 + for agricultural soil; Al and Si for natural soil; and SO 4 2− for urban resuspended dust. The enrichment factor analysis showed that Cu, Se, Sr and Ba were highly enriched by human activities in geological dust samples from south-west China. Elemental ratios were taken to highlight the features of geological dust from south-west China by comparing with northern urban fugitive dust, loess and desert samples. Low Si/Al and Fe/Al ratios can be used as markers to trace geological sources from southwestern China. High Pb/Al and Zn/Al ratios observed in urban areas demonstrated that urban geological dust was influenced seriously by non-crustal sources.

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Section: Contributions Of the Major Componentssupporting

confidence: 81%

Characteristics of PM10 Chemical Source Profiles for Geological Dust from the South-West Region of China

Liu

Zhang

et al. 2016

Atmosphere

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“…The TERRA algorithm, aircraft observations of total particulate matter number concentration and size close to the sources, and the fugitive dust speciation reported in Wang et al (2015) were used to estimate fugitive dust emissions for six oil sands facilities, for the 12 particle bin version of the GEM-MACH model (Zhang et al, 2017). We refer to these emissions and corrections to deposition based on them hereafter as "aircraft-observation-based".…”

Section: Estimates Of Primary Particulate Emissions and Resulting Bc mentioning

confidence: 99%

Estimates of Exceedances of Critical Loads for Acidifying Deposition in Alberta and Saskatchewan

Makar¹,

Akingunola²,

Aherne³

et al. 2018

Preprint

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Abstract.Estimates of potential harmful effects to ecosystems in the Canadian provinces of Alberta and Saskatchewan due to acidifying deposition were calculated, using a one year simulation of a high resolution implementation of the Global Environmental Multiscale -Modelling Air-quality and Chemistry (GEM-MACH) model, and estimates of aquatic and terrestrial ecosystem critical loads. The model simulation was evaluated against two different sources of deposition data; 20 total deposition in precipitation and total deposition to snowpack in the vicinity of the Athabasca oil sands. The model captured much of the variability of observed ions in wet deposition in precipitation (observed versus model sulphur, nitrogen and base cation R 2 values of 0.90, 0.76 and 0.72, respectively), while being biased high for sulphur deposition, and low for nitrogen and base cations (slopes 2.2, 0.89 and 0.40, respectively). Aircraft-observation-based estimates of fugitive dust emissions, shown to be a factor of ten higher than reported values (Zhang et al.., 2017), were used to estimate the impact of 25 increased levels of fugitive dust on model results. Model comparisons to open snowpack observations were shown to be biased high, but in reasonable agreement for sulphur deposition when observations were corrected to account for throughfall in needleleaf forests. The model-observation relationships for precipitation deposition data, along with the expected effects of increased (unreported) base cation emissions, were used to provide a simple observation-based correction to model deposition fields. Base cation deposition was estimated using published observations of base cation fractions in surface 30 collected particles .Both original and observation-corrected model estimates of sulphur, nitrogen and base cation deposition were used in conjunction with critical load data created using the NEG-ECP (2001) and CLRTAP (2004, 2016, 2017) Base cation deposition was shown to have a neutralizing effect on acidifying deposition, and the use of the aircraft and precipitation observation-based corrections to base cation deposition resulted in reasonable agreement with snowpack data collected in the oil sands area. However, critical load exceedances calculated using both observations and observationcorrected deposition suggest that the neutralization effect is limited in spatial extent, decreasing rapidly with distance from 10 emissions sources, due to the rapid deposition of emitted primary particles dust particles as a function of their size.

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“…That site was excluded from the organic-carbon range discussion in Wang et al (2015) and was excluded here as well.…”

Section: Epa Speciate V43 Database (See Section 3) Wang Et Al (201mentioning

confidence: 99%

“…Their analysis showed that PM speciation is clearly different between the dust collected from the facility sites and from the forest sites. For this study, the new AOSR-15 specific fugitive-dust PM speciation profile was compiled by averaging the site-specific profiles from all 17 facility sites from Wang et al (2015) to represent surface PM speciation with the following three exceptions: 1) For the unpaved-road site S16, the elemental-carbon percentage seemed to be too large, which might be an artefact due to dry deposition from heavy-duty diesel exhaust (Wang et al, 2015). This site was excluded from the facility profile average in their study and was excluded in this study too.…”

Section: Epa Speciate V43 Database (See Section 3) Wang Et Al (201mentioning

confidence: 99%

“…One of these simulations focussed on the impact of the increases of VOC and primary OM emissions on total organic aerosol and the formation of secondary organic aerosol (Stroud et al, 2017, this special issue). The second examined the impact of the increased crustalmaterial emissions on acid deposition by making use of the Wang et al (2015) PM speciation profile to further speciate the model's crustal material into a base-cation fraction (Makar et al, 2017, this special issue). Both 15 studies suggest that these improvements to emissions have a significant impact on model performance.…”

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confidence: 99%

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Emissions Preparation and Analysis for Multiscale Air Quality Modelling over the Athabasca Oil Sands Region of Alberta, Canada

Zhang¹,

Moran²,

Zheng³

et al. 2018

Preprint

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Abstract. The oil sands of Alberta, Canada are classified as unconventional oil, but they are also the thirdlargest oil reserves in the world, behind only Venezuela and Saudi Arabia. We describe here a six-year effort to improve the emissions data used for air quality (AQ) modelling of the roughly 100 km x 100 km oil extraction 15 and processing industrial complex operating in the Athabasca Oil Sands Region (AOSR) of north-eastern Alberta. The objective of this work was to review the available emissions data, provide information for comparison with observation-based emissions estimates, and generate model-ready emissions files for the Global Environmental Multiscale-Modelling Air-quality and CHemistry (GEM-MACH) AQ modelling system for application to the AOSR. GEM-MACH was used to produce nested AQ forecasts during an AQ field study 20 carried out in the AOSR in summer 2013 as well as ongoing experimental forecasts since then and retrospective model simulations and analyses for the field-study period. This paper discusses the generation of GEM-MACH emissions input files, in particular for a high-resolution model domain with 2.5-km grid spacing covering much of western Canada and centred over the AOSR. Prior to the field study, ten pre-2013 national, provincial, or sub-provincial emissions inventories for up to seven criteria-air-contaminant species (NO x , VOC, SO 2 , NH 3 , 25 CO, PM 2.5 , and PM 10 ) that covered the AOSR study area and that had been compiled for various purposes were reviewed, and then a detailed hybrid emissions inventory was created by combining the best available emissions data from some of these ten inventories. After the field study, additional sources of emissions-related data became available, including 2013 hourly SO 2 and NO X emissions and stack characteristics for large point sources measured by Continuous Emission Monitoring Systems, 2013-specific national inventories, daily 30 and 20%, respectively, and a ten-fold increase in PM 2.5 emissions based on aircraft observations. In addition, standard emissions processing approaches could not provide an accurate representation of emissions from such large, unconventional emissions sources as AOSR surface mines. In order to generate more accurate highresolution, model-ready emissions files, AOSR-specific improvements were made to the emissions processing 10 methodology. To account for the urban-scale spatial extent of the AOSR mining facilities and the highresolution 2.5-km model grid, novel facility-specific gridded spatial surrogate fields were generated using spatial information from GIS (geographic information system) shapefiles and satellite images to allocate emissions spatially within each mining facility. Facility-and process-specific temporal profiles and VOC speciation profiles were also developed. The pre-2013 vegetation and land-use data bases normally used to 15 estimate biogenic emissions and meteorological surface properties were modified to account for the rapid change of land use in the study area due to marked, year-by...

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Characterization of PM_2.5 and PM₁₀ fugitive dust source profiles in the Athabasca Oil Sands Region

Cited by 64 publications

References 63 publications

Characteristics of PM10 Chemical Source Profiles for Geological Dust from the South-West Region of China

Characteristics of PM10 Chemical Source Profiles for Geological Dust from the South-West Region of China

Estimates of Exceedances of Critical Loads for Acidifying Deposition in Alberta and Saskatchewan

Emissions Preparation and Analysis for Multiscale Air Quality Modelling over the Athabasca Oil Sands Region of Alberta, Canada

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Characterization of PM2.5 and PM10 fugitive dust source profiles in the Athabasca Oil Sands Region

Cited by 64 publications

References 63 publications

Characteristics of PM10 Chemical Source Profiles for Geological Dust from the South-West Region of China

Characteristics of PM10 Chemical Source Profiles for Geological Dust from the South-West Region of China

Estimates of Exceedances of Critical Loads for Acidifying Deposition in Alberta and Saskatchewan

Emissions Preparation and Analysis for Multiscale Air Quality Modelling over the Athabasca Oil Sands Region of Alberta, Canada

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Characterization of PM_2.5 and PM₁₀ fugitive dust source profiles in the Athabasca Oil Sands Region