Receptor Modeling of Epiphytic Lichens to Elucidate the Sources and Spatial Distribution of Inorganic Air Pollution in the Athabasca Oil Sands Region

Landis, Matthew S.; Pancras, J. Patrick; Graney, Joseph R.; Stevens, Robert K.; Percy, Kevin E.; Krupa, Sagar V.

doi:10.1016/b978-0-08-097760-7.00018-4

Cited by 62 publications

(109 citation statements)

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“…Metals, such as Cu, Zn, Ni, Cr, and Pb, have been found to be higher in the Athabasca River, its tributaries, and snowpack near the oil sands developments than several hundred kilometers away (Kelly et al, 2010). Furthermore, epiphytic lichens have experienced increases in Ti, Al, Si, and Ba (Landis et al, 2012). In summary, the available evidence suggests that contamination may already be occurring in this region and that some of this may be due to the transport of trace elements present within PM 2.5 .…”

Section: Introductionmentioning

confidence: 99%

“…One such receptor model is positive matrix factorization (PMF), which uses a weighted multivariate statistical approach to identify pollution sources (called factors) by examining the correlations in the PM 2.5 speciation matrix over time (Paatero, 1996). In past receptor modeling, openpit mining, upgrading, and fugitive dust have been identified as major emission factors in the oil sands region (Landis et al, 2012;Bari and Kindzierski, 2017). However, these emission factors were identified based solely on long-term, low time-resolution data.…”

Section: Introductionmentioning

confidence: 99%

“…2): uncentered r = 1.00 for the long-term campaign; Fig. 3) of its elemental profile with an average profile derived from samples of PM 2.5 taken from main upgrader stacks in the area (Landis et al, 2012). Specifically, the elemental profile contained significant portions of the S, V, As, Br, and Pb (Fig.…”

Section: Upgrader Emissions Imentioning

confidence: 99%

“…In open-pit mining, large hydraulic shovels lift the oils sand into trucks for transport to a nearby wet crusher which reduces the size of the soil and adds water, allowing the soil slurry to be piped to an upgrading facility (Syncrude Canada Ltd., 2017;Canadian Association of Petroleum Producers, 2014). Once at the upgrading facility the bitumen is separated from the slurry in large settling vessels, after which it is upgraded into different hydrocarbon streams using steam, vacuum distillation, fluid cokers, and hydrocrackers; these processes produce aerosol particles which are emitted to the air through both the main stack (Landis et al, 2012) as well as numerous other secondary stacks. Within these stacks, some particles are directly emitted from the upgrading processes, while others are created by the pollution control devices installed within the stacks (Wang et al, 2012).…”

Section: Field Measurement Sitesmentioning

confidence: 99%

“…Past research on PM 2.5 within the Athabasca region has included overall and comparative emission and air quality analyses Bari, 2011, 2012;Wang et al, 2012Wang et al, , 2015Percy et al, 2012;Howell et al, 2014;Landis et al, 2017). Further studies have developed into modeling the emission sources through both computer-based (Cho et al, 2012) and measurement-based methods (Landis et al, 2012) No previous studies have examined short-term variability in the elemental composition of PM 2.5 in this region. However, compositional analysis of PM 2.5 has helped elucidate sources and processes that contribute to PM 2.5 mass concentrations in other regions.…”

Section: Introductionmentioning

confidence: 99%

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Sources of particulate matter components in the Athabasca oil sands region: investigation through a comparison of trace element measurement methodologies

et al. 2017

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Abstract. The province of Alberta, Canada, is home to three oil sands regions which, combined, contain the third largest deposit of oil in the world. Of these, the Athabasca oil sands region is the largest. As part of Environment and Climate Change Canada's program in support of the Joint CanadaAlberta Implementation Plan for Oil Sands Monitoring program, concentrations of trace elements in PM 2.5 (particulate matter smaller than 2.5 µm in diameter) were measured through two campaigns that involved different methodologies: a long-term filter campaign and a short-term intensive campaign. In the long-term campaign, 24 h filter samples were collected once every 6 days over a 2-year period (December 2010-November 2012) at three air monitoring stations in the regional municipality of Wood Buffalo. For the intensive campaign (August 2013), hourly measurements were made with an online instrument at one air monitoring station; daily filter samples were also collected. The hourly and 24 h filter data were analyzed individually using positive matrix factorization. Seven emission sources of PM 2.5 trace elements were thereby identified: two types of upgrader emissions, soil, haul road dust, biomass burning, and two sources of mixed origin. The upgrader emissions, soil, and haul road dust sources were identified through both the methodologies and both methodologies identified a mixed source, but these exhibited more differences than similarities. The second upgrader emissions and biomass burning sources were only resolved by the hourly and filter methodologies, respectively. The similarity of the receptor modeling results from the two methodologies provided reassurance as to the identity of the sources. Overall, much of the PM 2.5 -related trace elements were found to be anthropogenic, or at least to be aerosolized through anthropogenic activities. These emissions may in part explain the previously reported higher levels of trace elements in snow, water, and biota samples collected near the oil sands operations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Upgrader Emissions Imentioning

confidence: 99%

Section: Field Measurement Sitesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sources of particulate matter components in the Athabasca oil sands region: investigation through a comparison of trace element measurement methodologies

et al. 2017

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Multivariate receptor modeling with widely dispersed Lichens as bioindicators of air quality

et al. 2022

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Biomonitoring studies evaluating air quality via airborne element accumulation patterns in lichens typically control variability by focusing on narrow geographic regions and short time windows. Using samples of the widespread "rock-posy" lichen sampled across the Intermountain Region of the United States, we investigate whether accumulation patterns of generic pollution sources are detectable on broad geographic and temporal scales. We develop a novel Bayesian multivariate receptor modeling (BMRM) approach that sharpens detection and discrimination of candidate pollution sources through (i) regularization of source contributions to each sample and (ii) incorporating estimated lichen secondary chemistry as a factor. Through a simulation study, we demonstrate a distinct advantage in shrinking contributions when they are truly sparse, as would be expected with heterogeneous samples from dispersed collection sites. We contrast analyses employing both standard and sparse BMRMs, and positive matrix factorization (PMF). The sparse model better maintains source identity, as specified though informative prior distributions on elemental profiles. We advocate quantitative profile matching, which reveals that PMF primarily captures variations of the baseline profile for lichen secondary chemistry. Both PMF and BMRM results suggest that the most detectable signatures relate to aeolian dust deposition, while spatial patterns hint at sporadic anthropogenic influence.

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Developing and applying control charts to detect changes in water chemistry parameters measured in the Athabasca River near the oil sands: A tool for surveillance monitoring

Arciszewski

Hazewinkel

Munkittrick

et al. 2018

Enviro Toxic and Chemistry

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Control charting is a simple technique to identify change and is well suited for use in water quality programs. Control charts accounting for covariation associated with discharge and in some cases time were used to explore example and representative variables routinely measured in the Athabasca River near the oil sands area for indications of change. The explored variables include 5 major ions (chloride, sodium, sulfate, calcium, magnesium), 5 total metals (aluminum, iron, thallium, molybdenum, vanadium), and total suspended solids at two sites straddling the developments north of Fort McMurray. Regression equations developed from reference data (1988-2009) were used to predict observations and calculate residuals from later test data (2010-2016). Evidence of change was sought in the deviation of residual errors from the test period compared with the patterns expected and defined from probability distributions of the reference residuals using the odds ratio. In most cases, the patterns in test residuals were not statistically different from those expected from the reference period at either site, especially when data were examined annually. However, differences were found at both locations, more were found at the downstream site, and more differences emerged as data accumulated and were analyzed over time. In sum, the analyses at the downstream site suggest higher concentrations than predicted in most major ions, but the source of the changes is uncertain. In contrast, the concentrations of most metals at during the test period were lower than expected, which may be related to deposition patterns of materials or weathering of minerals during construction activities of the 2000s which influence the reference data used. The analyses also suggest alternative approaches may be necessary to understand change in some variables. Despite this, the results support the use of control charts to detect changes in water chemistry parameters and the value of the tool in surveillance phases of long-term and adaptive monitoring programs. Environ Toxicol Chem 2018;37:2296-2311. © 2018 SETAC.

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Receptor Modeling of Epiphytic Lichens to Elucidate the Sources and Spatial Distribution of Inorganic Air Pollution in the Athabasca Oil Sands Region

Cited by 62 publications

References 50 publications

Sources of particulate matter components in the Athabasca oil sands region: investigation through a comparison of trace element measurement methodologies

Sources of particulate matter components in the Athabasca oil sands region: investigation through a comparison of trace element measurement methodologies

Multivariate receptor modeling with widely dispersed Lichens as bioindicators of air quality

Developing and applying control charts to detect changes in water chemistry parameters measured in the Athabasca River near the oil sands: A tool for surveillance monitoring

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