Under the Air Component of JOSM, the municipality of Wood Buffalo was selected for the monitoring of air pollutants associated with oil sands activities because it is home to both mining and in situ extraction operations. In the long-term filter study, element concentrations were monitored around the Athabasca river valley at three WBEA air monitoring stations (AMSs): AMS13 – Fort McKay South (SYN); AMS5 – Mannix (MAN); and AMS11 – Lower Camp (LOW) (Fig. 1). The AMS13 site is located between three oil companies in the area, all of which perform extensive mining, upgrading, and in situ processing (Fig. 1): Canadian Natural Resources Limited (CNRL) is to the north, Syncrude is to the south, and Suncor is to the southeast. All three companies extract bitumen through both open-pit mining and in situ methods within the Athabasca region. The other two measurement sites are located farther south, directly between Syncrude and Suncor, with AMS11 to the north of AMS5.

Within the municipality of Wood Buffalo, open-pit mining is the predominant method of bitumen extraction. 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). Other known sources of particles are the large fleets of on- and off-road vehicles, dust resuspended by mining activities, windblown dust from the tailings ponds and dikes (Wang et al., 2015), and dust resuspended from open petroleum coke piles.

The filter measurements were carried out in accordance with the standard operating protocols that were in place and care was taken to ensure that quality assurance and control programs (ISO17025 accredited) were followed.

Quality assurance and quality control (QA/QC) for the Xact measurements was based on protocols implemented before, during, and after the intensive campaign. Prior to the intensive campaign, the Xact instrument was calibrated using 12 high-concentration metal standards. Three metal standards: Cr, Pb, and Cd, were selected to represent the three energy levels employed by the Xact instrument. These metal standards were measured on-site at the beginning of the intensive measurement campaign (Table S1 in the Supplement). Throughout the campaign, the internal Pd, Cr, Pb, and Cd upscale values were recorded after the instrument's daily programmed test, and the PM10 and PM2.5 cyclones were cleaned weekly. A sample of filtered air was measured daily to determine both the detection limits (DLs) and baseline biases of the metals the instrument measured. Further QA/QC that occurred during the campaign can be found in the Supplement. After the campaign, the performance of the Xact metals monitor was further evaluated through three methods: re-testing with the high-concentration standards and new medium-concentration standards, as well as a comparison of the Xact data to co-measured data from filter samples and other collocated high time-resolution instruments (Supplement Sect. S1).

Average element concentrations from the filter data were compared to measurements taken by the NAPS program at seven different Canadian sites (Environment and Climate Change Canada, 2015) (Table S6), which, due to a lack of comparable rural or remote baseline data, corresponded to different Canadian cities. Prior to averaging, blank values were removed as described in Sect. S2, and below detection limit (BDL) values were replaced by half the detection limit. Overall, the average concentrations of some of the elements measured through the long-term campaign were lower than those observed at the sites as shown in Table S6. This is not overly surprising as the cities are impacted by a range of anthropogenic activities such as heavy traffic and industrial factories. What was surprising was the number of elements measured in the largely unpopulated oil sands region, which exhibited similar or higher concentrations to those seen across the various sites. In particular, levels of Si, Ti, K, Fe, Ca, and Al appeared to be higher near the oil sands operations. However, these averages do not fully capture the differences between the sites and the oil sands region. In the oil sands, large swaths of forest are broken up by the occasional mine or upgrader. When the wind comes from one of these directions, particularly the upgraders, there is a noticeable difference in the air quality. To illustrate this large variability, the 90th percentile of the various elements was calculated and analyzed, as species that show a high degree of variability are more likely to be from these intermittent pollution sources. The results of this showed that the previously discussed elevated elements showed peaks indicating large variability. Additionally, at the highest peaks the concentrations of S, Ba, Br, and Mn also showed large increases, which indicates that in the oil sands, they are likely caused by anthropogenic sources.

Each technique identified five unique factors through PMF analysis. Comparison of these factors led to the identification of seven factors: two types of upgrader emissions, soil, haul road dust, biomass burning, and two factors of presumably mixed origins. Three of these factors were identified by both methodologies and two by only one of the methodologies, and the two mixed sources showed more differences than similarities between the two methodologies. The results of the PMF factor profiles (F matrix) from the two methodologies can be seen in Fig. 2 (hourly data from the intensive campaign in August 2013) and in Fig. 3 (24 h filter data from the combined long-term campaign, December 2010–November 2012, and intensive filter campaign in August 2013). Distinctive marker elements were evident in some profiles while other elements were surprisingly ubiquitous, appearing in most or all of the factors. Here the high diversity of Ni and Se across the source profiles is interesting as it implies that these metal(oid)s are present in most of the sources, perhaps as a result of greater natural homogeneity in this region or contamination of the region through anthropogenic activities.

After analysis with PMF, the resultant factor time series (G matrix) of the combined filter data was split into three distinct factor time series, one corresponding to each site (AMS5, AMS11, or AMS13), in order to examine both the temporal and spatial trends (Fig. S10). The time-series contributions were then averaged to assess each site's seasonality (Fig. 4) from December 2010 to November 2012. Finally, the factor contributions derived from the Xact data were compared to the averaged contributions from the August 2013 portion of the long-term campaign at AMS13 (Fig. 5).

As can be seen from Fig. 4, there were clear differences in mass contributions between the three sites. Of the three sites, AMS5 consistently exhibited the highest element concentrations, followed by AMS11 and then AMS13, which was largely due to the high amounts of haul road dust measured at AMS5. In contrast, AMS11 had higher average contributions from the upgrader emissions and soil than both AMS5 and AMS13. This can be explained by the prevalent winds at AMS11, which were often from the southeast, the direction of the upgrader processes (Fig. 1). Overall, these local differences between the sites may have been due to a combination of the proximity of the sites to the various emission sources, as well as the direction of the prevailing winds at each site (Fig. S11). In particular, the predominant winds at AMS13 came from the north and the southeast, which point towards the Syncrude and the more distant CNRL mines. In contrast, at AMS5 the wind came from many directions in which there were roads and open mines.

Fig. 4 also depicts the seasonal trends in the five factors identified in the long-term campaign. The total contribution was largest in the spring, then lower in the winter, then summer, and then fall. A major part of this overall trend was due to the higher contribution from biomass burning, a natural source, in the spring. However, even without the inclusion of biomass burning, the combined mass loading of the four remaining factors followed the same overall seasonal trend. Of the five factors, only the mixed factor was relatively consistent throughout the seasons (within 0.08 µg m-3). Both the soil and the haul road dust factors were notably lower in the winter than in the other seasons, presumably due to freezing of the ground and snow cover in the winter. The concentrations were higher in spring than in fall. In the fall, the temperatures drop below 0 ∘C quickly (Fig. S12), which could result in the lower soil and haul road dust concentrations. In contrast, temperatures are higher in the spring (Fig. S12) and there is a surplus of sand on paved roads from ice treatment in the winter. Interestingly, the upgrader emissions factor's contribution to the total PM2.5 mass was highest in the winter and spring, which may be due to the differences in mixing height and wind direction and speeds across the seasons, or it may reflect true changes in upgrader activity. Overall, the combined seasonal concentration of the factors follows the same trend as the strength and directionality of the prevailing winds, in which the most prevalent, fast winds are present in the spring, then winter, then summer, and then fall (Fig. S11).

In addition to the wind speeds, mixing height plays a vital role in the impact of various factors throughout the seasons. In the colder months, inversions can occur, increasing concentrations and channeling emissions horizontally based on the local topography (Davison et al., 1981; Celo and Dabek-Zlotorzynska, 2010). This may result in higher contributions from emissions sources, such as soil and haul road dust, which occur close to the ground. In contrast, in the warmer months, more vertical mixing tends to occur, which may result in the observance of higher contributions from tall emission stacks. Thus, the seasonality apparent within the source contributions is presumably in part due to this seasonality in mixing. However, the low contributions of the soil and haul road dust factor in winter, when mixing is the lowest, are not likely due to low mixing, unless the mixing was so low that it prevents transport from the sources to the nearby measurement sites. This indicates that it is more likely that emissions of soil and haul road dust are lower in winter.

A closer look at the contributions during the summer of 2013 is shown in Fig. 5. Good agreement of the upgrader I emissions can be seen between the PMF factor contributions to the total element concentrations calculated from the AMS13 August 2013 filter and hourly data (52 and 53 %, respectively). While there is disagreement between the two independent PMF runs in the magnitude of the contribution from soil and haul road dust (38 % for filter vs. 31 % for Xact), the time series for the soil and haul road dust factor from the filter measurements taken at AMS13 during August 2013 vs. that from the Xact measurements did exhibit good temporal correlations (Spearman r=0.77). It is possible that the filter data only allowed limited separation of the haul road dust factor from the soil factor: the 24 h timescale of the filter data would not enable distinguishing changes that occur on a smaller timescale, such as the changing mixing height and/or wind direction throughout the day. Alternatively, by combining the data from the three sites prior to running PMF, the factor contributions may have been biased towards sites more impacted by the crustal factors. Despite these differences in relative contributions, the factors identified by the two different methodologies were broadly similar. Finally, as the filter data measured at AMS13 during August 2013 were added to those measured between December 2010 and November 2012 prior to analyzing it with PMF, the small contribution (∼ 6 %) of the biomass burning factor seen in this figure arose from the forest fire factor resolved through other times in the long-term campaign, and given its low magnitude it is uncertain if this is an accurate indication of emissions from biomass burning during the intensive measurement campaign.

Whereas the long-term campaign was able to identify the overall seasonal trends in the factors, the lower time resolution made identification of some sources more difficult (Fig. S9). In contrast, the higher time resolution employed during the intensive measurement campaign revealed diurnal patterns (Figs. 6 and S9). From Fig. S9, it is clear that both the upgrader emissions were observed episodically, which indicates that they likely came from specific point sources and could thus be measured only when the wind was favorable. Interestingly, both the haul road dust and soil exhibited similar but slightly offset, diurnal trends (Fig. 6). This is in contrast to the daily trends in the mixing height in the area, which are highest during the day and lowest during the night (Davies, 2012). This trend also disagrees with the average diurnal wind speeds that occur at AMS13, which experience their peak at 15:00, while both the soil and the haul road dust factors experienced their highest peaks at 11:00 (Fig. 6). Overall, this suggests that the daytime increases for both the soil and the haul road dust factors were not due to natural processes such as decreases in mixing height or increases in windblown dust. Further, the daytime increase pointed to on-road vehicles (e.g., through track-out) rather than off-road mining activities as the more dominant source, as off-road operations at most mining sites occur around the clock.

In addition to diurnal patterns, the higher time resolution used by the Xact instrument resulted in more detailed CPF plots (Figs. 7 and S13). Whereas the Xact data were able to point towards distinct emission sources with the CPF plots, the filter data time resolution was too low. Despite this, the multiple sites used by the filter study allowed for approximate triangulation of the sources (Fig. S13). The results of the two CPF profiles indicate that both the upgrader I and II emissions came from the direction of known upgraders and that the mixed factors came from the direction of known mines. The haul road dust factor appeared to come from the direction of the three major roads closest to the receptor site(s). Further, the CPF profile of the soil factor exhibited broad peaks which surrounded known mines, perhaps as a result of vehicular traffic leading to and from the mines. Finally, the biomass burning factor appeared to come into the valley from the north and south. Because of this, HYSPLIT analysis was run on the three highest-concentration days for both the biomass burning and the soil factors in order to determine if they were local or regional in origin. The long-range transport of the biomass burning factor, which experienced its highest-concentration days during periods of known biomass burning just north of the measurement sites (USDA Forest Service, 2011), appeared to largely come from specific regions in northern Alberta that contained additional forest fires (Fig. S14). Combined, the local and regional forest fires greatly affected the air quality, as during this period (May–June 2011), the average PM2.5 concentration increased by 328 %. Back trajectory analysis of the highest-concentration soil days of each receptor site suggested that it predominantly came from the direction of known mines (Fig. S15); these trajectories largely crossed uninhabited, forested, areas in northern Canada prior to reaching the open-pit mines. Overall, these finding support the conclusions that the upgrader emissions came from the bitumen upgrading process, the soil and the haul road dust factors came from on-road transportation coupled with track-out, and the mixed factors likely originated in the open-pit mines.

The methodologies employed in the long-term and the intensive campaigns each had their own strengths and weaknesses. The lower DL and higher number of element species measured by the filter samples resulted in more detailed factor profiles (Figs. 2 and 3). Further, the longer duration enabled the identification of seasonal trends, while the use of multiple sampling sites improved the geographic resolution of sources. However, the limited number of co-measured aerosol and gaseous species enabled fewer comparisons. Additionally, the longer 24 h sampling time of the filter limited the separation of close-proximity sources, such as upgrader emissions I and II.

In contrast, the higher time resolution employed by the Xact instrument resulted in better-defined temporal patterns, which supported the separation of similar sources (Figs. 6 and S9), which in turn led to more precise CPF profiles and gaseous species comparisons (Fig. 7, Table 2). Furthermore, the higher time-resolution measurements accomplished all this within a much shorter time frame. However, without the long-term filter sampling, it would have been unclear how representative this intensive period was of the norm. Because of this, it is important to take more time-resolved element measurements in this area in order to see if there are further, unresolved sources. This is important as it can help to guide decisions made about regulation and control in the area.

In addition to their measurement capabilities, each campaign had its own requirements in terms of energy, difficulty in setup and measurement, and quality control. While the intensive measurements by the Xact instrument had more energy and housing requirements on-site, the filter analysis required much more follow-up laboratory analysis. Finally, despite the quality assurance and control measures employed by the Xact instrument, comparison to co-measured species indicated a possible linear bias in certain elements measured. In contrast, the filter protocol employed was well established and followed stringent quality assurance and quality control protocols. This led to a high level of confidence in these measurements.

Overall, the average concentration of some element species measured in the Athabasca region was either equal to or higher than that measured in urban/industrial locations in Canada (Table S6). In particular, the concentrations of Si, Ti, K, Fe, Ca, and Al were, on average, higher than those measured in major Canadian cities. At their highest peaks the concentrations of S, Ba, Br, and Mn also exhibited large increases in concentrations. Of these elements, Ti, Fe, Cu, and Zn all showed periods of higher concentration during the intensive measurement campaign. Of these species, Al, Ca, Si, Mn, Ba, and Fe were predominantly observed in the soil and haul road dust factors, suggesting that they originated from vehicle-related emissions or associated anthropogenic dust production in the area. These species have been previously seen to be elevated in epiphytic lichens (Landis et al., 2012). The elements As, Pb, and Tl were associated with upgrader emissions, and Be and Ba were associated with the haul road dust; these metals and metalloids were also previously found to be higher in snow or biota near the oil sands operation (Landis et al., 2012).

There were some differences between the campaign data as to the dominant source(s) of some elements. For example, V was apportioned to soil and upgrader emissions I in the long-term campaign, while, based on the intensive measurement campaign, it was almost entirely apportioned to the upgrader emissions II factor. While this difference appeared to create some ambiguity it actually highlighted the enhanced separation of factors allowed by the higher time-resolution Xact data: V was associated with a type of anthropogenic emissions that the filter sampling had trouble identifying. Interestingly, Ni, Zn, Cr, Ag, and Cu were grouped into mixed factors. As these factors represented a combination of multiple sources, the individual sources causing the elevation of these elements is still not known; this limitation may help direct further studies. More generally, the elements used to create the factor profiles and thereby identify sources accounted for only a small portion of the total PM2.5 mass. This limitation will be addressed in a follow-up analysis combining the Xact data with other concurrent, time-resolved, measurements of non-refractory components. Combining these data will provide a more complete mass reconstruction so as to allow the apportionment of PM2.5, and further sources may be revealed by leveraging the perspective given by the additional composition information.