advances in science and applications of air …pubs.awma.org/flip/2019 critical...

2019 ANNUAL A&WMA CRITICAL REVIEW

Advances in science and applications of air pollution monitoring: A case studyon oil sands monitoring targeting ecosystem protectionJ.R. Brooka, S.G. Coberb, M. Freemarkc, T. Harnerb, S.M. Lib, J. Liggiob, P. Makarb, and B. Paulic

aDalla Lana School of Public Health and Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto,Ontario, Canada; bAir Quality Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada; cNational WildlifeResearch Centre, Environment and Climate Change, Ottawa, Canada

ABSTRACTThe potential environmental impact of air pollutants emitted from the oil sands industry in Alberta,Canada, has received considerable attention. The mining and processing of bitumen to producesynthetic crude oil, and the waste products associated with this activity, lead to significant emissionsof gaseous and particle air pollutants. Deposition of pollutants occurs locally (i.e., near the sources) andalso potentially at distances downwind, depending upon each pollutant’s chemical and physical proper-ties andmeteorological conditions. The Joint Oil SandsMonitoring Program (JOSM)was initiated in 2012by the Government of Canada and the Province of Alberta to enhance or improve monitoring ofpollutants and their potential impacts. In support of JOSM, Environment and Climate Change Canada(ECCC) undertook a significant research effort via three components: the Air, Water, and Wildlifecomponents, which were implemented to better estimate baseline conditions related to levels ofpollutants in the air and water, amounts of deposition, and exposures experienced by the biota. Thecriteria air contaminants (e.g., nitrogen oxides [NOx], sulfur dioxide [SO2], volatile organic compounds[VOCs], particulate matter with an aerodynamic diameter

Introduction

The Canadian Oil Sands (OS) are predominantlylocated in the northern half ofAlberta, with a small portion in cen-tral-western Saskatchewan. In size,the OS is 142,000 km2 and is esti-mated to include approximately 1.7trillion barrels of oil in the form ofbitumen, although the recoverableamount of oil is only about 10% ofthat amount, or 163.4 billion barrels(Natural Resources Canada, Canada

2017). This still makes the OS the third largest known

reserve of oil on earth. Once recovered, bitumen, whichis highly viscous and enriched in sulfur, carbon, nitro-gen, and metals and deficient in hydrogen comparedwith conventional and heavy crude oil, requiresupgrading. The bitumen extraction, separation, andupgrading processes that ultimately produce syntheticcrude oil consume energy and resources and producewaste, which can pose environmental risks.

Two major rivers, the Peace River and the AthabascaRiver (AR), both originating from headwaters in the RockyMountains, flow through the OS region. The glacial-fedAthabasca River is the longest in Alberta; its watershedencompasses nearly one quarter of the province. From its

Figure 1. Northern Alberta showing the Athabasca River, the Peace River, the Peace-Athabasca Delta (PAD), and the oil sandsdeposits.

J.R. Brook

662 J.R. BROOK ET AL.

mountainous origins, it flows for approximately 1000 kmbefore encountering the large, near-surface deposits ofbitumen in the area just north of Fort McMurray, thelargest city in the region (population of the RegionalMunicipality of Wood Buffalo, which includes the city,was 71,589 in 2016). The Athabasca River is a source ofdrinking water for Fort McMurray, and the river is anessential source of fresh water for bitumen recovery andprocessing. Both rivers empty into Lake Athabasca; to thewest of Lake Athabasca they form the Peace-AthabascaDelta (PAD), which is also 200 km downstream fromwhere the Athabasca River flows through the near-surfaceOS deposits. The PAD is one of the largest freshwater deltason earth and has been designated as a wetland of interna-tional importance (through the Ramsar Convention) anda United Nations Educational, Scientific and CulturalOrganization (UNESCO) World Heritage Site. The PADis also partially withinWood Buffalo National Park. Figure1 includes a map that provides perspective for the area.

There is tremendous biodiversity in the PAD, withmillions of migratory birds passing through annually.The Lower Athabasca River (LAR) subbasin containsFort McMurray, the majority of the OS deposits, theMcMurray Formation, and the PAD. Indigenous inhabi-tants of the region include the Mikisew Cree First Nationand Athabasca Chipewyan First Nation, with traditionallands in the region downstream of Fort McMurray,including the PAD. Others include the Fort McKay FirstNation, the Fort McMurray First Nation, and MétisLocals who have traditional lands near Fort McMurrayand in and around the active oil sands surface miningregion. Oil sands development poses potential risks to theenvironmental health of this part of Canada as well as tothe populations residing there. Consequently, effectiveenvironmental management of the OS development isan essential responsibility of all stakeholders.

The potential value of the OS has been recognized forover a century, but economically viable processes torecover this “unconventional” oil from deposits near thesurface became available in the second half of the 1960s.Advances in technology for extraction and in environ-mental protection have been continual since that time,including approaches to access what constitutes themajority (approximately 80%) of the bitumen that isfarther below the surface using in situ techniques (becauseOS deposits that start deeper than about 70 m are notaccessible through open-pit mining, in situ extractionapproaches are required). Collectively, through thesetwo processes, production in the OS has been yieldingon the order of 2.7 million barrels of oil per day (NaturalResources Canada, Canada 2017) from 0.45 million cubicmeters (m3) of raw bitumen processed per day (AlbertaEnergy Regulator, Canada 2017).

An important driver for technical advance in the OSindustry has been to increase the ratio of bitumen produc-tion to energy input, which represents both an economicand an environmental benefit. Water consumption hasbeen another key driver, with the main options forenhanced environmental performance being reduction inthe amount of freshwater required for bitumen recoveryand processing; water recycling, which leads to innovationin the clearing in fine suspended tailings in the ponds; anduse of more deep, saline groundwater in the in situ process.Cleanup of tailings pond water, for reuse and eventualrelease into the watershed so that the land can be reclaimed,is another key challenge.With these and other accomplish-ments, the current OS industry represents an impressiveengineering achievement for this important economic dri-ver of the Canadian economy.

Motivation for the Joint Oil Sands MonitoringProgram (JOSM)

The environmental performance of OS development hasbeen under considerable public scrutiny. The prevailingnarrative continually positions their significant contribu-tion to Canada’s economy and energy security againstpotential environmental damage and impact to FirstNations communities (Dowdeswell et al. 2010).Understanding the extent of the potential environmentaldamage so that its characteristics, magnitude, and long-term implications can inform public debate and publicpolicy decisions about development is critical. However,there has been debate concerning the availability of open,transparent, and credible data sources that could be used inmaking sound, evidence-based policy and regulatory deci-sions in the OS region. The scale and scope of the OSdevelopment means that environmental impact is likely,and some level of management of this impact is required.

Given the importance to Canada, a Royal Society ofCanada (RSC) panel was tasked with undertakinga comprehensive, evidence-based assessment of themajor environmental and health impacts of Canada’sOS industry. The RSC panel sought to offer Canadiansan independent review assessing the available evidenceand identifying knowledge gaps (The Royal Society ofCanada 2010; Weinhold 2011). The Canadian FederalMinister of the Environment also established a panel(OS Advisory panel), charged with “Documenting,reviewing and assessing the current body of scientificresearch and monitoring” and “Identifying strengthsand weaknesses in the scientific monitoring, and thereasons for them” (Dowdeswell et al. 2010). The findingsof these two panels, released in 2010, provide context forthe initiation of JOSM (Joint Canada-AlbertaImplementation Plan for Oil Sands Monitoring 2012).

JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION 663

Their full reports are available (Dowdeswell et al. 2010;The Royal Society of Canada 2010), and an overview oftheir findings in the context of this critical review isprovided in Section S1.1 of the supplemental material.

The OS Advisory panel’s overarching recommenda-tion was that a shared national vision and managementframework of aligned priorities, policies, and programsbe developed collaboratively by relevant jurisdictionsand stakeholders based upon four key fundamentals:“An holistic and integrated approach, An adaptiveapproach, A credible scientific approach, andA transparent and accessible approach.” The RSC panel’sview was that the lack of availability of environmentaldata collected by current developments and operations inthe OS region meant that timely, comprehensive assess-ments of the data were not taking place and that, con-sistent with the OS Advisory panel’s findings, providingwider access to monitoring data was a priority forimproving cumulative impact assessment. JOSM wasestablished, at least partially, in response to this priority.

The scope of this critical review

This critical review covers the main objectives and find-ings of the (now) Environment and Climate ChangeCanada (ECCC) research and monitoring supportingJOSM, with an emphasis on atmospheric emissionsand their potential impacts on air quality and depositionand linkages to water quality and potential impacts onwildlife. The conclusions and gaps summarized fromthis ECCC work are generally reflective of reports andpublications up through approximately 2018. In addi-tion to ECCC scientific results, some of the existingknowledge and activities prior to the enhanced effortsbrought about by the implementation of JOSM, andsome of the non-JOSM work, are discussed in this cri-tical review. Landscape disruption and habitat loss havelong-term influences on wildlife and ecosystems, andattention is being paid to this issue in the OS (AlbertaBiodiversity Monitoring Institute, Canada 2019), but arenot discussed in this review. Greenhouse gas emissionsare also a critical issue for the OS but are outside thescope of this review. Human health effects are alsoa potential concern and were discussed by the RSCpanel, but they are also not covered in this review.

JOSM is a partnership involving both the Provinceof Alberta and the Government of Canada. Given thatthe focus of this review is largely on scientific workundertaken at ECCC in the context of air emissions, itdoes not represent a full review of the JOSM science orprogram. Nonetheless, recognizing that it is helpful totake stock of scientific progress on a regular basis toguide future work, it is hoped that this critical review

can play a role in ECCC’s integrated planning and mayalso contribute to a future full JOSM science integra-tion and assessment (i.e., federal and provincial find-ings), ultimately supporting adaptive management ofOS ecosystem impact monitoring.

This critical review consists of (1) objectives of JOSMand evidence of impacts; (2) challenges of assessing eco-system effects; (3) main findings from the AirComponent; Air Component applications to the (4)Water and (5) Wildlife Contaminants and ToxicologyComponent; and (6) concluding remarks. Two “integrat-ing themes” were of interest across components: polycyc-lic aromatic compounds (PACs) and mercury.A summary on PAC integration (Harner et al. 2018) isreported in this critical review. The issue of acid deposi-tion was considered in an integrated manner, buildingupon a long monitoring history in this area (i.e., the AcidRain Program beginning in the 1970s), and results arehighlighted in this critical review (Makar et al. 2018).

JOSM objectives

Expanding upon the recommendations of the EC Panel,JOSM’s main objectives (Joint Canada-AlbertaImplementation Plan for Oil SandsMonitoring 2012) were

(1) Support sound decision-making by govern-ments as well as stakeholders

(2) Ensure transparency through accessible, com-parable, and quality-assured data

(3) Enhance science-based monitoring forimproved characterization of the state of theenvironment and collect the information neces-sary to understand cumulative effects

(4) Improve analysis of existing monitoring data todevelop a better understanding of historicalbaselines and changes

(5) Reflect the transboundary nature of the issue andpromote collaboration with the governments ofSaskatchewan and the Northwest Territories

At the beginning of JOSM implementation therewas extensive existing monitoring in the OS region(Figure S1.1), and the starting goals for JOSM were toenhance/improve these activities. In practice, multiplefocused monitoring or research projects, mainly per-tinent to objectives 3 and 4, were initiated by ECCC tocharacterize general baseline conditions and developmethods useful to detect changes in the environmentin order to make progress on understanding cumula-tive effects.


Evidence of potential impacts

There were at least five lines of evidence that informedJOSM studies:

● Snowpack measurements● Historical cores of lake sediments and peat● Air monitoring● Samples of the biota (e.g., lichen or wildlife)● Atmospheric modeling results

Kelly et al. (Kelly et al. 2010, 2009) measured PACs andmetals in snow atmultiple locations in theOS developmentarea. There was a clear decrease in the amount deposited inthe snowpack in relation to distance from the OS opera-tions. The work also demonstrated that pollutants from the

OS activities entering aquatic ecosystems during snowmelt,although the fate of these pollutants as they traveled fromthe atmosphere to the land and to the local streams, tribu-taries, and rivers required more study, as did the potentialfor impacts on biota. Willis et al. (2018) revealed a similarpattern for mercury deposition. Key questions arising fromthese snowpack studies were the following: Do thesedeposition patterns occur every winter? What is the spatialpattern of the deposition and how far away from thesources are these pollutants deposited at levels above back-ground? Are aquatic species affected when these pollutantsreach aquatic ecosystems?

Long-term trends in PAC deposition (Kurek et al.2013) provided further evidence that pollutants werebeing transported away from OS operations. As shownin Figure 2, dating the layers in sediment cores showed

Figure 2. Long-term trend in PACs in lake sediment cores sampled from the five to six lakes proximate to major oil sands operations.Data represented as standardized values (Z scores). Upper graph (A) shows a change in visible reflectance spectroscopy (VRS) ofchlorophyll (indicative of productivity). Middle graph (B) shows the total polycyclic aromatic hydrocarbon (PAH) concentrations, andthe bottom graph (C) shows the total dibenzothiophene (DBT) concentrations. The lines are from two segmented, piecewise linearregression models to identify the timings of breakpoints (from Kurek et al. 2013).


that deposition and accumulation of PACs in the envir-onment started to increase around 1970, congruentwith the time that oil sands industrial activity and oilproduction began to increase. There has been a cleartrend of increasing PACs since that time.

Enhanced air monitoring using passive samplersundertaken by the Wood Buffalo EnvironmentalAssociation (WBEA) found that pollutants related toacid deposition (i.e., sulfur dioxide [SO2], nitrogen diox-ide [NO2], and nitric acid [HNO3]) were moving fromsource areas to natural ecosystems (Hsu 2013). The RSCreport identified monitoring results even farther down-wind in Saskatchewan, and although there was no evi-dence of OS-related SO2 in the available data, NO2 waselevated up to 150 km east of the provincial border withAlberta (The Royal Society of Canada 2010). Satelliteproducts derived from multiple years of overpasses(Figure 3) show that NO2 is elevated over a large area intheOS region. Comparison of images across time revealedthe magnitude of the increase in NO2 concentrations overthe northern part of the near-surface deposit region andthe size of the area impacted (see also Figure S3.5).

WBEA was and continues to be responsible forcompliance-oriented monitoring in the region. Withsupport from the OS industries, WBEA has also estab-lished a range of research programs designed to helpclose the knowledge gaps relevant to studying the fateand impact of OS air pollutant emissions (WoodBuffalo Environmental Association, Canada 2018).

Early results of this work are summarized by Percy(2012). Of relevance here are the measurements ofmetals and PACs in lichen collected at multiple loca-tions and distances from the emission sources(Studabaker et al. 2012). This early report againdemonstrated that air pollutants were being depositedinto the environment downwind of the OS activities.

Atmospheric models have frequently been applied tothe OS area for a variety of purposes (e.g., Jung and Chang2012), but particularly for air quality management (Davies2012). Although much modeling work was undoubtedlydone to assess the impacts of specific new sources as part ofthe development approvals process, larger-scale modelshave provided estimates of the regional transport and fateof emissions from the OS emissions. They demonstratethat primary air emissions and/or their secondary productscan move and be deposited far downwind. During thedevelopment of the ECCC-JOSM Air Component scienceplan, deposition estimates were produced from ECCC’smodeling system and compared with the available aquaticand terrestrial critical load maps (Aherne 2011). Theseanalyses suggested that critical loads were potentiallybeing exceeded (Figure S1.2), especially in the acid-sensitive areas located in northern Saskatchewan.

Given clear evidence of the movement of a variety ofair pollutants into the environment “beyond the fencelines,” the critical questions were (and remain) the follow-ing: Are effects occurring? If yes, how significant are they?Can they be attributed to air pollutants from the OS? If

Figure 3. Increase in average column nitrogen dioxide (NO2) over the oil sands region between 2005–2007 and 2008–2010 observedfrom the “OMI satellite.” Upper right images show spatial patterns in column NO2, and the lower accompanying images show thegrowth in development between 2005 and 2009 from Landsat. The background image is 2005–2010 average column NO2 from OMIover northwestern United States and western Canada.(adapted from McLinden et al. 2012)


no, given the long-term accumulation of deposited pollu-tants in the ecosystems, and the expected growth in devel-opments in the OS, are significant effects expected in thefuture? And finally, is the monitoring system in placeadequate for early detection of these effects to ensurethat they can be managed and possibly mitigated? Interms of this latter question, the perspectives of the ECand RSC panels were that the monitoring system neededimprovement. Also, an important concept for futuremonitoring, as expressed by the EC Panel, and recognizedin implementing JOSM, was that it needs to be adaptive(e.g., able to change to address new evidence or monitor-ing needs in a timely manner).

The challenge of measuring ecosystem effects

Although the origin of potentially harmful contaminants inthe ecosystem could be direct release or contaminatedgroundwater seepage to the watershed or possibly spillageonto land areas, atmospheric deposition is a well-documented pathway in the OS, as discussed above.However, once taken up by the biota, the original pathwayis difficult to discern, and measuring and interpreting theeffects of these exposures represents an ongoing challenge.

Given what science is beginning to appreciate aboutlow-dose effects and the effects of combinations ofstressors (Dziedek et al. 2016; Gerner et al. 2017; Liesset al. 2016), it is uncertain whether single-pollutant or -stressor guidelines can be sufficient, even if set withprecautionary margins, to serve the desired cumulativeeffects management approach. Nonetheless, applyingsingle-indicator measures requires an evaluation pro-cess (The Royal Society of Canada 2010). For toxics inaquatic ecosystems, as an example, this generallyinvolves chemical and biological measurements to char-acterize water quality using best available criteria andsetting effects-based objectives in the context of back-ground conditions (e.g., specific to the LowerAthabasca River). To determine whether there is unac-ceptable risk, thresholds or critical effect sizes (CESs)for ecosystem safety are needed. Ideally, biologicallyrelevant CESs should be defined a priori and shouldconsider the type and magnitude of change that is likelyto be of concern (Munkittrick et al. 2009). What istraditionally available, in Canada at least, are guidelinesset by government agencies such as the CanadianCouncil for Ministers of the Environment (CCME),which has established guidelines for surface water qual-ity (CCME, Canada 1999). U.S. EnvironmentalProtection Agency (EPA) guidelines also support eva-luation of the potential level of risk (U. S. EPA 2019a).

There are numerous aspects of ecosystems that could bemonitored for evidence of potential impacts and that need

to be considered to meet the RSC’s recommendationregarding cumulative effects management. In general, airpollutants that can elicit an ecosystem or biotic responsethrough deposition are classified into the following cate-gories: acidifying pollutants, eutrophying pollutants, traceelements, and polycyclic aromatic compounds (PACs)(Wright et al. 2018). In order to monitor these, and inves-tigate potential impacts and “leading-edge” indicators ofecosystem effects, certain indicators or biotic responsemeasures have been examined and/or proposed. Forinstance, some examples of ecosystem effect or healthindicators that may be relevant in the OS include criticalload, critical level, acid neutralizing capacity, ground-levelozone exposure indices (Accumulated Ozone exposureover a Threshold of 40 ppb [AOT40], The sum of hourlyozone concentrations equal to or greater than 60 ppb overthe daylight period 08:00 – 19:59 [SUM60]), eutrophicload, nitrogen saturation, algae bloom, acidity of ombro-trophic bogs, biodiversity of plants or animals, forest resi-lience, toxicity to biota, chemical burden in animal tissuesand embryos, reproductive success of animals, animalstress and death, human health arising from multipleexposure routes, and human stress (fear of direct pollutanteffects or of food security and food and water safety). Theseexamples largely involve biological (e.g., biodiversity,health of animal and vegetation species and humans) andchemical-physical (e.g., atmospheric deposition amounts,water chemistry) indicators and do not reflect potentialsystems-based indicators (e.g., adaptive capacity, resilience)and traditional ecological knowledge (TEK).

In terms of systems-based indicators, combinations ofthe indicators in the example list may provide insight intothe state of the whole system, a concept that could bedeveloped in the future. Monitoring forest health repre-sents a form of a systems-based indicator. Thus, theTerrestrial Environmental Effects Monitoring (TEEM)program operated by WBEA (Jacques and Legge 2012;Percy, Maynard, and Legge 2012) strives to obtain a widerange of measures at multiple forest plots, including atmo-spheric inputs and is a valuable resource for trackingchange over the long term, which may then trigger follow-up to identify potential causes. TEK must also be includedin an adaptive monitoring program to provide insight onecosystem health. As an example,WBEA has been workingwith local indigenous harvesters to examine concernsregarding the health of local wild berry plants and safetyissues regarding the consumption of wild berries.Perceptions of appearance and taste are being exploredwith data on chemical composition and potential contami-nant load (Wood Buffalo Environmental Association,Canada 2019).

In terms of the biological and chemical-physical indica-tors, clear criteria regarding thresholds of effects are


difficult to determine and may be outdated. Chemical-physical indicators exist to provide an easier-to-monitorand early warning approach for tracking biologicalresponse (i.e., the chemical-physical and biologicalresponse indicators are correlated but are not the biologicalresponse per se). Munkittrick and Arciszewski (2017) con-sidered the case of changes in PACs in sediment cores inthe Cold Lake, Alberta, area as reported by Korosi et al.(2016) They pointed out that as our capacity to detect anychange advances, we also require a counterbalance toaccount for “trivial” change. Their suggestion was thatthis could be done through an interpretative frameworkbased on contextualization of differences; the goal is togenerate meaningful information for environmental mon-itoring programs and potential actions. A critical part ofthe proposed framework is data on normal ranges, con-sidering site-specific, local, and regional (distant) levels.Difficulties remain in contextualizing the levels of expo-sure, complicated by noisy baselines or small changes thatare or may be well below expected levels for ecologicalimpact (Willis et al. 2018; Summers et al. 2016). Ideally,such a framework would be developed and would beroutinely applied to monitoring data to determine whena change has occurred that is considered “significant” andthat warrants further study (Arciszewski et al. 2017a).

Examples of air pollution–related indicators

A critical load is defined as “a quantitative estimate of anexposure to one ormore pollutants belowwhich significantharmful effects on specified sensitive elements of the envir-onment do not occur, according to present knowledge”(Nilsson and Grennfelt 1988). A critical level for vegetationis defined as the “concentration, cumulative exposure orcumulative stomatal flux of atmospheric pollutants abovewhich direct adverse effects on sensitive vegetation mayoccur according to present knowledge” (Convention onLong-Range Transboundary Air Pollution [CLRTAP]2017). Most of the currently specified critical levels identifya threshold meant to protect a certain percentage of speciesat a given confidence level, usually set to a level where theimpacts will become discernible (e.g., 5–10% damage).However, they are still single-stressor (e.g., ozone) indica-tors that do not take into account the impact of climate orsoil and plant factors associated with ozone uptake. More-detailed calculations that, for instance, estimate the phyto-toxic ozone dose (POD) above a given threshold are pre-ferred where possible (CLRTAP 2017).

Internationally recognized procedures for the gen-eration and use of critical level and critical load datahave been set out in the United Nations EconomicCommission for Europe’s (UNECE) Convention onLong-Range Transboundary Air Pollution (CLRTAP

2017). Development of location-specific critical loadsfor sulfur and nitrogen deposition that reflect thepotential for an ecosystem response or a biologicaleffect required years of monitoring and research onacid deposition and eutrophication. Through thiswork, exposure models were developed to estimate anecosystem-specific critical load based upon terrestrialor aquatic ecosystem parameters. For acidifying deposi-tion, these include the Simple Mass Balance (SMB)model for terrestrial ecosystems and the Steady-StateWater Chemistry (SSWC) and First-Order AcidityBalance (FAB) models for aquatic ecosystems(CLRTAP 2017). These models are based on the con-cept of determining the charge balance of ions in soilwater (terrestrial ecosystems) or within lakes (aquaticecosystems); exceedances are thus with respect to theextent to which strong anion deposition that can’t bebuffered by cations present in and/or being depositedto the ecosystem, is above an anion threshold, whichwill depend on the sensitive plant or animal specieswithin the ecosystem. It should be noted that theseUNECE-recommended models for critical loads are“steady-state” models; they only indicate that ecosystemdamage at a given total deposition level (or calibratedto a specific wet deposition amount) will occur at somepoint from the present time to some point in the future.They do not provide the time frame to when the effectswill become noticeable (which could potentially be any-where from an immediate impact to years or evencenturies in the future). This is a drawback of themethodology, but exceedances of critical loads havenevertheless been considered, at least in Europe, suffi-cient cause to enact legislation designed to reduce acid-ifying emissions.

Dynamic critical load modeling has been attemptedas another approach with the potential to estimate thetime-to-effect for critical load exceedances; these mod-els were originally intended as a means to estimate thetime-to-recovery of damaged ecosystems (CLRTAP2017). However, the CLRTAP protocols stress thedependence of dynamic models on very accurate localdata, and recent work in Canada suggests that dynamicmodels are so poorly constrained by lack of this infor-mation to preclude their use for policy decisions relat-ing to acidifying deposition (Whitfield and Watmough2015). Variations on the CLRTAP (2017) acidifyingdeposition critical load estimating procedures and for-mulae have been constructed, usually employing sim-plifying assumptions and/or local information. Anexample is the protocol agreed upon by the NewEngland Governors–Eastern Canadian Premiers (NEG-ECP 2001; Ouimet 2005) that has been used in the pastto create Canada-wide acidifying deposition critical


load data sets (Aherne and Posch 2013; Carou et al.2008; Jeffries et al. 2010). These data are used in thecontext of the OS later in this review.

Critical loads may also be calculated for the deposi-tion of toxic heavy metals (cadmium, lead, and mer-cury) (CLRTAP 2017). As for acidifying deposition,critical loads for toxic metals are calculated based onthe receiving ecosystem (terrestrial or aquatic), but arefurther subdivided into the metals’ impact on humanhealth versus ecosystem functioning. The human healthimpacts result from uptake of metals into human foodsources and groundwater (metal content in food/foddercrops, grass, and animal products, the total metal con-tent in soil water below the rooting zone, and the metalconcentration in fish). The impacts on ecosystem func-tioning include the free metal ion concentration in soilsolution (impacts on invertebrates, plants, and soilmicroorganisms), the total metal concentrations in for-est humus (impacts on invertebrates and microorgan-ism impacts), and the total metal concentration infreshwater (impacts on the food chain, from algaethrough to top predators). As with acidifying pollu-tants, exceedances for metal critical loads only indicatethat at this estimated critical input flux rates, harmfuleffects will eventually occur, but not when they willoccur. Metal critical load calculations have additionalconstraints or limiting factors: (1) they may not becalculated for locations where more water is lost thangained (preventing soil leaching and leading to theaccumulation of salts and high pH), and for soils withreducing conditions such as wetlands; and (2) they donot include weathering inputs of metals (which areusually of low relevance and are difficult to calculateaccurately but may influence metal levels at locationswhere the geological content of metals is high).Interactions between heavy metals and acidity, whetherwhen in the atmosphere (i.e., on aerosols) or upondeposition, are also challenging to consider but maybe important given that acids can convert metals intomore bioavailable forms (e.g., water soluble).

Even though reasonably well developed, thereremain uncertainties and data limitations with criticalloads and levels, as highlighted above. In terms ofnitrogen deposition, before an ecosystem is declaredto be in a state of nitrogen saturation (Earl, Valett, andWebster 2006; Jung and Chang 2012) that can lead togreater risk of acidification, there are increases innutrient load or eutrophic load (Smith, Tilman, andNekola 1999). Beneficial to some plant and tree spe-cies and not to others, shifts in plant success andbiodiversity can occur, disrupting the natural state(Kwak, Chang, and Naeth 2018), which may requirea long period of time to reverse. It is difficult to

determine the level of perturbation that is acceptablebecause it is happening over a continuum and theform of the nitrogen (e.g., reduced, oxidized, ororganic), and interactions with base cations, also hascritical roles such that there is high diversity in thelevel of nitrogen sensitivity among ecosystems(Bobbink et al. 2010). Excess nutrients can also havean impact on the allocation of belowground resources(Varma, Catherin, and Sankaran 2018) such as devel-opment of root systems (Majdi and Kangas 1997),which may increase vulnerability or resilience toother stressors such as frost, drought, fire, and winddamage (Bobbink et al. 2010). Thus, assessing andprojecting the impacts of nitrogen deposition and set-ting a nutrient nitrogen critical load for the OS regionremains challenging (Murray, Whitfield, andWatmough 2017). Similarly, in regard to fertilization(Mullan-Boudreau et al. 2017) or neutralization ofacidity in bogs due to input of basic material (e.g.,dust from soil erosion), an acceptable amount ofdecrease in acidity is challenging to determine.Setting thresholds for nutrient loads in aquatic eco-systems (eutrophication) is also challenging given thevariability among ecosystems. However, within certaintypes of environments, critical loads have been estab-lished and in some cases an unacceptable point(“threshold”) can be obvious because an undesirableoutcome such as excess algae is highly visible.

Challenges in setting thresholds

Although thresholds or critical loads for some heavymetals exist, there is less information on toxicitythresholds based upon the levels of chemicals measuredwithin biota, and there is variability among species. Foroverall ecosystem protection, this necessitates identifi-cation of sentinel species, which could be a plant oranimal in any ecosystem (Cruz-Martinez and Smits2012). Species selection criteria include feasibility,reproducibility, sensitivity, ability for laboratory valida-tion, capability for long-term monitoring, noninvasive-ness or nonlethality, ability to set/measure an effects-based threshold, and cost-effectiveness.

For a selected sentinel species, death (e.g., the “can-ary in a coal mine” concept) is an obvious threshold,but given the availability of different assessment meth-ods and the sensitivity of modern analytical equipment,it is possible to use measures not based on lethality;new monitoring methods continue to change what ispossible to measure and observe. Although contami-nant load in a range of species has been used exten-sively, such as mercury levels in fish or colonialwaterbird eggs (Campbell et al. 2013; Evans and


Talbot 2012) and/or persistent organic pollutants(POPs) in mammals (Metcalfe 2012), laboratory-basedanalytical measures are becoming more precise.Metabolites in animal blood or tissue, gene expressionmeasures (Gagné et al. 2012; Marentette et al. 2017;Simmons and Sherry 2016), and epigenetic changes(Brander, Biales, and Connon 2017) can be measuredand can show evidence of change before the animal’shealth and survival are compromised. Other cellularapproaches extending from macroscale measures oforgans (liver, gonads, thyroid) and immune measures(e.g., Gagné et al. 2017) to telomere dynamics (Molleret al. 2018) are also available or being explored. Thesecellular or molecular markers may also respond ina dose-dependent manner, with or without an apparentthreshold.

Much like particulate air pollution effects in humans,with no discernible threshold in relation to prematuremortality and an increasing number of preclinical mea-sures, the appropriate safe threshold for some indicatorsand most molecular markers of biological effects in nature(i.e., wild animals) remains unclear. This is even morecomplicated in the context of the challenge of chronic, low-dose exposure, which is an ongoing process occurring inthe OS. Precaution, regular reassessment, and continuousimprovement is the prudent approach. Ultimately, indica-tors based upon metabolomics, proteomics, epigenomics,etc., may be the preferred approach given that trackingsingle chemicals is not fully reflective of the mixtures thatoccur in reality. Bradley et al. (2019) recently assessedmultiple indicators of cumulative contaminant effects(hazard) for in-stream biota, including in silico approachessuch as ToxCast (EPA 2017). High-throughput methodsfor wildlife based on gene arrays and microarrays are alsobeing developed. Bradley et al. (2019) point out that giventhe 80,000+ parent compounds estimated to be in currentuse globally and the “inestimable chemical-space of poten-tial metabolites and degradates” from these compounds,toxicity assessment remains a major challenge.

The adverse outcome pathway (AOP) is a conceptualframework for organizing existing knowledge concern-ing biologically plausible, and empirically supported,links between molecular-level perturbation ofa biological system and an adverse outcome at a levelof biological organization of regulatory relevance(Villeneuve et al. 2014). This resembles the exposomeconcept (Wild 2012) being explored to improve under-standing of how environmental factors lead to chronicdisease in humans (Rappaport and Smith 2010; Wild2012). To be repeatable and adaptable to multiple typesof ecosystems (or individuals), AOPs must be devel-oped in accordance with a consistent set of core prin-ciples (Villeneuve et al. 2014).

Potential of remote sensing

The indicators discussed above require tracking effects “onthe ground,” through repeated monitoring. The exceptionmight be using atmospheric models to identify areas ofcritical load exceedances and setting new emission regula-tions so that future deposition is deemed acceptably belowexceedance levels. However, field observations are stillnecessary to verify that the desired outcomes are beingachieved. The cost of this tracking or monitoring may beconsiderable, especially in remote yet sensitive areas, andcould benefit from more efficient approaches. Remotesensing is receiving attention in this regard (Andrew,Wulder, and Nelson 2014; De Araujo Barbosa, Atkinson,and Dearing 2015; Kerr and Ostrovsky 2003; Knox et al.2013; Sioris et al. 2018). If satellite observations could beused for early warnings of change, then there could be costsavings while also increasing the size of the area possible tohave under surveillance. Mapping ecosystem services,including in natural wetlands, is one potential possibilityfor this information (Radeva, Nedkov, andDancheva 2018;Zergaw-Ayanu et al. 2012). For satellite observations, suffi-cient temporal, spatial, and spectral resolutions are needed.Also, for such indices to be sensitive to important charac-teristics of the ecosystems and their functional attributes,satellite data need to provide the ability to track phenolo-gical changes and understand interannual variability ofecosystem processes (Paruelo et al. 2016). Although satel-lites or other types of remote sensing (e.g., from aircraft-based aerial surveys) are not capable of providing all that isneeded for cumulative effects monitoring, including theneed for strong empirical data allowing early detection ofecological change (Lindenmayera et al. 2010), they couldplay a valuable role in remote areas such as the OS.

Main findings from the ECCC Air Componentprogram of JOSM

Four overarching questions were posed to guide scien-tific activities toward meeting the Air Componentobjectives:

(1) What is being emitted from the oil sandsoperations, how much, and where?

(2) What is the atmospheric fate (transport, trans-formation, deposition) of oil sands emissions?

(3) What are the impacts of oil sands operationson ecosystem and human health?

(4) What additional impacts on ecosystem healthand human exposure are predicted as a resultof anticipated future changes in oil sandsdevelopment?


Focused studies involving short- and long-term fieldmeasurements (ground and airborne) were undertakento answer the first two questions and to support waterand wildlife research in answering the third question.In addition to these monitoring and research activities,an approach to integrate the information gathered fromthe ambient and emission monitoring using air qualitymodels, as well as satellite-based information, wasincluded in the Integrated Monitoring Plan(Environment Canada 2011). Information from airquality models provide essential input to ecosystem-and health-based models, ultimately providing insightinto the potential human and ecosystem health impactsfrom the OS (fourth question) (Environment Canada2011).

Improving understanding of emissions to theatmosphere

Among the complex open-pit mining and oil extrac-tion processes in the surface mining facilities of theOS, pollutants are mainly emitted from five pro-cesses: (1) exhaust from off-road vehicles used forremoval of the surface overburden and for excava-tion and transportation of the oil sands ores to anextraction plant; (2) ore processing at the extractionand upgrading plants, resulting in stack emissions;(3) fugitive volatile organic compound (VOC) emis-sions from mine faces, tailings ponds, and extractionplants and volatilization of fuels used for industrialactivities and vehicles; (4) fugitive dust emissionsfrom surface disturbances by the large fleet ofmining and transportation vehicles; and (5) wind-blown dust emissions from open surfaces such asmine faces and tailings pond periphery beaches.These emissions are superimposed on other emis-sions, such as on-road vehicle exhaust, wildfires,residential wood combustion, and other industries(e.g., cement, construction), several of which areengendered by population growth owing to OSemployment.

The National Air Pollutant Release Inventory(NPRI) and the complementary Air PollutantEmissions Inventory (APEI) contain annual emissionestimates for the region. NPRI (Government ofCanada, Canada 2019a) includes data reported byfacilities on releases, disposals, and recycling of over300 pollutants. NPRI collects data from over 9000industrial facilities nationwide, including the OS,that meet specified reporting criteria and whoseemissions meet or exceed reporting thresholds forNPRI-listed substances. The APEI expands on theofficial, annually reported data, quantifying emissions

from a range of other important sources (e.g., motorvehicles, agricultural activities, natural and opensources, etc.) for several common air pollutants, byprovince/territory, and for all of Canada(Government of Canada, Canada 2019b). Accordingto the 2013 NPRI, which was the year available at thestart of the Air Component program, emissions fromAlberta’s OS sector accounted for 61%, 34%, and 14%of the provincial total reported VOC, SO2, and nitro-gen oxide (NOx) emissions, respectively. The OS sec-tor was also a large source of particulate matter (PMor total PM [TPM]) and carbon monoxide (CO)emissions in 2013.

NPRI specifies reporting thresholds for “listed sub-stances,” that extends beyond the criteria air contami-nants (CACs) and includes range of VOCs such asbenzene, toluene, ethylbenzene, and xylenes (BTEX)and some polycyclic aromatic hydrocarbons (PAHs)(Li et al. 2017). Given the complexity of the OS pro-cesses that produce atmospheric emissions of primarypollutants and the potential for secondary formation ofother pollutants, it was suspected that other,“unknown” or “unmeasured” pollutants would be pre-sent in the air over and downwind of the region.Therefore, a key part of addressing the first question(“What is emitted”) was the need for more detailedambient measurements from the air and the ground.These new data were expected to help determine whatother pollutants might be important to understand inthe context of emission reporting, future long-termmonitoring needs, and potential for ecosystem andhuman health effects.

The NPRI and APEI have traditionally been usedfor the fine-scale air quality monitoring and modelingnecessary to characterize the air emission sources andtheir associated impact on air quality. However, inthe development of the Air Component program, itwas recognized that this inventory did not contain themultipollutant and multiscale air quality informationat the finer spatial and temporal scales necessary tosatisfy the JOSM objectives. Therefore, a review of 10available national, provincial, and subprovincial emis-sion inventories in 2012 (Alberta Environment andSustainable Resource Development 2013) was under-taken, leading to a new hybrid inventory (JOSM,Canada 2016; Zhang et al. 2018). The hybrid inven-tory, which also included better representation ofspatial and temporal emission patterns, was expectedto improve results from the daily air quality modelruns. These runs commenced in 2013 for the firstintensive field study in August and September ofthat year and have continued since that time(Figure S3.3).


Uncertainties in the OS emissionsThe criteria air contaminant (CAC) emissions from theOS (including NOx, VOCs, SO2, ammonia [NH3], CO,and PM with aerodynamic diameters

aircraft-based measurements to estimate facility-totalemissions for several VOCs. They found that the valuesof the summed VOC emissions, quantified for four ofthe key facilities in the surface mining region: SyncrudeMildred Lake (SML), Suncor Millenium and Steepbank(SUN), Canadian Natural Resources Ltd Horizon(CNRL), and Shell Albian Sands and Jackpine (SAJ)—now operated by CNRL, were factors of 2.0 ± 0.6,3.1 ± 1.1, 4.5 ± 1.5, and 4.1 ± 1.6 higher, respectively,when scaled to annual totals compared with the datacontained in the NPRI.

Figure 6 shows differences in measured (TERRA)versus reported (NPRI) annualized emission estimatesfor 93 separate VOC species included in annual emis-sion reports (totals among the four facilities) groupedby the reporting categories (i.e., Part 1, Part 5) ofinterest to NPRI. Only 11 of the 93 species had aircraft-observed annualized emissions that were similar toreported values, whereas 82 species had lower reportedemissions than aircraft-based emission estimates(TERRA) by a factor of 2 to 27,800 (Li et al. 2017).Looking closely at some specific species, the total aro-matic emission rates were 9.7 ± 1.5, 7.9 ± 0.5, 2.1 ± 0.3,1.5 ± 0.2, 0.53 ± 0.06, and 0.15 ± 0.02 tons day−1 atSML, SUN, CNRL, SAJ, Syncrude Aurora (SAU), andImperial Kearl Lake (IKL), respectively. These quanti-ties were composed of similar proportions of aromaticsat SML, SUN, and CNRL, but different proportions atSAJ, SAU, and IKL. The higher than previously esti-mated aromatic emission rates, coupled with the simi-larities in the aromatic compositions, are thought toreflect the naphtha-type solvents used in the bitumen

extraction process at SML, SUN, and CNRL.Conversely, at SAJ, SAU, and IKL, paraffinic solventsare used (Alberta Environment and Parks [AEP] 2016),and the lower aromatic emission rates detected forthese facilities is consistent with this knowledge. Largecontributions from alkanes, which peak between C4and C8, were measured for most of the facilities, andthis reflects the use of naphtha and paraffinic solventsused in bitumen-sand-water separation. Naphtha sol-vents have higher-carbon alkanes (>C6) and a higharomatic content, whereas paraffinic hydrocarbonscontain carbon numbers around C6 as the effectiveingredients (AEP 2016; Davies 2012). The aircraft andground data were able to detect these differences, sug-gesting that VOC ratios may be useful as near-fieldtracers associated with each facility.

PM emissions from the facilities originate mainlyfrom four major source categories: (1) emissions fromplant stacks; (2) tailpipe emissions from the off-roadmining fleet; (3) fugitive dust originating from variousmining and transportation activities, such as excavationof oil sands ore, loading and unloading trucks, andwheel abrasion of surfaces by off-road vehicles; and(4) wind-blown dust. Emission data from plant stacksand fugitive dust source categories are available inNPRI, whereas emissions from tailpipe emissions areprovided from other sources (APEI). Although theseemissions are uncertain, the most significant uncer-tainty in the PM emission inventories for the OS regionis associated with fugitive dust.

TERRA results have been reported for PM2.5 (Zhanget al. 2018), and, as shown in Figure 7, the reported

Figure 5. Interpolated observations of PM2.5 obtained from thebox aircraft flight around the Syncrude Mildred Lake (SML)facility during flight F12 on August 24, 2013. The arrows showthe mean wind direction at different flight altitudes corre-sponding to the maximum plume concentrations on the boxwalls. Plumes for PM2.5 can be seen moving northward awayfrom the facility, and there appears to be multiple sourcesrelated to the plants and surface mining activities.

Figure 6. Comparison of 2013 emission rates for the individualspecies reported to the Canadian National Pollutant ReleaseInventory (NPRI) with the measurement-based emission ratesfor the same species. Each dot represents a reported speciesunder either Part 1 or Part 5 of the NPRI reporting require-ments. The horizontal bars represent the uncertainty range ofthe measurement-based emission rates (Li et al. 2017).


base case PM2.5 emissions are considerably less than theestimates derived from the aircraft measurements forfive of the six facilities studied. Although these discre-pancies are for PM2.5, Figure S3.4 shows that 65–95% ofPM2.5 emissions are in PM size bin 8 (diameter rangefrom 1.28 to 2.56 μm), implying that the majority of thePM2.5 mass emissions are from fugitive dust areasources (Eldering and Cass 1996), either from dustkicked up by off-road mining vehicles or from wind-blown dust. It is reasonable to expect that this increasein mass toward the larger PM2.5 sizes continues intolarger sizes, which are likely associated with fugitivedust emissions. This has implications for acidic deposi-tion given that in these sizes basic material (e.g., cal-cium [Ca]) is typically present (Wang et al. 2015; Zhanget al. 2018). Given the presence of petroleum coke(petcoke) stockpiles at the facilities where bitumen isupgraded, there is the potential that these large particlesalso contain PACs.

In addition to potential differences seen throughdirect comparison (i.e., Figure 7), there has often beena discrepancy between initial inventory estimates ofprimary PM emissions and the amount of PM actuallydetected in the atmosphere downwind (i.e., “transpor-table fraction”). This tends to depend on the type ofland cover and indicates that a fraction of the emittedPM is deposited locally and thus does not escape intothe boundary layer or free troposphere for transportdownwind (Pace 2005). The range of uncertainty asso-ciated with the estimate of the transportable fraction ishigh, and TERRA estimates in the OS region may helpconstrain the value of this parameter.

In terms of PM chemical constituents, total blackcarbon (BC) emissions from the OS surface miningfacilities were estimated using the hourly emission rates

(TERRA) to be 707 ± 117 tons yr−1. The total annual BCemissions reported to the UNECE by ECCC (2016) aresimilar to these measurements, lending some confidenceto both results. However, the relative contributions ofoff-road vehicles versus stacks in the UNECE reportdiffer from the TERRA estimates, with the latter attri-buting the majority of BC emission to off-road vehiclesversus only 50% for the UNECE report. These differ-ences suggest that the UNECE reported total amount,which is derived using the standard approach (i.e., fromPM2.5 mass emission estimates for the oil sands surfacemining facilities in conjunction with the EPA SPECIATEdatabase [EPA 2014] BC/PM2.5 fractions [Cheng et al.2019]) is reasonably accurate, but differences by sourcecategory suggest a potential need for improvements.

Low-molecular-weight organic acids (LMWOAs)and isocyanic acid (HNCO) had never been reportedfor the OS, or for most other sources in Canada orglobally. They are both products of secondary forma-tion in the atmosphere but are also directly emitted.The transportation sector is an important source ofHNCO (Brady et al. 2014; Wentzell et al. 2013; Wrenet al. 2018), and emission rates were estimated from theaircraft measurements to be 2.2 ± 0.8 kg hr−1 fromSUN, followed by that from the SML facility of1.5 ± 0.5 kg hr−1. Figures S3.2a and S3.2b show thatby tracking a specific integrated plume downwind,increases in HNCO and LMWOAs can be observed(Liggio et al. 2017). Additionally, for HNCO, there isalignment of the plume emanating from SML and thelocation of active open-pit mining, which is consistentwith the expectation of the source being the off-roadheavy-duty diesel fleet. Isocyanates, of which HNCO isthe simplest stable and volatile species, have recentlybeen classified as being in the highest inhalation

-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

Tonnes

Base Case (Annual Total) Aircraft (Aug & Sept)

Figure 7. Comparison of PM2.5 emissions between base case annual emissions obtained from all available bottom-up emissioninventory information and the aircraft-observation-based (top-down) estimates for the two summer months (August andSeptember 2013) for the six oil sands mining facilities (Zhang et al. 2018).


toxicological potency class in an assessment of 296inhalable species of concern (Schüürmann et al. 2016).

LMWOA emissions (Figure S3.2a) for the SUN,SML, SAU, SAJ, CNRL, and IKL facilities were esti-mated to be 162 ± 22, 108 ± 15, 45 ± 6, 56 ± 8, 60 ± 8,and 19 ± 3 kg hr−1, respectively, or approximately 12tons day−1 of primary LMWOAs (Liggio et al. 2017).From the atmospheric chemical process perspective,LMWOAs could be contributors to precipitation acid-ity and ionic balance, particularly in remote areas(Khare et al. 1999; Stavrakou et al. 2012). Althoughimportant in their own right, their relative contributionto acidic deposition could become more important ifanthropogenic NOx and SOx emissions decrease.LMWOAs are also key participants in the aqueous-phase chemistry of clouds and contribute to secondaryorganic aerosol formation through various reactionswithin the aqueous portion of the particle phase(Carlton et al. 2007; Ervens et al. 2004; Lim et al.2010). Furthermore, since organic acids are also formedin photochemical reactions, their measurements serveas indicators of atmospheric transformation processes.Thus, measurements of LMWOAs can help evaluate theGlobal Environmental Multiscale–Modeling Air-qualityand Chemistry model (GEM-MACH), specifically thechemical mechanisms within the model. From anenvironmental health perspective, deposition ofLMWOAs may have ecosystem impacts, as they havebeen shown to be toxic to various marine invertebrates(Staples et al. 2000; Sverdrup et al. 2001), phytotoxic(Himanen et al. 2012; Lynch 1977), and interfere withthe uptake and mobilization of heavy metals by micro-bial communities in soils (Menezes-Blackburn et al.2016; Song et al. 2016). However, studies on thehuman toxicity of LMWOAs are sparse and the resultsunclear (Azuma et al. 2016; Rydzynski 1997).

It is important to note some of the limitations in thecurrent emission validation findings derived from thetop-down approach. Because of limitations in the mini-mum aircraft flying altitude, there is larger uncertaintyin the emission estimates associated with surfacesources; 20% versus elevated stack emissions at about10% (Gordon et al. 2015). However these uncertaintylevels are small compared with those expected for bot-tom-up inventory estimates from large and complexarea sources such as OS facilities.

The largest uncertainty regarding comparison of thetop-down results and the reported inventory is poten-tially due to the limited number of flights around eachfacility. These were also limited in time (i.e., August–September 2013); thus, the top-down estimates in gen-eral needed to be temporally extrapolated for compar-ison with data in the NPRI and APEI databases. These

extrapolations were done with caution, taking into con-sideration potential uncertainties and with notedcaveats (Li et al. 2017). Another limitation is thatalthough TERRA can theoretically be applied to anysize volume (i.e., could isolate a single stack), aircraftflights become logistically challenging to capture smal-ler elements within the OS facilities. Thus, the emissiondata reported thus far are for mainly whole facilities,recognizing that there is heterogeneity in the emissionsacross these relatively large areas and that more-resolved measurement would be desirable.

Ground-based measurements have also been analyzedto assess consistency with known emissions. For example,Parajulee and Wania (Parajulee and Wania 2014) sug-gested that a significant amount of PAH emissions fromtailings ponds would be necessary to explain their model-ing results. The Galarneau et al. (2014) study of tailingspond water supported this finding, demonstrating thatgiven known water concentrations, there is a potential forPAHs to partition into the air. Harner et al. (2018) alsohighlighted that potential; in order for the inverse model-ing of Parajulee andWania (2014) to explain the observedambient concentrations of phenanthrene, pyrene, andbenzo[a]pyrene in 2009, their emissions would need tobe 2–3 orders of magnitude higher than those reported inthe NPRI and APEI databases. More recent emissionestimates, also based on inverse modeling, but fora larger amount of ambient monitoring data (Schusteret al. 2015), also concluded that PAH emissions areunderestimated (Qiu et al. 2018). This work foundthat benzothiophene emissions needed to be morethan an order of magnitude higher than the currentlyavailable estimates in order to explain the observations.Qiu et al. (2018) also estimated what the emissions ofalkylated PAHs (alk-PAHs), which are not required tobe reported, needed to be to fit the observations: 160 tonsyr−1 for C1-naphthalenes; 130 tons yr

−1 for C2-naphthalenes; 52 tons yr−1 for C3-naphthalenes; 19 tonsyr−1 for C1-fluorenes; and 35 tons yr

−1 for C1-phenanthracene/anthracenes.

Remote sensing observations are being used exten-sively to monitor air pollutants over the OS region;SO2, NO2, CO, NH3, methanol (CH3OH), and formicacid (HCOOH) have been observed from 2004 onwardon the Aura satellite. Figure 3 shows how the amountof NO2 increased over the northern parts of the surfaceminable region in the late 2000s. The ECCC satelliteresearch has led to improved methods to derive emis-sions and for retrievals of SO2 (Fioletov et al. 2017,2015) and ammonia (Shephard et al. 2015). McLindenet al. (2012) examined the annual trends from 2005 to2011 and showed that there is good agreement betweenthe trend derived from satellite (vertical column density


and area-integrated NO2 mass) and ground NO2 obser-vations and bitumen production (Figure S3.5), suggest-ing that such analyses could provide anotherindependent validation of the reported emissions.Currently, 3-yr running average annual SO2 emissionsfrom the OS region for 2006–2017 are being analyzedfor trends for comparison with the NPRI reports forthe same time period (McLinden, personal communi-cation, 2019).

Greenhouse gases (GHG) are generally not a pollutantof interest in regard to long-term ecosystem effects due todeposition/exposure (i.e., the topic of this paper), as is thecase for the other pollutants discussed in this section.However, they will contribute to climate change effects.Liggio et al. (2019) report that the aircraft-derived carbondioxide (CO2) emissions intensities are, at times, largerthan what would be derived using publicly available datain the Greenhouse Gas Reporting Program (GHGRP).The difference in calculation results translates intoa potential gap in CO2 emissions of approximately 17Mt annually, which could correspond to a 64% increaserelative to reported emissions for the four major surfacemining operations in the OS. Similarly, methane (CH4)annual emissions estimated using aircraft hourly emissionrates from the five major facilities in the surface miningregion was found to be 48 ± 8% higher than that extractedfor 2013 from the GHGRP (Baray et al. 2018). Theseestimates were based upon examination of emissionsfrom mine faces and tailings ponds, which are the majorsources of CH4 on the facilities. Clearly, the discrepanciesbetween the aircraft-based (top-down) emission estimatesand the methods used to estimate emissions reported tothe GHGRP indicate a need for reconciliation betweenthe bottom-up (used to report to inventories) and top-down estimates.

How do the emissions transform in theatmosphere?—Assessment of changes in pollutantsduring atmospheric transport

Given the large emissions of volatile organic com-pounds (VOCs) and other pollutants (e.g., NOx) fromOS sources, it was anticipated that as they are trans-ported away from the source area, they would trans-form into both gaseous- and particle-phase oxygenatedproducts. Thus, four of the 2013 aircraft flights exam-ined the formation rates of and/or total quantities ofsecondary organic aerosols (SOAs), particle organicnitrates (pONs), gas-phase low-molecular-weightorganic acids (LMWOAs), and isocyanic acid(HNCO) downwind of the OS. In general, ozone levelsnear and downwind of the OS region are relatively low

(i.e., hourly maxima typically less than 60 ppbv) andthus were not a focus of these transformation studies.

SOA formation was hypothesized to be importantgiven that bitumen is composed of lower-volatility hydro-carbons, and open-pit extraction and subsequent proces-sing could release a disproportionately large fraction ofSOA precursors (semivolatile organic compounds andintermediate volatility organic compounds [SVOCs/IVOCs]) into the atmosphere. Should even a smallamount of the bitumen volatilize during production,there would be a strong potential for SOA formationdownwind of the region. However, although oil and gasproduction and processing, including OS production,were known to be a significant source of VOC emissions(Simpson et al. 2010), SVOC/IVOC emissions were onlysuspected. Liggio et al. (2016) reported large amounts ofSOAs forming downwind (Figure 8) suggestive of suchemissions from the OS activities. After correcting fordispersion of the plume as it spread downwind, a 6-foldrelative increase in organic aerosol mass (as SOAs) wasobserved over 4 hr of transport away from the OS facil-ities. In terms of the amount of SOAs formed, they foundthat during the summer season of the aircraft flightsformation was on the order of 55–101 tons day−1 andlikely higher given that SOA formation beyond the lastflight screens and at night were not considered. Thesequantities are comparable to what has been observedforming downwind of major cities (Liggio et al. 2016).

Based upon laboratory experiments, the characteris-tics of the newly formed SOAs were found to be similarto the hydroxyl (OH) oxidation products of bitumenvapors (Liggio et al. 2016). To determine how much

S

A

C

B

D

Figure 8. Organic aerosol (OM) observations at varying dis-tances and times downwind from the main oil sands surfacemining region (S). The aircraft flew at multiple heights perpen-dicular to the wind direction to capture the complete plume asit dispersed and transformed. Clear increases in OM after thefirst transect (A) can be seen by more red, yellow, and greencolors in B, C, and D. The yellow text indicates estimates of theamount of secondary organic aerosol (SOA) formed betweenthe separate transects (Liggio et al. 2016).


SVOCs/IVOCs would need to be present to explain theobserved SOAs, a Lagrangian box model was set up forthe OS conditions. This initially only considered theknown VOC emissions along with the other primaryemissions (e.g., NOx). However, oxidation products ofthe speciated alkanes, alkenes, and aromatic hydrocar-bons could only explain

predominantly found in the gas phase. In contrast,quinones were more abundant in the particle phase(Wnorowski 2017; Wnorowski and Charland 2017).The average 24-hr concentrations were greatest for 2-and 3-ring quinones, average 5.3 ± 7. 8 ng m–3 (rangeof 0.1–32.5 ng m–3); average for 4- and 5-ring species of0.4 ± 0.6 ng m–3 (range of 0.1–2.6 ng m–3); and lowestfor 6-ring quinines, average 0.1 ± 0.1 ng m–3 (range of0.01–0.3 ng m–3). These concentrations are of the sameorder of magnitude as reported elsewhere for industrialsites (Harner et al. 2018). Diurnal measurementsshowed a higher abundance of quinones during day-time than nighttime, indicating that some PAH sourcesare linked to daytime local activity and favorable photo-chemical conditions for the oxidative transformationsof quinone precursors. Correlations of quinone andPAH concentrations with colocated primary pollutantmeasurements (e.g., NOx) suggested that unsubstitutedPAHs originate from primary emission sources asso-ciated with OS activities (Wnorowski 2017; Wnorowskiand Charland 2017). In contrast, the temperature-dependent formation of the quinones corresponded toa decrease in PAH and NO2 levels, suggesting gas-phase oxidation of quinone precursors by free radicals(Wnorowski 2017; Wnorowski and Charland 2017).

Given their semivolatile nature, many PACs in theregion can be expected to cycle between gas and parti-cle phases depending upon temperature and otheratmospheric conditions. This can happen over short(i.e., diurnal) and long (i.e., seasonal) timescales. Hsuet al. (2015) presented evidence of evaporation of PAHsfrom Lake Gregoire, which may be sufficiently large asto control local atmospheric concentrations in summer,with peak levels occurring in May. They suggest thatPAHs that were atmospherically deposited during thewinter months may have run off to the lake’s surfacewaters during snow melt, from which evaporationoccurred when temperatures increased in spring.These partitioning processes could have a significantinfluence on where some PACs accumulate in theenvironment, in what chemical form, and their atmo-spheric processing. However, these processes have yetto be explored in a systematic manner in the OS region.A modeling study examining the fate of PAHs relativeto trace metals (i.e., inert tracer) around the Sudbury(Ontario, Canada) smelter provides some insight intothe impact of these processes (Thuens et al. 2014). Astheory would predict, lower-molecular-weight PAHswere observed to have shorter lifetimes than the higher-molecular-weight PAHs. Travel distances in colderwinters were more than twice those in the hotter sum-mers, indicative of the longer travel distances possiblefor particles. Overall, travel distances of up to 500 km

were found for the trace metals, whereas they wereshorter for the semivolatile PAHs.

Model evaluation and improvement—Towardbetter tools for integration of information toestimate impacts of OS emissions

The Global Environmental Multiscale–Modeling Air-quality and Chemistry model (GEM-MACH) has beenused extensively for applications in the OS region (seeSection S3.1.2 of the supplemental material). GEM-MACH (Makar et al. 2015b, 2015a; Moran et al. 2010)stems from a longer history of prediction model develop-ment involving the Acid Deposition and Oxidants Model(ADOM), which has its roots in the acid deposition era(Venkatram and Karamchandani 1986), followed byA Unified Regional Atmospheric Modeling System(AURAMS), which was initially developed to incorporateparticulate matter in the ECCC’s modeling tools (Brookand Moran 2000; Gong et al. 2006). In parallel to dailyGEM-MACH runs based upon the grid and domainshown in Figure S3.3, a number of components in themodel have been modified, including emissions, andtested against observations from the aircraft and ground,to determine whether they lead to improved predictions.This iterative process of model evaluation, improvement,and testing is critical given the ongoing role of GEM-MACH in identifying areas of potential concern (e.g.,above critical loads) and in a range of other applicationsin the region (e.g., emission reduction scenarios, air qual-ity forecasting).

Incorporation of observed emission rates fromaircraft dataTo determine whether GEM-MACH predictionsimprove through incorporation of the aircraft-basedemission estimates (Li et al. 2017), new VOC and size-resolved PM emissions files were created and modelsensitivity analyses were conducted (Zhang et al.2018). In addition to speciating VOCs and PM andadjusting their emission rates to reflect the aircraftobservations, spatial allocation of the facility-totalemissions to specific locations within the facilities(e.g., stacks, tailings ponds, mine faces) was improved(Figure S3.6). Stroud et al. (2018) found that with theaircraft-based VOC and organic PM emission esti-mates, changes in the model predictions for theAugust–September 2013 period was sometimes con-siderable, particularly based on a comparison of 99thpercentiles of aircraft-observed and modeled VOCand organic aerosols (Figure 9). This statistic isa quantitative estimate of whether the model capturesthe plume maxima mixing ratios, whereas the model


median value is more representative of the regionalbackground concentrations. The modeled di/trisubsti-tuted aromatics (AROM) median and 99% percentilemixing ratios were both closer to the aircraft observa-tions. The statistical analysis with the monosubstitutedaromatic (TOLU) species showed comparable results(i.e., similar performance with the original and revisedemissions).

For the C4+ alkanes (ALKA), which are emitted inlarge quantities given their use in bitumen processing(Li et al. 2017), the mean bias and median value werefound to be higher with the revised emissions.However, the 99th percentile was closer to the observedvalue with the revised emissions. In terms of SOAformation, the modeled PM1 organic aerosol meanbias and root mean square error improved significantlywith the revised emissions, although the organic aerosolmean bias still remained negative. One likely cause ofthe continued underestimate of organic aerosols isinsufficient organic aerosol enhancement from second-ary formation in the modeled plumes, particularly dueto missing contributions from IVOCs/SVOCs (Liggioet al. 2016). Overall, Stroud et al. (2018) concluded thatthe use of the aircraft estimates of emission ratesresulted in comparable or higher, and potentiallyimproved, results compared with predictions based

upon the original inventory (i.e., bottom-up inventory)in GEM-MACH.

Increasing resolution in the PM size distributionTwo-bin (size cuts of 0.01, 2.56, and 10.24 μm dia-meter) and 12-bin (size cuts of 0.01, 0.02, 0.04, 0.08,0.16, 0.32, 0.64, 1.28, 2.56, 5.12, 10.24, 20.48, and 40.96μm diameter) particle schemes were implemented inGEM-MACH, and the predicted PM concentrationswere compared with surface PM2.5 observations. The12-bin version led to improvements, with the mean biasdecreasing from −2.6 to −1.7 μg m−3 and the fraction ofobservations within a factor of 2 increased from 0.39 to0.45 (Akingunola et al. 2018). These observations sug-gest that a sizeable fraction of particulate underpredic-tions in 2-bin simulations may be due to poorrepresentation of particle microphysics, despite the sub-binning used in some of the algorithms simulatingmicrophysics processes. Figure 10, which providesinformation on the PM2.5 in the region during theAugust–September 2013 study period, compares themodeled PM2.5 with the surface observations. The 12-bin scheme did improve predictions, although negativebiases for the frequency of low concentration eventsand positive biases for the frequency of high concen-tration events were still evident.

Figure 9. Comparison of the observed and model histograms of VOC categories simulated by the GEM-MACH model. The firstcolumn is from aircraft observations obtained from flights during two summer months (August and September 2013). The secondcolumn is from GEM-MACH using the available bottom-up emission inventory information for VOCs and organic aerosol. The thirdcolumn is model estimates using revised VOC emissions obtained from the aircraft-observation-based (top-down) emission estimates(Li et al. 2017). The 99th percentile values for the different VOC groups and total organic aerosol are displayed in each graph (Stroudet al. 2018).


Impact of meteorology on local-scale motions—Vertical and horizontal placement of plumesGEM-MACH and most comparable models continue touse empirical plume rise formulae based on observationscollected between 1969 and 1985 (Briggs 1969, 1975,1984, 1985). Comparisons between model predictions ofthe height of SO2 plumes and the aircraft observationsindicated that GEM-MACH had a tendency to under-predict the height of buoyant plumes and overestimatethe frequency and intensity of surface fumigation events.To understand possible reasons for this discrepancy andto improve the model, meteorological observations wereused to “drive” the standard Briggs plume rise algorithms,and then these and alternative formulations (Gordon et al.2018) were evaluated using aircraft observations of SO2plumes. This confirmed that the plume rise algorithmsunderestimate plume rise, with 50% or more of the pre-dicted plume heights falling below half of the observedvalues in the OS region. In addition, computations ofplume rise using different sources of meteorological data(aircraft, two tall meteorological towers, and surface-based remote sensing) sometimes resulted in differentestimates of plume height, suggesting that plume riseestimates can be influenced by the high degree of spatialheterogeneity in meteorology in the OS region.Meteorological observations close to or at the stacksmight therefore be required for observation-basedimprovements to estimates of plume rise, which can ulti-mately influence where the emissions are deposited.

Akingunola et al. (2018) compared results from theplume rise algorithms using inputs from GEM-MACH(i.e., as opposed to from meteorological observations)to the aircraft observations. These simulations revealedthat the predicted temperature profiles and planetary

boundary layer (PBL) heights (key determining factorsin plume rise) were different between the locationscontaining the meteorological observations and thelocations of the stacks. That is, the model also revealedthe potential for spatial heterogeneity in conditionsbetween meteorological observation locations, againimplying that at-stack meteorological observations areneeded to obtain optimal results from the plume risealgorithms. Akingunola et al. (2018) also found thatonce these meteorological differences were taken intoaccount using model predictions at the actual stacklocations, a new plume rise estimation methodologythat makes use of successive residual buoyancy calcula-tions gave significantly improved results for both plumeheight and SO2 concentrations in plumes. However,excessive fumigation relative to observations stilloccurred at times. This appeared to be related to themeteorological model tending to systematically under-predict the temperatures in the lowest 2 km of theatmosphere, resulting in an overestimation of the tem-perature gradient and excessive fumigation.

Slight errors in the horizontal direction of advection,which will displace the location of plumes, can result inlarge errors when predictions and observations are com-pared. This can be accentuated in more spatially resolvedmodels such that the advantages of the finer resolution,which can be significant for simulating a range of pro-cesses, may be difficult to quantify in model diagnosticevaluation (Fox 1981, 1984; Hanha 1988). Russell et al.(2018) presented a new approach to account for this mis-alignment in their analysis of the potential benefits ofa more spatially resolved model (i.e., 1.0 vs. 2.5 km resolu-tion). They accounted for the fact that when the modelresolution is relatively low, the plumes spread over a largercross-sectional distance and wind direction errors havea smaller impact than when model resolution is high andplumes are less spatially distributed (Landers et al. 2010).These techniques have the potential to help evaluatewhether improvements to the model, ultimately to predictlong-term conditions and undertake emission scenariowork, are having the intended benefits. However, for airquality forecasting to provide advanced, short-term warn-ings (i.e., for location populations), misalignment errorsremain problematic, highlighting the ongoing need formore accurate simulations of meteorology. Incorporationof meteorological observations into model forecasts viadata assimilation may ultimately be needed to improveair quality forecasting in the OS region.

Two-way air-surface exchange processes—Aircraftand satellite evidence for ammonia bidirectional fluxConcentrations of ammonia (NH3) measured from theaircraft along with satellite data showed that background

Figure 10. Histogram of surface PM2.5 using Wood BuffaloEnvironmental Association (WBEA) surface monitoring data(blue), and the 2-bin (red) and 12-bin (purple) configurationsof the GEM-MACH model (Akingunola et al. 2018).


NH3 concentrations in the region were approximately 0.6ppbv throughout the boundary layer and into the lowertroposphere (Shephard et al. 2015). However, initialGEM-MACH simulations underpredicted backgroundNH3 relative to these observations. Bidirectional fluxes ofNH3, in which natural emissions as well as deposited NH3stored within vegetated surfaces is released once concen-trations drop below a vegetation-dependent “compensa-tion point” concentration, were tested in GEM-MACHand found to be capable of accounting for the deficit withobservations (Whaley et al. 2018b). Bidirectional fluxesmay have an impact on net nitrogen deposition, and two-way exchange processes will likely be necessary to bettersimulate the movement of PACs in the OS region.

Integration of Air Component findings—Twoexamples

Acid deposition and the potential for critical loadexceedancesThe most up-to-date estimates of total S andN deposition and critical load exceedances overAlberta and Saskatchewan were provided by Makaret al. (2018). These were derived through an annualrun (August 1, 2013–July 31, 2014) of the 2-bin, 2.5 kmresolution version of GEM-MACHv2, combined withavailable wet deposition observations and revised fugi-tive dust emissions derived through application of thelocal 12-bin version of GEM-MACH2.5 and informedby the aircraft observations. Relative to the first set ofmodel estimates (i.e., GEM-MACH2.5 runs initiated at

the start of JOSM), these new model-measurementfusion corrections (Figures S3.7–S3.9) resulted inincreased base cation and decreased anion deposition,respectively. These new deposition estimates were com-pared with the estimated critical loads (i.e., an updateto Figure S1.2). Areas potentially experiencing criticalload exceedances, implying potential future ecosystemdamage assuming continuation of emissions at thelevels used in the air quality model, can be seen inFigures 11 and 12 for terrestrial and aquatic ecosys-tems, respectively.

Two different approaches, the simplified NEG-ECP(2001) protocol and the CLRTAP (2017) approach(see “Examples of air pollution–related indicators”),were considered in the determination of critical loads.The estimated terrestrial critical load exceedances(NEG-ECP protocol) cover an area of1.20 × 104 km2. In contrast, the total area estimatedto be in exceedance using the CLRTAP (2017) criticalloads is more than 5 times higher, 6.99 × 104 km2,encompassing about 16% of the region for which datawere available within the Province of Alberta. Theselatter critical load exceedance values include data onprovince-specific vegetation and soil updates and theuse of a more rigorous protocol (see “Examples of airpollution–related indicators”). The inclusion of theobservation-corrected base cation deposition esti-mates derived from the aircraft measurements led toestimates of predictions of more neutralization in thevicinity of the oil sands (circled regions in Figures 11and 12) relative to initial model estimates (Makar

Figure 11. Predicted terrestrial ecosystem critical load exceedances with respect to Sdep + Ndep (deposition), using (a) NEG-ECP(2001) and (b) CLRTAP (2017) methodologies (eq ha−1 yr−1). (from Makar et al., 2018). Lower left of each panel: percentage of theentire critical load data area which is in exceedance, and the total area in exceedance, in km2. Circled region: 140 km radius circlearound the Athabasca oil sands.


et al. 2018). This is hypothesized to better reflect thedust emissions from the oil sands activities (Makaret al. 2018). However, although this enhanced basecation deposition, which may be interpreted as pro-tective for acidification, it may occur simultaneouslywith dust deposition of toxics (i.e., PACs and metals),which can also originate from area sources associatedwith OS development.

Assessment of the current knowledge on PACsGiven the snowpack and lake sediment observations(Kelly et al. 2009; Willis et al. 2018), their known and/or potential toxicity and presence in air, water, and thebiota, an integrated program was undertaken for PACs.Monitoring conducted within the ECCC WaterComponent of JOSM indicated that the amount ofPACs and other toxics (e.g., naphthenic acid, metals)seeping from tailings ponds into the watershed is smallrelative to other sources (Bickerton et al. 2018), sug-gesting the need to focus on atmospheric pathwaysbetween the OS industry and the surrounding environ-ment. Harner et al. (2018) assessed the current knowl-edge on PACs (before ~2017) in regard to theiremissions and transformation, concentrations in air,wet and dry deposition, and insights from source attri-bution and modeling studies. Three main categories ofPACs have been the focus of monitoring in the OSregion: polycyclic aromatic hydrocarbons (PAHs), alky-lated PAHs (alk-PAHs), and dibenzothiophenes (DBTsand alk-DBTs). These are defined in Section S3.2 of thesupplemental material.

Due to the complexity of the transport, transforma-tion, and deposition processes (e.g., particle deposition,deposition with snow, rain, etc.) and lack of knowledge,a comprehensive model to assess local to regionaltransport of PACs and their by-products and to predicttheir net influence on ecological systems, as has beendone for acidifying pollutants, is not available for theOS region. However, simplified modeling approacheshave been applied in the OS region to gain insight intoemissions of PACs (Parajulee and Wania 2014; Qiuet al. 2018). These indicate that fugitive emissions ofPAHs and alk-PAHs represent the major source to theatmosphere. These include resuspended dust frommine faces, unpaved roads, and petroleum coke storagepiles, as well as volatilization from tailings ponds(Figure 13).

Schuster et al. (2019) documented the long-termconcentrations of the three PAC classes across 15 sitesin the OS region covering the period 2010–2016. Thesepassive data serve as a key baseline for assessing theimpact of future and expanded mining projects in theregion on ambient levels of PACs. They have helpedevaluate the contribution of forest fire combustionemissions to the PAH levels in air as well as the con-tribution from revolatilization (during forest fires) ofambient PAHs, which were previously taken up byforests (Rauert, Kananathalingham, and Harner 2017).Forest fire emissions currently play an important rolein the PAC levels in the region, one that could increasedue to climate change. However, beyond the parentPAHs, there is a lack of information on wood

Figure 12. (a) Predicted lake ecosystem critical load excee-dances with respect to Sdep. GEM-MACH Sdep scaled usingprecipitation deposition observations and NEG-ECP (2001)methodology (eq ha−1 yr−1). (b) Predicted aquatic ecosystemcritical load exceedances with respect to Sdep, corrected tomatch precipitation observations, CLRTAP (2017) methodology(eq ha−1 yr−1). (c) Predicted aquatic ecosystem critical loadexceedances with respect to Sdep +Ndep, corrected to matchprecipitation observations (eq ha−1 yr−1) (Makar et al. 2018).


combustion emission factors for alk-PAHs and theDBTs, complicating assessment of the contributionfrom the OS industry to their levels in water, sediment,and biological compartments (Schuster et al. 2019).

Although forest fires are important, the spatial pat-terns in PACs observed in the passive sampling net-work (sites shown Figure S3.10) demonstrate that PACsin air are largely attributed to OS production opera-tions, although levels in different mining-type regionshave not been assessed (i.e., PACs arising from in situversus open-pit mining extraction). The alk-PAHs arethe dominant group in the air, and they are attributedto petrogenic sources (i.e., geological sources asopposed to combustion or pyrogenic sources).Observations have shown that other PAC classes, suchas DBTs, quinones, transformation products (e.g.,nitro- and oxy-PAHs), and a wide range of otherPACs (e.g., heterocyclic aromatics), also contribute tothe PAC levels in air (Wnorowski 2017).

Consistent with the spatial pattern in air concentra-tions (Schuster et al. 2019), deposition occurs ona gradient, with higher deposition near oil sands pro-duction activity. Spatial patterns of PAC depositionhave been explored using a variety of methods, includ-ing snow (Manzano et al. 2016), bulk deposition

collectors (Bari, Kindzierski, and Cho 2014), lichen(Graney et al. 2017; Landis et al. 2019), moss sampling(Zhang et al. 2016), and a passive dry deposition sam-pler (Jariyasopit et al. 2018). However, quantitativelinkage between concentrations in air and/or precipita-tion and the levels in these different forms of collectionmedia is difficult to assess (Zhang et al. 2016).Precipitation samples using wet-only collectors havealso been deployed in the OS region (Muir et al.2012). Three “near-field” sites have been operatedwith monthly collection since 2010. The highest load-ings or atmospheric wet deposition fluxes (µg m−2) ofPACs were observed in the winter months (January–March) in 2011 and 2012 at the two sites closest to theOS operations, whereas loadings at the third site,although only ~10 km north of SML, were 4- to6-fold lower in winter, but similar from May toOctober 2012. PAHs (17 unsubstituted) in precipitationat these two close sites were lower than the total annualwinter snow fluxes observed at the sites on theAthabasca River, which were within 5 km of theselocations (next paragraph). This is likely due to con-tributions from other sources, such as petcoke andhaul-road dust, in the snow relative to the wet-onlysamplers.

UTM easting (m)

ab

c

UTM

nor

advances in science and applications of air …pubs.awma.org/flip/2019 critical...

Documents