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Record W4241348392 · doi:10.5194/acp-2021-296

Impact of Athabasca oil sands operations on mercury levels in air and deposition

2021· preprint· en· W4241348392 on OpenAlexafffundabout
Ashu Dastoor, Andrei Ryjkov, Junhua Zhang, Jane L. Kirk, Matthew Parsons, A. Steffen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsConcordia UniversityEnvironment and Climate Change Canada
FundersEnvironment and Climate Change Canada
KeywordsOil sandsEnvironmental scienceMercury (programming language)Deposition (geology)Environmental chemistryEnvironmental engineeringAtmospheric sciencesHydrology (agriculture)ChemistryGeographyGeologyArchaeologyStructural basin

Abstract

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Abstract. Oil sands upgrading facilities in the Athabasca Oil Sands Region (AOSR) in Alberta, Canada, have been reporting mercury (Hg) emissions to public government databases (National Pollutant Release Inventory (NPRI)) since the year 2000, yet the relative contribution of these emissions to ambient Hg deposition remains unknown. A 3D process-based global Hg model, GEM-MACH-Hg, was applied to simulate the Hg burden in and around the AOSR using NPRI reported oil sands Hg emissions from 2012 (59 kg) to 2015 (25 kg) and other regional and global Hg emissions. The impact of oil sands emissions (OSE) on Hg levels in the AOSR, relative to contributions from sources such as global anthropogenic and biomass burning emissions (BBE), was assessed. In addition, the relative importance of year-to-year changes in Hg emissions from the above sources and meteorological conditions to inter-annual variations in Hg deposition was examined. Model simulated surface air concentrations of Hg species and annually accumulated Hg in snowpacks were found comparable to independently obtained measurements in the AOSR, suggesting consistency between reported Hg emissions from oil sands activities and Hg levels in the region. As a result of global-scale transport of gaseous elemental Hg (Hg(0)), surface air concentrations of Hg(0) in the AOSR reflected the background Hg(0) levels in Canada (1.4 ng m−3, AOSR; 1.2 1.6 ng m−3, Canada) with negligible impact from OSE. Highly spatiotemporally variable wildfire Hg emission events led to episodes of high ambient Hg(0) air concentrations of up to 2.5 ng m−3 during the burning season. By comparison, average air concentrations of total oxidised Hg (gaseous plus particulate; efficiently deposited Hg species) in the AOSR were elevated by 60 % above background levels (2012–2013) within 50 km of the oil sands major upgraders as a result of OSE. Annual average Hg deposition fluxes in the AOSR were within the range of the deposition fluxes measured for the entire province of Alberta (15.6–18.3 µg m−2 y−1, AOSR (2012–2015); ~14–25 µg m−2 y−1, Alberta (2015)). Winter (November–April) and summer (June–August), respectively, accounted for 20 % and 50 % of the annual Hg deposition in the AOSR. On a broad spatial scale, imported Hg from global sources dominated the annual Hg deposition in the AOSR, with present-day global anthropogenic emissions contributing to 40 % (< 1 % from Canada excluding OSE), and geogenic emissions and re-emissions of legacy mercury deposition contributing to 60 % of the background Hg deposition. Further, wildfire events contributed to regional Hg deposition with enhancements of 1–13 % across 200 km range of major oil sands sources. In contrast, oil sands Hg emissions were responsible for significant Hg deposition enhancements in the immediate vicinity of oil sands Hg emission sources, up to 100 km in winter and up to 30 km in summer. Hg deposition enhancements related to oil sands emissions were about 10 times larger in winter than summer (average enhancement of 250–350 % in winter and ~35 % in summer within 10 km of OSE, 2012–2013). In addition, snowpack Hg loadings and wintertime Hg deposition displayed significantly higher inter-annual variations compared to summertime deposition due to changes in meteorological conditions (such as precipitation amounts, wind speed, surface air temperature, solar insolation, and snowpack dynamics) as well as oil sands emissions. For example, a large snowmelt event at the end of February in 2015 effectively removed about half of the accumulated mercury in snow, contributing to (observed and modeled) low annual snow Hg loadings. Inter-annual variations in meteorological conditions were found to both exacerbate and diminish the impacts of OSE on Hg deposition in the AOSR, which can confound the interpretation of trends in short-term environmental Hg monitoring data. In winter, within 10 km of major oil sands sources, variations in meteorology led to Hg deposition reduction by 17 % in 2014 and increase by 10 % in 2015 and decline in OSE lowered Hg deposition by 35 % (2014) and 56 % ( 2015), resulting in overall reductions in wintertime Hg deposition of 52 % (2014) and 46 % (2015), relative to 2012. By comparison, annually, changes in meteorology and BBE in 2014–2015 (relative to 2012) led to Hg deposition increases of 1–6 % and 2 %, respectively, and decline in OSE lowered deposition by 15–22 %, resulting in overall reduction in Hg deposition of 7–20 % within 10 km of oil sands sources. Hg runoff in spring flood, comprising the majority of annual Hg runoff, is mainly derived from seasonal snowpack Hg loadings and mobilization of Hg deposited in surface soils, both of which are sensitive to Hg emissions from oil sands developments in proximity of sources. Model results suggest that sustained efforts to reduce anthropogenic Hg emissions from both global and oil sands sources are required to reduce Hg deposition in the AOSR.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.468

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.308
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2021
Admission routes3
Has abstractyes

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