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

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

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

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineEnvironmental Science
ThématiqueMercury impact and mitigation studies
Établissements canadiensConcordia UniversityEnvironment and Climate Change Canada
Organismes subventionnairesEnvironment and Climate Change Canada
Mots-clésOil sandsEnvironmental scienceMercury (programming language)Deposition (geology)Environmental chemistryEnvironmental engineeringAtmospheric sciencesHydrology (agriculture)ChemistryGeographyGeologyArchaeologyStructural basin

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,232
Score d'incertitude au seuil0,468

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,308
Écart entre enseignants0,279 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2021
Routes d'admission3
Résumé présentoui

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