A decadal synthesis of atmospheric emissions, ambient air quality, and deposition in the oil sands region
Notice bibliographique
Résumé
Abstract This review is part of a series synthesizing peer-reviewed literature from the past decade on environmental monitoring in the oil sands region (OSR) of northeastern Alberta. It focuses on atmospheric emissions, air quality, and deposition in and downwind of the OSR. Most published monitoring and research activities were concentrated in the surface-mineable region in the Athabasca OSR. Substantial progress has been made in understanding oil sands (OS)-related emission sources using multiple approaches: airborne measurements, satellite measurements, source emission testing, deterministic modeling, and source apportionment modeling. These approaches generally yield consistent results, indicating OS-related sources are regional contributors to nearly all air pollutants. Most pollutants exhibit enhanced air concentrations within ~20 km of surface-mining activities, with some enhanced >100 km downwind. Some pollutants (e.g., sulfur dioxide, nitrogen oxides) undergo transformations as they are transported through the atmosphere. Deposition rates of OS-related substances primarily emitted as fugitive dust are enhanced within ~30 km of surface-mining activities, whereas gaseous and fine particulate emissions have a more diffuse deposition enhancement pattern extending hundreds of kilometers downwind. In general, air quality guidelines are not exceeded, although these single-pollutant thresholds are not comprehensive indicators of air quality. Odor events have occurred in communities near OS industrial activities, although it can be difficult to attribute events to specific pollutants or sources. Nitrogen, sulfur, polycyclic aromatic compounds (PACs), and base cations from OS sources occur in the environment, but explicit and deleterious responses of organisms to these pollutants are not as apparent across all study environments; details of biological monitoring are discussed further in other papers in this special series. However, modeling of critical load exceedances suggests that, at continued emission levels, ecological change may occur in future. Knowledge gaps and recommendations for future work to address these gaps are also presented. Integr Environ Assess Manag 2022;18:333–360. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). KEY POINTS Air emissions from oil sands activities are regional contributors to nearly all air pollutants, with most exhibiting enhanced concentrations within ~20 km of surface-mining activities, and some enhanced at greater distances (>100 km) downwind. Temporal trends identified in ambient air-monitoring data vary with the statistical analysis applied; existing ambient air quality guidelines and standards are rarely exceeded; however, single-pollutant thresholds are not comprehensive indicators of air quality. Co-located deposition- and ecological-effects monitoring identify a link between nitrogen deposition and ecological changes in jack pine, bog, and poor fen ecosystems—there is limited evidence of acidification to date, but predictive modeling indicates areas exceeding critical loads of acidification. Knowledge gaps are synthesized and recommendations for future work to address these gaps are presented.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».