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Record W3207931546 · doi:10.1002/ieam.4539

A decadal synthesis of atmospheric emissions, ambient air quality, and deposition in the oil sands region

2021· review· en· W3207931546 on OpenAlexaffabout
Erin Horb, Gregory R. Wentworth, Paul A. Makar, John Liggio, Katherine Hayden, Elisa I. Boutzis, Danielle Beausoleil, Roderick O. Hazewinkel, Ashley Mahaffey, Diogo Sayanda, Faye Wyatt, Monique G. Dubé

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsEnvironment and Climate Change CanadaCumulative Environmental Management AssociationAlberta Environment and Protected Areas
Fundersnot available
KeywordsPollutantAir quality indexEnvironmental scienceDeposition (geology)ParticulatesOil sandsAir pollutionEnvironmental chemistryAtmosphere (unit)Emission inventoryAir pollutantsEnvironmental engineeringAtmospheric sciencesMeteorologyChemistryGeologyStructural basinGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.292
Teacher spread0.272 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations26
Published2021
Admission routes2
Has abstractyes

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