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Contributions of the oil sands sources to the ambient concentrations and deposition of particulate elements in the Canadian Athabasca oil sands region

2023· article· en· W4384008980 on OpenAlexafffundabout
Fuquan Yang, Abdulla Al Mamun, Irene Cheng, Xin Qiu, Leiming Zhang

Bibliographic record

VenueThe Science of The Total Environment · 2023
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsEnvironment and Climate Change Canada
FundersEnvironment and Climate Change Canada
KeywordsOil sandsParticulatesDeposition (geology)Environmental scienceEnvironmental chemistryGeologyGeochemistryMining engineeringChemistryAsphaltArchaeologyGeomorphologyGeographySediment

Abstract

fetched live from OpenAlex

In this study, model sensitivity tests were conducted to investigate the relative contributions between emission sources of oil sands (OS) activities and other sources to the ambient concentrations and deposition of 29 particulate elements in the Athabasca oil sands region (AOSR) of Canada. Element emission sources from a recently developed emission database were grouped into three source sectors for elements in PM2.5 (OS-Industrial, OS-Dust, and Non-OS) and two source sectors for elements in PM2.5–10 (OS-All and Non-OS). The OS-Dust and OS-Industrial sectors (combined as one sector for PM2.5–10; OS-All) included element sources linked to dust and other industrial activities from the OS activities, respectively, whereas the Non-OS sector included remaining sources in the region, unrelated to the OS activities. The OS-Industrial, OS-Dust, and Non-OS emissions (tonnes/year) of all elements in PM2.5 were 326, 1430, and 562, respectively. The OS-All and Non-OS emissions (tonnes/year) of all elements in PM2.5–10 were 5890 and 2900, respectively. The element concentrations were simulated by the CALPUFF dispersion model. The sum of the domain averaged annual mean concentrations of all elements in PM2.5 and PM2.5–10 from all sources were 57.3 ng/m3 and 30.4 ng/m3, respectively. Except for Co (PM2.5 and PM2.5–10), Sb (PM2.5–10), and Sn (PM2.5–10), major proportions (≥ 59 %) of the ambient concentrations of the individual elements were linked to the OS source sector. Overall, the OS sector was responsible for 78 % and 68 % of the sum of the mean ambient concentrations of all elements in PM2.5 and PM2.5–10, respectively, which are close to the corresponding emission contributions (76 % and 67 %, respectively). Likewise, the bulk proportion (~74 %) of the sum of the total atmospheric deposition of all elements was also associated with the OS sources. Carcinogenic and non-carcinogenic risks associated with inhalation exposure to airborne elements were below the recommended threshold risk levels.

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 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)0.001
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 score0.016
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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".

Quick stats

Citations9
Published2023
Admission routes3
Has abstractno

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