MétaCan
Menu
Back to cohort
Record W2181180697 · doi:10.1080/10962247.2015.1100693

Characterization of PM<sub>2.5</sub> and PM<sub>10</sub> fugitive dust source profiles in the Athabasca Oil Sands Region

2015· article· en· W2181180697 on OpenAlex
Xiaoliang Wang, Judith C. Chow, Steven D. Kohl, Kevin E. Percy, A.H. Legge, John G. Watson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of the Air & Waste Management Association · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsCumulative Environmental Management Association
FundersWood Buffalo Environmental Association
KeywordsOil sandsSoil waterEnvironmental chemistryEnvironmental scienceSulfateParticulatesCarbon fibersTotal organic carbonMineralogyTailingsInorganic ionsOrganic matterIsotopes of nitrogenChemistryNitrogenSoil scienceIonAsphalt

Abstract

fetched live from OpenAlex

UNLABELLED: Geological samples were collected from 27 representative locations in the Athabasca Oil Sands Region (AOSR) in Alberta, Canada. These samples were resuspended onto filter substrates for PM2.5 and PM10 size fractions. Samples were analyzed for 229 chemical species, consisting of elements, ions, carbon, and organic compounds. These chemical species are normalized to gravimetric mass to derive individual source profiles. Individual profiles were grouped into six categories typical of those used in emission inventories: paved road dust, unpaved road dust close to and distant from oil sand operations, overburden soil, tailings sands, and forest soils. Consistent with their geological origin, the major components are minerals, organic and elemental carbon, and ions. The sum of five major elements (i.e., Al, Si, K, Ca, and Fe) and their oxidized forms account for 25-40% and 45-82% of particulate matter (PM) mass, respectively. Si is the most abundant element, averaging 17-18% in the Facility (oil sand operations) and 23-27% in the Forest profiles. Organic carbon is the second most abundant species, averaging 9-11% in the Facility and 5-6% in the Forest profiles. Elemental carbon abundance is 2-3 times higher in Facility than Forest profiles. Sulfate abundance is ~7 times higher in the Facility than in the Forest profiles. The ratios of cation/anion and base cation (sum of Na+, Mg2+, K+, and Ca2+)/nitrogen- and sulfur-containing ions (sum of NH4+, NO2-, NO3-, and SO4(2-)) exceed unity, indicating that the soils are basic. Lead (Pb) isotope ratios of facility soils are similar to the AOSR stack and diesel emissions, while those of forest soils have much lower 206Pb/207Pb and 208Pb/207Pb ratios. High-molecular-weight n-alkanes (C25-C40), hopanes, and steranes are more than an order of magnitude more abundant in Facility than Forest profiles. These differences may be useful for separating anthropogenic from natural sources of fugitive dust at receptors. IMPLICATIONS: Several organic compounds typical of combustion emissions and bitumen are enriched relative to forest soils for fugitive dust sources near oil sands operations, consistent with deposition uptake by biomonitors. AOSR dust samples are alkaline, not acidic, indicating that potential acid deposition is neutralized. Chemical abundances are highly variable within emission inventory categories, implying that more specific subcategories can be defined for inventory speciation.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.409
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.022
GPT teacher head0.241
Teacher spread0.219 · 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