Using Pb isotope ratios of particulate matter and epiphytic lichens from the Athabasca Oil Sands Region in Alberta, Canada to quantify local, regional, and global Pb source contributions
Bibliographic record
Abstract
Ambient air particulate matter (PM) was collected at the Wood Buffalo Environmental Association Bertha Ganter Fort McKay monitoring station in the Athabasca Oil Sand Region (AOSR) in Alberta, Canada from February 2010 to July 2011 as part of an air quality source assessment study. Daily 24-hour duration fine (PM2.5) and coarse (PM10–2.5) PM was collected using a sequential dichotomous sampler. 100 pairs of PM2.5 and PM10–2.5 were selected for lead (Pb) concentration and isotope analysis. Pb isotope and concentration results from 250 epiphytic lichen samples collected as far as 160 km from surface mining operations in 2008, 2011, and 2014 were analyzed to examine longer term spatial variations in Pb source contributions. A key finding was recognition of thorogenic 208Pb from eastern Asia in the springtime in the PM2.5 in 2010 and 2011. 206Pb/207Pb and 208Pb/207Pb isotope ratios were used in a three-component mixing model to quantify local, regional, and global Pb sources in the PM and lichen data sets. 47 ± 3% of the Pb in the PM2.5 at AMS-1 was attributed to sources from eastern Asia. Combined results from PM10–2.5 and PM2.5 indicate PM2.5 Pb contributions from eastern Asia (34%) exceed local AOSR sources of PM2.5 Pb (20%), western Canada sources of PM2.5 Pb (19%), and PM10–2.5 Pb from fugitive dust including oil sands (14%), tailings (10%), and haul roads (3%). The lichen analysis indicates regional sources contribute 46% of the Pb, local sources 32%, and global sources 22% over the 2008–2014 timeframe. Local sources dominate atmospheric Pb deposition to lichens at near field sites (0–30 km from mining operations) whereas regional Pb sources are prevalent at distal sites (30–160 km). The Pb isotope methodology successfully quantified trans-Pacific transport of Pb to the AOSR superimposed over the aerosol footprint of the world's largest concentration of bitumen mining and upgrading facilities.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".