Industrial Sources Influence Air Concentrations of Hydrogen Sulfide and Sulfur Dioxide in Rural Areas of Western Canada
Bibliographic record
Abstract
A survey of monthly average concentrations of sulfur dioxide (SO2) and hydrogen sulfide (H2S) at rural locations in western Canada (provinces of Alberta, British Columbia, and Saskatchewan) was conducted in 2001-2002, as part of an epidemiological study of the effects of oil and gas industry emissions on the health of cattle. Repeated measurements were obtained at some months and locations. We aimed to develop statistical models of the effect of oil and gas infrastructure on air concentrations. The regulatory authorities supplied the information on location of the different oil and gas facilities during the study period and, for Alberta, provided data on H2S content of wells and flaring volumes. Linear mixed effects models were used to relate observed concentrations to proximity and type of oil and gas infrastructure. Low concentrations were recorded; the monthly geometric mean was 0.1-0.2 ppb for H2S, and 0.3-1.3 ppb for SO2. Substantial variability between repeated measurements was observed. The precision of the measurement method was 0.005 ppb for both contaminants. There were seasonal trends in the concentrations, but the spatial variability was greater. This was explained, in part, by proximity to oil/gas/bitumen wells and (for SO2) gas plants. Wells within 2 km of monitoring stations had the greatest impact on measured concentrations. For H2S, 8% of between-location variability was explained by proximity to industrial sources of emissions; for SO2 this proportion was 18%. In Alberta, proximity to sour gas wells and flares was associated with elevated H2S concentrations; however, the estimate of the effect of sour gas wells in the immediate vicinity of monitoring stations was unstable. Our study was unable to control for all possible sources of the contaminants. However, the results suggest that oil and gas extraction activities contribute to air pollution in rural areas of western Canada.
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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 | 0.001 | 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 | 0.000 |
| 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".