The development and application of a new method for quantifying total atmospheric sulfur in the Alberta Oil Sands
Bibliographic record
Abstract
Continuous ambient sulfur measurements are routinely conducted around the globe at numerous monitoring sites impacted by industrial sources, such as gas and oil processing facilities, pulp and paper mills, smelters, sewage treatment facilities, and concentrated animal feeding operations, as well as by natural sources, such as volcanoes. Various jurisdictions have or plan to establish air ambient quality objectives, guidelines, or standards for total reduced sulfur (TRS) based on odor perception and/or health effects. A conventional TRS monitoring technique is widely used, but few studies have looked at potential biases in the resulting TRS measurements. This paper presents a novel method to quantify total sulfur (TS) concentrations to investigate odor events caused by sulfur compounds and to construct the sulfur budget for sulfur dioxide (SO2), particle sulfate, hydrogen sulfide (H2S), and the sum of all remaining reduced sulfur compounds (non-H2S RSCs). This methodology was tested and improved through multi-year monitoring (2013–2017) at the Oski-ôtin site in the indigenous community of Fort McKay, in the Alberta Oil Sands Region (AOSR). Comparisons with SO2 and conventional TRS data from two long-term monitoring sites located within five kilometers of Oski-ôtin suggest that the conventional approach for TRS is biased, being low by 20% on average. Based on this new method, SO2 was observed to be responsible for about 40% of the TS mass in Fort McKay, whereas TRS and particle sulfate were 50% and 10%, respectively. During winter months, when SO2 plumes emitted from stacks tend to remain elevated due to diminished vertical mixing, TRS dominated the distribution. During periods with TS below 5 ppb, which was 84% of the time, TRS (with H2S) accounted for 55% of the sulfur mass observed in Fort McKay. Implications: Reduced sulfur compounds have a significant impact on the air quality near various types of industrial emission sources, but their accurate quantification has been hindered by technical problems inherent in standard sulfur measurement methods. We have developed, evaluated and applied a new method for measuring total sulfur. Comparisons in the Alberta Oil Sands suggest that standard methods underestimate total sulfur by typically 20% at this location. Sulfur dioxide (SO2) was observed to be responsible for about 40% of the total sulfur mass in Fort McKay, while total reduced sulfur and particulate sulfate made up 50% and 10%, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".