Contributions of natural and anthropogenic sources to ambient ammonia in the Athabasca Oil Sands and north-western Canada
Bibliographic record
Abstract
Atmospheric ammonia (NH 3 ) is a short-lived pollutant that plays an important role in aerosol chemistry and nitrogen deposition. Dominant NH 3 emissions are from agriculture and forest fires, both of which are increasing globally. Even remote regions with relatively low ambient NH 3 concentrations, such as northern Alberta and Saskatchewan in northern Canada, may be of interest because of industrial oil sands emissions and a sensitive ecological system. A previous attempt to model NH 3 in the region showed a substantial negative bias compared to satellite and aircraft observations. Known missing sources of NH 3 in the model were re-emission of NH 3 from plants and soils (bidirectional flux) and forest fire emissions, but the relative impact of these sources on NH 3 concentrations was unknown. Here we have used a research version of the high-resolution air quality forecasting model, GEM-MACH, to quantify the relative impacts of semi-natural (bidirectional flux of NH 3 and forest fire emissions) and direct anthropogenic (oil sand operations, combustion of fossil fuels, and agriculture) sources on ammonia volume mixing ratios, both at the surface and aloft, with a focus on the Athabasca Oil Sands region during a measurement-intensive campaign in the summer of 2013. The addition of fires and bidirectional flux to GEM-MACH has improved the model bias, slope, and correlation coefficients relative to ground, aircraft, and satellite NH 3 measurements significantly. By running the GEM-MACH-Bidi model in three configurations and calculating their differences, we find that averaged over Alberta and Saskatchewan during this time period an average of 23.1 % of surface NH 3 came from direct anthropogenic sources, 56.6 % (or 1.24 ppbv) from bidirectional flux (re-emission from plants and soils), and 20.3 % (or 0.42 ppbv) from forest fires. In the NH 3 total column, an average of 19.5 % came from direct anthropogenic sources, 50.0 % from bidirectional flux, and 30.5 % from forest fires. The addition of bidirectional flux and fire emissions caused the overall average net deposition of NH x across the domain to be increased by 24.5 %. Note that forest fires are very episodic and their contributions will vary significantly for different time periods and regions. This study is the first use of the bidirectional flux scheme in GEM-MACH, which could be generalized for other volatile or semi-volatile species. It is also the first time CrIS (Cross-track Infrared Sounder) satellite observations of NH 3 have been used for model evaluation, and the first use of fire emissions in GEM-MACH at 2.5 km resolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".