Assessing the impact of shipping emissions on air pollution in theCanadian Arctic and northern regions: current and future modelledscenarios
Bibliographic record
Abstract
Abstract. A first regional assessment of the impact of shipping emissions on air pollution in the Canadian Arctic and northern regions was conducted in this study. Model simulations were carried out on a limited-area domain (at 15-km horizontal resolution) centred over the Canadian Arctic, using the Environment and Climate Change Canada's on-line air quality forecast model (GEM-MACH), to investigate the contribution from the marine shipping emissions over the Canadian Arctic waters (at both present and projected future levels) to ambient concentrations of criteria pollutants (O3, PM2.5, NO2, and SO2), atmospheric deposition of sulphur and nitrogen, atmospheric loading and deposition of black carbon in the Arctic. Several model upgrades were introduced for this study, including the treatment of sea-ice in the dry deposition parameterization, chemical lateral boundary conditions, and the inclusion of North American wildfire emissions. The model is shown to have similar skills in predicting ambient O3 and PM2.5 concentrations in the Canadian Arctic and northern regions as the current operational air quality forecast models in North America and Europe. In particular, the model is able to simulate well the observed O3 and PM components at the Canadian high Arctic site, Alert. The model assessment shows that, at the current (2010) level, Arctic shipping emissions contribute to less than 1 % of ambient O3 concentration over the eastern Canadian Arctic and between 1 and 5 % of ambient PM2.5 concentration over the shipping channels. Arctic shipping emissions make a much greater contributions to the ambient NO2 and SO2 concentrations, at 10–50 % and 20–100 %, respectively. At the projected 2030 business-as-usual (BAU) level, the impact of Arctic shipping emissions is predicted to increase to up to 5 % in ambient O3 concentration over a broad region of the Canadian Arctic and to 5–20 % in ambient PM2.5 concentration over the shipping channels. In contrast, if emission controls such as the ones implemented in the current North American Emission Control Area (NA ECA) are to be put in place over the Canadian Arctic waters, the impact of shipping to ambient criteria pollutants would be significantly reduced. For example, with NA-ECA-like controls, the shipping contributions to population-weighted concentration of SO2 and PM2.5 would be brought down to below the current level. The contribution of Canadian Arctic shipping to the atmospheric deposition of sulphur and nitrogen is small at the current level,
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".