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Record W2802493372 · doi:10.5194/acp-18-16653-2018

Assessing the impact of shipping emissions on air pollution in the Canadian Arctic and northern regions: current and future modelled scenarios

2018· article· en· W2802493372 on OpenAlexaffabout
Wanmin Gong, S. R. Beagley, Sophie Cousineau, Mourad Sassi, Rodrigo Muñoz‐Alpizar, Sylvain Ménard, Jacinthe Racine, Junhua Zhang, Jack Chen, Heather A. Morrison, Sangeeta Sharma, Lin Huang, Pascal Bellavance, Jim Ly, Paul Izdebski, Lynn Lyons, Richard I. G. Holt

Bibliographic record

VenueAtmospheric chemistry and physics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsArcticEnvironmental scienceAir quality indexCurrent (fluid)Deposition (geology)Air pollutionPollutionThe arcticClimatologyPollutantAtmospheric sciencesMeteorologyOceanographyGeographyGeology

Abstract

fetched live from OpenAlex

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 (Global Environmental Multi-scale – Modelling Air quality and CHemistry), 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 (O 3 , PM 2.5 , NO 2 , and SO 2 ), atmospheric deposition of sulfur (S) and nitrogen (N), and atmospheric loading and deposition of black carbon (BC) 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 O 3 and PM 2.5 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 the observed O 3 and PM components well 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 O 3 concentration over the eastern Canadian Arctic and between 1 and 5 % of ambient PM 2.5 concentration over the shipping channels. Arctic shipping emissions make a much greater contributions to the ambient NO 2 and SO 2 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 O 3 concentration over a broad region of the Canadian Arctic and to 5 %–20 % in ambient PM 2.5 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 the population-weighted concentrations of SO 2 and PM 2.5 would be brought down to below the current level. The contribution of Canadian Arctic shipping to the atmospheric deposition of sulfur and nitrogen is small at the current level, < 5 %, but is expected to increase to up to 20 % for sulfur and 50 % for nitrogen under the 2030 BAU scenario. At the current level, Canadian Arctic shipping also makes only small contributions to BC column loading and BC deposition, with < 0.1 % on average and up to 2 % locally over the eastern Canadian Arctic for the former, and between 0.1 % and 0.5 % over the shipping channels for the latter. The impacts are again predicted to increase at the projected 2030 BAU level, particularly over the Baffin Island and Baffin Bay area in response to the projected increase in ship traffic there, e.g., up to 15 % on BC column loading and locally exceeding 30 % on BC deposition. Overall, the study indicates that shipping-induced changes in atmospheric composition and deposition are at regional to local scales (particularly in the Arctic). Climate feedbacks are thus likely to act at these scales, so climate impact assessments will require modelling undertaken at much finer resolutions than those used in the existing radiative forcing and climate impact assessments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.258
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations56
Published2018
Admission routes2
Has abstractyes

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