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Record W2281299640 · doi:10.5194/acp-2015-1032

Ship emissions measurement in the Arctic from plume intercepts of the Canadian Coast Guard <i>Amundsen</i> icebreaker from the <i>Polar 6</i> aircraft platform

2016· article· en· W2281299640 on OpenAlexaboutno aff
A. A. Aliabadi, J. L. Thomas, A. Herber, Ralf M. Staebler, W. R. Leaitch, Kathy S. Law, L. Marelle, Julia Burkart, Megan D. Willis, Jonathan P. D. Abbatt, Heiko Bozem, Peter Hoor, Franziska Köllner, Johannes Schneider, M. Levasseur

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
Fundersnot available
KeywordsPlumeEnvironmental scienceArcticOceanographyAtmospheric sciencesMeteorologyClimatologyGeologyGeography

Abstract

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Abstract. Decreasing sea ice and increasing marine navigability in northern latitudes have changed Arctic ship traffic patterns in recent years and are predicted to increase annual ship traffic in the Arctic in the future. Development of effective regulations to manage environmental impacts of shipping requires an understanding of ship emissions and atmospheric processing in the Arctic environment. As part of the summer 2014 NETCARE (Network on Climate and Aerosols) campaign, the plume dispersion and gas and particle emission factors of emissions originating from the Canadian Coast Guard Amundsen icebreaker operating near Resolute Bay, NU, Canada have been investigated. The Amundsen burnt distillate fuel with 1.5 wt % sulfur. Emissions were studied via plume intercepts using aircraft measurements, an analytical plume dispersion model, and using the FLEXPART-WRF Lagrangian particle dispersion model. The first plume intercepts by research aircraft were carried out on 19 July 2014 during the operation of the Amundsen in the open water. The second and third plume intercept measurements were carried out on 20 and 21 July 2014 when the Amundsen had reached the ice edge and operated under icebreaking conditions. Typical of Arctic marine navigation, the engine load was low compared to cruising conditions for all of the plume intercepts. The measured species included mixing ratios of CO2, NOx, CO, SO2, particle number concentration (CN), refractory Black Carbon (rBC), and Cloud Condensation Nuclei (CCN). The results were compared to similar experimental studies in mid latitudes. Plume expansion rates (γ) were calculated using the analytical model and found to be γ = 0.75 ± 0.80, 0.93 ± 0.37, and 1.19 ± 0.39 for plumes 1, 2, and 3, respectively. These rates are smaller than prior studies conducted at mid latitudes, likely due to polar boundary layer dynamics, including reduced turbulent mixing compared to mid latitudes. All emission factors were in agreement with prior observations at low engine loads in mid latitudes. Icebreaking increased the NOx emission factor from EFNOx = 22.3 ± 8.0 to 57.8 ± 11.0 and 65.8 ± 4.0 g kg–diesel−1 for plumes 1, 2, and 3, likely due to change in combustion temperatures. The CO emission factor was EFCO = 6.4 ± 11.7, 6.8 ± 2.2 and 5.0 ± 1.0 g kg–diesel−1 for plumes 1, 2, and 3. The rBC emission factor was EFrBC = 0.20 ± 0.04 and 0.25 ± 0.12 g kg–diesel−1 for plumes 1 and 2. The CN emission factor was reduced while icebreaking from EFCPC = 1.96 ± 0.41 to 0.43 ± 0.11 and 0.47 ± 0.04 × 1016 kg–diesel−1 for plumes 1, 2, and 3. At 0.6 % supersaturation, the CCN emission factor was lower than observations in mid latitudes at low engine loads with EFCCN = 1.63 ± 0.41 to 1.06 ± 0.32 and 0.28 ± 0.07 × 1014 kg–diesel−1 for plumes 1, 2, and 3.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.202
Teacher spread0.172 · 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 designObservational
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

Citations2
Published2016
Admission routes1
Has abstractyes

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