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Record W2784737026 · doi:10.5194/acp-18-13787-2018

Characterization of trace gas emissions at an intermediate port

2018· article· en· W2784737026 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAtmospheric chemistry and physics · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsSaint Mary's University
FundersCanada Foundation for Innovation
KeywordsTrace gasEnvironmental sciencePlumeHarbourSpectrometerTroposphereAtmospheric sciencesRemote sensingMeteorologyOceanographyGeologyOpticsGeographyPhysics

Abstract

fetched live from OpenAlex

Abstract. Growing ship traffic in Atlantic Canada strengthens the local economy but also plays an important role in greenhouse gas and air pollutant emissions in this coastal environment. A mobile open-path Fourier transform infrared (OP-FTIR; acronyms defined in Appendix A) spectrometer was set up in Halifax Harbour (Nova Scotia, Canada), an intermediate harbour integrated into the downtown core, to measure trace gas concentrations in the vicinity of marine vessels, in some cases with direct or near-direct marine combustion plume intercepts. This is the first application of the OP-FTIR measurement technique to real-time, spectroscopic measurements of CO2, CO, O3, NO2, NH3, CH3OH, HCHO, CH4 and N2O in the vicinity of harbour emissions originating from a variety of marine vessels, and the first measurement of shipping emissions in the ambient environment along the eastern seaboard of North America outside of the Gulf Coast. The spectrometer, its active mid-IR source and its detector were located on shore while the passive retroreflector was on a nearby island, yielding a 455 m open path over the ocean (910 m two-way). Atmospheric absorption spectra were recorded during day, night, sunny, cloudy and substantially foggy or precipitating conditions, with a temporal resolution of 1 min or better. A weather station was co-located with the retroreflector to aid in the processing of absorption spectra and the interpretation of results, while a webcam recorded images of the harbour once per minute. Trace gas concentrations were retrieved from spectra by the MALT non-linear least squares iterative fitting routine. During field measurements (7 days in July–August 2016; 12 days in January 2017) AIS information on nearby ship activity was manually collected from a commercial website and used to calculate emission rates of shipping combustion products (CO2, CO, NOx, HC, SO2), which were then linked to measured concentration variations using ship position and wind information. During periods of low wind speed we observed extended (∼9 h) emission accumulations combined with near-complete O3 titration, both in winter and in summer. Our results compare well with a NAPS monitoring station ∼1 km away, pointing to the extended spatial scale of this effect, commonly found in much larger European shipping channels. We calculated total marine sector emissions in Halifax Harbour based on a complete AIS dataset of ship activity during the cruise ship season (May–October 2015) and the remainder of the year (November 2015–April 2016) and found trace gas emissions (tonnes) to be 2.8 % higher on average during the cruise ship season, when passenger ship emissions were found to contribute 18 % of emitted CO2, CO, NOx, SO2 and HC (0.5 % in the off season due to occasional cruise ships arriving, even in April). Similarly, calculated particulate emissions are 4.1 % higher during the cruise ship season, when passenger ship emissions contribute 18 % of the emitted particulate matter (PM) (0.5 % in the off season). Tugs were found to make the biggest contribution to harbour emissions of trace gases in both cruise ship season (23 % NOx, 24 % SO2) and the off season (26 % of both SO2 and NOx), followed by container ships (25 % NOx and SO2 in the off season, 21 % NOx and SO2 in cruise ship season). In the cruise ship season cruise ships were observed to be in third place regarding trace gas emissions, whilst tankers were in third place in the off season, with both being responsible for 18 % of the calculated emissions. While the concentrations of all regulated trace gases measured by OP-FTIR as well as the nearby in situ NAPS sensors were well below maximum hourly permissible levels at all times during the 19-day measurement period, we find that AIS-based shipping emissions of NOx over the course of 1 year are 4.2 times greater than those of a nearby 500 MW stationary source emitter and greater than or comparable to all vehicle NOx emissions in the city. Our findings highlight the need to accurately represent emissions from the shipping and marine sectors at intermediate ports integrated into urban environments. Emissions can be represented as pseudo-stationary and/or pseudo-line sources.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0250.000

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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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