Characterization of trace gas emissions at an intermediate port
Bibliographic record
Abstract
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 our coastal environment. A mobile open-path Fourier transform infrared (OP-FTIR) 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 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 minute or better. A weather station was co-located with the retroreflector to aid in processing of absorption spectra and 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 Jul–Aug, 2016; 12 days in Jan, 2017) Automatic Identification System (AIS) information on nearby ship activity was collected manually 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 hr) 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–Oct 2015) and the remainder of the year (Nov 2015–Apr 2016) and found trace gas emissions (tonnes) to be on average 2.8 % higher during the cruise ship season, when passenger ship emissions were found to contribute 18 % of emitted CO2, CO, NOx, SO2 and HC (0.5 % off season). Similarly calculated particulate emissions are 4.1 % higher during the cruise ship season, when passenger ship emissions contribute 18 % of emitted PM (0.5 % 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 off season (26 % of both SO2 and NOx), followed by container ships (25 % NOx and SO2 in off season, 21 % NOx and SO2 in cruise ship season), but then either cruise ships in third place in season or tankers in third place off season, both responsible for 18 % of trace gas 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 measurement days, we find that AIS-based shipping emissions of NOx over the course of one 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 of the shipping and marine sectors at intermediate ports integrated into urban environments. Emissions can be represented as pseudo-stationary and/or pseudo-line sources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".