Assessing the air pollution co-impacts of hybridizing coastal ferry powertrains on ports and coastal communities
Bibliographic record
Abstract
Diesel-electric large passenger ferries play a crucial role in connecting coastal communities. Their current diesel-electric powertrain configuration makes them prime candidates for hybridization, providing a cost-effective and accessible route to decarbonization. While reductions in fuel consumption and CO₂ emissions are well-established benefits of hybrid systems, the primary aim of this study is to quantify and evaluate the associated air pollution co-benefits, particularly in and around ferry terminals where population exposure is high and nearby coastal communities are most affected. The study assesses the impact of emissions from British Columbia (BC) Ferry Corporation's Coastal class ferries, equipped with baseline diesel-electric powertrains, and when operating with plug-in hybrid propulsion systems, on local air quality in Tsawwassen Port, BC, Canada. A GT-SUITE™ engine model was developed for the marine diesel engines used in Coastal class ferries to estimate fuel consumption and nitrogen oxide (NOx) emissions. Using ferry traffic data and emission factors for cruising, maneuvering, and berth modes, annual NOx emissions were calculated through a fleet activity-based method. Calculated NOx, local meteorology, and coastal land use data were utilized in the AERMOD model. The model evaluated pollutant concentrations, assessed concentration in both marine and residential zones, and quantified the air quality benefits associated with hybrid powertrains. The results show that hybridizing coastal ferry powertrains may reduce NOₓ concentrations by up to 45 %, offering substantial air quality improvements alongside decarbonization benefits, particularly in densely populated and environmentally sensitive coastal zones.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".