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Record W2079468658 · doi:10.1080/10962247.2012.697974

Demonstration of fuel switching on oceangoing vessels in the Gulf of Mexico

2012· article· en· W2079468658 on OpenAlexaboutno aff
Louis Browning, Seth Hartley, Angela Bandemehr, Kenneth Gathright, W.J. Miller

Bibliographic record

VenueJournal of the Air & Waste Management Association · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsnot available
FundersUniversity of California, Riverside
KeywordsPort (circuit theory)Fuel oilOn boardEngineeringStack (abstract data type)Environmental protectionEnvironmental scienceWaste managementEnvironmental engineering

Abstract

fetched live from OpenAlex

UNLABELLED: Switching fuels from high-sulfur heavy fuel oils (HFO) to lower sulfur marine gas oils (MGO) on an oceangoing vessel (OGV) can substantially reduce both PM and SO(x) ship stack emissions, potentially resulting in significant human health and environmental benefits in Gulf of Mexico port communities. The International Maritime Organization (IMO) established an emission control area (ECA) within 200 nautical miles of the US. and Canadian coastlines and French territories off the coast of Canada with lower fuel sulfur standards effective beginning August 2012, where OGVs will need to switch from HFO to MGO. However some operators and other stakeholders, particularly in the Gulf of Mexico, may be unfamiliar with the benefits and requirements and ship operators may be concerned over potential implications for cost and operations. This first-ever US. Environmental Protection Agency (EPA)-sponsored fuel switching demonstration in the Gulf of Mexico was initiated to showcase the environmental and health benefits of as well as operational issues associated with, fuel switching through the following activities: (1) Fuel switching was conducted on typical container ships operating the Gulf of Mexico, as routine fuel switching has been demonstrated in California in recent years. Two vessels were employed in the demonstration: the Maersk Roubaix, which switched fuels entering Port of Houston, TX, and the Port of Progreso, Mexico, and the Hamburg Süd vessel Cap San Lorenzo, which switched fuels entering the Port of Houston and the Mexican Ports of Veracruz and Altamira. Operational and cost aspects were also noted. (2) Emissions reductions were quantified through both a calculation approach based on fuel use of the Maersk Line vessel Roubaix and in-stack monitoring of emissions from the Hamburg Süd Cap San Lorenzo. Pollutant emissions including PM, SO(x), NO(x), and PM component speciation were sampled during use of both fuels. These observations showed reductions (1-6%) in NO(x) and substantial reductions in PM (50-70%) and SO(x) (> 99%) depending on engine and load. (3) Emission inventories at three ports were calculated. (4) Potential health and environmental benefits to communities and ecosystems in the Gulf of Mexico were estimated through screening-level modeling of air dispersion and atmospheric deposition of ship emissions at the Port of Veracruz to indicate the substantial reductions available through fuel switching. IMPLICATIONS: The United States and Canada have recently adopted a North American Emission Control Area that will require a shift from high-sulfur to low-sulfur fuels in the marine transportation sector. Cleaner fuels are expected to reduce sulfur and particulate emissions. This paper verifies the feasibility of such switching operations, the potential to reduce emissions if extended to the Gulf of Mexico, and the associated environmental impacts of these emission reductions inland and to sensitive ecosystems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.215
Teacher spread0.208 · 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

Citations29
Published2012
Admission routes1
Has abstractyes

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