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Record W2902058827

ANALYSING ATMOSPHERIC IMPACTS OF REGIONAL TRUCK EMISSIONS USING AN INTEGRATED MODELLING APPROACH

2018· dissertation· en· W2902058827 on OpenAlexaboutno aff
Wilson Wang

Bibliographic record

VenueUWSpace (University of Waterloo) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTruckEnvironmental scienceEngineeringTransport engineeringEnvironmental planningAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

Transportation technology is providing new ways to mitigate multipollutant emissions co-emitted from on-road sources. Zero-emission vehicles (ZEV) are more common in passenger vehicles and other light-duty vehicles; however, they remain a relatively new technology for most medium-duty and heavy-duty vehicles. As more trucks are adopting zero-emission technology, we need to evaluate whether these mitigation strategies are sufficient in meeting regional reduction goals. Previous studies have evaluated the multipollutant impacts of trucks and other vehicles; however, these methods estimate vehicle activity by empirical data such as surveys, which, unlike process-based models, are not amenable to evaluating significant future technology adoption. \n \nThis research presents a new method to quantify the atmospheric impacts and evaluate mitigation strategies of zero-emission technology in trucks at a regional scale using an integrated assessment model (IAM). This model establishes a connection between EMME, a travel demand model, MOVES, a mobile emissions simulator, and EASIUR, a regression model that produces marginal damage estimates. The IAM estimates a baseline and compares the total damages of alternative scenarios, using different ZEV adoption rates applied to trucks. The annual, ground-level emissions were estimated for the following pollutants using the developed IAM: primary PM2.5, NOX, SO2, NH3, CO2, CH4, and N2O. \n \nThe results from the application of the IAM to the baseline scenario show that the total annual damages resulting from atmospheric emissions from trucks for the Province of Ontario in 2012 is approximately $1.82 Billion (2005 USD). Most of these damages are in Southern Ontario, with Toronto, Peel and York being the top three contributors. Adoption of ZEV decreases these damages linearly. Ontario has an adoption rate goal for ZEV of 5% by 2020. This rate is assumed to hold true for trucks in this transportation network. This goal would yield approximately $89 Million (2005 USD) in benefits annually from trucks alone. This result varies by up to ±25% according to the sensitivity analysis related to the travel and emissions models. Future work should focus on the relationship between emissions to damages, which likely remain the largest source of uncertainty.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.050
GPT teacher head0.268
Teacher spread0.217 · 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 designSimulation or modeling
Domainnot available
GenreOther

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

Citations1
Published2018
Admission routes1
Has abstractyes

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