Assessment of Air Pollution and Carbon Emission from Fuel Consumption Activities
Bibliographic record
Abstract
The consumption of fuel is associated with a number of environmental issues. One of the major environmental concerns is the increasing greenhouse gas (GHG) emissions and air pollution. GHG emissions and air pollution are mainly from the human activities such as transportation, non-renewable electricity production, oil and gas production, and heating and cooling of buildings. In this thesis, a comprehensive review was conducted to assess the impact of elements in urban form on on-road vehicles GHG emissions. A small-medium North American city case study was given to track the progress in reducing real-world emissions over time and to estimate the future air quality impacts based on the trends of fleet mixes. It helps gain understanding of detailed source apportionment information to quantify the contributions to total emission made by different vehicle body types, different fuels, and manufacturer models in recent decades and how the fuel economy of the vehicle fleet has changed over the years. Following that, an assessment was conducted to analyze the impact of COVID-19 pandemic on GHG emissions from urban transportation and air quality in Canadian cities. The reduced traffic experienced throughout several lockdowns offers a glimpse of what air quality in cities would look like if the country switched to low-carbon transportation modes. Finally, the reductions in NO2 emissions from thermal power plants in Canada were assessed to evaluate the government commitment of switching from fossil fuels to clean energy. The satellite observation was developed as a supplementary information management tool to verify the effect of technologies and policies on emissions changes from threshold perspective on a smaller spatial scale. Overall, this thesis provides some new insights on assessing air pollution and carbon emission levels from transportation and electricity sectors.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".