Traffic NO<sub>x</sub> Pollution Prediction and Health Cost Estimation Using Machine Learning: A Case Study of Toronto, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Road traffic is a significant source of air pollution that has a harmful impact on human health. To reduce the health and environmental impacts of fossil fuel consumption in the transportation sector, many countries have implemented policies to promote the deployment of electric vehicles (EVs). A vital factor to consider when designing policies to support EV use is the monetized health impacts of fossil fuel consumption. This research aims to investigate the health benefit of replacing internal combustion engine vehicles (ICEVs) with zero-emission vehicles in the city of Toronto, Canada. A long short-term memory (LSTM) model is developed in this work to predict future NO<sub>x</sub> concentrations considering the effect of the traffic volume, weather, time of day, and historical NO<sub>x</sub> concentrations. The developed model is then used to predict long-term NO<sub>x</sub> concentrations and annual average NO<sub>x</sub> reduction from zero-emission vehicle deployment in four different scenarios in Toronto. Additionally, interpolation methods are used to predict the pollution reduction in all Dissemination Areas (DA) of Toronto, and a health cost assessment is conducted to estimate the health benefit in all the scenarios. The results of the modeling in this work show that the western areas of Toronto experience higher NO<sub>x</sub> concentration reduction in all scenarios. These reductions are the result of the higher correlation between traffic volume and pollution in those areas. The results also show that with a 10% reduction in ICEV traffic volume, 70 premature deaths can be prevented annually, equivalent to 560 million CAD in health benefits per year.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it