Air Quality: Assessment of Pollutant Levels and Chemistry in Kitchener, ON using multisensor pods
Bibliographic record
Abstract
Air quality is a growing concern amongst governmental bodies worldwide. A large number of scientific studies accumulated over the past 25 years suggest that poor ambient air quality is attributed to adverse health effects, especially in vulnerable communities that exhibit pre-existing conditions. The United Nations Children’s Fund (UNICEF) reported around 600 000 deaths globally in children under the age of 5 as a result of acute lower respiratory infections caused by poor air quality. With the current statistics on air quality impacts, it is clear that more needs to be done. This MSc work aims to put into perspective the current state of air quality in Ontario, Canada and provide insight into mitigation strategies. Here, we focus on 1) characterizing the impacts of COVID-19 on air quality across Southern Ontario (Chapter 2), where emission levels were shown have a significant decrease in the majority of sites studied, 2) exploring the state of air quality near elementary schools in a medium-sized sub-urban city, where locations near major roads and highways exhibited the poorest air quality conditions (Chapter 3), 3) incorporating a machine learning algorithm to disentangle the multitude of variables that influence the state of air quality, where meteorology was found to have the greatest influence, with anthropogenic sources contributing to an extent (Chapter 4), and 4) studies on the dark reaction pathways of aminophenol derivatives (nitrogen-containing aromatic carbons; NOCs), where hygroscopicity growth factors (κ) of these insoluble products under sub- and super-saturated conditions ranged from 0.4-0.6, higher than that of levoglucosan, which is a prominent proxy for biomass burning organic aerosol (BBOA) (Chapter 5). These individual studies highlight the importance of atmospheric chemistry and the need for frequent monitoring and routine studies on mitigation strategies and their formation pathways.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".