Mobile monitoring of air pollution in cities: the case of Hamilton, Ontario, 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
Air pollution in urban centres is increasing, with burgeoning population and increased traffic and industry. The detrimental impact on population health has been the focus of many epidemiological studies. Some cities are fortunate to have one, or at most a few, sparsely spaced fixed air quality monitors, which provide much needed daily data. However, fixed monitors do not accurately depict the spatial distribution of air pollution over the extent of an urban area nor can they target areas for focused surveys. We have used mobile monitoring to improve spatial coverage of pollution concentrations over the city of Hamilton, Ontario and to enhance our knowledge of the short-term bursts of pollution to which the population is exposed. Mobile surveys have been carried out in the city of Hamilton, Ontario, Canada since 2005. Results for two pollutants, oxides of nitrogen (NO(x)) representing traffic sources, and sulfur dioxide (SO2) representing industry sources, are presented. The data demonstrate very high levels of NO(x) exceeding 600 ppb, near major highways with SO2 levels up to 249 ppb near industrial sources. Both values significantly exceed the hourly maxima recorded by fixed monitors. The results also highlight the effect of wind direction on SO2 and NO(x) levels, and the affected population in each scenario.
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.
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 | 0.001 | 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