Spatial Analysis of Fine Particulate Matter (PM2.5) in South St. Boniface and Mission Industrial Area, Winnipeg, Manitoba, Canada
Bibliographic record
Abstract
Particulate matter smaller than 2.5 microns (PM2.5) stays airborne for long periods and can enter the lungs, increasing respiratory and cardiovascular risks. Metal shredders are known sources of PM2.5, lead and other heavy metals. Winnipeg residents of South Saint Boniface (SSB) in Manitoba, Canada, live downwind of the Mission Industrial Area (MIA), which includes a metal shredder, train tracks and other industries. Residents are concerned about the MIA air and noise pollution and wanted ambient air quality monitoring in their mixed land-use area to understand its impact on their health. We measured and mapped the daytime PM2.5, from the MIA and South St. Boniface (SSB) neighborhoods using the Dylos DC 1700 PM over seven months. The Dylos air quality data for PM2.5 was validated by the two federal reference monitors in the city, finding a moderate to very strong correlation (r = 0.52 to 0.83; p-value 0.001), confirming good accuracy. A spatial analysis of the emission data showed that the highest pollution concentration was downwind of the scrap metal shredder in MIA. One-way ANOVA and Pearson correlation analysis revealed significantly higher levels of PM2.5 at MIA and SSB than at the reference sites, which are away from pollution sources. The PM2.5 Canadian Ambient Air Quality Standard (CAAQS) of 27 μg/m3 was exceeded downwind of the property line of the scrap metal shredder in the MIA for five of the 35 monitoring days averaging between 28.9 μg/m3 to 38.1 μg/m3 over eight hours. The standard was not exceeded in the residential area, although PM2.5 levels higher than background levels increased SSB residents exposure levels. This exceedance of regulatory standards requires action to reduce emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".