Enhancing PM2.5 monitoring: Calibrating low-cost monitors and analysing community-wide variability
Bibliographic record
Abstract
Fine particulate matter (PM2.5) air pollution is a significant health concern for the global population. The increasing frequency, intensity, and duration of wildfire smoke events continues to worsen people’s exposure, regardless of their proximity to other major sources such as industry and roadways. To mitigate this exposure, it is crucial to understand the variability of PM2.5 in both space and time. However, the current PM2.5 monitoring network in Canada is limited to major population centres, due to the prohibitive costs of maintenance and deployment. In this study, we propose that low-cost air quality monitors are a viable solution to supplementing this monitoring network. Chapter 2 presents a comprehensive evaluation of these low-cost monitors by comparison with monitors from regulatory networks, including the development of a general-use bias correction model to improve their data quality. The focus of this analysis was to ensure optimal performance in the moderate to high concentration range, where variations in the concentrations have the greatest impact on human health. Chapter 3 demonstrates the application of this bias correction model to a network of low-cost monitors in a northern Canadian city. The data generated by this network was then combined with a novel interpolation method to assess the spatial and temporal variation in PM2.5 concentrations. This analysis represents a valuable resource for any population centre with a sufficient number of monitors installed, and has the potential to inform the siting of new regulatory monitors in locations without existing coverage.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".