Comparison of Remote Sensing, Land-use Regression, and Fixed-site Monitoring Approaches for Estimating Exposure to Ambient Air Pollution Within a Canadian Population-based Study of Respiratory and Cardiovascular Health
Bibliographic record
Abstract
O-31A5-5 Background/Aims: Remote sensing (RS) has emerged as a cutting edge approach for estimating ground-level concentrations of ambient air pollution. While the validity of RS has been demonstrated through comparisons to values obtained from fixed-site monitoring, no previous epidemiological studies have investigated the implications of using RS to characterize health risks. We examined respiratory and cardiovascular health outcomes associated with longer -term exposure measures of air pollution in a national population-based survey (N = 125,574), using estimates of annual average based on RS, land-use regression (LUR) models, and measured concentrations at the nearest fixed site monitor station. Methods: RS estimates of NO2 and PM2.5 were derived using satellite measurements from OMI, MODIS, and MISR. Multi-city LUR estimates were based on spatial models incorporating land-use characteristics such as traffic and industrial sources. Measured concentrations at the nearest regulatory continuous monitoring site were obtained from the National Air Pollution Surveillance Network. Self-reported health outcomes including diagnosis, age of onset, symptoms, and medication use for: asthma, bronchitis, COPD, heart disease, hypertension, congestive heart failure, angina, heart attack, and diabetes were collected through the Canadian Community Health Survey, a representative sample of Canadians 12 years of age and older. Results: RS estimates of PM2.5 and NO2 were highly correlated with ground-based measurements in North America (R = 0.9 and 0.8, respectively). Long-term exposures to ambient NO2 and PM2.5 were significantly associated with respiratory and cardiovascular health outcomes (OR = 1.1–1.4, P < 0.05) adjusting for age, sex, socioeconomic status, smoking status, and second-hand smoke. Effect estimates for RS were similar to those obtained using LUR and nearest fixed site monitor. Conclusion: These results suggest that RS can provide useful estimates of individual long-term exposure to ambient air pollution in epidemiologic studies, particularly in remote and rural areas for which monitoring and modeled air quality data are unavailable.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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".