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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".