Spatial modeling of ambient concentrations of volatile organic compounds in Montreal, Canada
Bibliographic record
Abstract
Volatile organic compounds (VOCs) are components of the complex mixture of air pollutants within cities and can cause various adverse health effects. Therefore, it is necessary to understand their spatial distribution for exposure assessment in epidemiological studies. Objectives: The objective was to model measured concentrations of five VOCs within the city of Montreal, Canada, developing spatial prediction models that can be used in health studies. Methods: We measured concentrations using 3M 3500 Organic Vapor Monitors, over 2-week periods, for three monitoring campaigns between 2005 and 2006 in over 130 locations in the city. Using GC/MSD (Gas Chromatography/Mass Selective Detector), we measured concentrations of benzene, n-decane, ethylbenzene, hexane, and trimethylbenzene. We fitted four different models that combine land-use regression and geostatistical methods to account for the potential spatial structure that remains after accounting for the land-use variables. The fitted models also accounted for possible variations in the concentration of air pollutants across campaigns. Results: The highest concentrations for all VOCs were found in December with hexane being the most abundant followed by ethylbenzene. We obtained predicted surfaces for the VOCs for the three campaigns and mean surfaces across campaigns. We found higher concentrations of some VOCs along highways and in the Eastern part of Montreal, which is a highly industrialized area. Conclusions: Each of the fitted models captured the spatial and across-campaigns variability for each VOC, and we found that different VOCs required different model structures.
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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 | 0.001 | 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) | 0.007 | 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".