Spatial Modeling for Air Pollution Monitoring Network Design: Example of Residential Woodsmoke
Bibliographic record
Abstract
The purpose of this paper is to demonstrate how to develop an air pollution monitoring network to characterize small-area spatial contrasts in ambient air pollution concentrations.Using residential woodburning emissions as our case study, this paper reports on the first three stages of a four-stage protocol to measure, estimate, and validate ambient residential woodsmoke emissions in Vancouver, British Columbia.The first step is to develop an initial winter nighttime woodsmoke emissions surface using inverse-distance weighting of emissions information from consumer woodburning surveys and property assessment data.Second, fireplace density and a compound topographic index based on hydrological flow regimes are used to enhance the emissions surface.Third, the spatial variation of the surface is used in a location-allocation algorithm to design a network of samplers for the woodsmoke tracer compound levoglucosan and fine particulate matter.Measurements at these network sites are then used in the fourth stage of the protocol (not presented here): a mobile sampling campaign aimed at developing a high-resolution surface of woodsmoke concentrations for exposure assignment in health effects studies.Overall the results show that relatively simple data inputs and spatial analysis can be effective in capturing the spatial variability of ambient air pollution emissions and concentrations. IMPLICATIONSRecent research highlights the importance of small-area contrasts in air pollution emissions and concentrations as predictors of health impacts.Using the example of residential woodburning emissions, this paper shows that relevant spatial covariates can be used to identify the microgeography intraurban air pollution gradients before deployment of more costly monitoring resources.
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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 | 0.005 | 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) | 0.000 | 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".