Modeling Residential Woodsmoke With Socioeconomic Variables Extracted From Hydrologically Based Buffers
Bibliographic record
Abstract
P-663 Introduction: Researchers and policy-makers are showing increasing concern over exposure to wood smoke and its associations to chronic bronchitis, emphysema, and asthma. North Americans, particularly in western Canada and the northwestern United States, have increasingly turned to woodburning as an alternative method for domestic heating. This research attempts to model ambient woodsmoke for the Border Air Quality Study (BAQS) using socioeconomic variables and a new hydrologically based buffering approach. Methods: Hydrological catchment basins were used to represent atmospheric drainage flows during calm, cold winter nights. Catchment basins were created using a 25km2 minimum size threshold. Consecutive adjacent uphill catchments were identified whose centroids were between one and ten kilometers of the catchment centroid of interest (lowest elevation catchment). The resulting boundary of the consecutive adjacent catchments was then used as the buffer to extract spatial and socioeconomic variables to assign to the catchment of interest. The spatial covariates were extracted using these buffers as well as uniform rectangular buffers. Extracted values were regressed in a stepwise manner against average fine particle levels for the catchment of interest. This latter variable was computed from a set of spatially intensive light scattering measurements. Results: Highest correlations between extracted variables and average light scattering values were observed when using an uphill hydrological search distance of 9 km. Socioeconomic variables, such as percent of the population employed in manufacturing and number of low income households, explained 50–70% of the measured woodsmoke concentration, while only 30–60% of the measured woodsmoke concentration were explained when the same spatial variables were extracted using a standard uniform buffer. Discussion and Conclusions: Spatial regression models using catchment-based buffers were more effective than traditional uniform grid methods at estimating ambient woodsmoke in hilly terrain. It is reasonable to assume that the drainage flow/catchment-based buffering approach would be applicable to all urban areas with complex topography. In combination with readily available socioeconomic variables this modeling technique can improve predictions of ambient woodsmoke exposure in urban areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".