Characterizing residential wood smoke at the neighbourhood scale: An evaluation of five communities in British Columbia.
Bibliographic record
Abstract
The experience of residential wood burning becoming an increasingly popular space heating method due to rising energy costs and interest in renewable energy resources raises both air quality and public health concerns. This work demonstrates a novel method that incorporates mobile and fixed-site monitoring to characterize the spatial and temporal distribution of residential wood smoke and identify persistent wood smoke hot spots within five communities situated in north-western British Columbia. High density measurements were collected throughout the communities with an integrated nephelometer during evenings (November 2007 and April 2008) when wood smoke was expected to be prevalent. Gravimetric PM[subscript 2.5] samples were collected at the central monitoring station in each community (October 2007 - April 2009) and analyzed for PM[subscript 2.5], levoglucosan (wood smoke tracer), and light absorbance (black carbon indicator). Slash burning activity was also assessed as a potential confounder of residential wood smoke. Measurements at the central monitoring stations confirmed that wood smoke is a prevalent (levoglucosan/PM[subscript 2.5] = 0.06± 0.03) and consistent (levoglucosan-PM[subscript 2.5] r[subscript]spear = 0.78-0.92) source of PM[subscript 2.5] in the communities. Comparisons between the 2007-08 and 2008-09 heating seasons suggest residential wood smoke concentrations may be declining. Persistent wood smoke hotspots were identified via mobile monitoring with mean estimated PM[subscript 2.5] ranging 13-59 μg m⁻³ and maximum values > 200 μg m⁻³. The majority of these areas were associated with single family dwellings followed by housing types typically associated with lower socioeconomic statuses. Central monitoring stations were representative of seasonal average community-wide concentrations for heating season evenings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".