Improving residential wood burning emission inventories with the integration of readily available data sources
Bibliographic record
Abstract
Residential wood burning (RWB) is the largest anthropogenic source of PM2.5 in many North American and European cities in the winter. The current lack of information on the locations, types, and intensity of use of wood burning appliances limits the ability to accurately assess the exposure of the population to RWB emissions. In this study, we generated a high spatial resolution emission inventory for RWB in the province of Quebec, Canada using a novel data driven approach. The method first combines real estate and socioeconomic census data through machine learning models to estimate ownership rates of fireplaces and wood stoves. These ownership rates are then combined with household survey data (on wood consumption and types of appliances), emission factors and building registry data to generate the emission inventory at a 1Km2 resolution. Our proposed approach was able to capture spatial patterns in RWB appliance ownership and intensity of use, which may be overlooked by using simple urban vs rural or population based spatial proxies. The machine learning models explained 80.3 % and 63 % of the variability in wood stove and fireplace ownership rates with each appliance type exhibiting different spatial trends. Wood stoves were common in rural areas and among lower income households, whereas fireplaces were more common in urban areas. Additionally, we observed large spatial and regional variability in emissions per residence due to differences in wood consumption, appliance ownership rates, and appliance mixes (e.g. conventional vs certified). Our results suggest that using simple spatial proxies based on population, urbanization levels or residence type are not enough to explain the spatial distribution of RWB emissions as they might overlook other factors such as socioeconomic factors or regional heating preferences. Finally, our spatially distributed emissions were strongly correlated (r = 0.86) with the increase in PM2.5 concentrations during peak-RWB hours on winter weekends at 42 reference stations across the province of Quebec.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".