Characterizing Particulate and Condensable Emissions from a Wood-Burning Insert
Bibliographic record
Abstract
Wood combustion in domestic appliances is a key contributor to renewable energy in France, accounting for 31% of primary renewable energy production in 2023.However, it remains a significant source of particulate and gaseous emissions, including total suspended particles (TSP), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs).This study evaluates particulate and condensable organic emissions from a modern wood-burning insert under nominal and degraded combustion conditions.The experimental setup accords to NF EN 16510-1 standards, using gravimetric methods for TSP quantification and gas chromatography coupled to mass spectrometry (GCMS) for chemical characterization of organic compounds.TSP concentrations mainly remained low under both combustion conditions.Higher filter temperatures (180 °C) lead to lower TSP collected mass by inhibiting the condensation of volatile species.Condensable compounds were captured using a series of impingers.Chemical analysis revealed the presence of heavy alkanes, PAHs, and oxygenated PAHs (O-PAHs), especially during degraded combustion.Phenolic compounds, indicative of lignin degradation, were also identified.Temperature and combustion phase significantly influence the partitioning of organic molecules between particle and gas phases.This study highlights the importance of characterizing condensable organic compounds to better understand their role in air quality and health impacts.The findings emphasize the need for further studies on the chemical composition of emissions from wood-burning appliances, especially under real-world conditions, to optimize their environmental performance and compliance with evolving regulations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".