Volatile organic compounds emissions from traditional and clean domestic heating appliances in Guanzhong Plain, China: Emission factors, source profiles, and effects on regional air quality
Bibliographic record
Abstract
Solid (biomass and coal) fuels burned for residential heating are major sources of atmospheric volatile organic compounds (VOCs). In this study, VOC samples were collected in-situ from chimneys in 10 typical heating scenarios in rural areas of the Guanzhong Plain. A modified SUMA canister approach was then employed, followed by gas chromatography/mass spectrometry analysis. Emission factors (EFs) (as received basis) of targeted non-methane VOCs (NMVOCs) varied from 90.3 ± 29.3 to 12300 ± 1510 mg kg−1 in descending order of fuel wood > maize straw > bitumite ≫ anthracite (p < 0.05). Both clean stove and coal briquetting technologies effectively reduced VOC EFs compared with traditional heating methods. The EFs of methane (CH4) had similar characteristics to those of NMVOCs. However, they yielded different correlations with CO because of their differing mechanisms of formation. Coefficient of divergence (CD) values showed that a semi-gasifier has a limited effect on changing VOC profiles compared with a traditional stove using the same fuels. However, different types of fuel produce CD values over 0.50, which should therefore be classified as different sub-categories in source apportionment models. Correlation analysis showed that volatile matter content (V%) and modified combustion efficiency (MCE) were the two primary factors influencing NMVOC and CH4 emissions. A stepwise linear regression analysis showed that V%, MCE, and element nitrogen content (N%) can be used to predict total VOC (TVOCs, including CH4 and NMVOCs) emissions with regression coefficients of 0.23, −72.8 and −6.53, respectively (R2 = 0.92, p < 0.001). Ozone formation potential (OFP) EFs from burning solid fuel ranged from 72 to 18680 mg kg−1, with an approximate 50% contribution from alkenes. VOCs from burning solid fuel were equivalent to 62 to 22200 mg kg−1 secondary organic aerosol formation potential (SOAP), most of which (>95%) were contributed by aromatics. A semi-gasifier and coal briquettes were effective in reducing TVOC emissions, even when used in conjunction with a traditional stove and fuels. It is estimated that over 15,000 ton year−1 emissions can be reduced in Guanzhong Plain by adopting a semi-gasifier and coal briquettes, resulting in a 57,000 and 65,000 ton year−1 reduction of OFP and SOAP emissions, respectively. These results demonstrate that the use of clean heating technologies in Guanzhong Plain has considerable potential in relation to emissions reduction and thus provides a feasible solution to mitigate VOCs and related secondary pollutants emitted by residential solid fuel burning.
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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 | 0.000 | 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) | 0.003 | 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".