SOURCE PM2.5 CHARACTERIZATION ? INITIAL RESULTS AND SOME IMPORTANT PARAMETERS IN FINE PARTICULATE MEASUREMENT FROM OIL AND COAL COMBUSTION
Bibliographic record
Abstract
The new North American ambient air quality standards introduce limits on fine particles below 2.5-micrometer size range, known as PM2.5, due to their possible associations with adverse human health. Subsequent controversies over lack of conclusive results have reinforced the need for more scientific data on particle properties and their health effects. Fossil fuel combustion is a known source of particulate emissions and many industries will be affected by these rules. To develop effective emissions reduction measures, and to provide signature profiles for source apportionment of regional ambient pollutants, accurate identification and quantification of source emissions are essential. Existing industrial emission inventories for stationary sources are no longer adequate to deal with new regulations. A new fine particulate measurement methodology for stationary sources was recently developed using a source dilution approach. The plume-simulating method protocol promotes formation of near-ambient particulates and allowed for subsequent characterization to provide size and chemical information of PM2.5, PM10 and PMTotal fractions. Particle constituents were examined using different instrumental techniques. No. 4 type residual oil and pulverized coal combustion-derived particles showed a good mass balance agreement of particulate loading between gravimetric determination and by particle constituent analysis. The effects of variables such as relative humidity, dilution air, and fuel composition on particle formation were also studied. Increased fuel sulphur content appeared to ptomote high particulate mass emission. Diluted sample humidity has an apparent effect on particle concentration but residence time and dilution chamber size are also important contributing factors in particle condensation.
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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 | 0.001 | 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) | 0.000 | 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".