Characterization of Atmospheric Organic and Elemental Carbon of PM2.5 in a Typical Semi-Arid Area of Northeastern China
Bibliographic record
Abstract
In the spring of 2006, daily particulate matter (PM2.5) aerosol samples were collected in Tongyu, a semi-arid area in northeastern China. The concentrations of organic carbon (OC) and elemental carbon (EC) were determined with a thermal/optical carbon analyzer in the filter samples. The average concentrations of OC and EC in PM2.5 were 14.1 ± 8.7 and 2.0 ± 1.3 μg/m3, respectively. A good correlation between OC and EC was observed during the spring season, suggesting that they might be derived from similar sources. The correlation between OC and K+ was high (R = 0.74), and the K+/OC ratio, as determined from their linear regression slope, reached 2.57. The good correlation and high K+/OC ratio indicated that biomass-burning was probably one of the major sources of OC in this region. The concentrations of estimated secondary organic carbon (SOC) in PM2.5 in Tongyu ranged from below the detection limit to 26.1 μg/m3 (mean, 5.9 μg/m3). The percentages of SOC in OC and in PM2.5 mass were 42.0% and 2.1%, respectively. The SOC concentrations during dust storm (DS) periods were higher than those during non-dust storm (NDS) ones, suggesting that chemical reaction processes involving gas-particle conversion occurred during the long-distance transport of aerosol particles.
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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 | 0.001 |
| 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 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".