Characteristics of Atmospheric Particulate Matter and Metals in Industrial Sites in Korea
Bibliographic record
Abstract
The distribution of metals in atmospheric particulates less than 10 µm was studied at a petrochemical refinery site and at a non-ferrous heavy metals industrial site in the city of Ulsan, South Korea in both the summer and fall seasons. The samples were collected with a high volume sampling system equipped with a 9 stage cascade impactor, which effectively separated the particulate matter into 9 size ranges. Total PM10 was 59 ± 14 µg/m3 in summer and 56 ± 18 µg/m3 in fall at the petrochemical site whereas it was 52 ± 14 µg/m3 in summer and 88 ± 36µg/m3 in fall at the non-ferrous heavy metals site. The particle size fractionation in less than 10 µm showed a typical bimodal distribution, with one peak corresponding to the particle size range of 1.1-4.7 µm and the other to the range of 4.7-10 µm. Five heavy metals (Ni, Cu, Zn, Pb, and Cd) were measured in the composite mixture of particulates (0.1-1.1, 1.1-4.7 and 4.7-10 µm). The heavy metals concentrations were found to be higher in the 1.1-4.7 µm fraction followed by 4.7-10 and 0.1-1.1 µm. Among the metals Pb showed particle size dependent whereas Zn was homogeneously mixed in all sizes. The obtained data are important for an estimation of level pollution with heavy metals in industrial sites.
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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) | 0.001 | 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".