Patterns of VOC and BTEX concentration in ambient air around industrial sources in Daegu, Korea
Bibliographic record
Abstract
Patterns of VOC and BTEX (Benzene, Toluene, Ethylvenzene, and Xylene) distribution at industrial emission sources, proximal residential areas of industrial estates, and ambient air were studied in Daegu, Korea. Daytime and night-time sampling was done at 12 sites and 9 emission sources to provide samples for analyses, using the TO-14 method. Measured BTEX component ratios B/T, T/EB, T/X and EB/X in ambient air were found to be 2.6 g, 11.3 g, 1.0 g and 1.2 g in the residential area; 2.2 g, 11.0 g, 1.0 g and 1.6 g in the commercial area; and 1.0 g, 14.9 g, 1.0 g and 1.3 g in the industrial area. The significant difference observed between the ratios for the residential and commercial areas implies that the two areas have different emission sources. This is also indicated by the significant differences observed between daytime and nighttime BTEX concentrations. Toluene and xylene were detected at very high concentrations, at the sampling sites. This pattern reflects the type of industrial processes and materials that are managed at the emission sources, as well as topographic/climatic factors that impact upon pollutant transport processes in the atmosphere. The BTEX distribution pattern in Daegu is observed to be similar to that of several Asian cities, particularly Hong Kong. These results are useful in the design of emission source control measures for VOCs and BTEX in Daegu.
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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 | 0.001 | 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".