Temporal variations and sources of organic pollutants in two urban atmopsheres: Ankara and Ottawa
Bibliographic record
Abstract
This study aimed at providing a thorough understanding of temporal and spatial variations of VOCs and underlying factors in different microenvironments in two different urban atmospheres, with different degrees of regulatory enforcement. The VOC data were collected in field campaigns conducted in Ankara, Turkey, and Ottawa, Canada over the years 2000-2004. Insight into the sources of VOCs in different urban atmospheres was sought by using three commonly used receptor models namely; Positive Matrix Factorization (PMF), Chemical Mass Balance (CMB) Model and Conventional Factor Analysis (CFA). Motor vehicle related source profiles were developed to use in receptor modeling. Motor vehicles are the most abundant VOC sources with about 60% and 95% contributions to ambient levels in Ankara and Ottawa, respectively. Residential heating (31%) during winter season, biogenic (9%) and architectural coating (12%) emissions during summer season and solvent use (about 12%) emissions are the next abundant VOC sources in Ankara. In addition, a new method to estimate the contribution of sources from wind sectors in urban atmosphere was developed and implemented in this study. The comparison of the results of these two cities demonstrated the influence of control measures on ambient levels and sources of VOCs observed in different urban atmospheres. VOC levels in Ankara exceed EU levels and they are about factor of two higher than that are measured in Ottawa owing to lack of implementation of emission control regulations for VOCs in Ankara compared to well adopted regulations in Ottawa.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".