Semi-volatile Organic Contaminants in the Urban Atmosphere: Spatial and Seasonal Distributions and Implications for Contaminant Transport
Bibliographic record
Abstract
Spatial and temporal patterns of semi-volatile organic contaminant (SVOC) concentrations in air and precipitation were investigated at the urban scale in order to improve our understanding of emission sources and factors affecting intra-urban variability. Toronto, Canada was used as a case study. Advances were made in two methods used to examine intra-urban variability, namely passive air sampling and land use regression analysis. The study showed that these methods are useful for assessing local-scale variability, and that passive air sampler concentrations are most reliable when using homologue-specific sampling rates obtained from a co-located low volume sampler. The results of the spatially and temporally distributed sampling demonstrated that the highest atmospheric concentrations of SVOCs were associated with the highest density regions of the urban area. Temporal patterns of elevated concentrations of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and polycyclic aromatic hydrocarbons (PAHs) in warm seasons were consistent with temperature-related emission processes such as volatilization and/or ventilation of indoor air. Spatial patterns in air concentrations were influenced by local sources on a scale of <5 km and were explained by factors related to human population activities such as building volume (PBDEs), population density (polycyclic musks, or PCMs), residential, commercial and transportation infrastructure (PAHs), and chemical inventory (PCBs). Industrial activities were not important factors. The link between elevated environmental concentrations and the in-use stock of banned chemicals, such as PCBs and PBDEs, suggest that efforts to control emissions and reduce environmental concentrations must address the removal of current use products, in addition to the bans on new uses of the SVOCs.
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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".