Evaluation of PAH diagnostic ratios as source apportionment tools for air particulates collected in an urban-industrial environment
Bibliographic record
Abstract
A variety of polycyclic aromatic hydrocarbon (PAH) diagnostic ratios were examined as source apportionment tools in the analysis of a PAH data set associated with atmospheric particulate matter collected in an urban-industrial environment. Seventy-six PM(10) samples were collected concurrently at 4 sampling sites over a one-month period in Hamilton, Ontario, Canada, a city of 500 000 people that is home to two integrated steel companies, associated industries and a network of roadways and major highways. Samples collected under well defined meteorological conditions were categorized as being 'upwind' or 'downwind' of the industrial sector. All sample extracts were analyzed for 48 parent PAH, methylphenanthrenes and sulfur-containing aromatics and showed a thousand-fold range of total PAH concentrations (0.23-172 ng m(-3)). Of all PAH diagnostic ratios examined, the two most useful were the anthracene/(anthracene+phenanthrene) and benz[a]anthracene/(benz[a]anthracene+chrysene/triphenylene) ratios. These afforded the best discrimination of samples that had significant industrial impacts. This work is the first example of the use of a linear combination of PAH ratios, coupled with total PAH data and well defined local samples to determine the relative impacts of mobile and industrial emissions in an urban-industrial environment. Use of a linear combination of PAH ratios allowed us to categorize 95% of the data as 'upwind' or 'downwind' of the industrial sector. It is important to determine PAH ratio threshold values based on data from well defined local samples rather than relying on literature values alone.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".