Characterization of Polycyclic Aromatic Compounds in Commercial Pavement Sealcoat Products for Enhanced Source Apportionment
Bibliographic record
Abstract
Coal tar-based sealcoat (CTSC) products are an urban source of polycyclic aromatic compounds (PACs) to the environment. However, efforts to assess the environmental fate and impacts of CTSC-derived PACs are hindered by the ubiquity of (routinely monitored) PACs released from other environmental sources. To advance source identification of CTSC-derived PACs, we use comprehensive two-dimensional gas chromatography-high resolution mass spectrometry (GC × GC/HRMS) to characterize the major and minor components of CTSC products in comparison to those in other sources of PACs, viz., asphalt-based sealcoat products, diesel particulate, diesel fuel, used motor oil and roofing shingles. GC × GC/HRMS analyses of CTSC products led to the confident assignment of compounds with 88 unique elemental compositions, which includes a set of 240 individual PACs. Visualization of the resulting profiles using Kendrick mass defect plots and hierarchical cluster analysis highlighted compositional differences between the sources. Profiles of alkylated PAHs, and heteroatomic (N, O, S) PACs enabled greater specificity in source differentiation. Isomers of specific polycyclic aromatic nitrogen heterocycles (PANHs) were diagnostic for coal tar-derived PAC sources. The compounds identified and methods used for this identification are anticipated to aid in future efforts on risk assessment and source apportionment of PACs in environmental matrices.
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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".