Application of Land Use Regression to Identify Sources and Assess Spatial Variation in Urban SVOC Concentrations
Bibliographic record
Abstract
Land use regression (LUR), a geographic information system (GIS), and measured air concentrations were used to identify potential sources of semivolatile organic contaminants (SVOCs) within an urban/suburban region, using Toronto, Canada as a case study. Regression results suggested that air concentrations of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), polycyclic aromatic hydrocarbons (PAHs), and polycyclic musks (PCMs) were correlated with sources at a scale of <5 km. LUR was able to explain 73-90% of the variability in PCBs and PCMs, and 36-89% of PBDE and PAH variability, suggesting that the latter have more spatially complex emission sources, particularly for the lowest and highest molecular weight compounds/congeners. LUR suggested that ~75% of the PCB air concentration variability was related to the distribution of PCBs in use/storage/building sealants, ~60% of PBDE variability was related to building volume, ~55% of the PAH variability was related to the distribution of transportation infrastructure, and ~65% of the PCM variability was related to population density. Parameters such as population density and household income were successfully used as surrogates to infer sources and air concentrations of SVOCs in Toronto. This is the first application of LUR methods to explain SVOC concentrations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".