Land-Use Regression Models for Metals Associated with Airborne Particulate Matter in Calgary, Alberta
Bibliographic record
Abstract
Background: Fine airborne particulate matter has been associated with cardiovascular and respiratory morbidity and mortality, and there is evidence that metals may contribute to these adverse health effects. We developed seasonal land-use regression (LUR) models to characterize the spatial distribution of PM-associated metals in Calgary, Alberta. Previous studies have successfully modeled PM and gaseous pollutants; however, to our knowledge this is the first study to develop LUR models for metals. Methods: Particulate matter with <1.0 µm in aerodynamic diameter (PM1.0) was measured at 25 sites during 2-week periods in August 2010 and January 2011. PM1.0 filters were analyzed using inductively-coupled plasma mass spectrometry. Industrial sources were obtained through the National Pollutant Release Inventory and verified using Google Maps. Traffic and zoning data were obtained from the City of Calgary. Predictor variables were generated using ArcMap-10.1. LUR models for arsenic, chromium, copper, lead, manganese, mercury, nickel, vanadium, and zinc were developed using SAS-EG-4.2. Results: Preliminary summer models explained 60-90% of the variability in arsenic, chromium, copper, lead, manganese, mercury, nickel, vanadium, and zinc, while winter models explained 40-80% of the variability in metals concentrations. Industrial sources and industrial land-use zoning were the strongest predictors (p<0.05). However, traffic was not a major predictor for most metals. These findings contrast with LUR models for PM and gaseous pollutants in which traffic variables were highly influential. There was an average improvement of 5-10% in model efficacy when wind speed and direction were included. Conclusions: These results suggest that airborne metals vary spatially with the distribution of local industrial sources and that LUR modeling can be used to predict local metals concentrations. Future analyses will include LUR modeling of the remaining PM-components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".