Ambient Ultrafine Particle Levels at Residential and Reference Sites in Urban and Rural Switzerland
Bibliographic record
Abstract
Although there is evidence that ultrafine particles (UFP) do affect human health there are currently no legal ambient standards. The main reasons are the absence of spatially resolved exposure data to investigate long-term health effects and the challenge of defining representative reference sites for monitoring given the high dependence of UFP on proximity to sources. The objectives of this study were to evaluate the spatial distribution of UFP in four areas of the Swiss Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) and to investigate the representativeness of routine air monitoring stations for residential sites in these areas. Repeated UFP measurements during three seasons have been conducted at a total of 80 residential sites and four area specific reference sites over a median duration of 7 days. Arithmetic mean residential PNC scattered around the median of 10,800 particles/cm(3) (interquartile range [IQR] = 7800 particles/cm(3)). Spatial within area contrasts (90th/10th percentile ratios) were around two; increased contrasts were observed during weekday rush-hours. Temporal UFP patterns were comparable at reference and residential sites in all areas. Our data show that central monitoring sites can represent residential conditions when locations are well chosen with respect to the local sources--namely traffic. For epidemiological research, locally resolved spatial models are needed to estimate individuals' long-term exposures to UFP of outdoor origin at home, during commute and at work.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".