Elucidating multipollutant exposure across a complex metropolitan area by systematic deployment of a mobile laboratory
Bibliographic record
Abstract
Abstract. This study evaluates a deployment strategy of a heavily instrumented mobile lab for characterizing multipollutant spatial patterns based upon a limited number of measurement days spread over different seasons. The measurements obtained through this deployment strategy are used to gain insight into average pollutant levels between routine monitoring sites and in relation to emission sources in the region, as well as to assess correlations between pollutant patterns to better understand the nature of urban air pollutant mixtures. A wide range of locations were part of the deployment in order to characterize the distribution of chronic exposures potentially allowing development of exposure models. Comparison of the mobile lab averages to the available adjacent air quality monitoring network stations to evaluate their representativeness showed that they were in reasonable agreement with the annual averages at the monitoring sites, thus providing some evidence that, through the deployment approach, the mobile lab is able to capture the main features of the average spatial patterns. The differences between mobile lab and network averages varied by pollutant with the best agreement for NO2 with a percentage difference of 20%. Sharp differences in the average spatial distribution were found to exist between different pollutants on multiple scales, particularly on the sub-urban scale, i.e., the neighborhood to street scales. For example, NO2 was observed to be 210–265% higher by the main highway in the study region compared to the nearby urban background monitoring site, while black carbon was higher by 180–200% and particle number concentration was 300% higher. The repeated measurements of near-roadway gradients showed that the rate of change differed by pollutant with elevated concentrations detected up to 600–700 m away for some pollutants. These results demonstrate that through systematic deployment mobile laboratory measurements can be used to characterize average or typical concentration patterns, thus providing data to assess monitoring site representativeness, spatial relationships between pollutants, and chronic multipollutant exposure patterns useful for evaluating and developing exposure models for outdoor concentrations in an urban environment.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".