ArticleEnvironmental science & technology2023
Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology.
Article in Environmental science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Mobile Monitoring and Land Use Regression Modeling of Ultrafine Particle Spatial Distribution in Beijing, China, during August 2022-July 2023.Environmental science & technology · 2026Article
- Influence of on-road mobile monitoring design on ultrafine particle exposure models and cognitive health inferences.Journal of exposure science & environmental epidemiology · 2026Article
- Optimizing Air Pollution Exposure Assessment with Application to Cognitive Function.Research report (Health Effects Institute) · 2025Article
- Ultrafine particles and late-life cognitive function: Influence of stationary mobile monitoring design on health inferences.Environmental pollution (Barking, Essex : 1987) · 2025Article
- Ultrafine Particle Mobile Monitoring Study Designs for Epidemiology: Cost and Performance Comparisons.Environmental health perspectives · 2025Article
- Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.Environmental science & technology · 2024Article
- Traffic-related air pollution and dementia incidence in the Adult Changes in Thought Study.Environment international · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.