Ultrafine Particle Mobile Monitoring Study Designs for Epidemiology: Cost and Performance Comparisons

Sun-Young Kim ^1,2, Amanda J Gassett ², Magali N Blanco ²

⁰Department of Public Health & AI, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.

Environmental Health Perspectives +

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March 5, 2025

View abstract on PubMed

Summary

Mobile monitoring helps assess long-term air pollution exposure and health effects. Temporally balanced designs with 12 visits per site offer a cost-effective balance between reduced costs and good prediction accuracy for air quality studies.

Area Of Science

Environmental Health Sciences
Epidemiology
Air Quality Monitoring

Background

Accurate long-term air pollution exposure assessment is crucial for investigating health effects.
Mobile monitoring offers a solution for collecting individual-level air pollution data.
Cost-effectiveness is a key consideration for mobile monitoring study designs.

Purpose Of The Study

To compare the costs and predictive model performance of different mobile monitoring designs.
To provide practical guidance for optimizing future air pollution monitoring campaigns.

Main Methods

Utilized data from the Adult Changes in Thought Air Pollution (ACT-AP) study on ultrafine particle monitoring and costs.
Assessed various mobile monitoring designs by varying the number of sites, visits, seasons, days, and hours.
Developed exposure prediction models for each design and evaluated performance using cross-validation (CV) statistics.

Main Results

Fewer visits per site reduced costs but slightly decreased model performance (CV R-squared), with minimal impact above 12 visits.
Reducing the number of sites to at least 100 sites also lowered costs with minimal performance reduction.
Temporal restrictions (fewer seasons, days, or hours) in designs with constant cost negatively impacted model performance.

Conclusions

Temporally balanced mobile monitoring designs with 12 visits per site represent a cost-effective approach.
These designs achieve a good balance between prediction accuracy and reduced operational costs.
Findings offer practical guidance for planning efficient mobile monitoring studies for health effect assessments.