Related Experiment Video
Updated: Aug 10, 2026
Real-World M3-BREATHE: Toward Multimodal Mobile Monitoring of Behaviour, Respiration, and Exposures for Treatment and Health Evaluation
Published on: June 5, 2026
Characterizing human exposure to traffic-related air pollution through daily activities using an agent-based
Michele Tirico1, Valentin Le Bescond2, Delphine Sengelin3
1INSA Lyon, CNRS, Ecole Centrale de Lyon, Univ. Claude Bernard Lyon 1, LMFA, UMR5509, 69621, Villeurbanne, France; EMob-Lab, ENTPE, Univ. Gustave Eiffel, Lyon, France.
Accurately assessing traffic-related air pollution exposure requires dynamic human activity data. An integrated modeling framework reveals intra-day exposure variations and activity-specific patterns, offering a more detailed understanding than static approaches.
Area of Science:
- Environmental Science
- Public Health
- Computational Modeling
Background:
- Accurate human exposure assessment to traffic-related air pollution (TRAP) necessitates integrating pollutant spatio-temporal variability with diverse human activities.
- Agent-based mobility models and synthetic populations offer a unified framework for this integration.
Purpose of the Study:
- To develop and apply an integrated modeling chain for dynamic human exposure assessment to TRAP.
- To compare the proposed dynamic approach with conventional static exposure methods.
Main Methods:
- An integrated modeling chain combining EQASim (synthetic population), MATSim (agent-based transport), COPERT (emission), SIRANE (dispersion), and a dedicated exposure module was developed.
- The framework was applied to the Lyon metropolitan area, comparing dynamic and static exposure analyses.
Main Results:
- Cumulative daily exposure indicators were similar between static and dynamic approaches at the metropolitan scale.
- The dynamic framework captured intra-day exposure variability, activity-specific patterns, and population redistribution.
- Individual trajectories showed how activities like work and leisure modify exposure, with many experiencing high pollution at home, following daily traffic cycles.
Conclusions:
- Dynamic mobility patterns enhance exposure analyses by characterizing exposure based on daily human activities.
- The proposed framework provides a basis for future research on travel exposure, multi-day mobility, environmental inequalities, and health impact assessments.
