Assessing personal exposure using Agent Based Modelling informed by sensors technology.
Dimitris Chapizanis1, Spyros Karakitsios2, Alberto Gotti3
1Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki, 54124, Greece.
Environmental Research
|September 21, 2020
Summary
This study introduces Agent Based Modelling (ABM) to simulate human behavior for detailed exposure assessment. The novel method reveals significant personal exposure variations due to differing daily activities and sociodemographics.
Area of Science:
- Environmental Health Sciences
- Computational Epidemiology
- Urban Planning
Background:
- Technological advancements enable extensive exposure data collection, but population-wide individual data acquisition faces significant challenges.
- Existing exposure assessment methods often lack detailed spatiotemporal resolution and fail to capture behavioral nuances.
- Agent Based Modelling (ABM) offers a promising approach to simulate complex human behaviors and interactions for exposure assessment.
Purpose of the Study:
- To develop and validate a city-scale Agent Based Model (ABM) for population-based exposure assessment.
- To simulate human movement and interaction behavior to generate refined time-activity diaries and exposure profiles.
- To assess personal exposure to air pollutants, specifically PM2.5, considering microenvironments and sociodemographic factors.
Main Methods:
- Developed a city-scale ABM for Thessaloniki, Greece, integrating population statistics, road/building networks, and time-use survey data.
- Transformed real-world data into agents (human, road, building) with defined rules and behaviors based on survey outputs.
- Coupled agent trajectories with spatially resolved pollution data from a local sensor campaign to estimate personal exposure.
Main Results:
- Simulated individuals exhibited diverse spatiotemporal behaviors based on sociodemographic background and agent-specific decision-making.
- Personal PM2.5 exposure varied significantly; housemates showed a 56.5% difference, and neighbors up to 87%, due to behavioral disparities.
- The model successfully generated refined daily exposure profiles, accounting for microenvironments and individual characteristics.
Conclusions:
- Agent Based Modelling provides a cost-effective methodology for detailed exposure assessment and understanding population health risks.
- The developed ABM addresses vulnerable subgroups and can inform public health policy by simulating intervention impacts.
- This approach enhances exposure science by integrating behavioral simulation with environmental data for more accurate personal exposure estimations.
Keywords:
Agent based modellingAir qualityPersonal exposure assessmentSensors technologySocioeconomic statusMore Related Videos
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