Open AccessSTHAM: an agent based model for simulating human exposure across high resolution spatiotemporal domains
Author(s) -
Albert M. Lund,
Ramkiran Gouripeddi,
Julio C. Facelli
Publication year - 2020
Publication title -
journal of exposure science and environmental epidemiology/journal of exposure science and environmental epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.155
H-Index - 92
eISSN - 1559-064X
pISSN - 1559-0631
DOI - 10.1038/s41370-020-0216-4
Subject(s) - environmental science , agent based model , high resolution , computer science , geography , remote sensing , artificial intelligence
Human exposure to particulate matter and other environmental species is difficult to estimate in large populations. Individuals can encounter significant and acute variations in exposure over small spatiotemporal scales. Exposure is strongly tied to both the environmental and activity contexts that individuals experience. Here we present the development of an agent-based model to simulate human exposure at high spatiotemporal resolutions. The model is based on simulated activity and location trajectories on a per-person basis for large geographical areas. We demonstrate that the model can successfully estimate trajectories and that activity patterns have been validated against traffic patterns and that can be integrated with exposure-agent geographical distributions to estimate total human exposure.