Abstract
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.
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Acknowledgements
We thank members of Professor John Horelâs group at the Department of Atmospheric Sciences for providing their expertise and for developing the PM 2.5 statistical model. The research reported in this publication was supported (in part or in full) by NIBIB/NIH under Award Number 1U54EB021973 and NCATS/NIH under Award Number UL1TR001067. Computational resources were provided by the Utah Center for High Performance Computing, which has been partially funded by the NIH Shared Instrumentation Grant 1S10OD021644-01A1. Map data copyrighted by OpenStreetMap contributors and is available from https://www.openstreetmap.org.
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Lund, A.M., Gouripeddi, R. & Facelli, J.C. STHAM: an agent based model for simulating human exposure across high resolution spatiotemporal domains. J Expo Sci Environ Epidemiol 30, 459â468 (2020). https://doi.org/10.1038/s41370-020-0216-4
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DOI: https://doi.org/10.1038/s41370-020-0216-4