STHAM: an agent based model for simulating human exposure across high resolution spatiotemporal domains

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.