Toward a Bayesian Approach for Self-Tracking Personal Pollution Exposures

Pollution exposure assessment at the population level is an established enterprise for environmental scientists and public health officials but efforts to help individuals monitor and track their personal pollution exposures have just begun to garner research interest. Self-tracking pollution exposure is challenging for several reasons, including current limitations in sensor size, accuracy, and cost, frequent calibration requirements, and that people's daily activities often interfere with data quality in wearable sensing. The goal of this research is to develop a human-centered computing framework for the emerging field of personal pollution exposure assessment. To that aim, in this position paper, we propose a Bayesian approach to combine environmental sensing data from different spatiotemporal resolutions, such as from citywide national monitoring stations, neighborhood-wide lightweight sensing nodes, and personal wearables.