HazeDose: Design and Analysis of a Personal Air Pollution Inhaled Dose Estimation System using Wearable Sensors

Nowadays air pollution becomes one of the biggest world issues in both developing and developed countries. Helping individuals understand their air pollution exposure and health risks, the traditional way is to utilize data from static monitoring stations and estimate air pollution qualities in a large area by government agencies. Data from such sensing system is very sparse and cannot reflect real personal exposure. In recent years, several research groups have developed participatory air pollution sensing systems which use wearable or portable units coupled with smartphones to crowd-source urban air pollution data. These systems have shown remarkable improvement in spatial granularity over government-operated fixed monitoring systems. In this paper, we extend the paradigm to HazeDose system, which can personalize the individuals' air pollution exposure. Specifically, we combine the pollution concentrations obtained from an air pollution estimation system with the activity data from the individual's on-body activity monitors to estimate the personal inhalation dosage of air pollution. Users can visualize their personalized air pollution exposure information via a mobile application. We show that different activities, such as walking, cycling, or driving, impact their dosage, and commuting patterns contribute to a significant proportion of an individual's daily air pollution dosage. Moreover, we propose a dosage minimization algorithm, with the trial results showing that up to 14.1% of a biker's daily exposure can be reduced while using alternative routes the driver can inhale 25.9% less than usual. One heuristic algorithm is also introduced to balance the execution time and dosage reduction for alternative routes scenarios. The results show that up to 20.3% dosage reduction can be achieved when the execution time is almost one seventieth of the original one.