  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.
