Quantifying personal exposure to air pollution from smartphone-based location data

Personal exposure assessment is a challenging task that requires both measurements of the state of the environment as well as individual’s movements and their activity patterns. While ambient exposure is well studied, learning people movements represents an open issue. We show how location data collected by smartphone applications are exploited to quantify the individual exposure of a large group of people to air pollution. A Bayesian approach that blends air quality monitoring data with individual location data is proposed to assess the personal exposure over time, under uncertainty on both pollutant level and individual location. A comparison with personal exposure obtained assuming fixed locations for the individuals is also provided. Location data collected by the Earthquake Network research project are employed to quantify the dynamic exposure to fine particulate matter of around 2500 people living in Santiago (Chile) over a 4-month period. For around 30% of individuals, the personal exposure based on people movements emerges significantly different over the static exposure. The approach is flexible and can be adopted to quantify the personal exposure based on any location-aware smartphone application