Mapping Traffic Pollution Exposure: the Quantified Self

The purpose of this paper is to apply recent developments in the areas of the ‘Quantified Self’ and the ‘Internet of Things’ in order to explore the use of low-cost sensors for the collection of a variety of spatially-referenced biometric and environmental data from participants. This is achieved using a specially designed application on the Android mobile platform, which has been developed for this project in order to log serial sensor data via USB against GPS location and timestamp, allowing the spatio-temporal mapping of a variety of personal data. In this case, traffic pollution data is collected and compared with a measures of nasal airflow, permitting an estimate of exposure to traffic pollution for an individual on a given journey. These data will be fed back to individuals in order to assess the level of correlation with their reported perception of exposure to pollution, as well the extent to which this influences subsequent behaviour; such as whether or not an individual will modify their route for a given journey based upon their new knowledge of pollution levels. In this way, personal pollution exposure can be empirically monitored at the individual level, allowing for a more in-depth understanding of these issues than has so far been possible.