Abstract
Introduction: Procedures for measuring continuous data on geolocation, physical activity (PA), and air pollution (AP) have improved greatly in recent years but storing, cleaning and analyzing these can be challenging. Data collected from mobile phones is useful to estimate for example geolocation, transport mode, and PA levels, but more data does not always lead to better outcomes.Method: We use the results from three different studies (AirMAP, CAVA and PASTA) to illustrate the challenges in managing datasets for AP exposure estimates, and comment on their strengths and weaknesses. The datasets vary greatly: AirMAP was part of the Telecom Italia Big Data challenge 2015 which combined aggregated data from cellular towers (~8 million users) in grid cells of varying sizes (10 – 300m) in seven Italian cities; in CAVA, GPS and accelerometer readings derived from a smartphone app were collected from 180 participants in Barcelona; PASTA collected detailed geolocation, PA, and AP exposure data from 120 free-living individuals in three European cities using separate monitors for each in a relatively burdensome and resource intensive approach. For each study, daily exposures estimated as a function of daily activity patterns were compared to estimates based on home location.Results: Processing large and complex datasets to identify people’s daily activities and account for microenvironmental concentrations in estimating personal exposures is still challenging. Mode for example is key in daily pollutant intake (modal choice may result in differences of up to 20%) but identifying it is a difficult task. AirMAP data suggests that home-based exposures overestimate daily activity-based exposures (by 6%), contrary to the other two studies (~18%).Conclusion: The comparison gives insights into difficulties of collecting, processing and interpreting data from current state of the art wearable devices, and into opportunities for large scale ubiquitous sensing.
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