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

Francesco Finazzi1, Lucia Paci2

  • 1Department of Management, Information and Production Engineering, University of Bergamo, Dalmine, Italy.

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|June 11, 2019
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Smartphone location data can track personal air pollution exposure, revealing significant differences for 30% of individuals compared to static estimates. This method improves exposure assessment, even with location inaccuracies.

Keywords:
Dynamic modelsMarkov chain Monte Carloparticulate matterspace-time modeling

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Area of Science:

  • Environmental Science
  • Public Health
  • Data Science

Background:

  • Assessing personal exposure to air pollution is complex, requiring environmental data and individual movement tracking.
  • Existing methods often rely on fixed locations, potentially misrepresenting actual exposure levels.

Purpose of the Study:

  • To quantify personal exposure to air pollution using smartphone location data.
  • To develop a Bayesian approach integrating air quality and individual mobility data.
  • To compare dynamic exposure assessment with static location-based methods.

Main Methods:

  • Utilized location data from the Earthquake Network project for ~2500 individuals in Santiago, Chile.
  • Employed a Bayesian framework to model individual exposure over time, accounting for location and pollutant uncertainties.
  • Compared dynamic exposure estimates with those derived from fixed individual locations.

Main Results:

  • Personal exposure to fine particulate matter varied significantly for approximately 30% of individuals when considering their movements versus static locations.
  • The proposed method demonstrated improved personal exposure assessment even with non-negligible location errors.
  • The approach proved flexible for various location-aware smartphone applications.

Conclusions:

  • Dynamic personal exposure assessment using smartphone location data offers a more accurate representation of air pollution exposure.
  • Integrating mobility data significantly enhances exposure assessment accuracy for a substantial portion of the population.
  • This methodology provides a scalable and adaptable tool for large-scale personal exposure studies.