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

Lucia Paci, Francesco Finazzi

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Personal exposure assessment is a challenging task that requires both measurements of the state of the environment as well as the individual's movements. In this paper, we show how location data collected by smartphone applications can be exploited to quantify the personal 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 individual exposure over time, under uncertainty of both the pollutant level and the 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 personal 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. On the basis of this result and thanks to a simulation study, we claim that even when the individual location is known with nonnegligible error, this helps to better assess personal exposure to air pollution. The approach is flexible and can be adopted to quantify the personal exposure based on any location-aware smartphone application.
Original languageEnglish
Pages (from-to)1356-1366
Number of pages11
JournalBiometrics
Volume75
DOIs
Publication statusPublished - 2019

Keywords

  • Dynamic models
  • Markov chain Monte Carlo
  • particulate matter
  • space-time modeling

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