On the potential of iPhone significant location data to characterize individual mobility for air pollution health studies

Highlights

● We evaluated the accuracy of iPhone data in capturing time-activity patterns.

● iPhone data captured the most important microenvironments and time spent in them.

● iPhone data also accurately captured daily exposure to ambient PM pollution.

● A considerable fraction of the population in the USA may have iPhone data available.

● iPhone data has great potential in air pollution health studies.

Abstract

In many air pollution health studies, the time-activity pattern of individuals is often ignored largely due to lack of data. However, a better understanding of this location-based information is expected to decrease uncertainties in exposure estimation. Here, we showcase the potential of iPhone’s Significant Location (iSL) data in capturing the user’s historical time-activity patterns in order to estimate exposure to ambient air pollutants. In this study, one subject carried an iPhone in tandem with a reference GPS tracking device for one month. The GPS device recorded locations in 10 second intervals while the iSL recorded the time spent in locations the subject visited frequently. Using GPS data as a reference, we then evaluated the accuracy of iSL data in capturing the subject’s time-activity patterns and time-weighted air pollution concentration within the study time period. We found the iSL data accurately captured the time the subject spent in 16 microenvironments (i.e. locations the subject visited more than once), which was 93% of the time during the study period. The average error of time-weighted aerosol optical depth value, a surrogate of particle pollution, is only 0.012%. To explore the availability of iSL data among iPhone users, an online survey was conducted. Among the 349 surveyed participants, 72% of them have iSL data available. Considering the popularity of iPhones, iSL data may be available for a significant portion of the general population. Our results suggest iSL data have great potential for characterizing historical time-activity patterns to improve air pollution exposure estimation.

Graphical abstract

Keywords

Air pollution exposure / Human mobility / iPhone / Significant Location / Smartphone data

References

[1] Apte J S , Messier K P , Gani S , Brauer M , Kirchstetter T W , Lunden M M , Marshall J D , Portier C J , Vermeulen R C H , Hamburg S P . (2017). High-resolution air pollution mapping with Google street view cars: Exploiting big data. Environmental Science & Technology, 51( 12): 6999– 7008

[2] Bell M L , Banerjee G , Pereira G . (2018). Residential mobility of pregnant women and implications for assessment of spatially-varying environmental exposures. Journal of Exposure Science & Environmental Epidemiology, 28 : 470– 480

[3] Bernstein J A , Alexis N , Barnes C , Bernstein I L , Bernstein J A , Nel A , Peden D , Diaz-Sanchez D , Tarlo S M , Williams P B . (2004). Health effects of air pollution. Journal of Allergy and Clinical Immunology, 114( 5): 1116– 1123

[4] GBD 2016 Risk Factors Collaborators . (2017). Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet, 390( 10100): 1345– 1422

[5] Glasgow M L , Rudra C B , Yoo E H , Demirbas M , Merriman J , Nayak P , Crabtree-Ide C , Szpiro A A , Rudra A , Wactawski-Wende J , Mu L . (2016). Using smartphones to collect time-activity data for long-term personal-level air pollution exposure assessment. Journal of Exposure Science & Environmental Epidemiology, 26( 4): 356– 364

[6] Goodwin R ( 2021). How To Find, Track & Manage Your iPhone Location History: Everything You Need to Know. London: Know Your Mobile

[7] Ipeirotis P G ( 2010). Demographics of mechanical turk. In: NYU Stern School of Business Research Paper Series. New York: New York University

[8] Keusch F , Struminskaya B , Antoun C , Couper M P , Kreuter F . (2019). Willingness to participate in passive mobile data collection. Public Opinion Quarterly, 83( S1): 210– 235

[9] Klepeis N E , Nelson W C , Ott W R , Robinson J P , Tsang A M , Switzer P , Behar J V , Hern S C , Engelmann W H . (2001). The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants. Journal of Exposure Science & Environmental Epidemiology, 11( 3): 231– 252

[10] Lyapustin A Wang Y Laszlo I Kahn R Korkin S Remer L Levy R Reid J ( 2011). Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. Journal of Geophysical Research, D, Atmospheres, 116: D03211

[11] Park Y M , Kwan M P . (2017). Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored. Health & Place, 43 : 85– 94

[12] Statista ( 2021). Subscriber share held by smartphone operating systems in the United States from 2012 to 2021. New York: Statista

[13] Yu H , Russell A , Mulholland J , Huang Z . (2018). Using cell phone location to assess misclassification errors in air pollution exposure estimation. Environmental Pollution, 233 : 261– 266

[14] Yu X Stuart A L Liu Y Ivey C E Russell A G Kan H Henneman L R F Sarnat S E Hasan S Sadmani A Yang X Yu H ( 2019). On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies. Environmental Pollution, 252(Pt A): 924− 930

RIGHTS & PERMISSIONS

2022 Higher Education Press