A critical question in environmental epidemiology is whether air pollution exposures of large populations can be refined using individual mobile-device-based mobility patterns. Cellular network data has become an essential tool for understanding the movements of human populations. As such, through inferring the daily home and work locations of 407,435 mobile phone users whose positions are determined, we assess exposure to PM2.5. Spatiotemporal PM2.5 concentrations are predicted using an Aerosol Optical Depth- and Land Use Regression-combined model. Air pollution exposures of subjects are assigned considering modeled PM2.5 levels at both their home and work locations. These exposures are then compared to residence-only exposure metric, which does not consider daily mobility. In our study, we demonstrate that individual air pollution exposures can be quantified using mobile device data, for populations of unprecedented size. In examining mean annual PM2.5 exposures determined, bias for the residence-based exposures was 0.91, relative to the exposure metric considering the work location. Thus, we find that ignoring daily mobility potentially contributes to misclassification in health effect estimates. Our framework for understanding population exposure to environmental pollution could play a key role in prospective environmental epidemiological studies.
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WHO (World Health Organization). Over 7 million premature deaths annually linked to air pollution. 2016; http://www.who.int.
Brunekreef B, Holgate ST. Air pollution and health. Lancet. 2002;360:1233–42.
Liao D, Duan Y, Whitsel EA, Zheng ZJ, Heiss G, Chinchilli VM, et al. Association of higher levels of ambient criteria pollutants with impaired cardiac autonomic control: a population-based study. Am J Epidemiol. 2003;159:768–77.
Pope CA III, Dockery DW. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag. 2006;56:709–42.
Laden F, Schwartz J, Speizer FE, Dockery DW. Reduction in fine particulate air pollution and mortality extended follow-up of the Harvard six cities study. Am J Resp Crit Care. 2006;173:667–72.
Pope CA III, Ezzati M, Dockery DW. Fine-particulate air pollution and life expectancy in the United States. N Engl J Med. 2009;360:376–86.
Gurjar BR, Sharma JA, Agarwal A, Gupta P, Nagpure AS, Lelieveld J. Human health risks in megacities due to air pollution. Atmos Environ. 2010;44:4606–13.
Nyhan M, Misstear BD, McNabola A. Comparison of particulate matter dose and acute heart rate variability response in cyclists, pedestrians, bus and train passengers. Sci Total Environ. 2014;468-469:821–31.
Brook RD, Rajagopalan S, Pope CA, Brook JR, Bhatnagar A, Diez-Rouz AV, et al. Particulate matter air pollution and cardiovascular disease, an update to the scientific statement from the American Heart Association. Circulation. 2010;121:2331–78.
Burnett RT, Goldberg MS. Size-fractionated particulate mass and daily mortality in eight Canadian cities. Revised Analyses of Time-Series of Air Pollution and Health. Special Report, Health Effects Institute: Boston, 2003, pp 85–90.
Dominici F, McDermott A, Zeger SL, Samet JM. National maps of the effects of particulate matter on mortality: exploring geographical variation. Environ Health Persp. 2003;111:39–43.
Jerrett M, Burnett RT, Ma R, Pope CA, Krewski D, Newbold KB, et al. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology. 2005;16:727–36.
Puett RC, Hart JE, Yanosky JD, Paciorek C, Schwartz J, Suh H, et al. Chronic fine and coarse particulate exposure, mortality and coronary heart disease in the Nurses’ Health Study. Environ Health Persp. 2009;117:1697–701.
Zanobetti A, Schwartz J. The effects of fine and coarse particulate air pollution on mortality: a national analysis. Environ Health Persp. 2009;117:898–903.
Avery CL, Mills KT, Williams R, McGraw KA, Poole C, Smith RL, et al. Estimating error in using ambient PM2.5 concentrations as proxies for personal exposures: a review. Epidemiology. 2010;21:215–23.
Ma Y, Chen R, Pan G, Xu X, Song W. Fine particulate air pollution and daily mortality in Shenyang, China. Sci Total Environ. 2011;409:2473–7.
Lepeule J, Laden F, Dockery DW, Schwartz J. Chronic exposure to fine particles and mortality: an extended follow-up of the Harvard Six Cities Study from 1974 to 2009. Environ Health Persp. 2012;120:965–70.
Lindström J, Szpiro AA, Sampson PD, Sheppard L, Oron A, Richards M et al. A flexible spatio-temporal model for air pollution: allowing for spatio-temporal covariates (January 19, 2011). UW Biostatistics Working Paper Series. Working Paper 370. 2010; http://www.bepress.com/uwbiostat/paper370
Szpiro AA, Sampson PD, Sheppard L, Lumley T, Adar SD, Kaufman JD. Predicting intra-urban variation in air pollution concentrations with complex spatio-temporal dependencies. Environmetrics. 2010;21:606–31.
Kloog I, Melly SJ, Ridgway WL, Coull BA, Schwartz J. Using new satellite based exposure methods to study the association between pregnancy PM2.5 exposure, premature birth and birth weight in Massachusetts. Environ Health. 2012;11:40.
Hajat A, Diez-Roux AV, Adar SD, Auchincloss AH, Lovasi GS, O’Neill MS, et al. Air pollution and individual and neighborhood socioeconomic status: evidence from the Multi-Ethnic Study of Atherosclerosis. Environ Health Perspect. 2013;121:1325–33.
Gonzalez M, Hidalgo C, Barabasi AL. Understanding individual human mobility patterns. Nature. 2008;453:779–82.
Candia J, Gonzalez MG, Wang P, Schoenharl T, Madey G, Barabasi A-L. Uncovering individual and collective human dynamics from mobile phone records. J Phys A-Math Theor. 2008;41:224015(pp 11).
Calabrese F, Diao Mi, Di Lorenzo G, Ferreira JJr, Ratti C. Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transp Res C-EMER. 2013;26:301–13.
Jiang S, Fiore GA, Yang Y, Ferreira J Jr, Frazzoli E, Gonzalez MC. A review of urban computing for mobile phone traces: Current methods, challenges and opportunities. Chicago, IL, USA: Proceedings of the ACM SIGKDD International Workshop on Urban Computing; 2013.
Setton E, Marshall JD, Brauer M, Lundquist KR, Hystad P, Keller P, et al. The impact of daily mobility on exposure to traffic-related air pollution health effect estimates. J Expo Sci Environ Epidemiol. 2011;21:42–8.
Ozkaynak H, Baxter LK, Dionisio KL, Burke J. Air pollution exposure prediction approaches used in air pollution epidemiology studies. J Expo Sci Environ Epidemiol. 2013;23:566–72.
Baxter LK, Dionisio KL, Burke J, Sarnet SE, Sarnet JA, Hodas N, et al. Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations. J Expo Sci Environ Epidemiol. 2013;23:654–9.
Breen MS, Long TC, Schultz BD, Williams RW, Richard-Bryant J, Breen M, et al. Air pollution exposure model for individuals (EMI) in health studies: evaluation for ambient PM2.5 in Central North America. Environ Sci Technol. 2015;49:14184–94.
Burke JM, Zufall M, Özkaynak H. A population exposure model for particulate matter: case study results for PM2.5 in Philadelphia, PA. J Expo Sci Environ Epidemiol. 2001;11:470–89.
Beckx C, Int Panis L, Arentze T, Janssens D, Torfs R, Broekx S, et al. A dynamic activity-based population modelling approach to evaluate exposure to air pollution: methods and application to a Dutch urban area. Environ Impact Asses. 2009;29:179–85.
Hatzopoulou M, Miller EJ. Linking an activity-based travel demand model with traffic emission and dispersion models: transport’s contribution to air pollution in Toronto. Transp Res D- TR E. 2010;15:315–25.
Dons E, Int Panis L, Van Poppel M, Theunis J, Willems H, Torfs R, et al. Impact of time–activity patterns on personal exposure to black carbon. Atmos Environ. 2011;45:3594–602.
Dhondt S, Beckx C, Degraeuwe B, Lefebvre W, Kochan B, Bellemans T, et al. Health impact assessment of air pollution using a dynamic exposure profile: implications for exposure and health impact estimates. Environ Impact Asses. 2012;36:42–51.
USEPA (United States Environmental Protection Agency). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model (APEX) Documentation: TRIM.Expo/APEX, Version 4; User Guide, 2012.
Smith JD, Mitsakou C, Kitwiroon N, Barratt BM, Walton HA, Taylor JG, et al. London hybrid exposure model: improving human exposure estimates to NO2 and PM2.5 in an urban setting. Environ Sci Technol. 2016;50:11760–8.
Nyhan M, Grauwin S, Britter R, Laden F, McNabola A, Misstear B, et al. Exposure track -the impact of mobile device based mobility patterns on quantifying population exposure to air pollution. Environ Sci Technol. 2016; https://doi.org/10.1021/acs.est.6b02385
Dewulf B, Neutens T, Lefebvre W, Seyneave G, Vanpoucke C, Beckx C, et al. Dynamic assessment of exposure to air pollution using mobile phone data. Int J Health Geogr. 2016;15:14.
De Nazelle A, Seto E, Donaire-Gonzalez D, Mendez M, Matamala J, Nieuwenhuijsen MJ, Jerrett M. Improving estimates of air pollution exposure through ubiquitous sensing technologies. Environ Pollut. 2013;176:92–9.
Glasgow ML, Rudra CB, Yoo E, Demirbas M, Merriman J, Nayak P, et al. Using smartphones to collect time-activity data for long-term personal-level air pollution exposure assessment. J Expo Sci Environ Epidemiol. 2014;26:356–64.
Su JG, Jerrett M, Meng Y, Pickett M, Ritz B. Integrating smart-phone based momentary location tracking with fixed site air quality monitoring for personal exposure assessment. Sci Total Environ. 2015;506-507:518–26.
USEPA (United States Environmental Protection Agency). Air quality system data; United States Environmental Protection Agency Website. 2016; www.epa.gov.
Lyapustin A, Martonchik J, Wang Y, Laszlo I, Korkin S. Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look-up tables. J Geophys Res Atmos. 2011;116:D03210.
Lyapustin A, Wang Y, Laszlo I, Kahn R, Korkin S, Remer L, et al. Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. J Geophys Res Atmos. 2011;116:D03211.
Lyapustin A, Wang Y, Frey R. An automatic cloud mask algorithm based on time series of MODIS measurements. J Geophys Res Atmos. 2008;113:D16207.
Kloog I, Koutrakis P, Coull BA, Lee HJ, Schwartz J. Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmos Environ. 2011;45:6267–75.
Kloog I, Nordio F, Coull BA, Schwartz J. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states. Environ Sci Technol. 2012;46:11913–21.
Armstrong BG. The effects of measurement on relative risk regressions. Am J Epidemiol. 1990;132:1176–84.
Wacholder S. When measurement errors correlate with truth - surprising effects of nondifferential misclassification. Epidemiology. 1995;6:157–61.
The authors thank Airsage who have provided data for this study through their support of the Senseable City Laboratory at Massachusetts Institute of Technology. This publication was made possible by USEPA grant (RD-835872-01) through the Harvard University United States Environmental Protection Agency sponsored Air, Climate & Environment (ACE) Centre. The contents of the study are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication.