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Distributions of human exposure to ozone during commuting hours in Connecticut using the cellular device network. (English) Zbl 07225440
Summary: Epidemiologic studies have established associations between various air pollutants and adverse health outcomes for adults and children. Due to high costs of monitoring air pollutant concentrations for subjects enrolled in a study, statisticians predict exposure concentrations from spatial models that are developed using concentrations monitored at a few sites. In the absence of detailed information on when and where subjects move during the study window, researchers typically assume that the subjects spend their entire day at home, school, or work. This assumption can potentially lead to large exposure assignment bias. In this study, we aim to determine the distribution of the exposure assignment bias for an air pollutant (ozone) when subjects are assumed to be static as compared to accounting for individual mobility. To achieve this goal, we use cell-phone mobility data on approximately 400,000 users in the state of Connecticut, USA during a week in July 2016, in conjunction with an ozone pollution model, and compare individual ozone exposure assuming static versus mobile scenarios. Our results show that exposure models not taking mobility into account often provide poor estimates of individuals commuting into and out of urban areas: the average 8-h maximum difference between these estimates can exceed 80 parts per billion (ppb). However, for most of the population, the difference in exposure assignment between the two models is small, thereby validating many current epidemiologic studies focusing on exposure to ozone. Supplementary materials accompanying this paper appear online.
MSC:
62P12 Applications of statistics to environmental and related topics
Software:
CODA; spTimer
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References:
[1] Bakar, Ks; Sahu, Sk, sptimer: Spatio-temporal Bayesian modelling using R, Journal of Statistical Software, 63, 15, 1-32 (2015)
[2] Baldwin, N.; Gilani, O.; Raja, S.; Batterman, S.; Ganguly, R.; Hopke, P.; Berrocal, V.; Robins, T.; Hoogterp, S., Factors affecting pollutant concentrations in the near-road environment, Atmospheric Environment, 115, 223-235 (2015)
[3] Bayir, M. A., Demirbas, M., and Eagle, N. (2009). Discovering spatiotemporal mobility profiles of cellphone users. In World of Wireless, Mobile and Multimedia Networks & Workshops, 2009, pages 1-9. IEEE.
[4] Becker, R.; Cáceres, R.; Hanson, K.; Isaacman, S.; Loh, Jm; Martonosi, M.; Rowland, J.; Urbanek, S.; Varshavsky, A.; Volinsky, C., Human mobility characterization from cellular network data, Communications of the ACM, 56, 1, 74-82 (2013)
[5] Benson, Pe, A review of the development and application of the CALINE3 and 4 models, Atmospheric Environment. Part B. Urban Atmosphere, 26, 3, 379-390 (1992)
[6] Brauer, M.; Hoek, G.; Smit, H.; De Jongste, J.; Gerritsen, J.; Postma, Ds; Kerkhof, M.; Brunekreef, B., Air pollution and development of asthma, allergy and infections in a birth cohort, European Respiratory Journal, 29, 5, 879-888 (2007)
[7] Byun, D.; Schere, Kl, Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system, Applied Mechanics Reviews, 59, 2, 51-77 (2006)
[8] Calabrese, F.; Di Lorenzo, G.; Liu, L.; Ratti, C., Estimating origin-destination flows using opportunistically collected mobile phone location data from one million users in boston metropolitan area, IEEE Pervasive Computing, 10, 4, 36-44 (2011)
[9] Chen, C-H; Xirasagar, S.; Lin, H-C, Seasonality in adult asthma admissions, air pollutant levels, and climate: A population-based study, Journal of Asthma, 43, 4, 287-292 (2006)
[10] Cressie, N. and Wikle, C. K. (2011). Statistics for spatio-temporal data. Hoboken. · Zbl 1273.62017
[11] Delamater, Pl; Finley, Ao; Banerjee, S., An analysis of asthma hospitalizations, air pollution, and weather conditions in Los Angeles county, California, Science of the Total Environment, 425, 110-118 (2012)
[12] Gent, Jf; Triche, Ew; Holford, Tr; Belanger, K.; Bracken, Mb; Beckett, Ws; Leaderer, Bp, Association of low-level ozone and fine particles with respiratory symptoms in children with asthma, Journal of the American Medical Association, 290, 14, 1859-1867 (2003)
[13] Jerrett, Michael; Burnett, Richard T.; Ma, Renjun; Pope, C. Arden; Krewski, Daniel; Newbold, K. Bruce; Thurston, George; Shi, Yuanli; Finkelstein, Norm; Calle, Eugenia E.; Thun, Michael J., Spatial Analysis of Air Pollution and Mortality in Los Angeles, Epidemiology, 16, 6, 727-736 (2005)
[14] Lu, S.; Fang, Z.; Zhang, X.; Shaw, S-L; Yin, L.; Zhao, Z.; Yang, X., Understanding the representativeness of mobile phone location data in characterizing human mobility indicators, ISPRS International Journal of Geo-Information, 6, 1, 7 (2017)
[15] Marques-Neto, H. T., Xavier, F. H., Xavier, W. Z., Malab, C. H. S., Ziviani, A., Silveira, L. M., and Almeida, J. M. (2018). Understanding human mobility and workload dynamics due to different large-scale events using mobile phone data. Journal of Network and Systems Management, pages 1-22.
[16] Munir, S.; Chen, H.; Ropkins, K., Modelling the impact of road traffic on ground level ozone concentration using a quantile regression approach, Atmospheric environment, 60, 283-291 (2012)
[17] Neidell, Mj, Air pollution, health, and socio-economic status: the effect of outdoor air quality on childhood asthma, Journal of Health Economics, 23, 6, 1209-1236 (2004)
[18] Nyhan, M., Kloog, I., Britter, R., Ratti, C., and Koutrakis, P. (2018). Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data . Journal of Exposure Science and Environmental Epidemiology Epidemiology.
[19] Özkaynak, H.; Frey, Hc; Burke, J.; Pinder, Rw, Analysis of coupled model uncertainties in source-to-dose modeling of human exposures to ambient air pollution: A pm2. 5 case study, Atmospheric environment, 43, 9, 1641-1649 (2009)
[20] Pedersen, M.; Giorgis-Allemand, L.; Bernard, C.; Aguilera, I.; Andersen, A-Mn; Ballester, F.; Beelen, Rm; Chatzi, L.; Cirach, M.; Danileviciute, A., Ambient air pollution and low birthweight: a European cohort study (ESCAPE), The Lancet Respiratory Medicine, 1, 9, 695-704 (2013)
[21] Peel, Jl; Tolbert, Pe; Klein, M.; Metzger, Kb; Flanders, Wd; Todd, K.; Mulholland, Ja; Ryan, Pb; Frumkin, H., Ambient air pollution and respiratory emergency department visits, Epidemiology, 16, 2, 164-174 (2005)
[22] Plummer, M.; Best, N.; Cowles, K.; Vines, K., CODA: convergence diagnosis and output analysis for MCMC, R News, 6, 1, 7-11 (2006)
[23] Pope, Ca III; Burnett, Rt; Thun, Mj; Calle, Ee; Krewski, D.; Ito, K.; Thurston, Gd, Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution, Journal of the American Medical Association, 287, 9, 1132-1141 (2002)
[24] Pope, Ca III; Hansen, Ml; Long, Rw; Nielsen, Kr; Eatough, Nl; Wilson, We; Eatough, Dj, Ambient particulate air pollution, heart rate variability, and blood markers of inflammation in a panel of elderly subjects, Environmental Health Perspectives, 112, 3, 339-345 (2004)
[25] Rage, E.; Siroux, V.; Künzli, N.; Pin, I.; Kauffmann, F., Air pollution and asthma severity in adults, Occupational and Environmental Medicine, 66, 3, 182-188 (2009)
[26] Sacks, Jd; Rappold, Ag; Davis, Ja Jr; Richardson, Db; Waller, Ae; Luben, Tj, Influence of urbanicity and county characteristics on the association between ozone and asthma emergency department visits in North Carolina, Environmental Health Perspectives, 122, 5, 506-512 (2014)
[27] Thuillier, E.; Moalic, L.; Lamrous, S.; Caminada, A., Clustering weekly patterns of human mobility through mobile phone data, IEEE Transactions on Mobile Computing, 17, 817-830 (2018)
[28] Turner, Mc; Jerrett, M.; Pope, Ca III; Krewski, D.; Gapstur, Sm; Diver, Wr; Beckerman, Bs; Marshall, Jd; Su, J.; Crouse, Dl, Long-term ozone exposure and mortality in a large prospective study, American Journal of Respiratory and Critical Care Medicine, 193, 10, 1134-1142 (2016)
[29] US Census Bureau (2017). 2016 tiger/line shapefiles [internet database] available at. https://www.census.gov/geo/maps-data/data/tiger-line.html, Accessed September 20.
[30] US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, National Climatic Data Center (2017). Quality controlled climatological database [internet database] available at. https://www.ncdc.noaa.gov/cdo-web/webservices/v2, Accessed September 20.
[31] US Environmental Protection Agency (2015). National Ambient Air Quality Standards for Ozone (80 FR 65291). Federal Register, 80(206):65292-65468.
[32] US Environmental Protection Agency (2017). Air quality system data mart [internet database] available at. http://www.epa.gov/ttn/airs/aqsdatamart, Accessed September 20.
[33] Warren, J. L., Son, J.-Y., Pereira, G., Leaderer, B. P., and Bell, M. L. (2017). Investigating the impact of maternal residential mobility on identifying critical windows of susceptibility to ambient air pollution during pregnancy. American journal of epidemiology.
[34] Zanobetti, A.; Schwartz, J., The effect of particulate air pollution on emergency admissions for myocardial infarction: a multicity case-crossover analysis, Environmental Health Perspectives, 113, 8, 978-982 (2005)
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