1932

Abstract

The exposome comprises all environmental exposures that a person experiences from conception throughout the life course. Here we review the state of the science for assessing external exposures within the exposome. This article reviews () categories of exposures that can be assessed externally, () the current state of the science in external exposure assessment, () current tools available for external exposure assessment, and () priority research needs. We describe major scientific and technological advances that inform external assessment of the exposome, including geographic information systems; remote sensing; global positioning system and geolocation technologies; portable and personal sensing, including smartphone-based sensors and assessments; and self-reported questionnaire assessments, which increasingly rely on Internet-based platforms. We also discuss priority research needs related to methodological and technological improvement, data analysis and interpretation, data sharing, and other practical considerations, including improved assessment of exposure variability as well as exposure in multiple, critical life stages.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-publhealth-082516-012802
2017-03-20
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/publhealth/38/1/annurev-publhealth-082516-012802.html?itemId=/content/journals/10.1146/annurev-publhealth-082516-012802&mimeType=html&fmt=ahah

Literature Cited

  1. Aceña J, Stampachiacchiere S, Pérez S, Barcelo D. 1.  2015. Advances in liquid chromatography–high-resolution mass spectrometry for quantitative and qualitative environmental analysis. Anal. Bioanal. Chem. 407:216289–99 [Google Scholar]
  2. Almanza E, Jerrett M, Dunton G, Seto E, Pentz MA. 2.  2012. A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data. Health Place 18:146–54 [Google Scholar]
  3. Álvarez-Romero JG, Devlin M, Teixeira da Silva E, Petus C, Ban NC. 3.  et al. 2013. A novel approach to model exposure of coastal-marine ecosystems to riverine flood plumes based on remote sensing techniques. J. Environ. Manag. 119:194–207 [Google Scholar]
  4. Arsand E, Muzny M, Bradway M, Muzik J, Hartvigsen G. 4.  2015. Performance of the first combined smartwatch and smartphone diabetes diary application study. J. Diabetes Sci. Technol. 9:3556–63 [Google Scholar]
  5. Austen K. 5.  2015. Pollution patrol. Nature 517:136–38 [Google Scholar]
  6. Balshaw DM, Kwok RK. 6.  2012. Innovative methods for improving measures of the personal environment. Am. J. Prev. Med. 42:5558–59 [Google Scholar]
  7. Beckerman BS, Jerrett M, Serre M, Martin RV, Lee SJ. 7.  et al. 2013. A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States. Environ. Sci. Technol. 47:137233–41 [Google Scholar]
  8. Beekhuizen J, Vermeulen R, Kromhout H, Burgi A, Huss A. 8.  2013. Geospatial modelling of electromagnetic fields from mobile phone base stations. Sci. Total Environ. 445–446:202–9 [Google Scholar]
  9. Benson AC, Bruce L, Gordon BA. 9.  2015. Reliability and validity of a GPS-enabled iPhone “app” to measure physical activity. J. Sports Sci. 33:141421–28 [Google Scholar]
  10. Bolte JFB, Eikelboom T. 10.  2012. Personal radiofrequency electromagnetic field measurements in the Netherlands: exposure level and variability for everyday activities, times of day and types of area. Environ. Int. 48:133–42 [Google Scholar]
  11. Boyle SA, Kennedy CM, Torres J, Colman K, Pérez-Estigarribia PE, de la Sancha NU. 11.  2014. High-resolution satellite imagery is an important yet underutilized resource in conservation biology. PLOS ONE 9:1e86908 [Google Scholar]
  12. Brook RD, Cakmak S, Turner MC, Brook JR, Crouse DL. 12.  et al. 2013. Long-term fine particulate matter exposure and mortality from diabetes mellitus in Canada. Diabetes Care 36:103313–20 [Google Scholar]
  13. Charreire H, Mackenbach JD, Ouasti M, Lakerveld J, Compernolle S, Ben-Rebah M. 13.  2014. Using remote sensing to define environmental characteristics related to physical activity and dietary behaviours: a systematic review (the SPOTLIGHT project). Health Place 25:1–9 [Google Scholar]
  14. Chen H, Burnett RT, Kwong JC, Villeneuve PJ, Goldberg MS. 14.  et al. 2013. Risk of incident diabetes in relation to long-term exposure to fine particulate matter in Ontario, Canada. Environ. Health Perspect. 121:7804–10 [Google Scholar]
  15. Christine PJ, Auchincloss AH, Bertoni AG, Carnethon MR, Sanchez BN. 15.  et al. 2015. Longitudinal associations between neighborhood physical and social environments and incident type 2 diabetes mellitus: the Multi-Ethnic Study of Atherosclerosis (MESA). JAMA Intern. Med. 175:81311–20 [Google Scholar]
  16. Costello S, Cockburn M, Bronstein J, Zhang X, Ritz B. 16.  2009. Parkinson's disease and residential exposure to maneb and paraquat from agricultural applications in the Central Valley of California. Am. J. Epidemiol. 169:8919–26 [Google Scholar]
  17. Crouse DL, Peters PA, van Donkelaar A, Goldberg MS, Villeneuve PJ. 17.  et al. 2012. Risk of nonaccidental and cardiovascular mortality in relation to long-term exposure to low concentrations of fine particulate matter: a Canadian national-level cohort study. Environ. Health Perspect. 120:5708–14 [Google Scholar]
  18. Cui Y, Balshaw DM, Kwok RK, Thompson CL, Collman GW, Birnbaum LS. 18.  2016. The exposome—embracing the complexity for discovery in environmental health. Environ. Health Perspect. 124:8A137–40 [Google Scholar]
  19. Dadvand P, Ostro B, Figueras F, Foraster M, Basagaña X. 19.  et al. 2014. Residential proximity to major roads and term low birth weight: the roles of air pollution, heat, noise, and road-adjacent trees. Epidemiology 25:4518–25 [Google Scholar]
  20. de Nazelle A, Seto E, Donaire-Gonzalez D, Mendez M, Matamala J. 20.  et al. 2013. Improving estimates of air pollution exposure through ubiquitous sensing technologies. Environ. Pollut. 176:92–99 [Google Scholar]
  21. Dennis KK, Auerbach SS, Balshaw DM, Cui Y, Fallin MD. 21.  et al. 2016. The importance of the biological impact of exposure to the concept of the exposome. Environ. Health Perspect. 124:101504–10 [Google Scholar]
  22. Dennis KK, Marder ME, Balshaw DM, Cui Y, Lynes MA. 22.  et al. 2016. Biomonitoring in the era of the exposome. Environ. Health Perspect. https://doi.org/10.1289/EHP474
  23. Deville Cavellin L, Weichenthal S, Tack R, Ragettli MS, Smargiassi A, Hatzopoulou M. 23.  2016. Investigating the use of portable air pollution sensors to capture the spatial variability of traffic-related air pollution. Environ. Sci. Technol. 50:1313–20 [Google Scholar]
  24. Dewulf B, Neutens T, Lefebvre W, Seynaeve G, Vanpoucke C. 24.  et al. 2016. Dynamic assessment of exposure to air pollution using mobile phone data. Int. J. Health Geogr. 15:14 [Google Scholar]
  25. Doherty ST, Lemieux CJ, Canally C. 25.  2014. Tracking human activity and well-being in natural environments using wearable sensors and experience sampling. Soc. Sci. Med. 106:83–92 [Google Scholar]
  26. Doña C, Chang NB, Caselles V, Sánchez JM, Camacho A. 26.  et al. 2015. Integrated satellite data fusion and mining for monitoring lake water quality status of the Alburera de Valencia in Spain. J. Environ. Manag. 151:416–26 [Google Scholar]
  27. Donaire-Gonzalez D, de Nazelle A, Seto E, Mendez M, Nieuwenhuijsen MJ, Jerrett M. 27.  2013. Comparison of physical activity measures using mobile phone-based calfit and actigraph. J. Med. Internet Res 156e111 [Google Scholar]
  28. Dons E, Götschi T, Nieuwenhuijsen M, de Nazelle A, Anaya E. 28.  et al. 2015. Physical Activity through Sustainable Transport Approaches (PASTA): protocol for a multi-centre, longitudinal study. BMC Public Health 15:11126 [Google Scholar]
  29. Dunton GF, Dzubur E, Kawabata K, Yanez B, Bo B, Intille S. 29.  2014. Development of a smartphone application to measure physical activity using sensor-assisted self-report. Front. Public Health 2:12 [Google Scholar]
  30. Dunton GF, Liao Y, Intille S, Wolch J, Pentz MA. 30.  2011. Physical and social contextual influences on children's leisure-time physical activity: an ecological momentary assessment study. J. Phys. Act. Health 8:Suppl. 1s103–8 [Google Scholar]
  31. Dzubur E, Li M, Kawabata K, Sun Y, McConnell R. 31.  et al. 2015. Design of a smartphone application to monitor stress, asthma symptoms, and asthma inhaler use. Ann. Allergy Asthma Immunol. 114:4341–42 [Google Scholar]
  32. Eskenazi B, Quirós-Alcalá L, Lipsett JM, Wu LD, Kruger P. 32.  et al. 2014. mSpray: a mobile phone technology to improve malaria control efforts and monitor human exposure to malaria control pesticides in Limpopo, South Africa. Environ. Int. 68:219–26 [Google Scholar]
  33. Evenson KR, Wen F, Metzger JS, Herring AH. 33.  2015. Physical activity and sedentary behavior patterns using accelerometry from a national sample of United States adults. Int. J. Behav. Nutr. Phys. Act. 12:20 [Google Scholar]
  34. Fahnrich C, Denecke K, Adeove OO, Benzler J, Claus H. 34.  et al. 2015. Surveillance and Outbreak Response Management System (SORMAS) to support the control of the Ebola virus disease outbreak in West Africa. Euro Surveill 20:12 pii:21071 [Google Scholar]
  35. Geddes JA, Martin RV, Boys BL, van Donkelaar A. 35.  2016. Long-term trends worldwide in ambient NO2 concentrations inferred from satellite observations. Environ. Health Perspect. 124:3281–89 [Google Scholar]
  36. Goedhart G, Kromhout H, Wiart J, Vermeulen R. 36.  2015. Validating self-reported mobile phone use in adults using a newly developed smartphone application. Occup. Environ. Med. 72:11812–18 [Google Scholar]
  37. Goedhart G, Vrijheid M, Wiart J, Hours M, Kromhout H. 37.  et al. 2015. Using software-modified smartphones to validate self-reported mobile phone use in young people: a pilot study. Bioelectromagnetics 36:7538–43 [Google Scholar]
  38. 38. GPS. 2011. What is GPS Updated Sept. 26, GPS, Washington, DC. http://www.gps.gov/systems/gps/
  39. Grimes DJ, Ford TE, Colwell RR, Baker-Austin C, Martinez-Urtaza J. 39.  et al. 2014. Viewing marine bacteria, their activity and response to environmental drivers from orbit: satellite remote sensing of bacteria. Microb. Ecol. 67:3489–500 [Google Scholar]
  40. Grundy A, Tranmer J, Richardson H, Graham CH, Aronson KJ. 40.  2011. The influence of light at night exposure on melatonin levels among Canadian rotating shift nurses. Cancer Epidemiol. Biomark. Prev. 20:112404–12 [Google Scholar]
  41. Guski R. 41.  1999. Personal and social variables as co-determinants of noise annoyance. Noise Health 1:345–56 [Google Scholar]
  42. Herbreteau V, Salem G, Souris M, Hugot JP, Gonzalez JP. 42.  2007. Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration. Health Place 13:2400–3 [Google Scholar]
  43. Hoff RM, Christopher SA. 43.  2009. Remote sensing of particulate pollution from space: Have we reached the promised land?. J. Air Waste Manag. Assoc. 59:6645–75 [Google Scholar]
  44. Hoffmann S, Guihenneuc C, Laroche P, Ancelet S. 44.  2016. Modeling effect modification and exposure uncertainty in the association between lung cancer mortality and radon exposure in a cohort of uranium miners via a Bayesian hierarchical approach. Abstr. 2016 Conf. Int. Soc. Environ. Epidemiol. (ISEE) Abstr. 3332 Research Triangle Park, NC: Environ. Health Perspect http://dx.doi.org/10.1289/ehp.isee2016 [Google Scholar]
  45. Hurley S, Goldberg D, Nelson D, Hertz A, Horn-Ross PL. 45.  et al. 2014. Light at night and breast cancer risk among California teachers. Epidemiology 25:5697–706 [Google Scholar]
  46. Intille SS. 46.  2007. Technological innovations enabling automatic, context-sensitive ecological momentary assessment. The Science of Real-Time Data Capture: Self-Reports in Health Research A Stone, S Shiffman, A Atienza, L Nebeling 308–37 Oxford, UK: Oxford Univ. Press [Google Scholar]
  47. Jarjour S, Jerrett M, Westerdahl D, de Nazelle A, Hanning C. 47.  et al. 2013. Cyclist route choice, traffic-related air pollution, and lung function: a scripted exposure study. Environ. Health 12:14 [Google Scholar]
  48. Jerrett M, Almanza E, Davies M, Wolch J, Dunton G. 48.  et al. 2013. Smart growth community design and physical activity in children. Am. J. Prev. Med. 45:4386–92 [Google Scholar]
  49. Jerrett M, Turner MC, Beckerman B, Pope CA III, van Donkelaar A. 49.  et al. 2016. Comparing the health effects of ambient particulate matter estimated using ground-based versus remote sensing exposure estimates. Environ. Health Perspect. https://doi.org/10.1289/EHP575
  50. Joseph W, Aerts S, Vandenbossche M, Thielens A, Martens L. 50.  2016. Drone based measurement system for radiofrequency exposure assessment. Bioelectromagnetics https://doi.org/10.1002/bem.21964
  51. Juarez PD, Matthews-Juarez P, Hood DB, Im W, Levine RS. 51.  et al. 2014. The public health exposome: a population-based, exposure science approach to health disparities research. Int. J. Environ. Res. Public Health 11:1212866–95 [Google Scholar]
  52. Kawamoto T, Nitta H, Murata K, Toda E, Tsukamoto N. 52.  et al. 2014. Rationale and study design of the Japan Environment and Children's Study (JECS). BMC Public Health 14:25 [Google Scholar]
  53. Lahoz WA, Schneider P. 53.  2014. Data assimilation: making sense of Earth observation. Front. Environ. Sci. 2:16 [Google Scholar]
  54. Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T. 54.  et al. 2010. A survey of mobile phone sensing. IEEE Commun. Mag. 48:9140–50 [Google Scholar]
  55. Lee M, Kloog I, Chudnovsky A, Lyapustin A, Wang Y. 55.  et al. 2016. Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011. J. Expo. Sci. Environ. Epidemiol. 26:4377–84 [Google Scholar]
  56. Lioy PJ, Weisel C. 56.  2014. Exposure Science: Basic Principles and Applications Oxford, UK: Academic/Elsevier
  57. Liu HY, Kobernus M, Broday D, Bartonova A. 57.  2014. A conceptual approach to a citizens' observatory—supporting community-based environmental governance. Environ. Health 14:107 [Google Scholar]
  58. Loveday A, Sherar LB, Sanders JP, Sanderson PW, Esliger DW. 58.  2015. Technologies that assess the location of physical activity and sedentary behavior: a systematic review. J. Med. Internet Res 178e192 [Google Scholar]
  59. Manrai AK, Cui Y, Bushel PR, Hall M, Karakitsios S. 59.  et al. 2017. Informatics and data analytics to support exposome-based discovery for public health. Annu. Rev. Public Health 38:279–94 [Google Scholar]
  60. Maxwell SK, Meliker JR, Goovaerts P. 60.  2010. Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research. J. Expo. Sci. Environ. Epidemiol. 20:2176–85 [Google Scholar]
  61. Mead MI, Popoola OAM, Stewart GB, Landshoff P, Calleja M. 61.  et al. 2013. The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmos. Environ. 70:186–203 [Google Scholar]
  62. Miller GW. 62.  2014. The Exposome: A Primer. Waltham, MA: Academic/Elsevier [Google Scholar]
  63. Mooney SJ, Sheehan DM, Zulaika G, Rundle AG, McGill K. 63.  et al. 2016. Quantifying distance overestimation from Global Positioning System in urban spaces. Am. J. Public Health 106:4651–53 [Google Scholar]
  64. Morello-Frosch R, Varshavsky J, Liboiron M, Brown P, Brody JG. 64.  2015. Communicating results in post-Belmont era biomonitoring studies: lessons from genetics and neuroimaging research. Environ. Res. 136:363–72 [Google Scholar]
  65. Murphy E, King EA. 65.  2016. Smartphone-based noise mapping: integrating sound level meter app data into the strategic noise mapping process. Sci. Total Environ. 562:852–59 [Google Scholar]
  66. Nelson TA, Denouden T, Jestico B, Laberee K, Winters M. 66.  2015. BikeMaps.org: a global tool for collision and near miss mapping. Front. Public Health 3:53 [Google Scholar]
  67. Nieuwenhuijsen MJ. 67.  2015. Introduction to exposure assessment. Exposure Assessment in Environmental Epidemiology M Nieuwenhuijsen 3–22 Oxford, UK: Oxford Univ. Press [Google Scholar]
  68. Nieuwenhuijsen MJ, Donaire-Gonzalez D, Foraster M, Martinez D, Cisneros A. 68.  2014. Using personal sensors to assess the exposome and acute health effects. Int. J. Environ. Res. Public Health 11:87805–19 [Google Scholar]
  69. Nieuwenhuijsen MJ, Donaire-Gonzalez D, Rivas I, de Castro M, Cirach M. 69.  et al. 2015. Variability in and agreement between modeled and personal continuously measured black carbon levels using novel smartphone and sensor technologies. Environ. Sci. Technol. 49:52977–82 [Google Scholar]
  70. Nieuwenhuijsen MJ, Kruize H, Gidlow C, Andrusaityle S, Anto JM. 70.  et al. 2014. Positive health effects of the natural outdoor environment in typical populations in different regions in Europe (PHENOTYPE): a study programme protocol. BMJ Open 4:4e004951 [Google Scholar]
  71. O'Connell SG, Kincl LD, Anderson KA. 71.  2014. Silicone wristbands as personal passive samplers. Environ. Sci. Technol. 48:63327–35 [Google Scholar]
  72. Papantoniou K, Pozo OJ, Espinosa A, Marcos J, Castaño-Vinyals G. 72.  et al. 2014. Circadian variation of melatonin, light exposure, and diurnal preference in day and night shift workers of both sexes. Cancer Epidemiol. Biomarkers Prev. 23:71176–86 [Google Scholar]
  73. Patel CJ, Bhattacharya J, Butte AJ. 73.  2010. An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus. PLOS ONE 5:5e10746 [Google Scholar]
  74. Patel CJ, Manrai AK. 74.  2015. Development of exposome correlation globes to map out environment-wide associations. Pac. Symp. Biocomput. 2015:231–42 [Google Scholar]
  75. Paul KC, Sinsheimer JS, Rhodes SL, Cockburn M, Bronstein J, Ritz B. 75.  2016. Organophosphate pesticide exposures, nitric oxide synthase gene variants, and gene-pesticide interactions in a case-control study of Parkinson's disease, California (USA). Environ. Health Perspect. 124:5570–77 [Google Scholar]
  76. Peters A, Hoek G, Katsouyanni K. 76.  2012. Understanding the link between environmental exposures and health: Does the exposome promise too much?. J. Epidemiol. Community Health 66:2103–5 [Google Scholar]
  77. Rager JE, Strynar MJ, Liang S, McMahen RL, Richard AM. 77.  et al. 2016. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. Environ. Int. 88:269–80 [Google Scholar]
  78. Rajkovich NB, Larsen L. 78.  2016. A bicycle-based field measurement system for the study of thermal exposure in Cuyahoga County, Ohio, USA. Int. J. Environ. Res. Public Health 13:2159 [Google Scholar]
  79. Ramanathan N, Lukac M, Ahmed T, Kar A, Praveen PS. 79.  et al. 2011. A cellphone based system for large-scale monitoring of black carbon. Atmos. Environ. 45:4481–87 [Google Scholar]
  80. Rappaport SM. 80.  2011. Implications of the exposome for exposure science. J. Expo. Sci. Environ. Epidemiol. 21:15–9 [Google Scholar]
  81. Robinson O, Basagana X, Agier L, De Castro M, Hernandez-Ferrer C. 81.  et al. 2015. The pregnancy exposome: multiple environmental exposures in the INMA-Sabadell birth cohort. Environ. Sci. Technol. 49:1710632–41 [Google Scholar]
  82. Robinson O, Vrijheid M. 82.  2015. The pregnancy exposome. Curr. Environ. Health Rep. 2:2204–13 [Google Scholar]
  83. Schootman M, Nelson EJ, Werner K, Shacham E, Elliott M. 83.  et al. 2016. Emerging technologies to measure neighborhood conditions in public health: implications for interventions and next steps. Int. J. Health Geogr. 15:120 [Google Scholar]
  84. Schymanski EL, Singer HP, Slobodnik J, Ipolyi IM, Oswald P. 84.  et al. 2015. Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis. Anal. Bioanal. Chem. 407:216237–55 [Google Scholar]
  85. Seltenrich N. 85.  2014. Remote-sensing applications for environmental health research. Environ. Health Perspect. 122:10A268–75 [Google Scholar]
  86. Shim E, Kim D, Woo H, Cho Y. 86.  2016. Designing a sustainable noise mapping system based on citizen scientists smartphone sensor data. PLOS ONE 11:9e0161835 [Google Scholar]
  87. Shoval N, Isaacson M. 87.  2006. Application of tracking technologies to the study of pedestrian spatial behavior. Prof. Geogr. 58:172–83 [Google Scholar]
  88. Silva de Lima AL, Hahn T, de Vries NM, Cohen E, Bataille L. 88.  et al. 2016. Large-scale wearable sensor deployment in Parkinson's patients: the Parkinson@Home study protocol. JMIR Res. Protoc. 5:3e172 [Google Scholar]
  89. Smolders R, de Boever P. 89.  2014. Perspectives for environment and health research in Horizon 2020: Dark Ages or Golden Era?. Int. J. Hyg. Environ. Health 217:8891–96 [Google Scholar]
  90. Smolders R, Den Hond E, Koppen G, Govarts E, Willems H. 90.  et al. 2015. Interpreting biomarker data from the COPHES/DEMOCOPHES twin projects: using external exposure data to understand biomarker differences among countries. Environ. Res. 141:86–95 [Google Scholar]
  91. Snik F, Rietjens JHH, Apituley A, Volten H, Mijling B. 91.  et al. 2014. Mapping atmospheric aerosols with a citizen science network of smartphone spectropolarimeters. Geophys. Res. Lett. 41:7351–58 [Google Scholar]
  92. Snyder EG, Watkins TH, Solomon PA, Thoma ED, Williams RW. 92.  et al. 2013. The changing paradigm of air pollution monitoring. Environ. Sci. Technol. 47:2011369–77 [Google Scholar]
  93. Steinle S, Reis S, Sabel CE. 93.  2013. Quantifying human exposure to air pollution—moving from static monitoring to spatio-temporally resolved personal exposure assessment. Sci. Total Environ. 443:184–93 [Google Scholar]
  94. Stingone JA, Buck Louis GM, Nakayama SF, Vermeulen RCH, Kwok RK. 94.  et al. 2017. Toward greater implementation of the exposome research paradigm within environmental epidemiology. Annu. Rev. Public Health 38:315–27 [Google Scholar]
  95. Su JG, Apte JS, Lipsitt J, Garcia-Gonzales DA, Beckerman BS. 95.  et al. 2015. Populations potentially exposed to traffic-related air pollution in seven world cities. Environ. Int. 78:82–89 [Google Scholar]
  96. Sudlow C, Gallacher J, Allen N, Beral V, Burton P. 96.  et al. 2015. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med 12:3e1001779 [Google Scholar]
  97. Teeguarden JG, Tan YM, Edwards SW, Leonard JA, Anderson KA. 97.  et al. 2016. Completing the link between exposure science and toxicology for improved environmental health decision making: the aggregate exposure pathway framework. Environ. Sci. Technol. 50:4579–86 [Google Scholar]
  98. Thomas DG, Gaheen S, Harper SL, Fritts M, Klaessig F. 98.  et al. 2013. ISA-TAB-Nano: a specification for sharing nanomaterial research data in spreadsheet-based format. BMC Biotechnol 13:2 [Google Scholar]
  99. van Donkelaar A, Martin RV, Brauer M, Boys BL. 99.  2015. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ. Health Perspect. 123:2135–43 [Google Scholar]
  100. van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R. 100.  et al. 2010. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Perspect. 118:6847–55 [Google Scholar]
  101. van Donkelaar A, Martin RV, Spurr RJ, Burnett RT. 101.  2015. High-resolution satellite-derived PM2.5 from optimal estimation and geographically weighted regression over North America. Environ. Sci. Technol. 49:1710482–91 [Google Scholar]
  102. van Tongeren M, Cherrie JW. 102.  2012. An integrated approach to the exposome. Environ. Health Perspect. 120:3A103–4 [Google Scholar]
  103. Vineis P, Chadeau-Hyam M, Gmuender H, Gulliver J, Herceg Z. 103.  et al. 2016. The exposome in practice: design of the EXPOsOMICS project. Int. J. Hyg. Environ. Health https://doi.org/10.1016/j.ijheh.2016.08.001
  104. Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M. 104.  et al. 2014. The Human Early-Life Exposome (HELIX): project rationale and design. Environ. Health Perspect. 122:6535–44 [Google Scholar]
  105. Weichenthal S, Villeneuve PJ, Burnett RT, van Donkelaar A, Martin RV. 105.  et al. 2014. Long-term exposure to fine particulate matter: association with nonaccidental and cardiovascular mortality in the agricultural health study cohort. Environ. Health Perspect. 122:6609–15 [Google Scholar]
  106. Weis BK, Balshaw D, Barr JR, Brown D, Ellisman M. 106.  et al. 2005. Personalized exposure assessment: promising approaches for human environmental health research. Environ. Health Perspect. 113:7840–48 [Google Scholar]
  107. Wild CP. 107.  2005. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol. Biomarkers Prev. 14:81847–50 [Google Scholar]
  108. Wild CP. 108.  2012. The exposome: from concept to utility. Int. J. Epidemiol. 41:124–32 [Google Scholar]
  109. Wishart D, Arndt D, Pon A, Sajed T, Guo AC. 109.  et al. 2015. T3DB: the toxic exposome database. Nucleic Acids Res 43:D1D928–34 [Google Scholar]
  110. Zidek JV, Wong H, Le ND, Burnett R. 110.  1996. Causality, measurement error and multicollinearity in epidemiology. Environmetrics 7:441–51 [Google Scholar]
/content/journals/10.1146/annurev-publhealth-082516-012802
Loading
/content/journals/10.1146/annurev-publhealth-082516-012802
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error