[ 1 ] Wilson EK. Wearable sweat sensors. Engineering 2019;5:359–60. link1

[ 2 ] Loh M, Sarigiannis D, Gotti A, Karakitsios S, Pronk A, Kuijpers E, et al. How sensors might help define the external exposome. Int J Environ Res Public Health 2017;14(4):E434. link1

[ 3 ] Donaire-Gonzalez D, Valentín A, van Nunen E, Curto A, Rodriguez A, Fernandez-Nieto M, et al. ExpoApp: an integrated system to assess multiple personal environmental exposures. Environ Int 2019;126:494–503. link1

[ 4 ] Perchoux C, Chaix B, Cummins S, Kestens Y. Conceptualization and measurement of environmental exposure in epidemiology: accounting for activity space related to daily mobility. Health Place 2013;21:86–93. link1

[ 5 ] Wild CP. The exposome: from concept to utility. Int J Epidemiol 2012;41:24–32. link1

[ 6 ] Chaix B. Mobile sensing in environmental health and neighborhood research. Annu Rev Public Health 2018;39:367–84. link1

[ 7 ] Openshaw S. A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modeling. Trans Inst Br Geogr 1977;2 (4):459–72. link1

[ 8 ] Parenteau MP, Sawada MC. The modifiable areal unit problem (MAUP) in the relationship between exposure to NO2 and respiratory health. Int J Health Geogr 2011;10(1):58. link1

[ 9 ] Morgenstern V, Zutavern A, Cyrys J, Brockow I, Koletzko S, Krämer U, et al. Atopic diseases, allergic sensitization, and exposure to traffic-related air pollution in children. Am J Respir Crit Care Med 2008;177(12):1331–7. link1

[10] Höfler M. The effect of misclassification on the estimation of association: a review. Int J Methods Psychiatr Res 2005;14(2):92–101. link1

[11] Park YM, Kwan MP. Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored. Health Place 2017;43:85–94. link1

[12] Kumar P, Morawska L, Martani C, Biskos G, Neophytou M, Di Sabatino S, et al. The rise of low-cost sensing for managing air pollution in cities. Environ Int 2015;75:199–205. link1

[13] Donaire-Gonzalez D, Curto A, Valentín A, Andrusaityte S, Basagaña X, Casas M, et al. Personal assessment of the external exposome during pregnancy and childhood in Europe. Environ Res 2019;174:95–104. link1

[14] Cornet VP, Holden RJ. Systematic review of smartphone-based passive sensing for health and wellbeing. J Biomed Inform 2018;77:120–32. link1

[15] Huck JJ, Whyatt JD, Coulton P, Davison B, Gradinar A. Combining physiological, environmental and locational sensors for citizen-oriented health applications. Environ Monit Assess 2017;189(3):114. link1

[16] Schlink U, Kindler A, Großmann K, Schwarz N, Franck U. The temperature recorded by simulated mobile receptors is an indicator for the thermal exposure of the urban inhabitants. Ecol Ind 2014;36:607–16. link1

[17] Finn S, O’Fallon L. The emergence of environmental health literacy—from its roots to its future potential. Environ Health Perspect 2017;125(4):495–501. link1

[18] Yeboah G, Alvanides S. Route choice analysis of urban cycling behaviors using OpenStreetMap: evidence from a British urban environment. In: Jokar Arsanjani J, Zipf A, Mooney P, Helbich M, editors. OpenStreetMap in GIScience. Cham: Springer; 2015. p. 189–210. link1

[19] Brody JG, Dunagan SC, Morello-Frosch R, Brown P, Patton S, Rudel RA. Reporting individual results for biomonitoring and environmental exposures: lessons learned from environmental communication case studies. Environ Health 2014;13(1):40. link1

[20] Deville Cavellin L, Weichenthal S, Tack R, Ragettli MS, Smargiassi A, Hatzopoulou M. Investigating the use of portable air pollution sensors to capture the spatial variability of traffic-related air pollution. Environ Sci Technol 2016;50(1):313–20. link1

[21] Kardous CA, Shaw PB. Evaluation of smartphone sound measurement applications (apps) using external microphones—a follow-up study. J Acoust Soc Am 2016;140(4):EL327–33. link1

[22] Kardous CA, Shaw PB. Evaluation of smartphone sound measurement applications. J Acoust Soc Am 2014;135(4):EL186–92. link1

[23] Hägerstrand T. Reflections on what about people in regional science. Pap Reg Sci Assoc 1989;66(1):1–6. link1

[24] Richardson DB, Volkow ND, Kwan MP, Kaplan RM, Goodchild MF, Croyle RT. Spatial turn in health research. Science 2013;339(6126):1390–2. link1

[25] Mazaheri M, Clifford S, Yeganeh B, Viana M, Rizza V, Flament R, et al. Investigations into factors affecting personal exposure to particles in urban microenvironments using low-cost sensors. Environ Int 2018;120:496–504. link1

[26] Jeong H, Park D. Characteristics of elementary school children’s daily exposure to black carbon (BC) in Korea. Atmos Environ 2017;154:179–88. link1

[27] Ryan PH, Son SY, Wolfe C, Lockey J, Brokamp C, LeMasters G. A field application of a personal sensor for ultrafine particle exposure in children. Sci Total Environ 2015;508:366–73. link1

[28] Gall ET, Chen A, Chang VWC, Nazaroff WW. Exposure to particulate matter and ozone of outdoor origin in Singapore. Build Environ 2015;93:3–13. link1

[29] Adams C, Riggs P, Volckens J. Development of a method for personal, spatiotemporal exposure assessment. J Environ Monit 2009;11(7):1331–9. link1

[30] Labrador MA, Lara Yejas OD. Human activity recognition: using wearable sensors and smartphones. Boca Raton: CRC Press; 2014. link1

[31] Rabinovitch N, Adams CD, Strand M, Koehler K, Volckens J. Withinmicroenvironment exposure to particulate matter and health effects in children with asthma: a pilot study utilizing real-time personal monitoring with GPS interface. Environ Health 2016;15(1):96. link1

[32] Lee M, Brauer M, Wong P, Tang R, Tsui TH, Choi C, et al. Land use regression modelling of air pollution in high density high rise cities: a case study in Hong Kong. Sci Total Environ 2017;592:306–15. link1

[33] Van den Bossche J, De Baets B, Verwaeren J, Botteldooren D, Theunis J. Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment. Environ Modell Softw 2018;99:58–69. link1

[34] Oke TR. Initial guidance to obtain representative meteorological observations at urban sites. Report. Geneva: World Meteorological Organization; 2006. Report No.: WMO/TD-No. 1250. link1

[35] Liu MK, Avrin J, Pollack RI, Behar JV, McElroy JL. Methodology for designing air quality monitoring networks: I. theoretical aspects. Environ Monit Assess 1986;6(1):1–11. link1

[36] Al-Adwani S, Elkamel A, Duever TA, Yetilmezsoy K, Abdul-Wahab SA. A surrogate-based optimization methodology for the optimal design of an air quality monitoring network: surrogate-based optimization monitoring network. Can J Chem Eng. 2015;93(7):1176–87. link1

[37] Mofarrah A, Husain T. A holistic approach for optimal design of air quality monitoring network expansion in an urban area. Atmos Environ 2010;44 (3):432–40. link1

[38] Pope R, Wu J. Characterizing air pollution patterns on multiple time scales in urban areas: a landscape ecological approach. Urban Ecosyst 2014;17 (3):855–74. link1

[39] ISO-21748: Guidance for the use of repeatability, reproducibility and trueness estimates in measurement uncertainty evaluation. ISO standard. Geneva: International Organization for Standardization; 2017.

[40] Ueberham M, Schlink U. Wearable sensors for multifactorial personal exposure measurements—a ranking study. Environ Int 2018;121(Pt 1):130–8. link1

[41] Willmott CJ, Ackleson SG, Davis RE, Feddema JJ, Klink KM, Legates DR, et al. Statistics for the evaluation and comparison of models. J Geophys Res 1985;90 (C5):8995–9005. link1

[42] Willmott CJ. Some comments on the evaluation of model performance. Bull Am Meteorol Soc 1982;63(11):1309–13. link1

[43] Office of Environmental Information. Guidance for preparing standard operating procedures (SOPs). Washington, DC: US Environmental Protection Agency; 2007.

[44] Reis S, Seto E, Northcross A, Quinn NW, Convertino M, Jones RL, et al. Integrating modelling and smart sensors for environmental and human health. Environ Model Softw 2015;74:238–46. link1

[45] Ueberham M, Schmidt F, Schlink U. Advanced smartphone-based sensing with open-source task automation. Sensors 2018;18(8):E2456. link1

[46] Atzori L, Iera A, Morabito G. The internet of things: a survey. Comput Netw 2010;54(15):2787–805. link1

[47] Tomasini M, Mahmood B, Zambonelli F, Brayner A, Menezes R. On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors. Pervasive Mob Comput 2017;38:215–32. link1

[48] Meier F, Fenner D, Grassmann T, Otto M, Scherer D. Crowdsourcing air temperature from citizen weather stations for urban climate research. Urban Clim 2017;19:170–91. link1

[49] Yu HL, Christakos G. Science-based spatiotemporal statistics: practical guide with environmental and human exposure applications. J Therm Biol 2016;17 (3):149–53. link1

[50] Evans J, Jones P. The walking interview: methodology, mobility and place. Appl Geogr 2011;31(2):849–58. link1

[51] Tamayo-Uria I, Maitre L, Thomsen C, Nieuwenhuijsen MJ, Chatzi L, Siroux V, et al. The early-life exposome: description and patterns in six European countries. Environ Int 2019;123:189–200. link1

[52] Tonne C, Basagaña X, Chaix B, Huynen M, Hystad P, Nawrot TS, et al. New frontiers for environmental epidemiology in a changing world. Environ Int 2017;104:155–62. link1

[53] Nikolopoulou M, Kleissl J, Linden PF, Lykoudis S. Pedestrians’ perception of environmental stimuli through field surveys: focus on particulate pollution. Sci Total Environ 2011;409(13):2493–502. link1