Advertisement

Towards an integrated framework for air quality monitoring and exposure estimation—a review

  • Savina Singla
  • Divya Bansal
  • Archan Misra
  • Gaurav Raheja
Article
  • 107 Downloads

Abstract

For the health and safety of the public, it is essential to measure spatiotemporal distribution of air pollution in a region and thus monitor air quality in a fine-grain manner. While most of the sensing-based commercial applications available until today have been using fixed environmental sensors, the use of personal devices such as smartphones, smartwatches, and other wearable devices has not been explored in depth. These kinds of devices have an advantage of being with the user continuously, thus providing an ability to generate accurate and well-distributed spatiotemporal air pollution data. In this paper, we review the studies (especially in the last decade) done by various researchers using different kinds of environmental sensors highlighting related techniques and issues. We also present important studies of measuring impact and emission of air pollution on human beings and also discuss models using which air pollution inhalation can be associated to humans by quantifying personal exposure with the use of human activity detection. The overarching aim of this review is to provide novel and key ideas that have the potential to drive pervasive and individual centric and yet accurate pollution monitoring techniques which can scale up to the future needs.

Keywords

Crowdsensing Activity recognition Sensing Air pollution maps Smart environments Ubiquitous sensing Mobile sensing Air pollution monitoring Exposure estimation 

Notes

Funding information

This work has been undertaken as a part of the project “Cityprobe” supported by IMPRINT India Initiative.

References

  1. Aberer, K., Sathe, S., Chakraborty, D., Martinoli, A., Barrenetxea, G., Faltings, B., Thiele, L. (2010). Opensense: open community driven sensing of environment. In Proceedings of the ACM SIGSPATIAL international workshop on GeoStreaming (pp. 39–42). ACM.Google Scholar
  2. Air Quality Egg. (2016). Airqualityegg. http://airqualityegg.com/. Accessed 22 Dec 2016.
  3. Airsensortoolbox. (2017). Epa air sensor toolbox. https://www.epa.gov/air-sensor-toolbox. Accessed 23 Sept 2017.
  4. Al-Ali, A., Zualkernan, I., Aloul, F. (2010). A mobile gprs-sensors array for air pollution monitoring. IEEE Sensors Journal, 10(10), 1666–1671.CrossRefGoogle Scholar
  5. Alphasense. (2016). Alphasense. http://www.alphasense.com/. Accessed 16 May 2017.
  6. Ama, M., & et al. (2016). Activity recognition on smartphones: efficient sampling rates and window sizes. In 2016 IEEE international conference on pervasive computing and communication workshops (PerCom Workshops) (pp. 1–6). IEEE.Google Scholar
  7. Amft, O., Junker, H., Troster, G. (2005). Detection of eating and drinking arm gestures using inertial body-worn sensors. In Ninth IEEE international symposium on wearable computers (ISWC’05) (pp. 160–163). IEEE.Google Scholar
  8. Amft, O., & Tröster, G. (2008). Recognition of dietary activity events using on-body sensors. Artificial Intelligence in Medicine, 42(2), 121–136.CrossRefGoogle Scholar
  9. Amft, O., & Tröster, G. (2009). On-body sensing solutions for automatic dietary monitoring. IEEE Pervasive Computing, 8(2), 62–70.CrossRefGoogle Scholar
  10. Ando, M., Katagiri, K., Tamura, K., Yamamoto, S., Matsumoto, M., Li, Y., Cao, S., Ji, R., Liang, C. (1996). Indoor and outdoor air pollution in tokyo and Beijing supercities. Atmospheric Environment, 30(5), 695–702.CrossRefGoogle Scholar
  11. Araki, S., Yamamoto, K., Kondo, A. (2015). Application of regression kriging to air pollutant concentrations in Japan with high spatial resolution. Aerosol and Air Quality Research, 15(1), 234–241.CrossRefGoogle Scholar
  12. Aram, S., Troiano, A., Pasero, E. (2012). Environment sensing using smartphone. In S2012 IEEE sensors applications symposium (SAS) (pp. 1–4). IEEE.Google Scholar
  13. Bayat, A., Pomplun, M., Tran, D.A. (2014). A study on human activity recognition using accelerometer data from smartphones. Procedia Computer Science, 34, 450–457.CrossRefGoogle Scholar
  14. Bernstein, J.A., Alexis, N., Barnes, C., Bernstein, I.L., 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.CrossRefGoogle Scholar
  15. Bisio, I., Lavagetto, F., Marchese, M., Sciarrone, A. (2014). Comparison of situation awareness algorithms for remote health monitoring with smartphones. In 2014 IEEE global communications conference (pp. 2454–2459). IEEE.Google Scholar
  16. Brauer, M., Freedman, G., Frostad, J., Van Donkelaar, A., Martin, R.V., Dentener, F., Dingenen, R.V., Estep, K., Amini, H., Apte, J.S., et al. (2015). Ambient air pollution exposure estimation for the global burden of disease 2013. Environmental Science & Technology, 50(1), 79–88.CrossRefGoogle Scholar
  17. Broderick, B., Byrne, M., McNabola, A., Gill, L., Pilla, F., McGrath, J., McCreddin, A. (2015). Palm: a personal activity-location model of exposure to air pollution environmental protection agency. Ireland: Wexford.Google Scholar
  18. Brunekreef, B., & Holgate, S.T. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242.CrossRefGoogle Scholar
  19. Copert. (2016). Copert. http://emisia.com/products/copert. Accessed 22 Dec 2016.
  20. Costa, J., Fazendeiro, P., Ferreira, F. (2016). A mobile application to improve the quality of life via exercise. In 2016 IEEE 12th international conference on intelligent computer communication and processing (ICCP) (pp. 55–62). IEEE.Google Scholar
  21. Dawson, S.V., & Schenker, M.B. (1979). Health effects of inhalation of ambient concentrations of nitrogen dioxide1. American Review of Respiratory Disease, 120(2), 281– 292.Google Scholar
  22. De Nazelle, A., Fruin, S., Westerdahl, D., Martinez, D., Ripoll, A., Kubesch, N., Nieuwenhuijsen, M. (2012). A travel mode comparison of commuters’ exposures to air pollutants in Barcelona. Atmospheric Environment, 59, 151–159.CrossRefGoogle Scholar
  23. Devarakonda, S., Sevusu, P., Liu, H., Liu, R., Iftode, L., Nath, B. (2013). Real-time air quality monitoring through mobile sensing in metropolitan areas. In Proceedings of the 2nd ACM SIGKDD international workshop on urban computing (p. 15). ACM.Google Scholar
  24. Dewulf, B., Neutens, T., Van Dyck, D., De Bourdeaudhuij, I., Panis, L.I., Beckx, C., Van de Weghe, N. (2016). Dynamic assessment of inhaled air pollution using gps and accelerometer data. Journal of Transport & Health, 3(1), 114–123.CrossRefGoogle Scholar
  25. Doraiswamy, P., Davis, W.T., Miller, T.L., Fu, J.S., Lam, Y.-F. (2005). Measuring air pollution inside and outside of diesel truck cabs. Prepared for the US Environmental Protection Agency by Department of Civil and Environmental Engineering, University of Tennessee.Google Scholar
  26. EPA Standards. (2017). Epa standards. https://www.epa.gov/criteria-air-pollutants/naaqs-table. Accessed 26 July 2017.
  27. EPASensors. (2017). Epasensors. https://www3.epa.gov/ttn/amtic/inorg.html. Accessed 23 Nov 2017.
  28. Europe Standard. (2017). Europe standard. http://ec.europa.eu/environment/air/quality/standards.htm. Accessed 26 July 2017.
  29. Faulkner, M., Olson, M., Chandy, R., Krause, J., Chandy, K.M., Krause, A. (2011). The next big one: detecting earthquakes and other rare events from community-based sensors. In 2011 10th international conference on information processing in sensor networks (IPSN) (pp. 13–24). IEEE.Google Scholar
  30. Folinsbee, L.J. (1993). Human health effects of air pollution. Environmental Health Perspectives, 100, 45.CrossRefGoogle Scholar
  31. Frank, N. (1964). Studies on the effects of acute exposure to sulphur dioxide in human subjects. Proceedings of the Royal Society of Medicine, pp. 1029–1033.Google Scholar
  32. GEOS-chem. (2017). Geos. http://acmg.seas.harvard.edu/geos/. Accessed 25 July 2017.
  33. Greenwald, R., Hayat, M.J., Barton, J., Lopukhin, A. (2016). A novel method for quantifying the inhaled dose of air pollutants based on heart rate, breathing rate and forced vital capacity. PloS One, 11(1), e0147578.CrossRefGoogle Scholar
  34. Hasenfratz, D., Saukh, O., Sturzenegger, S., Thiele, L. (2012). Participatory air pollution monitoring using smartphones. In Mobile Sensing. 2nd International Workshop on Mobile Sensing.Google Scholar
  35. Heagle, A.S., Body, D.E., Heck, W.W. (1973). An open-top field chamber to assess the impact of air pollution on plants 1. Journal of Environmental Quality, 2(3), 365–368.CrossRefGoogle Scholar
  36. Hojaiji, H., Kalantarian, H., Bui, A.A., King, C.E., Sarrafzadeh, M. (2017). Temperature and humidity calibration of a low-cost wireless dust sensor for real-time monitoring. In 2017 IEEE sensors applications symposium (SAS), (pp. 1–6). IEEE.Google Scholar
  37. Honicky, R., Brewer, E.A., Paulos, E., White, R. (2008). N-smarts: networked suite of mobile atmospheric real-time sensors. In Proceedings of the second ACM SIGCOMM workshop on networked systems for developing regions (pp. 25–30). ACM.Google Scholar
  38. Hu, K., Davison, T., Rahman, A., Sivaraman, V. (2014a). Air pollution exposure estimation and finding association with human activity using wearable sensor network. In Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis (p. 48). ACM.Google Scholar
  39. Hu, K., Wang, Y., Rahman, A., Sivaraman, V. (2014b). Personalising pollution exposure estimates using wearable activity sensors. In 2014 IEEE ninth international conference on intelligent sensors, sensor networks and information processing (ISSNIP), (pp. 1–6). IEEE.Google Scholar
  40. India Standard. (2017). India standard. http://www.arthapedia.in/index.php. Ambient air Quality Standards in India. Accessed 26 July 2017.
  41. Jafari, H., Li, X., Qian, L., Chen, Y. (2015). Community based sensing: a test bed for environment air quality monitoring using smartphone paired sensors. In 2015 36th IEEE Sarnoff symposium (pp. 12–17). IEEE.Google Scholar
  42. Jain, V., Goel, M., Maity, M., Naik, V., Ramjee, R. (2018). Scalable measurement of air pollution using cots iot devices. In 2018 10th international conference on communication systems & networks (COMSNETS) (pp. 553–556). IEEE.Google Scholar
  43. Jha, D.K., Sabesan, M., Das, A., Vinithkumar, N., Kirubagaran, R. (2011). Evaluation of interpolation technique for air quality parameters in port blair, India. Universal Journal of Environmental Research and Technology, 1(3), 301–310.Google Scholar
  44. Junker, H., Amft, O., Lukowicz, P., Tröster, G. (2008). Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition, 41(6), 2010–2024.CrossRefGoogle Scholar
  45. Kamionka, M., Breuil, P., Pijolat, C. (2006). Calibration of a multivariate gas sensing device for atmospheric pollution measurement. Sensors and Actuators B: Chemical, 118(1), 323–327.CrossRefGoogle Scholar
  46. Kampa, M., & Castanas, E. (2008). Human health effects of air pollution. Environmental Pollution, 151(2), 362–367.CrossRefGoogle Scholar
  47. Kaur, A., Bansal, D., Singla, S. (2017a). A review on estimating the effects of inhaling airborne pollutants and air quality monitoring. In 2017 8th international conference on computing, communication and networking technologies (ICCCNT) (pp. 1–7). IEEE.Google Scholar
  48. Kaur, A., Singla, S., Bansal, D. (2017b). Quantifying personal exposure to spatio-temporally distributed air pollutants using mobile sensors. In Proceedings of the first ACM workshop on mobile crowdsensing systems and applications (pp. 1–6). ACM.Google Scholar
  49. Khot, R., & Chitre, V. (2017). Survey on air pollution monitoring systems. In 2017 international conference on innovations in information, embedded and communication systems (ICIIECS) (pp. 1–4). IEEE.Google Scholar
  50. Kim, S.-Y., Yi, S.-J., Eum, Y.S., Choi, H.-J., Shin, H., Ryou, H.G., Kim, H. (2014). Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities. Environmental Health and Toxicology, 29, e2014012.CrossRefGoogle Scholar
  51. Klumpp, A., Klumpp, G., Domingos, M. (1994). Plants as bioindicators of air pollution at the serra do mar near the industrial complex of Cubatão, Brazil. Environmental Pollution, 85(1), 109–116.CrossRefGoogle Scholar
  52. Kwapisz, J.R., Weiss, G.M., Moore, S.A. (2011). Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter, 12(2), 74–82.CrossRefGoogle Scholar
  53. Larkin, A., & Hystad, P. (2017). Towards personal exposures: how technology is changing air pollution and health research. Current Environmental Health Reports, 4(4), 463–471.CrossRefGoogle Scholar
  54. Lee, S., & Chang, M. (2000). Indoor and outdoor air quality investigation at schools in Hong Kong. Chemosphere, 41(1), 109–113.CrossRefGoogle Scholar
  55. Li, J.J., Faltings, B., Saukh, O., Hasenfratz, D., Beutel, J. (2012). Sensing the air we breathe-the opensense Zurich dataset. In Proceedings of the national conference on artificial intelligence, (Vol. 1 pp. 323–325).Google Scholar
  56. Liu, X., Song, Z., Ngai, E., Ma, J., Wang, W. (2015). Pm2.5 monitoring using images from smartphones in participatory sensing. In 2015 IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 630–635). IEEE.Google Scholar
  57. Manning, W., & Godzik, B. (2004). Bioindicator plants for ambient ozone in Central and Eastern Europe. Environmental Pollution, 130(1), 33–39.CrossRefGoogle Scholar
  58. Matte, T.D., Ross, Z., Kheirbek, I., Eisl, H., Johnson, S., Gorczynski, J.E., Kass, D., Markowitz, S., Pezeshki, G., Clougherty, J.E. (2013). Monitoring intraurban spatial patterns of multiple combustion air pollutants in New York City: design and implementation. Journal of Exposure Science and Environmental Epidemiology, 23(3), 223–231.CrossRefGoogle Scholar
  59. Mattmann, C., Amft, O., Harms, H., Troster, G., Clemens, F. (2007). Recognizing upper body postures using textile strain sensors. In 2007 11th IEEE international symposium on wearable computers (pp. 29–36). IEEE.Google Scholar
  60. McDonnell, W.F., Horstman, D.H., Hazucha, M., Seal, E. Jr, Haak, E., Salaam, S., House, D. (1983). Pulmonary effects of ozone exposure during exercise: dose-response characteristics. Journal of Applied Physiology, 54(5), 1345–1352.CrossRefGoogle Scholar
  61. Moltchanov, S., Levy, I., Etzion, Y., Lerner, U., Broday, D.M., Fishbain, B. (2015). On the feasibility of measuring urban air pollution by wireless distributed sensor networks. Science of the Total Environment, 502, 537–547.CrossRefGoogle Scholar
  62. Mordukhovich, I., Beyea, J., Herring, A.H., Hatch, M., Stellman, S.D., Teitelbaum, S.L., Richardson, D.B., Millikan, R.C., Engel, L.S., Shantakumar, S., et al. (2016). Vehicular traffic-related polycyclic aromatic hydrocarbon exposure and breast cancer incidence: the long island breast cancer study project (libcsp). Environmental Health Perspectives, 124(1), 30.Google Scholar
  63. Moves. (2016). Moves. https://www.epa.gov/moves. Accessed 22 Dec 2016.
  64. Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M., Howard, E., West, R., Boda, P. (2009). Peir, the personal environmental impact report, as a platform for participatory sensing systems research. In Proceedings of the 7th international conference on mobile systems, applications, and services (pp. 55–68). ACM.Google Scholar
  65. Opensense II. (2017). Opensense ii. https://pdfs.semanticscholar.org/8df0/a038b0cddf68e0274db19f7b78476754db5e.pdf. Accessed 9 June 2017.
  66. Ott, W.R. (1982). Concepts of human exposure to air pollution. Environment International, 7(3), 179–196.CrossRefGoogle Scholar
  67. Ouidir, M., Giorgis-Allemand, L., Lyon-Caen, S., Morelli, X., Cracowski, C., Pontet, S., Pin, I., Lepeule, J., Siroux, V., Slama, R. (2015). Estimation of exposure to atmospheric pollutants during pregnancy integrating space-time activity and indoor air levels: does it make a difference? Environment International, 84, 161–173.CrossRefGoogle Scholar
  68. Panis, L.I., De Geus, B., Vandenbulcke, G., Willems, H., Degraeuwe, B., Bleux, N., Mishra, V., Thomas, I., Meeusen, R. (2010). Exposure to particulate matter in traffic: a comparison of cyclists and car passengers. Atmospheric Environment, 44(19), 2263–2270.CrossRefGoogle Scholar
  69. Predić, B., Yan, Z., Eberle, J., Stojanovic, D., Aberer, K. (2013). Exposuresense: integrating daily activities with air quality using mobile participatory sensing. In 2013 IEEE international conference on pervasive computing and communications workshops (PERCOM workshops) (pp. 303–305). IEEE.Google Scholar
  70. Radhakrishnan, M., Sen, S., Vigneshwaran, S., Misra, A., Balan, R. (2016). Iot+ small data: transforming in-store shopping analytics & services. In 2016 8th international conference on communication systems and networks (COMSNETS) (pp. 1–6). IEEE.Google Scholar
  71. Rakha, H., Ahn, K., Trani, A. (2003). Comparison of mobile5a, mobile6, vt-micro, and cmem models for estimating hot-stabilized light-duty gasoline vehicle emissions. Canadian Journal of Civil Engineering, 30(6), 1010–1021.CrossRefGoogle Scholar
  72. Rana, R.K., Chou, C.T., Kanhere, S.S., Bulusu, N., Hu, W. (2010). Ear-phone: an end-to-end participatory urban noise mapping system. In Proceedings of the 9th ACM/IEEE international conference on information processing in sensor networks (pp. 105–116). ACM.Google Scholar
  73. Ravi, N., Dandekar, N., Mysore, P., Littman, M.L. (2005). Activity recognition from accelerometer data. In AAAI, (Vol. 5 pp. 1541–1546).Google Scholar
  74. Rodriguez, J.H., Pignata, M.L., Fangmeier, A., Klumpp, A. (2010). Accumulation of polycyclic aromatic hydrocarbons and trace elements in the bioindicator plants tillandsia capillaris and lolium multiflorum exposed at pm10 monitoring stations in stuttgart (Germany). Chemosphere, 80(3), 208–215.CrossRefGoogle Scholar
  75. Santamouris, M. (2013). Energy and climate in the urban built environment. Evanston: Routledge.CrossRefGoogle Scholar
  76. Seaton, A., Godden, D., MacNee, W., Donaldson, K. (1995). Particulate air pollution and acute health effects. The Lancet, 345(8943), 176–178.CrossRefGoogle Scholar
  77. Sen, S., Rachuri, K.K., Mukherji, A., Misra, A. (2016). Did you take a break today? detecting playing foosball using your smartwatch. In 2016 IEEE international conference on pervasive computing and communication workshops (PerCom Workshops) (pp. 1–6). IEEE.Google Scholar
  78. Sen, S., Subbaraju, V., Misra, A., Balan, R.K., Lee, Y. (2015). The case for smartwatch-based diet monitoring. In 2015 IEEE international conference on pervasive computing and communication workshops (PerCom workshops) (pp. 585–590). IEEE.Google Scholar
  79. SensorsNews. (2017). Sensorsnews. https://actu.epfl.ch/news/air-quality-sensors-take-a-ride-on-city-buses/. Accessed 9 June 2017.
  80. Shankari, K., Yin, M., Culler, D., Katz, R. (2015). E-mission: automated transportation emission calculation using smartphones. In 2015 IEEE international conference on pervasive computing and communication workshops (PerCom workshops) (pp. 268–271). IEEE.Google Scholar
  81. Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J. (2015). A survey of online activity recognition using mobile phones. Sensors, 15(1), 2059–2085.CrossRefGoogle Scholar
  82. Singla, S. (2018). Air quality friendly route recommendation system. In Proceedings of the 2018 workshop on MobiSys 2018 Ph. D. forum (pp. 9–10). ACM.Google Scholar
  83. Singla, S., Bansal, D., Misra, A. (2016). Poster: air quality friendly route recommendation system. In Proceedings of the 14th annual international conference on mobile systems, applications, and services companion (pp. 79–79). ACM.Google Scholar
  84. Singla, S., & Misra, A. (2016). Indoor location error-detection via crowdsourced multi-dimensional mobile data. In Proceedings of the first workshop on mobile data (pp. 19–24). ACM.Google Scholar
  85. Sirsikar, S.V, Priya Karemore, A.V.D., Kamble, P.A. (2015). Design and implementation of geographically pollution monitoring system. International Journal on Recent and Innovation Trends in Computing and Communication, 3(2), 4984–4989.Google Scholar
  86. Snyder, E.G., Watkins, T.H., Solomon, P.A., Thoma, E.D., Williams, R.W., Hagler, G.S., Shelow, D., Hindin, D.A., Kilaru, V.J., Preuss, P.W. (2013). The changing paradigm of air pollution monitoring. Environmental Science & Technology, 47(20), 11369–11377.CrossRefGoogle Scholar
  87. Solomon, G.M., Campbell, T.R., Feuer, G.R., Masters, J., Samkian, A., Paul, K.A. (2001). No breathing in the aisles: diesel exhaust inside school buses, ERIC. https://files.eric.ed.gov/fulltext/ED450878.pdf.
  88. SPEERS, O.M., & UTELL, M.J. (1991). Effects of nitrogen dioxide exposure on pulmonary function and airway reactivity in normal humans. American Review of Respiratory Disease, 143, 522–527.CrossRefGoogle Scholar
  89. Spinelle, L., Gerboles, M., Kok, G., Persijn, S., Sauerwald, T. (2017). Review of portable and low-cost sensors for the ambient air monitoring of benzene and other volatile organic compounds. Sensors, 17(7), 1520.CrossRefGoogle Scholar
  90. Spirn, A., & Whiston, A. (1986). Air quality at street-level strategies for urban design. Boston Redevelopment Authority, 80. https://archive.org/details/airqualityatstre00bost.
  91. Stewart, R.D. (1975). The effect of carbon monoxide on humans. Annual Review of Pharmacology, 15(1), 409–423.CrossRefGoogle Scholar
  92. Talampas, M.C.R., & Low, K.-S. (2012). Maximum likelihood estimation of ground truth for air quality monitoring using vehicular sensor networks. In TENCON 2012-2012 IEEE region 10 conference (pp. 1–6). IEEE.Google Scholar
  93. Tecer, L.H., Alagha, O., Karaca, F., Tuncel, G., Eldes, N. (2008). Particulate matter (pm2. 5, pm10-2.5, and pm10) and children’s hospital admissions for asthma and respiratory diseases: a bidirectional case-crossover study. Journal of Toxicology and Environmental Health, Part A, 71(8), 512–520.CrossRefGoogle Scholar
  94. Thomas, M.D. (1961). Effects of air pollution on plants. Air Pollution, 239, 233–278.Google Scholar
  95. Treshow, M. (1984). Air pollution and plant life. United States: N. p., https://www.osti.gov/biblio/6013660.
  96. Tsujita, W., Ishida, H., Moriizumi, T. (2004). Dynamic gas sensor network for air pollution monitoring and its auto-calibration. In Sensors, 2004. Proceedings of IEEE (pp. 56–59). IEEE.Google Scholar
  97. Ustev, Y.E., Durmaz Incel, O., Ersoy, C. (2013). User, device and orientation independent human activity recognition on mobile phones: challenges and a proposal. In Proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication (pp. 1427–1436). ACM.Google Scholar
  98. Valli, G., Internullo, M., Ferrazza, A.M., Onorati, P., Cogo, A., Palange, P. (2013). Minute ventilation and heart rate relationship for estimation of the ventilatory compensation point at high altitude: a pilot study. Extreme Physiology & Medicine, 2(1), 1.CrossRefGoogle Scholar
  99. Variable inc. (2018). Variable inc. http://shop.variableinc.com/collections/sensor-modules-1. Accessed 18 Dec 2016.
  100. Vigneshwaran, S., Sen, S., Misra, A., Chakraborti, S., Balan, R.K. (2015). Using infrastructure-provided context filters for efficient fine-grained activity sensing. In 2015 IEEE international conference on pervasive computing and communications (PerCom) (pp. 87–94). IEEE.Google Scholar
  101. Wallace, L.A., Mitchell, H., T OConnor, G., Neas, L., Lippmann, M., Kattan, M., Koenig, J., Stout, J.W., Vaughn, B.J., Wallace, D., et al. (2003). Particle concentrations in inner-city homes of children with asthma: the effect of smoking, cooking, and outdoor pollution. Environmental Health Perspectives, 111(9), 1265.CrossRefGoogle Scholar
  102. Wang, D., Tan, A.-H., Zhang, D. (2015). Non-intrusive robust human activity recognition for diverse age groups. In 2015 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT), Vol. 2 pp. 368–375). IEEE.Google Scholar
  103. Wen, T.-H., Jiang, J.-A., Sun, C.-H., Juang, J.-Y., Lin, T.-S. (2013). Monitoring street-level spatial-temporal variations of carbon monoxide in urban settings using a wireless sensor network (wsn) framework. International Journal of Environmental Research and Public Health, 10(12), 6380–6396.CrossRefGoogle Scholar
  104. WHO Standard. (2017). Who standard. http://apps.who.int/iris/bitstream/10665/69477/1/WHO_SDE_PHE_OEH_06.02_eng.pdf. Accessed 26 July 2017.
  105. Wiese, J., Saponas, T.S., Brush, A. (2013). Phoneprioception: enabling mobile phones to infer where they are kept. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 2157–2166). ACM.Google Scholar
  106. Wilhelm, E., Siby, S., Zhou, Y., Ashok, X.J.S., Jayasuriya, M., Foong, S., Kee, J., Wood, K.L., Tippenhauer, N.O. (2016). Wearable environmental sensors and infrastructure for mobile large-scale urban deployment. IEEE Sensors Journal, 16(22), 8111–8123.CrossRefGoogle Scholar
  107. Yi, W.Y., Lo, K.M., Mak, T., Leung, K.S., Leung, Y., Meng, M.L. (2015). A survey of wireless sensor network based air pollution monitoring systems. Sensors, 15(12), 31392–31427.CrossRefGoogle Scholar
  108. Young, P., Pilcher, J., Patel, M., Cameron, L., Braithwaite, I., Weatherall, M., Beasley, R. (2013). Delivery of titrated oxygen via a self-inflating resuscitation bag. Resuscitation, 84(3), 391–394.CrossRefGoogle Scholar
  109. Zhang, L., Ou, M., Fu, X., Yan, X. (2016). Using smartphones to estimate vehicle emission under urban traffic levels-of-service. In 2016 12th world congress on intelligent control and automation (WCICA) (pp. 1758–1763). IEEE.Google Scholar
  110. Zhang, L., Wu, X., Luo, D. (2015). Real-time activity recognition on smartphones using deep neural networks. In 2015 IEEE 12th international conference on ubiquitous intelligence and computing and 2015 IEEE 12th international conference on autonomic and trusted computing and 2015 IEEE 15th international conference on scalable computing and communications and its associated workshops (UIC-ATC-ScalCom) (pp. 1236–1242). IEEE.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Punjab Engineering CollegeChandigarhIndia
  2. 2.Singapore Management UniversitySingaporeSingapore
  3. 3.Indian Institute of TechnologyRoorkeeIndia

Personalised recommendations