Development of an IOT-Based Atmospheric Fine Dust Monitoring System

  • N. Kavitha
  • P. MadhumathyEmail author
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 266)


The major perilous type of air pollution is due to particulate matter since it is the primary leading factor in affecting the human health as well as it is having major impact even on the earth’s typical weather condition and precipitation levels. The proposed methodology yields in developing a system which is user friendly, low cost module used for measuring the fine dust particle, amount of CO (carbon monoxide), in addition to that temperature and humidity is also been estimated for weather forecasting. The information about the pollutant from the environment is collected through fine dust sensor, humidity sensor and temperature sensor. The collected statistics about the environment is been forwarded to the Node MCU. ESP8266 is been used to operate Node MCU. The cost-effective Wi-Fi microchip ESP8266 is used to processes the data. The IoT server collects the processed data. IoT server is used to fetch data whenever there is a request from mobile application for the data. This proposed system after measuring the dust particle it analyses the dust levels in real world scenario in addition it tests and collects the pattern change of dust at different location. The analysed data is given to the users in the form off instant alerts to the subscribers. Preventive measures can be taken in prior based on the instant alert of the analysed data given to the subscribers. This preventive measures yields in providing better environment and leading a healthy life.


Node MCU (ESP8266) Temperature sensor Fine dust sensor Gas sensor Humidity sensor 


  1. 1.
    Kaivonen, S., Ngai, E.: Real-time air pollution monitoring with sensors on city bus. Digit. Commun. Netw. (2019) (Science Direct)Google Scholar
  2. 2.
    Binsy, M.S., Sampath, N.: Self configurable air pollution monitoring system using IoT and data mining techniques. In: ICICI: International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI), pp. 786–798 (2018)Google Scholar
  3. 3.
    Zhalgasbekova, A., Zaslavsky, A., Saguna, S., Mitra, K., Jayaraman, P.P.: Opportunistic data collection for IoT-based indoor air quality monitoring. In: Internet of Things, Smart Spaces, and Next Generation Networks and Systems. Springer (2017)Google Scholar
  4. 4.
    Park, T.J.: LPWA IoT network technology trends. Electron. Telecommun. Trends ETRI 32(1), 46–53 (2017)Google Scholar
  5. 5.
    Eric Wang, Y.-P., Lin, X., Adhikary, A.: A primer on 3GPP narrowband internet of things. IEEE Commun. Mag. 55(3), 117–123 (2017)CrossRefGoogle Scholar
  6. 6.
    Amann, M.: Health Risks of Ozone from Long-Range Transboundary Air Pollution. Copenhagen WHO Regional Office Europe (2008)Google Scholar
  7. 7.
    Lai, X., Yang, T., Wang, Z., Chen, P.: IoT Implementation of Kalman filter to improve accuracy of air quality monitoring and prediction. Appl. Sci. 9(9), 1831 (2019). Scholar
  8. 8.
    Brar, S.K., Verma, M.: Measurement of nanoparticles by light-scattering techniques. TrAC Trends Anal. Chem. 30, 4–17 (2011)CrossRefGoogle Scholar
  9. 9.
    Oh, J.S., Park, S.H., Kwak, M.K., HaePyo, C., et al.: Ambient particulate matter and emergency department visit for chronic obstructive pulmonary disease. J. Korean Soc. Emerg. Med. 28(1), 32–39 (2017)Google Scholar
  10. 10.
    Lu, Z., Young, Y.: On-line size measurement of fine dust through digital imaging. In: 2015 IEEE 3rd international conference on smart instrumentation, measurement and applications (ICSIMA) (2015)Google Scholar
  11. 11.
    Kang, D., Kim, J.-E.: Fine, ultrafine, and yellow dust: emerging health problem in Korea. J. Korean Med. Sci. 29(5), 621–622 (2014)CrossRefGoogle Scholar
  12. 12.
    Li, J.F., Xu, L.H., Cai, X.S.: Smoke dust emission monitoring system of more integrated measurement methods. China Powder Sci. Technol. 15, 24–27 (2009)Google Scholar
  13. 13.
    Compact Optical Dust Sensor. GP2Y1010AU0F, Sharp [cited Dec 2006].
  14. 14.
    Korea has the worst environment among OECD countries, The Dong-Ailbo. Accessed 20 Oct 2016
  15. 15.
    Air Quality Standards and Air Pollution Level. Ministry of Environment [cited 2013]. Accessed 20 Oct 2016
  16. 16.
    Kim, S.H.: Development of an IoT-based atmospheric environment monitoring system. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC) (2017)Google Scholar
  17. 17.
    Ghafghazi, S., Sowlati, T., Sokhansanj, S., Bi, X., Melin, S.: PM2.5 in China measurements sources visibility and health effects and mitigation. Renew. Sustain. Energy Rev. 15, 3019–3028 (2011)CrossRefGoogle Scholar
  18. 18.
    Carter, R.M., Yan, Y.: An instrumentation system using combined sensing strategies for online mass flow rate measurement and particle sizing, vol. 54, pp. 1433–1437 (2005)Google Scholar
  19. 19.
    Zhang, J.Q., Yan, Y.: On-line continuous measurement of particle size using electrostatic sensors. In: Proceedings of the I2MTC International Instrumentation and Measurement Technology Conference, Vail, CO, USA, pp. 877–880 (2003)Google Scholar
  20. 20.
    Zhang, H., Cai, X.S.: The measurement of size velocity and flow angle of coarse water with image method. Therm. Turbin 37, 26–29 (2008)Google Scholar
  21. 21.
    Wang, L., Zhang, L., Yan, Y.: Imaging-based size measurement of fine particles from industrial stacks. In: 2014 12th International Conference on Signal Processing (ICSP), pp. 19–23 (2014)Google Scholar
  22. 22.
    Zhang, J.Q., Yan, Y.: On-line continuous measurement of particle size using electrostatic sensors. In: Proceedings of the 20th IEEE Instrumentation and Measurement Technology Conference, pp. 164–168, 20–22 May 2003Google Scholar
  23. 23.
    Mazumder, M.K., Ware, R.E., Yokoyama, T., Rubin, B.J., Kamp, D.: Measurement of particle size and electrostatic charge distributions on toners using E-SPART analyzer. IEEE Trans. Ind. Appl. 27, 611–618 (1991)CrossRefGoogle Scholar
  24. 24.
    Carter, R.M., Yan, Y.: On-line particle sizing of pulverized and granular fuels using digital imaging techniques. Meas. Sci. Technol. 14, 1099–1109 (2003)CrossRefGoogle Scholar
  25. 25.
    Carter, R.M., Yan, Y.: The effect of illumination wavelength on the measurement of size distribution of very small particles using a novel imaging based system. Part. Part. Syst. Charact. 25, 298–305 (2008)CrossRefGoogle Scholar
  26. 26.
    Yan, Y.: Recent advances in imaging based instrumentation for combustion plant optimization. In: Proceedings of 2010 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 148–151 (2010)Google Scholar
  27. 27.
    Holoubek, J.: Some applications of light scattering in materials science. J. Quant. Spectrosc. Radiat. Transf. 106, 104–121 (2007)CrossRefGoogle Scholar
  28. 28.
    Carter, R.M., Yan, Y.: A novel imaging system for concurrent measurement of particle velocity and size distribution in a pneumatic suspension. In: Proceedings of Instrumentation and Measurement Technology Conference, pp. 2050–2054, 12–15 May 2008Google Scholar
  29. 29.
    Yamamoto, N., Tomita, K., Sugita, K.: Measurement of xenon plasma properties in an ion thruster using laser Thomson scattering technique. Rev. Sci. Instrum. (2012)Google Scholar
  30. 30.
    Carter, R.M., Yan, Y.: An instrumentation system using combined sensing strategies for online mass flow rate measurement and particle sizing. IEEE Trans. Instrum. Meas. 54, 1433–1437 (2005)CrossRefGoogle Scholar
  31. 31.
    Schaller, A., Mueller, K.: Motorola’s experience in designing the internet of things. Int. J. Ambient Comput. Intell. (IJACI) 1(1), 75–85 (2009)CrossRefGoogle Scholar
  32. 32.
    Carter, R.M., Yan, Y., Cameron, S.D.: On-line measurement of particle size distribution and mass flow rate of particles in a pneumatic suspension using combined imaging and electrostatic sensors, Flow Meas. Instrum. 16, 309–314 (2005)CrossRefGoogle Scholar
  33. 33.
    Muller, C.L., Chapman, L., Johnston, S., Kidd, C., Illingworth, S., Foody, G., Overeem, A., Leigh, R.R.: Crowd sourcing for climate and atmospheric sciences: current status and future potential. Int. J. Climatol. 35, 3185–3203 (2015)CrossRefGoogle Scholar
  34. 34.
    Dey, N., Wagh, S., Pathan, M.S.: Applied Machine Learning for Smart Data Analysis. CRC Press, Boca Raton (2019). Scholar
  35. 35.
    Durst, F., Melling, A., Whitelaw, J.H.: Principles and Practice of Laser-Doppler Anemometry, 2nd edn. Academic Press, London (1981)Google Scholar
  36. 36.
    Gao, L., Yan, Y., Lu, G., Carter, R.M.: On-line measurement of particle size and shape distributions of pneumatically conveyed particles through multi-wavelength based digital imaging. Flow Meas. Instrum. 27, 20–28 (2012)CrossRefGoogle Scholar
  37. 37.
    Black, D.L., McQuay, M.Q., Bonin, M.P.: Laser-based techniques for particle-size measurement: a review of sizing methods and their industrial applications. Prog. Energy Combust. Sci. 22, 267–306 (1996)CrossRefGoogle Scholar
  38. 38.
    Bhatt, C., Dey, N., Ashour, A.S.: Internet of Things and Big Data Technologies for Next Generation Healthcare (2017)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Electronics and CommunicationDayananda Sagar Academy of Technology and ManagementBengaluruIndia

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