International Journal of Environmental Monitoring and Analysis

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Development and Calibration of A Particulate Matter Measurement Device with Wireless Sensor Network Function

Received: 23 December 2012    Accepted:     Published: 20 February 2013
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Abstract

A Zigbee-based ubiquitous sensor network (USN) has many industrial applications and provides flexible measuring environments. In particular, the USN system can replace existing measuring devices in harsh environments such as subway stations. To monitor the intensities of various pollutants and air qualities in subway tunnels, this study applied the USN technique. A novel wireless sensor module, PMX, was designed and manufactured to simultaneously detect PM10 and PM2.5. Measurements were conducted at a subway station in Seoul. The PM concentrations using PMX were measured, analyzed, and compared with those obtained using an established commercial dust spectrometer (Grimm Aerosol Technik, 1.109). The measurements were performed from 24 March 2010 to 9 April 2010. PMX and the dust spectrometer measured PM10 levels of 98.3 and 40.7 ㎍/㎥, respectively, and PM2.5 concentrations of 86.5 and 16.6 ㎍/㎥, respectively. The monitored PM levels were investigated in a bimodal form during the sampling period. The PM10 and PM2.5 average correlations between PMX and the dust spectrometer were r2=0.81 and r2= 0.97, respectively. The two systems showed a similar time series trend, even though the measured values differed. A simple correlation analysis of the two data groups showed coefficients of determination of 0.7 for PM10 and 0.9 for PM2.5. The PMX data were mostly concentrated around the trend curve. Therefore, calibration of PMX data was required prior to use in the field. For the calibration, simple linear regression and nonlinear regression were used. The resulting correlation coefficients of simple linear regressions were 0.8 for PM10 and 0.9 for PM2.5, whereas those for nonlinear regressions were 0.7 for PM10 and 0.9 for PM2.5. The higher correlation coefficient for PM10 by the nonlinear regression indicates that it is the better method for calibrating the system developed in this study

DOI 10.11648/j.ijema.20130101.12
Published in International Journal of Environmental Monitoring and Analysis (Volume 1, Issue 1, February 2013)
Page(s) 15-20
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Particulate Matter, Ubiquitous Sensor Network, Subway, Indoor Air Quality

References
[1] Ahn, Y.J., Kwon, W.T., Kim, Y.W., 2004. Estimation of tool life by simple & multiple linear regression analysis of Si3N4 ceramic cutting tools, Transaction of the Korean Society of Machine Tool Engineers, 13(4), 23-29.
[2] Chow, J.C., Watson, J.G., 1998. Guideline on speciated particulate monitoring, Office of Air Quality Planning and Standards U.S. EPA, NC 27711.
[3] Coffey, C.C., Pearce, T.A., 2010. Direct-reading methods for workplace air monitoring, J. of Chemical Health & Safety, 17(3), 10-21.
[4] Dockery, D.W., Pope, C.A., Xu, X.P., Spengler, J.D., Ware, J.H., Fay, M.E., Ferris, B., Speizer, F.E., 1993. An association between air-pollution and mortality in 6 United States cities, New England J. of Medicine, 329, 1753-1759.
[5] Fox, J., 2002. Nonlinear regression and nonlinear least squares, Appendix to an R and S-PLUS companion to applied regression.
[6] Imre S., Weidinger, T., Maenhaut W., 2007. Time-resolved mass concentration, composition and sources of aerosol particles in a metropolitan underground railway station, Atmospheric Environment, 41(37), 8391-8405.
[7] Jung, C.H., Cho, Y.S., Hwang, S.M., Jung, Y.G., Ryu, J.C., Shin, D.S., 2007. Analysis of measurement error for PM10 mass concentration by inter-comparison study, J. KOSAE, 23(6), 689-698.
[8] Kim, S.J., Son, Y.S., Kang, H.S., Kim, J.C., Lee, J.H., Kim, G.S., Kim, I.W., 2009 Proceeding of the 49th Meeting of KOSAE, 613-615.
[9] Kwon, J.W., Kim, J.C., Kim, G.S., 2009. Air quality monitoring system using NDIR-CO2 sensor for underground space based in wireless sensor network, J. of the IEEK, 46(4), 23-38.
[10] Pope, C.A., Bates, D.V., Raizenne, M.E., 1995. Health effects of particulate air pollution: time for reassessment, Environmental Health Perspectives, 103, 472-480.
[11] Seber, G.A.F., 1997. Linear regression analysis, John Wiley & Sons, New York.
Author Information
  • Eco-Transport Research Division, Korea Railroad Research Institute

  • Eco-Transport Research Division, Korea Railroad Research Institute

  • Eco-Transport Research Division, Korea Railroad Research Institute

  • Eco-Transport Research Division, Korea Railroad Research Institute

  • Eco-Transport Research Division, Korea Railroad Research Institute

  • Eco-Transport Research Division, Korea Railroad Research Institute

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    Duckshin Park, Duckshin Park, Soon-Bark Kwon, Soon-Bark Kwon, Youngmin Cho, et al. (2013). Development and Calibration of A Particulate Matter Measurement Device with Wireless Sensor Network Function. International Journal of Environmental Monitoring and Analysis, 1(1), 15-20. https://doi.org/10.11648/j.ijema.20130101.12

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    ACS Style

    Duckshin Park; Duckshin Park; Soon-Bark Kwon; Soon-Bark Kwon; Youngmin Cho, et al. Development and Calibration of A Particulate Matter Measurement Device with Wireless Sensor Network Function. Int. J. Environ. Monit. Anal. 2013, 1(1), 15-20. doi: 10.11648/j.ijema.20130101.12

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    AMA Style

    Duckshin Park, Duckshin Park, Soon-Bark Kwon, Soon-Bark Kwon, Youngmin Cho, et al. Development and Calibration of A Particulate Matter Measurement Device with Wireless Sensor Network Function. Int J Environ Monit Anal. 2013;1(1):15-20. doi: 10.11648/j.ijema.20130101.12

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  • @article{10.11648/j.ijema.20130101.12,
      author = {Duckshin Park and Duckshin Park and Soon-Bark Kwon and Soon-Bark Kwon and Youngmin Cho and Youngmin Cho},
      title = {Development and Calibration of A Particulate Matter Measurement Device with Wireless Sensor Network Function},
      journal = {International Journal of Environmental Monitoring and Analysis},
      volume = {1},
      number = {1},
      pages = {15-20},
      doi = {10.11648/j.ijema.20130101.12},
      url = {https://doi.org/10.11648/j.ijema.20130101.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijema.20130101.12},
      abstract = {A Zigbee-based ubiquitous sensor network (USN) has many industrial applications and provides flexible measuring environments. In particular, the USN system can replace existing measuring devices in harsh environments such as subway stations. To monitor the intensities of various pollutants and air qualities in subway tunnels, this study applied the USN technique. A novel wireless sensor module, PMX, was designed and manufactured to simultaneously detect PM10 and PM2.5. Measurements were conducted at a subway station in Seoul. The PM concentrations using PMX were measured, analyzed, and compared with those obtained using an established commercial dust spectrometer (Grimm Aerosol Technik, 1.109). The measurements were performed from 24 March 2010 to 9 April 2010. PMX and the dust spectrometer measured PM10 levels of 98.3 and 40.7 ㎍/㎥, respectively, and PM2.5 concentrations of 86.5 and 16.6 ㎍/㎥, respectively. The monitored PM levels were investigated in a bimodal form during the sampling period. The PM10 and PM2.5 average correlations between PMX and the dust spectrometer were r2=0.81 and r2= 0.97, respectively. The two systems showed a similar time series trend, even though the measured values differed. A simple correlation analysis of the two data groups showed coefficients of determination of 0.7 for PM10 and 0.9 for PM2.5. The PMX data were mostly concentrated around the trend curve. Therefore, calibration of PMX data was required prior to use in the field. For the calibration, simple linear regression and nonlinear regression were used. The resulting correlation coefficients of simple linear regressions were 0.8 for PM10 and 0.9 for PM2.5, whereas those for nonlinear regressions were 0.7 for PM10 and 0.9 for PM2.5. The higher correlation coefficient for PM10 by the nonlinear regression indicates that it is the better method for calibrating the system developed in this study},
     year = {2013}
    }
    

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  • TY  - JOUR
    T1  - Development and Calibration of A Particulate Matter Measurement Device with Wireless Sensor Network Function
    AU  - Duckshin Park
    AU  - Duckshin Park
    AU  - Soon-Bark Kwon
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    JF  - International Journal of Environmental Monitoring and Analysis
    JO  - International Journal of Environmental Monitoring and Analysis
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ijema.20130101.12
    AB  - A Zigbee-based ubiquitous sensor network (USN) has many industrial applications and provides flexible measuring environments. In particular, the USN system can replace existing measuring devices in harsh environments such as subway stations. To monitor the intensities of various pollutants and air qualities in subway tunnels, this study applied the USN technique. A novel wireless sensor module, PMX, was designed and manufactured to simultaneously detect PM10 and PM2.5. Measurements were conducted at a subway station in Seoul. The PM concentrations using PMX were measured, analyzed, and compared with those obtained using an established commercial dust spectrometer (Grimm Aerosol Technik, 1.109). The measurements were performed from 24 March 2010 to 9 April 2010. PMX and the dust spectrometer measured PM10 levels of 98.3 and 40.7 ㎍/㎥, respectively, and PM2.5 concentrations of 86.5 and 16.6 ㎍/㎥, respectively. The monitored PM levels were investigated in a bimodal form during the sampling period. The PM10 and PM2.5 average correlations between PMX and the dust spectrometer were r2=0.81 and r2= 0.97, respectively. The two systems showed a similar time series trend, even though the measured values differed. A simple correlation analysis of the two data groups showed coefficients of determination of 0.7 for PM10 and 0.9 for PM2.5. The PMX data were mostly concentrated around the trend curve. Therefore, calibration of PMX data was required prior to use in the field. For the calibration, simple linear regression and nonlinear regression were used. The resulting correlation coefficients of simple linear regressions were 0.8 for PM10 and 0.9 for PM2.5, whereas those for nonlinear regressions were 0.7 for PM10 and 0.9 for PM2.5. The higher correlation coefficient for PM10 by the nonlinear regression indicates that it is the better method for calibrating the system developed in this study
    VL  - 1
    IS  - 1
    ER  - 

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