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Towards a Smart City: Development and Application of an Improved Integrated Environmental Monitoring System

Author

Listed:
  • Man Sing Wong

    (Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong, China)

  • Tingneng Wang

    (Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong, China)

  • Hung Chak Ho

    (Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong, China)

  • Coco Y. T. Kwok

    (Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong, China)

  • Keru Lu

    (Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong, China)

  • Sawaid Abbas

    (Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong, China)

Abstract

Environmental deprivation is an issue influencing the urban wellbeing of a city. However, there are limitations to spatiotemporally monitoring the environmental deprivation. Thus, recent studies have introduced the concept of “Smart City” with the use of advanced technology for real-time environmental monitoring. In this regard, this study presents an improved Integrated Environmental Monitoring System (IIEMS) with the consideration on nine environmental parameters: temperature, relative humidity, PM 2.5 , PM 10 , CO, SO 2 , volatile organic compounds (VOCs), UV index, and noise. This system was comprised of a mobile unit and a server-based platform with nine highly accurate micro-sensors in-coupling into the mobile unit for estimating these environmental exposures. A calibration test using existing monitoring station data was conducted in order to evaluate the systematic errors. Two applications with the use of the new system were also conducted under different scenarios: pre- and post-typhoon days and in areas with higher and lower vegetation coverage. Linear regressions were applied to predict the changes in environmental quality after a typhoon and to estimate the difference in environmental exposures between urban roads and green spaces. The results show that environmental exposures interact with each other, while some exposures are also controlled by location. PM 2.5 had the highest change after a typhoon with an estimated 8.0 μg/m³ decrease that was controlled by other environmental factors and geographical location. Sound level and temperature were significantly higher on urban roads than in urban parks. This study demonstrates the potential to use IIEMS for environmental quality measurements under the greater framework of a Smart City and for sustainability research.

Suggested Citation

  • Man Sing Wong & Tingneng Wang & Hung Chak Ho & Coco Y. T. Kwok & Keru Lu & Sawaid Abbas, 2018. "Towards a Smart City: Development and Application of an Improved Integrated Environmental Monitoring System," Sustainability, MDPI, vol. 10(3), pages 1-16, February.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:623-:d:133816
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    References listed on IDEAS

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    1. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1352-1362, August.
    2. Mahmoud AL-HADER & Ahmad RODZI, 2009. "The Smart City Infrastructure Development & Monitoring," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 4(2(11)), pages 87-94, May.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 524-524, March.
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