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Introduction of IoT-Based Surrogate Parameters in the Ex-Post Countermeasure of Industrial Sectors in Integrated Permit Policy

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  • Jae Hong Park

    (Environmental Resources Research Department, National Institute of Environmental Research (NIER), Gyeongseo-dong, Seo-gu, Incheon 22689, Korea)

  • Phil Goo Kang

    (Environmental Resources Research Department, National Institute of Environmental Research (NIER), Gyeongseo-dong, Seo-gu, Incheon 22689, Korea)

  • Eunseok Kim

    (Environmental Resources Research Department, National Institute of Environmental Research (NIER), Gyeongseo-dong, Seo-gu, Incheon 22689, Korea)

  • Tae Woo Kim

    (Environmental Resources Research Department, National Institute of Environmental Research (NIER), Gyeongseo-dong, Seo-gu, Incheon 22689, Korea)

  • Gahee Kim

    (Environmental Resources Research Department, National Institute of Environmental Research (NIER), Gyeongseo-dong, Seo-gu, Incheon 22689, Korea)

  • Heejeong Seok

    (Environmental Resources Research Department, National Institute of Environmental Research (NIER), Gyeongseo-dong, Seo-gu, Incheon 22689, Korea)

  • Jinwon Seo

    (Environmental Resources Research Department, National Institute of Environmental Research (NIER), Gyeongseo-dong, Seo-gu, Incheon 22689, Korea)

Abstract

New approaches and techniques are required for environmental management in workplaces that emit pollutants. The best available techniques (BATs) that are suggested by the integrated environmental permit system (IEPS) play a pivotal role in providing state-of-the-art approaches and techniques to workplaces. For the workplaces that install facilities with the BATs, it is necessary to perform real-time monitoring to determine whether pollutant emission facilities and treatment facilities are operating normally and whether such facilities can maintain the optimal condition during the emission of pollutants. The environmental management measures used before the IEPS had limitations for workplaces that are subjected to integrated environmental management because they consider the entire workplace as a single pollution source by combining all media. In addition, only the emission concentrations and amounts of pollutants were monitored because only the points at which most pollutants are discharged, i.e., stacks for air pollutant emission facilities and final effluent outlets for water pollutant emission facilities, are monitored. These monitoring approaches in a variety of facilities involve considerable costs, and most of them (95% for stacks in workplaces under IEPS, Korea) cannot perform real-time observation even though some of them are capable of it (5% for stacks managed by Tele Monitoring System, TMS). In this study, the applicability of a wireless sensor monitoring network was examined as a new approach for environmental management in workplaces. Based on the sensor monitoring network, surrogate parameters that can indirectly monitor the critical operating factors of pollutant emission and prevention facilities were developed and then measured in real time to examine the status of these facilities. Surrogate parameters can be used instead of directly measuring pollutants as the former has high efficiency, such as cost reduction, and can provide reliable data on the emission characteristics of pollutants. The wireless network is based on an Internet of Things (IoT) platform under real-time conditions in the emission and production facilities in a workplace. This study suggested that a wireless network using the IoT platform improves the BAT assessment methodology to understand the efficiency of environmental pollution reduction and the economic contribution to the workplace.

Suggested Citation

  • Jae Hong Park & Phil Goo Kang & Eunseok Kim & Tae Woo Kim & Gahee Kim & Heejeong Seok & Jinwon Seo, 2021. "Introduction of IoT-Based Surrogate Parameters in the Ex-Post Countermeasure of Industrial Sectors in Integrated Permit Policy," Sustainability, MDPI, vol. 13(23), pages 1-22, December.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13466-:d:695811
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    References listed on IDEAS

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    4. Ayaz Hussain & Umar Draz & Tariq Ali & Saman Tariq & Muhammad Irfan & Adam Glowacz & Jose Alfonso Antonino Daviu & Sana Yasin & Saifur Rahman, 2020. "Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach," Energies, MDPI, vol. 13(15), pages 1-22, August.
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