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Environmental Particulate Matter (PM) Exposure Assessment of Construction Activities Using Low-Cost PM Sensor and Latin Hypercubic Technique

Author

Listed:
  • Muhammad Khan

    (School of Architecture and Building Science, Chung Ang University, Seoul 06974, Korea)

  • Numan Khan

    (School of Architecture and Building Science, Chung Ang University, Seoul 06974, Korea)

  • Miroslaw J. Skibniewski

    (Department of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD 20742, USA
    Department of Engineering, 8000 Aarhus, Denmark
    Institute for Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland
    Department of Civil and Environmental Engineering & Architecture, 85-796 Bydgoszcz, Poland)

  • Chansik Park

    (School of Architecture and Building Science, Chung Ang University, Seoul 06974, Korea)

Abstract

Dust generation is generally considered a natural process in construction sites; ergo, workers are exposed to health issues due to fine dust exposure during construction work. The primary activities in the execution of construction work, such as indoor concrete and mortar mixing, are investigated to interrogate and understand the critical high particulate matter concentrations and thus health threats. Two low-cost dust sensors (Sharp GP2Y1014AU0F and Alphasense OPC N2) without implementing control measures to explicitly evaluate, compare and gauge them for these construction activities were utilized. The mean exposures to PM 10 , PM 2.5 and PM 1 during both activities were 3522.62, 236.46 and 47.62 µg/m 3 and 6762.72, 471.30 and 59.09 µg/m 3 , respectively. The results show that PM 10 and PM 2.5 caused during the concrete mixing activity was approximately double compared to the mortar. The Latin Hypercube Sampling method is used to analyze the measurement results and to predict the exposure concentrations. The high dust emission and exposure from mixing activities fail to meet the World Health Organization and Health and Safety Commission standards for environmental exposure. These findings will leverage the integration of low-cost dust sensors with Building Information Modelling (BIM) to formulate a digital twin for automated dust control techniques in the construction site.

Suggested Citation

  • Muhammad Khan & Numan Khan & Miroslaw J. Skibniewski & Chansik Park, 2021. "Environmental Particulate Matter (PM) Exposure Assessment of Construction Activities Using Low-Cost PM Sensor and Latin Hypercubic Technique," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7797-:d:593120
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    References listed on IDEAS

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    2. He Zhang & Ravi Srinivasan & Vikram Ganesan, 2021. "Low Cost, Multi-Pollutant Sensing System Using Raspberry Pi for Indoor Air Quality Monitoring," Sustainability, MDPI, vol. 13(1), pages 1-15, January.
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