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Monitoring Air Quality and Estimation of Personal Exposure to Particulate Matter Using an Indoor Model and Artificial Neural Network

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  • Hyeon-Ju Oh

    (Department of Public Health Sciences, Korea University, Seoul 02841, Korea
    Department of Materials Science and Engineering, Kumoh National Institute of Technology, 61 Daehak-ro (yangho-dong), Gumi 39177, Korea
    Department of Environmental Sciences, Rutgers, The State University of New Jersey, 14 College Farm Road, New Brunswick, NJ 08901, USA)

  • Jongbok Kim

    (Department of Materials Science and Engineering, Kumoh National Institute of Technology, 61 Daehak-ro (yangho-dong), Gumi 39177, Korea
    Andlinger Center for Energy and Environment, Princeton University, Princeton, NJ 08544, USA)

Abstract

Exposure to particulate materials (PM) is known to cause respiratory and cardiovascular diseases. Respirable particles generated in closed spaces, such as underground parking garages (UPGs), have been reported to be a potential threat to respiratory health. This study reports the concentration of pollutants (PM, TVOC, CO) in UPGs under various operating conditions of heating, ventilation and air-conditioning (HVAC) systems using a real-time monitoring system with a prototype made up of integrated sensors. In addition, prediction of the PM concentration was implemented using modeling from vehicle traffic volumes and an artificial neural network (ANN), based on environmental factors. The predicted PM concentrations were compared with the level acquired from the real-time monitoring. The measured PM 10 concentrations of UPGs were higher than the modeled PM 10 due to short-term sources induced by vehicles. The average inhalable and respirable dosage for adult was calculated for the evaluation of health effects. The ANN predicted PM concentration showed a close correlation with measurements resulting in R 2 ranging from 0.69 to 0.87. This study demonstrates the feasibility of the use of the air quality monitoring system for personal-exposure to vehicle-induced pollutant in UPGs and the potential application of modeling and ANN for the evaluation of the indoor air quality.

Suggested Citation

  • Hyeon-Ju Oh & Jongbok Kim, 2020. "Monitoring Air Quality and Estimation of Personal Exposure to Particulate Matter Using an Indoor Model and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3794-:d:354886
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    References listed on IDEAS

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    1. Liang Yu & Ning Kang & Weikuan Wang & Huiyu Guo & Jia Ji, 2020. "Study on the Influence of Air Tightness of the Building Envelope on Indoor Particle Concentration," Sustainability, MDPI, vol. 12(5), pages 1-20, February.
    2. Jae-joon Chung & Hyun-Jung Kim, 2020. "An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    3. Yslene Kachba & Daiane Maria de Genaro Chiroli & Jônatas T. Belotti & Thiago Antonini Alves & Yara de Souza Tadano & Hugo Siqueira, 2020. "Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America," Sustainability, MDPI, vol. 12(7), pages 1-15, March.
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    Cited by:

    1. Paulo S. G. de Mattos Neto & Manoel H. N. Marinho & Hugo Siqueira & Yara de Souza Tadano & Vivian Machado & Thiago Antonini Alves & João Fausto L. de Oliveira & Francisco Madeiro, 2020. "A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition," Sustainability, MDPI, vol. 12(18), pages 1-33, September.
    2. Armando Pelliccioni & Paolo Monti & Giorgio Cattani & Fabio Boccuni & Marco Cacciani & Silvia Canepari & Pasquale Capone & Maria Catrambone & Mariacarmela Cusano & Maria Concetta D’Ovidio & Antonella , 2020. "Integrated Evaluation of Indoor Particulate Exposure: The VIEPI Project," Sustainability, MDPI, vol. 12(22), pages 1-25, November.

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