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Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning

Author

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  • Zhijian Liu

    (Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China
    These authors contributed equally.)

  • Hao Li

    (Department of Chemistry, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA
    Institute for Computational and Engineering Sciences, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA
    These authors contributed equally.)

  • Guoqing Cao

    (Institute of Building Environment and Energy, China Academy of Building Research, Beijing 100013, China)

Abstract

Indoor airborne culturable bacteria are sometimes harmful to human health. Therefore, a quick estimation of their concentration is particularly necessary. However, measuring the indoor microorganism concentration (e.g., bacteria) usually requires a large amount of time, economic cost, and manpower. In this paper, we aim to provide a quick solution: using knowledge-based machine learning to provide quick estimation of the concentration of indoor airborne culturable bacteria only with the inputs of several measurable indoor environmental indicators, including: indoor particulate matter (PM 2.5 and PM 10 ), temperature, relative humidity, and CO 2 concentration. Our results show that a general regression neural network (GRNN) model can sufficiently provide a quick and decent estimation based on the model training and testing using an experimental database with 249 data groups.

Suggested Citation

  • Zhijian Liu & Hao Li & Guoqing Cao, 2017. "Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning," IJERPH, MDPI, vol. 14(8), pages 1-9, July.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:8:p:857-:d:106342
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    References listed on IDEAS

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    1. Zhijian Liu & Kejun Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-16, December.
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    7. Senthaamarai Rogawansamy & Sharyn Gaskin & Michael Taylor & Dino Pisaniello, 2015. "An Evaluation of Antifungal Agents for the Treatment of Fungal Contamination in Indoor Air Environments," IJERPH, MDPI, vol. 12(6), pages 1-14, June.
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    Cited by:

    1. Piotr Boniecki & Małgorzata Idzior-Haufa & Agnieszka A. Pilarska & Krzysztof Pilarski & Alicja Kolasa-Wiecek, 2019. "Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm," IJERPH, MDPI, vol. 16(18), pages 1-9, September.
    2. Anyu Yu & Guangshe Jia & Jianxin You & Puwei Zhang, 2018. "Estimation of PM 2.5 Concentration Efficiency and Potential Public Mortality Reduction in Urban China," IJERPH, MDPI, vol. 15(3), pages 1-19, March.
    3. Wenxing Wang & Guoqi Dang & Imran Khan & Xiaobin Ye & Lei Liu & Ruqing Zhong & Liang Chen & Teng Ma & Hongfu Zhang, 2022. "Bacterial Community Characteristics Shaped by Artificial Environmental PM2.5 Control in Intensive Broiler Houses," IJERPH, MDPI, vol. 20(1), pages 1-16, December.

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