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Predicting indoor PM2.5/PM10 concentrations using simplified neural network models

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Abstract

Neural network models were presented for prediction of indoor concentrations of particulate matters (PMs). Indoor PM concentrations are generally determined by the outdoor concentration, the indoor generation rate, and the air change rate of a building. In this study, indoor PM2.5 and PM10 concentrations were modelled in a single office room installed with a portable air purifier (AP) and a heat recovery ventilator (HRV), using three different neural network models with various input variables. The relative importance of individual input variables indicated that as opposed to PM10, PM2.5 is more affected by outdoor origin than indoor source. This is generally consistent with previous findings explaining that the main source of PM2.5 is outdoor environment, whereas that for PM10 is indoor human activities. The simplified models can be easily applied in practice based on the CO2 concentration measured in a room and the outdoor PM concentration data acquired from public data.

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Acknowledgments

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1 B01009625) and by Global scholarship of Kookmin University.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Kookmin University, Seoul, 136-702, Korea

    Muhammad Hatta & Hwataik Han

Authors
  1. Muhammad Hatta
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  2. Hwataik Han
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Correspondence to Hwataik Han.

Additional information

Muhammad Hatta received his M.S. degree in Mechanical Engineering from Kookmin University in 2020. He joined the TEE (Thermal Environmental Engineering) Lab in Kookmin University after he received his M.E. degree from University of Riau, Indonesia in 2017. Currently, he is working for an IoT company in South Korea. His main research interest lies in developing a predictive model for industrial machinery using machine learning.

Hwataik Han received B.S. and M.S. from Seoul National University and Ph.D. degree from University of Minnesota in 1988. Currently, he is a Professor in Kookmin University. He is a registered PE at Minnesota State Board of AELSLAGID. He served as the President of SAREK in 2014 and the Editorin-Chief of KACA (Korea Air Cleaning Association) since 2005. He organized the Indoor Air 2020 Conference as the President. Now, he is a fellow of ASHRAE and of SAREK.

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Hatta, M., Han, H. Predicting indoor PM2.5/PM10 concentrations using simplified neural network models. J Mech Sci Technol 35, 3249–3257 (2021). https://doi.org/10.1007/s12206-021-0645-6

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  • DOI: https://doi.org/10.1007/s12206-021-0645-6

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