DOI QR코드

DOI QR Code

Indoor air quality analysis based on genetic algorithm for childhood facilities

유전 알고리즘을 이용한 어린이 시설의 실내 공기질 분석

  • 박서연 (한국기술교육대학교 기계공학부) ;
  • 우창규 (한국기술교육대학교 기계공학부)
  • Received : 2024.02.23
  • Accepted : 2024.03.19
  • Published : 2024.03.31

Abstract

Children are vulnerable to bad indoor air quality, and many researches on indoor air quality have been done with various methodologies. Herein, we used the genetic algorithm, one of the optimization methods, for the analysis based on better estimation values that are not easy to measure. A children playroom and a Taekwondo gym were chosen for the different degree of physical activity. After estimation of the number of occupants, the generation degree of CO2 and PM2.5 were determined from the data of the indoor air quality monitors. Relative errors were below 1% for all cases. Due to many air-treating electronics, the PM2.5 in the children playroom was well-managed compared to that in the Taekwondo gym. The PM2.5-generating activities were calculated and that of the Taekwondo gym was higher than that of the children playroom. The PM2.5 generating values were on the positive relationship with CO2 generating values. This means that we can obtain meaningful information from limited measurement data. For the numerous children facilities, indoor air quality can be easily analyzed and this might contribute to enhancing the children health.

Keywords

Acknowledgement

이 연구는 한국기계연구원의 기관연구사업의 지원을 받아 수행되었으며, 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 지자체-대학 협력기반 지역혁신사업(RIS)(2021RIS-004)과 산업통상자원부의 바이오산업기술개발사업 (20018463) 지원을 받아 수행된 결과입니다.

References

  1. Argunhan, Z. and Avci, A. S. (2018). Statistical evaluation of indoor air quality parameters in classrooms of a university. Advances in Meteorology, 2018. doi: 10.1155/2018/4391579
  2. Cooper, N., Green, D., Guo, Y., and Vardoulakis, S. (2020). School children's exposure to indoor fine particulate matter. Environmental Research Letters, 15(11), 115003. doi: 10.1088/1748-9326/abbafe
  3. Diapouli, E., Chaloulakou, A., and Koutrakis, P. (2013). Estimating the concentration of indoor particles of outdoor origin: A review. Journal of the Air & Waste Management Association, 63(10), 1113-1129. doi: 10.1080/10962247.2013.791649
  4. Elbayoumi, M., Ramli, N. A., and Yusof, N. F. F. M. (2015). Development and comparison of regression models and feedforward backpropagation neural network models to predict seasonal indoor PM2.5-10 and PM2.5 concentrations in naturally ventilated schools. Atmospheric Pollution Research, 6(6), 1013-1023. doi: 10.1016/j.apr.2015.09.001
  5. Guak, S., Kim, K., Yang, W., Won, S., Lee, H., and Lee, K. (2021). Prediction models using outdoor environmental data for real-time PM10 concentrations in daycare centers, kindergartens, and elementary schools. Building and Environment, 187, 107371. doi: 10.1016/j.buildenv.2020.107371
  6. Kampa, M., and Castanas, E. (2008). Human health effects of air pollution. Environmental pollution, 151(2), 362-367. doi: 10.1016/j.envpol.2007.06.012
  7. Kim, J., Hong, T., Lee, M., and Jeong, K. (2019). Analyzing the real-time indoor environmental quality factors considering the influence of the building occupants' behaviors and the ventilation. Building and Environment, 156, 99-109. doi: 10.1016/j.buildenv.2019.04.003
  8. Macarulla, M., Casals, M., Carnevali, M., Forcada, N., and Gangolells, M. (2017). Modelling indoor air carbon dioxide concentration using grey-box models. Building and Environment, 117, 146-153. doi: 10.1016/j.buildenv.2017.02.022
  9. Persily, A., and Jonge, L. (2017). Carbon dioxide generation rates for building occupants, Indoor Air, 27, 868-879. doi: 10.1111/ina.12383
  10. Singh, V., Sokhi, R. S., and Kukkonen, J. (2020). An approach to predict population exposure to ambient air PM2.5 concentrations and its dependence on population activity for the megacity London. Environmental pollution, 257, 113623. doi: 10.1016/j.envpol.2019.113623
  11. Tang, C. H., Garshick, E., Grady, S., Coull, B., Schwartz, J., and Koutrakis, P. (2018). Development of a modeling approach to estimate indoor-to-outdoor sulfur ratios and predict indoor PM2.5 and black carbon concentrations for Eastern Massachusetts households. Journal of exposure science & environmental epidemiology, 28(2), 125-130. doi: 10.1038/jes.2017.11
  12. Um, C. Y., Zhang, N., Kang, K., Na, H., Choi, H., and Kim, T. (2022). Occupant behavior and indoor particulate concentrations in daycare centers. Science of The Total Environment, 824, 153206. doi: 10.1016/j.scitotenv.2022.153206