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- In relation to this article, we declare that there is no conflict of interest.
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Received August 17, 2014
Accepted February 25, 2015
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Localized indoor air quality monitoring for indoor pollutants’ healthy risk assessment using sub-principal component analysis driven model and engineering big data
Department of Environmental Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 446-701, Korea
Korean Journal of Chemical Engineering, October 2015, 32(10), 1960-1969(10), 10.1007/s11814-015-0042-x
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Abstract
Indoor air quality (IAQ) in subway systems shows periodic dynamics due to the number of passengers, train schedules, and air pollutants accumulated in the system, which are considered as an engineering big data. We developed a new IAQ monitoring model using a sub-principal component analysis (sub-PCA) method to account for the periodic dynamics of the IAQ big data. In addition, the IAQ data in subway systems are different on the weekdays and weekend due to weekly effect, since the patterns of the number of passengers and their access time on the weekdays and weekend are different. Sub-PCA-based local monitoring was developed for separating the weekday and weekend environmental IAQ big data, respectively. The monitoring results for the test data at the Y-subway station clearly showed that the proposed method could analyze an environmental IAQ big data, improve the monitoring efficiency and greatly reduce the false alarm rate of the local on-line monitoring by comparison with the multi-way PCA.
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References
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Kang O, Liu H, Kim M, Kim JT, Wasewar KL, Yoo C, Indoor Built Environ., 22, 77 (2013)
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Lu NY, Gao FR, Wang FL, AIChE J., 50(1), 255 (2004)
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Johnson RA, Wichern DW, Applied multivariate statistical analysis, Prentice Hall, Upper Saddle River, New Jersey (2002).
Kim Y, Kim M, Lim J, Kim JT, Yoo C, J. Hazard. Mater., 183(1-3), 448 (2010)
Nomikos P, Macgregor JF, AIChE J., 40(8), 1361 (1994)
Wang S, Chang Y, Zhao Z, Wang F, Int. J. Control Autom., 10, 1136 (2012)
