NDLI: Localized indoor air quality monitoring for indoor pollutants’ healthy risk assessment using sub-principal component analysis driven model and engineering big data

Content Provider	Springer Nature Link
Author	Shi, Honglan Kim, MinJeong Lee, SeungChul Pyo, SeHee Esfahani, Iman Janghorban Yoo, ChangKyoo
Copyright Year	2015
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.
Starting Page	1960
Ending Page	1969
Page Count	10
File Format	PDF
ISSN	02561115
Journal	Korean Journal of Chemical Engineering
Volume Number	32
Issue Number	10
e-ISSN	19757220
Language	English
Publisher	Springer US
Publisher Date	2015-06-23
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Engineering Data Big Data Sub-principal Component Analysis (Sub-PCA) Indoor Air Quality (IAQ) Weekly Effect Periodic Dynamics Industrial Chemistry/Chemical Engineering Catalysis Materials Science Biotechnology
Content Type	Text
Resource Type	Article
Subject	Chemistry Chemical Engineering

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