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Online monitoring and interpretation of periodic diurnal and seasonal variations of indoor air pollutants in a subway station using parallel factor analysis (PARAFAC)
Highlights PARAFAC monitoring is suggested for the IAQ periodic characteristics. It captures both the time-correlations and the variable-correlation of IAQ. A seasonal PARAFAC model can monitor and interpret the variations of IAQ. The source identification of the main air pollutants by a contributing plot.
Abstract Because indoor air pollutants are detrimental to public health, especially in public buildings or underground spaces, frequent monitoring of indoor air quality (IAQ) is necessary. IAQ undergoes seasonal and daily variations which are caused by anthropogenic emissions and weather conditions. Traditional IAQ monitoring methods, such as multiway principal component analysis (MPCA), which converts three-dimensional data of IAQ into one-dimensional data (a day) and two-dimensional data (IAQ variables by time) cannot capture the periodic characteristics of IAQ. In this paper, a new online monitoring and interpretation method of IAQ using parallel factor analysis (PARAFAC) is developed. PARAFAC is capable of capturing hourly variations of IAQ dynamics as well as dealing with the seasonal variations of IAQ. The experimental results in a subway station showed that the proposed method provides more accurate online monitoring and a more physically meaningful interpretation of IAQ than other univariate and MPCA monitoring methods. When online monitoring of PARAFAC detects an abnormal measurement of IAQ, the source of the main air pollutants was identified based on a contributing plot of PARAFAC, which is a useful advantage that enables IAQ control through a subway ventilation system.
Online monitoring and interpretation of periodic diurnal and seasonal variations of indoor air pollutants in a subway station using parallel factor analysis (PARAFAC)
Highlights PARAFAC monitoring is suggested for the IAQ periodic characteristics. It captures both the time-correlations and the variable-correlation of IAQ. A seasonal PARAFAC model can monitor and interpret the variations of IAQ. The source identification of the main air pollutants by a contributing plot.
Abstract Because indoor air pollutants are detrimental to public health, especially in public buildings or underground spaces, frequent monitoring of indoor air quality (IAQ) is necessary. IAQ undergoes seasonal and daily variations which are caused by anthropogenic emissions and weather conditions. Traditional IAQ monitoring methods, such as multiway principal component analysis (MPCA), which converts three-dimensional data of IAQ into one-dimensional data (a day) and two-dimensional data (IAQ variables by time) cannot capture the periodic characteristics of IAQ. In this paper, a new online monitoring and interpretation method of IAQ using parallel factor analysis (PARAFAC) is developed. PARAFAC is capable of capturing hourly variations of IAQ dynamics as well as dealing with the seasonal variations of IAQ. The experimental results in a subway station showed that the proposed method provides more accurate online monitoring and a more physically meaningful interpretation of IAQ than other univariate and MPCA monitoring methods. When online monitoring of PARAFAC detects an abnormal measurement of IAQ, the source of the main air pollutants was identified based on a contributing plot of PARAFAC, which is a useful advantage that enables IAQ control through a subway ventilation system.
Online monitoring and interpretation of periodic diurnal and seasonal variations of indoor air pollutants in a subway station using parallel factor analysis (PARAFAC)
Lee, SeungChul (author) / Liu, Hongbin (author) / Kim, MinJeong (author) / Kim, Jeong Tai (author) / Yoo, ChangKyoo (author)
Energy and Buildings ; 68 ; 87-98
2013-09-16
12 pages
Article (Journal)
Electronic Resource
English
Sensor Validation for Monitoring Indoor Air Quality in a Subway Station
| SAGE Publications | 2012
Sensor Validation for Monitoring Indoor Air Quality in a Subway Station
| Online Contents | 2012