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

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.

Investigation of A Potted Plant (Hedera helix) with Photo-Regulation to Remove Volatile Formaldehyde for Improving Indoor Air Quality

“…Serious health problems are related to sick building syndrome, which is usually associated with the quality of indoor air (Mullen et al, 2016;Mandin et al, 2017). In urban areas, most citizens have long-term exposure to large amounts of harmful chemicals indoors, whether it's at home or working at the office (Zheng et al, 2011;Shi et al, 2015;Lukcso et al, 2016). People are usually exposed to a higher intake or breathe in a greater concentration of air pollutants because these pollutants are more prevalent in indoor than outdoor environments (Zhang et al, 2017).…”