Predictive monitoring and diagnosis of periodic air pollution in a subway station

The purpose of this study was to develop a predictive monitoring and diagnosis system for the air pollutants in a subway system using a lifting technique with a multiway principal component analysis (MPCA) which monitors the periodic patterns of the air pollutants and diagnoses the sources of the co...

Full description

Saved in:
Bibliographic Details
Published in:Journal of hazardous materials 2010-11, Vol.183 (1), p.448-459
Main Authors: Kim, YongSu, Kim, MinJung, Lim, JungJin, Kim, Jeong Tai, Yoo, ChangKyoo
Format: Article
Language:English
Subjects:
Air Pollution
Air Pollution, Indoor
Air quality
Air quality monitoring
Applied sciences
Atmospheric pollution
Climate Change
Diagnosis
Environmental Monitoring - methods
Exact sciences and technology
Forecasting
Hoisting
Indoor
Indoor pollution and occupational exposure
Korea
Lifted model
Mathematical models
Monitoring
Multiway principal component analysis (MPCA)
Periodic pattern
Pollutants
Pollution
Predictive monitoring
Principal Component Analysis
Railroads
Subways
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The purpose of this study was to develop a predictive monitoring and diagnosis system for the air pollutants in a subway system using a lifting technique with a multiway principal component analysis (MPCA) which monitors the periodic patterns of the air pollutants and diagnoses the sources of the contamination. The basic purpose of this lifting technique was to capture the multivariate and periodic characteristics of all of the indoor air samples collected during each day. These characteristics could then be used to improve the handling of strong periodic fluctuations in the air quality environment in subway systems and will allow important changes in the indoor air quality to be quickly detected. The predictive monitoring approach was applied to a real indoor air quality dataset collected by telemonitoring systems (TMS) that indicated some periodic variations in the air pollutants and multivariate relationships between the measured variables. Two monitoring models – global and seasonal – were developed to study climate change in Korea. The proposed predictive monitoring method using the lifted model resulted in fewer false alarms and missed faults due to non-stationary behavior than that were experienced with the conventional methods. This method could be used to identify the contributions of various pollution sources.
ISSN:0304-3894
1873-3336
DOI:10.1016/j.jhazmat.2010.07.045