Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/7301
Title: Determination of asbestos cement rooftop surface composition using regression analysis and hyper-spectral reflectance data in the visible and near-infrared ranges
Authors: Sabat, Mira 
Fares, Noura
Mitri, George 
Kfoury, Adib 
Affiliations: Department of Mathematics 
Institute of the Environment 
Department of Environmental Science 
Keywords: Asbestos-cement
NIR
Outlier filter
Reflectance
Regression analysis
SDG 11
Urban pollution
VIS
Issue Date: 2024-03-12
Publisher: Elsevier
Part of: Journal of Hazardous Materials
Volume: 469
Abstract: 
The effects of asbestos on human health have spurred numerous studies examining its risks in urban environments. Recent works have shifted towards less-invasive techniques for remote detection and classification of asbestos-cement. In this context, this study combines visible (VIS) and near-infrared (NIR) reflectance data collected in-situ with reference signals from the USGS spectral library, utilizing optimized regression analysis to determine the surface composition of corrugated asbestos-cement rooftops. An outlier filter was successfully implemented to enhance the accuracy of regression calculations, achieving a high level of agreement with actual field observations. The regression analysis revealed varying proportions of weathered cement, hazardous asbestos fibers (specifically chrysotile and cummingtonite), and biological growth (such as lichens and moss). These results are consistent with previous research on the composition of asbestos-cement rooftops, including a comparable field study and XRD analysis conducted in 2019. This underscores the importance of using regression analysis, preceded by an outlier filtering step, on VIS and NIR reflectance data to ascertain the surface composition of asbestos-cement rooftops. This methodology holds potential for application to larger hyperspectral datasets across more extensive sample surfaces and areas.
URI: https://scholarhub.balamand.edu.lb/handle/uob/7301
ISSN: 03043894
DOI: 10.1016/j.jhazmat.2024.134006
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Institute of the Environment
Department of Environmental Science
Department of Mathematics

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