Literature DB >> 25025736

Volatile organic compound identification and characterization by PCA and mapping at a high-technology science park.

Cheng-Hang Lan1, Yu-Li Huang2, Sheng-Huei Ho3, Chiung-Yu Peng4.   

Abstract

High-technology industries have grown continuously in Taiwan and elsewhere in the world. Volatile organic compounds (VOCs) comprise the highest percentage of emissions in these industries. The objectives of this study were to identify VOC sources and to apportion their contributions by using a three-step approach. These included estimating concentration distributions, performing principal component analysis (PCA), and mapping concentration contours. The results showed that the dominant compound groups were aromatic and aliphatic compounds. The PCA resolved four emission sources: vehicular traffic, industrial solvents, waste water plants, and cleaning/degreasing agents. Spatial distributions showed that concentrations of vehicular traffic-related compounds (benzene and isooctane) were highest at the entrances to the science park, and strongly related to traffic volume, and that the emissions of industry-related compounds (xylene and ethylbenzene) were closest to the associated sources. This study provided an accurate, practical and efficient method of characterizing emission sources in an industrial complex.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Aromatics; Chlorinated compounds; Principal component analysis (PCA); Spatial distribution

Mesh:

Substances:

Year:  2014        PMID: 25025736     DOI: 10.1016/j.envpol.2014.06.014

Source DB:  PubMed          Journal:  Environ Pollut        ISSN: 0269-7491            Impact factor:   8.071


  3 in total

1.  E-nose based rapid prediction of early mouldy grain using probabilistic neural networks.

Authors:  Xiaoguo Ying; Wei Liu; Guohua Hui; Jun Fu
Journal:  Bioengineered       Date:  2015-02-25       Impact factor: 3.269

2.  Fluorescence and photophysical properties of xylene isomers in water: with experimental and theoretical approaches.

Authors:  Muhammad Farooq Saleem Khan; Jing Wu; Bo Liu; Cheng Cheng; Mona Akbar; Yidi Chai; Aisha Memon
Journal:  R Soc Open Sci       Date:  2018-02-07       Impact factor: 2.963

3.  Pitavastatin slows tumor progression and alters urine-derived volatile organic compounds through the mevalonate pathway.

Authors:  Luqi Wang; Yue Wang; Andy Chen; Meghana Teli; Rika Kondo; Aydin Jalali; Yao Fan; Shengzhi Liu; Xinyu Zhao; Amanda Siegel; Kazumasa Minami; Mangilal Agarwal; Bai-Yan Li; Hiroki Yokota
Journal:  FASEB J       Date:  2019-10-04       Impact factor: 5.834

  3 in total

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