title: Characterizing and locating air pollution sources in a complex industrial district using optical remote sensing technology and multivariate statistical modeling.
abstract: Emissions of volatile organic compounds (VOCs) are most frequent environmental nuisance complaints in urban areas, especially where industrial districts are nearby. Unfortunately, identifying the responsible emission sources of VOCs is essentially a difficult task. In this study, we proposed a dynamic approach to gradually confine the location of potential VOC emission sources in an industrial complex, by combining multi-path open-path Fourier transform infrared spectrometry (OP-FTIR) measurement and the statistical method of principal component analysis (PCA). Close-cell FTIR was further used to verify the VOC emission source by measuring emitted VOCs from selected exhaust stacks at factories in the confined areas. Multiple open-path monitoring lines were deployed during a 3-month monitoring campaign in a complex industrial district. The emission patterns were identified and locations of emissions were confined by the wind data collected simultaneously. N,N-Dimethyl formamide (DMF), 2-butanone, toluene, and ethyl acetate with mean concentrations of 80.0 ± 1.8, 34.5 ± 0.8, 103.7 ± 2.8, and 26.6 ± 0.7 ppbv, respectively, were identified as the major VOC mixture at all times of the day around the receptor site. As the toxic air pollutant, the concentrations of DMF in air samples were found exceeding the ambient standard despite the path-average effect of OP-FTIR upon concentration levels. The PCA data identified three major emission sources, including PU coating, chemical packaging, and lithographic printing industries. Applying instrumental measurement and statistical modeling, this study has established a systematic approach for locating emission sources. Statistical modeling (PCA) plays an important role in reducing dimensionality of a large measured dataset and identifying underlying emission sources. Instrumental measurement, however, helps verify the outcomes of the statistical modeling. The field study has demonstrated the feasibility of using multi-path OP-FTIR measurement. The wind data incorporating with the statistical modeling (PCA) may successfully identify the major emission source in a complex industrial district.
authors.affiliation: Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan.
authors.forename: Pao-Erh Paul
authors.initials: PE
authors.lastname: Chang
authors.forename: Jen-Chih Rena
authors.initials: JC
authors.lastname: Yang
authors.forename: Walter
authors.initials: W
authors.lastname: Den
authors.forename: Chang-Fu
authors.initials: CF
authors.lastname: Wu
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D007221:Industry; D015233:Models, Statistical; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D058998:Remote Sensing Technology / Q000379:methods*; D017550:Spectroscopy, Fourier Transform Infrared; D013624:Taiwan; D055549:Volatile Organic Compounds / Q000032:analysis*; D014919:Wind
chemical_list: D000393:Air Pollutants; D055549:Volatile Organic Compounds