Applications of Open-Path Fourier Transform Infrared for Identification of Volatile Organic Compound Pollution Sources and Characterization of Source Emission Behaviors
Abstract An open-path Fourier transform infrared spectroscopy (OP-FTIR) system was set up for 3-day continuous line-averaged volatile organic compound (VOC) monitoring in a paint manufacturing plant. Seven VOCs (toluene, m-xylene, p-xylene, styrene, methanol, acetone, and 2-butanone) were identified in the ambient environment. Daytime-only batch operation mode was well explained by the time-series concentration plots. Major sources of methanol, m-xylene, acetone, and 2-butanone were identified in the southeast direction where paint solvent manufacturing processes are located. However, an attempt to uncover sources of styrene was not successful because the method detection limit (MDL) of the OP-FTIR system was not sensitive enough to produce conclusive data. In the second scenario, the OP-FTIR system was set up in an industrial complex to distinguish the origins of several VOCs. Eight major VOCs were identified in the ambient environment. The pollutant detected wind-rose percentage plots that clearly showed that ethylene, propylene, 2-butanone, and toluene mainly originated from the tank storage area, whereas the source of n-butane was mainly from the butadiene manufacturing processes of the refinery plant, and ammonia was identified as an accompanying reduction product in the gasoline desulfuration process. Advantages of OP-FTIR include its ability to simultaneously and continuously analyze many compounds, and its long path length monitoring has also shown advantages in obtaining more comprehensive data than the traditional multiple, single-point monitoring methods.
Characterizing and locating air pollution sources in a complex industrial district using optical remote sensing technology and multivariate statistical modeling
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.
Monitoring and Optimization of Internal Grinding Process
Summary The grinding process is influenced by many factors such as grinding wheel characteristics, dressing conditions and, course, grinding conditions. Therefore, it is difficult to perform the grinding operation in an optimum state. In this study, in-process monitoring methods using power and acoustics emission sensors are proposed to detect malfunctions in the internal grinding process. In addition, a new internal grinding cycle featuring rapid indeed is proposed to minimize the grinding cycle time.