Although most coke oven research is focused on the emission of polycyclic aromatic hydrocarbons, well-known carcinogens, little has been done on the emission of volatile organic compounds, some of which are also thought to be hazardous to workers and the environment. To profile coke oven gas (COG) emissions, we set up an open-path Fourier transform infrared (OP-FTIR) system on top of a battery of coke ovens at a steel mill located in Southern Taiwan and monitored average emissions in a coke processing area for 16.5 hr. Nine COGs were identified, including ammonia, CO, methane, ethane, ethylene, acetylene, propylene, cyclohexane, and o-xylene. Time series plots indicated that the type of pollutants differed over time, suggesting that different emission sources (e.g., coke pushing, quench tower, etc.) were involved at different times over the study period. This observation was confirmed by the low cross-correlation coefficients of the COGs. It was also found that, with the help of meteorological analysis, the data collected by the OP-FTIR system could be analyzed effectively to characterize differences in

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.

This paper presents the simulation and field evaluation results of two approaches to localize pollutant emission sources with open-path Fourier transform infrared (OP-FTIR) spectroscopy. The first approach combined the plume's peak location information reconstructed from the Smooth Basis Function Minimization (SBFM) algorithm and the wind direction data to calculate source projection lines. In the second approach, the plume's peak location was determined with the Monte Carlo methodology by randomly sampling within the beam segment having the largest path-integrated concentration. We first conducted a series of simulation studies to investigate the sensitivity of using different basis functions in the SBFM algorithm. It was found that fitting with the beta and Weibull basis functions generally gave better estimates of the peak locations than with the normal basis function when the plumes were mainly within the OP-FTIR's monitoring line. However, for plumes that were symmetric to the peak position or spread over the OP-FTIR, fitting with the normal basis function gave better performance. In the field experiment, two tracer gases were released simultaneously from two locations and the OP-FTIR collected data downwind from the sources with a maximum beam path length of 97 m. For the first approach, the release locations were within the 0.25-to 0.5-probability area only after the uncertainty of the peak locations was included in the calculation process. The second approach was easy to implement and still performed as satisfactorily as the first approach. The distances from the sources to the best-fit lines (i.e., the regression lines) of the estimated locations were smaller than 10 m. INTRODUCTIONLocalizing (i.e., locating) toxic air pollutant sources is an important issue in many environment and health-related fields. To minimize the air pollutants' impact on the environment and the public health, it is necessary to identify the releasing sources in a timely fashion so that an effective control strategy can be implemented. For example, in homeland security applications, the release locations of harmful airborne contaminants usually are unknown. Their locations must be estimated from the postrelease concentration observations. 1 Another example is the leak detection of volatile organic compounds (VOCs) from the refinery or petrochemical industries. Detecting the source of a leak from thousands of possible components is always a challenging task; nevertheless, fixing the identified leaking components not only protects public health and the environment but also reduces waste and any potential liability.For a large area, one way to locate the fugitive emission source(s) of hazardous chemicals is placing a matrix of point samplers at the monitoring site. In Chen et al., 2,3 3-hr canister samples were taken from 25 sites inside a petrochemical plant, twice per season for 1 yr. These samples were then analyzed with gas chromatography/ mass spectrometry (GC/MS) according to the U.S. Environmental Protection Agency (E...

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