Remote Measurement of Emissions by Scanning Imaging Infrared Spectrometry

Transcription

1 Remote Measurement of Emissions by Scanning Imaging Infrared Spectrometry R. Harig *, M. Grutter 2, G. Matz, P. Rusch, J. Gerhard Hamburg University of Technology, Harburger Schlossstr. 2, 279 Hamburg, Germany, 2 Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México (UNAM), 45 México D.F., México Abstract Remote sensing by Fourier-transform infrared spectrometry allows identification and quantification of emissions from long distances. Imaging spectrometry allows to characterise emissions spatially as well as automated surveillance of large and potentially inaccessible areas. In this work, scanning imaging remote sensing spectrometers (SIGIS and SIGIS 2) have been deployed to identify and visualise industrial emissions. The systems are based on Michelson interferometers with a single detector element in combination with telescopes and synchronised scanning mirrors. For simple interpretation of the results, the systems are equipped with video cameras and the results of the analyses of the spectra are displayed by an overlay of a false colour image on the video image. This allows a simple evaluation of the position and the size of an exhaust plume. The analysis method is based on the evaluation of the radiance spectrum and the brightness temperature spectrum. In order to perform the radiometric calibration that is required to calculate the radiance spectrum, the spectrometers are equipped with automated calibration systems. The analysis is performed by modelling the measured spectrum using reference spectra of the target compounds as well as reference spectra of atmospheric species. In order to obtain high signal-to-noise ratios and short measurement times, measurements are performed with a relatively low spectral resolution. The low-resolution spectra (spectral resolution of the measurement) are modelled using highresolution reference spectra in combination with an instrument line shape model that is defined by five parameters. Measurements of emissions were performed at various industrial sites. Introduction Remote sensing by Fourier-transform infrared spectrometry allows identification and quantification of emissions from long distances. Imaging spectrometry allows to characterise emissions spatially as well as automated surveillance of large and potentially inaccessible areas. In this work, the deployment of scanning imaging remote sensing spectrometers (SIGIS and SIGIS 2) to identify and visualise industrial emissions is reported. 2 Radiative Transfer Model Figure illustrates the measurement set-up. The radiation measured by the spectrometer contains the spectral signatures of the background of the field of view, the pollutant cloud, and the atmosphere. The basic characteristics of spectra measured by a passive infrared spectrometer may be described by a model in which the atmosphere is divided into planeparallel homogeneous layers along the optical path. In many cases, a simple model with three layers can be used (Figure ). Radiation from the background, for example, the sky or a surface (Layer 3) propagates through the vapour cloud (Layer 2) and the atmosphere between the cloud and the spectrometer (Layer ). The layers and 2 are considered homogeneous with regard to all physical and chemical properties within each layer. The radiation containing the signatures of all layers is measured by the spectrometer.

2 Spectrometer Layer Layer 2 Layer 3 T,τ T 2,τ 2 Atmosphere Cloud L 3 T bg Background Fig. : Measurement set-up of infrared remote sensing of airborne pollutants. In this model, the spectral radiance at the entrance aperture of the spectrometer L is ) B + τ 2 ) [( τ B τ ] L = ( τ + L, () where τ i is the transmittance of layer i, B i is the spectral radiance of a blackbody at the temperature of the layer. L 3 is the radiance that enters the layer of the cloud from the background. All quantities in Equation () are frequency dependent. If the background of the field of view is a surface, the radiation entering the cloud contains radiation emitted by the surface and reflected radiation, i.e. ambient radiation and radiation from the sky. Because radiation in the infrared region is considered, the contribution of scattering is small in many cases and thus it is neglected in this model. If the distance between the background surface and the cloud is long, i. e. the transmittance of the atmosphere is not negligible, another layer may be added. If the temperatures of the layers and 2 are equal, Equation () can be simplified (B =B 2 ): The radiance difference L = L -L 3 is given by 2 ( L ) L = B + τ 2 3 B 2 3 τ (2) L = ( τ L, (3) where L 3 = B -L 3 = B 2 -L 3 (Because a confusion in terms is unlikely, in this work the term radiance is used as a simplifying synonym for the term spectral radiance.). τ 2 ) 3 2 Identification and Quantification The identification method is based on the approximation of a measured spectrum using reference spectra. First, the spectrum of the brightness temperature T Br (σ) is calculated [Harig 22]. The spectrum is analysed sequentially for all target compounds contained a spectral library. The identification is performed in three steps. In the first step, the mean brightness temperature is subtracted and the signatures of one target compound, atmospheric gases (e.g. H 2 O), and potential interferents are fitted to the resulting spectrum using a linear least squares fitting procedure. The fitting procedure includes an approximation of the baseline by slowly varying functions. In the next step, the contributions of all fitted signatures (i.e. interferents, atmospheric species, and baseline) except the signature of the target compound are subtracted from the measured spectrum. Then, in order to decide if the target compound is present, the coefficient of correlation between the corrected spectrum, i.e. the result of the subtraction, and a reference spectrum is calculated in a compound-specific number of spectral windows. Moreover, the coefficient of correlation between the fitted spectrum and the measured spectrum is calculated. The signal-to-noise ratio is calculated by division of the maximum brightness temperature difference caused by the target compound (determined by the least squares fitting procedure) by the noise equivalent temperature difference of the spectrum. If all coefficients of correlation

3 and the signal-to-noise ratio are greater than compound-specific threshold values, the target compound is identified. The calculation is performed for three different column densities of the target compound. The reference spectra with different column densities are calculated by convolution of highresolution transmittance spectra (e.g. calculated with absorption cross sections computed using Hitran [Rothman et al. 25]) with an instrument line shape function [Harig 24]. In this work, a quantification method that is based on the assumption that the gas temperature may be estimated by the ambient temperature is applied. The temperature of the gas is estimated by analysis of signatures of water that is present in the atmosphere. The quantification of the column density is performed by minimization of the difference between the measured spectrum and a spectrum calculated using a model. A nonlinear model that contains the column density of the target compound as a parameter is applied. The model consists of two sub-models, a radiative transfer model and a model for the instrument (instrument line shape model) [Harig et al. 22, 24]. The best-fit parameters are determined by the Levenberg-Marquardt method. The quantification is only performed if the compound has been identified automatically and the estimated brightness temperature difference is greater than 3 K. 3 Scanning Imaging Remote Sensing Systems The scanning imaging remote sensing systems (SIGIS and SIGIS 2) are based on the combination of an interferometer with a single detector element and a scanning mirror [Harig et al., 25]. The systems comprise an interferometer, a telescope, an azimuth-elevation-scanning mirror actuated by stepper motors, a video camera, and a PC for control, data analysis, and display of the results. Rotating head GPS Video camera Scanning mirror Telescope Figure 2: CAD-drawing of SIGIS 2. An azimuth-elevation scanning mirror is used to scan the field of regard. In addition, the system SIGIS 2 is equipped with a rotating head that is used for coarse orientation of the field of regard (Fig. 2) and allows 36 -surveillance. The measurement scene is recorded by a video camera and displayed on the PC. The video image and the interferograms measured by the interferometer are transferred to the PC. For the visualisation of gas clouds, the scanning mirror is sequentially set to all positions within the field of regard. The size and the direction of the field of regard and the spatial resolution (i.e. the angle between adjacent fields of view) are variable. The operator may define the field of view interactively using the displayed video image and the mouse. Each interferogram measured by the interferometer is transferred to the PC. After the Fourier-transformation, the spectrum is analysed and the results are visualised by the video image, overlaid by false colour images. This direct display of the cloud image in the video image allows simple assessment of the position and the size of the cloud or the exhaust plume.

4 Table : Specifications of the scanning imaging FTIR systems SIGIS and SIGIS 2. SIGIS SIGIS 2 Interferometer Bruker OPAG 22 Bruker OPAG 33 Spectral range 68 5 cm - (6-6 cm - max.) Maximum spectral resolution (max. opt. path difference D).5 cm - (D =.8 cm) Spectral resolution (this work) 4 cm - (D =.225 cm) 68 5 cm - (6-6 cm - max.).5 cm - (D =.8 cm, D = 4.4 cm opt.) 4 cm - (D =.225 cm) Field of view 7.5 mrad mrad Field of regard Maximum spectral rate NE T per scan (triangular apodization, cm -, σ = 4 cm - ) (practical limit: field of view of video camera) 6 spectra/s ( σ = 4 cm -, twosided interferograms) mk 2 mk Mass 65 kg 75 kg Power (without PC) W 75 W 6 spectra/s ( σ = 4 cm -, twosided interferograms) For identification and visualisation of emissions, a spectral resolution of 4 cm - is used preferably. The choice of resolution is a trade-off between the goals of a low limit of detection and a short measurement time (low resolution), and the goal of high selectivity (high resolution). The signal-to-noise ratio improves with decreasing spectral resolution for constant measurement time [Harig, 24]. 4 Measurement of Emissions Measurements of industrial emissions at various sites were performed. Figure 3 and figure 4 show results of measurements of emissions of ammonia in Germany. A large cloud of ammonia was identified, visualised and quantified. Ammonia cloud Coefficient of correlation R 85 Signal-to-noise ratio Figure 3: Measurement of ammonia. Left: False colour representation of the coefficient of correlation between the measured spectrum (after processing) and a reference spectrum of ammonia. Right: Signal-to-noise ratio.

5 Ammonia identified Column Density cl / ppm m 24 Figure 4: Identification (left) and quantification (right) of ammonia. In Hamburg, emissions of a stack at a distance of approximately km were imaged. Figure 5 shows the results of the identification algorithm. Ammonia identified Coefficient of correlation R Figure 5: Identification of ammonia (left) and false colour image of the coefficient of correlation between the measured spectrum (after processing) and a reference spectrum of ammonia (right). Figure 6 shows results of measurements of sulfur dioxide in Mexico. Sulfur dioxide was automatically identified in three measurements. However, the plume can clearly be observed in the false colour image of the coefficient of correlation.

6 SO 2 plume Sulfur dioxide identified Coefficient of correlation R Figure 6: Identification of sulfur dioxide (left) and false colour image of the plume of sulfur dioxide (right). 5 Summary and Conclusions Industrial emissions at various sites in Germany and Mexico were measured from long distances. Emitted gases were identified and visualised. The retrieval of the column density was demonstrated. The measurement technique allows measurements independent of solar radiation. Thus, passive remote sensing by infrared spectrometry allows remote monitoring industrial sites. In the case of an accident, the released gas and the dispersion of the plume can be determined on-site. This information is essential for emergency response personnel. 6 Acknowledgements The work described in this paper was in part supported by the German civil defence agency BBK. The authors thank the BBK, in particular Karin Braun, Bernhard Preuss, Roman Trebbe, and Udo Bachmann for good cooperation. The authors thank the emergency response forces (task forces) in Hamburg and Mannheim for providing parts of the data. In particular we would like to thank Mario König, Ralf Rudolph, Daniel Wormer, Georg Prat-Caralt, Oliver Hey (Fire Department Mannheim), Knut Storm, Thomas Lübbe-Horn, Michael Nagel (Fire Department Hamburg) for good cooperation. References Harig, R., Matz, G., Rusch, P., Gerhard, H.-H., Gerhard, J.-H., Schlabs, V. (25), New scanning infrared gas imaging system (SIGIS 2) for emergency response forces, Proc. SPIE, Harig, R. (24), Passive remote sensing of pollutant clouds by FTIR spectrometry: Signal-to-noise ratio as a function of spectral resolution, Applied Optics, 43 (23), Harig, R., Matz, G., Rusch, P. (22), Scanning Infrared Remote Sensing System for Identification, Visualization, and Quantification of Airborne Pollutants, Proc. SPIE, 4574, Rothman, L.S., Jacquemart, D., Barbe, A., Chris Benner, D., Birk, M., Brown, L.R., Carleer, M.R., Chackerian Jr., C., Chance, K., Coudert, L.H., Dana, V., Devi, V.M., Flaud, J.-M., Gamache, R.R., Goldman, A., Hartmann, J.-M., Jucks, K.W., Maki, A.G., Mandin, J.-Y., Massie, S.T., Orphal, J., Perrin, A., Rinsland, C.P., Smith, M.A.H., Tennyson, J., Tolchenov, R.N., Toth, R.A., Vander Auwera, J., Varanasi, P., Wagner, G. (25) The HITRAN 24 molecular spectroscopic database, Journal of Quantitative Spectroscopy and Radiative Transfer, 96 (2),