The importance of elucidating the direct and indirect effects of aerosol radiative forcing is now recognized as a significant component of our global climate forecasting models that must be better understood and quantified. Of particular interest is that of aerosol forcing through the so-called "direct" effect of aerosol absorption and scattering. This forcing can be of a magnitude comparable to those induced by anthropogenically-released greenhouse gases, yet can be either the opposite sign [negative (cooling)] or same sign [positive (warming)]. However, despite focused work on this issue, significant discrepancies on aerosol absorption still exist between measurements inferred from remote sensing and those obtained by in situ techniques. This is due, in large part, to the simple fact that the scattering channel dominates aerosol extinction, and thereby, makes measurement of the absorption difficult.
An alternative method to measuring aerosol absorption will be presented: measurement of the thermal dissipation of the spectrally absorbed energy through interferometry. The use of this coherent optical detection technique is particularly well suited to measuring the refractive index change that accompanies this energy transfer process. [1,2] This technique was even demonstrated towards measuring aerosol absorption in the mid-1980s . Attractive features of this technique for measuring aerosol absorption include its insensitivity to aerosol scattering, its ability to conduct the measurement in situ, its inherent high sensitivity, and near real-time response.
A discussion on the theoretical basis for this technique along with some preliminary data will be presented. Potential applications of this instrument to environmental security problems will also be discussed.
Brookhaven National Laboratory (BNL), Edgewood Chemical and Biological Center (ECBC) and ITT Industries Advanced Engineering and Sciences Division (AES) have been collaborating on the transitioning and subsequent development of a short-range, non-contact Raman lidar system specifically designed to detect and identify chemical agents on the battlefield. [The instrument, referred to as LISA (Laser Interrogation of Surface Agents), will the subject of an accompanying paper.] As part of this collaboration, BNL has the responsibility for developing a spectral database (library) of surrogates and precursors for use with LISA’s pattern recognition algorithms. In this paper, the authors discuss the phenomenon of UV Raman and resonance-enhanced Raman spectroscopy, the development of an instrument-independent Raman spectral library, and highlight the exploitable characteristics present in the acquired spectral signatures that suggest potential utility in our country’s efforts on Homeland Security.
Laser Interrogation of Surface Agents (LISA) is a UV-Raman technique that provides short-range standoff detection and identification of surface-deposited chemical agents. ITT Industries, Advanced
Engineering and Sciences Division, is currently developing and expanding the LISA technology under several programs that span a variety of missions for homeland defense. We will present and discuss some of these applications, while putting in perspective the overall evolution undergone by the technique within the last years. These applications include LISA-Recon (now called the Joint Contaminated Surface Detector--JCSD) which was developed under a cost-sharing arrangement with the U.S. Army Soldier and Biological Chemical Command (SBCCOM) for incorporation on the Army’s future reconnaissance
vehicles, and designed to demonstrate single-shot on-the-move measurements of chemical contaminants at concentration levels below the Army's requirements. In parallel, LISA-Shipboard is being developed to optimize the sensor technique for detection of surface contaminants in the operational environment of a ship. The most recently started activity is LISA-Inspector that is being developed to provide a transportable sensor in a 'cart-like' configuration.
Laser Interrogation of Surface Agents (LISA) is a new technique which exploits Raman scattering to provide standoff detection and identification of surface-deposited chemical agents. ITT Industries, Advanced Engineering and Sciences Division is developing the LISA technology under a cost-sharing arrangement with the US Army Soldier and Biological Chemical Command for incorporation on the Army's future reconnaissance vehicles. A field-engineered prototype LISA-Recon system is being designed to demonstrate on-the- move measurements of chemical contaminants. In this article, we will describe the LISA technique, data form proof-of- concept measurements, the LISA-Recon design, and some of the future realizations envisioned for military sensing applications.
The application of a novel mini-Raman Lidar to the standoff detection and identification of chemical spills is discussed. The new chemical sensor combines the spectral fingerprintign of solar-blind UV Raman spectroscopy with the principles of lidar to open a new venue of short-range, non- contact detection and identification of unknown substances on surfaces. In addition to discussing experimental result collected with a 'proof-of-principle' system, a next generation system, currently under development, is also presented.
A novel method of performing DIAL (Differential Absorption Lidar) measurements of airborne chemicals is presented. The technique, called RaDIAL, utilizes the Raman returns from atmospheric nitrogen and oxygen as the `on' and `off' wavelengths for a particular chemical species. Both laboratory and field tests of RaDIAL for molecules with absorption bands in the UV solar blind demonstrate the utility of this detection scheme. The advantages of RaDIAL for range-resolved chemical species detection/monitoring include insensitivity of the measurement to laser pulse-to- pulse energy fluctuations and variations in aerosol burden. The RaDIAL technique offers the desired high sensitivity associated with DIAL while keeping the data reduction simple and free of complex approximations.
The Mini-Raman Lidar System (MRLS) is a `proof-of-principle' chemical sensor that combines the spectral fingerprinting of solar-blind UV Raman spectroscopy with the principles of lidar to open a new venue of short-range (meters to tens of meters), non-contact detection and identification of unknown substances on surfaces. The device has potential application to `first responders' at the site of a chemical spill. The MRLS is portable and has been used both in the lab and in the field. Theoretical estimates and actual laboratory data suggest the possibility of detecting contaminants with a surface coverage of < 1 g/m2 at a distance of three meters for one second of signal integration. Increasing the optical throughput efficiency, integrating pattern recognition software, and incorporating a laser with a wavelength near 250 nm are the primary goals for the development of a prototype system.
The Mini-Raman Lidar System (MRLS) is a 'proof-of-principle' chemical sensor that combines the spectral fingerprinting of solar- blind UV Raman spectroscopy with the principles of lidar to open a new venue of short-range (meters to tens of meters), non-contact detection and identification of unknown substances on surfaces. The device has potential application to 'first responders' at the site of a chemical spill. The MRLS is portable and has been used both in the lab and in the field. Theoretical estimates and actual laboratory data suggest the possibility of detecting contaminants with a surface coverage of less than 1g/m2 at a distance of three meters for one second of signal integration. Increasing the optical throughput efficiency, integrating pattern recognition software, and incorporating a laser with a wavelength near 250 nm are the primary goals for the development of a prototype system.
BNL has been developing a remote sensing technique for the detection of atmospheric pollutants based on the phenomenon of resonance Raman LIDAR that has also incorporated a number of new techniques/technologies designed to extend its performance envelope. When the excitation frequency approaches an allowed electronic transition of the molecule, an enormous enhancement of the inelastic scattering cross- section can occur, often up to 2 to 4 orders-of-magnitude, and is referred to as resonance Raman, since the excitation frequency is in 'resonance' with an allowed electronic transition. Exploitation of this enhancement along with new techniques such as pattern recognition algorithm to take advantage of the spectral fingerprint and a new laser frequency modulation technique designed to suppress broadband fluorescence, referred to as frequency modulated excitation Raman spectroscopy and recent developments in liquid edge filter technology, for suppression of the elastic channel, all help increase the overall performance of Raman LIDAR.
BNL has been developing a remote sensing technique for the detection of atmospheric pollutants using resonance Raman LIDAR that has also incorporated a number of new techniques/technologies designed to extend it performance envelope. Chief among these new techniques is the use of pattern recognition to take advantage of the spectral fingerprint and a new laser frequency modulation technique, referred to as Frequency Modulated Excitation Raman Spectroscopy, designed to suppress broadband fluorescence. In the laboratory, broadband fluorescence suppression approaching 3 orders-of-magnitude has been achieved. In addition, the application of a BNL designed knife-edge Rayleigh filter has also bee demonstrated using our LIDAR system where spectral features as close as 200 cm-1 from the excitation line were observed. How all these features help increase the overall performance of Raman LIDAR will be discussed.
We introduce an adaptive mixing algorithm for estimating the relative ratios of chemicals in a mixture spectrum. This procedure is particularly well suited to mixtures with a large dynamic range of mixture weights. It has the advantage of being able to be used in conjunction with a band-pass (difference-to-Gaussian or DOG) filter, and a correction of baseline off-set and tilting of the spectrum. Output of these filtering techniques is a cleaner signal retaining most of the relevant Raman spectral signature while minimizing artifacts due primarily to Rayleigh, dust, and atmospheric aerosols. We will describe the results of applying these algorithm to mixture spectra with both real and simulated additive noise.