An urban-oriented emergency assessment system for airborne Chemical, Biological, and Radiological (CBR) threats, called CT-Analyst and based on new principles, gives greater accuracy and much greater speed than possible with current alternatives. This paper explains how this has been done. The increased accuracy derives from detailed, three-dimensional CFD computations including, solar heating, buoyancy, complete building geometry specification, trees, wind fluctuations, and particle and droplet distributions (as appropriate). This paper shows how a very finite number of such computations for a given area can be extended to all wind directions and speeds, and all likely sources and source locations using a new data structure called Dispersion Nomographs. Finally, we demonstrate a portable, entirely graphical software tool called CT-Analyst that embodies this entirely new, high-resolution technology and runs effectively on small personal computers. Real-time users don't have to wait for results because accurate answers are available with near zero-latency (that is 10 - 20 scenarios per second). Entire sequences of cases (e.g. a continuously changing source location or wind direction) can be computed and displayed as continuous-action movies. Since the underlying database has been precomputed, the door is wide open for important new real-time, zero-latency functions such as sensor data fusion, backtracking to an unknown source location, and even evacuation route planning. Extensions of the technology to sensor location optimization, buildings, tunnels, and integration with other advanced technologies, e.g. micrometeorology or detailed wind field measurements, will be discussed briefly here.

Networked groups of sensors that detect Chemical, Biological, and Radiological (CBR) threats are being developed to defend cities and military bases. Due to the high cost and maintenance of these sensors, the number of sensors deployed is limited. It is vital for the sensors to be deployed in optimal locations for these sensors to be effectively used to analyze the scope of the threat. A genetic algorithm, along with the instantaneous plume prediction capabilities of CT-Analyst has been developed to meet these goals. CT-Analyst’s time dependant plumes, upwind danger zone, and sensor capabilities are used to determine the fitness of sensor networks generated by the genetic algorithm. The optimization and the requirements for the evaluation of sensor networks in an urban region are examined along with the number of sensors required to detect these plumes.

The terrorist threat coupled with a global military mission necessitates quick and accurate identification of environmental hazards, and CBRN early warning. The Air Force Institute for Operational Health (AFIOH) provides fundamental support to protect personnel from and mitigate the effects of untoward hazards exposures. Sustaining healthy communities since 1955, the organizational charter is to enhance warfighter mission effectiveness, protect health, improve readiness and reduce costs, assess and manage risks to human heath and safety, operational performance and the environment. The AFIOH Surveillance Directorate provides forward deployed and reach-back surveillance, agent identification, and environ-mental regulatory compliance testing. Three unique laboratories process and analyze over two million environmental samples and clinical specimens per year, providing analytical chemistry, radiological assessment, and infectious disease testing, in addition to supporting Air Force and Department of Defense (DoD) clinical reference laboratory and force health protection testing. Each laboratory has an applied or investigational testing section where new technologies and techniques are evaluated, and expert consultative support to assist in technology assessments and test analyses. The Epidemiology Surveillance Laboratory and Analytical Chemistry Laboratory are critical assets of the Centers for Disease Control and Prevention (CDC) National Laboratory Response Network. Deployable assets provide direct support to the Combatant Commander and include the Air Force Radiological Assessment Team, and the Biological Augmentation Team. A diverse directorate, the synergistic CBRN response capabilities are a commander’s force protection tool, critical to maintaining combat power.

An ever-expanding global military mission necessitates quick and accurate identification of biological hazards, whether naturally occurring or man-made. Coupled with an ever-present threat of biological attack, an expanded U.S. presence in worn-torn locations like Southwest Asia presents unique public health challenges. We must heed modern day "lessons learned" from Operation Desert Shield and the Soviet Afghanistan Campaign and guard against rapid incapacitation of troop strength from endemic disease and biological attack. To minimize readiness impacts, field hygiene is enforced, and research on better medical countermeasures such as antibiotics and vaccines continues. However, there are no preventions or remedies for all military-relevant infectious diseases or biological agents. A deployable, streamlined, self-contained diagnostic and public health surveillance laboratory capability with a reach-back communication is critical to meeting global readiness challenges. Current deployable laboratory packages comprise primarily diagnostic or environmental sample testing capabilities. Discussion will focus on critical components needed to improve existing laboratory assets, and to facilitate deployment of small, specialized packages far forward. The ideal laboratory model described will become an essential tool for the Combatant or Incident Commander to maintain force projection in the expeditionary environment.

A novel linear ion trap (LIT) system for protein bio-marker identification that provides significant improvement in sensitivity and dynamic range over current mass spectrometry approaches is being developed. The improvements arise from 1-2 orders of magnitude improvement in ion collection efficiency and 1-2 orders of magnitude improvement in ion storage capacity. These improvements will translate directly into improved sensitivity and dynamic range. The end goal is an instrument capable of identifying low femtomole levels of bio-agent protein markers present in complex mixtures. A unique aspect of the proposed instrument will be the potential for detection and identification of a priori unknowns. This approach is not restricted to searching for targeted agents and can be used to identify unanticipated threats. This effort leverages previous work with three-dimensional ion trap mass spectrometry that has recently been demonstrated to provide the capability for concentration, purification, and identification of protein biomarkers within the ion trap. This effort will take advantage of significant potential improvements in overall performance. This paper will discuss the historical work with three-dimensional ion traps and show preliminary results from the linear ion trap.

Photoacoustic spectroscopy is a useful monitoring technique that is well suited for trace gas detection. The technique also possesses favorable detection characteristics when the system dimensions are scaled to a micro-system design. The objective of present work is to incorporate two strengths of the Army Research Laboratory (ARL), piezoelectric microelectromechanical systems (MEMS) and chemical and biological sensing into a monolithic MEMS photoacoustic trace gas sensor. A miniaturized macro-cell design was studied as a means to examine performance and design issues as the photoacoustics is scaled to a dimension approaching the MEMS level. Performance of the macro-cell was tested using standard organo-phosphate nerve gas simulants, Dimethyl methyl phosphonate (DMMP) and Diisoprpyl methyl phosphonate (DIMP). Current MEMS work centered on fabrication of a multi-layer cell subsystem to be incorporated in the full photoacoustic device. Preliminary results were very positive for the macro-cell sensitivity (ppb levels) and specificity indicating that the scaled cell maintains sensitivity. Several bonding schemes for a three-dimension MEMS photoacoustic cavity were investigated with initial results of a low temperature AuSn bond proving most feasible.

A new sensor for highly toxic species including chemical warfare (CW) agents has been developed. This sensor is based on a unique CW indicating chromophore (CWIC) developed by Professor Tim Swager at MIT. The CWIC was designed to be sensitive to the reactivity that makes these chemicals so toxic. Since it requires the reactivity of the agent to be detected, the CWIC technology has shown remarkable selectivity for nerve agent surrogates and some other highly toxic species, thereby demonstrating the potential to provide low false alarm rate detection. Since the chromophore has mini-mal fluorescence prior to reaction with an electrophilic and toxic chemical, the sensor acts in a dark field fluorescence mode. This provides the sensor with exceptional sensitivity and a potential to detect priority analytes well below levels detected by current hand held sensors. Finally, it is based on a simple optical detection scheme that enables small and rugged sensors to be developed and produced at a low enough cost so they can be widely utilized.

Precision micro-dispensing based upon ink jet technology has been used in medical diagnostics since the early nineties, and now is moving into a wide range of applications. Ink-jet printing technology can reproducibly dispense spheres of fluid with diameters of 15 to 100 μm (2pl to 5nl) at rates of 0 - 25,000 per second from a single drop-on-demand printhead. The deposition is non-contact, data-driven and can dispense a wide range of fluids. It is a key enabling technology in the development of Bio-MEMS devices, Sensors, Micro-fluidic devices and Micro-optical systems. In this paper, we will discuss the use of this technology for miniature chemical and bio-molecular sensors and will review in detail specific applications.

There is an urgent need for efficient, rapid, and inexpensive collection techniques for pathogen detection in environmental samples. For over 40 years membrane filters have been played an important role in the collection of radiological and chemical samples from the environment. Recently inorganic mesoporous alumina membranes have been developed with high densities of highly uniform size pores. Measurements of the physical properties of membranes with 100 nm and 200 nm pores revealed that a transition state hydrodynamic condition exists in the pores that enhanced the permeability of the membranes to gases. These membranes were also found to maintain a high permeability at relative humidities as high as 98% and to be capable of supporting pressures as high as 65 psi. A high density poly(ethylene glycol) monolayer was grafted to the alumina membranes to minimize the adhesion of aerosols to the membranes. This hybrid membrane allowed B. globigii spores to be extracted from aqueous solutions with 96.7% efficiencies. Multi-day collection runs with a prototype collector demonstrated that an instrument based on these membranes could be operated in complex environmental conditions.

Surface enhanced Raman spectroscopy (SERS) has been used as a tool to investigate spectral differences of bacterial endospores. Ultimately, this method could be used as a smart and rapid on-site detector for biological warfare agents. However, due to the spectral complexity and the relative size of spores to the substrate features, a rigidly defined substrate is necessary for reproducible characterization. We are investigating many of the reported substrate classes such as: Nano-sphere lithography (NSL), Film over nano-sphere (FONS), nano-shells, electrochemically roughened metals, and dispersed and immobilized colloids. The key aspects of this work include discerning what architectural pattern provides the largest enhancement and reproducibility when sampling the spore coat and whether some method of immobilization, or attraction, of the spores to the surface is necessary. We will present preliminary results of bacterial spore identification as well as a comparison of the substrates studied.

DNA capture elements (DCEs; aptamers) are artificial DNA sequences, from a random pool of sequences, selected for their specific binding to potential biological warfare agents. These sequences were selected by an affinity method using filters to which the target agent was attached and the DNA isolated and amplified by polymerase chain reaction (PCR) in an iterative, increasingly stringent, process. Reporter molecules were attached to the finished sequences. To date, we have made DCEs to Bacillus anthracis spores, Shiga toxin, Venezuelan Equine Encephalitis (VEE) virus, and Francisella tularensis. These DCEs have demonstrated specificity and sensitivity equal to or better than antibody.

This paper presents an alternative, computational intelligence based paradigm for biological attack detection. Conventional approaches to this difficult problem include sensor technologies and analytical modeling approaches. However, the processes that constitute the environmental background as well as those which occur as the result of an attack are extremely complex. This phenomenological complexity, in terms of both physics and biology aspects, is a challenge difficult to overcome by conventional approaches. In contrast to such approaches, the proposed approach is centered on automatic learning to discriminate between sensor signals that are in a normal range from those that are likely to represent a biological attack. It is argued that constructing machine learning methods robust enough to perform such a task is often more feasible than constructing an adequate model that could form a basis for bioattack detection. The paper discusses machine learning and multisensor information fusion methods in the context of biological attack detection in a subway environment, including recognition architecture and its components. However, the applicability of the proposed approach is much broader than the subway bioattack protection case, extending to a wide range of CBR defense applications.

Biosensors could consist of hybrids such as a biological nerve cell grown on a suitable silicon substrate. We will assume a hybrid system consisting of a dendritic tree for input, a cell soma and an axon for output transmission. Such a system is almost achievable with current technology. We will discuss how to model the action potential of the nerve cell in such a hybrid system so that we can efficiently recognize toxins introduced on the input side (the dendritic subsystem) from changes we observe on the output side. We first discuss an abstract model of how a given toxin would influence the structure of the action potential of a biological nerve cell. It is known that the action potential of such a cell is influenced at several times scales: (1) milliseconds: changes in ion flux due to alterations in standard Hodgkin - Huxley voltage activated gates and (2) tens to hundreds of milliseconds: changes in the structure of ligand operated gates due to the creation of new proteins via requests to the nerve cell's nuclear material (genome). The classical Hodgkin - Huxley model consists of a number of nonlinear gating coefficients that give rise in even a simple model to 38 independently modifiable parameters. We discuss how the influences of type one and two can be modeled using a alterations to these parameters and show that a given toxin can be associated with a toxin signature corresponding to perturbations from the standard values of these coefficients. Finally, we show how these ideas can be used to determine low dimensional feature vectors for recognition purposes. We also discuss how a low dimensional biological feature vector could be used to obtain similar results.

This paper demonstrates the use of a high fidelity hyperspectral scene simulation tool, called MCScene, to generate realistic thermal infrared scenes that can be used for algorithm development efforts, such as gas plume detection algorithms. MCScene is based on a Direct Simulation Monte Carlo (DSMC) approach for modeling 3D atmospheric radiative transport, as well as spatially inhomogeneous surfaces including surface BRDF effects. Synthetic “groundtruth” is specified as surface and atmospheric property inputs, and it is practical to consider wide variations of these properties. The model includes treatment of land and ocean surfaces, 3D terrain and bathymetry, 3D surface objects, and effects of finite clouds with surface shadowing. The computed hyperspectral data cubes can supplement field validation data for algorithm development. Sample calculations presented in this paper include a thermal infrared simulation for a desert scene that includes a gas plume produced by an industrial complex. This scene was derived from an AVIRIS visible to SWIR HSI data collect over the Virgin Mountains in Nevada. The data has been extrapolated to the thermal IR and a representative industrial site and plume have been added to the scene.

Laser-induced fluorescence provides a real-time technique for detecting airborne pathogens. Discrimination between biological and non-biological particles can be improved by simultaneously testing for more than one of the several common biofluorophores. Typically, this requires excitation with two or more laser wavelengths, considerably increasing the cost, size and complexity of sensors based on mainframe lasers. Recent advances in UV-emitting AlGaN diode lasers present an opportunity for compact and inexpensive multi-wavelength excitation. In this paper, we will present a model for choosing the best excitation wavelengths and emission bands for discriminating between biological and non-biological particles. We will discuss recent advances in detection, and present fluorescence photon counting experimental results. We will describe techniques for simultaneous excitation and detection at multiple wavelengths to improve selectivity and guard against false positives.

A compact ultraviolet lidar stand-off sensor was recently developed and field-tested for detection of bio warfare (BW) agent stimulant aerosols and interferents. It employed a low-power (~5mW), continuous-wave, 375nm semiconductor ultraviolet optical source (SUVOS) laser diode that was modulated at high-speed with a pseudo-random (PR) code to provide range-resolved lidar detection of both aerosol elastic scattering and fluorescence. The sensor incorporated a 150mm diameter receiver telescope and 3 photon-counting detection channels centered at 375nm, 440nm, and 550nm. Aerosol elastic and fluorescence lidar profiles were obtained by correlating the signal photon-counts with the PR code. Tests of the lidar were performed first with simulants released in the Large Aerosol Chamber at Edgewood Chemical and Biological Center, MD at a lidar range of only 7.5m. The second phase of testing was done at Dugway Proving Ground, UT. Here the lidar was continuously scanned (± 13°) in a horizontal plane to detect downwind simulant and interferent aerosol disseminations at ranges of several hundred meters. Preliminary analyses of these tests show that the lidar detected fluorescence from the BW simulants at ranges up to 100m, and elastic scattering from aerosols up to 350m.

The US Army Edgewood Chemical Biological Center is the leader in development of military systems for chemical and biological defense, in collaboration with all Services, other Government laboratories, academia, and industry. Chemical and biological optical sensing principles, unique capabilities, state-of-the-art sensors, and emerging technologies will be discussed.

The overall objective is to assess the detection performance of LWIR (long wavelength infrared) coherent Lidar systems that potentially possess enhanced effluent detection capabilities. Previous work conducted by Los Alamos has demonstrated that infrared DIfferential Absorption Lidar (DIAL) is capable of detecting chemicals in plumes from long standoff ranges. Our DIAL approach relied on the reflectivity of topographical targets to provide a strong return signal. With the inherent advantage of applying heterodyne transceivers to approach single-photon detection in LWIR, it is projected that marked improvements in detection range or in spatial coverage can be attained. In some cases, the added photon detection sensitivity could be utilized for sensing “soft targets”, such as atmospheric and threat aerosols where return signal strength is drastically reduced, as opposed to topographical targets. This would allow range resolved measurements and could lead to the mitigation of the limiting source of noise due to spectral/spatial/temporal variability of the ground scene. The ability to distinguish normal variations in the background from true chemical signatures is crucial to the further development of sensitive remote chemical sensing technologies. One main difficulty in demonstrating coherent DIAL detection is the development of suitable heterodyne transceivers that can achieve rapid multi-wavelength tuning required for obtaining spectral signature information. LANL has recently devised a novel multi-wavelength heterodyne transceiver concept that addresses this issue. A 5-KHz prototype coherent CO₂ transceiver has been constructed and is being now used to help address important issues in remote CBW agent standoff detection. Laboratory measurements of signal-to-noise ratio (SNR) will be reported. Since the heterodyne detection scheme fundamentally has poor shot-to-shot signal statistics, in order to achieve sensitive detection limits, favorable averaging statistics have to be validated. The baseline coherent DIAL detection sensitivity that can be achieved averaging multiple laser pulses and by comparisons of different wavelengths will be demonstrated. Factors that are presently limiting performance and attempts to circumvent these issues will be discussed.

In a continuing series of experiments designed to determine the spectral extinction cross-section of bacterial aerosols, a White cell transmissometer was constructed to obtain stable, long path length measurements. Laser transmittance at different aerosol concentrations allows calculation of the extinction cross-sections. Using three lasers, a HeNe at 543 nm, a diode pumped solid state at 1.064 μm, and a fiber laser at 1.558 μm, the data on the spectral cross section of Bacillus globigi (BG) was extended into the NIR. The extinction cross-section was estimated to be 2.58 x 10^-8 cm² at 543 nm wavelength during the 2003 measurements, which is consistent with previous measurements at this wavelength. In the NIR, the cross section of BG was determined to be 2.71 x 10^-8 cm² and 2.32 x 10^-8 cm² at 1.064 and 1.558 μm, respectively. To validate the measurements, Mie calculations were used to continuously represent the extinction and backscatter cross-sections from the UV to NIR. The efforts described herein are intended to explore the various optical scatter features that may allow discrimination of biological pathogens from naturally occurring aerosol constituents such as pollen, dust, etc.

Analytical results and tradeoffs are reported for an aerosol lidar system that is intended to serve as a referee during testing of standoff bio-aerosol detection systems. The lidar system is still under development by Dugway Proving Grounds -- results from the operational system are not included in this paper. The recommended configuration of the lidar system is to use a 1064 nm lidar in elastic mode to measure the concentration of the aerosol, and a 355 nm excitation to measure the fluorescence of the bio-aerosol. Both of these measurements are important in scoring the performance of the systems that will be tested at DPG. Performance tradeoffs and predictions are presented primarily for the elastic mode lidar. The elastic mode lidar is designed to make measurements out to ranges of approximately 15 km. The UV fluorescence mode of operation is intended to support discrimination of bio-aerosols from non-biological aerosols, and is only required to operate at a range of 1 km. The optical design of the proposed telescope supports dual wavelength operation, allows for effective TV camera imaging for test and alignment support, and tailors the optical overlap function for the UV and near IR lidar to optimize the performance of both subsystems.

Detection and estimation of materials in the atmosphere by lidar has heretofore required that the spectral dependence of the relevant cross section coefficients -- backscatter in the case of aerosols and absorptivity for vapors -- be known in advance. While this typically is a reasonable assumption in the case of vapor, the aerosol backscatter coefficients are complicated functions of particle size, shape, and refractive index, and are therefore usually not well characterized a priori. Using incorrect parameters will give biased concentration estimates and impair discrimination ability. This paper describes an approach for estimating both the spectral dependence of the aerosol backscatter and relative concentration range-dependence of a set of materials using multi-wavelength lidar. The approach is based on state-space filtering that applies a Kalman filter in range for concentration, and updates the backscatter spectral estimates through a sequential least-squares algorithm at each time step. The method is illustrated on aerosol-release data of the bio-simulant ovalbumin collected by ECBC during field tests in 2002, as well as synthetic data sets.

Infrared spectroscopy is routinely employed for the identification of organic molecules and, more recently, for the classification of biological materials. We have developed a sample collection method that facilitates infrared analysis of airborne particulates using a diffuse reflectance (DR) technique. Efforts are underway to extend the method to include simultaneous analysis of vapor phase organics by using adsorbent substrates compatible with the DR technique. This series of laboratory results provides proof-of-principle for both the sample collection and data collection processes. Signal processing of the DR spectra is shown to provide rapid qualitative identification of representative aerosol materials, including particulate matter commonly found in the environment. We compare the results for such materials as bacterial spores, pollens and molds, clays and dusts, smoke and soot. Background correction analysis is shown to be useful for differentiation and identification of these constituents. Issues relating to complex mixtures of environmental samples under highly variable conditions are considered. Instrumentation development and materials research are now underway with the aim of constructing a compact sampling system for near real-time monitoring of aerosol and organic pollutants. A miniature, tilt-compensated Fourier transform spectrometer will provide spectroscopic interrogation. A series of advanced digital signal processing methods are also under development to enhance the sensor package. The approach will be useful for industrial applications, chemical and biological agent detection, and environmental monitoring for chemical vapors, hazardous air pollutants, and allergens.

Pacific Advanced Technology has developed a small hand held imaging spectrometer, Sherlock, for gas leak and aerosol detection and imaging. The system is based on a patented technique, (IMSS Image Multi-spectral Sensing), that uses diffractive optics and image processing algorithms to detect spectral information about objects in the scene of the camera. This cameras technology has been tested at Dugway Proving Ground and Dstl Porton Down facilities looking at Chemical and Biological agent simulants. In addition to Chemical and Biological detection, the camera has been used for environmental monitoring of green house gases and is currently undergoing extensive laboratory and field testing by the Gas Technology Institute, British Petroleum and Shell Oil for applications for gas leak detection and repair. In this paper we will present some of the results from the data collection at the TRE test at Dugway Proving Ground during the summer of 2002 and laboratory testing at the Dstl facility at Porton Down in the UK in the fall of 2002.

Performance capabilities of the developed multifrequency lidar system based on the pulsed chain chemical DF laser are presented in the paper. The purpose of the research and development is the creation of a lidar for the detection and recognition of aerosol impurities in the atmosphere, and the main attention is paid to realization of these resources of the system. Based on the numerical modeling data, the restrictions to the statement of the recognition problem are formulated. It is shown that the lidar performance characteristics allow to detect and to recognize aerosol impurities in the atmosphere for distances of up to 10 - 15 km.

A remote atmospheric breakdown (RAB) is a very rich source of ultraviolet (UV) and broadband visible light that could provide the early warning to the presence of CW/BW agents through spectroscopic detection, identification and quantification at extended standoff distances. A low-intensity negatively chirped laser pulse propagating in air compresses in time due to linear group velocity dispersion and focuses transversely due to non-linear effects resulting in rapid laser intensity increase and ionization near the focal region that can be located kilometers away from the laser system. Proof of principle laboratory experiments are being performed at the Naval Research Laboratory on the generation of RAB and the spectroscopic detection of mock BW agents. We have demonstrated pulse compression and focusing up to 105 meters in the laboratory using femtosecond pulses generated by a high power Ti:Sapphire laser. We observed nonlinear modifications to the temporal frequency chirp of the laser pulse and their effects on the laser compression and the positions of the final focus. We have generated third harmonics at 267 nm and white light in air from the compressed pulse. We have observed fluorescence emission from albumin aerosols as they were illuminated by the compressed femtosecond laser pulse.

Standoff detection, identification and quantification of chemicals require sensitive spectrometers with calibration capabilities. Recent developments in LWIR focal plane arrays combined with the mastering of Fourier-Transform Spectrometer technology allow the realization of an imaging spectrometer specifically designed for chemical imaging. The spectral and radiometric calibration of the instrument enables the processing of the data to detect the chemicals with spectral signatures in the 8-12 μm region. Spectral images are processed and the contrast between different pixels is used to map the chemicals. Telops is building the field-portable instrument. This paper presents the requirements for chemical detection in the LWIR, how the system is broken down into different modules and the details of each of these modules: calibration, interferometer, datacube acquisition and processing, and the main controller. The system has real-time processing capabilities of the measured data. Performance prediction is presented as well.

A Process Query System (PQS) has the capability of filtering large volumes of real time data originating from a field of networked Physical Sensors. Modern air quality monitoring techniques such as Fourier Transform Infra-Red (FTIR) spectroscopy will eventually provide massively distributed real time contamination data at high fidelity. As large networks of these sensors are deployed, improved techniques of data retrieval and assimilation will be required. The case of detecting a diffusion event such as a hazardous chemical plume is considered. In this scenario, a plume model based on an Ensemble Kalman Filter (EnKF)is submitted to the PQS which manages multiple hypotheses explaining the current observations. The feasibility of such an application is demonstrated and results from preliminary simulations are presented.