Detection of BG Aerosol by Passive Standoff FTIR Radiometry

The proliferation of chemical and biological (CB) weapons continues to be a serious threat to world security. There is a growing need for tactical and strategic surveillance systems that are capable of providing timely and accurate standoff CB threat assessments. There are currently a number of passive infrared standoff techniques that show promise for the detection of chemical warfare agents; however, their implementation in the detection of biological agents is a relatively new venture and a greater effort is required in determining their potential role in this field. Hence, a study has been undertaken that focuses on the passive standoff detection and identification of the biological warfare simulant, Bacillus subtilis (BG), with the Compact ATmospheric Sounding Interferometer (CATSI) sensor. The CATSI sensor is a double-beam Fourier-transform infrared (FTIR) spectrometer having optical background suppression capabilities that provides a spectrally simplified signature of the remote cloud. This system is particularly suited for the remote monitoring of weak IR emissions in the presence of a strong atmospheric background, as in the case of bio-aerosol detection. This study is based on recent spectral measurements of BG clouds obtained during the Technology Readiness Evaluation trial held at Dugway Proving Ground in July 2002. This study demonstrates that the standoff detection and identification of bio-aerosols with passive IR systems is very challenging; however, the results appear to be promising. We have shown that BG clouds can be detected passively at a distance of up to 3 km for near-horizontal path scenarios. It has been found that the low thermal contrast (few tenths of a degree) between the BG cloud and the background yields weak but observable spectral signatures. The processing of the spectral signatures has provided a rough estimate of BG amounts with column densities (cl) varying from 300 to 3000 mg/m2. It has also been shown through simulations using the Line-by-line Radiative Transfer Model (LBLRTM) that there exist a variety of specific surveillance scenarios for which current and future passive hyperspectral sensors can be optimized to adequately detect and identify bio-aerosols.