Narrow Results

We investigate the use of multiple scattering via Multiple-Field-Of-View (MFOV) lidar signals to characterize bioaerosol particles size and concentration from ground based lidar over distances shorter than a few kilometers. The MFOV lidar signal is calculated for background aerosols at a wavelength 355 nm for a visibility of 30 km. The optical depths studied are small and the calculations are restricted to second order scattering. Also since background aerosols are constituted of relatively small particles which diffuse the light at large angles, the fields of view (FOV) range from 1 to 100 mrad full angle. We show that the MFOV lidar measurements contain exploitable information on particle size and extinction.

Standoff LIDAR detection of BW agents depends on accurate knowledge of the infrared and ultraviolet optical elastic scatter (ES) and ultraviolet fluorescence (UVF) signatures of bio-agents and interferents. MIT Lincoln Laboratory has developed the Standoff Aerosol Active Signature Testbed (SAAST) for measuring polarization-dependent ES cross sections from aerosol samples at all angles including 180° (direct backscatter) [1]. Measurements of interest include the dependence of the ES and UVF signatures on several spore production parameters including growth medium, sporulation protocol, washing protocol, fluidizing additives, and degree of aggregation. Using SAAST, we have made measurements of the polarization-dependent ES signature of Bacillus globigii (atropheaus, Bg) spores grown under different growth methods. We have also investigated one common interferent (Arizona Test Dust). Future samples will include pollen and diesel exhaust. This paper presents the details of the apparatus along with the results of recent measurements.

Identification of aerosol type and chemical composition may help to trace their origin and estimate their impact on land and people. Aerosols chemical composition, size distribution and particles shape, manifest themselves in their spectral scattering cross-section. In order to make a reliable identification, comprehensive spectral analysis of aerosol scattering should be carried out. Usually, spectral LIDAR measurements of aerosols are most efficiently performed using an Nd:YAG laser transmitter in the fundamental frequency and its 2^nd, 3^rd and 4^th harmonics. In this paper we describe automatic detection and identification of several aerosol types and size distributions, using a multispectral lidar system operating in the IR, NIR and UV spectral regions. The LIDAR transmitter is based on a single Nd:YAG laser. In addition to the 3^rd and 4^th harmonics in the UV, two optical parametric oscillator units produce the eye-safe 1.5 μm wavelength in the near IR and up to 40 separable spectral lines in the 8-11 μm IR. The combination of a wide spectral coverage required for backscattering analysis combined with fluorescence data, enable the generation of a large spectral data set for aerosols identification. Several natural and anthropogenic aerosol types were disseminated in controlled conditions, to test system capabilities. Reliable identification of transient and continuous phenomena demands fast and efficient control and detection algorithms. System performance, using the specially designed algorithms, is described below.

Fluid modeling covers a wide range of principles describing the motion of matter and energy in dependence on spatial scales, time scales and other attributes. In order to provide efficient numeric calculations, the information systems have to be developed for management, pre-processing, post-processing and visualization. In spite of that many software tools contain sophisticated subsystems for data management and implement advanced numerical algorithms, there is still need to standardize data inputs/outputs, wide used data analyses, and case oriented computational tools under one roof. Thus, the geographic information system (GIS) is used to satisfy all the requirements. As an example, the case study focused on dust dispersion above the surface coal mine documents the GIS ability to solve all the tasks. The input data are represented by terrain measurements of meteorological conditions and by estimates of the emission rates of potential surface dust sources. Remote sensing helps to identify and classify the coal mine surface in order to map erosion sites and other surface objects. GPS is used to improve the accuracy of the erosion site boundaries and to locate other point emission sources such as excavators, storage sites, and line emission sources such as conveyors and roads. The 3D mine surface for modeling of wind flows and dust dispersion is based on GPS measurements and laser scanning. All data inputs are integrated together with simulation outputs in the spatial database in the framework of the GIS project. In case of dispersion modeling, a few ways can be used to provide numeric calculations together with GIS analyses. The traditionally used way represents using of standalone simulation tools and the input/output data linkage through shared data files. The more advanced way is the implementation of dispersion models in the GIS environment. The methods are demonstrated by using U.S. EPA modeling tools and by linking standalone numerical calculations in the GIS environment with using case oriented programming libraries and GIS development tools.