Avishai Ben-David

Remote detection of biological aerosols at a distance of 3 km with a passive Fourier transform infrared (FTIR) sensor

Bio-aerosols containing Bacillus subtilis var. niger (BG) were detected at a distance of 3 km with a passive Fourier Transform InfraRed (FTIR) spectrometer in an open-air environment where the thermal contrast was low (~ 1 K). The measurements were analyzed with a new hyperspectral detection, identification and estimation algorithm based on radiative transfer theory and advanced signal processing techniques that statistically subtract the undesired background spectra. The results are encouraging as they suggest for the first time the feasibility of detecting biological aerosols with passive FTIR sensors. The number of detection events was small but statistically significant. We estimate the false alarm rate for this experiment to be 0.0095 and the probability of detection to be 0.61 when a threshold of detection that minimizes the sum of the probabilities of false alarm and of missed detection is chosen.

Estimation method for serial dilution experiments

Titration of microorganisms in infectious or environmental samples is a corner stone of quantitative microbiology. A simple method is presented to estimate the microbial counts obtained with the serial dilution technique for microorganisms that can grow on bacteriological media and develop into a colony. The number (concentration) of viable microbial organisms is estimated from a single dilution plate (assay) without a need for replicate plates. Our method selects the best agar plate with which to estimate the microbial counts, and takes into account the colony size and plate area that both contribute to the likelihood of miscounting the number of colonies on a plate. The estimate of the optimal count given by our method can be used to narrow the search for the best (optimal) dilution plate and saves time. The required inputs are the plate size, the microbial colony size, and the serial dilution factors. The proposed approach shows relative accuracy well within ±0.1log10 from data produced by computer simulations. The method maintains this accuracy even in the presence of dilution errors of up to 10% (for both the aliquot and diluent volumes), microbial counts between 10(4) and 10(12) colony-forming units, dilution ratios from 2 to 100, and plate size to colony size ratios between 6.25 to 200.

Cloud-droplet-size distribution from lidar multiple-scattering measurements.

A method for calculating droplet-size distribution in atmospheric clouds is presented, based on measurement of laser backscattering and multiple scattering from water clouds. The lidar uses a Nd:YAG laser that emits short pulses at a moderate repetition rate. The backscattering, which is composed mainly of single scattering, is measured with a detector pointing along the laser beam. The multiple scattering, which is mainly double scattering, is measured with a second detector, pointing at a specified angle to the laser beam. The domain of scattering angles that contribute to the doublescattering signal increases monotonically as the pulse penetrates the cloud. The water droplets within the probed volume are assumed to have a constant size distribution. Hence, from the double-scatteringmeasured signal as a function of penetration depth within the cloud, the double-scattering phase function of the scattering volume is derived. Inverting the phase function results in a cloud-droplet-size distribution in the form of a log-normal function.

Simultaneous estimation of aerosol cloud concentration and spectral backscatter from multiple-wavelength lidar data

We present a sequential algorithm for estimating both concentration dependence on range and time and backscatter coefficient spectral dependence of optically thin localized atmospheric aerosols using data from rapidly tuned lidar. The range dependence of the aerosol is modeled as an expansion of the concentration in an orthonormal basis set whose coefficients carry the time dependence. Two estimators are run in parallel: a Kalman filter for the concentration range and time dependence and a maximum-likelihood estimator for the aerosol backscatter wavelength and time dependence. These two estimators exchange information continuously over the data-processing stream. The state model parameters of the Kalman filter are also estimated sequentially together with the concentration and backscatter. Lidar data collected prior to the aerosol release are used to estimate the ambient lidar return. The approach is illustrated on atmospheric backscatter long-wave infrared (CO2) lidar data.

High pulse repetition frequency, multiple wavelength, pulsed CO 2 lidar system for atmospheric transmission and target reflectance measurements

A multiple wavelength, pulsed CO(2) lidar system operating at a pulse repetition frequency of 200 Hz and permitting the random selection of CO(2) laser wavelengths for each laser pulse is presented. This system was employed to measure target reflectance and atmospheric transmission by using laser pulse bursts consisting of groups with as many as 16 different wavelengths at a repetition rate of 12 Hz. The wavelength tuning mechanism of the transversely excited atmospheric laser consists of a stationary grating and a flat mirror controlled by a galvanometer. Multiple wavelength, differential absorption lidar (DIAL) measurements reduce the effects of differential target reflectance and molecular absorption interference. Examples of multiwavelength DIAL detection for ammonia and water vapor show the dynamic interaction between these two trace gases. Target reflectance measurements for maple trees in winter and autumn are presented.

Backscattering measurements of atmospheric aerosols at CO 2 laser wavelengths: implications of aerosol spectral structure on differential-absorption lidar retrievals of molecular species

The volume backscattering coefficients of atmospheric aerosol were measured with a tunable CO2 lidar system at various wavelengths in Utah (a desert environment) along a horizontal path a few meters above the ground. In deducing the aerosol backscattering, a deconvolution (to remove the smearing effect of the long CO2 lidar pulse and the lidar limited bandwidth) and a constrained-slope method were employed. The spectral shape beta(lambda) was similar for all the 13 measurements during a 3-day period. A mean aerosol backscattering-wavelength dependence beta(lambda) was computed from the measurements and used to estimate the error Delta(CL) (concentration-path-length product) in differential-absorption lidar measurements for various gases caused by the systematic aerosol differential backscattering and the error that is due to fluctuations in the aerosol backscattering. The water-vapor concentration-path-length product CL and the average concentration C = /L for a path length L computed from the range-resolved lidar measurements is consistently in good agreement with the water-vapor concentration measured by a meteorological station. However, I was unable to deduce, reliably, the range-resolved water-vapor concentration C(r), which is the derivative of the range-dependent product CL, because of the effect of residual noise caused mainly by errors in the deconvolved lidar measurements.

MUELLER MATRICES AND INFORMATION DERIVED FROM LINEAR POLARIZATION LIDAR MEASUREMENTS : THEORY

A Mueller matrix M is developed for a single-scattering process such that G(theta, phi) = T (phi(a))M T (phi(p))u, where u is the incident irradiance Stokes vector transmitted through a linear polarizer at azimuthal angle phi(p), with transmission Mueller matrix T (phi(p)), and G(theta, phi) is the polarized irradiance Stokes vector measured by a detector with a field of view F, placed after an analyzer with transmission Mueller matrix T (phi(a)) at angle phi(a). The Mueller matrix M is a function of the Mueller matrix S (theta) of the scattering medium, the scattering angle (theta, phi), and the detector field of view F. The Mueller matrixM is derived for backscattering and forward scattering, along with equations for the detector polarized irradiance measurements (e.g., cross polarization and copolarization) and the depolarization ratio. The information that can be derived from the Mueller matrix M on the scattering Mueller matrixS (theta) is limited because the detector integrates the cone of incoming radiance over a range of azimuths of 2pi for forward scattering and backscattering. However, all nine Mueller matrix elements that affect linearly polarized radiation can be derived if a spatial filter in the form of a pie-slice slit is placed in the focal plane of the detector and azimuthally dependent polarized measurements and azimuthally integrated polarized measurements are combined.

Multiple-scattering transmission and an effective average photon path length of a plane-parallel beam in a homogeneous medium

A two-stream radiative transfer model is used to derive expressions for the multiple-scattered transmitted flux (including single-scattering contributions) and the total effective average photon path length on transmission of a normally incident plane-parallel beam on a homogeneous layer characterized by the optical depth, the single-scattering albedo, and the asymmetry parameter of the scatterers. The results are simple analytical expressions that are useful for modifying the Beer-Lambert transmission law for a thick scattering medium in which the multiple-scattering contribution to the transmission is not negligible.

Probability theory for 3-layer remote sensing radiative transfer model: univariate case

A probability model for a 3-layer radiative transfer model (foreground layer, cloud layer, background layer, and an external source at the end of line of sight) has been developed. The 3-layer model is fundamentally important as the primary physical model in passive infrared remote sensing. The probability model is described by the Johnson family of distributions that are used as a fit for theoretically computed moments of the radiative transfer model. From the Johnson family we use the SU distribution that can address a wide range of skewness and kurtosis values (in addition to addressing the first two moments, mean and variance). In the limit, SU can also describe lognormal and normal distributions. With the probability model one can evaluate the potential for detecting a target (vapor cloud layer), the probability of observing thermal contrast, and evaluate performance (receiver operating characteristics curves) in clutter-noise limited scenarios. This is (to our knowledge) the first probability model for the 3-layer remote sensing geometry that treats all parameters as random variables and includes higher-order statistics.

Multiple-scattering effects on differential absorption for the transmission of a plane-parallel beam in a homogeneous medium

The transmission, including all scattering orders, of a plane-parallel beam in a homogeneous scattering medium containing aerosols (e.g., water cloud) mixed with an absorbing gas (e.g., ozone) is computed with a two-stream radiative transfer model. From differential transmission the concentration of the gas is deduced. The effect of multiple-scattering on the deduced concentration is shown for conservative scattering aerosols for which the multiple scattering by the aerosols is differentially absorbed by the gas and for nonconservative scattering aerosols for which the multiple scattering is differentially absorbed by the aerosols as well as differentially absorbed by the gas. The two-stream analytical model (with no dependence on the field of view) shows good qualitative agreement (especially for a small field of view) with a numerical radiative transfer model in which the trace gas concentration is computed for the different detectors field of view.