Modified version of the Chahine algorithm to invert spectral extinction data for particle sizing

Abstract

A modified version of the nonlinear iterative Chahine algorithm is presented and applied to the inversion of spectral extinction data for particle sizing. Simulated data were generated in a λ range of 0.2-2 µm,and particle-size distributions were recovered with radii in the range of 0.14-1.4 µm. Our results show that distributions and sample concentrations can be recovered to a high degree of accuracy when the indices of refraction of the sample and of the solvent are known. The inversion method needs no a priori assumptions and no constraints on the particle distributions. Compared with the algorithm originally proposed by Chahine, our method is much more stable with respect to random noise, permits a better quality of the retrieved distributions, and improves the overall reliability of the fitting. The accuracy and resolution of the method as functions of noise were investigated and showed that the retrieved distributions are quite reliable up to noise levels of several rms percent in the data. The sensitivity to errors in the real and imaginary parts of the refraction index of the particles was also examined.

Night-Time Ground Hyperspectral Imaging for Urban-Scale Remote Sensing of Ambient PM. I. Aerosol Optical Thickness Acquisition

“…Moreover, optimally, to obtain the best retrieval the spectral range for analysis should be dynamic and change according to the characteristics of the true environmental (i.e., aerosol) conditions. Many studies in the past 30 years aimed at retrieving bi-or tri-modal atmospheric aerosol PSD from spectral optical thicknesses in the UV-visible and the NIR ranges, acquired by satellite and airborne mounted instruments (Heintzenberg et al 1981;Ferri et al 1995;Wang et al 1996;Franssens 2001;Kocifaj and Horvath 2005;Kuzmanoski et al 2007) or by ground-based radiometers (Dubovik et al 2002;Wang et al 2002). In general, correlations between spectral optical thickness and ground-level fine PM concentrations are highly dependent on site-specific attributes, seasonality, and meteorological conditions such as the relative humidity (RH) and mixing layer height (Schäfer et al 2008).…”

Particle analysis by transmission fluctuation spectrometry with temporal correlation in multiphase flow

“…The vector M j is a quantity of the spectrum corresponding to the jth correlation time τ j , and it can be obtained from the measurement. The particle size distribution and the concentration, expressed as C i , can be retrieved from the linear equation by using the well-known inversion algorithm [15][16][17]. Including the high concentration effects, Eq.…”

An efficient gradient method for maximum entropy regularizing retrieval of atmospheric aerosol particle size distribution function

“…To overcome the oscillations in recovering of the particle size distribution function n(r), various techniques have been developed, e.g., direct regularization methods (Bockmann, 2001;Phillips, 1962;Shaw, 1979;Shifrin & Zolotov, 1996;Twomey, 1963;Wang, Fan, Feng, Yan, & Guan, 2006) and iterative methods (Bockmann & Kirsche, 2006;Chahine, 1970;Ferri, Bassini, & Paganini, 1995;Grassl, 1971;Lumme & Rahola, 1994;Twomey, 1975;Voutilainenand & Kaipio, 2000;Wang, Fan, & Feng, 2007;Yamamoto & Tanaka, 1969). We consider an entropy constrained regularization model in this paper, which is usually used for moment problems in different applications (Wang, 2007).…”