Optimization of measurement angles for soot aggregate sizing by elastic light scattering, through design-of-experiment theory

Abstract

In multiangle elastic light scattering (MAELS) experiments, the morphology of aerosolized particles is inferred by shining collimated radiation through the aerosol and then measuring the scattered light intensity over a set of angles. In the case of soot-laden-aerosols MAELS can, in principle, be used to recover the size distribution of soot aggregates, although this involves solving an ill-posed inverse problem. This paper presents a design-of-experiment methodology for identifying the set of angles that maximizes the information content of the angular scattering measurements, thereby minimizing the ill-posedness of the underlying inverse problem. While the optimized angles highlight the physical significance of the scattering regimes, they do not improve the accuracy of size distributions reconstructed from simulated experimental data.

The Absorption Ångström Exponent of black carbon: from numerical aspects

“…Geometries of numerically generated BC aggregates with different particle geometries, i.e. loose aggregate (left) for Fresh BC, compact aggregate (middle) for Compact BC, and coated aggregate (right) for Coated BC, and some examples of realistic BC images for comparison (Burr et al, 2012;Lewis et al, 2009;Freney et al, 2010). …”

“…A tunable particle-cluster aggregation algorithm is applied to generate 30 the FAs (Filippov et al, 2000;Liu et al, 2012), and the coating sphere is added with its center located at the mass center of compact FA. Three transmission or scanning electron microscope images of BC particles are also given in the figure for comparison (Burr et al, 2012;Lewis et al, 2009;Freney et al, 2010), and we can see that the numerically generated 1997; Sorensen, 2000;Brasil et al, 2000;Chakrabarty et al, 2014) have been widely used for numerical studies, and are applied here to represent realistic BC particles (Liu and Mishchenko, 2005;Smith and Grainger, 2014;Li et al, 2016). The diameter of the same-sized monomers is set to 30 nm, and the fractal prefactor of 1.2 is used.…”

T-matrix modeling of linear depolarization by morphologically complex soot and soot-containing aerosols

“…They can also limit atmospheric visibility and have a highly negative effect on human health [12]. Owing to the notoriously complex morphology of such particles [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], theoretical modeling of their scattering and absorption properties is a highly nontrivial task and has often been based on approximate approaches with poorly defined accuracy and range of applicability. However, the growing need for much improved knowledge of BC and BC-containing aerosols and their climatic, ecological, and visibility effects imposes strict limitations on quantitative uncertainties in particle scattering and absorption properties entering optical characterization and remote sensing applications as well as atmospheric radiation budget computations [31][32][33][34][35][36][37][38][39][40][41][42].…”

“…This characteristic is often measured in laboratory experiments [110,111] and, in the case of the exact backscattering direction, in lidar remote-sensing research [112][113][114][115], but appears to be studied inadequately on the basis of first-principle numerical computations. Our paper is motivated by the recent publication [98] in which the FDTDM was used to compute the LDR for smoke clusters of up to four monomers in order to analyze implications of depolarization lidar observations from the Cloud-Aerosol Lidar and [29], (b) Scanning electron microscope image of a collapsed (fractal dimension approaching 3) chamise smoke particle [24], (c) TEM images of nearly spheroidal soot particles [28], (d) TEM image of an aggregate formed by a soot cluster and an ammonium sulfate particle [15] and (e) TEM image of internally mixed soot aggregates. Arrows show solid phases inside aqueous droplets [27].…”

Sizing aerosolized fractal nanoparticle aggregates through Bayesian analysis of wide-angle light scattering (WALS) data

“…Recovering the aggregate distribution parameters from ELS measurements is complicated by the fact that the measurement equation is a Fredholm integral equation of the first kind (IFK) [18,19]. It is straightforward to predict the angular distribution of scattered light for a particular aggregate morphology and size distribution by integrating the scattered light contributed by a particular aggregate over all size classes.…”