Optical properties of coated black carbon aggregates: numerical simulations, radiative forcing estimates, and size-resolved parameterization scheme

The formation of black carbon fractal aggregates (BCFAs) from combustion and subsequent ageing involves several stages resulting in modifications of particle size, morphology, and composition over time. To understand and quantify how each of these modifications influences the BC radiative forcing, the optical properties of BCFAs are modelled. Owing to the high computational time involved in numerical modelling, there are some gaps in terms of data coverage and knowledge regarding how optical properties of coated BCFAs vary over the range of different factors (size, shape, and composition). This investigation bridged those gaps by following a state-of-the-art description scheme of BCFAs based on morphology, composition, and wavelength. The BCFA optical properties were investigated as a function of the radius of the primary particle (ao), fractal dimension (Df), fraction of organics (forganics), wavelength (λ), and mobility diameter (Dmob). The optical properties are calculated using the multiple-sphere T-matrix (MSTM) method. For the first time, the modelled optical properties of BC are expressed in terms of mobility diameter (Dmob), making the results more relevant and relatable for ambient and laboratory BC studies. Amongst size, morphology, and composition, all the optical properties showed the highest variability with changing size. The cross sections varied from 0.0001 to 0.1 µm2 for BCFA Dmob ranging from 24 to 810 nm. It has been shown that MACBC and single-scattering albedo (SSA) are sensitive to morphology, especially for larger particles with Dmob > 100 nm. Therefore, while using the simplified core–shell representation of BC in global models, the influence of morphology on radiative forcing estimations might not be adequately considered. The Ångström absorption exponent (AAE) varied from 1.06 up to 3.6 and increased with the fraction of organics (forganics). Measurement results of AAE ≫ 1 are often misinterpreted as biomass burning aerosol, it was observed that the AAE of purely black carbon particles can be ≫ 1 in the case of larger BC particles. The values of the absorption enhancement factor (Eλ) via coating were found to be between 1.01 and 3.28 in the visible spectrum. The Eλ was derived from Mie calculations for coated volume equivalent spheres and from MSTM for coated BCFAs. Mie-calculated enhancement factors were found to be larger by a factor of 1.1 to 1.5 than their corresponding values calculated from the MSTM method. It is shown that radiative forcings are highly sensitive to modifications in morphology and composition. The black carbon radiative forcing ΔFTOA (W m−2) decreases up to 61 % as the BCFA becomes more compact, indicating that global model calculations should account for changes in morphology. A decrease of more than 50 % in ΔFTOA was observed as the organic content of the particle increased up to 90 %. The changes in the ageing factors (composition and morphology) in tandem result in an overall decrease in the ΔFTOA. A parameterization scheme for optical properties of BC fractal aggregates was developed, which is applicable for modelling, ambient, and laboratory-based BC studies. The parameterization scheme for the cross sections (extinction, absorption, and scattering), single-scattering albedo (SSA), and asymmetry parameter (g) of pure and coated BCFAs as a function of Dmob were derived from tabulated results of the MSTM method. Spanning an extensive parameter space, the developed parameterization scheme showed promisingly high accuracy up to 98 % for the cross sections, 97 % for single-scattering albedos (SSAs), and 82 % for the asymmetry parameter (g).

Black carbon aerosol number and mass concentration measurements by picosecond short-range elastic backscatter lidar

Black carbon aerosol emissions are recognized as contributors to global warming and air pollution. There remains, however, a lack of in-situ techniques to remotely quantify black carbon aerosol particles with high range and time resolution. This article presents for the first time, to our knowledge, a direct and contact-free remote measurement of black carbon aerosol number and mass concentration less than ten of meters from the emission source. This is done with a novel picosecond short-range elastic backscatter lidar (PSR-EBL) technique. To address the complexity of retrieving lidar products at short measurement ranges, we apply a forward inversion method featuring radiometric lidar calibration. Our method is based on an extension of a well-established light-scattering model, the Rayleigh-Debye-Gans for Fractal-Aggregates (RDG-FA) theory, which computes an analytical expression for lidar parameters. These parameters are the backscattering cross-sections and the lidar ratio for black carbon fractal aggregates. Using a small-scale Jet A-1 kerosene pool fire, it is shown that our technique can quantify the aerosol number and mass concentration with centimetre range-resolution and millisecond time-resolution.

Predicting the Optical Properties of Arbitrarily Shaped Black Carbon Aerosols with Graph Neural Networks

<p>Aerosols sourced from combustion such as black carbon (BC) are important short-lived climate forcers whose direct radiative forcing and atmospheric lifetime depend on their morphology. These aerosols are typically fractal aggregates consisting of ~20-80 nm spheres. This complex morphology makes modeling their optical properties difficult, contributing to uncertainty in both their direct and indirect climate effects. Accurate and fast calculations of BC optical properties are needed for remote sensing inversions and for radiative forcing calculations in atmospheric models, but current methods to accurately calculate the optical properties of these aerosols such as the multi-sphere T-matrix method or generalized multiple-particle Mie Theory are computationally expensive and must be compiled in extensive data-bases off-line and then used as a look-up table. Recent advances in machine learning approaches have applied the graph convolutional neural network (GCN) to various physical science applications, demonstrating skill in generalizing beyond initial training data by exploiting and learning internal properties and interactions inherent to the larger system. Here we demonstrate for the first time that a GCN trained to predict the optical properties of numerically-generated BC fractal aggregates can accurately generalize to arbitrarily shaped aerosol particles, even over much larger aggregates than in the training dataset, providing a fast and accurate method to calculate aerosol optical properties in atmospheric models and for observational retrievals. This approach could be integrated into atmospheric models or remote sensing inversions to more realistically predict the physical properties of arbitrarily-shaped aerosol and cloud particles. In addition, GCN’s can be used to gain physical intuition on the relationship between large-scale properties (here of the radiative properties of aerosols) and small-scale interactions (here of the spheres’ positions and their interactions).</p>