Applying machine learning to estimate the optical properties of black carbon fractal aggregates

2018 ◽  
Vol 215 ◽  
pp. 1-8 ◽  
Author(s):  
Jie Luo ◽  
Yongming Zhang ◽  
Feng Wang ◽  
Jinjun Wang ◽  
Qixing Zhang
Keyword(s):  
Machine Learning ◽  
Optical Properties ◽  
Black Carbon ◽  
Fractal Aggregates

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

2021 ◽  
Vol 21 (17) ◽  
pp. 12989-13010
Author(s):  
Baseerat Romshoo ◽  
Thomas Müller ◽  
Sascha Pfeifer ◽  
Jorge Saturno ◽  
Andreas Nowak ◽  
...  
Keyword(s):  
Optical Properties ◽  
Black Carbon ◽  
Cross Sections ◽  
Asymmetry Parameter ◽  
Radiative Forcing ◽  
Single Scattering ◽  
Parameterization Scheme ◽  
Fractal Aggregates ◽  
Mobility Diameter ◽  
The Cross

Abstract. 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).

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

2021 ◽  
Author(s):  
Kara D. Lamb ◽  
Pierre Gentine
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Black Carbon ◽  
Radiative Forcing ◽  
Training Data ◽  
Radiative Properties ◽  
Training Dataset ◽  
Small Scale ◽  
Atmospheric Models ◽  
Fractal Aggregates

<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>

scholarly journals Two-step machine learning enables optimized nanoparticle synthesis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Flore Mekki-Berrada ◽  
Zekun Ren ◽  
Tan Huang ◽  
Wai Kuan Wong ◽  
Fang Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Properties ◽  
Materials Science ◽  
Nanoparticle Synthesis ◽  
Bayesian Optimization ◽  
Absorbance Spectrum ◽  
Nanomaterial Synthesis ◽  
Target Spectrum ◽  
Colour Palette ◽  
Algorithmic Framework

AbstractIn materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

scholarly journals Sensitivity of aerosol radiative effects to different mixing assumptions in the AEROPT 1.0 submodel of the EMAC atmospheric-chemistry–climate model

2014 ◽  
Vol 7 (5) ◽  
pp. 2503-2516 ◽  
Author(s):  
K. Klingmüller ◽  
B. Steil ◽  
C. Brühl ◽  
H. Tost ◽  
J. Lelieveld
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Black Carbon ◽  
Atmospheric Chemistry ◽  
Radiative Forcing ◽  
Climate Model ◽  
Mixing Rule ◽  
Aerosol Optical Properties ◽  
Internal Mixing ◽  
Aerosol Radiative

Abstract. The modelling of aerosol radiative forcing is a major cause of uncertainty in the assessment of global and regional atmospheric energy budgets and climate change. One reason is the strong dependence of the aerosol optical properties on the mixing state of aerosol components, such as absorbing black carbon and, predominantly scattering sulfates. Using a new column version of the aerosol optical properties and radiative-transfer code of the ECHAM/MESSy atmospheric-chemistry–climate model (EMAC), we study the radiative transfer applying various mixing states. The aerosol optics code builds on the AEROPT (AERosol OPTical properties) submodel, which assumes homogeneous internal mixing utilising the volume average refractive index mixing rule. We have extended the submodel to additionally account for external mixing, partial external mixing and multilayered particles. Furthermore, we have implemented the volume average dielectric constant and Maxwell Garnett mixing rule. We performed regional case studies considering columns over China, India and Africa, corroborating much stronger absorption by internal than external mixtures. Well-mixed aerosol is a good approximation for particles with a black-carbon core, whereas particles with black carbon at the surface absorb significantly less. Based on a model simulation for the year 2005, we calculate that the global aerosol direct radiative forcing for homogeneous internal mixing differs from that for external mixing by about 0.5 W m−2.

scholarly journals Effects of internal mixing and aggregate morphology on optical properties of black carbon using a discrete dipole approximation model

2012 ◽  
Vol 12 (10) ◽  
pp. 26401-26434 ◽  
Author(s):  
B. Scarnato ◽  
S. Vahidinia ◽  
D. T. Richard ◽  
T. W. Kirchstetter
Keyword(s):  
Optical Properties ◽  
Black Carbon ◽  
Linear Polarization ◽  
Incident Radiation ◽  
Dipole Approximation ◽  
Approximation Model ◽  
Mixing State ◽  
Internal Mixing ◽  
Aggregate Morphology ◽  
Nm Range

Abstract. According to recent studies, internal mixing of black carbon (BC) with other aerosol materials in the atmosphere alters its aggregate shape, absorption of solar radiation, and radiative forcing. These mixing state effects are not yet fully understood. In this study, we characterize the morphology and mixing state of bare BC and BC internally mixed with sodium chloride (NaCl) using electron microscopy and examine the sensitivity of optical properties to BC mixing state and aggregate morphology using a discrete dipole approximation model (DDSCAT). DDSCAT predicts a higher mass absorption coefficient, lower single scattering albedo (SSA), and higher absorption Angstrom exponent (AAE) for bare BC aggregates that are lacy rather than compact. Predicted values of SSA at 550 nm range between 0.18 and 0.27 for lacy and compact aggregates, respectively, in agreement with reported experimental values of 0.25 ± 0.05. The variation in absorption with wavelength does not adhere precisely to a power law relationship over the 200 to 1000 nm range. Consequently, AAE values depend on the wavelength region over which they are computed. In the 300 to 550 nm range, AAE values ranged in this study from 0.70 for compact to 0.95 for lacy aggregates. The SSA of BC internally mixed with NaCl (100–300 nm in radius) is higher than for bare BC and increases with the embedding in the NaCl. Internally mixed BC SSA values decrease in the 200–400 nm wavelength range, a feature also common to the optical properties of dust and organics. Linear polarization features are also predicted in DDSCAT and are dependent on particle morphology. The bare BC (with a radius of 80 nm) presents in the linear polarization a bell shape feature, which is a characteristic of the Rayleigh regime (for particles smaller than the wavelength of incident radiation). When BC is internally mixed with NaCl (100–300 nm in radius), strong depolarization features for near-VIS incident radiation are evident, such as a decrease in the intensity and multiple modes at different angles corresponding to different mixing states. DDSCAT, being flexible on the geometry and refractive index of the particle, can be used to study the effect of mixing state and complex morphology on optical properties of realistic BC aggregates. This study shows that DDSCAT predicts morphology and mixing state dependent optical properties that have been reported previously and are relevant to radiative transfer and climate modeling and interpretation of remote sensing measurements.

Impact of Different Mixing Ways of Black Carbon and Non-Absorbing Aerosols on the Optical Properties

2013 ◽  
Vol 33 (8) ◽  
pp. 0829001 ◽  
Author(s):  
周晨 Zhou Chen ◽  
张华 Zhang Hua ◽  
王志立 Wang Zhili
Keyword(s):  
Optical Properties ◽  
Black Carbon ◽  
Absorbing Aerosols

scholarly journals Influence of light-absorbing particles on snow spectral irradiance profiles

2019 ◽  
Vol 13 (8) ◽  
pp. 2169-2187 ◽  
Author(s):  
Francois Tuzet ◽  
Marie Dumont ◽  
Laurent Arnaud ◽  
Didier Voisin ◽  
Maxim Lamare ◽  
...  
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Black Carbon ◽  
Mineral Dust ◽  
Surface Energy Budget ◽  
Spectral Irradiance ◽  
Light Extinction ◽  
Attractive Alternative ◽  
Chemical Measurements ◽  
Radiative Impact

Abstract. Light-absorbing particles (LAPs) such as black carbon or mineral dust are some of the main drivers of snow radiative transfer. Small amounts of LAPs significantly increase snowpack absorption in the visible wavelengths where ice absorption is particularly weak, impacting the surface energy budget of snow-covered areas. However, linking measurements of LAP concentration in snow to their actual radiative impact is a challenging issue which is not fully resolved. In the present paper, we point out a new method based on spectral irradiance profile (SIP) measurements which makes it possible to identify the radiative impact of LAPs on visible light extinction in homogeneous layers of the snowpack. From this impact on light extinction it is possible to infer LAP concentrations present in each layer using radiative transfer theory. This study relies on a unique dataset composed of 26 spectral irradiance profile measurements in the wavelength range 350–950 nm with concomitant profile measurements of snow physical properties and LAP concentrations, collected in the Alps over two snow seasons in winter and spring conditions. For 55 homogeneous snow layers identified in our dataset, the concentrations retrieved from SIP measurements are compared to chemical measurements of LAP concentrations. A good correlation is observed for measured concentrations higher than 5 ng g−1 (r2=0.81) despite a clear positive bias. The potential causes of this bias are discussed, underlining a strong sensitivity of our method to LAP optical properties and to the relationship between snow microstructure and snow optical properties used in the theory. Additional uncertainties such as artefacts in the measurement technique for SIP and chemical contents along with LAP absorption efficiency may explain part of this bias. In addition, spectral information on LAP absorption can be retrieved from SIP measurements. We show that for layers containing a unique absorber, this absorber can be identified in some cases (e.g. mineral dust vs. black carbon). We also observe an enhancement of light absorption between 350 and 650 nm in the presence of liquid water in the snowpack, which is discussed but not fully elucidated. A single SIP acquisition lasts approximately 1 min and is hence much faster than collecting a profile of chemical measurements. With the recent advances in modelling LAP–snow interactions, our method could become an attractive alternative to estimate vertical profiles of LAP concentrations in snow.

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