Particle size amplification of black carbon by scattering measurement due to morphology diversity

Cheng, Tianhai; Cheng, Tianhai; Zheng, Lijuan; Zhang, Yonggen; Zhang, Lili

doi:10.1088/1748-9326/acaede

Cited by 3 publications

(4 citation statements)

References 39 publications

(58 reference statements)

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“…The data is integrated over a radius ranging from 0.0125 to 0.8 μm based on the assumption of lognormal distribution, and the wavelength is set to be 0.49 μm. The mean radius is 0.1 μm and the standard deviation is 0.25 according to previous observations (Wu et al., 2023).…”

Section: Resultssupporting

confidence: 66%

“…With respect to larger size parameters, these differences could be more obvious, which was not illustrated in this study because large size parameters are uncommon even in the photosynthetically active radiation band (Wu et al, 2023). The IITM method allows for more complex morphological features, resulting in a more universally applicable coated fractal aggregates model.…”

Section: Optical Propertiesmentioning

confidence: 81%

“…As BC particles age, the fractal aggregates become more compact, with an increased value of D f and k 0 . Microscopic measurements have shown that D f values typically range from ∼1.8 to ∼2.8 (China et al., 2013; Kelesidis et al., 2017; Wu et al., 2023). In general, the differences in the single‐scattering properties between fractal aggregates with different Df values are much smaller compared to those between fractal aggregates and spheres (Cheng et al., 2013).…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Single‐Scattering Properties of Encapsulated Fractal Black Carbon Particles Computed Using the Invariant Imbedding T‐Matrix Method and Deep Learning Approaches

Wang,

Bi,

Han

et al. 2023

JGR Atmospheres

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Efficient and accurate computation of the single‐scattering properties of black carbon (BC) aerosols is fundamental in various fields, including remote sensing and climate simulations. In this study, we developed a composite model of fractal aggregates of BC encapsulated with hygroscopic aerosols to represent the ambient BC. We used the invariant imbedding T‐matrix method to compute the optical properties of fully and partially encapsulated BC aerosols. In this new model, the traditional assumption of unoverlapped surfaces in the super‐position T‐matrix method is unnecessary. After extensive simulations, we established a database of single‐scattering properties, including the extinction efficiency, the single‐scattering albedo, the asymmetry factor and six phase matrix elements. Moreover, we obtained deep neural networks (DNNs) from this database using a deep learning method. These DNN models provide a universal interface for predicting the optical properties of ambient BC aerosols. Specifically, through a modified architecture of the DNN, we trained two models based on the database to predict three integrated optical properties (extinction efficiency, single‐scattering albedo, and asymmetry factor) and six phase matrix elements. We performed statistical assessments based on the true values in the database and the predicted values from the DNNs, demonstrating that the DNNs accurately predicted all single‐scattering properties. Therefore, the developed DNN models can be conveniently implemented in aerosol optical parameterization for remote sensing studies and atmospheric models.

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Section: Resultssupporting

confidence: 66%

Section: Optical Propertiesmentioning

confidence: 81%

Section: Methodsmentioning

confidence: 99%

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Single‐Scattering Properties of Encapsulated Fractal Black Carbon Particles Computed Using the Invariant Imbedding T‐Matrix Method and Deep Learning Approaches

Wang,

Bi,

Han

et al. 2023

JGR Atmospheres

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“…To obtain accurate optical properties of BC aerosols, researchers have employed more realistic models that account for varied particle sizes, chemical components, mixing states and emission sources [9][10][11][12]. Previous studies suggested that the scattering errors of the single sphere model using the MIE method may reach to ∼50% [13][14][15]. Recently, substantial improvements have been made in the optical modeling of BC particles by incorporating their fractal aggregate morphologies using the multi-sphere superposition T-matrix method (MSTM) and the discrete dipole approximation (DDA).…”

Section: Introductionmentioning

confidence: 99%

Optical modeling of atmospheric black carbon aerosol ensembles with complex particle morphology

Wu,

Zheng,

et al. 2024

Environ. Res. Lett.

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Black carbon (BC) aerosol is one of the most important factor in global warming. BC radiative forcing remains unconstrained, mainly because of the uncertain parameterizations of its absorption and scattering properties in the atmosphere. The single sphere model is widely used in current climate assessment of BC aerosols due to its computational convenience, however, their complex morphologies in particle level are excessively simplified which leads to computed inaccuracy. In this study, we present a dynamic model for optical calculations of BC aerosol ensembles considering their complex fractal aggregate morphologies with the constraint of max monomer numbers (Ns, max) and radius (amax). We show that the simulation accuracy of the dynamic model with suitable values of Ns, max and amax may achieve ~95% while the computation time may reduce to ~6%. We find that optical properties of BC aerosol ensembles can be simulated for higher accuracy or faster calculation by performing different selections of monomer numbers and radius in their size distributions. This method enables extensive and accurate optical calculations of BC particles with complex morphologies, which would be useful for the remote sensing inversion and the assessment of climate.

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Numerical investigation on retrieval errors of mixing states of fractal black carbon aerosols using single-particle soot photometer based on Mie scattering and the effects on radiative forcing estimation

Liu,

Wang,

Zhu

et al. 2023

Atmos. Meas. Tech.

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Abstract. The mixing state of black carbon (BC) aerosols, which is the diameter ratio of coated particle to BC core (Dp/Dc), can be retrieved by the single-particle soot photometer (SP2). However, the retrieved Dp/Dc contains errors, because the core–shell model and Mie scattering calculation are normally employed in the retrieval principle of SP2 and the spherical core–shell structure seriously deviated from the real morphology of coated BC. In this study, fractal models are constructed to represent thinly and thickly coated BC particles for optical simulations, the differential scattering cross-sections are selected as references to conduct optical retrieval of particle diameter (Dp) based on Mie theory, just like the retrieval principle of SP2, and the volume equivalent diameter of BC core (Dc) is the same for fractal and spherical models. Then, the retrieval errors of the mixing state (Dp/Dc) of BC are investigated from numerical aspects, and the estimation accuracy of BC radiative forcing is analyzed through the simple forcing efficiency (SFE) equation with SP2 retrieval results taken into consideration. Results show that SP2 retrieved Dp/Dc based on Mie theory underestimates the realistic Dp/Dc of coated BC at most particle sizes. The retrieval errors of Dp/Dc of thinly coated BC for both single particles and particle groups are larger than those of thickly coated BC. In addition, evaluation errors of radiative forcing of coated BC caused by retrieval errors of SP2 are up to about 55 % and 95 % at 1064 and 532 nm, respectively. This study provides meaningful referential understandings of the retrieved Dp/Dc of SP2 based on Mie scattering.

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Particle size amplification of black carbon by scattering measurement due to morphology diversity

Cited by 3 publications

References 39 publications

Single‐Scattering Properties of Encapsulated Fractal Black Carbon Particles Computed Using the Invariant Imbedding T‐Matrix Method and Deep Learning Approaches

Single‐Scattering Properties of Encapsulated Fractal Black Carbon Particles Computed Using the Invariant Imbedding T‐Matrix Method and Deep Learning Approaches

Optical modeling of atmospheric black carbon aerosol ensembles with complex particle morphology

Numerical investigation on retrieval errors of mixing states of fractal black carbon aerosols using single-particle soot photometer based on Mie scattering and the effects on radiative forcing estimation

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