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

Luo, Jie; Zhang, Yongming; Wang, Feng; Wang, Jinjun; Zhang, Qixing

doi:10.1016/j.jqsrt.2018.05.002

Cited by 44Atmospheric chemistry and aerosolsAtmospheric aerosols and cloudsRadiative Heat Transfer Studies

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

Luo, Jie, Zhang, Yongming, Wang, Feng, Wang, Jinjun, Zhang, Qixing·2018·Journal of Quantitative Spectroscopy and Radiative Transfer

DOI 10.1016/j.jqsrt.2018.05.002

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.jqsrt.2018.05.002

Cites

58 of 58 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

ML has had recent success in predicting specific chemical, material, and optical properties without the need for expensive experimentation or brute-force simulation, including in aerosols.
“ML has had recent success in predicting specific chemical, material, and optical properties without the need for expensive experimentation or brute-force simulation [105,299,490,653], including in aerosols [465,500].”
From Tackling climate change with machine learning (2023) · cites this paper as [500]

Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Support-Vector Networks
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
Beiträge zur Optik trüber Medien, speziell kolloidaler Metallösungen
Mie, G. (1908) · Annalen der Physik · doi:10.1002/andp.19083300302 · Cited by 11,537 · see citing works
Bounding the role of black carbon in the climate system: A scientific assessment
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., et al. (2013) · Journal of Geophysical Research: Atmospheres · doi:10.1002/jgrd.50171 · Cited by 6,385 · see citing works
Support Vector Regression Machines
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. N. (1996) · Cited by 4,654 · see citing works
The Nature of Statistical Learning Theory
Sain, S. R., & Vapnik, V. N. (1996) · Technometrics · doi:10.2307/1271324 · Cited by 4,097 · see citing works
Discrete-Dipole Approximation For Scattering Calculations
Draine, B. T., & Flatau, P. J. (1994) · Journal of the Optical Society of America A · doi:10.1364/josaa.11.001491 · Cited by 3,671 · see citing works
Light Absorption by Carbonaceous Particles: An Investigative Review
Bond, T. C., & Bergstrom, R. W. (2006) · Aerosol Science and Technology · doi:10.1080/02786820500421521 · Cited by 2,980 · see citing works
A satellite view of aerosols in the climate system
Kaufman, Y. J., Tanré, D., & Boucher, O. (2002) · Nature · doi:10.1038/nature01091 · Cited by 2,216 · see citing works
Scattering, Absorption, and Emission of Light by Small Particles
Mishchenko, M. I., Lacis, A. A., & Travis, L. D. (2002) · Cited by 2,155 · see citing works
On the algorithmic implementation of multiclass kernel-based vector machines
Crammer, K., & Singer, Y. (2002) · Cited by 1,902 · see citing works
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
Keerthi, S. S., & Lin, C. (2003) · Neural Computation · doi:10.1162/089976603321891855 · Cited by 1,623 · see citing works
Light Scattering by Fractal Aggregates: A Review
SORENSEN, C. M. (2001) · Aerosol Science and Technology · doi:10.1080/02786820117868 · Cited by 1,185 · see citing works
On the algorithmic implementation of multiclass kernel-based vector machines
CrammerKoby, & SingerYoram (2002) · Journal of Machine Learning Research · doi:10.5555/944790.944813 · Cited by 876 · see citing works
Calculation of the T matrix and the scattering matrix for ensembles of spheres
Mackowski, D. W., & Mishchenko, M. I. (1996) · Journal of the Optical Society of America A · doi:10.1364/josaa.13.002266 · Cited by 774 · see citing works
Strong present-day aerosol cooling implies a hot future
Andreae, M. O., JONES, C. D., & Cox, P. M. (2005) · Nature · doi:10.1038/nature03671 · Cited by 664 · see citing works
Black-carbon absorption enhancement in the atmosphere determined by particle mixing state
Liu, D., Whitehead, J. D., Alfarra, M. R., Reyes‐Villegas, E., Spracklen, D. V., Reddington, C. L. S., Kong, S., Williams, P. I., Ting, Y.-C., Haslett, S. L., et al. (2017) · Nature Geoscience · doi:10.1038/ngeo2901 · Cited by 524 · see citing works
A multiple sphere T-matrix Fortran code for use on parallel computer clusters
Mackowski, D. W., & Mishchenko, M. I. (2011) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2011.02.019 · Cited by 510 · see citing works
Climate Forcing by Aerosols--a Hazy Picture
Anderson, T. L., Charlson, R. J., Schwartz, S. E., Knutti, R., Boucher, O., Rodhe, H., & Heintzenberg, J. (2003) · Science · doi:10.1126/science.1084777 · Cited by 344 · see citing works
A study of radiative properties of fractal soot aggregates using the superposition T-matrix method
Liu, L., Mishchenko, M. I., & Arnott, W. P. (2008) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2008.05.001 · Cited by 253 · see citing works
Morphology and mixing state of aged soot particles at a remote marine free troposphere site: Implications for optical properties
China, S., Scarnato, B. V., Owen, R. C., et al. (2015) · Geophysical Research Letters · doi:10.1002/2014gl062404 · Cited by 241 · see citing works
Enhanced Light Absorption and Scattering by Carbon Soot Aerosol Internally Mixed with Sulfuric Acid
Khalizov, A. F., Xue, H., Wang, L., Zheng, J., & Zhang, R. (2009) · The Journal of Physical Chemistry A · doi:10.1021/jp807531n · Cited by 240 · see citing works
Variation of the radiative properties during black carbon aging: theoretical and experimental intercomparison
He, C., Liou, K. N., Takano, Y., Zhang, R., Zamora, M. L., Yang, P. X., Li, Q., & Leung, L.-Y. R. (2015) · Atmospheric chemistry and physics · doi:10.5194/acp-15-11967-2015 · Cited by 198 · see citing works
Effects of aggregation on scattering and radiative properties of soot aerosols
Liu, L., & Mishchenko, M. I. (2005) · Journal of Geophysical Research Atmospheres · doi:10.1029/2004jd005649 · Cited by 181 · see citing works
A generalized multiparticle Mie-solution: further experimental verification
Xu, Y.-L., & Gustafson, B. Å. S. (2001) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/s0022-4073(01)00019-x · Cited by 171 · see citing works
Comparison of methods to derive morphological parameters of multi-fractal samples of particle aggregates from TEM images
Woźniak, M., Onofri, F. R. A., Barbosa, S., et al. (2011) · Journal of Aerosol Science · doi:10.1016/j.jaerosci.2011.12.008 · Cited by 143 · see citing works
A general superposition solution for electromagnetic scattering by multiple spherical domains of optically active media
Mackowski, D. W. (2013) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2013.08.012 · Cited by 127 · see citing works
On the radiative properties of soot aggregates part 1: Necking and overlapping
Yon, J., Bescond, A., & Liu, F. (2015) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2015.03.027 · Cited by 113 · see citing works
A cluster-cluster aggregation model with tunable fractal dimension
Thouy, R., & Jullien, R. (1994) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/27/9/012 · Cited by 111 · see citing works
Fractal Dimension of Generalized Diffusion-Limited Aggregates
Hentschel, H. G. E. (1984) · Physical Review Letters · doi:10.1103/physrevlett.52.212 · Cited by 109 · see citing works
Coagulation–Agglomeration of Fractal-like Particles: Structure and Self-Preserving Size Distribution
Goudeli, E., Eggersdorfer, M. L., & Pratsinis, S. E. (2015) · Langmuir · doi:10.1021/la504296z · Cited by 108 · see citing works
Stochastic parameterization for light absorption by internally mixed BC/dust in snow grains for application to climate models
Liou, K. N., Takano, Y., He, C., et al. (2014) · Journal of Geophysical Research Atmospheres · doi:10.1002/2014jd021665 · Cited by 104 · see citing works
Morphology and mobility diameter of carbonaceous aerosols during agglomeration and surface growth
Kelesidis, G. A., Goudeli, E., & Pratsinis, S. E. (2017) · Carbon · doi:10.1016/j.carbon.2017.06.004 · Cited by 97 · see citing works
Scattering and radiative properties of semi-external versus external mixtures of different aerosol types
Mishchenko, M. I., Liu, L., Travis, L. D., et al. (2004) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2003.12.032 · Cited by 84 · see citing works
On the radiative properties of soot aggregates – Part 2: Effects of coating
Liu, F., Yon, J., & Bescond, A. (2015) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2015.08.005 · Cited by 83 · see citing works
Calculation of the Addition Coefficients in Electromagnetic Multisphere-Scattering Theory
Xu, Y.-L. (1996) · Journal of Computational Physics · doi:10.1006/jcph.1996.0175 · Cited by 82 · see citing works
Optical properties of black carbon aggregates with non-absorptive coating
Liu, C., Li, J., Yin, Y., et al. (2016) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2016.10.023 · Cited by 76 · see citing works
Models for integrated and differential scattering optical properties of encapsulated light absorbing carbon aggregates
Kahnert, M., Nousiainen, T., & Lindqvist, H. (2013) · Optics Express · doi:10.1364/oe.21.007974 · Cited by 68 · see citing works
Influence of complex component and particle polydispersity on radiative properties of soot aggregate in atmosphere
Yin, J., & Liu, L. H. (2010) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2010.05.016 · Cited by 60 · see citing works
Simplifying the calculation of light scattering properties for black carbon fractal aggregates
Smith, A., & Grainger, R. G. (2014) · Atmospheric chemistry and physics · doi:10.5194/acp-14-7825-2014 · Cited by 51 · see citing works
Modelling the optical and radiative properties of freshly emitted light absorbing carbon within an atmospheric chemical transport model
Kahnert, M. (2010) · Atmospheric chemistry and physics · doi:10.5194/acp-10-1403-2010 · Cited by 44 · see citing works
Intercomparison of the GOS approach, superposition T-matrix method, and laboratory measurements for black carbon optical properties during aging
He, C., Takano, Y., Liou, K., Yang, P. X., Li, Q., & Mackowski, D. W. (2016) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2016.08.004 · Cited by 42 · see citing works
Multifractal structure of the harmonic measure of diffusion-limited aggregates
Jensen, M. H., Levermann, A., Mathiesen, J., et al. (2002) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.65.046109 · Cited by 41 · see citing works
Models for the optical simulations of fractal aggregated soot particles thinly coated with non-absorbing aerosols
Wu, Y., Cheng, T., Zheng, L., & Chen, H. (2016) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2016.05.011 · Cited by 41 · see citing works
Numerically exact computation of the optical properties of light absorbing carbon aggregates for wavelength of 200 nm–12.2 μm
Kahnert, M. (2010) · Atmospheric chemistry and physics · doi:10.5194/acp-10-8319-2010 · Cited by 39 · see citing works
A model study of aggregates composed of spherical soot monomers with an acentric carbon shell
Luo, J., Zhang, Y., & Zhang, Q. (2017) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2017.10.024 · Cited by 39 · see citing works
Self-citation
A Study of Optical Properties of Soot Aggregates Composed of Poly-Disperse Monomers Using the Superposition T-Matrix Method
Wu, Y., Cheng, T., Zheng, L., et al. (2015) · Aerosol Science and Technology · doi:10.1080/02786826.2015.1083938 · Cited by 38 · see citing works
Single scattering properties of semi-embedded soot morphologies with intersecting and non-intersecting surfaces of absorbing spheres and non-absorbing host
Wu, Y., Cheng, T., Zheng, L., et al. (2015) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2015.02.006 · Cited by 30 · see citing works
Effects of polydispersity of chainlike aggregates on light-scattering properties and data inversion
Charalampopoulos, T. T., & Shu, G. (2002) · Applied Optics · doi:10.1364/ao.41.000723 · Cited by 21 · see citing works
Empirical relationships between optical properties and equivalent diameters of fractal soot aggregates at 550 nm wavelength
Pandey, A., Chakrabarty, R. K., Liu, L., et al. (2015) · Optics Express · doi:10.1364/oe.23.0a1354 · Cited by 18 · see citing works
Characteristics of light scattering by smoke particles based on spheroid models
Xie, Q., Zhang, H., Wan, Y., et al. (2007) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2007.01.059 · Cited by 16 · see citing works
Discrete-dipole-approximation-based light-scattering calculations for particles with a real refractive index smaller than unity
Laczik, Z. J. (1996) · Applied Optics · doi:10.1364/ao.35.003736 · Cited by 14 · see citing works
Remarks on the state of the art in automatic fire detection
Luck, H. O. (1997) · Fire Safety Journal · doi:10.1016/s0379-7112(96)00030-6 · Cited by 10 · see citing works
Cross-validation of predictors for a chronic pain control program.
Js, R., Hain, J. D., Hr, V., et al. (1982) · PubMed · Cited by 1 · see citing works

Flagged first, then by citations

Sorted by citations

Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works

Differences in molar absorptivity of 4-NP with the reaction solution and apparatus affect ALP measurement.
Huang, W. F., Yang, M. Q., Zeng, J., et al. (1997) · PubMed
[The thermodynamic molar absorptivity of coordination color reaction of indium(III) with 4,5-dihydroxybenzene-1,3-disufornic acid].
Guo, Y. C., Chen, L., Zhang, D., et al. (1999) · PubMed
Absorptivity and Solid State and Solution Fluorescence of Mixed Aflatoxin Standards Stored in Solution and as Dry Films
Pons, W. A., Robertson, J. A., Cucullu, A. F., et al. (1970) · Journal of AOAC INTERNATIONAL · doi:10.1093/jaoac/53.2.293
[Li@C 60 ]PF 6 : Infrared spectra from 90K to 523K; Determination of the molar extinction coefficients and integrated molar absorptivity
García-Hernández, D. A., Manchado, A., & Cataldo, F. (2019) · Fullerenes Nanotubes and Carbon Nanostructures · doi:10.1080/1536383x.2019.1703696 · Cited by 6 · see citing works
Metal powder absorptivity: modeling and experiment
Boley, C. D., Mitchell, S., Rubenchik, A. M., et al. (2016) · Applied Optics · doi:10.1364/ao.55.006496 · Cited by 163 · see citing works

Authors: Luo, J., Zhang, Y., Wang, F., Wang, J., & Zhang, Q.
Affiliations: University of Science and Technology of China (CN)
Corresponding author: Zhang, Qixing
ORCID: Luo, Jie
Wang, Feng
Zhang, Qixing
Venue: Journal of Quantitative Spectroscopy and Radiative Transfer
Publisher: Elsevier BV
Published: 2018-08
Volume: 215
Pages: 1-8
ISSN: 0022-4073 (ISSN-L)
DOI: 10.1016/j.jqsrt.2018.05.002
OpenAlex: W2801505543
Cited by: 44
References: 58
Citations by year: 2026: 1 · 2025: 3 · 2024: 12 · 2023: 4 · 2022: 9 · 2021: 8 · 2020: 4 · 2019: 2 · 2018: 1
FWCI: 1.77
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Quantitative Spectroscopy and Radiative Transfer (publishedVersion)
Funders: National Natural Science Foundation of China (41675024, U1733126)
Anhui Provincial (1704a0902030)
Topics: Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Atmospheric chemistry and aerosols
Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
Physical Sciences › Engineering › Computational Mechanics › Radiative Heat Transfer Studies
Keywords: black carbon, fractal aggregates, optical properties, support vector machine, extinction efficiency, absorption efficiency, scattering efficiency, asymmetry factor, aerosol optical properties, machine learning parameterization

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks