Evidence for particle size–shape correlations in the optical properties of silicate clay aerosol

Meland, B. S.; Alexander, Jennifer M.; Wong, C. S.; Grassian, Vicki H.; Young, Mark A.; Kleiber, P. D.

doi:10.1016/j.jqsrt.2012.01.012

Cited by 15 publications

(35 citation statements)

References 45 publications

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“…T‐matrix calculations are carried out for a single particle shape parameter and then the results are averaged, as described in Meland et al . [], to simulate the optical properties for a distribution of particle shapes. Details of the assumed particle shape distributions for each mineral component are reviewed in the Appendix, and the model parameters are summarized in Table and Figure .…”

Section: Modeling Analysismentioning

confidence: 98%

“…Similarly, Meland et al . [] showed that simulations of the optical data for illite and kaolinite could obtain fits of similar quality for a range of bimodal size‐shape distribution parameters. Thus, these particle shape distribution models are not unique, but place reasonable limits on the range of shape parameters necessary to adequately describe the IR extinction and visible light scattering data for these mineral dust components.…”

Section: Modeling Analysismentioning

confidence: 99%

“…For a given mineral composition, a forward simulation of the IR spectral extinction and visible light scattering data can be run for each mineral species in the sample. The IR extinction coefficient, C ext , and scattering matrix elements, F αβ (θ), for each mineral component can then be combined through a weighted average to determine the cumulative properties of the externally mixed sample using equations –:

〈C_{ext}〉 = \sum_{i} (N^{((), i)} C_{italicext}^{((), i)})

〈F_{italicαβ} (θ)〉 = [\frac{\sum_{i} (N^{((), i)} C_{italicscat}^{((), i)} F_{italicαβ}^{((), i)})}{\sum_{i} (N^{((), i)} C_{italicscat}^{((), i)})}]

subject to

\sum_{i} N^{((), i)} = 1

where the summations are over the different mineral components, N (i) is the fractional number of particles, and C (i) scat ( C (i) ext ) is the average scattering (extinction) cross‐section per particle for the i th mineral component [ Meland et al ., ]. The scattering phase function is then given by < F 11 (θ)> and the linear polarization by P = −< F 12 (θ)>/< F 11 (θ)>.…”

Section: Modeling Analysismentioning

confidence: 99%

“…More recently, Meland et al . [] extended this work to model the absorption and scattering properties of kaolinite and illite, two important silicate clay minerals. Interestingly, the best overall agreement with experimental data was obtained by assuming a bimodal size shape distribution for the clay particles.…”

Section: Introductionmentioning

confidence: 99%

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A combined laboratory and modeling study of the infrared extinction and visible light scattering properties of mineral dust aerosol

et al. 2013

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[1] Optical properties, including infrared (IR) extinction and visible light scattering of mineral dust aerosol, are measured experimentally and compared to modeling results using T-matrix theory. The work includes studies of complex, authentic field samples of Saharan sand, Iowa loess, and Arizona road dust (ARD). Particle size distributions and aerosol optical properties are measured simultaneously. These authentic dust samples are treated as external mixtures of mineral components. The mineral compositions for the Saharan sand and Iowa loess samples have been reported by Laskina et al. [2012], and the mineralogy for ARD is derived here using a similar method. T-matrix-based simulations, using measured particle size distributions and a priori particle shape models, are carried out for each mineral component of the authentic samples. The simulated optical properties for the complex dust mixtures are obtained by a weighted average of the properties of the mineral components, based on a given sample mineralogy. T-matrix simulations are then directly compared with the measured IR extinction spectra and visible light scattering phase function and linear polarization profiles for each sample. Generally good agreement between experiment and theory is obtained. Model simulations that account for differences in particle shape with mineralogy and include a broad range of eccentric spheroid shape parameters offer a significant improvement over more commonly applied models that ignore variations in particle shape with size or mineralogy and include only a moderate range of shape parameters.

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Section: Modeling Analysismentioning

confidence: 98%

Section: Modeling Analysismentioning

confidence: 99%

〈C_{ext}〉 = \sum_{i} (N^{((), i)} C_{italicext}^{((), i)})

〈F_{italicαβ} (θ)〉 = [\frac{\sum_{i} (N^{((), i)} C_{italicscat}^{((), i)} F_{italicαβ}^{((), i)})}{\sum_{i} (N^{((), i)} C_{italicscat}^{((), i)})}]

subject to

\sum_{i} N^{((), i)} = 1

Section: Modeling Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A combined laboratory and modeling study of the infrared extinction and visible light scattering properties of mineral dust aerosol

et al. 2013

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“…Recently, a laboratory-scale instrument was developed that uses an elliptical mirror to reflect scattered light onto a chargecoupled device (CCD) linear array detector (Curtis et al 2007(Curtis et al , 2008Meland et al 2011Meland et al , 2012. The elliptical mirror and CCD detector method offers high angular resolution and relatively fast measurement times without moving parts except for a rotator to control the laser incident polarization.…”

Section: Introductionmentioning

confidence: 99%

A Portable High-Resolution Polar Nephelometer for Measurement of the Angular Scattering Properties of Atmospheric Aerosol: Design and Validation

McCrowey

Tinilau

Calderón

et al. 2013

Aerosol Science and Technology

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Modeling of scattering properties of mineral aerosols using modified beta function

Tang

Lin

2013

JGR Atmospheres

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[1] In the combined experiment from Amsterdam and modeling study, the spheroid is applied to represent the real mineral aerosols, and the single scattering properties of spheroids are computed using the T matrix combined with the improved geometric optical method in the wider size range and aspect ratio range. After that a modeling method for the scattering matrices of the feldspar, quartz, and red clay is proposed. In this approach, the ensemble-average scattering matrix is calculated by using the number density distribution deduced from the fit of the measured normalized projected surface area distribution with the modified beta function and the shape distribution obtained by the least squares fit of all the six measured scattering elements. On the other hand, the models of scattering matrices using the lognormal function to fit the measured normalized number distribution and fit the measured normalized projected surface area distribution are also conducted as comparison. Furthermore, the asymmetry parameter is calculated in order to further verify the reliability of the proposed method. Simulation experiments indicate that the simulated scattering matrices with the proposed method are rather close to the measured scattering matrices for the three mineral aerosols, and this proposed numerical model can provide a simple, reliable, and efficient method to reproduce the scattering properties of the mineral aerosols based on the Amsterdam database.Citation: Tang, H., and J.-Z. Lin (2013), Modeling of scattering properties of mineral aerosols using modified beta function,

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Evidence for particle size–shape correlations in the optical properties of silicate clay aerosol

Cited by 15 publications

References 45 publications

A combined laboratory and modeling study of the infrared extinction and visible light scattering properties of mineral dust aerosol

A combined laboratory and modeling study of the infrared extinction and visible light scattering properties of mineral dust aerosol

A Portable High-Resolution Polar Nephelometer for Measurement of the Angular Scattering Properties of Atmospheric Aerosol: Design and Validation

Modeling of scattering properties of mineral aerosols using modified beta function

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