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Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy
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    A new light transport model improves remote sensing in fog by using a weak angular dependence approximation. This model covers moderate to highly scattering conditions, enhancing situational awareness and system uptime.

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    Area of Science:

    • Optical Physics
    • Atmospheric Optics
    • Computational Imaging

    Background:

    • Aerosols like fog scatter and absorb light, degrading situational awareness and system performance.
    • Efficient light transport models are crucial for remote sensing in optically challenging environments.

    Purpose of the Study:

    • To develop a computationally efficient light transport model applicable to both moderate and highly scattering regimes.
    • To validate the model's performance using experimental fog data.

    Main Methods:

    • Developed a model based on a weak angular dependence approximation to the Boltzmann/radiative transfer equation.
    • Derived an analytic solution and validated it with experimental data from a fog chamber.
    • Measured fog particle density and size distribution to determine optical properties using Mie theory.
    • Utilized a three-band transmissometer to measure fog density changes across different wavelengths.

    Main Results:

    • The model is applicable in both moderate and highly scattering regimes, bridging the gap between small angle and diffusion approximations.
    • Experimental validation confirmed the model's accuracy in predicting light transport through fog.
    • Demonstrated a relationship between the meteorological optical range and the transport mean free path length.

    Conclusions:

    • The developed weak angular dependence model offers a computationally efficient approach for light transport in aerosols.
    • The model enhances remote sensing capabilities in degraded optical conditions.
    • Provides a refined physical interpretation of the meteorological optical range parameter.