Abstract

This study explored a novel approach for estimating the variations in the refractive index (RI) of aerosols, which is a crucial optical parameter that contains information about particle scattering and absorption. RI is often subject to constraints related to observation time, wavelength selection, and analysis methods. To overcome these limitations, we employed real-time continuous measurements using a camera and optical particle counter (OPC) equipment and applied optimization techniques based on the Mie theory to reverse-calculate changes in the refractive index. Our methodology uses a camera to measure the aerosol extinction coefficients at three different wavelengths within the RGB spectrum (597 nm, 534 nm, and 459 nm), whereas OPC observations provide information on particle size distribution (0.127–17.58 μm as radius). Because the extinction efficiency at known wavelengths varies with the refractive index according to the Mie theory, we retrieved the refractive index, which is an unknown variable, from the extinction coefficient equation. We focused on the real part of the RI because it is challenging to discern the imaginary part of the RI from the visible wavelengths. Our observations were conducted in areas near Busan Harbor and in mixed residential regions. The real part of the RI was determined to be 1.53 ± 0.12. During periods of observed dust events, the refractive index's real part was 1.62 ± 0.05, while during periods anticipated to have emissions of fine aerosols, it was 1.51 ± 0.10. The retrieved RI fell within a range similar to that in other studies, and the changes in the refractive index showed meaningful patterns when analyzed for variations in the particle size distribution and changes in the scattering coefficient. This approach has the potential to advance our understanding of aerosol optical properties and can be applied to real-time analyses with a relatively simple equipment setup, thereby opening new possibilities for its use as a critical variable in optical measurements and environmental models.

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