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Images based on RGB pixel values were used to measure the extinction coefficient of aerosols suspended in an atmospheric state. The pixel values of the object-image depend on the target-object reflection ratio, reflection direction, object type, distances, illumination intensity, atmospheric particle extinction coefficient, and scattering angle between the sun and the optical axes of the camera, among others. Therefore, the imaged intensity cannot directly provide information on the aerosol concentration or aerosol extinction coefficient. This study proposes simple methods to solve this problem, which yield reasonable extinction coefficients at the three effective RGB wavelengths. Aerosol size information was analogized using the RGB Ångström exponent measured at the three wavelengths for clean, dusty, rainy, Asian dust storm, and foggy days. Additionally, long-term measurements over four months showed reasonable values compared with existing PM2.5 measurements and the proposed method yields useful results.

In this study, we explore an innovative approach to calculate Lidar ratios, an important element of atmospheric science that is generally assumed to be constant. By integrating 532nm laser lidar with camera data, we can derive extinction coefficients from Lidar and backscatter signals from laser images captured by the camera to calculate relative Lidar ratios under different atmospheric conditions. The relative Lidar ratio here is not the actual Lidar ratio because it is obtained from the backscatter signal and not the backscatter coefficient. Our method also overcomes the near-field limitation of Lidar by using camera-derived pixel values to restore the near-field extinction coefficient. These advances present more accurate techniques for determining atmospheric properties and demonstrate the potential of combining Lidar and camera data in environmental monitoring.

In this study, we attempted to calculate the extinction parameters of PM2.5 using images from a commercial camera. The photo pixels provided information on the characteristics of the objects (i.e., the reflectivity, transmittance, or extinction efficiency) and ambient brightness. Using the RGB values of pixels, we calculated the extinction coefficient and efficiency applied to the mass concentration of PM2.5. The calculated extinction coefficient of PM2.5 determined from the camera images had a higher correlation with the PM2.5 mass concentration (R2 = 0.7) than with the visibility data, despite the limited mass range. Finally, we identified that the method of calculating extinction parameters using the effective wavelength of RGB images could be applied to studies of changes in the atmosphere and aerosol characteristics. The mass extinction efficiency of PM2.5, derived from images, and the mass concentration of PM2.5 was (10.8 ± 6.9) m2 g−1, which was higher than the values obtained in Northeast Asia by previous studies. We also confirmed that the dry extinction efficiency of PM2.5, applied with a DRH of 40%, was reduced to (6.9 ± 5.0) m2 g−1. The extinction efficiencies of PM2.5, calculated in this study, were higher than those reported in previous other studies. We inferred that high extinction efficiency is related to changes in size or the composition of aerosols; therefore, an additional long-term study must be conducted.

This study focuses on the technology for real-time water droplets removal using two-wavelength scanning LiDAR monitoring. The LiDAR measures the Ångström exponent, using extinction coefficients at 1064 nm and 532 nm wavelengths, to assess particle size, and calculates depolarization ratio from backscatter signals at 532 nm to determine particle shape. This technology allowed for the identification and classification of particle types, focusing on fine particles while excluding water droplets. The aim of this study is to use scanning LiDAR to distinguish fine particles and water droplets across a wide area and to identify their movement and origin when pollutant concentrations are high.

Hygroscopic growth of aerosols significantly affects radiative forcing and visibility, yet remains challenging due to the interplay among size, composition, and humidity. This study integrates in-situ PM and lidar data to address discrepancies between dry mass concentrations and wet optical measurements. Using machine learning inversion (XGBoost, R 2  = 0.98), dry-state size distributions were retrieved from PM data. Mie theory was applied to derive the dry extinction coefficient, and the lidar-based wet extinction coefficient yielded the hygroscopic growth. Aerosol types were classified using Random Forest (accuracy 83.4%), revealing dominance of coarse hygroscopic aerosols in this coastal urban region. Optical response varied with hygroscopicity: wet extinction coefficient increased with RH for hydrophilic types but remained low for hydrophobic aerosols. Notably, clean conditions occasionally showed a sharp increase in wet extinction coefficient despite low PM, highlighting limitations of mass-only assessments. This approach suggests improved aerosol characterization algorithms considering size and hygroscopicity, supporting advanced air quality and climate modeling studies.

The quantitative analysis of measurements with horizontally scanning aerosol lidar instruments faces two major challenges: the background correction can be affected by abnormal signal peaks, and the choice of a reference extinction coefficient αref is complicated if aerosols are ubiquitous in the sampled volume. Here, we present the newly developed multi-section method for the stable solution of extinction coefficient retrievals from horizontally scanning lidar measurements. The algorithm removes irregular peaks related to signal noise based on an experimentally derived fitting model. A representative value for αref is inferred from converging retrievals along different scan axes and over multiple scans of 10 to 15 min under the assumption that they are only related to ambient aerosols without distinct emission sources. Consequently, αref obtained through the multi-section method reflects typical atmospheric aerosols unaffected by emissions and noise. When comparing αref to the PM2.5 mass concentrations at national monitoring stations near the measurement area, a significant correlation with an r2 value exceeding 0.74 was observed. The presented case studies show that the new method allows for the retrieval and visualization of spatio-temporal aerosol distributions and subsequent products such as PM2.5 concentrations.