This study investigates the applicability of using the sky information from an all-sky imager (ASI) to retrieve aerosol optical properties and type. Sky information from the ASI, in terms of Red-Green-Blue (RGB) channels and sun saturation area, are imported into a supervised machine learning algorithm for estimating five different aerosol optical properties related to aerosol burden (aerosol optical depth, AOD at 440, 500 and 675 nm) and size (Ångström Exponent at 440–675 nm, and Fine Mode Fraction at 500 nm). The retrieved aerosol optical properties are compared against reference measurements from the AERONET station, showing adequate agreement (R: 0.89–0.95). The AOD errors increased for higher AOD values, whereas for AE and FMF, the biases increased for coarse particles. Regarding aerosol type classification, the retrieved properties can capture 77.5% of the total aerosol type cases, with excellent results for dust identification (>95% of the cases). The results of this work promote ASI as a valuable tool for aerosol optical properties and type retrieval.

Quality-assured aerosol optical properties (AOP) with high spatiotemporal resolution are vital for the accurate estimation of direct aerosol radiative forcing and solar irradiance under clear skies. In this study, the sky information from an all-sky imager (ASI) is used with machine learning (ML) synergy to estimate aerosol optical depth (AOD) and the Ångström Exponent (AE). The retrieved AODs (AE) revealed good accuracy, with a dispersion error lower than 0.07 (0.15). The retrieved ML AOPs are used to estimate the DNI by applying radiative transfer modeling. The estimated ML DNI calculations revealed adequate accuracy to reproduce reference measurements with relatively low uncertainties.

Particulate matter (PM) is one of the major air pollutants that has adverse impacts on human health. The aim of this study is to present an alternative approach for retrieving fine PM (particles with an aerodynamic diameter less than 2.5 μm, PM_2.5) using artificial intelligence. Ground-based instruments, including a hand-held Microtops II sun photometer (for aerosol optical depth), a PurpleAir sensor (for PM_2.5), and Rotronic sensors (for temperature and relative humidity), are used for the machine learning algorithm training. The retrieved PM_2.5 reveals an adequate performance with an error of 0.08 μg m⁻³ and a Pearson correlation coefficient of 0.84.

Radiative transfer modeling is used to investigate the effect of aerosol optical properties and water vapor on cloud-free sky radiances at various atmospheric conditions. Simulations are generated by changing the most critical aerosol optical properties, namely aerosol optical depth, Ångström exponent, the single-scattering albedo, the precipitable water, and the solar zenith angle (SZA) in three different spectral ranges: ultraviolet A, visible, and near-infrared.