Abstract. In this paper particle categorization and absorption properties were discussed to understand transport mechanisms at different geographic locations and possible radiative impacts on climate. The long-term Aerosol Robotic Network (AERONET) data set (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) is used to estimate aerosol optical depth (AOD), single scattering albedo (SSA), and the absorption Ångström exponent (α abs ) at eight locations in North Africa and the Middle East. Average variation in SSA is calculated at four wavelengths (440, 675, 870, and 1020 nm), and the relationship between aerosol absorption and physical properties is used to infer dominant aerosol types at different locations. It was found that seasonality and geographic location play a major role in identifying dominant aerosol types at each location. Analyzing aerosol characteristics among different sites using AERONET Version 2, Level 2.0 data retrievals and the Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) backward trajectories shows possible aerosol particle transport among different locations indicating the importance of understanding transport mechanisms in identifying aerosol sources.

In geotechnical engineering, in-situ tests, like standard penetration test (SPT) and cone penetration test (CPT), are essential to evaluate the underground conditions. SPT is a frequently used geotechnical approach, while CPT is considered one of the best exploration apparatuses. However, there is a demand for a correlation of SPT-CPT that can be used in the geotechnical design. This research aimed to find the relationships between the standard penetration test’s blow count (N) and cone penetration test’s tip resistance (qc) for different groups of sandy soils in Port Sudan city, Sudan. The data used in this study was attained from numerous projects conducted in the city to identify the underground conditions. The values of the SPT and CPT tests were compared and tried to generate specific statistical data. The correlations were established between qc and N for various soils. Uphill linear relationships wear found between qc and N for different sandy soils. These results were interpreted based on their correlation coefficients. The study proposed formulas to predict qc from N for three soil groups, with a high correlation coefficient. Besides, qc/N ratios were calculated for different soil groups; the current research results agree with those in the published literature with less high values.

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