Document Type


Publication Date


Publication Title

Transactions on Engineering and Computer Science






1-13 (Article 119)


In this article, we present a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear models (GLM) and regression tree techniques are used to further analyze the performance of the LIDAR parameter-based aerosol classification methods. The goal of GLM and regression tree analyses is to compare and contrast distinct classification data schemes, and compare the results with the measured aerosol reflection data in the atmosphere. The detailed statistical comparisons and analyses shows that the optical methods adopted in this study for classification and prediction of various harmful aerosol types such as soot, carbon monoxide (CO), sulfates (SOx), and nitrates (NOx) are efficient under appropriate functional distributions. The article offers a method for natural ordering of the aerosol types.


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Original Publication Citation

Alqawba M., Diawara N., Afrifa K.G., et al. (2021). Statistical analysis and comparison of optical classification of atmospheric aerosol lidar data. Transactions on Engineering and Computer Science, 2(1), Article 119.


0000-0002-8403-6793 (Diawara), 0000-0002-8125-5413 (Elbakary), 0000-0003-2003-9343 (Cetin)