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Open AccessArticle

Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering

1
Laboratory of Atmospheric Physics, Physics Department, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Aosta Valley Regional Environmental Protection Agency (ARPA), 11020 Saint-Christophe, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 965; https://doi.org/10.3390/rs12060965
Received: 20 February 2020 / Revised: 10 March 2020 / Accepted: 13 March 2020 / Published: 17 March 2020
(This article belongs to the Section Atmosphere Remote Sensing)
In this study, we present an aerosol classification technique based on measurements of a double monochromator Brewer spectrophotometer during the period 1998–2017 in Thessaloniki, Greece. A machine learning clustering procedure was applied based on the Mahalanobis distance metric. The classification process utilizes the UV Single Scattering Albedo (SSA) at 340 nm and the Extinction Angstrom Exponent (EAE) at 320–360 nm that are obtained from the spectrophotometer. The analysis is supported by measurements from a CIMEL sunphotometer that were deployed in order to establish the training dataset of Brewer measurements. By applying the Mahalanobis distance algorithm to the Brewer timeseries, we automatically assigned measurements in one of the following clusters: Fine Non Absorbing Mixtures (FNA): 64.7%, Black Carbon Mixtures (BC): 17.4%, Dust Mixtures (DUST): 8.1%, and Mixed: 9.8%. We examined the clustering potential of the algorithm by reclassifying the training dataset and comparing it with the original one and also by using manually classified cases. The typing score of the Mahalanobis algorithm is high for all predominant clusters FNA: 77.0%, BC: 63.9%, and DUST: 80.3% when compared with the training dataset. We obtained high scores as well FNA: 100.0%, BC: 66.7%, and DUST: 83.3% when comparing it with the manually classified dataset. The flags obtained here were applied in the timeseries of the Aerosol Optical Depth (AOD) at 340 nm of the Brewer and the CIMEL in order to compare between the two and also stress the future impact of the proposed clustering technique in climatological studies of the station. View Full-Text
Keywords: aerosol classification; clustering; machine learning; spectrophotometer; sunphotometer aerosol classification; clustering; machine learning; spectrophotometer; sunphotometer
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MDPI and ACS Style

Siomos, N.; Fountoulakis, I.; Natsis, A.; Drosoglou, T.; Bais, A. Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering. Remote Sens. 2020, 12, 965. https://doi.org/10.3390/rs12060965

AMA Style

Siomos N, Fountoulakis I, Natsis A, Drosoglou T, Bais A. Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering. Remote Sensing. 2020; 12(6):965. https://doi.org/10.3390/rs12060965

Chicago/Turabian Style

Siomos, Nikolaos; Fountoulakis, Ilias; Natsis, Athanasios; Drosoglou, Theano; Bais, Alkiviadis. 2020. "Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering" Remote Sens. 12, no. 6: 965. https://doi.org/10.3390/rs12060965

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