Aerosol classification in Cyprus using active and passive remote sensing techniques

Limassol region in Cyprus, located in the Eastern Mediterranean, represents a major crossroads for various air masses, making the place a hub for mixing particles from both local and remote aerosol sources. The unique atmospheric conditions of the area offer an ideal place to study the vertical atmospheric structure. This study utilizes active and passive remote sensing techniques, such as the sun-photometer AERONET CUT-TEPAK station (Aerosol Robotic Network) and the Polly XT Raman LIDAR depolarization system available in Limassol (34.7°N, 33°E). An extended analysis of long-term ground-based measurements using AERONET Level 2.0 solar products is presented. The study focuses on the classification method proposed by Toledano et al. (2007) for different aerosol types. Aerosol optical depth at 440 nm (AOD) and Ångström Exponent at 440-870 nm (AE) are examined for 14 years (2010 - 2023). The results show a strong contribution of dust particles in spring months and continental particles in summer periods. Marine particles were found to be extremely dominant according to the classification. Subsequently, to examine the presence of dust particles in the marine’s classification, the study incorporates the particle depolarization ratio (PDR) from the LIDAR vertical profiles at 532 nm using the Klett method. Thus, a new aerosol scheme has been developed concluding in four aerosol categories (dominating conditions of marine aerosol (M), mineral dust (D), anthropogenic haze/ biomass burning (H+S), mixed aerosol).

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Published
2024-09-13
DOI
https://doi.org/10.1117/12.3037304
Primary Topic
Atmospheric aerosols and clouds
Type
article
Field-Weighted Citation Impact
0.00

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article

Aerosol classification in Cyprus using active and passive remote sensing techniques

Diofantos G. Hadjimitsis, Albert Ansmann, Argyro Nisantzi, Rodanthi Elisavet Mamouri et al.
Atmospheric aerosols and clouds
article

Aerosol classification in Cyprus using active and passive remote sensing techniques

Diofantos G. Hadjimitsis, Albert Ansmann, Argyro Nisantzi, Rodanthi Elisavet Mamouri, Athina Savva
article en

Abstract

Limassol region in Cyprus, located in the Eastern Mediterranean, represents a major crossroads for various air masses, making the place a hub for mixing particles from both local and remote aerosol sources. The unique atmospheric conditions of the area offer an ideal place to study the vertical atmospheric structure. This study utilizes active and passive remote sensing techniques, such as the sun-photometer AERONET CUT-TEPAK station (Aerosol Robotic Network) and the Polly XT Raman LIDAR depolarization system available in Limassol (34.7°N, 33°E). An extended analysis of long-term ground-based measurements using AERONET Level 2.0 solar products is presented. The study focuses on the classification method proposed by Toledano et al. (2007) for different aerosol types. Aerosol optical depth at 440 nm (AOD) and Ångström Exponent at 440-870 nm (AE) are examined for 14 years (2010 - 2023). The results show a strong contribution of dust particles in spring months and continental particles in summer periods. Marine particles were found to be extremely dominant according to the classification. Subsequently, to examine the presence of dust particles in the marine’s classification, the study incorporates the particle depolarization ratio (PDR) from the LIDAR vertical profiles at 532 nm using the Klett method. Thus, a new aerosol scheme has been developed concluding in four aerosol categories (dominating conditions of marine aerosol (M), mineral dust (D), anthropogenic haze/ biomass burning (H+S), mixed aerosol).

Cyprus University of Technology (CY), Leibniz Institute for Tropospheric Research (DE)
Cyprus University of Technology, University of Cyprus, European Commission, Horizon 2020 Framework Programme
Openalex Percentile: Top 86%
Atmospheric aerosols and clouds
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Aerosol classification in Cyprus using active and passive remote sensing techniques — Diofantos G. Hadjimitsis, Albert Ansmann, et al. · (2024) | TGRS Research Map | TGRS