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PM2.5 Retrieval Using Aerosol Optical Depth, Meteorological Variables, and Artificial Intelligence

Logothetis, Stavros-Andreas; Kosmopoulos, Georgios; Salamalikis, Vasileios; Kazantzidis, Andreas


Tidsskrift: Environmental Sciences Proceedings, vol. 26, 136, 2023

Doi: doi.org/10.3390/environsciproc2023026136

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, PM2.5) using artificial intelligence. Ground-based instruments, including a hand-held Microtops II sun photometer (for aerosol optical depth), a PurpleAir sensor (for PM2.5), and Rotronic sensors (for temperature and relative humidity), are used for the machine learning algorithm training. The retrieved PM2.5 reveals an adequate performance with an error of 0.08 μg m−3 and a Pearson correlation coefficient of 0.84.