Application of a physically informed neural network for the recovery of vertical greenhouse gas profiles in the Mediterranean Basin

During March 2025, three intrusions of Saharan dust affected southern Italy, with observable effects on atmospheric composition and, in particular, on greenhouse gases. A recent study conducted by the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (CNR-IMAA) documented these events through integrated in situ and remote sensing observations. Significant variations in CH₄ and CO₂ concentrations were detected in correspondence with the dust transport episodes. In this work, we propose an approach based on Physics-Informed Neural Networks (PINNs) to retrieve the vertical profile of CH₄. The results are evaluated against high-precision ground-based measurements from CNR-IMAA, in order to assess the model’s predictive accuracy and its sensitivity to atmospheric variations associated with the presence of mineral aerosols.

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Published
2025-10-29
DOI
https://doi.org/10.1117/12.3069890
Primary Topic
Atmospheric and Environmental Gas Dynamics
Type
article
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Application of a physically informed neural network for the recovery of vertical greenhouse gas profiles in the Mediterranean Basin

Fabio Della Rocca, Pamela Pasquariello, Guido Masiello, Maria Ragosta et al.
Atmospheric and Environmental Gas Dynamics
article

Application of a physically informed neural network for the recovery of vertical greenhouse gas profiles in the Mediterranean Basin

Fabio Della Rocca, Pamela Pasquariello, Guido Masiello, Maria Ragosta, Carmine Serio, Giuliano Liuzzi, Lucia Mona, Sara Martínez, Francesco Carbone, Christian N. Gencarelli, Carlos Morillas, Rocco Giosa, Marco D’Emilio, Italia De Feis, Lorenzo Cassini, Isabella Zaccardo
article en

Abstract

During March 2025, three intrusions of Saharan dust affected southern Italy, with observable effects on atmospheric composition and, in particular, on greenhouse gases. A recent study conducted by the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (CNR-IMAA) documented these events through integrated in situ and remote sensing observations. Significant variations in CH₄ and CO₂ concentrations were detected in correspondence with the dust transport episodes. In this work, we propose an approach based on Physics-Informed Neural Networks (PINNs) to retrieve the vertical profile of CH₄. The results are evaluated against high-precision ground-based measurements from CNR-IMAA, in order to assess the model’s predictive accuracy and its sensitivity to atmospheric variations associated with the presence of mineral aerosols.

University of Basilicata (IT), Institute of Environmental Geology and Geoengineering (IT), IMA (Italy) (IT), Institute of Informatics and Telematics (IT), Sapienza University of Rome (IT), Universidad Politécnica de Madrid (ES), IAC (United States) (US)
Openalex Percentile: Top 71%
Atmospheric and Environmental Gas Dynamics
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