Air pollution and its effects on human health pose a significant challenge in modern society. The PRIMARY (PRIsma for Monitoring AiR quality) research project aims to utilize the capabilities of the Italian Space Agency's (ASI) PRISMA (PRecursor HyperSpectral Application Mission) to enhance air quality monitoring, particularly in urban areas. In particular, the project focuses on the exploitation of the hyperspectral PRISMA data to obtain detailed qualitative and quantitative data on atmospheric aerosol load and composition in urban environments. Current satellite-based characterization of particulate matter is limited due to spatial resolution constraints and to the complexities of the underlying inverse problem involving multiple variables. The PRIMARY project addresses the first issue through the decametric spatial resolution of PRISMA images, while the second issue is tackled by leveraging artificial intelligence approaches.
De Santis, D., Sasidharan, S.t., Del Frate, F., Curci, G., Barnaba, F., Di Liberto, L., et al. (2023). The ‘Primary’ Project: air quality monitoring at urban scale with prisma hyperspectral data. In IGARSS 2023: 2023 IEEE International Geoscience and Remote Sensing Symposium (pp.2576-2579). New York : IEEE [10.1109/igarss52108.2023.10282391].
The ‘Primary’ Project: air quality monitoring at urban scale with prisma hyperspectral data
De Santis, Davide;Del Frate, Fabio;
2023-01-01
Abstract
Air pollution and its effects on human health pose a significant challenge in modern society. The PRIMARY (PRIsma for Monitoring AiR quality) research project aims to utilize the capabilities of the Italian Space Agency's (ASI) PRISMA (PRecursor HyperSpectral Application Mission) to enhance air quality monitoring, particularly in urban areas. In particular, the project focuses on the exploitation of the hyperspectral PRISMA data to obtain detailed qualitative and quantitative data on atmospheric aerosol load and composition in urban environments. Current satellite-based characterization of particulate matter is limited due to spatial resolution constraints and to the complexities of the underlying inverse problem involving multiple variables. The PRIMARY project addresses the first issue through the decametric spatial resolution of PRISMA images, while the second issue is tackled by leveraging artificial intelligence approaches.File | Dimensione | Formato | |
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