Stereoscopic Remote Sensing of Atmospheric Ozone and Its Precursors and Its Applications
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: closed (30 July 2024) | Viewed by 4286
Special Issue Editors
Interests: satellite remote sensing; ground based remote sensing (MAX-DOAS, FTS, Lidar); deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: solar radiation; clouds; aerosols; water vapor; ozone
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Ozone pollution is becoming an increasingly prominent problem, which is mainly derived from atmospheric photochemical reactions of its precursors (VOCs, NOx, etc.), as well as stratospheric invasion. The monitoring of the atmospheric ozone and its precursors is critical to understanding the sources and causes of ozone pollution, which can support air quality management and reduce human exposure. Despite the current monitoring networks being insufficient for full understanding of the formation and source attribution of ozone pollution at the surface, the need for the development of an international stereoscopic monitoring strategy is emphasized due to the inhomogeneity of ozone pollution in both the horizontal and vertical directions. The stereoscopic monitoring and analysis strategy based on technologies such as multi-platform remote sensing (satellites, ground-based and mobile) and modeling will help us to more effectively characterize the formation of ozone pollution, leading to an advanced diagnostic understanding and prediction of ozone pollution.
This Special Issue aims to present studies on stereoscopic remote sensing and model simulation and analysis of the atmospheric ozone and its precursors. Topics can cover all aspects related to the monitoring, modeling and analysis of the atmospheric ozone and its precursors.
Prof. Dr. Cheng Liu
Dr. Manuel Antón
Guest Editors
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Keywords
- satellite remote sensing
- ground-based remote sensing
- ozone
- ozone precursors
- NOx
- VOCs
- monitoring
- chemistry
- modeling
- multi-source data fusion
- machine learning
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