Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: 26 May 2024 | Viewed by 679
Special Issue Editors
2. Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA
Interests: aerosols; air quality monitoring; forecasting; PM2.5; NO2; O3; remote sensing; satellites, machine leaning; deep learning
Interests: satellite remote sensing; aerosols; air quality; wild fire; urban heatwave; drought; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Long-term exposure to air pollution not only adversely affects human health but also has serious impacts on the climate and ecosystems. It is important to continuously monitor and predict air quality to assess its impact on human health and the environment. Air quality monitoring is usually conducted through point-based in situ measurements, but the spatial coverage is limited due to the lack of stations covering a large area. Satellite-based aerosol information is widely used as the key parameter to monitor and forecast air quality, not only the local scale but also on the global scale. Many previous studies have adopted various approaches (e.g., data assimilations (DA), artificial intelligence (AI), and a combination of DA and AI) to improve the their model’s performance. The aim of this Special Issue is to explore the latest advancements in satellite remote sensing of atmospheric aerosols for air quality applications. We invite submissions that showcase innovative methodologies, techniques, and applications related to remote sensing analysis in the context of aerosol monitoring. We welcome research articles, reviews, and case studies that address the following topics:
- Satellite remote sensing data acquisition and preprocessing techniques for aerosol monitoring.
- Development and validation of algorithms and models for satellite-based retrieval of aerosol properties.
- Integration of remote sensing data with other sources of information, such as ground-based observations, for comprehensive aerosol assessments.
- Applications of satellite remote sensing in air quality monitoring, including source attribution and regional-scale variability analysis.
- Evaluation of the effectiveness of satellite remote sensing in informing regulatory and policy actions to reduce air pollution.
Dr. Seohui Park
Prof. Dr. Jungho Im
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- satellite remote sensing
- atmospheric aerosols
- air pollution
- PM2.5
- O3
- NO2
- methane
- air quality monitoring and forecasting
- atmospheric components
- artificial intelligence
- machine learning
- deep learning
- data assimilation
