Remote Sensing in Air Quality Monitoring
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 192
Special Issue Editors
Interests: automatic matching and fusion of multi-source geospatial data; application of GIS, RS and machine learning technology in environmental science
Special Issues, Collections and Topics in MDPI journals
Interests: geography information system; remote sensing; matching learning; deep learning; image super resolution; pattern recognition
Special Issue Information
Dear Colleagues,
Over the last decade, air quality monitoring has become a critical focus of environmental research, driven by growing concerns about public health and climate change. Traditional monitoring methods, primarily relying on ground-based stations, offer valuable insights, but often suffer from limitations in spatial coverage and temporal resolution. Rapid advancements in remote sensing technologies, particularly satellite-based sensors, has transformed air quality monitoring by enabling large-scale real-time assessments of atmospheric conditions. Remote sensing provides a powerful tool to monitor air pollution across extensive regions, offering a broader perspective and improved data accessibility compared to traditional methods. Such developments in remote sensing techniques have significant implications for our ability to track and understand air quality dynamics on a global scale. Hence, this Special Issue aims to explore the latest advancements in the use of remote sensing for air quality monitoring, focusing on novel methods, technologies, and applications that improve our understanding of air pollution dynamics. We welcome papers on a wide range of topics, including, but not limited to, the following:
- Remote sensing methods for air pollution detection and monitoring;
- Development of air quality models based on remote sensing data;
- Use of remote sensing and machine/deep learning techniques in air quality trend analysis;
- Air quality prediction and forecasting based on remote sensing data.
We invite contributions that delve into both the technical challenges and practical applications of remote sensing in air quality monitoring, aiming to provide insights into how this technology can help inform policy, improve public health outcomes, and contribute to sustainable environmental practices.
Prof. Dr. Meng Zhang
Dr. Bo Zhang
Prof. Dr. Naifang Bei
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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
- remote sensing
- air pollution detection and monitering
- machine learning and deep learning
- multispectral satellite data
- primary air pollutants
- estimation, prediction and forecasting
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