Application of Emerging Methods in Aerosol Research
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 963
Special Issue Editors
Interests: sulfate aerosol; atmospheric nitrate; mass independent fractionation of oxygen; chemical mechanism
Interests: nitrogen-containing aerosols; nitrogen isotope analysis; carbon isotope analysis; organic nitrogen
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the field of aerosol research, a series of innovative methods have emerged, providing us with unprecedented perspectives and tools to reveal the mysteries of the atmosphere. These methods cover laboratory experiments, field observations, and model simulations, such as technologies in high-resolution mass spectrometry, synchrotron radiation, lidar detection, machine learning, and artificial intelligence. This Special Issue of Atmosphere titled “Application of Emerging Methods in Aerosol Research” aims to facilitate the development of innovative methods in aerosol studies and to provide researchers with a comprehensive platform sharing their recent work. Therefore, we encourage researchers to apply emerging methods in various aspects of aerosol science, including aerosol properties, formation mechanisms, toxicities, sources, etc. Through collaboration, this Special Issue aims to deepen our understanding of atmospheric aerosols and open new avenues for aerosol research.
The topics of this Special Issue include but are not limited to the following:
- Innovative methods of aerosol measurement;
- Development of aerosol-relevant models;
- Probes in tracing aerosol formation mechanisms;
- New technologies in aerosol source apportionment;
- Strategies in future aerosol studies.
Dr. Pengzhen He
Dr. Libin Wu
Guest Editors
Manuscript Submission Information
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Keywords
- aerosol measurement
- model development
- formation mechanism
- machine learning
- artificial intelligence
- high-resolution mass spectrometry
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