Multiple Data Analyses of Air Quality Diagnosis
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".
Deadline for manuscript submissions: closed (25 January 2022) | Viewed by 4262
Special Issue Editors
Interests: atmospheric chemistry; air pollution; air quality; polar environment; meteorology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Nowaday, massive volumes of data have been produced from multiple platforms: e.g., Eularian and Lagrangina style in-situ measurements, ground-based or satellite remote sensing, model simulations, reanalysis results from data assimilation. Analyses based on a single data set may not provide sufficient information for air quality diagnose.. Sophisticated analyses based on data from multiple resources are strongly necessary for reliable diagnosis of local, regional, and global air quality. Meanwhile, novel methods of data analyses (e.g. machine learning, artificial intelligence, etc.) have been utilized widely to leverage these “big data” for air quality diagnosis.
This special issues seeks studies on the use of “big data” from multiple resources and novel data analysis methods to multi-scale air quality diagnose. We also welcome studies on the use of machine learning, artificial intelligence, deep learning for air quality diagnosis.
Prof. Ja-Ho Koo
Dr. Guanyu Huang
Guest Editors
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Keywords
- air quality
- big data
- machine learning
- AI
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