Advances in Air Quality Data Analysis and Modeling
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".
Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 25207
Special Issue Editor
Interests: air quality modeling; indoor air quality; environmental information technology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The field of air quality data analysis and modelling has grown exponentially after the passage of the Clean Air Act in 1970. This growth is largely driven by the need to protect public health and to solve environmental problems as a result of the release of emissions in the atmosphere around the globe. Those efforts led to field studies which collected air pollution data and the development of computational resources and analytical techniques. This issue aims to provide readers state-of-the art solutions on model development using advanced predictive and cognitive analytical techniques and the analysis of air quality and associated health data using the concepts of big data, machine learning, artificial intelligence (AI), geographical information systems, and statistics. This issue invites authors to submit papers that exploit the use of advanced analytics in solving air pollution related problems. It is strongly recommended that the authors provide a detailed description of the relevant models/mathematical algorithms and procedures adopted in their respective studies. The papers may range from database development to emerging air quality models incorporating AI.
This Special Issue on innovative data analysis and modelling invites you to submit papers across the broader spectrum of air pollution science and engineering (e.g., air quality modelling, climate change, risk, exposure assessment, remote sensing, air monitoring, greenhouse gases, and online learning). The submission of research work by interdisciplinary teams and multi-country groups are of significant interest.
Prof. Dr. Ashok Kumar
Guest Editor
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
- Air quality modelling and health risk
- Atmospheric chemistry
- Climate change
- Exposure Assessment and Health Effects
- Regulatory modelling
- Air pollution measurements and monitoring
- Fence line monitoring
- Monitoring networks
- Satellite data analysis
- Greenhouse gas inventories
- Landfill
- Waste-to-Energy
- Virus analysis and its impact
- Advanced Analytics
- Machine Learning
- Big Data
- Statistics
- Artificial Intelligence
- Geographic Information Systems
- Pollution Information Technology
- Environmental Management Systems
- Artificial Intelligence
- Emission Rate Modeling
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