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Manuscript deadline
01 July 2021

Cover image - Journal of the Air & Waste Management Association

Journal of the Air & Waste Management Association

Special Issue Editor(s)

Dr. Marina Astitha, Department of Civil & Environmental Engineering, School of Engineering, University of Connecticut
[email protected]

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Emerging technologies for monitoring, assessment and prediction of air quality

Artificial intelligence in the form of machine learning, deep learning or neural networks has formed new emerging technologies that begin to appear frequently in atmospheric sciences research and operations. These non-traditional methodologies are gaining interest in the scientific community and operational agencies worldwide, as new research outcomes strengthen the confidence to employ such tools and study atmospheric physical and chemical processes.

The Special Issue “Emerging technologies for monitoring, assessment and prediction of air quality” aims to bring together current state-of-the-science research about the utilization, successes and challenges of artificial intelligence in the realm of air quality monitoring, prediction and/or forecasting. It is a unique opportunity to consolidate our current knowledge and understanding of these methodologies as they relate to air quality, either by complementing numerical prediction/forecasting, correcting prediction errors, identifying sources of air pollutants or building new tools altogether. Applications that include various atmospheric pollutants, from tropospheric ozone and particulate matter to emerging contaminants will be considered for publication with the Special Issue.

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Submission Instructions

Please select the “2021 Artificial Intelligencemanuscript type when submitting to the journal's ScholarOne site.

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