Special Issue "Advances in Air Quality Spatio-temporal Mapping"

Dear Colleagues,

The development of real-time and time-averaged air pollutants concentration maps is a crucial step in identifying hotspot exposure areas and designing control and mitigation plans on sources to reduce the public health risk. However, all concentration maps are subject to uncertainties. Recently, advances in computer techniques to obtain larger dataset, such as those offered by machine learning and artificial intelligence, application of drone-based sensors to facilitate sampling from any terrain, and rapid growth in development of low-cost sensors, to establish denser air-quality monitoring networks have set the stage to visualize reliable spatio-temporal concentration maps. These new tools have reduced both the uncertainty and cost of data acquisition. Therefore, this Special Issue seeks advances in the above-mentioned approaches leading to improved resolution in air-quality concentration mapping or reduction of the computational cost, as well as more accurate estimation of the health end-points, health hazards, and exposure risks.

Dr. Nima Afshar-Mohajer
Guest Editor

Manuscript Submission Information

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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 2000 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.

• low-cost sensors
• numerical modeling
• hazard maps
• remote sensing
• machine learning
• deep learning
• land use regression modeling