Remote Sensing of Air Pollution

Dear Colleagues,

The World Health Organization (WHO) indicates that 12.6 million deaths are associated with unhealthy environments each year across the globe, particularly in South-East Asia and Western Pacific regions, where the majority of air-pollution-linked deaths have been recorded. Meanwhile, the urbanization process has a significant negative effect on air pollutant concentrations. Thus, the accurate monitoring of air pollution with continuous spatiotemporal coverage is urgently required. Spaceborne remote sensing has been employed widely for the retrieval of information on various air pollutants, especially particulate matter. However, there are still limited studies on retrieving data on trace gases (e.g., O3, NO2, SO2, CO) and other aerosols (e.g., organic carbons) which significantly affect the ecosystem and climate. The spatiotemporal distribution of air pollutants and how they are affected by urbanization require still more research. Advanced techniques such as machine learning provide unprecedented opportunities to aggregate multi-source data for air pollution monitoring and estimation, which benefits further studies of air pollution exposure and deepens the understanding of the spatiotemporal characteristics of air pollutants.

This Special Issue aims to discuss the satellite-based monitoring and estimation of air pollution at urban, national or global scales for trace gases and aerosols and the interaction between pollutants and human activities or urbanization. Authors are encouraged to use multi-source data and advanced techniques such as machine learning models to improve the retrieval accuracy.

The potential topics include but are not limited to the following:

• Improving air pollution retrieval techniques by artificial intelligence and machine learning algorithms.
• Investigating the variables, relations of pollutions and spatiotemporal characteristics for improving air pollution retrieval accuracy.
• Synergizing multi-source data for air pollution retrieval.
• Long-term historical air pollution data reconstruction.
• Air pollution near-real-time monitoring.
• Investigating the relation between pollution and human activity or landscape patterns.
• Analysis of effect of urbanization on spatiotemporal changes of air pollutants.

Dr. Ziyue Chen
Dr. Xing Yan
Dr. Zhen Wang
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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.