Special Issue "Urban Air Quality Monitoring using Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Dr. Pawan Gupta
E-Mail Website
Guest Editor
NASA Goddard Space Flight Center, Greenbelt, United States
Interests: satellite remote sensing of aerosols, and clouds and applications to air quality and climate change
Prof. Sagnik Dey
E-Mail Website
Guest Editor
Centre for Atmospheric Sciences, IIT Delhi, India
Interests: aerosol-cloud-radiation interaction; air quality; climate change and health
Dr. Jason Blake Cohen
E-Mail Website
Guest Editor
School of Atmospheric Sciences, Sun Yat-Sen University, China
Interests: integration of data across multiple satellites; inverse modeling of atmospheric composition and emissions sources; in-situ aerosol modeling; air quality extremes

Special Issue Information

Dear Colleagues,

Air pollution around the world is a growing problem, and achieving clean air for breathing is one of the top priorities of the United Nation’s Sustainable Development Goals (SDGs). Remote sensing methods from space or ground, over the last two decades, have advanced, and can provide useful information on the state of the air. The focus of this Special Issue is on the monitoring and forecasting of surface air quality using the remote sensing observations of aerosols and trace gases at local, regional, and global scales. We encourage authors to submit contributions that describe original research methods, data, and the results of studies conducted on aerosols and trace gases (i.e., NO2, SO2, O3, HCHO, CH4, NH3, etc.) products from ground- and space-based remote sensing sensors. The specific topics include (but are not limited to) the following: PM2.5/PM10 measurements and estimates from satellite and surface; regional trends of atmospheric composition; assimilation of satellite data into regional and global models; transport of aerosols; role of biomass burning; dust aerosols and anthropogenic emissions in air quality; boundary layer processes and their impact on satellite estimations; and the physical and statistical modeling of air quality, population health, and ecological impact assessments driven by satellite data. Air quality product development, validation, and inter-comparison with models from current satellite and sensors in LEO (TROPOMI, MODIS, MISR, OMI, VIIRS, and OMPS), GEO (GOES-R, GOES-S, Himawari-8/9, GOCI, anf INSAT), and L1 (EPIC) orbits are encouraged. Studies discussing upcoming satellite missions (i.e., TEMPO, MAIA, GEMS, and 3MI) are also welcome.

Dr. Pawan Gupta
Prof. Sagnik Dey
Dr. Jason Blake Cohen
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 papers will be 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. 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 1800 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.


  • air pollution
  • air quality
  • satellite
  • space
  • particulate matter
  • trace gases

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


Open AccessLetter
Variability of Major Aerosol Types in China Classified Using AERONET Measurements
Remote Sens. 2019, 11(20), 2334; https://doi.org/10.3390/rs11202334 - 09 Oct 2019
Aerosol type is a critical piece of information in both aerosol forcing estimation and passive satellite remote sensing. However, the major aerosol types in China and their variability is still less understood. This work uses direct sun measurements and inversion derived parameters from [...] Read more.
Aerosol type is a critical piece of information in both aerosol forcing estimation and passive satellite remote sensing. However, the major aerosol types in China and their variability is still less understood. This work uses direct sun measurements and inversion derived parameters from 47 sites within the Aerosol Robotic Network (AERONET) in China, with more than 39,000 records obtained between April 1998 and January 2017, to identify dominant aerosol types using two independent methods, namely, K means and Self Organizing Map (SOM). In total, we define four aerosol types, namely, desert dust, scattering mixed, absorbing mixed and scattering fine, based on their optical and microphysical characteristics. Seasonally, dust aerosols mainly occur in the spring and over North and Northwest China; scattering mixed are more common in the spring and summer, whereas absorbing aerosols mostly occur in the autumn and winter during heating period, and scattering fine aerosols have their highest occurrence frequency in summer over East China. Based on their spatial and temporal distribution, we also generate seasonal aerosol type maps that can be used for passive satellite retrieval. Compared with the global models used in most satellite retrieval algorithms, the unique feature of East Asian aerosols is the curved single scattering albedo spectrum, which could be related to the mixing of black carbon with dust or organic aerosols. Full article
(This article belongs to the Special Issue Urban Air Quality Monitoring using Remote Sensing)
Show Figures

Figure 1

Back to TopTop