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Remote Sensing
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Satellite Observations for Particulate Matter and Gaseous Pollutants Research
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Satellite Observations for Particulate Matter and Gaseous Pollutants Research

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2014

Special Issue Editors

Prof. Dr. Zhengqiang Li

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Guest Editor
State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: aerosol and PM2.5 polarimetry
Special Issues, Collections and Topics in MDPI journals
Dr. Jintai Lin

E-Mail Website
Guest Editor
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Interests: air pollutants; satellite remote sensing
Dr. Cheng Chen

E-Mail Website
Guest Editor
Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Interests: remote sensing; atmospheric modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Yan Yu

E-Mail Website
Guest Editor
Department of Atmospheric and Oceanic Sciences, School of Physics, Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Interests: dust; fire; aerosol remote sensing

Special Issue Information

Dear Colleagues,

Atmospheric components, such as aerosols and gases, affect Earth’s climate and environment in various ways. Among them, particulate matters (e.g., PM1, PM2.5, and PM10) and gaseous pollutants are key elements that affect air quality and Earth–atmosphere radiation balance and are thus of great interest in various research fields. Monitoring aerosol and gaseous pollutants, as well as understanding their interactions, is crucial for both environmental governance and climate change mitigation.

Satellite signals from the reflected solar radiation or reception of emitted radiation (such as lasers and microwaves) passing through the atmosphere are altered by the presence of particulate matters and gaseous pollutants simultaneously. Thus, satellite observations can be used to derive the amount, volume, and characteristics of these atmospheric components and thus become an important topic in atmospheric remote sensing. Furthermore, an increasing number of satellite instruments have been deployed in orbit or are being designed to monitor aerosol and gaseous pollutants. However, the absorption spectra of gaseous pollutants and aerosols may partially overlap, and it is still challenging to derive them simultaneously with high accuracy from satellite observations.

This Special Issue is directed at studies covering different aspects related to satellite remote sensing of atmospheric particulate matters, gaseous pollutants, and their interactions. We welcome papers focusing on the development of remote sensing retrieval algorithms and/or applications of remote sensing datasets with respect to aerosols, PM2.5, PM10, gaseous pollutants, and their interactions.

  • Remote sensing of aerosol and particulate matters;
  • Remote sensing of gaseous pollutants;
  • Interactions of particulate matters and gaseous pollutants based on satellite observations.

Prof. Dr. Zhengqiang Li
Dr. Jintai Lin
Dr. Cheng Chen
Dr. Yan Yu
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 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 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.

Keywords

  • particulate matters
  • gaseous pollutants, such as SO2, NO2, O3, etc.
  • aerosol and chemical compositions
  • satellite observation
  • interaction
  • PM2.5, PM10, PM1, and dust

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

21 pages, 9315 KiB  
Article
An Extension of Ozone Profile Retrievals from TROPOMI Based on the SAO2024 Algorithm
by Juseon Bak, Xiong Liu, Gonzalo González Abad and Kai Yang
Remote Sens. 2025, 17(5), 779; https://doi.org/10.3390/rs17050779 - 23 Feb 2025
Viewed by 547
Abstract
We investigate the retrieval of ozone (O3) profiles, with a particular focus on tropospheric O3, from backscattered ultraviolet radiances measured by the TROPOspheric Monitoring Instrument (TROPOMI), using the UV2 (300–332 nm) and UV3 (305–400 nm) channels independently. An optimal [...] Read more.
We investigate the retrieval of ozone (O3) profiles, with a particular focus on tropospheric O3, from backscattered ultraviolet radiances measured by the TROPOspheric Monitoring Instrument (TROPOMI), using the UV2 (300–332 nm) and UV3 (305–400 nm) channels independently. An optimal estimation retrieval algorithm, originally developed for the Ozone Monitoring Instrument (OMI), was extended as a preliminary step toward integrating multiple satellite ozone profile datasets. The UV2 and UV3 channels exhibit distinct radiometric and wavelength calibration uncertainties, leading to inconsistencies in retrieval accuracy and convergence stability. A yearly “soft” calibration mitigates overestimation and cross-track-dependent biases (“stripes”) in tropospheric ozone retrievals, enhancing retrieval consistency between UV2 and UV3. Convergence stability is ensured by optimizing the measurement error constraints for each channel. It is shown that our research product outperforms the standard product (UV1 and UV2 combined) in capturing the seasonal and long-term variabilities of tropospheric ozone. An agreement between the retrieved tropospheric ozone and ozonesonde measurements is observed within 0–3 DU ± 5.5 DU (R = 0.75), which is better than that of the standard product by a factor of two. Despite lacking Hartley ozone information in UV2 and UV3, the retrieved stratospheric ozone columns have good agreement with ozonesondes (R = 0.96). Full article
(This article belongs to the Special Issue Satellite Observations for Particulate Matter and Gaseous Pollutants Research)
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25 pages, 50172 KiB  
Article
Improvement of Space-Observation of Aerosol Chemical Composition by Synergizing a Chemical Transport Model and Ground-Based Network Data
by Zhengqiang Li, Zhiyu Li, Zhe Ji, Yisong Xie, Ying Zhang, Zhuolin Yang, Zheng Shi, Lili Qie, Luo Zhang, Zihan Zhang and Haoran Gu
Remote Sens. 2024, 16(23), 4390; https://doi.org/10.3390/rs16234390 - 24 Nov 2024
Viewed by 997
Abstract
Aerosol chemical components are critical parameters that influence the atmospheric environment, climate effects, and human health. Retrieving global columnar atmospheric aerosol components from satellite observations provides foundational data and practical value. This study develops a method for retrieving aerosol component composition from polarized [...] Read more.
Aerosol chemical components are critical parameters that influence the atmospheric environment, climate effects, and human health. Retrieving global columnar atmospheric aerosol components from satellite observations provides foundational data and practical value. This study develops a method for retrieving aerosol component composition from polarized satellite data by synergizing a chemical transport model with ground-based remote sensing data. The method enables the rapid acquisition of columnar mass concentrations for seven aerosol components on a global scale, including black carbon (BC), brown carbon (BrC), organic carbon (OC), ammonium sulfate (AS), aerosol water (AW), dust (DU), and sea salt (SS). We first establish a remote sensing model based on the multiple solution mixing mechanism (MSM2) to obtain aerosol chemical components using AERONET ground-based measurements. We then employ a cross-layer adaptive fusion (CAF)-Transformer model to learn the spatial distribution characteristics of aerosol components from the MERRA-2 model. Furthermore, we optimize the retrieval model by transfer learning from the ground-based composition data to achieve satellite remote sensing of aerosol components. Residual analysis indicates that the retrieval model exhibits robust generalization capabilities for components such as BC, OC, AS, and DU, achieving a coefficient of determination of 0.7. Moreover, transfer learning effectively enhances the consistency between satellite retrievals and ground-based remote sensing results, with an average improvement of 0.23 in the correlation coefficient. We present annual and seasonal means of global distributions of the retrieved aerosol component concentrations, with a major focus on the spatial and temporal variations of BC and DU. Additionally, we analyze three typical atmospheric environmental cases, wildfire, dust storm, and particulate pollution, by comparing our retrievals with model data and other datasets. This demonstrates the ability of satellite remote sensing to identify the location, intensity, and impact range of environmental pollution events. Satellite-retrieved aerosol component data offers high spatial resolution and efficiency, particularly providing significant advantages for near-real-time monitoring of regional atmospheric environmental events. Full article
(This article belongs to the Special Issue Satellite Observations for Particulate Matter and Gaseous Pollutants Research)
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