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Atmosphere
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Study of Air Pollution Based on Remote Sensing (2nd Edition)
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Study of Air Pollution Based on Remote Sensing (2nd Edition)

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1382

Special Issue Editors

Dr. Haoran Liu

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Guest Editor
Institute of Physical Science and Information Technology, Anhui University, Hefei 230601, China
Interests: remote sensing; air pollution; trace gases; vertical distribution; pollution transport
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Tan

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Guest Editor
Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Interests: vertical distribution; air pollution transport; emission flux
Special Issues, Collections and Topics in MDPI journals
Dr. Wenjing Su

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Guest Editor
Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China
Interests: satellite remote sensing; air pollution; trace gases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second volume in a series of publications dedicated to the “Study of Air Pollution Based on Remote Sensing” (https://www.mdpi.com/journal/atmosphere/special_issues/5KD97OR884).

High-concentration atmospheric aerosol, ozone, VOCs, nitrogen oxide, sulfur dioxide, and other air pollutants pose a great threat to both the ecosystem and human health. In order to fully clarify the process of air pollution, advanced monitoring technology is needed. Remote sensing methods have unique advantages for monitoring the horizontal and vertical distribution of air pollutants, which can make up for the lack of spatial distribution monitoring of in situ monitoring networks. With their rapid development in recent decades, multiplatform remote sensing technologies, such as satellites, ground-based tools, mobile observation methods, etc., have been widely used in atmospheric environment monitoring applications. Remote sensing data on high-spatial–temporal-resolution air pollutants can be used to study the spatial–temporal distribution characteristics, transmission characteristics, and evolution mechanisms of air pollution.

We are pleased to announce the launch of a new Special Issue titled the “Study of Air Pollution Based on Remote Sensing”, which invites contributions presenting research on atmospheric environment remote sensing technology and its applications. This topic covers the design of atmospheric monitoring instruments, retrieval algorithms, observation experiments, data analysis research, health impact assessments, etc.

Dr. Haoran Liu
Dr. Wei Tan
Dr. Wenjing Su
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. 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 2400 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

  • air pollution
  • remote sensing
  • ozone
  • VOCs
  • aerosols
  • atmospheric trace gaces
  • monitoring

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Published Papers (2 papers)

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Research

21 pages, 12008 KiB  
Article
Fine-Grained Air Pollution Inference at Large-Scale Region Level via 3D Spatiotemporal Attention Super-Resolution Model
by Changqun Li, Shan Tang, Jing Liu, Kai Pan, Zhenyi Xu, Yunbo Zhao and Shuchen Yang
Atmosphere 2025, 16(2), 166; https://doi.org/10.3390/atmos16020166 - 31 Jan 2025
Viewed by 305
Abstract
Air pollution presents a serious hazard to human health and the environment for the global rise in industrialization and urbanization. While fine-grained monitoring is crucial for understanding the formation and control of air pollution and their effects on human health, existing macro-regional level [...] Read more.
Air pollution presents a serious hazard to human health and the environment for the global rise in industrialization and urbanization. While fine-grained monitoring is crucial for understanding the formation and control of air pollution and their effects on human health, existing macro-regional level or ground-level methods make air pollution inference in the same spatial scale and fail to address the spatiotemporal correlations between cross-grained air pollution distribution. In this paper, we propose a 3D spatiotemporal attention super-resolution model (AirSTFM) for fine-grained air pollution inference at a large-scale region level. Firstly, we design a 3D-patch-wise self-attention convolutional module to extract the spatiotemporal features of air pollution, which aggregates both spatial and temporal information of coarse-grained air pollution and employs a sliding window to add spatial local features. Then, we propose a bidirectional optical flow feed-forward layer to extract the short-term air pollution diffusion characteristics, which can learn the temporal correlation contaminant diffusion between closeness time intervals. Finally, we construct a spatiotemporal super-resolution upsampling pretext task to model the higher-level dispersion features mapping between the coarse-grained and fined-grained air pollution distribution. The proposed method is tested on the PM2.5 pollution datatset of the Yangtze River Delta region. Our model outperforms the second best model in RMSE, MAE, and MAPE by 2.6%, 3.05%, and 6.36% in the 100% division, and our model also outperforms the second best model in RMSE, MAE, and MAPE by 3.86%, 3.76%, and 12.18% in the 40% division, which demonstrates the applicability of our model for different data sizes. Furthermore, the comprehensive experiment results show that our proposed AirSTFM outperforms the state-of-the-art models. Full article
(This article belongs to the Special Issue Study of Air Pollution Based on Remote Sensing (2nd Edition))
34 pages, 41034 KiB  
Article
The Dynamics of Air Pollution in the Southwestern Part of the Caspian Sea Basin (Based on the Analysis of Sentinel-5 Satellite Data Utilizing the Google Earth Engine Cloud-Computing Platform)
by Vladimir Tabunshchik, Aleksandra Nikiforova, Nastasia Lineva, Polina Drygval, Roman Gorbunov, Tatiana Gorbunova, Ibragim Kerimov, Cam Nhung Pham, Nikolai Bratanov and Mariia Kiseleva
Atmosphere 2024, 15(11), 1371; https://doi.org/10.3390/atmos15111371 - 14 Nov 2024
Viewed by 770
Abstract
The Caspian region represents a complex and unique system of terrestrial, coastal, and aquatic environments, marked by an exceptional landscape and biological diversity. This diversity, however, is increasingly threatened by substantial anthropogenic pressures. One notable impact of this human influence is the rising [...] Read more.
The Caspian region represents a complex and unique system of terrestrial, coastal, and aquatic environments, marked by an exceptional landscape and biological diversity. This diversity, however, is increasingly threatened by substantial anthropogenic pressures. One notable impact of this human influence is the rising concentration of pollutants atypical for the atmosphere. Advances in science and technology now make it possible to detect certain atmospheric pollutants using remote Earth observation techniques, specifically through data from the Sentinel-5 satellite, which provides continuous insights into atmospheric contamination. This article investigates the dynamics of atmospheric pollution in the southwestern part of the Caspian Sea basin using Sentinel-5P satellite data and the cloud-computing capabilities of the Google Earth Engine (GEE) platform. The study encompasses an analysis of concentrations of seven key pollutants: nitrogen dioxide (NO2), formaldehyde (HCHO), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), methane (CH4), and the Aerosol Index (AI). Spatial and temporal variations in pollution fields were examined for the Caspian region and the basins of the seven rivers (key areas) flowing into the Caspian Sea: Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan. The research methodology is based on the use of data from the Sentinel-5 satellite, SRTM DEM data on absolute elevations, surface temperature data, and population density data. Data processing is performed using the Google Earth Engine cloud-computing platform and the ArcGIS software suite. The main aim of this study is to evaluate the spatiotemporal variability of pollutant concentration fields in these regions from 2018 to 2023 and to identify the primary factors influencing pollution distribution. The study’s findings reveal that the Heraz and Gorgan River basins have the highest concentrations of nitrogen dioxide and Aerosol Index levels, marking these basins as the most vulnerable to atmospheric pollution among those assessed. Additionally, the Gorgan basin exhibited elevated carbon monoxide levels, while the highest ozone concentrations were detected in the Sunzha basin. Our temporal analysis demonstrated a substantial influence of the COVID-19 pandemic on pollutant dispersion patterns. Our correlation analysis identified absolute elevation as a key factor affecting pollutant distribution, particularly for carbon monoxide, ozone, and aerosol indices. Population density showed the strongest correlation with nitrogen dioxide distribution. Other pollutants exhibited more complex distribution patterns, influenced by diverse mechanisms associated with local emission sources and atmospheric dynamics. Full article
(This article belongs to the Special Issue Study of Air Pollution Based on Remote Sensing (2nd Edition))
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