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Special Issue "Satellite Observations of Air Pollution, Analyses with Models and Applications"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: 31 October 2023 | Viewed by 5527

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Special Issue Editors

Dr. Zhe Jiang

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Guest Editor

School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Interests: air pollution; chemistry transport model; data assimilation; machine learning

Dr. Chi Li

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Guest Editor

Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
Interests: air quality; satellite remote sensing; atmospheric chemistry; data science and machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Benjamin Gaubert

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Guest Editor

National Center for Atmospheric Research, Boulder, CO, USA
Interests: atmospheric composition; chemistry-transport model; data assimilation

Special Issue Information

Dear Colleagues,

Remotely sensed measurements provided by satellite instruments have been widely used in the field of atmospheric environment science and have led to dramatic improvements in our understanding of atmospheric pollutants. Chemistry transport models are powerful tools that are used to understand atmospheric pollutant sources and atmospheric fate. Data assimilation techniques, integrating models and observations, allow us to constrain the sources and sinks of atmospheric pollutants and provide better forecasts of air quality evolution. Recent advancements in data-driven machine learning techniques have provided new opportunities for the integration and extension of atmospheric observations, with the rapid rise in applications in atmospheric environment studies.

This Special Issue proposes to document recent advancements in the applications of satellite observations to monitor air pollution, methods to optimally combine satellite observations and chemical transport models, as well as the developments of inverse analyses, data assimilations and machine learning techniques.

Potential topics for this Special Issue include but are not limited to the following:

Monitoring and analyses of air pollutants using satellite observations.
Interpretation of atmospheric pollutants using satellite observations and chemistry transport models.
Global and regional data assimilation of satellite observations of atmospheric composition.
Inverse modeling to optimize fluxes by assimilating satellite observations.
Applications of artificial intelligence and machine learning algorithms to extend and enhance satellite observations.

Dr. Zhe Jiang
Dr. Chi Li
Dr. Benjamin Gaubert
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

satellite remote sensing
air pollution
chemistry transport models
data assimilation
machine learning
emissions

Published Papers (4 papers)

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Research

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Open AccessArticle

Multi-Scale Analysis of PM2.5 Concentrations in the Yangtze River Economic Belt: Investigating the Combined Impact of Natural and Human Factors

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Remote Sens. 2023, 15(13), 3356; https://doi.org/10.3390/rs15133356 - 30 Jun 2023

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Abstract

Air pollutants, primarily PM_2.5, have inflicted significant harm on public health and sustainable urban development in the Yangtze River Economic Belt (YREB). Previous studies often neglected the coordinated measurement of PM_2.5 human and natural factors in this area. Therefore, this paper focuses on the YREB. Using a geographic information system (GIS) platform, along with remote sensing and statistical data spanning from 2000 to 2020, this study employs spatial analysis to uncover the spatial-temporal characteristics of PM_2.5 and its spatial agglomeration patterns. Furthermore, this study further employs the spatial panel Durbin model to investigate the natural and anthropogenic factors driving PM_2.5 concentrations across multiple scales. The analysis of the results reveals an “M”-shaped change trend in PM_2.5 concentrations within the YREB. PM_2.5 concentrations exhibit significant spatial agglomeration characteristics, whereby most urban agglomerations are high-pollution areas. Moreover, the changes in PM_2.5 concentrations are jointly influenced by several factors, including the secondary industry, urban built-up area, population density, annual precipitation, and NDVI. Furthermore, the dominant factors influencing PM_2.5 concentrations in the three major urban agglomerations exhibit both similarities and differences. In addition, for effective governance coordination across regions, policymakers should diligently consider both the shared predominant factors and the varying factors specific to each region in the future. This study expands the research content of watershed PM_2.5 collaborative governance, and further provides practical support for other watershed environmental governance and urban sustainable management. Full article

(This article belongs to the Special Issue Satellite Observations of Air Pollution, Analyses with Models and Applications)

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Open AccessArticle

Air Quality Index (AQI) Did Not Improve during the COVID-19 Lockdown in Shanghai, China, in 2022, Based on Ground and TROPOMI Observations

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Remote Sens. 2023, 15(5), 1295; https://doi.org/10.3390/rs15051295 - 26 Feb 2023

Cited by 2 | Viewed by 2870

Abstract

The lockdowns from the coronavirus disease of 2019 (COVID-19) have led to a reduction in anthropogenic activities and have hence reduced primary air pollutant emissions, which were reported to have helped air quality improvements. However, air quality expressed by the air quality index (AQI) did not improve in Shanghai, China, during the COVID-19 outbreak in the spring of 2022. To better understand the reason, we investigated the variations of nitrogen dioxide (NO₂), ozone (O₃), PM_2.5 (particular matter with an aerodynamic diameter of less than 2.5 μm), and PM₁₀ (particular matter with an aerodynamic diameter of less than 10 μm) by using in situ and satellite measurements from 1 March to 31 June 2022 (pre-, full-, partial-, and post-lockdown periods). The results show that the benefit of the significantly decreased ground-level PM_2.5, PM₁₀, and NO₂ was offset by amplified O₃ pollution, therefore leading to the increased AQI. According to the backward trajectory analyses and multiple linear regression (MLR) model, the anthropogenic emissions dominated the observed changes in air pollutants during the full-lockdown period relative to previous years (2019–2021), whereas the long-range transport and local meteorological parameters (temperature, air pressure, wind speed, relative humidity, and precipitation) influenced little. We further identified the chemical mechanism that caused the increase in O₃ concentration. The amplified O₃ pollution during the full-lockdown period was caused by the reduction in anthropogenic nitrogen oxides (NO_x) under a VOC-limited regime and high background O₃ concentrations owing to seasonal variations. In addition, we found that in the downtown area, ground-level PM_2.5, PM₁₀, and NO₂ more sensitively responded to the changes in lockdown measures than they did in the suburbs. These findings provide new insights into the impact of emission control restrictions on air quality and have implications for air pollution control in the future. Full article

(This article belongs to the Special Issue Satellite Observations of Air Pollution, Analyses with Models and Applications)

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Open AccessEditor’s ChoiceArticle

Investigating the Long-Term Variation Trends of Absorbing Aerosols over Asia by Using Multiple Satellites

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Remote Sens. 2022, 14(22), 5832; https://doi.org/10.3390/rs14225832 - 17 Nov 2022

Cited by 1 | Viewed by 1028

Abstract

Absorbing aerosols, consisting of smoke (black carbon (BC) and other organics) and dust (from windblown sources), can have a strong warming effect on the climate and impact atmospheric circulation due to localized heating. To investigate the spatiotemporal and vertical changes of absorbing aerosols across Asia, collocation data from OMI, MODIS, and CALIPSO were used to compare two periods: 2006–2013 and 2014–2021. This study revealed a significant temporal and spatial contrast of aerosol loading over the study region, with a drop in total aerosol concentration and anthropogenic smoke concentration recorded across the Eastern China region (all seasons) and a concurrent increase in the Indian sub-continent region (especially in autumn). The range of aerosol diffusion is affected by the height of the smoke and aerosol plumes, as well as the wind force, and is dispersed eastwards because of the Hadley circulation patterns in the Northern Hemisphere. Smoke from Southeast Asia typically rises to a height of 3 km and affects the largest area in contrast to other popular anthropogenic zones, where it is found to be around 1.5–2 km. The dust in Inner Mongolia had the lowest plume height of 2 km (typically in spring) compared to other locations across the study region where it reached 2–5 km in the summer. This study showed, by comparison with AERONET measurements, that combining data from MODIS and OMI generates more accuracy in detecting aerosol AOD from smoke than using the instruments singularly. This study has provided a comprehensive assessment of absorbing aerosol in Asia by utilizing multiplatform remote-sensed data and has summarized long-term changes in the spatiotemporal distribution and vertical structure of absorbing aerosols. Full article

(This article belongs to the Special Issue Satellite Observations of Air Pollution, Analyses with Models and Applications)

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Open AccessTechnical Note

Window-Based Filtering Aerosol Retrieval Algorithm of Fine-Scale Remote Sensing Images: A Case Using Sentinel-2 Data in Beijing Region

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Remote Sens. 2023, 15(8), 2172; https://doi.org/10.3390/rs15082172 - 20 Apr 2023

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Abstract

The satellite-based Aerosol Optical Depth (AOD) retrieval algorithms are generally needed to construct Land Surface Reflectance (LSR) database. However, errors are unavoidable due to the surface complexity, especially for the short observation period and high-resolution images, such as Sentinel-2 Multi-Spectral Instrument (MSI) data. To address this, reference day images are used instead of the LSR database. The surface is assumed to be Lambertian; however, the fact is that not all pixels meet it well. Therefore, we proposed a window-based AOD retrieval algorithm, which can ignore the unreliable/non-Lambertian pixels in a retrieval window based on two main filtering processes. Finally, using Sentinel-2 Band 1 (60 m), the AODs (120 m) of 134 reference images to 43 reference images were retrieved by this algorithm from 2017 to 2021 in Beijing region, China. The results show that the retrieved AOD with the proposed algorithm exhibits good agreement with the ground-based measured AOD (R > 0.97). The high-resolution AOD presents comparable spatial distributions to the Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm AOD (1 km) products. Moreover, the very little noise and very high spatial continuity of retrieval AOD imply that this algorithm could be ported to other algorithms as part of improving AOD quality. Full article

(This article belongs to the Special Issue Satellite Observations of Air Pollution, Analyses with Models and Applications)

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