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Satellite Observations of Air Pollution, Analyses with Models and Applications
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Satellite Observations of Air Pollution, Analyses with Models and Applications

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: closed (31 October 2023) | Viewed by 14542

Special Issue Editors

Dr. Zhe Jiang

E-Mail Website
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

E-Mail Website
Guest Editor
Atmospheric Composition Analysis Group, Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
Interests: air quality; atmospheric chemistry; environmental health; emission inventory; radiative transfer; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Benjamin Gaubert

E-Mail Website
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

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Published Papers (5 papers)

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26 pages, 3631 KiB  
Article
Global Scale Inversions from MOPITT CO and MODIS AOD
by Benjamin Gaubert, David P. Edwards, Jeffrey L. Anderson, Avelino F. Arellano, Jérôme Barré, Rebecca R. Buchholz, Sabine Darras, Louisa K. Emmons, David Fillmore, Claire Granier, James W. Hannigan, Ivan Ortega, Kevin Raeder, Antonin Soulié, Wenfu Tang, Helen M. Worden and Daniel Ziskin
Remote Sens. 2023, 15(19), 4813; https://doi.org/10.3390/rs15194813 - 3 Oct 2023
Cited by 8 | Viewed by 2746
Abstract
Top-down observational constraints on emissions flux estimates from satellite observations of chemical composition are subject to biases and errors stemming from transport, chemistry and prior emissions estimates. In this context, we developed an ensemble data assimilation system to optimize the initial conditions for [...] Read more.
Top-down observational constraints on emissions flux estimates from satellite observations of chemical composition are subject to biases and errors stemming from transport, chemistry and prior emissions estimates. In this context, we developed an ensemble data assimilation system to optimize the initial conditions for carbon monoxide (CO) and aerosols, while also quantifying the respective emission fluxes with a distinct attribution of anthropogenic and wildfire sources. We present the separate assimilation of CO profile v9 retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument and Aerosol Optical Depth (AOD), collection 6.1, from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. This assimilation system is built on the Data Assimilation Research Testbed (DART) and includes a meteorological ensemble to assimilate weather observations within the online Community Atmosphere Model with Chemistry (CAM-chem). Inversions indicate an underestimation of CO emissions in CAMS-GLOB-ANT_v5.1 in China for 2015 and an overestimation of CO emissions in the Fire INventory from NCAR (FINN) version 2.2, especially in the tropics. These emissions increments are consistent between the MODIS AOD and the MOPITT CO-based inversions. Additional simulations and comparison with in situ observations from the NASA Atmospheric Tomography Mission (ATom) show that biases in hydroxyl radical (OH) chemistry dominate the CO errors. Full article
(This article belongs to the Special Issue Satellite Observations of Air Pollution, Analyses with Models and Applications)
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25 pages, 9838 KiB  
Article
Multi-Scale Analysis of PM2.5 Concentrations in the Yangtze River Economic Belt: Investigating the Combined Impact of Natural and Human Factors
by Shuoshuo Li, Guoen Wei, Yaobin Liu and Ling Bai
Remote Sens. 2023, 15(13), 3356; https://doi.org/10.3390/rs15133356 - 30 Jun 2023
Cited by 5 | Viewed by 1781
Abstract
Air pollutants, primarily PM2.5, 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 PM2.5 human and natural factors in this area. Therefore, this [...] Read more.
Air pollutants, primarily PM2.5, 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 PM2.5 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 PM2.5 and its spatial agglomeration patterns. Furthermore, this study further employs the spatial panel Durbin model to investigate the natural and anthropogenic factors driving PM2.5 concentrations across multiple scales. The analysis of the results reveals an “M”-shaped change trend in PM2.5 concentrations within the YREB. PM2.5 concentrations exhibit significant spatial agglomeration characteristics, whereby most urban agglomerations are high-pollution areas. Moreover, the changes in PM2.5 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 PM2.5 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 PM2.5 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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21 pages, 4700 KiB  
Article
Air Quality Index (AQI) Did Not Improve during the COVID-19 Lockdown in Shanghai, China, in 2022, Based on Ground and TROPOMI Observations
by Qihan Ma, Jianbo Wang, Ming Xiong and Liye Zhu
Remote Sens. 2023, 15(5), 1295; https://doi.org/10.3390/rs15051295 - 26 Feb 2023
Cited by 11 | Viewed by 5075
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 [...] Read more.
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), PM2.5 (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 PM2.5, 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 (NOx) 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 PM2.5, 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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16 pages, 8305 KiB  
Article
Investigating the Long-Term Variation Trends of Absorbing Aerosols over Asia by Using Multiple Satellites
by Ding Li, Yong Xue, Kai Qin, Han Wang, Hanshu Kang and Lizhang Wang
Remote Sens. 2022, 14(22), 5832; https://doi.org/10.3390/rs14225832 - 17 Nov 2022
Cited by 5 | Viewed by 2267
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 [...] Read more.
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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15 pages, 4738 KiB  
Technical Note
Window-Based Filtering Aerosol Retrieval Algorithm of Fine-Scale Remote Sensing Images: A Case Using Sentinel-2 Data in Beijing Region
by Jian Zhou, Yingjie Li, Qingmiao Ma, Qiaomiao Liu, Weiguo Li, Zilu Miao and Changming Zhu
Remote Sens. 2023, 15(8), 2172; https://doi.org/10.3390/rs15082172 - 20 Apr 2023
Viewed by 1595
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. [...] Read more.
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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