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Satellite Remote Sensing of Atmospheric Composition and Monitoring Spatiotemporal Variabilities

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 9108

Special Issue Editors

Sustainable System Research Laboratory (SSRL), Central Research Institute of Electric Power Industry (CRIEPI), Abiko 2701194, Japan
Interests: numerical modeling simulation; air pollution; deposition
Special Issues, Collections and Topics in MDPI journals
Universities Space Research Association (USRA), National Aeronautics and Space Administration (NASA), Huntsville, AL 21046, USA
Interests: remote sensing of atmosphere; air quality; climate change
Special Issues, Collections and Topics in MDPI journals
Department of Atmospheric Science, The University of Alabama in Huntsville, 320 Sparkman Dr., Huntsville, AL 35805, USA
Interests: air pollution; greenhouse gases; modelling studies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring the change in the atmospheric composition at varying spatial and temporal scales using satellite retrievals is one of the keys to promoting our understanding of the Earth–Atmosphere system. The targeted species are short-lived climate pollutants (SLCPs) and greenhouse gases. Satellite data have been serving as important observational information to understand satellite behavior in the atmosphere, through the capturing of their emissions status and atmospheric fates. It is vitally important to understand the changes of the atmospheric composition over long- and short-term time periods. Based on the accumulation of the satellite dataset, a trend analysis of atmospheric composition can provide us with an idea of their variations over long-term periods. Additionally, satellite measurements can also help us to understand the short-term dramatic variations in atmospheric composition during specific events such as the economic recession and the restrictions of human activities during COVID-19.

This Special Issue is calling for scientific papers which contribute to understanding of the variations of atmospheric composition based on the satellite retrievals both for long- and short-term time periods. Contributions on the improvements on retrieval algorithms toward the precise monitoring of the atmospheric composition are also welcomed.

The Special Issue “Satellite Remote Sensing of Atmospheric Composition and Monitoring Spatiotemporal Variabilities” is jointly organized between “Remote Sensing” and “Climate” journals. Contributors are required to check the website below and follow the specific instructions for authors:
https://www.mdpi.com/journal/remotesensing/instructions
https://0-www-mdpi-com.brum.beds.ac.uk/journal/climate/instructions

The other special issue could be found at: https://0-www-mdpi-com.brum.beds.ac.uk/journal/climate/special_issues/Climate_A_C_M_S_V. You will have the opportunity to choose to publish your papers in Climate, which will offer a lot of discounts or full waivers for your papers based on peer-review results.

Dr. Syuichi Itahashi
Dr. Pawan Gupta
Dr. Prabir K. Patra
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

  • atmospheric composition
  • air pollution
  • climate change

Published Papers (4 papers)

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Research

21 pages, 8045 KiB  
Article
Spatiotemporal Variation, Driving Mechanism and Predictive Study of Total Column Ozone: A Case Study in the Yangtze River Delta Urban Agglomerations
by Peng Zhou, Youyue Wen, Jian Yang, Leiku Yang, Minxuan Liang, Tingting Wen and Shaoman Cai
Remote Sens. 2022, 14(18), 4576; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184576 - 13 Sep 2022
Cited by 1 | Viewed by 1339
Abstract
Total column ozone (TCO) describes the amount of ozone in the entire atmosphere. Many scholars have used the lower resolution data to study TCO in different regions, but new phenomena can be discovered using high-precision and high-resolution TCO data. This paper used the [...] Read more.
Total column ozone (TCO) describes the amount of ozone in the entire atmosphere. Many scholars have used the lower resolution data to study TCO in different regions, but new phenomena can be discovered using high-precision and high-resolution TCO data. This paper used the long time, high accuracy, and high-resolution MSR2 dataset (2000–2019) to analyze the spatial and temporal variation characteristics of TCO over the Yangtze River Delta Urban Agglomeration to explore the relationship between the TCO and meteorological and socio-economic factors. The correlations between the TCO and climatic factors and the driving forces of meteorological and socio-economic factors on the spatial and temporal variability of TCO were also analyzed, and different mathematical models were constructed to fit the TCO for the past 20 years and predict the future trend of the TCO. The results show the following. (1) The TCO over the study area exhibited a quasi-latitudinal distribution, following a slight downtrend during 2000–2019 (0.01 ± 0.18 DU per year) and achieved its maximum in 2010 and minimum in 2019; throughout the year, an inverted V-shaped cycle characterizes the monthly variability of TCO; TCO was significantly higher in spring than in summer and autumn than winter. (2) Precipitation and the absorbed aerosol index (AAI) had critical effects on the spatial distribution of TCO, but meteorological factors were weakly correlated with the annual variation of TCO subject to the game interactions between different external driving factors. The monthly changes in the TCO were not in synergy with that of other meteorological factors, but with a significant hysteresis effect by 3–5 months. Socio-economic factors had a significant influence on TCO over the study area. (3) The Fourier function model can well describe the history and future trend of the annual TCO over the study area. The TCO over the study area shows a fluctuating upward trend (0.27 ± 1.35 DU per year) over the next 11 years. This study enriches the theoretical and technical system of ozone research, and its results can provide the necessary theoretical basis for ozone simulation and forecasting. Full article
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20 pages, 12540 KiB  
Article
Fifteen-Year Trends (2005–2019) in the Satellite-Derived Ozone-Sensitive Regime in East Asia: A Gradual Shift from VOC-Sensitive to NOx-Sensitive
by Syuichi Itahashi, Hitoshi Irie, Hikari Shimadera and Satoru Chatani
Remote Sens. 2022, 14(18), 4512; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184512 - 09 Sep 2022
Cited by 6 | Viewed by 1672
Abstract
To mitigate tropospheric ozone (O3) pollution with proper and effective emission regulations, diagnostics for the O3-sensitive regime are critical. In this study, we analyzed the satellite-measured formaldehyde (HCHO) and nitrogen dioxide (NO2) column densities and derived the [...] Read more.
To mitigate tropospheric ozone (O3) pollution with proper and effective emission regulations, diagnostics for the O3-sensitive regime are critical. In this study, we analyzed the satellite-measured formaldehyde (HCHO) and nitrogen dioxide (NO2) column densities and derived the HCHO to NO2 ratio (FNR) from 2005 to 2019. Over China, there was a clear increase in the NO2 column during the first 5-year period and a subsequent decrease after 2010. Over the Republic of Korea and Japan, there was a continuous decline in the NO2 column over 15 years. Over the entire East Asia, a substantial increase in the HCHO column was identified during 2015–2019. Therefore, FNR increased over almost all of East Asia, especially during 2015–2019. This increasing trend in FNR indicated the gradual shift from a volatile organic compound (VOC)-sensitive to a nitrogen oxide (NOx)-sensitive regime. The long-term changes in HCHO and NO2 columns generally corresponded to anthropogenic non-methane VOC (NMVOC) and NOx emissions trends; however, anthropogenic sources did not explain the increasing HCHO column during 2015–2019. Because of the reduction in anthropogenic sources, the relative importance of biogenic NMVOC sources has been increasing and could have a larger impact on changing the O3-sensitive regime over East Asia. Full article
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23 pages, 6297 KiB  
Article
An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia
by Marie Shaylor, Helen Brindley and Alistair Sellar
Remote Sens. 2022, 14(11), 2664; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112664 - 02 Jun 2022
Cited by 7 | Viewed by 2326
Abstract
We present an evaluation of Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Australia covering the period 2001–2020. We focus on retrievals from the Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms, showing how these [...] Read more.
We present an evaluation of Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Australia covering the period 2001–2020. We focus on retrievals from the Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms, showing how these compare to one another in time and space. We further employ speciated AOD estimates from Copernicus Atmospheric Monitoring Service (CAMS) reanalyses to help diagnose aerosol types and hence sources. Considering Australia as a whole, monthly mean AODs show similar temporal behaviour, with a well-defined seasonal peak in the Austral summer. However, excepting periods of intense biomass burning activity, MAIAC values are systematically higher than their DB counterparts by, on average, 50%. Decomposing into seasonal maps, the patterns of behaviour show distinct differences, with DB showing a larger dynamic range in AOD, with markedly higher AODs (ΔAOD∼0.1) in northern and southeastern regions during Austral winter and summer. This is counter-balanced by typically smaller DB values across the Australian interior. Site level comparisons with all available level 2 AOD data from Australian Aerosol Robotic Network (AERONET) sites operational during the study period show that MAIAC tends to marginally outperform DB in terms of correlation (RMAIAC = 0.71, RDB = 0.65) and root-mean-square error (RMSEMAIAC = 0.065, RMSEDB = 0.072). To probe this behaviour further, we classify the sites according to the predominant surface type within a 25 km radius. This analysis shows that MAIAC’s advantage is retained across all surface types for R and all but one for RMSE. For this surface type (Bare, comprising just 1.2% of Australia) the performance of both algorithms is relatively poor, (RMAIAC = 0.403, RDB = 0.332). Full article
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19 pages, 7963 KiB  
Article
Assessment of the Performance of TROPOMI NO2 and SO2 Data Products in the North China Plain: Comparison, Correction and Application
by Chunjiao Wang, Ting Wang, Pucai Wang and Wannan Wang
Remote Sens. 2022, 14(1), 214; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010214 - 04 Jan 2022
Cited by 12 | Viewed by 2699
Abstract
The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite has been used to detect the atmospheric environment since 2017, and it is of great significance to investigate the accuracy of its products. In this work, we present comparisons between TROPOMI tropospheric NO [...] Read more.
The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite has been used to detect the atmospheric environment since 2017, and it is of great significance to investigate the accuracy of its products. In this work, we present comparisons between TROPOMI tropospheric NO2 and total SO2 products against ground-based MAX-DOAS at a single site (Xianghe) and OMI products over a seriously polluted region (North China Plain, NCP) in China. The results show that both NO2 and SO2 data from three datasets exhibit a similar tendency and seasonality. In addition, TROPOMI tropospheric NO2 columns are generally underestimated compared with collocated MAX-DOAS and OMI data by about 30–60%. In contrast to NO2, the monthly average SO2 retrieved from TROPOMI is larger than MAX-DOAS and OMI, with a mean bias of 2.41 (153.8%) and 2.17 × 1016 molec cm−2 (120.7%), respectively. All the results demonstrated that the TROPOMI NO2 as well as the SO2 algorithms need to be further improved. Thus, to ensure reliable analysis in NCP area, a correction method has been proposed and applied to TROPOMI Level 3 data. The revised datasets agree reasonably well with OMI observations (R > 0.95 for NO2, and R > 0.85 for SO2) over the NCP region and have smaller mean biases with MAX-DOAS. In the application during COVID-19 pandemic, it showed that the NO2 column in January-April 2020 decreased by almost 25–45% compared to the same period in 2019 due to the lockdown for COVID-19, and there was an apparent rebound of nearly 15–50% during 2021. In contrast, a marginal change of the corresponding SO2 is revealed in the NCP region. It signifies that short-term control measures are expected to have more effects on NO2 reduction than SO2; conversely, we need to recognize that although the COVID-19 lockdown measures improved air quality in the short term, the pollution status will rebound to its previous level once industrial and human activities return to normal. Full article
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