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Application of Satellite Aerosol Remote Sensing in Air Quality
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Application of Satellite Aerosol Remote Sensing in Air Quality

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

Deadline for manuscript submissions: closed (30 July 2024) | Viewed by 5970

Special Issue Editors

Dr. Jinhui Gao

E-Mail Website
Guest Editor
School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: ozone; aerosol; numerical simulation; source apportionment
Special Issues, Collections and Topics in MDPI journals
Dr. Cheng Chen

E-Mail Website
Guest Editor
GRASP-SAS, LOA/University of Lille, 59650 Villeneuve-d’Ascq, France
Interests: satellite remote sensing; atmospheric aerosol; inverse modelling; chemical transport model; data assimilation
Dr. Fangwen Bao

E-Mail Website
Guest Editor
Department of Ocean Sciences and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: aerosol remote sensing; satellite retrievals; air quality observations
Special Issues, Collections and Topics in MDPI journals
Dr. Kerstin Stebel

E-Mail Website
Guest Editor
Atmosphere and Climate Department (ATMOS), NILU - Norwegian Institute for Air Research, Instituttveien 18, 2027 Kjeller, Norway
Interests: ground-based and satellite remote sensing; imaging; satellite validation; aerosol and trace gases; air quality; volcanic gas emissions

Special Issue Information

Dear Colleagues,

Aerosols have received a great deal of attention since the recognition of their serious impacts on climate and air quality. Satellite remote sensing has become a well-established tool for characterizing and monitoring the properties of aerosols, and makes it possible to study the impacts of aerosols on air quality at high spatial resolution and large scale. Particularly, more and more advanced satellite aerosol retrieval algorithms have been extensively proposed, which continuously improve the quality and integrity of satellite aerosol remote sensing products. Such products can be popularized to other air quality monitoring and research methods (e.g., ground-based observations and numerical simulations) with unprecedented flexibility, which will have great potential for assessing the detailed distributions of aerosols, estimating aerosol emissions and sinks, finding the effects on air quality and forecasting the pollution trend.

This Special Issue aims to bring together the latest studies focused on the applications of satellite aerosol remote sensing and comprehensive studies of aerosol impacts on air quality (emissions, transport, interactions with other air pollutants, sinks, etc.). We also welcome papers related to multiscale aerosol retrieval algorithms (physical models, deep learning models, etc.) and applications focused on long-term air quality monitoring or aerosol pollution episodes.

Articles may address, but are not limited, to the following topics:

  • Aerosol impacts evaluation using satellite aerosol products;
  • Combination of numerical models and satellite products;
  • Top-down aerosol emission estimation;
  • Long-term variations and spatial differences of aerosols;
  • New satellite aerosol retrieval algorithms;
  • Remote sensing of aerosol components;
  • Validations of satellite aerosol products;
  • Aerosol data assimilation.

Dr. Jinhui Gao
Dr. Cheng Chen
Dr. Fangwen Bao
Dr. Kerstin Stebel
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

  • aerosol
  • remote sensing
  • satellite
  • air quality
  • emissions
  • retrieval algorithms

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Published Papers (5 papers)

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15 pages, 5639 KiB  
Article
Identification Method for Spring Dust Intensity Levels Based on Multiple Remote Sensing Parameters
by Qi Jiang, Linchang An, Fei Wang, Guozhou Wu, Jianwei Wen, Bin Li, Yuchen Jin and Yapeng Wei
Remote Sens. 2024, 16(14), 2606; https://doi.org/10.3390/rs16142606 - 17 Jul 2024
Viewed by 700
Abstract
The advancement of more precise remote sensing inversion technology for dust aerosols has long been a hot topic in the field of the atmospheric environment. In 2023, China experienced 18 dust-related weather events, predominantly in spring. These high-intensity and frequent dust events have [...] Read more.
The advancement of more precise remote sensing inversion technology for dust aerosols has long been a hot topic in the field of the atmospheric environment. In 2023, China experienced 18 dust-related weather events, predominantly in spring. These high-intensity and frequent dust events have attracted considerable attention. However, gridded observation data of dust intensity levels are not collected in current dust monitoring and forecasting operations. Based on the Himawari 9 geostationary satellite data, this study establishes a new method to identify spring dust events. This method integrates the brightness temperature difference method and the multiple infrared dust index, taking into account the response discrepancies of the multiple infrared dust index under various underlying surfaces. Furthermore, by obtaining dynamic background brightness temperature values eight times a day, threshold statistics are applied to analyze the correlation between the infrared difference dust index and ground-observed dust level, so as to establish a satellite-based near-surface dust intensity level identification algorithm. This algorithm aims to improve dust detection accuracy, and to provide more effective gridded observation support for dust forecasting and monitoring operations. The test results indicate that the algorithm can effectively identify the presence or absence of dust, with a misjudgment rate of less than 3%. With regard to dust intensity, the identification of blowing sand and floating dust aligns relatively well with ground-based observations, but notable uncertainties exist in determining a dust intensity of sand-storm level or above. Among these uncertainties, the differences between ground-based observations and satellite identification caused by non-grounded dust in the upper air, and the selection of dust identification thresholds, are two important error sources in the dust identification results of this study. Full article
(This article belongs to the Special Issue Application of Satellite Aerosol Remote Sensing in Air Quality)
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20 pages, 7579 KiB  
Article
AIRS and MODIS Satellite-Based Assessment of Air Pollution in Southwestern China: Impact of Stratospheric Intrusions and Cross-Border Transport of Biomass Burning
by Puyu Lian, Kaihui Zhao and Zibing Yuan
Remote Sens. 2024, 16(13), 2409; https://doi.org/10.3390/rs16132409 - 1 Jul 2024
Viewed by 975
Abstract
The exacerbation of air pollution during spring in Yunnan province, China, has attracted widespread attention. However, many studies have focused solely on the impacts of anthropogenic emissions while ignoring the role of natural processes. This study used satellite data spanning 21 years from [...] Read more.
The exacerbation of air pollution during spring in Yunnan province, China, has attracted widespread attention. However, many studies have focused solely on the impacts of anthropogenic emissions while ignoring the role of natural processes. This study used satellite data spanning 21 years from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) to reveal two natural processes closely related to springtime ozone (O3) and PM2.5 pollution: stratospheric intrusions (SIs) and cross-border transport of biomass burning (BB). We aimed to assess the mechanisms through which SIs and cross-border BB transport influence O3 and PM2.5 pollution in Southwestern China during the spring. The unique geographical conditions and prevalent southwest winds are considered the key driving factors for SIs and cross-border BB transport. Frequent tropopause folding provides favorable dynamic conditions for SIs in the upper troposphere. In the lower troposphere, the distribution patterns of O3 and stratospheric O3 tracer (O3S) are similar to the terrain, indicating that O3 is more likely to reach the surface with increasing altitude. Using stratospheric tracer tagging methods, we quantified the contributions of SIs to surface O3, ranging from 6 to 31 ppbv and accounting for 10–38% of surface O3 levels. Additionally, as Yunnan is located downwind of Myanmar and has complex terrain, it provides favorable conditions for PM2.5 and O3 generation from cross-border BB transport. The decreasing terrain distribution from north to south in Yunnan facilitates PM2.5 transport to lower-elevation border cities, whereas higher-elevation cities hinder PM2.5 transport, leading to spatial heterogeneity in PM2.5. This study provides scientific support for elucidating the two key processes governing springtime PM2.5 and O3 pollution in Yunnan, SIs and cross-border BB transport, and can assist policymakers in formulating optimal emission reduction strategies. Full article
(This article belongs to the Special Issue Application of Satellite Aerosol Remote Sensing in Air Quality)
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17 pages, 5623 KiB  
Article
Potential Modulation of Aerosol on Precipitation Efficiency in Southwest China
by Pengguo Zhao, Xiaoran Liu and Chuanfeng Zhao
Remote Sens. 2024, 16(8), 1445; https://doi.org/10.3390/rs16081445 - 18 Apr 2024
Viewed by 987
Abstract
The aerosol–cloud–precipitation correlation has been a significant scientific topic, primarily due to its remarkable uncertainty. However, the possible modulation of aerosol on the precipitation capacity of clouds has received limited attention. In this study, we utilized multi-source data on aerosol, cloud properties, precipitation, [...] Read more.
The aerosol–cloud–precipitation correlation has been a significant scientific topic, primarily due to its remarkable uncertainty. However, the possible modulation of aerosol on the precipitation capacity of clouds has received limited attention. In this study, we utilized multi-source data on aerosol, cloud properties, precipitation, and meteorological factors to investigate the impact of aerosols on precipitation efficiency (PE) in the Sichuan Basin (SCB) and Yun-nan-Guizhou Plateau (YGP), where the differences between terrain and meteorological environment conditions were prominent. In the two study regions, there were significant negative correlations between the aerosol index (AI) and PE in spring, especially in the YGP, while the correlations between the AI and PE in other seasons were not as prominent as in spring. In spring, aerosol significantly inhibited both the liquid water path (LWP) and the ice water path (IWP) in the YGP, but negatively correlated with the IWP and had no significant relationship with the LWP in the SCB. Aerosol inhibited precipitation in the two regions mainly by reducing cloud droplet effective radius, indicating that warm clouds contributed more to precipitation in spring. The suppressive impact of aerosols on precipitation serving as the numerator of PE is greater than that of the cloud water path as the denominator of PE, resulting in a negative correlation between aerosol and PE. The AI–PE relationship is significantly dependent on meteorological conditions in the YGP, but not in the SCB, which may be related to the perennial cloud cover and stable atmosphere in the SCB. In the future, as air quality continues to improve, precipitation efficiency may increase due to the decrease in aerosol concentration, and of course, the spatio-temporal heterogeneity of the aerosol–cloud–precipitation relationship may become more significant. Full article
(This article belongs to the Special Issue Application of Satellite Aerosol Remote Sensing in Air Quality)
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14 pages, 4477 KiB  
Article
Comparative Analysis of Aerosol Vertical Characteristics over the North China Plain Based on Multi-Source Observation Data
by Fei Wang, Zhanqing Li, Qi Jiang, Xinrong Ren, Hao He, Yahui Tang, Xiaobo Dong, Yele Sun and Russell R. Dickerson
Remote Sens. 2024, 16(4), 609; https://doi.org/10.3390/rs16040609 - 6 Feb 2024
Cited by 2 | Viewed by 1253
Abstract
In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain [...] Read more.
In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σabs), scattering coefficients (σsca), and extinction coefficients (σext) gradually decrease with altitude, with their maximum values near HPBL. The vertical profiles of σext depended most on the vertical distribution of measured σsca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, HPBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted HPBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions. Full article
(This article belongs to the Special Issue Application of Satellite Aerosol Remote Sensing in Air Quality)
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15 pages, 9486 KiB  
Technical Note
In-Flight Preliminary Performance of GF-5B/Absorbing Aerosol Sensor
by Yongmei Wang, Zhuo Zhang, Jinghua Mao, Houmao Wang, Entao Shi, Xiaohong Liu, Pengda Li and Jiu Liu
Remote Sens. 2023, 15(17), 4343; https://doi.org/10.3390/rs15174343 - 3 Sep 2023
Cited by 2 | Viewed by 1074
Abstract
The Absorbing Aerosol Sensor (AAS) is carried on the Gao-Fen 5B (GF-5B) satellite, and it allows for the measurement of solar backscatter radiation by the atmosphere in the UV–Vis bands. The AAS is an imaging spectrometer that employs CCD for capturing both a [...] Read more.
The Absorbing Aerosol Sensor (AAS) is carried on the Gao-Fen 5B (GF-5B) satellite, and it allows for the measurement of solar backscatter radiation by the atmosphere in the UV–Vis bands. The AAS is an imaging spectrometer that employs CCD for capturing both a continuous spectrum and the cross-track orientation with a 114° wide swath. The broad field of view provides daily global envelopment with a 4 km spatial resolution at the nadir. This paper mainly analyzes the initial working status of the instrument in orbit, including wavelength calibration, radiometric calibration, detector performance, and product availability. Preliminary observations indicate the ability of the AAS to monitor absorbing aerosols like dust, biomass burning, volcano ash, and some pollution aerosols and to identify the aerosol events in China and other regions with high spatial resolution. Full article
(This article belongs to the Special Issue Application of Satellite Aerosol Remote Sensing in Air Quality)
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