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Remote Sensing of Particulate Matter, Its Components and Air Pollution...
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Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment

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

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 3129

Special Issue Editors

Dr. Ying Li

E-Mail Website
Guest Editor
Associate Professor, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Interests: air pollution; ocean remote sensing; air–sea interactions; satellite remote sensing
Prof. Dr. Ismail Gultepe

E-Mail Website
Guest Editor
Faculty of Engineering and Applied Science, Ontario Technical University, Oshawa, ON L1G 0C5, Canada
Interests: clouds; cold weather systems; cloud microphysics; precipitation; arctic weather; aviation meteorology; aircraft and ground based in-situ and remote sensing observations of the atmosphere, including satellites, radars, lidars, as well as microwave radiometers
Special Issues, Collections and Topics in MDPI journals
Dr. Pradeep Khatri

E-Mail Website
Guest Editor
Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Sendai City 980-8578, Japan
Interests: field observation; remote sensing; atmospheric radiation; cloud; aerosol

Special Issue Information

Dear Colleagues,

Air pollution is an increasingly important topic of global concern. It harms ecological protection and the advancement of human health, while also inducing global climate change, etc. Therefore, this Special Issue emphasizes the importance of atmospheric particulate matter and the measurement of its components (PM, such as PM2.5, PM10, dust, dirt, soot, or smoke, black carbon, organic carbon, etc.) to provide decision support to improve air quality, understand regional climate impact and ensure sustainable development.

In recent years, remote sensing technologies have been widely used in atmospheric composition retrieval and air quality monitoring. Given their characteristics of high resolution, full-time, etc., remote sensing can provide more comprehensive, timely, and accurate data sets for use to assess air pollution. Meanwhile, with the development of artificial intelligence (AI), remote sensing data can be analyzed more efficiently and accurately to estimate and assess air pollution. This Special Issue aims to measure and estimate the PM and its components in the atmosphere and assess air pollution using satellite remote sensing, whether alone or together with AI and numerical models.

We encourage authors to submit articles describing original research results from measuring and estimating PM and its components in the atmosphere and assessing the environmental and climatic impacts of air pollution using satellite remote sensing, AI, and numerical models.

Topics include (but are not limited to):

  • New algorithms for PM and its component remote sensing from different satellites (such as MODIS, MISR, OMI, VIIRS, OMPS, GOES-R, GOES-S, Himawari-8/9 GOCI, GEMS );
  • New method for the estimation of PM and its component, applying satellite remote sensing, AI, and numerical models.
  • Innovative approaches for analyzing remote sensing data for air quality and air pollution assessment;
  • Air quality product development, validation, and inter-comparison;
  • Research into important aspects of air pollution control based on remote sensing data (remote sensing observation data analysis, source emission inventory estimation, air quality model improvement, source resolution, health exposure, ).

Dr. Ying Li
Prof. Dr. Ismail Gultepe
Dr. Pradeep Khatri
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

  • remote sensing
  • aerosol components
  • numerical models
  • machine learning
  • deep learning

Published Papers (4 papers)

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20 pages, 4738 KiB  
Article
Trans-Boundary Dust Transport of Dust Storms in Northern China: A Study Utilizing Ground-Based Lidar Network and CALIPSO Satellite
by Zhisheng Zhang, Zhiqiang Kuang, Caixia Yu, Decheng Wu, Qibing Shi, Shuai Zhang, Zhenzhu Wang and Dong Liu
Remote Sens. 2024, 16(7), 1196; https://doi.org/10.3390/rs16071196 - 29 Mar 2024
Viewed by 464
Abstract
During 14–16 March 2021, a large-scale dust storm event occurred in the northern region of China, and it was considered the most intense event in the past decade. This study employs observation data for PM2.5 and PM10 from the air quality monitoring station, [...] Read more.
During 14–16 March 2021, a large-scale dust storm event occurred in the northern region of China, and it was considered the most intense event in the past decade. This study employs observation data for PM2.5 and PM10 from the air quality monitoring station, the HYSPLIT model, ground-based polarized Lidar networks, AGRI payload data from Fengyun satellites and CALIPSO satellite Lidar data to jointly explore and scrutinize the three-dimensional spatial and temporal characteristics of aerosol transport. Firstly, by integrating meteorological data for PM2.5 and PM10, the air quality is assessed across six stations within the Lidar network during the dust storm. Secondly, employing a backward trajectory tracking model, the study elucidates sources of dust at the Lidar network sites. Thirdly, deploying a newly devised portable infrared 1064 nm Lidar and a pulsed 532 nm Lidar, a ground-based Lidar observation network is established for vertical probing of transboundary dust transport within the observed region. Finally, by incorporating cloud imagery from Fengyun satellites and CALIPSO satellite Lidar data, this study revealed the classification of dust and the height distribution of dust layers at pertinent sites within the Lidar observation network. The findings affirm that the eastward movement and southward compression of the intensifying Mongolian cyclone led to severe dust storm weather in western and southern Mongolia, as well as Inner Mongolia, further transporting dust into northern, northwestern, and northeastern parts of China. This dust event wielded a substantial impact on a broad expanse in northern China, manifesting in localized dust storms in Inner Mongolia, Beijing, Gansu, and surrounding areas. In essence, the dust emanated from the deserts in Mongolia and northwest China, encompassing both deserts and the Gobi region. The amalgamation of ground-based and spaceborne Lidar observations conclusively establishes that the distribution height of dust in the source region ranged from 3 to 5 km. Influenced by high-pressure systems, the protracted transport of dust over extensive distances prompted a gradual reduction in its distribution height owing to sedimentation. The comprehensive analysis of pertinent research data and information collectively affirms the precision and efficacy of the three-dimensional aerosol monitoring conducted by the ground-based Lidar network within the region. Full article
(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)
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23 pages, 9387 KiB  
Article
Cloud–Aerosol Classification Based on the U-Net Model and Automatic Denoising CALIOP Data
by Xingzhao Zhou, Bin Chen, Qia Ye, Lin Zhao, Zhihao Song, Yixuan Wang, Jiashun Hu and Ruming Chen
Remote Sens. 2024, 16(5), 904; https://doi.org/10.3390/rs16050904 - 04 Mar 2024
Viewed by 755
Abstract
Precise cloud and aerosol identification hold paramount importance for a thorough comprehension of atmospheric processes, enhancement of meteorological forecasts, and mitigation of climate change. This study devised an automatic denoising cloud–aerosol classification deep learning algorithm, successfully achieving cloud–aerosol identification in atmospheric vertical profiles [...] Read more.
Precise cloud and aerosol identification hold paramount importance for a thorough comprehension of atmospheric processes, enhancement of meteorological forecasts, and mitigation of climate change. This study devised an automatic denoising cloud–aerosol classification deep learning algorithm, successfully achieving cloud–aerosol identification in atmospheric vertical profiles utilizing CALIPSO L1 data. The algorithm primarily consists of two components: denoising and classification. The denoising task integrates an automatic denoising module that comprehensively assesses various methods, such as Gaussian filtering and bilateral filtering, automatically selecting the optimal denoising approach. The results indicated that bilateral filtering is more suitable for CALIPSO L1 data, yielding SNR, RMSE, and SSIM values of 4.229, 0.031, and 0.995, respectively. The classification task involves constructing the U-Net model, incorporating self-attention mechanisms, residual connections, and pyramid-pooling modules to enhance the model’s expressiveness and applicability. In comparison with various machine learning models, the U-Net model exhibited the best performance, with an accuracy of 0.95. Moreover, it demonstrated outstanding generalization capabilities, evaluated using the harmonic mean F1 value, which accounts for both precision and recall. It achieved F1 values of 0.90 and 0.97 for cloud and aerosol samples from the lidar profiles during the spring of 2019. The study endeavored to predict low-quality data in CALIPSO VFM using the U-Net model, revealing significant differences with a consistency of 0.23 for clouds and 0.28 for aerosols. Utilizing U-Net confidence and a 532 nm attenuated backscatter coefficient to validate medium- and low-quality predictions in two cases from 8 February 2019, the U-Net model was found to align more closely with the CALIPSO observational data and exhibited high confidence. Statistical comparisons of the predicted geographical distribution revealed specific patterns and regional characteristics in the distribution of clouds and aerosols, showcasing the U-Net model’s proficiency in identifying aerosols within cloud layers. Full article
(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)
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18 pages, 2907 KiB  
Article
Emission-Based Machine Learning Approach for Large-Scale Estimates of Black Carbon in China
by Ying Li, Sijin Liu, Reza Bashiri Khuzestani, Kai Huang and Fangwen Bao
Remote Sens. 2024, 16(5), 837; https://doi.org/10.3390/rs16050837 - 28 Feb 2024
Viewed by 554
Abstract
Tremendous efforts have been made to construct large-scale estimates of aerosol components. However, Black Carbon (BC) estimates over large spatiotemporal scales are still limited. We proposed a novel approach utilizing machine-learning techniques to estimate BC on a large scale. We leveraged a comprehensive [...] Read more.
Tremendous efforts have been made to construct large-scale estimates of aerosol components. However, Black Carbon (BC) estimates over large spatiotemporal scales are still limited. We proposed a novel approach utilizing machine-learning techniques to estimate BC on a large scale. We leveraged a comprehensive gridded BC emission database and auxiliary variables as inputs to train various machine learning (ML) models, specifically a Random Forest (RF) algorithm, to estimate high spatiotemporal BC concentration over China. Different ML algorithms have been applied to a large number of potential datasets and detailed variable importance and sensitivity analysis have also been carried out to explore the physical relevance of variables on the BC estimation model. RF algorithm showed the best performance compared with other ML models. Good predictive performance was observed for the training cases (R2 = 0.78, RMSE = 1.37 μgm−3) and test case databases (R2 = 0.77, RMSE = 1.35 μgm−3) on a daily time scale, illustrating a significant improvement compared to previous studies with remote sensing and chemical transport models. The seasonal variation of BC distributions was also evaluated, with the best performance observed in spring and summer (R2 ≈ 0.7–0.76, RMSE ≈ 0.98–1.26 μgm−3), followed by autumn and winter (R2 ≈ 0.7–0.72, RMSE ≈ 1.37–1.63 μgm−3). Variable importance and sensitivity analysis illustrated that the BC emission inventories and meteorology showed the highest importance in estimating BC concentration (R2 = 0.73, RMSE = 1.88 μgm−3). At the same time, albedo data and some land cover type variables were also helpful in improving the model performance. We demonstrated that the emission-based ML model with an appropriate auxiliary database (e.g., satellite and reanalysis datasets) could effectively estimate the spatiotemporal BC concentrations at a large scale. In addition, the promising results obtained through this approach highlight its potential to be utilized for the assessment of other primary pollutants in the future. Full article
(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)
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14 pages, 11225 KiB  
Technical Note
3-D Changes of Tropospheric O3 in Central and Eastern China Induced by Tropical Cyclones over the Northwest Pacific: Recent-Year Characterization with Multi-Source Observations
by Yongcheng Jiang, Tianliang Zhao, Kai Meng, Xugeng Cheng and Qiaoyi Lv
Remote Sens. 2024, 16(7), 1178; https://doi.org/10.3390/rs16071178 - 28 Mar 2024
Viewed by 578
Abstract
In this study, the multi-year data of meteorology and O3 from remote sensing and ground observations are applied to characterize the 3-D changes of O3 in the troposphere over central and eastern China (CEC) induced by the tropical cyclones (TCs) in [...] Read more.
In this study, the multi-year data of meteorology and O3 from remote sensing and ground observations are applied to characterize the 3-D changes of O3 in the troposphere over central and eastern China (CEC) induced by the tropical cyclones (TCs) in the tropical and subtropical ocean regions over Northwest Pacific. The CEC-regional average of near-surface O3 levels is significantly elevated with 6.0 ppb in the large coverage by the TCs in the subtropical ocean, while the TCs in the tropical ocean alter near-surface O3 weakly, indicating the latitudinal-located TCs in the subtropical offshore ocean could largely influence the O3 variations over CEC. The sub-seasonal change with the positive and negative anomalies of near-surface O3 is induced by the tropical TCs from June to July and from August to October. The peripheral circulation of TCs in the subtropical offshore ocean persistently enhances the O3 concentrations over CEC during the season of East Asian summer monsoons. The positive O3 anomalies maintain from the entire troposphere to the lower stratosphere over CEC in the peripheries of subtropical TCs, while the tropical TCs cause the positive O3 anomalies merely in the lower troposphere. The O3 transport and accumulation, photochemical production and stratospheric intrusion are climatologically confirmed as the major meteorological mechanisms of TCs affecting the O3 variations. This study reveals that the downward transport of stratospheric O3 of TCs in the subtropical ocean exerts a large impact on the atmospheric environment over CEC, while the regional O3 transport and photochemical productions dominate the lower troposphere over CEC with less impact of stratospheric intrusion from the TCs in the tropical ocean region. These results present the climatology of tropospheric O3 anomalies in China induced by the TCs over the Northwest Pacific with enhancing our comprehension of the meteorological impact on O3 variations over the East Asian monsoon region. Full article
(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)
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