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Atmosphere
Special Issues
Dust Detection and Long-Term Transport in High Spatiotemporal Resolution
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Dust Detection and Long-Term Transport in High Spatiotemporal Resolution

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Aerosols".

Deadline for manuscript submissions: closed (10 July 2021) | Viewed by 5791

Special Issue Editors

Dr. Hui Xu

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Guest Editor
ESSIC/CISESS, University of Maryland, College Park, MD 20740, USA
Interests: atmospheric remote sensing; fundamental climate data record (FCDR); satellite calibration and validation
Dr. Manzhu Yu

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Guest Editor
Department of Geography, Penn State University, State College, PA 16801, USA
Interests: GIScience; spatiotemporal analysis; natural hazards/extreme weather events; spatial data science and deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Qian Liu

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Guest Editor
NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
Interests: patiotemporal statistics; applications in remote sensing; environmental science and climatic analytics; leveraging artificial intelligence (AI) methodologies in the research of natural phenomena; the ability to use the above to solve pressing issues in natural disaster and sustainability
Prof. Dr. David Cappelletti

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Guest Editor
Dipartimento Di Chimica, Biologia e Biotecnologie, Università Degli Studi di Perugia, Via Elce Di Sotto 8, 06123 Perugia, Italy
Interests: elementary processes in the gas phase; molecular clusters; chemical and physical characterization of atmospheric aerosols; remote environments; vertical profiles of aerosol properties; aerosol source apportionment; chemical transport models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Dust detection and intensity estimation are among the significant tasks in aerosol remote sensing. Advances in remote sensing technologies have enabled the monitoring of dust with a higher spatial, temporal, and spectral resolution. Various detection methods, including split-window techniques, probabilistic methods, and fixed- or machine-learned thresholds for brightness temperature differences, have been proposed for detecting dust from single- or multi-sensor/platform observations. Many of these methods focus on using an index to indicate the existence of dust, while others attempt to estimate the intensity or concentration of dust. However, challenges exist in the parameter sensitivity, cross-region adaptability, and long-term robustness of these methods. These challenges further hinder the quality of long-term dust transport, such as transatlantic dust transport. New advanced methods are expected to be developed by leveraging the increasing capability of computational power, machine learning techniques, and big data frameworks.

This Special Issue aims to bring together the latest dust detection and transport tracking techniques; to highlight the high spatiotemporal resolution in dust detection; and to explore the potential of using high-performance computing, machine learning, and big data frameworks in advancing this topic. Applications of dust products are also welcomed, as are review papers, that summarize the current state-of-the-art.

Potential topics include but are not limited to the following:

  • Novel quantitative methods for dust detection, retrieval, and tracking
  • Long-term (e.g., transatlantic) dust transport understanding
  • Interaction of airborne dust, cloud, and precipitation
  • Machine learning approaches for dust monitoring
  • Applications of advanced computational capabilities for dust detection and tracking

Dr. Hui Xu
Dr. Manzhu Yu
Dr. Qian Liu
Prof. Dr. David Cappelletti
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • Dust monitoring
  • Climate change
  • Geo AI
  • Spatiotemporal computing

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Published Papers (2 papers)

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Research

15 pages, 2951 KiB  
Article
Passive versus Active Transport of Saharan Dust Aerosols by African Easterly Waves
by Dustin F. P. Grogan and Terrence R. Nathan
Atmosphere 2021, 12(11), 1509; https://doi.org/10.3390/atmos12111509 - 16 Nov 2021
Cited by 2 | Viewed by 1790
Abstract
Theory and modeling are combined to reveal the physical and dynamical processes that control Saharan dust transport by amplifying African easterly waves (AEWs). Two cases are examined: active transport, in which the dust is radiatively coupled to the circulation; passive transport, in which [...] Read more.
Theory and modeling are combined to reveal the physical and dynamical processes that control Saharan dust transport by amplifying African easterly waves (AEWs). Two cases are examined: active transport, in which the dust is radiatively coupled to the circulation; passive transport, in which the dust is radiatively decoupled from the circulation. The theory is built around a dust conservation equation for dust-coupled AEWs in zonal-mean African easterly jets. The theory predicts that, for both the passive and active cases, the dust transports will be largest where the zonal-mean dust gradients are maximized on an AEW critical surface. Whether the dust transports are largest for the radiatively passive or radiatively active case depends on the growth rate of the AEWs, which is modulated by the dust heating. The theoretical predictions are confirmed via experiments carried out with the Weather Research and Forecasting model, which is coupled to a dust conservation equation. The experiments show that the meridional dust transports dominate in the passive case, while the vertical dust transports dominate in the active case. Full article
(This article belongs to the Special Issue Dust Detection and Long-Term Transport in High Spatiotemporal Resolution)
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13 pages, 10890 KiB  
Article
Characteristics of Dust Events in China from 2015 to 2020
by Lili Yang, Shuwen Zhang, Zhongwei Huang, Yanping Yang, Lina Wang, Wenyu Han and Xiaoyun Li
Atmosphere 2021, 12(8), 952; https://doi.org/10.3390/atmos12080952 - 24 Jul 2021
Cited by 21 | Viewed by 3145
Abstract
As the main source of dust in Asia, China often suffers from dust events. The temporal and spatial characteristics of dust events change with the variations of geography, climate and human activities. Based on the criteria of selecting dust events proposed recently by [...] Read more.
As the main source of dust in Asia, China often suffers from dust events. The temporal and spatial characteristics of dust events change with the variations of geography, climate and human activities. Based on the criteria of selecting dust events proposed recently by the China Environmental Monitoring Station, the hourly concentration of PM10 and PM2.5 of 336 cities in China from 2015 to 2020 were used to study the temporal and spatial characteristics of dust events more accurately and objectively. The results showed that all of the dust events in China clearly decreased, but the strong dust events did not decrease. There were 334 cities that had dust events except Shenzhen and Dongguan, 299 cities were seriously polluted due to dust events, 134 cities encountered dust level III and 56 cities encountered dust level IV. The high frequencies of dust events were mainly distributed in Northern China, especially in Northwest China. The dust contribution of PM10 to the cities in Northwest China was more than 10% and about 5–10% for PM2.5. The most likely month for dust was May. The starting time of dust was bimodally distributed, and the most common starting time was 10:00–11:00 BJT, followed by 22:00–23:00 BJT. According to the PSCF (Potential Source Contribution Function) results, the dust potential source contribution of different cities mainly came from the northwest, and was mainly affected by Mongolia in addition to the local dust in China. In addition, Beijing was obviously affected by dust recirculation. This study is of great significance to the improvement of the forecast of dust weather and the warning of heavy pollution caused by dust events. Full article
(This article belongs to the Special Issue Dust Detection and Long-Term Transport in High Spatiotemporal Resolution)
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