{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:32:45Z","timestamp":1717806765862},"reference-count":52,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T00:00:00Z","timestamp":1717718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Regional Collaborative Innovation Program of Xinjiang Province","award":["2022E01014"]},{"name":"Tianchi Talent Introduction Programme","award":["Young Doctor"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42030612"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Third Xinjiang Scientific Expedition and Research program","award":["2021xjkk030501"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Central Asia (CA) is one of the major sources of global dust aerosols. They pose a serious threat to regional climate change and environmental health and also make a significant contribution to the global dust load. However, there is still a gap in our understanding of dust transport in this region. Therefore, this study utilizes Cloud\u2013Aerosol LiDAR with Orthogonal Polarization (CALIOP) data from 2007 to 2022 to depict the three-dimensional spatiotemporal distribution of dust aerosols over CA and to analyze their transport processes. In addition, the Tropospheric Monitoring Instrument (TROPOMI) was employed to assist in monitoring the movement of typical dust events, and the trajectory model was utilized to simulate the forward and backward trajectories of a dust incident. Additionally, a random forest (RF) model was employed to rank the contributions of various environmental factors. The findings demonstrate that high extinction values (0.6 km\u22121) are mostly concentrated within the Tarim Basin of Xinjiang, China, maintaining high values up to 2 km in altitude, with a noticeable decrease as the altitude increases. The frequency of dust occurrences is especially pronounced in the spring and summer seasons, with dust frequencies in the Tarim Basin and the Karakum and Kyzylkum deserts exceeding 80%, indicating significant seasonal and regional differences. The high values of dust optical depth (DOD) in CA are primarily concentrated in the summer, concurrent with the presence of a stable aerosol layer of dust in the atmosphere with a thickness of 0.62 km. Furthermore, dust from CA can traverse the Tianshan mountains via the westerlies, transporting it eastward. Additionally, skin temperature can mitigate regional air pollution. Our results contribute to a deeper understanding of the dynamic processes of dust in CA and provide scientific support for the development of regional climate regulation strategies.<\/jats:p>","DOI":"10.3390\/rs16122049","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T12:05:17Z","timestamp":1717761917000},"page":"2049","source":"Crossref","is-referenced-by-count":0,"title":["Three-Dimensional Structure and Transport Properties of Dust Aerosols in Central Asia\u2014New Insights from CALIOP Observations, 2007\u20132022"],"prefix":"10.3390","volume":"16","author":[{"given":"Jinglong","family":"Li","sequence":"first","affiliation":[{"name":"College of Geographic Science and Tourism, Xinjiang Normal University, Urumqi 830054, China"},{"name":"Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Urumqi 830054, China"}]},{"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China"},{"name":"National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China"},{"name":"Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China"},{"name":"Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China"},{"name":"Key Laboratory of Tree-Ring Physical and Chemical Research, China Meteorological Administration, Urumqi 830002, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0006-1024-2187","authenticated-orcid":false,"given":"Yonghui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geographic Science and Tourism, Xinjiang Normal University, Urumqi 830054, China"},{"name":"Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Urumqi 830054, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9456-0065","authenticated-orcid":false,"given":"Xiaofei","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Research Centre for Ecology and Environment of CA, Chinese Academy of Sciences, Urumqi 830011, China"}]},{"given":"Xueqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Research Centre for Ecology and Environment of CA, Chinese Academy of Sciences, Urumqi 830011, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7059-368X","authenticated-orcid":false,"given":"Yongkang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3884","DOI":"10.1021\/acs.est.1c04380","article-title":"Projected aerosol changes driven by emissions and climate change using a machine learning method","volume":"56","author":"Li","year":"2022","journal-title":"Environ. 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