Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia
The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the va...
Ausführliche Beschreibung
Autor*in: |
Na Wu [verfasserIn] Yongxiao Ge [verfasserIn] Jilili Abuduwaili [verfasserIn] Gulnura Issanova [verfasserIn] Galymzhan Saparov [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Remote Sensing - MDPI AG, 2009, 14(2022), 13, p 3201 |
---|---|
Übergeordnetes Werk: |
volume:14 ; year:2022 ; number:13, p 3201 |
Links: |
---|
DOI / URN: |
10.3390/rs14133201 |
---|
Katalog-ID: |
DOAJ039135667 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ039135667 | ||
003 | DE-627 | ||
005 | 20240414072928.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230227s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/rs14133201 |2 doi | |
035 | |a (DE-627)DOAJ039135667 | ||
035 | |a (DE-599)DOAJad0e8f0963ae4c27932c362e1d043c38 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 0 | |a Na Wu |e verfasserin |4 aut | |
245 | 1 | 0 | |a Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia |
264 | 1 | |c 2022 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin. | ||
650 | 4 | |a long-range transport | |
650 | 4 | |a atmospheric aerosol | |
650 | 4 | |a CALIPSO | |
650 | 4 | |a Aral Sea | |
650 | 4 | |a Central Asia | |
653 | 0 | |a Science | |
653 | 0 | |a Q | |
700 | 0 | |a Yongxiao Ge |e verfasserin |4 aut | |
700 | 0 | |a Jilili Abuduwaili |e verfasserin |4 aut | |
700 | 0 | |a Gulnura Issanova |e verfasserin |4 aut | |
700 | 0 | |a Galymzhan Saparov |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Remote Sensing |d MDPI AG, 2009 |g 14(2022), 13, p 3201 |w (DE-627)608937916 |w (DE-600)2513863-7 |x 20724292 |7 nnns |
773 | 1 | 8 | |g volume:14 |g year:2022 |g number:13, p 3201 |
856 | 4 | 0 | |u https://doi.org/10.3390/rs14133201 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/ad0e8f0963ae4c27932c362e1d043c38 |z kostenfrei |
856 | 4 | 0 | |u https://www.mdpi.com/2072-4292/14/13/3201 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2072-4292 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2108 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2119 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4392 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 14 |j 2022 |e 13, p 3201 |
author_variant |
n w nw y g yg j a ja g i gi g s gs |
---|---|
matchkey_str |
article:20724292:2022----::nihsnoaitosnptnilogagtasotftopeiarslfot |
hierarchy_sort_str |
2022 |
publishDate |
2022 |
allfields |
10.3390/rs14133201 doi (DE-627)DOAJ039135667 (DE-599)DOAJad0e8f0963ae4c27932c362e1d043c38 DE-627 ger DE-627 rakwb eng Na Wu verfasserin aut Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin. long-range transport atmospheric aerosol CALIPSO Aral Sea Central Asia Science Q Yongxiao Ge verfasserin aut Jilili Abuduwaili verfasserin aut Gulnura Issanova verfasserin aut Galymzhan Saparov verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 13, p 3201 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:13, p 3201 https://doi.org/10.3390/rs14133201 kostenfrei https://doaj.org/article/ad0e8f0963ae4c27932c362e1d043c38 kostenfrei https://www.mdpi.com/2072-4292/14/13/3201 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 13, p 3201 |
spelling |
10.3390/rs14133201 doi (DE-627)DOAJ039135667 (DE-599)DOAJad0e8f0963ae4c27932c362e1d043c38 DE-627 ger DE-627 rakwb eng Na Wu verfasserin aut Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin. long-range transport atmospheric aerosol CALIPSO Aral Sea Central Asia Science Q Yongxiao Ge verfasserin aut Jilili Abuduwaili verfasserin aut Gulnura Issanova verfasserin aut Galymzhan Saparov verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 13, p 3201 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:13, p 3201 https://doi.org/10.3390/rs14133201 kostenfrei https://doaj.org/article/ad0e8f0963ae4c27932c362e1d043c38 kostenfrei https://www.mdpi.com/2072-4292/14/13/3201 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 13, p 3201 |
allfields_unstemmed |
10.3390/rs14133201 doi (DE-627)DOAJ039135667 (DE-599)DOAJad0e8f0963ae4c27932c362e1d043c38 DE-627 ger DE-627 rakwb eng Na Wu verfasserin aut Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin. long-range transport atmospheric aerosol CALIPSO Aral Sea Central Asia Science Q Yongxiao Ge verfasserin aut Jilili Abuduwaili verfasserin aut Gulnura Issanova verfasserin aut Galymzhan Saparov verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 13, p 3201 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:13, p 3201 https://doi.org/10.3390/rs14133201 kostenfrei https://doaj.org/article/ad0e8f0963ae4c27932c362e1d043c38 kostenfrei https://www.mdpi.com/2072-4292/14/13/3201 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 13, p 3201 |
allfieldsGer |
10.3390/rs14133201 doi (DE-627)DOAJ039135667 (DE-599)DOAJad0e8f0963ae4c27932c362e1d043c38 DE-627 ger DE-627 rakwb eng Na Wu verfasserin aut Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin. long-range transport atmospheric aerosol CALIPSO Aral Sea Central Asia Science Q Yongxiao Ge verfasserin aut Jilili Abuduwaili verfasserin aut Gulnura Issanova verfasserin aut Galymzhan Saparov verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 13, p 3201 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:13, p 3201 https://doi.org/10.3390/rs14133201 kostenfrei https://doaj.org/article/ad0e8f0963ae4c27932c362e1d043c38 kostenfrei https://www.mdpi.com/2072-4292/14/13/3201 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 13, p 3201 |
allfieldsSound |
10.3390/rs14133201 doi (DE-627)DOAJ039135667 (DE-599)DOAJad0e8f0963ae4c27932c362e1d043c38 DE-627 ger DE-627 rakwb eng Na Wu verfasserin aut Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin. long-range transport atmospheric aerosol CALIPSO Aral Sea Central Asia Science Q Yongxiao Ge verfasserin aut Jilili Abuduwaili verfasserin aut Gulnura Issanova verfasserin aut Galymzhan Saparov verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 13, p 3201 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:13, p 3201 https://doi.org/10.3390/rs14133201 kostenfrei https://doaj.org/article/ad0e8f0963ae4c27932c362e1d043c38 kostenfrei https://www.mdpi.com/2072-4292/14/13/3201 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 13, p 3201 |
language |
English |
source |
In Remote Sensing 14(2022), 13, p 3201 volume:14 year:2022 number:13, p 3201 |
sourceStr |
In Remote Sensing 14(2022), 13, p 3201 volume:14 year:2022 number:13, p 3201 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
long-range transport atmospheric aerosol CALIPSO Aral Sea Central Asia Science Q |
isfreeaccess_bool |
true |
container_title |
Remote Sensing |
authorswithroles_txt_mv |
Na Wu @@aut@@ Yongxiao Ge @@aut@@ Jilili Abuduwaili @@aut@@ Gulnura Issanova @@aut@@ Galymzhan Saparov @@aut@@ |
publishDateDaySort_date |
2022-01-01T00:00:00Z |
hierarchy_top_id |
608937916 |
id |
DOAJ039135667 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ039135667</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414072928.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs14133201</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ039135667</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJad0e8f0963ae4c27932c362e1d043c38</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Na Wu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">long-range transport</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">atmospheric aerosol</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CALIPSO</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aral Sea</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Central Asia</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yongxiao Ge</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jilili Abuduwaili</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gulnura Issanova</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Galymzhan Saparov</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Remote Sensing</subfield><subfield code="d">MDPI AG, 2009</subfield><subfield code="g">14(2022), 13, p 3201</subfield><subfield code="w">(DE-627)608937916</subfield><subfield code="w">(DE-600)2513863-7</subfield><subfield code="x">20724292</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:13, p 3201</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/rs14133201</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ad0e8f0963ae4c27932c362e1d043c38</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2072-4292/14/13/3201</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2072-4292</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4392</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2022</subfield><subfield code="e">13, p 3201</subfield></datafield></record></collection>
|
author |
Na Wu |
spellingShingle |
Na Wu misc long-range transport misc atmospheric aerosol misc CALIPSO misc Aral Sea misc Central Asia misc Science misc Q Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia |
authorStr |
Na Wu |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)608937916 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
20724292 |
topic_title |
Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia long-range transport atmospheric aerosol CALIPSO Aral Sea Central Asia |
topic |
misc long-range transport misc atmospheric aerosol misc CALIPSO misc Aral Sea misc Central Asia misc Science misc Q |
topic_unstemmed |
misc long-range transport misc atmospheric aerosol misc CALIPSO misc Aral Sea misc Central Asia misc Science misc Q |
topic_browse |
misc long-range transport misc atmospheric aerosol misc CALIPSO misc Aral Sea misc Central Asia misc Science misc Q |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Remote Sensing |
hierarchy_parent_id |
608937916 |
hierarchy_top_title |
Remote Sensing |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)608937916 (DE-600)2513863-7 |
title |
Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia |
ctrlnum |
(DE-627)DOAJ039135667 (DE-599)DOAJad0e8f0963ae4c27932c362e1d043c38 |
title_full |
Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia |
author_sort |
Na Wu |
journal |
Remote Sensing |
journalStr |
Remote Sensing |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
txt |
author_browse |
Na Wu Yongxiao Ge Jilili Abuduwaili Gulnura Issanova Galymzhan Saparov |
container_volume |
14 |
format_se |
Elektronische Aufsätze |
author-letter |
Na Wu |
doi_str_mv |
10.3390/rs14133201 |
author2-role |
verfasserin |
title_sort |
insights into variations and potential long-range transport of atmospheric aerosols from the aral sea basin in central asia |
title_auth |
Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia |
abstract |
The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin. |
abstractGer |
The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin. |
abstract_unstemmed |
The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 |
container_issue |
13, p 3201 |
title_short |
Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia |
url |
https://doi.org/10.3390/rs14133201 https://doaj.org/article/ad0e8f0963ae4c27932c362e1d043c38 https://www.mdpi.com/2072-4292/14/13/3201 https://doaj.org/toc/2072-4292 |
remote_bool |
true |
author2 |
Yongxiao Ge Jilili Abuduwaili Gulnura Issanova Galymzhan Saparov |
author2Str |
Yongxiao Ge Jilili Abuduwaili Gulnura Issanova Galymzhan Saparov |
ppnlink |
608937916 |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3390/rs14133201 |
up_date |
2024-07-03T21:47:50.302Z |
_version_ |
1803596092202287104 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ039135667</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414072928.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs14133201</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ039135667</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJad0e8f0963ae4c27932c362e1d043c38</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Na Wu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">long-range transport</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">atmospheric aerosol</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CALIPSO</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aral Sea</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Central Asia</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yongxiao Ge</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jilili Abuduwaili</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gulnura Issanova</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Galymzhan Saparov</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Remote Sensing</subfield><subfield code="d">MDPI AG, 2009</subfield><subfield code="g">14(2022), 13, p 3201</subfield><subfield code="w">(DE-627)608937916</subfield><subfield code="w">(DE-600)2513863-7</subfield><subfield code="x">20724292</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:13, p 3201</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/rs14133201</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ad0e8f0963ae4c27932c362e1d043c38</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2072-4292/14/13/3201</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2072-4292</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4392</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2022</subfield><subfield code="e">13, p 3201</subfield></datafield></record></collection>
|
score |
7.401636 |