Mongolia Contributed More than 42% of the Dust Concentrations in Northern China in March and April 2023
Dust storms are one of the most frequent meteorological disasters in China, endangering agricultural production, transportation, air quality, and the safety of people’s lives and property. Against the backdrop of climate change, Mongolia’s contribution to China’s dust cannot be ignored in recent yea...
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Published in | Advances in atmospheric sciences Vol. 40; no. 9; pp. 1549 - 1557 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Heidelberg
Science Press
01.09.2023
Springer Nature B.V Key Laboratory for Semi-Arid Climate Change of the Ministry of Education,Lanzhou University,Lanzhou 730000,China%Institute of Desert Meteorology,China Meteorological Administration,Urumqi 830002,China |
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Abstract | Dust storms are one of the most frequent meteorological disasters in China, endangering agricultural production, transportation, air quality, and the safety of people’s lives and property. Against the backdrop of climate change, Mongolia’s contribution to China’s dust cannot be ignored in recent years. In this study, we used the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), along with dynamic dust sources and the HYSPLIT model, to analyze the contributions of different dust sources to dust concentrations in northern China in March and April 2023. The results show that the frequency of dust storms in 2023 was the highest observed in the past decade. Mongolia and the Taklimakan Desert were identified as two main dust sources contributing to northern China. Specifically, Mongolia contributed more than 42% of dust, while the Taklimakan Desert accounted for 26%. A cold high-pressure center, a cold front, and a Mongolian cyclone resulted in the transport of dust aerosols from Mongolia and the Taklimakan Desert to northern China, where they affected most parts of the region. Moreover, two machine learning methods [the XGBoost algorithm and the Synthetic Minority Oversampling Technique (SMOTE)] were used to forecast the dust storms in March 2023, based on ground observations and WRF-Chem simulations over East Asia. XGBoost-SMOTE performed well in predicting hourly PM
10
concentrations in China in March 2023, with a mean absolute error of 33.8 µg m
−3
and RMSE of 54.2 µg m
−3
. |
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AbstractList | Dust storms are one of the most frequent meteorological disasters in China,endangering agricultural production,transportation,air quality,and the safety of people's lives and property.Against the backdrop of climate change,Mongolia's contribution to China's dust cannot be ignored in recent years.In this study,we used the Weather Research and Forecasting model coupled with Chemistry(WRF-Chem),along with dynamic dust sources and the HYSPLIT model,to analyze the contributions of different dust sources to dust concentrations in northern China in March and April 2023.The results show that the frequency of dust storms in 2023 was the highest observed in the past decade.Mongolia and the Taklimakan Desert were identified as two main dust sources contributing to northern China.Specifically,Mongolia contributed more than 42%of dust,while the Taklimakan Desert accounted for 26%.A cold high-pressure center,a cold front,and a Mongolian cyclone resulted in the transport of dust aerosols from Mongolia and the Taklimakan Desert to northern China,where they affected most parts of the region.Moreover,two machine learning methods[the XGBoost algorithm and the Synthetic Minority Oversampling Technique(SMOTE)]were used to forecast the dust storms in March 2023,based on ground observations and WRF-Chem simulations over East Asia.XGBoost-SMOTE performed well in predicting hourly PM10 concentrations in China in March 2023,with a mean absolute error of 33.8μg m-3 and RMSE of 54.2μg m-3. Dust storms are one of the most frequent meteorological disasters in China, endangering agricultural production, transportation, air quality, and the safety of people’s lives and property. Against the backdrop of climate change, Mongolia’s contribution to China’s dust cannot be ignored in recent years. In this study, we used the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), along with dynamic dust sources and the HYSPLIT model, to analyze the contributions of different dust sources to dust concentrations in northern China in March and April 2023. The results show that the frequency of dust storms in 2023 was the highest observed in the past decade. Mongolia and the Taklimakan Desert were identified as two main dust sources contributing to northern China. Specifically, Mongolia contributed more than 42% of dust, while the Taklimakan Desert accounted for 26%. A cold high-pressure center, a cold front, and a Mongolian cyclone resulted in the transport of dust aerosols from Mongolia and the Taklimakan Desert to northern China, where they affected most parts of the region. Moreover, two machine learning methods [the XGBoost algorithm and the Synthetic Minority Oversampling Technique (SMOTE)] were used to forecast the dust storms in March 2023, based on ground observations and WRF-Chem simulations over East Asia. XGBoost-SMOTE performed well in predicting hourly PM10 concentrations in China in March 2023, with a mean absolute error of 33.8 µg m−3 and RMSE of 54.2 µg m−3. Dust storms are one of the most frequent meteorological disasters in China, endangering agricultural production, transportation, air quality, and the safety of people’s lives and property. Against the backdrop of climate change, Mongolia’s contribution to China’s dust cannot be ignored in recent years. In this study, we used the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), along with dynamic dust sources and the HYSPLIT model, to analyze the contributions of different dust sources to dust concentrations in northern China in March and April 2023. The results show that the frequency of dust storms in 2023 was the highest observed in the past decade. Mongolia and the Taklimakan Desert were identified as two main dust sources contributing to northern China. Specifically, Mongolia contributed more than 42% of dust, while the Taklimakan Desert accounted for 26%. A cold high-pressure center, a cold front, and a Mongolian cyclone resulted in the transport of dust aerosols from Mongolia and the Taklimakan Desert to northern China, where they affected most parts of the region. Moreover, two machine learning methods [the XGBoost algorithm and the Synthetic Minority Oversampling Technique (SMOTE)] were used to forecast the dust storms in March 2023, based on ground observations and WRF-Chem simulations over East Asia. XGBoost-SMOTE performed well in predicting hourly PM 10 concentrations in China in March 2023, with a mean absolute error of 33.8 µg m −3 and RMSE of 54.2 µg m −3 . |
Author | Lou, Gaotong Du, Shikang Huang, Jianping Zhang, Yue Bi, Hongru Yang, Fan Zhao, Dan Chen, Junyan Chen, Yu Chen, Siyu He, Jiaqi |
AuthorAffiliation | Key Laboratory for Semi-Arid Climate Change of the Ministry of Education,Lanzhou University,Lanzhou 730000,China%Institute of Desert Meteorology,China Meteorological Administration,Urumqi 830002,China |
AuthorAffiliation_xml | – name: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education,Lanzhou University,Lanzhou 730000,China%Institute of Desert Meteorology,China Meteorological Administration,Urumqi 830002,China |
Author_xml | – sequence: 1 givenname: Siyu surname: Chen fullname: Chen, Siyu organization: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University – sequence: 2 givenname: Dan surname: Zhao fullname: Zhao, Dan organization: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University – sequence: 3 givenname: Jianping surname: Huang fullname: Huang, Jianping email: hjp@lzu.edu.cn organization: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University – sequence: 4 givenname: Jiaqi surname: He fullname: He, Jiaqi organization: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University – sequence: 5 givenname: Yu surname: Chen fullname: Chen, Yu organization: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University – sequence: 6 givenname: Junyan surname: Chen fullname: Chen, Junyan organization: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University – sequence: 7 givenname: Hongru surname: Bi fullname: Bi, Hongru organization: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University – sequence: 8 givenname: Gaotong surname: Lou fullname: Lou, Gaotong organization: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University – sequence: 9 givenname: Shikang surname: Du fullname: Du, Shikang organization: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University – sequence: 10 givenname: Yue surname: Zhang fullname: Zhang, Yue organization: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University – sequence: 11 givenname: Fan surname: Yang fullname: Yang, Fan organization: Institute of Desert Meteorology, China Meteorological Administration |
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Keywords | Mongolian dust dust aerosol 沙尘气溶胶 HYSPLIT model 蒙古沙尘 transboundary contribution 沙尘跨境贡献 HYSPLIT模型 WRF-Chem |
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Title | Mongolia Contributed More than 42% of the Dust Concentrations in Northern China in March and April 2023 |
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