Mongolia Contributed More than 42%of the Dust Concentrations in Northern China in March and April 2023  

作　　者：Siyu CHEN Dan ZHAO Jianping HUANG Jiaqi HE Yu CHEN Junyan CHEN Hongru BI Gaotong LOU Shikang DU Yue ZHANG Fan YANG 

机构地区：[1]Key Laboratory for Semi-Arid Climate Change of the Ministry of Education,Lanzhou University,Lanzhou 730000,China [2]Institute of Desert Meteorology,China Meteorological Administration,Urumqi 830002,China

出　　处：《Advances in Atmospheric Sciences》2023年第9期1549-1557,共9页大气科学进展（英文版）

基　　金：This work was jointly supported by a project supported by the Joint Fund of the National Natural Science Foundation of China and the China Meteorological Administration(Grant No.U2242209);the National Natural Science Foundation of China(Grant No.42175106).

摘　　要：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.

关 键 词：dust aerosol Mongolian dust transboundary contribution WRF-Chem HYSPLIT model 

分 类 号：P425.55[天文地球—大气科学及气象学]