Variation characteristics and prediction of pollutant concentration during winter in Lanzhou New District, China

作     者：DongYu Jia XiaoXia Li XiaoQing Gao LiWei Yang Dong Yu Jia;Xiao Xia Li;Xiao Qing Gao;Li Wei Yang

作者机构：College of Geography and Environmental EngineeringLanzhou City UniversityLanzhouGansu 730070China Northwest Regional Climate Center/Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province/Key Open Laboratory of Arid Climatic Change and Disaster of China Meteorological Administration/Institute of Arid MeteorologyLanzhouGansu 730020China Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions/Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouGansu 730000China 

出 版 物：《Research in Cold and Arid Regions》 (寒旱区科学（英文版）)

年 卷 期：2020年第12卷第5期

页      面：317-328页

核心收录：

学科分类：07[理学] 070602[理学-大气物理学与大气环境] 0706[理学-大气科学] 

基　　金：research and development plan of Gansu Province in 2018"Experimental study on atmospheric environment characteristics of near-ground boundary layer in Lanzhou New District serving fine functional zoning"(18YF1FA100) the Opening Fund of Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions,CAS(Grant No.LPCC2018006) the Lanzhou City University Doctoral Research Initiation Fund(Grant No.LZCU-BS2019-13) 

主　　题：PM2.5 PM10 WRF Wavelet neural network Lanzhou New District 

摘      要：PM2.5 and PM10 were the main air pollutants during winter in Lanzhou New District,*** this paper,WRF model output combined with hourly monitoring data of pollutant concentration was used to analyze characteristics of the concentration change and to study the relationship between meteorological elements and PM10/PM2.5 in Lanzhou New District in January,***,the concentration changes of PM2.5 and PM10 were predicted by wavelet analysis combined with BP neural *** results show that:(1)Due to the cold front process in winter,PM2.5 was negatively correlated with the water vapor mixing ***10 was positively correlated with air temperature and negatively correlated with air pressure.(2)There was an inversion layer in the atmosphere near the high value day of PM2.5 and PM10,the surface was controlled by low pressure,low wind speed,and the situation of low value day of PM2.5 was the *** the day of high value of PM10,the air temperature below 600 hPa was higher,and the wind speed near the surface was also higher.(3)Wavelet analysis combined with BP(Back Propagation)neural network had a good prediction effect on PM2.5,which could basically reflect the hourly change of PM2.5 ***,the simulation effect of PM10 was poor,and the input parameters of surrounding pollutants should be added to improve the prediction effect.