摘要
综合利用多源数据,应用HYSPLIT4轨迹分析、颗粒物区域传输分析等方法对2022年末安徽一次大气重污染过程变化特征及成因进行分析。结果表明:污染前期,受弱冷空气影响,有利于污染物输送至安徽地区;污染期间,地面处于弱气压场,混合层厚度较低,存在持续性逆温,且地面风速小、相对湿度较高、基本无降水,不利于污染物的扩散和清除,受区域输送叠加本地污染排放累积共同影响,污染程度加剧;2023年1月2日地面以东北、偏东风为主,风速增大、相对湿度下降、混合层厚度抬升,扩散条件转好,污染程度减轻。在此次污染过程中,山东、江苏、河南对安徽累积的区域输送贡献率占比为48.5%,高于安徽本地污染的贡献率(21.4%)。
Using multi-source data,HYSPLIT4 trajectory analysis and particle matter regional transport analysis,the characteristics and causes of a heavy air pollution process in Anhui at the end of 2022 were analyzed.The results showed that in the early stage of pollution,affected by weak cold air,it was conducive to the transport of pollutants to Anhui.During the pollution period,the ground was in a weak pressure field,the thickness of the mixed layer was low and there was a persistent temperature inversion.The ground wind speed was small,the relative humidity was high,and there was basically no precipitation,which was not conducive to the diffusion and removal of pollutants.The pollution level was aggravated due to the common influence of regional transport and local pollution emissions accumulation.On January 2,2023,the surface wind direction was mainly northeast and easterly,the wind speed increased,the relative humidity decreased,the thickness of the mixed layer increased,the diffusion conditions were improved and the pollution level was reduced.During this pollution process,the cumulative regional transport contribution rate from Shandong,Jiangsu and Henan to Anhui was 48.5%,which was larger than that from local pollution in Anhui(21.4%).
作者
张浩
于彩霞
杨关盈
石春娥
ZHANG Hao;YU Caixia;YANG Guanying;SHI Chune(Anhui Institute of Meteorological Sciences,Anhui Key Laboratory of Atmospheric Sciences and Satellite Remote Sensing,Hefei,Anhui 230031,China;Shouxian National Climatology Observatory,Huaihe River Basin Typical Farm Eco-meteorological Experiment Field of CMA,Shouxian,Anhui 232200,China)
出处
《环境监测管理与技术》
CSCD
北大核心
2024年第5期20-26,共7页
The Administration and Technique of Environmental Monitoring
基金
国家自然科学基金资助项目(No.41875171)
中国气象局创新发展专项基金资助项目(No.CXFZ2022J070)。