摘要
探究全国大气气溶胶分布及变化特征,准确了解中国地区气溶胶光学特性对研究大气环境污染、应对全球气候变化是非常重要的。对2008~2016年的MODIS MAIAC气溶胶光学厚度数据在中国的适用性进行验证,并采用Mann-Kendall方法,从不同的时空尺度和气溶胶类型上分析中国地区AOD值的时空变化特征。结果表明:(1)验证表明C6的MAIAC反演结果在中国AERONET匹配点上表现良好,C6的MAIAC反演AOD结果适用于中国区域;(2)从年际尺度上看,2008~2016年AOD年均值整体呈波动下降;从季节尺度上看,AOD季节变化呈春季整体高、夏季中心高、秋冬季水平低的特点,各省AOD平均值及各省份划区AOD平均值随季节变化趋势相似。(3)在空间上,AOD呈东南高、西北低、高值中心聚集的特征。(4)中国AOD变化整体呈现出东部减少且集聚,西部增加且分散的变化特征。可进一步探究不同种类气溶胶分布和气溶胶与典型大气污染物分布关系,以期为中国环境污染治理提供更好的决策指导。
Exploring the distribution and change characteristics of atmospheric aerosols across the country and accurately understanding the optical characteristics of aerosols in China are very important for studying atmospheric environmental pollution and coping with global climate change.The applicability of the MODIS MAIAC aerosol optical thickness data from 2008 to 2016 in China was verified,and the Mann-Kendall method was used to analyze the spatiotemporal characteristics of AOD values in China from different spatiotemporal scales and aerosol types.The results show that:(1)The verification shows that the C6 MAIAC inversion results perform well on the Chinese AERONET matching point,and the C6 MAIAC inversion AOD results are applicable to the Chinese region;(2)From the perspective of seasonal scale,the seasonal change of AOD is characterized by high overall spring,high summer center,and low autumn and winter levels.The average AOD of each province and the average AOD of each province are similar with seasonal changes.(3)Spatially,AOD is characterized by high southeast,low northwest,and high value centers.(4)The change of AOD in China as a whole shows the characteristics of decreasing and agglomeration in the east and increasing and scattered in the west.It can further explore the distribution of different types of aerosols and the relationship between aerosols and typical atmospheric pollutants,with a view to providing better decision-making guidance for environmental pollution control in China.
作者
王浩天
汪源
袁强强
Wang Haotian;Wang Yuan;Yuan Qiangqiang(School of Surveying and Mapping,Wuhan University,Wuhan 430079,China)
出处
《遥感技术与应用》
CSCD
北大核心
2021年第1期217-228,共12页
Remote Sensing Technology and Application
基金
中国科学院A类战略性先导科技专项“地球大数据科学工程”子课题(XDA19090104)。