Literature DB >> 34032060

[MAIAC AOD and PM2.5 Mass Concentrations Characteristics and Correlation Analysis in Beijing-Tianjin-Hebei and Surrounding Areas].

Jian-Nan Jin1, Xing-Chuan Yang2, Xing Yan2, Wen-Ji Zhao1.   

Abstract

Based on the MAIAC AOD and PM2.5 mass concentration data for the Beijing-Tianjin-Hebei region and surrounding areas from 2014 to 2018, the temporal and spatial differences in Aerosol Optical Depth (AOD) and PM2.5 mass concentrations were explored and their correlation was analyzed by linear regression. The results show that the daily average concentration of PM2.5 exceeds the standard for 33% and 57% of measurements based on the daily average standard values of the World Health Organization IT.1 and IT.2, respectively, indicating serious pollution levels. The annual average concentrations of PM2.5 and Terra and Aqua MAIAC AOD all show downward trends. The PM2.5 concentrations are high in winter and spring and low in summer and autumn; Terra and Aqua AOD values are high in spring and summer and low in autumn and winter. The seasonal and annual average concentrations of PM2.5 and AOD all show the regional pattern of "low in the north and high in the south". High-value areas are mainly located in southern Hebei, southwestern Shanxi, western Shandong, and northern Henan, while low-value areas are mainly located in northwestern Shanxi, northern Hebei, and eastern Shandong. The annual average concentration of PM2.5 is between 27 and 99μg·m-3, and the annual average AOD is between 0.20 and 0.69. The correlation between Aqua AOD and PM2.5 concentration is strong whereas and the correlations between Terra AOD, Aqua AOD, and PM2.5vary significantly in different seasons; overall, correlations are strongest in spring and winter and weakest in summer and autumn. After vertical-humidity correction, the correlation between satellite AOD and PM2.5 data is significantly improved.

Keywords:  Beijing-Tianjin-Hebei and surrounding areas; MAIAC AOD; PM2.5; correlation; temporal and spatial distribution

Year:  2021        PMID: 34032060     DOI: 10.13227/j.hjkx.202009200

Source DB:  PubMed          Journal:  Huan Jing Ke Xue        ISSN: 0250-3301


  1 in total

1.  A Model Study on Raw Material Chemical Composition to Predict Sinter Quality Based on GA-RNN.

Authors:  Yifan Li; Qunwei Zhang; Yi Zhu; Aimin Yang; Weixing Liu; Xinfeng Zhao; Xinying Ren; Shilong Feng; Zezheng Li
Journal:  Comput Intell Neurosci       Date:  2022-04-12
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.