文章基本信息

标题：Understanding global changes in fine-mode aerosols during 2008–2017 using statistical methods and deep learning approach
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作者：Xing Yan ; Zhou Zang ; Chuanfeng Zhao 等
期刊名称：Environment International
印刷版ISSN：0160-4120
电子版ISSN：1873-6750
出版年度：2021
卷号：149
页码：106392
DOI：10.1016/j.envint.2021.106392
出版社：Pergamon
摘要：Despite their extremely small size, fine-mode aerosols have significant impacts on the environment, climate, and human health. However, current understandings of global changes in fine-mode aerosols are limited. In this study, we employed newly developed satellite retrieval data and an attentive interpretable deep learning model to explore the status, changes, and association factors of the global fine-mode aerosol optical depth (fAOD) and aerosol fine-mode fraction (FMF) from 2008 to 2017. At the global scale, the results show a significant increasing trend in land FMF (2.34 × 10 −3 /year); however, the FMF over the ocean and the fAOD over land and ocean did not reveal significant trends. Between 2008 and 2017, high levels of both fAOD (>0.30) and FMF (>0.75) were identified over China, southeastern Asia, India, and Africa. Seasonally, global land FMF showed high values in summer (>0.70) and low values in spring (0.15) but low in winter (<0.13). Importantly, Australia and Mexico experienced significant increasing trends in FMF during all four seasons. At the regional scale, a significant decline in fAOD was identified in China, which indicates that government emission controls and reductions have been effective in recent decades. The deep learning model was used to interpret the result and showed that O 3 was significantly associated with changes in both the FMF and fAOD. This finding suggests the importance of synergizing the regulations for both O 3 and fine particles. Our work comprehensively examined global spatial and seasonal fAOD and FMF changes and provides a holistic understanding of global anthropogenic impacts.
关键词：Satellite ; Aerosol ; fAOD ; FMF