Understanding global changes in fine-mode aerosols during 2008–2017 using statistical methods and deep learning approach Article Swipe
Xing Yan
,
Zhou Zang
,
Chuanfeng Zhao
,
Husi Letu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.envint.2021.106392
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.envint.2021.106392
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 (<0.65), while land fAOD was high in summer (>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 O3 was significantly associated with changes in both the FMF and fAOD. This finding suggests the importance of synergizing the regulations for both O3 and fine particles. Our work comprehensively examined global spatial and seasonal fAOD and FMF changes and provides a holistic understanding of global anthropogenic impacts.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.envint.2021.106392
- OA Status
- gold
- Cited By
- 38
- References
- 110
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3126630374Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.envint.2021.106392Digital Object Identifier
- Title
-
Understanding global changes in fine-mode aerosols during 2008–2017 using statistical methods and deep learning approachWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-01-28Full publication date if available
- Authors
-
Xing Yan, Zhou Zang, Chuanfeng Zhao, Husi LetuList of authors in order
- Landing page
-
https://doi.org/10.1016/j.envint.2021.106392Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.envint.2021.106392Direct OA link when available
- Concepts
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Aerosol, Environmental science, Satellite, Climatology, Mode (computer interface), China, Atmospheric sciences, Meteorology, Geography, Geology, Computer science, Aerospace engineering, Archaeology, Engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
38Total citation count in OpenAlex
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2025: 7, 2024: 5, 2023: 8, 2022: 11, 2021: 7Per-year citation counts (last 5 years)
- References (count)
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110Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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