Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning Article Swipe
Wenyuan Wang
,
Bochi Liu
,
Qi Tian
,
Xinglu Xu
,
Yun Peng
,
Shitao Peng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.envpol.2024.124053
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.envpol.2024.124053
Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate prediction of dust pollution helps identify risks early and take preventive measures. However, there remain challenges in solving non-stationary time series and selecting relevant features. Besides, existing studies rarely consider impacts of port operations on dust pollution. Therefore, a hybrid approach based on data decomposition and deep learning is proposed to predict dust pollution from dry bulk ports. Port operational data is specially integrated into input features. A secondary decomposition and recombination (SDR) strategy is presented to reduce data non-stationarity. A dual-stage attention-based sequence-to-sequence (DA-Seq2Seq) model is employed to adaptively select the most relevant features at each time step, as well as capture long-term temporal dependencies. This approach is compared with baseline models on a dataset from a dry bulk port in northern China. The results reveal the advantages of SDR strategy and integrating operational data and show that this approach has higher accuracy than baseline models. The proposed approach can mitigate adverse effects of dust pollution from dry bulk ports on urban residents and help port authorities control dust pollution.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.envpol.2024.124053
- OA Status
- hybrid
- Cited By
- 11
- References
- 91
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4395446252
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4395446252Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.envpol.2024.124053Digital Object Identifier
- Title
-
Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-25Full publication date if available
- Authors
-
Wenyuan Wang, Bochi Liu, Qi Tian, Xinglu Xu, Yun Peng, Shitao PengList of authors in order
- Landing page
-
https://doi.org/10.1016/j.envpol.2024.124053Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.envpol.2024.124053Direct OA link when available
- Concepts
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Pollution, Baseline (sea), Port (circuit theory), Environmental science, Decomposition, Air quality index, Air pollution, Computer science, Meteorology, Engineering, Geography, Oceanography, Biology, Geology, Electrical engineering, Ecology, Organic chemistry, ChemistryTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
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2025: 7, 2024: 4Per-year citation counts (last 5 years)
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91Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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