Publications by authors named "Kuiquan Duan"
Article Synopsis
- The study introduces a machine learning model to enhance the accuracy of emission estimates by identifying operational phases of ships' engines and boilers, using AIS data from bulk carriers.
- The random forest (RF) model outperformed other machine learning models, achieving high accuracy in operational phase identification and significantly improving NOx emission estimation.
- The proposed approach could be adopted by entities like the International Maritime Organization, benefiting port authorities and potentially applying to other ship types, ultimately contributing to better management of global shipping emissions.
In the winter of 2018-2019, 75 air samples were collected through four ship-borne measurements in the Yellow Sea (YS) to assess the levels, confinement processes, and source distribution of volatile organic compounds (VOCs). A total of 41 were eventually detected, which mainly were non-methane hydrocarbons (NMHCs), volatile halogenated hydrocarbons (VHCs), oxygenated volatile organic compounds (OVOCs), and volatile organic sulfur compounds (VSCs). Aromatics (31.
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