Articles | Volume 19, issue 21
https://doi.org/10.5194/acp-19-13611-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-19-13611-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Nov 2019
Research article |
| 11 Nov 2019
| 11 Nov 2019
Surveillance of SO2 and NO2 from ship emissions by MAX-DOAS measurements and the implications regarding fuel sulfur content compliance
Yuli Cheng
Shanghai Key Laboratory of Atmospheric Particle Pollution and
Prevention (LAP), Department of Environmental Science and Engineering,
Fudan University, Shanghai, 200433, China
Shanshan Wang
CORRESPONDING AUTHOR
Shanghai Key Laboratory of Atmospheric Particle Pollution and
Prevention (LAP), Department of Environmental Science and Engineering,
Fudan University, Shanghai, 200433, China
Shanghai Institute of Eco-Chongming (SIEC), No.3663 Northern
Zhongshan Road, Shanghai, 200062, China
Jian Zhu
Shanghai Key Laboratory of Atmospheric Particle Pollution and
Prevention (LAP), Department of Environmental Science and Engineering,
Fudan University, Shanghai, 200433, China
Yanlin Guo
Shanghai Key Laboratory of Atmospheric Particle Pollution and
Prevention (LAP), Department of Environmental Science and Engineering,
Fudan University, Shanghai, 200433, China
Ruifeng Zhang
Shanghai Key Laboratory of Atmospheric Particle Pollution and
Prevention (LAP), Department of Environmental Science and Engineering,
Fudan University, Shanghai, 200433, China
Yiming Liu
Shanghai Key Laboratory of Atmospheric Particle Pollution and
Prevention (LAP), Department of Environmental Science and Engineering,
Fudan University, Shanghai, 200433, China
Yan Zhang
Shanghai Key Laboratory of Atmospheric Particle Pollution and
Prevention (LAP), Department of Environmental Science and Engineering,
Fudan University, Shanghai, 200433, China
Shanghai Institute of Eco-Chongming (SIEC), No.3663 Northern
Zhongshan Road, Shanghai, 200062, China
Institute of Atmospheric Sciences, Fudan University, Shanghai,
200433, China
Qi Yu
Shanghai Key Laboratory of Atmospheric Particle Pollution and
Prevention (LAP), Department of Environmental Science and Engineering,
Fudan University, Shanghai, 200433, China
Shanghai Institute of Eco-Chongming (SIEC), No.3663 Northern
Zhongshan Road, Shanghai, 200062, China
Weichun Ma
Shanghai Key Laboratory of Atmospheric Particle Pollution and
Prevention (LAP), Department of Environmental Science and Engineering,
Fudan University, Shanghai, 200433, China
Shanghai Institute of Eco-Chongming (SIEC), No.3663 Northern
Zhongshan Road, Shanghai, 200062, China
Bin Zhou
CORRESPONDING AUTHOR
Shanghai Key Laboratory of Atmospheric Particle Pollution and
Prevention (LAP), Department of Environmental Science and Engineering,
Fudan University, Shanghai, 200433, China
Shanghai Institute of Eco-Chongming (SIEC), No.3663 Northern
Zhongshan Road, Shanghai, 200062, China
Institute of Atmospheric Sciences, Fudan University, Shanghai,
200433, China
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- Robust Co(II)-Based Metal–Organic Framework for the Efficient Uptake and Selective Detection of SO2 V. López-Cervantes et al. 10.1021/acs.chemmater.3c02715
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- Significant contribution of inland ships to the total NOx emissions along the Yangtze River X. Zhang et al. 10.5194/acp-23-5587-2023
- Investigation of emission characteristics of a marine cargo ship in real-world conditions Y. Zhang et al. 10.1016/j.scitotenv.2024.174991
- Impacts of Omicron associated restrictions on vertical distributions of air pollution at a suburb site in Shanghai S. Zhang et al. 10.1016/j.atmosenv.2022.119461
- Detection of Sulfur Dioxide by Broadband Cavity-Enhanced Absorption Spectroscopy (BBCEAS) R. Thalman et al. 10.3390/s22072626
- Monitoring the compliance of sailing ships with fuel sulfur content regulations using unmanned aerial vehicle (UAV) measurements of ship emissions in open water F. Zhou et al. 10.5194/amt-13-4899-2020
- Ground-Based MAX-DOAS Observations of Tropospheric NO2 and HCHO During COVID-19 Lockdown and Spring Festival Over Shanghai, China A. Tanvir et al. 10.3390/rs13030488
- MAX-DOAS observation in the midlatitude marine boundary layer: Influences of typhoon forced air mass R. Zhang et al. 10.1016/j.jes.2021.12.010
- MAX-DOAS observations of ship emissions in the North Sea A. Mahajan et al. 10.1016/j.marpolbul.2024.116761
- Ship fuel sulfur content prediction based on convolutional neural network and ultraviolet camera images K. Cao et al. 10.1016/j.envpol.2021.116501
- Determination of NOx emission rates of inland ships from onshore measurements K. Krause et al. 10.5194/amt-16-1767-2023
- Development of a spectrum-based ship fuel sulfur content real-time evaluation method H. Wu et al. 10.1016/j.marpolbul.2022.114484
- Trace Gases over Land and Ocean Surfaces of China: Hotspots, Trends, and Source Contributions M. Ali et al. 10.1007/s41748-023-00354-0
- Validation of Water Vapor Vertical Distributions Retrieved from MAX-DOAS over Beijing, China H. Lin et al. 10.3390/rs12193193
- Observations by Ground-Based MAX-DOAS of the Vertical Characters of Winter Pollution and the Influencing Factors of HONO Generation in Shanghai, China S. Xu et al. 10.3390/rs13173518
- Vertical distributions of atmospheric HONO and the corresponding OH radical production by photolysis at the suburb area of Shanghai, China S. He et al. 10.1016/j.scitotenv.2022.159703
- Deep learning bias correction of GEMS tropospheric NO2: A comparative validation of NO2 from GEMS and TROPOMI using Pandora observations M. Ghahremanloo et al. 10.1016/j.envint.2024.108818
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- Impact Assessment of COVID‐19 Lockdown on Vertical Distributions of NO2 and HCHO From MAX‐DOAS Observations and Machine Learning Models S. Zhang et al. 10.1029/2021JD036377
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- Constructing the 3D Spatial Distribution of the HCHO/NO2 Ratio via Satellite Observation and Machine Learning Model Z. Jiang et al. 10.1021/acs.est.4c12362
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- Protocol development for real-time ship fuel sulfur content determination using drone based plume sniffing microsensor system A. Anand et al. 10.1016/j.scitotenv.2020.140885
- Monitoring Sulfur Content in Marine Fuel Oil Using Ultraviolet Imaging Technology Z. Zhang et al. 10.3390/atmos12091182
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Latest update: 13 Jul 2025
Short summary
Owing to the gradual implementation of emission control zone (ECA) regulations, feasible technology for the surveillance of compliance with respect to fuel sulfur content is in high demand. We presented shore-based MAX-DOAS measurements of ship-emitted SO2 and NO2 under different traffic conditions. The results of this study indicate that this technique has high potential as a fast and accurate way to surveil ship emissions and fuel sulfur content.
Owing to the gradual implementation of emission control zone (ECA) regulations, feasible...
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