Light Penetrating the Seawater Column as the Indicator of Oil Suspension—Monte Carlo Modelling for the Case of the Southern Baltic Sea
Abstract
:1. Introduction
2. Materials
3. Method
4. Results and Discussion
5. Conclusions and Perspectives
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Merlin, F.; Zhu, Z.; Yang, M.; Chen, B.; Lee, K.; Boufade, M.C.; Isaacman, L.; Zhang, B. Dispersants as marine oil spill treating agents: A review on mesoscale tests and field trials. Environ. Syst. Res. 2021, 10, 37. [Google Scholar] [CrossRef]
- Lai, Q.; Xie, Y.; Wang, C.; Wang, M.; Tan, J. Multiband directional reflectance properties of oil-in-water emulsion: Application for identification of oil spill types. Appl. Opt. 2021, 60, 6902–6909. [Google Scholar] [CrossRef]
- IMO. The International Convention for the Prevention of Pollution from Ships (MARPOL), 1973 as Modified by the Protocol of 1978. Available online: http://www.imo.org/en/About/conventions/listofconventions/pages/international-convention-forthe-prevention-of-pollution-from-ships-(marpol).aspx (accessed on 10 November 2022).
- Nowak, P.; Kucharska, K.; Kamiński, M. Ecological and Health Effects of Lubricant Oils Emitted into the Environment. Int. J. Environ. Res. Public Health 2019, 16, 3002. [Google Scholar] [CrossRef] [Green Version]
- Chen, D.; Zhang, G.; Dong, D.; Zhao, M.; Wang, X. Widespread fluid seepage related to buried submarine landslide deposits in the northwestern South China Sea. Geophys. Res. Lett. 2022, 49, e2021GL096584. [Google Scholar] [CrossRef]
- Sun, S.; Hu, C. The Challenges of Interpreting Oil–Water Spatial and Spectral Contrasts for the Estimation of Oil Thickness: Examples From Satellite and Airborne Measurements of the Deepwater Horizon Oil Spill. IEEE Trans. Geosci. Remote Sens. 2019, 57, 2643–2658. [Google Scholar] [CrossRef]
- Lednicka, B.; Kubacka, M.; Freda, W.; Haule, K.; Dembska, G.; Galer-Tatarowicz, K.; Pazikowska-Sapota, G. Water Turbidity and Suspended Particulate Matter Concentration at Dredged Material Dumping Sites in the Southern Baltic. Sensors 2022, 22, 8049. [Google Scholar] [CrossRef]
- Carpenter, A. Monitoring Oil Pollution from Oil and Gas Installations in the North Sea. In Oil Pollution in the North Sea. The Handbook of Environmental Chemistry; Carpenter, A., Ed.; Springer International Publishing: Berlin/Heidelberg, Germany, 2015; Volume 41, pp. 209–235. ISBN 978-3-319-23900-2. [Google Scholar]
- Najoui, Z.; Amoussou, N.; Riazanoff, S.; Aurel, G.; Frappart, F. Oil slicks in the Gulf of Guinea—10 years of Envisat Advanced Synthetic Aperture Radar observations. Earth Syst. Sci. Data 2022, 14, 4569–4588. [Google Scholar] [CrossRef]
- European Maritime Safety Agency (EMSA). Available online: https://www.emsa.europa.eu/earth-observation-products/ how-does-sar-detection-work.html (accessed on 10 November 2022).
- Skrunes, S.; Johhanson, A.M.; Brekke, C. 2019 Synthetic Aperture Radar Remote Sensing of Operational Platform Produced Water Releases. Remote Sens. 2019, 11, 2882. [Google Scholar] [CrossRef] [Green Version]
- Hook, S.; Batley, G.; Holloway, M.; Irving, P.; Ross, A. Oil Spill Monitoring Handbook; CSIRO Publishing: Clayton, Victoria, 2016; 288p. [Google Scholar]
- Graham, L.; Hale, C.; Maung-Douglass, E.; Sempier, S.; Swann, L.; Wilson, M. Oil Spill Science: Chemical Dispersants and Their Role in Oil Spill Response; MASGP-15-015; The Gulf of Mexico Research Initiative: Biloxi, MS, USA, 2016. [Google Scholar]
- Bailey, D.; Dannreuther, M.N.; Maung-Douglass, E.; Partyka, M.; Sempier, S.; Skelton, T.; Wilson, M. Dispersant Use and Impacts after the Deepwater Horizon Oil Spill; GOMSG-G-21-008; The Gulf of Mexico Research Initiative: Biloxi, MS, USA, 2021. [Google Scholar]
- Baszanowska, E.; Otremba, Z.; Piskozub, J. Modelling a Spectral Index to Detect Dispersed Oil in a Seawater Column Depending on the Viewing Angle: Gulf of Gdansk Case Study. Sensors 2020, 20, 5352. [Google Scholar] [CrossRef]
- Dera, J. Marine Physics, 2nd ed.; PWN: Warszawa, Poland, 2003; p. 541. (In Polish) [Google Scholar]
- Fingas, M. Oil Spill Science and Technology, 2nd ed.; Elsevier: Amsterdam, The Netherlands, 2017. [Google Scholar]
- Zhou, Z.; Guo, L.; Shiller, A.M.; Lohrenz, S.; Asper, V.L.; Osburn, C. Characterization of oil components from the Deepwater Horizon oil spill in the Gulf of Mexico using fluorescence EEM and PARAFAC techniques. Mar. Chem. 2013, 148, 10–21. [Google Scholar] [CrossRef]
- Hou, Y.; Li, Y.; Liu, B.; Liu, Y.; Wang, T. Design and Implementation of a Coastal-Mounted Sensor for Oil Film 5 Detection on Seawater. Sensors 2017, 18, 70. [Google Scholar] [CrossRef] [Green Version]
- Hu, C.; Weisberg, R.H.; Liu, Y.; Zheng, L.; Daly, K.L.; English, D.; Zhao, J.; Vargo, G.A. Did the northeastern Gulf of Mexico become greener after the Deepwater Horizon oil spill? Geophys. Res. Lett. 2011, 38, L09601. [Google Scholar] [CrossRef] [Green Version]
- Leifer, I.; Lehr, W.J.; Simecek-Beatty, D.; Bradley, E.; Clark, R.; Dennison, P.E.; Hu, Y.; Matheson, S.; Jones, C.E.; Holt, B.; et al. State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill. Remote Sens. Environ. 2012, 124, 185–209. [Google Scholar] [CrossRef] [Green Version]
- Sun, S.; Lu, Y.; Liu, Y.; Wang, M.; Hu, C. Tracking an Oil Tanker Collision and Spilled Oils in the East China Sea Using Multisensor Day and Night Satellite Imagery. Geophys. Res. Lett. 2018, 45, 3212–3220. [Google Scholar] [CrossRef]
- Schifter, I.; Sánchez-Reyna, G.; González-Macías, C.; Salazar-Coria, L.; González-Lozano, C. Fluorescence characteristics in the deep waters of South Gulf of México. Mar. Pollut. Bull. 2017, 123, 165–174. [Google Scholar] [CrossRef]
- Otremba, Z.; Piskozub, J. Modelling the Spectral Index to Detect a Baltic-Type Crude Oil Emulsion Dispersed in the Southern Baltic Sea. Remote Sens. 2021, 13, 3927. [Google Scholar] [CrossRef]
- Sagan, S. The inherent water optical properties of Baltic waters. In Rozprawy i Monografie; IOPAN Sopot: Sopot, Poland, 2008; p. 244. (In Polish) [Google Scholar]
- Baszanowska, E.; Otremba, Z.; Piskozub, J. Modelling the Visibility of Baltic-Type Crude Oil Emulsion Dispersed in the Southern Baltic Sea. Remote Sens. 2021, 13, 1917. [Google Scholar] [CrossRef]
- Cox, C.; Munk, W.H. Statistics of the sea surface derived from sun glitter. J. Mar. Res. 1954, 13, 198–227. [Google Scholar]
- Gregg, W.W.; Carder, K.L. A simple spectral solar irradiance model for cloudless maritime atmospheres. Limnol. Oceanogr. 1990, 35, 1657–1675. [Google Scholar] [CrossRef]
- Lednicka, B.; Kubacka, M. Semi-empirical model of remote-sensing reflectance for chosen areas of the southern Baltic. Sensors 2022, 22, 1105. [Google Scholar] [CrossRef]
- Otremba, Z.; Piskozub, J. Monte Carlo Radiative Transfer Simulation to Analyze the Spectral Index for Remote Detection of Oil Dispersed in the Southern Baltic Sea Seawater Column: The Role of Water Surface State. Remote Sens. 2022, 14, 247. [Google Scholar] [CrossRef]
Wavelength [nm] | Absorption Coefficient [m−1] | ||
---|---|---|---|
0–5 m | 5–30 m | 30–50 m | |
412 | 0.596 | 0.536 | 0.476 |
440 | 0.398 | 0.348 | 0.298 |
488 | 0.218 | 0.178 | 0.148 |
510 | 0.188 | 0.158 | 0.138 |
532 | 0.163 | 0.143 | 0.123 |
555 | 0.149 | 0.139 | 0.119 |
650 | 0.391 | 0.381 | 0.371 |
676 | 0.517 | 0.497 | 0.467 |
Wavelength [nm] | Scattering Coefficient [m−1] | ||
---|---|---|---|
0–5 m | 5–30 m | 30–50 m | |
412 | 0.63 | 0.39 | 0.14 |
440 | 0.60 | 0.37 | 0.13 |
488 | 0.60 | 0.37 | 0.14 |
510 | 0.60 | 0.37 | 0.14 |
532 | 0.60 | 0.37 | 0.14 |
555 | 0.59 | 0.37 | 0.15 |
650 | 0.54 | 0.34 | 0.14 |
676 | 0.51 | 0.32 | 0.14 |
Wavelength [nm] | Absorption Coefficient [m−1] |
---|---|
412 | 0.299 |
440 | 0.114 |
488 | 0.052 |
510 | 0.042 |
532 | 0.029 |
555 | 0.029 |
650 | 0.0125 |
676 | 0.0087 |
Wavelength [nm] | Scattering Coefficient [m−1] |
---|---|
412 | 7.81 |
440 | 7.97 |
488 | 7.98 |
510 | 7.95 |
532 | 7.91 |
555 | 7.87 |
650 | 7.60 |
676 | 7.48 |
Wavelength [nm] | Direct Solar Irradiance Percentage |
---|---|
412 | 64.7 |
440 | 66.2 |
488 | 68.2 |
510 | 68.8 |
532 | 69.4 |
555 | 69.9 |
650 | 71.2 |
676 | 71.4 |
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Lednicka, B.; Otremba, Z.; Piskozub, J. Light Penetrating the Seawater Column as the Indicator of Oil Suspension—Monte Carlo Modelling for the Case of the Southern Baltic Sea. Sensors 2023, 23, 1175. https://doi.org/10.3390/s23031175
Lednicka B, Otremba Z, Piskozub J. Light Penetrating the Seawater Column as the Indicator of Oil Suspension—Monte Carlo Modelling for the Case of the Southern Baltic Sea. Sensors. 2023; 23(3):1175. https://doi.org/10.3390/s23031175
Chicago/Turabian StyleLednicka, Barbara, Zbigniew Otremba, and Jacek Piskozub. 2023. "Light Penetrating the Seawater Column as the Indicator of Oil Suspension—Monte Carlo Modelling for the Case of the Southern Baltic Sea" Sensors 23, no. 3: 1175. https://doi.org/10.3390/s23031175