Mueller matrix-based calculating model for extracting polarization parameters in the analysis system for sea surface oil-spill

2019 ◽  
Vol 102 ◽  
pp. 103015 ◽  
Author(s):  
Kan Ren ◽  
Cheng Cheng ◽  
Guohua Gu ◽  
Qian Chen
Keyword(s):  
Oil Spill ◽  
Mueller Matrix ◽  
Sea Surface ◽  
Analysis System ◽  
Calculating Model

scholarly journals Revisit of a Case Study of Spilled Oil Slicks Caused by the Sanchi Accident (2018) in the East China Sea

2021 ◽  
Vol 9 (3) ◽  
pp. 279
Author(s):  
Zhehao Yang ◽  
Weizeng Shao ◽  
Yuyi Hu ◽  
Qiyan Ji ◽  
Huan Li ◽  
...  
Keyword(s):  
East China Sea ◽  
Oil Spill ◽  
Oil Spills ◽  
Ocean Model ◽  
Sea Surface ◽  
East China ◽  
The East China Sea ◽  
Sar Images ◽  
China Sea ◽  
Spatial Coverage

Marine oil spills occur suddenly and pose a serious threat to ecosystems in coastal waters. Oil spills continuously affect the ocean environment for years. In this study, the oil spill caused by the accident of the Sanchi ship (2018) in the East China Sea was hindcast simulated using the oil particle-tracing method. Sea-surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF), currents simulated from the Finite-Volume Community Ocean Model (FVCOM), and waves simulated from the Simulating WAves Nearshore (SWAN) were employed as background marine dynamics fields. In particular, the oil spill simulation was compared with the detection from Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) images. The validation of the SWAN-simulated significant wave height (SWH) against measurements from the Jason-2 altimeter showed a 0.58 m root mean square error (RMSE) with a 0.93 correlation (COR). Further, the sea-surface current was compared with that from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2), yielding a 0.08 m/s RMSE and a 0.71 COR. Under these circumstances, we think the model-simulated sea-surface currents and waves are reliable for this work. A hindcast simulation of the tracks of oil slicks spilled from the Sanchi shipwreck was conducted during the period of 14–17 January 2018. It was found that the general track of the simulated oil slicks was consistent with the observations from the collected GF-3 SAR images. However, the details from the GF-3 SAR images were more obvious. The spatial coverage of oil slicks between the SAR-detected and simulated results was about 1 km2. In summary, we conclude that combining numerical simulation and SAR remote sensing is a promising technique for real-time oil spill monitoring and the prediction of oil spreading.

scholarly journals Sacrificial amphiphiles: Eco-friendly chemical herders as oil spill mitigation chemicals

2015 ◽  
Vol 1 (5) ◽  
pp. e1400265 ◽  
Author(s):  
Deeksha Gupta ◽  
Bivas Sarker ◽  
Keith Thadikaran ◽  
Vijay John ◽  
Charles Maldarelli ◽  
...  
Keyword(s):  
Crude Oil ◽  
Oil Spill ◽  
Oil Spills ◽  
Cold Water ◽  
Marine Ecosystem ◽  
Hot Water ◽  
Sea Surface ◽  
Marine Biota ◽  
Polar Group ◽  
Branched Chain

Crude oil spills are a major threat to marine biota and the environment. When light crude oil spills on water, it forms a thin layer that is difficult to clean by any methods of oil spill response. Under these circumstances, a special type of amphiphile termed as “chemical herder” is sprayed onto the water surrounding the spilled oil. The amphiphile forms a monomolecular layer on the water surface, reducing the air–sea surface tension and causing the oil slick to retract into a thick mass that can be burnt in situ. The current best-known chemical herders are chemically stable and nonbiodegradable, and hence remain in the marine ecosystem for years. We architect an eco-friendly, sacrificial, and effective green herder derived from the plant-based small-molecule phytol, which is abundant in the marine environment, as an alternative to the current chemical herders. Phytol consists of a regularly branched chain of isoprene units that form the hydrophobe of the amphiphile; the chain is esterified to cationic groups to form the polar group. The ester linkage is proximal to an allyl bond in phytol, which facilitates the hydrolysis of the amphiphile after adsorption to the sea surface into the phytol hydrophobic tail, which along with the unhydrolyzed herder, remains on the surface to maintain herding action, and the cationic group, which dissolves into the water column. Eventual degradation of the phytol tail and dilution of the cation make these sacrificial amphiphiles eco-friendly. The herding behavior of phytol-based amphiphiles is evaluated as a function of time, temperature, and water salinity to examine their versatility under different conditions, ranging from ice-cold water to hot water. The green chemical herder retracted oil slicks by up to ~500, 700, and 2500% at 5°, 20°, and 35°C, respectively, during the first 10 min of the experiment, which is on a par with the current best chemical herders in practice.

BRRDF Simulation Research on Detection Parameters of Oil-in-Water Emulsion of Oil Spill on Sea Surface

2020 ◽  
Vol 40 (17) ◽  
pp. 1701001
Author(s):  
张晓丹 Zhang Xiaodan ◽  
孔德明 Kong Deming ◽  
袁丽 Yuan Li ◽  
孔德翰 Kong Dehan ◽  
孔令富 Kong Lingfu ◽  
...  
Keyword(s):  
Oil Spill ◽  
Sea Surface ◽  
Simulation Research ◽  
Water Emulsion ◽  
Oil In Water ◽  
Oil In Water Emulsion

scholarly journals Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data

2018 ◽  
Vol 10 (12) ◽  
pp. 4408 ◽  
Author(s):  
Yu Li ◽  
Yuanzhi Zhang ◽  
Zifeng Yuan ◽  
Huaqiu Guo ◽  
Hongyuan Pan ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Crude Oil ◽  
Oil Spill ◽  
Sea Surface ◽  
Synthetic Aperture ◽  
Polarimetric Sar ◽  
Classification Result ◽  
Marine Oil ◽  
Oil Spill Detection ◽  
Biogenic Slicks

As a major marine pollution source, oil spills largely threaten the sustainability of the coastal environment. Polarimetric synthetic aperture radar remote sensing has become a promising approach for marine oil spill detection since it could effectively separate crude oil and biogenic look-alikes. However, on the sea surface, the signal to noise ratio of Synthetic Aperture Radar (SAR) backscatter is usually low, especially for cross-polarized channels. In practice, it is necessary to combine the refined detail of slick-sea boundary derived from the co-polarized channel and the highly accurate crude slick/look-alike classification result obtained based on the polarimetric information. In this paper, the architecture for oil spill detection based on polarimetric SAR is proposed and analyzed. The performance of different polarimetric SAR filters for oil spill classification are compared. Polarimetric SAR features are extracted and taken as the input of Staked Auto Encoder (SAE) to achieve high accurate classification between crude oil, biogenic slicks, and clean sea surface. A post-processing method is proposed to combine the classification result derived from SAE and the refined boundary derived from VV channel power image based on special density thresholding (SDT). Experiments were conducted on spaceborne fully polarimetric SAR images where both crude oil and biogenic slicks were presented on the sea surface.

Real-Time, High-Resolution, Space–Time Analysis of Sea Surface Temperatures from Multiple Platforms

2007 ◽  
Vol 135 (9) ◽  
pp. 3158-3173 ◽  
Author(s):  
Steven M. Lazarus ◽  
Corey G. Calvert ◽  
Michael E. Splitt ◽  
Pablo Santos ◽  
David W. Sharp ◽  
...  
Keyword(s):  
Bias Correction ◽  
Atmospheric Model ◽  
Current Data ◽  
Sea Surface ◽  
Loop Current ◽  
Background Error ◽  
Analysis System ◽  
Spatially Varying ◽  
Cloud Mask ◽  
The Impact

Abstract A sea surface temperature (SST) analysis system designed to initialize short-term atmospheric model forecasts is evaluated for a month-long, relatively clear period in May 2004. System inputs include retrieved SSTs from the Geostationary Operational Environmental Satellite (GOES)-East and the Moderate Resolution Imaging Spectroradiometer (MODIS). The GOES SSTs are processed via a sequence of quality control and bias correction steps and are then composited. The MODIS SSTs are bias corrected and checked against the background field (GOES composites) prior to assimilation. Buoy data, withheld from the analyses, are used to bias correct the MODIS and GOES SSTs and to evaluate both the composites and analyses. The bias correction improves the identification of residual cloud-contaminated MODIS SSTs. The largest analysis system improvements are obtained from the adjustments associated with the creation of the GOES composites (i.e., a reduction in buoy/GOES composite rmse on the order of 0.3°–0.5°C). A total of 120 analyses (80 night and 40 day) are repeated for different experimental configurations designed to test the impact of the GOES composites, MODIS cloud mask, spatially varying background error covariance and decorrelation length scales, data reduction, and anisotropy. For the May 2004 period, the nighttime MODIS cloud mask is too conservative, at times removing good SST data and degrading the analyses. Nocturnal error variance estimates are approximately half that of the daytime and are relatively spatially homogeneous, indicating that the nighttime composites are, in general, superior. A 30-day climatological SST gradient is used to create anisotropic weights and a spatially varying length scale. The former improve the analyses in regions with significant SST gradients and sufficient data while the latter reduces the analysis rmse in regions where the innovations tend to be well correlated with distinct and persistent SST gradients (e.g., Loop Current). Data thinning reduces the rmse by expediting analysis convergence while simultaneously enhancing the computational efficiency of the analysis system. Based on these findings, an operational analysis configuration is proposed.

scholarly journals Enrichment of Fusobacteria in Sea Surface Oil Slicks from the Deepwater Horizon Oil Spill

2016 ◽  
Vol 4 (3) ◽  
pp. 24 ◽  
Author(s):  
Tony Gutierrez ◽  
David Berry ◽  
Andreas Teske ◽  
Michael Aitken
Keyword(s):  
Oil Spill ◽  
Deepwater Horizon ◽  
Sea Surface ◽  
Deepwater Horizon Oil Spill

Deepwater Blowouts: Modeling for Oil Spill Contingency Planning, Monitoring, and Response

1999 ◽  
Vol 1999 (1) ◽  
pp. 295-303 ◽  
Author(s):  
Mark Reed ◽  
Øistein Johansen ◽  
Henrik Rye ◽  
Narve Ekrol ◽  
Ivar Singsaas ◽  
...  
Keyword(s):  
Oil Spill ◽  
Mechanical Response ◽  
Hydrate Formation ◽  
Field Trials ◽  
Sea Surface ◽  
Contingency Planning ◽  
Depth Of Penetration ◽  
Droplet Distribution ◽  
Vertical Density ◽  
Oil Gas

ABSTRACT This paper describes work in progress in the Environmental Engineering Division of SINTEF's Applied Chemistry Institute in Trondheim, Norway. The modeling work relies heavily on past and planned future field and laboratory work for calibration and verification data. Because these data remain incomplete, the results presented are tentative. The deepwater blowout model DeepBlow includes hydrate formation, gas dissolution, and separation of bubbles and oil droplets from the oil-gas-water plume as key processes which have been ignored in previous models, and which appear extremely important in determining the dynamics of the blowout plume. Time-variable vertical current profiles and the vertical density profile affect the depth of penetration of the plume. The oil droplet distribution in the blowout plume, combined with the above factors, determines to a large extent the time-varying footprint of the oil on the sea surface. Oil Spill Contingency And Response (OSCAR) model simulations demonstrate that mechanical response may be reasonably effective, or not, depending on the eventual correct values of key underlying parameters. Future field trials will be critical in establishing such values. The results demonstrate the usefulness of these models as tools for guidance of environmental monitoring and response activities during an actual blowout event.

Numerical Modeling of the Oil Spill Trajectory and Real-Time Tracking Using Intelligent Swarm Robots

2013 ◽  
Vol 446-447 ◽  
pp. 1261-1265 ◽  
Author(s):  
Mohsen Pashna ◽  
Rubiyah Yusof ◽  
Zool H. Ismail
Keyword(s):  
Numerical Modeling ◽  
Numerical Model ◽  
Crude Oil ◽  
Oil Spill ◽  
Water Surface ◽  
Sea Surface ◽  
Sea Waves ◽  
Robot Locomotion ◽  
Swarm Robots ◽  
Real Time Tracking

An oil spill is discharge of fluid petroleum such as crude oil or its by-product derivations such as diesel and gasoline on the water surface. In this paper, a numerical model of the oil spill has been introduced as a simulation of releasing oil on the sea surface. Meantime, the influence of sea waves and wind has been considered and shown. Moreover, a swarm of robots is engaged in order to track the spreading boundaries of the slicked oil, so that a novel schedule of robot locomotion is presented, based on the online sharing information in the flock network. Therefore, the swarm of robots tracks the oil spill margins intelligently and successfully.

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