Spill Characteristics Research Articles

Modeling oil spill disasters using system dynamics: A case study on the MT Princess Empress oil spill in Oriental Mindoro, Philippines

Oil spill events pose potentially irreversible threats to the environment and economy of affected communities, necessitating a comprehensive analysis of their dynamics. This study employs system dynamics (SD) modeling to enhance the current understanding of the complex dynamics associated with oil spill incidents, focusing on the case of the MT Princess Empress oil spill in the Philippines last February 2023. The research integrates quantitative and qualitative data to develop a dynamic simulation model that captures the interdependencies among key variables, such as spill characteristics, environmental factors, response measures, resource allocation, and socio-economic impacts. Model validation was performed by comparing the results of the simulation with historical data from the MT Princess Empress incident. Sensitivity analysis was then conducted to identify critical factors influencing the system's behavior. Among oil spill size, initial recovery resources, resource support delay, and cash assistance to affected individuals, oil spill size was determined to have the most significant effect on the oil spill system owing to the amplification of negative consequences via reinforcing feedback loops. The research findings thus underscore the effectiveness of preventive measures as the primary strategy for mitigating the consequences of oil spills. Sustainable recovery for communities affected by such incidents also necessitates a shift towards long-term rehabilitation initiatives and proactive community engagement rather than relying on short-term solutions.

Energy approach to the description of the dynamics of hydrocarbon spills

An energy approach to the problem of theoretical description of axisymmetric and quasi-one-dimensional oil product spills is presented. Within the framework of these models, approximate equations describing the evolution of the size of oil spots over time were derived. Solution of these equations describing the characteristics of spills of both a limited area (for machine oil stains) and unlimited spills of crude oil is obtained. A number of laboratory experiments have been carried out to study the dynamics of spreading of a spot of reference engine oil and crude oil for axisymmetric and quasi-one-dimensional flows. The presented theoretical and experimental results are in good agreement for both spreading regimes.

Long-Wave Infrared Polarization-Based Airborne Marine Oil Spill Detection and Identification Technology

In this paper, infrared polarization detection information acquisition technology is proposed, and the polarization characteristics of oil spills are modeled and studied. A set of long-wave infrared polarization detection equipment for oil spills is designed and built, and modeling research on oil spill polarization characteristics is carried out to accurately detect and identify oil spill types and for the faster processing of oil spill events. Oil spill accuracy is increased by defining the polarization maintenance method of the polarization optical system and reducing the polarization measurement error brought on by the imaging system. As a result, a higher than 3% contrast exists between the polarization degree image and the corrected infrared intensity image. Outdoor tests using oil, palm oil, crude oil, gasoline, and diesel oil spill types are carried out in a controlled environment to collect data on the polarization of various oil species. According to the findings, each oil species’ infrared polarization contrast with seawater is typically greater than its infrared intensity contrast. However, the polarization data of saltwater, diesel, and palm oil, which are difficult to identify in intensity data, show a noticeable difference, further proving the viability of utilizing polarization to discern oil spills.

DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation

Successful implementation of oil spill segmentation in synthetic aperture radar (SAR) images is vital for marine environmental protection. In this article, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images and the other is the generative module to produce oil spill segmentation maps by drawing the latent feature variables as inputs. Thus, to yield accurate segmentation, we take into account the intrinsic distribution of backscatter values in SAR images and embed it in our segmentation model. The intrinsic distribution originates from SAR imagery, describing the physical characteristics of oil spills. In the training process, the formulated intrinsic distribution guides efficient learning of optimal latent feature variable inference for oil spill segmentation. The efficient learning enables the training of our proposed DGNet with a small amount of image data. This is economically beneficial to oil spill segmentation where the availability of oil spill SAR image data is limited in practice. Additionally, benefiting from optimal latent feature variable inference, our proposed DGNet performs accurate oil spill segmentation. We evaluate the segmentation performance of our proposed DGNet with different metrics, and experimental evaluations demonstrate its effective segmentations.

Oil Spill Identification based on Dual Attention UNet Model Using Synthetic Aperture Radar Images

Oil spills cause tremendous damage to marine, coastal environments, and ecosystems. Previous deep learning-based studies have addressed the task of detecting oil spills as a semantic segmentation problem. However, further improvement is still required to address the noisy nature of the Synthetic Aperture Radar (SAR) imagery problem, which limits segmentation performance. In this study, a new deep learning model based on the Dual Attention Model (DAM) is developed to automatically detect oil spills in a water body. We enhanced a conventional UNet segmentation network by integrating a dual attention model DAM to selectively highlight the relevant and discriminative global and local characteristics of oil spills in SAR imagery. DAM is composed of a Channel Attention Map and a Position Attention Map which are stacked in the decoder network of UNet. The proposed DAM-UNet is compared with four baselines, namely fully convolutional network, PSPNet, LinkNet, and traditional UNet. The proposed DAM-UNet outperforms the four baselines, as demonstrated empirically. Moreover, the EG-Oil Spill dataset includes a large set of SAR images with 3000 image pairs. The obtained overall accuracy of the proposed method increased by 3.2% and reaches 94.2% compared with that of the traditional UNet. The study opens new development ideas for integrating attention modules into other deep learning tasks, including machine translation, image-based analysis, action recognition, and speech recognition.

Oil spills: impacts and perspectives of treatment technologies with focus on the use of green surfactants.

Oil spills into the oceans cause irreparable damage to marine life and harms the coastal population of the affected areas. The main measures to be taken in response to an oil spill are to reduce the impact on marine life, prevent oil from reaching the shore through its recovery, and accelerate the degradation of unrecovered oil. Any environmental damage can be reduced if the spilled oil is removed from the water quickly and efficiently. Therefore, it is essential to know the treatment strategies for spilled oils. Several technologies are currently available, including booms, skimmers, in situ burning, use of adsorbents, dispersants/surfactants, and bioremediation. The selection of the type of treatment will depend not only on the effectiveness of the technique, but mainly on the type of oil, amount spilled, location, weather, and sea conditions. In this review, the characteristics of oil spills, their origin, destination, and impacts caused, including major accidents around the world, are initially addressed. Then, the main physical, chemical, and biological treatment technologies are presented, describing their advances, advantages, and drawbacks, with a focus on the use of green surfactants. These agents will be described in detail, showing the evolution of research, recent studies, patents, and commercialized products. Finally, the challenges that remain due to spills, the necessary actions, and the prospects for the development of existing treatment technologies are discussed, which must be linked to the use of combined techniques.

Oil Spill SAR Image Segmentation via Probability Distribution Modeling

Segmentation of marine oil spills in synthetic aperture radar (SAR) images is a challenging task because of the complexity and irregularities in SAR images. In this work, we aim to develop an effective segmentation method which addresses marine oil spill identification in SAR images by investigating the distribution representation of SAR images. To seek effective oil spill segmentation, we revisit the SAR imaging mechanism in order to attain the probability distribution representation of oil spill SAR images, in which the characteristics of SAR images are properly modelled. We then exploit the distribution representation to formulate the segmentation energy functional, by which oil spill characteristics are incorporated to guide oil spill segmentation. Moreover, the oil spill segmentation model contains the oil spill contour regularization term and the updated level set regularization term which enhance the representational power of the segmentation energy functional. Benefiting from the synchronization of SAR image representation and oil spill segmentation, our proposed method establishes an effective oil spill segmentation framework. Experimental evaluations demonstrate the effectiveness of our proposed segmentation framework for different types of marine oil spill SAR image segmentation.

Design and validation of a synchrotron proton beam line for FLASH radiotherapy preclinical research experiments.

The main purpose of this work was to generate and validate the dosimetric accuracy of proton beams of dimensions that are appropriate for in vivo small animal and in vitro ultrahigh dose rate (FLASH) radiotherapy experiments using a synchrotron-based treatment delivery system. This study was performed to enable future investigations of the relevance of a spread-out Bragg peak (SOBP) under FLASH conditions. The spill characteristics of the small field fixed horizontal beam line were modified to deliver accelerated protons in times as short as 2ms and to control the dose delivered. A Gaussian-like transverse beam profile was transformed into a square uniform one at FLASH dose rates, while avoiding low-dose regions, a crucial requirement to protect normal tissue during FLASH irradiation. Novel beam-shaping devices were designed using Monte Carlo techniques to produce up to about 6cm3 of uniform dose in SOBPs while maximizing the dose rate. These included a scattering foil, a conical flattening filter to maximize the flux of protons into the region of interest, energy filters, range compensators, and collimators. The shapes, sizes, and positions of the components were varied to provide the required field sizes and SOBPs. The designed and fabricated devices were used to produce 10-, 15-, and 20-mm diameter, circular field sizes and 10-, 15-, and 20-mm SOBP modulation widths at uniform physical dose rates of up to 375Gy/s at the center of the SOBP and a minimum dose rate of about 255Gy/s at the entrance, respectively, in cylindrical volumes. The flatness of lateral dose profiles at the center could be adjusted to within ±1.5% at the center of the SOBP. Assessment of systematic uncertainties, such as impact of misalignments and positioning uncertainties, was performed using simulations, and the results were used to provide appropriate adjustments to ensure high-accuracy FLASH beam delivery for both in vitro and in vivo preclinical experiments. It is feasible to use synchrotron-generated proton beams of sufficient dimensions for FLASH radiobiology experiments. We expect to use the system we developed to acquire in vitro and in vivo small animal FLASH radiobiology data as a function of dose, dose rate, oxygen content, and linear energy transfer to help us understand the underlying mechanisms of the FLASH phenomenon.

Ultra-High Dose-Rate Particle Therapy: On the Delivery of Active Beam Scanning at a Synchrotron-Based Facility With p, 4He, 12C and 16O

<h3>Purpose/Objective(s)</h3> Despite being a principal research interest, feasibility of raster-scanned ultra-high dose-rate particle beams remains largely unknown. Moreover, with the increasing evidence of potential normal tissue sparing with uHDR delivery, there is an urgent need for comprehensive assessments of facility-specific delivery capacity and characteristics to ultimately drive forward radiobiological investigations of the FLASH effect and subsequent modeling. This work aims to establish and assess the range and limits of uHDR delivery for raster-scanned particle therapy at a synchrotron-based facility for "light" (p, He) and "heavy" (C, O) ion beams. <h3>Materials/Methods</h3> For p, He, C and O ions, different settings of the synchrotron are tested to assess the linearity window for clinically ideal spill characteristics at FLASH dose levels and dose-rates. For instance, different parameters require modification and/or monitoring for each delivery scenario, e.g., monitor chamber current, delivery time, field size, the number of scanned spot positions, the number of particles and thus the achievable dose level. The dose can be assessed in two particular positions, either the entrance channel of a pristine peak or within a spread-out Bragg peak using a range modulator. Limitations in dose level and dose-rate were investigated within the entrance channel where, conceptually, normal tissues reside within the irradiation field. In addition, the influences of the field size and number of scanned-spots on spatially dependent dose-time structure and delivery time are investigated. <h3>Results</h3> For the entrance channel, parameters which primarily impact dose delivery characteristics were both the number of particles to be delivered within a single spill by the synchrotron and the requested beam intensities. Parameter settings influence delivery characteristics differently for "light" and "heavy" particles: for "light ions" maximizing a number of particles per spill to FLASH dose levels can be limited while for "heavy" ions, extraction intensity is the main obstacle in achieving FLASH dose-rates. Characterized for the four ions, the dose-time evolution is spatially dependent and varies with the number of spots and scanning patterns. <h3>Conclusion</h3> In parallel with mean delivery time and mean dose-rate measured live via monitoring systems, temporal delivery aspects of in-field dose distributions can be assessed and verified (experimentally or in-silico) for active beam delivery that could complement further bio-modelling. The results of this work make evident the importance of a proper assessment of dosimetry and delivery characteristics prior to biological experimentation, as demonstrated at our synchrotron-based facility where delivery parameters may fluctuate between treatment sessions.

Experimental Study on the Synergistic Effect between Evaporation Weathering and Emulsification of Oil Spills

First, a range of experiments using a stainless steel shallow plate in a thermostatic oscillator were carried out to simulate evaporation weathering of oil spills under different temperatures, wind velocities, oil film thicknesses, and wave conditions. The information on influencing factors of evaporation weathering could be obtained. Then, evaporation experiments of four oil samples with three emulsification states were conducted, and the effect of emulsification on evaporation of oil spills was investigated. The characteristics of each operation were described, mass loss characteristics of oil spills with time were plotted, and the effects of evaporative weathering processes under different conditions were quantitatively compared. A high-precision visualization system was utilized to simulate experimentally oil spill emulsification processes, and the effect of evaporative weathering on emulsification weathering of oil spills was investigated. The results of evaporation experiments showed that the increase of temperature could promote the evaporative weathering. The thin film thickness was beneficial to evaporation of oil spills. The increment of the wind speed could promote evaporation behavior when the wind velocity was small, but the increase of velocity had little effect on oil evaporation when the wind speed was large. Wave conditions had little effect on oil evaporation under the conditions of this experiment. The effect of different emulsification states on oil evaporation was not consistent. Unstable or semistable water-in-oil emulsions inhibited oil evaporation at the initial stage of evaporation, but water evaporation would increase oil-phase evaporation with the destruction of the emulsion structure. Stable water-in-oil emulsions inhibited evaporation weathering. The evaporation weathering of oil was conducive to the emulsification of oil.

Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training

We investigate the problem of training an oil spill detection model with small data. Most existing machine-learning-based oil spill detection models rely heavily on big training data. However, big amounts of oil spill observation data are difficult to access in practice. To address this limitation, we developed a multiscale conditional adversarial network (MCAN) consisting of a series of adversarial networks at multiple scales. The adversarial network at each scale consists of a generator and a discriminator. The generator aims at producing an oil spill detection map as authentically as possible. The discriminator tries its best to distinguish the generated detection map from the reference data. The training procedure of MCAN commences at the coarsest scale and operates in a coarse-to-fine fashion. The multiscale architecture comprehensively captures both global and local oil spill characteristics, and the adversarial training enhances the model’s representational power via the generated data. These properties empower the MCAN with the capability of learning with small oil spill observation data. Empirical evaluations validate that our MCAN trained with four oil spill observation images accurately detects oil spills in new images.

Detailed chemical characterization of the composition and variability of soil gas at remediated residential heating oil discharges

Underground storage tanks containing petroleum or other hazardous substances are used widely for residential storage of home heating oil. Spills and leaks of fuel from these tanks are common, and resulting subsurface petroleum vapors may pose health risks. However, understanding of this risk is limited by a lack of observational data on the chemical composition of vapors from discharged fuel. We present here the composition of soil gas sampled at 66 remediated residential sites of underground heating oil discharges throughout Virginia using a newly developed data analysis technique that allows characterization of hydrocarbons by carbon number and degree of unsaturation. Measured concentrations of total petroleum hydrocarbons exceeded 100,000 μg/m3 at 12 sites, but its composition varied widely between sites. Concentrations of hydrocarbons from chemical classes differing by more than a few carbon numbers or degrees of unsaturation are found to be poorly correlated. Furthermore, differences in composition are poorly described by metrics expected to indicate subsurface weathering (e.g., discharge year, or ratio of n-heptadecane to pristane). These results suggest that the composition and magnitude of residual contamination at remediated subsurface discharges is driven by rarely documented spill characteristics (e.g., age and composition of source material, discharge rate, etc.).

Oil Spill Simulation and Analysis of Its Behavior Under the Effect of Weathering and Chemical Dispersant: a Case Study of the Bacia de Campos—Brazil

Offshore oil spills are a critical form of marine pollution, requiring researchers to mitigate their impacts. In this sense, this paper contributes to a better understanding of oil behavior after spillage and the improvement of contingency measures. Using Bacia de Campos as the study area, which accounts for approximately 32% of daily oil and gas production in Brazil, information regarding the properties of light, medium, and heavy oils, seawater, wind and wave weather conditions, and oil spill characteristics was used to perform simulations to identify the percentage of oil remaining after the offshore spill and the effectiveness of the chemical dispersant in dispersing this oil. The results show that after the weathering simulation, on average, 55% of the spilled oil remains at sea in dispersed and remaining form and that the use of chemical dispersant under the conditions defined in this study did not result in significant oil removal.

Evaluation of the fate of carcasses and dummies deployed in the nearshore and offshore waters of the northern Gulf of Mexico

In response to the Deepwater Horizon oil spill, federal and state agencies conducted field studies to develop inputs for a shoreline deposition model used to estimate nearshore avian mortality resulting from the spill. A 2011 carcass drift study was designed to generate data on the likelihood that birds that died on the water would deposit along the northern Gulf of Mexico coast (rather than becoming lost at sea). In the case of the Deepwater Horizon oil spill, carcass losses at sea accounted for a significant portion of nearshore avian mortality. We evaluate the data collected during the Deepwater Horizon carcass drift study and compare the results obtained from the use of avian carcasses versus dummy carcasses (dummies) and the differences between those deployed nearshore versus further offshore. We conclude that, although the use of dummies provided valuable confirmation on the drift patterns of dead birds, dummies drifted greater distances, for longer periods of time, and were more likely to be observed beached compared to avian carcasses, with 64.6% of dummies beaching compared to 17.2% of carcasses. In response to future spills, researchers should account for these potential biases when incorporating dummy drift data into estimates of avian carcass loss. Further, none of the avian carcasses and dummies released more than 40 km from the shoreline made it to shore. In the northern Gulf of Mexico, carcasses that die on the waters farther offshore are unlikely to make it to shore to be captured in a deposition model; therefore, it may be appropriate to utilize a separate methodology to estimate offshore mortality. The applicability of these results to other spill events should be evaluated in the context of the specific spill characteristics.

Spatial Distribution Characteristics of Oil Spills in the Bohai Sea Based on Satellite Remote Sensing and GIS

Bing, L.; Xing, Q.-G.; Liu, X., and Zou, N.-N., 2019. Spatial distribution characteristics of oil spills in the Bohai Sea based on satellite remote sensing and GIS. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 164-170. Coconut Creek (Florida), ISSN 0749-0208.In recent years, on account of the vast and persistent damage of oil spill accidents, it becomes essential to carry out a further study on oil spill distribution characteristics on sea. Routine satellite remote sensing surveillance on oil spills with Synthetic Aperture Radar (SAR) proved to be ideal for analyzing distribution of oil pollution in macro scale. In this research work, considering the presence of “look-alikes” phenomena on SAR images as well as current operational application of “confidence level”, a confidence-oriented oil spill geodatabase is initially designed and built for quantification analysis. Then, in view of the requirement of marine grid management, a common framework based on remote sensing and Geographic Information System (GIS) is proposed to map and reveal the spatial distribution of oil pollution, in the process of which, oil pollution index (OPI) is put forward to evaluate oil pollution levels, then oil pollution distribution map can be compiled in terms of OPI, and the relationship between oil pollution and oil spill risk source can be further analyzed. Finally, a case study of the Bohai Sea and the north of the Yellow Sea with five-year's inter-annual satellite monitoring data was studied. The results showed that, during the period between 2009 and 2013, the high frequency grids of potential oil spills mainly distributed in the Bohai Bay and the south of the Liaodong Bay. 77.03 % of detected oil spills were within a certain distance along the sea lanes or near the offshore platforms, indicating a high risk of oil pollution of these areas. Generally, this research work also benefits for yield for the first time a rather comprehensive distribution characteristics of potential oil spills in macro scale in the Bohai Sea combined with remote sensing and GIS technique, which will be beneficial for oil spill response, preparedness and risk management.

Characterizing and modeling environmental emergency of unconventional oil and gas spills in the USA: Life-year versus spill factors

Abstract Significantly reducing consumers' electric bills and producing more jobs in USA, the remarkable growth of unconventional oil and gas (UOG) especially shale gas production in the last decade has made an impressive accomplishment. However, threatening the environment caused by UOG spills, UOG has caused enormous concerns about public health risks, there is minimized research examining the UOG spills’ causal mechanism and its spatial and temporal characteristics, which could play pivotal roles in risk control and environmental protection. Using two states Colorado (CO) and New Mexico (NM) in the USA with detailed UOG spill observations from 2005 to 2014, this study designs multi-categorical statistical tests and models to examine the factors that characterize UOG spills including spilled volume, life-year, cause, pathway, and spilled material. The ANOVA of spilled volumes across life-years has a p values 0.517, and hence the differences in spilled volumes among life-years are not significant in both CO or NM, but spilled materials are significantly between life-year 0 and other life-years with a p-value 0.0001. Based on a series of Poisson regression models for the association between pathway and spilled material and the conditional association given causal mechanism, the Chi-squared tests have p-values less than 0.00001, which shows both joint dependence and conditional dependence of pathway and spilled materials by controlling causal factors are significant in both CO and NM. Furthermore, spatiotemporal hot and cold spots of UOG spills are significant in CO, but they are not significant at p-value 0.01 in NM. This study is the first time to analyze and model the multivariate factors of UOG spills, which provides the first-hand insight to the characteristics of spills and to the monitoring and mitigation of potential risks in the lifetime of UOG operations.

An Investigation into the Capability of Compact Polarized SAR to Classify Multi-Sea-Surface Characteristics

ABSTRACTThis paper assesses the capability of Compact Polarized (CP) Synthetic Aperture Radar (SAR) to classify multi-sea-surface characteristics (defined as typical sea surface suspended sediments, such as oil spills algal look-alikes and algal blooms). CP reconstruction is carried out on two C-band RADARSAT-2 quad-polarized (QP) SAR images using Nord's algorithm. The accuracies of the reconstructed polarized signatures of the multi-sea-surface characteristics of open water, oil spills and algal blooms derived from QP original quad-polarized data (quad-pol) and CP reconstructed approximate quad-polarized data (namely pseudo quad-pol) are then compared and analyzed. In addition, 10 types of polarization parameters are chosen in order to investigate the accuracy of the reconstruction for open water, oil spills and algal blooms. A comparison of the results shows that, of the chosen parameters, the reconstruction accuracies for σ0HH, σ0VV, ρ, l1, l2 and α are the highest whereas the accuracies obtained for σ0HV, l3, H and A are unsatisfactory. The influence of reconstructed CP polarization parameters on the classification of multi-sea-surface characteristics is also assessed. Based on an assessment of this classification, it is concluded that the hybrid polarization (CTLR) mode should be adopted for the reconstruction of σ0HH, σ0VV, l1, H and α whereas the π/4 mode should be used for the reconstruction of σ0HV, ρ, l2 and l3.

Distillate marine fuel oil and ULSFO spills in cold climate - oil spill characteristics and response options

In 2014 the Norwegian Coastal Administration (NCA) conducted an environmental risk and oil spill response analysis related to possible oil spills from shipping in the areas of Svalbard and Jan Mayen. One of the key findings were that due to regulations to ban heavy fuel oil in protected areas, the most likely spill scenarios are spills of distillate marine fuel oils. Furthermore, the cold climate is expected to slow down oil weathering processes, and in calm weather situations this may call for active response, even to spills of light fuel oils. Also along the coast of mainland Norway, response options to spills of light fuel oils is an emerging topic. This includes not only MGO/MDO, but also several new products formulated to meet the 2015 Emission Control Areas (ECA) sulphur limit, also referred to as hybrid fuel oil / ultra low sulfur fuel oil (ULSFO). Previous experiences from spills of light fuel oils in Norwegian waters have been summarized; however, some recommendations for response remain inconclusive. Hence, the need for increased knowledge of the characteristics of light fuel oils and relevant response options is recognized. SINTEF analyzed a range of light fuel oils on behalf of NCA. This initial screening included chemical characterization (GC-MS/GC-FID) and identification of physical properties, i.e. viscosity, density, pour point, flash point, as well as emulsifying properties. Based on these results, five different fuel oils were selected for further examination, including:- Weathering predictions and improved trajectory modeling- Chemical and toxicological characterization of water accommodated fraction (WAF)- Laboratory tests of properties related to dispersant use and ignition, both in order to explore the applicability of dispersants and in-situ burning as response techniques, and to determine windows of opportunity for the different oil types. Laboratory tests are performed at 2 °C and 13 °C, reflecting “arctic” / cold climate conditions and North Sea summer conditions. Furthermore, mechanical recovery will be tested on the same oil types in the NCA test facility (abstract submitted by Holt &amp; Frost). The results from this ongoing project will be presented from an operational viewpoint. They are expected to give insights useful to response planning, decision making during spill incidents, and enhanced response options for future spills of distillate marine fuel oils and ULSFO, especially in cold climates and arctic environment.

Congestion spill effects of Heathrow and Frankfurt airports on connection traffic in European and Gulf hub airports

We develop two stage fixed-effects single-spill and double-spill models for congestion connection spills of London Heathrow and Frankfurt airports on 9 hub airports in Europe and the Gulf. Our panel data covers connection traffic from 1997 to 2013 for Heathrow and 1997 to 2011 for Frankfurt. The single-spill results support strongly that the connection spills from Heathrow’s capacity limitations do strengthen competing hub airports of major alliance groups and to a lesser degree one Gulf hub. The double-spill model for Heathrow and Frankfurt shows nearly asymmetric overall spill characteristics between the two airports. Our results underline the influence of airline network strategies on congestion spills as European airline networks are shaped by alliances and umbrella mergers. Thus, the airline network perspective in airport capacity expansion decisions needs to play a greater role, as indicated by our asymmetric results for overall spill effects between Heathrow and Frankfurt airports.

Research on polarization of oil spill and detection

The SAR (Synthetic Aperture Radar) has the capabilities for all-weather day and night use. In the case of determining the effects of oil spill dumping, the oil spills areas are shown as dark spots in the SAR images. Therefore, using SAR data to detect oil spills is becoming progressively popular in operational monitoring, which is useful for oceanic environmental protection and hazard reduction. Research has been conducted on the polarization decomposition and scattering characteristics of oil spills from a scattering matrix using allpolarization of the SAR data, calculation of the polarization parameters, and utilization of the CPD (Co-polarized Phase Difference) of the oil and the sea, in order to extract the oil spill information. This method proves to be effective by combining polarization parameters with the characteristics of oil spill. The results show that when using Bragg, the oil spill backscattering machine with Enopy and a mean scatter α parameter. The oil spill can be successfully identified. However, the parameter mechanism of the oil spill remains unclear. The use of CPD can easily extract oil spill information from the ocean, and the polarization research provides a base for oil spill remote sensing detection.