The Deepwater Horizon (DWH) oil spill in the Gulf of Mexico (GoM) released an estimated 4.9 million barrels of oil between April 20, 2010 and July 15, 2010. An estimated 36% of the oil formed a neutrally buoyant intrusion, containing both dissolved compounds and oil microdroplets, between 1000 and 1300 m depth. This study used geographic information systems software, and data from water samples that were collected as part of the National Resource Damage Assessment (NRDA), to determine that an area of at least 1,600 km2 was exposed to DWH oil. Toxic BTEX (benzene, toluene, ethylbenzene, and xylenes) compounds and polycyclic aromatic hydrocarbons (PAHs) reached concentrations 950 and 50 times higher than maximum background concentrations, respectively. BTEX and n-alkane concentrations above pre-2010 values were present through late August, more than a month after the wellhead was capped. This study is the first to examine the DWH intrusion over such a large temporal and spatial extent. We further estimated that an area between 500 and 1000 km2 may have been exposed to harmful PAH concentrations, based on studies of PAH toxicity and U.S. Environmental Protection Agency (EPA) guidelines. We also found evidence of aggregation and deposition of oil near the DWH wellhead, as well as an area of 400 km2 where the intrusion may have impinged on the seafloor. While relative rates of dilution, degradation, and deposition in the intrusion are unknown, we have shown evidence that supports two previously proposed processes that may have deposited DWH oil from this deep intrusion onto sediments, where toxic compounds could be resuspended and continue to be bioavailable to benthic organisms.

Oil extraction in Nigeria has caused extensive environmental degradation and health problems in many Nigerian communities, particularly in the ecologically sensitive Niger Delta where nearly all of the oil extraction takes place. The reasons for this are complex and have roots in Nigeria’s colonial past. The Nigerian economy is largely reliant on its petroleum resources which, in conjunction with governmental corruption and high international demand for Nigerian oil, has created a system where environmental externalities are largely ignored. Multinational oil companies with little stake in the development and environment of Nigeria are responsible for most of the extraction projects and subsequent environmental damage. However, the Nigerian federal government has failed to effectively regulate these projects. Communities in the Niger Delta bear nearly all of the environmental burden of oil extraction, but see very little of the economic benefits. The main environmental impacts of oil extraction are oil spills, land use change, and gas flaring. Oil spills are very common in the Niger Delta. Cleanup efforts are often inadequate, resulting in loss of delicate ecosystems as well as fisheries and farmland. Large tracts of rainforest and mangrove ecosystems have been cleared or degraded by the oil extraction process. Nigeria flares more gas per barrel of oil extracted than any other country in the world, contributing to global warming and creating serious health hazards for communities located near gas flares. Diversification of the Nigerian economy would help to alleviate many of the factors that lead to environmental degradation, including the dependence of the government on oil revenues, high unemployment, and rampant oil theft. Curbing government corruption is also vital to effective regulation of oil extraction. International consumers can help Nigeria head towards a less petroleum-driven future through an increased awareness of the origins of their oil and pressure on the Nigerian federal government and the multinational oil companies to extract oil more conscientiously or even to discontinue oil extraction. But most importantly, the solution to Nigeria’s economic concerns must ultimately come from Nigerians as international influence has been a major contributor to the environmental degradation in the first place.

Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application.

Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application.

The synthetic aperture radar (SAR) onboard Earth observing satellites has been acknowledged as an integral tool for many applications in monitoring the marine environment. Some of these applications include regional sea-ice monitoring and detection of illegal or accidental oil discharges from ships. Nonetheless, a practicality of the usage of SAR images is greatly hindered by the presence of speckle noises. Such noise must be eliminated or reduced to be utilized in real-world applications to ensure the safety of the marine environment. Thus this thesis presents a novel two-phase total variation optimization segmentation approach to tackle such a challenging task. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise smooth state by minimizing the total variation constraints. In the finite mixture model classification phase, an expectation-maximization method was performed to estimate the final class likelihoods using a Gaussian mixture model. Then a maximum likelihood classification technique was utilized to obtain the final segmented result. For its evaluation, a synthetic image was used to test its effectiveness. Then it was further applied to two distinct real SAR images, X-band COSMO-SkyMed imagery containing verified oil-spills and C-band RADARSAT-2 imagery mainly containing two different sea-ice types to confirm its robustness. Furthermore, other well-established methods were compared with the proposed method to ensure its performance. With the advantage of a short processing time, the visual inspection and quantitative analysis including kappa coefficients and F1 scores of segmentation results confirm the superiority of the proposed method over other existing methods.

Green marketing has been a main topic of discussion for several years. Most studies conducted in this area have focused on the benefits of green marketing for a normal company. However, no studies have considered the effectiveness of green marketing by a negative brand like BP. This study aims to reveal the effectiveness of green marketing after pollution. An experiment was conducted to evaluate participants’ attitudes toward the marketing strategy. Although the results were not significant, some interesting findings were revealed and are addressed at the end of the paper. They imply that individuals with green awareness are less influenced by both green marketing and an economic-assistance strategy in BP’s case, compared to individuals with lower green awareness. Overall, all participants in this study preferred the economic-assistance strategy, which means that green marketing is not effective for a corporation involved in pollution issues. / text

Drawing on social movement literature, my thesis examines if news media, NGO, business and government engagement of the BP Oil Spill in the Gulf of Mexico is affected by issue or event complexity, visuality, or issue build-up. To engage this, data from English language newspaper articles in the US, Canada, and the UK, press releases by Greenpeace and Sierra Club, press releases by BP, ExxonMobil, and Shell as well as press releases by the White House are analyzed using both quantitative and qualitative methods. I find that as an issue or event’s casual narrative becomes less complicated and as it becomes easier to portray visually its engagement by social and political actors increases. I also find that issue engagement is influenced by whether or not social and political actors signal an issue or an event’s importance to others.

In April 2010, approximately 4.9 million barrels of oil were accidentally released into the Gulf of Mexico during the Deepwater Horizon Macondo Mc252 Oil Spill. Some of the surface oil was carried by prevailing winds and currents and reached the coast of Louisiana impacting marsh and marine ecosystems. One and a half years after this incident, a set of oiled marsh samples (2 grab samples) coupled with nearby subtidal and intertidal cores (12 cores) were collected from Barataria Bay, Louisiana to determine the probable source of petroleum residues present and to characterize the chemical composition of the oil. Plus, pre-spill core which was collected from Barataria Bay in 2007 was analyzed to identify the background hydrocarbon composition of the area. Polycyclic aromatic hydrocarbons (PAH), total petroleum hydrocarbons (TPH), biomarker, and stable carbon isotope compositions of selected samples were detected using a GC-MS and an elemental analyzer Conflo system coupled to a DeltaPlusXP isotope ratio mass spectrometer. The comprehensive chemical data allowed us to classify the pre and post-spill samples into 4 Groups. According to this classification, Group 1 and Group 2 samples had the highest concentrations of petroleum-derived hydrocarbons. Group 3 and background samples, on the other hand, was dominated by biogenic signatures. Although a direct connection between the detected and spilled Macondo oils results are complicated due to confounding factors (e.g., already present hydrocarbons and weathering processes), our biomarker data indicates that both oils have similar signatures. This close genetic relationship was also identified by stable carbon isotope analysis. The impact of the Macondo Mc252 Oil Spill in Barataria Bay appears to be limited to areas closer to the source. The oil has undergone moderate weathering and has penetrated into, the at least, the top 9 cm sediments. Additionally, to examine the decadal-scale history of sedimentation in these marshes, a sediment core was analyzed for the radioisotope 137Cs. The observed sedimentation rate of 0.39 cm/yr shows that oil pollutant input into Barataria Bay has been ongoing for at least 50-60 years.

The synthetic aperture radar (SAR) onboard Earth observing satellites has been acknowledged as an integral tool for many applications in monitoring the marine environment. Some of these applications include regional sea-ice monitoring and detection of illegal or accidental oil discharges from ships. Nonetheless, a practicality of the usage of SAR images is greatly hindered by the presence of speckle noises. Such noise must be eliminated or reduced to be utilized in real-world applications to ensure the safety of the marine environment. Thus this thesis presents a novel two-phase total variation optimization segmentation approach to tackle such a challenging task. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise smooth state by minimizing the total variation constraints. In the finite mixture model classification phase, an expectation-maximization method was performed to estimate the final class likelihoods using a Gaussian mixture model. Then a maximum likelihood classification technique was utilized to obtain the final segmented result. For its evaluation, a synthetic image was used to test its effectiveness. Then it was further applied to two distinct real SAR images, X-band COSMO-SkyMed imagery containing verified oil-spills and C-band RADARSAT-2 imagery mainly containing two different sea-ice types to confirm its robustness. Furthermore, other well-established methods were compared with the proposed method to ensure its performance. With the advantage of a short processing time, the visual inspection and quantitative analysis including kappa coefficients and F1 scores of segmentation results confirm the superiority of the proposed method over other existing methods.

The Western Tropical North Atlantic (WTNA) was once thought to be a net source of carbon dioxide (CO₂) to the atmosphere, but recent studies have shown that this Amazon River influenced region may actually act as a net sink for CO₂. During a 2010 research cruise to the WTNA, we characterized the impact of the Amazon River on offshore diazotrophy (N₂-fixation) and the resulting stimulation of biological carbon export from surface waters. Through the delivery of phosphate- and silicate-replete waters to the nitrogen (N) limited surface waters of the WTNA, the aging Amazon River plume promotes the growth of diatom-diazotoph associations (DDAs). Regions supporting large DDA blooms were associated with increased pCO₂ and DIC drawdown in the surface waters, reflecting the net export of carbon from the mixed layer. The existence of this biologically mediated linkage between the C and N cycles in productive surface waters is well known, but we have only recently discovered a stimulatory relationship in deep waters between oil/gas release and N₂-fixation. This association was first observed after the Deepwater Horizon oil spill in 2010 and we again saw evidence for it in the days following the Hercules 265 natural gas blowout. This blowout event was characterized by the release of an unknown quantity of natural gas into the shelf waters of the Northern Gulf of Mexico, but we detected a response from the marine microbial community within days. We observed a significant drawdown of dissolved oxygen and found biogeochemical evidence for the incorporation of methane-carbon into the food web, along with a modest stimulation of N₂-fixation. The episodic nature of anthropogenic blowouts makes them difficult to study, so we use cold seeps in the Gulf of Mexico as natural analogues. Interestingly, we have measured both methane oxidation and N₂-fixation at depth above some of the more active seeps. Using NanoSIMS analyses, we have taken the first steps towards physically characterizing the organisms utilizing these metabolisms. It appears that different organisms are carrying out these processes, with CH₄-assimilation occurring primarily in individual particles or small aggregates, whereas N₂-fixtion was associated with larger, sulfur-containing aggregates. Continued NanoSIMS work in combination with the use of microbial ID techniques will help to further characterize these unique deepwater diazotrophs.