Terrestrial Oil Spills Research Articles

Oil spill soil remediation using thermal desorption: Project synthesis and outcomes

AbstractFarmland within the Williston Basin of North Dakota was the site of the largest terrestrial oil spill to date in the United States in 2013. Over 3200 m3 of oil was released into the topsoil and subsoil, creating a risk to soil, water, and air resources. The purpose of this document is to provide a summary of results from a 7‐year project investigating the impacts of how thermal desorption (TD), the method used to remediate topsoil and subsoil, impacted contaminant reduction, soil function, and plant productivity simultaneously with site remediation. Soil disturbance and TD decreased soil organic matter and microbial communities, resulting in decreased soil function and plant production. However, TD did not reduce soil microbial recovery 4 years after treatment. Blending TD‐treated soil with uncontaminated topsoil appeared to minimize these negative effects and promote recovery of soil function. These findings provided critical information to stakeholders in the understanding of soil remediation and reclamation in this region.

Terrestrial oil spill mapping using satellite earth observation and machine learning: A case study in South Sudan

Terrestrial oil spills are a major threat to environmental and human well-being. Rapid, accurate, and remote spatial assessment of oil contamination is critical to implementing countermeasures that prevent potentially lasting ecological damage and irreversible harm to local communities. Satellite remote sensing has been used to support such assessments in inaccessible regions, although mapping small terrestrial oil spills is challenging – partly due to the pixel size of remote sensing systems, but also due to the distinguishability of small oil spill areas from other land cover types. We assessed the usability of freely available Sentinel satellite images to map terrestrial oil spills with machine learning algorithms. Using two test sites in South Sudan, we demonstrated that information from the Sentinel-1 and -2 instruments can be used to map oil spills with more than 90 % classification accuracy. Classification accuracy was significantly increased (>95 %) with the addition of multi-temporal information and spatial predictor variables that quantify proximity to oil production infrastructure such as pipelines and oil pads. The mapping of terrestrial oil spills with freely available Sentinel satellite images may thus represent an accurate and efficient means for the regular monitoring of oil-impacted areas.

The effects of biostimulation and bioaugmentation on crude oil biodegradation in two adjacent terrestrial oil spills of different age, in a hyper-arid region

This study deals with two adjacent terrestrial oil spills, with similar properties, located in a hyper-arid region in Israel, one from 1975 and the other from 2014. It tests the effect of biostimulation on crude oil degradation in both spills and whether biostimulated sediments from the 1975 spill can bioaugment crude oil degradation in the 2014 spill. Soil hydrophobicity, expressed as Water Drop Penetration Time (WDPT), and Gasoline Range Organics (GRO) and Diesel Range Organics (DRO) content in sediments were measured in one-month ex-situ experiments. No significant reduction in hydrophobicity and GRO + DRO content was observed in non-biostimulated controls. A combined treatment of mineral fertilization at t0 and maintaining 50% water saturation, significantly accelerated the decrease in hydrophobicity and GRO + DRO content in sediments of both spills. The addition of biostimulated sediments from the 1975 spill failed to accelerate the reduction of GRO + DRO content and hydrophobicity in the 2014 spill. Surprisingly, the GRO + DRO degradation rate in biostimulated sediments from the 2014 spill was 36% higher than in biostimulated sediments from the 1975 spill. Crude oil composition in both spills changes during its degradation and is characterized by an increase in the GRO fraction. To a certain range, WDPT was found to serve as a reliable indicator for oil content in the soil. We conclude that even in a hyper-arid region, oil bio-degradation capabilities develop in a relatively short time. Moreover, while biostimulation was effective in accelerating biodegradation, bioaugmentation with biostimulated sediments from a nearby older spill was found ineffective.

The Impact of Terrestrial Oil Pollution on Parasitoid Wasps Associated With Vachellia (Fabales: Fabaceae) Trees in a Desert Ecosystem, Israel.

Oil is a major pollutant of the environment, and terrestrial oil spills frequently occur in desert areas. Although arthropods account for a large share of animal diversity, the effect of oil pollution on this group is rarely documented. We evaluated the effects of oil pollution on parasitoid wasps associated with Vachellia (formerly Acacia) tortilis (Forssk.) and Vachellia raddiana (Savi) trees in a hyper-arid desert that was affected by two major oil spills (in 1975 and 2014). We sampled the parasitoid populations between 2016 and 2018 in three sampling sites and compared their abundance, diversity, and community composition between oil-polluted and unpolluted trees. Parasitoid abundance in oil-polluted trees was lower in one of the sites affected by the recent oil spill, but not in the site affected by the 1975 oil spill. Oil-polluted trees supported lower parasitoid diversity than unpolluted trees in some sampling site/year combinations; however, such negative effects were inconsistent and pollution explained a small proportion of the variation in parasitoid community composition. Our results indicate that oil pollution may negatively affect parasitoid abundances and diversity, although the magnitude of the effect depends on the tree species, sampling site, and the time since the oil spill.

Decontaminating Terrestrial Oil Spills: A Comparative Assessment of Dog Fur, Human Hair, Peat Moss and Polypropylene Sorbents

Terrestrial oil spills have severe and continuing consequences for human communities and the natural environment. Sorbent materials are considered to be a first line of defense method for directly extracting oil from spills and preventing further contaminant spread, but little is known on the performance of sorbent products in terrestrial environments. Dog fur and human hair sorbent products were compared to peat moss and polypropylene sorbent to examine their relative effectiveness in adsorbing crude oil from different terrestrial surfaces. Crude oil spills were simulated using standardized microcosm experiments, and contaminant adsorbency was measured as percentage of crude oil removed from the original spilled quantity. Sustainable-origin absorbents made from dog fur and human hair were equally effective to polypropylene in extracting crude oil from non- and semi-porous land surfaces, with recycled dog fur products and loose-form hair showing a slight advantage over other sorbent types. In a sandy terrestrial environment, polypropylene sorbent was significantly better at adsorbing spilled crude oil than all other tested products.

A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing

One of the most ubiquitous and detrimental types of environmental contamination in the world is crude oil pollution. When released into either the aquatic or terrestrial environments, this pollution can negatively impact flora and fauna, as well as human health. Hence, a rapid and affordable spatial assessment of the pollution is favored to limit the spill’s effects. Using airborne hyperspectral remote sensing (HRS) for crude oil detection in terrestrial areas has been investigated in previous studies, which mainly relied on heavily oiled artificial samples. These studies and others based their methodologies on the premise that the spectral features of petroleum hydrocarbon (PHC) are clearly observable, which might not be true in all cases. In this study, we aimed at assessing the true potential of using HRS for terrestrial oil spill mapping in a real disaster site in southern Israel, where laboratory and controlled conditions do not apply. Using the AISA SPECIM Fenix1 K sensor, we collected airborne image of the study site and analyzed the data with advanced data mining techniques. Various challenges and limitations arose from the airborne HRS image being taken two and a half years after the crude oil had been released into the environment and exposed to the surface. Here, no spectral features of PHC were detectable in the spectrum, preventing the use of PHC indices and spectral methods developed by others. Nevertheless, by using standardization techniques, vicarious band selection, dimension reduction, multivariate calibration, and supervised machine-learning, we were able to successfully distinguish between contaminated pixels from non-contaminated ones. Classification accuracy metrics of overall accuracy, sensitivity, specificity, and Kappa yielded good results of 0.95, 0.95, 0.95 and 0.9, respectively, for cross-validation, and 0.93, 0.91, 0.94 and 0.85, for the validation dataset. Classified image and test scenes also showed strong agreement with an orthophoto image taken several days after the disaster, when the pollution was clearly visible. Thus, we conclude that HRS technology can detect PHC traces in an oil spill site, even under the most challenging conditions.

Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: a case site within the Niger Delta region of Nigeria

Terrestrial oil pollution is one of the major causes of ecological damage within the Niger Delta region of Nigeria and has caused a considerable loss of mangroves and arable croplands since the discovery of crude oil in 1956. The exact extent of landcover loss due to oil pollution remains uncertain due to the variability in factors such as volume and size of the oil spills, the age of oil, and its effects on the different vegetation types. Here, the feasibility of identifying oil-impacted land in the Niger Delta region of Nigeria with a machine learning random forest classifier using Landsat 8 (OLI spectral bands) and Vegetation Health Indices is explored. Oil spill incident data for the years 2015 and 2016 were obtained from published records of the National Oil Spill Detection and Response Agency and Shell Petroleum Development Corporation. Various health indices and spectral wavelengths from visible, near-infrared, and shortwave infrared bands were fused and classified using the machine learning random forest classifier to distinguish between oil-free and oil spill–impacted landcover. This provided the basis for the identification of the best variables for discriminating oil polluted from unpolluted land. Results showed that better results for discriminating oil-free and oil polluted landcovers were obtained when individual landcover types were classified separately as opposed to when the full study area image including all landcover types was classified at once. Similarly, the results also showed that biomass density plays a significant role in the characterization and classification of oil contaminated and oil-free pixels as tree cover areas showed higher classification accuracy compared to cropland and grassland.

Spatio-temporal analysis of vegetation and oil spill intensity in Ogoniland

Ogoniland, Niger Delta Nigeria is an area that has been subjected to terrestrial oil spills from the beginning of oil exploration in the 1950s till date. Despite many studies and practical efforts by different organizations and multinational companies, oil spills continue to occur and their impacts persist. These oil spills are regarded as the major cause of environmental degradation in the study area. The objective of this study was to ascertain the fact that oil spill is the major cause of environmental changes in Ogoniland using Normalized Difference vegetation Index (NDVI) and statistical methods. Remote sensing satellite data (Landsat 5TM (1984), Landsat 7ETM+ (2013, 2014 and 2015), and geographical coordinates of spill locations were used to observe vegetation dynamics with respect to the intensity of oil spills. For vegetation dynamics, the geographical coordinates were used to observe temporal variations of NDVI values at each spill point while statistical analyses were used to identify the relationships between the intensity of the spills and the changes in vegetation. It was observed that changes in vegetation quality and quantity can be attributed to oil spill occurrences, however, the level of change in vegetation cannot be ascribed to the frequency or intensity of the oil spills. Finally, this study asserts that oil spill is the major cause of environmental changes in the study area. (C) 2017 The Authors. Published by IASE.

Geochemical markers of soil anthropogenic contaminants in polar scientific stations nearby (Antarctica, King George Island)

The organic contamination of Antarctic soils and terrestrial sediments from nearby of five polar scientific stations on King George Island (Antarctica) was investigated. Gas chromatography–mass spectrometry (GC–MS) was applied to find composition of dichloromethane extracts of soil and terrestrial sediments. The presence of geochemical markers, such as n-alkanes, steranes, pentacyclic triterpenoids, and alkyl PAHs, their distribution types, and values of their ratios indicates the predominating source of organic fossil fuels and products of their refining rather than from the natural Antarctic environment. Fossil fuel-originated compounds well survived in conditions of Antarctic climate over long times thus enabling to characterize geochemical features of source fossil fuel identified as petroleum expelled from kerogen II of algal/bacterial origins deposited in sub-oxic conditions and being in the middle of catagenesis. Both microbial activity and water leaching play an important role in degradation of terrestrial oil spills in the Antarctica climate, and petroleum alteration occurs lowly over long periods of time. Synthetic anthropogenic compounds found in terrestrial Antarctica sediments included diisopropylnaphthalenes, products of their sulfonates degradation in paper combustion, and organophosporus compounds used as retardants and plasticizers.

High spatial resolution image object classification for terrestrial oil spill contamination mapping in West Siberia

This work is a part of the OSCaR pilot study (Oil Spill Contamination mapping in Russia). A synergetic concept for an object based and multi temporal mapping and classification system for terrestrial oil spill pollution using a test area in West Siberia is presented. An object oriented image classification system is created to map contaminated soils, vegetation and changes in the oil exploration well infrastructure in high resolution data. Due to the limited spectral resolution of Quickbird data context information and image object structure are used as additional features building a structural object knowledge base for the area. The distance of potentially polluted areas to industrial land use and infrastructure objects is utilized to classify crude oil contaminated surfaces. Additionally the potential of Landsat data for dating of oil spill events using change indicators is tested with multi temporal Landsat data from 1987, 1995 and 2001. OSCaR defined three sub-projects: (1) high resolution mapping of crude oil contaminated surfaces, (2) mapping of industrial infrastructure change, (3) dating of oil spill events using multi temporal Landsat data. Validation of the contamination mapping results has been done with field data from Russian experts provided by the Yugra State University in Khanty-Mansiyskiy. The developed image object structure classification system has shown good results for the severely polluted areas with good overall classification accuracy. However it has also revealed the need for direct mapping of hydrocarbon substances. Oil spill event dating with Landsat data was very much limited by the low spatial resolution of Landsat TM 5 data, small scale character of oil spilled surfaces and limited information about oil spill dates.

Monitoring Soil Ecosystem Recovery Following Bioremediation of a Terrestrial Crude Oil Spill With and Without a Fertilizer Amendment

The effect of fertilizer as an amendment in the bioremediation of a terrestrial crude oil spill has been investigated in terms of the subsequent recovery of the soil ecosystem following bioremediation. Two different spills in the same area with different initial hydrocarbon concentrations (33,500 mg kg-1 and 4,800 mg kg-1) were compared. At the higher initial hydrocarbon concentration fertilizer addition increased the rate of bioremediation (first-order rate constant of 0.0033 days-1 with fertilizer amendment vs. 0.0020 days-1 without) and resulted in more rapid recovery of soil bacteria (numbers, community structure, diversity) and nematodes (trophic diversity and community structure). The effect of the fertilizer amendment was more significant at the higher initial concentration of crude oil hydrocarbons, presumably due to greater depletion of soil nutrient pools in the absence of the amendment. A second objective of this work was to identify sensitive and cost-effective ecological indicators useful for monitoring the recovery of soil ecosystems impacted by crude oil. Ecological indicators used included: microbial numbers, community structure, and activity as revealed by biomarker analysis (phospholipid fatty acids); nitrogen availability; nematode numbers and community structure (trophic groups and colonizer-persister classes); and ultimately, plant cover and diversity. All ecological indicators investigated were sensitive to disturbances in the soil food web in a hydrocarbon-impacted site. However, nematode community structure analysis offered the greatest sensitivity coupled with low cost and readily available sources for the analysis.

Evaluation of The Efficiency of Various Commercial Products for The Bioremediation of Hydrocarbon Contaminated Soil

Bioremediation has become an important method for the treatment of terrestrial oil spills and is often favoured over strictly physical-chemical methods. In this study, enzymatic analyses and signature lipid biomarkers were employed to evaluate the efficacy of selected bioremediation products on control and oil contaminated soil plots. It is envisioned that these biological indicators may be used as possible adjuncts to the strictly physical-chemical criteria most commonly employed. The application of the enzymatic and signature biomarker methods for product evaluation proved successful. The enzymatic assays provided a valuable insight into shifts in the functional diversity of the soil microbial communities resultant from the various treatments. Stimulation or inhibition of the microbial communities as a result of the various treatments was also demonstrated, particularly with regards to dehydrogenase activity. Phospholipid fatty acid profiles proved sufficiently sensitive to allow differentiation between products and resultant microbial communities that corresponded to satisfactory and unsatisfactory petroleum hydrocarbon removal.

CLEANUP OF A COLD WEATHER TERRESTRIAL PIPELINE SPILL

ABSTRACT On February 15, 1978, sabotage of the Trans-Alaska Pipeline near Fairbanks resulted in a large terrestrial crude oil spill. Rapid response of trained personnel limited the spread of oil to an area of 2.1 acres. A 63-day cleanup effort led to the recovery of most of the crude oil which was later re-injected into the pipeline system at three different locations. The remaining crude was subsequently burned at the spill site and the area was fertilized. The intent of this paper is to examine the implementation of the pipeline oil spill contingency plan for that area and the associated problems in and advantages of dealing with oil recovery under typical northern latitude winter conditions.

Terrestrial Oil Spills in Alaska: Environmental Effects and Recovery

ABSTRACT Damage and natural recovery of terrestrial ecosystems affected by refined petroleum spills along the Haines to Fairbanks military pipeline in Alaska have been investigated. Since the 20-cm-diameter, 1007-km-long pipeline was opened in 1956, there have been 40 reported ruptures along it. Mosses and trees were completely killed, and vegetation is now sparse in the drier portions of the spill areas. Some new vegetation is growing in drainage pathways. The dead organic mat has provided adequate insulation, and only a few instances of slope instability, where slopes exceed 20 percent, have occurred. Through laboratory studies on the rates of microbial respiration in Fairbanks silt containing 15 percent Prudhoe crude, it has been determined that microbial activity is increased by inoculation with mixed-culture oil-degrading microorganisms, increased pH, and phosphorus additions. Microbial activity also responds positively to nitrogen addition after an initial negative response. The negative response varied with treatments, and was not caused by either ammonia or nitrite toxicity. These observations were tested in the field along the pipeline where nitrogen and phosphorus fertilizers were applied in several rate combinations to small plots in a selected automotive gasoline spill area. In addition the plots were seeded with a commercial grass mixture. The objective of this experiment was to determine if microbial degradation of the fuel and revegetation could be enhanced by the addition of fertilizer. There was a positive response in terms of microbial activity and plant growth in the treated plots. It has not been determined at this point if the increase in vegetation response was solely a fertilizer response or related to the increased rate of petroleum degradation.