Effects Of Oil Spill Research Articles

Risk Assessment of the Impact of Oil Spill on the Heavy Metal Content of Santa Barbara River in Bayelsa State, Nigeria

This study was aimed at assessing both the effect (risk) and impact of a recent oil spill on heavy metal concentration in the Santa Babara River in Bayelsa State, Nigeria. Samples were collected and preserved based on standard methods. The heavy metals of interest were Mercury (Hg), Arsenic (As), Cadmium (Cd), Chromium (Cr) and Copper (Cu) due to their levels of toxicities and presence in water bodies while the five study stations were Worokuma (W), Uwanga (W), Tuweni (T), Shellikiri (S), Inarakiri (I) and Esenfakiri (E). The solar thermos elemental flame atomic absorption spectrometer (AAS), model SE-71096 made in Germany with detection limit of 0.001 mg·kg−1 was used for the heavy metal analysis. The trend of heavy metal abundance was found in this order Cr>Cu>Cd>Pb>Hg=As. All the detectable metals had risk index of greater than 5 but the highest was found in the order, Cd> Pb > Cu > Cr (149, 35, 26 and 12 respectively) which meant extremely high level of pollution. This also indicated the probability of non-carcinogenic effects occurring and increases as HI increases signifying carcinogenic risk via direct ingestion of water. Graphs were used to show trends in both mean heavy metal concentrations and risk assessment indices. Chromium and Pb had moderate contaminations for CF and CDI values, hence regular monitoring of the Santa Barbara River is of essence as most of the inhabitants take this as the only source of potable water as their wells have very high iron content.

Simulation of Oil Spill Trajectory and Fate at the Southern Entrance of the Suez Canal, Red Sea, Egypt

The Suez Canal suffers from heavy maritime traffic, especially oil tankers, due to its strategic location between the Mediterranean and the Red Sea. As a result, it is prone to accidental oil spills, which might obstruct the maritime lane via the canal and severely harm the marine and coastal ecosystems. This study aims to forecast an oil spill trajectory and fate under the influence of different wind regimes using the General NOAA Operational Modeling Environment (GNOME) and the Automated Data Inquiry for Oil Spills (ADIOS2) models to define the potentially affected regions. Hence, four scenarios were simulated, assuming a spill of one thousand metric tons of Arabian light crude oil into the seawater about two kilometers from the Suez Canal’s southern entrance. The results highlight that wind direction and sea currents substantially affect the movement of oil spills. The trajectory maps show that the north-west wind forces the spilled oil to move toward the southeast direction, threatening the navigation lane through the Suez Canal and about 38 km of beaches south of the canal, which has several vital projects such as the Ayoun Mousse power plant and a lot of resorts. In the case of northern winds, the oil moved south in the center of the Gulf, which may allow response teams more time to clean up the spill. However, in the case of north-east winds, the oil drifted southwesterly and threatened the Green Island and western shores of the Gulf, which has many tourist villages. About a quarter of the oil evaporated, and more than two-thirds of the oil emulsified in all four scenarios. For the first time, this study has provided an understanding of oil spill forecasting and trajectory modeling for the Suez Canal’s southern entrance. Also, it can be considered a prediction tool for Egypt’s policymakers and Suez Canal Authority (SCA) to develop adequate and practical strategies to mitigate crude oil spill consequences.

Application of Vegetation Indices for Detection and Monitoring Oil Spills in Ahoada West Local Government Area of Rivers State, Nigeria

The study evaluated the environmental effects of an oil spill in Joinkrama 4 and Akimima Ahoada West LGA, Rivers State, Nigeria, using various vegetation indices. Location data for the spill were obtained from the Nigeria Oil Spill Detection and Response Agency, and Landsat imagery was acquired from the United States Geological Survey. Three soil samples were collected from the affected area, and their analysis included measuring total petroleum hydrocarbons (TPH), total hydrocarbons (THC), and polycyclic aromatic hydrocarbons (PAH). The obtained data were processed with ArcGIS software, utilizing different vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Atmospheric Resistant Vegetation Index (ARVI), Soil Adjusted Vegetation Index (SAVI), Green Short Wave Infrared (GSWIR), and Green Near Infrared (GNIR). Statistical analysis was performed using SPSS and Microsoft Excel. The results consistently indicated a negative impact on the environment resulting from the oil spill. A comparison of spectral reflectance values between the oil spill site and the non-oil spill site showed lower values at the oil spill site across all vegetation indices (NDVI 0.0665-0.2622, ARVI –0.0495-0.1268, SAVI 0.0333-0.1311, GSWIR –0.183-0.0517, GNIR –0.0104-–0.1980), indicating damage to vegetation. Additionally, the study examined the correlation between vegetation indices and environmental parameters associated with the oil spill, revealing significant relationships with TPH, THC, and PAH. A t-test with a significance level of p < 0.05 indicated significantly higher vegetation index values at the non-oil spill site compared to the oil spill site, suggesting a potential disparity in vegetation health between the two areas. Hence, this study emphasizes the harmful effect of oil spills on vegetation and highlights the importance of utilizing vegetation indices and spectral reflectance analysis to detect and monitor the impact of oil spills on vegetation.

Online detection of alkanes by a biological-phase microextraction and biosensing (BPME-BS) device

Oil spill incidents occur frequently and threaten ecosystems and human health. Solid-phase microextraction allows direct alkane extraction from environmental matrices to improve the limit of detection but is unable to measure alkanes on site. A biological-phase microextraction and biosensing (BPME-BS) device was developed by immobilising an alkane chemotactic Acinetobacter bioreporter ADPWH_alk in agarose gel to achieve online alkane quantification with the aid of a photomultiplier. The BPME-BS device had a high enrichment factor (average 7.07) and a satisfactory limit of detection (0.075 mg/L) for alkanes. The quantification range was 0.1–100 mg/L, comparable to a gas chromatography flame ionisation detector and better than a bioreporter without immobilisation. ADPWH_alk cells in the BPME-BS device maintained good sensitivity under a wide range of environmental conditions, including pH (4.0–9.0), temperature (20–40 °C), and salinity (0.0–3.0%), and its response remained stable within 30 days at 4 °C. In a 7-day continual measurement, the BPME-BS device successfully visualised the dynamic concentration of alkanes, and a 7-day field test successfully captured an oil spill event, helping in source apportionment and on-scene law enforcement. Our work proved that the BPME-BS device is a powerful tool for online alkane measurement, showing substantial potential for fast detection and rapid response to oil spills on site and in situ.

Cyber-physical oil spill monitoring and detection for offshore petroleum risk management service

Petroleum industry has started to embrace the advanced petroleum cyber-physical system (CPS) technologies. Offshore petroleum CPS is particularly hard to build, mainly due to the difficulty in detecting and preventing offshore oil leaking. During the oil exploration and transportation process, the remote multi-sensing technology is typically employed for emerging service. It can be utilized for leak detection by enabling the underwater modeling of an offshore petroleum CPS. However, such a technology suffers from insufficient remote sensing resources and expensive computational overhead. In this work, a cross-entropy based leak detection technique is proposed to detect the oil leak, which facilitates the understanding of the oil leak induced marine pollution. Furthermore, a hierarchical parallel approach is proposed on the super computer Tianhe-2 to improve the efficiency of the proposed leak detection technique. Experimental results on Penglai oil spill events demonstrate that the proposed method can effectively identify the sources of oil spilling with accuracy up to 100%.

Hindcast of oil spill pollution in the East China Sea in January 2018

In this study, an integrated model is developed for studying the Sanchi oil spill event, which occurred in the East China Sea in January 2018. The results of the Advanced Research Weather Research and Forecasting model (WRF-ARW) as well as the Princeton Ocean Model (POM) are used for meteorological forecasting and the hydrodynamic simulations, respectively. These data are adopted as inputs for the OpenOil, a sub-module of OpenDrift, for the oil spill model. Some reference experiments are examined for short-term hindcast. The satellite image is used to validate the numerical result. The oil slicks of the satellite image and the numerical result are of similar shapes. Quantitatively, the simulated oil slick and that from the satellite image are located closely and have similar dimensions of 56 km by 34 km and 54 km by 29 km, respectively. It is found that the accurate results can be obtained by the proposed integrated model with the high-frequency (hourly) and high-spatial-resolution data as inputs, and the wind drift factor has to be added. The long-term 1-month simulation showed that most of the oil particles would move to the northeast of the sinking location and be trapped by the Kuroshio current.

Oil spills detection from SAR Earth observations based on a hybrid CNN transformer networks

Oil spills are the main threats to marine and coastal environments. Due to the increase in the marine transportation and shipping industry, oil spills have increased in recent years. Moreover, the rapid spread of oil spills in open waters seriously affects the fragile marine ecosystem and creates environmental concerns. Effective monitoring, quick identification, and estimation of the volume of oil spills are the first and most crucial steps for a successful cleanup operation and crisis management. Remote Sensing observations, especially from Synthetic Aperture Radar (SAR) sensors, are a very suitable choice for this purpose due to their ability to collect data regardless of the weather and illumination conditions and over far and large areas of the Earth. Owing to the relatively complex nature of SAR observations, machine learning (ML) based algorithms play an important role in accurately detecting and monitoring oil spills and can significantly help experts in faster and more accurate detection. This paper uses SAR images from ESA's Copernicus Sentinel-1 satellite to detect and locate oil spills in open waters under different environmental conditions. To this end, a deep learning framework has been presented to identify oil spills automatically. The SAR images were segmented into two classes, the oil slick and the background, using convolutional neural networks (CNN) and vision transformers (ViT). Various scenarios for the proposed architecture were designed by placing ViT networks in different parts of the CNN backbone. An extensive dataset of oil spill events in various regions across the globe was used to train and assess the performance of the proposed framework. After the detection performance assessments, the F1-score values for the standard DeepLabV3+, FC-DenseNet, and U-Net networks were 75.08 %, 73.94 %, and 60.85, respectively. In the combined networks models (combination of CNN and ViT), the best F1-score results were obtained as 78.48 %. Our results showed that these hybrid models could improve detection accuracy and have a high ability to distinguish oil spill borders even in noisy images. Evaluation metrics are increased in all the combined networks compared to the original CNN networks.

Effect of oil spills and gas flaring on agriculture and environment: A case of crop farmers in Niger Delta Region of Nigeria (2000 – 2022)

Effect of oil spills and gas flaring on agriculture and environment: A case of crop farmers in Niger Delta Region of Nigeria (2000 – 2022)

Response of dung beetle diversity to remediation of soil ecosystems in the Ecuadorian Amazon.

Efforts to alleviate the negative effects of oil spills in the Ecuadorian Amazon include remediation activities such as cleaning, reshaping, and revegetation of polluted areas. However, studies of the diversity of biological communities in these hydrocarbon-degraded ecosystems have never been carried out. Here, we evaluated the diversity of dung beetles on remediated soil ecosystems (Agricultural Soils and Sensitive Ecosystems) and on non-contaminated soils (Natural Forests and Palm Plantations). The study was conducted in Sucumbíos and Orellana provinces, in the Ecuadorian Amazon at four sampling sites per ecosystem type (a total of 16 sites). At each sampling site, six pitfall traps remained active for 120 consecutive h per month for 1 year. We collected 37 species and 7,506 individuals of dung beetles. We observed significant differences in mean species abundance, richness, and diversity between non-contaminated soil ecosystems and remediated soil ecosystems, with Natural Forests presenting the highest values, and Agricultural Soils the lowest values. Regarding sampling month, we also found significant differences among ecosystems, which were also higher in Natural Forests. The results suggest that hydrocarbon-degraded ecosystems tend to conserve lower beetle diversity one year after remediation highlighting the importance of Natural Forests for the conservation of tropical biodiversity. Therefore, dung beetle diversity could be used for future landscape management of these hydrocarbon-degraded ecosystems.

Bioremediation: Review on oil spill management using oil eating microorganisms, and spill effects on marine and terrestrial environment

With never ending increase in usage and demand of petroleum products worldwide chances of its spill also increase. Oil spills directly affect marine as well as terrestrial ecosystem, as petroleum product became source of water pollution. Marine shorelines serve as home to variety of wildlife oil spills thus destroying habitat of different animals by polluting coastline. Fishes and birds being the main victims. Different physical and chemical methods are used to manage oil spills. However bioremediation is considered as best procedure to manage oil spills around the world. Bioremediation uses the oil loving microbes (lyophilic) to cope oil spills, microbes are cable of destroying complex chemical compounds, some bacteria consume hydrocarbon as their ultimate energy and carbon source. Although process is considered as highly effective the rate at which degradation occur is very slow to prevent oil effecting shore and marine ecosystem, generally due to limitation of availability of nutrients which are generally nitrogen and phosphorous. Two type of methods are generally involved in bioremediation, these include bio augmentation and bio stimulation. In bio augmentation biodegradable bacteria are added to existing population at effected site while in bio stimulation, is addition of nutrients to make process more effective and efficient. Latest technology introduce use of nanoparticles in bioremediation. Along with bacteria some microalgae are under consideration for effective bioremediation. Challenges involve in biodegradation include slow bioremediation of polycyclic hydrocarbons of high molecular weight, i.e. asphaltenes can be difficult to deal .While failure of some techniques which are successful in laboratory at effected site is another problem .The review focus on oil spill effects, new trends in bioremediation developing technique and microbial bioremediation agents.

Ecological Risk Assessment of Oil Spill in Seribu Islands Coastal Area (SICA)

The Study of Ecological Risk Assessment (ERA) of oil spill in Seribu Islands Coastal Area (SICA) was done in Oil Company near Pertamina’s authorization mining territory. From the simulation of chance release model using fault tree analysis, the rate of chance release at 4.95 tons of particles of oil is acquired in an amount of 105.009 x 10 −7 . Supported by data on the oceanographic dynamics existing in the SICA which consist of climate, physical and chemistry characteristics are processed using Gnome Analysis was predicted. The final position of oil particle from oil company near Pertamina. From the simulation using Gnome for the locations of SICA at North of Jakarta, the endpoint of the spill for the simulation period is two months after the oil spill event in both. The results of the simulation of oil spills in part of SICA (East Karawang waters) where the location of the Pertamina Oil Refinery was leaked off the East Karawang coast showed the distribution of crude oil headed to the northwest and touched Bekasi Beach and Happy be achin the SICA in two months of observation. There are oceanographic dynamics and characteristics of oil spilled on 29 July 2019 in Karawang Waters provided information to coastal communities in both locations to give a negative perception of the impact of ecological risks. Nearly 85% of the Karawang coastal communities to the Thousand Islands border provide negative perspectives to the government, especially polluters, in this case Pertamina YYA-1 East Karawang.

Acute and Chronic Effects of Crude Oil Water-Accommodated Fractions on the Early Life Stages of Marine Medaka (Oryzias melastigma, McClelland, 1839)

Oil spill is a major marine environmental pollution issue. Research regarding the long-term effects of oil spills on the early life stage of marine fish is still limited. In this study, the potential adverse impact of crude oil from one oil spill accident which occurred in the Bohai Sea on the early life stages of marine medaka (Oryzias melastigma, McClelland, 1839) was evaluated. A 96-h acute test (larvae) and a 21-d chronic test (embryo-larvae) of water-accommodated fractions (WAFs) from crude oil were conducted, respectively. The results of the acute test showed that only the highest concentration of WAFs (100.00%) significantly affected the mortality of larvae (p < 0.01) and that the 96 h-LC50 was 68.92% (4.11 mg·L-1 expressed as total petroleum hydrocarbons (TPHs)). Larval heart demonstrated histopathological alterations in all WAF-exposed groups. The chronic test results showed that, except for larval mortality, the total hatching success (%)/hatching time of embryos in WAF treatments was not significantly different from those of the control group (p > 0.05), and no malformation was found in surviving larvae after 21 d of exposure. Nevertheless, the exposed embryos and larvae in the highest concentration of WAFs (60.00%) demonstrated significantly reduced heart rate (p < 0.05) and increased mortality (p < 0.01), respectively. Overall, our results indicated that both acute and chronic WAF exposures had adverse impacts on the survival of marine medaka. In the early life stages, the heart of the marine medaka was the most sensitive organ which showed both structural alteration and cardiac dysfunction.

Effect of Wind-Wave-Current Interaction on Oil Spill in the Yangtze River Estuary

Oil spills are major threat to marine ecosystem and have long-lasting effect on marine life and water quality. In this study, a two-dimension hydrodynamic and oil spill transport model of the Yangtze River Estuary is established based on MIKE21 with a special attention to wind-wave-current interaction (WWCI). The model agrees well with the observed data on water level, current velocity, and the real oil spill event during Typhoon Fongwong. This study is mainly focused on the oil particle trajectory and spread. The model results show: (1) when the oil spill occurs during a typhoon period, the oil film can rapidly deposit under high WWCI, decreasing the swept area to about 20% compared with the normal weather condition; (2) strong current and large wave enlarge the oil film coverage whereas high wind speeds cause the oil particles to deposit in the shallow water area; and (3) the oil particles move farther and the swept area is far greater under the winter wind than under the summer wind, and the two times of the winter wind show the greatest effect on the oil spill. This study considers the drift, dispersion, evaporation, and emulsification of oil during the moving period under different wind, wave and current conditions, providing a good guidance for the oil spill prevention and mitigation in other estuaries.

Semi-quantitative risk assessment of marine mammal oil exposure: A case study in the western Gulf of Mexico

Marine mammals are highly vulnerable to oil spills, although the effects at both individual and population levels are not fully understood. A first approximation to evaluate the possible consequences of oil spills on marine life is using ecological risk assessments, which are analytical tools used to assess the likelihood of adverse environmental effects due to exposure to stressors derived from human activities. We developed a semi-quantitative framework to evaluate the risk of oil spill exposure on marine mammals that combines the likelihood of exposure based on species-specific biological and ecological traits, and the feasibility of encounter, which considers not only the overlap between the distribution of the species and the total affected area by a spill but also considers the distribution of spilled oil within this area, thus reducing the uncertainty in the estimate. We applied our framework to assess the risk of exposure of eight cetaceans to scenarios of large heavy oil (API gravity&amp;lt;22) spills originating from three hypothetical deep-water wells in the western Gulf of Mexico. High habitat suitability areas obtained using the maximum entropy (MaxEnt) modeling approach were used as a proxy for the geographic regions where each species is likely to be distributed, and oil spill scenarios were generated using numerical models incorporating transport, dispersion, and oil degradation. The analysis allowed identifying those species for which there is a significant risk of exposure in each spill scenario. However, our results suggest that the risk does not appear to be high for any species under any scenario. The information generated by our risk assessment is key to developing management plans in those areas of the Gulf of Mexico where deep-water activities of the hydrocarbon industry are currently being developed or planned.

Establishing a benthic macrofaunal baseline for the sandy shoreline ecosystem within the Gulf Islands National Seashore in response to the DwH oil spill

Sandy shorelines present a first line of defense against the catastrophic effects of storms and oil spills within the coastal zone of the northern Gulf of Mexico. Immediately following the DwH oil spill prior to any spill related impacts, we conducted a rapid response survey of the sandy shoreline benthic macrofauna from throughout the National Park Service - Gulf Islands National Seashore (GINS) in Mississippi and Florida. To characterize pre-spill macrofaunal assemblages, we surveyed seven barrier island or peninsular areas comprising nine exposed and 12 protected shoreline sites. A comparable benthic macrofaunal inventory had been conducted 17 years earlier using a parallel study design. The primary objective of this study was to distinguish hierarchical spatiotemporal scales of macrofaunal variation within the 1993 and 2010 GINS data. We hypothesized that the 1993 GINS macrofaunal inventory baseline was stable, despite multiple disturbances by large storms within the intervening 17-year period. Additionally, the relative importance of hierarchical spatial scales of macrofaunal dissimilarity was examined so suitable scales of macrofaunal variation could be identified for assessments of stressor effects at commensurate scales. An Implicit Nested Mixed Model PERMANOVA using Type 1 sequential Sum of Squares delineated variation components of nested scales which ranked Station &amp;gt; Shore Side &amp;gt; Site &amp;gt; Habitat &amp;gt; District &amp;gt; Year. The Year main factor had the smallest effect on macrofaunal variation, confirming that the 1993 GINS macrofaunal inventory can serve as the foundation for a robust baseline including both the 1993 and the 2010 macrofaunal data for the GINS. A literal Hierarchical Nested Mixed Model PERMANOVA using Type 1 sequential Sum of Squares (SS) partitioned effects among nested factors and their interactions. Definitive macrofaunal variation was expressed for all combinations of two levels for each of the three spatially nested fixed factors, District, Shore Side, and Habitat. Variation in macrofaunal dissimilarity for combined levels of fixed factors reflected corresponding differences in the macrofauna. The use of sandy shoreline macrofaunal assemblages as ecological indicators would fulfill the need to focus on cumulative effects of oil spills and should be eminently tractable when responses and impacts are considered on commensurate scales.

The Red Tide Organism Chaetoceros sp. Responding to Exposure to Oil and Dispersant

Laboratory experiments were conducted to study the effects of oil spills and dispersants on the growth of the red tide organism Chaetoceros sp. Crude oil produced from the Chinese Bohai Sea, diesel oil, and the chemical dispersant (GM-2) produced in China were added into Chaetoceros sp. cultures. The results showed that both crude oil and diesel oil could enhance the growth of Chaetoceros sp. Data were analyzed by one-way ANOVA, and the confidence interval was 95%. At a concentration of 20 mg L−1 crude oil and a concentration of 10 μL L−1 diesel oil, Chaetoceros sp. bloomed to 1.57 × 105 cells mL−1 (p &lt; 0.01) at day 14 and 3.55 × 104 cells mL−1 (p &lt; 0.05) at day 10, respectively. A concentration of 10 μL L−1 diesel oil stimulated the specific growth rate for Chaetoceros sp. of 0.49 d−1 over 10 days. The specific growth rate of Chaetoceros sp. with 20 mg L−1 crude oil alone was 0.46 d−1 over 14 days. However, the mixture of oil and dispersant did not enhance the growth of Chaetoceros sp. as significantly as oil alone. These results implied that oil spills in coastal waters can stimulate Chaetoceros sp., and the specific dispersant GM-2 applied following oil spills may be unlikely to further enhance the growth of Chaetoceros sp.

Bridging the lab to field divide: Advancing oil spill biological effects models requires revisiting aquatic toxicity testing

Oil fate and exposure modeling addresses the complexities of oil composition, weathering, partitioning in the environment, and the distributions and behaviors of aquatic biota to estimate exposure histories, i.e., oil component concentrations and environmental conditions experienced over time. Several approaches with increasing levels of complexity (i.e., aquatic toxicity model tiers, corresponding to varying purposes and applications) have been and continue to be developed to predict adverse effects resulting from these exposures. At Tiers 1 and 2, toxicity-based screening thresholds for assumed representative oil component compositions are used to inform spill response and risk evaluations, requiring limited toxicity data, analytical oil characterizations, and computer resources. Concentration-response relationships are employed in Tier 3 to quantify effects of assumed oil component mixture compositions. Oil spill modeling capabilities presently allow predictions of spatial and temporal compositional changes during exposure, which support mixture-based modeling frameworks. Such approaches rely on summed effects of components using toxic units to enable more realistic analyses (Tier 4). This review provides guidance for toxicological studies to inform the development of, provide input to, and validate Tier 4 aquatic toxicity models for assessing oil spill effects on aquatic biota.Evaluation of organisms’ exposure histories using a toxic unit model reflects the current state-of the-science and provides an improved approach for quantifying effects of oil constituents on aquatic organisms. Since the mixture compositions in toxicity tests are not representative of field exposures, modelers rely on studies using single compounds to build toxicity models accounting for the additive effects of dynamic mixture exposures that occur after spills. Single compound toxicity data are needed to quantify the influence of exposure duration and modifying environmental factors (e.g., temperature, light) on observed effects for advancing use of this framework. Well-characterized whole oil bioassay data should be used to validate and refine these models.

Towards relevant ecological experiments and assessments of coastal oil spill effects: Insights from the 2010 Deepwater Horizon oil spill

Towards relevant ecological experiments and assessments of coastal oil spill effects: Insights from the 2010 Deepwater Horizon oil spill

Calibration of an acute toxicity model for the marine crustacean, Artemia franciscana, nauplii to support oil spill effect assessments

Oil spill risk and impact assessments rely on time-dependent toxicity models to predict the hazard of the constituents that comprise crude oils and petroleum substances. Dissolved aromatic compounds (ACs) are recognized as a primary driver of aquatic toxicity in surface spill exposure scenarios. However, limited time-dependent toxicity data are available for different classes of ACs to calibrate such models. This study examined the acute toxicity of 14 ACs and 3 binary AC mixtures on Artemia franciscana nauplii at 25 °C. Toxicity tests for 3 ACs were also conducted at 15 °C to evaluate the role of temperature on toxicity. The ACs investigated represented parent and alkylated homocyclic and nitrogen-, sulfur- and oxygen-containing heterocyclic structures with octanol-water partition coefficients (log Kow) ranging from 3.2 to 6.6. Passive dosing was used to expose and maintain concentrations in toxicity tests which were confirmed using fluorometry, and independently validated for 6 ACs using GC–MS analysis. Mortality was assessed at 6, 24, and 48 h to characterize the time course of toxicity. No mortality was observed for the most hydrophobic AC tested, 7,12-dimethylbenz[a]anthracene, due to apparent water solubility constraints. Empirical log LC50 s for the remaining ACs were fit to a linear regression with log Kow to derive a critical target lipid body burden (CTLBB) based on the target lipid model. The calculated 48 h CTLBB of 47.1 ± 8.1 μmol/g octanol indicates that Artemia nauplii exhibited comparable sensitivity to other crustaceans. A steep concentration-response was found across all compounds as evidenced by a narrow range (1.0–3.1) in the observed LC50 /LC10 ratio. Differences in toxicokinetics were noted, and no impacts of temperature-dependence of AC toxicity were found. Toxicity data obtained for individual ACs yielded acceptable predictions of observed binary AC mixture toxicity. Results from this study advance toxicity models used in oil spill assessments.

The effects of oil spills on marine life and coastal communities

The effects of oil spills on marine life and coastal communities