There was an average of 2.2 large oil spills from tanker incidents every year in the decade from 2020 onward. In 2024, six oil spills were reported where more than 700 metric tons of oil leaked. In the years since the 1970s, the number of oil tanker spills has been notably reduced from an excess of 20 large oil spills per year. Largest ever oil spills The Gulf war oil spill in January 1991 is the largest global oil spill to ever take place since commercial drilling took off. An estimated 380 to 520 million gallons of oil were intentionally dumped into the ocean by the Iraqi government, which had invaded Kuwait and was trying to prevent the arrival of a UN-coalition navy force. The second largest oil spill is also one of the more recent disasters, the Deepwater Horizon wellhead blowout in 2010. Over 200 million gallons of oil were released into the Gulf of Mexico, while 11 people were killed in the accident. Oil tanker spill causes Oil tankers are the prevailing means of transporting the commodity over distances greater than can be covered by pipelines. Running aground is the most common cause of large oil spills from tankers. 31 percent of large oil tanker spills occurring between 1970 and 2024 were due to grounding.

The amount of oil spilled from oil tankers worldwide was approximately 10,000 metric tons in 2024. This was a notable increase compared to the previous year. In 2018, a total of 116,000 metric tons of oil was leaked from oil tanker incidents, the largest quantity leaked in 24 years. Most of the quantity leaked in 2018 was attributable to the incident involving the MT Sanchi in the East China Sea. Since the 1970s and 1980s, the average annual amount of oil spilled from tankers has decreased significantly.

With increasing oil exploration and ship traffic in the U.S. Arctic, there is concern about the increased potential for an oil spill event in this region of the world. Baseline exposure levels of oil-spill related contaminants are lacking for marine mammals, particularly endangered or threatened populations (e.g., ice seals, bowhead whales). Identification of the appropriate tissues/fluids to assess recent exposure of marine mammals to oil components must be determined, as well as the type of oil spill-related contaminant (e.g., parent polycyclic aromatic hydrocarbons (PAHs), metabolites of PAHs). To help address these data gaps, various matrices of Arctic marine mammals will be collected during subsistence harvests and from fresh dead stranded animals over the next year and these samples will be analyzed for oil-spill related compounds. These tissues will also be analyzed for additional oil-spill related components after the methods have been developed and validated. Under the guidance of NOAA Fisheries Office of Protected Resources, we will collaborate with the National Institute of Standards and Technology to develop and test appropriate standard reference materials and control materials for these analyses to ensure that the chemical contaminant data generated for this project are of known and acceptable quality. Concentrations of PAHs and alkylated PAHs in Arctic marine mammal tissues.

BP plc was responsible for 376 thousand liters of oil being spilled in 2024, a decrease of nearly 74 percent compared to the previous year. A significant portion of this amount, 294,000 liters, was unrecovered. In the period of consideration, oil spill volume has seen fluctuations even though the number of BP oil spills decreased.

Oil spills are a major source of marine pollution that affect the environment, economy, and marine ecosystems. Toxic chemicals from oil spills can remain in the ocean for years and even sink down to the seabed affecting sedimentation rates. While many oil spills are accidental, some are caused deliberately by cargo ships dumping waste oil and bilge water. It is very difficult to identify, detect and remove oil from the ocean surface and routine monitoring can help prevent illegal dumping and aid with remediation efforts.This deep learning model automates the task of detecting potential oil spills from Sentinel-1 SAR data. In addition to being inexpensive, SAR data is collected day and night in all weather conditions without getting affected by cloud cover. Use this model to identify potential oil spills that need to be reviewed or monitored, reducing time and effort required significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band Sentinel-1 C band SAR GRD VV polarization band raster.OutputFeature layer representing oil slick.Applicable geographiesThe model is expected to work globally.Model architectureThe model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe model has an average precision score of 0.69.Training dataThis model is trained on 381 Sentinel-1 scenes downloaded from the ASF portal, and the ground truth data from NESDIS Marine Pollution Products. Sample resultsHere are a few results form the model.

In accordance with the Black Sea Strategic Action Plan (1996), the Ukrainian Law on a National Programme for the Protection and Rehabilitation of the Azov and Black Seas (2001), and the Regional Contingency Plan for Combating Pollution of the Black Sea by Oil (2003), the Ministry of Environmental Protection of Ukraine initiated a project to create a Marine Accident Oil Spill Information System (MAOSIS), based at the Ukrainian Scientific Centre of Ecology of the Sea. MAOSIS combines existing data from different sources: analyses, studies, projects and statistics that are collected and updated annually. Most of the data are collected as part of the routine work of UkrSCES. However, some data are obtained from ESRI, the European Union and Black Sea Environment Recovery Project. MAOSIS can be used to view different combinations of datasets related to oil spills over a common background map. MAOSIS allows those who are responsible for decision making to access and assess this information in various areas. The system includes the basic datasets (coastlines, administrative boundaries, coastal cities, sea basin, lakes and estuaries, coastal oblasts, etc.), a tool to view maps of the datasets, and documentation about the datasets. The layers consist of a number of datasets: * Human usage, * Dangerous sites, * Maritime traffic, * Ports and terminals, * Zones of response, * Coastal sensitivity, * Protected areas. MAOSIS provides a single and easy to use interface. Users can visualize oil spills and oil transportation networks in the Black and Azov Seas, focusing on the risk of oil pollution, recreational potential and oil spill response equipment. The users of this system are mainly decision makers, and oil pollution response authorities of the Ukrainian Black and Azov Seas coastal areas.

This dataset contains records of spills of petroleum and other hazardous materials. Under State law and regulations, spills that could pollute the lands or waters of the state must be reported by the spiller (and, in some cases, by anyone who has knowledge of the spill). Examples of what may be included in a spill record includes: Administrative information (DEC region and unique seven-digit spill number). Program facility name. Spill date/time. Location. Spill source and cause. Material(s) and material type spilled. Quantity spilled and recovered. Units measured. Surface water bodies affected. Close date (cleanup activity finished and all paperwork completed).

In 2023, there were 30 cases of oil spills across the Gulf of Thailand, which increased from the previous year. The oil spills have a negative impact on the marine ecosystem and organisms caused by the contamination of petroleum hydrocarbons.

The Historical Incidents database contains reports and images from oil and chemical spills that occurred between 1968 and 2002. The database includes reports on incidents to which NOAA responded, as well as some significant incidents in which NOAA was not involved. The database includes mainly U.S. incidents, but also significant incidents that occurred elsewhere. Generally, it includes inciden...

Conductivity Temperature and Depth (CTD) measurements were collected aboard NOAA Ship Pisces, Cruise 12, Leg 2, to determine physical oceanographic parameters of the water column, and in some cases used to help guide sample collection as part of the Deepwater Horizon oil spill sampling effort. Temperature, conductivity/salinity, depth, dissolved oxygen, and fluorometry data were collected onboard NOAA Ship Pisces, Cruise 10, Leg 1. The final product is a series of NetCDF files containing every CTD cast that has been processed and quality checked.

The dataset contains the numerical results of the 2010 Deepwater Horizon oil spill incident at Macondo well in the Gulf of Mexico, as estimated from the simulations using the latest updated version of the oil application of the Connectivity Modeling System (CMS) or oil-CMS. This contains additional data that complements the dataset that is available at GRIIDC under Unique Dataset Identifier (UDI) R4.x267.000:0084 (DOI: 10.7266/N7KD1WDB). In this version of the oil-CMS model, the specified hydrocarbon pseudo-components are in the same droplet. The post-processing analysis yielded 4-D spatiotemporal data of the oil concentrations and oil mass on a regular horizontal and vertical grid. There are two sets of simulations that last 167 days and 100 days (a shorter sensitivity run). CMS has a Lagrangian, particle-tracking framework, computing particle evolution and transport in the ocean interior. CMS simulations start date: April 20, 2010, 0000 UTC, and particles were tracked for 167 days or 100 days. Oil particles release location: 28.736N, 88.365W, depth is 1222m or 300m above the oil well. 3000 particles were released every 2 hours, for 87 days, equivalent to a total of 3132000 oil particles released during the simulation. Initial particle sizes were determined at random by the CMS in the range of 1-500 micron, and are scaled during post-processing to represent the chosen droplet size distribution (DSD). Each particle contained three (3) pseudo-components accounting for the differential oil density as follows: 10% of light oil with a density of 800kg/m^3, 75% of the oil with 840 kg/m^3, and 15% of heavy oil with 950 kg/m^3 density. The half-life decay rates of oil fractions were 30 days, 40 days, and 180 days, respectively. The surface evaporation half-life was set to 250 hours; horizontal diffusion was set to 10 m^2/s in the present case. Ocean hydrodynamic forcing for the CMS model was used from the HYbrid Coordinate Ocean Model (HYCOM) for the Gulf of Mexico region on a 0.04-deg. horizontal grid and 40 vertical levels from the surface to 5500m. It provided daily average 3-D momentum, temperature and salinity forcing fields to the CMS model. The surface wind drift parameterization used surface winds and wind stressed from the 0.5-degree Navy Operational Global Atmospheric Prediction System (NOGAPS). The transport and evolution of the oil particles were tracked by the oil-CMS model during the 167 days of the main simulation (100 days for a sensitivity run), recording each particle’s horizontal position, depth, diameter, and density into the model output every 2 hours. Model data needed to be post-processed to obtain oil concentrations and oil mass estimates. The post-processing algorithm took into account the total amount of oil spilled during the 87-day incident as estimated from the reports (730000 tons), and the assumptions about the oil particle size distribution at the time of the release as estimated in the prior studies. The current dataset contains post-processed gridded and non-gridded analyses for the cases of untreated oil and cases of oil treated with the chemical dispersants at the oil release location.

There were a total of 10 oil tanker spills worldwide in 2024. Six incidents had a release volume of more than 700 metric tons. In the last two decades, the amount of oil leaked by tanker spills generally declined, although 2018 stood out as an unusually perilous year for oil shipping. In 2018, the Sanchi tanker collision resulted in the death of all crew members, and spilled a quantity of 113,000 metric tons of condensate, polluting the East China Sea.

The dataset contains the numerical results of alternative scenarios of oil spills similar to the Deepwater Horizon (DWH) in 2010. Two scenarios of alternative locations - the east (27.0N, 85.168W) and the west (26.66N, 93.19W) Gulf of Mexico - were run 2010-04-20 to 2010-07-17. The third scenario is run at the DWH location for 2010-09-01 to 2010-11-28. Oil dispersal and concentrations were simulated using the updated oil application of the Connectivity Modeling System (oil-CMS). Post-processing analysis yielded 4-D spatiotemporal data on a 0.02-degree regular horizontal grid. Daily-averaged oil concentrations are provided for a 1-m deep surface layer and 125 20-m thick layers extending to 2500m; the summed oil mass within a layer is provided for 125 20-m thick layers extending to 2500m on a two-hour interval.

CMS has a Lagrangian, particle-tracking framework, computing particle evolution and transport in the ocean interior. Ocean hydrodynamic forcing for the CMS model was used from the HYbrid Coordinate Ocean Model (HYCOM) for the Gulf of Mexico region on a 0.04-deg. horizontal grid and 40 vertical levels from the surface to 5500m. It provided daily average 3-D momentum, temperature and salinity forcing fields to the CMS model. The surface wind drift parameterization used surface winds and wind stressed from the 0.5-degree Navy Operational Global Atmospheric Prediction System (NOGAPS). 3000 particles were released every 2 hours for 87 days, for a total of 3,132,000 oil particles. The depth of release was 1222 m or 300m above the oil well. Initial particle sizes were determined at random by the CMS in the range of 1-500 micron, with a model peak between 50-70 microns, consistent with oil left untreated by dispersants. Each particle contained three (3) pseudo-components accounting for the differential oil density as follows: 10% light oil of density of 800kg/m^3, 75% oil with a density of 840 kg/m^3, and 15% heavy oil of 950 kg/m^3 density. The half-life decay rates of oil fractions were 30 days, 40 days, and 180 days, respectively. The surface evaporation half-life was set to 250 hours; horizontal diffusion was set to 10 m^2/s in the present case.

The transport and evolution of the oil particles were tracked by the oil-CMS model during the 90 days of the simulation, recording each particle’s horizontal position, depth, diameter, and density into the model output every 2 hours. Model data need to be post-processed to obtain oil concentrations estimates. The post-processing algorithm took into the account the total amount of oil spilled during the 87-day incident as estimated from the reports (730000 tons), and the assumptions about the oil particle size distribution at the time of the release as estimated in the prior studies. A hindcast of the DWH spill can be downloaded in the related dataset available under GRIIDC Unique Dataset Identifier (UDI): R4.x267.000:0084 (DOI: 10.7266/N7KD1WDB).

In response to the Gulf of Mexico oil spill, scientists from the Commonwealth Scientific and Industrial Research Organization (CSIRO) conducted on-site monitoring of dispersed oil at the request of BP.

This dataset contains structured content and unstructured content. The structured content is the CSIRO shipboard measurements including CTD vertical profiles, towed fluorometry, onboard hydrocarbon sensors and GCMS data. Unstructured content is associated with shipboard measurements, including photos, raw data files/casts, cast profiles, sonar contacts, daily reports and cruise summaries. Included in this data product are CSIRO documentation with detailed information in a database data dictionary and manifest.

In response to the BP oil spill, EPA monitored air near the spill. While emergency response data collection has ended, results continue to be available on this site.

The droplet size distribution (DSD) formed by gas-saturated oil jets is one of the most important characteristics of the flow to understand and model the fate of uncontrolled deep-sea oil spills. The shape of the DSD, generally modeled as a theoretical lognormal, Rosin-Rammler or non-fundamental distribution function, defines the size and the mass volume range of the droplets. Yet, the fundamental DSD shape has received much less attention than the volume median size (d50) and range of the DSD during ten years of research following the Deepwater Horizon (DWH) blowout. To better understand the importance of the distribution function of the droplet size we compare the oil rising time, surface oil mass, and sedimented and beached masses for different DSDs derived from the DWH literature in idealized and applied conditions, while keeping d50 constant. We highlight substantial differences, showing that the probability distribution function of the DSD for far-field modeling is, regardless of the d50, consequential for oil spill response. This research was made possible by a grant from the Gulf of Mexico Research Initiative (GoMRI) C-IMAGE III and RECOVER2 Consortia. This dataset supports the publication: Faillettaz, R., Paris, C. B., Vaz, A. C., Perlin, N., Aman, Z. M., Schlüter, M., & Murawski, S. A. (2021). The choice of droplet size probability distribution function for oil spill modeling is not trivial. Marine Pollution Bulletin, 163, 111920. doi:10.1016/j.marpolbul.2020.111920

The Satellite Analysis Branch (SAB) of NOAA/NESDIS detects oil slicks in satellite imagery over U.S. waters (and international waters when requested by OR&R) in order to meet the NOAA Office of Response & Restoration (OR&R) need for oil spill information. The Marine Pollution Surveillance Report (MPSR) is issued by SAB satellite analysts when accidental or intentional marine oil spills are detected in imagery. The Marine Pollution Surveillance Program consists of manual detection and mapping of oil slicks primarily through the use of available moderate to high resolution optical and Synthetic Aperture Radar (SAR) satellite imagery.

In 2010, the Deepwater Horizon oil spill occurred in the Gulf of Mexico and the Natural Resources Damage Assessment (NRDA) was initiated to determine the extent of damage to the resources and habitat of the area impacted by the spill. The Southeast Fisheries Science Center Mississippi Laboratories has collected standardized data in the Gulf of Mexico since the 1980s through various fisheries...

Analysis of ‘Oil Spill Incident Tracking [ds394]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ed8efaf2-6dd4-4612-b1aa-df743e906dbc on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The Office of Spill Prevention and Response (OSPR) Incident Tracking Database is a statewide oil spill tracking information system. The data are collected by OSPR Field Response Team members for Marine oil spills and by OSPR Inland Pollution Coordinators and Wardens for Inland incidents.

--- Original source retains full ownership of the source dataset ---