There was an average of *** large oil spills from tanker incidents every year in the decade from 2020 onward. In 2024, *** oil spills were reported where more than *** 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 ** 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 *** to *********** 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 *********** 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. ** percent of large oil tanker spills occurring between 1970 and 2024 were due to grounding.

This dataset provides a comprehensive overview of the number of large and medium oil spills (defined as spills greater than 700 tonnes and between 7 to 700 tonnes, respectively) that occurred globally from 1970 to 2023. The data is sourced from OurWorldInData.org, a project by Hannah Ritchie, Veronika Samborska, and Max Roser, and is part of their comprehensive analysis on oil spills.

Source: Hannah Ritchie, Veronika Samborska, and Max Roser (2022) - “Oil spills ” published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/oil-spil ls' [Online Resource].

Citation: Ritchie, H., Samborska, V., & Roser, M. (2022). Oil spills. OurWorldInData. Retrieved from: https://ourworldindata.org/oil-spills

Background: Oil spills are environmental disasters that can have severe social, economic, and ecological impacts. They typically result from the release of crude oil or refined petroleum products from tankers, rigs, wells, and offshore platforms. These spills are most common in marine environments but can also occur on land. Over the decades, there has been a significant reduction in the number and volume of oil spills, particularly from tankers. However, not all oil spills come from tankers; they can also originate from other sources such as offshore oil rigs and damaged pipelines. The most notable incident in recent history is the Deepwater Horizon disaster in the Gulf of Mexico in 2010, which released an estimated 4.9 million barrels (approximately 700,000 tonnes) of oil. Monitoring and tracking oil spills from all sources, including non-tanker incidents, is crucial for global environmental data and safety.

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

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.

Content

Selected oil spills off US coastal waters and other incidents where NOAA's Office of Response and Restoration (OR&R) provided scientific support for the spill response.

The file is in the UTF-8 character set with Excel formatting conventions. Null values are represented by "" (an empty cell). Boolean values are represented by "1" (true) and "0" (false). Dates are in "YYYY-MM-DD" format. Embedded newlines in text fields are replaced by a vertical tab character (ctrl-K, \v, 0xb) because a few programs mistake them for end-of-record markers.

Fields

id (integer) Our incident ID.

open_date (date) The date OR&R was notified of the spill. (For pre-1985 spills from third-party databases, the spill date in the source database.)

name (text) Incident name.

location (text) Incident location. Usually "City, State".

lat (float) Latitude. Positive is north, negative is south. Value may be approximate.

lon (float) Longitude. Positive is east, negative is west. Value may be approximate.

threat (text) The primary threat: "Oil", "Chemical", or "Other". "Oil" means an oil spill. "Chemical" means a chemical or biological agent spill. "Other" means some other kind of incident.

tags (text) The tags assigned to the incident. These are mostly causes (what caused the incident). The current list of tags are:

Adrift Collision Coral Derelict Grounding Historic Wreck Hurricane Marine Debris Marine Mammal Mystery Substance Pipeline Railcar Search + Rescue Tsunami Wellhead Multiple tags are delimited by a pipe symbol (|).

commodity (text) The item spilled. (Freeform text.)

is_measure_skim (boolean) True if On-Water Recovery (skimming) was used as a countermeasure.

is_measure_shore (boolean) True if Shoreline Cleanup was used as a countermeasure.

is_measure_bio (boolean) True if Bioremediation was used as a countermeasure.

is_measure_disperse (boolean) True if Dispersants were used as a countermeasure.

is_measure_burn (boolean) True if In-Situ Burning was used as a countermeasure.

max_ptl_release_gallons (float) Maximum potential release in gallons. This may be a known quantity or the upper bound of a minimum-maximum estimate. Note that different commodities have different toxicities and reaction characteristics, so 10,000 gallons of one material may be more significant than 10,000 gallons of another.

Blank values indicate that the potential release is unknown or can't be converted to gallons. As of May 2016, 2% of the releases were reported in a mass unit (e.g., pounds) that can't be converted to gallons because the database doesn't know the commodity's density.

posts (integer) Number of posts in IncidentNews.

description (text) Text description of the incident.

Acknowledgements

National Oceanic and Atmospheric Administration

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.

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).

This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The raw data were collected during 9 flights aboard a NASA ER-2 aircraft over the Gulf of Mexico and portions of California, Colorado, Arizona, Utah, Idaho, New Mexico, Texas, Arkansas, Illinois, Wisconsin, Michigan, Louisiana, Mississippi, and Florida from 2010-07-31 to 2010-09-01. A primary purpose of this deployment was to collect imagery related to the Deepwater Horizon-BP Oil Spill that occurred in late April 2010 in the Gulf of Mexico. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.

This data set contains Oil Spill Financial Responsibility (OSFR)

This U.S. Geological Survey (USGS) Data Release provides data from samples and measurements completed at the National Crude Oil Spill Fate and Natural Attenuation Research Site near Bemidji, Minnesota (Site) since 1983. This is version 3.0 of this data release, and it now contains 11 data sets. The content of these data sets include inorganic and organic chemistry data from water, oil, and sediment samples, hydraulic conductivity data from well slug tests, sediment grain-size distribution data from core samples, and water- and oil-level data. Most of these data sets have been described in previously published peer-reviewed reports. This data release provides data sets that were not included with the original publications in a tabular, database-ready format. Each result value in the data sets is coded to describe the kind of sample collected, the material that was analyzed, the method of analysis, and the publication where the value was originally published. Some sample codes are taken from the U.S. Geological Survey's National Water Information System (NWIS, https://waterdata.usgs.gov/nwis) and the remaining codes were developed specifically for Site data. Data dictionaries containing code definitions are available at a companion data release titled "Sampling site information, well construction details, and data dictionaries for data sets associated with the National Crude Oil Spill Fate and Natural Attenuation Site near Bemidji, Minnesota", available at https://doi.org/10.5066/F7736PDR. The National Crude Oil Spill Fate and Natural Attenuation Research Site is located where a high-pressure pipeline carrying crude oil burst in 1979 and spilled approximately 1.7 million liters (10,700 barrels) of crude oil into glacial outwash deposits. Since 1983, scientists with the U.S. Geological Survey, in collaboration with scientists from academic institutions, industry, and the regulatory community have conducted extensive investigations of multiphase flow and transport, volatilization, dissolution, geochemical interactions, microbial populations, and biodegradation with the goal of providing an improved understanding of the natural processes limiting the extent of hydrocarbon contamination. Long-term field studies at Bemidji have illustrated that the fate of hydrocarbons evolves with time, and a snap-shot study of a hydrocarbon plume may not provide information that is of relevance to the long-term behavior of the plume during natural attenuation. The research at the site has been supported primarily by the U.S. Geological Survey's Toxic Substances Hydrology Program.

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.

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...

The largest oil spill affecting U.S. waters since 1969 is the Deepwater Horizon spill. On April 20, 2010, an explosion on the BP-operated Deepwater Horizon drilling platform caused some 134 million gallons, or 3.2 million barrels, of oil to spill into the Gulf of Mexico until the well was eventually declared sealed on September 19, 2010. This event is also the largest marine oil spill in history.

In 2024, Shell recorded operational spills of some 1,230 metric tons, compared with around 2,000 metric tons spilled due to sabotage. This was an increase compared to the previous year. The company indicates that all of its sabotage spills were related to its operations in Nigeria. In 2008 and 2009, equipment failure and criminal activity resulted in the company's most significant oil spills, when around 30,000 metric tons of oil flooded the Niger Delta swamps.

The Exxon Valdez oil spill area includes the area enclosed by the maximum extent of the oiled shorelines, severly affected communities and their immediate human use areas and adjacent uplands to the watershed divide. The intended use of this dataset is as a resource for damage assessment and future restoration activities. Data available from the Alaska Department of Natural Resources.

this was created in R,locker studio and OurDataWorld:

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Oil spills are disasters that can have severe social, economic, and environmental impacts.

They are the release of crude oil or refined petroleum products from tankers, rigs, wells, and offshore platforms.

These spills are most common in marine environments but can also occur on land. They can have disastrous consequences for local ecosystems, and be expensive due to the loss of oil and the costs involved in their clean-up.

The number of oil spills and the quantity of oil that is spilled from tankers has fallen substantially in recent decades.1

On this page, you can find all our data, visualizations, and writing relating to oil spills. Specifically, this refers to oil spills from tankers – container ships transporting oil – where consistent, high-quality global data is available.

But not all oil spills come from tankers. They can also come from other sites, such as offshore oil rigs and damaged pipelines. The world’s largest (and most well-known) event was Deepwater Horizon in the Gulf of Mexico in 2010. This disaster was caused by an explosion in a drilling rig. The US Government estimates that 4.9 million barrels of oil were released (equivalent to around 700,000 tonnes).

Tracking non-tanker oil spills is essential, but we are unaware of any global, updated databases that include this. Filling this gap would be critical to global environmental data and monitoring.

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...

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 2024, there were ** cases of oil spills across the Gulf of Thailand, which decreased from the previous year. The oil spills have a negative impact on the marine ecosystem and organisms caused by contamination of petroleum hydrocarbons.