In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

The largest reported data leakage as of January 2025 was the Cam4 data breach in March 2020, which exposed more than 10 billion data records. The second-largest data breach in history so far, the Yahoo data breach, occurred in 2013. The company initially reported about one billion exposed data records, but after an investigation, the company updated the number, revealing that three billion accounts were affected. The National Public Data Breach was announced in August 2024. The incident became public when personally identifiable information of individuals became available for sale on the dark web. Overall, the security professionals estimate the leakage of nearly three billion personal records. The next significant data leakage was the March 2018 security breach of India's national ID database, Aadhaar, with over 1.1 billion records exposed. This included biometric information such as identification numbers and fingerprint scans, which could be used to open bank accounts and receive financial aid, among other government services.

Cybercrime - the dark side of digitalization As the world continues its journey into the digital age, corporations and governments across the globe have been increasing their reliance on technology to collect, analyze and store personal data. This, in turn, has led to a rise in the number of cyber crimes, ranging from minor breaches to global-scale attacks impacting billions of users – such as in the case of Yahoo. Within the U.S. alone, 1802 cases of data compromise were reported in 2022. This was a marked increase from the 447 cases reported a decade prior. The high price of data protection As of 2022, the average cost of a single data breach across all industries worldwide stood at around 4.35 million U.S. dollars. This was found to be most costly in the healthcare sector, with each leak reported to have cost the affected party a hefty 10.1 million U.S. dollars. The financial segment followed closely behind. Here, each breach resulted in a loss of approximately 6 million U.S. dollars - 1.5 million more than the global average.

During the third quarter of 2024, data breaches exposed more than *** million records worldwide. Since the first quarter of 2020, the highest number of data records were exposed in the first quarter of ***, more than *** million data sets. Data breaches remain among the biggest concerns of company leaders worldwide. The most common causes of sensitive information loss were operating system vulnerabilities on endpoint devices. Which industries see the most data breaches? Meanwhile, certain conditions make some industry sectors more prone to data breaches than others. According to the latest observations, the public administration experienced the highest number of data breaches between 2021 and 2022. The industry saw *** reported data breach incidents with confirmed data loss. The second were financial institutions, with *** data breach cases, followed by healthcare providers. Data breach cost Data breach incidents have various consequences, the most common impact being financial losses and business disruptions. As of 2023, the average data breach cost across businesses worldwide was **** million U.S. dollars. Meanwhile, a leaked data record cost about *** U.S. dollars. The United States saw the highest average breach cost globally, at **** million U.S. dollars.

Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine Mark Schwabacher, NASA Ames Research Center Robert Aguilar, Pratt & Whitney Rocketdyne Fernando Figueroa, NASA Stennis Space Center Abstract The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically “learns” a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to “train” and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it “learned” a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location. Introduction The J-2X rocket engine will be tested on Test Stand A-1 at NASA Stennis Space Center (SSC) in Mississippi. A team including people from SSC, NASA Ames Research Center (ARC), and Pratt & Whitney Rocketdyne (PWR) is developing a prototype end-to-end integrated systems health management (ISHM) system that will be used to monitor the test stand and the engine while the engine is on the test stand[1]. The prototype will use several different methods for detecting and diagnosing faults in the test stand and the engine, including rule-based, model-based, and data-driven approaches. SSC is currently using the G2 tool http://www.gensym.com to develop rule-based and model-based fault detection and diagnosis capabilities for the A-1 test stand. This paper describes preliminary results in applying the data-driven approach to detecting and diagnosing faults in the J-2X engine. The conventional approach to detecting and diagnosing faults in complex engineered systems such as rocket engines and test stands is to use large numbers of human experts. Test controllers watch the data in near-real time during each engine test. Engineers study the data after each test. These experts are aided by limit checks that signal when a particular variable goes outside of a predetermined range. The conventional approach is very labor intensive. Also, humans may not be able to recognize faults that involve the relationships among large numbers of variables. Further, some potential faults could happen too quickly for humans to detect them and react before they become catastrophic. Automated fault detection and diagnosis is therefore needed. One approach to automation is to encode human knowledge into rules or models. Another approach is use data-driven methods to automatically learn models from historical data or simulated data. Our prototype will combine the data-driven approach with the model-based and rule-based appro

Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine Mark Schwabacher, NASA Ames Research Center Robert Aguilar, Pratt & Whitney Rocketdyne Fernando Figueroa, NASA Stennis Space Center Abstract The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically “learns” a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to “train” and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it “learned” a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location. Introduction The J-2X rocket engine will be tested on Test Stand A-1 at NASA Stennis Space Center (SSC) in Mississippi. A team including people from SSC, NASA Ames Research Center (ARC), and Pratt & Whitney Rocketdyne (PWR) is developing a prototype end-to-end integrated systems health management (ISHM) system that will be used to monitor the test stand and the engine while the engine is on the test stand[1]. The prototype will use several different methods for detecting and diagnosing faults in the test stand and the engine, including rule-based, model-based, and data-driven approaches. SSC is currently using the G2 tool http://www.gensym.com to develop rule-based and model-based fault detection and diagnosis capabilities for the A-1 test stand. This paper describes preliminary results in applying the data-driven approach to detecting and diagnosing faults in the J-2X engine. The conventional approach to detecting and diagnosing faults in complex engineered systems such as rocket engines and test stands is to use large numbers of human experts. Test controllers watch the data in near-real time during each engine test. Engineers study the data after each test. These experts are aided by limit checks that signal when a particular variable goes outside of a predetermined range. The conventional approach is very labor intensive. Also, humans may not be able to recognize faults that involve the relationships among large numbers of variables. Further, some potential faults could happen too quickly for humans to detect them and react before they become catastrophic. Automated fault detection and diagnosis is therefore needed. One approach to automation is to encode human knowledge into rules or models. Another approach is use data-driven methods to automatically learn models from historical data or simulated data. Our prototype will combine the data-driven approach with the model-based and rule-based appro

Oil and Gas Pipeline Leak Detection Market Outlook

The global oil and gas pipeline leak detection market size is projected to experience significant growth, with an expected valuation rising from USD 2.37 billion in 2023 to USD 3.89 billion by 2032, reflecting a healthy compound annual growth rate (CAGR) of 5.6% from 2024 to 2032. This market expansion is largely fueled by the increasing emphasis on safety and environmental regulations, the growing complexity of pipeline networks, and the dire need for efficient and reliable leak detection systems. As governments and organizations worldwide become more aware of and committed to reducing the environmental impacts of fossil fuel extraction and transportation, the demand for advanced leak detection technologies has intensified, driving market growth.

One of the primary factors contributing to the growth of the oil and gas pipeline leak detection market is the stringent regulatory frameworks being implemented globally to prevent environmental disasters. These regulations mandate the installation of sophisticated leak detection systems to minimize the risk of oil spills and gas leaks, which can have catastrophic environmental and economic consequences. The increasing public awareness and pressure on governments to ensure the safety and integrity of oil and gas infrastructure have also played a crucial role in driving the market's expansion. Furthermore, the adoption of best practices and international standards in pipeline monitoring and maintenance is further propelling the demand for innovative and reliable leak detection technologies.

Technological advancements in the oil and gas industry have paved the way for the development of more efficient and accurate leak detection systems. Innovations such as acoustic/ultrasonic sensors, fiber optic technologies, and advanced data analytics are improving the precision and reliability of leak detection, thereby reducing operational risks and potential losses. The integration of Internet of Things (IoT) and artificial intelligence (AI) in pipeline monitoring systems enhances real-time data collection and analysis, enabling prompt detection and response to leaks. These cutting-edge technologies are not only enhancing the effectiveness of leak detection but also reducing the overall costs associated with pipeline monitoring and maintenance, making them increasingly attractive to oil and gas companies.

The growing global energy demand and the expansion of oil and gas pipeline networks, especially in emerging economies, are also driving the need for efficient leak detection systems. As countries endeavor to secure their energy supply and improve infrastructure, significant investments are being made in the construction and maintenance of extensive pipeline networks. This expansion necessitates robust leak detection solutions to ensure the safe and efficient transportation of oil and gas resources. Additionally, the shift towards unconventional oil and gas resources, such as shale gas and deepwater drilling, presents new challenges in leak detection, further increasing the demand for advanced technologies.

Pipeline Leak Detectors play a crucial role in ensuring the safety and efficiency of oil and gas transportation. These detectors are designed to identify leaks quickly and accurately, minimizing the risk of environmental damage and economic loss. By utilizing advanced technologies such as acoustic sensors and fiber optics, pipeline leak detectors can provide real-time monitoring and immediate alerts, allowing operators to respond swiftly to any potential issues. This capability is particularly important in complex pipeline networks, where undetected leaks can lead to significant operational challenges. As the industry continues to evolve, the integration of pipeline leak detectors with digital technologies like AI and IoT is enhancing their effectiveness, offering more precise detection and predictive maintenance capabilities.

Technology Analysis

The technology segment of the oil and gas pipeline leak detection market encompasses various sophisticated systems, each offering unique advantages in detecting leaks with precision. Acoustic/ultrasonic technology, for instance, stands out for its ability to detect leaks through sound waves. This method is particularly effective in situations where traditional methods may fall short, as it can monitor for changes in noise levels along pipeline routes, indicating potential leaks. The sensitivity of acoustic/ultrasonic systems to sound variations makes th

Water loss due to undetected pipeline leaks is a critical issue in urban infrastructure and smart utility networks. In water transport systems, small leaks can escalate into major inefficiencies, driving up operational costs and wasting precious resources—especially in arid or high-demand regions like the UAE, where this project was inspired.

This dataset simulates real-world IoT sensor data from a smart water transport network, combining geolocation (latitude, longitude) and telemetry values (pressure, flow rate, vibration, RPM, and operational hours) to detect potential pipeline leakage. It supports the development of machine learning models that can power real-time monitoring systems and interactive GIS dashboards.

📡 Sources:

This dataset is synthetically generated but carefully modeled after real-world industrial systems and smart utility practices. Sensor behaviors (e.g., pressure drops, abnormal flow rates) are crafted to mimic patterns observed in real leakage events.

Sensor types: Pressure, flow rate, temperature, vibration, RPM, operational hours

GPS values simulate pipeline segment locations in a grid-style zone system

Labels were generated using a rule-based thresholding logic to indicate leak conditions

If you are working with actual utility providers or have IoT devices, this dataset can serve as a foundation for building real-time predictive models and dashboards.

💡 Inspiration:

This dataset was created to power a complete ML + API + Dashboard workflow, including:

A machine learning model using XGBoost for binary classification

A Flask API for real-time leakage prediction

A Streamlit dashboard with an interactive GIS map to visualize detected leaks

The goal was to build a portfolio-ready, real-world project for smart cities, IoT analytics, and geospatial machine learning—particularly targeting applications in water transport, infrastructure monitoring, and predictive maintenance.

Use Cases:

Build real-time ML pipelines for leakage detection

Visualize water transport failures on interactive maps

Experiment with anomaly detection in geospatial sensor data

Extend into MQTT or real sensor integration for smart cities

This dataset is about books. It has 1 row and is filtered where the book is Data leaks for dummies. It features 7 columns including author, publication date, language, and book publisher.

This is the first fully labeled open dataset for leak detection and localization in water distribution systems. This dataset includes two hundred and eighty signals acquired from a laboratory-scale water distribution testbed with four types of induced leaks and no-leak. The testbed was 47 m long built from 152.4 mm diameter PVC pipes. Two accelerometers (A1 and A2), two hydrophones (H1 and H2), and two dynamic pressure sensors (P1 and P2) were deployed to measure acceleration, acoustic, and dynamic pressure data. The data were recorded through controlled experiments where the following were changed: network architecture, leak type, background flow condition, background noise condition, and sensor types and locations. Each signal was recorded for 30 seconds. Network architectures were looped (LO) and branched (BR). Leak types were Longitudinal Crack (LC), Circumferential Crack (CC), Gasket Leak (GL), Orifice Leak (OL), and No-leak (NL). Background flow conditions included 0 L/s (ND), 0.18 L/s, 0.47 L/s, and Transient (background flow rate abruptly changed from 0.47 L/s to 0 L/s at the second 20th of 30-second long measurements). Background noise conditions, with noise (N) and without noise (NN), determined whether a background noise was present during acoustic data measurements. Accelerometer and dynamic pressure data are in ‘.csv’ format, and the hydrophone data are in ‘.raw’ format with 8000 Hz frequency. The file “Python code to convert raw acoustic data to pandas DataFrame.py” converts the raw hydrophone data to DataFrame in Python.

This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, currently under review.

The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis.

The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.

According to Cognitive Market Research, the Global Information Security Consulting Market is expected to have a market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.

The Asia-Pacific region has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
North America is the fastest growing with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
Cloud Security has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
The cloud segment has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
Large Enterprise has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
The BFSI segment has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.

Market Dynamics

Key drivers

The increasing number of cyber-attacks globally is favoring market growth

Strong security solutions are in more demand as a result of the growing anxiety that cyber assaults are causing among both individuals and enterprises. Any hostile action directed towards computer networks, infrastructures, personal computers, smartphones, or computer information systems is called a cyberattack. Because of this and the need for more stringent security and regulatory compliance, the information security consulting industry is growing quickly. For instance, according to McKinsey and company, cyberattacks are on track to cause $10.5 trillion a year in damage by 2025. That’s a 300 percent increase from 2015 levels. To protect against the onslaught, organizations around the world spent around $150 billion on cybersecurity in 2021, and this sum is growing by 12.4 percent a year. In all industries combined, the average cost of a single data breach as of 2022 was approximately 4.35 million US dollars. The healthcare industry was shown to be the most expensive for this, with each leak estimated to have cost the impacted party a whopping 10.1 million dollars. The segment on finances was closely followed. The Cam4 data breach in March 2020, which revealed over 10 billion data records, was the largest known data leak as of January 2024. The Yahoo data breach, which happened in 2013, is currently the second-largest data breach in history. To compact these increasing data breaches and cybercrimes, many company solutions have been in development and adopted. Cloud migration will remain a key component of many organizations' technological agendas. For this reason, cloud providers must be able to safeguard both standard and customized cloud configurations. Furthermore, there is a sharp rise in the demand for cyber security in the fields of healthcare, banking and financial services, aviation, and automobiles. Some of the main factors driving the demand for technologically advanced information security solutions among businesses are the emergence of IoT and connected technologies, the quick adoption of smartphones for digital payments, and the use of unsecured networks for accessing organizational servers. Therefore, the market is expected to grow significantly in the coming years.

(Source-http://https://www.statista.com/statistics/290525/cyber-crime-biggest-online-data-breachesworldwide/#:~:text=The%20largest%20reported%20data%20leakage,data%20breach%2C%20occurred%20in%202013.)

The rise in the number of regulations and developments has favoured the market growth

As cyber risks continue to grow, information security has become a key concern for both individuals and enterprises. The laws and regulatory requirements that are propelling the information security market's expansion are intended to strengthen cybersecurity defenses and shield private information from nefarious individuals. For instance, The United States government enacted two cybersecurity laws into law in June 2022. The first bill, the State and Local Government Cybersecurity Act of 2022, aims to improve cooperation between state, territorial, local, and tribal governments as well as the Cybersecurity and Infrastructure Security Agency (CISA). It is anticipated that these important actions will boost the i...

Explore the historical Whois records related to leaked.today (Domain). Get insights into ownership history and changes over time.

The size of the Leak Detection market was valued at USD XX Million in 2024 and is projected to reach USD XXX Million by 2033, with an expected CAGR of 6% during the forecast period.The leakage detection process aims to identify the position of leakages that happen in diverse systems, ranging from pipelines, equipment in industry processes, to building structures. Various systems affected by leaks may be within water supply and distribution, oil and gas transportation, chemical processes, heating or cooling distribution lines, among many more.Leak detection technologies use several methods, which include acoustic emission monitoring, ground penetrating radar, infrared thermography, and fiber optic sensing. Use of these technologies makes sure that leak location is precisely determined, hence preventing extended downtime and environmental damage.A few factors have been pushing the leak detection market forward. These factors are increasing environmental regulations, a need for resource-saving, and a heightened call to ensure safety and efficiency in all sectors.

Closed system drug-transfer devices (CSTDs) are devices which replace traditional needles, septa, and other connectors used for transferring hazardous drugs (HDs). NIOSH recommends using CSTDs to limit occupational exposure to hazardous materials and sharps when compounding and administering these drugs (NIOSH 2004). One of the engineering challenges associated with CSTDs is management of the headspace that is either compressed or displaced when transferring liquids in and out of drug vials. CSTD designs and components employing various technologies include creating a physical barrier to contain the displaced volume of air or filters to clean the displaced volume of air when transferring HD solutions. In 2015, NIOSH developed a protocol to test material containment of barrier type CSTDs (NIOSH 2015). NIOSH presented a plan to update the testing protocol so that it was applicable to both barrier and air cleaning types of CSTDs (NIOSH 2016). Both barrier type CSTDs and air cleaning type CSTDs may be susceptible to either liquid or headspace vapor leaks. Air-cleaning type CSTDs allow free passage of air but are expected to remove semi-volatile hazardous drugs (HD)s from the exiting airstream. Barrier type CSTDs have been designed to contain air rather than clean it, and it is reasonable to conclude that a headspace leak with a barrier type CSTD would contain the drug at the same concentration as the headspace inside the vial. As a result, the procedure described in this paper can adequately assess the efficacy of barrier type CSTDs based on the volume of liquid and headspace vapor leak measured. However, the volatile compounds used in this procedure will readily pass through an air-cleaning CSTD, regardless of the ability to retain a semi-volatile HD. Therefore, testing the efficacy of an air-cleaning CSTD requires coupling the procedure described herein with an assessment of the ability of air cleaning CSTDs to retain an appropriate semi-volatile surrogate when volumes of headspace containing that surrogate are passed through the CSTDs. The difference in the amount of HD contained in liquid versus headspace vapor leaks may be several orders of magnitude. The work presented herein is a test method that can distinguish the origin and volumetric quantity of liquid and headspace vapor leaked.

CSTD evaluation involves operation of CSTDs during normal use tasks, such as transferring a solution between two drug vials (NIOSH 2015). A test solution containing two volatile organic compounds (VOCs), acetone and methyl t-butyl ether (MTBE), was used in the evaluation. Leaks were measured by detecting the VOCs in a glove chamber using Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) as the detector. Liquid and headspace leaks are differentiated by the ratios of the two VOCs measured as a result of leaks from the CSTD. The compounds, acetone and MTBE, at equal concentrations in a test solution have a concentration ratio in the headspace vapor of the test solution that is very different, as predicted by their Henry’s constants. The ratio of acetone to MTBE detected in the glove chamber can be used to elucidate the source, liquid or headspace, and the magnitude of a leak. The analytical strategy is similar to stable isotope mixing models used to determine contributions from various sources by measuring isotopic ratios (Phillips and Gregg 2001). Propylene glycol (PG) was included in the testing solution as a surrogate for a HD component, though it was not quantified. Fluorescein was included as a visual qualitative indicator of a liquid leak location. SIFT-MS offers low limits of detection and real-time response. The real-time response has the benefit of enabling leaks to be temporally correlated with tasks involving manipulation of CSTD components.

Between 2004 and April 2025, internet users in the United States experienced many significant data breach incidents. In these incidents, passwords were the most frequently leaked type of data, with more than two billion passwords being leaked in the research period. Names of the cities where the users were located ranked second, followed by first names.

This dataset shows the location of storage tanks and associated piping used for petroleum and certain hazardous substances that have experienced leaks as determined by RIDEM.

The dataset was designed for the inventory and mapping of hazardous material in Rhode Island.

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH4) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ∼15 000 measurements from 18 prior studies, we show that all available natural gas leakage data sets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of the total leakage volume. While prior studies used log-normal model distributions, we show that log-normal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH4 emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of data sets to increase sample size is not recommended due to apparent deviation between sampled populations. Understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies.

Leaking Underground Storage Tank (LUST) sites where petroleum contamination has been found. There may be more than one LUST site per UST site.

A typical leaking underground storage tank (LUST) scenario involves the release of a fuel product from an underground storage tank (UST) that can contaminate surrounding soil, groundwater, or surface waters, or affect indoor air spaces. Early detection of an UST release is important, as is determining the source of the release, the type of fuel released, the occurrence of imminently threatened receptors, and the appropriate initial response. The primary objective of the initial response is to determine the nature and extent of a release as soon as possible.PROHIBITED USES: KSA 45-230 prohibits the use of names and addresses contained in public records for certain commercial purposes. By submitting this request, you are signing the following written certification that you will not use the information in the records for any purpose prohibited by law.

DATA LIMITATIONS:

This data set is not designed for use as a regulatory tool in permitting or citing decisions; it may be used as a reference source. Carefully consider the provisional or incomplete nature of these data before using them for decisions that concern personal safety or involves substantial monetary consequences.

This dataset contains one facility point per LUST data record. The points will be stacked if multiple LUST occurred at the same facility.

A new facility point is added when a new facility is added to the origination database.

Data is replicated on a nightly basis for public consumption. KDHE is not responsible for database integrity following download.

The facility point is not the exact location of the tank, but a general representative somewhere in the property of the Storage Tank Facility.

KDHE makes no assurances of the accuracy or validity of information presented in the Spatial Data. KDHE Tanks have been located using a variety of locational methods. More recent points are geocoded and validated with accuracy of 3-10 meters. Many inactive/old facilities only had a Legal description to calculate point placement on a map, with an accuracy of 250 – 2000 meters.For users who wish to interact with the data in a finished product, KDHE recommends using our Kansas Environmental Interest Finder . More information about KDHE can be found on the Kansas Department of Health and Environment website .More information about KDHE Storage Tanks can be found on the Kansas Department of Health and Environment website Storage Tanks Division .ATTRIBUTES description: Start Date/End Date: The LUST is considered finished when the remediation has occurred and the environment is back to pre-contamination state. A new LUST will be recorded if the Tank Leaks again. Approved TRUST: Flag Yes if approved for EPA TRUST: In 1986, Congress created the Leaking Underground Storage Tank (LUST) Trust Fund to address petroleum releases from federally regulated underground storage tanks (USTs) by amending Subtitle I of the Solid Waste Disposal Act. In 2005, the Energy Policy Act expanded eligible uses of the Trust Fund to include certain leak prevention activities.

Water Leak Detection Sensors Market Outlook

The global water leak detection sensors market size was valued at $1.5 billion in 2023 and is projected to reach $2.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.1% from 2024 to 2032. The rapid growth of this market can be attributed to increasing urbanization, the need for efficient water management, and rising awareness about water conservation.

One of the primary growth factors in the water leak detection sensors market is the increasing scarcity of water resources globally. With growing populations and expanding urban areas, the demand for water is skyrocketing, necessitating advanced systems for its effective management. Water leak detection sensors are becoming essential tools in urban planning and infrastructure development, helping to minimize wastage and ensuring sustainable water supply systems. Governments and environmental organizations are actively promoting innovations in water management technologies, further accelerating the market growth.

Another significant driver is technological advancement in sensor technology. Innovations such as the Internet of Things (IoT) have revolutionized the water leak detection landscape. IoT-enabled sensors provide real-time data and remote monitoring capabilities, allowing for instantaneous leak detection and reducing response times significantly. These advanced solutions are not only more efficient but also more cost-effective in the long term, driving their adoption across various sectors including residential, commercial, and industrial applications.

Furthermore, stringent regulatory frameworks and policies aimed at water conservation and management are propelling the adoption of water leak detection systems. Governments worldwide are implementing regulations that mandate the use of efficient water management systems to curb wastage. For instance, regions prone to water scarcity issues, such as the Middle East & Africa and parts of Asia Pacific, have stringent policies that enforce the usage of advanced leak detection systems, thereby driving market growth. These regulatory measures are not only limited to water utilities but extend to other sectors like oil & gas, manufacturing, and data centers, contributing to the market expansion.

The integration of Smart Home Water Sensor and Controller systems is revolutionizing the way homeowners manage water usage and detect leaks. These systems not only provide real-time alerts but also allow users to remotely control water flow, significantly reducing the risk of water damage. By leveraging IoT technology, smart home water sensors can communicate with other devices in the home, creating a seamless and efficient water management system. This not only enhances convenience but also contributes to water conservation efforts by allowing users to monitor and adjust their water usage patterns. As more consumers adopt smart home technologies, the demand for integrated water management solutions is expected to rise, further driving the growth of the water leak detection sensors market.

From a regional perspective, North America and Europe are expected to be leading markets for water leak detection sensors, driven by advanced infrastructure and high levels of awareness regarding water conservation. However, Asia Pacific is anticipated to exhibit the highest growth rate during the forecast period, owing to rapid urbanization and significant investments in smart city projects. Emerging economies in Latin America and the Middle East & Africa are also expected to witness substantial growth, supported by increasing government initiatives focusing on water management and infrastructural improvements.

Product Type Analysis

In the realm of product types, acoustic sensors have gained significant traction due to their high accuracy and reliability in detecting leaks. These sensors work by picking up sound waves generated by leaks and are particularly effective in detecting small leaks that might not be immediately visible. Acoustic sensors are widely used in both residential and commercial applications due to their efficiency in pinpointing the exact location of leaks, minimizing damage, and reducing repair costs. Their ability to integrate seamlessly with IoT-based systems further enhances their appeal, making them a popular choice in advanced water management solutions.

Pressure sensors represent another critical segment in the wa