Statistical analysis of environmental consequences of hazardous liquid pipeline accidents

Abstract

Although pipelines are the safest method to transport fuels, they are associated with risks due to failures, leading to significant negative consequences. This paper investigates pipeline accident data provided by PHMSA (Pipeline and Hazardous Material Safety Administration) between 2010 and 2017, with a focus on environmental consequences of hazardous liquid pipeline accidents. The average amount of released product, the average time elapsed between the accident, the emergency response from the oil company, and the average costs of environmental remediation are estimated. The impact on soil, water, and wildlife is investigated for frequency and magnitude, where possible. It was found that, on average, 85% of product released after an accident remained unrecovered, 53% of accidents led to soil contamination, 41% of accidents impacted environmentally sensitive areas, and 92% of water crossing pipelines involved in accidents were uncased. From an annual average total cost of USD 326 million, annual average environmental damage and remediation costs were USD 140 million. This analysis assists in the diagnosis of challenges that might be addressed with improved maintenance and inspection programs, especially for pipelines at higher risk of negative environmental consequences. Finally, the performance of safety management systems should be improved to efficiently respond to emergencies.

Predicting Environmental Impact of Hazardous Liquid Pipeline Accidents: Application of Intelligent Systems

“…Currently, comprehensive design information must be collected by regulators after pipeline accidents, including pipeline material; despite this, a large number of missing data points makes it challenging to include certain design variables as inputs for the model. Belvederesi et al (2018) show that, over time, regulators are becoming stricter with regard to reporting and the quality and quantity of information collected after an accident. For this reason, future iterations of this model should include more explanatory variables as they become available, such as pipeline material, and the model should be updated with newly collected data to ensure that predictions will be accurate in addition to the model being computationally efficient.…”

“…The quality and quantity of information provided by PHMSA have increased over time. To ensure consistency and significance for model training and, therefore, predictions, this study focuses on information provided by PHMSA between January 1, 2010, and October 31, 2018, because it was collected under the same requirements and reporting criteria, enabling a more robust analysis of hazardous liquid pipeline failures in the US (Belvederesi et al 2018). Moreover, this study considers both offshore and onshore gathering and transmission hazardous liquid pipelines regulated by PHMSA.…”

“…Pipelines have the lowest failure rates among fuel transportation methods, on the order of 10 −3 and 10 −4 accidents per km per year for onshore and offshore pipelines, respectively (Belvederesi 2017;Green and Jackson 2015;Singleton 2013). However, the receiving environments, communities, and oil companies can be significantly impacted by a single accident (Belvederesi et al 2018;TransCanada 2017;PHMSA 2018b;EPA 2015). The objective of this study is to predict environmental consequences due to hazardous liquid pipeline accidents based on pipeline explanatory variables, such as commodity transported, accident location, nominal diameter, nominal wall thickness, and material-release method.…”

“…The objective of this study is to predict environmental consequences due to hazardous liquid pipeline accidents based on pipeline explanatory variables, such as commodity transported, accident location, nominal diameter, nominal wall thickness, and material-release method. These explanatory variables were selected following a thorough statistical analysis of the relationships between pipeline design and failure (Belvederesi et al 2018), which informs on the data availability and significance. Moreover, this set of explanatory variables represents the basic pipeline design and the information most commonly gathered by regulatory agencies after pipeline accidents worldwide (Belvederesi 2017), which helps model reproducibility in other countries.…”

“…To develop a predictive model, it is necessary to have a robust, complete, and informative data set containing information gathered from past accidents. Belvederesi et al (2017Belvederesi et al ( , 2018 investigated the limitations that derive from inadequate data and their implications for accident response actions, and conducted a thorough statistical analysis of trends in hazardous liquid pipeline accidents in relation to the pipeline design characteristics. Major findings led to the development of the model presented in this study.…”