An inventory of greenhouse gas emissions due to natural gas pipeline incidents in the United States and Canada from 1980s to 2021

Lu, Hongfang; Xu, Zhong; Cheng, Y. Frank; Peng, Haoyan; Xi, Dongmin; Jiang, Xinmeng; Ma, Xin; Dai, Jun; Shan, Yuli

doi:10.1038/s41597-023-02177-0

Cited by 19 publications

(2 citation statements)

References 33 publications

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“…These pipelines, due to their immense scale and connectivity, play an essential role in facilitating energy supplies and economic growth of the country 3 . However, occurrence of accidents within this intricate pipeline system can pose significant environmental challenges, particularly cause release of substantial amount of greenhouse gases (GHGs) 4 , 5 . Methane is the primary GHG released from crude oil pipelines when accidents occur.…”

Section: Background and Summarymentioning

confidence: 99%

Greenhouse gas emissions from U.S. crude oil pipeline accidents: 1968 to 2020

Lu,

Xu,

Song

et al. 2023

Sci Data

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Crude oil pipelines are considered as the lifelines of energy industry. However, accidents of the pipelines can lead to severe public health and environmental concerns, in which greenhouse gas (GHG) emissions, primarily methane, are frequently overlooked. While previous studies examined fugitive emissions in normal operation of crude oil pipelines, emissions resulting from accidents were typically managed separately and were therefore not included in the emission account of oil systems. To bridge this knowledge gap, we employed a bottom-up approach to conducted the first-ever inventory of GHG emissions resulting from crude oil pipeline accidents in the United States at the state level from 1968 to 2020, and leveraged Monte Carlo simulation to estimate the associated uncertainties. Our results reveal that GHG emissions from accidents in gathering pipelines (~720,000 tCO2e) exceed those from transmission pipelines (~290,000 tCO2e), although significantly more accidents have occurred in transmission pipelines (6883 cases) than gathering pipelines (773 cases). Texas accounted for over 40% of total accident-related GHG emissions nationwide. Our study contributes to enhanced accuracy of the GHG account associated with crude oil transport and implementing the data-driven climate mitigation strategies.

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Section: Background and Summarymentioning

confidence: 99%

Greenhouse gas emissions from U.S. crude oil pipeline accidents: 1968 to 2020

Lu,

Xu,

Song

et al. 2023

Sci Data

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“…of natural gas pipelines around the world has also increased rapidly [5]. According to statistics, as of 2021, the United States has built 330 000 km of natural gas pipelines, while Canada has approximately 84 000 km [6]. The natural gas network established by the European Union comprises transportation pipelines spanning over 200 000 km and distribution pipelines exceeding 2 million km [7].…”

Section: Introductionmentioning

confidence: 99%

An intelligent feature recognition method of natural gas pipelines based on shapelet and blending fusion model

Ma,

Hu,

Wang

et al. 2023

Meas. Sci. Technol.

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Despite the availability of pipeline bending strain detection technologies based on inertial measurement unit (IMU), there is a lack of intelligent and efficient methods for accurately identifying pipeline features by bending strain. Therefore, this paper proposes a novel method for identifying features in natural gas pipelines based on shapelet and blending fusion model. Specifically, the shape features of the bending strain data are extracted and transformed by shapelet. Then a blending fusion model with SVM, Decision Tree and Gradient Boosting as base learners and Random Forest as meta-learner is constructed. Finally, the extracted features are fed into the blending fusion model for pipeline feature recognition. The model is trained with bending strain data obtained from a real natural gas pipeline, the results indicate that the recognition accuracy of the proposed method is 97.17%. Compared with other models, the superiority of the proposed model is verified, and it is proved that the proposed method has better accuracy than the existing models (over 1.3%). Overall, the method proposed in this paper can be effectively combined with the in-line inspection system to provide a reference for pipeline companies to carry out pipeline integrity management.

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