Nordstream Pipelines CH4 Leak Estimates and Transport Uncertainty Using ICOS Data and the FLEXPART Lagrangian Particle Dispersion Model

Ignacio Pisso,Stephen Platt,Norbert Schmidbauer,Sabine Eckhardt,Nikolaos Evangeliou, Erik Marthinsen,Rona Thompson,Massimo Cassiani

crossref（2024）

Cited 0|Views28

Abstract

Following the sabotage of the Nord Stream 1 and 2 subsea pipelines on 26 September 2022, natural gas leaks resulted in unprecedented emissions of methane that were detected by several ICOS stations. As the plume traveled North, the detections occurred mainly in Scandinavia. NILU’s initial modeling activities provided a preliminary estimate of 155 KtonCH4 for the leaks that was made public as a press release. A recent collaborative effortorganized by the United Nations Environment Programme’s International Methane Emissions Observatory (UNEP’s IMEO) provided new model-based pipeline rupture outflow rates. In combination with updated ICOS CH4 time series we updated the estimated release values produced. We discuss the uncertainties associated with the atmospheric modelling for this updated analysis with emphasis on the Lagrangian transport aspects of the problem and the associated uncertainties.

Translated text

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Data Disclaimer

The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn

Chat Paper

【要点】：本文通过整合ICOS站点的甲烷监测数据和FLEXPART模型，估算了北溪管道损坏导致的甲烷泄漏量，并探讨了运输过程中的不确定性，创新点在于结合了实时大气监测数据与先进的粒子扩散模型进行泄漏估算。

【方法】：利用ICOS大气监测网络提供的甲烷浓度数据，并结合FLEXPART拉格朗日粒子扩散模型，对北溪管道泄漏的甲烷排放量进行估算。

【实验】：通过对2022年9月26日北溪管道被破坏后，由ICOS站点监测到的甲烷浓度变化进行分析，结合FLEXPART模型的模拟结果，更新了甲烷泄漏量估算，并讨论了模型的不确定性。论文未明确提及使用的具体数据集名称，但可以推断数据来源于ICOS大气监测网络。