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Environ Sci Technol. 2016 Nov 15;50(22):12512-12520. Epub 2016 Oct 26.

Methane Leaks from Natural Gas Systems Follow Extreme Distributions.

Author information

1
Department of Energy Resources Engineering, Stanford University , Stanford California 94305, United States.
2
National Renewable Energy Laboratory, Golden, Colorado 80401, United States.
3
Colorado State University , Fort Collins, Colorado 80523, United States.

Abstract

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.

PMID:
27740745
DOI:
10.1021/acs.est.6b04303
[Indexed for MEDLINE]

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