Literature DB >> 28326761

Rapid, Vehicle-Based Identification of Location and Magnitude of Urban Natural Gas Pipeline Leaks.

Joseph C von Fischer1, Daniel Cooley2, Sam Chamberlain1, Adam Gaylord1, Claire J Griebenow1, Steven P Hamburg3, Jessica Salo4, Russ Schumacher5, David Theobald6, Jay Ham7.   

Abstract

Information about the location and magnitudes of natural gas (NG) leaks from urban distribution pipelines is important for minimizing greenhouse gas emissions and optimizing investment in pipeline management. To enable rapid collection of such data, we developed a relatively simple method using high-precision methane analyzers in Google Street View cars. Our data indicate that this automated leak survey system can document patterns in leak location and magnitude within and among cities, even without wind data. We found that urban areas with prevalent corrosion-prone distribution lines (Boston, MA, Staten Island, NY, and Syracuse, NY), leaked approximately 25-fold more methane than cities with more modern pipeline materials (Burlington, VT, and Indianapolis, IN). Although this mobile monitoring method produces conservative estimates of leak rates and leak counts, it can still help prioritize both leak repairs and replacement of leak-prone sections of distribution lines, thus minimizing methane emissions over short and long terms.

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Year:  2017        PMID: 28326761     DOI: 10.1021/acs.est.6b06095

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  3 in total

1.  An open source algorithm to detect natural gas leaks from mobile methane survey data.

Authors:  Zachary D Weller; Duck Keun Yang; Joseph C von Fischer
Journal:  PLoS One       Date:  2019-02-13       Impact factor: 3.240

2.  Large Fugitive Methane Emissions From Urban Centers Along the U.S. East Coast.

Authors:  Genevieve Plant; Eric A Kort; Cody Floerchinger; Alexander Gvakharia; Isaac Vimont; Colm Sweeney
Journal:  Geophys Res Lett       Date:  2019-07-29       Impact factor: 4.720

3.  Methods for quantifying methane emissions using unmanned aerial vehicles: a review.

Authors:  Jacob T Shaw; Adil Shah; Han Yong; Grant Allen
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2021-09-27       Impact factor: 4.226

  3 in total

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