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Rapid, Vehicle-Based Identification of Location and Magnitude of Urban Natural Gas Pipeline Leaks.
von Fischer, Joseph C; Cooley, Daniel; Chamberlain, Sam; Gaylord, Adam; Griebenow, Claire J; Hamburg, Steven P; Salo, Jessica; Schumacher, Russ; Theobald, David; Ham, Jay.
Afiliação
  • von Fischer JC; Department of Biology, Colorado State University , 200 West Lake Street, Fort Collins, Colorado 80523, United States.
  • Cooley D; Department of Statistics, Colorado State University , Fort Collins, Colorado 80523, United States.
  • Chamberlain S; Department of Biology, Colorado State University , 200 West Lake Street, Fort Collins, Colorado 80523, United States.
  • Gaylord A; Department of Biology, Colorado State University , 200 West Lake Street, Fort Collins, Colorado 80523, United States.
  • Griebenow CJ; Department of Biology, Colorado State University , 200 West Lake Street, Fort Collins, Colorado 80523, United States.
  • Hamburg SP; Environmental Defense Fund , 18 Tremont Street, Boston, Massachusetts 02108, United States.
  • Salo J; Department of Geography, University of Northern Colorado , Greeley, Colorado 80639, United States.
  • Schumacher R; Department of Atmospheric Science, Colorado State University , Fort Collins, Colorado 80523, United States.
  • Theobald D; Conservation Science Partners , 5 Old Town Square, Suite 205 Fort Collins, Colorado 80524, United States.
  • Ham J; Department of Soil and Crop Sciences, Colorado State University , Fort Collins, Colorado 80523, United States.
Environ Sci Technol ; 51(7): 4091-4099, 2017 04 04.
Article em En | MEDLINE | ID: mdl-28326761
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2017 Tipo de documento: Article