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Quantification of Methane Emissions From Street Level Data
Published
Author(s)
Kuldeep R. Prasad
Abstract
Kuldeep Prasad(1), Brian Lamb(2), Maria Obiminda Cambaliza(3), Tegan Lavoie(3), Olivia E Salmon(3), Paul Shepson(3), Thomas Lauvaux(4), Ken Davis(4), and James R. Whetstone(1) (1) National Institute of Standards and Technology, Gaithersburg, MD. (2) Department of Civil and Environmental Engineering, Washington State University, Pullman, WA. (3) Department of Chemistry, Purdue University, West Lafayette, IN. (4) Department of Meteorology, The Pennsylvania State University, University Park, PA. The problem of identifying, attributing, and quantifying methane emissions from urban sources such as landfills, waste-water treatment facilities and natural gas distribution systems is an active area of research. This interest is fueled, in part, by recent measurements indicating that urban emissions are a significant source of methane (CH4, a potent greenhouse gas) and in fact may be substantially higher than current inventory estimates. As a result, developing methods for locating and quantifying emissions from urban methane sources is of great interest to industries such as landfill owners, and governmental agencies. In an attempt to identify major methane source locations and emissions in the city of Indianapolis, systematic measurements of CH4 concentrations and meteorology data were made at street level using multiple vehicles equipped with cavity ring-down spectrometers. A number of discrete sources were detected at methane molar ratios in excess of 15 times background levels. The street level data is analyzed with plume inversion models including Weather Research and Forecasting (WRF) software, Fire Dynamics Simulator (FDS) and backward Lagrangian Simulations (bLS) to identify source location and emission rates. The methodology for analyzing the street level data and our estimates of CH4 emissions from various sources in the city of Indianapolis will be presented.
Prasad, K.
(2014),
Quantification of Methane Emissions From Street Level Data, Proceedings of the AGU 2013 Fall Meeting, San Francisco, CA
(Accessed October 14, 2024)