04216aam 2200517I 450 991071116750332120160926090654.0GOVPUB-C13-0353deb555be1a558f57d6b82368ba75(CKB)5470000002480334(OCoLC)958885804(EXLCZ)99547000000248033420160921d2015 ua 0engrdacontentrdamediardacarrierGreenhouse gas emissions and dispersion 3. reducing uncertainty in estimating source strangth and location through plume inversion models /Kuldeep Prasad; Adam Pintar; Heming Hu; Israel Lopez-Coto; Dennis Ngo; James R. WhetstoneGaithersburg, MD :U.S. Dept. of Commerce, National Institute of Standards and Technology,2015.1 online resource (28 pages) illustrations (color)NIST special publication ;1175Contributed record: Metadata reviewed, not verified. Some fields updated by batch processes.September 2015.Title from PDF title page (viewed September 30, 2015).Includes bibliographical references.Recent development of accurate instruments for measuring greenhouse gas concentrations and the ability to mount them in ground-based vehicles has provided an opportunity to make temporally and spatially resolved measurements in the vicinity of suspected source locations, and for subsequently estimating the source location and strength.^The basic approach of using downwind atmospheric measurements in an inversion methodology to predict the source strength and location is an ill-posed problem and results in large uncertainty.^In this report, we present a new measurement methodology for reducing the uncertainty in predicting source strength from downwind measurements associated with inverse modeling.^In order to demonstrate the approach, an inversion methodology built around a plume dispersion model is developed.^Synthetic data derived from an assumed source distribution is used to compare and contrast the predicted source strength and location.^The effect of introducing various levels of noise in the synthetic data or uncertainty in meteorological variables on the inversion methodology is studied.^Results indicate that the use of noisy measurement data had a small effect on the total predicted source strength, but gave rise to several spurious sources (in many cases 8-10 sources were detected, while the assumed source distribution only consisted of 2 sources).^Use of noisy measurement data for inversion also introduced large uncertainty in the location of the predicted sources.^A mathematical model for estimating an upper bound on the uncertainty, and a bootstrap statistical approach for determining the variability in the predicted source distribution is demonstrated.^The new measurement methodology, which involves using measurement data from two or more wind directions, combined together as part of a single inversion process is presented.^Results of the bootstrap process indicated that the uncertainty in locating sources reduced significantly when measurements are made using the new proposed measurement approach.^The proposed measurement system can be significant in determining emission inventories in urban domains at a high level of reliability, and for studying the role of remediation measures.Greenhouse gas emissions and dispersion Greenhouse gas emissionsQuality controlGreenhouse gas emissions.Quality control.Prasad Kuldeep1394938Hu Heming1397615Lopez-Coto Israel1397616Ngo Dennis1397617Pintar Adam1397618Prasad Kuldeep1394938Whetstone James R1390336National Institute of Standards and Technology (U.S.).Engineering Laboratory.NBSNBSGPONBSBOOK9910711167503321Greenhouse gas emissions and dispersion3459506UNINA