LEADER 04216aam 2200517I 450 001 9910711167503321 005 20160926090654.0 024 8 $aGOVPUB-C13-0353deb555be1a558f57d6b82368ba75 035 $a(CKB)5470000002480334 035 $a(OCoLC)958885804 035 $a(EXLCZ)995470000002480334 100 $a20160921d2015 ua 0 101 0 $aeng 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aGreenhouse gas emissions and dispersion $e3. reducing uncertainty in estimating source strangth and location through plume inversion models /$fKuldeep Prasad; Adam Pintar; Heming Hu; Israel Lopez-Coto; Dennis Ngo; James R. Whetstone 210 1$aGaithersburg, MD :$cU.S. Dept. of Commerce, National Institute of Standards and Technology,$d2015. 215 $a1 online resource (28 pages) $cillustrations (color) 225 1 $aNIST special publication ;$v1175 300 $aContributed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aSeptember 2015. 300 $aTitle from PDF title page (viewed September 30, 2015). 320 $aIncludes bibliographical references. 330 3 $aRecent 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. 517 $aGreenhouse gas emissions and dispersion 606 $aGreenhouse gas emissions 606 $aQuality control 615 0$aGreenhouse gas emissions. 615 0$aQuality control. 700 $aPrasad$b Kuldeep$01394938 701 $aHu$b Heming$01397615 701 $aLopez-Coto$b Israel$01397616 701 $aNgo$b Dennis$01397617 701 $aPintar$b Adam$01397618 701 $aPrasad$b Kuldeep$01394938 701 $aWhetstone$b James R$01390336 712 02$aNational Institute of Standards and Technology (U.S.).$bEngineering Laboratory. 801 0$bNBS 801 1$bNBS 801 2$bGPO 801 2$bNBS 906 $aBOOK 912 $a9910711167503321 996 $aGreenhouse gas emissions and dispersion$93459506 997 $aUNINA