LEADER 01331cam--2200385---450 001 990001277750203316 005 20180413085656.0 035 $a000127775 035 $aUSA01000127775 035 $a(ALEPH)000127775USA01 035 $a000127775 100 $a20031124d1980----km-y0itay5003----ba 101 0 $aita 102 $aIT 105 $ay|||||||001yy 200 1 $a<> Pietà dei carcerati$econfraternite e società a Roma nei secoli XVI-XVIII$fVincenzo Paglia$gpremessa di Gabriele De Rosa 210 $aRoma$cEdizioni di storia e letteratura$d1980 215 $aXII, 370 p.$d26 cm 225 2 $aBiblioteca di storia sociale$v11 410 0$12001$aBiblioteca di storia sociale$v11 606 0 $aArciconfraternita della Pietà dei carcerati $xStoria$2BNCF 676 $a267.24245632 700 1$aPAGLIA,$bVincenzo$0205949 702 1$aDE ROSA,$bGabriele$f<1917-2009> 801 0$aIT$bsalbc$gISBD 912 $a990001277750203316 951 $aX.2.B. 2627(A 1 2 4)$b6674 CSSM$cX.2.$d411083 951 $aX.2.B. 2627a(III D Coll. 43/11)$b9403 L.M.$cX.2.$d411083 959 $aBK 969 $aUMA 979 $aSIAV4$b10$c20031124$lUSA01$h1656 979 $aPATRY$b90$c20040406$lUSA01$h1731 979 $aCOPAT3$b90$c20050405$lUSA01$h1058 996 $aPietà dei carcerati$9139270 997 $aUNISA LEADER 04181aam 2200505I 450 001 9910711167503321 005 20241213181716.0 024 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 R.$f1964-$01415551 701 $aHu$b Heming$01397615 701 $aLopez-Coto$b Israel$01397616 701 $aNgo$b Dennis$01397617 701 $aPintar$b Adam L$01394651 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$94302046 997 $aUNINA