Vai al contenuto principale della pagina
Autore: | Prasad Kuldeep |
Titolo: | Greenhouse 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. Whetstone |
Pubblicazione: | Gaithersburg, MD : , : U.S. Dept. of Commerce, National Institute of Standards and Technology, , 2015 |
Descrizione fisica: | 1 online resource (28 pages) : illustrations (color) |
Soggetto topico: | Greenhouse gas emissions |
Quality control | |
Altri autori: | HuHeming Lopez-CotoIsrael NgoDennis PintarAdam PrasadKuldeep WhetstoneJames R |
Note generali: | Contributed record: Metadata reviewed, not verified. Some fields updated by batch processes. |
September 2015. | |
Title from PDF title page (viewed September 30, 2015). | |
Nota di bibliografia: | Includes bibliographical references. |
Sommario/riassunto: | 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. |
Altri titoli varianti: | Greenhouse gas emissions and dispersion |
Titolo autorizzato: | Greenhouse gas emissions and dispersion |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910711167503321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |