Greenhouse Gas Emissions and Dispersion 3. Reducing Uncertainty in Estimating Source Strength and Location through Plume Inversion Models
Kuldeep R. Prasad, Adam L. Pintar, Heming Hu, Israel Lopez Coto, Dennis T. Ngo, James R. Whetstone
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 us- ing downwind atmospheric measurements in an inversion methodology to predict the source strength and location is an ill-posed problem and results in high degree in 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 form an assumed source distribution is used to compare and contrast the predicted source strength and location. The e ect of introducing various levels of noise in the synthetic data or uncertainty in meteorological variables on the inversion method- ology is studied. Results indicate that the use of noisy measurement data had a small e ect 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 un- certainty, 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.
, Pintar, A.
, Hu, H.
, Lopez, I.
, Ngo, D.
and Whetstone, J.
Greenhouse Gas Emissions and Dispersion 3. Reducing Uncertainty in Estimating Source Strength and Location through Plume Inversion Models, Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.SP.1175
(Accessed December 1, 2021)