Variational Inverse Modeling of Regional Nitrogen Oxides Emissions

Inverse modeling methods have been widely used for model performance improvement and parameter estimation.For air quality studies, inverse modeling is often used for emission inversion as emissions are associated with significant amount of uncertainties.Emission inversion aims to find the optimized perturbations for emissions referred to as scaling factors that reduce the distance between model output and observations.Nitrogen oxides emissions are estimated through a four dimensional variational (4D-Var) inverse modeling approach using Ozone Monitoring Instrument (OMI) and ground-network observations.The modeling period was chosen July 2007 over North America domain.The Community Multiscale Air Quality model is used along with Sparse Matrix Operator Kernel Emissions (SMOKE) and Weather Research and Forecasting model (WRF) for emissions and meteorological modeling.In the non-temporal set-ups, NO emissions are adjusted by assimilating NO columns from satellite (OMI) and NO from ground based observations separately.The results indicate that the average scaling factors vary from 0.41 to 1.74 or from 0.45 to 2.52 when ground observations and OMI observations are used, respectively.The average scaling factors are in the range of 0.43 to 1.79 when both types of observations were used simultaneously.The total amount of emissions increase 3.5% when inversion is based on ground observations only, 17.3% when inversion includes only OMI observations and 13.6% when both observations are used simultaneously.Under the temporal set-up, NO emissions are adjusted by using OMI and modified surface observations to estimate hourly profiles of emissions at each location.Hourly profiles show that iii in large urban locations, CMAQ tends to overestimate the NO 2 column densities as an example at Los Angeles during the simulation period the emissions are reduced about %40 Lightning is one of the more uncertain sources of NO .While inverted emissions included point, mobile, biogenic, and other sources in the CMAQ, lightning as one of the most significant natural sources of NO was excluded in previous inversion setups.To account for the impact of this source, lightning emissions are parameterized as inputs for the CMAQ model from the observations of Lightning Detection Networks flash rates.Places and observations that were dominated by anthropogenic sources were filtered.However, scarcity of data did not allow us to extend emission inversions for lightning events across the entire domain and during all episodes.iv ACKNOWLDEGMENT I deeply appreciate the tremendous support and help I have received from many individuals during my Ph.D. career.First of all, I would like to thank my supervisor Dr. Amir Hakami

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