Adjoint Inverse Modeling of PM2.5 Emissions in Order to Improve Performance of Air Quality Models, Ph.D. Dissertation Sharif University of Technology ; Hosseini, Vahid (Supervisor) ; Mozafari, Ali Asghar (Supervisor)
Abstract
In atmospheric studies, chemical transport models are formulated to simulate the spatial and temporal distribution of pollutant concentrations. However, the performance of these models is strongly dependent on the input parameters such as emissions. Inverse modeling is a widely used mathematical approach for estimating model parameters by minimizing the discrepancy between model output and observations. For air quality studies, inverse modeling is often used for emission inversion as emissions are associated with significant amount of uncertainties.This research aims to estimate optimal values for anthropogenic PM2.5 emission through a four-dimensional variational (4D-Var) inverse modeling...
Cataloging briefAdjoint Inverse Modeling of PM2.5 Emissions in Order to Improve Performance of Air Quality Models, Ph.D. Dissertation Sharif University of Technology ; Hosseini, Vahid (Supervisor) ; Mozafari, Ali Asghar (Supervisor)
Abstract
In atmospheric studies, chemical transport models are formulated to simulate the spatial and temporal distribution of pollutant concentrations. However, the performance of these models is strongly dependent on the input parameters such as emissions. Inverse modeling is a widely used mathematical approach for estimating model parameters by minimizing the discrepancy between model output and observations. For air quality studies, inverse modeling is often used for emission inversion as emissions are associated with significant amount of uncertainties.This research aims to estimate optimal values for anthropogenic PM2.5 emission through a four-dimensional variational (4D-Var) inverse modeling...
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