List of Figures

• 1.1 Schematic depiction of the outline of this thesis. The shaded areas indicate the contributions of this work
• 2.1 Types of uncertainty present in transport-transformation modeling applications and their interrelationships (adapted from Georgopoulos, 1995)
• 2.2 A schematic depiction of the propagation of uncertainties in transport-transformation models
• 3.1 Schematic depiction of the Stochastic Response Surface Method
• 3.2 Schematic depiction of the steps involved in the application of conventional Monte Carlo method
• 3.3 Schematic depiction of the steps involved in the application of the Stochastic Response Surface Method
• 3.4 Application of the web based SRSM tool: specification of input uncertainties
• 3.5 Application of the web based SRSM tool: recommended sample points and the corresponding model outputs
• 3.6 Application of the web based SRSM tool: estimates of the pdfs of the outputs
• 4.1 Rationale for coupling of the SRSM with a sensitivity analysis method
• 4.2 Schematic illustration of the Automatic Differentiation method
• 4.3 FORTRAN derivative code generation using ADIFOR: (a) Sample code fragment and (b) ADIFOR generated derivative code
• 4.4 Schematic depiction of the steps involved in the application of the SRSM-ADIFOR
• 5.1 Schematic representation of a PBPK model for PERC
• 5.2 Evaluation of DEMM/PCM: uncertainty in the cumulative amount of PERC metabolized, over a period of one day, resulting from 6 uncertain parameters
• 5.3 Evaluation of DEMM/PCM: uncertainty in the area under the PERC concentration-time curve in (a) arterial blood (AUCA) and in (b) liver (AUCL), over a period of one day, resulting from 6 uncertain parameters
• 5.4 Evaluation of ECM: uncertainty in the area under the PERC concentration-time curve in (a) arterial blood (AUCA) and in (b) liver (AUCL), over a period of one day, resulting from 6 uncertain parameters
• 5.5 Evaluation of ECM: uncertainty in the cumulative amount of PERC metabolized, over a period of one day, resulting from 6 uncertain parameters
• 5.6 Evaluation of ECM: uncertainty in the cumulative amount of PERC metabolized, over a period of one day, resulting from 11 uncertain parameters
• 5.7 Evaluation of ECM: uncertainty in the area under the PERC concentration-time curve in (a) arterial blood (AUCA) and in (b) liver (AUCL), over a period of one day, resulting from 11 uncertain parameters
• 5.8 Evaluation of SRSM (regression based) and LHS: uncertainty in the the CML resulting from 11 uncertain parameters
• 5.9 Evaluation of SRSM (regression based) and LHS: uncertainty in (a) AUCL and in (b) AUCA (Figure b) resulting from 11 uncertain parameters
• 5.10 Evaluation of SRSM-ADIFOR: uncertainty in the cumulative amount of PERC metabolized, over a period of one day, resulting from 11 uncertain parameters
• 5.11 Evaluation of SRSM-ADIFOR: uncertainty in the area under the PERC concentration-time curve in (a) arterial blood (AUCA) and in (b) liver (AUCL), over a period of one day, resulting from 11 uncertain parameters
• 5.12 Evaluation of ECM: uncertainty in the predicted ozone concentrations at (a) 2 km and (b) 20 km downwind from the source
• 5.13 Evaluation of SRSM (regression based): uncertainty in the predicted ozone concentrations at (a) 2 km and (b) 20 km downwind from the source
• 5.14 Evaluation of SRSM-ADIFOR: uncertainty in the predicted ozone concentrations at (a) 2 km and (b) 20 km downwind from the source
• 5.15 Origins and types of uncertainty present in Photochemical Air Quality Simulation Models
• 5.16 The Philadelphia/New Jersey modeling domain used for uncertainty analysis
• 5.17 Probability distribution of the daily maximum ozone concentration
• 5.18 Probability distribution of the daily average ozone concentration
• 5.19 Probability distribution of the daily maximum 8 hr running average ozone concentration
• 5.20 Probability distribution of the pervasiveness of the ozone episode
• 5.21 Uncertainty in estimated maximum tritium concentration in a receptor well
• 5.22 Uncertainty in estimated time of occurrence of maximum tritium concentration in a receptor well
• 6.1 Schematic depiction of the evolution of an atmospheric plume (adapted from Stewart et al.,1981)
• 6.2 Schematic depiction of entrainment and detrainment steps simulated in the RPM (adapted from Stewart et al., 1981)
• 6.3 Discretization of the plume cross section by RPM-IV (left) and RPM-3D (right)
• 6.4 Dependence of plume average O$_3$ concentration on horizontal resolution of the RPM-IV in a VOC dominant regime
• 6.5 Dependence of plume average O$_3$ concentration on vertical resolution of the RPM-3D in a VOC dominant regime (4 horizontal cells)
• 6.6 Horizontal profile of the O$_3$ concentration in the plume in a VOC dominant regime (6 horizontal cells, RPM-IV)
• 6.7 Vertical profile of the O$_3$ concentration at the plume centerline (w.r.t. horizontal) in a VOC dominant regime (4 horizontal cells and 6 vertical cells, RPM-3D)
• 6.8 Dependence of plume average O$_3$ concentration on horizontal resolution of the RPM-IV in a NO$_x$ dominant regime
• 6.9 Dependence of plume average O$_3$ concentration on vertical resolution of the RPM-3D in a NO$_x$ dominant regime
• 6.10 Horizontal profile of the O$_3$ concentration in the plume in a NO$_x$ dominant regime (6 horizontal cells, RPM-IV)
• 6.11 Vertical profile of the O$_3$ concentration at the plume centerline (w.r.t. horizontal) in a VOC limited (NO$_x$ dominant) regime (4 horizontal cells and 6 vertical cells, RPM-3D)
• C.1 Major species in the CB-IV and their hierarchical relationship