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- 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
concentration on horizontal
resolution
of the RPM-IV in a VOC dominant regime
- 6.5 Dependence of plume average O
concentration on vertical resolution
of the RPM-3D in a VOC dominant regime (4 horizontal cells)
- 6.6 Horizontal profile of the O
concentration in the plume in a VOC
dominant regime (6 horizontal cells, RPM-IV)
- 6.7 Vertical profile of the O
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
concentration on horizontal
resolution of the RPM-IV in a NO
dominant regime
- 6.9 Dependence of plume average O
concentration on vertical resolution
of the RPM-3D in a NO
dominant regime
- 6.10 Horizontal profile of the O
concentration in the plume in a
NO
dominant regime (6 horizontal cells, RPM-IV)
- 6.11 Vertical profile of the O
concentration at the plume centerline
(w.r.t. horizontal) in a VOC limited (NO
dominant) regime (4 horizontal
cells and 6 vertical cells, RPM-3D)
- C.1 Major species in the CB-IV and their hierarchical relationship
Sastry S. Isukapalli
1999-01-19