EPA/600/R-01-068
December 2001
Computational Methods for Sensitivity and
Uncertainty Analysis for Environmental and Biological
Models
By
Sastry S. Isukapalli and Panos G. Georgopoulos
Computational Chemodynamics Laboratory
Environmental and Occupational Health Sciences Institute
Rutgers University and UMDNJ RWJ Medical School
170 Frelinghuysen Road, Piscataway, NJ 08854
Cooperative Agreement CR823717
Project Officer
Daewon W. Byun
Atmospheric Modeling Division
National Exposure Research Laboratory
Research Triangle Park, NC 27711
National Exposure Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711

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DISCLAIMER
~> This document has been reproduced from the best copy furnished
by the sponsoring agency. It is being released in the interest of
making available as much information as possible.

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NOTICE
THE U.S. Environmental Protection Agency through its Office of Research and De-
velopment partially funded and collaborated in the research described here under
Cooperative Agreement CR823717 to the Environmental and Occupational Health
Sciences Institute, Rutgers University. It has been subjected to the Agency's peer
and administrative review and has been approved for publication as an EPA docu-
ment.

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ABSTRACT
Characterization of uncertainty associated with transport-transformation models
is often of critical importance, as for example in cases where environmental and bio-
logical models are employed in risk assessment. However, uncertainty analysis using
conventional methods such as standard Monte Carlo or Latin Hypercube Sampling
may not be efficient, or even feasible, for complex, computationally demanding mod-
els.
This work introduces a computationally efficient alternative method for uncer-
tainty propagation, the Stochastic Response Surface Method (SRSM). The SRSM
approximates uncertainties in model outputs through a series expansion in normal
random variables (polynomial chaos expansion). The unknown coefficients in series
expansions are calculated using a limited number of model simulations. This method
is analogous to approximation of a deterministic system by an algebraic response
surface.
Further improvements in the computational efficiency of the SRSM are accom-
plished by coupling the SRSM with AD IFOR, which facilitates automatic calculation
of partial derivatives in numerical models coded in Fortran. The coupled method,
SRSM-ADIFOR, uses the model outputs and their derivatives to calculate the un-
known coefficients.
The SRSM and the SRSM-ADIFOR are general methods, and are applicable to
any model with random inputs. The SRSM has also been implemented as a black-box,
web-based tool for facilitating its easy use.
The SRSM and the SRSM-ADIFOR have been applied to a set of environmental
and biological models. In all the case studies, the SRSM required an order of magni-
tude fewer simulations compared to conventional methods, and the SRSM-ADIFOR
required even fewer simulations. In addition to their computational efficiency, these
methods directly provide sensitivity information and individual contributions of in-
put uncertainties to output uncertainties; conventional methods require substantially
larger numbers of simulations to provide such information. Thus, the SRSM and the
SRSM-ADIFOR provide computationally efficient means for uncertainty and sensi-
tivity analysis.
Finally, this research addresses uncertainties associated with model structure and
resolution with application to photochemical air quality modeling. A three dimen-
sional version of the regulatory Reactive Plume Model (RPM), RPM-3D, has been
developed and applied to understand model uncertainty.

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ACKNOWLEDGMENTS
• U.S. EPA National Exposure Research Laboratory (NERL)
(Co-operative Agreement CR823717)
• NJDEP funded Ozone Research Center at EOHSI
• U.S. DOE Consortium for Risk Evaluation with Stakeholder Participation
(CRESP)
(Cooperative Agreement DE-FC01-95EW55084)

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Contents
1	INTRODUCTION	1
1.1	Uncertainty Analysis		1
1.2	Transport-Transformation Models		2
1.3	Limitations in Performing Uncertainty Analysis		3
1.4	Objective of this Research		4
1.5	Outline of the Document 		5
2	BACKGROUND	8
2.1	Types and Origins of Uncertainty in Transport-Transformation Models . .	8
2.1.1	Natural Uncertainty and Variability		8
2.1.2	Model Uncertainty		9
2.1.3	Parametric/Data Uncertainty		10
2.2	Reducible and Irreducible Uncertainty		11
2.3	Approaches for Representation of Uncertainty		12
2.3.1	Interval Mathematics 		13
2.3.2	Fuzzy Theory 		14
2.3.3	Probabilistic Analysis 		16
2.4	Sensitivity and Sensitivity/Uncertainty Analysis		19
2.5	Conventional Sensitivity/Uncertainty Analysis Methods		19
2.5.1	Sensitivity Testing Methods		21
2.5.2	Analytical Methods 		21
2.5.3	Sampling Based Methods		23
2.5.4	Computer Algebra Based Methods 		27
2.6	Need for Alternative Sensitivity/Uncertainty Analysis Methods		29
3	THE STOCHASTIC RESPONSE SURFACE METHOD: DEVELOP-
MENT AND IMPLEMENTATION	30
3.1	Overview of the Method		30
3.2	Step I: Representation of Stochastic Inputs		32
3.2.1	Direct Transformations 		33
3.2.2	Transformation via Series Approximation		34
3.2.3	Transformation of Empirical Distributions		34
3.2.4	Transformation of Correlated Inputs		35
3.3	Step II: Functional Approximation of Outputs		37
3.4	Step III: Estimation of Parameters in the Functional Approximation ...	38
iv

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3.4.1	Deterministic Equivalent Modeling Method/Probabilistic Colloca-
tion Method (DEMM/PCM) 		39
3.4.2	Efficient Collocation Method (ECM) 		41
3.4.3	Regression Based SRSM		42
3.5	Step IV: Estimation of the Statistics of the Output Metrics 		42
3.6	Step V: Evaluation of Convergence, Accuracy and Efficiency of Approxi-
mation 		44
3.7	An Illustration of the Application of the SRSM		44
3.8	Computational Implementation 		47
3.8.1	Implementation of the Conventional Uncertainty Propagation Meth-
ods 		47
3.8.2	Implementation of the Stochastic Response Surface Method ...	49
3.9	Implementation of a Web Interface to the SRSM		50
4	COUPLING OF THE SRSM WITH SENSITIVITY ANALYSIS METH-
ODS: DEVELOPMENT AND IMPLEMENTATION OF SRSM-ADIFOR	54
4.1	Methods for Estimation of Partial Derivatives		56
4.2	Automatic Differentiation using ADIFOR		59
4.3	Coupling of SRSM and ADIFOR		59
4.3.1 Selection of Sample Points 		62
4.4	An Illustration of the Application of the SRSM-ADIFOR		62
4.5	Implementation of the SRSM-ADIFOR		64
5	CASE STUDIES FOR THE EVALUATION OF THE SRSM AND THE
SRSM-ADIFOR	66
5.1	Case Study I: A Zero Dimensional Physiological System 		67
Uncertainty Analysis of a Physiologically Based PharmacoKinetic Model
for Perchloroethylene 		67
5.1.1	Description of the Case Study		67
5.1.2	Specification of Parameter Uncertainty		70
5.1.3	Implementation of the PERC PBPK Model		71
5.1.4	Results for the PBPK case study		71
5.2	Case Study II: A Two-Dimensional Photochemical Air Quality Model . .	82
Uncertainty Analysis of the Reactive Plume Model (RPM-IV) 		82
5.2.1	Description of RPM-IV 		82
5.2.2	Uncertainty Analysis of RPM-IV		84
5.2.3	Results and Discussion		84
5.3	Case Study III: A Three-Dimensional Urban/Regional Scale Photochemical
Air Quality Model		89
Uncertainty Analysis of the Urban Airshed Model (UAM-IV) for the New
Jersey/Philadelphia Domain		89
v

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5.3.1	Uncertainties Associated with Biogenic Emission Estimates ....	90
5.3.2	Uncertainty Analysis		91
5.3.3	Estimation of Uncertainties		91
5.3.4	Results of the Case Study		94
5.4 Case Study IV: A Ground Water Model with Discontinuous Probability
Distributions		99
Uncertainty Analysis of Tritium Contamination in a Landfill using the
EPACMTP Model		99
5.4.1	EPACMTP Model		99
5.4.2	Data Sources for the Application of EPACMTP		101
5.4.3	Implementation of the Monte Carlo Method in EPACMTP ....	101
5.4.4	Uncertainty Analysis		102
5.4.5	Results and Discussion		103
6	CHARACTERIZATION AND REDUCTION OF MODEL UNCERTAINTY:
AN ATMOSPHERIC PLUME MODEL STUDY	105
6.1	Introduction and Background		105
6.2	Photochemical Air Pollution Modeling		106
6.2.1 Mathematical and Numerical Formulation of PAQSMs		107
6.3	Formulation of The Reactive Plume Model (RPM)		108
6.3.1	Initial Physical Dimensions of the Plume		110
6.3.2	RPM-IV Model Equations		Ill
6.3.3	Limitations of the RPM-IV 		112
6.4	Formulation of the RPM-3D		113
6.5	Case Studies		115
6.6	Discussion		120
7	CONCLUSIONS AND DISCUSSION	122
7.1	Development and Application of the SRSM		122
7.2	Development and Application of the SRSM-ADIFOR		123
7.3	Consideration of Uncertainties Beyond Parametric/Data Uncertainty . . .	124
7.4	Directions for Future Work 		124
7.4.1	Improvements to the Stochastic Response Surface Method ....	124
7.4.2	Further evaluation of the SRSM and the SRSM-ADIFOR		125
7.4.3	Addressing uncertainty propagation under constraints 		125
7.4.4	Random processes and random fields		126
7.4.5	Uncertainties Associated with Evaluation Data		127
vi

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List of Figures
1.1 Outline of the Document 		7
2.1	Types of uncertainty in transport-transformation modeling		9
2.2	A schematic depiction of the propagation of uncertainties in transport-
transformation models		17
3.1	Schematic depiction of the Stochastic Response Surface Method ....	32
3.2	Schematic depiction of the steps involved in the application of conventional
Monte Carlo method		47
3.3	Schematic depiction of the steps involved in the application of the Stochas-
tic Response Surface Method		49
3.4	Application of the web based SRSM tool: specification of input uncertainties	51
3.5	Application of the web based SRSM tool: recommended sample points
and the corresponding model outputs		52
3.6	Application of the web based SRSM tool: estimates of the pdfs of the
outputs		53
4.1	Rationale for coupling of the SRSM with a sensitivity analysis method . .	55
4.2	Schematic illustration of the Automatic Differentiation method 		58
4.3	FORTRAN derivative code generation using ADIFOR - An Example ...	60
4.4	Schematic depiction of the steps involved in the application of the SRSM-
ADIFOR		64
5.1	Schematic representation of a PBPK model for PERC		68
5.2	Evaluation of DEMM/PCM: uncertainty in CML (6 uncertain inputs) . .	72
5.3	Evaluation of DEMM/PCM: uncertainty in AUCA and AUCL (6 uncertain
inputs)		73
5.4	Evaluation of ECM: uncertainty in AUCA and AUCL (6 uncertain inputs)	74
5.5	Evaluation of ECM: uncertainty in CML (6 uncertain inputs)		75
5.6	Evaluation of ECM: uncertainty in CML (11 uncertain inputs)		76
5.7	Evaluation of ECM: uncertainty in AUCA and AUCL (11 uncertain inputs)	77
5.8	Evaluation of SRSM and LHS: uncertainty in CML (11 uncertain inputs)	78
5.9	Evaluation of SRSM and LHS: uncertainty in AUCA and AUCL (11 uncer-
tain inputs)		79
5.10	Evaluation of SRSM-ADIFOR: uncertainty in CML		80
5.11	Evaluation of SRSM-ADIFOR: uncertainty in AUCA and AUCL		81
5.12	Evaluation of ECM: uncertainty in downwind ozone concentrations ...	86
vii

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5.13	Evaluation of SRSM: uncertainty in downwind ozone concentrations ... 87
5.14	Evaluation of SRSM-ADIFOR: uncertainty in downwind ozone concentrations 88
5.15	Origins and types of uncertainty present in Photochemical Air Quality
Simulation Models	 90
5.16	The Philadelphia/New Jersey modeling domain used for uncertainty analysis 93
5.17	Probability distribution of the daily maximum ozone concentration . ... 94
5.18	Probability distribution of the daily average ozone concentration	 95
5.19	Probability distribution of the daily maximum 8 hr running average ozone
concentration	 95
5.20	Probability distribution of the pervasiveness of the ozone episode .... 96
5.21	Uncertainty in estimated maximum tritium concentration in a receptor well 103
5.22	Uncertainty in estimated time of occurrence of maximum tritium concen-
tration in a receptor well	 104
6.1	Schematic depiction of the evolution of an atmospheric plume		109
6.2	Schematic depiction of the entrainment and detrainment steps in the RPM	110
6.3	Discretization of the plume cross section by RPM-IV and RPM-3D . . .	113
6.4	Dependence of plume average 03 concentration on horizontal resolution
of the RPM-IV in a VOC dominant regime	 115
6.5	Dependence of plume average 03 concentration on vertical resolution of
the RPM-3D in a VOC dominant regime (4 horizontal cells)	 116
6.6	Horizontal profile of the O3 concentration in the plume in a VOC dominant
regime (6 horizontal cells, RPM-IV)	 116
6.7	Vertical profile of the O3 concentration at the plume centerline (w.r.t.
horizontal) in a VOC dominant regime (4 horizontal cells and 6 vertical
cells, RPM-3D)	 117
6.8	Dependence of plume average O3 concentration on horizontal resolution
of the RPM-IV in a NOx dominant regime 	 118
6.9	Dependence of plume average 03 concentration on vertical resolution of
the RPM-3D in a NOx dominant regime 	 119
6.10	Horizontal profile of the 03 concentration in the plume in a NOx dominant
regime (6 horizontal cells, RPM-IV)	 119
6.11	Vertical profile of the O3 concentration at the plume centerline (w.r.t.
horizontal) in a VOC limited (NOx dominant) regime (4 horizontal cells
and 6 vertical cells, RPM-3D)	 120
viii

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List of Tables
1.1 Transport-transformation components in biological and environmental
models		3
2.1	Sources of uncertainty in transport-transformation models		11
2.2	Selected probability density functions useful in representing uncertainties
in environmental and biological model inputs		18
2.3	A summary of sensitivity measures employed in sensitivity analysis ....	20
2.4	A representative list of packages available for automatic differentiation	27
3.1 Representation of common univariate distributions as functionals of normal
random variables		33
5.1	Deterministic and uncertain parameters used in the uncertainty analysis of
the PERC PBPK model		69
5.2	Contribution of individual parameters to the overall uncertainty in the
UAM-IV		97
5.3	Uncertain parameters in the EPACMTP model		100
5.4	Deterministic parameters in the EPACMTP model		102
ix

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Chapter 1
INTRODUCTION
1.1 Uncertainty Analysis
Mechanistic modeling of physical systems is often complicated by the presence of un-
certainties. Environmental and biological modeling, for example, entails uncertainties
in the estimates of toxicant emissions, transformation and transport parameters, etc.,
that impact the estimates of related health risks. The implications of these uncer-
tainties are particularly important in the assessment of several potential regulatory
options, for example, with respect to the selection of a strategy for the control of
pollutant levels. Even though significant effort may be needed to incorporate uncer-
tainties into the modeling process, this could potentially result in providing useful
information that can aid in decision making.
A systematic uncertainty analysis provides insight into the level of confidence in
model estimates, and can aid in assessing how various possible model estimates should
be weighed. Further, it can lead to the identification of the key sources of uncertainty
(such as data gaps) which merit further research, as well as the sources of uncertainty
that are not important with respect to a given response.
The purpose of quantitative uncertainty analysis is to use currently available in-
formation to quantify the degree of confidence in the existing data and models. The
purpose is not to somehow "reduce" uncertainty - reduction in uncertainty can only
come from gathering additional information and filling "data gaps". Even though the
applicability of a model is limited by the model assumptions and the uncertainties
in the evaluation data, understanding the judgments associated with the modeling
process is more valuable than side-stepping the uncertainty analysis. In fact, it is pre-
cisely for problems where data are limited and where simplifying assumptions have
been used that a quantitative uncertainty analysis can provide an illuminating role, to
help identify how robust the conclusions about model results are, and to help target
data gathering efforts [70].
The following stages are involved in the uncertainty analysis of a model: (a) es-
timation of uncertainties in model inputs and parameter (characterization of input
uncertainties), (b) estimation of the uncertainty in model outputs resulting from
the uncertainty in model inputs and model parameters (uncertainty propagation),
(c) characterization of uncertainties associated with different model structures and
model formulations (characterization of model uncertainty), and (d) characterization
1

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Chapter: I Section: 2
2
of the uncertainties in model predictions resulting from uncertainties in the evaluation
data.
1.2 Transport-Transformation Models
A wide range of environmental and biological models involve the fate and trans-
port of a chemical species that originates from a source, travels through a medium
("transport"), undergoes changes due to chemical or radioactive processes ("trans-
formation"), and eventually comes in contact with a receptor (e.g., a human, or
specifically an organ, or a tissue).
In this report, the term "transport-transformation models" refers to a wide range
of biological, environmental, and engineering problems. These models are similar
in structure, often described by ordinary or partial differential equations. These
models are based on the continuity equation, and on a mass-balance of chemical or
radioactive species in a control volume. Even though the complexity of these models
varies significantly, they are categorized together because the underlying physical
and chemical processes, and the mathematical equations describing these systems,
are similar.
The term "transport" refers to the movement of material through the surroundings
as a result of associated flows and diffusive processes. The inhalation of chemicals,
the movement of toxic substances along with the blood flow, or the absorption of
pollutants by the layers of skin, are examples of transport. The flow of gaseous
pollutants along with the wind, and the flow of contaminants through and along with
surface and ground water are also examples of transport processes. Additionally,
the diffusion of chemical species across a concentration gradient is also a transport
process.
The term "transformation" refers to the change of a species (physical, chemical, or
biological). In biological systems,the enzymatic reactions account for transformations.
In the atmosphere, transformation can result from a series of nonlinear photochem-
ical reactions. In groundwater systems, transformation can sometimes result from
radioactive processes, and sometimes due to chemical reactions.
Many transport-transformation models can be represented in a simplified form by
the following equation:
^ = F + R + S
at
where — is the rate of change of a species concentration in a control volume
at
under consideration, F is the net inflow of the species into the control volume, R is
the net rate of production of the species due to chemical or radioactive processes, and
S is the net rate of injection of the species into the control volume (also called as

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Chapter: I Section: 3
3
Table 1.1: Examples of transport-transformation components in biological and envi-
ronmental models
Model
Component
Flow
Reaction
Source/Sink
Biological
Air Quality
Water Qual-
ity
Blood
Wind
S u rface water/groundwater
Enzymatic
Photochemica
Radioactive
Absorption into tis-
sues
Emissions/Deposition
Leachate
source/sink term). Table 1.1 presents examples of the components of some transport-
transformation models of environmental and biological systems.
Uncertainty occurs in transport-transformation models due to a number of fac-
tors. The randomness inherent in natural systems, errors in the estimates of transport
properties of a species in a medium, and inaccurate estimates of transformation prop-
erties, such as the reaction rates, contribute to the overall uncertainty in these models.
The main sources of uncertainty in transport-transformation systems are presented
in more detail in Chapter 2.
1.3 Limitations in Performing Uncertainty Analysis
The main limitation in performing comprehensive uncertainty analyses of transport-
transformation models is the associated cost and effort. The computer resources
required for uncertainty propagation using conventional methods (to be discussed
in the following paragraph) can sometimes be prohibitively expensive. Further, the
incorporation of uncertainty associated with structural and formulation aspects of
the models requires significant effort. Finally, the data needs for characterizing input
uncertainties are often substantial, and not necessarily available.
Conventional methods for uncertainty propagation typically require several model
runs that sample various combinations of input values. The number of model runs can
sometimes be very large, i.e., of the order of many thousands, resulting in substantial
computational demands. On the other hand, in order to estimate the uncertainties
associated with model formulation, several different models, each corresponding to a
different formulation of the mathematical problem corresponding to the original phys-
ical system, have to be developed. The model results corresponding to all the possible
combinations give an estimate of the range of the associated model uncertainty. De-
velopment and application of several alternative computational models can require
substantial time and effort. Thus, the costs associated with uncertainty analysis may
sometimes be prohibitively high, necessitating a large number of model simulations

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Chapter: I Section: 4
4
and/or the development of several alternative models.
1.4 Objective of this Research
The primary objective of this research is the development of computationally effi-
cient methods for uncertainty propagation. This is addressed from the perspective of
(a) computational requirements of the methods, (b) applicability of the methods to
a wide range of models, and (c) ease of use of the methods.
Conventional methods for uncertainty propagation, such as the standard Monte
Carlo and Latin Hypercube Sampling methods, may be computationally prohibitively
expensive, as described in more detail in Chapter 2. The development of alternative
methods that are applicable to a wide range of transport-transformation models could
substantially reduce the computational costs associated with uncertainty propagation
(in terms of both the time and the resources required). In fact, such methods could
facilitate the uncertainty analysis of complex, computationally demanding models
where the application of traditional methods may not be feasible due to computational
and time limitations. Additionally, there is a need for an extensive evaluation of the
alternative methods with realistic case studies. Such an evaluation addresses the
issues associated with the wider applicability of the methods. Further, the ease of use
of any method is an important factor in its applicability. The extra effort required
in understanding and applying a new method can sometimes offset other advantages
that the method has to offer. Hence, attention must be paid to the development of
auxiliary tools that facilitate the easy use of the new methods.
Thus, the primary objective of this research can be stated as
"the development of computationally efficient alternative methods for un-
certainty propagation that are applicable to a wide range of transport-
transformation models, and the development of auxiliary tools that facil-
itate easy use of these methods."
Another objective of this research is to study the uncertainties associated with
model structure and formulation, which fall beyond the scope of input uncertainty
propagation. Such a study provides help in the selection of the appropriate level of
model detail. Further, in conjunction with uncertainty propagation, it can be used
to identify the relative magnitudes of uncertainties associated with model inputs and
model formulation. This provides information as to where available resources should
be focused: filling data gaps versus refining the model used for the problem.
These objectives are accomplished via the development of the Stochastic Response
Surface Method (SRSM), and its evaluation for a range of transport-transformation
models. Additionally, an easy to use, web-based interface is developed to facilitate
black-box use of this method. Furthermore, the SRSM is coupled with an existing sen-
sitivity analysis method, ADIFOR (Automatic Differentiation of FORtran), in order

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Chapter: I Section: 5
5
to further improve the computational efficiency of the SRSM. Finally, uncertainties
associated with model structure and formulation are addressed in the framework of
photochemical air quality modeling.
1.5 Outline of the Document
Chapter 1 has presented a brief introduction to the concepts of uncertainty, and
the importance of uncertainty analysis in the context of transport-transformation
models. It has also discussed the steps involved in a systematic uncertainty analysis,
and the associated limitations. Additionally, Chapter 1 summarizes the objectives
and presents a brief overview of this document (Figure 1.1).
Chapter 2 presents the relevant background information in the following order:
•	classification of uncertainty into natural, model, and parametric/data uncertain-
ties, and into reducible and irreducible uncertainties,
•	approaches for representing parametric/data uncertainty, such as, Interval
Mathematics, Fuzzy Theory, and Probabilistic Analysis, and
•	conventional methods for sensitivity and sensitivity/uncertainty analysis, clas-
sified here as sensitivity testing methods, analytical methods, sampling based
methods, and computer algebra based methods.
Additionally, Chapter 2 presents several applications of the conventional meth-
ods to the uncertainty analysis of transport-transformation models. The merits and
limitations of each method are discussed, and an argument is made for alternative
methods that can be used for combined sensitivity and uncertainty analysis. This
argument is made both from the perspective of the computational cost reduction,
and from the perspective of obtaining sensitivity information.
Chapter 3 presents the Stochastic Response Surface Method (SRSM), which is de-
veloped here as a computationally efficient alternative method for uncertainty analysis
of numerical models. The formulation, implementation, and application of the SRSM
are discussed in detail. Individual sections elaborate on the steps involved in the ap-
plication of the SRSM: (a) representation of random inputs in a "standardized man-
ner", (b) expression of model outputs in a parametric form (e.g., series expansions),
(c) estimation of the parameters of output approximation (e.g., coefficients in a series
expansion), (d) calculation of the statistical properties of outputs, and (e) evaluation
of the approximation. Chapter 3 also presents an example problem that illustrates
the application of the SRSM, explaining all the steps in detail; case studies for the
evaluation of the SRSM are presented in Chapter 5. Additionally, Chapter 3 presents
a web-interface for the application of the SRSM through a browser, without requiring
additional effort in studying the mathematical details of the method.

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Chapter: I Section: 5
6
Chapter 4 presents the coupling of the SRSM with the Automated Differentiation
method, AD IFOR (Automatic Differentiation of FORtran). AD IFOR is a computer
algebra based method that reads the Fortran source code of a model and constructs
the code that calculates first order partial derivatives of model outputs with respect to
model inputs or parameters. In Chapter 4, first the rationale for coupling the SRSM
with ADIFOR is presented, and is followed by a description of ADIFOR. Then, the
coupling of the SRSM with ADIFOR is presented, and the application of the coupled
method, SRSM-ADIFOR, is illustrated with an example problem.
Chapter 5 presents a comprehensive evaluation of both the SRSM and the SRSM-
ADIFOR methods. This evaluation consists of four case studies, covering a wide
range of transport-transformation models, as follows:
I: A zero-dimensional physiologically-based pharmacokinetic model.
II: A two-dimensional reactive atmospheric plume model.
Ill: A three-dimensional urban/regional scale photochemical air quality model.
IV: A one-dimensional ground water model with discontinuous probability density
functions for inputs and parameters.
The SRSM is evaluated for all the four models, whereas the SRSM-ADIFOR is eval-
uated for the first two models. Each case study description contains the information
about the model and its application in exposure/risk assessment. Then, the un-
certainties associated with the model are described, and the application of different
uncertainty propagation methods is presented. The estimates from the uncertainty
propagation methods are then presented graphically, in the form of probability density
functions (pdf s), along with a brief discussion at the end.
Chapter 6 addresses the issues involved with model uncertainty, which arises due
to uncertainties in model formulation and model structure. This chapter describes
how such uncertainty can be addressed in the framework of photochemical air quality
modeling. A study is presented involving a regulatory atmospheric photochemical
plume model, the Reactive Plume Model (RPM). The development and implemen-
tation of the RPM-3D, a three-dimensional version of the RPM, is presented in this
Chapter. Subsequently, case studies are presented to compare the estimates of RPM-
3D and RPM, in order to characterize uncertainties associated with model resolution.
Chapter 7 presents the conclusions of this research, and recommendations for
future work.

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Chapter: I Section: 5
7
Implementation
of SRSM
(cli. 3)
Implementation
of MC and
LHS (ch. 3)
Implementation
of SRSM-ADIFOR
(ch. 4)
Air Quality Model
(local scale)
Photochemical Plume
	 (ch. 5.2)	„
Biological Model
(Human PBPK)
(ch. 5.1)
Development and Application
of RPM-3D. a 3D Version of
	 RPM-IV (ch. 6)
Air Quality Model
(urban scale)
Airshed Model
_ (ch. 5.3) _
Groundwater M
(Leachate)
(ch. 5.4)
Introduction
(ch. 1)
Automatic
Differentiation
(ADIFOR)
(ch. 2, ch. 4)
Model Uncertainty (ch.2)
Input Uncertainty
and its Representation (ch.2)
Novel Methods
SRSM (ch. 3)
SRSM-ADIFOR (ch. 4)
Non-Probabilistic
Methods:
Fuzzy Theory
Interval Methods
(ch. 2)
Stochastic Response
Surface Method
(SRSM) (cli.3)
Coupling of SRSM and ADIFOR
(SRSM-ADIFOR) (cli.4)
Background: Uncertainties and Classification (ch.2)
Conventional Probabilistic
Methods:
Monte Carlo (MC) and
Latin Hypercube Sampling
(LHS) (ch. 2)
Addressing Uncertainties
Associated with Model Formulation
and Resolution (ch. 6)
Figure 1.1: Schematic depiction of the outline of this document. The shaded areas
indicate the contributions of this research

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Chapter 2
BACKGROUND
2.1 Types and Origins of Uncertainty in Transport-Transformation
Models
Uncertainties in transport-transformation models can be classified as Natural, Model,
and Data uncertainties, depending on their origins and on how they can be addressed.
They are briefly described in the following.
2.1.1 Natural Uncertainty and Variability
Environmental and biological systems are inherently stochastic due to unavoidable
unpredictability (randomness). Some quantities are random even in principle, while
some quantities that are precisely measurable are modeled as "random" quantities
as a practical matter (due to cost and effort involved with continuous and precise
measurement). For example, in air pollution systems, the turbulent atmosphere and
unpredictable emission-related activities contribute to "natural uncertainty". In such
cases, a precise estimation of system properties is not possible, and the uncertainty
can be characterized through ensemble averages. On the other hand, the modeling
of emissions from a source are sometimes modeled via a mean value and a "random
error", since it is impractical to continuously monitor the emissions.
Further, some quantities vary over time, over space, or across individuals in a
population; this is termed "variability". Variability is the heterogeneity between in-
dividual members of a population of some type, and is typically characterized through
a frequency distribution. It is possible to interpret variability as uncertainty under
certain conditions, since both can be addressed in terms of "frequency" distributions.
However, the implications of the differences in uncertainty and variability are
relevant in decision making. For example, the knowledge of the frequency distribution
for variability can guide the identification of significant subpopulations which merit
more focused study. In contrast, the knowledge of uncertainty can aid in determining
areas where additional research or alternative measurement techniques are needed to
reduce uncertainty.
8

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Chapter: 11 Section: 1	9
Evaluation Data Uncertainty
JNatural Uncertainty
Model Uncertainty
Input Data Uncertainty
Figure 2.1: Types of uncertainty present in transport-transformation modeling appli-
cations and their interrelationships (adapted from [75])
2.1.2 Model Uncertainty
Mathematical models are necessarily simplified representations of the phenomena be-
ing studied and a key aspect of the modeling process is the judicious choice of model
assumptions. The optimal mechanistic model will provide the greatest simplifica-
tions while providing an adequately accurate representation of the processes affecting
the phenomena of interest. Hence, the structure of mathematical models employed
to represent transport-transformation systems is often a key source of uncertainty.
In addition to the significant approximations often inherent in modeling, sometimes
competing models may be available. Furthermore, the limited spatial or temporal
resolution (e.g., numerical grid cell size) of many models is also a type of approxima-
tion that introduces uncertainty into model results. In this context, different sources
of model uncertainties are summarized in the following:
Model Structure: Uncertainty arises when there are alternative sets of scientific
or technical assumptions for developing a model. In such cases, if the results from
competing models result in similar conclusions, then one can be confident that the
decision is robust in the face of uncertainty. If, however, alternative model formula-
tions lead to different conclusions, further model evaluation might be required.
Model Detail: Often, models are simplified for purposes of tractability. These in-
clude assumptions to convert a complex nonlinear model to a simpler linear model
in a parameter space of interest. Uncertainty in the predictions of simplified models

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Chapter: 11 Section: 1
10
can sometimes be characterized by comparison of their predictions to those of more
detailed, inclusive models.
Extrapolation: Models that are validated for one portion of input space may be com-
pletely inappropriate for making predictions in other regions of the parameter space.
For example, a dose-response model based on high-dose, short duration animal tests
may incur significant errors when applied to study low-dose, long duration human
exposures. Similarly, in atmospheric modeling, models that are evaluated only for
base case emission levels, may introduce uncertainties when they are employed in
the study of future scenarios that involve significantly different emissions levels (also
known as AE caveat).
Model Resolution: In the application of numerical models, selection of a spatial
and/or temporal grid size often involves uncertainty. On one hand, there is a trade-
off between the computation time (hence cost) and prediction accuracy. On the other
hand, there is a trade-off between resolution and the validity of the governing equa-
tions of the model at such scales. Very often, a coarse grid resolution introduces
approximations and uncertainties into model results. Sometimes, a finer grid res-
olution need not necessarily result in more accurate predictions. For example, in
photochemical grid modeling, the atmospheric diffusion equation (ADE) (see Equa-
tion 6.1) is valid for grid cell sizes between 2 km and 20 km [134], In this case, a
coarse-grid model ignores significant "sub-grid" detail, and a fine-grid model would
require more computer resources, and may even produce incorrect results, as the
governing equation may not be appropriate. This type of uncertainty is sometimes
dealt with through an appropriate selection of model domain parameter values, or by
comparing results based on different grid sizes.
Model Boundaries: Any model may have limited boundaries in terms of time, space,
number of chemical species, types of pathways, and so on. The selection of a model
boundary may be a type of simplification. Within the boundary, the model may be
an accurate representation, but other overlooked phenomenon not included in the
model may play a role in the scenario being modeled.
2.1.3 Parametric/Data Uncertainty
Uncertainties in model parameter estimates stem from a variety of sources. Even
though many parameters could be measurable up to any desired precision, at least
in principle, there are often significant uncertainties associated with their estimates.
Some uncertainties arise from measurement errors; these in turn can involve (a) ran-
dom errors in analytic devices (e.g., the imprecision of continuous monitors that mea-
sure stack emissions), (b) systematic biases that occur due to imprecise calibration,
or (c) inaccuracies in the assumptions used to infer the actual quantity of interest
from the observed readings of a "surrogate" or "proxy" variable. Other potential
sources of uncertainties in estimates of parameters include misclassification, estima-

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Chapter: 11 Section: 2
11
Table 2.1: Examples of the sources of uncertainty in the formulation and application
of transport-transformation models
Uncertainty in Model Formulation
(Structural Uncertainty)
Uncertainty in Model Application
(Data/Parametric Uncertainty)
Simplifications in Conceptual Formulation
Simplifications in Mathematical Formulation
Ergodic-type Hypotheses
Idealizations in Constitutive Relations
Independence Hypotheses
Spatial Averaging
Temporal Averaging
Process Decoupling
Lumping of Parameters
Discretization
Numerical Algorithm/Operator Splitting
Approximations in Computer Coding
Constitutive Parameter Selection
Design/Structural Parameter Selection
Input Data Development/Selection
Source Information
Meteorology (air quality models)
Hydrology (water quality models)
Initial and Boundary Conditions
Operational Model Evaluation
Uncertainty in model estimates
Uncertainty in observations
Nonexistence of observations
Response Interpretation
AE caveat and other implicit hypotheses
tion of parameters through a small sample, and estimation of parameters through
non-representative samples. Further, uncertainty in model application arises from
uncertainties associated with measurement data used for the model evaluation.
Table 2.1 lists some of the sources of model and parametric uncertainty associated
with the formulation and the application of transport-transformation models.
2.2 Reducible and Irreducible Uncertainty
Uncertainty associated with model formulation and application can also be classified
as "reducible" and "irreducible". Natural uncertainty is "inherent" or irreducible,
whereas data and model uncertainty contain both reducible and irreducible compo-
nents. The irreducible uncertainty in data and models is generally a result of the
presence of natural uncertainty. Reducible uncertainty can be lowered, e.g., by better
inventorying methods, improved instrumentation, improvements in model formula-
tion, etc. Nevertheless, the distinction between reducible and irreducible model and
data uncertainties is to a great extent a matter of convention since it may not be
feasible to eliminate the presence of an error (reducible uncertainty) in measurement
or modeling beyond a certain level. Furthermore, what is perceived as irreducible
natural uncertainty may be quantified in a statistical sense, and via mechanistic mod-
eling, better than "artificial" reducible uncertainty. Irreducible modeling uncertainty

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Chapter: 11 Section: 3
12
reflects the "current" model formulation and may actually change when improved
theories describing the phenomena under consideration become available. Also, the
averaging processes involved in model formulation unavoidably "lump" together nat-
ural and modeling uncertainty and only a quantification of this lumped uncertainty
may be possible or desirable. Figure 2.1 depicts schematically the types of uncertain-
ties present in transport-transformation models, and their interrelationships.
2.3 Approaches for Representation of Uncertainty
Various approaches for representing uncertainty in the context of different domains of
applicability are presented by Klir [122], and are briefly summarized in the following:
•	Classical set theory: Uncertainty is expressed by sets of mutually exclusive alter-
natives in situations where one alternative is desired. This includes diagnostic,
predictive and retrodictive uncertainties. Here, the uncertainty arises from the
nonspecificity inherent in each set. Large sets result in less specific predictions,
retrodictions, etc., than smaller sets. Full specificity is obtained only when one
alternative is possible.
•	Probability theory: Uncertainty is expressed in terms of a measure on subsets of
a universal set of alternatives (events). The uncertainty measure is a function
that, according to the situation, assigns a number between 0 and 1 to each subset
of the universal set. This number, called probability of the subset, expresses
the likelihood that the desired unique alternative is in this subset. Here, the
uncertainty arises from the conflict among likelihood claims associated with the
subsets of the universal set, each consisting of exactly one alternative. Since
these alternatives are mutually exclusive, nonzero probabilities assigned to two
or more events conflict with one another since only one of them can be the
desired one.
•	Fuzzy set theory: Fuzzy sets, similar to classical sets, are capable of express-
ing nonspecificity. In addition, they are also capable of expressing vagueness.
Vagueness is different from nonspecificity in the sense that vagueness emerges
from imprecision of definitions, in particular definitions of linguistic terms. In
fuzzy sets, the membership is not a matter of affirmation or denial, but rather
a matter of degree.
•	Fuzzy measure theory: This theory considers a number of special classes of mea-
sures, each of which is characterized by a special property. Some of the measures
used in this theory are plausibility and belief measures, and the classical proba-
bility measures. Fuzzy measure theory and fuzzy set theory differ significantly:
in the fuzzy set theory, the conditions for the membership of an element into a

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Chapter: 11 Section: 3
13
set are vague, whereas in the fuzzy measure theory, the conditions are precise,
but the information about an element is insufficient to determine whether it
satisfies those conditions.
• Rough set theory: A rough set is an imprecise representation of a crisp set in
terms of two subsets, a lower approximation and upper approximation. Further,
the approximations could themselves be imprecise or fuzzy.
Some of the widely used uncertainty representation approaches used in transport-
transformation modeling include interval mathematics, fuzzy theory, and probabilistic
analysis. These approaches are presented in the following sections.
2.3.1 Interval Mathematics
Interval mathematics is used to address data uncertainty that arises (a) due to im-
precise measurements, and (b) due to the existence of several alternative methods,
techniques, or theories to estimate model parameters. In many cases, it may not be
possible to obtain the probabilities of different values of imprecision in data; in some
cases only error bounds can be obtained. This is especially true in case of conflict-
ing theories for the estimation of model parameters, in the sense that "probabilities"
cannot be assigned to the validity of one theory over another. In such cases, interval
mathematics can be used for uncertainty estimation, as this method does not require
information about the type of uncertainty in the parameters [5,24],
The objective of interval analysis is to estimate the bounds on various model out-
puts based on the bounds of the model inputs and parameters. In the interval math-
ematics approach, uncertain parameters are assumed to be "unknown but bounded",
and each of them has upper and lower limits without a probability structure [178];
every uncertain parameter is described by an interval. If a parameter Xi of a model is
known to be between xi — q and xi + the interval representation of Xi is given by
\xi — ti, xi + ti\. Correspondingly, the model estimates would also belong to another
interval. Special arithmetic procedures for calculation of functions of intervals used
in this method are described in the literature [5,146,147,150]. For example, if two
variables, a and b are given by [cii,au] and [bi,bu], where a/ < au and 6/ < bui simple
arithmetic operations are given by the following:
a + b
[ai + bhau + bu]
a — b
a ฆ b
IP'i by, CLU 6/]
[min(aik, aibu, aubi,aubu),max(aibi,aibu, aubh aubu)\	(2.1)
a/b
[ahau]- —;0 i[bhbu\
Furthermore, a range of computational tools exist for performing interval arith-
metic on arbitrary functions of variables that are specified by intervals [119,129].

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Chapter: 11 Section: 3
14
Symbolic computation packages such as Mathematica [10] and Maple [91] support
interval arithmetic. In addition, extensions to FORTRAN language with libraries for
interval arithmetic are also available [117,118]. Therefore, in principle, the ranges of
uncertainty in any analytical or numerical model (FORTRAN model) can be analyzed
by existing tools.
Various applications of interval analysis in the literature include the treatment of
uncertainty in the optimal design of chemical plants [67], in the cost benefit analysis
of power distribution [178], and in decision evaluation [24], Interval analysis has also
been applied to estimate the uncertainty in displacements in structures due to the
uncertainties in external loads [126]. Dong et al. presented a methodology for the
propagation of uncertainties using intervals [50]. Hurme et al. presented a review on
semi qualitative reasoning based on interval mathematics in relation to chemical and
safety engineering [101]. Applications in transport-transformation modeling include
uncertainty analysis of groundwater flow models [51,138].
The primary advantage of interval mathematics is that it can address problems
of uncertainty analysis that cannot be studied through probabilistic analysis. It
may be useful for cases in which the probability distributions of the inputs are not
known. However, this method does not provide adequate information about the
nature of output uncertainty, as all the uncertainties are forced into one arithmetic
interval [131]. Especially when the probability structure of inputs is known, the
application of interval analysis would in fact ignore the available information, and
hence is not recommended.
2.3.2 Fuzzy Theory
Fuzzy theory is a method that facilitates uncertainty analysis of systems where uncer-
tainty arises due to vagueness or "fuzziness" rather than due to randomness alone [63].
This is based on a superset of conventional (Boolean) logic that has been extended
to handle the concept of partial truth - truth values between "completely true" and
"completely false". It was introduced by Zadeh as a means to model the uncer-
tainty of natural language [212], Fuzzy theory uses the process of "fuzzification" as a
methodology to generalize any specific theory from a crisp (discrete) to a continuous
(fuzzy) form.
Classical set theory has a "crisp" definition as to whether an element is a member
of a set or not. However, certain attributes of systems cannot be ascribed to one set
or another. For example, an attribute of a system can be specified as either "low" or
"high". In such a case, uncertainty arises out of vagueness involved in the definition
of that attribute. Classical set theory allows for either one or the other value. On the
other hand, fuzzy theory provides allows for a gradual degree of membership. This
can be illustrated as follows:
In the classical set theory, the truth value of a statement can be given by the

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Chapter: 11 Section: 3
15
membership function /j,a(x), as
"-M = { J is Ha	<2-2'
On the other hand, fuzzy theory allows for a continuous value of ha between 0 and
1, as
(	1 iff x E A
Ha(x) = <	0 iff x ^ A	(2.3)
[p; 0 < p < 1 if x partially belongs to A
In fuzzy theory, statements are described in terms of membership functions, that
are continuous and have a range [0,1]. For example, given the measured value of a
parameter, the membership function gives the "degree of truth" that the parameter
is "high" or "low".
Further, fuzzy logic is defined by the following the set relationships:
fJLA'(x) = 1.0 -ha (x)
Hahb(x) = min(fj,A(x),fJ,B(x))	(2.4)
Haub(x) = max(/j,A(x),iiB(x))
Using fuzzy arithmetic, based on the grade of membership of a parameter of
interest in a set, the grade of membership of a model output in another set can be
calculated. Fuzzy theory can be considered to be a generalization of the classical set
theory. It must be noted that if the membership grades are restricted to only 0 and
1, the fuzzy theory simplifies to classical set theory.
A vast amount of literature is available on fuzzy theory, including extensive in-
troductions to the concepts involved by Bezdek and by Smithson [15, 185]. Fuzzy
arithmetic and its applications are described by Klir et al., by Kauffmann et al., and
by Puri et al. [116,123,161]. Kraslawski et al. applied fuzzy set theory to study un-
certainty associated with incomplete and subjective information in process engineer-
ing [127]. Ayyub et al. studied structural reliability assessment by using fuzzy theory
to study the uncertainty associated with ambiguity [9]. Juang et al. demonstrated the
applicability of fuzzy theory in the modeling and analysis of non-random uncertain-
ties in geotechnical engineering [112]. Further literature on industrial applications of
fuzzy theory is available for the interested reader [169,211], Specifically, it has been
applied to characterize uncertainty in engineering design calculations [128,209], in
quality control [162], in sludge application land selection [44], and in solute transport
modeling [51,52,138]. However, drawbacks in its applicability to uncertainty analy-
sis has been noted by some researchers [187,203]. Fuzzy theory appears to be more
suitable for qualitative reasoning, and classification of elements into a fuzzy set, than
for quantitative estimation of uncertainty.

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Chapter: 11 Section: 3
16
2.3.3 Probabilistic Analysis
In the probabilistic approach, uncertainties are characterized by the probabilities as-
sociated with events. The probability of an event can be interpreted in terms of the
frequency of occurrence of that event. When a large number of samples or experiments
are considered, the probability of an event is defined as the ratio of the number of
times the event occurs to the total number of samples or experiments. For example,
the statement that the probability that a pollutant concentration c lies between c\
and C2 equals p means that from a large number of independent measurements of the
concentration c, under identical conditions, the number of times the value of c lies
between c\ and c2 is roughly equal to the fraction p of the total number of samples.
Probabilistic analysis is the most widely used method for characterizing uncer-
tainty in physical systems, especially when estimates of the probability distributions
of uncertain parameters are available. This approach can describe uncertainty arising
from stochastic disturbances, variability conditions, and risk considerations. In this
approach, the uncertainties associated with model inputs are described by probability
distributions, and the the objective is to estimate the output probability distributions.
This process comprises of two stages:
•	Probability encoding of inputs: This process involves the determination of the
probabilistic distribution of the input parameters, and incorporation of random
variations due to both natural variability (from, e.g., emissions, meteorology)
and "errors." This is accomplished by using either statistical estimation tech-
niques or expert judgments. Statistical estimation techniques involve estimating
probability distributions from available data or by collection of a large number
or representative samples. Techniques for estimating probability distributions
from data are presented in the literature [36,100]. In cases where limited data
are available, an expert judgment provides the information about the input
probability distribution. For example, a uniform distribution is selected if only
a range of possible values for an input is available, but no information about
which values are more likely to occur is available. Similarly, normal distribution
is typically used to describe unbiased measurement errors. Table 2.2 presents a
list of some commonly used probability distributions in the uncertainty analysis
of transport-transformation models.
•	Propagation of uncertainty through models: Figure 2.2 depicts schematically
the concept of uncertainty propagation: each point of the response surface (i.e.,
each calculated output value) of the model to changes in inputs "1" and "2" will
be characterized by a probability density function (pdf) that will depend on the
pdf s of the inputs. The techniques for uncertainty propagation are discussed in
the following.
A number of excellent texts on probabilistic analysis are available in the literature

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Chapter: 11 Section: 3
17
response 1-2
probability density of response 1-2
input 2
probability density of input 2
input 1
probability density of input 1
Figure 2.2: A schematic depiction of the propagation of uncertainties in transport-
transformation models
for the interested reader [73,155,194],

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Chapter: 11 Section: 3
18
Table 2.2: Selected probability density functions useful in representing uncertainties
in environmental and biological model inputs
Distribution
Parameters
& Conditions
Probability density
function
Moments
Uniform
a, b
1
b — a
a + b
Mean =	
12


(x - fl)2
' e 2a2
(TV 2tt

Normal
H, a , a > 0
Mean = /j
Var = a2
Mode = fi
Lognormal
fi, a , a > 0
(\og(x) - il)2
' e 2a2
XOsJ 2tt
Mean = + a /^)
Var = (eฐ"2 — l)e^^ + a )
Mode = ~
Gamma
a, b,
a > 0, b > 0
X
,, xa~le b x > 0
Y(a)ba
Mean = ab
Var = ab2
Mode = (a — 1)6
Exponential
A, A > 0
Xe~Xx , x > 0
1
Mean = —
1A
Var = —
Mode = 0
Weibull
a
axa ~ ^e~x (x > 0)
Mean = F(1 + -)
2 a 1
Var = P(1 H—) — P(1 H—)
a a



1
Mode = (1	)a,a > 1
a



Extreme Value

g—x — e~x
Mean = 0
Var = 1
Mode = 0

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Chapter: 11 Section: 4
19
2.4	Sensitivity and Sensitivity/Uncertainty Analysis
The aim of sensitivity analysis is to estimate the rate of change in the output of a
model with respect to changes in model inputs. Such knowledge is important for
(a) evaluating the applicability of the model, (b) determining parameters for which
it is important to have more accurate values, and (c) understanding the behavior of
the system being modeled. The choice of a sensitivity analysis method depends to a
great extent on (a) the sensitivity measure employed, (b) the desired accuracy in the
estimates of the sensitivity measure, and (c) the computational cost involved.
In general, the meaning of the term "sensitivity analysis" depends greatly on the
sensitivity measure that is used. Table 2.3 presents some of the sensitivity measures
that are often employed in the sensitivity analysis of a mathematical model of the
form
Jr(u,k) = 0	(2.5)
where k is a set of m parameters, and u is a vector of n output variables.
Based on the choice of a sensitivity metric and the variation in the model pa-
rameters, sensitivity analysis methods can be broadly classified into the following
categories:
•	Variation of parameters or model formulation: In this approach, the model is
run at a set of sample points (different combinations of parameters of concern)
or with straightforward changes in model structure (e.g., in model resolution).
Sensitivity measures that are appropriate for this type of analysis include the
response from arbitrary parameter variation, normalized response and extrema.
Of these measures, the extreme values are often of critical importance in envi-
ronmental applications.
•	Domain-wide sensitivity analysis: Here, the sensitivity involves the study of the
system behavior over the entire range of parameter variation, often taking the
uncertainty in the parameter estimates into account.
•	Local sensitivity analysis: Here, the focus is on estimates of model sensitivity to
input and parameter variation in the vicinity of a sample point. This sensitivity
is often characterized through gradients or partial derivatives at the sample
point.
2.5	Conventional Sensitivity/Uncertainty Analysis Methods
Conventional methods for sensitivity analysis and uncertainty propagation can be
broadly classified into four categories: (a) "sensitivity testing", (b) analytical meth-
ods, (c) sampling based methods, and (d) computer algebra based methods.

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Chapter: 11 Section: 5
20
Table 2.3: A summary of sensitivity measures employed in sensitivity analysis,
adapted from McRae et al., 1982 [143]
Sensitivity Measure
Definition
Response from arbitrary parameter variation
u = u(k + 5k) — u{k)
Normalized Response
D,= iUi
Ui{k)
Average Response
/... / Ui(k)dk
Uiik) -
hldk
Expected Value
(ut(k)) = j-J ut(k)P(k)dk
Variance
5f(k) = Mk)2) - (ut(k))2
Extrema
max [ui(k)], min [Ui(k)]
Local Gradient Approximation
dv ฆ
Su [S]5k ; = ^
Normalized Gradient
kj dui
13 Ui(k) Ok,
Sensitivity testing involves studying model response for a set of changes in model
formulation, and for a selected model parameter combinations. Analytical methods
involve either the differentiation of model equations and subsequent solution of a
set of auxiliary sensitivity equations, or the reformulation of original model using
stochastic algebraic/differential equations. On the other hand, the sampling based
methods involve running the original model for a set of input/parameter combinations
(sample points) and estimating the sensitivity/uncertainty using the model outputs at

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Chapter: 11 Section: 5
21
those points. Yet another sensitivity analysis method is based on direct manipulation
of the computer code of the model, and is termed automatic differentiation. These
methods are elaborated in the following.
2.5.1	Sensitivity Testing Methods
In this approach, the model is run for a set of sample points for the parameters
of concern or with straightforward changes in model structure (e.g., in model res-
olution). This approach is often used to evaluate the robustness of the model, by
testing whether the model response changes significantly in relation to changes in
model parameters and structural formulation of the model. The application of this
approach is straightforward, and it has been widely employed in conjunction with
transport-transformation modeling.
Sistla et al. [183] and Al-Wali et al. [4] used this approach for the sensitivity
analysis of urban air quality models. Roselle [168] used this approach to study the
effect of biogenic emission uncertainties by performing model simulations at three
emission biogenic estimate levels. Vieux et al. [199] and Vanderperk [198] have used
a similar approach in water quality modeling.
The primary advantage of this approach is that it accommodates both qualitative
and quantitative information regarding variation in the model. However, the main
disadvantage of this approach is that detailed information about the uncertainties is
difficult to obtain using this approach. Further, the sensitivity information obtained
depends to a great extent on the choice of the sample points, especially when only a
small number of simulations can be performed.
2.5.2	Analytical Methods
Some of the widely used analytical methods for sensitivity/uncertainty are: (a) dif-
ferential analysis methods, (b) Green's function method, (c) spectral based stochastic
finite element method, and (d) coupled and decoupled direct methods.
Differential Analysis Methods
Differential analysis methods include the Neumann expansion [3,190], and the pertur-
bation method [3,191]. The Neumann expansion method involves finding the inverse
of the model operator through the expansion of the model equations, and hence has
limitations on the type of model equations it can address. The perturbation method
involves expansion of model outputs as a series in terms of small random perturbations
in model parameters, and the subsequent solution of the series coefficients. The Neu-
mann expansion and perturbation based methods have been applied in uncertainty
analysis of ground water models [1,2,214], and in the design of structures [26,115,135].
The main limitation of these methods is the requirement that the perturbation terms

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Chapter: 11 Section: 5
22
be small. Further, these methods are in general difficult to apply in conjunction
with the modeling of complex, nonlinear systems, as the model equations are often
mathematically intractable.
Green's Function Method
In the Green's function method, the sensitivity equations of a model are obtained
by differentiating the model equations. The sensitivity equations are then solved
by constructing an auxiliary set of Green's functions. This method minimizes the
number of differential equations that are solved for sensitivity, and replaces them
with integrals that can be easily evaluated [53,54], This approach has been applied
to the sensitivity analysis of atmospheric chemistry models [103,201].
Spectral Based Stochastic Finite Element Method
This method relies on the use of representing stochastic processes in terms of a series
expansion, specifically the Karhunen-Loeve expansion [80,155]. For finite element
method problems, this approach results in a set of linear matrix equations with de-
terministic matrices multiplied by random vectors. The matrix equations are solved
either using operator expansions or by using the Galerkin's method [200]. One of the
main features of this method is the representation of random parameters in terms of
orthogonal functions of a set of standardized random variables; the expansion is also
known as "polynomial chaos expansion", and forms the basis for the development of
the "Stochastic Response Surface Method" (SRSM). The polynomial chaos expansion
and the SRSM are discussed in detail in Chapter 3.
Coupled/Decoupled Direct Method
The direct method involves the differentiation of model equations and the subse-
quent solution of the sensitivity equations. The sensitivity equations are then solved
along with the original model equations (Coupled Direct Method) [193], or separately
(Decoupled Direct Method) [58,160]. The decoupled method is advantageous both
in terms of computational efficiency and stability of the solution. This method has
evolved into a standard module in conjunction with commercial sensitivity analy-
sis codes for chemical kinetics, such as CHEMKIN [40], and GCKP86 [163]. The
decoupled method is also reported to be more efficient than the Green's function
method [58].
The analytical methods require access to the governing model equations, and may
involve writing additional computer code for the solution of the auxiliary equations,
which may be impractical and sometimes impossible. For example, reformulating an
existing computational model developed by others could require prohibitive amounts of
resources.

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Chapter: 11 Section: 5
23
2.5.3 Sampling Based Methods
Sampling based methods do not require access to model equations or even the model
code. These methods involve running a set of model at a set of sample points, and
establishing a relationship between inputs and outputs using the model results at
the sample points. Some of the widely used sampling based sensitivity /uncertainty
analysis methods are: (a) Monte Carlo and Latin Hypercube Sampling methods,
(b) Fourier Amplitude Sensitivity Test (FAST) (c) reliability based methods, and
(d) response surface methods.
Monte Carlo and Latin Hypercube Sampling Methods
Monte Carlo (MC) methods are the most widely used means for uncertainty analysis,
with applications ranging from aerospace engineering [12] to zoology [28]. These meth-
ods involve random sampling from the distribution of inputs and successive model
runs until a statistically significant distribution of outputs is obtained. They can
be used to solve problems with physical probabilistic structures, such as uncertainty
propagation in models or solution of stochastic equations, or can be used to solve
non-probabilistic problems, such as finding the area under a curve [47,73]. Monte
Carlo methods are also used in the solution of problems that can be modeled by
the sequence of a set of random steps that eventually converge to a desired solution.
Problems such as optimization and the simulation of movement of fluid molecules are
often addressed through Monte Carlo simulations [73]. For the interested reader, a
wide range of literature describing the methodology, tools, and the applicability of
the Monte Carlo methods is available in the literature [49,69,113,172,186].
Since these methods require a large number of samples (or model runs), their ap-
plicability is sometimes limited to simple models. In case of computationally intensive
models, the time and resources required by these methods could be prohibitively ex-
pensive.*.
A degree of computational efficiency is accomplished by the use of Modified Monte
Carlo (MMC) methods that sample from the input distribution in an efficient manner,
so that the number of necessary solutions compared to the simple Monte Carlo method
is significantly reduced.
The Latin Hypercube Sampling [104,141,188] is one such widely used variant of
the standard Monte Carlo method. In this method, the range of probable values for
each uncertain input parameter is divided into ordered segments of equal probability.
Thus, the the whole parameter space, consisting of all the uncertain parameters, is
partitioned into cells having equal probability, and they are sampled in an "efficient"
*For example, in order to perform the uncertainty analysis of a grid-based photochemical model,
such as the Urban Airshed Model (UAM-IV), which requires about six hours of CPU time for the
simulation of air quality near the vicinity of New Jersey (see the example in Section 5.3), a thousand
samples would require more than eight months of computer time

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Chapter: 11 Section: 5
24
manner such that each parameter is sampled once from each of its possible segments.
The advantage of this approach is that the random samples are generated from all
the ranges of possible values, thus giving insight into the extremes of the probability
distributions of the outputs. A detailed description of the implementation of Latin
Hypercube Sampling is presented in Section 3.8.1.
Monte Carlo methods and Latin Hypercube Sampling have been applied in proba-
bilistic assessment of risk in physiologically based pharmacokinetic models by consid-
ering uncertainty in the estimates of the model parameters [43,65,181]. These meth-
ods have also been applied in groundwater contamination models [14,86,138,152,167],
in air pollution modeling [33,46,72,81,173,184], in process engineering [164,205], and
in the reliability assessment of structures [8].
Guidelines for the application of Monte Carlo and Latin Hypercube Sampling
methods in risk assessment are provided by the U.S.EPA [196,197]. Several computer
packages containing routines for Monte Carlo and Latin Hypercube Sampling methods
are reported in the literature [105,109]. The EPA approved FORTRAN package
for Latin Hypercube Sampling by Iman et al. [105] is available through the EPA's
Exposure Models Library [195]. Commercial plugins based on spreadsheet software
include Crystal ball [45], @RISK [208], RISKMAN [157], and MonteCarlo [159]. This
list is only representative of the available software tools. In the present work, a
Latin Hypercube Sampling utility in FORTRAN has been developed as a part of the
implementation of the Stochastic Response Surface Method, and is freely available to
the research community.
Fourier Amplitude Sensitivity Test (FAST)
Fourier Amplitude Sensitivity Test (FAST) is a method based on Fourier transfor-
mation of uncertain model parameters into a frequency domain, thus reducing the a
multi-dimensional model into a single dimensional one. For a model with m model
parameters, ki,k2, ฆ ฆ ฆ ,km, and n outputs, U\, u2, ฆ ฆ ฆ , um, such that
the FAST method involves the transformation of the parameters into a frequency
domain spanned by a scalar s, as follows:
fi(tj ^1 > ^2) • • • j	i 1) 2, • • • , Tlj
h = Gi(smwis), l = l,2,...,m
The outputs are then approximated as:

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Chapter: 11 Section: 5
25
These integrals are evaluated by repeatedly sampling the parameter space of s,
which corresponds to the sampling in the multidimensional model parameter space.
The details of the transformation of model parameters into the frequency domain,
and the subsequent sampling are explained by Koda et al. [124], and a computational
implementation of FAST is presented by McRae et al. [144],
FAST has been applied in the sensitivity /uncertainty analysis of atmospheric pho-
tochemical mechanisms [64], in conjunction with land surface processes in global cli-
mate models models [39,140], and in the disposal of radioactive waste [121], A review
article by Helton [93] reports that Monte Carlo methods are more widely applicable
than FAST.
Reliability Based Methods (FORM and SORM)
First- and second-order reliability methods (FORM and SORM, respectively) are ap-
proximation methods that estimate the probability of an event under consideration
(typically termed "failure"). For example, these methods can provide the probabil-
ity that a contaminant concentration exceeds a target level at a location (or, the
probability of failure). In addition, these methods provide the contribution to the
probability of failure from each input random variable, at no additional computa-
tional effort [89,114,142], These methods are useful in uncertainty analysis of models
with a single failure criterion.
For a model with random parameters
the objective of the reliability based approach is to estimate the probability of failure.
In case of pollutant contamination exceedance, the failure condition can be defined
as
where Cr is a pre-specified maximum permissible concentration at a location of in-
terest.
If the joint probability density function for the set X is given by /x, then the
probability of failure is given by the n-fold integral:
where the integration is carried out over the failure domain. The evaluation of this
integral becomes computationally demanding as the number of random variables (the
dimension of the integration) increases; in fact if m is the number of function calls
X = (Xi,X2,..., Xn)
and a failure condition
g(x1}x2,... , xn) < o
g(X) = CR-C(X) < 0,
PF = P{g(X) < 0} = P{CR < C(X)}

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Chapter: 11 Section: 5
26
of the integrand per dimension, and n is the dimension, the computation time grows
as mn [94], In addition, since the value of the integrand is small, the numerical
inaccuracies can be considerably magnified when integrated over a multi-dimensional
space [23].
FORM and SORM use analytical schemes to approximate the probability integral,
through a series of the following simple steps, as illustrated by Bjerager [20]:
•	mapping the basic random variables X and the failure function g(X), into a
vector of standardized and uncorrelated normal variates U, as X(U) and G(U)
respectively,
•	approximating the function G(U) by a tangent (FORM) or a paraboloid (SORM)
at a failure point u* closest to the origin, and
•	calculating the probability of failure as a simple function of u*.
These methods are reported to be computationally very efficient compared to
Monte Carlo methods, especially for scenarios corresponding to low probabilities of
failure [89]. Further, SORM is more accurate than FORM, but computationally more
intensive, since it involves a higher order approximation. These methods have been
applied to problems of ground-water flow and contaminant transport [87-89,108] and
to problems in safety assessment [38,142],
The main drawbacks of FORM and SORM are that the mapping of the failure
function on to a standardized set, and the subsequent minimization of the function,
involve significant computational effort for nonlinear black box numerical models [79].
In addition, simultaneous evaluation of probabilities corresponding to multiple failure
criteria would involve significant additional effort. Furthermore, these methods im-
pose some conditions on the joint distributions of the random parameters [89], thus
limiting their applicability.
Response Surface Methods
The response surface methods consist of (i) screening to determine a subset of im-
portant model input parameters, (ii) making multiple runs of the computer model
using specific values and pairings of these input parameters, and (iii) fitting a general
polynomial model to the model data (using the method of least squares). This fitted
response-surface is then used as a replacement or proxy for the computer model, and
all inferences related to sensitivity/uncertainty analysis for the original model are
derived from this fitted model. This approach is sometimes termed as a "secondary
model technique" [66]. Box et al. [21,22] describe the adaptive nature of these meth-
ods, and the methodology for identifying the sampling points and for subsequent
analysis is presented in detail in the literature [57,68,120].

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Chapter: 11 Section: 5
27
Helton [93] studied the applicability of response surface methods in performance
assessment of radioactive waste disposal. It has also been used in conjunction with
soil erosion modeling [32], with vegetative plant growth modeling [136], and with
structural reliability problems [27].
2.5.4 Computer Algebra Based Methods
Table 2.4: A representative list of packages available for automatic differentiation
Package
Authors
Year
Language
Applications/Features
ADIC
ADIFOR
ADGEN
ADOL-C
ADOL-F
ATOM FT
GRESS
Odyssee
PADRE2
PCOMP
SMS
Bischof et al. [19]
Bischof et al. [17]
Worley et al. [210]
Griewank [84]
Shiriaev et al. [180]
Change et al. [31]
Horwedel [97]
Rostaing et al. [170]
Kubota [130]
Dobmann et al. [48
Korelc [125]
TEXPANDER Ozaki et al. [153]
Unnamed Rich et al. [166]
1997
1996
1989
1996
1996
1994
1993
1991
1995
1997
1993
1992
C
Fortran
Fortran
C/C++
Fortran 90
Fortran
Fortran
Fortran
Fortran
Fortran
Mathematics
Fortran
Matlab
3-D CFD grid generator [19]
A wide range of
applications [29,98,102,107]
Radionuclide modeling [95]
Groundwater flow [41]
Solution of ODEs [31]
Radionuclide
transport [96,151,156]
Process engineering [145]
Estimates rounding errors
Automatic derivation of finite
element formulae [125]
Optimization of structures [153]
Computer algebra based methods involve the direct manipulation of the com-
puter code, typically available in the form of a high level language code (such as C
or FORTRAN), and estimation of the sensitivity and uncertainty of model outputs
with respect to model inputs. These methods do not require information about the
model structure or the model equations, and use mechanical, pattern-matching algo-
rithms, to generate a "derivative code" based on the model code. One of the main
computer algebra based methods is the automatic differentiation, which is sometimes
also termed automated differentiation.
Automatic differentiation involves direct manipulation of a model code to gener-
ate a corresponding "derivative calculating code". Given the source code, and the
information about what the dependent and independent variables of interest are, the
automatic differentiation tools can generate derivative information without further
user intervention. This approach is essentially a "black-box" approach, as it does not

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Chapter: 11 Section: 5
28
require access to the model formulation or model equations. The main advantages
of this approach are that partial derivatives can be calculated with the accuracy of
the machine precision [17], and this approach results in substantial computer time
savings [102,110].
An overview of the methodology and applications of automatic differentiation is
presented by Griewank [82,83]. In addition, various reviews of automatic differenti-
ation in the fields of engineering design, process engineering and numerical analysis
can be found in the literature [13,34,106]. A large number of packages for automatic
differentiation of model codes written in various programming languages are currently
available. Table 2.4 lists a majority of the automatic differentiation tools currently
available, along with some of their applications reported in the literature.
Two of the notable automatic differentiation tools that have been applied in con-
junction with transport-transformation models are GRESS and ADIFOR. GRESS
(GRadient Enhanced Software System) [97] is a Fortran preprocessor system that
searches the model equations based on the appearance of the "=" symbol, and an-
alyzes the functional dependence of independent and dependent variables. The dif-
ferentiation is carried out for each statement and the results are stored numerically
at each stage of computation. This method has been applied to models describing
transport of radionuclides through groundwater [151,156]. It has also been applied a
model describing the atmospheric dispersion of radionuclides, AIRDOS [95,147]. The
main limitation of GRESS is that the particulars of every operation performed in the
code are stored in the memory at each step. This interpretation overhead associated
with the storage, and the potentially great memory demands, can sometimes limit its
applicability [16].
ADIFOR (Automatic Differentiation of FORtran) [17] applies the rules of auto-
matic differentiation, and rewrites the original code, inserting statements for com-
puting the first-order derivatives. The generated code can then be compiled just like
a normal program, and can be executed to calculate derivatives in the model, along
with the model outputs. This approach avoids the limitation of GRESS, with respect
to the interpretation overhead.
In the area of photochemical air quality modeling, Carmichael et al. [29] applied
the ADIFOR method to calculate the sensitivity of ozone levels to the initial con-
centrations (84 species) and all reaction rate constants (178 chemical reactions) for
six different chemical regimes. Further, Hwang et al. [102] studied the applicability
of automatic differentiation techniques in numerical advection schemes in air quality
models. They calculated the sensitivity of concentration to a global perturbation of
wind velocity in advection models and comparing that with other methods. Their
results indicate that ADIFOR-generated code can produce exact sensitivity informa-
tion up to the machine epsilon, and that the CPU requirements were lower for the
ADIFOR method.
ADIFOR has also been applied in several other research areas, such as groundwa-

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Chapter: 11 Section: 6
29
ter modeling [204], in weather modeling [18], in biostatistics [107], and in aeronau-
tics [98].
The details of the ADIFOR approach and its application are presented in Chap-
ter 4 where the coupling of the Stochastic Response Surface Method and ADIFOR is
described.
2.6 Need for Alternative Sensitivity/Uncertainty Analysis Meth-
ods
Traditional sampling methods for sensitivity and uncertainty analysis, such as the
Monte Carlo and Latin Hypercube Sampling, require a substantial number of model
runs to obtain a good approximation of the output pelfs, especially for cases involv-
ing several inputs. On the other hand, analytical methods require the information
about the mathematical equations of a model, and often are restricted in their ap-
plicability to cases where the uncertainties are small. Therefore there is a need for
a computationally efficient method for uncertainty propagation that is robust and
also applicable to a wide range of complex models. The following chapter describes
the development of the Stochastic Response Surface Method (SRSM), which is a
computationally efficient uncertainty analysis method developed as part of this work.

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Chapter 3
THE STOCHASTIC RESPONSE SURFACE
METHOD: DEVELOPMENT AND
IMPLEMENTATION
3.1 Overview of the Method
The Stochastic Response Surface Method (SRSM), as the name suggests, can be
viewed as an extension to the classical deterministic Response Surface Method (RSM),
on which extensive literature is available [21,22,68,120]. The main difference between
the SRSM and the RSM is that in the former the inputs are random variables, where
as in the latter, the inputs are deterministic variables.
The SRSM was developed as a part of this work with the objective of reducing
the number of model simulations required for adequate estimation of uncertainty,
as compared to conventional methods. This is accomplished by approximating both
inputs and outputs of the uncertain system through series expansions of standard
random variables; the series expansions of the outputs contain unknown coefficients
which can be calculated from the results of a limited number of model simulations.
Conceptually, the propagation of input uncertainty through a model using the
SRSM consists of the following steps: (1) input uncertainties are expressed in terms
of a set of standard random variables, (2) a functional form is assumed for selected
outputs or output metrics, and (3) the parameters of the functional approximation
are determined.
The SRSM is founded on the principle that random variables with well-behaved
(square-integrable) pdf s can be represented as functions of independent random vari-
ables [25,47,80,191]. The above integrability requirement is usually satisfied by
uncertain quantities of interest, so this method is applicable to a wide variety of
of transport-transformation models. Random variables with normal distributions,
N(0,1), are often selected to represent input uncertainties due to the mathematical
tractability of functions of these random variables [47,155]. In the present work these
random variables are referred to as "Standard Random Variables" (srvs).
Once the inputs are expressed as functions of the selected srvs, the output metrics
can also be represented as functions of the same set of srvs. The minimum number of
srvs needed to represent the inputs is defined as the "number of degrees of freedom" in
input uncertainty. Since model outputs are deterministic functions of model inputs,
30

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Chapter: 111 Section: 1
31
they have at most the same number of degrees of freedom in uncertainty.
Consider a model y = T_ (x), where the set of random inputs is represented by the
vector x, and a set of selected random outputs or output metrics is represented by the
vector y. First, the vector of input random variables is expressed as a function of the
form x = /i(ฃ), where ฃ denotes the vector of the selected srvs. Then, a functional
representation for outputs, of the form y = /(ฃ,a), where a denotes a parameter
vector, is assumed. The specific method employed in the estimation of parameters
of approximation depends on the complexity of the model, which can be broadly
categorized into the following:
•	Models consisting of simple linear algebraic operators: the outputs can be di-
rectly obtained as analytical functions of srvs, as y = ฃ_1 (x) = 
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Chapter: 111 Section: 2
32
inputs (X)
computational model
outputs (Y)
express inputs as functions of srvs
X = F<&	X=F(&
(for complex cases) J
(exact, if possible)
explicit systems A
Y=G(X))
direct solution A
) =<1<1 (I) t J
mathematically tractable
equations
le^
estimate a by standard
error minimization methods
(e.g., Galerkin's method)
implicit systems A
(1 (X, Y) =0 J
assume a form for Y >
(e.g., series expansion)
Y= h ฃ,a) >
black-box models or
complex numerical codes
D
estimate a using:	>
collocation methods	regression methods
(e.g., ECM or	(if collocation methods
\orthogonal collocation)	don't converge) j
evaluate
) = lain)
Figure 3.1: Schematic depiction of the Stochastic Response Surface Method
3.2 Step I: Representation of Stochastic Inputs
The first step in the application of the SRSM is the representation of all the model
inputs in terms of a set of "standardized random variables". This is analogous to nor-
malization process used in transforming deterministic variables. In this work, normal
random variables are selected as srvs as they have been extensively studied and their
functions are typically well behaved [25,47,80,155]. Here, the srvs are selected from
a set of independent, identically distributed (iid) normal random variables, Ld
where n is the number of independent inputs, and each ^ has zero mean and unit
variance. When the input random variables are independent, the uncertainty in the
zth model input is expressed directly as a function of the ith srv, i.e., a
)

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Chapter: 111 Section: 2
33
transformation of Xj to is employed. Such transformations are useful in the stan-
dardized representation of the random inputs, each of which could have very different
distribution properties.
One of two approaches may be taken to represent the uncertain inputs in terms
of the selected srvs: (a) direct transformation of inputs in terms of the srvs, and
(b) series approximations in srvs. Devroye [47] presents transformation techniques
and approximations for a wide variety of random variables. Input random variables
whose transformations are not found in literature can be approximated either by alge-
braic manipulation or by series expansion techniques. These techniques are described
in detail in the following sections.
Table 3.1: Representation of common univariate distributions as functionals of normal
random variables
Distribution Type
Transformationฎ
Uniform (a, b)
a + (b - a) Q + ^erf^/v^
Normal (n,cr)

Lognormal (n,a)
exp(/x + O
/ I	 \ ^
Gamma (a,b)
ab[iyh+l~ i)
Exponential (A)
~ilog(}+rfK/vi))
Weibull (a)
y~a
Extreme Value
- log(y)
3 ฃ is normal (0,1) and y is exponential (1) distributed
3.2.1 Direct Transformations
Direct transformations for expressing a given random variable as a function of another
random variable are typically derived by employing the following relation [155]:
If Y = h(X), where X is a random variable with the pdf fx(x), then the pdf of
Y, fr(y), is given by
fr(v) = +'''+ ji^\+"'	(:11)
\h'{x i)|	\h'(xn) |
where X\,... , xn,... are the real roots of the equation y = h(x).

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Chapter: 111 Section: 2
34
Using the above equation, given fy(y), and fx(x), the transformation Y = h(X)
is identified by means of trial and error. Transformations are available in the literature
for some common distributions. For example, if X is uniform [0,1], then — — logX is
A
exponential (A) distributed, and if X is normal, the X2 is gamma (1,1) distributed.
Although a large number of transformations for distributions exist in the lit-
erature, they are not directly applicable for the case of normal random variables
because of the following reasons: (a) these transformations are optimized for compu-
tational speed as they are used as random number generators, and (b) a majority of
these transformations are in terms of the uniform random numbers. However, some
transformations in terms of the standard normal random variables are presented by
Devroye [47]. In addition, some transformations were derived during the course of
the development of the SRSM. Table 3.1 presents a list of transformations for some
probability distributions commonly employed in transport-transformation modeling.
3.2.2	Transformation via Series Approximation
When a model input follows a non-standard distribution, direct transformation in
terms of standard random variables is often difficult to obtain. This may be true for
some distributions for which direct transformations are not available in the literature,
and are difficult to derive. In such cases, the approximation of the random variable
can be obtained as follows:
•	the moments of the random variable are computed based on the given informa-
tion (e.g., measurements or the given non-standard or empirical pdf),
•	a series expansion in terms of an srv is assumed to represent the random variable,
•	the moments of the corresponding series expansion are derived as functions of
unknown coefficients,
•	the error between the derived moments and the calculated moments is mini-
mized (e.g., through least squares), thus resulting in a set of equations in the
unknown coefficients, and
•	the process is repeated using a higher order approximation, until the desired
accuracy is obtained.
3.2.3	Transformation of Empirical Distributions
In the case of empirical distributions, the probability distribution function of an input
is specified in terms of a set of measurements (i.e., a sample of random data points, or
a frequency histogram). In some cases the uncertainty in a model input x is specified

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Chapter: 111 Section: 2
35
by an empirical cumulative distribution function (cdf), in the form Fx(x) = g(x),
where g(x) can be one of the following: (a) an algebraic function, or (b) a look-up
table, or (c) a numerical subroutine. In such cases, x can be approximated in terms
of an srv ฃ, as follows:
•	Generate a sample of srv ฃ
•	Generate a uniform random variate z from ฃ as
(z corresponds to Uniform(0,l))
•	For the calculated value of z, obtain the value of the input x, for which Fx(x)
equals the sample value of z
•	Repeat the above steps for additional samples.
In this process, x is sampled uniformly from the all its percentiles, and hence the
samples represent the true distribution of x. These steps can be represented by the
following transformation equation:
x = ,-'(ซ), where , = (I + IerfK/^)) , and ,(*) i„ the cdf of * (3.2)
3.2.4 Transformation of Correlated Inputs
For cases in which the random inputs are correlated, the interdependence of a set of n
correlated random variables is often described by a covariance matrix. The covariance
matrix, S, is a symmetric matrix with the following elements:
where rjXi and rjx. are the means of Xi and Xj, respectively.
The transformation process for n correlated random variables with zero mean, and
similar distribution functions, is presented by Devroye [47], and is described briefly
here:
To generate a random vector A, with n components, with mean 0 and covariance
C, we start with a vector B comprising of n iid unit random variables. Then,
assuming a linear transformation A = HB, the following conditions are satisfied:
Vxi){xj Vx3)},
E {BBt} = 1
E{A} = HE{B} = 0
E {AAt} = HE{BBt}Ht = HHt = C

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Chapter: 111 Section: 2
36
The problem reduces to finding a nonsingular matrix H such that HHT = C. Since
covariance matrices are positive semidefinite, one can always find a nonsingular H.
The above method is generalized here for the case of transformation of an arbitrary
random vector X with a covariance matrix X. If the zth random variable, Xi, has
a probability density function fi(x), mean fii, and standard deviation  0
f(x;a) = l r(a/2)2ฐ/2
I 0, elsewhere
The mean of the x2 distributions is a, and is related to the mean of the zth component,
Xi>m, and the Dirichlet distribution parameter 0, as follows:

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Chapter: 111 Section: 3
37
The zth component can now be directly obtained as:
X
Xi =			 where n is the number of components in the Dirichlet distribution.
X1 + x2 +... + xn
3.3 Step II: Functional Approximation of Outputs
An output of a model may be influenced by any number of model inputs. Hence, any
general functional representation of uncertainty in model outputs, should take into
account uncertainties in all inputs. For a deterministic model with random inputs,
if the inputs are represented in terms of the set	the output metrics can also
be represented in terms of the same set, as the uncertainty in the outputs is solely
due to the uncertainty of the inputs [191]. This work addresses one specific form of
representation, the series expansion of normal random variables, in terms of Hermite
polynomials; the expansion is called "polynomial chaos expansion" [80].
When normal random variables are used as srvs, an output can be approximated
by a polynomial chaos expansion on the set {ฃ;ป}?= 1; given by:
n	n i\
y Clo + ^ ] ^rifei) ~l~ ^ ^	6^2)
ii = l	ii = l ^2 = 1
n i\ %2
+ ^2 ^2 ^2 ^1*2^3(61,62; 63) + • • • )	(3-3)
i 1 = 1 12 = 1 13 = 1
where, y is any output metric (or random output) of the model, the a^.Js are de-
terministic constants to be estimated, and the rp(^il,... , ฃip) are multi-dimensional
Hermite polynomials of degree p, given by
rr(€u,...,^) = (-1)" etft * e-ift,	(3.4)
where ^ is the vector of p iid normal random variables {iik}pk=1, that are used to
represent input uncertainty. Hermite polynomials on {^}^1are random variables,
since they are functions of the random variables	Furthermore, the Hermite
polynomials defined on l=laxe orthogonal with respect to an inner product defined
as the expectation of the product of two random variables [80]. Thus,
E[TpTq] = 0 iff Tp^Tq ,
It is known that the set of multi-dimensional Hermite polynomials forms an orthog-
onal basis for the space of square-integrable pelfs, and that the polynomial chaos
expansion is convergent in the mean-square sense [80]. In general, the accuracy of
the approximation increases as the order of the polynomial chaos expansion increases.

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Chapter: 111 Section: 4
38
The order of the expansion can be selected to reflect accuracy needs and computa-
tional constraints.
For example, an uncertain model output U, can be expressed as second and third
order Hermite polynomial approximations, U2 and U3 as follows:
n	n	n—1 n
U2 = ซ0,2 + ^ <^,26 + ^ au,2(ฃi — 1) + ^ ^ aij,2^j	(3.5)
i= 1	i= 1	i= 1 j>i
n	n
U3 — ao,3 + ^	^ aii,3(Ci ~ 1) + ^ 0-iii,3(Ci ~ 3ฃi)
1=1	1=1	^=1
n—1 n	n n	n—2 n—1 n
E E^.3ซ4-a + E E E Clijk^CiCjCk (3-6)
i= 1	i=l j = l	1
where n is the number of srvs used to represent the uncertainty in the model
inputs, and the coefficients a^m2, a^m3, aij>m2, ay>m3, and 3 are the coefficients to
be estimated.
From the above equation, it can be seen that the number of unknowns to be
determined for second and third order polynomial chaos expansions of dimension n,
denoted by N2 and N3, respectively, are:
N2 = l + 2„,+ ^>	(3.7)
JV3 = 1 + 3n +	+ ป(" - !) (" - 2)	(3 8)
2	6
3.4 Step III: Estimation of Parameters in the Functional Approx-
imation
The unknown coefficients in the polynomial chaos expansion (a^...'s) can be estimated
by one of the following methods, depending on the complexity of the model:
•	For cases in which the model is invertible, output metrics can be directly calcu-
lated as explicit functions of the srvs (ฃjs); the expressions for input variables in
terms of s are substituted into the inverted model to obtain explicit functions
for the output metrics in terms of s. In such simple cases, there is no need to
assume a functional form for the output metrics.
•	For nonlinear operators with mathematically manipulable equations, the un-
known coefficients in the polynomial chaos expansion can be determined by
minimizing an appropriate norm of the residual, after substituting the trans-
formed inputs into the model equations. Gelarkin's method [200] is commonly
used with weight function corresponding to the expectation of the random vari-
ables [191].

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Chapter: 111 Section: 4
39
• For cases in which the model equations are not easy to manipulate, or when
the model is of "black-box" type, the unknown coefficients can be obtained by
a collocation method [191,200]. This method imposes the requirement that the
estimates of model outputs are exact at a set of selected collocation points, thus
making the residual at those points equal to zero. The unknown coefficients are
estimated by equating model outputs and the corresponding polynomial chaos
expansion, at a set of collocation points in the parameter space; the number
of collocation points should be equal to the number of unknown coefficients to
be found. Thus, for each output metric, a set of linear equations results with
the coefficients as the unknowns; these equations can be readily solved using
standard linear solvers.
3.4.1 Deterministic Equivalent Modeling Method/Probabilistic
Collocation Method (DEMM/PCM)
The Deterministic Equivalent Modeling Method (DEMM) [191] incorporates the poly-
nomial chaos expansion technique, along with symbolic computation methods and
compiler technology, to provide a prototype language for automated uncertainty anal-
ysis. This method uses the Galerkin's method to relate the input and output approx-
imations for models with tractable equations. Depending on the complexity of the
model, DEMM uses either the Galerkin's method or the Probabilistic Collocation
Method (PCM), described here.
As a part of DEMM, the Probabilistic Collocation Method (PCM) [154, 192],
is used for applying DEMM in the case of black-box models. The coefficients of
the polynomial chaos expansion are obtained using the model outputs at selected
collocation points. Then, the next order polynomial chaos expansion is employed, and
the solution process is repeated. If the estimates of the pdf s of the output metrics of
concern, are approximately equal, the expansion is assumed to have converged; the
higher order approximation is used to estimate the pdfs of output metrics.
In DEMM/PCM the collocation points are selected following the orthogonal col-
location method suggested by Villadsen and Michelsen [200]. The collocation points
correspond to the roots of the polynomial of one degree higher than the order of
the polynomial chaos expansion. For one-dimensional problems, this method gives
the same results as Galerkin's method [200], and hence is regarded as an "optimal
method". This approach is adapted for multi-dimensional cases in DEMM [191]. For
example, in order to solve for a two dimensional second order polynomial chaos expan-
sion, the roots of the third order Hermite polynomial, i/3,— V3 and 0 are used, hence
the possible collocation points are (0,0), (—1/3,0), (0,i/3), (0,—i/3), (~V3,—V3),
(1/3,1/3), (1/3,0), (—1/3,1/3) and (i/3, — i/3). There are nine possible collocation
points, but from Equations 3.5 and 3.6 with n=2 and taking terms only up to T2,
there are only six unknowns. Similarly, for higher dimension systems and higher or-

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Chapter: 111 Section: 4
40
der approximations, the number of available collocation points is always greater than
the number of collocation points needed, which introduces a problem of selecting the
appropriate collocation points. In the absence of selection criteria at present, the
collocation points are typically selected at random from the set of available points.
Limitations of DEMM/PCM
As noted above, the selection of the required collocation points in an efficient manner
from the large number of possible points is not addressed in the present implemen-
tation of DEMM. In fact, as the number of degrees of freedom increases, the number
of available collocation points increases exponentially. For example, for the case of
n = 6, using Equation 3.3, the number of collocation points required for second and
third order expansions are 28 and 84 respectively. Since the collocation points are
selected from the combinations of the roots of one order higher Hermite polynomial,
the number of collocation points available for second and third order expansions are
36 = 729 and 46 = 4096, respectively - there are three roots for a third order Hermite
polynomial (used for obtaining collocation points for a second order expansion), and
there are six variables, hence the number of possible collocation points is 36, and
similarly 46 for a third order expansion. As the number of inputs and the order of
expansion increase, the number of available collocation points increases exponentially.
In principle, any choice of collocation points from the available ones, should give
adequate estimates of the polynomial chaos expansion coefficients. However, different
combinations of collocation points may result in substantially different estimates of
the pdf s of output metrics; this poses the problem of the optimal selection of colloca-
tion points. Furthermore, in a computational setting, some of the collocation points
could be outside the range of the algorithmic or numerical applicability of the model,
and model results at these points cannot be obtained. These issues are addressed in
the following section, where an algorithm for collocation point selection method is
described.
Another shortcoming of the standard collocation technique suggested in DEMM is
that sometimes the roots of the Hermite polynomials do not correspond to high prob-
ability regions and these regions are therefore not adequately represented in the poly-
nomial chaos expansion. For example, for a third order approximation, the roots of
the fourth order Hermite polynomial, namely \J3 + \/6, — s/3 + \/6, \]3 — \/6, and —
\J3 — -\/6 are used as the collocation points. However, the origin corresponds to the
region of highest probability for a normal random variable. Hence, the exclusion of
the origin as a collocation point could potentially lead to a poor approximation.
The limitations of DEMM/PCM are shown in the context of a case study involving
a human Physiologically Based Pharmacokinetic (PBPK) Model, and the results are
presented in Section 5.1.4.

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Chapter: 111 Section: 4
41
3.4.2 Efficient Collocation Method (ECM)
This method addresses some limitations of DEMM/PCM. Here, the collocation points
for the estimation of the coefficients of the polynomial chaos expansion are selected
based on a modification of the standard orthogonal collocation method of [191,200].
The points are selected so that each standard normal random variable takes the
values of either zero or of one of the roots of the higher order Hermite-polynomial.
A simple heuristic technique is used to select the required number of points from
the large number of potential candidates: for each term of the series expansion,
a "corresponding" collocation point is selected. For example, the collocation point
corresponding to the constant is the origin; i.e., all the standard normal variables (ฃ's)
are set to value zero. For terms involving only one variable, the collocation points
are selected by setting all other ฃ's to zero value, and by letting the corresponding
variable take values as the roots of the higher order Hermite polynomial. For terms
involving two or more random variables, the values of the corresponding variables
are set to the values of the roots of the higher order polynomial, and so on. If more
points "corresponding" to a set of terms are available than needed, the points which
are closer to the origin are preferred, as they fall in regions of higher probability.
Further, when there is still an unresolved choice, the collocation points are selected
such that the overall distribution of the collocation points is more symmetric with
respect to the origin. If still more points are available, the collocation point is selected
randomly.
The advantage of this method is that the behavior of the model is captured rea-
sonably well at points corresponding to regions of high probability. Furthermore,
singularities can be avoided in the resultant linear equations for the unknown co-
efficients, as the collocation points are selected to correspond to the terms of the
polynomial chaos expansion. Thus, the pdfs of the output metrics are likely to be
approximated better than by a random choice of collocation points, while following
a technique similar to the "orthogonal collocation" approach.
Applicability and Limitations of ECM
The ECM method was applied to a case study involving a human Physiologically
Based Pharmacokinetic (PBPK) model, which is presented in Section 5.1.4. This
method resulted in accurate estimates of the output pdf s while requiring much fewer
model simulations as compared to standard Monte Carlo methods, as shown in Fig-
ures 5.4-5.7 in Section 5.1.4.
The ECM method was applied to a more complex model, the two-dimensional at-
mospheric photochemical plume model, the Reactive Plume Model, version 4, (RPM-
IV), as described in Section 5.2.3. For that case study, the ECM method failed to
converge for a third order polynomial chaos approximation. This is attributed to the
inherent instability of the collocation based approaches, as described by Atkinson [6].

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Chapter: 111 Section: 5
42
3.4.3 Regression Based SRSM
Collocation methods are inherently unstable, especially with polynomial approxima-
tions of high orders, since the requirement that the polynomial (curve or a surface)
has to pass through all the collocation points. Thus any one collocation point in
the model space could significantly alter the behavior of the polynomial [6]. In this
context, regression-based methods provide slightly more computationally expensive,
but robust means of estimation of coefficients of the functional approximation, be-
cause model results at more points are used in this approach, and the influence of
each sample point is moderated by all other sample points. This extension to the
collocation method uses regression in conjunction with an improved input sampling
scheme to estimate the unknown coefficients in the polynomial expansion. In the
regression based method, a set of sample points is selected in the same manner as the
ECM method, as described earlier. The number of sample points selected must be
higher than the number of unknown coefficients to be estimated; selecting a number
of points equaling twice the number of coefficients is recommended for obtaining ro-
bust estimates, and is the approach used in the case studies presented in this work.
The model outputs at the selected sample points are equated with the estimates from
the series approximation, resulting in a set of linear equations with more equations
than unknowns. This system of equations is then solved using the singular value
decomposition method [158].
The regression method has the advantage that it is more robust than the colloca-
tion method, but the method requires a higher number of model simulations to obtain
estimates of output uncertainty. The advantages of this method are demonstrated by
comparing the results of this method with those of ECM for a the case study involving
RPM-IV, an atmospheric photochemical plume model, described in Section 5.2.3.
Based on preliminary case studies (presented in Chapter 5), the regression based
method is found to be robust and the method resulted in good approximations of
model outputs. Hence this method is recommended for use in general. The higher
computational burden, compared to the ECM, is offset by the robustness this method
provides for many cases. Throughout the rest of this document, the ECM method is
denoted as "ECM" and the regression based method is denoted simply as "SRSM".
Unless otherwise specified, the term SRSM in the case studies refers to a regression
based SRSM.
3.5 Step IV: Estimation of the Statistics of the Output Metrics
Once the coefficients used in the series expansion of model outputs are estimated, the
statistical properties of the outputs, such as the density functions, moments; joint
densities; joint moments; correlation between two outputs, or between an output and
an input; etc., can be readily calculated. One way of accomplishing this is through

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Chapter: 111 Section: 5
43
the generation of a large number of realizations of the srvs, and the calculation of
the values of inputs and outputs from the transformation equations. This results
in a large number of samples of inputs and outputs. These samples can then be
statistically analyzed using standard methods.
It must be noted that the calculation of model inputs and outputs involves eval-
uation of simple algebraic expressions, and does not involve model runs, and hence
substantial savings in the computer time are accomplished.
As an example, if the inputs Xi s are represented as Xi =	and if the outputs
Uj's are estimated as Xi = Gi(ฃ 1,^2 • • •ฃซ), then the following steps are involved in
the estimation of the statistics of the inputs and outputs.
•	generation of a large number of samples of (ฃ1 ฃ2 • • • ฃn,i),
•	calculation of the values of input and output random variables from the samples,
•	calculation of the moments using Equation 3.9, and
•	calculation of density functions and joint density functions using the sample
values.
al
(3.9)
From a set of N samples, the moments of the distribution of an output t/j can be
calculated as follows:
1 N
Vyi = Mean (yj =	E{yJ	=
3 = 1
1 N
Var(yi) = Ej^ - %i)2} =	- r]yi)2
3 = 1
1	1 N
71.W = Skew (yi) = — E{(yi - r/yi)3} =	- r]yif
aVi	ฐVi j=1
1	1 N
l2,Vi = Kurt(yi) = — EKyi-^.)4} =	^(yij - r]Vif
ayi	ayi j=1
Further, the correlation coefficient of an input Xk and an output yi can be calcu-
lated using the following:
Txm = E{(*>-ป.)(ป, -h,)} = 1_ j^{xk j _	(3 10)
axkayi	^axkayi j=l
Similarly, higher moments of outputs, or the correlation between two outputs, or
between an input and an output can be directly calculated.

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Chapter: 111 Section: 6
44
3.6	Step V: Evaluation of Convergence, Accuracy and Efficiency
of Approximation
Once the coefficients of the polynomial chaos expansion are obtained using one of
the methods described above, the convergence of the approximation is determined
through comparison with the results from a higher order approximation. The next
order polynomial chaos expansion is used, and the process for the estimation of un-
known coefficients is repeated. If the estimates of pdf s of output metrics agree closely,
the expansion is assumed to have converged, and the higher order approximation is
used to calculate the pdf s of output metrics. If the estimates differ significantly, yet
another series approximation, of the next order, is used, and the entire process is
repeated until convergence is reached.
In the present work, the accuracy of the approximation is evaluated by comparing
the results from SRSM with the results obtained from Monte Carlo analyses. This
verification is performed to ensure that the approximation converges to the true dis-
tribution. The efficiency of the SRSM is further evaluated by comparing the number
of simulations required for the SRSM method, with the number required for a Latin
Hypercube sampling method.
3.7	An Illustration of the Application of the SRSM
As an illustration, consider a computational model with three independent random
inputs Xi, X2, and X3, and two outputs Y\ and Y2, where the input random variables
X\, X2 and X3 are given by
The input random variables can be represented by three srvs, ฃi, ฃ2, ฃ3, using Table 3.1
as follows:
Xi = Uniform(pi, q\)
X2 = Lognormal(p2, Q2)
X3 = Gamma (p3,(?3)
(3.11)
X2 = exp(p2 + 926)
(3.12)
Where ฃ1, ฃ2, ฃ3 are Hd N(0,1) random variables. A second order polynomial chaos

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Chapter: 111 Section: 7
45
approximation for Yx and Y2 in terms of ฃ1, ฃ2> ฃ3 is given by
Yi = a0 + &iฃi + + a3^3 + ^4(^1 — 1) + a${^2 — 1) + as(&i ~ 1)
+<37^1^2 + a8^,2^,3 + ^9^,1^,3
bo + 6lฃl + &2^2 + ^3^3 + — 1) + M^2 — 1) + ^(ฃ3 ~~ 1)
+&7C1C2 + ^^2^3 + ^^1^3
(3.13)

In order to estimate the 10 unknown coefficients (for each output) from the above
equation, the selection of a set of N sample points (equaling about twice the number
of sample points, i.e., 20 in this case) is recommended for regression based SRSM, in
the form
(6,i 6,1 ฃ3,1); (6,2 ฃ2,2 ฃ3,2), • • • , (6,w &,n 6,w)
These sample points can be readily generated using a simple algorithm developed as
a part of the SRSM implementation. The selection of the sample points is discussed
in detail in Section 3.4.2, in the context of the ECM method.
These sample points correspond to the original model input samples, (xi;i ฃ2,1 ฃ3,1)... (x^n %2,n %3,n
as follows:
( t \
U ,i
ฃ2 ,i
V J
^ Xi,i ^
^3 yi j
' Pi + (Qi -Pi) Q + ^erf(ฃlii/v/2)^ ^
exp(p2 + 926,0
V
UWg^ + l-g^
(3.14)

for i = 1... N. After obtaining the original model input sample points, the model
simulation is performed at the points given by (x^i x2>\ x3;i)... (xitN x2>^ %3,n)- Then
the outputs at these sample points, 2/1,1 •• • 2/1, n and 2/2,1 •• • J/2,n, are used to calculate
the coefficients a\... a 10 and b\... b\o by solving the following linear equations through
singular value decomposition.
ZTx
eta bo
CL\ b\
a2 b2
as bg
CIq bg
V1,1 2/2,1
2/1,2 2/2,2
Ul,N 2/2, N
, where
(3.15)

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Chapter: 111 Section: 7
46
1
1	1	...	1
6,2	6,3	• • •	6 ,N
6,2	6,3	• • •	6 ,N
6,2	6,3	• • •	6 ,N
6,1
6,1
6,i
62,i -1
62,i -1
62,i -1
6,16,1
(3.16)
6,16,1
6,16,1
In the above equations, Z can be calculated from the values of ฃ's at each sample
point, whereas, yi^ and j/2,i are the corresponding model outputs. Thus, the only
unknowns in Equation 3.15, the coefficients a^s and bi s, can be readily estimated.
Once the coefficients are estimated, the distributions of Yi and Y2 are fully described
by the polynomial chaos expansions as shown in Equation 3.14.
The next step involves determination of the statistical properties of the outputs,
such as the individual moments, joint moments, correlations between the outputs
and correlations between inputs and outputs. This process is done by numerically
generating a large number of random samples of (ฃ1^ ฃ2,i 6,ป)> calculating the values of
model inputs and outputs for those samples, and computing the statistical properties
from the values of inputs and outputs as described in Section 3.5.

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Chapter: 111 Section: 8
47
3.8 Computational Implementation
Input Distributic
Model
ฆ*-	it Distributi
(
Generate a set of
random inputs
)
Figure 3.2: Schematic depiction of the steps involved in the application of conventional
Monte Carlo method
3.8.1 Implementation of the Conventional Uncertainty Propaga-
tion Methods
Modules for the application of conventional Monte Carlo and the Latin Hypercube
Sampling methods were developed as part of this work. Though these modules are
mainly used in this work for the evaluation of alternative methods, they can be used
in isolation.
As shown in Figure 3.2, the conventional Monte Carlo method involves the fol-
lowing steps: (a) obtaining random samples from the probability distributions of the
inputs, (b) performing model simulations for the combination of the sampled inputs,
and (c) statistically analyzing the model outputs.
The random numbers for sampling the input distributions were generated depend-
ing on the case study considered. For all the cases, the normal random numbers were
generated through the function GASDEV provided with the mathematical routines of
the Numerical Recipes [158]. Gamma random variables were generated by first gener-
ating a normal random number, and applying the transformation shown in Table 3.1.
In the case of Dirichlet distributions, the independent random numbers were gener-
ated first, and the corresponding ratios were calculated using Equation 3.2.4. In one
specific case, in the study involving a physiologically based pharmacokinetic model,
described in Section 5.1, the model was implemented on the SimuSolv modeling plat-
form [56], and the Monte Carlo method involved sampling from random numbers
generated by the pseudo-gaussian random number generator function in SimuSolv,
R-GAUSS.
In all the case studies described in this work, the original numerical models were
modified accordingly to accept the uncertain model inputs and parameters as inputs
from a file, and model simulations were performed at the sampled values of the inputs.

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Chapter: 111 Section: 8
48
The outputs of the simulations were then analyzed by "binning" them into a set of
intervals; a bin number associated with an interval contains the number of outputs
realizations that belong to that interval, from all the model simulations performed.
The ratio of the bin number to the total number of samples, gives the probability
that the output belongs to that interval. The ratio of this probability to the interval
size gives the probability density at the mid-point of the interval. The probability
density is then plotted against the range of values that the output can belong to, as
presented in the case studies. Further, FORTRAN routines to calculate the mean,
variance, and higher moments were also implemented as part of the SRSM; these were
based on equations presented in Section 3.5.
The Latin Hypercube Sampling (LHS) method was developed and implemented
from basic principles, mainly because of the lack of availability of a standardized
routine readily usable on Unix operating system. The EPA approved routine for the
LHS, developed by Iman et al. [105], is developed for the VAX/VMS operating system,
and other commercial software such as Crystal ball [45] and @RISK [208], for the PCs
have LHS routines embedded in the modeling platform (typically spreadsheet based),
and thus cannot be used in conjunction with stand-alone numerical codes. The main
aspects of the LHS implementation developed here are presented in the following.
In order to generate samples from input distributions, the range of probable values
for each uncertain input parameter is divided into M segments of equal probability.
Thus, the whole parameter space, consisting of N parameters, is partitioned into
MN cells, with each cell corresponding to equal probability regions from the input
sampling space. For example, for the case of 3 input parameters and 4 segments, the
parameter space is divided into 4x4x4 cells. The next step involves the generation
of M samples from MN cells. This is accomplished as follows: first, a random sample
is generated, and its cell number is calculated. The cell number indicates the segment
number the sample belongs to, with respect to each of the parameters. For example,
a cell number (2,1,2) indicates that the sample lies in the segment 2 with respect
to first parameter, segment 1 with respect to second parameter, and segment 2 with
respect to third parameter. At each successive step, a random sample is generated,
and is accepted only if it does not agree with any previous sample on any of the
segment numbers. Thus, no two samples have any input corresponding to the same
segment. The advantage of this approach is that the random samples are generated
from all the ranges of possible values, thus providing samples from the tails of the
probability distributions (i.e., regions that have very low probabilities of occurrence,
but correspond to extreme values). Further, for every M samples, each parameter is
sampled from each of its M subranges.
The LHS FORTRAN subroutine developed here requires the following inputs:
(a) the total number of samples to be generated, A^0t, (b) the number of random
inputs, N, and (c) the input probability distributions, consisting of the distribution
type (a number), and the distribution parameters (dependent on the distribution

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Chapter: 111 Section: 8
49
Input Distributions
Output Distributions
Model
Select a set of srvs
and transform inputs
in terms of srvs
Express outputs as
series in srvs with
unknown coefficients
Generate a set of
regression points
Estimate the coefficients
of output approximation
Figure 3.3: Schematic depiction of the steps involved in the application of the Stochas-
tic Response Surface Method
type). Further, the number of ranges the parameter range of each input should be
divided (M) is also required as an input. The routine generates a set of M sample
points in each iteration, till samples are generated. A typical value of M used in
the case studies is about 50. A much higher value results in too small a range for each
sampled input, requiring a large number of trial samples before an acceptable sample
is generated, whereas much smaller values do not adequately represent all ranges of
possible values, especially the tails of the distributions.
3.8.2 Implementation of the Stochastic Response Surface Method
The steps involved in the application of the SRSM are presented in Figure 3.3. The
implementation of the SRSM is based on the following routines:
•	A symbolic calculation routine in Maple [91], to generate polynomial chaos
expansions of arbitrary order, consisting of an arbitrary number of parameters.
•	A FORTRAN subroutine to generate polynomial chaos expansions of up to fifth
order for an arbitrary number of parameters. The extension to higher orders is
straightforward, but not pursued because in all the case studies considered, the
third order expansion was the highest required. This routine is developed using
the results from the Maple routine.
•	A FORTRAN subroutine to generate the sample points based on the input
distributions and the order of the polynomial chaos expansion selected.
•	A set of FORTRAN utilities to sample distributions in the form of the srvs, to

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Chapter: 111 Section: 9
50
calculate the corresponding transformed random variables, and to calculate the
numerical values of the polynomial chaos expansions.
•	A FORTRAN subroutine to perform collocation or regression (based on the
scenario) on a set of sample points and the corresponding model outputs. These
are used to estimate the coefficients of a polynomial chaos expansion.
•	A FORTRAN subroutine to generate the probability density functions from
the coefficients of a polynomial chaos expansion in srvs, by obtaining a large
number of samples of srvs, and calculating the statistics of the corresponding
outputs.
•	FORTRAN subroutines to calculate the moments, percentiles, and other statis-
tical properties of both the inputs and outputs.
3.9 Implementation of a Web Interface to the SRSM
All numerical routines associated with the development of the SRSM have been incor-
porated into a stand-alone application, that could be used for uncertainty propagation
with black-box models. This has also been implemented as a "Web Based" tool, that
can be run from remote web browsers. The interface was implemented by using
the Common Gateway Interface (CGI) programming using the Perl language. An
operational version can be accessed through the location
http://www.ccl.rutgers.edu/srsm.html
The web server is located in the Computational Chemodynamics Laboratory, at the
Environmental and Occupational Sciences Institute, a joint project of University of
Medicine and Dentistry, New Jersey, and Rutgers, The State University of New Jersey.
Here, the application of the SRSM method for a simple black-box model is pre-
sented. In order to use the SRSM, first the number of inputs, their probability
distributions, the number of outputs, and the order of polynomial chaos expansion
desired, have to be specified, as shown in Figure 3.4. The program responds with
the recommended sample points, and the model outputs at these sample points have
to be input, as shown in Figure 3.5. Additionally, a range for plotting the output
probability density functions is also required as input. The program uses the model
outputs at these sample points and calculates, and displays the probability density
functions for each of the outputs, as shown in Figure 3.6.

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Chapter: 111 Section: 9
51
nle Edit View 3d Communicator	Help
r '	>-ฆ	!.ฆ ฆ_ ' ii tp //nenny rutqers edu/s3i/bin/5r3m c :	ฆฆ!. !ฆ., iled
A V/pii Interface. ro the SKSM Methodology
for computationally efficient uncertainly propagation
To use the SRSM method for the uncertainty analysis of a model.
Please fill in the following table:
Number of outputs: fCurrently up to 3 5upportedi : i2
! Distributionlype | ParainaterA i Paramater B i
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Bugs, comments/suggestions; vasin'i-' i sut a;;a'i
Last modified: Wed Nov 26 16:00:26 EST 1998

Figure 3.4: Application of the web based SRSM tool: specification of input uncer-
tainties

-------
Chapter: 111 Section: 9
52
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Figure 3.5: Application of the web based SRSM tool: recommended sample points
and the corresponding model outputs

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Chapter: 111 Section: 9
53
These are Hie probability distributions for the outputs
ฆฆฆฆฆฆฆฆฆฆฆฆฆฆฆฆฆฆ
Output 2

Figure 3.6: Application of the web based SRSM tool: estimates of the pdfs of
outputs
the

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Chapter 4
COUPLING OF THE SRSM WITH SENSITIVITY
ANALYSIS METHODS: DEVELOPMENT AND
IMPLEMENTATION OF SRSM-ADIFOR
The objective of this effort is to couple the Stochastic Response Surface Method (SRSM)
with sensitivity analysis methods, to obtain savings in the computer resources re-
quired for uncertainty propagation. In the construction of an approximate model,
sensitivity analysis methods can provide information that can help reduce the num-
ber of model simulations required to estimate the approximation parameters. Conse-
quently, the use of sensitivity information in conjunction with uncertainty propagation
methods can reduce the model simulations required to estimate uncertainties in model
outputs.
The rationale for the coupling of the SRSM with a sensitivity analysis method
is illustrated in Figure 4.1 for the simple case of approximation of a curve in a 2-D
Euclidean space using a set of sample points. Figure 4.1 (A) represents the model re-
sponse, which can be approximated accurately using the five sample points, as shown
in Figure 4.1 (B). In contrast, using three sample points, only a poor approximation
as shown in Figure 4.1 (C) can be obtained. However, using the derivative informa-
tion at the three sample points (tangents at these points), a good approximation,
as shown in Figure 4.1 (D) can be obtained. As an extension of this reasoning to
multi-dimensional systems, the partial derivatives of model outputs with respect to
model inputs (i.e., tangents in the multi-dimensional space) can be used to reduce the
number of sample points required to construct an adequately approximate model.
An overview of various sensitivity metrics is presented in Section 2.4, and the
metrics are listed in Table 2.3. Of these sensitivity metrics, the derivative information
can be directly utilized in constructing an approximate model. For example, consider
a model
* = f(x,y)
for which the output z is to be approximated as a function of inputs x and y through
the expression:
z = a0 + a\x + ci2y + a%x2 + a^y2 + a$xy
In order to estimate the coefficients a^s, the model outputs for z are needed at six
sample points, requiring six model runs. This results in a set of six equations with
54

-------
Chapter: IV	55
B
(A) Model Response
(B)
Approximation with
sufficient data
(C)
Approximation with
fewer data
(D)
Approximation with
fewer data and
derivative information
Figure 4.1: Rationale for coupling of the SRSM with a sensitivity analysis method
the left hand side consisting of the model outputs and the right hand side consisting
of linear equations in the coefficients.
For the same case, if all the first order partial derivatives are available at each
sample point, the number of model runs can be reduced to two, and the derivative
information at two points, (x\,yi) and (ฃ2,2/2) can be used to construct the following

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Chapter: IV Section: 1
56
closed system of equations:
z\{xi,yi)
dz
o,q + tt\X\ + ct,2yi + o 3X1^ + Q4J/12 + CL5X1I/1
9X (x1>yi)
dz
0\ + 2a,3Xi + CI5J/1
&2 + 2d4j/i + CI5X1
do + CliX2 + O2I/2 + 03X2^ + O4I/22 + O5X2I/2
9X (x2)OT)
dz
0\ + 203X2 + CI5J/2
ci2 + 2o^y2 + 05X2
Similarly, for a model with M parameters, the number of model runs required to
estimate the unknown coefficients decreases by a factor of M + 1, since the first order
partial derivatives with respect to M parameters result in M additional equations at
each sample point, whereas calculation of the model output at a given point results
in only one equation.
4.1 Methods for Estimation of Partial Derivatives
The following approaches are commonly used to estimate derivatives for a model [16,
(a)	Variational Equations: When the model equations are available, one can, in princi-
ple, compute the derivatives from these equations by directly differentiating the equa-
tions and performing the necessary algebraic manipulations. This approach results
in an additional set of equations that can be solved either in a coupled manner [193],
or in a decoupled manner [58]. For lower dimensional, linear models, these equations
can be solved directly along with the model equations to estimate the derivatives.
However, for nonlinear functions the derivatives are generally more complicated than
the function itself. Further, if the original model has n outputs and m model param-
eters, in order to estimate the partial derivatives of all the outputs with respect to all
the parameters, the number of equations required would be m x n. Hence, this ap-
proach requires a considerable amount of effort for generating all the necessary model
equations. Further, this approach requires access to the original model formulation
equations.
(b)	"Brute-force" approach: The two commonly used techniques in this approach are:
29]:

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Chapter: IV Section: 1
57
Central differences: For the model represented by Equation 2.5, the partial
derivative of Ui with respect to a parameter kj at a point (fci;o, • • • , kjto,... , km>o)
is estimated by solving the model at two points (fci,o, • • • , fcj,o — h,... , km>o) and
(fci;o, • • • , kjfl + h,... , kmfl), and the respective outputs are obtained as Uij and
Ui>r. The partial derivative is then given by
diti Ui r Uij
—2h-'h^ฐ
One-sided differences: Here, the model is solved at a point (fci;o, • • • , • • • , km>o),
resulting in output -u^o and either at the point (/ci,o, • • • , kj)o — h,... , km,o) or at
the point (fci,o, • • • , kj)o + h,... , km
-------
Chapter: IV Section: 1
58
Automatic
Derivative
Calculator
(e.g. ADIFOR)
Compile
and Link
Further Analysis
DERIVATIVE
CODE
Derivative
Computing
Code
COMPUTER MODEL
(To Be Analyzed)
Figure 4.2: Schematic illustration of the Automatic Differentiation method (adapted
from Bischof et al., 1994 [16])
(d) Automated Differentiation: Automated differentiation, also known as automatic
differentiation, relies on the fact that every function is executed on a computer as a
sequence of elementary operations such as additions, multiplications, and elementary
functions. By applying the chain rule

t=t o
s=g(t0)
t=t o.
over and over again to the composition of those elementary operations, one can com-
pute, in a completely mechanical fashion, derivatives of / that are correct up to the
machine precision [16]. This approach essentially consists of using a computational
tool that can "understand" the model code and subsequently produce the code for
estimating partial derivatives. The main advantage of this method is that the par-
tial derivatives can be estimated using a single model run; another advantage is that
this method can be used in conjunction with complex numerical models, that can be
considered as "black-box" structures.
The automatic differentiation method has been widely applied in sensitivity anal-
ysis of transport/transformation modeling, as mentioned in Section 2.5.4. ADI-
FOR (Automatic Differentiation of FORtran) is used in this work for coupling with
the SRSM. The following sections describe the ADIFOR methodology and the cou-
pling with the SRSM.

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Chapter: IV Section: 2
59
4.2	Automatic Differentiation using AD I FOR
ADIFOR (Automatic Differentiation of FORtran) [17] transforms the model source
code based on the specification of the dependent and independent variables, and
produces source code that calculates the derivatives of the dependent variables with
respect to the independent variables. Figure 4.2 presents a schematic depiction of the
automatic differentiation procedure using ADIFOR.
Figure 4.3 presents an example of the transformation of source code using ADI-
FOR. Figure 4.3 (a) shows a segment of a sample Fortran program for which deriva-
tives are desired. The dependent variable is y and the independent variables are x(l)
through x(10). Figure 4.3 (b) shows the derivative code produced through the appli-
cation of ADIFOR. Using the resultant code, the partial derivatives of y with respect
to independent variable x(i) can be obtained through the corresponding element
dy(i) from the array dy.
This example illustrates some key advantages of using ADIFOR over other meth-
ods: ADIFOR steps through the conditional execution loops and iterative loops (e.g.,
the if and the do statements in Figure 4.3 (a)), and ADIFOR can calculate deriva-
tives for assignment and iteration statements (e.g., the statement y =x(i) * y *y).
Further, the iterative loops and conditional loops are common in computer codes,
while they do not appear in algebraic or differential equations. It must be noted that
the value of the variable y depends to a great extent the "sign" of the various x(i) 's,
specifically of x(n); such situations are difficult to express in algebraic forms, whereas
they can be readily expressed in computer code. Since ADIFOR methodically follows
the model code, all the complex logic of the model code is followed exactly by the
derivative code.
In principle, this method can be easily extended to compute derivatives for com-
puter code of arbitrary length and complexity. Furthermore, in the application of
ADIFOR, the user has to only (a) specify the information about the dependent and
independent variables, and (b) add statements to the derivative code to output the
derivatives.
4.3	Coupling of SRSM and ADIFOR
The coupling of SRSM and ADIFOR follows the same steps as the SRSM with re-
spect to input and output transformations. The coupled method, SRSM-ADIFOR,
SRSM approximates uncertain model outputs in terms of a set of "standard random
variables" (srvs), denoted by the set	The following steps are involved in the
application of the SRSM-ADIFOR:
• the model inputs are expressed as functions of selected srvs, as described in
Section 3.2,

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Chapter: IV Section: 3
60
dimension x(10)
y = 1.0
do i = 1, n
if (x(i) .gt. 0.0) tbjen
y = x(i) * y * y
endif
enddo
(a)
dimension x(10)
dimension dy(10),
dx(10,10)
do j = 1,10
dx(i,i) = 1.0
enddo
do j = 1, 10
dy(j) = 0.0
enddo
y = 1.0
do i = 1, n
if (x(i) .gt. 0.0) then
a = x(i)*y
b = y*y
c = a + y*x(i)
do j = 1, 10
dy(j) = b*dx(j,i) +
c*dy(j)
enddo
y = a*y
endif
enddo
(b)
Figure 4.3: FORTRAN derivative code generation using ADIFOR: (a) Sample code
fragment and (b) ADIFOR generated derivative code
• an approximation for the model outputs is assumed in the same form as pre-

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Chapter: IV Section: 3
61
sented in Section 3.3:
n
yr Clr^0+ ^ ^	^ ^ ^ ^ &ryiii2 ^2	)
n %\
ii = l	%i = l ^2 — 1
n i\ %2
+ EEE ^(61,62> 6s) + • • • (4-i)
%1 = \ ป2 = 1 13 = 1
where, yr is the rth output metric (or random output) of the model, the ar;il...'s
are deterministic constants to be estimated, and the	... , ฃip) are multi-
dimensional Hermite polynomials of degree p, given by
rp(&i,... ,Cip) = (~l)p	9 e~^, (4.2)
O^i 1 ... o^tp
• correspondingly, the first order partial derivatives of the rth model output with
respect to jth srv^j, given by —are expressed as
dyr _	^ ^ ^2(^,62)
dt	dt	dฃ
J J	—1	J J	—1 — 1	J J
n ^l
a
ii = l	^J	ii = l ^2 = 1
n ii 12
+ E E E+ ฆฆฆ (ซ)
il = l ป2 = 1 13 = 1
•	the model derivative code is generated using the original model code and ADI-
FOR and modified so that it can calculate the first order partial derivatives of
model outputs with respect to the srvs,
•	a set of sample points is selected using the sampling algorithm presented in
Section 3.4.2; the number of sample points selected is smaller than the number
of coefficients to be estimated, as shown in Equation 4.4,
•	the model outputs and the partial derivatives with respect to the srvs are cal-
culated at these sample points,
•	outputs at these points are equated with the polynomial chaos approximation
(Equation 4.1, resulting in a set of linear equations in s, and partial derivatives
at these points are equated with the corresponding approximation given by
Equation 4.3,
•	The resultant equations are solved using singular value decomposition, to obtain
estimates of a^s, as illustrated in Section 4.4.

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Chapter: IV Section: 4
62
4.3.1 Selection of Sample Points
In the application of SRSM-ADIFOR to the uncertainty analysis of a model with M
inputs, P outputs, for a given order of expansion, Equation 4.1 is used to calculate
the number of coefficients to be estimated (say K). Thus, K coefficients need to
be estimated for each output. The execution of the model derivative code at one
sample point gives the model calculations for the outputs and M first order partial
derivatives for each output. Thus, equations 4.1 and 4.3 in conjunction with these
calculations result in M + 1 linear equations at each sample point.
Here, the number of recommended sample points is based on the rationale behind
the regression based SRSM: the number of resulting equations should be higher than
the number of coefficients estimated in order to obtain robust estimates of the coef-
ficients. Here, the recommended number of equations is about twice the number of
coefficients to be estimated.
Since for each sample point, the number of resultant equations is M + 1, the
number of sample points required for SRSM-ADIFOR, N, is approximately given
4.4 An Illustration of the Application of the SRSM-ADIFOR
This section presents an illustration of the SRSM-ADIFOR method by revisiting
the example presented in Section 3.7 involving the application of the SRSM. The
probability distributions of the three model inputs, X1,X2, and X3, are given by
Equation 3.12, and the two model outputs, Yi and Y2, are approximated by polynomial
chaos expansions
by*:
2 K
(4.4)
N
M + 1
Y\ = ao + aiฃi + a2^2 + <^3^3 + ~ 1) + (^2 ~~ 1) + ~ 1)
+ซ7Cl^2 + <28^2^3 +
Y2 = b0 + &iฃi + b2ฃ2 + 63^3 + 64(^1 — 1) + — 1) + ^(ฃ3 — 1)
+&7C1C2 + ^^2^3 +
(4.5)
*the number is approximate because 2K may not always be exactly divisible by M + 1

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Chapter: IV Section: 4
63
Clearly, the partial derivatives of Yi and Y2 with respect to the srvs are given by:
dYl
<96
dYi
<96
dYi
<96
dY2
<96
<96
9Y2
<96
=	ai + 2a^i + 0,7^2 +
=	a2 + 2a5ฃ2 + <276 + ฎs6
= + 2a6^3 + ^6 + <^9^1
=	61 + 2646 + ^6 + ^9^3
=	62 + 2656 + ^6 + ^6
=	63 + 26e6 + &s6 + 696
(4.6)
Based on Equation 4.4, the number of sample points, N, required to estimate the
10 unknown coefficients equals 5. Thus, using SRSM-ADIFOR, the uncertainty in
the model outputs can be estimated by obtaining the model outputs at N = 5 sample
points, compared to 20 sample points required in the application of the SRSM.
The model equations corresponding to the estimation of unknown coefficients, are
given by the following equations:
ZTx
ao	bo
ai	bi
a2	b2
as	bs
ag	bg
yi,i
2/2,1
<9yi
dy2
<96,1
<9yi
<96,1
dy2
<96,1
dyi
#6,1
<9?/2
<96,i
<96,1
vi,n
y2,N
dyi
dy2
<96 ,N
dyi
<96,w
dy2
<96,w
dyi
<96,w
dy2
- <96 ,N
<96 ,N .
, where
(4.7)

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Chapter: IV Section: 5
64
Input Distributions
Output Distributions
Model
Select a set of srvs
and transform inputs
in terms of srvs
ADIFOR
application
Estimate the coefficients
of output approximation
Express outputs as
series in srvs with
unknown coefficients
Derivative Code
Generate a set of
regression points
Figure 4.4: Schematic depiction of the steps involved in the application of the SRSM-
ADIFOR
1
0
0
0 ...
1
0
0
0
6,i
1
0
0 ...
6 ,N
1
0
0
6,i
0
1
0 ...
6 ,N
0
1
0
6,i
0
0
1
6 ,N
0
0
1
62,i -1
26,i
0
0 ...
••• ฃLv-i
26,JV
0
0
62,1 -1
0
26,i
1
• • • ฃ2,N ~~ 1
0
26, N
1
ฃ!,i -1
0
0
26,1 •••
• • • d,N ~ i
0
0
26, N
6,16,1
6,i
0
6,1 • • •
• • • 6,w6,w
6 ,N
0
6 ,N
ฃ2,16,1
6,1
6,1
0 ...
• • • 6,w6,w
6 ,N
6 ,N
0
6,16,1
0
6,1
6,1 • • •
• • • 6,w6,w
0
6 ,N
6 ,N
4.5 Implementation of the SRSM-ADIFOR
The implementation of the SRSM-ADIFOR involves the modification of the model
source code, by first processing the code using ADIFOR, and then making changes in
the resultant code so that the derivatives with respect to the srvs can be calculated.
Subsequently the derivative code has to be coupled with a main routine that produces

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Chapter: IV Section: 5
65
the derivatives of concern as program outputs. The calculated derivatives are then
coupled with the SRSM to estimate the coefficients of the polynomial chaos approx-
imations of the outputs. In order to accomplish this, a set of routines is developed
to generate expressions for the derivatives of a polynomial chaos expansion, and to
generate a matrix similar to the illustration shown in Equation 4.7. The subsequent
procedure is similar to the application of the SRSM. Figure 4.4 presents the steps
involved in the application of the SRSM-ADIFOR.

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Chapter 5
CASE STUDIES FOR THE EVALUATION OF THE
SRSM AND THE SRSM-ADIFOR
This chapter presents four case studies to evaluate the SRSM and the SRSM-
ADIFOR. These models cover a range of applications, both from the perspective
of model application (such as biological, air quality, and groundwater) and from the
perspective of model complexity (ranging from a zero dimensional model to a three-
dimensional urban/regional scale photochemical model). The execution times of the
models also vary from a fraction of a second to a few hours per model run. The main
objective of these case studies is to evaluate the applicability of the SRSM and the
SRSM-ADIFOR to a wide range of transport-transformation models, and potentially
to numerical models used in other areas.
The case studies are presented here in the following order:
I: A zero-dimensional pharmacokinetic model.
II: A two-dimensional atmospheric plume model.
Ill: A three-dimensional urban/regional scale air quality model.
IV: A one-dimensional ground water model with discontinuous probability density
functions.
The order corresponds to both the chronological evaluation of the SRSM method,
as well as to the associated complexity. The computational time for each model
simulation in the first three cases is of the order of a fraction of a second (case I),
through a few seconds (case II), to approximately 6 hours (case III). The groundwater
model required about a few seconds per model simulation, but is presented at the
end, since the complexity with respect to the specification of uncertainties in inputs
is high.
Each case study description contains the information about the model and its
application in exposure/risk assessment. Then, the uncertainties associated with
the model are described, and the application of different uncertainty propagation
methods is presented. The estimates from the uncertainty propagation methods are
then presented graphically, in the form of probability density functions (pdf s), along
with a brief discussion at the end.
66

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Chapter: V Section: 1
67
5.1 Case Study I: A Zero Dimensional Physiological System
Uncertainty Analysis of a Physiologically Based Pharmacokinetic
Model for Perchloroethylene
5.1.1 Description of the Case Study
There is often significant uncertainty associated with human Physiologically Based
PharmacoKinetic (PBPK) model parameters since human in vivo experimental data
are not usually available for toxic chemicals. Thus many of the parameters in human
PBPK models are generally estimated by in vitro experimentation and by inter-species
scale-up of animal PBPK model parameters. The uncertainty in human PBPK pa-
rameters includes a significant amount of natural variability, reflecting the interindi-
vidual variability inherent in human populations. It is desirable to estimate the effects
that uncertainties (including variability) associated with model inputs and parameters
have on output metrics such as internal and biologically effective doses.
Here, a PBPK model for perchloroethylene (PERC) [59] is considered for uncer-
tainty analysis. Human exposure to PERC, and the subsequent metabolism of PERC
in the body, is of concern since PERC is a potential carcinogen [59]. Further, PERC
is used as a cleaning solvent in the dry cleaning industry, and hence human exposures
to it are very common. Three dose surrogates for PERC inhalation are considered in
this study: (a) area under the arterial blood concentration (AUCA), (b) area under
venous blood from liver (AUCL), and (c) the cumulative amount metabolized in the
liver (CML).
CML is considered to be an alternative indicator of risk than simply the total up-
take of PERC, since the health risks are thought to be associated with the metabolism
of PERC in the body. AUCA and AUCL are indicators of the amount of time PERC
resides in the arterial blood and liver, respectively, and thus are also considered as
alternative indicators of risk.
The formulation of a PBPK model can be found in the literature [75,171]. Fig-
ure 5.1 presents the schematic of the PERC PBPK model used in this case study; the
human body is considered to consist of a set of compartments corresponding to dif-
ferent organs and tissues. These are modeled as a series of interconnected continuous
stirred tank reactors (CSTRs).
The effect of uncertainty in the parameters of the PERC PBPK on the three dose
surrogates is analyzed using the following:
•	Standard Monte Carlo approach,
•	Latin Hypercube Sampling (LHS),
•	Deterministic Equivalent Modeling Method/Probabilistic Collocation Method
(DEMM/PCM),

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Chapter: V Section: 1
68
rP
rp
sp
sp
Qfat
fat
Ql
1VW
Cliv
-ca
Rapidly Perfused [~
Slowly Perfused
Fat
Liver
rp
-sp
Qfat
Ql
IV
-~ metabolism
Lungs
Cinh| Qalv Cexh Qalv
r
Cca
Figure 5.1: Schematic representation of a PBPK model for PERC
•	Stochastic Response Surface Method with Efficient Collocation (ECM),
•	Regression based SRSM, also referred to simply as SRSM, and
•	Combined Application of the Stochastic Response Surface Method and Auto-
matic Differentiation Method (SRSM-ADIFOR).
The uncertainty analysis consisted of two stages: In the first stage, the effect of
uncertainty in the metabolic constants (Km and Vma_y) and the partition coefficients
are considered (see Table 5.1). The uncertainty in these six parameters are described
by independent log-normally distributed random variables. In the second stage, the
uncertainty in compartmental volume proportions are included in the analysis. These
five additional parameters are described by a set of mutually dependent Dirichlet
distributed random variables. In the first stage (6 parameters), the DEMM approach
and the ECM are evaluated through comparison with the results from a standard
Monte Carlo simulation. For the second stage (11 parameters), the ECM, Regression
Based SRSM and SRSM-ADIFOR are evaluated by comparing the results from these
methods with the results from the standard Monte Carlo and LHS methods.
In an earlier study, Farrar et al. [65] examined the effect of PBPK model param-
eter uncertainty on the three dose surrogates, CML, AUCA, and AUCL. The PBPK
model parameter uncertainties used in the present work are adopted from their work,
and are specified by the preferred values (PVs) and uncertainty factors (UFs) given

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Chapter: V Section: 1
69
Table 5.1: Deterministic and uncertain parameters used in the uncertainty analysis
of the PERC PBPK model


Preferred

Symbol
Description
Value
UF
BW
body weight [Kg]
70
—
Partition Coefficients
-Pblood / air
blood:air partition coefficient
12
1.7
Pfat/blood
fat:blood partition coefficient
102
2.15
Psp/blood
slowly perfused tissue:blood partition coefficient
2.66
11.0
Prp/blood
rapidly perfused tissue:blood partition coefficient
5.05
5.69
-Pliv/blood
liver:blood partition coefficient
5.05
9.37
Blood Flows
Qc
cardiac output [liters/hr]
348
1.12
Qp
alveolar ventilation [liters/hr]
288
1.50
Q fat
blood flow to fat [liters/hr]
17.4
1.09
Qsp
blood flow to slowly perfused tissue [liters/hr]
87.0
1.04
Qrp
blood flow to rapidly perfused tissue [liters/hr]
153
1.25
Qliv
blood flow to liver [liters/hr]
90.6
1.35
Compartment Mass Fractions
Vfat
mass of fat compartment [Kg]
23.0%
1.09
Kp
mass of slowly perfused tissue compartment [Kg]
62.0%
1.04
Kp
mass of rapidly perfused tissue compartment [Kg]
5.0%
1.25
Miv
mass of liver compartment [Kg]
2.6%
1.35
Metabolic Constants

Michaelis-Menten constant for metabolism [mg/liter]
1.47
12.3
Kia x,c
maximum rate of metabolism [mg/hr]
0.33
2.84
in Table 5.1. The preferred values for all parameters other than metabolic constants,
correspond to the nominal values in the PERC PBPK model [59], updated as sug-
gested by Reitz and Nolan [165]. The preferred values of metabolic constants are
given by the geometric mean of values suggested by Hattis et al [90], and by Reitz
and Nolan [165]. The uncertainty factor for a parameter is defined such that the inter-
val from Xp/UF to Xp UF is the 95% confidence interval, where Xp is the preferred
value of parameter X.

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Chapter: V Section: 1
70
5.1.2 Specification of Parameter Uncertainty
Partition coefficients and metabolic constants are assumed to be lognormally
distributed, according to Farrar et al [65]. The pdf for a given lognormally distributed
parameter is specified by assuming that the preferred value of the parameter corre-
sponds to the median of the distribution. This is convenient, since the mean and
standard deviation that specify the corresponding normal distribution are then given
by lnXp and ln(UF)/1.96 respectively.
Compartment volume fractions are assumed to be specified by a Dirichlet distri-
bution [65]. This implies that each compartment volume is specified by a x2 distri-
bution [111, 194], the pdf of which is given by:
x > 0
f(x-,a) = ^ r(a/2)2"/2	'	(5.1)
0, elsewhere
The mean of the x2 distributions is a, and is related to the preferred value and the
Dirichlet distribution parameter 0, as follows:
E[Xj] = xipO
where Xj is the ith compartment volume, E[Xj] is its mean, and xip is the preferred
value of the fraction Xi} as given in Table 5.1. From the values of 0 and the preferred
values for each compartment volume fraction, the parameters of the pdfs of the
compartment volumes can be obtained.
Compartment blood flows are assumed to be specified as deterministic functions of
compartment volumes and preferred values of compartmental blood flows. In sleeping
humans, the blood flow to compartment j is given by:
Vj
Qj = Qj,p^
Vj,P '
where the notation Xp for the preferred value of the parameter X is employed. The
total cardiac output is given by
Qc Qliv "I" Qfat "I" Qsp "I" Qrp ?
and the total cardiac output in waking humans is given by:
Qcw Qc (Qcw,p Qc,p)qci i
where qce is a lognormally distributed random variable, the preferred value and un-
certainty factor for which are given in Table 5.1. The form of the expression for Qcw
ensures that Qcw > Qc always. The pdf for qce is constructed in a manner analogous
to that of the other lognormally distributed parameters as described above.

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Chapter: V Section: 1
71
The increase in cardiac output in waking humans, is accounted for by an increase
in blood flow to the slowly perfused compartment, as follows:
Q spw QsP,p (Qcw,p Qc,p)q ce*
Alveolar ventilation rate is a deterministic function of the cardiac output, and the
preferred values for ventilation are given in Table 5.1. Alveolar ventilation rates in
sleeping and waking humans are given by:
^ 	p. Qc i p. 	p. Qcw
^%p — Wp,P s~\ )	^%pw — ^pw,P
lc,P	Qcw,P
5.1.3	Implementation of the PERC PBPK Model
The PERC PBPK model has been implemented in SimuSolv [56], a software environ-
ment for numerical modeling and simulation. The PBPK model consisted of a set of
coupled ordinary differential equations, which describe the dynamic variation in the
amount of PERC in each of the compartments shown in Figure 5.1, resulting from
uptake, distribution, metabolism and excretion processes. The random parameters in
the model have been sampled for the simulations using the pseudo-gaussian random
number generator function in SimuSolv, RGAUSS. The representation of pdf s of uncer-
tain parameters in terms of standard gaussian random variables, each with zero mean
and unit variance, is described here. Daily values of dose surrogates, CML, AUCA,
and AUCL, are computed using the PBPK model to simulate a 24 hour inhalation ex-
posure to 10 ppb of PERC, which is a typical high concentration in urban/industrial
areas [99].
A lognormally distributed random variable X, with median fix, and an uncer-
tainty factor UFx, can be represented in terms of a standard normal random variable
ฃ = N(0,1), having zero mean and unit variance, as follows [194]:
X = exp (\n(nx) +	^
A x2 distributed random variable with parameter a, can be approximated in terms
of a normal random variable ฃ as follows [207]:
y=a (?\/I+1 - ฃ)	<5-3)
5.1.4	Results for the PBPK case study
Stage I (Six Uncertain Parameters): Evaluation of DEMM/PCM and ECM
The effect of six uncertain PBPK model parameters on the dose surrogate pdfs is
studied in the first stage of uncertainty analysis. The number of model simulations

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Chapter: V Section: 1
72
used for DEMM/PCM and the ECM are equal, as both these methods differ only with
respect to the selection of collocation points. Second and third order approximations
are used for both DEMM/PCM and the ECM. The number of simulations required
for both these methods for the case of six uncertain inputs are 28 and 84, for 2nd and
3rd order approximations, respectively. Additionally, 10,000 Monte Carlo simulations
are used in the first stage for evaluation of the DEMM/PCM and ECM.
Figures 5.2 and 5.3 present the pdfs estimated from four different sets of colloca-
tion points selected from the same set of original 4096 points (DEMM Trial 1, 2, 3 and
4), for the dose surrogates AUCA, AUCL and CML, respectively. They also present
the pdfs estimated by standard Monte Carlo approach. Here, the four DEMM trails
involved the same number of sample points, but different samples. Further, the pdfs
estimated by the Monte Carlo simulation are considered to be the "correct" pdfs.
From these figures, it can be seen that the DEMM/PCM approach cannot con-
sistently guarantee convergence of the approximation to the true probability density.
Since the DEMM/PCM method failed to produce consistent results for a simple model
with independent input probability distributions, this method was not used in other
case studies, which involved more complex models or input distributions.
DEMM Trial 1
DEMM Trial 2
DEMM Trial 3
DEMM Trial 4
Monte Carlo - 10,000 runs
40
20
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
Cumulative Amount Metabolized [mg] (normalized by mass of liver)
Figure 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

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Chapter: V Section: 1
73
250
DEMM Trial 1
DEMM Trial 2
DEMM Trial 3
DEMM Trial 4
Monte Carlo - 10,000 runs
200
O)
A
CO
150
100
0.02
0
0.005
0.01
0.015
0.025
0.03
Area Under the Arterial Concentration Curve [ng.hr/cm^]
(a)
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DEMM Trial 1
DEMM Trial 2
DEMM Trial 3
DEMM Trial 4
Monte Carlo - 10,000 runs
0.05	0.1	0.15	0.2	0.25
Area Under Liver Concentration Curve [ng.hr/cm^]
(b)
Figure 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

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Chapter: V Section: 1
74
200
Monte Carlo - 10,000 runs
ECM - 2nd (28 runs)
ECM - 3rd (84 runs)
150
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100
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Chapter: V Section: 1
75
Monte Carlo - 10,000 runs
ECM - 2nd (28 runs)
ECM - 3rd (84 runs)
45
40
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25
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Chapter: V Section: 1
76
Monte Carlo - 100,000 runs
ECM - 2nd (28 runs)
ECM - 3rd (84 runs)
40
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& 25
CO
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Chapter: V Section: 1
77
180
Monte Carlo - 100,000 runs
ECM - 2nd (28 runs)
ECM - 3rd (84 runs)
160
140
O)
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120
CO
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Chapter: V Section: 1
78
Monte Carlo - 100,000 runs
LHS- 1,000 runs
SRSM - 2nd (130 runs)
SRSM - 3rd (600 runs)
40
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& 25
CO
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Chapter: V Section: 1
79
O)
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180
160
140
120
100
80
60
40
20
0
Monte Carlo - 100,000 runs
LHS- 1,000 runs
SRSM - 2nd (130 runs)
SRSM - 3rd (600 runs)
0.002 0.004 0.006 0.008 0.01 0.012
Area Under the Arterial Concentration Curve [|ig.hr/cm
0.014 0.016 0.018
,3i
(a)
18
Monte Carlo - 100,000 runs
LHS - 1,000 runs
SRSM - 2nd (130 runs)
SRSM - 3rd (600 runs)
16
14
12
10
8
6
4
2
0
0
0.05
0.1 0.15 0.2 0.25
Area Under Liver Concentration Curve (jag)
0.3
0.35
0.4
(b)
Figure 5.9: Evaluation of SRSM (regression based) and LHS: uncertainty in (a) AUCL
and in (b) AUCA (Figure b) resulting from 11 uncertain parameters

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Chapter: V Section: 1
80
Monte Carlo - 100,000 runs
SRSM - 3rd Order (600 runs)
LHS- 1,000 runs
SRSM-ADIFOR - 3rd Order (70 runs)
45
40
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Chapter: V Section: 1
81
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Chapter: V Section: 2
82
5.2 Case Study II: A Two-Dimensional Photochemical Air Quality
Model
Uncertainty Analysis of the Reactive Plume Model (RPM-IV)
The Reactive Plume Model, version IV (RPM-IV), is a standard regulatory model
used for calculating pollutant concentrations and establishing causal relationships
between ambient pollutant concentrations and the emissions from point sources such
as industrial stacks [148,189]. It uses either point source emission estimates or ini-
tial plume concentrations as inputs, and calculates downwind concentrations, as the
plume expands.
RPM-IV is often applied for regulatory purposes to calculate the ozone (03) con-
centrations at locations downwind of industrial point sources, since high ozone concen-
trations in the ambient environment lead to adverse health effects. Ozone is primarily
formed in the atmosphere through a series of complex chemical reactions involving
oxides of nitrogen (NO^) and volatile organic compounds (VOCs) in the presence of
sunlight. Some of the major point sources of NO^ and VOCs are industrial units,
such as the power plants (NO^ sources) and refineries (VOC sources). The appli-
cation of RPM-IV helps in establishing a quantitative causal relationship between
emissions and ambient pollutant concentrations, which is useful in assessing various
control strategies for emission reductions.
However, there are significant uncertainties in developing estimates of the emis-
sions from industrial sources. These uncertainties occur with respect to the amounts
of emissions (e.g., total amounts of VOCs and NO^), and with respect to their chem-
ical compositions (or speciations, i.e., the fractions of various chemicals within these
groups of compounds). These uncertainties arise due to a variety of reasons: for ex-
ample, emission estimates are typically derived from hourly averages projected from
annual or seasonal averages [37]. Since there could be a significant variation in the
load and operating conditions of an industrial unit, emission estimates and chemical
compositions for specific days under consideration may differ significantly from the
averages, and thus result in significant uncertainties. Hence, an uncertainty analy-
sis that takes into account the emission estimate uncertainties is useful for a better
understanding of the effects of control strategies for emission reductions.
5.2.1 Description of RPM-IV
A brief description of the RPM-IV model is presented here; additional details are
presented in Chapter 6, which deals with the model uncertainty associated with RPM-
IV, and which presents an improved, three-dimensional version of the model.
RPM-IV simulates mechanistically the complex nonlinear photochemistry and dis-
persion processes occurring in an expanding plume. The nonlinear atmospheric gas

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Chapter: V Section: 2
83
phase photochemistry is described by the Carbon Bond IV (CB-IV) mechanism [206],
which consists of a set of 95 chemical reactions among 35 surrogate chemical species
corresponding to organic bonds and functional groups. This model follows the tra-
jectory of an expanding, moving plume and simulates its evolution. In this model,
the pollutant mass is initially divided into cells containing equal amounts of pollu-
tants. As the plume expands, the individual cells expand in volume and pollutant
mass is transferred across cell boundaries in two phases: (a) an "entrainment" phase,
where the expanding cell boundaries entrain the pollutants from other cells, and (b)
a "detrainment" phase, where the pollutants diffuse across cell boundaries, due to
concentration gradients. Further, the pollutants in each cell undergo chemical trans-
formation governed by the Carbon Bond-IV mechanism.
The equation describing the concentration changes within a cell is given by:
dc) {dc)\	{ 1 dwj\ ,• { 1 dhj\ ,• .	.
~r = -r 		r-\uci~\i	r1 ) uc) + F	5-4
dt \ dt J chem \WJ ds J \hJ ds )
where c* denotes the concentration of chemical species % in cell j. F- is the net
flux into the cell, and is given by Fj = E* — Dj, where Ej is the entrainment as the
cell expands, and Dj is the detrainment due to the diffusion of the species across the
boundaries of the cell. Expressions for entrainment and detrainment are given by the
following equations:

n* = — (k—\ 2 fK ( c)+i~c) \ _ K (c)~c)-i
1 dy\ dy) y3-y3-1\ J \yi+1 - y3- J ^ [y3 - y3-2
where yj is the distance from the plume centerline to the far most side of the cell
j, in the horizontal direction. An additional condition is imposed on the expansion
of the cells: the cells are allowed to expand in a way such that the amount of an
inert species remains constant within each cell, i.e., no net transfer of an inert species
occurs from a cell. This condition results in the following equation for expansion of
the cells, as the plume travels downwind:
Ej-D^O
RPM-IV was selected for evaluation of the SRSM and SRSM-ADIFOR because it
is sufficiently complex to represent a wide range of environmental models, and at the
same time it is computationally feasible to perform a large number of Monte Carlo
simulations with this model. Further, the complex nonlinear photochemistry of this
model is employed by a number of photochemical models that are computationally
very demanding. Thus, evaluation of the SRSM and SRSM-ADIFOR with RPM-IV
could potentially serve as a preliminary test of applicability of this method to a wide
range of complex photochemical models.

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Chapter: V Section: 2
84
5.2.2	Uncertainty Analysis of RPM-IV
The present work studies the effect of uncertainty in emission estimates on predicted
downwind secondary pollutant concentrations; here, the specific focus is on the un-
certainties in the calculated ozone concentrations resulting from the uncertainties in
amounts and the chemical composition of the emissions. This study uses emission
estimates and field data measured near Marathon oil refinery at Robinson, Illinois,
during June and July, 1977 by Sexton et al. [177]. Since there was no characteriza-
tion of uncertainty, and since the focus of this work is to evaluate the applicability of
SRSM-ADIFOR, only representative probability distributions to describe the uncer-
tainties in emissions were assumed.
The following distributions for the emissions of VOCs and NOx are used: (a) the
amounts of VOCs and NO^ released are assumed to have normal distributions with
a standard deviation of 20% of the mean value, and (b) the chemical compositions
of VOCs and NOx are assumed to follow a Dirichlet distribution [194], The Dirichlet
distribution satisfies the condition that the sum of mole fractions is unity. According
to this distribution the mole fraction of the zth compound, is given by:
Vi =	(5-7)
i= 1
where Xi is an independent random variable, representing the the amount (in moles)
of the zth compound; here a normal distribution is assumed for all a^s, with a nominal
standard deviation of 40% of mean value. In this case study, the VOC group consists
of five subgroups (paraffins, ethylene, olefins, toluene, and xylene), and the NO^
group consists of NO and NO2. Thus, the total number of uncertain parameters for
the model is nine (including two parameters representing the total amounts of VOCs
and NOx). Thus, a total of 9 uncertain parameters with 7 degrees of freedom were
used in this case study; these were represented by 9 srvs.
The output metrics considered are the average ozone concentration in the plume
for selected distances downwind from the source.
The original RPM-IV model was implemented in Fortran and obtained from the
EPA Exposure Models Library [195]. The derivative code was obtained using the
ADIFOR system on RPM-IV code. For Monte Carlo, LHS, and the SRSM, the model
was run at selected sample points, whereas for the SRSM-ADIFOR, the derivative
model was run.
5.2.3	Results and Discussion
From equations 3.5 and 3.6 for the number of unknown coefficients for a second and
third order polynomial chaos expansions, for n=9, the number of coefficients to be

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Chapter: V Section: 2
85
determined is 78 and 364 for second and third order approximations, respectively. For
a regression based method, 300 model simulations were selected for a second order
approximation, and 600 model simulations were selected for a third order approxima-
tion, in order to facilitate regression with an adequate number of model outputs. The
estimates of pdfs for these cases were evaluated with the results obtained from 10,000
Monte Carlo simulations and 10,000 Latin Hypercube sampling methods. For the case
of the SRSM-ADIFOR, 80 simulations were used for a third order approximation.
Two representative output metrics were considered here: (a) ozone concentration
at a downwind distance of 2 km, representative of near-source transport and transfor-
mation, and (b) ozone concentration at a downwind distance of 20 km, representative
of larger scale transport and transformation. The output pdfs were obtained first
using second and third order approximations of the SRSM/ECM. Figure 5.12 shows
the pdfs of ozone concentration at a downwind distance of 2 km and of 20 km, as
estimated by the ECM, and the conventional Monte Carlo method.
Figure 5.13 shows the pdfs of the predicted ozone concentrations at downwind
distances of 2 km and 20 km, as estimated by the regression based SRSM, traditional
Monte Carlo and Latin Hypercube Sampling.
As shown in Figures 5.12 and 5.13, although the regression based-method required
significantly fewer runs than the Monte Carlo method, the results agree very closely
with the Monte Carlo results. On the other hand, the predictions of ECM become
inaccurate as the order of approximation increases, indicating the lack of robustness in
the collocation method. This behavior is more prominent for large downwind distance,
indicating that the collocation method may not converge when used with highly
nonlinear models (the near-source behavior is expected to be less nonlinear than far-
source behavior). On the other hand, a regression based method resulted in similar
estimates for both second and third order approximations and was consistent for all
ranges of downwind distances. The results indicate that, although the regression
methods require a higher number of model simulations for uncertainty propagation,
compared to the collocation methods, their robustness makes them a more viable tool
for uncertainty analysis of complex environmental models.
For the evaluation of the SRSM-ADIFOR method with RPM-IV, Figure 5.14
shows the uncertainty estimates obtained. As shown in the figure, SRSM/ADIFOR
gave closer estimates with only 80 model runs, while 10000 Latin Hypercube sam-
ples were not sufficient to achieve agreement with the results from the Monte Carlo
methods.

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Chapter: V Section: 2
86
Evaluation of The ECM Method
Monte Carlo Method (10,000 runs
/\ ECM - 3rd Order (364 runs
, \ ECM - 2nd Order (78 runs
160
140
120
100
80
60
40
20
0
0.065
0.07
0.075
0.08
0.085
0.09
0.095
Average Plume 03 Concentration (ppm) at 2 km downwind from the source
(a)
Evaluation of The ECM Method
160
Monte Carlo Method (10,000 runs)
ECM - 3rd Order (364 runs)
ECM - 2nd Order (78 runs)
140
E 120
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Chapter: V Section: 2
87
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160
140
120
100
80
60
40
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Monte Carlo : 10,000 runs
LHS- 10,000 runs
SRSM - 2nd order (91 runs)
SRSM - 3rd order (600 runs)
0.07
0.075
0.08
0.085
0.09
0.095
Average Plume 03 Concentration (ppm) 2 km (left) from the Source
(a)
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100
Monte Carlo : 10,000 runs
LHS - 10,000 runs
SRSM - 2nd order (91 runs)
SRSM - 3rd order (600 runs)
0.11
0.115
0.12
0.125
0.13
0.135
0.14
0.145
Average Plume 03 Concentration (ppm) 20 km (left) from the Source
(b)
Figure 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

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Chapter: V Section: 2
88
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jy*\\ SRSM - 3rd order (600 runs) 	
\\ LHS- 10,000 runs 	
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Average Plume 03 Concentration (ppm) 2 km from the Source
(a)
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Chapter: V Section: 3
89
5.3 Case Study III: A Three-Dimensional Urban/Regional Scale
Photochemical Air Quality Model
Uncertainty Analysis of the Urban Airshed Model (UAM-IV) for
the New Jersey/Philadelphia Domain
Photochemical modeling of ozone (O3) levels in the atmosphere is complicated by
the fact that ozone is a secondary pollutant; it is formed through complex chemi-
cal reactions involving oxides of nitrogen (NO2 and NO), and volatile organic com-
pounds (VOCs). The modeling process is further complicated by the presence of
natural, model, and data uncertainties, as shown in Figure 5.3. Uncertainties in the
input data and parameters (such as emission estimates and reaction rates) become es-
pecially significant when photochemical models are used to evaluate control strategies
for the reduction of ozone levels [42],
Biogenic emissions have a significant impact on atmospheric ozone concentra-
tions, depending on the availability of NOx- Earlier studies on the effects of biogenic
emissions on O3 concentrations indicate that typically O3 concentrations increase in
response to increases in biogenic hydrocarbons. However, modeling studies suggest
that in some extremely NOx-limited areas, increases in biogenic emissions decrease
O3 concentrations [168]. Sensitivity tests in a case study show that local biogenic
emissions are an important contributor to local O3 production, relative to anthro-
pogenic hydrocarbons (AHCs) [137]. Isoprene forms a main component of biogenic
emissions, and affects the chemistry of the troposphere because its oxidation products
are precursors for the photochemical production of ozone [11]. It is one of the most
abundant phytogenic chemical species found in the ambient air [71], and accounts for
a substantial portion of the atmospheric hydrocarbon load [85,179].
However, biogenic emission estimates are laden with large uncertainties [168]. In
addition, there are significant uncertainties associated with the chemical mechanisms
employed in photochemical modeling, such as uncertainty in the chemical reaction
rates and uncertainties arising from the lumping of chemical species [7]. The focus
of the present work is on the comparative evaluation of the effects of uncertainty in
the biogenic emission estimates and uncertainties in the reaction rate coefficients on
the estimated ozone levels, with respect to isoprene (2-methyl-l,3-butadiene), since
it is one of the major components of biogenic emissions. More specifically, the aim is
to compare the effects of uncertainties associated with the emission rates of isoprene
with the effects of uncertainties in the rates of reactions involving isoprene. Such
uncertainty analysis could provide information that could be useful in identifying
the area to focus resources: studying the reaction mechanisms in more detail versus
improving the inventorying methods.

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Chapter: V Section: 3
90
Atmospheric Turbulence
Variabilty
Model
Uncertainty
Natural
Uncertainty
Data Uncertainty
Simplifications in Model Formulation
Spatial and Temporal Averaging
Lumping in Chemical Mechanisms
Emission Estimate Uncertainty
Errors in Measurement
Insufficient Data
Figure 5.15: Origins and types of uncertainty present in Photochemical Air Quality
Simulation Models
5.3.1 Uncertainties Associated with Biogenic Emission Estimates
There are significant uncertainties associated with biogenic emission estimates, and
specifically with isoprene emission estimates. These uncertainties occur mainly due
to the following factors:
•	variability in the vegetation: the amounts of biogenic emissions differ signifi-
cantly from one plant species to other, and there is a wide range of plant species
in an urban/regional scale modeling area.
•	variability in emission rates: the emission rates depend on factors such as sun-
light, temperature, nitrogen and water availability, and are associated with
significant spatial and temporal variability.
Currently employed models and associated inventories for estimating biogenic emis-
sions typically cannot treat the above variability in adequate detail. Hence significant
uncertainties occur in the emission estimates.
The total U.S. emission estimates of biogenics are reported to range from 22 to 50
Tg/yr depending upon the formulation of different emission rate factors [132], Some
studies report the uncertainty in the biogenic emission estimates of an inventory
for the United States to be of the order of 300% [133,168]. In another study, the
uncertainties in the emission estimates of isoprene in Europe are reported to be of
the order of 500% [182],
In addition, there are significant uncertainties associated with the description
of isoprene chemistry. Recently reported studies show that different photochemical

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Chapter: V Section: 3
91
mechanisms with different treatments of isoprene chemistry give significantly different
estimates of ozone levels [215]. The main sources of uncertainties in existing chemical
mechanisms are: (a) lumping of various biogenic compounds, and (b) insufficient
data to estimate reaction rate parameters associated with biogenic compounds. It is
reported in the literature that some reaction rate parameters associated with isoprene
chemistry are uncertain up to a range of 150% [7].
5.3.2	Uncertainty Analysis
Considering the importance of biogenic emissions, and the uncertainties involved in
their estimates and reaction rates, it is important to study how these uncertainties
propagate through the model and affect the predicted 03 concentrations. For simple
models, uncertainty propagation can be accomplished through conventional meth-
ods such as Monte Carlo methods. However, grid-based photochemical models are
typically very demanding computationally, thus making conventional Monte Carlo
methods prohibitively expensive.
Due to the lack of computationally efficient uncertainty analysis methods, a num-
ber of studies reported in the literature with respect to uncertainty in photochemical
models involved running the model for selected sets of parameter or input values, and
thus obtaining some estimates on the "bounds" of uncertainty in model results. The
main advantage of this approach is that it is simple and requires few model runs.
However, this approach ("sensitivity/uncertainty" testing) does not provide insight
into the distribution of the model results. Here, the regression based SRSM is applied
to estimate the probability distributions of the estimated ozone concentrations.
As mentioned earlier, one goal of this approach is to characterize individual contri-
butions of uncertainties in chemistry and emissions on the ambient ozone levels, and
to identify important contributors to uncertainty, so that resources could be focused
to reduce uncertainties in those factors. Another goal is to obtain an approximate
estimate of the range of uncertainty in the predicted ozone concentration, resulting
from the above mentioned input uncertainties; such an estimate could potentially
assist in answering some regulatory questions.
5.3.3	Estimation of Uncertainties
The present work involves the study of the effects of uncertainty in the emission and
reaction rates of isoprene, on the ozone levels in the atmosphere. This is pursued as
follows:
• a single cell ("box") model is used as a screening tool to identify parameters
that could potentially affect the results of the grid-based PAQSMs. The param-
eters considered include the the initial concentration of isoprene, and the rate
constants for reactions involving isoprene in the CB-IV chemical mechanism.

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Chapter: V Section: 3
92
•	approximate estimates of uncertainty in the parameters thus identifies are ob-
tained from the literature, and are then propagated through the Urban Airshed
Model (UAM-IV) to study urban/regional scale effects of these uncertainties.
More specifically, a photochemical box model is used to perform preliminary
screening based on the effects of uncertainty in isoprene reaction rate constants and
initial concentrations of isoprene on the time-dependent concentrations of ozone. The
results from this exercise are utilized in identifying key factors contributing to uncer-
tainty in the application of a grid-based PAQSM, specifically the UAM-IV.
A box-model consists of a single well mixed reactor cell, and the evolution of
chemical species depends only on time dependent photochemical reaction rates. In
the present work, a single cell option of the Reactive Plume Model (RPM-IV) is used
for the screening runs. A single cell trajectory model is equivalent to a box model,
since there is no transfer of material occurring from outside the cell. The RPM-
IV was used here since the same chemical mechanism, the Carbon-Bond IV (CB-
IV) mechanism [206], is employed to describe the chemistry in both RPM-IV and
UAM-IV. The CB-IV mechanism consists of a set of 95 chemical reactions among 35
surrogate chemical species corresponding to organic bonds and functional groups.
The Urban Airshed Model, version IV, [149] is a photochemical grid model that
has been applied to several urban areas in the United States, Europe and the Far
East [61,75]. This model solves the atmospheric diffusion equation (ADE) [175] on
a three dimensional grid covering the airshed of interest. Atmospheric chemistry is
described by the CB-IV mechanism. This model requires meteorological inputs and
the emission information for both anthropogenic and biogenic emissions.
This case study considers the modeling of the Philadelphia-New Jersey region
for July 19 and 20, 1991, when a severe ozone episode occurred in this region. The
domain and the modeling grid (at a horizontal resolution of 5 kmx5 km) for the case
study are shown in Figure 5.16.
The meteorological inputs were developed using the Diagnostic Wind Model
(DWM) [55], the anthropogenic emissions were obtained from State-provided in-
ventories, processed using the Emission Processing System (EPS) [30]. The bio-
genic emissions were obtained from the EPA's Biogenic Emissions Inventory Sys-
tem (BEIS) model [30]. These inputs were obtained from the input data from Geor-
gopoulos et al. [75]
The procedure used for uncertainty analysis consisted of the following steps:
•	Performing systematic screening tests to study the sensitivity of the results of
the box-model with respect to selected reaction rate constants of isoprene, and
initial concentration of isoprene
•	Identifying the factors that have clear effects of the predicted ozone concentra-
tion
•	Obtaining approximate uncertainty estimates for these factors from the litera-
ture [7]

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Chapter: V Section: 3
93
Philadelphia/New Jersey Modeling Domain
County Locations
UTM ZONE 18
UTM East (km)
Figure 5.16: The Philadelphia/New Jersey modeling domain used for uncertainty
analysis

-------
Chapter: V Section: 3
94
1400
July 19
July 20
1200
1000
ฃฆ 800
(/)
c

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Chapter: V Section: 3
95
July 19
July 20
ฎ 2500
1 2000
ฃ 1500

0.07	0.08
Daily Average 03 Concentration (ppm)
Figure 5.18: Probability distribution of the daily average ozone concentration
Here, 2.5 is used as a truncation factor to specify the probability distribution uniquely,
and to limit the range of values x could have.
2000
1800
1600
1400
1200
ฎ 1000
o
800
600
400
200
0.11

July 19 	

July 20 	
k

0.12
0.13	0.14
Maximum 8hr 03 Running Average (ppm)
0.15
0.16
Figure 5.19: Probability distribution of the daily maximum 8 hr running average
ozone concentration
More specifically, the uncertainties in isoprene emission estimates, and the rate

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Chapter: V Section: 3
96
0.014
0.012
S" 0.01
'c
3
O
> 0.008
'<75
c

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Chapter: V Section: 3
97
Table 5.2: Contribution of uncertainties in the individual parameters to the overall
uncertainty in the UAM-IV, as estimated by the SRSM

Jul 19
Mean OH NO., ISOP
Jul 20
Mean OH NO., ISOP
Max (ppm)
Ave (ppm)
8hr-Ave (ppm)
Pervasiveness
0.143 0.0003 0.0000 0.0009
0.062 0.0000 0.0000 0.0002
0.121 0.0000 -0.0001 0.0005
341.71 6.32 -1.22 47.53
0.178 0.0003 -0.0001 0.0003
0.072 -0.0001 0.0000 0.0001
0.149 -0.0003 0.0000 0.0001
4075.0 31.30 -9.17 157.14
hour running average concentration, for the same case, whereas Figure 5.20 shows
the uncertainty in the pervasiveness.
In summary, the following conclusions were reached:
•	The daily average 03 concentration was not affected significantly by the input
uncertainties considered.
•	The daily maximum eight hour running average was affected mildly by the input
uncertainties.
•	The daily maximum hourly average was also affected mildly.
•	The pervasiveness was affected significantly by the uncertainty in the inputs
considered.
•	The probability distributions of output metrics differed significantly for the two
episode days considered, implying that the uncertainty analysis method needs
to be applied on a case by case basis.
•	The variance in the concentration metrics for all cases was less than 1% of the
mean value.
•	The variance in pervasiveness was of the order of 5%-f0% of the mean value.
Table 5.2 summarizes the uncertainty in the uncertainty estimates resulting from
this analysis. The coefficients in the table indicate the uncertainty contributed to a
metric, due to the uncertainty in the individual parameter. The coefficients indicate
that the uncertainty contribution is very small relative to the mean value of the metric.
Further, they indicate that the uncertainty in the isoprene emission rate contributes
significantly to the overall uncertainty, and that the uncertainty in the reaction rate of
the NC>3-isoprene reaction has an almost negligible effect. However, the uncertainty
in the OH-isoprene reaction constant contributes to an extent comparable to the
isoprene emission uncertainty. So, the above analysis indicates that the isoprene
emission rates and the reaction rate constant of the isoprene-OH reaction should be
studied in more detail.

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Chapter: V Section: 3
98
This case study also demonstrates how the SRSM, while requiring a small number
of simulations, can be used to estimate the uncertainties in the outputs of a complex
model such as UAM-IV. The execution time, about five days (20 model runs x 6 hours
per model run) is much less when compared with a Monte Carlo analysis involving,
for example, 500 runs, requiring 125 days of computer time.

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Chapter: V Section: 4
99
5.4 Case Study IV: A Ground
Probability Distributions
Uncertainty Analysis of Tritium
the EPACMTP Model
Water Model with Discontinuous
Contamination in a Landfill using
Characterization of exposure due to leakage of hazardous contaminants from a land
disposal unit involves significant uncertainty due to inherent variability in the hydro-
geologic properties of the site, and due to incomplete understanding of the processes
involved in transport of contaminants. Since the exposure estimates are utilized in
making policy decisions, it is important that any exposure characterization should
take into account the variability and uncertainty involved with the physical system,
and with the modeling process.
Here, a case study is presented in relation to characterization of uncertainty in
estimated tritium exposure at a receptor well. The main source for the contamination
is a hypothetical landfill unit in the southern United States. The fate and transport
model used in this work is the EPA's Composite Model for leachate Migration and
Transformation Products (EPACMTP). In the following sections, a brief description
of the model used, the estimates of the model parameters and the uncertainty analysis
methods is given. The variability and uncertainty associated with the hydrogeologic
parameters and with the physical properties of the landfill unit are considered to
characterize the uncertainty in the calculated concentrations of tritium. Environ-
mental metrics considered are the estimated maximum concentration of tritium in a
"receptor" well and the estimated time of occurrence of the maximum concentration.
5.4.1 EPACMTP Model
The EPA's Composite Model for leachate Migration and Transformation Products
(EPACMTP) provides estimates of potential human exposure to hazardous chem-
icals leaching from land disposal facilities [62], EPACMTP simulates the subsur-
face fate and transport of contaminants released from land disposal sites, and pre-
dicts the associated groundwater exposure in a domestic drinking water receptor
well. This model is an improvement over the EPA's Composite Model for Land-
fills (EPACML) [60]. EPACML accounts for the first-order decay and sorption of
chemicals, but disregards the formation and transport of transformation products. In
addition, EPACML can describe only uniform, unidirectional groundwater flow. On
the other hand, EPACMTP can take into consideration: (i) chain decay reactions
and transport of daughter and grand-daughter products, (ii) effects of water-table
mounding on groundwater flow and contaminant migration, (iii) finite source as well
as continuous source scenarios, and (iv) metals transport.
EPACMTP consists of two modules: an unsaturated zone module called Finite

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Chapter: V Section: 4
100
Table 5.3: Probability distributions used for the EPACMTP model parameters for
the uncertainty analysis
Pa ran
Dist."
Distribution Parameters'"
units
Description
Ks
LN
f] = 0.343, a = 0.989
cm/hr
Saturated hydraulic conductivity
K
U
min = 1500, max =
2114
m/yr
Hydraulic conductivity
a
LN
r] = 0.019, (7 = 0.012
cm"1
Moisture retention parameter
(3
SB
r] = 1.409, a = 1.629
-
Moisture retention parameter
9r
SB
rj = 0.068, a = 0.071
-
Residual water content
%OM
SB
r] = 0.105, a = 5.88
-
Percentage of organic matter
d
LN
r] = 0.09, a = 4.0
cm
Particle diameter

LN
•q = 10.0, a = 5.0
m
Longitudinal dispersivity
pH
LN
r] = 6.5, a = 0.75
-
Aquifer pH
foe
SB
r] = 0.000432, a =
0.0456
-
Fractional organic carbon content
m
N
r] = 800, a = 150
m
Radial distance of observation well
e
U
min = 0ฐ, max = 90ฐ
-
Angle off-center
B
U
miri = 30.0, max = 35.0
m
Aquifer thickness
"Distribution types : LN - Lognormal, U - Uniform, N - Normal, and SB - Johnson SB.
6Distribution parameters: r/ - mean, a - standard deviation, min - minimum value, and max -
maximum value
Element and semi-analytical Contaminant Transport in the Unsaturated Zone (FEC-
TUZ), and a saturated zone module called Combined Analytical-Numerical SAtu-
rated Zone in 3-Dimensions (CANSAZ-3D). FECTUZ is a one-dimensional model
that simulates vertically downward steady-state flow and contaminant transport
through the unsaturated zone above an unconfined aquifer. CANSAZ-3D simulates
3-D steady-state groundwater flow and transient or steady state contaminant trans-
port. EPACMTP currently uses a simplified 2-D version of the CANSAZ-3D, and
the modules are optimized for computational efficiency.

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Chapter: V Section: 4
101
5.4.2	Data Sources for the Application of EPACMTP
Data for the site characteristics, infiltration rates, the volume and area of the landfills,
and the probability distributions for the hydrogeologic parameters are obtained from a
review conducted by the Hydrogeologic Inc. [62], In this review, a number of different
sources were used for the development of this site-based approach. Four of these sets
were selected to derive the regional characteristics of important parameters for each
sampled site:
•	the OPPI survey of waste management units (EPA, 1986),
•	the infiltration and recharge analysis performed by OSW,
•	the USGS state-by-state inventory of groundwater resources, and
•	the Hydrogeologic Database for Modeling (HGDB), developed from a survey of
hazardous waste field sites in the U.S.
These datasets were used in conjunction with the soil mapping database provided by
the Soil Conservation Service (SCS), the data sets from the National Oceanic and
Atmospheric Administration, and simulations from the HELP model [174], The data
for the hypothetical scenario in this case study were adapted from a data set provided
by Hydrogeologic Inc.
5.4.3	Implementation of the Monte Carlo Method in EPACMTP
The Monte Carlo method requires that for each input parameter that has associated
uncertainty or variability, a probability distribution (or a frequency distribution) be
provided. The method involves the repeated generation of pseudo-random values of
the uncertain input variables (drawn from the known distribution and within the
range of any imposed bounds) and the application of the model using these values to
generate a set of model responses or outputs (for example, the receptor well concen-
tration, Crw)• These responses are then statistically analyzed to yield the probability
distribution of the model output. The various steps involved in the application of a
Monte Carlo simulation are:
•	selection of representative cumulative probability distribution functions for the
relevant input variables,
•	generation of a pseudo-random number from the selected distributions, repre-
senting a possible set of values (a realization) for the input variables,
•	checking whether the realization satisfies the constraints or bounds on the dis-
tribution (and resampling till a reasonable realization is obtained),
•	application of the model to compute the derived inputs and outputs,
•	repeated application of the above steps for a specified number of iterations, and
•	statistical analysis of the series of the output values generated.

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Chapter: V Section: 4	102
Table 5.4: Deterministic Parameters in the EPACMTP model for the case study
Pa ram
Value
Units
Description
Aw
3.5xl05
„_2
m
Area of the source
xw
304.8
m
Length of the source
Yw
1143.0
m
Width of the source
Ir
0.381
m/yr
A real recharge rate
T
17.5
ฐC
Aquifer temperature
A
0.0564
1/yr
Radioactive decay coefficient
ts
20
yr
Source leaching duration
(XT
8.0
m
Transverse dispersivity
dv
160.0
m
Vertical dispersivity
0s
0.45
-
Saturated water content
Dw
10.67
m
Depth to water table
S
0.0086
-
Hydraulic gradient
Rs
1.0
-
Retardation factor
pb
1.65
g/cm3
Bulk density
5.4.4 Uncertainty Analysis
This case study consists of a site based landfill modeling for tritium contamination at
a groundwater well resulting from a hypothetical landfill unit in the southern United
States. The landfill is a finite source, of 0.35 km2 area, with a leaching duration
of 20 years, and a recharge rate of 0.381 m/yr. The receptor well is located at a
distance of 1635 m from the landfill. The EPACMTP model is used to simulate the
radioactive decay and transport of tritium through the saturated and unsaturated
zone underlying the landfill. The data used here are adapted from data provided by
Hydrogeologic Inc.
The probability distributions of the site-specific parameters for the land fill at
the Old Burial Ground are given in Table 5.3, and the values of the constant model
parameters for EPACMTP are given in Table 5.4.

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Chapter: V Section: 4
103
Uncertainty in Max. Tritium Concentration
50
SRSM : 350 Runs 	
1000 Runs 	
5000 Runs 	
40
0
0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
Predicted Maximum Concentration of Tritium At Well N1 (Normalized)
Figure 5.21: Uncertainty in estimated maximum tritium concentration in a receptor
well
5.4.5 Results and Discussion
Figure 5.21 shows the uncertainty associated with the estimation of maximum tritium
concentration in the ground water, as a result of the leaching from the landfill unit.
Figure 5.22 shows the uncertainty associated with the estimated time of occurrence
of the maximum tritium concentration in the ground water at the well. The figures
show the pdf s estimated by the Monte Carlo simulations and by the SRSM. Results of
the SRSM with 350 model runs are compared with those of Monte Carlo simulations
with 1000, 3000 and 5000 model runs.
The results indicate that the SRSM shows close agreement with the Monte Carlo
results, while requiring much fewer number of runs. Further, there are certain other
advantages in using the SRSM, that are focused in the ongoing research:
• The relationships between inputs and outputs can be easily estimated, since
both the input and output pdfs are expressed as a series expansion in srvs.
This implies that SRSM provides insight into the relative contribution of each
uncertain input each output, thus giving extensive sensitivity and uncertainty
information. Conventional Monte Carlo methods do not provide such informa-
tion.
• Correlation between outputs can be easily estimated since all outputs are ex-

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Chapter: V Section: 4
104
Uncertainty in Time of Occurrence of Max. Tritium Concentration
0.12
0.1
0.08
>.
'
C

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Chapter 6
CHARACTERIZATION AND REDUCTION OF
MODEL UNCERTAINTY: AN ATMOSPHERIC
PLUME MODEL STUDY
6.1 Introduction and Background
Uncertainty in the application of transport-transformation models arises not only due
to uncertainty in model inputs or parameters (i.e., parametric or data uncertainty),
but also due to uncertainty in model formulation (i.e., model uncertainty). As men-
tioned in Chapter 1, model uncertainty arises under several conditions, including the
following: (a) when alternative sets of scientific or technical assumptions for devel-
oping a model exist (Model Structure), (b) when models are simplified for purposes
of tractability (Model Detail), and (c) when a coarse numerical discretization is used
to reduce the computation demands of the model (Model Resolution). Some of the
major sources of model uncertainty in transport-transformation modeling are listed
in Table 2.1.
When a model application involves both model and data uncertainties, it is im-
portant to identify the relative magnitudes of the uncertainties associated with data
and model formulation. Such a comparison is useful in focusing resources where it is
most appropriate (e.g., data gaps versus model refinement).
The approach followed here for characterizing model uncertainty here is based on
the development of models corresponding to different model formulations subsequent
comparison of the model results. The availability of the alternative models, ranging
from simplified to more detailed, aids in evaluating the applicability of the low reso-
lution models - if the results of the low resolution models agree closely with those of
the high resolution models, then the low resolution models are preferable, since they
typically require fewer computational resources and lesser input data. Furthermore,
the availability of the alternative models aids in the characterization of the model
uncertainty associated with the application of those models - the bounds on model
calculations corresponding to different model formulations provide an estimate of the
uncertainty associated with the model formulation. For example, if the bounds are
narrow the uncertainty associated with the model formulation can be considered to
be relatively small.
This chapter focuses on the model uncertainties associated with photochemical air
105

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Chapter: VI Section: 2
106
quality modeling applications. Specifically, the uncertainties associated with model
resolution and assumptions of "well mixedness" are addressed here. A case study is
presented involving an EPA regulatory atmospheric photochemical trajectory model,
the Reactive Reactive Plume Model (RPM) [189], which is briefly described in Sec-
tion 5.2, and is described in more detail in the following sections.
As mentioned in Section 5.2, the RPM describes the evolution of a photochemical
plume from "point sources" of emissions, such as the stacks of refineries and of power
plants. The currently used version of RPM, RPM-IV, lacks vertical resolution, as
the model assumptions include uniform mixing in the vertical direction; the lack of
resolution is highlighted in Figure 6.3. Here, two aspects of the model are studied:
(a) the uncertainty associated with the assumption of uniform vertical concentration
profile, and (b) the uncertainty associated with the choice of horizontal resolution.
In this process, the results of the RPM at varying horizontal resolutions are studied
first. Then a "three-dimensional version" of the RPM, termed RPM-3D, is developed
by incorporating vertical resolution into the RPM-IV. The results from the RPM and
the RPM-3D are subsequently compared with each other in order to characterize the
uncertainty associated with the vertical resolution in the RPM.
The relevant background information on photochemical modeling, required for a
detailed understanding of the RPM, is presented here. That is followed by a descrip-
tion of the formulation of RPM-IV and RPM-3D.
6.2 Photochemical Air Pollution Modeling
The main objective of Photochemical Air Quality Simulation Models (PAQSMs) is
to establish causal relations among emission levels, meteorological conditions and
ambient air quality. These are numerical models that simulate the transport and
chemical transformation of pollutants in the atmosphere. Air quality can be modeled
via prognostic modeling, that is based on the fundamental physiochemical principles
governing air pollution, and as via diagnostic modeling, that is statistical description
of observed data.
These models have been applied to local scale (e.g., plume models), to urban
and regional scales (e.g., airshed models). The applications of PAQSMs range from
simulation of transport and transformation of chemicals over a few kilometers over
few hours (local scale) to over a few thousand kilometers over a few weeks (regional
scale).
In the application of PAQSMS, one of the main pollutants of concern is ozone, as
mentioned in Chapter 5.2. Ozone is a "secondary pollutant" formed through nonlinear
chemical reactions between precursor species, oxides of nitrogen (NOx) and volatile
organic compounds (VOCs) that are emitted by a wide variety of both anthropogenic
and natural (biogenic) emissions sources. Certain meteorological conditions can sig-
nificantly enhance the ambient concentrations of ozone in specific locales, leading

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Chapter: VI Section: 2
107
to substantial health risks [75,176]. The PAQSMs are employed to study the effec-
tiveness of various alternative strategies for reductions in emissions with respect to
reductions in ambient ozone levels.
6.2.1 Mathematical and Numerical Formulation of PAQSMs
Many PAQSMs are based on the fundamental description of atmospheric transport
and chemical processes. The atmospheric diffusion equation, given by
d{ci) _ d{d) _ d f d{d}\	/
~sr+u'~si~ - d7s {)+ R'( ฆ ฆ ฆ ฆ • M) + Si  (fU)
is typically employed to describe the pollutant concentration in a selected control
volume. This equation expresses the conservation of mass of each pollutant in a tur-
bulent fluid in which chemical reactions occur. In the above equation, (•) represents
the ensemble average, Ci is the concentration, Ri is the rate of formation of the zth
species, Kjj is the turbulent dispersion coefficient (obtained by approximating turbu-
lent transport with a gradient diffusion scheme), and Si is the rate of emissions of the
zth species into the control volume. The Einstein summation convention is used here
with respect to the subscript j.
The assumptions leading to the atmospheric diffusion equation above include: (a)
first order closure approximation (i.e., turbulent fluxes are approximated by a gradient
driven fluxes), (b) negligence of molecular diffusion compared to turbulent dispersion,
(c) incompressibility of the atmosphere, and (d) approximation of ensemble average
of reaction rates with the reaction rates for ensemble averages.
Air quality models can be broadly categorized into the following types:
Box models: These are the simplest of the numerical models for air quality mod-
eling. The region to be modeled is treated as a single cell, or box, bounded by the
ground at the bottom, and some upper limit to the mixing on the top, and the east-
west and north-south boundaries on the sides. Here, the pollutant concentrations
in a volume of air, a "box", are spatially homogeneous and instantaneously mixed.
Under these conditions, the pollutant concentrations can be described by a balance
among the rates they are transported in and out of the volume, their rates of emis-
sions, and the rates at which pollutants react chemically or decay. Since these models
lack spatial resolution, they cannot be used in situations where the meteorological or
emissions patterns vary significantly across the modeling region.
Grid Models: Grid models employ a fixed Cartesian reference system and divide
the modeling region into a two- or three-dimensional array of uniform grid cells.
Horizontal dimensions of each cell usually measure on the order of kilometers, while
vertical dimensions can vary from a few meters to a few hundred meters. One example
of the grid models is the Urban Airshed Model (UAM), briefly described in Section 5.3.

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Chapter: VI Section: 3
108
Grid models as currently designed are generally not capable of resolving pollutant
concentrations at the microscale - that is, at scales smaller than the size of a grid cell.
Thus, important local effects, such as high pollutant concentrations in the immediate
vicinity of point sources, tend to become obscured by the averaging of pollutant
concentrations over the grid cell volume.
There are practical and theoretical limitations to the minimum grid cell size.
Increasing the number of cells increases computing and data acquisition effort and
costs. In addition, choice of a grid cell implies that the input data, such as the
wind flows, turbulence, and emissions, are resolved to that scale. In practice, most
applications employ grid cell sizes of a few kilometers. Further, the assumptions
involved in the formulation of the atmospheric diffusion equation make it applicable
for grid models with horizontal resolution coarser than 2 km [134],
These models are used to assess the effectiveness of emissions reductions at a
urban/regional scale. In essence, considering the domain-wide effects of emissions
reductions. As these models assume uniform conditions within each grid cell, all the
local scale processes are lumped into one average quantity.
Trajectory Models: The trajectory models use a moving coordinate approach to
describe pollutant transport. Here, the atmospheric diffusion equation is solved in an
air parcel of interest, which is assumed to travel solely with the horizontal wind. These
models give results only on the path of traversed by the air parcel described, and do
not permit construction of the spatial and temporal variation of concentrations over
the entire region. However, their utility is in the calculation of extreme concentrations
that are typically found in the plumes from point sources.
Since the numerical grids used in grid models are considerably large in size (on
the length scales of a few kilometers), an understanding of the "sub-grid" effects
is important in order to assess local scale effects. In order to describe the sub-grid
phenomena, with specific emphasis on establishing causal relationships between emis-
sions from point sources (such as industrial stacks) and the pollutant concentrations
in the neighborhood of the point sources, plume models are employed. Plume models
are trajectory models that describe short term behavior of the plume of emissions
from a point source. The typical time scales range from 1 hour to 24 hours, with
length scales ranging from a few hundred meters to a few kilometers. Such detail
cannot be achieved by increasing the resolution of grid-based models, since the model
formulation of the grid-based models is not valid for finer resolutions (i.e., less than
2 km length scales). The formulation of the grid models and of the plume models is
discussed extensively in the literature [75,77,139,175,213].
6.3 Formulation of The Reactive Plume Model (RPM)
The Reactive Plume Model, version IV (RPM-IV), is a standard regulatory model
used for estimating pollutant concentrations in the atmosphere, resulting from the

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Chapter: VI Section: 3
109
Touch
Down
L = Depth of mixing layer
Z = Effective stack height
TD
Source
Downwind Distance
Figure 6.1: Schematic depiction of the evolution of an atmospheric plume (adapted
from [189])
emissions from point sources such as industrial stacks [148,189]. It uses either point
source emission estimates or initial plume concentrations as inputs, and calculates
downwind concentrations, as the plume expands. RPM-IV makes use of a diffu-
sion/reaction equation similar in form to the atmospheric diffusion equation (Equa-
tion 6.1), but with dispersion parameters that evolve with time. This model considers
a control volume that moves downwind along the plume trajectory, and solves the
diffusion/reaction equation by considering the nonlinear photochemistry and diffusion
of chemical species from one cell into another cell.
In this model, the plume is modeled by a set of "cells" consisting of equal masses
of pollutants, by assuming a Gaussian distribution of the pollutant mass along the
plume centerline. As the plume expands, the individual cells expand in volume cor-
respondingly. The transfer of pollutant mass across cell boundaries is modeled in two
phases: (a) an "entrainment" phase, where the expanding cell boundaries entrain
the pollutants from other cells, and (b) a "detrainment" phase, where the pollutants
diffuse across cell boundaries due to concentration gradients. Further, the cells are
modeled to expand in a manner such that the amount of an inert species remains
constant within each cell. The expansion of the boundaries, and the governing equa-
tions for pollutant concentrations within each cell are as described in the following
section.

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Chapter: VI Section: 3
110
Plume Centerline
Entrainment
Detrainment
• •
• •
o
o

j = -2
j = -l

o
o
j = l
j = 2
t = t
\
/ i \
1

•
•

1
> •
o
•
•
o
• >
1 •
o
•
•
o
• 1
1

•
•

1
j = -2
j =
-1
j = l

]=2
t = tQ+ dt
t = tQ+ dt
Figure 6.2: Schematic depiction of entrainment and detrainment steps simulated in
the RPM (adapted from [189])
6.3.1 Initial Physical Dimensions of the Plume
The total width W(s) and the total depth H(s) of the photochemical plume can be
estimated as functions of the horizontal and vertical dispersion parameters, as follows:
W(s) = fx crx(s) and H(s) = fyaz(s),	(6.2)
where s is the total distance traveled by the plume up to time t under consideration,
and fx, and fz are selected as cutoff values of the plume boundaries. Based on the
Gaussian distribution of initial plume mass [175], fx and fz indicate the amount of
pollutant mass that is included in the model boundaries, as functions of standard
normal quantiles. A typical value of fx = fy = 4.0 implies that 95.4% of the total
pollutant mass is included within the plume boundaries.
For a plume described by M cells in the horizontal direction, with only one cell in
the vertical, at the start of the simulation each cell contains equal amount of pollutant
mass. Since the pollutant mass is assumed to follow a Gaussian distribution, the width

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Chapter: VI Section: 3
111
of the cell j, Wj, can be calculated using the recursive equation
2
M
erfte)-erfte)' (6-3)
where Wj is the width of the jth cell [189].
6.3.2 RPM-IV Model Equations
Based on mass balance, the governing equation of the Reactive Plume Model is given
by:
B
D
dc) (dc)\	( 1 dwA ,• ( 1 dhj , ,• ฆ ,	.
—- = —1			p Uc - 		-1)uc)+ F	6.4
dt \ dt J chem \WJ ds J \hJ ds
"V	V
A	C
Here, the term (A) describes the change in pollutant concentrations due to chem-
ical transformation, term (B) the dilution as the jth cell expands in the horizontal,
term (C) the dilution as the jth cell expands in the vertical, and term (D) the net
flux of the zth contaminant into the cell.
In this equation, c* denotes the concentration of chemical species i in cell j, u
denotes the speed at which the cells travel downwind, and Wj and hj denote the
width and height of the jth, respectively. Additionally, F- is the net flux into the
cell, and is given by F- = Fj — Dj, where F] is the entrainment as the cell expands,
and Dlj is the detrainment due to the diffusion of the species across the boundaries of
the cell. Figure 6.2 describes the process of entrainment and detrainment in detail.
Expressions for entrainment and detrainment are given by the following equations:

Di_9(KdC\_ 2 f f Q,, Q \	/
J dy{ dy) ,n ,n , I	//, J ? 1U //, 2
where yj is the distance from the plume centerline to the far most side of the cell
j, in the horizontal. An additional condition is imposed on the expansion of the cells:
the cells are allowed to expand in a way such that the amount of an inert species
remains constant within each cell, i.e., no net transfer of an inert species occurs from
a cell. This condition results in the equation E1- — D1- = 0, for the expansion of the

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Chapter: VI Section: 3
112
cells, as the plume travels downwind. This condition leads to a recursive relationship
to compute Kj, as follows:
i-1
^i\ r1 fdyj-l\ r1 = ? I k I ฐIj+1 ~ ฐIj 1 k I ฐIj ~
dt ) 3+1 V dt ) 3 | 3 ^-+1 - Vj_x J J_1 y Vj - Vj—2
where Cj is the concentration of an inert species I in the jth cell. Further,
the condition of zero concentration gradient at the plume centerline results in the
equation:
k - ( c2 V2 ~ Vo \ dyi	rfi
The values of Kj can then be recursively obtained using the above two equations.
Once KjS are computed, Equations 6.4, 6.5, and 6.6 can be used to compute the
concentration in each cell at different times.
In RPM-IV, the nonlinear plume photochemistry is modeled through the carbon-
bond mechanism, version IV (CB-IV) [206]. CB-IV describes the complex non-linear
gas-phase atmospheric photochemistry through a set of 95 reactions among 35 surro-
gate chemical species corresponding to organic bonds/functional groups [206]. This
mechanism lumps similarly bonded carbon atoms, resulting in a condensed mecha-
nism of that is used widely in the regulatory photochemical models.
6.3.3 Limitations of the RPM-IV
Figure 6.1 presents a schematic depiction of the physical structure of a plume as
it evolves from a stack, and travels downwind. The assumption of uniform verti-
cal concentration in the RPM (hence, only one "well mixed" cell in the vertical) is
valid only at distances much greater than Xtd from the point source. This poses a
limitation, since the typical touchdown distance could vary between a few hundred
meters to a few kilometers, depending on the meteorological conditions, the height
of the stack, and the exit velocity at the plume source. When the value of Xtd is
large, the well-mixedness assumption is not valid at the vicinity of the plume, thus
lessening the main advantage of using a plume model to study the "sub-grid" scale,
local phenomena. In such cases, if the RPM is used with the uniform vertical mixing
assumption, significant "errors" could result in the model calculations.
In order to address this limitation of the RPM-IV, a corresponding "three-
dimensional" version of the RPM, called RPM-3D, is developed as part of this work.
This RPM-3D simulates the evolution of a plume by dividing the plume cross-section
into rectangular regions consisting of equal initial pollutant mass; the plume cross
section is divided into columns of cells, as in the case of RPM-IV, and each column
is further subdivided into rows.

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Chapter: VI Section: 4
113


	




I
II
1
1


C'
B'
A' A
B
C


i
i
i
i




i
i


	
	
••*#>•• i ••
	
	


i







R3
B'
R2
jA"
Y'
_x:
X"
Y
Z
RPM-IV
RPM-3D
Figure 6.3: Discretization of the plume cross section by RPM-IV (left) and RPM-3D
(right)
Figure 6.3 illustrates the physical structure of the plume cross section in the RPM-
IV and RPM-3D. In the figure, R1 is the region containing the cell closest to the
plume centerline, R2 contains the ground-level cell along the plume centerline in the
horizontal, and R3 contains the centerline cells with respect to both the horizontal and
the vertical. Here, R1 corresponds to the representation used by RPM-IV, whereas
R2 and R3 correspond to representations used by RPM-3D. In typical cases, the
extreme pollutant concentration occurs along the plume centerline, and the pollutant
concentration decreases as the distance of the cells from the centerline increases.
Hence, the extreme pollutant concentration (e.g., concentration of ozone) calculated
by the RPM-IV is an average over the region Rl. This provides a concentration for
the estimation of exposure to ozone. However, as shown in the figure, the region R2
corresponds to the most likely ground level maximum ozone concentration (typically
covering the region from the ground to up to less than a hundred meters), whereas
the region R3 corresponds to the maximum ozone level concentration at the plume
centerline. Clearly, the RPM-3D provides estimates of pollutant concentrations at a
detailed spatial resolution that is appropriate for estimating human exposure.
6.4 Formulation of the RPM-3D
The equations describing the transport of chemical species in RPM-IV are modified
accordingly to accommodate the three dimensional description of the plume. Thus,
Equation 6.4 is modified as follows:

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Chapter: VI Section: 4
114
dcj,k _ ( dcj,k\	( 1 dwjtk\ i ( 1 dhjtk . j	„
,	,	"f-i t T rVit +
dt ~\dt )n^m \wj>k ds J^'k \hj)k ds
(6.9)
where cj fc is the concentration of the zth species in the cell (j, k); j denotes the cell
number in the horizontal direction, and k denotes the cell number in the vertical. Fy • k
and Flz • fc denote the fluxes along the horizontal and vertical directions respectively.
The equations for the calculation of the fluxes, are similar to Equations 6.5 and 6.6,
and are as follows:
T?l			T?% T^l
rY,j,k	~	UYj,k
jji		 en 	 r\i
Z,j,k	~	^ZJ.k Z,j,k
^	=	(6'10)
pi	_	^ j ( cj+l,k ~ Cj,k j-r (Cj,k ~ Cj-l,k
F'y.j.k	— 	 \ y,j,k 	 ~ KY,j-l,k 	
Vj,k Uj-l,k ^	yj/j+l,fc Uj-l,k J	\ Vj,k Uj—2,k
ZM Zi,k-:,,k-1 H dt J 1Ml \ dt )
Dh,k = 	^	(I* ) - KZJ,k.. (d'-k ~ d,'k->
Zj,k Zj,k—1 I	\ Zj,k+1 Zj,k—1 J	\^j,k ^j,k—2
where yj>k, and Zj>k denote the distances from the plume centerlines to the far most
sides, in the horizontal and vertical directions respectively. KZjtk and KYj,k are solved
using recursive relationships similar to Equation 6.7: the relationships are obtained
from the following conditions: (a) Fyjk and FZ j k are equal to zero for an inert species
/, (b) there is a zero concentration gradient at the plume centerline in both horizontal
and vertical directions.
The RPM-IV uses the Gear algorithm [74] to solve the stiff set of equations de-
scribing the chemical reactions. In this method, the step size for each successive
calculation is adjusted according to the local truncation errors. The same approach
is followed in the RPM-3D.

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Chapter: VI Section: 5
115
0.13
0.125
0.12
0.115
11
0.105
0.1
0.095
0.09
2 Cells
4 Cells
6 Cells
8 Cells
10 Cells
0.085
0.08
0.075
0
20
40
Plume Evolution Time (min)
60
80
100
120
Figure 6.4: Dependence of plume average 03 concentration on horizontal resolution
of the RPM-IV in a VOC dominant regime
6.5 Case Studies
The RPM-IV and RPM-3D are both applied to two significantly different case studies.
An attempt is made to understand the extent of the uncertainty associated with the
model assumptions and model resolution, by comparing the predictions of these two
models.
The first case study uses emission estimates and field data measured near
Marathon Oil Refinery at Robinson, Illinois, during June and July, 1977 [177]. This
case study involves a primarily VOC dominant source (i.e., the volatile organic com-
pounds emissions are significantly higher than the NO/NO2 emissions). The me-
teorological data and initial plume concentrations were obtained from the work of
Georgopoulos and Roy [76], which was based on the measurements reported by Sex-
ton et al. [177].
Figure 6.4 shows the average ozone concentrations in the plume, calculated by
RPM-IV, as a function of the plume evolution time for different horizontal resolutions,
ranging from two cells to ten cells in the horizontal. Similarly, Figure 6.5 shows the
average plume concentrations calculated by RPM-3D for different vertical resolutions
with four cells in the horizontal. The calculations from RPM-IV, which has no vertical
resolution, and RPM-3D with vertical resolution corresponding to two cells, four
cells, and six cells, are shown in the figure. The results indicate that the different
vertical and horizontal resolutions produce similar estimates of the average plume

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Chapter: VI Section: 5
116
0.13
0	20	40	60	80	100	120
Plume Evolution Time (min)
Figure 6.5: Dependence of plume average 03 concentration on vertical resolution of
the RPM-3D in a VOC dominant regime (4 horizontal cells)
0.14
0.13
0.12
E
CL
CL
c
o
ro
c
<1)
O
C
o
O
0.09
Plume Average
Outermost Horizontal Cell
Middle Horizontal Cell
Centerline Cell
0.08
0.07
0
20
40
60
80
100
120
Plume Evolution Time (min)
Figure 6.6: Horizontal profile of the O3 concentration in the plume in a VOC dominant
regime (6 horizontal cells, RPM-IV)
concentrations. Hence, it is sufficient to use a low-resolution two-dimensional model,
for studying the plume average concentrations in such cases.
Figure 6.6 shows the ozone concentrations within each horizontal cell calculated

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Chapter: VI Section: 5
117
Centerline Cell (Horizontal)
Outermost Vertical Cell
Middle Vertical Cell
Centerline Cell
Plume Evolution Time (min)
Figure 6.7: Vertical profile of the 03 concentration at the plume centerline (w.r.t.
horizontal) in a VOC dominant regime (4 horizontal cells and 6 vertical cells, RPM-
3D)
by RPM-IV when the plume cross section is divided into six cells. The plots indicate
the average ozone concentrations in the different horizontal cells (indicated by "A",
"B", and "C" in Figure 6.3 for RPM-IV). Similarly, Figure 6.7 shows the vertical
profile of the ozone concentrations calculated by RPM-3D for the centerline horizontal
cell when the plume is divided into four horizontal cells and each cell is in turn
divided into six vertical cells. The plots indicate the average of the vertical cells
along the horizontal centerline, and individual vertical cells at the horizontal line.
The cells are illustrated in Figure 6.3. The horizontal centerline cell is given by
"A", and the corresponding vertical slices are "AZ" (outermost), "AY" (middle),
and "AX" (centerline). Examining the results, it is clear that in a NOx dominant
regime, this would clearly produce an under-estimation of the ground level ozone
levels. Examining the results, it is clear that by ignoring the vertical structure of a
plume, the RPM-IV approximates the highest possible ground level concentration by
the average in the vertical. Hence, in a VOC dominant regime, this would clearly
produce an over-estimation of the ground level ozone levels.

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Chapter: VI Section: 5
118
0.08
0.07

0.06
0.05
0.04
0.03
0.02
2 Cells
4 Cells
6 Cells
8 Cells
10 Cells
0.01
0
20
40
60
80
100
Plume Evolution Time (min)
Figure 6.8: Dependence of plume average 03 concentration on horizontal resolution
of the RPM-IV in a NOx dominant regime
The second case study considers a single point source in Mercer County, New
Jersey. The simulation is performed for the period of July 7, 1988, 8:00 am to
10:00 am, corresponding to the occurrence of a severe ozone episode in the region. This
case study involves a NO/NO2 dominant regime. Ambient pollutant concentrations
were obtained from the results of an Urban Airshed Model (UAM-IV) simulation for
the Philadelphia/New Jersey modeling domain for the time period of July 6 to July 8,
1988, described by Georgopoulos et al. [78]. The meteorological inputs were obtained
from the model inputs of the UAM-IV simulation for the modeling domain [78]. The
model results are analyzed in the same manner as described in the earlier case study
involving a VOC dominant regime.
Figure 6.8 shows the average ozone concentrations in the plume, calculated by
RPM-IV, as a function of the plume evolution time for different horizontal resolutions,
ranging from two cells to ten cells in the horizontal. Similarly, Figure 6.9 shows the
average plume concentrations calculated by RPM-3D for different vertical resolutions
with four cells in the horizontal. The calculations from RPM-IV, which has no vertical
resolution, and RPM-3D with vertical resolution corresponding to two cells, four
cells, and six cells, are shown in the figure. The results indicate that the different
vertical and horizontal resolutions produce similar estimates of the average plume
concentrations. Hence, it is sufficient to use a low-resolution two-dimensional model,
for studying the plume average concentrations in such cases.
Figure 6.10 shows the ozone concentrations within each horizontal cell calculated

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Chapter: VI Section: 5
119
0.08
0.07
0.06
0.05
0.04
0.03
0.02
RPM-IV
2 Cells
4 Cells
6 Cells
0.01
0
20
40
60
80
100
Plume Evolution Time (min)
Figure 6.9: Dependence of plume average 03 concentration on vertical resolution of
the RPM-3D in a NOx dominant regime
0.08
0.07
0.06
0.05
0.04
0.03
0.02
Plume Average
Outermost Horizontal Cell
Middle Horizontal Cell
Centerline Cell
0.01
0
0
20
40	60
Plume Evolution Time (min)
80
100
Figure 6.10: Horizontal profile of the O3 concentration in the plume in a NOx domi-
nant regime (6 horizontal cells, RPM-IV)
by RPM-IV when the plume cross section is divided into six cells. Similarly, Fig-
ure 6.11 shows the vertical profile of the ozone concentrations calculated by RPM-3D

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Chapter: VI Section: 6
120
0.08
ฆfj 0.04

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Chapter: VI Section: 6
121
knowledge is very useful in building an "optimal model", one that produces outputs
similar to more detailed models, but requires much less detailed inputs and much
fewer computational resources. Further, results from models with varying detail pro-
vide an estimate of the range of model calculations, thus helping in characterizing
the uncertainty associated with model formulation and detail.

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Chapter 7
CONCLUSIONS AND DISCUSSION
The application of transport-transformation models involves significant uncertainties
that may have implications on the confidence in model estimates and on their use
in the assessment of possible alternative scenarios in a regulatory framework, for
example, with respect to control strategy selection for decreasing pollutant levels.
Hence, it is important to address these uncertainties. However, the main limitations
in performing comprehensive uncertainty analyses of transport-transformation models
are the associated cost and effort, and the increased data needs for characterizing
uncertainties in inputs. Clearly, there is a need for methods that can reduce the cost
and effort associated with uncertainty analysis of transport-transformation models.
7.1 Development and Application of the SRSM
One of the main contributions of this research is the development of computation-
ally efficient methods for uncertainty propagation, specifically, the development of
the Stochastic Response Surface Method (SRSM) (Chapter 3). The SRSM approxi-
mates the model inputs and the outputs through a series of "well behaved" standard
random variables; the series expansions of the outputs contain unknown coefficients
which are calculated by a method that uses the results of a limited number of model
simulations. The SRSM is based on the Stochastic Finite Element Method [80] and
the Deterministic Equivalent Modeling Method [191].
The SRSM has been implemented as a modular and readily portable stand-alone
tool that can be used by researchers as a black-box tool, without requiring the math-
ematical details of the SRSM. In fact, the SRSM has been implemented as a web-
based tool, that can be accessed through a web browser, as shown towards the end of
Chapter 3. This implementation includes modules for (a) the transformation of non-
standard distributions into functions of the srvs, (b) the construction of polynomial
chaos approximations for the model outputs, (c) the identification of sample points
for model runs, (d) the estimation of the unknown coefficients in the approximations,
and (e) the subsequent calculation of the statistical properties of the model outputs.
Further, mathematical formulae have been developed and presented for the exten-
sion of this method for dealing with arbitrary empirical distributions (with correlated
and uncorrelated inputs), and for the estimation of correlations between two model
outputs, and between a model input and a model output.
122

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Chapter: VII Section: 2
123
The development and the application of the SRSM meets the need for fast and
accurate estimates of the uncertainties associated with the model outputs, and also
the need for identification of inputs that contribute to the output uncertainties the
most. The main impact of this research is the facilitation of fast uncertainty analyses
of complex, computationally intensive models, and the estimation of the relative
contribution of each model input to the uncertainties in the outputs.
The SRSM has been evaluated for case studies involving the following models:
(a)	a biological model (a Physiologically Based PharmacoKinetic (PBPK) model,
i.e., a lumped system described by a set of stiff ODEs) describing the uptake
and metabolism of perchloroethylene in humans,
(b)	a two-dimensional atmospheric photochemical plume model that calculates
pollutant concentrations downwind of point sources, the Reactive Plume
Model (RPM),
(c)	a three-dimensional urban/regional scale air quality model, the Urban Airshed
Model (UAM), version IV, and
(d)	a one-dimensional ground water model with discontinuous probability density
functions, the EPA's Composite Model for leachate Migration and Transforma-
tion Products (EPACMTP).
In the first two cases, the performance of the SRSM was significantly superior
to both the Monte Carlo and the Latin Hypercube Sampling methods - the SRSM
application required up to an order of magnitude fewer simulations for estimating
the uncertainties in the model outputs. In the third case study, involving the Urban
Airshed Model, it was impractical to perform hundreds of Monte Carlo simulations,
whereas, the SRSM required just twenty (20) simulations to estimate the output
uncertainties: this demonstrates the potential of the SRSM to address problems that
are beyond the scope of conventional methods (in terms of computational and time
limitations). In the last case study involving the ground water model, EPACMTP,
the SRSM, as before, required significantly fewer model runs compared to the Monte
Carlo method.
7.2 Development and Application of the SRSM-ADIFOR
In order to further improve the computational efficiency of the SRSM, this method
was coupled with a state of the art automated sensitivity analysis method, AD IFOR
(Automatic Differentiation of FORtran) [17]. ADIFOR produces fast and accurate
estimates of first order partial derivatives of model outputs with respect to model
inputs or any intermediate quantities in the model code. ADIFOR accomplishes this
by rewriting the model code and producing code that calculates first order partial
derivatives. Hence, the coupling of the SRSM and the ADIFOR, where the series

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Chapter: VII Section: 3
124
expansions of the SRSM are used in conjunction with outputs and partial derivatives
from the AD IFOR generated code, provides the potential to substantially reduce the
computer demands.
The coupled method, SRSM-AD IFOR, was applied to two of the case studies used
for evaluating SRSM: a human PBPK model, and the Reactive Plume Model (RPM).
The case studies indicate substantial computer time savings; up to two orders of mag-
nitude reductions in the required number of model simulations compared to "con-
ventional" methods were achieved. This demonstrates the advantages of combining
sensitivity analysis methods with uncertainty propagation methods. The application
of the SRSM-ADIFOR involves processing the original model code using ADIFOR,
and subsequent use of the derivative code for uncertainty analysis. For this reason,
developing a "black-box tool" for using the combined SRSM-ADIFOR approach (as
was done for the "stand-alone" SRSM) is not possible.
7.3	Consideration of Uncertainties Beyond Parametric/Data Un-
certainty
In addition to the uncertainties associated with model inputs, there are often uncer-
tainties associated with model formulation and structure. Model uncertainty arises
mainly when competing models, ranging from simplified to more detailed models,
exist. When a model application involves both model and data uncertainties, it is
important to identify the relative magnitudes of the uncertainties associated with
data and model formulation. Such a comparison is useful in focusing resources where
it is most appropriate (e.g., filling data gaps versus refining a model).
The case study involving the development of a three dimensional version of the
Reactive Plume Model (RPM-3D, Chapter 6) examines the issues associated with
the characterization of uncertainties associated with model formulation. Lack of
sufficient model resolution could in fact result in model predictions that may either
significantly over-estimate or under-estimate the quantities that are important for
decision making. The case study stresses the need for comprehensive hierarchical
models, for the systematic evaluation of the simplified models.
7.4	Directions for Future Work
7.4.1 Improvements to the Stochastic Response Surface Method
In the present form, the SRSM includes modules for the transformation of probability
distributions commonly used to represent uncertainties in the inputs of environmental
and biological models. The transformations are listed in Table 3.1. However, this list
is not extensive, since model inputs can follow one of several other distributions. In
order to extend this list, the methods outlined for transforming random variables that

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Chapter: VII Section: 4
125
follow empirical distributions (Section 3.2.3) and for transforming random variables
that are correlated (Section 3.2.4) can be incorporated into the SRSM tool.
The SRSM calculates probability density functions (pdfs) to quantify the uncer-
tainties in model outputs. Additionally, several other statistical metrics, such as the
cumulative density function, moments of the output metrics, and percentile estimates,
can be calculated. This can be accomplished by following the techniques outlined in
Section 3.5.
Finally, the methods for calculating correlations between outputs and between
an input and an output, presented in Section 3.5, can also be incorporated into the
SRSM tool. This would facilitate the identification of individual contributions of
model inputs to the uncertainties in model outputs, in a more rigorous manner than
that presented in relation to the case study in Chapter 5 (Table 5.2).
These improvements could further enhance the already wide range of applicability
of the SRSM. Furthermore, the same improvements translate into the improvements
in the SRSM-ADIFOR method, and even in the wed-based SRSM tool.
7.4.2	Further evaluation of the SRSM and the SRSM-ADIFOR
Further rigorous, statistically based evaluation of the SRSM and the SRSM-ADIFOR,
that would be based not only on the pdfs of model outputs, but also on the moments,
cumulative densities, percentiles and correlation coefficients, would aid in the wider
acceptance of this method. Additionally, application of the SRSM and the SRSM-
ADIFOR to more complex models could also aid in the identification of areas where
these methods could be further improved.
7.4.3	Addressing uncertainty propagation under constraints
The case study for the evaluation of the SRSM with a groundwater model showed
certain limitations in the application of the SRSM to models with discontinuous prob-
ability distributions. Transport-transformation models sometimes have constraints on
the values the model inputs can assume; often, the constraints are based on the values
of other model inputs. For instance, the sample value of random input x may affect
the range from which another random input y is sampled. While many constraints
are defined in terms of joint pdfs, in some cases, the constraints could follow a dis-
continuous pattern. For example, the diameter and porosity of a particle may not
assume certain combinations in the EPACMTP model (Chapter 5).
The Monte Carlo method can address such constraints by following the rules listed
below:
• generate sample point by randomly sampling all inputs from their respective
distributions ignoring constraints,

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Chapter: VII Section: 4
126
•	ignore the sample point if any constraints are not satisfied, and repeat the above
step till an acceptable sample point is obtained,
•	run the model at the accepted sample point,
•	repeat the above procedure till required number of sample points are obtained,
and
•	statistically analyze the model outputs corresponding to all the sample points.
It must be noted that samples are drawn from the given pelfs, and they also obey
the constraints. That is an advantage of using Monte Carlo methods in a brute-force
manner.
In the SRSM, such an approach does not appear to be readily applicable, because
the inputs are represented as algebraic functions of the srvs. This means that the
inputs have pdf s that are continuous and well behaved (as opposed to the actual pdf s
which are discontinuous). One possible approach that could be followed is suggested
here:*
•	express inputs in terms of srvs, ignoring constraints,
•	approximate the outputs using the polynomial chaos expansions, and
•	generate the sample points for the srvs without considering the constraints,
•	estimate the unknown coefficients in the series expansion through regression on
the model outputs at these sample points, and
•	calculate the statistical properties of the model outputs from the srvs using the
methods listed in Chapter 3.5 and in the process rejecting the combinations of
srvs that do not obey the constraints.
One potential focus of the future efforts could be on the evaluation of this approach, in
addition to the identification of other techniques to address uncertainty propagation
under constraints.
7.4.4 Random processes and random fields
The SRSM, in its present form addresses only random variables, i.e., random quan-
tities that do not vary with time or space. From the perspective of the uncertain-
ties that occur in the nature, the random variables are analogous to points in a
multi-dimensional space. Random processes and random fields are generalizations of
random variables to multi-dimensional spaces.
A random process can be considered as a function of time in the random space,
as
y(t,0
*This approach has not been tested, and its applicability is still unknown.

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Chapter: VII Section: 4
127
where t denotes the time dimension, and ( is used to denote the randomness. Here, a
particular realization of y can be considered as a deterministic function y(t, Q), where
(i denotes one realization of the various possible functions that the random process
y can assume. Further, at a given time, U, y reduces to a random variable. The
relationship between random processes, random variables, deterministic functions,
and deterministic variables can be summarized as follows [155]:
•	x(t, ฃ) is a stochastic process, or a family of time functions,
•	x(U,() is a random variable,
•	x(t,(i) is a single time function, and
•	x(U,(i) is a single number.
Similarly, random fields are random functions of spatial coordinates and time.
Random processes and random fields are common in environmental and biological
systems: for example, the flow rates or emissions from a point source are random
processes, and emissions from area sources, wind patterns over a domain are examples
of random fields. Further research could focus on extending the SRSM so that it can
address uncertainty propagation involving model inputs defined by random processes
and random fields.
7.4.5 Uncertainties Associated with Evaluation Data
Characterization of uncertainty associated with the evaluation data is an important
component of a comprehensive uncertainty analysis. The uncertainties in the evalua-
tion data may sometimes in fact impact the selection of an appropriate model from a
set of alternative models. Comparisons of parametric and evaluation data uncertain-
ties can provide insight into where available resources must be focused (input data
versus evaluation data). Various techniques (such as those used in the spatio-temporal
analysis of environmental data, e.g., Vyas and Christakos [35,202]) could be explored
for their potential to improve the characterization of uncertainties in environmental
databases that are used to evaluate transport-transformation models.

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