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E-grāmata: Modeling and Simulation in Ecotoxicology with Applications in MATLAB and Simulink

(The Institute of Environmental and Human Health, Lubbock, USA)
  • Formāts: 270 pages
  • Izdošanas datums: 19-Apr-2016
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781040161395
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Modeling and Simulation in Ecotoxicology with Applications in MATLAB and SimulinkPalielināt
  • Formāts: 270 pages
  • Izdošanas datums: 19-Apr-2016
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781040161395
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Exploring roles critical to environmental toxicology, Modeling and Simulation in Ecotoxicology with Applications in MATLAB® and Simulink® covers the steps in modeling and simulation from problem conception to validation and simulation analysis. Using the MATLAB and Simulink programming languages, the book presents examples of mathematical functions and simulations, with special emphasis on how to develop mathematical models and run computer simulations of ecotoxicological processes.

Designed for students and professionals with little or no experience in modeling, the book includes:











General principles of modeling and simulation and an introduction to MATLAB and Simulink Stochastic modeling where variability and uncertainty are acknowledged by making parameters random variables Toxicological processes from the level of the individual organism, with worked examples of process models in either MATLAB or Simulink Toxicological processes at the level of populations, communities, and ecosystems Parameter estimation using least squares regression methods The design of simulation experiments similar to the experimental design applied to laboratory or field experiments Methods of postsimulation analysis, including stability analysis and sensitivity analysis Different levels of model validation and how they are related to the modeling purpose

The book also provides three individual case studies. The first involves a model developed to assess the relative risk of mortality following exposure to insecticides in different avian species. The second explores the role of diving behavior on the inhalation and distribution of oil spill naphthalene in bottlenose dolphins. The final case study looks at the dynamics of mercury in Daphnia that are exposed to simulated thermal plumes from a hypothetical power plant cooling system.

Presented in a rigorous yet accessible style, the methodology is versatile enough to be readily applicable not only to environmental toxicology but a range of other biological fields.
Preface xiii
Acknowledgments xv
About the Author xvii
Chapter 1 Introduction
1(8)
1.1 Theories Underlying Predictive Models
1(1)
1.3 Reasons for Modeling and Simulation
2(3)
1.2.1 Alternatives and Their Consequences
3(1)
1.2.2 Relative Predictive Ability
3(1)
1.2.3 Instruction
3(1)
1.2.4 Hypothesis and Theory Construction
3(1)
1.2.5 Nonexistent Universes
4(1)
1.2.6 Cost
4(1)
1.2.7 Planning and Management Decision Aid
4(1)
1.2.8 System Identification
4(1)
1.2.9 Unanticipated Effects
5(1)
1.3 What Does It Take To Be a Modeler?
5(1)
1.4 Why Models Fail: A Cautionary Note
6(3)
1.4.1 Poor Data for Parameter Estimation
6(1)
1.4.2 Uncertainty Not Considered
6(1)
1.4.3 Bias (Political, Social, Economic)
6(1)
1.4.4 Lack of Understanding of Real-World Systems
6(1)
1.4.5 Misuse of Mathematics
7(1)
References
7(2)
Chapter 2 Principles of Modeling and Simulation
9(22)
2.1 Systems
9(2)
2.1.1 Definition
9(1)
2.1.2 System Input and Output
9(1)
2.1.3 Control Systems
9(1)
2.1.4 Feedback
10(1)
2.1.5 System States: Steady State versus Transient States
10(1)
2.1.6 Discrete versus Continuous
10(1)
2.1.7 Linear versus Nonlinear
11(1)
2.2 Modeling
11(10)
2.2.1 Equations
11(3)
2.2.1.1 Solution of Ordinary First-Order Differential Equations
14(2)
2.2.1.2 Steady-State and Transient Response
16(1)
2.2.1.3 Difference Equation Approximation to Differential Equation
16(1)
2.2.1.4 Numerical Solutions to Differential Equations
17(1)
2.2.2 Block Diagrams
18(3)
2.2.3 Stochastic Models
21(1)
2.2.4 Individual-Based Models
21(1)
2.2.5 Aggregated Models
21(1)
2.3 Simulation
21(10)
2.3.1 Principles of Simulation
23(1)
2.3.1.1 Principle of Communication
23(1)
2.3.1.2 Principle of Modularity
23(1)
2.3.1.3 A Modified Principle of Parsimony
23(1)
2.3.2 Steps in Simulation
23(1)
2.3.2.1 Problem Definition
24(1)
2.3.2.2 Model Development
24(1)
2.3.2.3 Model Implementation
24(3)
2.3.2.4 Data Requirements
27(1)
2.3.2.5 Model Validation
27(1)
2.3.2.6 Design of Simulation Experiments
28(1)
2.3.2.7 Analyze Results of Simulation Experiments
28(1)
2.3.2.8 Presentation and Implementation of Results
28(1)
References
28(3)
Chapter 3 Introduction to MATLAB® and Simulink®
31(16)
3.1 MATLAB
31(12)
3.1.1 Matrix Algebra
32(2)
3.1.2 Data Input
34(3)
3.1.3 Solving Equations
37(2)
3.1.4 Saving Data
39(1)
3.1.5 Plotting Data
40(3)
3.2 Simulink
43(4)
Exercises
44(1)
References
45(2)
Chapter 4 Introduction to Stochastic Modeling
47(26)
4.1 Introduction to Probability Distributions
47(3)
4.2 Example Probability Distributions
50(15)
4.2.1 Continuous Distributions
50(1)
4.2.1.1 Uniform
50(1)
4.2.1.2 Exponential
51(1)
4.2.1.3 Gamma
51(2)
4.2.1.4 Weibull
53(1)
4.2.1.5 Normal
53(1)
4.2.1.6 Lognormal
54(1)
4.2.1.7 Beta
54(2)
4.2.1.8 Triangular
56(1)
4.2.1.9 Logistic
56(2)
4.2.2 Discrete Distributions
58(1)
4.2.2.1 Bernoulli
58(1)
4.2.2.2 Binomial
59(1)
4.2.2.3 Discrete Uniform
60(1)
4.2.2.4 Geometric
60(2)
4.2.2.5 Negative Binomial
62(1)
4.2.2.6 Poisson
62(2)
4.2.3 Empirical Distributions
64(1)
4.3 Discrete-State Markov Processes
65(4)
4.4 Monte Carlo Simulation
69(4)
Exercises
71(1)
References
72(1)
Chapter 5 Modeling Ecotoxicology of Individuals
73(36)
5.1 Toxic Effects on Individuals
73(36)
5.1.1 The Dose-Response Relationship
73(1)
5.1.1.1 Quantal Response
73(5)
5.1.1.2 Graded Response
78(1)
5.1.2 Toxicokinetics
78(1)
5.1.3 Physiological Processes
79(1)
5.1.3.1 Uptake
79(3)
5.1.3.2 Absorption
82(4)
5.1.3.3 Distribution
86(1)
5.1.3.4 Excretion
87(1)
5.1.4 Biological Processes
88(1)
5.1.4.1 Reproduction
88(2)
5.1.4.2 Growth
90(5)
5.1.4.3 Death
95(3)
5.1.4.4 Movement
98(2)
5.1.4.5 Homeostasis
100(5)
Exercises
105(1)
References
106(3)
Chapter 6 Modeling Ecotoxicology of Populations, Communities, and Ecosystems
109(16)
6.1 Effects of Toxicants on Aggregated Populations
109(4)
6.2 Effects of Toxicants on Age-Structured Populations
113(2)
6.3 Effects of Toxicants on Communities
115(4)
6.4 Effects of Toxicants on Ecosystems
119(6)
Exercises
123(1)
References
124(1)
Chapter 7 Parameter Estimation
125(22)
7.1 Linear Regression
125(11)
7.1.1 Function: regress
126(3)
7.1.2 Function: polyfit
129(3)
7.1.3 Function: regstats
132(4)
7.2 Nonlinear Regression
136(8)
7.2.1 Function: nlinfit
136(8)
7.3 Comparison between Linear and Nonlinear Regressions
144(3)
Exercises
145(1)
References
145(2)
Chapter 8 Designing Simulation Experiments
147(12)
8.1 Factorial Designs
147(4)
8.1.1 Full Factorial Designs
147(2)
8.1.2 Fractional Factorial
149(2)
8.2 Response Surface Designs
151(8)
8.2.1 Central Composite Designs
152(3)
8.2.2 Box-Behnken Designs
155(3)
Exercises
158(1)
References
158(1)
Chapter 9 Analysis of Simulation Experiments
159(16)
9.1 Simulation Output Analysis
159(3)
9.1.1 Types of Simulations
159(1)
9.1.2 Output Analysis Methods
159(3)
9.2 Stability Analysis
162(4)
9.2.1 Linear Systems
163(2)
9.2.2 Nonlinear Systems
165(1)
9.2.3 Relative Stability
165(1)
9.2.4 Resilience
166(1)
9.3 Sensitivity Analysis
166(2)
9.4 Response Surface Methodology
168(7)
Exercises
173(1)
References
174(1)
Chapter 10 Model Validation
175(16)
10.1 Validation and Reasons for Modeling and Simulation
175(1)
10.2 Testing Hypotheses
176(3)
10.2.1 Accept the Null Hypothesis When It Is True
177(1)
10.2.2 Reject the Null Hypothesis When It Is True
177(1)
10.2.3 Accept the Null Hypothesis When It Is False
177(1)
10.2.4 Reject the Null Hypothesis When It Is False
177(1)
10.2.5 Accept the Null Hypothesis When It Is True
178(1)
10.2.6 Reject the Null Hypothesis When It Is True
178(1)
10.2.7 Accept the Null Hypothesis When It Is False
178(1)
10.2.8 Reject the Null Hypothesis When It Is False
179(1)
10.3 Statistical Techniques
179(1)
10.4 Some MATLAB Methods
180(11)
10.4.1 Paired t-test
180(1)
10.4.2 Wilcoxon Nonparametric Signed Rank Test
180(3)
10.4.3 Linear Regression
183(1)
10.4.4 Theil's Inequality Coefficient
184(2)
10.4.5 Analysis of Variance
186(1)
10.4.6 Kruskal-Wallis Nonparametric ANOVA
186(3)
Exercises
189(1)
References
190(1)
Chapter 11 A Model to Predict the Effects of Insecticides on Avian Populations
191(18)
11.1 Problem Definition
191(1)
11.2 Model Development
191(1)
11.3 Model Implementation
191(5)
11.3.1 Model Description
192(1)
11.3.1.1 Ingestion in Food
192(1)
11.3.1.2 Consumption of Chlorpyrifos Granules
193(1)
11.3.1.3 Avian Loss Rates
194(1)
11.3.1.4 Mortality
195(1)
11.3.2 Model Structure Validation
195(1)
11.3.3 Programming the Computer Code
196(1)
11.4 Data Requirements
196(4)
11.4.1 Ingestion
196(1)
11.4.1.1 Proportion of Components in Diet
196(1)
11.4.1.2 Granule Consumption Rate
196(1)
11.4.1.3 Time Spent in Treated Areas
197(1)
11.4.1.4 Residues in Diet Components
198(1)
11.4.2 Avian Loss Rates
199(1)
11.4.3 Mortality
199(1)
11.5 Model Validation
200(1)
11.6 Design Simulation Experiments
200(1)
11.7 Analyze Results of Simulation Experiments
201(8)
11.7.1 Predicted Dose
201(1)
11.7.1.1 Ring-Necked Pheasant
201(1)
11.7.1.2 Northern Bobwhite
201(1)
11.7.1.3 Red-Winged Blackbird
201(2)
11.7.1.4 House Sparrow
203(1)
11.7.2 Predicted Mortality
203(4)
References
207(2)
Chapter 12 Case Study: Predicting Health Risk to Bottlenose Dolphins from Exposure to Oil Spill Toxicants
209(14)
12.1 Problem Definition
209(1)
12.2 Model Development
209(2)
12.3 Model Implementation
211(5)
12.3.1 Differential Equations
211(5)
12.4 Data Requirements
216(1)
12.5 Model Validation
217(1)
12.6 Design of Simulation Experiments
217(1)
12.7 Analyze Results of Simulation Experiments
217(3)
12.7.1 Simulation Output
217(2)
12.7.2 Sensitivity Analysis
219(1)
12.8 Presentation and Implementation of Results
220(3)
References
222(1)
Chapter 13 Case Study: Simulating the Effects of Temperature Plumes on the Uptake of Mercury in Daphnia
223(16)
13.1 Problem Definition
223(1)
13.2 Model Development
223(1)
13.3 Model Implementation
223(2)
13.4 Data Requirements
225(8)
13.4.1 Plot Data
225(1)
13.4.2 Plot Edited Data
226(1)
13.4.3 Estimate Model Parameters
226(1)
13.4.4 Gross Uptake Model
227(1)
13.4.5 Estimate Parameters for Gross Uptake Model
228(2)
13.4.6 Differential Equation for Mercury Dynamics
230(1)
13.4.7 Parameters as Functions of Temperature
230(1)
13.4.8 Estimate Thermal Plume Temperatures
231(2)
13.5 Model Validation
233(1)
13.6 Design of Simulation Experiments
233(2)
13.7 Analyze Results of Simulation Experiments
235(1)
13.8 Presentation and Implementation of Results
235(4)
References
237(2)
Index 239
Dr. Kenneth R. Dixons current research interests include developing and applying computer simulation models to predict the movement of toxic chemicals in the environment and their effects on human and wildlife populations. He also studies the spatial distribution of toxicants and effects at ecosystem, landscape, and regional scales by integrating models with geographic information systems. Current research projects include developing food-chain models to predict the uptake and effects of pesticides, perchlorate, and explosives; developing spatial models of the spread of infectious diseases; and a mathematical programming model of the effects of pollutants on optimal feeding strategies. Dr. Dixon has taught courses in modeling, geographic information systems, ecosystems analysis, biometry, and wildlife management.
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