ISBN-10:
1118059972
ISBN-13:
9781118059975
Pub. Date:
07/22/2013
Publisher:
Wiley
GIS Based Chemical Fate Modeling: Principles and Applications / Edition 1

GIS Based Chemical Fate Modeling: Principles and Applications / Edition 1

by Alberto Pistocchi
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Product Details

ISBN-13: 9781118059975
Publisher: Wiley
Publication date: 07/22/2013
Pages: 504
Product dimensions: 8.80(w) x 11.10(h) x 1.20(d)

About the Author

ALBERTO PISTOCCHI, MSc Eng, MSc Phil, PhD, is AdjunctProfessor of Spatial Decision Support Systems at the University ofTrento, Italy, and the author of several scientific contributionsto the fields of hydrology, environmental assessment, chemical fateand transport modeling, and spatial decision support systems. As aresearcher, environmental analyst, and project manager, he has beenworking for the European Commission's Joint Research Centre, theEmilia Romagna regional government, and other private and publicorganizations. He is a founding partner (2001) and the scientificdirector of GECOsistema, a research spin-off from the University ofBologna, Italy.

Table of Contents

Preface xiii

Contributors xvii

Chapter 1 | Chemicals, Models, and GIS: Introduction 1

1-1 Chemistry, Modeling, and Geography 1

1-2 Mr. Palomar and Models 2

1-3 What Makes a Model Different? 4

1-4 Simple, Complex, or Tiered? 7

Compatibility of Emissions and Concentrations 9

Spatiotemporal Variability 10

Spatial Patterns 12

More Complex Models and the Tale of Horatii and Curiatii 15

1-5 For Whom is this Book Written? 17

References 19

Chapter 2 | Basics of Chemical Compartment Models and TheirImplementation with GIS Functions 23

2-1 Introduction 23

2-2 Phase Partitioning 24

Air Compartment 24

Surface Water Compartment 25

Soil Compartment 25

2-3 Diffusion, Dispersion, and Advection 26

2-4 Fluxes at the Interfaces 28

Air–Ground Surface Interface 28

Water–Air and Water–Bottom Sediment Interface 28

Soil–Air and Soil–Water Interface 29

Parameterization of Advection Velocities andDiffusion/Dispersion Rates 29

2-5 Reactions 32

2-6 Transport Within an Environmental Medium: TheAdvection–Diffusion Equation (ADE) 33

Soils 37

Surface Water 38

Atmosphere 39

2-7 Analytical Solutions 40

Example: The Domenico Model 40

Example: Implementation of a River Plug Flow Model in aSpreadsheet 45

2-8 Box Models, Multimedia and Multispecies Fate and Transport47

Example: Implementing a Box Model of Soil Contamination andWater Pollution Loading in a Spreadsheet 51

2-9 Spatial Models: Implicit, Explicit, Detailed Explicit, andGIS-Based Schemes 57

References 65

Chapter 3 | Basics of GIS Operations 71

3-1 What is GIS? 71

3-2 GIS Data 72

Coordinate Systems 72

Example: Coordinate Transformation 75

Example: Georeference a Map from a Paper Using ArcGIS 77

GIS Formats 81

3-3 GIS Software 92

3-4 GIS Standards 93

Exercise: Browse and Export Geographic Objects in KML andCombine Them with Layers from a WMS 94

3-5 A Classification of GIS Operations for Chemical FateModeling 99

3-6 Spatial Thinking 100

3-7 Beyond GIS 103

3-8 Further Progress on GIS 104

References 104

Chapter 4 | Map Algebra 107

4-1 Map Algebra Operators and Syntaxes 109

4-2 Using Map Algebra to Compute a Gaussian Plume 112

Example: Using Map Algebra to Compute Volatilization Rates fromWater Bodies 119

4-3 Using Map Algebra to Implement Isolated Box Models 121

References 124

Chapter 5 | Distance Calculations 127

5-1 Concepts of Distance Calculations 127

Example: Feature Buffering 127

Example: Join Based on Distance 129

5-2 Distance Along a Surface and Vertical Distance 134

5-3 Applications of Euclidean Distance in Pollution Problems135

5-4 Cost Distance 139

Exercise: Euclidean and Cost distance Calculations 140

References 148

Chapter 6 | Spatial Statistics and Neighborhood Modeling inGIS 149

6-1 Variograms: Analyzing Spatial Patterns 149

Exercise: Computing Variograms of Observed AtmosphericContaminants 154

6-2 Interpolation 160

6-3 Zonal Statistics 163

6-4 Neighborhood Statistics and Filters 164

Exercise: Creating a Population Map from Point and Polygon Data169

References 170

Chapter 7 | Digital Elevation Models, Topographic Controls,and Hydrologic Modeling in GIS 171

7-1 Basic Surface Analysis 171

7-2 Drainage 178

Example: Pit Filling, Flow Direction, Flow Accumulation, andFlow Length in ArcGIS 178

Example: Catchment Population in India 183

Example: Travel Time 185

7-3 Using GIS Hydrological Functions in Chemical Fate andTransport Modeling 187

7-4 Non-D8 Methods and the TauDEM Algorithms 190

7-5 ESRI’s ‘‘Darcy Flow’’ and‘‘Porous Puff’’ Functions 191

References 193

Chapter 8 | Elements of Dynamic Modeling in GIS 195

8-1 Dynamic GIS Models 195

8-2 Studying Time-Dependent Effects With Simple Map Algebra200

Intermittent Emissions 200

Lagged Release from Historical Stockpiles 201

Stepwise Constant Emission and Removal Processes 202

8-3 Decoupling Spatial and Temporal Aspects of Models: The MappeGlobal Approach 203

References 206

Chapter 9 | Metamodeling and Source–ReceptorRelationship Modeling in GIS 209

9-1 Introduction 209

9-2 Metamodeling 210

9-3 Source–Receptor Relationships 213

References 215

Chapter 10 | Spatial Data Management in GIS and the Couplingof GIS and Environmental Models 217

10-1 Introduction 217

10-2 Historical Perspective of Emergence of Spatial Databases inEnvironmental Domain 218

10-3 Spatial Data Management in GIS: Theory and History 221

Spatial Database Definition 221

Relational Data Model Foundations 221

Object Relational Concepts: A Foundation Model for SpatialDatabases—Theoretical Background 224

PostgreSQL/PostGIS Object Relational Support 225

Oracle Object Relational Support 225

10-4 Spatial Database Solutions 226

ESRI Geodatabase 226

PostgreSQL and PostGIS 229

Oracle Locator and Spatial 230

10-5 Simple Environmental Spatiotemporal Database Skeleton andGIS: Hands-On Examples 230

Simple PostgreSQL/PostGIS Environmental Spatiotemporal DatabaseSkeleton and QuantumGIS 231

Simple Oracle XE Environmental Spatiotemporal Database Skeleton237

10-6 Generalized Environmental Spatiotemporal Database Skeletonand Geographic Mashups 244

Spatiotemporal Database Skeleton 244

Geographic Mashup 246

References 249

Chapter 11 | Soft Computing Methods for the Overlaying ofChemical Data with Other Spatially Varying Parameters 253

11-1 Introduction 253

11-2 Fuzzy Logic and Expert Judgment 258

11-3 Spatial Multicriteria Analysis 262

11-4 An Example of Vulnerability Mapping of Water

Resources to Pollution 266

References 276

Chapter 12 | Types of Data Required for Chemical FateModeling 279

12-1 Climate and Atmospheric Data 280

12-2 Soil Data 286

12-3 Impervious Surface Area 289

12-4 Vegetation 289

12-5 Hydrological Data 291

12-6 Elevation Data 293

12-7 Hydrography 296

12-8 Lakes 298

12-9 Stream Network Hydraulic Data 298

12-10 Ocean Parameters 299

12-11 Human Activity 301

Land Use/Land Cover 303

Population 305

Stable Lights at Night 306

12-12 Using Satellite Images for the Extraction of EnvironmentalParameters 306

12-13 Compilations of Data for Chemical Fate and TransportModeling 307

References 307

Chapter 13 | Retrieval and Analysis of Emission Data311

13-1 Characterization of Emissions 311

13-2 Emissions based on Production Volumes 312

13-3 Estimation from Usage or Release Inventories 313

13-4 Emission Factors 313

13-5 Spatial and Temporal Distribution of Emissions 314

Diffuse Emissions at Local to Regional Scale 317

Example: Estimating Urban Runoff Contaminants from Land Use andPopulation Data in the Province
of Naples, Italy 318

Exercise: Apportionment of Emissions Using a Geographic Pattern318

13-6 Modeling Traffic Flows 322

References 326

Chapter 14 | Characterization of Environmental Properties andProcesses 329

14-1 Physicochemical Properties and Partition Coefficients329

14-2 Aerosol and Suspended Sediments 330

Exercise: Computing SPM in Rivers Using the Formula of Hakansonand Co-workers 332

14-3 Diffusive Processes 335

14-4 Dispersion 335

14-5 Advective Processes 336

Atmospheric Deposition 336

Soil Water Budget Calculations 338

Soil Erosion 344

14-6 River and Lake Hydraulic Geometry 344

References 350

Chapter 15 | Complex Models, GIS, and Data Assimilation353

15-1 Atmospheric Transport Models 353

Example: Dispersion Modeling of an Atmospheric Emission inAustralia 354

15-2 Transport in Groundwater and the Analytic Element Method361

15-3 GIS Functions of Modeling Systems and Data Assimilation361

References 363

Chapter 16 | The Issue of Monitoring Data and the Evaluationof Spatial Models of Chemical Fate 365

16-1 Existing Monitoring Programs 366

16-2 Distributed Sampling 366

16-3 Methods for the Comparison of Measured and ModeledConcentrations 367

Exercise: Comparison of Two PCB Soil Concentration Models368

References 375

Chapter 17 | From Fate to Exposure and Risk Modeling with GIS377

17-1 Exposure and Risk for Human Health 377

17-2 Models for the Quantification of Chemical Intake by Humans382

Exercise: Human Exposure, Intake, and Cancer Risk Related toIngestion of Aboveground Produce
Contaminated by Gas and Dust Deposition of 2,3,7,8-TCDD Emittedfrom an Industrial Emission Source 386

17-3 Ecological and Environmental Risk Assessment 393

Exercise: Mapping Patch Area and Ecotones in South America398

17-4 Data for GIS Based Risk Assessment 400

References 401

Chapter 18 | GIS Based Models in Practice: The MultimediaAssessment of Pollutant Pathways in the Environment (MAPPE) Model405

18-1 Introduction 405

18-2 Environmental Compartments Considered in the Model 407

Atmosphere Compartment 409

Soil Compartment 412

Inland Water Compartment 413

Seawater 415

18-3 Implementation in GIS: Example with Lindane 416

Scalar Input Quantities 416

Maps Describing Landscape and Climate Parameters 418

Air Compartment Calculations 419

Soil Compartment Calculations 422

Inland Water Compartment Calculations 427

Seawater Compartment Calculations 434

18-4 Using the Model For Scenario Assessment 436

References 441

Chapter 19 | Inverse Modeling and Its Application to WaterContaminants 443

19-1 Introduction 443

Exercise: Inverse Modeling of Caffeine in Europe 447

References 451

Chapter 20 | Chemical Fate and Transport Indicators and theModeling of Contamination Patterns 453

20-1 The Relative Risk Model 453

Example: Relative Risk Assessment for Coastal Ecosystems Due toWastewater Emission in South Africa 456

20-2 Use of Chemical Fate and Transport Indicators in theContext of Relative Risk Assessment:
An Example with Contaminants Applied to Soil 459

Example: Generic Modeling of Sewage Sludge Soil Application inMexico 464
References 472

Chapter 21 | Perspectives: The Challenge of CumulativeImpacts and Planetary Boundaries 475

References 478

Index 481

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