{"product_id":"gis-based-chemical-fate-modeling-isbn-9781118059975","title":"GIS Based Chemical Fate Modeling","description":"\u003cp\u003e\u003cb\u003eExplains how GIS enhances the development of chemical fate and transport models\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOver the past decade, researchers have discovered that geographic information systems (GIS) are not only excellent tools for managing and displaying maps, but also useful in the analysis of chemical fate and transport in the environment. Among its many benefits, GIS facilitates the identification of critical factors that drive chemical fate and transport. Moreover, GIS makes it easier to communicate and explain key model assumptions.\u003c\/p\u003e \u003cp\u003eBased on the author's firsthand experience in environmental assessment, \u003ci\u003eGIS Based Chemical Fate Modeling\u003c\/i\u003e explores both GIS and chemical fate and transport modeling fundamentals, creating an interface between the two domains. It then explains how GIS analytical functions enable scientists to develop simple, yet comprehensive spatially explicit chemical fate and transport models that support real-world applications. In addition, the book features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePractical examples of GIS based model calculations that serve as templates for the development of new applications\u003c\/li\u003e \u003cli\u003eExercises enabling readers to create their own GIS based models\u003c\/li\u003e \u003cli\u003eAccompanying website featuring downloadable datasets used in the book's examples and exercises\u003c\/li\u003e \u003cli\u003eReferences to the literature, websites, data repositories, and online reports to facilitate further research\u003c\/li\u003e \u003cli\u003eCoverage of important topics such as spatial decision support systems and multi-criteria analysis as well as ecological and human health risk assessment in a spatial context\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eGIS Based Chemical Fate Modeling\u003c\/i\u003e makes a unique contribution to the environmental sciences by explaining how GIS analytical functions enhance the development and interpretation of chemical fate and transport models. Environmental scientists should turn to this book to gain a deeper understanding of the role of GIS in describing what happens to chemicals when they are released into the environment.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePreface xiii\u003cbr\u003e \u003cbr\u003e Contributors xvii\u003cbr\u003e \u003cbr\u003e Chapter 1 | Chemicals, Models, and GIS: Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1-1 Chemistry, Modeling, and Geography 1\u003c\/p\u003e \u003cp\u003e1-2 Mr. Palomar and Models 2\u003c\/p\u003e \u003cp\u003e1-3 What Makes a Model Different? 4\u003c\/p\u003e \u003cp\u003e1-4 Simple, Complex, or Tiered? 7\u003c\/p\u003e \u003cp\u003eCompatibility of Emissions and Concentrations 9\u003c\/p\u003e \u003cp\u003eSpatiotemporal Variability 10\u003c\/p\u003e \u003cp\u003eSpatial Patterns 12\u003c\/p\u003e \u003cp\u003eMore Complex Models and the Tale of Horatii and Curiatii 15\u003c\/p\u003e \u003cp\u003e1-5 For Whom is this Book Written? 17\u003c\/p\u003e \u003cp\u003eReferences 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 | Basics of Chemical Compartment Models and Their Implementation with GIS Functions 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2-1 Introduction 23\u003c\/p\u003e \u003cp\u003e2-2 Phase Partitioning 24\u003c\/p\u003e \u003cp\u003eAir Compartment 24\u003c\/p\u003e \u003cp\u003eSurface Water Compartment 25\u003c\/p\u003e \u003cp\u003eSoil Compartment 25\u003c\/p\u003e \u003cp\u003e2-3 Diffusion, Dispersion, and Advection 26\u003c\/p\u003e \u003cp\u003e2-4 Fluxes at the Interfaces 28\u003c\/p\u003e \u003cp\u003eAir–Ground Surface Interface 28\u003c\/p\u003e \u003cp\u003eWater–Air and Water–Bottom Sediment Interface 28\u003c\/p\u003e \u003cp\u003eSoil–Air and Soil–Water Interface 29\u003c\/p\u003e \u003cp\u003eParameterization of Advection Velocities and Diffusion\/Dispersion Rates 29\u003c\/p\u003e \u003cp\u003e2-5 Reactions 32\u003c\/p\u003e \u003cp\u003e2-6 Transport Within an Environmental Medium: The Advection–Diffusion Equation (ADE) 33\u003c\/p\u003e \u003cp\u003eSoils 37\u003c\/p\u003e \u003cp\u003eSurface Water 38\u003c\/p\u003e \u003cp\u003eAtmosphere 39\u003c\/p\u003e \u003cp\u003e2-7 Analytical Solutions 40\u003c\/p\u003e \u003cp\u003eExample: The Domenico Model 40\u003c\/p\u003e \u003cp\u003eExample: Implementation of a River Plug Flow Model in a Spreadsheet 45\u003c\/p\u003e \u003cp\u003e2-8 Box Models, Multimedia and Multispecies Fate and Transport 47\u003c\/p\u003e \u003cp\u003eExample: Implementing a Box Model of Soil Contamination and Water Pollution Loading in a Spreadsheet 51\u003c\/p\u003e \u003cp\u003e2-9 Spatial Models: Implicit, Explicit, Detailed Explicit, and GIS-Based Schemes 57\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 | Basics of GIS Operations 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3-1 What is GIS? 71\u003c\/p\u003e \u003cp\u003e3-2 GIS Data 72\u003c\/p\u003e \u003cp\u003eCoordinate Systems 72\u003c\/p\u003e \u003cp\u003eExample: Coordinate Transformation 75\u003c\/p\u003e \u003cp\u003eExample: Georeference a Map from a Paper Using ArcGIS 77\u003c\/p\u003e \u003cp\u003eGIS Formats 81\u003c\/p\u003e \u003cp\u003e3-3 GIS Software 92\u003c\/p\u003e \u003cp\u003e3-4 GIS Standards 93\u003c\/p\u003e \u003cp\u003eExercise: Browse and Export Geographic Objects in KML and Combine Them with Layers from a WMS 94\u003c\/p\u003e \u003cp\u003e3-5 A Classification of GIS Operations for Chemical Fate Modeling 99\u003c\/p\u003e \u003cp\u003e3-6 Spatial Thinking 100\u003c\/p\u003e \u003cp\u003e3-7 Beyond GIS 103\u003c\/p\u003e \u003cp\u003e3-8 Further Progress on GIS 104\u003c\/p\u003e \u003cp\u003eReferences 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 | Map Algebra 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4-1 Map Algebra Operators and Syntaxes 109\u003c\/p\u003e \u003cp\u003e4-2 Using Map Algebra to Compute a Gaussian Plume 112\u003c\/p\u003e \u003cp\u003eExample: Using Map Algebra to Compute Volatilization Rates from Water Bodies 119\u003c\/p\u003e \u003cp\u003e4-3 Using Map Algebra to Implement Isolated Box Models 121\u003c\/p\u003e \u003cp\u003eReferences 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 | Distance Calculations 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5-1 Concepts of Distance Calculations 127\u003c\/p\u003e \u003cp\u003eExample: Feature Buffering 127\u003c\/p\u003e \u003cp\u003eExample: Join Based on Distance 129\u003c\/p\u003e \u003cp\u003e5-2 Distance Along a Surface and Vertical Distance 134\u003c\/p\u003e \u003cp\u003e5-3 Applications of Euclidean Distance in Pollution Problems 135\u003c\/p\u003e \u003cp\u003e5-4 Cost Distance 139\u003c\/p\u003e \u003cp\u003eExercise: Euclidean and Cost distance Calculations 140\u003c\/p\u003e \u003cp\u003eReferences 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 | Spatial Statistics and Neighborhood Modeling in GIS 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6-1 Variograms: Analyzing Spatial Patterns 149\u003c\/p\u003e \u003cp\u003eExercise: Computing Variograms of Observed Atmospheric Contaminants 154\u003c\/p\u003e \u003cp\u003e6-2 Interpolation 160\u003c\/p\u003e \u003cp\u003e6-3 Zonal Statistics 163\u003c\/p\u003e \u003cp\u003e6-4 Neighborhood Statistics and Filters 164\u003c\/p\u003e \u003cp\u003eExercise: Creating a Population Map from Point and Polygon Data 169\u003c\/p\u003e \u003cp\u003eReferences 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 | Digital Elevation Models, Topographic Controls, and Hydrologic Modeling in GIS 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7-1 Basic Surface Analysis 171\u003c\/p\u003e \u003cp\u003e7-2 Drainage 178\u003c\/p\u003e \u003cp\u003eExample: Pit Filling, Flow Direction, Flow Accumulation, and Flow Length in ArcGIS 178\u003c\/p\u003e \u003cp\u003eExample: Catchment Population in India 183\u003c\/p\u003e \u003cp\u003eExample: Travel Time 185\u003c\/p\u003e \u003cp\u003e7-3 Using GIS Hydrological Functions in Chemical Fate and Transport Modeling 187\u003c\/p\u003e \u003cp\u003e7-4 Non-D8 Methods and the TauDEM Algorithms 190\u003c\/p\u003e \u003cp\u003e7-5 ESRI’s ‘‘Darcy Flow’’ and ‘‘Porous Puff’’ Functions 191\u003c\/p\u003e \u003cp\u003eReferences 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 | Elements of Dynamic Modeling in GIS 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8-1 Dynamic GIS Models 195\u003c\/p\u003e \u003cp\u003e8-2 Studying Time-Dependent Effects With Simple Map Algebra 200\u003c\/p\u003e \u003cp\u003eIntermittent Emissions 200\u003c\/p\u003e \u003cp\u003eLagged Release from Historical Stockpiles 201\u003c\/p\u003e \u003cp\u003eStepwise Constant Emission and Removal Processes 202\u003c\/p\u003e \u003cp\u003e8-3 Decoupling Spatial and Temporal Aspects of Models: The Mappe Global Approach 203\u003c\/p\u003e \u003cp\u003eReferences 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 | Metamodeling and Source–Receptor Relationship Modeling in GIS 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9-1 Introduction 209\u003c\/p\u003e \u003cp\u003e9-2 Metamodeling 210\u003c\/p\u003e \u003cp\u003e9-3 Source–Receptor Relationships 213\u003c\/p\u003e \u003cp\u003eReferences 215\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 | Spatial Data Management in GIS and the Coupling of GIS and Environmental Models 217\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10-1 Introduction 217\u003c\/p\u003e \u003cp\u003e10-2 Historical Perspective of Emergence of Spatial Databases in Environmental Domain 218\u003c\/p\u003e \u003cp\u003e10-3 Spatial Data Management in GIS: Theory and History 221\u003c\/p\u003e \u003cp\u003eSpatial Database Definition 221\u003c\/p\u003e \u003cp\u003eRelational Data Model Foundations 221\u003c\/p\u003e \u003cp\u003eObject Relational Concepts: A Foundation Model for Spatial Databases—Theoretical Background 224\u003c\/p\u003e \u003cp\u003ePostgreSQL\/PostGIS Object Relational Support 225\u003c\/p\u003e \u003cp\u003eOracle Object Relational Support 225\u003c\/p\u003e \u003cp\u003e10-4 Spatial Database Solutions 226\u003c\/p\u003e \u003cp\u003eESRI Geodatabase 226\u003c\/p\u003e \u003cp\u003ePostgreSQL and PostGIS 229\u003c\/p\u003e \u003cp\u003eOracle Locator and Spatial 230\u003c\/p\u003e \u003cp\u003e10-5 Simple Environmental Spatiotemporal Database Skeleton and GIS: Hands-On Examples 230\u003c\/p\u003e \u003cp\u003eSimple PostgreSQL\/PostGIS Environmental Spatiotemporal Database Skeleton and QuantumGIS 231\u003c\/p\u003e \u003cp\u003eSimple Oracle XE Environmental Spatiotemporal Database Skeleton 237\u003c\/p\u003e \u003cp\u003e10-6 Generalized Environmental Spatiotemporal Database Skeleton and Geographic Mashups 244\u003c\/p\u003e \u003cp\u003eSpatiotemporal Database Skeleton 244\u003c\/p\u003e \u003cp\u003eGeographic Mashup 246\u003c\/p\u003e \u003cp\u003eReferences 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 | Soft Computing Methods for the Overlaying of Chemical Data with Other Spatially Varying Parameters 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11-1 Introduction 253\u003c\/p\u003e \u003cp\u003e11-2 Fuzzy Logic and Expert Judgment 258\u003c\/p\u003e \u003cp\u003e11-3 Spatial Multicriteria Analysis 262\u003c\/p\u003e \u003cp\u003e11-4 An Example of Vulnerability Mapping of Water\u003c\/p\u003e \u003cp\u003eResources to Pollution 266\u003c\/p\u003e \u003cp\u003eReferences 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 | Types of Data Required for Chemical Fate Modeling 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12-1 Climate and Atmospheric Data 280\u003c\/p\u003e \u003cp\u003e12-2 Soil Data 286\u003c\/p\u003e \u003cp\u003e12-3 Impervious Surface Area 289\u003c\/p\u003e \u003cp\u003e12-4 Vegetation 289\u003c\/p\u003e \u003cp\u003e12-5 Hydrological Data 291\u003c\/p\u003e \u003cp\u003e12-6 Elevation Data 293\u003c\/p\u003e \u003cp\u003e12-7 Hydrography 296\u003c\/p\u003e \u003cp\u003e12-8 Lakes 298\u003c\/p\u003e \u003cp\u003e12-9 Stream Network Hydraulic Data 298\u003c\/p\u003e \u003cp\u003e12-10 Ocean Parameters 299\u003c\/p\u003e \u003cp\u003e12-11 Human Activity 301\u003c\/p\u003e \u003cp\u003eLand Use\/Land Cover 303\u003c\/p\u003e \u003cp\u003ePopulation 305\u003c\/p\u003e \u003cp\u003eStable Lights at Night 306\u003c\/p\u003e \u003cp\u003e12-12 Using Satellite Images for the Extraction of Environmental Parameters 306\u003c\/p\u003e \u003cp\u003e12-13 Compilations of Data for Chemical Fate and Transport Modeling 307\u003c\/p\u003e \u003cp\u003eReferences 307\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 | Retrieval and Analysis of Emission Data 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13-1 Characterization of Emissions 311\u003c\/p\u003e \u003cp\u003e13-2 Emissions based on Production Volumes 312\u003c\/p\u003e \u003cp\u003e13-3 Estimation from Usage or Release Inventories 313\u003c\/p\u003e \u003cp\u003e13-4 Emission Factors 313\u003c\/p\u003e \u003cp\u003e13-5 Spatial and Temporal Distribution of Emissions 314\u003c\/p\u003e \u003cp\u003eDiffuse Emissions at Local to Regional Scale 317\u003c\/p\u003e \u003cp\u003eExample: Estimating Urban Runoff Contaminants from Land Use and Population Data in the Province\u003cbr\u003e of Naples, Italy 318\u003c\/p\u003e \u003cp\u003eExercise: Apportionment of Emissions Using a Geographic Pattern 318\u003c\/p\u003e \u003cp\u003e13-6 Modeling Traffic Flows 322\u003c\/p\u003e \u003cp\u003eReferences 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 | Characterization of Environmental Properties and Processes 329\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14-1 Physicochemical Properties and Partition Coefficients 329\u003c\/p\u003e \u003cp\u003e14-2 Aerosol and Suspended Sediments 330\u003c\/p\u003e \u003cp\u003eExercise: Computing SPM in Rivers Using the Formula of Hakanson and Co-workers 332\u003c\/p\u003e \u003cp\u003e14-3 Diffusive Processes 335\u003c\/p\u003e \u003cp\u003e14-4 Dispersion 335\u003c\/p\u003e \u003cp\u003e14-5 Advective Processes 336\u003c\/p\u003e \u003cp\u003eAtmospheric Deposition 336\u003c\/p\u003e \u003cp\u003eSoil Water Budget Calculations 338\u003c\/p\u003e \u003cp\u003eSoil Erosion 344\u003c\/p\u003e \u003cp\u003e14-6 River and Lake Hydraulic Geometry 344\u003c\/p\u003e \u003cp\u003eReferences 350\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 | Complex Models, GIS, and Data Assimilation 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15-1 Atmospheric Transport Models 353\u003c\/p\u003e \u003cp\u003eExample: Dispersion Modeling of an Atmospheric Emission in Australia 354\u003c\/p\u003e \u003cp\u003e15-2 Transport in Groundwater and the Analytic Element Method 361\u003c\/p\u003e \u003cp\u003e15-3 GIS Functions of Modeling Systems and Data Assimilation 361\u003c\/p\u003e \u003cp\u003eReferences 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 | The Issue of Monitoring Data and the Evaluation of Spatial Models of Chemical Fate 365\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16-1 Existing Monitoring Programs 366\u003c\/p\u003e \u003cp\u003e16-2 Distributed Sampling 366\u003c\/p\u003e \u003cp\u003e16-3 Methods for the Comparison of Measured and Modeled Concentrations 367\u003c\/p\u003e \u003cp\u003eExercise: Comparison of Two PCB Soil Concentration Models 368\u003c\/p\u003e \u003cp\u003eReferences 375\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 | From Fate to Exposure and Risk Modeling with GIS 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17-1 Exposure and Risk for Human Health 377\u003c\/p\u003e \u003cp\u003e17-2 Models for the Quantification of Chemical Intake by Humans 382\u003c\/p\u003e \u003cp\u003eExercise: Human Exposure, Intake, and Cancer Risk Related to Ingestion of Aboveground Produce\u003cbr\u003e Contaminated by Gas and Dust Deposition of 2,3,7,8-TCDD Emitted from an Industrial Emission Source 386\u003c\/p\u003e \u003cp\u003e17-3 Ecological and Environmental Risk Assessment 393\u003c\/p\u003e \u003cp\u003eExercise: Mapping Patch Area and Ecotones in South America 398\u003c\/p\u003e \u003cp\u003e17-4 Data for GIS Based Risk Assessment 400\u003c\/p\u003e \u003cp\u003eReferences 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18 | GIS Based Models in Practice: The Multimedia Assessment of Pollutant Pathways in the Environment (MAPPE) Model 405\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18-1 Introduction 405\u003c\/p\u003e \u003cp\u003e18-2 Environmental Compartments Considered in the Model 407\u003c\/p\u003e \u003cp\u003eAtmosphere Compartment 409\u003c\/p\u003e \u003cp\u003eSoil Compartment 412\u003c\/p\u003e \u003cp\u003eInland Water Compartment 413\u003c\/p\u003e \u003cp\u003eSeawater 415\u003c\/p\u003e \u003cp\u003e18-3 Implementation in GIS: Example with Lindane 416\u003c\/p\u003e \u003cp\u003eScalar Input Quantities 416\u003c\/p\u003e \u003cp\u003eMaps Describing Landscape and Climate Parameters 418\u003c\/p\u003e \u003cp\u003eAir Compartment Calculations 419\u003c\/p\u003e \u003cp\u003eSoil Compartment Calculations 422\u003c\/p\u003e \u003cp\u003eInland Water Compartment Calculations 427\u003c\/p\u003e \u003cp\u003eSeawater Compartment Calculations 434\u003c\/p\u003e \u003cp\u003e18-4 Using the Model For Scenario Assessment 436\u003c\/p\u003e \u003cp\u003eReferences 441\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19 | Inverse Modeling and Its Application to Water Contaminants 443\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19-1 Introduction 443\u003c\/p\u003e \u003cp\u003eExercise: Inverse Modeling of Caffeine in Europe 447\u003c\/p\u003e \u003cp\u003eReferences 451\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20 | Chemical Fate and Transport Indicators and the Modeling of Contamination Patterns 453\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20-1 The Relative Risk Model 453\u003c\/p\u003e \u003cp\u003eExample: Relative Risk Assessment for Coastal Ecosystems Due to Wastewater Emission in South Africa 456\u003c\/p\u003e \u003cp\u003e20-2 Use of Chemical Fate and Transport Indicators in the Context of Relative Risk Assessment:\u003cbr\u003e An Example with Contaminants Applied to Soil 459\u003c\/p\u003e \u003cp\u003eExample: Generic Modeling of Sewage Sludge Soil Application in Mexico 464\u003cbr\u003e References 472\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21 | Perspectives: The Challenge of Cumulative Impacts and Planetary Boundaries 475\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 478\u003c\/p\u003e \u003cp\u003eIndex 481\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eALBERTO PISTOCCHI, MSc Eng, MSc Phil, PhD,\u003c\/b\u003e is Adjunct Professor of Spatial Decision Support Systems at the University of Trento, Italy, and the author of several scientific contributions to the fields of hydrology, environmental assessment, chemical fate and transport modeling, and spatial decision support systems. As a researcher, environmental analyst, and project manager, he has been working for the European Commission's Joint Research Centre, the Emilia Romagna regional government, and other private and public organizations. He is a founding partner (2001) and the scientific director of GECOsistema, a research spin-off from the University of Bologna, Italy.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eExplains how GIS enhances the development of chemical fate and transport models\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOver the past decade, researchers have discovered that geographic information systems (GIS) are not only excellent tools for managing and displaying maps, but also useful in the analysis of chemical fate and transport in the environment. Among its many benefits, GIS facilitates the identification of critical factors that drive chemical fate and transport. Moreover, GIS makes it easier to communicate and explain key model assumptions.\u003c\/p\u003e \u003cp\u003eBased on the author's firsthand experience in environmental assessment, \u003ci\u003eGIS Based Chemical Fate Modeling\u003c\/i\u003e explores both GIS and chemical fate and transport modeling fundamentals, creating an interface between the two domains. It then explains how GIS analytical functions enable scientists to develop simple, yet comprehensive spatially explicit chemical fate and transport models that support real-world applications. In addition, the book features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePractical examples of GIS based model calculations that serve as templates for the development of new applications\u003c\/li\u003e \u003cli\u003eExercises enabling readers to create their own GIS based models\u003c\/li\u003e \u003cli\u003eAccompanying website featuring downloadable datasets used in the book's examples and exercises\u003c\/li\u003e \u003cli\u003eReferences to the literature, websites, data repositories, and online reports to facilitate further research\u003c\/li\u003e \u003cli\u003eCoverage of important topics such as spatial decision support systems and multi-criteria analysis as well as ecological and human health risk assessment in a spatial context\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eGIS Based Chemical Fate Modeling\u003c\/i\u003e makes a unique contribution to the environmental sciences by explaining how GIS analytical functions enhance the development and interpretation of chemical fate and transport models. Environmental scientists should turn to this book to gain a deeper understanding of the role of GIS in describing what happens to chemicals when they are released into the environment.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989291417829,"sku":"NP9781118059975","price":179.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118059975.jpg?v=1761783541","url":"https:\/\/k12savings.com\/es\/products\/gis-based-chemical-fate-modeling-isbn-9781118059975","provider":"K12savings","version":"1.0","type":"link"}