Environmental chemometrics : principles and modern applications / Grady Hanrahan

Includes bibliographical references and index

TABLE OF CONTENTS Preface Dedication Acknowledgements Author Biography 1. Introduction 1.1. Chemometrics ¿ An Overview 1.2. The Importance of Quantitative Environmental Analysis 1.3. Common Chemical and Biological Pollutants in Environmental Matrices 1.3.1. Air 1.3.2. Water 1.3.3. Soil and Sediments 1.4. Overview of Chemometric Methods used in Environmental Analysis 1.5. Chapter Summary 1.6. Key Terms 1.7. End of Chapter Problems 1.8. References 2. Review of Statistics and Analytical Figures of Merit 2.1. Error and Sampling Considerations 2.2. Descriptive Statistics 2.3. Distribution of Repeated Measurements 2.4. Data Quality Indicators (DQIs) 2.4.1. Primary DQIs 2.4.2. Secondary DQIs 2.5. Confidence Intervals 2.6. Statistical Tests 2.7. Outlying Results 2.8. Analysis of Variance 2.9. Regression and Calibration Methods 2.10. Sensitivity and Limit of Detection 2.11. Bayesian Statistics Considered 2.12. Expanded Research Application I-Statistical Merits of Calculating Transfer Coefficients between Environmental Media 2.13. Introduction to Excel 2.14. Chapter Summary 2.15. Key Terms 2.16. End of Chapter Problems 2.17. References 3. Quality Assurance in Environmental Analysis 3.1. The Role of Chemometrics in Quality Assurance 3.2. Quality Assurance Considerations 3.2.1. Project Planning and Preparation 3.2.2. Traceability 3.2.3. Sample Handling and Chain of Custody 3.2.4. Accreditation 3.2.5. Good Laboratory Practice 3.3. Environmental Sampling Protocol - General Considerations 3.4. Quality Assurance: Sample Collection, Preparation and Storage 3.4.1. Sampling Design 3.4.2. Sample Collection, Preservation and Storage 3.5. Field QA/QC Samples 3.6. Laboratory and Instrumental Methods: QA/QC Samples 3.7. Standard Addition and Internal Standard Methods 3.8. Certified Reference Materials 3.9. Statistical Quality Control Charts 3.10. Proficiency Testing 3.11. Data Archiving, Storage and Auditing 3.12. Multivariate Quality Assurance/Control ¿ Initial Considerations 3.13. Expanded Research Application II ¿ Monte Carlo Simulation for Estimating Uncertainly Intervals for the Determination of Nitrate in Drinking Water 3.14. Chapter Summary 3.15. Key Terms 3.16. End of Chapter Problems 3.17. References 4. Experimental Design and Optimization Techniques 4.1. System Theory 4.2. Review of Linear Regression Models and Matrix Notation 4.3. Experimental Design Considerations 4.3.1. Experimental Uncertainty and Replication 4.3.2. Sample Size and Power 4.3.3. Blocking and Randomization 4.3.4. Orthogonality 4.3.5. Confounding 4.3.6. Center Points 4.4. Single Factor Categorical Designs 4.4.1. Randomized Block Designs 4.4.2. Latin Square Design 4.4.3. Greco Latin Square Design 4.5. Screening Designs 4.5.1. Full Factorial Designs (Two Levels per Factor) 4.5.2. Fractional Factorial Designs (Two Levels per Factor) 4.5.3. Plackett-Burman and Taguchi Designs 4.6. Three-Level Designs: Response Surface Designs 4.6.1. Central Composite Designs 4.6.2. Box-Behnken Design 4.7. Doehlert Matrix Design 4.8. Mixture Designs 4.8.1. Simplex Centroid Designs 4.8.2. Simplex Lattice Designs 4.8.3. Split-Plot Mixture Designs 4.8.4. Simplex Optimization 4.9. Expanded Research Application III ¿ Optimized Separation of Benzo [a] pyrene- Quinone Isomers Using Liquid Chromatography-Mass Spectrometry (LC-MS) and Response Surface Methodology 4.10. Chapter Summary 4.11. Key Terms 4.12. End of Chapter Problems 4.13. References 5. Time Series Analysis 5.1. Introduction to Time Series Analysis 5.2. Smoothing and Digital Filtering 5.2.1. Moving Average Smoothing 5.2.2. Exponential Smoothing 5.3. Time Series and Forecast Modeling ¿ A Detailed Look 5.3.1. Model Choice and Diagnostics 5.3.2. Harmonic Analysis Techniques 5.4. Autoregressive Integrated Moving Average Models 5.4.1. Non-Seasonal ARIMA Models 5.4.2. Seasonal ARIMA Models 5.5. Outliers in Time Series Analysis 5.6. Export Coefficient Modeling 5.7. Expanded Research Application IV- Export Coefficient Modeling and Time Series Analysis of Phosphorus in a Chalk Stream Watershed 5.8. Chapter Summary 5.9. Key Terms 5.10. End of Chapter Problems 5.11. References 6. Multivariate Data Analysis 6.1. Introduction to Multivariate Data Analysis 6.2. Data Pre-Processing 6.3. Correlation Analysis 6.3.1. Correlation Matrix 6.3.2. Inverse Correlations, Partial Correlations and Covariance Matrix 6.3.3. Pairwise Correlations 6.3.4. Fit Functions 6.3.5. Scatterplot Matrix 6.3.6. Color Maps and 3D Ellipsoid Plots 6.3.7. Nonparametric Correlations 6.4. Pattern Recognition ¿ Unsupervised 6.4.1. Cluster Analysis 6.4.2. Factor Analytic Techniques 6.5. Pattern Recognition ¿ Supervised 6.5.1. Discriminant Analysis 6.6. K ¿ Nearest Neighbor Classification 6.7. Soft Independent Modeling of Class Analogy (SIMCA) 6.8. Multivariate Calibration Methods 6.8.1. Multivariate Linear Regression 6.8.2. Partial Least Squares 6.8.3. Principal Component Regression 6.9. Multivariate T2 Control Charts 6.10. Cusum Control Charts 6.11. Soft Computing Techniques 6.11.1. Artificial Neural Networks 6.11.2. Fuzzy Logic 6.12. Expanded Research Application V ¿ A Multivariate Study of the Relations between PM10 Composition and Cell Toxicity 6.13. Chapter Summary 6.14. Key Terms 6.15. End of Chapter Problems 6.16. References Appendix I. Common Excel Shortcuts and Key Combinations Appendix II. Symbols used in Escuder¿Gilabert and others (2007) Appendix III. Review of Basic Matrix Algebra Notation and Operations Appendix IV. Environmental Chain of Custody Example