Environmental Chemometrics: Principles and Modern Applications

Multivariate, heterogeneous data has been traditionally analyzed using the "one at a time" variable approach, often missing the main objective of discovering the relationships among multiple variables and samples. Enter chemometrics, with its powerful tools for design, analysis, and data interpretation of complex environmental systems. Delineating the rigors of modern environmental analysis and how to effectively solve limitations through multivariate approaches, Environmental Chemometrics: Principles and Modern Applications provides an introduction and practical guide to chemometric methods used in environmental chemical analysis.

The text begins with an overview of chemometrics in relation to quantitative environmental analysis and a review of descriptive statistical concepts. Building on this, the author covers environmental sampling considerations, experimental design and optimization techniques, multivariate analysis of environmental and chemical data sets, time series analysis, and quality assurance and method validation. Each chapter contains problem-oriented exercises and research applications from the author’s own work and from other experts in the field.

The author’s presentation of the basic principles of these methods together with real applications in the field of environmental chemistry makes the comprehension of complex environmental problems and chemically-related concepts more accessible. He covers all major areas of environmental analysis backed by studies from experts in the field. The book is a valuable tool for understanding the rapidly developing world of chemometric methods in environmental analysis.

Introduction
Chemometrics—An Overview The Importance of Quantitative Environmental Analysis
Common Chemical and Biological Pollutants in Environmental Matrices
Overview of Chemometric Methods Used in Environmental Analysis
Chapter Summary
End of Chapter Problems
References

Review of Statistics and Analytical Figures of Merit
Error and Sampling Considerations
Descriptive Statistics
Distribution of Repeated Measurements
Data Quality Indicators
Confidence Intervals
Statistical Tests
Outlying Results 1
Analysis of Variance
Regression and Calibration Methods
Sensitivity and Limit of Detection
Bayesian Statistics Considered
Expanded Research Application I—Statistical Merits of Calculating Transfer Coeffi cients Among Environmental Media
References
Quality Assurance in Environmental Analysis

The Role of Chemometrics in Quality Assurance
Quality Assurance Considerations
Environmental Sampling Protocol—General Considerations
Quality Assurance: Sample Collection, Preparation and Storage
Field QA/QC Samples
Laboratory and Instrumental Methods: QA/QC Samples
Standard Addition and Internal Standard Methods
Certified Reference Materials
Statistical Quality Control Charts
Proficiency Testing
Data Archiving, Storage, and Auditing
Multivariate Quality Assurance/Control—Initial Considerations
Expanded Research Application II—Monte Carlo Simulation for Estimating Uncertainty Intervals for the Determination of Nitrate in Drinking Water
Chapter Summary
End of Chapter Problems
References

Experimental Design and Optimization Techniques
System Theory
Review of Linear Regression Models and Matrix Notation
Experimental Design Considerations
Single Factor Categorical Designs
Screening Designs
Three-Level Designs: Response Surface Methodology
Doehlert Matrix Design
Mixture Designs
Expanded Research Application III—Optimized Separation of Benzo[a]Pyrene-Quinone Isomers Using Liquid Chromatography-Mass Spectrometry (LC-MS) and Response Surface Methodology
Chapter Summary
End of Chapter Problems
References
Time Series Analysis
Introduction to Time Series Analysis
Smoothing and Digital Filtering
Times Series and Forecast Modeling—A Detailed Look
ARIMA models
Outliers in Time Series Analysis
Export Coeffi cient Modeling
Expanded Research Application IV—Export Coefficient Modeling and Time Series Analysis of Phosphorus in a Chalk Stream Watershed
Chapter Summary
End of Chapter Problems
References 02

Multivariate Data Analysis
Introduction to Multivariate Data Analysis
Data Preprocessing
Correlation Analysis
Pattern Recognition—Unsupervised
Pattern Recognition—Supervised
K-Nearest Neighbor Classification
Soft Independent Modeling of Class Analogy (SIMCA)
Multivariate Calibration Methods
Multivariate T 2 Control Charts
Cusum Control Charts
Soft Computing Techniques
Expanded Research Application—A Multivariate Study of the Relations Between PM10 Composition and Cell Toxicity
Chapter Summary
End of Chapter Problems
References
Appendix I: Common Excel® Shortcuts and Key Combinations
Appendix II: Symbols Used in Escuder-Gilabert et al. (2007)
Appendix III: Review of Basic Matrix Algebra Notation and Operations
Appendix IV: Environmental Chain of Custody
Index