The book "Statistical Methods for Environmental Pollution Monitoring" by Richard O. Gilbert provides a comprehensive guide to statistical methods used in environmental pollution studies. It is designed for non-statisticians, including environmental scientists, engineers, and hydrologists, who have a basic understanding of statistics. The book covers various topics such as sampling designs, statistical analysis procedures, and the handling of skewed distributions and correlated data. Key chapters include: 1. **Introduction**: Discusses the types and objectives of environmental pollution studies, the importance of statistical design and analysis, and the process of designing an environmental sampling study. 2. **Sampling Environmental Populations**: Explains the space-time framework for environmental sampling, the concepts of target and sampled populations, and the sources of variability and error in environmental data. 3. **Environmental Sampling Design**: Outlines criteria for choosing a sampling plan, including study objectives, cost-effectiveness, patterns of environmental contamination, and practical considerations. 4. **Simple Random Sampling**: Introduces basic concepts, estimation techniques, and the impact of measurement errors. 5. **Stratified Random Sampling**: Focuses on estimating means and totals, selecting strata, and allocating samples. 6. **Two-Stage Sampling**: Discusses primary units of equal and unequal size, and the sampling process. 7. **Compositing and Three-Stage Sampling**: Covers equal and unequal-sized units, and the sampling process. 8. **Systematic Sampling**: Explains sampling along lines and over space, comparing systematic and random sampling, and estimating means and variances. 9. **Double Sampling**: Describes linear regression and ratio double sampling, and case studies. 10. **Locating Hot Spots**: Discusses grid spacing, likelihood of hitting hot spots, and probability calculations. 11. **Quantiles, Proportions, and Means**: Covers estimation techniques, confidence limits, and nonparametric methods. 12. **Skewed Distributions and Goodness-of-Fit Tests**: Introduces lognormal, Weibull, Gamma, and Beta distributions, and goodness-of-fit tests. 13. **Characterizing Lognormal Populations**: Focuses on estimating means, variances, medians, and quantiles. 14. **Estimating the Mean and Variance from Censored Data Sets**: Discusses data near detection limits and estimation techniques. 15. **Outlier Detection and Control Charts**: Covers data screening, treatment of outliers, and control charts. 16. **Detecting and Estimating Trends**: Discusses types of trends, statistical complexities, methods, and the Mann-Kendall test. 17. **Trends and Seasonality**: Introduces seasonal Kendall tests and slope estimators. 18. **Comparing Populations**: Discusses tests for paired and independent data sets. The book also includes appendices with statistical tables, a computer code for trend analysis, and a glossary. It aims to provide practical tools and methodsThe book "Statistical Methods for Environmental Pollution Monitoring" by Richard O. Gilbert provides a comprehensive guide to statistical methods used in environmental pollution studies. It is designed for non-statisticians, including environmental scientists, engineers, and hydrologists, who have a basic understanding of statistics. The book covers various topics such as sampling designs, statistical analysis procedures, and the handling of skewed distributions and correlated data. Key chapters include: 1. **Introduction**: Discusses the types and objectives of environmental pollution studies, the importance of statistical design and analysis, and the process of designing an environmental sampling study. 2. **Sampling Environmental Populations**: Explains the space-time framework for environmental sampling, the concepts of target and sampled populations, and the sources of variability and error in environmental data. 3. **Environmental Sampling Design**: Outlines criteria for choosing a sampling plan, including study objectives, cost-effectiveness, patterns of environmental contamination, and practical considerations. 4. **Simple Random Sampling**: Introduces basic concepts, estimation techniques, and the impact of measurement errors. 5. **Stratified Random Sampling**: Focuses on estimating means and totals, selecting strata, and allocating samples. 6. **Two-Stage Sampling**: Discusses primary units of equal and unequal size, and the sampling process. 7. **Compositing and Three-Stage Sampling**: Covers equal and unequal-sized units, and the sampling process. 8. **Systematic Sampling**: Explains sampling along lines and over space, comparing systematic and random sampling, and estimating means and variances. 9. **Double Sampling**: Describes linear regression and ratio double sampling, and case studies. 10. **Locating Hot Spots**: Discusses grid spacing, likelihood of hitting hot spots, and probability calculations. 11. **Quantiles, Proportions, and Means**: Covers estimation techniques, confidence limits, and nonparametric methods. 12. **Skewed Distributions and Goodness-of-Fit Tests**: Introduces lognormal, Weibull, Gamma, and Beta distributions, and goodness-of-fit tests. 13. **Characterizing Lognormal Populations**: Focuses on estimating means, variances, medians, and quantiles. 14. **Estimating the Mean and Variance from Censored Data Sets**: Discusses data near detection limits and estimation techniques. 15. **Outlier Detection and Control Charts**: Covers data screening, treatment of outliers, and control charts. 16. **Detecting and Estimating Trends**: Discusses types of trends, statistical complexities, methods, and the Mann-Kendall test. 17. **Trends and Seasonality**: Introduces seasonal Kendall tests and slope estimators. 18. **Comparing Populations**: Discusses tests for paired and independent data sets. The book also includes appendices with statistical tables, a computer code for trend analysis, and a glossary. It aims to provide practical tools and methods