Statistical Procedures for Analysis of Environmental Monitoring Data and Risk Assessment

For students and professionals in environmental, civil, and mechanical engineering, few tasks are as challenging as statistical analysis and interpretation.

In this book, two leaders in the field address these challenges head-on. They introduce each leading statistical analysis technique, downplaying mathematical notation in favor of sample environmental applications and explanations that make sense to non-statisticians. They also address common problems in data interpretation: small data sets; the need to correlate constituents to infill missing data or identify outliers; creating early warning systems with fewer "false positives," handling noise, and assessing risk.

Coverage includes:

• Characterizing environmental quality data with Normal, Lognormal, and other distributions.
• Characterizing coincident behavior using regression, correlation and multiple regression.
• Multiple comparisons using ANOVA and associated parametric analysis techniques.
• Testing differences between monitoring records when censored data records exist.
• Focuses on "real-world" situations where data sets may be imperfect.

Reflecting decades of experience in the field, the authors also show how to use statistical analysis as the input to realistic risk assessment. In particular, they demonstrate simulation procedures for risk characterization, using sampling methodologies from probability distributions of data. Whether you are concerned with issues of air quality, surface water, groundwater, or soil contamination, the techniques covered in this book will be invaluable.