Statistical Methods for Environmental Pollution MonitoringThis book discusses a broad range of statistical design and analysis methods that are particularly well suited to pollution data. It explains key statistical techniques in easytocomprehend terms and uses practical examples, exercises, and case studies to illustrate procedures. Dr. Gilbert begins by discussing a spacetime framework for sampling pollutants. He then shows how to use statistical sample survey methods to estimate average and total amounts of pollutants in the environment, and how to determine the number of field samples and measurements to collect for this purpose. Then a broad range of statistical analysis methods are described and illustrated. These include:

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Contents
Sampling Environmental Populations  5 
Environmental Sampling Design  17 
Simple Random Sampling  26 
Stratified Random Sampling  45 
TwoStage Sampling  58 
Compositing and ThreeStage Sampling  71 
Systematic Sampling  89 
Double Sampling  106 
Characterizing Lognormal Populations  164 
Estimating the Mean and Variance from Censored Data Sets  177 
Outlier Detection and Control Charts  186 
Detecting and Estimating Trends  204 
Trends and Seasonality  225 
Comparing Populations  241 
Statistical Tables  254 
TREND  274 
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Common terms and phrases
aliquots approximate arithmetic mean average batch bias BoxJenkins CALCULATE chisquare collected concentrations confidence interval confidence limits control chart cost data set data values datum detection determine double sampling ENOIF environmental Equation estimate the mean estimated mean example Figure gives grid spacing Hence homogeneous hot spot illustrated linear regression locations lognormal distribution MannKendall test mean and total measurements median nonparametric normal distribution null hypothesis number of data number of samples OATA obtained optimum order statistics outliers parameters pollution population units prespecified primary units probability plot procedure quantile randomly reject H0 sample mean sampling plans seasonal Kendall test Section selected serial correlation simple random sampling slope estimates spatial specified standard deviation standard error station statistical strata stratified random sampling stratum subgroup subsamples subunits Suppose systematic sampling Table A1 target population test for trend total amount total number true mean unbiased estimate underlying distribution Var(J VAR(S Var(x variability zero