Environmental pollution studies may be divided into the following broad and somewhat overlapping types.1. Monitoring. Data may be collected (a) to monitor or to characterize ambient concentrations in environmental media (air, water, soil, biota) or (b) to monitor concentrations in air and water effluents. The purpose may be to assess the adequacy of controls on the release or containment of pollutants, to detect long-tenn trends, unplanned releases, or accidents and their causes, to provide a spatial or temporal summary of average or extreme conditions, to demonstrate or enforce compliance with emission or ambient standards, to establish base-line data for future reference and long-range planning, to indicate whether and to what extent additional infonnation is required, or to assure the public that effluent releases or environmental levels are being adequately controlled. Research.Field and laboratory data may be collected (a) to study the transport of pollutants through the environment by means of food chains and aerial pathways to man and (b) to detennine and quantitate the cause-andeffect relationships that control the levels and variability of pollution concentrations over time and space.Many design and statistical analysis problems are common to monitoring and research studies. Environmental data sets also tend to have similar statistical characteristics. These problems and characteristics, discussed in the next section, motivate the topics discussed in this book. STATISTICAL DESIGN AND ANALYSIS PROBLEMSNumerous problems must be faced when applying statistical methods to environmental pollution studies. One problem is how to define the environmental "population" of interest. Unless the population is clearly defined and related to study objectives and field sampling procedures, the collected data may contain very little useful infonnation for the purpose at hand. Chapter 2 gives an approach for conceptualizing and defining populations that leads into the discussion of field sampling (survey) designs in Chapters 3-9. The important role that objectives play in detennining sampling designs is discussed in Chapter 3.Once data are in hand, the data analyst must be aware that many statistical procedures were originally developed for data sets presumed to have been drawn from a population having the symmetric, bell-shaped Gaussian ("nonnal") distribution. However, environmental data sets are frequently asymmetrical and skewed to the right-that is, with a long tail towards high concentrations, so the validity of classical procedures may be questioned. In this case, nonparametric (distribution-free) statistical procedures are often recommended. These procedures do not require the statistical distribution to be Gaussian. Alternatively, an asymmetrical statistical distribution such as the lognonnal may be shown or assumed to apply. Both of these approaches are illustrated in this book. Frequently, a right-skewed distribution can be transfonned to be approximately Gaussian by using a logarithmic or square-root transfon...

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Highlight: A double sampling procedure was employed for obtaining more reliable weight estimates for leaves, flowering stalks, live wood, dead wood, various combinations of the preceding, and total phytomass of sagebrush shrubs. Easily obtained dimension measurements were related to harvest categories using regression analyses. Volume (length x width x height) and length measurements were the most highly correlated to phytomass. Double sampling reduced the variance of the mean phytomass estimates ranging from 33% to 80% for the various categories assuming optimum allocation. The precision achieved by combining dimension measurements with harvesting is significantly higher than by harvests without supporting dimensional measurements. Efforts to obtain reliable phytomass estimates for rangeland shrubs by harvest methods are time consuming and costly. One approach is to establish a relationship between one or a few easily obtained plant measurements and harvest data. This approach has been termed double sampling or dimension analysis. Aboveground phytomass has seldom been measured in desert shrubs. Harniss and Murray (1976) found a relationship between foliage of big sagebrush and the independent variables, circumference, and height of plant. A correlation (Z?' = 0.93) was obtained with the developed dry weight predictor to determine total phytomass estimates of sagebrush. Chew and Chew (1965) determined shrub weights of creosotebush (Larrea divaricata) in Arizona, and Ludwig et al. (1975) used a double sampling method involving dimension analysis to estimate phytomass on eight species of desert shrubs in New Mexico. The results show that volume and canopy area were generally suitable estimators. Medin (1960) used a crown diameterweight relationship to predict foliage phytomass in mountainmahogany (Cercocarpus montanus) shrubs on Colorado mule deer range. There appears to be little published data on phytomass sampling in big sagebrush (Artemisia tridentutu) in the Pacific Northwest (Daubenmire 1970).

This paper reviews statistical procedures for estimating the mean p and stamdard deviation (S.D.) (T of radionuclide data sets containing negative, unreported, or "less-than" values resulting from concentrations being less than the analytical detection limit. Computational precedures leading to biased estimates are reviewed and alternate methods presented. The arithmetic mean, S.D. and median are compared with methods appropriate when the underlying distribution is lognormal and data sets are left-censored.

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