Global Approaches to Environmental Management on Military Training Ranges - Tracey Temple

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described in this chapter must be implemented in full to obtain reproducible data [12]. The authors have conducted extensive research into methods that were and are commonly used, and almost all of these methods do not result in reproducible data. Omitting one of the steps would be like leaving a wheel off an automobile: You can do it and maybe still get to where you want to go, but the results will be catastrophic. Even modifying one of the steps could result in bad data. Think of having all four wheels but modifying the tire pressure at one of the wheels by deflating the tire.

      The MIS process can be described as a series of five steps, all of which must be executed in order to obtain reproducible and defensible data [13, 14]. These steps are listed below:

       Step 1: Develop sample quality criteria

       Step 2: Determine material properties

       Step 3: Apply the principles of TOS to develop sampling protocol

       Step 4: Evaluate all data, including QC data

       Step 5: Make inference from analytical results to decision unit.

      The sample quality criteria (SQC) is formulation of the project objectives in such a manner that a sampling protocol can be developed [15]. The USEPA has also developed a similar approach called data quality objectives (DQO). The DQO process can be a bit complicated and makes many assumptions that may not be valid when investigating military ranges; therefore, the less complicated and more universal approach of the SQC process will be described. The three main parts of the SQC process are: (1) the Question that the study attempts to answer, (2) the Decision Unit (DU), and (3) the Confidence desired in the final decision.

       1) QuestionWhat is the analyte(s) of interest?– On a training range these may be pyrotechnics, energetics, or metals.What is the concentration of concern?– CoCs are risk-based and may be receptor-specific, i.e. human health or environmental risk.How are the data going to be used to make inference?– Data that is collected must be of sufficient quality and quantity to make the appropriate inferences (e.g. confidence intervals, means, medians, etc).

       2) Decision UnitThe DU is the specific mass/volume of material from which increments are collected and to which inference will be made [16, 17]. There may be one or many decision units within a military training complex, such as firing points, target areas, engineer training ranges and fuel or ammunition storage sites.

       3) ConfidenceThis is the assurance that the final decision is correct. Confidence can be based on statistical calculations or other techniques. Higher human health risk, public exposure and liability issues all impact the degree of confidence needed for a data set.

      For those new to sampling, the most difficult part of the SQC process is determining the parameters (physical and temporal boundaries) of a decision unit. Decision units are typically based either on risk or potential source areas. Risk-based DUs are based on exposure to a receptor in the area in which the receptor typically exists. In cases where there are multiple receptors, multiple-sized DUs may exist within the same area or the DUs may be based on the most sensitive receptor. While the concept of DUs is sometimes ignored, it is critical that the DU be identified or incorrect decisions will result. Much of the work performed by CRREL in applying the concept of MIS to military ranges was based on specifying DUs to best achieve the goals specific to the study. Earlier studies (before 2006) are not to be used as a starting point for determining DUs for an actual military range characterization. The other type of DU is a source-based DU. These DUs are based on the source area/volume and not on risk.

      There are two types of material properties that must be determined. This first is the nature of the elements: are they finite or infinite element materials. In the case of characterizing military ranges, all materials can be assumed to be infinite element materials and therefore the theory of sampling (TOS) must be applied.

      The second material property is the nature of the heterogeneity. There are two types of heterogeneity: compositional and distributional [18].

      Compositional heterogeneity exists when the individual elements (particles) that make up the DU do not have exactly the same concentration of the analyte of interest. Compositional heterogeneity (CH) always exists to some degree.

      Distributional heterogeneity (DH) exists when the individual elements that make up the decision unit are not randomly distributed throughout the entire DU. Examples of distributional heterogeneity are the settling of small, dense fines to the bottom of a container of soil and the uneven distribution of propellant residue across a weapons firing point. Like compositional heterogeneity, DH almost always exists and can be influenced by physical conditions. The magnitude and nature of CH and DH will vary for different materials.

      The theory of sampling (TOS) is used to develop a sampling protocol. This protocol is dependent on the SQC and the material properties. The TOS is used to determine the appropriate mass, number of increments and types of tools that must be employed. The sampling mass, number of increments and types of acceptable tools are driven by the amount of acceptable error, which in turn is based on the desired confidence determined in the SQC. Sampling errors can broadly be classified into three types: (1) fundamental sampling error, (2) grouping and segregation error, and (3) materialization error. Fortunately, for military bases, research done by CRREL, Envirostat, and Defence Research and Development Canada, Val Cartier (DRDC), has identified the starting point for mass, increments and tools.

      Fundamental sampling error

      Error attributable to compositional heterogeneity, the non-uniform composition of each particle in the DU or within the sample leads to a fundamental sampling error (FSE). FSE is a precision error that is controlled through the collection of sufficient mass to represent all the particles of varying composition. There are various formulas to estimate FSE, some quite complicated and others quite simple if certain assumptions can be made. The basic relationship of FSE to particle size, sample mass and compositional heterogeneity is as follows:

      FSE2∝Cd3ms

      where:

       FSE = Fundamental sampling error

       C = Sampling constant (g cm−3)

       d = diameter of largest particles (cm)

       ms = mass of sample (g).

      This equation is used to determine the mass necessary to control the FSE. The sampling constant (C) is unique for each type of material and needs to be determined. However, research done through the US Strategic Environmental Research and Development Program (SERDP) has determined that the mass necessary to control the FSE at an adequate level to make the appropriate and defensible inferences is approximately 2 kg.

      Grouping and segregation error

      Error attributable to distributional heterogeneity, the non-uniform distribution of particles of concern in the DU and the collected samples is called a grouping and segregation error. A grouping and segregation error (GSE) is a precision error that is controlled through the collection of an adequate number of random increments to make up the sample. An increment is defined as a group of particles selected during the single operation of a sampling tool.

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