Читать книгу Global Approaches to Environmental Management on Military Training Ranges - Tracey Temple - Страница 47

Before going to the field to obtain samples, a sampling plan must be created. A key element of the sampling plan is the establishment of objectives that the samples are to meet. In the US, the Environmental Protection Agency has established a method based on what it calls the ‘data quality objectives (DQO) process’ [28]. The DQO process is iterative and will evolve as the project progresses. The SQC process, described in section 2.3.1, is a more direct approach that pares the development of objectives down to its essentials. Other countries have processes similar to these. The key elements for any sampling plan are that the sample must represent the mean concentration of the analytes in the DU, the samples must be reproducible (replicate samples from the field, not subsamples in the lab), and the data resulting from the analyses of the samples must be defensible. Without a robust sampling plan, the samples collected will not fulfill the objectives of the project.

At this point, it is worth saying something about ‘hot spots’. The concept of hot spots has been around since samples were first collected. However, the definition of a hot spot has not [29, 30]. There are no mass, distribution, or availability criteria associated with a hot spot. Thus, hot spots are meaningless. If there is any analyte present in a DU, the highest concentration, the hottest hot spot, will be a million parts per million: a particle of pure substance (if it is present in that form). How many hot spots by this definition will occur within a DU? Many! Is there enough mass associated with these ‘hot spots’ to pose a risk to the receptor within the DU? The only way to determine this is to estimate a mean value of the analyte within the DU. Thus, the most important estimate that can be made within a DU is the mean concentration of the analyte. Hot spots are irrelevant.