2.4.1 Sampling Considerations—Microscale Heterogeneity

Heterogeneity at various scales can lead to large variability in data sets from areas that have traditionally been expected to be fairly uniform. Heterogeneities at very small, apparently inconsequential, spatial scales can create the impression that large hot spots are present when discrete sampling is used. However, it is just as likely that heterogeneity can cause true hot spots to be missed, even though a sample was taken from within the boundaries of a hot spot. Taking a sample from within a hot spot is no guarantee that the few grams actually analyzed will reflect the hot spot's true average concentration. Both micro- and short-scale heterogeneity complicate detecting and delineating hot spots. See Section 3.5 for a further discussion of hot spots.