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Statistically meaningful standards for contaminated sites using composite sampling
Author(s) -
Barnett Vic,
Bown Marion
Publication year - 2002
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.492
Subject(s) - sampling (signal processing) , population , statistics , verifiable secret sharing , limit (mathematics) , computer science , set (abstract data type) , mathematics , telecommunications , detector , programming language , mathematical analysis , demography , sociology
Current environmental standards commonly consist of a statement of some upper or lower limit to be met by pollutant levels ‘at large’ or of prescriptions of required outcomes from sampling procedures, with no consideration of the effects of uncertainty or variation. Barnett and O'Hagan (1997) classified these as ideal standards and realizable standards , respectively, concluding that neither form was satisfactory and recommending that they be replaced with statistically meaningful standards. Such standards should conjointly incorporate a regulatory limit coupled with a standard that provides some prescribed level of statistical assurance that the limit is actually being met, and are termed statistically verifiable ideal standards (SVISs). This recommendation was endorsed by the U.K. government Royal Commission on Environmental Pollution (1998). In many cases, realizable standards specify a particular sampling scheme and the outcome that is required from such a scheme to demonstrate compliance with the standard, but without consideration of the statistical interpretation of the outcome that might infer some property of the underlying population. The sampling methods take various forms and may feature composite sampling . One example of a set of realizable standards that consider composite sampling is found in the Australian Environmental Investigation Limits (EILs) for contaminated land sites (ANZECC/NHMRC, 1992). Under specific sampling guidelines (Standards Australia, 1997), composite sampling is allowed only if the conservative ‘divide‐by‐ n ’ principle is employed for adjustment of the standard limit. Using this set of standards and the associated sampling guidelines as motivation, we investigate an alternative SVIS that takes direct account of uncertainty and variation, and is statistically interpretable. The approach is developed initially for the illustrative case of independently and identically normally distributed pollution data, using a more statistically appropriate and less conservative principle than the ‘divide‐by‐ n ’ rule. We also consider other distributional assumptions for the pollution data. Copyright © 2002 John Wiley & Sons, Ltd.