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  • 1990-1994  (2)
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  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Ground water monitoring & remediation 14 (1994), S. 0 
    ISSN: 1745-6592
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Geosciences
    Notes: Current federal ground water monitoring statistical regulation dates from the revised RCRA Subtitle C Final Rule of 1988. That rule was a considerable advance over previous RCRA statistical rules. However, two major problem areas remained: facility-wide false positive rate (FWFPR) control and spatial variability. Progress has been made in the 1991 Subtitle D Final Rule and in guidance: the 1992 Addendum to Interim Final Guidance in particular includes a substantial conceptual advance toward resolving the FWFPR problem. Other areas of improvement include normality testing and distribution assumptions, dropping the four independent samples per monitoring period requirement, allowing a preliminary evaluation short of a 40 CFR Part 258 Appendix II assessment upon finding a statistically significant increase, and suggesting superior alternatives to analyses of variance (ANOVAs) and tests of proportions.The problem of dealing with natural spatial variability remains. Although certain techniques listed in the regulations can control for inherent spatial variability and the performance standards require doing so “when necessary,” little attention has been paid to the ubiquity of such spatial variation. Moreover, regulatory traditions favoring upgradient-downgradient comparisons often make control of natural spatial variation difficult and ineffective. With new. lined facilities easily implemented statistical solutions are available; however, dealing with the several existing solid waste facilities which will now be regulated under Subtitle D will present major challenges.In short, the 1988 revision of the Subtitle C rules made it more possible to provide statistically sound monitoring programs, and there has been steady progress since then. Challenges remain, however. These vary from slate to slate, particularly with regard to controlling false positives and false negatives in the presence of natural spatial variability.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Ground water monitoring & remediation 14 (1994), S. 0 
    ISSN: 1745-6592
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Geosciences
    Notes: Nonparametric prediction limits can be useful statistical tools for ground water monitoring at facilities regulated under RCRA Subtitle C. Subtitle D. and similar regulations. New, exact tables arc presented for both “1 of m” plans (m chances to gel one observation inbounds at each of r monitoring wells to avoid a statistically significant increase) and “California” plans (first or all of the next m-1 observations inbounds at each well). The tables provide per-constituent significance levels (false positive rates) as a function of the background sample size n. m. r, the prediction limit (the largest or the next to largest, background observation), and the confirmatory resampling plan selected.When used in a monitoring program, future observations from several wells are compared with a prediction limit obtained from a common background sample. The table significance levels therefore depend critically on having IID (independent and identically distributed) observations. In particular, the false positive rate computations are not valid, and the procedures should not be used, with constituents whose measurements exhibit inherent spatial or systematic temporal variability.Recent U.S. EPA guidance explicitly encourages controlling facility-wide false positive rates over both constituents and wells. Nonparametric prediction limits, particularly with California resampling plans, will have greater difficulty in meeting the new. lower per-constituent false positive rate goals than previous ones, especially if many constituents are involved. Nonetheless, nonparametric prediction limits remain superior to other commonly used procedures for dealing with data with high proportions of nondctects.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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