Regulatory purposes

Commonly, there are components that are not in any database of failure rates, or the data do not apply for the environment or test and maintenance at your plant. In addition, site specific data may be needed for regulatory purposes or for making the plant run safer and better. For both cases there is a need for calculating failure rate data from incident data, and the mechanics of database preparation and processing. 

In many industries, rule books have a tendency to become enshrined as policy statements, either for internal or external regulatory purposes. Unfortunately, the format that is appropriate for a regulatory or standards document is unlikely to fulfill the requirements of an effective operating instruction or procedure to provide assistance in carrying out a task effectively. 

For regulatory purposes the fuel economy assigned to the vehicle from these tests is based on the premise that the vehicle will accumulate 55 percent of its mileage on the urban schedule and 45 per-... 

The European Commission has mandated CEN/CENELEC to develop many standards to support manufacturers in the realisation of the essential requirements of the Directives. If the Commission determines that an adopted standard is fit for this purpose, then a reference is published in the Official Journal of the EU, which elevates the status of the standard to that of a harmonised standard for European regulatory purposes. 

Interest has been expressed in the possibility of using biomarker assays as a part of risk assessment for regulatory purposes, and some workers have suggested tiered testing procedures that follow this approach (see, for example, Handy et al. 2003). It is to be hoped that regulatory schemes, such as that of REACH (see European Union 2003), will be sufficiently flexible to incorporate new assays and testing strategies as the science advances. 

For regulatory purposes, food-based RMs play an important role in validating accuracy of analytical data from use of routine methodology. For example, the quality of data obtained by analytical measurements serves an important function with regard to ensuring nutritional label claims. Unfortunately, and historically in some cases, assay data for the same analyte can vary greatly from laboratory to laboratory. Evalua-... 

Because the conclusions that can be reached from an LSMBS depend critically on the choice of commodities, involvement of relevant regulatory agencies [e.g., EPA, USDA, and Food and Drug Administration (FDA)] in the USA, depending on the commodity and study objective] should be considered. Agreement on the choice of commodities by such authorities will ensure that the study and its outcome will be acceptable for regulatory purposes. 

Accurate, precise and sensitive analytical methods are important to the collection of data needed for regulatory decisions about pesticide registration. This article describes the various components of analytical method development, validation and implementation that affect the collection of pesticide residue distribution data for regulatory assessment of environmental fate and water quality impacts. Included in this discussion are both the technical needs of analytical methods and the attributes of study design and sample collection needed to develop data that are useful for regulatory purposes. 

A waste is toxic under 40 CFR Part 261 if the extract from a sample of the waste exceeds specified limits for any one of eight elements and five pesticides (arsenic, barium, cadmium, chromium, lead, mercury, selenium, silver, endrin, methoxychlor, toxaphene, 2,4-D and 2,4,5-TP Silvex using extraction procedure (EP) toxicity test methods. Note that this narrow definition of toxicity relates to whether a waste is defined as hazardous for regulatory purposes in the context of this chapter, toxicity has a broader meaning because most deep-well-injected wastes have properties that can be toxic to living organisms. 

Criteria for Evaluation of QSAR Models for Regulatory Purposes... 

Criteria which are suitable for regulatory purposes, to cope with a reduction of the risk complying with the EU regulations, the REACH legislation provides a good guidance on the requirements, since QSAR models are explicitly mentioned within the law, in Annex XI. 

For regulatory purposes greater attention should be given to models which avoid false negatives. Thus, in the evaluation, preference should be given when the model has lower false negatives. 

QSAR models with a regulatory purpose should mimic the in vivo (and occasionally, in vitro) data, which are typically used in the context identified by the law. As a consequence it should be very much preferable that also the data on the basis of the QSAR models are experimental data suitable for the regulation. In any case, their quality should be very high, and a check should be done on it. 

Other principles for the validation of QSAR models for regulatory purposes were edited by OECD in 2007 [8]. A good model must have ... 

Horwitz points out the universal recognition of irreproducible differences in supposedly identical method results between laboratories. It has even been determined that when the same analyst is moved between laboratories that the variability of results obtained by that analyst increases. One government laboratory study concluded that variability in results could be minimized only if one was to conduct all analyses in a single laboratory. .. by the same analyst . So if we must always have interlaboratory variability how much allowance in results should be regarded as valid - or legally permissible as indicating identical results. What are the practical limits of acceptable variability between methods of analysis - especially for regulatory purposes. 

Gerberick, G.F. et al., Local lymph node assay Validation assessment for regulatory purposes, Am. J. Contact Derm., 11,3, 2000. 

In the first section of my talk I hope to show why measurement of these effects as a function of regulatory actions under TSCA is not practical. If such effects are not measurable, then for regulatory purposes they must be estimated, usually by extrapolation from animal experiments. I will briefly indicate that quantitative extrapolation is an uncertain business. In the second section, I will summarize TSCA s probable impact on health, methodological difficulties in measurement notwithstanding. I will conclude with some remarks about recent regulatory pronouncements which seem to indicate that if past policies have had little discernible health impact, future ones may have even less. 

In animal experiments exposures can be carefully controlled, and dose-response curves can be formally estimated. Extrapolating such information to the human situation is often done for regulatory purposes. There are several models for estimating a lifetime cancer risk in humans based on extrapolation from animal data. These models, however, are premised on empirically unverified assumptions that limit their usefulness for quantitative purposes. While quantitative cancer risk assessment is widely used, it is by no means universally accepted. Using different models, one can arrive at estimates of potential cancer incidence in humans that vary by several orders of magnitude for a given level of exposure. Such variations make it rather difficult to place confidence intervals around benefits estimations for regulatory purposes. Furthermore, low dose risk estimation methods have not been developed for chronic health effects other than cancer. The... 

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