Meeting Data Quality Objectives with Interval Information

Bayne, C.K.; Dindal, Amy; Jenkins, Roger A.; Crumbling, Deana M.; Koglin, Eric N.

doi:10.1021/es001572m

Cited by 4 publications

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“…However, analytical uncertainty is only one aspect of a measurement. DQOs should also consider the uncertainty in health-based standards, forensic tolerances, sample collection, and exposure pathways since each contributes to the overall uncertainty of a decision [28]. Of particular note, many investigators realize that a sample that does not accurately represent study conditions or the population of interest contributes the majority of uncertainty in the data resulting from that sample, which may be as much as 90% [29].…”

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confidence: 99%

The Importance of Data Reliability and Usability When Assessing Impacts of Marine Mineral Oil Spills

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2021

Toxics

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Spilled mineral oils in the marine environment pose a number of challenges to sampling and analysis. Mineral oils are complex assemblages of hydrocarbons and additives, the composition of which can vary considerably depending on the source oil and product specifications. Further, the marine microbial and chemical environment can be harsh and variable over short times and distances, producing a rigorous source of hydrocarbon degradation of a mineral oil assemblage. Researchers must ensure that any measurements used to determine the nature and extent of the oil release, the fate and transport of the mineral oil constituents, and any resultant toxicological effects are derived using representative data that adhere to the study’s data quality objectives (DQOs). The purpose of this paper is to provide guidance for crafting obtainable DQOs and provide insights into producing reliable results that properly underpin researchers’ findings when scrutinized by others.

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confidence: 99%