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    Assessing Chemical Data Quality for Risk Assessment Through Data Validation

    Published: 01 January 1996

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    The chemical analytical data that are generated as part of every Superfund investigation are vital for the decisions that will be made regarding selection of a remedy and, ultimately, the future use of a site. Data validation is a process whereby laboratory data (and, to a limited extent, field data) are rigorously qualitatively and quantitatively scrutinized to determine the correctness of the results and to determine if the specified data quality objectives (DQOs) have been achieved. Data quality indicators are used as a gauge to determine if the quality of the data is adequate for the intended use.

    Although data validation has become recognized as an important aspect in assessing data quality, a tendency has evolved to turn the validation process into a “checklist” exercise. A case study provides compelling evidence to avoid such approaches, which “short circuit” rigorous data validation. In the case study, rigorous validation of raw instrument output revealed analytical problems that had a significant impact on the risk assessment. These analytical problems were not identified during the initial checklist data validation approach.


    data validation, data quality objectives, data quality indicators, hazardous waste sites, raw instrument output

    Author Information:

    Vitale, RJ
    Quality Assurance Specialist, Environmental Standards, Inc., Valley-Forge, PA

    Committee/Subcommittee: D18.14

    DOI: 10.1520/STP16015S