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W S Hitchcock,
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Data Quality
Assessment
Data Quality Assessment (DQA) is a rigorous scientific and statistical evaluation to determine if environmental data are of the right type, quality, and quantity to support their intended use. The process involves review of data quality objectives (DQO), sampling purpose, sampling design, sampling methods, documentation, analytical procedures, validation procedures, data reduction procedures, review of database procedures, and review of statistical methods used for decision making. The process is not limited to analytical data, but includes all data types used by the decision makers. Although data validation is often employed during DQA, it is not always necessary. The question that needs to be answered by the data quality assessment is "Are the data appropriate for the intended use?"
It is important to note that the DQA process covers all components of decision making including planning, implementation, data review, and decision making. It is not a process that is limited to review of analytical data (see data validation). While data validation and verification are important processes, they are activities that are often applied only to analytical data. High quality analytical data can be useless if the samples were collected improperly, if there are errors in the electronic database, if improper statistical test were selected, or if the sampling plan did not generate sufficient samples.
The five steps in the DQA process are:
1 - Review the DQOs and Sampling Design
2 - Conduct a Preliminary Data Review
3 - Select the Statistical Test
4 - Verify the Assumptions of the Statistical Test
5 - Draw Conclusions from the Data
The figure at the top of the page is an example of one of the techniques that can be used to draw conclusions from the data. The figure is a ranked order plot that contains the results of metal analyses for an area under investigation. For this data set, lead was used as the indicator parameter. The lead data were plotted from lowest to highest and the results of the other three metals were also plotted. All data were on a dry weight basis to eliminate moisture bias.
The five samples that had the highest lead values also had the highest values for copper and chromium. These samples were taken in an area that was visibly contaminated. Based on this information and knowledge of the waste, it was safe to assume the lead, copper, and chromium was associated with the waste. Arsenic, however did not appear to be associated with the waste. The arsenic was either from another waste or was the result of natural background. Based on other information, it was concluded that the arsenic was natural background. Natural background levels for all four metals can be easily estimated from the figure or more accurately from a probability plot such as a Mann-Whitney Rank Sum Test.
Information on DQA is available on the world wide web.
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Last update: April 15, 2005.