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e i- n .'e 1- n h s - t. is is 3. r- ly re a- >1- 96 ogy to assure the process is in control. The measures may be procedural, such as samples must be analyzed within 12 hours of instrument calibration, or nu- merical, such as the recovery of a spiked analyte must be 80 percent to 120 per- cent. Data validation verifies the labora- tory has complied with all QC require- menrs of an analytical method. The val- idation is performed, normally using a checklist, by making QC checks that cover the procedural and numerical re- quirements of an analytical method. For a given parameter, the data quality re- quirements can differ slightly or signifi- cantly for the major analytical methods (SW -846, CLP or other EPA series). QC checks are made by examining the laboratory data and comparing reported values against method requirements. Calculations from raw data are also made to verify the reported values. (Raw data are unprocessed and taken directly from the instrument printouts instead of the laboratory QC reports.) A high-profile remediation verifica- tion will normally require all the QC checks specified in the method. Progress monitoring may require only a fraction. Another variable is the amount of raw data calculations to perform. The deci- sion is based on the QC level and the project staffs confidence and familiarity with the laboratory. Raw data calcula- tions are labor-intensive and normally nor made on more than 20 percent of the data. Of course, if errors are found, more calculations are required. Data Qualification/ Review (Flagging) Laboratories normally apply qualifiers-- flags—to the analytical dare that directly relate to the concentration value report- ed. For example, a "U" flag is applied to the concentration value (in this case, the detection limit) of analytes that are not detected in the sample. Likewise, the laboratory applies "J" and "E" flags to concentration values that are below or above the calibration range of the instru- ment. For most analysis types, though, the laboratory does not apply flags that relate to the QC requirements of the an- alytical method. Data qualification—sometimes called data review—involves flagging analytical data, according to pre -established func- tional guidelines, to reflect any QC fail- ures. The procedure includes flagging each sample to reflect any failures for the September 1996 u n with rhe laboratory can sample itself, such as extended holding corramunrca o time, and any failureof a QC sample ref- often improve data quality. erenced to the sample, such as blank contamination. Normally, the procedures follow in the U.S. EPA Contract Laboratory Program National Functional Guidelines for Organic Data Review and Guidelines for Inorganic Data Review. The procedures, commonly referred to as NFG, produce flags to indicate if a concentration value is only an estimate of the actual value or if it is completely rejected for use. The Region III Modifications to NFG also produce flags to indicate if estimated val- ues are biased low or high. Alternatively, functional guidelines specific to the pro- ject can be developed and used. Like data validation, data qualification can be stan- dardized. Suitability Determination Depending on a particular project, ana- lytical data that is qualified—for exam- ple, as biased low—may or may not be suitable for use. A suitability determina- tion assesses the suitability of qualified analytical data for the intended use. It is the most critical step of data quality as- sessment and requires a thorough under- standing of both the analysis procedure and the environmental project. The determination is made by exam- ining the qualified data for each sample and the QC check completeness—per- centage of samples passing each check— for the data set. In some cases, it may be possible to use the NFG flags applied during data qualification to indicate data suitability. In other cases, the NFG flags may not be appropriate, and additional flagging is required. Data Rescue Based on a data validation, qualification and suitability determination, some an- alytical data cannot be used as reported by the laboratory. It can be quite ex- pensive to re-collecr and re -analyze the samples. Aa analytical chemist with broad interpretive experience asking the right questions can often uncover ways to rescue the current analytical data and/or improve the quality of future data. This may involve applying correc- tion factors or calculating sample -spe- cific detection limits for the data. Modifying the sampling or analysis technique or selecting a more appropri- ate analytical method may also be war- ranted. Simply establishing regular An Automated Future As an ultimate goal, data quality assess- ment procedures should be standardized and uniform throughout the environ- menral industry and applied to all envi- ronmental analysis data. Certain aspects of data quality assessment, namely data validation and qualification, lend them- selves to automation, and the only prac- rical approach is an automated software application that processes electronically formatted laboratory data. The software application must be able to process raw laboratory data; validate to control limits for all the major environ- mental methods; incorporate new meth- ods; easily accommodate changes in cur- rent methods; incorporate project -specif- ic control limits; accommodate different QC protocols and communicate with other software. A logical step would be to include data management functions— preparation of sampling documents, tracking of the data quality assessment, and storage and retrieval of technically valid, legally defensible data of known and documented quality. Once a system is set up to read a labo- ratory's data, it is possible to automatical- ly process all analysis data at a QC level that included all QC checks and 100 per- cent raw data calculations for less than it costs to manually process the same data with fewer QC checks and only 20 per- cent raw data calculations. Because data for both environmental and QC samples would reside in the system, it would Pro- vide a perfect environment for evaluation of historical data either by the engi- neer/scientist or QA personnel. M Taryn G. Scholz is a chemical engineer and tic,_ resident of Quality Assurance Associate' (QAA), College Station, Texas. Louise McGinley is a chemist and presi- dent of QAA. Donald A. Flory, Ph.D., is a principal of QAA. For more information on Quality Assur- ance Associates, circle 234 on card. What oo You Think? Please indicate your level of interest in this article by circling the appropriate number on the Reader Service Card. High 231 Medium 232 Low 233 Environmental PROTECTION 131