PARCC parameters

To encompass the seemingly incompatible qualitative and quantitative components of total error, we evaluate them under the umbrella of so-called data quality indicators (DQIs). DQIs are a group of quantitative and qualitative descriptors, namely precision, accuracy, representativeness, comparability, and completeness, summarily referred to as the PARCC parameters, used in interpreting the degree of acceptability or usability of data (EPA, 1997a). As descriptors of the overall environmental measurement system, which includes field and laboratory measurements and processes, the PARCC parameters enable us to determine the validity of the collected data. 

We will revisit the application of the PARCC parameters in Chapters 2, 4, and 5 of the Guide and discuss them in detail. The following brief definitions (EPA, 1997a) will help us understand their relation to the concepts of total error and data quality ... 

The PARCC parameters are meaningful only if compared to certain standards as a means to determine their acceptability. The EPA gives the following definition to these qualitative and quantitative standards, which are called acceptance criteria (EPA, 1997a) ... 

Similar to the PARCC parameters, acceptance criteria are expressed in qualitative and quantitative terms. Some of them are statistically derived values, while others are purely qualitative and represent industry standards and accepted practices. Quantitative parameters (precision, accuracy, and completeness) are evaluated mathematically and compared to numerical acceptance criteria representativeness, which is a qualitative parameter, is established by comparing documented field and laboratory procedures to applicable standards and specifications. Comparability is estimated as the closeness of analytical results obtained at two different laboratories, and is usually expressed qualitatively. In environmental project work, acceptance criteria for the PARCC parameters are documented in the SAP. 

A comparison of a data set PARCC parameters to the corresponding acceptance criteria enables us to evaluate all of the quantitative and qualitative aspects of total error. Because every component of total error affects at least one of the PARCC parameters, as shown in Table 1.1, we should make every effort to minimize their cumulative degrading influence on the PARCC parameters and consequently, on data quality. 

Acceptance criteria for the PARCC parameters are derived from an extensive system of practices and regulations that define the accepted standard for environmental data collection. Included in this comprehensive standard are environmental laws guidance documents from government agencies and local authorities, overseeing environmental work specifications developed by professional... 

Table 1.1 Total error components and PARCC parameters...

Acceptance criteria for the PARCC parameters 2.1.8 DQO process in simple terms... 

The quality of analytical data is assessed in terms of qualitative and quantitative data quality indicators, which are precision, accuracy, representativeness, comparability, and completeness or the PARCC parameters. The PARCC parameters are the principal DQIs the secondary DQIs are sensitivity, recovery, memory effects, limit of quantitation, repeatability, and reproducibility (EPA, 1998a). 

Qualitative and quantitative acceptance criteria for the PARCC parameters are derived in the planning phase. Whether they are specific statistical values or represent accepted standards and practices, they must be always selected based on the project objectives and be appropriate for the intended use of the data. The DQI acceptance criteria are documented in the SAP and serve as standards for evaluating data quality and quantity in the assessment phase of data collection process. The primary DQIs are established through the analysis of field and laboratory QC samples and by adhering to accepted standards for sampling and analysis. 

Completeness is a measure of whether all the data necessary to meet the project objectives have been collected. Completeness is calculated only after the rest of the PARCC parameters have been calculated or qualitatively evaluated to determine the validity of data. It is the final and all-inclusive indicator of data usability. The DQI of completeness enables us to determine whether data of acceptable quality have been collected in sufficient quantity to meet the project objectives. 

Why do we need extensive and complicated data validation packages instead of compact standard laboratory reports Because data validation is necessary to establish a certain level of confidence in the quality of obtained data. Data validation enables the data user to determine whether acceptance criteria for the PARCC parameters have been met and to make the quality of the data known. Standard laboratory reports support only cursory review of basic laboratory QC checks. Data that have not been reviewed or validated cannot be considered data of known quality. 

Data quality has a meaning only in relation to the intended use of the data and when defined by a certain standard. The standard comprises a system of applicable general and project-specific requirements for sampling and analysis, and when stated in the SAP, forms the qualitative and quantitative acceptance criteria for the PARCC parameters. 

Data evaluation is the process of establishing the collected data quality by confirming that the data meet the acceptance criteria for the PARCC parameters. Only after the quality of data has been established through a comparison to a standard, the quantity of valid data may be assessed. 

PARCC parameters (typically, accuracy, precision, and representativeness) for the samples in the reviewed data package. A checklist prompts a structured approach to data evaluation and allows documenting deficiencies. Appendices 23, 24, and 25 contain examples of such checklists for organic, trace element, and inorganic analyses. 

By the time Step 5 is conducted, accuracy, precision, and representativeness have been assessed and the data have been qualified accordingly. In DQA Step 5, the chemist will assess the data collected for each intended use in terms of the other remaining PARCC parameters, comparability and completeness. 

The emphasis of a DQAR prepared to this outline is on the DQIs (the PARCC parameters), their acceptance criteria, and their meaning for data usability. While focusing on data validity and relevancy relative to the project DQOs, the DQAR, in fact, establishes whether the data collection process has been successful in each of its three phases—planning, implementation and assessment. 

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