Data quality assessment

Data quality assessment is a scientific and statistical process that establishes whether the collected data are of the right type, quality, and quantity to support their intended use (EPA, 1997a). DQA consists of data evaluation, which may be conducted as data validation or review, and of reconciliation of the collected data with the project DQOs. 

In the course of DQA, the data will be evaluated and compared to the project DQOs to answer the four questions, first presented in Chapter 1.5  

Do the data have a well-defined need, use, and purpose  

The answer to the first question will establish the appropriateness of the collected data type and quantity or data relevancy, whereas the remaining three answers will establish the data quality or data validity. If the answers to all four questions are positive, the data may be confidently used for project decisions. A negative answer to any of them will reduce the level of confidence with which the data may be used or even make a data set unusable. 

Why do we need to perform DQA The need for DQA arises from the very existence of total error. The collection of planned data may go astray due to unforeseen field conditions, human errors or analytical deficiencies that may alter the type, quality, and quantity of the data compared to what has been planned. We use DQA as a tool that enables us to evaluate various components of total error and to establish their effect on the amount of valid and relevant data collected for each intended use. 

The quality of input data is important for the credibility of the study. No matter how performaint the methodology, it can never compensate for poor data input. The evaluation of data quality should  

Special attention should be paid to conditions that are specific to the considered operations. As an example, very few industrial plants operate in isolation some services, such as the treatment of water effluents and the provision of steam, are often common to several plants within the production site. If the distribution of these services is not well-defined, it can be very tricky to quantify the consumption of each plant or process. Furthermore, if a process produces several useful outputs, the consumption of energy and raw materials of the process must be distributed between these outputs in a rational and transparent marmer. 

Companies generally only keep the minimum of records necessary to run their plants with respect to processing conditions, legislative requirements and plant operation permits. The information necessary for a LCA may not be available, or it may be available but not in a useful form. For example, if emission figures are available in terms of concentrations it may be difficult to explain them, since it may not be clear at what distance from the source of emissions the concentration has been calculated or under what wind and weather conditions. 

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Data quality assessment requirements are related to precision and accuracy. Precision control limits are established, i.e., 4-10% of span value, as calculated from Eq. (15-1). The actual results of the may be used to calculate an average deviation (Eq. 15-3) ... 

Quality Assessment. Data validity is established through the application of data evaluation procedures their relevancy for making project decisions is determined in the course of the data quality assessment (DQA) process. 

What is the importance of the null and the alternative hypotheses They enable us to link the baseline and alternative condition statements to statistical testing and to numerically expressed probabilities. The application of a statistical test to the sample data during data quality assessment will enable us to decide with a chosen level of confidence whether the true mean concentration is above or below the action level. If a statistical test indicates that the null hypothesis is not overwhelmingly supported by the sample data with the chosen level of confidence, we will reject it and accept the alternative hypothesis as a true one. In this manner we will make a choice between the baseline and the alternative condition. 

Planning documents preparation —Laboratory procurement —Field and sampling equipment procurement —Preparation for mobilization —Field sampling —Field and laboratory audits —Sampling and laboratory oversight —Data evaluation —Data quality assessment... 

Data Quality Assessment Report (DQAR) preparation... 

D3 Reconciliation with user requirements 5.3 The seven steps of the data quality assessment... 

Preparation and analytical batches must be clearly identified with a unique number in laboratory bench sheets, notebooks, and computer systems. The same applies to QC check samples associated with each batch. During data quality assessment, the data user will determine the quality of the field sample data based in the results of the batch QC check samples that are part of the preparation and analytical batches. The data user will examine batch QC check samples first and, if they are acceptable, will proceed to individual sample QC checks. A complete examination of these QC checks will enable the data user to evaluate the quantitative DQIs (accuracy and precision). A combination of acceptable batch QC checks and individual QC checks makes the data valid on condition that the qualitative DQIs (representativeness and comparability) are also acceptable. 

At the top of the data collection pyramid shown in Figure 5.1 is assessment. By the time the data collection process enters assessment, its third and final phase, the provisions made in the planning phase have been already implemented in the field and at the laboratory as the requirements for sampling, analysis, and QA/QC activities. In the assessment phase, by conducting Task 6—Data Evaluation and Task 7—Data Quality Assessment, we will establish whether the collected data are valid, relevant, and may be used for project decisions. 

The EPA developed a document titled Guidance for Data Quality Assessment, Practical Methods for Data Analysis, EPA QA/G-9 (EPA, 1997a) as a tool for project teams for assessing the type, quality, and quantity of data collected for projects under the EPA oversight. This document summarizes a variety of statistical analysis techniques and is used primarily by statisticians. DQA, however, is not just a statistical evaluation of the collected data. It is a broad assessment of the data in the context of the project DQOs and the intended use of the data, which requires a... 

Data Quality Assessment Report (DQAR) is the last stone placed at the top of the data collection pyramid. The DQAR summarizes the data collection activities and states whether the data are of the right type, quantity, and quality to support their intended use. 

US Environmental Protection Agency, Guidance for Data Quality Assessment, Practical Method for Data Analysis, EPA QA/G-9, [US Environmental Protection Agency, 1997a]. 

DOD DOT DQA DQAR DQIs DQOs DRO Department of Defense Department of Transportation Data Quality Assessment Data Quality Assessment Report Data Quality Indicators Data Quality Objectives diesel range organics... 

FASTX-Toolkit http //hannonlab.cshl.edu/ f astx to o 1 k i t/i ndex.html Sequencing data quality assessment and preprocessing, for example, adapter sequence removal, sequence trimming, and conversion of sequence reads to reverse complement... 

FastQC http //www.bioinformatics. babraham.ac.uk/projects/fastqc/ Sequence data quality assessment... 

USEPA (2000) Guidance for data quality assessment. Practical methods for data analysis. Washington, DC, United States Environmental Protection Agency, Office of Research and Development, July (EPA/600/R-96/084 http //www.epa.gov/quality/qs-docs/g9-final.pdf). 

A fourth property of significance in data quality assessment is the limit of detection, that is, the lowest value detectable. This value has to be known in order to properly process very low concentration values. If the limit of detection for lithium is 0.015 mg/1, then a value of 0.07 mg/1 is analytically significant (this point is further discussed in section 5.8). 

In addition to the exposnre model documentation components noted above, the American Indnstrial Health Council (AIHC) (1994) and USEPA (1997a) have recommended data, particularly those based on Monte Carlo simulation, that are also relevant for simulation models being used as part of the overall assessment process. The USEPA has also issued guidelines for data quality assessment (USEPA, 1996b) relevant to model documentation. Some of these principles are listed below more details are provided in USEPA (1992a, 1997b), AIHC (1994) and Burmaster and Anderson (1994). 

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