Data Quality Objectives

In this example, the quadrupole is scanned to mass A. The electronics are allowed to settle (settling time), left to dwell for a fixed period of time at one or multiple points 

FIGU RE 12.8 Multielement scanning and peak measurement protocol used in a quadrupole. 

Practical Guide to ICP-MS A Tutorial for Beginners, Second Edition 

Dwell time x sweeps x elanents x rephcates I Dwell time x sweeps x elanents x rephcates)-E (Scanning/settling time x sweeps x elements x reps)  

FIGURE 12.9 Measurement duty cycle as a function of dwell time with varying scanning/ settling times. 

In this example, the quadrupole is scanned to mass A. The electronics are allowed to settle (settling time), left to dwell for a fixed period of time at one or multiple points on the peak (dwell time), and intensity measurements are taken (based on the dwell time). The quadrupole is then scanned to masses B and C, and the measurement protocol is repeated. The complete multielement measurement cycle (sweep) is repeated as many times as needed to make up the total integration per peak. It should be emphasized that this is a generalization of the measurement routine— management of peak integration by the software will vary slightly based on different instrumentation. 

To achieve the highest duty cycle, the nonanaly tical time must be kept to an absolute minimum. This leads to more time being spent counting ions and less time scanning and settling, which does not contribute to the quality of the analytical signal. This becomes critically important when a rapid transient peak is being quantified 

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The steps outlined below are intended to guide the development of data quality objectives for the sampling effort. These have been discussed in part by others (1,2). 

Fong SH, Alvarez JL. 1997. Data quality objectives for surface-soil cleanup operation using in situ gamma spectrometry for concentration measurements. Health Phys 72 286-295. 

Another method (EPA 3611) that focuses on the to separation of groups or fractions with similar mobility in soils is based on the use of alumina and silica gel (EPA 3630) that are used to fractionate the hydrocarbon into ahphatic and aromatic fractions. A gas chromatograph equipped with a boiling-point column (nonpolar capillary column) is used to analyze whole soil samples as weU as the aliphatic and aromatic fractions to resolve and quantify the fate-and-transport fractions. The method is versatile and performance based and therefore can be modified to accommodate data quality objectives. 

The Facility Closure Plan includes two sampling and analysis plans. The first, Sampling and Analysis Plan for HWMU Closure (Appendix 2 in the Plan), includes requirements and procedures for conducting field sampling operations and investigations of soils and structures associated with the MDB and the HWMUs, as well as data quality objectives and field sampling protocols that could be used to verify decontamination (U.S. Army, 2000a). 

The foundation for the collection of relevant and valid data is laid out in the planning phase through the completion of Task 1—Data Quality Objectives (DQOs) Development and Task 2—Sampling and Analysis Plan (SAP) Preparation. The DQO process enables the project participants to come to the understanding of the... 

Task 2. Sampling and Analysis Plan Preparation Task 1. Data Quality Objectives Development... 

Planning is the most critical phase of the data collection process as it creates a foundation for the success of the implementation and assessment phases. Two major tasks of the planning phase, as shown in Figure 2.1, are Task 1—Data Quality Objectives Development and Task 2—Sampling and Analysis Plan Preparation. The SAP summarizes the project objectives and requirements for environmental chemical data collection. 

Figure 2.2 The seven steps of the Data Quality Objectives process (EPA, 2000a).

A4 Project/task organization A5 Problem definition/background 2.1 What are the Data Quality Objectives 2.2 Definitive, screening, and effective data... 

Data validation may be conducted at two different levels of diligence Level 4 and Level 3. Level 4 validation is the most comprehensive of two. Different levels of data validation originate from different levels (1 through 5) of data quality objectives defined by the EPA in one of the older, outdated documents (EPA, 1987). The designation of these levels, nevertheless, became an industry standard. 

US Environmental Protection Agency, Development of Data Quality Objectives, Description of Stages I and II, July, [US Environmental Protection Agency, 1986]. 

US Environmental Protection Agency, Data Quality Objectives for Remedial Response Activities, EPA/540/G-87/003, [US Environmental Protection Agency, 1987]. 

US Environmental Protection Agency, Guidance for Data Quality Objectives Process, EPA QA/G-4, [US Environmental Protection Agency, 2000a]. 

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... 

US Environmental Protection Agency [USEPA]. 2000a. Guidance for the data quality objectives process. Washington (DC) US Environmental Protection Agency, Office of Environmental Information, 100 p. http //www.epa.gov/quality/qs-docs/g4-final.pdf (accessed December 28, 2007). 

Designated areas of competence of the model, including time, space, pathogens, pathways, exposed populations and acceptable ranges of values for each input and jointly among all inputs for which the model meets data quality objectives. 

A method intended to provide accurate exposure and risk using appropriately rigorous and scientifically credible methods. The purpose of such methods, models or techniques is to produce an accurate and precise estimate of exposure and/or risk consistent with data quality objectives and/or best practice. 

The property of a set of observations such that they are characteristic of the system from which they are a sample or which they are intended to represent, and thus appropriate to use as the basis for making inferences. A representative sample is one that is free of unacceptably large bias with respect to a particular data quality objective. 

Develop a detailed site investigation, sampling plan, and data quality objectives... 

Before sampling can start however, a sampling strategy needs to be developed. This strategy is based on the conceptual model (hypothesis). Further, the number of samples needed and the required sampling locations are also determined. A further determination is made whether the use of composite or grab samples is appropriate. One needs to know how the data will be used and evaluated. All these are part of the data quality objectives on which a sampling plan is based. 

If compositing reduces the number of samples collected below the statistical need of the data quality objectives, then those objectives will be compromised. 

This thus requires a sampling plan that reflects the data quality objectives and analytical measurement subjected to the laboratory quality system (Swyngedouw and Lessard, 2007). The measurement uncertainty can be controlled and evaluated (Eurachem, 2000). The sampling variance may contain systematic and random components of error from population representation and sampling protocol. Note that the errors are separate and additive. This means that the laboratory cannot compensate for sampling errors. 

All sampling and laboratory activities are aimed at one target the production of quality data that is reliable and has a minimum of errors. Further, reliable data must be produced consistently. To achieve this, an appropriate program of quality control (QC) is needed. Quality control includes the operational techniques and activities that are used to satisfy the quality requirements or data quality objectives (DQOs) (FAO, 1998). 

Figure 1 presents the overall steps of the various EPA evaluations for immunoassays. Immunoassays are submitted to the EMSL-LV for evaluation. Raw data as well as data analyses must be submitted for review prior to initiation of a method evaluation. With the aid of a statistician, individual evaluation studies will then be designed to test the specific immunoassay and ensure that data quality objectives are met. These studies will be based on EPA and AOAC protocols for conducting evaluation studies (3). 

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