Data Quality Objectives. The data of primary interest in this verification are the reductions in emissions of the FTP primary pollutants: NOx, hydrocarbons (HC), PM, and carbon monoxide (CO). The DQOs of this GVP are the requirements of the test methods specified in 40 CFR Part 86 (highway diesel engines) or 89 (nonroad diesel engines) when conducting the number and type of tests called for by the approved test/QA plan for the SCR. ETV tests that do not meet the FTP and SET QA requirements are invalid. The number of and type of FTP tests (cold- or hot-start) required for ETV is determined from the following criteria: First, a minimum of three tests is required to provide the basic ETV result of a mean emission reduction and the 95 percent confidence interval on that mean based on measured variability for each of the measured emissions and test parameters. For highway engines, this minimum is satisfied with one cold start test and three hot start tests. For nonroad engines, three replicates of the appropriate test sequence (i.e., three 8-mode tests or three 6-mode tests) are required. A three test minimum is currently the same as is required by the State of California for its program. Second, additional tests may be required to meet the ETV requirement that the test/QA plan provide a 90 percent probability of detecting the expected emissions reductions when computed using the expected experimental errors for the various measurements. These criteria become controlling for low emissions reductions and/or high test variability. This is a planning requirement for the test/QA plan. Third, additional tests may be desired by the applicant to reduce the width of the 95 percent confidence interval on the mean emission reduction. This third criterion is a consequence of applying standard statistical procedures to the ETV test design and data analysis. At a fixed measurement variability, normal statistical procedures lead to a small number of tests giving a broader 95 percent confidence interval than would a larger number of tests. To any regulator or potential technology user, an emission reduction of 40 ± 5 percent is better than 40 ± 20 percent and will be given more credence. Noncritical measurements, including ammonia slip, will also be made as described in later sections. These are not considered critical, and the methods and DQOs for them will be stated in the test/QA plan. The FTP tests referenced above are conducted following test cycles specified in 40 CFR. As discussed in Section ...
Data Quality Objectives. Provide data quality objectives that identify what data are needed and the intended use of the data.
Data Quality Objectives. The project objectives are to collect data in a manner that complies with WQCD guidance for surface-water quality monitoring programs, to support decisions related to TMAL development, stream standards modifications, permit decisions, water quality assessments and CRP ROD compliance. The following paragraphs define the measurement performance criteria necessary to support the project objectives.
Data Quality Objectives. The last sentence on page 1-14 is cut off and the missing text is found beneath Figure 1.5. but not visible to the reader. Please fix this issue.
Data Quality Objectives. The U.S. EPA has developed the Data Quality Objective (DQO) Process as the agency’s recommended planning process when environmental data are used to select between two alternatives or derive an estimate of contamination. The DQO Process is used to develop performance and acceptance criteria (or DQOs) that clarify study objectives, define the appropriate type of data, and specify tolerable levels of potential decision errors that will be used as the basis for establishing the quality and quantity of data needed to support decisions. Under this contract, the contractor shall implement the DQO process to ensure data of adequate quality are collected to support project decisions. Laboratories may be subject to on-site government audits of their Quality Assurance/Quality Control (QA/QC) protocols and procedures (not subject to the expense of the contractor). All laboratories shall meet DQOs specified in installation sampling and analysis requirements, and all laboratories shall perform QA/QC requirements as specified in the project-specific SAP.
Data Quality Objectives. The overall project data quality objective is to provide valid data of known and documented quality to characterize sources, determine location of contaminants at levels equal to or above screening or natural background quality levels, and screen for threats the site may pose to human health and/or the environment. Data gathered during the site investigation will provide the basis for decisions relating to future investigation requirements, human health and ecological risk screening or assessment, and remedial measures. The data quality assurance objectives for this project are to develop and implement procedures to collect representative samples and to provide chemical data of known quality. In order to meet these objectives, all field activities will be conducted according to the methods described in this SAP. Of particular importance will be to obtain data of sufficient quantity and quality, with appropriately low method detection limits, to support appropriate risk screening.
Data Quality Objectives. The DQO process is the application of systematic pla nning to generate performance and acceptance criteria for collecting environmental data. The out put of the DQO process is a set of qualitative and quantitative statements that describe s a data collection activity. Adherence to the DQO process ensures that data of known and appropriate quality support project decisions . The DQO planning process is the formalization of the normal process of planning, designing, and implementing environmental data collection activities. The output of the DQO process is a detailed sampling and analysis strategy. The relationship between the DQO process and the normal project li fecycle is illustrated in Table 2-1. (All tables appear at the end of this section.) The DQO process consists of determining what information is needed , why it is needed , how it will be used , and who will use it . The DQO process: Evaluates different sampling approaches based on cost and resource constraints . Selects the most cost -effective monitoring approach that will meet the needs of the ultimate data us er. Determines specific sampling and laboratory methodology requirements. The DQO process will facilitate data collection activities and will yield data meeting the needs of the user as defined in Guidance on Systematic Planning Using the Data Quality Obje ctives Process , EPA QA/ G -4, EPA/ 240/ B -06/ 001 (USEPA, 2006a). As defined in the above reference, the DQO process includes the following step s: Define Problem Statement . Identify the Goal of the Study . Identify Information Inputs . Define the Boundaries of th e Study . Develop Analytic Approach . Specify Performance or Acceptance Criteria . Develop the Detailed Plan for Obtaining Data . Additional guidance that may be helpful in developing project specific DQOs includes Systematic Planning: A Case Study for Hazardous Waste Site Investigations , EPA/ 240/ B -06/ 004 (USEPA, 2006b). Development of project DQOs is an iterative proc ess and should reflect a common -sense approach to environmental data collection and analysis. RWQCB anticipates that the general types of activit ies or steps that will be conducted using this QAPP will include, but will not be limited to: Initial site investigation. Site characterization. Remedial actions and site cleanup. Site closure. Figure 2-6 illustrates the outputs of the DQO process as it relates to site cleanup within RWQCB jurisdiction. Table 2-2 presents considerati...
Data Quality Objectives. Provide data quality objectives that identify what data are needed and the intended use of the data following the U.S. Environmental Protection Agency procedures in Guidance For The Data Quality Objectives Process, EPA QA/G-4, September 1994 or the most recent edition.
Data Quality Objectives
