Table of Contents
Step 1 | An Introduction to Developing a Volunteer Water Monitoring Program
Step 2 | Setting Goals and Objectives
Step 3 | Understanding Elements of Quality Assurance (QA) and Quality Control (QC) Protocols
Step 4 | Recruiting, Training, and Retaining Volunteers
Step 5 | Collecting Reliable Data
Step 6 | Data Collection, Analysis and Data Management
Step 7 | Evaluating the Success of Your Program
Additional Resources
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Step 3 | Understanding Elements of Quality Assurance (QA) and Quality Control (QC) ProtocolsAfter completing this section, you should be able to:
Quality Assurance (QA) and Quality Control (QC)
You’ve likely heard of Quality Assurance (QA) and Quality Control (QC) but you may not be sure what these terms mean. This step will provide you with basic definitions and why QA/QC procedures may be necessary in your volunteer monitoring program. Quality assurance ensures that the process you’re following, from the methods to be used to how data will be managed and analyzed to the final product, is adequate and appropriate for the project to meet its objectives. Some examples of QA procedures might include making sure that volunteers or staff are properly trained; instruments are calibrated according to manufacturer guidelines; and a set of standard operating procedures are clearly defined and available. Quality control consists of developing measures and protocols to make sure sample collection and analyses are consistent and correct. If there is a problem, good quality control will help you find out what the problem is. It also informs you whether or not the work your volunteers are performing is being done correctly. Quality control may include the number of replicate samples that need to be collected for chemical analyses, the use of field blanks, or including spiked samples. Replicate samples are two or more samples taken from the site being sampled. Procedures for taking the samples should be the same and occur near the same spot at approximately the same time. Field blanks usually contain distilled water but are treated in the same manner as the sample. When determining concentrations, the field blank should be free of the parameter being measured. Spiked samples or samples with known concentrations might also be used to help ensure that the preparation of the sample or the actual analysis did not cause a change in the concentration. Just as with field blanks, spiked samples are treated in the same manner as the sample in question. Both spiked samples and field blanks can be used to determine if there was any contamination from the sampling method, the laboratory testing or while shipping the sample. Generally, the rule of thumb is to have 10% of samples be quality control samples, which mean that they should be analyzed by a lab, independent of the volunteer doing the analysis. For non-chemical analyses, quality control measures may include standarization of sampling methods and habitat assessment, replicate readings of physical parameters, and taxonomic verification of species by a professional. As with chemical sampling, 10% of the sorted samples should be examined by trained professionals or a qualified co-worker. Your volunteer monitoring goals will also help determine whether you need a written document that provides detailed information on procedures for data collection and analyses, and other components of your project. The document is called a QAPP or a Quality Assurance Project Plan. If your project is funded through EPA then it is required and must be approved. But, even if it’s not required, a QAPP is beneficial to develop as it provides you and your volunteers with detailed written information that outlines procedures to obtain reliable data. The QAPP often contains detailed information concerning project management, measurement/data acquisition, assessment and oversight, and data validation and usability. It usually is developed in conjunction with EPA, a state agency, and/or quality assurance experts. Whether or not a QAPP is developed, it is important to have good QA and QC procedures in place which provide credibility to your data and help members of your group follow the selected protocols.
Data Quality Indicators
Due to variability in natural systems as well as variability in monitoring by individual volunteers there may be different results when analyzing duplicate samples. Associated with QA/QC procedures are various terms that can describe this variability and lead one to better understand the data. These measures are referred to as data quality indicators and are used to assess your data and its quality. These measures include: precision, accuracy and bias, representativeness, completeness, comparability, detection limit, and measurement range. Chapter Three from the US EPA manual, “The Volunteer Monitor’s Guide to Quality Assurance Project Plans” focuses on basic QA/QC concepts and provides a review of these terms and what they mean. A summary of that information appears below.
Self-Assignment:
Elements to a QAPP
There are a number of other elements or parameters that can help your program meet its goals and objectives. These include project management; collecting data, analyzing it, and managing it; project oversight; and validating and verifying the data. Chapter 4 of the EPA manual mentioned earlier provides more detailed information on these and provides examples from a fictional volunteer monitoring program. In your program, it is important to decide which of the elements or parameters are needed and which will give you confidence in the data you collect. Revisiting your Goals
In determining the degree of accuracy of your data, the type of equipment to use, and other QA/QC procedures, you need to revisit your goals and objectives for your monitoring program. If the goal is to provide education, the sampling protocol may only require volunteers to have minimal replicates and equipment with less sensitive detection limits than what a regulatory agency might require. If the goals are to share the data with state agencies or to provide them information on potential “hot spots”, then QA/QC procedures will be more stringent. Generally, as the use of data moves from educational purposes to regulatory use, the time involved to collect and process the data increases as do the costs and the quality control. With good QA/QC procedures in place, and proper training, you and your volunteers can feel more confident about the credibility of the data collected, analyzed, and managed. Whether the data will be used for educational purposes or by government agencies, volunteers can make a significant contribution in furthering the knowledge about a local stream, lake or watershed. Based on the numbered elements in Table 1 and the information presented in Chapter 4 of the EPA Manual, indicate the element that would best fit the procedure indicated the four statements below. You are not responsible for reading the entire chapter, but you will need to consult it to find the correct answers.
1. Project/Task Description (6)
2. Sampling Process Design (10)
3. Quality Control Requirements (14)
4. Training Requirements/Certification (8)
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- Home
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Steps
- Step 1 | An Introduction to Developing a Volunteer Monitoring Program
- Step 2 | Setting Goals and Objectives
- Step 3 | Understanding Elements of Quality Assurance (QA) and Quality Control (QC) Protocols
- Step 4 | Recruiting, Training and Retaining Volunteers
- Step 5 | Collecting Reliable Data
- Step 6 | Data Collection, Analysis and Data Management
- Step 7 | Evaluating the Success of Your Program
- Acknowledgements