«Guidelines for Quality Assurance and Quality Control in Surface Water Quality Programs in Alberta Guidelines for Quality Assurance and Quality ...»
Guidelines for Quality Assurance and
Quality Control in Surface Water
Quality Programs in Alberta
Guidelines for Quality Assurance and Quality
Control in Surface Water Quality Programs
Patricia Mitchell, M.Sc., P.Biol.
Patricia Mitchell Environmental Consulting
Pub. No: T/884
ISBN: 0-7785-5081-8 (Printed Edition)
ISBN: 0-7785-5082-6 (On-line Edition)
Web Site: http://www3.gov.ab.ca/env/info/infocentre/publist.cfm Any comments, questions or suggestions regarding the content of this document may be
Environmental Monitoring and Evaluation Branch Environmental Assurance Division Alberta Environment 12 th Floor, Oxbridge Place 9820 – 106 Street Edmonton, Alberta T5K 2J6 Fax: (780) 422-8606
Additional copies of this document may be obtained by contacting:
Information Centre Alberta Environment Main Floor, Oxbridge Place 9820 – 106 Street Edmonton, Alberta T5K 2J6 Phone: (780) 427-2700 Fax: (780) 422-4086 Email: firstname.lastname@example.org PREFACE Over the years, quality assurance for the acquisition of water quality data has become increasingly important. Development in Alberta is increasing at a rapid pace, but at the same time the people of Alberta are demanding that developers and the government protect the environment. Predevelopment studies and monitoring and assessment of impacts during and after development must be based on sound science. Sound science demands a good quality assurance program, so that government staff can demonstrate that the data collected are accurate and precise, ensuring that environmental decisions are valid.
The following document responds to the need to standardize quality assurance programs for all surface water quality studies conducted by and for the Alberta government.
Although the document focuses on surface water and sediments, the principles would be similar for monitoring of groundwater, drinking water and other environmental studies.
Very few similar documents are available in Canada. In the United States, the U.S.
Environmental Protection Agency has extensive and detailed documents on quality assurance, mainly because any studies using federal funding are legally bound to follow quality assurance procedures.
Much of this guideline document is based on several literature sources. It should be remembered that in the field of quality assurance, very little is set in stone, and quite often one literature source contradicts another. Therefore, Alberta Environment decided to draft its own QA/QC guidelines, which professional staff agreed upon. This document should be reviewed and updated periodically as new information becomes available.
It is the intention of Alberta Environment to establish measurement quality objectives for variables of concern, to be based on recent data for all sampling programs. These will be published in a separate document.
LIST OF TABLES
LIST OF FIGURES
LIST OF APPENDICIES
2. BASIC CONCEPTS OF QA/QC
Measurement Quality Objectives and Data Quality Objectives
Quality Control Indicators
Other Estimates of Quality Assurance
Quality Control Samples
3. SAMPLING PROGRAM DESIGN
Steps in Program Design
1. State the Problem
2. Identify the Information Needs
3. Define the Boundaries of the Study
4. Develop a Testable Hypothesis for Assessment Studies
5. Set Data Quality Objectives and Measurement Quality Objectives................. 13
6. Finalize the Study Design
Quality Control Samples
Examples of Study Design
4. DATA QUALITY ASSESSMENT
Quality Control Sample Results
6. CONCLUSIONS AND RECOMMENDATIONS
Table 2. False acceptance and false rejection decisions.
Table 3. Preliminary MQOs for phosphorus and chlorophyll a in lake water.
.... 15 Table 4. Example of measurement quality objectives for a hypothetical stream study in the State of Washington.
Table 5. General guidelines for types of quality control samples and their frequency of collection.
Table 6. Guidelines for Recommended Parameters for Different Coefficient of Variations and Censoring
Appendix B. Alberta Environment Project Plan
Appendix C. Detection Limits
Appendix D. Alberta Environment Data Validation Process
Technical review was provided by Anne-Marie Anderson, Doreen LeClair, and Darcy McDonald, of the Environmental Assurance Division (EAD). Final report formatting and preparation was done by Mary Raven (EAD).
Alberta Environment has sampled water and sediment quality in rivers, lakes and streams since the 1960s. The data collected are used to assess present conditions, compare data with water quality guidelines, investigate specific water quality issues, or determine longterm trends. Monitoring programs also assess whether regulatory processes are effective in protecting water bodies from excessive nutrients, metals, pesticides and toxic substances.
Another purpose in monitoring surface waters is in support of the Water for Life strategy of the Alberta government. Water for Life is a commitment to using scientific knowledge in decision-making to sustain water supplies, protect drinking water and ensure healthy aquatic ecosystems. Partnerships are a key direction in this initiative. The Surface Water Monitoring Subcommittee of the Water Quality Task Group has identified several key principles to ensure that good scientific information, which cannot be compromised, is
collected. These include the following steps (Anderson et al. 2005):
Step 1: Scoping and Design Competent program design requires a clear scientific understanding of the issues, the study objectives, appropriate methods and the natural dynamics of rivers, streams, lakes, wetlands and reservoirs.
Step 2: Sample Collection Sample collection requires expertise and skill, including adherence to well-defined methods, good data management standards and health and safety considerations.
Step 3: Sample Analysis Chemical, biological and physical analyses must be performed by competent laboratories and results must meet scientific criteria for the acceptability of results.
Step 4: Data Validation High quality, reliable data must be ensured before they are stored electronically. This is done by confirming field and lab methods, checking results and QC data and ensuring proper coding.
Step 5: Data Storage Information and data must be reliably stored over the long-term, and be easily accessible to all parties. It is undesirable to have many independent databases with separate validation procedures.
Step 6: Reporting Competent parties should convert the data to accurate, reliable and scientifically defensible information in a timely manner.
Guidelines for Quality Assurance and Quality Control in Surface Water Quality Programs in Alberta All of the sampling programs conducted by Alberta Environment (AENV) depend on reliable and accurate data. The consequences of using poor quality data include faulty decisions, higher risk to the environment or human health, wasted resources, loss of credibility and sometimes, legal liability (Lombard and Kirchmer 2004). Data quality, however, fundamentally depends on the intended use of the data. To be meaningful, the data quality must meet the desired level of confidence for the purpose of the sampling program. As well, the sampling design and data quality should be able to perform over a wide range of possible outcomes.
As a general policy, surface water and sediment sampling programs conducted by AENV use accredited laboratories, although new and emerging substances may not be accredited yet. Laboratory accreditation, however, does not guarantee good data. Many other factors can influence data quality.
To ensure that good data are collected, all sampling programs should include a quality assurance plan. Quality assurance (QA) is a system of activities designed to make sure that the data meet defined standards of quality. It pertains to the overall management of the sampling program, and includes planning, documentation, training, consistency in collecting and handling samples, analyses, validation and reporting. An important part of QA is quality control (QC). Quality control refers to the technical activities used to reduce errors throughout the sampling program. These activities measure the performance of a process against defined standards to verify that the data meet the expected quality. Errors can occur in the field, laboratory or while handling the data. QC should include both internal and external measures. Internal QC is a set of measures undertaken by the project’s own samplers and analysts. External QC involves people and laboratories outside of the project (USEPA 1996).
Table 1 shows how QA and QC differ.
Table 1. Comparison of quality assurance and quality control.
From Statistics Canada: http://www.statcan.ca/english/edu/power/ch3/quality/quality.htm The purpose of this document is to recommend QA/QC guidelines for all water and sediment sampling programs conducted by aquatic scientists in the Alberta government.
To make sure that all data generated on various projects are reliable, quality assurance Guidelines for Quality Assurance and Quality Control in Surface Water Quality Programs in Alberta and quality control must be included in sampling designs for all water quality monitoring projects; quality control data should be interpreted in project reports. This document is intended primarily for the staff of the Alberta government and their partners and consulting firms conducting studies for AENV. It would also be useful for anyone doing water quality studies.
Although several jurisdictions in North America have quality assurance guideline documents for surface water sampling, they vary considerably in methods and requirements. The information herein may differ somewhat from literature sources, but the guidelines were agreed upon by Alberta Environment staff and should be applied to Alberta government sampling programs.
These guidelines are intended for sampling of surface water and sediments only, including chemistry and a few biological variables, mainly fecal coliform bacteria and chlorophyll a. Other biological variables require different QA techniques, and likely different guidelines. This document does not address compliance monitoring for regulatory purposes, nor for groundwater monitoring, although the principles would be the same. A field sampling procedures manual, which includes field QC, is available (Alberta Environment 2006), and therefore specific field techniques to ensure quality data are not reported here, nor are those for certified analytical laboratories, which have their own QA/QC procedures. Appendix A provides information on the current state of quality assurance in Alberta Environment.
Guidelines for Quality Assurance and Quality Control in Surface Water Quality Programs in Alberta
2. BASIC CONCEPTS OF QA/QC Quality assurance is the overall management of a sampling program so that reliable and accurate data are produced. Variability occurs naturally in streams, lakes and rivers, but is also introduced during the collection and analysis of samples from these waters. For analytical results to be meaningful the total error contributed by all stages of sampling and analysis should be substantially less than the natural variability of the water or sediments being sampled. All of the following would apply to sediments as well as water.
Field quality assurance includes basic precautions that must be followed if variability (errors) in the data is to be minimized. The Alberta Environment field-sampling manual (Alberta Environment 2006) gives specific instructions to maintain consistency and ensure the staff are diligent while collecting, filtering, preserving and shipping samples.
Quality control (QC) samples are used to evaluate whether the sampling and processing system is functioning properly, and whether measurement quality objectives have been met. Analytical labs have their own quality control procedures, but QC samples submitted from the field will provide an estimation of the total study error. If necessary, QC samples can be used to pinpoint sources of error, especially those from contamination. New sampling programs should incorporate rigorous QC measures until an acceptable level of data quality has been demonstrated. This is particularly important if the program objectives are to assess trends or investigate an impact on aquatic life or human health.
Measurement Quality Objectives and Data Quality Objectives A practical distinction between error and uncertainty is that we can do something about error, but we have to live with uncertainty. Error has to do with the quality of measurements, while uncertainty has to do with what they represent. In practice we can identify sources of both, and both can be diminished. The level of effort depends on the amount of error we can tolerate. Error is how far out a particular measurement could be from the “truth” – it can be specified as a percentage of the true value, such as +/- 10% (Katznelson 1998). These are often referred to as Measurement Quality Objectives (MQOs). MQOs are specific units of measure, such as percent recovery (accuracy) and percent relative standard deviation (precision). They are usually listed in the same units as the real sample data, so they can be compared directly to QC sample results. They should be specified before a sampling program begins, and the QC data should be analyzed during the sampling program, so that problems that may arise can be corrected.