QA/QC FOR ENVIRONMENTAL MEASUREMENT

1. QA/QC FOR ENVIRONMENTAL MEASUREMENT Unit 4: Module 13, Lecture 2

2. Objectives • Introduce the why and how of Quality Control • Analysis of natural systems • Why do we need QC? • Introduce Data Quality Objectives (DQOs) • How do we evaluate quality of data ? • Emphasize the PARCC parameters • QC sample(s) applicable for each key parameter • QC sample collection and evaluation methods • Statistical calculation of percussion • Determination of accuracy and bias • Introduce Quality Assurance Project Plans

3. Quality Control • What is Quality Control (QC)? • The overall system of technical activities designed to measure quality and limit error in a product or service. • A QC program manages quality so that data meets the needs of the user as expressed in a Quality Assurance Program Plan (QAPP). - US EPA (1996) QC is used to provide QUALITY DATA

4. http://ma.water.usgs.gov/CapeCodToxics/photo-gallery/wq-sampling.htmhttp://ma.water.usgs.gov/CapeCodToxics/photo-gallery/wq-sampling.htm QC for environmental measurement • Evaluation of a natural system: • Collect environmental samples • Specified matrix – medium to be tested (e.g. soil, surface water, etc.) • Specified analytes – property or substance to be measured (e.g. pH, dissolved oxygen, bacteria, heavy metals)

5. http://climchange.cr.usgs.gov/info/lacs/watersampling.htm http://www.fe.doe.gov/techline/tl_hydrates_oregon.shtml QC for environmental measurement • QC is particularly critical in field data collection • often the most costly aspect of a project • data is never reproducible under the exact same condition or setting sechi readings logging sea cores field filtration

6. http://www.nrcs.usda.gov/programs/cta/ctasummary.html http://pubs.usgs.gov/fs/fs-0058-99 QC for environmental measurement • Natural systems are inherently variable • Variability of lakes vs. streams vs. estuaries • Changes in temperature, sunlight, flow, sediment load and inhabitants • Human introduction of error

7. QC for environmental measurement • Why do we need quality control? • To prevent errors from happening • To identify and correct errors that have taken place QC is used to PREVENT and CORRECT ERRORS

8. QC for environmental measurement • QC systems are used to: • Provide constant checks on sensitivity and accuracy of instruments. • Maintain instrument calibration and accurate response. • Provide real-time monitoring of instrument performance. • Monitor long-term performance of measurement and analytical systems (Control Charts) and correct biases when detected.

9. QC for environmental measurement • Data Quality Objectives (DQOs): • Unique to the goals of each environmental evaluation • Address usability of data to the data user(s) • Those who will be evaluating or employing data results • Specify quality and quantity of data needed • Include indicators such as precision, accuracy, representativeness, comparability, and completeness (PARCC); and sensitivity.

10. QC for environmental measurement • The PARCC parameters help evaluate sources of variability and error • Precision • Accuracy • Representativeness • Completeness • Comparability “PARCC” parameters increase the level of confidence in our data

11. QC for environmental measurement • Sensitivity • Ability to discriminate between measurement responses • Detection limit • Lowest concentration accurately detectable • Instrument detection limit • Method detection limit (MDL) • Measurement range • Extent of reliability for instrument readings • Provided by the manufacturer

12. Quality control methods: QC samples • Greater that 50% of all errors found in environmental analysis can be directly attributed to incorrect sampling • Contamination • Improper preservation • Lacking representativeness • Quality control (QC) samples are a way to evaluate the PARCC parameters.

13. http://ma.water.usgs.gov/CapeCodToxics/photo-gallery/wq-sampling.htmhttp://ma.water.usgs.gov/CapeCodToxics/photo-gallery/wq-sampling.htm Quality control methods: QC samples QC sample types include: • field blank • equipment or rinsate blank • duplicate/replicate samples • spiked samples • split samples • blind samples

14. Quality control methods: QC samples • Field blank sample collection • In the field, using a sample container supplied by the analytical laboratory, collect a sample of analyte free water (e.g. distilled water) • Use preservative if required for other samples • Treat the sample the same as all other samples collected during the designated sampling period • Submit the blank for analysis with the other samples from that field operation. • Field blanks determine representativeness

15. Quality control methods: QC samples • Equipment or rinsate blank collection • Rinse the equipment to be used in sampling with distilled water immediately prior to collecting the sample • Treat the sample the same as all others, use preservative if required for analysis of the batch • Submit the collected rinsate for analysis, along with samples from that sample batch • Rinsate blanks determine representativeness

16. Quality control methods: QC samples • Duplicate or Replicate sample collection • Two separate samples are collected at the same time, location, and using the same method • The samples are to be carried through all assessment and analytical procedures in an identical but independent manner • More that two duplicate samples are called replicate samples. • Replicates determine representativeness

17. http://pubs.usgs.gov/fs/fs-0058-99 QC methods: Representativeness • Representativeness - • extent to which measurements actually represent the true environmental condition or population at the time a sample was collected. • Representative data should result in repeatable data  Does this represent this?? 

18. Quality control methods: QC samples • Split and blind sample collection • A sample is collected and mixed thoroughly • The sample is divided equally into 2 or more sub-samples and submitted to different analysts or laboratories. • Field split • Lab split • Blinds - submitted without analysts knowledge • Split and blind samples determine precision

19. Quality control methods: QC samples • Spiked sample preparation • A known concentration of the analyte is added to the sample • Field preparation • Lab preparation • The sample is treated the same as others for all assessment and analytical procedures • Spiked samples determine accuracy • % recovery of the spiked material is used to calculate accuracy

20. Quality control methods: QC Samples • Precision - • degree of agreement between repeated measurements of the same characteristic • can be biased – meaning a consistent error may exist in the results

21. Precision – degree of agreement between results Statistical Precision - standard deviation, or relative percent difference from the mean value target images Adapted from Ratti and Garton (1994) Mean Value Key concepts of QA/QC: Precision

22. Key concepts of QA/QC: Precision How to quantify precision: • Determine the mean result of the data (the average value for the data) • the arithmetic mean will usually work. To determine arithmetic mean: • add up the value of each data point • divide by the total number of points “n” Mean Value

23. Mean Value SD1 SD1 SD2 SD2 Key concepts of QA/QC: Precision How to quantify precision: 2. Determine the first and second standard deviation (SD). • SD1 = approximately 68% of the data points included on either side of the mean • SD2 = approximately 95% of the data points included on either side of the mean

24. Mean Value (18.48) Key concepts of QA/QC: Precision • The lower diagrams show ‘scatter’ around the mean • The SD quantifies the degree of scatter (or spread of data) • Less scatter = smaller SD value and grater precision (target 1) Adapted from Ratti and Garton (1994)

25. 2.0 1.0 0 1.0 2.0 Key concepts of QA/QC: Precision • Improbable Data • Data values outside the 95th (2 SD) interval (below) • These are improbable

26. Mean Value (18.48) Key concepts of QA/QC: Precision • Below example: The mean value 18.480C • The standard deviation SD is 2.340C • The precision value is expressed 18.480C +/- 2.340C

27. accuracy = (average value) – (true value) precision represents repeatability bias represents amount of error low bias and high precision = statistical accuracy Key concepts of QA/QC: Accuracy http://www.epa.gov/owow/monitoring/volunteer/qappexec.html

28. Determine the accuracy and bias of this data: Key concepts of QA/QC: accuracy & bias

29. Hach DR2400 portable spectrophotometer Key concepts of QA/QC: Comparability • Comparability - • the extent to which data generated by different methods and data sets are comparable • Variations in the sensitivity of the instruments and analysis used to collect and assess data will have an effect upon comparability with other data sets.  Will similar data from these instruments be Comparable ?? 

30. Key concepts of QA/QC: Completeness • Completeness - • % comparison between the amount of data intended to be collected vs. actual amount of valid (usable) data collected. • In the QAPP design – do the goals of the plan meet assessment needs? • Will sufficient data be collected? Would this give usable data?? 

31. Sample design Will samples collected at an out flow characterize conditions in the entire lake? Statistically relevant number of data points Will analysis in ppm address analytes toxic at ppb? Valid data Would data be sufficient if high humidity resulted in “error” readings? Is data valid if the readings are outside the measurement range of the instrument? Key concepts of QA/QC: Completeness

32. The QAPP is a project-specific QA document. The QAPP outlines the QC measures to be taken for the project. QAPP guides: the selection of parameters and procedures data management and analysis steps taken to determine the validity of specific sampling or analysis procedures Review: Quality Assurance Project Plans

33. Review: Elements of a QAPP • The QAPP governs work conducted in the field, laboratory, and the office. • The QAPP consists of 24 elements generally grouped into four project areas: • Project management (office) • Measurement and data acquisition (field and lab) • Assessment and oversight (field, lab, and office) • Data validation and usability (field, lab, and office)

34. References • EPA 1996, Environmental Protection Agency Volunteer Monitor’s Guide to: Quality Assurance Project Plans. 1996. EPA 841-B-96-003, Sep 1996, U.S. EPA, Office of Wetlands, Washington, D.C. 20460, USA http://www.epa.gov/owowwtr1/monitoring/volunteer/qappexec.htm • EPA 1994, Environmental Protection Agency Requirements for Quality Assurance Project Plans for Environmental Data Operations. EPA QA/R-5, August 1994). U.S. EPA, Washington, D.C. 20460, USA • Ratti, J.T., and E.O. Garton. 1994. Research and experimental design. pages 1-23 in T.A. Bookhout, editor. Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, Md.