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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s2 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s3 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s4 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) cs/photo-gallery/wq-sampling.htm

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s5 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 hydrates_oregon.shtml rsampling.htm sechi readings field filtration logging sea cores

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s6 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s7 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s8 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.

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s9 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.

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s10 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s11 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s12 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.

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s13 Quality control methods: QC samples QC sample types include:  field blank  equipment or rinsate blank  duplicate/replicate samples  spiked samples  split samples  blind samples

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s14 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s15 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s16 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s17 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?? 

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s18 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s19 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s20 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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s21 Key concepts of QA/QC: Precision  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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s22 Key concepts of QA/QC: Precision How to quantify precision: 1. Determine the mean result of the data (the average value for the data)  the arithmetic mean will usually work. To determine arithmetic mean: 1. add up the value of each data point 2. divide by the total number of points “n” Mean Value

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s23 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 Key concepts of QA/QC: Precision Mean Value SD1 SD2 SD1

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s24 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) Mean Value (18.48)

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s25  Improbable Data  Data values outside the 95th (2 SD) interval (below)  These are improbable Key concepts of QA/QC: Precision

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s26 Key concepts of QA/QC: Precision  Below example: The mean value C  The standard deviation SD is C  The precision value is expressed C +/ C Mean Value (18.48)

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s27  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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s28  Determine the accuracy and bias of this data: Key concepts of QA/QC: accuracy & bias Example Data Collected - pH 7.0 Standard Group 1Group 2Group 3Group

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s29 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 ??  Hach DR2400 portable spectrophotometer

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s30 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 ?? 

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s31 Key concepts of QA/QC: Completeness  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?

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s32 Review: Quality Assurance Project Plans  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

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s33 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)

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s34 References  EPA 1996, Environmental Protection Agency Volunteer Monitor’s Guide to: Quality Assurance Project Plans EPA 841-B , Sep 1996, U.S. EPA, Office of Wetlands, Washington, D.C , USA ec.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 , USA  Ratti, J.T., and E.O. Garton 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.

Developed by: Zwiebel, Filbin Updated: June 14, 2005 U5-m13b-s35