QA/QC FOR ENVIRONMENTAL MEASUREMENT

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QA/QC FOR ENVIRONMENTAL MEASUREMENT

• Unit 4 Module 13, Lecture 2

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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

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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
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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)

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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
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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

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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
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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.

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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.

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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
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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

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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.

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Quality control methods QC samples

• QC sample types include
• field blank
• equipment or rinsate blank
• duplicate/replicate samples
• spiked samples
• split samples
• blind samples

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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

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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

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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

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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?? ?
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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

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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

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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

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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

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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
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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

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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)
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Key concepts of QA/QC Precision

• Improbable Data
• Data values outside the 95th (2 SD) interval
  (below)
• These are improbable

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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

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Key concepts of QA/QC Accuracy

• accuracy (average value) (true value)
• precision represents repeatability
• bias represents amount of error
• low bias and high precision statistical
  accuracy

http//www.epa.gov/owow/monitoring/volunteer/qappe
xec.html
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Key concepts of QA/QC accuracy bias

• Determine the accuracy and bias of this data

Example Data Collected - pH 7.0 Standard Example Data Collected - pH 7.0 Standard Example Data Collected - pH 7.0 Standard Example Data Collected - pH 7.0 Standard
Group 1 Group 2 Group 3 Group 4
7.5 7.2 6.5 7.0
7.4 6.8 7.2 7.4
6.7 7.3 6.8 7.2
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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 ?? ?
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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 ?? ?
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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?

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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

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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)

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References

• EPA 1996, Environmental Protection Agency
  Volunteer Monitors 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.

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