qa/qc for environmental measurement

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


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


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)

http://ma.water.usgs.gov/CapeCodToxics/photo-gallery/wq-sampling.htm



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

http://www.fe.doe.gov/techline/tl_hydrates_oregon.shtml

http://climchange.cr.usgs.gov/info/lacs/watersampling.htm

sechi readings field filtration logging sea cores



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

http://www.nrcs.usda.gov/programs/cta/ctasummary.htmlhttp://pubs.usgs.gov/fs/fs-0058-99



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



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.



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.



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



Sensitivity Ability to discriminate between measurement

responses Detection limit

Lowest concentration accurately detectable Instrument detection limitMethod detection limit (MDL)

Measurement range Extent of reliability for instrument readings Provided by the manufacturer


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.



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

http://ma.water.usgs.gov/CapeCodToxics/photo-gallery/wq-sampling.htm



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



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



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


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??

http://pubs.usgs.gov/fs/fs-0058-99



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



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


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


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



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


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


Mean ValueSD1

SD2SD2

SD1



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)


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


-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.0 1.0 0 1.0 2.0



Below example: The mean value 18.480C The standard deviation SD is 2.340C The precision value is expressed 18.480C +/- 2.340C

Mean Value (18.48)


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


Determine the accuracy and bias of this data:

Key concepts of QA/QC: accuracy & bias

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


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


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 ??


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?


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


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)


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.

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