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The Dirty Dozen 12 Ways Your Environmental Data Might be Flawed . WVONGA Annual Fall Meeting Wheeling, West Virginia September 11, 2013 Gerald L. Kirkpatrick, P.G. Bryce E. Stearns. Six Bad Field Team Habits. Poorly Designed Sampling Programs

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WVONGA Annual Fall Meeting Wheeling, West Virginia September 11, 2013

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The Dirty Dozen

12 Ways Your Environmental Data Might be Flawed

WVONGA Annual Fall Meeting

Wheeling, West Virginia

September 11, 2013

Gerald L. Kirkpatrick, P.G.

Bryce E. Stearns

Six Bad Field Team Habits

  • Poorly Designed Sampling Programs

  • Inconsistent Sample Collection Procedures

  • Improper Sampling Equipment

  • Untrained and Inexperienced Field Personnel

  • Absence of Documentation

  • Inadequate QA/QC Sampling


1. Poorly Designed Sampling Programs

  • What’s your objective in collecting the data?

  • What do you want to know?

  • Who wants to know it?

Thoughtful analysis up-front saves problems out-back


2.Inconsistent Sample Collection Procedures

  • Multiple sampling contractors result in multiple ways of collecting a sample.

  • Some of those ways are wrong and lead to litigation


Prepare Standard Operating Procedures and train field people


3.Improper Sampling Equipment

  • Using the proper equipment is critical to producing reliable sampling data.

  • Just because a sample was collected, doesn’t mean it was collected well.

Prepare Sampling and Analysis Plans Follow-up With Field Audits


4.Untrained and Inexperienced Field Personnel

  • Watching contractor minions in the field is entertaining, but can be scary.

  • Training field personnel on how to collect a sample is not bizarre and experience indicates it is badly needed.

Training and field audits are critical to the health of your sample data


5.Absence of Documentation

  • Document, document, document!

  • Be careful of what you write

  • Be careful of what you don’t write

Prepare a Quality Assurance Project Plan and use it


6.Inadequate QA/QC Sampilng

  • Not that hard

  • Not that expensive

  • Data can solve your worst nightmares

  • “QA/QC Insurance”

Submit some sort of QA/QC samples with your field samples


Six Laboratory Pitfalls

  • Lack of Traceability

  • Not Adhering to the Method

  • Failure to Calibrate Instrumentation

  • Inadequate Quality Control

  • Poor Documentation Practices

  • Insufficient Training


1.Lack of Traceability

You do not want your data generated “blind.”

  • Everything that comes in contact with the sample…

  • Materials

  • Reagents

  • Standards

  • Solvents

  • Sample Jars, etc.

Traceability is a key component to Quality Control


2.Not Adhering to the Method

Failure to follow the approved method or internal SOP

  • Provides a basis of how the result was generated


3.Failure to Calibrate Instruments

Failure to adequately calibrate monitoring and measuring devices used in the analytical processimpacts data quality.


Balances, thermometers, pipettes

Crucial to ensure measurement accuracy


4.Inadequate Quality Control

  • Basic Error Checking

  • Failure to review or verify results

  • Requires a formal and documented procedure

  • Vital for minimizing reporting errors


5.Poor Documentation Practices

  • Required information isn’t recorded or it’s recorded incorrectly.

  • Everything from SOPs, bench sheets, log books, to actual lab measurements.

  • If it’s not documented, it didn’t happen.


6.Insufficient Training

  • The devil is in the details.

  • Depth of understanding is required to produce accurate and precise data.

  • Fundamental need for the generation of viable results



  • Analytical work is complex and requires significant effort to be accurate and precise.

  • Demands are high and there can be a lot at stake.

  • Need data that will stand the test of time.

  • Because…In the end, all you have are data.






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