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An Overview of EPA’s Quality Assurance Guidance for Ambient Air Quality Monitoring Data

An Overview of EPA’s Quality Assurance Guidance for Ambient Air Quality Monitoring Data. Data Analysis and Interpretation February 12 – 14, 2008, Tempe, AZ Catherine Brown, EPA Region 9. What are the QA elements for ambient monitoring data?. Monitoring Objectives PQAO defined

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An Overview of EPA’s Quality Assurance Guidance for Ambient Air Quality Monitoring Data

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  1. An Overview of EPA’s Quality Assurance Guidance for Ambient Air Quality Monitoring Data Data Analysis and Interpretation February 12 – 14, 2008, Tempe, AZ Catherine Brown, EPA Region 9

  2. What are the QA elements for ambient monitoring data? • Monitoring Objectives • PQAO defined • Network Design • DQO process • EPA’s MQO’s for ambient monitoring (some new) • Data Quality Assessments • Tools

  3. EPA’s Ambient Air Quality Monitoring Program Objectives • Provide air pollution data to general public in a timely manner • Support compliance with air quality standards & emissions strategy development • Support air pollution research studies

  4. Primary Quality Assurance Organizations To aggregate monitoring data and assess DQO’s, should have common • SOPs, QAPPs • Team of operators with common training • Calibration facilities and standards • QA oversight • Management, lab or HQ

  5. Design of Ambient Monitoring Networks • Highest concentration areas • Population exposure • Source-oriented sampling • General background • Pollutant transport • Visibility and welfare effects * * Note: Appendix A requirements now apply to PSD monitoring

  6. EPA’s Quality System • Now fully based on DQO process (December 2006) • Decision makers must understand probability of incorrect NAAQS decision (data uncertainty) • Data Quality Assessments determine DQOs are achieved

  7. Measurement Quality Objectives • Table A-2 in 40 CFR 58 Appendix A • Note: Some statistics are new –Defines measurement quality samples required for manual and automated methods for each criteria pollutant

  8. Data Quality Assessments Evaluate each monitoring program or project for these indicators • Representativeness • Precision • Bias • Detectability • Completeness • Comparability

  9. Summary • QA requirements apply to environmental measurements used in decision-making • Data quality assessments help organize and understand complex datasets • Statistical tools available from EPA

  10. EPA References • Guidance on Systematic Planning Using the Data Quality Objectives Process EPA/240/B-06/001 Feb. 2006 http://www.epa.gov/quality/qa_docs.html • Guideline on the Meaning and Use of Precision and Bias Data Required by 40 CFR Part 58 Appendix A – Version 1.1 http://www.epa.gov/ttn/amtic/parslist.html

  11. EPA References (cont.) • EPA Quality Assurance Handbook http://www.epa.gov/ttn/amtic/qabook.html • Data Assessment Statistical Calculator (DASC) Software for calculating new precision and bias statistics http://www.epa.gov/ttn/amtic/parslist.html

  12. EPA References (cont.) • 2006 Criteria Pollutant Quality Indicator Summary Report for AQS Data http://www.epa.gov/ttn/amtic/parslist.html

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