1 / 27

Emission Inventory Quality Assurance/Quality Control (QA/QC)

Emission Inventory Quality Assurance/Quality Control (QA/QC). Melinda Ronca-Battista ITEP. Quality Control Terminology. Quality Control (QC) Documenting data sources Rechecking calculations Accuracy checks Use approved standardized procedures for emissions calculations

shira
Télécharger la présentation

Emission Inventory Quality Assurance/Quality Control (QA/QC)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Emission InventoryQuality Assurance/Quality Control (QA/QC) Melinda Ronca-Battista ITEP

  2. Quality Control Terminology • Quality Control (QC) • Documenting data sources • Rechecking calculations • Accuracy checks • Use approved standardized procedures for emissions calculations • Quality Assurance (QA) • External review and audit procedures by third party

  3. QA/QC: Where to Start? • Prepare QAPP that answers • What are you going to report in EI? • What will you use the EI data for? • How are you going to review the data? See example QAPP handout • Potential uses of EI data will define minimum level of QA/QC

  4. QA/QC Levels From US EPA’s Emissions Inventory Improvement Program (EIIP) Vol. 6, page 2.1-5 • Level 1 – supports enforcement, compliance, or litigation • Level 2 – supports strategic decision making • Level 3 – general assessment or research • Level 4 – Inventory compiled entirely from previously published data or other inventories See Emissions Inventory Levels handout

  5. Data Quality Objectives (DQOs) • DQOs • Broad statement on how “good” or “true” your EI results will be • EI ESTIMATES emissions. You can’t know exact “truth” about quantity or type of pollutants from a given source

  6. DQOs: Examples • EI DQO for Accuracy • 100% of data transcribed from paper forms to TEISS will be verified • EPA methods used to estimate emissions, ensure estimates as close to “truth” as possible • Track Accuracy Checks with TEISS QA/QC functions

  7. DQOs: Examples • DQO for Completeness • 100% of largest point sources of PM included in EI • DQO for Comparability • Emissions calculated represent “truth” on your reservation • Emissions comparable to similar sources or areas

  8. DQOs are set, now what’s the plan? • QC should be included in each EI task • QC for data collection • QC for calculations • QC for choosing estimation methods • Allocate at least 10% of resources for QA activities • Don’t wait until the end!

  9. QC: What is included? • Check transcription of data during inventory preparation and reporting • Transcription of data from raw data collection sheets into electronic spreadsheets or TEISS calculators • Transcription of data results from TEISS summary tables to EI report

  10. QC: What is included? (cont.) • Check calculations • Including calculation of throughput, if necessary • If not calculating emissions with TEISS calculator, check that throughput multiplied by EF equals emissions • Verify that unit conversions are correct • Verify that units of your data match units TEISS or equation asks for

  11. Unit Example Asks for data in units of 1000 gallons

  12. QC: What is included? (cont.) • Verify you’ve documented all data sources • Completeness checks • Consistency checks • Double counting • Reasonableness

  13. QC: How to track it • Keep a file for each source • Use checklist to monitor person and date for • Data collection • Data calculations • Evaluation of data reasonableness • Evaluation of data completeness • Data coding and recording • Data tracking

  14. QC Methods: Reality Checks • Most commonly used • Is this number reasonable? Does it make sense? • Never use the reality check as the sole criterion of quality • Find data for similar sources on EPA’s EIS Gateway system

  15. QC Methods: Peer Review • Independent review of calculations, assumptions, and/or documentation by person with moderate to high level of technical experience • QA is a form of peer review • Can also be included as part of QC

  16. QC Methods: Replication of Calculations • Most reliable way to detect computational errors • General rule, minimum of 10% of calculations checked, depending on • Complexity of calculations • Inventory DQOs • Rate of errors encountered

  17. QC Methods: Computerized Checks • Automated data checks can be • Built-in functions of databases, models, or spreadsheets, or stand-alone programs • Automate to • Check for data format errors (like Export to NEI component of TEISS) • Conduct range checks • Provide look-up tables to define permissible entries (like TEISS selection boxes)

  18. TEISS needs a Human Touch • TEISS is an excellent tool; however, it needs your guidance • Be familiar with emission methodologies on which TEISS calculators are based

  19. Calculator Methodology • Scroll down on summary screen to get to Reference and Online Link

  20. Why Review the Methodology? • What do I select here?

  21. Methodology has Answers • Methodology: used to calculate emissions for 4 different “station operations” (in most cases) • Underground tank filling • Underground tank breathing • Vehicle refueling displacement losses • Vehicle refueling spillage • Each operation should be included as a different Process in TEISS • If using an EPA model to calculate onroad emissions, make sure vehicle refueling emissions are not double counted

  22. Missing or duplicate facilities Improper facility locations Missing operating or technical data Erroneous technical data Double counting Errors in calculations Data entry and transposition errors; data coding errors QC Methods: Typical Errors

  23. Most Typical QC Error Letting it slide • Make sure to include time for QA/QC • Pressure to gather data and “get it done” can harm documentation & verification • Putting it off to project’s end

  24. QC Documentation • Ensure final written compilation of data accurately reflects inventory effort • Support QA assessments of inventory • Ensure reproducibility of inventory estimates • Enable inventory user or reviewer to assess quality of emission estimates and identify data references • Foundation for future inventories

  25. What about QA? • Independent review by third party • Checks effectiveness of your QC • Allocate 10% of project resources to QA • Again, don’t wait until the end • QA person checks a fraction of data entry, calculations, documentation, etc.

  26. Are you hungry for more? • Excellent resource and source for some of this presentation material • EIIP Volume 6: Quality Assurance/Quality Control http://www.epa.gov/ttn/chief/eiip/techreport/volume06/index.html

  27. Homework • Read EIIP Volume 3, Chapter 1: Introduction to Area Source Emission Inventory Development, Sections 6.1 and 6.2 • Answer questions on handout

More Related