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Emission Inventories QA/QC and Quality Assurance Project Plans

Emission Inventories QA/QC and Quality Assurance Project Plans. Angelique Luedeker and Melinda Ronca-Battista, ITEP/TAMS Center. Welcome To EI Advanced. This is the beginning of the second training, EI Advanced, in the EI/TEISS training series

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Emission Inventories QA/QC and Quality Assurance Project Plans

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  1. Emission Inventories QA/QC and Quality Assurance Project Plans Angelique Luedeker and Melinda Ronca-Battista, ITEP/TAMS Center

  2. Welcome To EI Advanced This is the beginning of the second training, EI Advanced, in the EI/TEISS training series This training is designed for those needing to collect their own data and use TEISS to estimate emissions for those sources Are there any questions from the first training?

  3. Definitions • Quality Control (QC) • Documenting data sources • Rechecking calculations • Accuracy checks • Use of approved standardized procedures for emissions calculations • Quality Assurance (QA) • External review by a third party

  4. 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 template QAPP • Potential uses of EI data will define minimum level of QA/QC

  5. QA/QC Levels • 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 From US EPA’s Emission Inventory Improvement Program (EIIP) Vol. 6, page 2.1-5

  6. 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

  7. 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!

  8. 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

  9. 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

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

  11. QC: What is included? (more cont.) • Verify you’ve documented all data sources-EI logbook! • Completeness checks • Consistency checks (ex: using same area for different nonpoint throughputs) • Double counting • Reasonableness (look for county EI online)

  12. QC: How to track it • Keep a file for each source (paper and computer) • Use checklist to record the person and date who did: • Data collection • Data calculations • Review of data reasonableness (initials, date, comments) • Review of data completeness • Data coding and recording • Data tracking (full file names and paths)

  13. 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

  14. 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

  15. 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

  16. 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)

  17. 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 (read at least the EPA info webpage accessible from the TEISS calculators)

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

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

  20. Because the 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

  21. 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 Typical Errors in EIs

  22. 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—set aside at least 4 hours a week for QC to start

  23. 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

  24. What about QA? • Independent review by third party, ITEP for example • 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.

  25. What is a QAPP Tool for project managers (YOU) and planners (YOU) to document the type and quality of information needed for environmental decisions (signatures commit to doing what it says) Describes the methods for collecting and assessing information Required by EPA

  26. Why is a QAPP important? • EIs are the Foundation of Decisions (agreements about who is going to do what are documented) • Sets Goals and Objectives • Road Map of How to Conduct EI • Provides Long-term Guidance • Makes Writing the EI Report Easier

  27. TEISS Inventory Preparation Plan (IPP) Wizard

  28. Determine the Use Air quality program planning Assess contributions of future new sources (document baseline) Assess need for tribal permitting program Support development of TIP Support participation in regional AQ planning efforts

  29. Next Step in a Level 1, 2, or 3 EI • Start with a QAPP • List sources on tribal land you will estimate emissions for • Use TEISS calculators to determine data you will need to collect to estimate emissions • The Level 4 EI you have been working on can be the off-reservation section of your Level 1, 2, or 3 EI • Data from off-reservation sources need to be in a separate section than data from on-reservation sources in QAPP and EI

  30. QAPP: Data Source Types Sections • For each source type (point, nonpoint, etc.), list the sources

  31. Data Quality Objectives and Indicators • Data Quality Objectives (DQOs) • General statements for accuracy, completeness, representativeness, and comparability • Example, Completeness DQO: Point and Nonpoint NEI data downloaded for area of concern • Data Quality Indicators (DQIs) • More specific measure of progress towards each DQO • Example, Completeness DQI: 100% of Title V sources in the NEI within the area of concern included in EI

  32. Examples from Approved QAPPs: DQI

  33. QAPP: QA/QC Section How are data collected? How are data documented? How are data checked? Where are the data stored? How are the data reported? Just type what makes sense that you can do; nothing fancy

  34. Example of Details –Data Management and Reporting 1+ sentence each • Describe how data will be stored • In TEISS • Paper and electronic filing system • Data collection and calculations on paper • Calculations done in spreadsheets • Final Products of EI • Completed TEISS project – has ALL the details • Paper report – gives general details, summarizes results • Presentation – summarizes results

  35. Just G.I.O.W. Do your homework now—the longer you wait the harder it gets

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