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 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 Definitions

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 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 QC: What is included?

9 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 QC: What is included? (cont.)

10 Unit Example Asks for data in units of 1000 gallons

11 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) QC: What is included? (more cont.)

12 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) QC: How to track it

13 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 QC Methods: Reality Checks

14 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 QC Methods: Peer Review

15 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 QC Methods: Replication of Calculations

16 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) QC Methods: Computerized Checks

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 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 QC Documentation

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

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29 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

30 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

31 QAPP: Data Source Types Sections For each source type (point, nonpoint, etc.), list the sources Facility NameAddressType of Source Colville Indian Precision Pine 373 Omak Riverside-Eastside Road, Omak Lumber Manufacturing, Title V Permit, Point Source Colville Indian Plywood and Veneer 1100 Eighth Avenue East, Omak Manufacturing Veneer, Plywood and Power, Title V Permit, Point Source Atlas Pellets 325 Omak Riverside-Eastside Road, Omak Manufacture Wood Pellets for Stoves Granite Northwest Inc. 249-B Rodeo Trail Road, Okanogan Asphalt Production and Paving Coulee Dam Concrete26 Canyon Street, Elmer City Ready Mix Concrete, Sand and Gravel 31

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33 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

34 Examples from Approved QAPPs: DQI 34

35 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?

36 Example of Details –Data Management and Reporting 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

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