1. 1 Elements of EI Data Management Melinda Ronca-Battista ITEP

2. 2 Data management is keeping track of information used to estimate emissions

3. 3 The “answer” you get from an EI is... Sources of air pollution Amount of air pollution from each source

4. 4 The “answer” you get... (cont.) Example Residential woodstoves create 10 tons per year of CO 50 tons per year of PM 10 … …and so on for other criteria pollutants

5. 5 Data Management Data management is describing how you got those numbers “Road map” to how you got answer

6. 6 Data Management—How? Keep information in notes Include information in EI report Store in spreadsheet or database

7. 7 Data Management— Why ? Proves quality of your emission estimates Helps when updating EI Don’t have to redo/refigure everything

8. 8 You start with... Amount of wood burned for residential heating (i.e., number of cords) Types of stoves people are using Emission factors Equation that puts all these numbers together to calculate your emissions estimate

9. 9 Data Management: Records How did you estimate # cords burned for residential heating? Guessed based on personal knowledge of your reservation Used population and estimate of percent of homes burning wood Did survey to count woodstove types and amount of wood burned Data Quality Least reliable  Most reliable

10. 10 Data Management: Records (cont.) Example formula E = EF x (W x H x % x D) E = emission rate EF = emission factor for given pollutant W = volume of wood burned annually for one home H = number of tribe households % = percent houses heating with wood D = Dry density of wood

11. 11 Data Management: Records (cont.) For each variable, identify source EF s from EPA AP-42, fifth edition W from tribal housing office estimate H and % from tribal housing office D from EPA AP-42, Fifth edition

12. 12 Including Formula in Report Just write it out! See example on handout

13. 13 Including Formula in Spreadsheet Onscreen example

14. 14 Crucial Data Collect some kind of data for every source you inventory Having these data allows your tribal sources to be included in National Emission Inventory (NEI) Regional air quality modeling Market trading negotiations

15. 15 Data from Point Sources Collect following information AT MINIMUM Name of facility North American Industry Classification System code (NAICS code) Complete street address Lat/long or UTM coordinates (+datum) Start and end date of emission process

16. 16 Data from Point Sources (cont.) Source Classification Code (SCC) Estimated emission for each inventoried pollutant Period of emission estimate (annual, weekly, daily, etc.) Specify units of emission estimate (tons/year, lbs/month, etc.)

17. 17 Point Source – Process Level Best if broken down to process level Example Site: ABC Gravel Company Units: Screening operation, excavating operation Processes: Loading, screening, output Release points: stacks, vents, fugitive Control devices and their efficiency

18. 18 Processes Example: Sand and Gravel Pit Excavating Operation Screening Operation Screening Loading Output Release Points Control Devices Release Points Control Devices Release Points Control Devices

19. 19 Area and Mobile Sources Collect the following information AT MINIMUM SCC Estimated emission for each inventoried pollutant Period of emission estimate (annual, weekly, daily, etc.)

20. 20 Area and Mobile Sources (cont.) Specify units of emission estimate (tons/year, lbs/month, etc) Control devices and their efficiency (if any) Start and end date of emission process Emission type (average annual, total for season, etc.)

21. 21 Data Management Summary Describe WHAT you are calculating HOW you calculated emissions estimate WHERE you got data WHEN you collected data WHO you got your data from Include mandatory data for NEI

22. 22 TEISS QA/QC Tools Place to document data sources Documents QA checks Can automatically list frequently entered names

23. 23 QA/QC Report QA/QC Report lists EPA methods used to calculate emissions

24. 24 TEISS QA/QC Functions Demonstrate TEISS QA/QC functions Use this functionality for data management