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Incomplete Ozone Data Handling Case Study: Knoxville, Tennessee, 2011

Incomplete Ozone Data Handling Case Study: Knoxville, Tennessee, 2011. Daniel Garver, Ryan Brown, and Stacy Harder US Environmental Protection Agency, Region 4. Overview. Background “Appendix P” Demonstration: Days not conducive to high ozone

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Incomplete Ozone Data Handling Case Study: Knoxville, Tennessee, 2011

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  1. Incomplete Ozone Data HandlingCase Study: Knoxville, Tennessee, 2011 Daniel Garver, Ryan Brown, and Stacy HarderUS Environmental Protection Agency, Region 4

  2. Overview • Background • “Appendix P” Demonstration: Days not conducive to high ozone • Prediction of missing data using regression model with nearby monitors • Results

  3. Background • Valid ozone data was not collected at both Knox County monitoring sites from March – mid June 2011 • Both monitors violated the NAAQS based on 2008-2010 data • Several monitors in the Southeast were able to show attainment based on 2009-2011 data, including other monitors in the Knoxville CBSA • The 2009-2011 design values for both Knox County monitors were incomplete and invalid

  4. Missing Days • The EPA Regional Administrator can give credit for missing days: “When computing whether the minimum data completeness requirements have been met, meteorological or ambient data may be sufficient to demonstrate that meteorological conditions on missing days were not conducive to concentrations above the level of the standard. Missing days assumed less then the level of the standard are counted for the purpose of meeting the data completeness requirement, subject to the approval of the appropriate Regional Administrator.” 40 CFR Part 50 Appendix P Section 2.3(b) (emphasis added).

  5. Identification of Missing Days Not Conducive to High Ozone • Both sites needed credit for 63 missing days in order to produce complete design values • Tennessee, Knox County, and EPA worked together to identify these days • Focused on relationships between historical ozone levels and temp, cloud cover, and precipitation data • Tennessee submitted a package to EPA requesting credit for missing days

  6. Selection of Days Ozone data used was the maximum 8-hr average collected in the Knoxville CSA

  7. What about missing days that were conducive to high ozone? Would the two incomplete sites have violated the NAAQS if they had collected complete data in 2011?

  8. Ozone Regression Analysis • Theil-Sen regression model that predicts missing data based on data collected at nearby sites • Paired daily maximum 8-hr average ozone values for each site • Calculates a confidence interval using a bootstrapping method • Results in a predicted 4th max and design value with an associated confidence interval

  9. Theil-Sen Regression Model • Nonparametric linear regression model • Less influenced by outliers than least squares regression • Does not assume normally distributed data • Does not assume error is only in y-direction • Spearman’s Rank Correlation Coefficient • Nonparametric equivalent of Pearson’s r • Measures positive or negative correlation

  10. Step 1: Screen Nearby Sites and Select the Best Predictor Sites • Selected predictor sites for each incomplete site that meet the following criteria: • At least 920 valid sample pairs (approx. 75% over the previous 5 years) • Spearman’s Rank Correlation Coefficient > 0.80 • At least 75% data completeness in the year to be predicted • Also calculated Pearson Correlation Coefficient and Theil-Sen Regression for each site pair

  11. Example Site Selection Matrix:Spring Hill Site (AQS ID: 47-093-1020)

  12. Example Regression Plots:Spring Hill vs. Selected Predictor Sites

  13. Step 2: Calculate Predicted Data and Design Value • For each selected predictor site: • Predicted missing daily maximum 8-hr ozone values for the incomplete site (up to 100% or as much as possible) • Calculated new 4th maximum for the incomplete year using new “complete” data set of collected and predicted values • Calculated 3-year design value using predicted 4th max and other measured 4th max values

  14. Step 3: Estimate Confidence Interval Using Resampling or “Bootstrapping” • Randomly resampledn sample pairs from original dataset • Performed Theil-Sen regression on resampled dataset • Repeated 1,000 times • Estimated confidence interval by taking 95th and 5th percentile slope and intercept values • Re-predicted missing data using 95th and 5th percentile slope and intercept values • Calculated confidence interval as 95th and 5th percentile 4th max and design value

  15. Results • This means that we can be 90% confident that the 2009-2011 design value for Knox County would have been 0.072 ppm. • So, the Knox County monitors would have attained the ozone standard. • Thus, EPA approved Tennessee’s request for credit for missing days, and the Knox County data was certified as complete and attaining the NAAQS.

  16. Questions?

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