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Corroborative and Weight-of-Evidence Development and Analyses 11-08CCOS

Corroborative and Weight-of-Evidence Development and Analyses 11-08CCOS. Envair Charles Blanchard Shelley Tanenbaum Alpine Geophysics James Wilkinson May 29, 2012. Overview of Presentation. Project objectives Weight-of-evidence framework Trends in emissions, O 3 precursors, and O 3

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Corroborative and Weight-of-Evidence Development and Analyses 11-08CCOS

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  1. Corroborative and Weight-of-Evidence Development and Analyses11-08CCOS Envair Charles Blanchard Shelley Tanenbaum Alpine Geophysics James Wilkinson May 29, 2012

  2. Overview of Presentation • Project objectives • Weight-of-evidence framework • Trends in emissions, O3 precursors, and O3 • Generalized additive model (GAM) • Using the GAM to project future O3 response • (Uncertainty analyses and design value variability) • (Model demonstration after presentation)

  3. Objectives • Identify innovative new methods to reduce uncertainty in O3 attainment demonstrations • Develop and demonstrate use of new methods for weight-of-evidence evaluation: • Enhance confidence in future year projections • Provide additional evidence for effectiveness of VOC and NOx emission reductions • Further assess local and regional influences • Provide data, software, and documentation suitable for ongoing Study Agency use

  4. Weight-of-Evidence Framework • Corroborate VOC and NOx emission reductions by comparison with trends in ambient NMOC & NOx concentrations • Quantify O3 reductions • Link O3 reductions to observed ambient VOC (NMOC) and NOx concentrations • Project O3 response(s) to future emission reductions and precursor concentrations • Reconcile weight-of-evidence analyses with modeling predictions

  5. Our Study Domain -Central California ShowingO3 Monitoring Sites in 15 Subregions

  6. Ambient CO, NO, NO2, NMOC Trends Are Downward and Significant Mean of 7 – 10 a.m. CO Mean of daily max NO All sites All sites Mean of 7 – 10 a.m. NO2 All sites Fresno 1st Central San Joaquin Valley (CSJ)

  7. Ambient CO Trends Are Consistent With Emission Trends in Most Subregions

  8. Ambient NO2 Trends Exceed NOx Emission Trends in Some Subregions

  9. Ambient NMOC Trends Exceed VOC Emission Trends, But Limited Data

  10. CSJ Peak 8-Hour O3 Metrics Comparison

  11. Downward Trends in Peak 8-Hour O3

  12. Conclusions from Trends Analyses • Ambient precursor trends confirm emission reductions • Peak 8-hour O3 is trending downward at rates of ~0.2 – 0.7 ppbv per year with exception of CBA (upward) and SCC (downward at 1.5 – 2 ppbv per year) • The top 10% of days and the top 60 days per subregion per year provide good subsets for study – trends are relevant to 4th-highest and to subregion mean daily excess of 75 ppbv • Season average subregion daily maximum peak 8-hour O3 is also useful metric

  13. Generalized Additive Model (GAM) • GAM developed by U.S. EPA to determine meteorologically-adjusted O3 trends • We adapted the GAM to link peak 8-hour O3 to ambient NO, NO2, and other precursors, while accounting for the influence of weather • Tested many meteorological and air-quality variables as predictors – focus on final model • We developed estimates of uncertainty and an approach for projecting future O3 response

  14. EPA published GAM in Atmospheric Environment,2007 • EPA used GAM to determine meteorologically-adjusted O3 trends • GAM generates sensitivity of O3 to each predictor variable Area of application was eastern US

  15. The Basic GAM log(O3)ik = m + Yk + Wd + f1(Ji) + f2(xik) + … • Model says that predicted log O3 on day “i” of year “k” is an additive function of: • Overall mean of data from all days of all years, m • Mean effects Y=year, W=day of week, J=julian day • Contributions due to nonlinear functions, f, of meteorological and air quality predictor variables • Log transform of O3 is useful but optional • Flexible choice of functions “fi” • GAM is set up to use natural splines • Natural splines are (special) cubic polynomials

  16. Programming Aspects of GAM • Original software written by EPA as R program (R is nonproprietary, available, runs under LINUX, Windows, MacOS ) • We modified program to generate output files • Graphs of annual average trends (various formats) • Statistical summaries (text files) • Daily data linking O3 to predictors (CSV files) • Output files can be manipulated to select subsets of data and develop projections

  17. GAM Application to Central California • Predict subregion max daily peak 8-hour O3 • Find daily peak 8-hour O3 for each site • Take maximum site for each day • Meteorological variables • Daily max T, 10 a.m. – 4 p.m. RH, 7 – 10 a.m. & 1 – 4 p.m. WS & WD, HYSPLIT 24-hour back trajectory distance & direction, solar radiation, 850 mb T, delta 850 mb T – surface min T, pressure gradients • Tested precipitation, 925 mb T, lagged met data • Air quality variables • Subregion mean daily max NO, 7 – 10 a.m. NO2 • Tested CO, NMOC, visibility, PM TC

  18. Data Used for Application • 1995 – 2010 O3 season (March – October): 3920 days (3424 – 3661 days data available) • One surface meteorological site per subregion (Redding, Sacramento, etc.) – also ran HYSPLIT for each surface met site • Nearest upper-air site (Medford, Oakland, San Diego) • Means of CIMIS data in each subregion • Means of NO, NO2 data in each subregion – (CO and NMOC data tested, not in final) • IMPROVE data in each subregion (tested)

  19. CIMIS Sites

  20. NMOC Data Limitations • Inconsistencies between measurement methods, changes in methods, incomplete canister sampling – longest, consistent record is for continuous NMOC coded as Method 164 (TEI 55 instrument) • 14 NMOC sites in 8 subregions • 5 Bay area sites with 5 – 6 years data plus 9 other sites with 11 – 13 years • Variability of NMOC data greater than variability of CO, NO, and NO2 measurements

  21. GAM Results • Fit • Sensitivity coefficients • Factors contributing to high O3 • Projections • Uncertainty

  22. SJV and Sequoia Bay Area Sacramento Valley & Sierra Coastal

  23. Which Variables Are Important? (Higher values of F-to-remove statistics indicate greater importance)

  24. Sacramento Valley and Sierra Bay Area SJV and Sequoia Coastal Sensitivity to Daily Max Temperature

  25. Sensitivity to Mid-day RH Sacramento Valley and Sierra Bay Area Bay Area SJV and Sequoia Coastal

  26. Sacramento Valley and Sierra Bay Area SJV and Sequoia Coastal Sensitivity to 850 mb Temperature

  27. Sensitivity to Daily Max NO Sacramento Valley and Sierra Bay Area SJV and Sequoia Coastal

  28. Sacramento Valley and Sierra Bay Area SJV and Sequoia Coastal Sensitivity to 7 – 10 a.m. NO2

  29. Sensitivity to Day of Week

  30. Declining NO2 Has Reduced Peak O3

  31. Declining NO Has Increased Peak O3

  32. Net Effect of Declining NOx Has Been to Decrease Mean Peak 8-Hour O3

  33. Net NOx Effect is Robust to Change in Model Formulation

  34. a. WBA b. CBA c. EBA Higher Peak O3 is Related to Stagnation (Shorter Transport Distances)

  35. Multiple Factors Enhance Peak O3 on High O3 Days (Top 60)

  36. Precursor Reductions Lowered O3 in CSJ CSJ Top 60 Days per Year

  37. Precursor Reductions Lowered O3 in NSJ NSJ Top 60 Days per Year

  38. Projecting Future Progress • Method I: Combine annual O3 sensitivities to NOx with projections of NOx emissions • Method II: Combine daily O3 sensitivities to NOx with projected ambient NOx concentrations generated from synthetic data • Implicit assumption in both methods: ratio of VOC/NOx remains constant or follows trends similar to historical trends

  39. Projection Method I Project historical trend lines to estimate effects of future basin NOx emissions

  40. Projection Method II • Use 2008 – 2010 as base period, utilizing daily monitoring data with daily R sensitivities • For each month and day of week, remove date with highest NO2 – for ties, remove date with highest NO • For each month and day of week, retain 5 dates using random selection • Recode data as 2011 • Repeat steps to generate 2012 – 2020 • Aggregate daily sensitivities to NO, NO2, NOx

  41. NO2 and NO Concentrations “Continue” Declining at Historical Rates

  42. Decreasing NOx Concentrations Will Continue to Decrease Peak O3

  43. Decreasing Peak O3 on High O3 Days NSJ High O3 Days (Top 60)

  44. Compare and Contrast Modeling and Weight-of-Evidence Analyses • Need to consider prediction uncertainties for modeling and weight-of-evidence analyses • GAM uncertainties quantified in two ways • Parameter standard errors from R • Bootstrap uncertainties • Design value variability assessment used to characterize one type of modeling uncertainty

  45. GAM Prediction Uncertainties • Parameter standard errors are computed for each day by R – but are they realistic? • Tested using bootstrap uncertainties • Leave out one year at a time (16 combinations) • Leave out one group of meteorological variables at a time (10 combinations) • Add AQ variables one at a time (4 combinations) • Generate variances from each • Sum variances

  46. Bootstrap and R Standard Errors are Comparable and ~10% of Coefficients SAC NBA NSJ CSJ

  47. Annual Effects of NO, NO2, and NOx on Peak O3 with Uncertainties, CSJ

  48. Design Value Variability Assessment – Baseline Design Values Vary 1 – 14 ppbv

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