1. Available Analytical Approaches for Estimating Fire Impacts on Ozone Formation Hilary Hafner Stephen Reid Clinton MacDonald Sonoma Technology, Inc. Petaluma, CA WESTAR Wildfire and Ozone Exceptional Events Workshop Sacramento, CA March 6, 2013 910417-5607

2. 2 Presentation Outline Statistical modeling approaches –Overview –Sample analysis (Northern CA wildfires) –Strengths and weaknesses Other tools –MetDat2 –AirNow-Tech

3. 3 Statistical Modeling Overview (1 of 4) Regression equations Represent a statistical method for quantifying the relationship among different variables (e.g., air quality and meteorological parameters) Have been successfully used to predict daily or sub-daily pollutant concentrations in many areas of the country Are developed using several years of data to describe the relationship between air quality and meteorology under typical emission patterns

4. Statistical Modeling Overview (2 of 4) Need to understand meteorological conditions leading to high ozone concentrations –Transport patterns, synoptic typing, meteorological “cut points” (e.g., max. temperature must be > 85°F) Typical predictor variables for ozone –Max temperature, AM/PM wind speed and direction, 850 mb temp, 500 mb geopotential height, % cloud cover 4

5. 5 Statistical Modeling Overview (3 of 4) Multi-linear regression: Ozone = c 1 V 1 + c 2 V 2 ……. c n V n + constant Where: Ozone = predictand c = coefficients (weighting factors) V = meteorological predictor variables

6. 6 Statistical Modeling Overview (4 of 4) Example equation and predictor variables: 1-hr Ozone = exp (13.72 – 0.03*Clouds – 0.04*WindSpeed1 + 0.01*WindSpeed2 + 0.0002*WindDirection – 0.01*Pressure – 0.02*DewPoint + 0.03*AloftTemperature – 0.009*AloftWindSpeed + 0.009*TemperatureDifference) Variable AbbreviationsDescription Clouds Average hourly cloud cover from 6:00 a.m. PST to 6:00 p.m. PST where clear = 0, partly cloudy = 1, mostly cloudy = 2, and overcast = 3 WindSpeed1Average wind speed from 6:00 a.m. PST to 12:00 p.m. PST in m/s WindSpeed2Surface wind speed at 00Z (4:00 p.m. PST on the previous day) in m/s WindDirectionSurface wind direction at 00Z (4:00 p.m. PST on the previous day) PressureSurface pressure at 12Z (4:00 a.m. PST) in mb DewPoint Surface dew point temperature at 12Z (4:00 a.m. PST) in °C AloftTemperature 925-mb temperature at 00Z (4:00 p.m. PST on the previous day) in °C AloftWindSpeed925-mb wind speed at 12Z (4:00 a.m. PST) in m/s TemperatureDifferenceTemperature difference from 850 mb to the surface at 12Z (4:00 a.m. PST) in °C

7. 7 Sample Analysis (1 of 6) Sacramento Regional Nonattainment Area (California Air Resources Board): From June 20-22, 2008, lightning strikes ignited a series of wildfires in Northern CA Over 1 million acres burned through mid-July, most within 200 miles of Sacramento The Sacramento region was covered in a thick layer of smoke from June 23 through much of July Sacramento area monitors recorded very high 1-hr ozone (>160 ppb) and 24-hr PM 2.5 concentrations (> 60 µg/m 3 ) during this time period

8. 8 Sample Analysis (2 of 6) Conceptual Model: 8-hr ozone concentrations above 95 ppb generally occur in Sacramento when: An aloft ridge of high pressure is located over California; and Lower surface pressure (a thermal trough) extends inland to Sacramento. Above: Plot of 500-mb heights for 8/15/08 indicating a ridge of high pressure over California. Right: Surface weather map for 8/15/08 indicating a thermal trough extending from the Sacramento region northward. Surface temperatures were warm.

9. 9 Sample Analysis (3 of 6) In 2004, STI developed a regression equation to assist SMAQMD with daily ozone forecasting: Obtained and processed 6 years (1997-2003) of ozone season (May-Oct) data for 7 sites in Sacramento County Determined proper inputs for the regression algorithm to yield statistically sound equations (stepwise procedure for adding and removing variables from the model) Evaluated output variables for physical sense (i.e., higher ozone correlated with lower wind speeds) Evaluated the statistical strength of the equation (T-test, P2- tail, standard coefficients of a variable, etc.) Tested final equation against observations from data set reserved for testing only (not model development)

10. Sample Analysis (4 of 6) Applying the model to exceptional events analysis: Tested the performance of the regression equation for the 2007 ozone season to evaluate the impacts of ozone precursor reductions since 2003 Found a positive bias of 8 to 13 ppb (depending on which NCEP Eta model results were used) for 2007 Made adjustments to account for this bias in the 2008 analyses Applied the regression equation to the 2008 ozone season and compared predicted and observed 1-hr ozone concentrations during the fire event 84 53 55 10

11. Sample Analysis (5 of 6) Evaluating uncertainty and error: Analysis of deviation distributions (observed – predicted) for data from 2007 and portions of 2008 ozone seasons showed that the 95 th percentile of the daily differences was 27.6 ppb 11

12. Sample Analysis (6 of 6) Evaluating uncertainty and error: Conservative thresholds of maximum predicted ozone concentrations were calculated by adding the 95 th percentile value of 27.6 ppb to daily results from the regression model (June 2008 results below) 12

13. 13 Method Strengths Provides an objective method that weights relationships that are difficult to quantify Leverages existing resources for regions that are currently forecasting ozone Provides a quantitative estimate of anticipated ozone concentrations in the absence of fires

14. 14 Method Weaknesses Requires air quality and meteorological data for a period of several years Requires analysis of met conditions leading to high ozone concentrations Requires expertise in regression analysis techniques, meteorology, and air quality May require updates (or bias testing) as emissions sources and land use changes May not be appropriate for ozone exceedances that are close to the standard

15. 15 Other Tools (1 of 6) EPA’s MetDat2 System Database of meteorological variables for the years 2001-2011 Includes surface and upper-air observational data from ISH and IGRA Includes gridded (32-km resolution) data from the North American Reanalysis (NARR) model Could be used to develop regression equations for ozone forecasting ISH = Integrated Surface Hourly database IGRA = Integrated Global Radiosonde Archive

16. 16 Other Tools (2 of 6) AirNow-Tech Navigator Multi-function, web-based air quality GIS used to: Run HYSPLIT trajectories Save multiple map configurations, set a default view View Hazard Mapping System fire locations and smoke plumes

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21. 21 Other Tools (6 of 6) AirNow-Tech Navigator Upcoming improvements –Satellite data layers including MODIS AOD and true color imagery –Connection with AQS to backfill AirNow with regulatory data –Additional data reporting and graphic output capabilities

22. 22 Summary Regression equations provide a method for quantifying what ozone concentrations would have been “but for” a fire event The method is data intensive and may not be applicable to all areas MedDat2 data system may be useful in regression equation development Other analytical tools exist, such as AirNow-Tech, to aid analysts in preparing exceptional event packets

23. Contact Information Hilary Hafner hilary@sonomatech.com (707) 665-9900 23