1. The Importance of MeteorologicalData in Exceptional Events Analysis Prepared by Hilary Hafner, Daniel Alrick, ShihMing Huang, and Adam Pasch Sonoma Technology, Inc. Petaluma, CA Presented at the 2010 National Air Quality Conferences Raleigh, NC March 15-18, 2010 3801

2. Outline Definition of an Exceptional Event Importance of meteorology in Exceptional Events; data sources and tools available Examples from past events Conclusions

3. What Are Exceptional Events? Exceptional Events are unusual or naturally occurring events that can affect air quality but are not reasonably preventable using techniques air agencies can implement to meet National Ambient Air Quality Standards (NAAQS). Three exceedance days in May 2007 were likely related to wildfires.

4. What Makes an Event Exceptional? To justify the exclusion of air quality data from NAAQS determination, the reporting agency must show A clear causalrelationship between the measured values under consideration and an identified source, Measured value(s) are in excess of normal historical fluctuations, and No exceedance or violation would have occurred “but-for” the event. Exceptional Events cannot be caused by air mass stagnation, temperature inversions, high temperatures, lack of precipitation, or transport from anthropogenic sources. Federal Register (72 FR 13560-13581). Treatment of Data Influenced by Exceptional Events; Final Rule. Vol.72, No. 55, Pages 13560-13581, March 22, 2007

5. Justifying an Exceptional Event Good meteorological data and useful or appropriate analysis tools must be available and put to use. Meteorological analyses help determine whether the event was unusual when compared to the historical record. Tools such as diffusion models and trajectory analyses are vital in examining pollution transport from unusual sources such as volcanoes, wildfires, and stratospheric ozone. No single data set necessarily has all the pertinent information. Surface meteorological and air quality instrumentation are usually not collocated. Sub-hourly information can be important (i.e., wind gusts). Surface meteorology does not represent important aloft meteorology. Models may not be accurate.

6. Examples of Meteorological Data

7. Examples of Meteorology Analysis Tools Model predictions of pollution concentrations based on meteorological predictions BlueSky Gateway NOAA Model NAAPS (Navy Aerosol Analysis and Prediction System) Spatial analysis tools that use meteorological and/or air quality data AIRNow-Tech DataFed HYSPLIT Example of output from a 12Z run of the NOAA model depicting predicted 1-hr maximum ozone concentrations (ppb) http://www.nws.noaa.gov/aq/

8. Example 1: Are Wind Speeds Outside the Historical Norm? Wind speeds during a PM10 event Comparison to historical meteorological data indicates this event was unique. Wind speed on 4/12 was very high compared to historical records.

9. Example 1: Are Wind Speeds Outside the Historical Norm? Wind speed maximum coincided with a jump in PM10 concentrations. Concentrations between 1400 and 1700 on 4/12 were among the highest observed over the 2005-2008 time period.

10. Example 2: Do Wind Gusts Coincide with High PM10 Concentrations? 4/12/2007 Interpolated gust map during cold frontal passageat 1400-1800 LST 10

11. Example 2: Do Wind Gusts Coincide with High PM10 Concentrations? 4/12/2007 Interpolated gust map during cold frontal passageat 1400-1800 LST

12. Example 2: Do Wind Gusts Coincide with High PM10 Concentrations? Note: Wind speed data are from AQS sites different from the meteorological sites that measure gust. 4/12/2007 Interpolated wind speed map during cold frontal passageat 1400-1800 LST 12

13. Example 2: Do Wind Gusts Coincide with High PM10 Concentrations? Note: Wind speed data are from AQS sites different from the meteorological sites that measure gust. 4/12/2007 Interpolated wind speed map during cold frontal passageat 1400-1800 LST

14. Example 2: Was the Wind Direction Different During the Event? Historical April wind rose suggests stronger winds come more frequently from the southwest and west. Strongest winds on the event day came from the northwest (circled in blue on second graph), indicating unique conditions. Average April Winds Event Day Winds

15. Example 3: Was Transport of PM from Wildfires? DataFed-generated air trajectories indicate air parcels over the southeast came from a large fire in southern Georgia. The satellite-based Ozone Monitoring Instrument (OMI) shows high PM concentrations in the suspected source region. These tools help analysts make a case for an exceptional event. DataFed-generated 24-hr backward trajectories and 24-hr average PM2.5 concentrations OMI Aerosol Index

16. Example 3: Was Transport of PM from Wildfires? Models predict various pollutant concentrations based on meteorological conditions. During this event, sulfate concentrations (important components of PM) were very low across the southeast U.S. Modeled smoke was highest at the site of fires, which affected downwind regions. Thus the PM event was most likely caused by the fire upwind rather than by anthropogenic sulfate. NAAPS (Navy Aerosol Analysis and Prediction System) from DataFed

17. Example 4: Was Pollutant Transport Important? Trajectory analysis identifies source regions and transport of pollutants. Example above includes Hazard Mapping System smoke plumes and fire locations. AIRNow-Tech allows integration and analysis of meteorology and air quality data sets.

18. Example 5: What Would Concentrations Have Been? BlueSky Gateway models fire and non-fire PM2.5 separately. It also provides information on air quality “but-for” a wildfire; this aids Exceptional Events analysis. The accuracy of model predictions is tied in part to the quality and availability of meteorological data. = +

19. Conclusions The integration of air quality and meteorological data is vital in “telling the story” of an Exceptional Event. Properly justifying the exclusion of data due to an Exceptional Event may require multiple meteorological and air quality-related data sets, as well as a variety of analysis tools and atmospheric models. Smoke Visible satellite showing extent of smoke plume from fires over southern Georgia. Red dot represents fire locationBlue dot represents receptor location

20. Selected Links to Data and Analysis Sources ASOS and Upper Air Data – www.weather.gov/observations.php AQS – www.epa.gov/ttn/airs/airsaqs BlueSky Gateway – www.getbluesky.org NOAA Air Quality Forecasting – www.arl.noaa.gov NAAPS Model and Satellite Analyses – www.nrlmry.navy.mil/aerosol DataFed – www.datafed.net AIRNow-Tech – www.airnowtech.org MADIS – madis.noaa.gov