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An Analysis of Ozone Monitoring Seasons in the U.S.

An Analysis of Ozone Monitoring Seasons in the U.S. Louise Camalier (not attending) (Presented by David Mintz) National Air Quality Conference Portland, Oregon April 8, 2008. Purpose. The ozone NAAQS level is now 0.075 ppm

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An Analysis of Ozone Monitoring Seasons in the U.S.

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  1. An Analysis of Ozone Monitoring Seasons in the U.S. Louise Camalier (not attending) (Presented by David Mintz) National Air Quality Conference Portland, Oregon April 8, 2008

  2. Purpose • The ozone NAAQS level is now 0.075 ppm Are states’ current official monitoring seasons adequate to protect against the adverse health effects protected by the future primary air quality standard?

  3. Analytical Plan • Use most recently certified 3-yr period • 2004-2006 • Look at ambient data, examine actual exceedences • Predict ozone to examine potential exceedences (across time) • Useful for monitors without year round data

  4. Official Monitoring SeasonsCurrent CFR vs. AQS • Current CFR (mandated season) • Generally, seasons are consistent within a state, excluding Texas and Louisiana (defined by AQCR) • AQS (mandated & modified season) • Seasons can be modified on a site-by-site basis, based on judgment of regional administrator • Examples: • California, Nevada, Arizona

  5. Wisconsin is April 15 - Sept 15 Apr-Nov Apr-Oct Jan-Dec Jun-Sep Mar-Nov Mar-Oct Apr-Sep Mar-Sep May-Oct May-Sep Official Ozone Monitoring SeasonsWhere is the year round monitoring? Monitoring Data in AQS Official seasons in AQS Monitoring Season Year round monitoring Good spatial representation

  6. Using Ambient Data What do we see? Ambient, year round data from 531 sites (~45% of total) Examine number of observed exceedances (8hr daily max) using as much data as possible • Full year • Partial year • Only within monitoring season With the data available, are we seeing exceedances occurring outside of a state’s official season?

  7. Exceedances: in or out of season? • Common (“core”) monitoring season across all states is June-Sept • Months displayed on the following maps are the “fringe” months • Feb-May (4 months before) • Nov-Jan (4 months after) Are there out of season exceedances when the concentration threshold is lowered? Scenarios: 0.075 ppm 0.060 ppm* *indicator for the yellow “AQI” level

  8. Ozone AQI Summary Used in conjunctionwith one another Changes are in red

  9. Using the new standard (0.075 ppm) to locate areas that may need seasonal modifications

  10. “in season” exceedances in blue • “out of season” exceedances in red May April Standard scenario: 0.075 ppm 4 months before common O3 season March February 3/30-3/31 2/1-2/27 This situation doesn’t in itself justify expanding the season for the entire month of March

  11. “in season” exceedances in blue • “out of season” exceedances in red October December November Standard scenario: 0.075 ppm 4 months after common O3 season January 1/24-1/31

  12. Using the AQI yellow level (0.060 ppm) to locate areas that may need seasonal modifications

  13. “in season” exceedances in blue • “out of season” exceedances in red Scenario: 0.060 ppm 4 months before common O3 season February March April May

  14. “in season” exceedances in blue • “out of season” exceedances in red Scenario: 0.060 ppm 4 months after common O3 season October November December January

  15. Estimating ozone at existing sites when data is not available Predicting during the off-season months

  16. Red dots are data in the predicted months Why Statistically predict ozone? Example for South Carolina • We would like to “fill” temporal gaps where little or no ozone data are available • Statistical model is used and tailored to accurately predict exceedance rates during off-season months* • Off season month ranges are area specific but typically include months such as February, March, October and November *Assumes that relationship between ozone and meteorology during other months is similar to data used in fitting (do not use core months)

  17. Statistical Prediction of Ozone(in non-monitored months) Case Study: Columbia, South Carolina • South Carolina’s official monitoring season: April - September • We want to predict ozone during months outside of the official season • Focusing on predicting for: February, March, and October • Core months for ozone season: June, July, and August • Ozone and meteorological relationships are different during “core” months, therefore we only use the surrounding (cooler) months (March, April, May, September, and October) in the model • Using the cooler months is best as this better represents the kind of meteorology and ozone response that occurs during the months which we are trying to predict

  18. About the model • Urban area ozone data is combined with meteorological data (1997-2006) • Relationship is developed between maximum 8-hr ozone values and meteorology • Maximum 8-hr ozone is modeled as a function of daily meteorological variables (max temperature, humidity, etc.) • Best predictions obtained when excluding summer months during the fitting process (June, July, Aug) • Summer relationship is different from spring/fall/winter relationship • Columbia has observed data for months which we are trying to predict (e.g., February, March, October, November) • Use these data to validate our model predictions • Results are shown for Columbia • For more details: • Camalier, L., Cox, B., and Dolwick, P., 2007. The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmospheric Environment 41, 7127-7137.

  19. Model Validation Example: Columbia, South Carolina Scatter plot of observed and predicted values for only the data used in the fitting process (March, April, May, Sept, Oct) Scatter plot of observed and predicted number of exceedances for all month/year combinations (2004-2006) with observed data Values in red not used in fitting process (February, November)

  20. June, July, and August are not used in the fitting process, however they are behaving the way we expect Columbia, South Carolina exceedances > 75 ppb occur in months outside of current monitoring season (red bars) Jan 0 Feb 0 Mar 0.6 April 3.4 May 4.8 June 3.9 July 3.7 Aug 1.2 Sep 0.7 Oct 0.1 Nov 0 Dec 0

  21. Using ambient & predicted data Case Example: South Carolina Season: April-September Used urban area with highest expected exceedences Ambient Data (2004-2006) On average, for 60 ppb ~10 exceedences/year between 2/15-3/31 We are predicting ~8 exceedences/year between February and March Predicted Exceedences, days above 60 ppb predicted months Feb: 1.2 March: 6.4

  22. Conclusions • One can use ambient, existing data along with statistically predicted data to guide informed decisions • Any modifications of the official season will be based on monitoring judgment and the results from this analysis

  23. Other Questions? Contact: • Louise Camalier Camalier.Louise@epa.gov (919) 541-0200 EPA,OAR,OAQPS,AQAD, Air Quality Analysis Group (RTP, NC)

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