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A Comparison of AirNow and AQS Particulate Matter Databases

A Comparison of AirNow and AQS Particulate Matter Databases. Katina Gracien Brian Hare Graduate Assistants : Atina Brooks, John White & Andrew Moore Faculty Advisor : William F. Hunt Jr. & Dr. Kimberly Weems Clients: USEPA Chet Wayland David Mintz Tim Hanley Lewis Weinstock

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A Comparison of AirNow and AQS Particulate Matter Databases

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  1. A Comparison of AirNow and AQS Particulate Matter Databases Katina Gracien Brian Hare Graduate Assistants : Atina Brooks, John White & Andrew Moore Faculty Advisor : William F. Hunt Jr. & Dr. Kimberly Weems Clients: USEPA Chet Wayland David Mintz Tim Hanley Lewis Weinstock Scott Jackson

  2. Background • Methods of Monitoring Fine Particulate Matter • FRM (federal reference method) • A single 24 hour measurement (midnight to midnight) • Continuous • TEOM ( Tapered Element Oscillating Microbalance) • Hourly measurements • RAMS • Speciated • Used Air Quality Index Reporting • Health Advisory • Reported in USA Today

  3. AQI Chart for Fine PM

  4. Purpose • Compare AQS FRM with AirNow concentration levels on a national level • How well does the AirNow prediction agree with the FRM measurement? • How well do methods in different regions and/or states compare during the summer and winter seasons? • Which regions and/or states need improvement in the way they predict fine PM? • Is an Early Warning System needed to improve the accuracy of AiRNow reporting?

  5. Analysis of the Federal Reference Method: Accuracy of AirNow Predictions

  6. Map of Percentage Correct using AQI District of Columbia

  7. Map of Percentage correct using AQI Percent Correct by Seasons (AirNow vs FRM)

  8. Differences in Concentration Levels (ug/m3)

  9. Differences in Concentration Levels Average difference of AirNow – FRM by County (ug/m3)

  10. Surrounding Lake Michigan Counties Average difference of AirNow – FRM

  11. Local ComparisonsMI and NC by County

  12. FRM vs. AirNow Correlations

  13. FRM Correlation Coefficients by Season

  14. Examples of AirNow Reporting

  15. Chronic Under-reporting of AirNow • District of Columbia • Wrong Level =72/342 = 24.8 % • AirNow < FRM = 331/342 = 96.7% • Texas • Wrong Level 153/1241 = 15.16% • AirNow < FRM 1156/1241 = 93.2%

  16. Chronic Over-reporting of AirNow • Michigan • Wrong Level 364/1913 = 19.02% • AirNow > FRM 1496/1913 = 78.2% • Indiana • Wrong Level 211/962 = 21.93% • AirNow > FRM 699/962 = 72.7%

  17. How Accurately Does AirNow Report the AQI?

  18. AQS FRM vs. AirNow with AQI code Black = difference in AQI level

  19. AirNow Levels vs. FRM by AQI Category 5 occurrences out of 8,666 obs. out of 23,262 obs. 7 occurrences 1 occurrence out of 286 obs. out of 19 obs.

  20. Recommendations • Redo the Fine PM episode in the Midwest occurring in February 2005. • Explore the possibility of developing a QC procedure to pair “problem sites” with sites that consistently report data accurately. • 3. Provide an early warning system that data has not been properly adjusted at problem site.

  21. Early Warning System Approach • Examine the correlation matrix for all PM fine sites using FRM(s): • Find sites in States with high reporting accuracy (using continuous PM fine instruments) that can be paired to sites in States with low reporting accuracy (Indiana, Michigan, etc.). • Develop regression equations predicting FRM measurements at a site in a county with poor reporting accuracy as a function of a PM fine measurements at an adjacent county with “good” reporting accuracy.

  22. Average Differences by CountyWinter Ingham Washtenaw Wayne

  23. Correlation Matrix for Selected FRM Sites 0.92821 0.92821 frm1=Washtenaw, frm2=Ingham, frm3=Wayne

  24. WINTERAn Early Warning System

  25. Winter Wake Wayne

  26. Average Differences by CountyWinter Ingham Washtenaw Wayne

  27. STEP 1

  28. STEP 1 Obtain the AirNow values for both of the sites, “good” and “bad”.

  29. STEP 2

  30. Step 2{pred.FRM Washtenaw=F(Airnow Washtenaw)}

  31. Step 2 Validation Predicted FRM Washtenaw, from the regression equation in previous slide Actual observed FRM Washtenaw

  32. STEP 3

  33. Step 3 {FRM Wayne= F(FRM Washtenaw)} Make sure this is predicted FRM Wayne value not the observed FRM Wayne value

  34. Step 3 Validation Predicted FRM Wayne from the regression equation in previous slide Actual observed FRM Wayne

  35. Next Steps & Recommendations • Continue to work on QC Procedure. • Further evaluate the effectiveness of this approach. • Identify other pairs of “good” & “bad” sites to apply to this QC procedure to.

  36. Additional Materials

  37. Locations of the 5 Occurrences of when AirNow Reported Unhealthy for Sensitive and FRM forecasted Good Air Quality

  38. Locations of the 7 Occurrences of when AirNow Reported Good and FRM forecasted Unhealthy for Sensitive Air Quality

  39. Location of the 1 Occurrence of when AirNow Reported Good and FRM forecasted Unhealthy Air Quality

  40. Map of Regression CoefficientsModel : AQS(frm-primary) = β*AirNow

  41. Regression Coefficients by EPA Region with Season Model : AQS(frm-primary) = β*AirNow

  42. Air Quality Monitoring Sites Accuracy AirNow Correctly Predicts AQI

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