Envair DRI Alpine Geophysics

1. Understanding Relationships Between Changes in Ambient Ozone and Precursor Concentrations and Changes in VOC and NOx Emissions from 1990 to 2004 in Central California Envair DRI Alpine Geophysics May 25, 2006

2. Today’s Meeting – Progress Report • Data acquisition and evaluation • Met classification results (version 2) • Ambient AQ trends • Next steps • Feedback?

3. Data Acquisition and Evaluation

4. Emissions Inventories • Obtained ARB monthly historical emissions • Have checked completeness & readability • Status: ready for re-aggregation into county or greater total VOC, NOx, CO

5. Ambient AQ Data • Hourly gas-phase (O3, NO, NOx, CO, HC) • Completeness • Rounding conventions • VOC: PAMS, ARB, SJVAQS (1990) • No combination of previous VOC validation provides complete spatial & temporal coverage • Start with EPA archives and validate data

6. Meteorological Data • Upper air: Oakland soundings 4 am & 4 pm • Pressure gradients: San Francisco to Medford, Reno, and Fresno • Surface wind speed and direction (applied QA steps recommended by BAAQMD)

7. Surface Met Data QA Issues Identified by BAAQMD • Site locations • Time zone • Zero values vs. missing data • Sudden increases or decreases in WS, T • Long periods of constant WS, WD, or T • Incorrect flag values • Redundant data

8. Surface Meteorological Stations

9. Met Classification Methods

10. Met Classification Approach • Goal is to stratify days, not to adjust ozone • Stratifications related to transport directions • Primary clustering method is based on met variables related to transport • Secondary method is spatial correlation of peak ozone (e.g., DRI CCOS clustering algorithm, BAAQMD analysis)

11. Met Classification Details • K-means clustering • Use all days May through October each year • Standardized variables • 850 mb T, height, u and v wind vector components • Pressure gradients SFO to Medford, Reno, Fresno • Surface u and v wind vector components, 53 stations • Limited to 12 cluster types

12. Classification Results

13. Summary of Findings • Met classifications yield clusters having differences in transport direction or distance • Different clusters exhibit different mean concentrations of air pollutants • Pollutant trends at a site may but do not always differ among clusters • Clusters account for about 30 – 40% of ozone variance

14. Four Met Types Dominate Jul - Oct

15. Which Clusters Have Highest Ozone? • Clusters 1, 3, 5, 6 (summer and fall) • Sometimes cluster 9 (Sep and Oct) • In southern SJV, also clusters 2, 7, 8, 11 (occur most frequently May and June)

16. Daily 8-Hour Peak Ozone, 1990-2004Livermore Meteorological Type (cluster number)

17. Daily 8-Hour Peak Ozone, 1990-2004Folsom Meteorological Type (cluster number)

18. Daily 8-Hour Peak Ozone, 1990-2004Parlier Meteorological Type (cluster number)

19. Daily 8-Hour Peak Ozone, 1990-2004Arvin Meteorological Type (cluster number)

20. Do clusters exhibit differences in transport direction and distance? … most easily seen using plots of U and V wind vector components (these show direction of origin)

21. Directional Differences

22. Directional Differences

23. Directional Differences

24. Directional Differences

25. Mean Directional Differences

26. 850 mb Directional Differences

27. Directional Differences

28. Main Finding of Cluster Analysis Clusters exhibit differences in transport directions or distances. The differences are more pronounced at some sites than others.

29. Can Clusters Be Improved? Maybe - Direct comparisons of met types (clusters) to groupings determined from wind speed and direction at each site suggest room for improvement.

30. Chabot 12 1 4 2 8 11 1 4 2 10 11 1 4 10 1 42 10 9 36 24 20 1 3 5 8 1 2 21 4 62 5 Cluster 7 1 100 5 6 16 5 166 23 5 1 3 31 126 4 4 7 4 16 2 3 12 64 109 44 55 2 6 2 41 12 1 83 8 1 12 34 3 2 5 Calm <1m/s NE 1-2m/s SE 1-2m/s SW 1-2m/s NW 1-2m/s NE >2m/s SE >2m/s SW >2m/s NW >2m/s Direction/Windspeed

31. Sacramento 12 2 1 38 11 1 62 10 7 4 9 1 5 69 2 9 24 9 55 1 99 8 20 5 85 23 7 82 12 Cluster 7 1 3 18 233 6 6 5 59 30 319 5 22 16 286 4 16 1 10 7 25 21 3 148 26 207 19 14 131 2 3 5 5 157 1 212 1 13 27 41 6 12 1 Calm <1m/s NE 1-2m/s SE 1-2m/s SW 1-2m/s NW 1-2m/s NE >2m/s SE >2m/s SW >2m/s NW >2m/s Direction/Windspeed

32. Fresno 12 1 40 11 39 1 13 6 10 3 1 12 79 9 77 1 1 90 15 8 2 30 198 Cluster 7 3 18 2 231 6 25 1 1 226 1 159 5 79 2 7 196 50 4 38 1 15 5 3 13 3 3 346 1 2 2 186 5 2 30 1 2 4 71 59 1 290 3 1 6 Calm <1m/s NE 1-2m/s SE 1-2m/s SW 1-2m/s NW 1-2m/s NE >2m/s SE >2m/s SW >2m/s NW >2m/s Direction/Windspeed

33. Preliminary Trends Assessment

34. Trends Methods • Split subregional top 60 days by cluster • For each site and each cluster, compute linear regression of AQ metric against day • Determine regression slope and SE

35. Do trends differ among clusters?

36. CO Trends

37. Daily Morning CO – All Sites

38. Error bars = 2 SE of slope

39. Error bars = 2 SE of slope

40. Error bars = 2 SE of slope

41. Error bars = 2 SE of slope

42. Error bars = 2 SE of slope

43. Error bars = 2 SE of slope

44. Error bars = 2 SE of slope

45. Error bars = 2 SE of slope

46. Error bars = 2 SE of slope

47. NOx Trends

48. Daily Morning NOx – All Sites

49. Error bars = 2 SE of slope

50. Error bars = 2 SE of slope