PhaseIMeetingMay2007 v1

Understanding Relationships Between Changes in Ambient Ozone and Precursor Concentrations and Changes in VOC and NOx Emissions from 1990 to 2004 in Central California : 

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 – C. Blanchard and S. Tanenbaum DRI – E. Fujita and D. Campbell Alpine Geophysics – J. Wilkinson Phase I Findings and Proposed Phase II Approach May 31, 2007

Acknowledgements: 

Acknowledgements CARB Dar Mims, Dwight Oda, Don Johnson, Martin Johnson, Larry Larsen, Cheryl Taylor BAAQMD Jim Cordova, Saffet Tanrikulu SJVAPCD Evan Shipp … plus everyone else for review and assistance

Today’s Topics: 

Today’s Topics Project overview Trend analysis summary Trends in ozone precursors Trends in precursors compared with county-level emissions Spatial variations of ozone precursors => Questions Ozone trends Meteorological classification Do met classes shed light on ozone trends? => Questions Phase II analyses and schedule

I. Project Overview: 

I. Project Overview Phase I Develop databases and methods Characterize ozone and precursor trends by site, subregion – compare precursor trends to county-level emission trends – characterize ozone trends by meteorological class Evaluate prospects for success in Phase II Phase II Grid emissions and relate ambient primary-pollutant trends to zone-of-influence emission trends Relate ozone trends to ambient and emission trends of precursors, and to meteorological conditions Submit final report

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II. Trends Summary

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AQ Sites and Subregions

AQ Metrics – Annual Averages: 

AQ Metrics – Annual Averages Ozone Annual 4th-highest daily 8-hour max, each site Annual mean of top-60 peak 8-hour days, each site (the top-60 days are determined for each subregion) CO and NOx Annual means from top-60 days, each site Morning (start hours 5 am – 10 am) Time of peak 8-hour ozone maxima (“mid-day”) NMOC Annual means from all days, by site Early morning - 5 am PST (most complete sampling)

1990 – 2004 AQ Trends Summary Results: 

1990 – 2004 AQ Trends Summary Results No trends significantly upward* NOx sig* down at 22 of 28 sites** CO sig* down at 21 of 25 sites** NMOC sig* down at 5 of 7 sites*** Ozone sig* down at 7 of 42 sites** Annual mean top-60 ozone trends similar to trends in annual 4th-highest 8-hour max * p < 0.05 ** At least 10 years data. One or both metrics. *** 7 - 10 years data

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III. Trends in Ozone Precursors

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Morning NOx decline: 0.66 ppbv/year (~10 ppbv over period) Mid-day NOx decline: 0.31 ppbv/year (~5 ppbv over period)

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Morning NOx decline: 0.39 ppbv/year (~4 ppbv over period) Mid-day NOx decline: 0.21 ppbv/year (~2 ppbv over period)

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Morning NMOC decline: 8.2 ppbC/year (~66 ppbv over period)

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Morning NMOC decline: 11.5 ppbC/year (~115 ppbv over period)

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Morning NMOC decline: 5.1 ppbC/year (~51 ppbv over period)

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Morning CO decline: 43 ppbv/year (~650 ppbv over period) Mid-day CO decline: 20 ppbv/year (~300 ppbv over period) (1990 data suspect)

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Similar results for Bakersfield and Sacramento Del Paso: ~0.3 ppbC NMOC per ppbv CO (some years differed) NMOC/CO from regression of NMOC against CO, by year. Annual r2 ranged from 0.65 to 0.94 (5 am samples)

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CO/NOx declines  so does NMOC/NOx

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Benzene/CO from regression of benzene against CO, by year. Annual r2 ranged from 0.68 to 0.94, except 1998 (5 am samples)

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IV. Comparison of AQ Trends to County-Level Emission Trends

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Emission trend (D 0.40) exceeds ambient trend (D 0.20)

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Ambient trend (D 0.80) exceeds emission trend (D 0.60)

What Did We Learn?: 

What Did We Learn? On average, ambient precursor decreases are comparable to county-level emissions decreases There is a possibility that emission decreases are overestimated or underestimated for some counties Confirmation requires comparison of site trends to spatially-resolved emission trends (Phase II)

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V. Spatial Variations of Ozone Precursors

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Sacramento Del Paso Mean Morning Concentrations CO NOx

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  Roseville Mean Morning Concentrations CO NOx

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  Fresno 1st St Mean Morning Concentrations CO NOx

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  Fresno Sierra Skypark Mean Morning Concentrations CO NOx

Phase II AQ vs. Emissions: 

Phase II AQ vs. Emissions Directional variations of primary species AQ concentrations imply significant local influences Comparison of site primary species AQ trends to emission inventory trends can be improved by using gridded inventories The Phase II comparisons will permit more robust conclusions about the differences between site and emission trends – eliminate the mismatch between spatial scales (replace county-level emissions with emissions from local zones of influence) Potential limitation is accuracy of gridding

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Questions and Comments on Primary Species Phase I Findings and Phase II Objectives

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VI. What Are the Ozone Trends?

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Annual 4th max decline: 0.58 ppbv/year (~9 ppbv over period) Mean Top 60 decline: 0.38 ppbv/year (~6 ppbv over period)

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 First Street | Rincon 

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* Livermore 1st St only

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VII. Meteorological Classification

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Why Examine Meteorological Data? Meteorological information may permit more complete reconciliation of ambient ozone trends with precursor and emissions trends. Phase I. Uses meteorological information to split days into groups with different meteorological characteristics. Initial evidence indicates that ozone trends vary by site, subregion, met type. Phase II. More detailed analyses.

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Met Classification*: Principal component analysis (PCA) of regional-scale met variables K-means clustering of PCs PCA applied to all days of all years from 1990 to 2004 (n = 5480** days) Clustering applied to all ozone-season days (n = 2790 days) ** 5441 with pressure gradient data; 4202 with 850 mb data

PCA of Regional-Scale Variables: 

PCA of Regional-Scale Variables San Francisco-to-Medford sea-level pressure gradient, daily average San Francisco-to-Reno sea-level pressure gradient, daily average San Francisco-to-Fresno sea-level pressure gradient, daily average San Francisco-to-Las Vegas sea-level pressure gradient, daily average Oakland 850 mb vector component (u) wind speed and direction at 4 am Oakland 850 mb vector component (v) wind speed and direction at 4 am Oakland 850 mb vector component (u) wind speed and direction at 4 pm Oakland 850 mb vector component (v) wind speed and direction at 4 pm Oakland 850 mb temperature and height at 4 am Oakland 850 mb temperature and height at 4 pm

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Three PCs explain 80% of variance of eight variables PC1 is westerly wind – PC2 is northerly wind – what is PC3?

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PC3 correlates with surface wind speeds – interpret as ventilation

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Split days into four groups using K-means clustering

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Clusters separate days into groups with different pressure gradients

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Clusters separate days into groups with different 850 mb wind directions

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Clusters separate days into groups with different 850 mb T

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Clusters exhibit persistence and preferred transitions (especially 4-to-1, 2-to-3, and 3-to-4)

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SBA NBA & EBA SAC NSJV CSJV SSJV Daily-average surface wind speeds, all sites

What Can We Learn From Met Clusters?: 

What Can We Learn From Met Clusters? Splitting days into groups having similar meteorological conditions is useful for reducing meteorological “noise” Potentially may reveal differences in response of ozone to precursor changes under different source-receptor conditions or different degrees of photochemical activity and “aging”

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VIII. Do Met Classes Shed Light on Ozone Trends?

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Mean peak 8-hour ozone concentrations varied among sites, subregions, and met types – these days are all top-60 peak 8-hour days

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The change in mean peak 8-hour ozone concentrations from 1995-1999 to 2000-2004 varied among sites, subregions, and met types

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(Order is SBA, NBA, EBA, NSJ, SAC, NSF, CSJ, SSJ)

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For all met classes, top-60 mean peak 8-hour ozone is down at Clovis but up at Parlier … the differences in ozone trends at Clovis and Parlier do not appear to be due to changes in meteorology or in the frequencies of occurrence of different meteorological types

Next Steps – Phase II Ozone Analyses: 

Next Steps – Phase II Ozone Analyses Phase I analyses demonstrate variations of ozone and of ozone trends but do not explain them Site-to-site variations and directional variations of mean concentrations imply significant local ozone formation Need to analyze ozone formation rates Potential limitation is signal-to-noise

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Questions and Comments on Ozone Phase I Findings and Phase II Objectives

Phase II: 

Phase II Phase II schedule – complete by December 2007 with final report and draft manuscript Why would Phase II be useful? Identify zones of emission influence - more accurate comparison of sites’ AQ trends with “zone-of-influence” emission trends Better understanding of ozone trends at sites within each subregion and the relation of ozone to precursor trends, differentiated by met class and subclass

Task 5: 

Task 5 Generate gridded inventories from county-level inventories and historical surrogate files Develop monitor-specific “zone-of-influence” emission trends using 3x3 to 7x7 arrays of grid cells around each long-term monitor

Task 6: 

Task 6 Compare “zone-of-influence” emission trends to ambient primary-pollutant trends. Identify consistencies and discrepancies. Evaluate evidence for inaccuracies in emission estimates. Subdivide met classes. Determine peak ozone changes by site, met class and subclass, and (if warranted) month and day of week. Relate ozone changes to precursor trends and meteorology.

Reporting: 

Reporting Task 7: Prepare final report and draft manuscript Task 8: Provide data, documentation, and software Task 9: Present findings at meetings