1. Clinton MacDonald 1, Kenneth Craig 1, Jennifer DeWinter 1, Adam Pasch 1, Brigette Tollstrup 2, and Aleta Kennard 2 1 Sonoma Technology, Inc., Petaluma, CA 2 Sacramento Metropolitan Air Quality Management District, Sacramento, CA Presented at the 2010 National Air Quality Conferences Raleigh, NC March 15-18, 2010 3807 Benefits of Forecast-Based Residential Wood Burning Bans on Air Pollution

2. 22 Sacramento’s PM 2.5 Problem Sacramento is designated “non-attainment” for 24-hr average PM 2.5 * *Daily PM 2.5 National Ambient Air Quality Standard = 35.5 μg/m 3 12/4/09 (hourly PM 2.5 concentration = 54  g/m 3 ) Based on daily maximum PM 2.5 concentration, Oct. 2002–Sep. 2009

3. 33 Main Causes of PM 2.5 Source apportionment of air samples shows that wood smoke is 26% of total PM 2.5

4. 44 Main Causes of PM 2.5 Surface and aloft high pressure Relatively warm aloft temperatures during a temperature inversion Cool nights Cloud-free skies Light winds Weather Sea Level Pressure Vertical Temperature Profile

5. 55 SMAQMD Wood Burning Rule – Check Before You Burn Episodic curtailment of burning from November 1 through February 28 (curtailment period is midnight to midnight) Four stages based on next-day forecast 24-hr average PM 2.5 ≤ 25 μg/m 3 Legal to Burn = No restrictions > 25 to ≤ 35 μg/m 3 Burning Discouraged = Voluntary curtailment > 35 to ≤ 40 μg/m 3 Stage 1 = No burning except in certified devices > 40 μg/m 3 Stage 2 = No burning in any device

6. 66 Key Questions How effective is the program in improving air quality? What is each county’s contribution to the woodsmoke PM 2.5 in Sacramento? Analyses conducted Cluster analyses: What do we observe? 3-D numerical grid modeling: What do models predict? Chemical mass balance analyses: What is possible? MM5/CAMx and TEAK: What are the contributions?

7. 7 7 Method – Cluster Analysis Compared PM 2.5 on unrestricted burning days (prior to CBYB) to burn ban days Used cluster and qualitative analysis of meteorology to determine days on which meteorology was very similar Differences in PM 2.5 concentration between days can be primarily attributed to a burn ban

8. 8 Method – 3D Numerical Grid Modeling Ran numerical model for 37 days with and without burning –MM5 meteorological model –Community Multiscale Air Quality (CMAQ) model with full chemistry –Sparse Matrix Operator Kernel Emissions (SMOKE) including residential wood combustion temporal profiles –Coarse (36-km) grid resolution Compared relative differences between model runs 8

9. 99 Method – CMB Analysis Chemical Mass Balance (CMB) modeling conducted on speciated PM 2.5 data CMB components – PM 2.5 species concentrations – Known abundances of chemical species from emission sources (source profiles) CMB results estimate the contribution from each source type to each PM 2.5 sample

10. 10 Method – MM5 and CAMx Tracked primary wood smoke emissions from the 21 source areas within and surrounding Sacramento Used MM5 and CAMx to simulate transport, diffusion, and deposition Analyzed relative contributions of primary wood smoke concentrations from each source region to receptor sites Performed analyses for all days from 12/15/2000 through 1/9/2001 (subset of California Regional Particulate Air Quality Study)

11. 11 Method – TEAK (1 of 4) Combined back trajectories and hourly-resolved wood smoke emissions to estimate contributions Calculated back trajectories –for each winter high PM 2.5 day in 2007-2009 –from each receptor back 36 hours –24 times per day –at three starting elevations (~25, 100, and 200 m agl) Air parcels “injected” during transit with wood smoke emissions coincident in time and space, provided the parcels were in the ABL at that time At arrival, omitted parcels above the ABL as contributors

12. 12 Method – TEAK (2 of 4) += + Parcel in ABL? Trajectories Emissions Thirty-six-hour backward trajectories ending at Del Paso Manor at 25 m agl every hour on December 10, 2008

13. 13 Method – TEAK (3 of 4) + Results for all elevations and days with high PM 2.5 concentrations = Daily Percent Contribution Gridded percent contribution to primary PM 2.5 at Del Paso Manor on December 10, 2008

14. 14 Method – TEAK (4 of 4) The percentage each county contributed to wood smoke primary PM 2.5 in Del Paso Manor when peak 24-hr PM 2.5 concentrations in Sacramento County were greater than 35.5 μg/m 3 (winters of 2007-08 and 2008-09) Average contribution for all days

15. 15 Results of Cluster Analysis: What Do We Observe at the Peak Site? Substantial benefit from wood-burning ban, especially in the evening Stage 2 Days Only Stage 1 Days Only 24-hr average benefit = 12 μg/m 3 24-hr average benefit = 4 μg/m 3 Benefit Stage 1 and Stage 2 (μg/m 3 ) Benefit Stage 2 (μg/m 3 ) Benefit Stage 1 (μg/m 3 ) 24-hr 9124 Morning 8113 Daytime -7-4-11 Evening 212319 Change from prior day 12175

16. 16 Results of Cluster Analysis: What Is the Potential Reduction in Exceedance Days? NAAQS exceedances in 2008/2009 20 days 33 days estimated without CBYB 40% reduction attributed to CBYB For this analysis, data collected by a beta attenuation monitor at Del Paso Manor were used to calculate NAAQS exceedances.

17. 17 Results of 3D Numerical Grid Modeling: What Does the CMAQ Model Predict? Average and maximum benefits of Stage 1 and Stage 2 burn bans. Concentration (μg/m 3 ) and percentage of total concentration

18. 18 Results of CMB Analyses: What Is Possible? On average, wood smoke contribution to total PM 2.5 is 12 μg/m 3, so a benefit of ~12 μg/m 3 is possible Contributions (μg/m 3 ) to total PM 2.5 Other 3.7 (8%) 12 μg/m 3 (26%) is wood smoke Ammonium sulfate 0.9 (2%) Ammonium nitrate 13.7 (29%) Dust 0.1 (0.2%) Organic carbon 7.8 (17%) Organic carbon 8.2 (18%) Wood burning (combined oak/eucalyptus) 12.1 (26%)

19. 19 Results of Source Attribution MM5-CAMx (2000-2001)TEAK (2007-2009)

20. 20 Conclusions Residential wood smoke is a major contributor to wintertime PM 2.5 Episodic burn ban is effective at reducing PM 2.5 (on average, 12 μg/m 3 ) Burn bans have led to an estimated 40% reduction in the number of exceedance days Results from analysis of observed data and modeling are consistent