Clinton MacDonald 1 , Kenneth Craig 1 , Jennifer DeWinter 1 ,

1. Benefits of Forecast-Based Residential Wood Burning Bans on Air Pollution Clinton MacDonald1, Kenneth Craig1, Jennifer DeWinter1, Adam Pasch1, Brigette Tollstrup2, and Aleta Kennard2 1Sonoma Technology, Inc., Petaluma, CA 2Sacramento Metropolitan Air Quality Management District, Sacramento, CA Presented at the 2010 National Air Quality Conferences Raleigh, NC March 15-18, 2010 3807

2. Sacramento’s PM2.5 Problem Sacramento is designated “non-attainment” for 24-hr average PM2.5* 12/4/09 (hourly PM2.5 concentration = 54 g/m3 ) Based on daily maximum PM2.5 concentration, Oct. 2002–Sep. 2009 *Daily PM2.5 National Ambient Air Quality Standard = 35.5 μg/m3 2

3. Main Causes of PM2.5 Source apportionment of air samples shows that wood smoke is 26% of total PM2.5 3

4. Sea Level Pressure Main Causes of PM2.5 Weather Vertical Temperature Profile • Surface and aloft high pressure • Relatively warm aloft temperatures during a temperature inversion • Cool nights • Cloud-free skies • Light winds 4

5. 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 PM2.5 5

6. Key Questions • How effective is the program in improving air quality? • What is each county’s contribution to the woodsmoke PM2.5in 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? 6

7. Method – Cluster Analysis • Compared PM2.5 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 PM2.5 concentration between days can be primarily attributed to a burn ban 7

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. Method – CMB Analysis • Chemical Mass Balance (CMB) modeling conducted on speciated PM2.5 data • CMB components • PM2.5 species concentrations • Known abundances of chemical species from emission sources (source profiles) • CMB results estimate the contribution from each source type to each PM2.5 sample 9

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) 10

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 PM2.5 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. Method – TEAK (2 of 4) Trajectories Emissions Parcel in ABL? + = + Thirty-six-hour backward trajectories ending at Del Paso Manor at 25 m agl every hour on December 10, 2008

13. Method – TEAK (3 of 4) Daily Percent Contribution Results for all elevations and days with high PM2.5 concentrations = + Gridded percent contribution to primary PM2.5 at Del Paso Manor on December 10, 2008

14. Method – TEAK (4 of 4) Average contribution for all days The percentage each county contributed to wood smoke primary PM2.5 in Del Paso Manor when peak 24-hr PM2.5 concentrations in Sacramento County were greater than 35.5 μg/m3 (winters of 2007-08 and 2008-09)

15. 24-hr average benefit = 4 μg/m3 Results of Cluster Analysis: What Do We Observe at the Peak Site? Stage 2 Days Only Substantial benefit from wood-burning ban, especially in the evening 24-hr average benefit = 12 μg/m3 Stage 1 Days Only 15

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. 16

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/m3) and percentage of total concentration 17

18. Results of CMB Analyses:What Is Possible? On average, wood smoke contribution to total PM2.5 is 12 μg/m3, so a benefit of ~12 μg/m3 is possible Other 3.7 (8%) Dust 0.1 (0.2%) Wood burning (combined oak/eucalyptus) 12.1 (26%) Organic carbon 8.2 (18%) 12 μg/m3 (26%) is wood smoke Ammonium sulfate 0.9 (2%) Organic carbon 7.8 (17%) Ammonium nitrate 13.7 (29%) Contributions (μg/m3) to total PM2.5 18

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

20. Conclusions • Residential wood smoke is a major contributor to wintertime PM2.5 • Episodic burn ban is effective at reducing PM2.5 (on average, 12 μg/m3) • 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 20