Clinton MacDonald1, Kenneth Craig1, Jennifer DeWinter1, Adam Pasch1, Brigette Tollstrup2, and Aleta Kennard2

1Sonoma Technology, Inc., Petaluma, CA2Sacramento Metropolitan Air Quality Management District, Sacramento, CA

Presented at the 2010 National Air Quality ConferencesRaleigh, NCMarch 15-18, 20103807Benefits of Forecast-Based Residential Wood Burning Bans on Air Pollution

*Sacramentos PM2.5 ProblemSacramento is designated non-attainment for 24-hr average PM2.5* *Daily PM2.5 National Ambient Air Quality Standard = 35.5 g/m312/4/09 (hourly PM2.5 concentration = 54 g/m3 )Based on daily maximum PM2.5 concentration, Oct. 2002Sep. 2009

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

*Main Causes of PM2.5Surface and aloft high pressure Relatively warm aloft temperatures during a temperature inversionCool nightsCloud-free skiesLight winds

WeatherVertical Temperature Profile

*SMAQMD Wood Burning Rule Check Before You BurnEpisodic 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

25 g/m3Legal to Burn = No restrictions> 25 to 35 g/m3Burning Discouraged = Voluntary curtailment> 35 to 40 g/m3Stage 1 = No burning except in certified devices> 40 g/m3Stage 2 = No burning in any device

*Key QuestionsHow effective is the program in improving air quality?What is each countys contribution to the woodsmoke PM2.5 in Sacramento?Analyses conductedCluster 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?

*Method Cluster AnalysisCompared PM2.5 on unrestricted burning days (prior to CBYB) to burn ban daysUsed cluster and qualitative analysis of meteorology to determine days on which meteorology was very similarDifferences in PM2.5 concentration between days can be primarily attributed to a burn ban

Method 3D Numerical Grid ModelingRan numerical model for 37 days with and without burningMM5 meteorological modelCommunity Multiscale Air Quality (CMAQ) model with full chemistrySparse Matrix Operator Kernel Emissions (SMOKE) including residential wood combustion temporal profilesCoarse (36-km) grid resolutionCompared relative differences between model runs*

*Method CMB AnalysisChemical 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

*Method MM5 and CAMxTracked primary wood smoke emissions from the 21 source areas within and surrounding SacramentoUsed MM5 and CAMx to simulate transport, diffusion, and depositionAnalyzed relative contributions of primary wood smoke concentrations from each source region to receptor sitesPerformed analyses for all days from 12/15/2000 through 1/9/2001 (subset of California Regional Particulate Air Quality Study)

Method TEAK (1 of 4)Combined back trajectories and hourly-resolved wood smoke emissions to estimate contributionsCalculated back trajectoriesfor each winter high PM2.5 day in 2007-2009from each receptor back 36 hours24 times per dayat 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 timeAt arrival, omitted parcels above the ABL as contributors

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

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

Method TEAK (4 of 4)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 g/m3 (winters of 2007-08 and 2008-09)Average contribution for all days

*Results of Cluster Analysis: What Do We Observe at the Peak Site?Substantial benefit from wood-burning ban, especially in the eveningStage 2 Days OnlyStage 1 Days Only24-hr average benefit = 12 g/m3

Benefit Stage 1 and Stage 2 (g/m3)Benefit Stage 2 (g/m3)Benefit Stage 1 (g/m3)24-hr9124Morning8113Daytime-7-4-11Evening212319Change from prior day12175

*Results of Cluster Analysis: What Is the Potential Reduction in Exceedance Days?

NAAQS exceedances in 2008/2009

20 days33 days estimated without CBYB40% reduction attributed to CBYBFor this analysis, data collected by a beta attenuation monitor at Del Paso Manor were used to calculate NAAQS exceedances.

*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 (g/m3) and percentage of total concentration

*Results of CMB Analyses:What Is Possible?On average, wood smoke contribution to total PM2.5 is 12 g/m3, so a benefit of ~12 g/m3 is possibleContributions (g/m3) to total PM2.5

*Results of Source AttributionMM5-CAMx (2000-2001)TEAK (2007-2009)

*ConclusionsResidential wood smoke is a major contributor to wintertime PM2.5Episodic burn ban is effective at reducing PM2.5 (on average, 12 g/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

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