1

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, NC

March 15-18, 2010

3807

Benefits of Forecast-Based Residential Wood Burning Bans on Air Pollution

22

Sacramento’s PM2.5 Problem

Sacramento is designated “non-attainment” for 24-hr average PM2.5*

*Daily PM2.5 National Ambient Air Quality Standard = 35.5 μg/m3

12/4/09 (hourly PM2.5 concentration = 54 g/m3 )Based on daily maximum PM2.5 concentration, Oct. 2002–Sep. 2009

19.0

13.5

8.8

6.9

12.6 13.1

15.8

12.4

16.9 16.6

20.3

17.1

6.0

1.5 0.1 0.6 0.4

1.1 0.3 0.1

6.0 6.8

0.6 0.5 0.1 1.0

0.0

5.0

10.0

15.0

20.0

25.0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Da

ys p

er

Mo

nth

Average Days per Month in Each AQI Category (Moderate and Above Only)

Moderate

USG

Unhealthy

33

Main Causes of PM2.5

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

44

Main Causes of PM2.5

• Surface and aloft high pressure

• Relatively warm aloft temperatures during a temperature inversion

• Cool nights

• Cloud-free skies

• Light winds

WeatherSea Level Pressure

Vertical Temperature Profile

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 PM2.5

≤ 25 μg/m3 Legal to Burn = No restrictions

> 25 to ≤ 35 μg/m3 Burning Discouraged = Voluntary curtailment

> 35 to ≤ 40 μg/m3 Stage 1 = No burning except in certified devices

> 40 μg/m3 Stage 2 = No burning in any device

66

Key Questions

• How effective is the program in improving air quality?

• What is each county’s contribution to the woodsmoke PM2.5 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?

77

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

0

20

40

60

80

100

120

140

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

PM

2.5

Co

ncen

trat

ion

(μg

/m3 )

Time (PST)

Del Paso Manor on Wednesday, Dec 22, 2004 - No Restrictions

Del Paso Manor on Thursday, Jan 29, 2009 - Stage 1

24-hr average benefit = 11 μg/m3

29

40

0

5

10

15

20

25

30

35

40

45

PM2.

5 Co

ncen

trati

on (μg/m

3)

24-hr average

29

40

0

5

10

15

20

25

30

35

40

45

PM2.

5 Co

ncen

trati

on (μg/m

3)

24-hr average

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

99

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

1010

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

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)

+ =+ 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

Method – TEAK (3 of 4)

+

Results for all elevations and days with high PM2.5 concentrations

=

Daily Percent Contribution

Gridded percent contribution to primary PM2.5 at

Del Paso Manor on December 10, 2008

14

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 μg/m3 (winters of 2007-08

and 2008-09)

Average contribution for all days

1515

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Time (PST)

PM

2.5 C

on

cen

tra

tion

(μ

g/m

3 )

No Restrictions days used for Stage 1 comparisons

All Stage 1 days

Benefit = 4 μg/m3

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Time (PST)

PM

2.5 C

on

cen

tra

tion

(μ

g/m

3 )

No Restrictions days used for Stage 1 comparisons

All Stage 1 days

Benefit = 4 μg/m3

Results of Cluster Analysis: What Do We Observe at the Peak Site?

0

10

20

30

40

50

60

70

80

90

100

110

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Time (PST)

PM

2.5 C

once

ntra

tion

(μg/

m3)

No Restrictions days used for Stage 2 comparisons

All Stage 2 days

Benefit = 12 μg/m3

0

10

20

30

40

50

60

70

80

90

100

110

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Time (PST)

PM

2.5 C

once

ntra

tion

(μg/

m3)

No Restrictions days used for Stage 2 comparisons

All Stage 2 days

Benefit = 12 μg/m3

Substantial benefit from wood-burning ban, especially in the evening

51712Change from the prior day

162320Evening

-10-3-6Daytime

31510Morning

412824-hr

BenefitStage 1 (μg/m3)

BenefitStage 2 (μg/m3)

BenefitStage 1 and 2 (μg/m3)

51712Change from the prior day

162320Evening

-10-3-6Daytime

31510Morning

412824-hr

BenefitStage 1 (μg/m3)

BenefitStage 2 (μg/m3)

BenefitStage 1 and 2 (μg/m3)

Stage 2 Days Only

Stage 1 Days Only

0

10

20

30

40

50

60

70

80

90

100

110

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

PM

2.5 C

on

cent

ratio

n (μ

g/m

3)

Time (PST)

No Restrictions days used for Stage 2 comparisons

All Stage 2 days

24-hr average benefit = 12 μg/m3

0

10

20

30

40

50

60

70

80

90

100

110

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

PM

2.5

Co

nce

ntr

atio

n (μ

g/m

3 )

Time (PST)

No Restrictions days used for Stage 1 comparisons

All Stage 1 days

24-hr average benefit = 4 μg/m3

Benefit Stage 1 and

Stage 2 (μg/m3)

Benefit Stage 2 (μg/m3)

Benefit Stage 1 (μg/m3)

24-hr 9 12 4

Morning 8 11 3

Daytime -7 -4 -11

Evening 21 23 19

Change from prior day 12 17 5

1616

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.

0

5

10

15

20

25

30

35

2004

/200

5

2005

/200

6

2006

/200

7

2007

/200

8

2007

/200

8*

2008

/200

9

2008

/200

9*

Year

Nu

mb

er o

f D

ays

*Estimate if Stage 1 and Stage 2 days were not called

0

5

10

15

20

25

30

35

2004

/200

5

2005

/200

6

2006

/200

7

2007

/200

8

2007

/200

8*

2008

/200

9

2008

/200

9*

Year

Nu

mb

er o

f D

ays

*Estimate if Stage 1 and Stage 2 days were not called

1717

Results of 3D Numerical Grid Modeling:What Does the CMAQ Model Predict?

Average and maximum benefits of Stage 1 and Stage 2 burn bans.

Stage 1Burn Ban

Stage 2Burn Ban

Average Benefit

5.2 (13.7%) 6.4 (16.9%)

MaximumBenefit

8.7 (18.4%) 10.8 (22.7%)

Concentration (μg/m3) and percentage of total concentration

1818

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

Contributions (μg/m3) to total PM2.5

Other3.7 (8%)

12 μg/m3 (26%) is wood smoke

Ammonium sulfate0.9 (2%)

Ammonium nitrate13.7 (29%)

Dust0.1 (0.2%)

Organic carbon7.8 (17%)

Organic carbon8.2 (18%)

Wood burning (combined oak/eucalyptus)12.1 (26%)

1919

Results of Source Attribution

MM5-CAMx (2000-2001) TEAK (2007-2009)

2020

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