Evaluation of the Effectiveness and Benefits of the Wood Burning Rule in 

the San Francisco Bay Area for Winter 2009-2010

1. Introduction

Research and Modeling Section staff have studied the effectiveness of the District’s 

wood burning rule for winter 2009-10, using the following five methods:

1. Chemical Mass Balance (CMB) analysis.

2. PM filter analysis for carbon-14 (

14C).

3. Meteorological cluster analysis.

4. Air quality modeling.

5. Health impacts analysis.

For each method, a brief description and a summary of findings are given below. The 

overall assessment of the effectiveness of the rule for winter 2009-10 is presented in 

the conclusion section.

2. Background

Seventeen days of interest with their 24-hr PM2.5 concentrations at selected Bay Area 

monitoring stations for winter 2009-10 are given in Table 1. Key features of the 

tabulated data include:

 Seven Spare the Air days, marked with an S (11/26, 12/9, 12/25, 1/5, 1/8-1/10).

 Nine 24-hour exceedance days, marked with an E (12/10, 12/19, 12/20, 12/26, 

1/5-1/8, 1/16).

 Five filter data analysis days for 14C, marked with an F (11/21, 12/3, 12/9, 1/8, 

1/14).

 Six CMB analysis days, not marked (12/3, 12/9, 12/15, 1/2, 1/8, 1/14).

Note the following observations: 

 Two of the Spare the Air days were also exceedance days (1/5, 1/8).

 Carbon-14 analysis was conducted for two Spare the Air days (12/9, 1/8) and 

three non-Spare the Air days (11/21, 12/3, 1/14).

 CMB analysis was conducted for two Spare the Air days (12/9, 1/8) and four nonSpare the Air days (12/3, 12/15, 1/2, 1/14).
Table 1. Observed 24-hr PM2.5 concentrations (μg/m3

) on days of interest for winter 2009-10. Exceedances are highlighted. (S= Spare the Air day. E = 

exceedance day. F=filter analysis day for 14C)

F S F S/F E E E S E S/E E E S/E/F S S F E

Station Location 11/21 11/26 12/3 12/9 12/10 12/19 12/20 12/25 12/26 1/5 1/6 1/7 1/8 1/9 1/10 1/14 1/16

San Rafael 4 7 23 15 19 25 34 30 28 34 35.7 41.0 40.6 47.0 24 26 9 35

San Francisco 4 11 23 18 20 28 35.6 25 30 27 36.5 41.1 45.8 34 22 26 17 31

Vallejo 2 8.4 33.3 14.3 20.7 18 26.2 37.1 33.5 38.6 23.5 31.0 25 31.7 29.7 17 9 23

San Jose 2 12.4 22.3 29.8 23.3 35 28.9 23.7 27.1 11.8 35.3 33.3 41.5 35.5 28.8 29.6 12.5 20

Concord 2 5.5 29.2 21.9 18.4 25.0 23.3 33.9 25.4 39.0 30.1 32.3 32.1 19.7 19.7 18.3 5.4 36.4

Livermore 1 9.2 19 16.9 21.5 36.3 24.5 30.7 29.9 32.2 30.1 28.4 24.7 10 13 8 4 22

Redwood City 1 7.7 17 24 21 32 28 26 24 16 28 30 36.7 31 22 23 10 20

Days over 

National 

Standard
3. Chemical Mass Balance Analysis

We performed CMB analyses for three sites – Livermore, Vallejo, and San Jose. At each 

site, speciated samples were available for the 12/9/09 and 1/8/10 Spare the Air days. 

Samples were also available for the 12/3/09, 12/15/09, 1/2/10, and 1/14/10 non-Spare 

the Air days. These latter days were used in comparisons with the Spare the Air days’ 

samples. Two types of comparisons were conducted. The first examined the fraction of 

total PM2.5 contributed by wood smoke. The second focused on the ratio of wood 

smoke to total carbonaceous PM2.5, which is an indication of the relative contribution 

of wood smoke to fossil fuel-based PM.

Figure 1a shows the wood smoke contribution to total PM2.5 for 12/9/09 compared 

with 12/3/09 and 12/15/09. Figure 1b shows the wood smoke contribution to total 

PM2.5 for 1/8/10 compared with 1/2/10 and 1/14/10. The bottom bar represents the 

estimated wood smoke portion within total PM2.5. Two observations are worth noting. 

First, wood smoke is a significant (40-60) percent of the total PM2.5 at most sites and 

days examined. These percentages are of similar magnitude to those found in previous 

CMB analyses. Second, the estimated differences in wood smoke between Spare the Air 

and non-Spare the Air days are insignificant. The contributions from wood smoke are 

similar to or higher on 12/9/09 (a Spare the Air day) than the non-Spare the Air days. On 

the other hand, the wood smoke percentages are lower on 1/8/10 (Spare the Air day) 

than the non-Spare the Air days.

To determine whether the results on 1/8/10 indicate lower levels of wood burning, we 

looked at wood smoke as a fraction of carbonaceous PM2.5. The results are presented in 

Figures 2a and 2b. In particular, Figure 2b shows that the proportion of carbonaceous 

PM2.5 due to wood smoke was not significantly lower on 1/8/10 (Spare the Air day). This 

suggests that the small wood smoke fractions shown in Figure 1b for 1/8/10 are more 

likely due to increased contributions from secondary PM rather than a big drop in wood 

burning activity. However, without the re-enforcement of a marker like 14C, there is a 

good deal of uncertainty in the CMB determination of the wood smoke and fossil fuel 

fractions so that comparisons of specific Spare the Air and non-Spare the Air days are 

not reliable.

In summary, the CMB analyses indicate that during the winter of 2009-10, wood smoke 

continued to be a large component of winter PM in the Bay Area. Furthermore, there is 

no clear evidence of large-scale compliance with the wood burning ban. Together these 

findings imply that while curbing wood burning has the potential to significantly affect 

Bay Area total PM2.5, the compliance rate was not sufficient for the benefits to be 

discernible.
0%

20%

40%

60%

80%

100%

12/3/2009 12/9/2009 12/15/2009 12/3/2009 12/9/2009 12/15/2009 12/3/2009 12/9/2009 12/15/2009

Livermore Vallejo SanJose Percent of PM2.5

marine

ammonium nitrate

ammonium sulfate

geological dust

fossil fuel

woodsmoke

Figure 1a. CMB-estimated percent contributions to total PM2.5 for the 12/9/09 Spare the 

Air day, compared with 12/3/09 and 12/15/09. 

0%

20%

40%

60%

80%

100%

1/2/2010 1/8/2010 1/14/2010 1/2/2010 1/8/2010 1/14/2010 1/2/2010 1/8/2010

Livermore Vallejo SanJose Percent of PM2.5

ship

marine

ammonium nitrate

ammonium sulfate

geological dust

fossil fuel

woodsmoke

Figure 1b. CMB-estimated percent contributions to total PM2.5 for the 1/8/10 Spare the 

Air day, compared with 1/2/10 and 1/14/10.
0

0.2

0.4

0.6

0.8

1

1.2

12/3/2009 12/9/2009 12/15/2009 12/3/2009 12/9/2009 12/15/2009 12/3/2009 12/9/2009 12/15/2009

Livermore Vallejo SanJose Percent of Carbonaceous PM2.5

wood smoke/carbonaceous_PM ratio STA day

STA day

STA day

Figure 2a. CMB-estimated percent contributions to carbonaceous PM2.5 for the 12/9/09 

Spare the Air day, compared with 12/3/09 and 12/15/09.

0

0.2

0.4

0.6

0.8

1

1.2

1/2/2010 1/8/2010 1/14/2010 1/2/2010 1/8/2010 1/14/2010 1/2/2010 1/8/2010

Livermore Vallejo SanJose Percent of Carbonaceous PM2.5

wood smoke/carbonaceous_PM ratio

STA 

STA 

STA 

Figure 2b. CMB-estimated percent contributions to carbonaceous PM2.5 for the 1/8/10 

Spare the Air day, compared with 1/2/10 and 1/14/10.
4. PM Filter Analysis for Carbon-14

Carbon-14 analysis of PM filters is a method used to identify the ratio of new carbon 

over stable carbon (

12C or 13C). It is assumed that the majority of new carbon (under 100 

years old) is coming from wood burning, cooking, agricultural burning, and biodiesel

consumption.

In order to study the effectiveness of the wood burning rule, we had the University of 

Arizona analyze PM filters for five days: two spare the Air days and three non-Spare the 

Air days. PM on Spare the Air days were compared against PM on non-Spare the Air 

days. The three non-Spare the Air days were selected for comparison because they had 

meteorological conditions and PM levels similar to the Spare the Air days.

Table 2 shows dates and locations for 14C analysis. Figure 3 shows results from the 

comparison among Spare the Air and non-Spare the Air days. The comparison of Spare 

the Air days 12/9 and 1/8 against non-Spare the Air days does not show statistically 

significant differences among them. Therefore, this method was inconclusive in 

identifying the effectiveness of the wood burning rule for winter 2009-10.

Table 2. Dates and locations for C14 analysis. An “x” indicates that 14C analysis was 

performed. Last column provides rationale for analyzing each day: selected Spare the Air 

days were paired against non-Spare the Air days.

Date Concord Napa San Jose San Rafael 

11/21 x x x x compare with 12/9 

12/03 x x x x compare with 12/9

12/09 x x x x Spare the Air call

01/08 x x x x Spare the Air call

01/14 x x x x compare with 1/8
Estimated Woodsmoke Percentage of Carbonaceous PM2.5

C-14 analysis of PM2.5 filters collected on Spare the Air and similar meteorology days

0

10

20

30

40

50

60

70

80

90

100

11/21/2009 12/3/2009 12/9/2009 1/8/2010 1/14/2010 Percent

Concord

Napa

San Jose 

San Rafael

Figure 3. Results of 14C sampling for dates and locations indicated in Table 2.

5. Meteorologically-Adjusted PM2.5 Trend Analysis

This method is applied for ten winter seasons (2000-01 through 2009-10) to estimate 

the number of PM-conducive and exceedance days. (Conduciveness is determined 

based on meteorological conditions.) Then for each season and each PM observation 

station, the percentages of conducive days with observed PM2.5 levels exceeding 35 

μg/m3 were calculated. Trends in percentages over these winters are expected to show 

whether air quality is improving, staying the same or getting worse.

Because the number of exceedance days in the Bay Area is small, we repeated the 

analysis above with observed PM2.5 levels exceeding 25 μg/m3as well. The above 

calculations are referred to as p35 and p25 below.

Lower percentages for the last two winters (after wood smoke rule promulgation) 

relative to 2000-01 through 2007-08 (before wood smoke rule promulgation) would 

provide partial evidence for the effectiveness of the rule. Note that the trend analysis 

described here applies to winter seasons only. Also, the sample size for Spare the Air 

days from the last two winters is not large enough to facilitate comparisons of Spare the 
Air days against non-Spare the Air days directly. It will be possible to make the Spare the 

Air day versus non-Spare the Air day comparison in the future after obtaining 

sufficiently large sample size for the Spare the Air days.

The results from this exercise are shown in Table 3. Excluding the outlier winters 2000-

01 and 2007-08, trends do appear evident. For most locations, the p25 and often also the 

p35 values were in a lower range for 2008-09 and 2009-10 as compared to the previous 

years. 

Trends for p25 and p35 at San Jose are plotted in Figure 4. The six winters before the 

wood smoke rule promulgation exhibited p25 ranging 42-85% with a mean of 70%. The 

two years after the wood smoke rule promulgation exhibited smaller p25 values of 35% 

and 36%. Likewise, p35 ranged 19-50% during 2001-02 through 2006-07. On average, 

before the wood smoke rule promulgation, there were around 5.8 days per winter 

season for which San Jose 24-hr PM2.5 level exceeded the NAAQS threshold. During the 

two winters after the wood smoke rule promulgation, only a single conducive day 

exhibited San Jose PM2.5 levels exceeding the NAAQS 24-hr PM2.5 threshold. San Jose 

was an ideal location to evidence the effectiveness of the wood smoke rule because this 

location was believed to be heavily impacted by wood smoke. Therefore, it would be 

expected to have benefited considerably from compliance with the rule. 

Locations that were less strongly impacted by wood smoke would be less likely to have 

benefited from the rule. For example, the p25 and p35 values for San Francisco were in 

the same range, and occasionally even lower, for 2001-02 through 2006-07 relative to 

the more recent years for which the wood smoke rule was in effect. These data do not 

provide any evidence for a trend around San Francisco. The lack of a trend at San 

Francisco likely resulted because wood smoke emissions were less of a contributor to 

local PM2.5 than at many other monitoring locations. Likewise, Vallejo exhibited some 

overlap for its ranges of p25 and p35 values before and after the wood smoke rule 

promulgation. The lack of a trend at Vallejo likely resulted because this location was 

believed to be the most strongly impacted by secondary PM2.5 transported from the 

Central Valley.
Figure 4. Time series for p25 and p35 for San Jose, taken from Table 3. Outlier winters 

2000-01 and 2007-08 to be excluded from the trend analysis are not shown.
Table 3. Number of December-January conducive days having PM2.5 level measurements (N) and percentages of these days for 

which 24-hr PM2.5 level exceeded 25 and 35 μg/m3 (p25 and p35, respectively) for six Bay Area monitoring locations across ten 

winters. Wood burning restrictions were in effect only for 2008-09 and 2009-10. Outlier winters to be excluded from trend analysis 

are: 2000-01 (red) was extremely favorable to PM2.5 buildup, and 2007-08 (blue) exhibited few conducive days. N/A indicates no 

measurements available for that location and winter.

Concord (FRM) Livermore Oakland Redwood City San Francisco San Jose Vallejo

N p25 p35 N p25 p35 N p25 p35 N p25 p35 N p25 p35 N p25 p35 N p25 p35

2000-01 20 80% 55% 21 90% 71% N/A N/A N/A N/A N/A N/A N/A N/A N/A 21 95% 90% N/A N/A N/A

2001-02 13 54% 15% 13 23% 0% N/A N/A N/A N/A N/A N/A 4 25% 0% 13 85% 38% N/A N/A N/A

2002-03 19 58% 37% 21 52% 33% 21 67% 52% N/A N/A N/A 20 55% 25% 21 81% 48% 18 72% 39%

2003-04 24 29% 8% 26 31% 8% 26 23% 8% 18 33% 11% 26 27% 15% 26 42% 19% 26 46% 15%

2004-05 22 55% 14% 25 32% 8% 25 60% 20% 25 60% 8% 25 52% 8% 20 65% 30% 25 40% 0%

2005-06 12 42% 25% 12 50% 25% 8 38% 13% 12 42% 17% 12 25% 17% 12 75% 33% 12 67% 50%

2006-07 9 56% 11% 10 70% 30% 10 80% 30% 10 50% 20% 10 40% 0% 10 70% 50% 10 60% 10%

2007-08 4 0% 0% 4 25% 0% 4 25% 0% 4 25% 0% 4 25% 0% 4 50% 25% 4 25% 0%

2008-09 23 26% 4% 21 14% 0% 23 30% 4% 23 26% 0% 23 52% 9% 23 35% 0% 23 43% 17%

2009-10 21 38% 5% 25 8% 0% 13 15% 0% 25 28% 4% 25 40% 16% 25 36% 4% 25 20% 0%
6. Simulations

To estimate the impact of Spare the Air days, two types of simulations were conducted

from 11/15/2009 to 1/20/2010 using CAMx. The simulation period contained all seven

Spare the Air days of the 2009-10 winter PM season. Two different model runs were 

performed: (1) with full Bay Area wood burning emissions and (2) with 50 percent 

reduced wood burning emissions, assuming a 50 percent compliance rate.

In the first run, ambient concentrations with full wood burning emissions were 

simulated for all days of the simulation period. In the second run, ambient 

concentrations were estimated will full wood burning emissions on burn days and with 

50 percent of wood burning emissions on no-burn days. 

Results for two Spare the days (12/9/2009 and 1/5/2010) are shown as an example in 

Figure 5. Without burning restrictions, peak wood smoke levels of 20-28 μg/m3 would 

have occurred over parts of the East Bay and Santa Clara County. Wood smoke levels 

would have been around 5 μg/m3or more for many of the remaining populated 

locations within the Bay Area. Without wood burning restrictions on 12/9/2009, wood 

smoke levels would have been highest in the South Bay. Without wood burning 

restrictions on 1/5/2010, wood smoke levels would have been highest in the East Bay. 

With burning restrictions on these Spare the Air days, considerable reductions in wood 

smoke levels would occur for most Bay Area locations near concentrated wood burning 

source areas. Peak benefits are estimated to be about 10 μg/m3of reduction in PM2.5

from wood smoke. Greater benefit would be obtained in the East Bay on 1/5/2010

whereas the South Bay would see slightly greater benefit on 12/9/2009. Wood smoke 

levels are not reduced to zero because carried over wood smoke from previous days 

impact the Spare the Air days. In summary, the burning restrictions have a significant 

effect which varies by location depending on the prevailing weather.

Two examples of estimated impacts are given in Figures 6 and 7 for San Jose and 

Concord, respectively. Figure 6a shows expected PM2.5 levels had the wood burning rule 

not been in effect (0 percent compliance) while Figure 6b shows expected PM2.5 levels 

had the compliance rate been 50 percent. These figures suggest if 50 percent 

compliance were achieved, one of the two official exceedances in San Jose might have 

been avoided. Figures 7a and 7b show analogous representations for Concord. At this 

location, no Spare the Air days were on the cusp of an exceedance. Nevertheless, larger 

benefits are expected at Concord than San Jose for the period modeled.
Figure 5. Wood smoke simulation results for Spare the Air days 12/9/2009 (top row) and 

1/5/2010 (bottom row). Simulation without wood burning rule (left column) shows 

estimated Bay Area wood smoke levels assuming full wood burning. Figures in the right 

column shows estimated benefit (reduction) of wood smoke levels resulting from 50

percent compliance with the burning restrictions.
0

5

10

15

20

25

30

35

40

45 11/15/2009 11/17/2009 11/19/2009 11/21/2009 11/23/2009 11/25/2009 11/27/2009 11/29/2009 12/1/2009 12/3/2009 12/5/2009 12/7/2009 12/9/2009 12/11/2009 12/13/2009 12/15/2009 12/17/2009 12/19/2009 12/21/2009 12/23/2009 12/25/2009 12/27/2009 12/29/2009 12/31/2009 1/2/2010 1/4/2010 1/6/2010 1/8/2010 1/10/2010 1/12/2010 1/14/2010 1/16/2010 1/18/2010 1/20/2010 PM2.5 (ug/m3)

0% Compliance

Observed

San Jose

(a)

0

5

10

15

20

25

30

35

40

45 11/15/2009 11/17/2009 11/19/2009 11/21/2009 11/23/2009 11/25/2009 11/27/2009 11/29/2009 12/1/2009 12/3/2009 12/5/2009 12/7/2009 12/9/2009 12/11/2009 12/13/2009 12/15/2009 12/17/2009 12/19/2009 12/21/2009 12/23/2009 12/25/2009 12/27/2009 12/29/2009 12/31/2009 1/2/2010 1/4/2010 1/6/2010 1/8/2010 1/10/2010 1/12/2010 1/14/2010 1/16/2010 1/18/2010 1/20/2010 PM2.5 (ug/m3)

Observed

50% Compliance

San Jose

(b)

Figure 6. Simulated impacts of wood burning rule at San Jose. (a) 0% compliance; (b) 

50% compliance
0

5

10

15

20

25

30

35

40

45 11/15/2009 11/17/2009 11/19/2009 11/21/2009 11/23/2009 11/25/2009 11/27/2009 11/29/2009 12/1/2009 12/3/2009 12/5/2009 12/7/2009 12/9/2009 12/11/2009 12/13/2009 12/15/2009 12/17/2009 12/19/2009 12/21/2009 12/23/2009 12/25/2009 12/27/2009 12/29/2009 12/31/2009 1/2/2010 1/4/2010 1/6/2010 1/8/2010 1/10/2010 1/12/2010 1/14/2010 1/16/2010 1/18/2010 1/20/2010 PM2.5 (ug/m3)

0% Compliance

Observed

Concord

(a)

0

5

10

15

20

25

30

35

40

45 11/15/2009 11/17/2009 11/19/2009 11/21/2009 11/23/2009 11/25/2009 11/27/2009 11/29/2009 12/1/2009 12/3/2009 12/5/2009 12/7/2009 12/9/2009 12/11/2009 12/13/2009 12/15/2009 12/17/2009 12/19/2009 12/21/2009 12/23/2009 12/25/2009 12/27/2009 12/29/2009 12/31/2009 1/2/2010 1/4/2010 1/6/2010 1/8/2010 1/10/2010 1/12/2010 1/14/2010 1/16/2010 1/18/2010 1/20/2010 PM2.5 (ug/m3)

Observed

50% Compliance

Concord

(b)

Figure 7. Simulated impacts of wood burning rule at Concord. (a) 0% compliance; (b) 

50% compliance
7. BenMap Analysis

BenMap was developed by the U.S. EPA to estimate the impacts of air pollution on 

human health, in terms of both reductions in the incidence of adverse health effects and 

monetary benefits. Staff applied BenMap to estimate the impacts of the District’s wood 

burning rule on public health in the San Francisco Bay Area.

PM concentrations from the base case simulation and simulation with assumed 50-

percent wood burning compliance rate were input to BenMap. The simulations were 

conducted for the winter PM season (November 15, 2009 through January 20, 2010). 

However, BenMap expects concentrations estimated for the entire year. In order to 

prepare inputs to BenMap, we made two assumptions. First, the mean of simulated 

daily average concentrations from January 12 through January 20, 2010 was assumed 

for each day from January 21 through February 28. Second, it was assumed that wood 

burning emissions are negligible from March 1 through November 14 and the 

concentration was set to zero for all days in that period. 

BenMap allows users to select from a wide array of health impact functions and 

valuation methods in order to estimate incidence of adverse health effects and 

monetary value. For this analysis, we used the combination of functions that was used 

by EPA to evaluate the benefits of the most recent PM2.5 National Ambient Air Quality 

Standards. We assumed the no-threshold scenario as recommended by the EPA Science 

Advisory Board Advisory Council for Clean Air Compliance.

Table 4 below shows the results of the BenMap analysis. The estimated total benefit of 

seven no-burn days with 50 percent compliance rate is about $256 million. This value 

accounts for the list of health effect endpoints shown in the table, including mortality. 

Furthermore, the monetary values shown represent the willingness-to-pay (WTP) which 

is at least the direct cost of illness, but can also include the amount a population is 

willing to pay to avoid the adverse health effects altogether. Table 5 shows the benefits 

by county. These trends are determined mainly by a combination of pollution level and 

population density. Age and income distributions are also influential factors.

Table 4. BenMap-estimated monetary benefits of the wood burning rule during the 

winter 2009-10.

Health Endpoint Group Valuation

Acute Bronchitis $ 1,996.00 

Acute Myocardial Infarction $ 2,823,666.00 

Acute Respiratory Symptoms $ 904,697.00 

Asthma Exacerbation $ 15,697.00 

Chronic Bronchitis $ 5,225,826.00 

Emergency Room Visits, Respiratory $ 2,177.00 

Hospital Admissions, Cardiovascular $ 189,849.00 
Hospital Admissions, Respiratory $ 43,049.00 

Lower Respiratory Symptoms $ 6,215.00 

Mortality $ 246,416,955.00 

Upper Respiratory Symptoms $ 7,805.00 

Work Loss Days $ 539,210.00 

Grand Total $ 256,177,142.00 

Table 5. Monetary benefits by county.

County Valuation

Alameda $ 57,932,056.00 

Contra Costa $ 60,102,734.00 

Marin $ 8,893,318.00 

Napa $ 2,688,549.00 

San Francisco $ 19,672,059.00 

San Mateo $ 20,972,445.00 

Santa Clara $ 63,976,877.00 

Solano $ 5,495,570.00 

Sonoma $ 16,443,534.00 

Grand Total $ 256,177,142.00 

Table 6 summarizes the number of averted incidents by health endpoint group. These 

estimates correspond to the valuations presented above, namely they represent the 

number of adverse health outcomes averted due to a 50 percent compliance rate for 

the wood burning rule during the 2009-10 winter season.

Table 6. Reductions in adverse health effect incidence due to 50 percent compliance 

rate.

Health Endpoint Group Reduction in Incidence

Acute Bronchitis 29

Acute Myocardial Infarction 22

Acute Respiratory Symptoms 15287

Asthma Exacerbation 309

Chronic Bronchitis 13

Emergency Room Visits, Respiratory 4

Hospital Admissions, Cardiovascular 8

Hospital Admissions, Respiratory 3

Lower Respiratory Symptoms 338

Mortality 37

Upper Respiratory Symptoms 253

Work Loss Days 2580
8. Conclusion

Five methods were applied to evaluate the effectiveness of the District’s wood burning 

rule for winter 2009-10. Some of these analyses aimed to quantify the benefits while 

others gave a measure of the potential benefits. CMB analysis and carbon-14 PM filter 

analysis did not confirm the impacts of the rule. Trends analysis sought to confirm the 

realized impacts of the rule for the season as a whole. Wood burning PM simulations 

and monetary benefits analysis using BenMap were geared toward estimating impacts 

of the wood burning rule in context.

CMB analysis did not detect significant differences in wood smoke levels between Spare 

the Air and non-Spare the Air days. Carbon-14 analyses of 20 filter samples representing 

two Spare the Air and three non-Spare the Air days also did not find differences 

between the two types of days. An analysis of total PM2.5 trends across several years 

showed an apparent drop in ambient levels on PM-conducive days with the adoption of 

the wood burning rule. But the two years of the rule’s implementation are insufficient 

to make conclusive statements regarding the trend. These findings may also suggest an 

overall drop in wood burning activity rather than specific effects of Spare the Air alerts. 

This is generally confirmed through surveys and surveillance efforts, which have been 

documented elsewhere.

The simulated impact with 50 percent compliance rate and the resulting BenMap 

analysis show significant benefits. Recent survey studies suggest that the actual 

compliance rate was about four percent, rather than 50 percent. In that case, the tables 

presenting the simulated PM values and the resulting health benefit may be about 12 

times less than the values presented in the tables for these two categories.

9. Discussion

The meteorologically-adjusted trend analysis suggested that PM2.5 levels decreased 

upon the promulgation of the wood smoke rule for many Bay Area locations. The 

decrease in PM2.5 levels was generally largest for locations believed to have been most 

heavily impacted from wood smoke. The trend was less strong or even absent for 

locations for which wood smoke was believed to have been less of a contributor to 

PM2.5. This spatial pattern for the PM2.5 trends to decrease more strongly around areas 

with more wood burning emissions suggested that decreasing (total) PM2.5 levels indeed 

reflected decreasing wood smoke PM2.5 levels. Thus, overall, the meteorologicallyadjusted trend analysis does suggest the rule has been effective to reduce both PM2.5

levels and the 24-hr PM2.5 NAAQS exceedance rate. The effectiveness of the rule varied 

strongly with location, however, presumably due to spatial variability for the wood 

burning emissions. It is cautioned that other factors such as changes in economic 

activity or non-wood burning emissions may have also contributed to the observed 

trends.
The meteorologically-adjusted trend analysis described herein provided the best 

available means to isolate changes in PM2.5 level trends over a relatively short twowinter period. Nonetheless, any trend analysis based on such a small sample size may 

be inaccurate or even misleading. This meteorologically-adjusted trend analysis will be 

repeated after future winters to increase the sample size. Conclusive evidence for a 

downward trend in PM2.5 levels would likely require at least an additional one to three 

years of measurements.

Note that although the CMB results show wood smoke to be a large fraction of total PM 

on some days, the modeling suggests that eliminating that fraction requires consecutive 

Spare the Air alerts on the preceding days and not just on the target day. For instance, 

CMB analysis shows wood smoke to be ~11 μg/m3in San Jose on 1/8/2010, but Figure 

6b shows the simulated benefit, at 50 percent compliance, to be nowhere near a 4-5 

μg/m3reduction in total PM2.5. Carryover effects are important as can be seen on 1/9

and 1/10, both Spare the Air days. The same phenomenon is present at Concord (Figure 

7b).

These analyses are planned for the next several years as the sample size (the number of 

no-burn days) increases. This will reduce uncertainties associated with them.