### 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 ( 14 C)

. 3. Meteorological cluster analysis.

4. Air quality m odeling.

5. Health impacts analysis.

For each method, a b rief 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 PM 2.5 concentrations at selected Bay Area monitoring s tations for winter 2009

- 10 are given in Table 1. K ey features of the
tabulated data include:

 Seven Spare the Air days , marked with an S (11/2 6

, 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 f ilter data analysis days for 14 C , 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 d ays (1/5, 1/8).

 Carbon

- 14 analys is 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 non

Spare the Air days (12/ 3 , 12/15, 1/ 2, 1/14).Table 1

. Observed 24

- hr PM 2.5 concentrations ( μ g/m 3 ) on days of interest for winter 2009

- 10

. E xceedances are highlighted. (S= Spare the Air day. E =

e xceedance day. F= f ilter a nalysis d ay for 14 C )

### 3

. Chemical Mass Balan ce 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 1/1 4/10 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 sh ows 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 PM 2.5 . Two observations are worth noting. First, wood smoke is a significant (40

- 60) percen t of the total PM 2.5 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. Th e 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 PM 2.5 . The results are presented in Figures 2a and 2b. In particular, Figure 2b shows that the proportion of carbonaceous

PM 2.5 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 14 C, 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 r eliable.

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. Toget her these findings imply that while curbing wood burning has the potential to significantly affect Bay Area total PM 2.5 , the compliance rate was not sufficient for the benefits to be
discernible.Figure 1a . CMB

- estimated percent contributions to total P M 2.5 for the 12/9/09 Spare the

Air day

, compared with 12/3/09 and 12/15/09

.

Figure 1b . CMB

- estimated percent contributions to total PM 2.5 for the 1/8/10 Spare the Air day , compared with 1/2/10 and 1/14/10

.Figure 2a . CMB

- estimated percent contributions to carbonaceous PM 2.5 for the 12/9/09

Spare the Air day

, compared with 12/3/09 and 12/15/09

.

Figure 2b . CMB

- estimated percent contributions to carbonaceous PM 2.5 for the 1/8/10 Spare the Air day , compared with 1/2/10 and 1/14/10

.

### 4

. PM Filter Analysis f or Carbon

- 14

Carbon

- 14 analysis of PM filters is a method used to identif y the ratio of new carbon

over stable carbon ( 12 C or 13 C) . It is assumed that the majority of new carbon (under 100 years old) is coming from wood burning, cooking, agricultural burn ing, 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

- S pare the Air days. PM on Spare the Air days were compare d 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 14 C analysis . Figure 3 shows results from the comparison among Spare the Air and non

- Spare the Air days

. The c omparison of Spare the Air days 12/9 and 1/8 against non

- Spare the Air days do es not show statistically significant difference s among the m . Therefore, this method wa s in conclusive in identifying the effectiveness of the wood burning rule for winter 2009

- 10

.

Table 2 . Dates and locations for C 14 analys i s. An “x” indicates that 14 C analysis was performed. Last column provides rationale for analyzing each day: selected Spar e 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 comp are with 1/8Figure 3 . Results of 14 C sampling for dates and locations indicated in Table 2

.



### 5

. Meteorologically

- Adjusted PM 2.5 

### Trend Analysis

This method is applied for ten winter seasons (2000

- 01 through 2009

- 10) to estimate
the number of PM

- cond ucive 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/m 3 were calculated. Trends in percentages over the se winters are expected to show whether air quality is improving, sta ying the same or getting wor se

.

Because the number of exceedance days in the Bay Area is small, we repeated the analysis above wit h observed PM2.5 levels exceeding 25 μg/m 3 as 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 pr omulgation) would provide partial evidence for the effectiveness of the rule. Note that the trend analysis described here appl ies to winter seasons only. Also, t he sample size for Spare the Air days from the last two winters is not large enough to facilita te compar isons of Spare theAir 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 p 25 and often also the

p 35 values were in a lower range for 2008

- 09 and 2009

- 10 as compared to the previous

years.

Trends for p 25 and p 35 at San Jose are plotted in Figure 4 . The six winters before the wood smoke rule promulgation exhibited p 25 ranging 42

- 85% with a mean of 70%. The two years after the wood smoke rule promulgation exhibited smaller p 25 values of 35% and 36%. Likewise, p 35 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 PM 2.5 level exceeded the NAAQS threshold. Durin g the two winters after the wood smoke rule promulgation, only a single conducive day
exhibited San Jose PM 2.5 level s exceeding the NAAQS 24

- hr PM 2.5 threshold. San Jose
was an ideal location to evidence the effectiveness of the wood smoke rule because thi s 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 fr om the rule. For example, t he p 25 and p 35 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 PM 2.5 than at many other monitoring locations . Likewise, Vallejo exhibited some overlap for it s ranges of p 25 and p 35 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 PM 2.5 transported from the Central Valley.Figure 4 . Time series for p 25 and p 35 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 PM 2.5 level measurements ( N ) and percentages of these days for

which 24

- hr PM 2.5 l evel exceeded 25 and 35 μ g/m3 ( p 25 and p 35 , 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 p 25 p 35 N p 25 p 35 N p 25 p 35 N p 25 p 35 N p 25 p 35 N p 25 p 35 N p 25 p 35
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 ( 1 2 / 9 /200 9 and 1/ 5 /20 1 0 ) are shown as an example in Figure 5 . Without burning restrictions, peak wood smoke level s of 20

- 2 8 μ g/m 3 would
have occurred over parts of the East Bay and Santa Clara County . Wood smoke l evels
would have been around 5 μ g/m 3 or more for many of the remaining populated locations within the Bay Area. Without wood burning restrictions on 12 / 9 /20 0 9 , woo d
smoke levels would have been high est in the South Bay. Without w ood burning restrictions on 1/5 /20 1 0, wood smoke levels would have been highest in the East Bay.

With burning restrictions on these Spare the Air days, considerable reductio ns in wo od
smoke levels would occur for most Bay Area locations near concentrated wood burning

source areas . Peak benefits are estimated to be about 10 μ g/m 3 of reduc tion in PM 2.5 from wood smoke. G reater benefit would be obtained in the East Bay on 1/ 5 /20 1 0 whereas the South Bay would see slightly greater benefit on 1 2 / 9 /2009 . Wood smoke
levels a re not reduced to zero because carried over wood smoke from previous days impact the Spare the Air days. In summary, t he burning restrictions ha ve a significant
effect which var ies by location depending on the prevailing weather.

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

Conc ord, respectively. Figure 6a shows expected PM 2.5 levels had the wood burning rule
not been in effect (0 percent compliance) while Figure 6b shows expected PM 2.5 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 r esults for Spare the Air days 12 / 9 /200 9 (top row) and

1/ 5 /20 1 0 (bottom row). Simulation with out wood burning rule (left column) sh ows 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 5 0 percent compliance with the burning restrictions.(a)

(b) Figure 6. Simulated im pacts of wood burning rule at San Jose. (a) 0% compliance; (b) 50% compliance(a)

(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 estimat e 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 Franc isco 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

. F irst, 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 PM 2.5 Natio nal 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

seve n 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

i s 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 lev el 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

Eme rgency Room Visits, Respiratory $ 2,177.00 Hospital Admissions, Cardiovascular $ 189,849.00Hospital 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

Sol ano $ 5,495,570.00

Sonoma $ 16,443,534.00

Grand Total $ 256,177,142.00

T able 6 summarizes the number of averted incidents by health endpoint group. These estimates correspond to the valuations presented above, namely they represent the

n umber 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 Reduct ion 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, Respi ratory 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 ana lyses 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. T rends analysis sought to confirm the realized impacts of the rule for the se ason 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
show ed 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

ove rall 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 t he 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 benef it 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 PM 2.5 levels decreased upon the promulgation of the wood smoke rule for many Bay Are a locations. The
decrease in PM 2.5 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 contr ibutor to

PM 2.5 . This spatial pattern for the PM 2.5 trends to decrease more strongly around areas with more wood burning emissions suggested that decreasing (total) PM 2.5 levels indeed reflected decreasing wood smoke PM 2.5 levels. Thus, overall, the meteor ologically

adjusted trend analysis does suggest the rule has been effective to reduce both PM 2.5 levels and the 24

- hr PM 2.5 NAAQS exceedance rate. The effectiveness of the rule varied strongly with location, however, presumably due to spatial variability f or 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 th e best

available means to isolate changes in PM 2.5 level trends over a relatively short two

winter 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 PM 2.5 levels would likely require at least an additional one to three years of measurements.

N ote 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 inst ance, CMB analysis shows wood smoke to be ~11 μg/m 3 in 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/m 3 reduction in total PM 2.5 . 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 plan ned for the next several years as the sample size (the number of

no

- burn days) increase s

. This will reduce uncertaint ies associated with them

.