SACRAMENTO METROPOLITAN AIR QUALITY MANAGEMENT DISTRICT

STAFF REPORT

Rule 421, Mandatory Episodic Curtailment of Wood an d Other Solid Fuel Burning

Proposed Amendments September 9, 2009

Prepared by: Marc Cooley Assistant Air Quality Engineer
Reviewed by: Kevin J. Williams, Ph.D. Program Coordinator

Aleta Kennard Program Supervisor
Approved by: Brigette Tollstrup Division ManagerStaff Report

Rule 421 September 9, 2009, Page 2

BACKGROUND
Particulate matter (PM) is a mixture of very small liquid droplets and solid particles. Adverse health effects are linked to particles that are les s than 10 microns in diameter (PM10), and fine particles that are less than 2.5 microns in diamete r (PM2.5).

Sacramento does not meet state or federal health st andards for fine particle air pollution (PM2.5). Sacramento will be formally designated no nattainment for the federal health standards later this year. Fine particulate matter is linked to serious adverse health effects, including aggravated asthma, heart attacks, and premature dea th in people with heart or lung disease. The largest single source of Sacramento's wintertim e PM2.5 emissions is wood and other solid fuel burning in fireplaces, wood inserts, wood stov es and pellet stoves. New health studies have increased the concern about wood smoke health impacts, including the impact to children.
The District has implemented three control measures to reduce emissions from wood burning: 1) provide financial incentives to remove or replac e fireplaces and dirty stoves with cleaner burning options, 2) prohibit installation of new fi replaces and dirty wood stoves, and 3) prohibit burning on days when weather conditions cause wood smoke to be trapped near the ground and build up to unhealthy levels.
Rule 421, Mandatory Episodic Curtailment of Wood an d Other Solid Fuel Burning, prohibits wood and other solid fuel burning when PM2.5 concen trations are forecast to exceed the federal health standard. Rule 421 is in effect from Novemb er 1 st through the end of February. When PM2.5 concentrations are forecast to exceed the fed eral health standard of 35 μg/m3 but will not exceed 40 μg/m3, burning is prohibited, except in E PA certified wood stoves or pellet stoves. This is called a Stage 1 No Burn day. When PM2.5 co ncentrations are forecast to exceed 40 μ g/m 3 , all burning is prohibited. This is called a Stag e 2 No Burn day. The rule does not apply to fireplaces and stoves that burn gaseous fuels (n atural gas and propane), and exempts burning that is the sole source of heat, or in situ ations when not burning would be a financial hardship to a resident.

Staff commissioned two studies of the effectiveness of our wood burning control efforts, specifically Rule 421. The first study, conducted b y Sonoma Technology, Inc. (STI) examined the air quality benefits on forecast No Burn days i n the last two seasons. The second study, conducted by Aurora Research Group, examined the pu blic awareness and response to the rule. These studies showed significant improvement s in air quality. The Aurora survey showed that 92% of the respondents were aware of burning r estrictions and from their actions Sacramento County saw up to a 23% reduction in PM2. 5 concentrations. The studies showed significant reductions and when curtailment days ar e called the largest air quality improvement occurred in the PM2.5 concentrations in the early e venings.
Although the public response to the rule and air qu ality improvements were significant, they are not sufficient to meet the health standards. There were 20 unhealthful days during the winter 2008/2009 season. The study also showed that two c ontributing factors to the unhealthful air quality days are the underprediction in the forecas ts and lower air quality benefit on Stage 1 days. Therefore, Staff is proposing to amend Rule 421 to:

• Maintain the two stage program but reduce the conc entration thresholds for prohibiting burning.Staff Report

Rule 421 September 9, 2009, Page 3
Other options for modifying the rule that are inclu ded for Board consideration are:

• Maintain the two-stage curtailment program but fur ther reduce the concentration thresholds for prohibiting burning than the Staff r ecommendation; and/or

• Eliminate the two-stage curtailment program and re place it with a single-stage curtailment program with a reduced concentration th reshold; and/or

• Incorporate a provision that automatically drops t he threshold to 20 ug/m3 if the District does not meet our federal air quality deadline (201 5 or 2019).

HEALTH IMPACTS
Particulate matter (PM) is a mixture of very small liquid droplets and solid particles. Adverse health effects are linked to particles that are les s than 10 microns in diameter (PM10), and fine particles that are less than 2.5 microns in diamete r (PM2.5). The District is currently designated as a nonattainment area for the state and federal 1 health standards for PM10 and for the state health standard for PM2.5. In addition, the Distri ct will be nonattainment for the federal PM2.5 health standard when formal designations are publis hed later this year.

Wood smoke also emits other air contaminants includ ing volatile organic compounds (VOCs) and nitrogen oxides (NOx) which are precursors to a erosol forms of PM2.5, and toxic and/or cancer-causing substances, such as benzene, formald ehyde and benzo-a-pyrene, a polycyclic aromatic hydrocarbon (PAH). In addition, wood smok e is a component of black carbon.
According to the U.S. Environmental Protection Agen cy (EPA), health studies have linked exposure to particulate matter, especially fine par ticles, to several significant health problems, including:

• increased respiratory symptoms, such as irritation of the airways, coughing, or difficulty breathing;

• decreased lung function;

• aggravated asthma;

• development of chronic bronchitis;

• irregular heartbeat;

• nonfatal heart attacks,

• premature death in people with heart or lung disea se; and

• increased risk of cardiovascular and cerebrovascul ar events in post-menopausal
women.
Exposure to particulate matter pollution can cause coughing, wheezing, and decreased lung function even in otherwise healthy children and adu lts. EPA estimates that thousands of elderly people die prematurely each year from exposure to f ine particles. In addition, a recent study (Dominici et al., 2006) of the correlation between PM2.5 concentrations and hospital admission rates concluded that short-term exposure to PM2.5 i ncreases the risk of hospitalization for cardiovascular and respiratory diseases.

1 Based on 1998-2000 monitoring data, EPA made a fi nding (February 15, 2002 Federal Register, Volume 67, Number 32, Page 7082 et seq.) that Sacra mento County attained the federal ambient PM10 standard by the applicable December 31, 2000 a ttainment deadline. Note that this EPA finding did not redesignate the Sacramento District to atta inment.Staff Report

Rule 421 September 9, 2009, Page 4
The California Air Resources Board (CARB) heard the results of several studies 2 that showed smoke from wood fires aggravates lung and heart dis ease and increases the number of hospital admissions. The research results indicate exposure to wood smoke may reduce lung function and reduce the blood’s ability to clot properly. I n addition, wood smoke exposure may also increase substances in the body that lead to cardio vascular and pulmonary inflammation. The studies also found that up to 70 percent of smoke f rom chimneys can re-enter a home or neighboring residences.

The research further found that children are more l ikely to be harmed by PM2.5 and wood smoke than adults because their respiratory systems are still developing, they breather more air per pound of body weight, and they are more active outdoors. Recent studies have concluded that children breathing PM2.5 and wood smoke are li kely to experience:

• more coughing and wheezing,

• reduced lung function if they have asthma 3

,
• permanent reduction in lung air capacity 4

,
• ten percent increased hospital admissions from res piratory problems 5 , and

• decreases in memory function and scores on intelli gence tests 6

. In 2008, CARB updated their studies of mortality ef fects from elevated PM2.5 concentrations. They now estimate that in the Sacramento Metropolit an Area, up to 600 premature deaths would be avoided at an economic benefit of over $0. 9 billion per year if the region met the state and federal annual PM2.5 standards.

In addition, studies have shown that with less air pollution, people will walk and bicycle more for recreation and transportation 7 . People who live in an area more conducive to wal king have been shown to walk more, weigh less, and have lower blood pressure 8,9 . Rule 421 has the potential to increase walking of residents by decre asing wood burning emissions. Furthermore, the analysis of Rule 421 effectiveness has shown th e largest air quality improvement occurred in the early evening, when people are more likely t o be active or commuting home from work or school. Staff has received feedback from some memb ers of the public that the reduction in wood smoke has made it nicer to walk and has reduce d health concerns from breathing wood smoke.

2 California Air Resources Board, The potential heal th impacts of residential wood burning, Jan. 2009, http://www.arb.ca.gov/research/health/healthup/Jan0 9.pdf 3 Allen R et al 2008, Changes in lung function and airway inflammation among asthmatic children residing in wood smoke-impacted urban area. Inhal Toxicol. 20(4):423-433 4 Gauderman WJ et al, The Effect of Air Pollution on Lung Development from 10 to 18 Years of Age. N Engl J Med 351;11:1057-1063, Sept 2004 5 Anderson ZJ et al, Ambient particle source apport ionment and daily hospital admissions among children and elderly in Copenhagen. J Expos Sci Environ Epidemiol. 17(7):625-636. 6 Suglia F et al, Association of Black Carbon with Cognition among Children in a Prospective Birth Coh ort Study. American Journal of Epidemiology . 2008:167:280-286 7 Pikora T et al, Developing a framework for assessme nt of the environmental determinants of walking and cycling. Social Science & Medicin e. 56 (2003): 1693-1703 8 Frank LD et al, Many Pathways from Land Use to Heal th. Journal of the American Planning Association. 72 (2006): 75-87 9 Fuzhong L et al, Built environment and changes in b lood pressure in middle aged and older adults. Preventive medicine . 48(2009)237-241.Staff Report

Rule 421 September 9, 2009, Page 5

LEGAL MANDATES

Federal Mandates: The District is currently designated as a nonatta inment area for the federal PM10 standards. Sacramento does not meet federal h ealth standards for fine particle air pollution (PM2.5). On December 22, 2008, the EPA A dministrator signed rules designating the Sacramento area as nonattainment for the federal 24 -hour PM2.5 health standard of 35 μg/m 3

. The designations are under review by the new admini stration and are expected to be published in the coming months. Attainment plans are expected to be due in late 2012 and the attainment deadline will be 2014, but may be extended to 2019 if properly justified to EPA.
The plan must include transportation conformity bud gets and control measures. Transportation conformity budgets will require that future transpo rtation projects stay within the specified emission levels to meet attainment and progress goa ls. Failure to submit a plan can result in withholding of federal transportation project appro vals and funding.

Attainment of the federal health standards is deter mined using the design value (three-year average of the 98 th percentile PM2.5 concentrations). When Rule 421 w as adopted, our design value was based on 2004-2006 data. Since that tim e, PM2.5 concentrations have increased, likely due to weather influences. Sacramento will l ikely need more reductions than previously estimated to attain. It is likely that the current Rule 421 alone will not be enough to attain before the plan is due. Strengthening Rule 421 by lowerin g the concentration thresholds will help to further reduce PM2.5 concentrations during winter m onths, potentially reducing the need for other, more costly regulatory measures.
PM2.5 Design Value 10 Monitoring Station μg/m 3 Del Paso Manor, Years 2004-2006 48.7 Del Paso Manor, Years 2006-2008 51.8 11 Federal Health Standard 35

State Mandates: The District is currently designated as a nonattain ment area for the state PM10 and PM2.5 standards. Senate Bill (SB) 656 req uired CARB to adopt a list of the most feasible and cost effective control measures to mak e progress towards state and federal PM10 and PM2.5 standards. The following table lists the District’s PM control measures for wood burning fireplaces and wood burning heaters.

PM Control Measure Status

1 Require use of USEPA-Certified Phase II or equivalent devices Implemented in Rule 417

2 Public Awareness Program with either a voluntary curtailment or mandatory curtailment Implemented in Rule 421

10 This data was acquired at Del Paso Manor station l ocated in Sacramento County that has a federal reference method monitor. The average value is cal culated according to procedures specified in Appendix N of Title 40, Code of Federal Regulations Part 50. The procedures require selection of the 98 th percentile monitored concentration. For example, in 2006 the 98 th percentile was the eighth highest reading. 11 Design value calculated with exceptional event dat a from 2008 forest fires removed.Staff Report

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PM Control Measure Status

3 Require replacement of non-certified units upon sal e of property Not implemented

4 Restrict number of wood burning fireplaces and wood stoves allowed in new residential developments Partially implemented in Rule 417

5 Control of wood moisture content. Prohibit burning materials that are not intended for use in fireplace/heater. Implemented in Rule 417

Rule 417, adopted on October 26, 2006, implemented control measures 1 and 5, and partially implemented control measure 4. Although Rule 417 e xceeds the control measures by prohibiting the installation of any wood burning fi replaces in new residential developments, it doesn’t limit the number of certified wood burning stoves in a residence. Rule 421 implemented control measure 2. Control measure 3, which addres ses replacement of noncertified units upon sale of property, has not yet been implemented. St aff recommended postponing the implementation of the control measures that have no t been fully implemented until the benefits could be assessed from the original adoption of Rul e 421. Staff is still considering postponement of these measures to assess the benefi ts of amendments to Rule 421. All of the PM2.5 measures will be reconsidered during developm ent of the federal PM2.5 attainment plan.
The District's strategy to improve particulate matt er air quality includes a three-pronged approach to reducing emissions from wood burning: p roviding financial incentives, regulating new wood burning installations, and reducing burnin g from existing fireplaces and wood stoves. The program to provide financial incentives to remo ve or replace existing fireplaces and dirty wood stoves was established in June 2006. On Octobe r 26, 2006 the Board of Directors approved Rule 417 to prohibit installing new firepl aces and dirty wood burning devices. The last prong was adopted on October 25, 2007, as Rule 421, Mandatory Episodic Curtailment of Wood and Other Solid Fuel Burning. Residents and busines ses that take advantage of the financial incentives to install gaseous fuel burning devices are exempt from our wood burning regulations.

2009 WOOD BURNING SURVEY
Aurora Research Group conducted a telephone survey of Sacramento County residents in April 2009. This survey was similar to the telephone surv ey Aurora previously conducted in April 2007. The purpose of this study was to gauge publi c awareness of mandatory No Burn restrictions and to gauge the effectiveness and com pliance rates of Rule 421 on Burning Discouraged, Stage 1 and Stage 2 No Burn days.
The 2009 Aurora phone survey received responses fro m 400 random digit dial telephone surveys from county residents, including a subset o f 6% who would be classified as low-income residents based on federal guidelines. Only those who have the capability to burn wood or pellets were included in the 400 surveys. To get a n accurate compliance rate only the respondents who burned last season were focused on. Of the burners, 57% complied with all the Stage 1 restriction days (this accounts for cer tified device owners who are allowed to burn) and 70% complied with all the Stage 2 restriction d ays.Staff Report

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Other key findings are:

• 50% of all respondents who had the capability to b urn wood or pellets did not burn at all last winter.

• 50% of all respondents burned sometime during the last winter season. 30% of all respondents burned wood less than once per week. 11% indicated burning more than once a week.

• No respondents indicated wood burning as their sol e source of heat.

• Among the 45% of those who reduced their burning o f wood last winter, 35% of those who burned less did so because of air quality or he alth concerns, or because they heard a request not to burn.

• 71% of all respondents know that it is their oblig ation to check to see if burning is permitted.

• 85% of those respondents aware of the mandatory cu rtailment restrictions saw or heard about a mandatory curtailment episode on TV and rad io.

• 59% of all respondents rated residential wood burn ing fireplaces as a serious part of wintertime air pollution. However, 82% rated traff ic as a serious part of wintertime air pollution with 20% more rating traffic as very seri ous compared to wood burning as very serious.
The final Aurora report is included in Appendix C.

Rule 421 Effectiveness Analysis and Results
The District contracted with STI to study the air q uality benefits from Rule 421. They conducted three different analyses; a cluster analysis, Chemi cal Mass Balance (CMB) modeling, and numerical modeling.

• Cluster Analysis Cluster analysis involves grouping days with simila r meteorology. For each group, the analysis then compares the PM2.5 concentrations on days prior to the adoption of Rule 421 to days after Rule 421 was in effect. The difference in PM2.5 concentrations can be largely attributed to the ben efits achieved by Rule 421.
A total of 11 day-pairs (22 days) with similar mete orological conditions was compared. Half of the 22 days occurred prior to ad option of Rule 421 and the other half of the days were either Stage 1 or Stage 2 No Burn days. The results of the cluster analysis showed an average reduction in 24- hour average PM2.5 concentration on a Stage 1 day of 4 μg/m3 (10% redu ction) and on a Stage 2 day of 12 μg/m3 (23% reduction.) The greatest reduction o f PM2.5 concentrations occurred during the evening hours.

• Chemical Mass Balance (CMB) model A CMB model uses analysis of the chemical species c ontained in particulate matter collected on filters to determine which emissions s ources contribute to the PM2.5 problem. Studies have established a "fingerprint" of chemical species that areStaff Report

Rule 421 September 9, 2009, Page 8

associated with different emissions sources. The CM B model then uses this information to quantify the contribution from each source type.
The results of the CMB modeling indicate the averag e wood smoke contribution on days when PM2.5 concentrations were high was at lea st 11 μg/m3 (27%) and as high as 19 μg/m3 of the total PM2.5 concentration 12 . These results indicate that it is possible to achieve a PM2.5 reduction on the order of 10 to 20 μg/m3 on a Rule 421 No Burn day. This percentage is consistent with em issions inventory 13

.

• Numerical Modeling STI runs a gridded air quality modeling system call ed BlueSky Gateway 14 . This model system provides hourly predictions of PM2.5 c oncentrations for the entire United States, including Sacramento, using weather information, emissions data, and an air quality model to represent the chemical reac tions that occur in the atmosphere. BlueSky Gateway’s sub-components includ e the weather model, called MM5, an emissions model, called Sparse Matrix Opera tor Kernel Emissions (SMOKE), and the Community Multiscale Air Quality m odel (CMAQ). STI estimated PM2.5 concentrations assuming 100% curtailment. Bec ause the model represents the entire United States, and consequently has a fa irly coarse resolution, peak concentrations may be under-predicted by the model.
The initial results of the modeling analysis show t hat the average PM2.5 reduction with complete curtailment was 5.2 μg/m3 or 21%.
STI updated the modeling for this analysis on Augus t 20 th to account for less than 100% compliance. The analysis was modified by appl ying the 57% emission reduction for Stage 1 No Burn days and the 70% emis sion reduction for Stage 2 No Burn days. The updated results of the modeling ana lysis show that the average PM2.5 reduction on a Stage 1 No Burn day was 5.2 μg /m3 or 13% and on a Stage 2 No Burn day was 6.4 μg/m3 or 17%.

• Other data analysis STI also determined how many days during the 2008/2 009 season that benefits from Rule 421 avoided an exceedance of the federal healt h standard. A total of 20 days exceeding the NAAQS violations were observed, and h ad Rule 421 not been in effect there would have been about 13 additional da ys on which the NAAQS was exceeded.

12 Along with PM2.5, burning wood emits nitrogen oxid es and organic compounds. Nitrogen oxides and organic compounds then react in the atmosphere to f orm secondary PM2.5. However, because there are many other sources of organic compounds and nit rogen oxides, it is not possible to specifically quantify the amount of this secondary PM2.5 that is from wood burning. Therefore, 11 μg/m 3 , is a conservative estimate of the wood smoke contributio n to total PM2.5 concentrations. 13 If all secondary PM2.5 compounds were excluded, th e percentage of PM2.5 associated with wood smoke would be 58%, similar to the percentage estim ated using emissions inventory methods. 14 The model grid resolution is 36 kilometers.Staff Report

Rule 421 September 9, 2009, Page 9

Finally, STI evaluated the potential for underpredi ctions in the air quality forecast. No Burn forecasts are made based on the air quality an d weather forecasts for each day. To provide greater certainty in avoiding fede ral health standard violations, STI recommends the thresholds be reduced to account for the potential underprediction.
The STI report is included in Appendix D.

SUMMARY OF PROPOSED AMENDMENTS
When Rule 421 was first adopted, forecasting error had not been quantified nor considered when determining the thresholds. The recent STI st udies analyzed the current program, including the forecasting error. With this additio nal information and consideration of actual Rule 421 air quality benefits and comments received at p ublic workshop, concentration thresholds can now be re-examined. Staff evaluated lowering t he thresholds to strengthen the rule for three reasons; 1) the studies indicate that althoug h the public awareness and air quality benefits are very good, we are still not meeting state or fe deral health standards, 2) when the thresholds were established Staff did not have information to consider the potential for underpredicting air quality forecasts or actual air quality benefit inf ormation, and 3) recent health studies indicate that serious health consequences can be specificall y attributed to wood smoke and that updated CARB health information estimates higher mortality rates in Sacramento from high PM2.5 concentrations.
Four amendment options are being evaluated:
Recommended Change - Reduce the Stage 1 threshold from 35 to 31 μg/m 3 to account for the potential underprediction in air quality foreca sts. Reduce the Stage 2 threshold from 40 to 35 μg/m 3 to account for the actual benefits that occur on S tage 1 No Burn days.
This is the Recommended Change because, as noted, a n adjustment is needed to account for the potential underprediction in air quality fo recasts and the actual Stage 1 benefit of 4 μg/m 3 . Staff originally estimated the Stage 1 benefit a t 5 μg/m 3 during the adoption of Rule 421.
Option A – Change to a single-stage program, thereby elimin ating the exemption for EPA certified wood stoves and pellet stoves. Set the N o Burn threshold to 30 μg/m 3 and either maintain the voluntary threshold at 25 μg/m 3 or reduce it to 20 μg/m 3 . These thresholds are consistent with the San Joaquin Valley APCD program , and are more likely to meet Clean Air Act requirements for reasonably available contr ol measures. Option A is expected to result in 8 more No Burn days.
This was the recommended change that was presented at the public workshops. However, many comments at the workshops expressed concern th at eliminating a two-stage program, with its exemption for EPA certified devices and pe llet stoves on Stage 1 days, would reduce the incentive for people to switch to cleane r devices and also be unfair to those who have already invested in cleaner devices.
Option B - Reduce the Stage 1 threshold to 25 μg/m 3 and the Stage 2 threshold to 30 μg/m 3 to be consistent with the San Joaquin Valley No Bur n threshold. Reduce the voluntary threshold to 20 μg/m 3 . Option B is expected to result in 21 additional No Burn days.Staff Report

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Contingency Option - Include a provision to automatically change to a single-stage (if not already) program with a reduced threshold of 20 μg/ m 3 if Sacramento does not meet the federal health standards by the required deadlines, either 2014 or 2019. Using the past 5 years of data, this would result in up to approxima tely 41 No Burn days. However, due to uncertainty in the effectiveness of future control measures to improve air quality the actual numbered days would likely be lower.
In addition, Staff recommends adopting the administ rative change that clarifies the intent that first-time violators must complete and pass (i.e. a test or other method) the smoke awareness
course.

IMPACTS AND BENEFITS FROM POTENTIAL RULE CHANGES

Staff used PM2.5 concentration data collected over the last 5 seasons to estimate the number of No Burn days that might be called each winter if the new potential thresholds were established.
Every additional No Burn day would be expected to h ave a 10% improvement in air quality for Stage 1 days, and 23% improvement in air quality fo r Stage 2 days.

• Staff recommendation – 5 more No Burn days than th e current rule

• Option A – 8 more No Burn days than the current ru le

• Option B – 21 more No Burn days than the current r ule

Staff estimated the number of additional exceedance s of the federal health standard that might be avoided by applying the STI estimated benefit. The estimation of the number of days avoided over the federal health standard was based on the 2007/2008 and 2008/2009 season daily forecasts.

The method was: 1) If the actual forecast concentration would have resulted in a change in No Burn status; e.g. from a Stage 1 day to a Stage 2 day, then 2) The observed concentration was adjusted downward by STI’s estimate of the average benefit on Stage 1 and Stage 2 No Burn days. 3) The adjusted concentration for each day was comp ared to the federal health standard. If the adjusted concentration was below the 35 μg/m 3 health standard it was counted as an avoided exceedance.

The table below shows the number of avoided exceeda nce days. The primary reason that the number of avoided exceedance days doesn’t vary between the options is because the analysis relies on historic observed air quality da ta and actual forecasts for 2007-2009, rather than a theoretical benefit that reflects the different projected emissions under each option. If there were no days where the air qualit y levels and forecasts fit into the boundaries that define the differences in the thres holds, then it was not counted as an avoided exceedance. There were only ten days (five per season) historically where the actual exceedance could have been avoided. The fut ure could see different air quality levels, possibly more preventable days.Staff Report

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The number of avoided exceedance days will likely b e higher in the future because, unfortunately, on our very worst days, a 23% air qu ality improvement hasn’t been enough to attain the standards. But as air quality improves, 23% may be enough and the actual number of days avoided will be higher.
Optional threshold changes (μg/m3)

Current rule Recommended Change Option A Option B

Voluntary threshold 25 25 20 or 25 20 Stage 1 threshold 35 31 Eliminated 25 Stage 2 threshold 40 35 30 30 Impacts and Benefits
Stage 1 Stage 2 Stage 1 Stage 2 Single Stage Stage 1 Stage 2
# of days in 2004/2005 7 9 9 16 29 17 29

# of days in 2005/2006 7 14 1 21 27 15 27

# of days in 2006/2007 5 24 4 29 37 10 37
# of days in 2007/2008 7 8 4 15 20 14 20 # of days in 2008/2009 7 26 6 33 43 10 43 Average # of No Burn days 7 16 5 23 31 13 31

Average total no. of mandatory No Burn days 23 28 31 44

Average additional # of No Burn days – 5 8 21

Average # of voluntary No Burn days 22 17 15 33 @ 20 μg/m3 13 @ 25 μg/m3 20
# of avoided days over the federal health standard 13 16 17 18

# of additional avoided days over the federal health standard – 3 4 5

EMISSIONS INVENTORY
The California Air Resources Board is responsible f or preparing the wood burning emission inventory for Sacramento County. Wood smoke is the single largest directly emitted PM2.5 emissions source, 49% as reported by the CARB’s 200 5 California Emission Forecast System

15 The number of days is lower than the current rule, even though the threshold has not changed, because some of the previous voluntary days would b ecome mandatory No Burn days.Staff Report

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(CEFS) wintertime PM2.5 emissions inventory for Sac ramento County. The 2005 emissions inventory is 8.30 tons on an average winter day fro m wood burning in Sacramento County. CARB estimates are based on a 1987 survey conducted in Healdsburg, California that suggested that 0.28 cords of wood is burned per hou sehold per year 16 . This wood usage amount was assumed statewide. The inventory using this methodology was calculated using heating degree days.

The residential wood smoke combustion emissions inv entory is being re-evaluated by CARB in preparation for upcoming federal PM2.5 plans. CARB is evaluating Staff’s 2007 rule development work which suggests the inventory is si gnificantly underestimated. Additionally, several relevant surveys have been conducted since CARB last modified the methodology. The revised methodology relies on breakdown of device t ypes, wood usage per device type per year, updated emission fireplace factor and wood co rd weight. The methodology will use regional information when appropriate for device br eakdowns to further refine a region’s inventory.
This revised CARB methodology estimates PM2.5 emiss ions from wood burning in Sacramento County of 2,036 tons per year and 10.14 tons on an average 17 winter day. Since not all wood burning occurs during the winter months (November t hrough April), the methodology uses a temporal profile to estimate wood usage for the ent ire year. The amount of wood burned during the 120 days of the CBYB program accounts for 69.1% of all wood fuel burned in a year and 90.9% of the fuel is burned in the months of Novemb er through April.

16 California Air Resources Board, Area Source Methodo logy, Section 7.1, Residential Wood Combustion, Revised July 1997 17 Based on a 120-day winter with 90.9% of wood burne d in the winter periodStaff Report

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2005 Revised Methodology Wintertime PM2.5 Inventory by Source

The changes represent a reduction in the Fireplace PM2.5 Emission Factor, use of cord weight, total number of devices, changes in the usage chara cteristics for the various wood devices into conventional (non-certified), Phase II certified no n-catalytic, Phase II certified catalytic, and pellet device. In Sacramento County the largest so urce of wintertime PM2.5 emissions is wood smoke.
For detailed calculations see Appendix E, Emissions Calculations.
The changes between the CARB CEFS inventory and the revised methodology are graphical shown below: Other Burning 2% (0.38 tpd)

Fugitive Dust 17% (3.23 tpd)

Cooking 3% (0.57 tpd)

Other Mobile 7% (1.37 tpd)

Farming

2% (0.41 tpd) Motor Vehicles 8% (1.45 tpd) Fuel Burning 4% (0.69 tpd) Other Sources 3% (0.42 tpd)

Wood Smoke 54% (10.14 tpd)Staff Report

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The overall increase in emissions are due to a vari ety of factors, some increase emissions, others decrease emissions relative to the old metho dology. Overall there is a significant reduction in projected fireplace emissions and incr ease in wood stove and fireplace insert emissions as shown below.

The graph below shows the revised methodology inven tory by device type which shows the largest portion of the wood smoke inventory is from uncertified wood stoves and inserts. In Sacramento County the devices that contribute most to PM2.5 emissions are wood inserts. Contribution for fireplaces has significantly decre ased based on a revised fireplace emission factor, percent of fireplaces used, amount of wood used and accounting for supplemental heating or aesthetic fireplace use. Sacramento Residential Wood Combustion 2005 PM2.5 Winter Planning Inventory

Old Versus New

OLD

OLD OLD

NEW NEW

NEW

0.00 2.00 4.00 6.00 8.00 10.00 12.00

Fireplaces Wood Stoves TotalPM2.5, Tons/Day

6.97 8.30

1.33 2.03 8.11 10.14Staff Report

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The new CARB information shows that replacing firep laces with EPA certified wood stoves or wood inserts and generally pellet stoves does not r educe overall emissions.

Device Type Emission Factor (lbs PM2.5/ton fuel burned) PM2.5 per

device per year (lbs/year) Fireplace – Aesthetic Heating 23.6 23.6 3

24 Uncertified wood stove Catalytic wood stove Non-Catalytic wood stove 30.6 20.4 14.6 71

47 34 Uncertified wood insert Catalytic wood insert Non-Catalytic wood insert 30.6 20.4 14.6 57

38 27 Pellet stove 3.06 6

Sacramento Residential Wood Combustion 2005 PM2.5 Winter Planning Inventory

New Inventory

uncertified uncertified

0.14 2.03 certified

certified

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

Fireplaces Wood Stoves Fireplace Inserts Pellet StovesPM2.5, Tons/Day

1.83

0.61 3.94

1.59Staff Report

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### Direct

Average Wintertime Observed PM2.5 Contributions

The PM2.5 emission inventory shown above only tells part of the story. As noted previously, PM2.5 includes both directly emitted particles char acterized in the emission inventory above, and PM2.5 aerosols formed by chemical reactions bet ween gaseous pollutants, called precursors, in the atmosphere, in particular VOC, N Ox and ammonia. Studies use chemical analyses of filters that have collected PM2.5 from Sacramento’s air to ascribe collected PM2.5 concentrations to precursor and directly emitted PM 2.5 emissions sources. These studies are generally referred to as chemical mass balances. T he results from STI’s recent chemical mass balance are shown below.
Along with PM2.5, burning wood emits nitrogen oxide s and organic compounds. Nitrogen oxides and organic compounds then react in the atmosphere to form secondary PM2.5. However, because there are many other sources of organic com pounds and nitrogen oxides, it is not possible to specifically quantify the amount of thi s secondary PM2.5 that is from wood burning. Therefore, 11 μg/m 3 is a conservative estimate of the wood smoke contr ibution to total PM2.5 concentrations.

EMISSION REDUCTIONS
The primary effect of the proposed amendments will be to reduce the PM2.5 concentrations on additional peak days, reduce adverse health effects , particularly for those in sensitive groups, and make additional progress to attain the state an d federal annual and 24-hour health standards.Staff Report

Rule 421 September 9, 2009, Page 17
The emissions reductions were calculated using resu lts from the 2009 Aurora Survey of burning behavior during the Rule 421 No Burn program. That survey reported 90% of those who burn were aware of the burning restrictions. The study results were used to determine that there was a 57% reduction in the number of people burning on Stage 1 No Burn days (EPA certified and pellet stoves are allowed to burn), and a 70% reduc tion in burning on Stage 2 No Burn days 18

. The Stage 1 No Burn reduction is assumed to be only on those who are not allowed to burn. All certified wood stoves, wood inserts and all pellet stoves are assumed to be burning on Stage 1. The emissions reduction for Stage 1 when accounting for all devices is 43%.

The emission reductions are based on the revised me thodology and use the Sacramento specific data from the Houck and 2007 Aurora survey , the methodology estimates that wood burning devices in Sacramento County:

• 155,649 residence have open hearth fireplaces

• 25,105 residences have wood stoves

• 80,335 residences have wood burning inserts

• 15,063 residences have pellet stoves
Of the homes with wood burning equipment:

• 98,059 residences with fireplaces burn wood or man ufactured logs

• 15,816 residences with wood stoves burn wood

• 50,611 residences with wood burning inserts burn w ood

• 9,490 residences with pellet stoves burn pellets

• 69% use wood burning fireplaces more for aesthetic s

• 31% use wood burning fireplaces more for supplemen tal heating

• 20% residences with wood burning fireplaces burn m anufactured logs

• 43% of wood stoves are Phase II certified

• 45% of wood inserts are Phase II certified
The average wood usage per residence:

• Aesthetic Fireplace Users – 0.069 cords per year 19

• Supplemental Heating Fireplace Users – 0.656 19

• Pellet Stove Users – 4,000 pounds per year 20

• Wood Stove Users – 1.5 cords per year 20

• Wood Insert Users – 1.2 cords per year 20

Based on the temporal profile used in the revised C ARB methodology for Residential Wood Combustion, 69.1% of the annual emissions from wood burning occur in the months of November through February, when Rule 421 is in effe ct. The table below summarizes the daily average emissions during November – February and th e estimated emission reductions from Stage 1 and Stage 2 days. Each option has the same emission reduction per No Burn day;

18 Cooley memo to Morley Chavero, “Burner Only Compli ance/Emission Reduction Rates”, July 1, 2009. 19 Houck, James E., Mangino Joseph, Brooks,Garry and H untley, Roy, “Recommended Procedure for Compiling Emission Inventory National, Regional and County Level Activity Data for Residential Wood Combustion Source Category”, U.S. EPA Emission Inve ntory Conference, 2001. 20 Broderick, D. and Houck, J., OMNI Consulting Servi ces, Inc, “Results of Wood Burning Survey – Sacramento, San Joaquin, and San Francisco Areas, U niversity of California Berkeley/CARB – GIS Study”, January 15, 2003.Staff Report

Rule 421 September 9, 2009, Page 18
however, the options differ in the additional numbe r of No Burn days that would occur. Detailed calculations and data sources are presented in Appe ndix E.
The table below summarizes the average winter day i nventory for the wintertime season and potential emission reductions. Detailed calculatio ns and data sources are presented in Appendix E.

Reductions (tons/day) Pollutant Nov. – Feb.

Daily Average Emissions (tons/day) Stage 1 Day Stage 2 Day

PM10 11.95 5.17 8.37 PM2.5 11.47 4.97 8.03 NOx 1.39 0.59 0.76 CO 78.5 22.3 54.8 VOC 35.2 18.9 24.6

Note: Each option has the same reduction per No B urn day but the number of No Burn days

differs in each option.

OTHER DISTRICTS
Since Rule 421 was adopted, three other California air districts have adopted or amended similar measures.
San Joaquin Valley Unified Air Pollution Control Di strict (SJVUAPCD) adopted its rule prohibiting wood burning on forecast poor air quali ty days on July 17, 2003. The threshold for prohibiting burning was set to the federal PM2.5 he alth standard at the time, 65 μg/m3 or greater. On October 16, 2008 they lowered their th reshold to 30 μg/m3 and will automatically lower that threshold to 20 μg/m 3 if they fail to attain the federal health standard of 65, by April 2015. The 20 μg/m 3 threshold would be implemented 60 days after the e ffective date of EPA’s published finding of failure to attain for PM2.5.
Bay Area Air Quality Management District (BAAQMD) a nd South Coast Air Quality Management District (SCAQMD) also recently adopted similar man datory No Burn rules. The BAAQMD threshold is 35 μg/m 3 and became effective on November 1, 2008. The SCA QMD rule becomes effective November 1, 2011, with a threshol d of 35 μg/m 3 . None of these three districts exempt EPA certified devices or pellet st oves from mandatory No Burn restrictions.
Feather River Air Quality Management District (FRAQ MD) has proposed amendments to Rule 3.17 – Wood Burning Appliances. A workshop is sche duled for the end of August. FRAQMD has proposed to allow the APCO issued voluntary adv isory to curtail emissions to an advisory with specific recommended actions such as allow, re duce, curtail, limit specific areas, or a request to cease. The advisory would continue to b e voluntary.
Additionally, Butte County AQMD is considering adop ting mandatory No Burn restrictions. The Chico City Council recommended Butte County AQMD ad opt a No Burn regulation with a threshold of 25 μg/m 3 . They propose to exempt EPA certified stoves.Staff Report

Rule 421 September 9, 2009, Page 19
Other areas with wood burning restrictions include the state of Oregon as well as specific counties and agencies in Oregon; the state of Washi ngton and specific jurisdictions; numerous areas of Idaho: Denver, Colorado; Mammoth Lakes, Ca lifornia; several counties in Utah; Maricopa County, Arizona; and Albuquerque, New Mexi co. Many other areas have voluntary restrictions or are considering adopting mandatory restrictions.
The restrictions in Oregon are based on PM10 and ha ve not yet been updated for PM2.5 standards. The state of Washington has a two stage curtailment with exemptions for certified devices and pellet stoves on Stage 1, with a Stage 1 threshold of 35 μg/m 3 . Several clean air agencies in Washington have adopted the state regul ation that includes exemptions for sole source, financial hardship and startup period.

COST IMPACT
Section 40703 of the California Health and Safety c ode requires that the District consider and make public its findings relating to the cost effec tiveness of implementing an emission control
measure.

Cost to Businesses: Businesses with solid fuel burning devices other than for cooking would have a cost savings from reduced wood use, because they generally burn wood for aesthetic purposes. An increase in the number of No Burn day s will increase the cost savings. Staff does not have information to quantify the impact, i f any, to these businesses due to the loss of ambience. There will be an additional impact to wo od retailers, because mandatory curtailment will reduce the amount of wood burned by the public , and therefore, the amount of wood sold by retailers. Using the current average number of No Burn days in the winter season (7 Stage 1 days and 16 Stage 2 days) as a baseline, the impact to wood retailers varies for each option. The estimated value of the wood products not purcha sed due to reduced burning on the additional mandatory curtailment days is shown in t he following table:

Average number of additional No Burn days for each option compared to current Rule 421 averages Value of wood products not purchased ($ per season)

Recommended Change 5 $577,781 Option A 8 $1,137,064 Option B 21 $1,620,987 Contingency Option 41 $4,211,867

Note: 1. Rule 421 average number of No Burn days is 23. 2. Percent of all wood burned that is purchased is 51.4% (Houck 2003).

Cost to Public: The majority of the people affected by the rule bur n wood for ambiance and would have cost savings from reduced wood use. For people who use wood as their primary or supplemental heat source, there will be a shift in costs from wood to an alternate source forStaff Report

Rule 421 September 9, 2009, Page 20
heat. Anecdotal information from comments during p ublic workshops suggests that some consumers may have reduced heating costs from using wood or pellets as a supplemental heat source. The data below shows that it costs less pe r unit of heat delivered to use natural gas or electricity than to use wood 21 . Reported cost savings may result from personal c omfort choices and/or because supplemental sources heat main livin g areas while other rooms remain cooler than the home’s alternative heat source would provi de, resulting in a net reduction in heat delivered. Staff cannot rely on anecdotal informat ion to estimate community scale impacts of the additional No Burn days.

The average cost of heating a home with a wood stov e for 30 days is $236 and a pellet stove is $172 while the cost is $153 for electricity and $88 for natural gas.

Overall Rule Amendment Cost Effectiveness: The cost effectiveness of the amendments can be calculated by using the cost (lost sales for wood suppliers). In this case the recommended option cost effectiveness is estimated to be $3.14-$5.32 per pound of PM2.5. To put these costs into perspective, it is useful to c ompare the cost effectiveness for other District rules. District Rule 417, Wood Burning Appliances, cost $4.19/lb of PM2.5 in today’s dollar. It should be noted that most of the District’s existin g rules are for controlling ozone precursors, usually VOC or NOx. Rule 421 also achieves NOx red uctions. The cost effectiveness of this rule is $2.80 - $4.86 per pound of NOx + PM2.5. Th e 2005 amendments to District Rule 411, NOx from Boilers, Process Heaters and Steam Generat ors cost $13.90/lb of NOx. The cost effectiveness of the gasoline dispensing regulation s (Rule 449, Transfer of Gasoline into Vehicle Fuel Tanks; 12/17/1991 rule amendments) is at the h igher end of the cost effectiveness range, costing $17/lb of VOC in today's dollars. Rule 452, Can Coating (8/21/1990 rule amendments), is at the low end of the range at a cost of $1/lb o f VOC in today's dollars.

21 In the case that free wood is delivered to the res idence at no cost, there would be an increase in fu el cost. 22 Socioeconomic Impact Analysis for SMAQMD Rule 421 Amendments, ERG, 2009

Heating Device Thermal Efficiency Fuel Cost per MMb tu Fireplace 7% $206.23 Certified Wood Stove 63% $22.91 Pellet Stove 76% $28.29 Propane Fireplace 75% $34.24 Natural Gas Fireplace 75% $14.54 Electric Fireplace >99% $25.23 Gas Central Heat & Air 80% $13.63 Electric Central Heat & Air 100% $25.23 Propane Central Heat & Air 80% $32.10
Note: 1. Used as primary source of heat 2. PG&E rates for natural gas (Winter 2009) 3. SMUD rates for electricity (Winter 2009) 4. Average cost of cord of wood 22 = $269, average cost of pellets 22 = $344/ton, and Cost of propane = $2.31/gallonStaff Report

Rule 421 September 9, 2009, Page 21

DISTRICT IMPACT

The amendments and costs to the District for Rule 4 21 fall into three general areas: 1) forecasting PM2.5 air quality, 2) public outreach t o continue to educate and inform individuals, and 3) enforcement/compliance.
Daily forecasting will continue. If amendments are adopted, the additional number of No Burn days will not cause an increase in forecasting cost s. Additionally, the Staff time and media outreach efforts that are utilized to educate the p ublic about the health effects of particulate matter pollution and the Check Before You Burn prog ram will continue without increased costs.
Compliance and enforcement will continue in the sam e manner. The original estimate of Staff resources when Rule 421 was adopted was 0.3 FTE com pared to the actual 1.2 FTE to administer the 10 Stage 1 days and 28 Stage 2 days experienced in the 2008/2009 season. Using a ratio of the options to last season, and ke eping in mind that last season was an above average year for the number of No Burn days, the es timated Staff resources for the additional No Burn days for the enforcement/compliance effort are 0.16 FTE, 0.25 FTE, or 0.66 FTE for the Recommended Change, Option A, or Option B respe ctively, specifically for the following:
1) Surveillance by inspection Staff on the addition al No Burn days for at least 1 hour per day per inspection Staff member (including weekends ). 2) Responding to the increase in reported burning o r smoke complaints from the public due to the additional No Burn days. 3) Handling of the increase in Notice of Violations (NOVs) from the additional No Burn days.

SOCIOECONOMIC IMPACT ANALYSIS
CHSC Section 40728.5 requires a district to perform an assessment of the socioeconomic impacts before adopting, amending, or repealing a r ule that will significantly affect air quality or emission limitations. The District Board is requir ed to actively consider the socioeconomic impacts of the proposal and make a good faith effor t to minimize adverse socioeconomic impacts.
CHSC Section 40728.5 requires discussion of:
1. The type of industry or business, including smal l business, affected by the proposed rule or rule amendments. 2. The impact of the proposed rule or rule amendmen ts on employment and the economy of the region. 3. The range of probable costs, including costs to industry or business, including small business. 4. The availability and cost-effectiveness of alter natives to the proposed rule or rule amendments. 5. The emission reduction potential of the rule or regulation. 6. The necessity of adopting, amending, or repealin g the rule or regulation to attain state and federal ambient air standards.Staff Report

Rule 421 September 9, 2009, Page 22
Eastern Research Group (ERG) prepared a report for the District that contains a detailed socioeconomic analysis of the proposed rule amendme nts. This report is included as Appendix G. The paragraphs below summarize the socioeconomi c findings.
Type of industry or business, including small busin ess, affected by the proposed rule: Rule 421 applies to the use of solid fuel burning appliances and fires. The rule has the potential to affect fuel suppliers as well as businesses (e.g., restaur ants, drinking establishments) and residents who burn wood for ambience or as a source of heat. Fuel suppliers include large retail stores (e.g., supermarkets and hardware stores) in additio n to small independent dealers that sell wood as either a primary source of income (wood lot s) or a secondary source of income (e.g., tree service or landscape companies). Companies th at produce manufactured logs are also potentially affected. For a more detailed descript ion of the businesses affected, refer to Appendix G.
Impact on employment and economy in the District of the proposed rule: The fuel suppliers, particularly the small independent dealers, are the group with the potential to be most affected by the proposed amendments, due to lost sales. Two scenarios were analyzed to determine worst case impacts on either suppliers or buyers of wood. Under the first scenario (the worst case for suppliers), it was assumed that the suppli ers would absorb the entire cost of the lost sales volume without the ability to raise prices to compensate. To estimate the impacts, it was assumed (again, worst case) that the total loss in sales for Sacramento County would be borne either entirely by the independent wood dealers or entirely by the large retail stores. Under this scenario, the lost revenue, percentage of total rev enue and the potential loss of jobs were determined, as presented in the following tables.

Small Independent Wood Dealers Recommended Option Option A Option B Contingency Op tion Total Revenue $7,683,905 $7,683,905 $7,683,905 $7,683,905 Lost Revenue $352,025 $669,899 $1,074,327 $2,622,768 % Lost Revenue 4.6% 8.7% 14.0% 34.1% Potential Job Loss 2.8 5.3 8.5 20.6

Large Retail Stores Recommended Option Option A Option B Contingency Op tion Total Revenue $112,145,169,000 $112,145,169,000 $112,145,169,000 $112,145,169,000 Lost Revenue $577,781 $1,137,064 $1,620,987 $4,211,867 % Lost Revenue 0.01% 0.03% 0.04$ 0.10% Potential Job Loss 0 0 0 0
Four companies were identified as producers of manu factured logs that are sold in Sacramento County. One of these companies, Duraflame, is loca ted in nearby Stockton. Staff was unable to determine the market share for Duraflame logs in the county. If Duraflame represented 100% of the market share (worst case), the potential imp act of lost sales to company revenues and associated employment would be:

• 0.5% of revenue and 1.2 jobs for the Recommended O ption

• 0.8% of revenue and 2.1 jobs for Option A

• 1.8% of revenue and 4.4 jobs for Option B

• 3.8% of revenue and 9.4 jobs for the Contingency O ptionStaff Report

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Residents and businesses that burn wood for ambienc e would experience a cost saving by not burning on the additional No Burn days. For reside nts who burn wood for heat, there could be a decrease or an increase in cost depending on the pa rticular circumstances. For residents who purchase their wood, there would be a decrease in c ost get by switching to an alternative heat source on No Burn days. The average cost to heat a home using wood is higher than either electricity or natural gas, as discussed in the Cos t Impact section of this staff report. Residents who get their wood for free would experience a cost increase; however, the maximum impact is less than 0.25% of the median household income for Sacramento County.
Under the second scenario, fuel suppliers would rai se prices to compensate for the loss in sales volume. In this case, those who get their wood for free would experience the same cost impacts as in the first scenario, but no additional cost im pacts. Those who pay for their wood would pay higher prices for the days they are allowed to burn , which would offset the savings they would experience from using alternative fuels on No Burn Days.
A more detailed economic analysis is included in Ap pendix G.
Range of probable costs, including costs to industr y or business, including small business of the proposed rule: As noted in the Cost Impact section of this staff report, the total cost, in terms of the value of wood not purchased, is $577,781 per ye ar for the Recommended Option and $1,137,064 - $4,211,867 per year for the other opti ons. If wood sellers cannot compensate for the loss in revenue by raising prices, these lost r evenues would be spread over a variety of wood sellers, including large retail stores, small businesses that sell wood as a primary or secondary income, and manufacturers of manufactured logs. A more detailed analysis of the probable costs is included in Appendix G.
Availability and cost effectiveness of alternatives to the proposed rule: Alternatives to the proposed amendments include: not amending the rule; adopting different thresholds for Rule 421 for the Recommend Option, such as Options A, B, or the Contingency Option; and adopting other measures to reduce emissions from wood burnin g devices, such as requiring replacement of noncertified devices upon sale of property and/o r limiting the number of devices allowed per acre. Not amending the rule would not increase cos ts to impacted businesses but would not increase the effectiveness of the rule in reducing the number of days that exceed the PM2.5 health standard. This could require the adoption o f more costly measures in the future, most of which have much lower potential for reducing emissi ons from wood burning, and therefore lower effectiveness in reducing the number of days that e xceed the PM2.5 health standard.
Alternatives to the Recommended Option for reducing the thresholds were analyzed as Options A, B, and the Contingency Options. These options h ave the same cost effectiveness to the proposed option, in the range of $$3.14-$5.32 per p ound of PM2.5. Each option reduces the number of days exceeding the PM2.5 health standard compared to the Recommended Option but also increases the number of No Burn days, and therefore the total cost, of the rule.
Additional wood burning control measures were analy zed as further study measures as part of the District’s response to SB 656. The cost effect iveness in 2005 for requiring replacement of non-certified units upon sale of property was estim ated to be $2.62 - $6.03/lb, however, the emission reductions are much less than those achiev able through mandatory No Burn days. San Joaquin Valley APCD estimated a reduction of 2% for replacement of non-certified units upon sale of property. Staff estimates a reduction of 3.1% for replacement of non-certified units upon sale of property using the same assumptions fr om 2005 but using updated emissionStaff Report

Rule 421 September 9, 2009, Page 24
inventory numbers. The cost effectiveness for repla cement is estimated to be $7.48. Using this assumption, the reduction for Sacramento County wou ld be 0.36 tons per day.

Other measures that are available to reduce PM2.5 e missions that were identified during the SB656 process include measures to reduce fugitive d ust from agricultural tilling; chipping and grinding activities; composting; paved and unpaved roads; street sweepers; and construction, grading, and demolition; as well as add-on controls for charbroilers. None of these measures approaches the potential of No Burn days in reducin g PM2.5 emissions. Strengthening Rule 421 now may avoid the need to adopt some of these m easures in the future.
Emission reduction potential of the proposed amendm ents: The proposed rule is estimated to achieve reductions of 4.97 tons of PM2.5 per Stage 1 day and 8.03 tons of PM2.5 per Stage 2 day. Each option has the same emission reduction p er No Burn day; however, the options differ in the additional number of No Burn days that would occur. (See discussion under Emission Reductions section of this staff report.)
Necessity of adopting the amendments: A more effective Rule 421 is necessary to reduce the number of unhealthy air quality days that violate t he federal 24-hour PM2.5 health standard and to provide further emission reductions that will co ntribute to attainment of the state and federal PM2.5 standards and state PM10 standards.

PUBLIC COMMENTS
Staff conducted five public workshops throughout Sa cramento County between July 13 th and July 22 nd . Each workshop started with a structured presenta tion of the four options with time available for questions and comments. The workshop s were held in downtown Sacramento, Carmichael, Folsom, Galt and at the District office

. Noticing for these public meetings included:

• Letters to all elected officials in Sacramento Cou nty

• Ad in the Our Region section of the Sacramento Bee

• Notices to newspapers across Sacramento County

• Notices to radio stations across the region

• Letters to homeowner associations

• Notice on the District’s Web site

• Notices by mail to the District’s list of parties interested in rule development.

Workshop Location Date Time La Sierra Community Center Room 800 5325 Engle Rd., Carmichael July 14 6:00 p.m.
Tsakopoulos Library Galleria 828 I Street, East Meeting Room, Sacramento July 15 5:30 p.m.
SMAQMD District Office 777 12 th Street, 3 rd Floor, Sacramento July 16 1:30 p.m.
Chabolla Center 610 Chabolla Avenue, Galt July 20 6:00 p.m. Folsom Community Center, Ballroom 52 Natoma Street, Folsom July 22 6:30 p.m.Staff Report

Rule 421 September 9, 2009, Page 25

In addition, Staff conducted stakeholder industry m eetings on July 9 th and August 17 th . The stakeholder meetings were attended by HBPA and vari ous retailers. Attendees, comments and responses are listed in Appendix F.
Staff received comments and questions at the worksh ops, through the mail, e-mail and at stakeholder meetings. Comments and responses are l isted in Appendix F.

ENVIRONMENTAL REVIEW AND COMPLIANCE
Rule 421 was created as part of the response to the requirements of Senate Bill 656 that the District implement cost-effective control measures for particulate matter emissions. Staff finds that the proposed rule is exempt from the Californi a Environmental Quality Act as an action by a regulatory agency for protection of the environment (Class 8 Categorical Exemption, Section 15308 State CEQA Guidelines) and because it can be seen with certainty that there is no possibility that the activity in question may have a significant adverse effect on the environment (Section 15061(b)(3), State CEQA Guidelines).
California Public Resources Code (Section 21159) re quires an environmental analysis of the reasonably foreseeable methods of compliance. Staf f anticipates that users of solid fuel burning devices will comply with No Burn day restrictions b y not burning for ambience and, where wood burning provides supplemental heat, by using altern ative sources such as natural gas or electricity. The use of these alternative fuels wi ll result in a decrease in the emissions of particulate matter and other air pollutants, includ ing greenhouse gasses, with a corresponding improvement in air quality and public health.

There has been some debate as to whether the burnin g of wood should be considered “carbon neutral,” because even though carbon dioxide is rel eased from the burning of wood, carbon dioxide was absorbed by the living trees. Burning simply returns that carbon dioxide to the atmosphere, thus the term “neutral.” However, the term “carbon neutral” is something of a misnomer, because other greenhouse gasses, includin g methane and nitrous oxide (much more potent greenhouse gasses than carbon dioxide) and b lack carbon particles (another global warming pollutant), are released when wood is burne d, while only the carbon dioxide is absorbed by living trees. Carbon neutrality, at le ast with respect to carbon dioxide emissions, depends on a number of factors, including whether t he wood has been harvested from a sustainable resource. Both BAAQMD 23 and SJVAPCD 24 have found that a large percentage of the firewood being burned comes from nonsustainable activities. In particular, there has been a loss in the acreage of oak woodlands in Northern Ca lifornia, including the foothills on the eastern side of the Central Valley, attributable to urban growth, firewood harvesting, and land clearing for vineyards and developments. Similar a ctivities have been conducted in Sacramento County and the surrounding areas. It may take as l ong as 200 years for young trees to approach the carbon storage capacity of old growth forests.

Nevertheless, Staff has examined the net effect on greenhouse gas emissions under two bounding scenarios: 1) the burning of wood is not c arbon neutral, and 2) the burning of wood is

23 BAAQMD, Draft Environmental Impact Report for the Bay Area Air Quality Management District’s Proposed Regulation 6, Rule 3 Wood-Burning Devices , May 2008 24 SJVAPCD, Final Staff Report Proposed Amendments to Rule 4901 , October 2008Staff Report

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carbon neutral with respect to carbon dioxide. It should be noted that there are greenhouse gas emissions associated with electricity due to fuel b urned at electrical power plants. The following table shows the results of this analysis, based on equal heat outputs using natural gas or electricity rather than wood combustion on No Burn days.

GHG Emissions (metric tons/yr CO 2 Equiv.) Heat Source Recommended Change Option A Option B Contingency Option Wood 6,249 11,891 19,070 46,557 Wood – Carbon (CO 2 ) neutral 552 1,051 1,686 4,116 Natural Gas 1,270 2,418 3,877 9,466 Electricity – Only CO 2 1,418 2,698 4,326 10,561
As shown in the table, if wood burning is not carbo n neutral, there is a net decrease in greenhouse gas emissions from using natural gas or electricity instead of wood on No Burn days. If wood burning is carbon (CO 2 ) neutral, there is a net increase in greenhouse ga s emissions from using the replacement fuels on No Bu rn days. Under the Recommended Option, assuming that wood burning is carbon (CO 2 ) neutral, and wood is replaced by electricity on No Burn days, there is as net increase in greenh ouse gas emissions of 866 metric tons per year of CO 2 equivalents, which represents only 0.005% of the g reenhouse gas emission inventory for Sacramento County and 0.0002% of the California inventory.
Even in the worst case (Contingency Option, using e lectricity instead of wood), the increase in greenhouse gas emissions represents only 0.04% of t he greenhouse gas emission inventory for Sacramento County and 0.001% of the California inve ntory.
Staff has concluded that no significant environment al impacts will be caused by compliance with the proposed rule.

FINDINGS
The California Health and Safety Code (HSC), Divisi on 26, Air Resources, requires local districts to comply with a rule adoption protocol a s set forth in Section 40727 of the Code. This section has been revised through legislative mandat e to contain six findings that the District must make when developing, amending, or repealing a rule. These findings, effective January 1, 1992, and their definitions are listed in the ta ble below. Rule 421 – Required Findings
Finding Finding Determination Authority: The District must find that a provision of law or of a state or federal regulation permits or requires the District to adopt, amend, or repeal th e rule. The District is authorized to amend Rule 421 by California Health and Safety Code (HSC) Sections 40001, 40702, 40716, 41013 and 42402.5. [HSC Section 40727(b)(2)]. Necessity: The District must find that the rulemaking demonstrates a need exists for the rule, or for its amendment or repeal. It is necessary to adopt the proposed amendments to Rule 421 to reduce the number of unhealthy air qual ity days that violate the federal 24-hour PM2.5 health standard. [HSC Section 40727(b)(1)]. Clarity: The District must find that the rule is written The District has reviewed the proposed rule andStaff Report

Rule 421 September 9, 2009, Page 27

Finding Finding Determination or displayed so that its meaning can be easily understood by the persons directly affected by it. determined that it can be understood by the affecte d parties. In addition, the record contains no evide nce that people directly affected by the rule cannot underst and the rule. [HSC Section 40727(b)(3)]. Consistency: The rule is in harmony with, and not in conflict with or contradictory to, existing statute s, court decisions, or state or federal regulations. The District has found the proposed rule does not c onflict with, and is not contradictory to, existing statute s, court decisions, or state or federal regulations. [HSC S ection 40727(b)(4)].

Non-Duplication: The District must find that either: 1) The rule does not impose the same requirements as an existing state or federal regulation; or (2) that the duplicative requirements are necessary or prope r to execute the powers and duties granted to, and imposed upon the District. The proposed rule does not duplicate any existing s tate or federal regulations. Rule 421 does not duplicat e the federal requirements because it requires curtailmen t of use of any fires or solid fuel burning device on ce rtain days. [HSC Section 40727(b)(5)].

Reference: The District must refer to any statute, court decision, or other provision of law that the District implements, interprets, or makes specific by adopting, amending or repealing the rule. In adopting the proposed rule, the District is implementing the requirements of HSC Section 39614 (SB 656). [HSC Section 40727(b)(6)].

Additional Informational Requirements: In complying with HSC Section 40727.2, the District must identify all federal requirements and District rules that apply to the same equipment or source type as the proposed rule or amendments. Appendix A includes a comparison with federal requirements. [HSC Section 40727.2].

REFERENCES
1. Morley Chavero, Dawn, and Holobow, Naomi, “2009 Wood Burn Awareness Survey,” May 2009
2. MacDonald, Clinton P., Craig, Kenneth J., DeWint er, Jennifer L., Pasch, Adam N., Brown, Steve G., and Wheeler, Neil J. M., “Evaluati on of Sacramento Metropolitan Air Quality Management District’s Check Before You Burn Program,” May 2009
3. Fields, Paula, Kaplan, Maureen, and Lamb-Janney, Lilli., “Socioeconomic Impact Analysis for SMAQMD Rule 421 Amendments,” August 20 09.
4. STAPPA-ALAPCO, EPA, Emissions Inventory Improvem ent Program. (EIIP) 2001. “Residential Wood Combustion”, Volume III, Chapter 2

5. Broderick, David R., and Houck, James E. 2003, Emissions Inventory Improvement Program (EIIP) Residential Wood Combustion Coordina tion Project”
6. Houck, James. 2003 “Results of Wood Burning Surv ey – Sacramento, San Joaquin, and San Francisco Areas, University of California B erkeley/California Air Resources Board – GIS Study”
7. U.S. Environmental Protection Agency, Compilatio n of Air Pollutant Emission Factors, Volume I Stationary Point and Area Sources , AP-42, Section 1.4 – Natural Gas Combustion, 1998Staff Report

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8. U.S. Environmental Protection Agency, Compilatio n of Air Pollutant Emission Factors, Volume I Stationary Point and Area Sources , AP-42, Section 1.9 – Residential Fireplaces, 1996
9. U.S. Environmental Protection Agency, Compilatio n of Air Pollutant Emission Factors, Volume I Stationary Point and Area Sources , AP-42, Section 1.10 – Residential Wood Stoves, 1996
10. 2006 Winter Emission Inventory for PM2.5, Sacra mento County, 2007 CARB Almanac, 2006 Baseline, extracted 4/15/2009, http://www.arb.ca.gov/app/emsinv/fcemssumcat2007.ph pAPPENDIX A

40727.2 Matrix

Comparative Requirements Elements of Comparison Specific

Provisions Proposed Rule 421 Rule 417 40CFR60 Subpart AAA Exemptions Cookstoves Sole source of heat Religious Activity Economic Hardship Gaseous fueled appliances Cookstoves Open masonry fireplaces constructed on site Boilers Furnaces Cookstoves Devices with air-to-fuel ratio > 35-to-1, minimum burn rate > 11 lb/hr, firebox > 20 cubic ft, or weight > 1,760 lb Averaging Provisions None none none

Units none g/hr g/hr

none Catalytic Units: 4.1 g/hr Non-Catalytic Units: 7.5 g/hr Catalytic Units: 4.1 g/hr Non-Catalytic Units: 7.5 g/hr Emissions Limits

Compliance

alternatives none Pellet stoves, masonry heaters none

Operating Parameters Prohibits operation on days with PM2.5 concentration projected above: Recommended Change - 31 μ g/m 3 Option A (A.1 or A.2) - 30 μ g/m 3

. Option B - 25 μ g/m 3 Prohibits burning trash and other specified fuels none

Work Practice Requirements None Wood advertised by retailers as “seasoned” or “dry” must contain less than 20% moisture. none

Recordkeeping none none none Monitoring/ Records Frequency none none none Monitoring/ Testing Test Methods none Wood Moisture Content, ASTM D4442-92 Test methods: -PM: Method 28 -Emissions Concentration, if a dilution tunnel sampling location uses: Method 5G -Emissions Concentration, if a stack location is used: Method 5HAPPENDIX B
List of Changes to Rules Rule 421, Episodic Curtailment of Wood and Other So lid Fuel Burning
Proposed rule changes implementing administrative c hange

NEW SECTION NUMBER EXISTING

SECTION NUMBER PROPOSED CHANGES

402.1 Same Clarify the intent that first-time viola tors must complete and pass (i.e. a test or other method) the smoke awareness c ourse.
Proposed rule changes implementing Recommended Chan ge

NEW SECTION NUMBER EXISTING

SECTION NUMBER PROPOSED CHANGES

301.2 Same Adjust Stage 1 threshold to 31 μg/m 3

. 301.3 Same Adjust Stage 2 threshold to 35 μg/m 3

. 302 Same Change Voluntary Curtailment section numbe ring to be

consistent with the Mandatory Curtailment section. 302.1 N/A Adjust Voluntary Curtailment to 25 μg/m 3 to 31 μg/m 3

. Proposed rule changes implementing Option A (A.1 or A.2)

NEW SECTION NUMBER EXISTING

SECTION NUMBER PROPOSED CHANGES

N/A 115 Eliminate exemption. N/A 116 Eliminate exemption. N/A 208 Eliminate definition no longer used. N/A 209 Eliminate definition no longer used. 208 210 Renumbered. 209 211 Renumbered. N/A 301.2 Eliminate Stage 1 Mandatory Curtailment. 301.2 301.3 Change Stage 2 to a single stage Mandat ory Curtailment and adjust threshold to 30 μg/m 3

. 302 Same Change Voluntary Curtailment section numbe ring to be

consistent with the Mandatory Curtailment section. 302.1 N/A Set Voluntary Curtailment Range to 25 μg/m 3 to 30 μg/m 3 ( A.1 ) or 20 μg/m 3 to 30 μg/m 3 ( A.2 ).
Proposed rule changes implementing Option B

NEW SECTION NUMBER EXISTING

SECTION NUMBER PROPOSED CHANGES301.2 Same Adjust Stage 1 threshold to 25 μg/m 3

.

301.3 Same Adjust Stage 2 threshold to 30 μg/m 3

. 302 Same Change Voluntary Curtailment section numbe ring to be

consistent with the Mandatory Curtailment section. 302.1 N/A Adjust Voluntary Curtailment to 20 μg/m 3 to 25 μg/m 3

. Additional proposed rule changes either Recommended Change or Option B with Contingency Option

NEW SECTION NUMBER EXISTING

SECTION NUMBER PROPOSED CHANGES

301.4 N/A Add a contingency mandatory curtailment. The contingency mandatory curtailment changes the rule to a single stage with a threshold of 20 μg/m 3

. 302.2 N/A Add a contingency voluntary curtailment. The contingency voluntary curtailment has a reduced threshold of 15 μg/m 3

. Additional proposed rule changes Option A (A.1 or A .2) with Contingency Option

NEW SECTION NUMBER EXISTING

SECTION NUMBER PROPOSED CHANGES

208 Same Maintain U.S. EPA definition. 209 210 Renumbered. 210 211 Renumbered. 301.3 N/A Add a contingency mandatory curtailment. The contingency mandatory curtailment changes the rule to a single stage with a threshold of 20 μg/m 3

. 302.2 N/A Add a contingency voluntary curtailment. The contingency voluntary curtailment has a reduced threshold of 15 μg/m 3

.APPENDIX C
Final Aurora Survey Report

(See May 2009 Board Letter)2618 Stoughton Way, Sacramento, CA 95827 www.auroraresearchgroup.com (916) 363-8682 

## Sacramento Metropolitan Air Quality Management District 

## 2009 W OOD 

## B URN 

## A WARENESS 

## S URVEY



## F INAL 

## R EPORT 

## M AY 

## , 2009

S UBMITTED BY 

### : 

## D AWN 

## M ORLEY 

## C HAVERO 

## &
N AOMI 

## E. H OLOBOW 

## , P H 

## .D.

F ORSacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 2 

## Table of Contents

E

XECUTIVE H IGHLIGHTS (3 P AGES ) .....................................................................................................3 P

ROJECT B ACKGROUND & O BJECTIVES ..............................................................................................6 R ESEARCH M ETHODOLOGY .................................................................................................................7 R ESULTS & C ONCLUSIONS ..................................................................................................................9 W

INTERTIME A IR Q UALITY I SSUES .......................................................................................................9 Residential Wood Bu rning Fir eplaces......................................................................................... 9 C URRENT R ESULTS ................................................................................................................... 9 C OMPARISON WITH P REVIOUS R ESULTS ................................................................................... 10 G ROUP D IFFERENCES ............................................................................................................. 10 Seriousness of Other Caus es of Air Polluti on........................................................................... 11 C URRENT R ESULTS ................................................................................................................. 11 C OMPARISON WITH P REVIOUS R ESULTS ................................................................................... 12 G ROUP D IFFERENCES ............................................................................................................. 13 K

NOWLEDGE OF P ARTICULATE M ATTER ............................................................................................14 CURRENT RESULTS .................................................................................................................. 14 C OMPARISON WITH P REVIOUS R ESULTS ................................................................................... 14 G ROUP D IFFERENCES ............................................................................................................. 15 W

OOD B URNING A CTIVITY ................................................................................................................16 Inventory...................................................................................................................... .............. 16 C URRENT R ESULTS ................................................................................................................. 16 C OMPARISON WITH P REVIOUS R ESULTS ................................................................................... 18 Usage of Device................................................................................................................ ........ 18 C URRENT R ESULTS ................................................................................................................. 18 C OMPARISON WITH P REVIOUS R ESULTS ................................................................................... 22 G ROUP D IFFERENCES ............................................................................................................. 24 A WARENESS OF THE C HECK B EFORE Y OU B URN P ROGRAM ..............................................................25 Unaided Awareness: Aware of No Burn Notice s ..................................................................... 25 C URRENT R ESULTS ................................................................................................................. 25 Unaided Awareness: Familiarity with t he Check Before You Burn Program ........................... 26 C URRENT R ESULTS ................................................................................................................. 26 Aided Awareness: Familiarity with t he Check Before Y ou Burn Pr ogram ............................... 29 C URRENT R ESULTS ................................................................................................................. 29 Awareness of Different Lev els of the Progra m ......................................................................... 31 C URRENT R ESULTS ................................................................................................................. 31 G ROUP D IFFERENCES ............................................................................................................. 32 Knowledge about Different As pects of t he Progr am................................................................. 34 C URRENT R ESULTS ................................................................................................................. 34 G ROUP D IFFERENCES ............................................................................................................. 35 C OMPLIANCE WITH THE C HECK B EFORE Y OU B URN P ROGRAM ..........................................................35 Among Those Aware at Each Level.......................................................................................... 35 C URRENT R ESULTS ................................................................................................................. 35 Among the Total Resp ondent Base .......................................................................................... 36 C URRENT R ESULTS ................................................................................................................. 36 G ROUP D IFFERENCES ............................................................................................................. 37 C OMMUNICATION C HANNELS .............................................................................................................39 Among Those Aware of the Check Before You Bu rn Levels .................................................... 39 C URRENT R ESULTS ................................................................................................................. 39 C OMPARISON WITH P REVIOUS R ESULTS ................................................................................... 40 G ROUP D IFFERENCES ............................................................................................................. 40 D EMOGRAPHICS

............................................................................................................................... 41Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 3 

## E

XECUTIVE 

## H IGHLIGHTS 

## (3 P AGES 

## )

These conclusions are based on the results of a random-digit-dial (RDD) telephone survey conducted with a random sample of Sacramento County residents who owned wood burning devices in March and early April 2009.



### Wintertime Air Quality Issues  Nearly six in ten (59%) Sacramento County respondents with wood burning devices acknowledged that residential wood burning was a serious cause of wintertime air pollution.

- There appears to be a move in the right direction in terms of improved understanding, with a 5% increase over the 2007 survey results, although this is not statistically significant.

- Significantly more females than males rated residential wood burning as a serious pollution problem.  However, residential wood burning was not felt to be as serious a problem as traffic in contributing to wintertime air pollution.

- The most serious cause of wintertime air pollution was viewed to be traffic (32% rated it a “very serious” contributor), followed by industry (19%) agricultural burning (15%), and finally residential wood burning (12%).

- Results this year are generally similar to those found in 2007.

- Significantly more females than males rated traffic and industry as serious causes of pollution. Caucasians were less likely than other ethnicities to rate industry and agricultural burning as serious causes of wintertime air pollution.  Approximately seven in ten respondents considered themselves to be knowledgeable about particulate matter (PM) pollution, although only 12% of these felt “very” knowledgeable.

- In terms of overall knowledge of PM pollution, the current results were similar to the results found in the 2007 survey.

- Older respondents were noticeably more knowledgeable than younger respondents, highlighting a need for better informing younger residents about PM pollution. Knowledge about PM pollution also varied by gender, household size, education, ethnicity, and income.



### Wood Burning Activity  By study design, all respondents had at least one wood burning device in their home, the majority (83%) of which included an indoor fireplace.

− The type of wood burning devices found in homes remains unchanged since 2007.

− Only a third of fireplace inserts (36%) and wood or pellet stoves (33%) were EPAcertified or pellet devices and 18% of all respondents had heard about the Change Out Incentive Program.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 4  Fifty percent (50%) of all respondents did not use their wood burning device last winter. Among those who did, the majority burned wood, pellets or manufactured logs less than once a week.

- Significantly fewer wood burning device owners lit up at least once during the 2008-09 winter season (50%) than two years ago (64% in the 2007 survey).

- Fewer burners in 2009 than in 2007 were lighting a fire on “most nights” while more are burning wood “mainly on weekends.” There was a slight drop in the total proportion of burners who lit fires at least once a week (from 58% in 2007 to 54% in 2009), although the difference was not statistically significant.

- Respondents who burned wood, pellets, or manufactured logs this past winter were more likely than those who did not to have access to the Internet, have some post secondary education, and live in wealthier households.  Forty-five percent (45%) said they burned less this past winter compared to a typical winter. Of these, one in five (21%) attributed the decrease to the existence of No Burn days. Combined with the 12% who were motivated to reduce their wood burning behavior because of air quality reasons, and the 2% who cited health-related reason, a total of 35% chose to burn less last winter in order to help decrease wintertime air pollution.

- The 5% who burned more wood, pellets and manufactured logs this winter did so because they felt it was cheaper than the heater, had wood, and felt that this winter was colder than normal.



### Awareness of the Check Before You Burn Program  The outreach messages have been successful in reaching the population: three in four (76%) respondents recalled the No Burn day messages that notified them not to burn wood (unaided awareness).

- Name recognition of Check Before You Burn is also high, with 72% claiming to be somewhat or very familiar with the Check Before You Burn program. However, not everyone was able to accurately describe it.  The program has already achieved successful visibility: when results of the unaided and aided awareness questions were combined, overall, 92% of all respondents were aware of the Check Before You Burn program.  In terms of awareness of different levels of the program, approximately seven in ten respondents (74%) who were aware of Check Before You Burn were also aware of the Burning Discouraged, Burn Cleanly and Stage 2- All Burning Prohibited levels.  However, recognition of the Stage 1 - No Burn Unless Exempt level was much lower, at just over half (54%) of these respondents. Although it could be attributed to the lower level of exposure to that level (there were fewer actual Stage 1 No Burn days), program organizers might want to consider increasing efforts aimed at educating the public specifically about Stage 1 requirements. Alternatively, they might also consider eliminating the Stage 1 category altogether in order to reduce confusion.

- There were very few demographic features that distinguished those aware of each of the levels from those not aware, indicating that the program can continue to be aimed at the general population rather than targeting specific groups of individuals.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 5  The majority of respondents (71%) recognize that it is their responsibility to see if it is permissible to burn wood and nearly half (47%) of the respondents who were aware of the Stage 1 and Stage 2 levels knew that it was illegal to burn manufactured logs on Stage 1 and Stage 2 No Burn days.

- Demographically speaking, there was no difference between those who were aware of the program details, such as the requirement to check before burning and the prohibition of manufactured logs being used in fireplaces during Stage 1 and Stage 2 No Burn days, and those who were not.



### Compliance with the Check Before You Burn Program  Compulsory measures appear to be more effective than voluntary requests: among those who were aware of each level, compliance with all Stage 1 and Stage 2 No Burn days was significantly higher (at nearly 90%) than voluntary compliance when burning was simply discouraged (only 26%).  In terms of the total base of respondents (all of whom owned wood burning devices), 32% complied with every voluntary Burning Discouraged request, 42% complied with every Stage 1-No Burn Unless Exempt directive; and 52% complied with all Stage 2-All Burning Prohibited days. In other words, compliance increased as the level of restrictions increased.

- Compliance was relatively independent of demographics -- there were only three features that distinguished compliers from others within each level of the program: home owners, those older than 35 years of age, and those whose ethnicity was Caucasian were more likely to comply.

- Among EPA-certified device owners only, compliance with Stage 1 No Burn Unless Exempt days was just as high as it was for Stage 2 days, even though they were legally allowed to burn on Stage 1 days. This indicates that there was no advantage to keeping Stage 1 and offers additional support for considering the possible elimination of Stage 1 from the program entirely

.



### Communication Channels  Using news media to announce No Burn days is an effective communication channel: the vast majority of respondents aware of the program heard about the No Burn days on television and radio (85%), followed by newspapers (58%).

- The general news media was also the preferred communication channel identified in the 2007 baseline survey.

- How respondents heard about whether or not they could burn wood was relatively independent of demographic features, indicating once again that the public education campaign can continue to be aimed at the population in general to be effective.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 6 

## P ROJECT 

## B ACKGROUND 

## & O BJECTIVES The mission of the Sacramento Metropolitan Air Quality Management District (SMAQMD) is to protect public health and the environment through innovative and effective programs that aim to improve air quality in Sacramento County. The winter season program runs from November 1 to February 28 and focuses on reducing the amount of fine particulate matter (PM) pollution that is caused by burning wood in fireplaces, woodstoves, and outside fire pits and chimeneas. The Check Before You Burn program has been in place since 2007 and expects residents to inform themselves of the day’s burn status before they consider burning wood, pellets or manufactured logs. It consists of four stages as illustrated in Table 1.

Table 1

P ROGRAM L EVEL D ESCRIPTION # OF O CCURRENCES IN 2008-09 SEASON 1

The public is allowed to burn and the burning of manufactured fire logs is acceptable. 

## 54

Residents are requested to voluntarily not burn and the burning of manufactured logs is acceptable. 

## 28

Burning is prohibited unless EPA-certified wood burning or pellet

devices are used. Manufactured fire logs in fireplaces are banned from use. 

## 10

Burning of any solid fuel, including wood, manufactured logs and pellets, is

prohibited. 

## 28

1 The specific number of occurrences at each program leve l was provided by Marc Cooley, Air Quality Engineer for the Sacramento Metropolitan Air Quality Management District in an e-mail received on April 29, 2009. The number of Burn Cleanly days was calculated by subtracting the num ber of Burning Discouraged, Stage 1 and Stage 2 days from the total number of days in the season (November 1 to February 28).Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 7 The current study was designed to conduct interviews with a representative sample of Sacramento County residents who own an indoor or outdoor wood or pellet burning device to assess public awareness, perceptions and behavior. More specifically, the objectives of this study were to assess:

− Wood burning activity,

− Overall awareness of the Check Before You Burn program,

− Message awareness and whether or not residents distinguish the various stages of the program,

− The effectiveness of the current program,

− The comparison of current results with the 2007 baseline survey results (when appropriate), and

− Relevant demographic information.



## R ESEARCH 

## M ETHODOLOGY Aurora Research Group was contracted to conduct this public opinion research study. For this study, random-digit-dialed (RDD) telephone interviews were completed with a representative sample of 400 Sacramento County residents who owned a wood burning device either inside or outside their home. The margin of error for the study as a whole was + or – 4.9%, at the 95% confidence level. In other words, we are 95% sure that the true population parameters lie within +/- 4.9% of the sample statistics. As an example, if a response category to a question were chosen by 50% of respondents, we would be 95% sure that the true population parameters would be between 45.1% and 54.9% (50.0% +/- 4.9%).
Using the 2007 survey as a first draft, Aurora Research Group designed the questionnaire which addressed the previously-mentioned objec tives, and SMAQMD staff approved the final survey. Most of the questions were asked in a closed-ended format, but two questions were asked as open-ended. Verbatim responses were captured and later categorized for quantitative analyses. (Transcripts of all the verbatim responses will be provided in the statistical binder). The questionnaire was translated into Spanish and 2% of the general base study interviews were conducted in Spanish. The questionnaire was programmed for a CATI system and interviews took approximately 14 minutes on average to administer. Respondents were screened for age (adults at least 18 years old 2 ), ownership of a wood burning device (indoor or outdoor) and to confirm residency in Sacramento County. Interviewing took place between March 26 and April 9, 2009. 

### Methods of Analysis Survey results were analyzed using univariate and bi-variate statistical techniques. The type of analysis depended upon the kind of variable analyzed and the hypotheses that were generated through an examination of the initial results. Unless otherwise noted, frequency

2 In order to speak with someone under 18 years of age, by law we would need to get the parents’ written permission.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 8 percentages cited in this document represent adjusted frequencies, meaning that percentages have been adjusted to account for any non-responses (refusals to answer) or non-qualified responses (questions not answered due to answers to previous questions). Researchers are interested in assessing whether or not the differences in observed percentages between certain groups of individuals are due to chance, or if they represent real differences among the subpopulations. Differences are identified by running statistical analyses and are discussed in the report. Statistical significance within crosstabulation tables was calculated using chi square ( χ 2) statistics. Tests of proportion were used to identify differences in responses between questions or groups of respondents. The level of significance was generally set to a p value of .01. 

### Caveat: The sole purpose of this report is to provide a collection, categorization and summary of public opinion data. Aurora Research Group intends to neither endorse nor criticize the Eastern Research Group, Inc (ERG) or Sacramento Metropolitan Air Quality Management District; or their policies, products, board of directors or staff. The Client shall be solely responsible for any modifications, revisions, or further disclosure/distribution of this report.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 9 

## R ESULTS 

## & C ONCLUSIONS The survey results are organized and presented as follows: wintertime air quality issues, knowledge of particulate matter, wood burning activity, awareness of and compliance with the Check Before You Burn program, and the use of communication channels. Within each section of the report, the current survey results (of wood burning device respondents) are first presented. Next, the current results are compared with the results of the baseline survey conducted in 2007 with a representative sample of Sacramento County residents, who owned or did not own wood burning devices. Finally, any statistically significant group differences due to demographic characteristics (age, income, ethnicity, gender, internet access, the number of people living in the house, home ownership, Air Alert subscription, or education 3 ) are presented. In other words, up to nine separate cross-tabulations will have been conducted for each question. If no group results are described, it is an indication that there were no significant differentiators for a particular question. Unless otherwise specified, the reported results exclude responses of “undecided” as well as refusals. The order of topics presented in the report was chosen as the most logical in terms of meeting the information requirement objectives of the study and does not necessarily conform to the order of the questions within the survey. 

## Wintertime Air Quality Issues Residential Wood Burning Fireplaces

C URRENT R ESULTS

 1 Nearly six in ten (59%) Sacramento County respondents with wood burning devices acknowledged that residential wood burning was a serious cause of wintertime air pollution. Respondents were asked to rate the seriousness of wintertime air pollution caused by residential wood burning fireplaces. Results, excluding responses of undecided/don’t know, are presented in Figure 1. It can be seen that nearly half (46%) thought it was a “somewhat” serious problem and a further 13% said it was a “very” serious problem, for a combined total of 59% of respondents who acknowledged wood burning to be a serious cause of air pollution. However, it can also be seen that about four in ten respondents (41%) thought residential wood burning was not a serious contributor, indicating an ongoing need for public education.

3 The reader is referred to the demographic characteristics se ction near the end of this report to see how the demographics

were categorized.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 10 F IGURE 1

– S ERIOUSNESS OF W INTERTIME A IR P OLLUTION C AUSED BY R ESIDENTIAL W OOD B URNING F IREPLACES

Very Serious

13%

Somewhat Serious

46%
Not at all Serious

41%

C OMPARISON WITH P REVIOUS R ESULTS  2 There appears to be a move in the right direction in terms of improved understanding – 5% more owners of wood burning devices this year than in 2007 rated residential wood burning as a serious cause of wintertime air pollution. However, this is not statistically significant. Two years ago (in 2007) respondents 4 were asked the same question about residential wood burning. Results indicated that, among those who owned a wood burning device, a combined total of 54% respondents in 2007 compared with 59% this year felt that residential wood burning was a serious cause of wintertime air pollution – a 5% increase. These results, although not statistically significant, nevertheless indicate a move in the right direction in terms of public education.

G ROUP D IFFERENCES  3 Significantly more females than males rated residential wood burning as a serious pollution problem. To see if there were any features that distinguished those respondents who said residential wood burning was not a serious cause of wintertime air pollution from those who said it was, results were dichotomized (percent “notat all serious” versus percent “somewhat + very serious”), and a series of chisquare analyses was conducted. Variables included in the analyses included: age, income, education, gender, ethnicity, home ownership, number of people in household, access to the internet, and Air Alert subscription.

4 It should be noted that respondents in 2007 represent ed the general population of Sacramento County and included

both wood burning households as well as non wood burning households. In order to make an appropriate comparison, we recalculated the 2007 results to include only those wh o had a wood burning device. In 2009 we spoke with only respondents who had wood burning devices. 59% combinedSacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 11 Only one feature showed a significant difference: gender. Significantly more females (66%) than males (55%) thought residential wood burning was a serious cause of air pollution. No other demographic characteristics emerged: neither ethnicity, education, age, inter net access, the number of people living in the house, nor whether or not they subscribed to Air Alerts were significant. In other words, older residents were just as likely as younger residents to say wood burning was a serious issue or not; better educated respondents responded similarly to less educated ones, etc. In short, because there were so few demographic differences, a public education campaign dealing with the sources and seriousness of wintertime air pollution that is designed to target residents of all descriptions in Sacramento County should be effective. However, if there were a way of educating males in particular about the pollution problems associated with wood-burning, there could be added benefits, as they demonstrated a significant information gap compared with females. Additionally, although not surprising, those who burned wood, pellets, or manufactured logs (50%) at least once last winter were significantly more likely than those who refrained (33%) to say that wood burning pollution was not a serious contributor to poor wintertime air quality.



### Seriousness of Other Causes of Air Pollution

C URRENT R ESULTS  4 The most serious cause of wintertime air pollution was viewed to be traffic (32% rated it a “very serious” contributor), followed by industry (19%), agricultural burning (15%), and finally residential wood burning (12%). In other words, residential wood burning was not felt to be as serious a problem as traffic in contributing to wintertime air pollution. In addition to residential wood burning fireplaces, respondents were also asked to rate how traffic, industry, and agricultural burning contributed to wintertime air pollution. Results, including responses of undecided/don’t know 5 , are presented in Figure 2. It can be seen, first of all, that traffic was considered to be the most serious cause of wintertime air pollution: approximately one third of respondents (32%) said it was a “very” serious problem. Only 19% of all respondents felt that industry was to blame and 15% felt that agricultural burning was responsible. Residential wood-burning fireplaces were seen as the least serious causes of wintertime air pollution: only 12% rated wood-burning as “very” serious.

5 Typically, in attitudinal surveys, the percentage of undeci ded/don’t know responses is low (between 0% and 4%). We

chose to present the percentage of undecided/don’t know responses in Figure 2 because two questions resulted in a relatively high percentages (8% and 9%) of respondents sayi ng they did not know, a volunteered response rather than an actual response category. These indicate specific areas where more public education on the causes of wintertime air pollution (industry and agricultural burning) could be beneficial.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 12 This indicates that further public education about the causes of wintertime air pollution is warranted. It also attests to the ability of successful public education campaigns to influence public perceptions, insofar as the majority of the general population understands that traffic causes air pollution, regardless of the season. For example, in the Sacramento area, the summertime Spare The Air program has been in effect since 1995 and, although it is not the only source of education, it is now highly recognizable among the local population in general. 6

Figure 2 – Seriousness: Source of Wintertime Air Pollution (includes undecided responses)



### 12 15 19 32

44 38 45 50

39 38 29 15

5 9 8

3

0% 20% 40% 60% 80% 100% Wood-Burning

Fireplaces Agricultural

Burning

Industry

Traffic

Very serious Somewhat serious Not at all serious Undecided

C OMPARISON WITH P REVIOUS R ESULTS  5 Results this year are generally similar to those found in 2007 with among respondents who owned a wood burning device. In order to compare this year’s results with those from 2007, the undecided responses as well as the responses from non wood burning device owners were eliminated and percentages were recalculated. The combined responses of “somewhat” plus “very serious” for each source of wintertime pollution in both survey years are presented in the next chart. It can be seen that, in general, results are very similar – traffic in both years was viewed as the most serious contributor to pollution in both 2007 and 2009. As previously mentioned, there is a 5% increase in the percentage of respondents who said residential wood burning was a serious polluter, but the difference is not statistically significant.

6 See: SMAQMD 2008 Air Quality & Transportation Te lephone Tracking Survey, Aurora Research Group, January 2009:

nearly three quarters (74%) of all respondents were familiar with the summertime Spare The Air program.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 13 Figure 3 – Seriousness: Source of Wintertime Air Pollution by Survey Year (Somewhat + Very Serious Combined; Excludes undecided responses among wood burning device owners)

87

65 61 54 84
69 58 59

0 25 50 75 100

Traffic Industry Agricultural Burning Residential Wood Burning Fireplaces 2007 2009

G ROUP D IFFERENCES  6 Significantly more females than males rated traffic and industry as serious causes of pollution. Caucasians were less likely than other ethnicities to rate industry and agricultural burning as serious causes of wintertime pollution. A total of 27 chi-square analyses (3 questions x 9 demographic features) were run to see if there were any demographic features that distinguished those who rated the causes of wintertime air pollution as serious from those who did not. There were only a few analyses that yielded significant differences:

• females were significantly more likely than males to say that traffic (88% vs. 80%) and industry (75% vs. 62%) were serious causes,

• respondents less than 55 years of age were more likely than those older than 55 to say that industry was a serious cause of winter air pollution (78% vs. 59%), and

• non-Caucasians were more likely than Caucasians to say that industry (82% vs. 64%) and agricultural burning (74% vs. 55%) were serious polluters.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 14 

## Knowledge of Particulate Matter

CURRENT RESULTS  7 Approximately seven in ten respondents considered themselves to be knowledgeable about PM pollution, although only 12% of these felt “very” knowledgeable. The survey asked respondents to rate their level of knowledge of particulate matter or PM pollution, using a three-point scale. Results are shown in the next pie chart. About six in ten respondents (59%) reported being “somewhat” knowledgeable, and a further 12% said they were “very” knowledgeable, for a combined total of 71%. Conversely, 29% did not feel they knew anything about the subject. There were no undecided responses.

F IGURE 4

– K NOWLEDGE OF P M P OLLUTION

Not At All Knowledgeable 29% Very Knowledgeable 12%

Somewhat Knowledgeable 59%

C OMPARISON WITH P REVIOUS R ESULTS  8 In terms of overall knowledge of particulate matter pollution, the current results were similar to the results found in the 2007 survey. The question about knowledge of PM pollution was also asked in the 2007 baseline survey. The next figure compares current results with those from the two years ago (respondents with wood burning devices only). There were no statistically significant differences between the two years – respondents’ levels of knowledge of PM pollution were generally similar. 71% CombinedSacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 15 F IGURE 5

– K NOWLEDGE OF P M P OLLUTION B Y S URVEY Y EAR

15% 12% 63% 59% 22% 29%

0% 20% 40% 60% 80% 100%



### 2007 2009 Not At All Knowledgeable

Somewhat Knowledgeable

Very Knowledgeable

G ROUP D IFFERENCES  9 Knowledge about PM pollution varied by many demographic features – gender, household size, age, education, ethnicity, and income. Older respondents were noticeably more knowledgeable than younger respondents, highlighting a need for better informing younger residents about PM pollution

. Results were dichotomized (responses of “somewhat” knowledgeable werecombined with “very” knowledgeable vs. “not at all” knowledgeable) and chisquare analyses were run to see if there were any demographic features that distinguished those who claimed to be knowledgeable about PM pollution from those who were not. Many differences emerged:

• males were significantly more likely than females to say they were knowledgeable (77% vs. 66%),

• respondents living in households with one or two people were more knowledgeable than households with three or more members (79% vs. 63%),

• older respondents were more knowledgeable than younger ones ( 85% of those 65 and older ; vs. 75% of those between 45 and 64 years of age; vs. 65% of those aged between 35 to 44; vs. only 34% of those under 35 years)

,
• better educated respondents were more knowledgeable than less educated (80% of those with college degrees vs. 60% with high school or some college),

• Caucasian respondents claimed more knowledge than nonCaucasians (78% vs. 51%), andSacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 16 

### • wealthier households were more knowledgeable than less wealthy (79% of those with household incomes of over $100,000 vs. 67% of those in households earning less than $100,000 per year). 

## Wood Burning Activity

Inventory

C URRENT R ESULTS  10 By study design, all respondents had at least one wood burning device in or outside their home, the majority (83%) of which included an indoor fireplace. However, only a third of wood or pellet stoves (33%) or fireplace inserts (36%) were reported to be EPA-certified. The sampling design included only those Sacramento County residents who owned either an indoor or an outdoor wood burning device. As a follow up to the screening question, a series of questions was asked to determine the types of devices owned. Each respondent was asked about each device and, as a result, the results in the following chart do not sum to 100%. It can be seen that most homes (83%) were equipped with an indoor fireplace. Other less common indoor devices include wood or pellet stoves (7%) and fireplace inserts (6%). In terms of outdoor devices, 11% said they had an outdoor pit, and a few respondents reported owning a chimenea (4%).
F IGURE 6

– T YPE OF W OOD B URNING D EVICE ( AMONG ALL RESPONDENTS )

83%

7% 6% 4% 11%

0% 20% 40% 60% 80% 100%

Indoor Fireplace Wood or pellet stove Fireplace insert Outdoor pit Chimenea

All respondents were asked if their wood burning or pellet burning device was certified by the Environmental Protecti on Agency (EPA) or not. Overall, only 11% of all respondents said their device was EPA-certified. The remaining majority either said it was not (62%) or were unsure (27%). Then the results were examined by type of device and these results are shown in the next chart. It can be seen that the one third of the wood burningSacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 17 devices were EPA-certified among fireplace insert owners (36%) and wood or pellet stove owners (33%).

F IGURE 7

– I NCIDENCE OF EPA-C ERTIFIED W OOD B URNING D EVICES

36% 44% 20% 27%

33% 40%

0% 20% 40% 60% 80% 100%

Fireplace Insert Wood/Pellet Stove EPA certified Not certified Don't know/Refused

Respondents were then asked a series of follow-up questions aimed at identifying how someone would find out whether or not a wood burning device was EPA-certified. In general, the majority of those surveyed said they would search online, either by conducting a Google search (73%) or visiting the District’s web site (www.airquality.org) (68%). Almost half said they would visit a retailer in the industry (48%). The results are shown in the next chart. F IGURE 8

T O F IND O UT IF W OOD B URNING D EVICE IS EPA- CERTIFIED , WOULD YOU

...

73%

68%

46%

37%

0% 20% 40% 60% 80% 100% Other contact

Visit retailer Visit www.airquality.org Google search

Friend/family, 6% EPA, 5% County, 4% Manufacturer, 3% The news, 3% Fire department, 3% Property mgr., 2% Local utility, 2% Other, 9%Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 18  11 Only 18% of all respondents had heard about the Change Out Incentive Program. All respondents were asked if they had heard about the Change Out Incentive Program, which provides funding to help Sacramento County residents replace older, more polluting wood stoves and fireplaces with cleaner-burning, EPA-certified or equivalent units. Only 18% said they were familiar with the program, while eight in ten said they had not heard of it.

F IGURE 9

– A WARENESS OF C HANGE O UT I NCENTIVE P ROGRAM ( EXCLUDING 1% OF UNDECIDEDS )

No 82% Yes 18%

C OMPARISON WITH P REVIOUS R ESULTS  12 The type of wood burning devices found in homes that have wood burning devices remains unchanged since 2007. Additional analyses compared the types of wood burning devices found in homes in 2007 and 2009. In 2007, this question was asked of the general population. In order to conduct a year-to-year comparison, the respondents who did not have a wood burning device in the 2007 were excluded and the percentages were recalculated. Results indicated that 80% of those who owned a wood burning device in 2007 had an indoor fireplace, which is very similar to the 83% found in the 2009 survey.


### Usage of Device

C URRENT R ESULTS  13 Fifty percent (50%) of all respondents did not use their wood burning device last winter. Among those who did, the majority burned wood, pellets or manufactured logs less than once a week, while 19% burned at least once a week. Respondents were asked about the frequency of burning wood last winter and the results are shown in the following graph. First of all, it can be seen that half of those surveyed said they did not burn wood, pellets orSacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 19 manufactured logs at all between November 2008 and February 2009. Among those who did, the largest group (30%) burned wood less than once a week (the 19% who said less than once a week plus the 7% who burned mainly on the weekends and the 4% who burned less than once a month). A combined total of 19% burned at least once a week, although only 3% of these recalled burning most nights.

F IGURE 10

– F REQUENCY OF U SE : H OW O FTEN D ID Y OU B URN W OOD , P ELLETS OR M ANUFACTURED L OGS L AST W INTER ?

Once a week 8% Most nights 3%
2-3 times/week 8%

Mainly weekends 7% Not at all

50% Burned wood, pellets, or mfg. logs last winter 50%

Other 1% Less than once a month

4% Less than once a week

19%

 14 Forty-five percent (45%) said they burned less last winter compared with a typical winter. The most common reason (21%) given for this change in behavior was the No Burn days. Combined with the 12% who were motivated to reduce their wood burning behavior because of air quality reasons, and the 2% who cited health related reasons, a total of 35% chose to burn less last winter in order to help decrease wintertime air pollution. In order to better understand the wood burning frequency results, we asked all respondents to compare this year’s level of wood burning activity with that of a typical winter. In other words, did they burn wood, pellets, and manufactured logs less, the same, or more from November 2008 to February 2009 than during a typical winter? As shown in the following chart, 45% of all respondents claimed to have reduced their burning activity last winter compared with a typical year. A similar percentage (41%) said they burned just as much as they have in the past. Only 5% said there had been an increase in amount of their wood burning last winter, and 9% were undecided.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 20 F IGURE 11

– F REQUENCY OF U SE C OMPARED WITH P REVIOUS W INTERS 

### Did You Burn Less, the Same, or More this Past Winter than a Typical Winter? (among all respondents)

45% 41%

5% 9%

0% 20% 40% 60% 80% 100%

Less The same More Don’t know/Refused

Those who burned less were asked to briefly explain why. When the openended responses were categorized, we found that 21% specifically mentioned No Burn days, 12% said they burned less for air quality reasons, and 2% mentioned health concerns. Ten percent said they no longer use their device, while 7% found wood burning to be too expensive now.

F IGURE 12

– R EASONS FOR B URNING L ESS T HIS W INTER T HAN A T YPICAL Y EAR

Air quality

reasons

12%

No longer use it 10% Financial

concerns

7% Other 6%

Not cold enough/No need 16% Health concerns 2% Have small kids 2%

No Burn days 21% Had less wood 2% Device not

working

3% Not at home a lot 5%
Inconvient 6%

Use heater instead

8%Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 21 Some of the categorized verbatim comments for the most common reasons for burning less wood included:
No Burn messages

- “Because of the burn regulations.

- Because of the air quality control messages of non-burn days and we try to follow that.

- Because of the rules we now check before we burn.

- For the contamination and also that we have to call before burning.

- Stupid laws because we burn wood to heat home.

- The Air Alerts and there were a lot of days I couldn’t burn.

- The new laws in effect.

- They put quite a few restrictions on burning, so we're trying to do something else.

- We burned less to do to the wood burning la ws in effect.

- We were watching for which days were okay to burn.

- You have to look before you burn. I just don't have the time.”
Poor air quality

- “Air pollution.

- Because I don't want to put the pollution in the air. I only burn when it rains.

- Due to the contamination to the world environment.

- I'm aware of the carbon footprints burning wood leaves behind.

- I realized it was bad for the environment and haven't burned since.

- Just because we were concerned about air quality.

- Pollution concerns.

- The environment. All the other pollution. Fires in your home are nice but the smoke is going in the air.”

No need/Not as cold this winter

- “I don't think the winter was a harsh this year.

- Global warming, It wasn't that cold this last winter.

- I personally think it was a warmer winter and was gone a lot for work functions.

- It seemed like it was less cold.

- It started to get warmer earlier here.

- It was a warm winter with no rain.

- It wasn't as cold.

- The mild winter.”

Financial reasons

- “Because I couldn't afford it.

- Because I have to pay for it. The Dura-Flame logs are a rare treat for me because of the expense.

- Can't afford it.

- I did not have the money to buy the wood.

- The cost of the wood and the fire burning logs, we were cutting back on money, because it doesn't really heat our house.

- The cost of wood went up.

- The economy, buying the wood.”Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 22 Increased heater use

- “Because I use my heater more.

- My husband died and I don't have anybody to bring in the wood and set the fire. I don't need the fire going because I have the heating.

- I use my heater. Time consuming.

- I use the heater much more than the fireplace because you have to gather the wood. I don't have time even it's cheaper.

- We were using central air.”

Health-related reasons

- “Because my wife is allergic to the smell and to the smoke.

- Sometimes the smell causes headaches.

- I have a lung problem.”

 15 The 5% who burned more wood, pellets and manufactured logs this winter than in the past did so because they felt it was cheaper than using the heater, they had wood, and they felt that this winter was colder than normal. Twenty respondents (or 5% of all respondents surveyed) said they burned more wood this winter than a typical winter. Two respondents were undecided as to why. Reasons given for increasing wood burning activity included: 7

- the lower cost than the heater (5 respondents);

- the availability of wood this year (4);

- the lower temperature (3); and

- other reasons (6).

C OMPARISON WITH P REVIOUS R ESULTS  16 Overall wood burning activity has dropped since 2007, with significantly fewer wood burning device owners lighting up at least once during the wintertime (50%) than in the previous survey (64%). The 2007 results indicated that the majority (64%) of wood-burning device
owners burned wood last winter at least once during the winter months, which is significantly higher than the 50% found in the 2009 survey.

7 These results should be treated with caution as there are so few respondents, which is why the results are not presented in percentages but actual number of respondents.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 23 F IGURE 13

– U SAGE A MONG D EVICE O WNERS (by survey year)

50% 36% 50%

64%

0% 25% 50% 75% 100%

2007 2009 Non-Users (Have, But Do NOT Use) Users (Have & Use)



### *

(* indicates a statistically significant difference)

 17 Further analyses indicated that fewer burners in 2009 than in 2007 were lighting a fire on “most nights” and more are burning wood “mainly on weekends.” The total proportion of users who burned at least once a week dropped slightly from 58% in 2007 to 54%, although the difference was not found to be statistically significant. In terms of wood burning frequency, the results of year-to-year comparison analyses indicated that significantly fewer respondents who used their wood burning devices were lighting a fire “most nights” (6%) in 2009 than in 2007 (16%) and significantly more were burning “mainly on the weekends” (from 5% in 2007 to 15% in 2009). However, in terms of weekly behavior, there was no significant difference found between the total percent of device-owning respondents who said they burned at least once a week this winter (54%) and those who gave a similar response in the 2007 survey (58%).Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 24 F IGURE 14

– F REQUENCY OF W OOD B URNING A CTIVITY ( AMONG W OOD B URNING U SERS E XCLUDING UNDECIDED RESPONSES ) ( BY SURVEY YEAR )

16% 21%

16% 31%

11% 15% 16% 15%

5% 32%

6% 17%

0% 10% 20% 30% 40% 50%

Most nights * 2-3 times a week Mainly on weekends

* Once a week Less than

once a week Once a month or

less 2007 2009

(* indicates a statistically significant difference)

G ROUP D IFFERENCES  18 There were only three demographic characteristics that distinguished burners from non-burners: more burners than non burners had access to the Internet , some post secondary education , and lived in wealthier households. In order to try and characterize “burner” households in Sacramento County from those that did not burn any wood, pellets or manufactured logs last winter, a series of chi-square analyses were conducted, including all demographic features. Results indicated that those who were categorized as “burners” were significantly more likely to have:

− Access to the Internet (52% vs. no access, 24%),

− Had some post-secondary education (50% vs. not, 38%), and

− Household earnings of at least $50,000 (54% vs. lower, 34%).Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 25 F IGURE 15

– D ISTINCTIVE D EMOGRAPHIC C HARACTERISTICS OF “B URNERS ” VS

. N ON -B URNERS (2009 S URVEY R ESULTS )

76%

62%

38% 66%

34% 52% 50% 54%

24% 50% 48% 48%

0% 20% 40% 60% 80% 100%

No access Internet

access High school At least some college Income under $50,000 Income $50,000 or

more Non-burners Burners

Additional analyses compared the responses of “burners” and “non-burners” in terms of why they burned less this winter compared with a typical year.Results indicated that “burners” were significantly more likely than nonburners to say that it wasn’t cold enough (17% vs. 3%). 

## Awareness of the Check Before You Burn Program

Unaided Awareness: Aware of No Burn Notices

C URRENT R ESULTS  19 The outreach messages have been successful in reaching the population: three in four (76%) respondents recalled the No Burn day messages that notified them not to burn wood. When respondents were asked if they had heard, read, or seen any news stories, radio commercials, or e-mails informing them not to use their wood burning fireplaces or outdoor fire pits because of poor air quality last winter, the majority recalled some form of exposure (76%). One in five respondents (21%) said they did not recall hearing or seeing a No Burn message and 3% were undecided for a total of 24% who were unaware. Those who were aware of the No Burn messages were asked if they decided not to burn wood because of them. About half said they had not reduced their burning (48%) or were undecided (3%). The other half (49%) reduced the number of fires they burned last winter because of the notices they heard or saw. This translates into 37% of all respondents that reduced wood burning because of the publicity , as shown in the next graph.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 26 F IGURE 16

– A WARENESS OF M ESSAGES AND C HANGE IN B EHAVIOR ( AMONG ALL RESPONDENTS )

Unaware or Undecided 24%

NonResponse

(3%) Aware, but still burned wood (36%) Aware, and

reduced

wood burning (37% of total population) Aware of No Burn messaging 76%



### Unaided Awareness: Familiarity with the Check Before You Burn Program

C URRENT R ESULTS  20 Name recognition of Check Before You Burn is also high, with 72% claiming to be familiar with the Check Before You Burn program. However, not everyone was able to accurately describe it. All respondents were asked about their familiarity with the Check Before You Burn program, using a three-point scale. This question was designed to evaluate respondents’ unaided awareness of the program name. Results, as shown in Figure 16, are fairly evenly split among the three responses: 38% said they were very familiar with the program and 34% said they were “somewhat” familiar with it for a total of 72% who claimed to be familiar. The remaining 28% admitted to being not at all familiar with the Check Before You Burn program.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 27 F IGURE 17

– U NAIDED A WARENESS : F AMILIARITY WITH THE C HECK B EFORE Y OU B URN P ROGRAM ( EXCLUDING U NDECIDED R ESPONSES )

Very Familiar

38% Not At All Familiar

28%

Somewhat

Familiar

34%

Verification of what was actually known about the program was determined by asking those who said they were “s omewhat” or “very” familiar with the Check Before You Burn program to briefly describe it. Responses were recorded and then categorized and coded. Overall the vast majority accurately described aspects of the program, but a few responses indicated some misperceptions. Complete transcripts of all descriptions will be available in the statistical report.
41% mentioned the need to check to see if wood burning is permitted on a particular day . A sample of some of the verbatim responses includes:

- “Basically, you check in the newspaper to see if you can burn or not. There are two conditions to check. There are volunt ary restrictions and then there are days you can not burn at all unless it is your only heat source.

- Check the paper or web site if it’s a burn day or not.

- Everyday we check the paper to see if it is burn or no burn day.

- I call before I burn.

- I know that you can look in the paper or online to see if burning is allowed those days.

- I know there is an alert system on the news that tells you when not to burn.

- I have the number in my cell phone and I would call before I burn.

- On a particular day somebody decides whether or not we can have a fire due to the quality of air and we have to call a number to find out if we can have a fire that night, or we can check online too.

- That we have to call to see if we can use our chimenea.

- We are supposed to check everyday before we do any burning that's allowed by the county to help the air quality.

- We usually check online and I think there is also a number you can call and they tell you if it is a non-burn day. We watc h a morning news show and it mentions it too. Our neighbors often talk about it.

- You can be fined if you're burning on a day you're not supposed to burn, and you can check online to find out. 72% combinedSacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 28 23% mentioned the no burn days in general. A sample of these verbatim responses includes:

− “Days that recommended not to burn.

− Depending upon the air quality that day is the condition whether we can burn wood on that day or not. I would assume somebody makes that decision whether we can burn a fire or not. I get that information in the paper daily.

− If it is limited or restricted to burn. Or no burning what so ever.

− On certain burn you can burn and some days you can't.

− Some days you can burn and some you can’t. And some days it is just discouraged.

− That there were days that we should no t burn and days that we are allowed to burn. I tried to abide by them but there were days when it was very cold and it was more economical to burn than to use other heating sources.

− There's a day that you can burn and if ai r quality is bad you can't burn. There's an index or something.

− There are no burn days.”
17% indicated that the program told them when to burn and not to burn. They mentioned the messages they see on the news or read in the newspaper, although didn’t specifically say they had to check before they burned wood.

− “We were notified that there are certain day s that we can burn, and they notify us if air quality is poor.

− Control the smog in the area. On the new s it tells you if you can burn on that day.

− I know that the news on ce rtain channels they will gi ve you an update on the status.

− On the news, they tell when to burn and not to burn.

− That they notify when you’re supposed to burn and not to burn.

− That there is a warning that's issued in newspapers, TV, and radio.

− They announce on the TV what days to burn or not to burn wood.

− They check the day before to put up a sign or notice for the next day to let people know about when it's okay to burn.

− They forecast what they think the air qu ality is going to be for that day and then tell us whether we can burn or not. They give us a scale. I don't feel they are honest about it.

− They have notices on the nightly news on TV telling you whether or not it's okay to burn.

− They just tell us day to day whether we can burn or not.

− They just tell you what certain days are high danger for that day, so they tell you not to burn.

− They put it in the Bee that you shouldn't burn on a particular day because of the air quality.

− They put it in the paper everyday with the weather forecast, if you can burn or not.

− They tell you on T.V. not to burn.

− Well I just know that they advertise it on the news and when they do, I don’t burn fires. I don’t check online or anything.”Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 29 However, 3% may feel that the program is optional and voluntary. A sample of these comments includes:

− “Days that recommended not to burn.

− I heard that there are some burn days t hat they suggest you don't burn because of air quality. I believe it showed in the paper those days.

− It is an optional program with notices in the local newspapers regarding whether or not fire burning is recommended on a particular day. It would be helpful if they put the info about it in the same place in the news paper (The Sacramento Bee).

− The Sacramento Bee and the TV Stations & NPR mention when there is a no burn day. Right now the program is voluntary and they are probably not going to be able to enforce it and there is a waiver if you are poor.

− I know the program is out there, and they are trying to restrict burning because it causes particulates in the air.

− The city encourages non-burning and certain days of the week and an incentive for converting to a better solution to eliminate the harmful stuff.

− It provides educational information about knowing the air quality before you burn.”
A few (2%) had misconceptions about the focus of the No Burn program. A sample of those comments includes:

− “It is about burning of substances and what it produces.

− Make sure to keep the chimney clean and check the shaft and make sure it is open.

− You are supposed to have your flue check ed, not sure of the frequency. Make sure the flue is open.

− You can burn leaves and garden waste and you are supposed to check to see if it is okay to burn.

− You have to get a permit for outside fires fo r burning leaves, trash. Anything you burn has to be in a container.”
6% said they were familiar with the program, but when asked to describe it, they simply gave (mostly negative) commentary. A sample of these comments includes:

− “That they are nosy and it is none of their damn business.

− I just know that there are times when the air pollution is really bad.

− I don't believe it means anything. I think it is a bunch of boohoo. There are not that people that burn uncontrollably, to a poi nt that would matter to the air quality control.

− I just don't burn anymore, air quality doesn't allow us. It's just not fair.

− It is a pain in the rear end.

− It sucks. We have more cars blowing out more smoke than fireplaces burning for a couple of hours.

− The conditions that need to prevail in order to be looked at to see what is going to build up in the air so as long as we have a breeze i don't worry about it.

− The risk of burning and not burning.” 

### Aided Awareness: Familiarity with the Check Before You Burn Program

C URRENT R ESULTS All respondents, regardless of their familiarity with the program were then read the following description:Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 30 You may or may not have heard that in Sacramento County, it is now law that from November to February residents and businesses are prohibited from using indoor or outdoor fireplaces, wood stoves, fire pits and chimeneas that burn wood, pellets, manufactured logs or any other solid fuel on days when air quality is forecast to be unh ealthy to breathe. It is your re sponsibility to Check Before You Burn, to see if it is permissible to light a fire.
 21 Among respondents who previously were not at all familiar with Check Before You Burn, the majority said it sounded familiar to them after being read a description. The 28% of respondents who were previously unfamiliar with the program were asked if it sounded it familiar after hearing the brief description. Six in ten (60%) said it did.

F IGURE 18

– A IDED A WARENESS : F AMILIARITY WITH P ROGRAM AFTER H EARING A D ESCRIPTION (A MONG THOSE NOT FAMILIAR IN THE UNAIDED QUESTION )

No 40% Yes 60%

 22 The program has already achieved successful visibility: when results of the unaided and aided awareness questions were combined, overall, 92% of all respondents were aware of the Check Before You Burn program. Overall awareness of the program was measured through a combination of responses. To be considered “aware”, a respondent: remembered seeing or hearing notices not to burn; or said they were somewhat or very familiar with the Check Before You Burn program (unaided awareness); or said that the program sounded familiar to them after hearing a description of it (aided awareness). Combining the results of these unaided and aided awareness questions indicated that the vast majority of all 400 respondents (370 respondents, or 92%) were aware of the program. Only 8% were still unfamiliar (or undecided or refused to answer) with the Check Before You Burn program, as shown in the next chart.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 31 F IGURE 19

– O VERALL A WARENESS : U NAIDED + A IDED A WARENESS (A MONG A LL 400 SURVEY RESPONDENTS )

Unaware, Undecided,

or Refused 8% Aware

92%



### Awareness of Different Levels of the Program

C URRENT R ESULTS  23 Approximately seven in ten respondents (74%) who were aware of Check Before You Burn were also aware of the Burn Cleanly, Burning Discouraged and Stage 2- All Burning Prohibited levels. However, recognition of the Stage 1- No Burn Unless Exempt level was much lower, at just over half (54%) of these respondents. Although it could be attributed to less exposure to that level (there were fewer actual Stage 1 days), program organizers might want to consider increasing efforts aimed at educating the public specifically about Stage 1 requirements. Alternatively, they might also consider eliminating the Stage 1 category altogether in order to reduce confusion. The 370 respondents who were aware of the Check Before You Burn program (either in the aided or unaided questions) were asked more specifically about their awareness of each of the four levels in the program, using the scale: not at all, somewhat, or very aware. The four levels of the Check Before You Burn program include:

 Burn Cleanly . The public is allowed to burn and the burning of manufactured fire logs is acceptable at this level.

 Burning Discouraged. This is when residents are requested to voluntarily not burn.

 Stage 1 – No Burn Unless Exempt . At this level, burning is prohibited unless EPA-certified wood burning or pellet devices are used. First time violations will result in a $50 fine or a requirement to take a compliance course. Fines for subsequent violations will be higher.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 32  Stage 2 – All Burning Prohibited. At this level the burning of any solid fuel, including wood, manufactured logs and pellets, is prohibited with the same penalties as in Stage 1. Results of respondents’ awareness of each of the four levels are shown in the next chart. It can be seen, first of all, that the voluntary restriction (Burning Discouraged) is the most widely recognized level – 74% of the respondents who were aware of Check Before You Burn were also aware of the Burning Discouraged level (combined responses of “somewhat” and “very” aware). This was followed closely by awareness of the Burn Cleanly level (71%), and Stage 2 – All Burning Prohibited level (67%). However, it can also be seen that awareness of Stage 1 – No Burn Unless Exempt is significantly lower than the other three levels – only 54% of these respondents were aware of this level. It is recommended that further efforts be made to educate the population about this particular level. Alternatively, they might also consider eliminating the Stage 1 category altogether in order to reduce confusion. It should be noted that this lower level of awareness of Stage 1 No Burn days may be due to the decreased exposure to this particular level. There were fewer Stage 1 No Burn days in the 2008-09 season than Stage 2 and Burning Discouraged: only 10 days compared with 28 Stage 2 No Burn days and 28 Burning Discouraged days.
F IGURE 20

– A WARENESS OF S PECIFIC P ROGRAM L EVELS (A MONG THOSE AWARE OF C HECK B EFORE Y OU B URN ; EXCLUDING UNDECIDED RESPONSES )

43% 43% 32% 44% 28% 31%
22% 23% 29% 26% 46% 33%

0% 20% 40% 60% 80% 100%

Burn Cleanly (71% aware) Burning Discouraged (74% aware) Stage 1 No Burn Unless Exempt (54%

aware) * Stage 2 All Burning Prohibited (67% aware) Very aware Somewhat aware Not at all aware

* indicates a statistically significant di fference from the other three levels

G ROUP D IFFERENCES  24 There were very few demographic features that distinguished those aware of each of the levels from those not aware, indicating that the program can continue to be aimed at the general population rather than targeting specific groups of individuals.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 33 Results were dichotomized (responses of “somewhat” aware were combined with “very” aware vs. “not at all” aware) and 36 chi-square analyses (4 levels x 9 demographic variables) were run to see if there were any demographic features that distinguished those who were aware of each level from those who were not. Results indicated that in general, awareness of the four levels of the program did not vary by demographics – males were just as aware (and unaware) as females, better educated respondents were just as aware as less educated, households with high incomes were just as aware as those with lower incomes, those living with alone were just as aware as those living with other people, etc. The few differences that did emerge indicated that:

• owners were significantly more aware of all four levels than were those who rented,

• those older than 35 years were significantly more aware of the Burn Cleanly and Burning Discouraged levels than were those younger than 35 (73% vs. 50%; and 77% vs. 47% respectively),

• those who had Internet access were significantly more aware of the Stage 2 – All Burning Prohibited level than those who did not have access (70% vs. 46%), and

• Caucasians were significantly more aware of the Burn Cleanly level than non-Caucasians (75% vs. 59%).

Additional analyses were conducted among the small group of respondents who reported having an EPA-certified wood burning device to see if awareness of each program level varied. In general, awareness among those with EPA-certified devices was similar across the four program levels. Any differences shown in the chart below were not found to be statistically significant, which may be attributed to the small size of this subgroup: only 45 respondents.
F IGURE 21

– A WARENESS OF S PECIFIC P ROGRAM L EVELS (A MONG THOSE WITH AN EPA- CERTIFIED D EVICE ; EXCLUDING UNDECIDED RESPONSES )

56% 60% 57% 60 % 33% 28% 19% 21 % 11% 12% 24% 19 %

0% 20% 40% 60% 80% 100%

Burn Cl eanl y Burn ing Discou raged Stage 1 No Burn Unl ess

Exempt Stag e 2 All Bu rning Pro hibi ted Very aware Somewhat aware Not at all awareSacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 34 

### Knowledge about Different Aspects of the Program

C URRENT R ESULTS

 25 The majority of respondents (71%) recognize that it is their responsibility to see if it is permissible to burn wood. All 400 respondents were asked: “Before today, did you know that it was your obligation to check to see if you were permitted to burn wood on any given day or night during the winter?” Seventy-one percent said they did, while the remaining 29% were unaware of this fact.
F IGURE 22

– A WARENESS OF P ERSONAL O BLIGATION TO C HECK B EFORE B URNING (A MONG A LL 400 SURVEY RESPONDENTS )

No 29% Yes 71%

 26 Nearly half (47%) of the respondents who were aware of the Stage 1 and Stage 2 levels knew that it was illegal to burn manufactured logs on Stage 1 and Stage 2 No Burn days. Respondents who were aware of the Stage 1 and Stage 2 levels were asked if they knew that it was illegal to burn manufactured logs on Stage 1 and Stage 2 No Burn days. More than half (53%) were unaware of this fact, while the remaining 47% said they knew this information.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 35 F IGURE 23

– A WARENESS OF R ULE A GAINST B URNING M ANUFACTURED L OGS ON S TAGE 1 AND S TAGE 2 N O B URN D AYS (A MONG T HOSE WHO WERE AWARE OF THE S TAGE 1 AND S TAGE 2 L EVELS )

No 53% Yes 47%

G ROUP D IFFERENCES  27 Demographically speaking, there was really no difference between those who were aware of the program details from those who were not.
There were no demographic characteristics that distinguished those who knew it was their responsibility to check before they burned from those who did not. Similarly, no statistically significant differences were found in the demographic responses between those who knew it was illegal to burn manufactured logs on Stage 1 and Stage 2 No Burn days and those who did not. 

## Compliance with the Check Before You Burn Program
Among Those Aware at Each Level

C URRENT R ESULTS  28 Compulsory measures appear to be more effective than voluntary requests: among those who were aware of each level, compliance with all Stage 1 and Stage 2 No Burn days was significantly higher (at nearly 90%) than voluntary compliance when burning was simply discouraged (only 26%). Those who were aware of each of the levels higher than the Burn Cleanly level were asked how frequently they complied with the notices during the past winter. It can be seen in the following chart that respondents were more willing to comply with the law (illegal to burn) than with a request (burning discouraged), with 87% saying they complied with all Stage 1 No Burn days and a similar (89%) percent indicating compliance with Stage 2 No Burn days, compared with only 26% who said they chose not to burn each time burning was discouraged. Conversely, the percentage of respondents who said they did not comply with any burn days was highest when burning was discouraged (55% of respondents) than when it was compulsory – only 7% defied the Stage 1 No Burn days and 6% defied the Stage 2 No Burn days.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 36 F IGURE 24

– C OMPLIANCE WITH THE C HECK B EFORE Y OU B URN P ROGRAM BY S PECIFIC L EVEL (A MONG THOSE AWARE OF EACH LEVEL ; EXCLUDING UNDECIDED RESPONSES )

26% 19% 5% 5%

55% 7% 6%

89% 87%

0% 20% 40% 60% 80% 100%

Burning Discouraged Stage 1 No Burn Unless Exempt Stage 2 All Burning Prohibited All of them At least one, but not all None



### Among the Total Respondent Base

C URRENT R ESULTS  29 In terms of the total base of respondents (all of whom owned wood burning devices), 32% complied with every voluntary Burning Discouraged request, 42% complied with every Stage 1-No Burn Unless Exempt directive; and 52% complied with all Stage 2-All Burning Prohibited days. In other words, compliance increased as the level of restrictions increased. In order to generalize to the entire base of respondents, compliance within each level was re-calculated: those who said they either complied with all burn days or at least one of them were coded as “compliers”; all other respondents (those who did not comply, those who were undecided, those who were unaware of each level and those who refused to answer) were combined and coded as “non compliers.” When based on the total population of respondents, it can be seen in the next chart that of the total base of respondents, all of whom owned wood-burning devices, 52% complied with all Stage 2 No Burn days this past winter and a further 3% complied with at least one of them, indicating that over half of the population complied with the mandatory Burning Prohibited level of the Check Before You Burn program. It can also be seen that compliance with all notices increased as the restrictions increased. In other words, significantly fewer respondents refrained from burning on all voluntary Burning Discouraged days (32%) than with all Stage 1 No Burn days (42%); and in turn, significantly more respondents complied with all Stage 2 No Burn days (52%).Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 37 F IGURE 25– C OMPLIANCE WITHIN THE T OTAL G ROUP OF R ESPONDENTS (I NCLUDES A LL 400 R ESPONDENTS )

32% 42% 52% 12% 2% 3% 56% 56% 45%

0% 20% 40% 60% 80% 100%

Burning Discouraged (44% compliers) Stage 1 No Burn Unless Exempt (44% compliers) Stage 2 All Burning Prohibited (55% compliers) Complied with all Complied with at least one but not all Non-complier

G ROUP D IFFERENCES  30 Compliance was relatively independent of demographics -- there were only three features that distinguished compliers from others within each level of the program: home owners, those older than 35 years, and those whose ethnicity was Caucasian were more likely to comply. Respondents who complied all the time were combined with those who complied at least once within each of the three program levels and analyses comparing compliers versus all other respondents were run to determine if there were any distinguishing demographic features. In total, 27 analyses were run (3 levels x 9 demographic variables). Results indicated that, in general, those who were aware of and complied with each of the three Check Before You Burn levels had the same demographics as those who did not comply – both males and females complied or did not with Burning Discouraged, or Stage 1 or Stage 2; less well educated respondents were just as likely (or not) to comply as those better educated, households with low incomes were just as likely to comply within each level as those with higher incomes, those living with alone were just as likely to comply as those living with other people, etc. The few differences that did emerge indicated that more compliers within each of the three levels:

• owned their homes than rented them:

 Burning Discouraged - (47% owners vs. 31% renters),

 Stage 1 – (47% vs. 28%), and

 Stage 2 – (59% vs. 42%),Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 38 • were over 35 years of age than under 35 years of age:

 Burning Discouraged - (47%% older vs. 21% younger),

 Stage 1 – (46% vs. 21%), and

 Stage 2 – (58% vs. 30%),

• were Caucasian than non-Caucasian

 Burning Discouraged - (49% Caucasian vs. 30% nonCaucasian), and

 Stage 2 – (61% vs. 41%).

 31 Among EPA-certified device owners, compliance with Stage 1 No Burn days was just as high as it was for Stage 2 days, even though they were exempt and legally allowed to burn on Stage 1 No Burn days. Additional analyses were conducted among the small group (45 respondents) who reported having an EPA-certified wood burning device to see if their level of compliance varied by program level. It can be seen in the following chart that EPA-certified device owners were just as likely to comply with Stage 1 No Burn days when they were exempt as they were with Stage 2 days, indicating that there is no advantage to keeping the Stage 1 No Burn Unless Exempt level in the program 8 . They were also significantly more likely to comply with all Stage 1 and Stage 2 No Burn days (both 84%) as they were with Burning Discouraged days (55%).
F IGURE 26

– C OMPLIANCE WITH S PECIFIC P ROGRAM L EVELS

(A MONG THOSE WITH AN EPA- CERTIFIED D EVICE ; EXCLUDING UNDECIDED RESPONSES )

55% 24% 6% 6% 21% 9% 9%

84% 84%

0% 20% 40% 60% 80% 100%

Burning Discouraged Stage 1 No Burn Unless Exempt Stage 2 All Burning Prohibited Complied with all Complied with at least one, but not all Non-complier

8 Given that there is already some confusion about Stage 1 qua lifiers, this analysis offers additional support for possibly

eliminating the Stage 1 level from the Check Before You Burn program.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 39 

## Communication Channels Among Those Aware of the Check Before You Burn Levels

C URRENT R ESULTS  32 Using news media to announce No Burn days is an effective communication channel: the vast majority of respondents aware of the program heard about the No Burn days on television and radio (85%), followed by newspapers (58%). Respondents who were aware of any of the Check Before You Burn levels (i.e. those who said they were either “somewhat” or “very” aware of the Burn Cleanly, Burning Discouraged, Stage 1-No Burn Unless Exempt, or Stage 2- All Burning Prohibited levels) were asked how they heard about the No Burn days during the past winter. The exact wording of the question was: I am going to read you a list of possible ways that residents could have found out about the air quality and whether or not they could burn wood on a particular day. I’d like you to tell me how you might have heard about the No Burn days during this past season. Did you ... Results are presented in the next chart. It can be seen that the vast majority of these respondents (85%) heard about No Burn days via television and radio. Nearly six in ten (58%) read about the No Burn days in the newspaper, and approximately one fifth (21%) saw the information on a web site. Eight percent (8%) called a telephone number to hear a recorded message and only 2% of these respondents were Air Alert subscribers – that is, those who received either an e-mail or a text message from the District. Further efforts could be made to increase the number of Air Alert subscribers and to better publicize the phone-in telephone number. Of those who said they learned about the No Burn days some other way, the most frequently mentioned channel was via word of mouth, through friends, neighbors and colleagues. F IGURE 27

– C OMMUNICATION C HANNELS (A MONG T HOSE A WARE OF P ROGRAM , EXCLUDING UNDECIDED RESPONSES )

85

58

21

15

8

2 0 20 40 60 80 100

See or hear it in the general news media, such as TV and radio?

Newspaper

See the information on a website?

Other? (Heard about it by word of mouth)

Call a telephone number and listen to a recorded message?
Receive an email or a text on your cell phone?Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 40 C OMPARISON WITH P REVIOUS R ESULTS  33 The general news media was also the preferred communication channel identified in the 2007 baseline survey. In the 2007 baseline survey, respondents were read a list of possible ways that residents could find out about whether or not they could burn wood in the event that a mandatory no-burn rule was adopted, and were asked to rate each one in terms of how effective it would be for them personally, using a four-point scale: poor, fair, good, or excellent. Results indicated that 84% of respondents said they would prefer to hear about No Burn days (combined ratings of “good” plus “excellent”) via the general news media of television, radio, and newspapers; the same percentage who actually did hear about the No Burn days through television and radio this year. However, the percentages of respondents who, in 2007, thought they would prefer to visit a web site (43%), receive an e-mail (41%), or call a telephone number (40%) were in all cases substantially higher than the percentage of respondents in 2009 who actually used these means to find out about No Burn days (21%, 2%, and 8% respectively).

F IGURE 28

– 2007 H YPOTHETICAL P REFERRED C OMMUNICATION C HANNELS V S

. 2009 A CTUAL U SE

84

43

41

40 85

21

2

8 0 20 40 60 80 100

General news media

Posting information on a web site

Receive an e-mail or a text on your cell phone

Call a telephone number and listen to a recorded message

2007 (Good + Excellent) 2009 (Yes)

G ROUP D IFFERENCES  34 How respondents heard about whether or not they could burn wood was relatively independent of demographic features, indicating that the public education campaign can continue to be aimed at the population in general to be effective. Chi square analyses were run to determine if there were any demographic features that distinguished those who heard about the No Burn days via television and radio, via newspaper, or via the web site from those who did not. 9

9 The percentages who heard the messages via email or by calling a telephone number were too small to be analyzed by

demographics.Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 41 Results indicated that in general, the communication channels respondents used to hear about whether they could burn wood were independent of demographic features – both males and females learned about the No Burn days via the general media, newspaper, or via the web site; less well educated respondents were just as likely to have used the three channels as those better educated, households with low incomes were just as likely to have used the channels as those with higher incomes, those living with alone were just as likely as those living with other people, etc. The few differences that did emerge indicated that:

• those who learned about the No Burn days from the newspaper were more likely to own their homes (61%) than to rent them (40%); and to be older than 45 years of age (62%) than younger than 45 years (42%), and

• those who accessed the web site were more likely to be younger than 45 years of age (34%) than those aged between 45 and 64 years (21%) or 65 years and older (8%).


## Demographics

 35 A plurality of respondents who owned wood burning devices and lived in Sacramento County were: females who live with one to three other people in a home they own. They hold a college degree, have access to the Internet, are non-subscribers to Air Alert, are at least 45 years of age, self-identify as Caucasian, and have an annual household income of at least $50,000. Table 2 provides the reader with the respondent demographics from the interviews conducted with the sample of Sacramento county residents who had wood burning devices either inside or outside their homes.

Table 2

G ENDER P ERCENT
Female 52%
Male 48%
Total 100%

S URVEY L ANGUAGE P ERCENT English 98% Spanish 2%
Total 100%Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 42 A IR A LERT S UBSCRIPTION P ERCENT
No 94%
Yes 4% Non-response 2%
Total 100%

E THNIC B ACKGROUND P ERCENT
African-American 5%
Asian/Pacific Islander 6%
Caucasian 70% Hispanic/Latino 11% Something else 3% Non-response 5%
Total 100%

H OUSEHOLD S IZE P ERCENT
Live alone 15%
Two members 35%
Three members 18%
Four members 20%
Five or more members 11% Non-response 1%
Total 100%

A CCESS TO THE I NTERNET P ERCENT
Yes 89%
No 10% Non-response 1%
Total 100%Sacramento Metropolitan Air Quality Management District 2009 Wood Burn Awareness Survey Final Results Report May, 2009

Dawn Morley Chavero & Naomi E. Holobow, Ph.D. Page 43 H OME O WNERSHIP P ERCENT
Own 86%
Rent 16% Non-response 1%
Total 100%

E DUCATION P ERCENT High school or less 17% Some college 24%
Trade/vocational school 4% College degree 37% Post-graduate degree 16% Non-response 2%
Total 100%

A GE P ERCENT
18-24 3%

25-34 8%

35-44 17%

45-54 26%

55-64 21%
65 and older 23%
Refused 2%
Total 100%

2008 A NNUAL H OUSEHOLD I NCOME P ERCENT

Under $20,000 6% $20,000 to $49,999 16% $50,000 to $99,999 37% $100,000 or more 27% Non-response 14%
Total 100%APPENDIX D
STI Final Report Evaluation of SMAQMD’s Check Before You Burn Progra m

(See May 2009 Board Letter)

# EVALUATION OF SACRAMENTO METROPOLITAN AIR QUALITY MANAGEMENT DISTRICT’S “CHECK BEFORE YOU BURN” PROGRAM

FINAL REPORT STI-909014.06-3612-FR2

By: Clinton P. MacDonald Kenneth J. Craig Jennifer L. DeWinter Adam N. Pasch Steven G. Brown Neil J. M. Wheeler Sonoma Technology, Inc. 1455 North McDowell Blvd., Suite D Petaluma, CA 94954-1169

Prepared for: Sacramento Metropolitan Air Quality Management District 777 12th Street, 3rd Floor

Sacramento, CA 95814

May 6, 2009 Updated August 24, 2009

### iii ACKNOWLEDGMENTS

The authors of this report thank several Sonoma Technology, Inc. st aff who contributed to this project: Hilary Hafner and Paul Roberts for their scientific advice and guidance; Charley Knoderer for creating the air qual ity climatology plots used in th is report; and Steve Reid and Sandy Smethurst for editing this report.

### v TABLE OF CONTENTS

Section Page

ACKNOWLEDGMENTS ............................................................................................................. iii LIST OF FIGURES ................................................................................................................ ...... vii LIST OF TABLES................................................................................................................. ........ ix

1. INTRODUCTION.............................................................................................................. 1- 1

2. SUMMARY OF FINDINGS ............................................................................................. 2-1

3. METHODS..................................................................................................................... .... 3-1 3.1 Program Effectiveness.............................................................................................. 3-1 3.2 CMB Modeling......................................................................................................... 3-6 3.2.1 CMB Background......................................................................................... 3-7 3.2.2 Data Processing and Quality Assurance....................................................... 3-7 3.2.3 Source Profiles and Model Certainty ......................................................... 3-10 3.3 Season Representativeness ..................................................................................... 3-12 3.4 No-burn Thresholds................................................................................................ 3-13 3.5 Build-up Day Rule.................................................................................................. 3-13

4. FINDINGS .................................................................................................................... ..... 4-1 4.1 Program Effectiveness.............................................................................................. 4-1 4.2 CMB Modeling......................................................................................................... 4-4 4.3 Season Representativeness ..................................................................................... 4-11 4.4 No-burn Thresholds................................................................................................ 4-14 4.5 Build-up Day Rule.................................................................................................. 4-15

5. REFERENCES.................................................................................................................. . 5-1

APPENDIX A: ADDITIONAL FINDINGS ON THE EFFECTIVENESS OF THE BURNING CURTAILMENT PROGRAM TOWARD MEETING THE NAAQS A-1

### vii LIST OF FIGURES

Figure Page

3-1. Example of surface and 500-mb weather patterns used to determine matching d day-pairs...................................................................................................................... .... 3-4
3-2. Reconstructed mass of sample s during winter 2008/2009.............................................. 3-8
3-3. Average reconstructed concentrations plus the difference between the total reconstructed and measured concentrations for all winter 2008-2009 sample dates...... 3-9

3-4. Allocation of annual wood-bur ning emissions by month............................................. 3-16
3-5. BlueSky Gateway model grid in the Sacramento County region ................................. 3-17
4-1. Average hourly PM 2.5 

### time series for the Stage 2 and Stage 1 day pairs........................ 4-3
4-2. Source contribution estimates for each pr ofile averaged across all winter 2008-2009 sample dates, all 2008/2009 Stage dates, St age 1 days with matching no-restriction days, Stage 2 days with matching no-rest riction days, no-rest riction days with meteorology similar to Stage days.................................................................................. 4-5

4-3. Percent of contributions to total calculated concentra tions for the eucalyptus, oak, almond, and combined wood burning source profile...................................................... 4-8
4-4. Number of days that PM 2.5 

### concentrations exceeded the NAAQS from the 2004/2005 through 2008/2009 winter seasons, an d estimated number of days PM 2.5 

### concentrations would have exceeded the NAAQS without the CBYB program........................................................................................................................ . 4-12
4-5. Number of meteorologica lly conducive days for high PM 2.5 

### concentrations for the 2004/2005 through 2008/2009 winter seasons ............................................................. 4-12
4-6. Distribution of peak daily PM 2.5 

### AQI by category for the 2008/2009 season observed and estimated if th ere was no CBYB program.............................................. 4-13
4-7. Average PM 2.5 

### pollution episode length for the 2004/2005 through 2008/2009 winter seasons ............................................................................................................... 4 -13
4-8. Time series of BlueSky Gateway PM 2.5 

### concentrations in northeast Sacramento County for November 29 through Novemb er 30, 2008, for one-day and two-day residential wood-burning curt ailment scenarios ........................................................... 4-17
4-9. Time series of observed average hourly PM 2.5 

### concentrations using data from Folsom and Del Paso Manor for Novemb er 29 through November 30, 2008.............. 4-17

### viii LIST OF FIGURES

Figure Page
4-10. Time series of BlueSky Gateway PM 2.5 

### concentrations in northeast Sacramento County for December 9, 2008, through December 10, 2008, for one-day and two-day residential wood-burning curtailment scenarios............................................. 4-18
4-11. Time series of observed average hourly PM 2.5 

### concentrations using data from Folsom and Del Paso Manor for Decembe r 9, 2008, through December 10, 2008...... 4-18
A-1. BlueSky Gateway model grid in the Sacramento County region .................................. A-7

### ix LIST OF TABLES

Table Page
3-1. The 12 meteorological variables us ed in the cluster analysis......................................... 3-2

3-2. Meteorological conditions for a matching day-pair: December 2, 2006, a no-restriction day, and January 11, 2009, a Stage 2 day.............................................. 3-3
3-3. Matching days when meteorologi cal conditions were similar........................................ 3-5

3-4. PM 2.5 

### concentrations comparisons for a day-pair: December 2, 2006, a no-restrictions day, and Janua ry 11, 2009, a Stage 2 day ............................................ 3-6
3-5. Species data below method detection limits for each sample date in winter 2008-2009............................................................................................................ 3- 9
3-6. Chemical species included in CMB as fitting or floating species ................................ 3-11
3-7. Cases selected for modeling analysis............................................................................ 3-14
3-8. 2007 Sacramento County residentia l wood combustion emission estimates used in BlueSky Gateway simulations.......................................................................... 3-15
3-9. 2009 Sacramento County residentia l wood combustion emission estimates from the CEFS database................................................................................................ 3-15

3-10. SMOKE PM 2.5 

### speciation profile.................................................................................. 3-16
4-1. Average reduction (benefit) for all day-pairs, Stage 2 day-pairs, and Stage 1 day-pairs............................................................................................................. 4-2

4-2. The 24-hour PM 2.5 

### concentration reduction for each day-pair ....................................... 4-4
4-3. Reduction in wood smoke contributions for the cluster sample dates............................ 4-7

4-4. Performance metrics for the combined oak/eucalyptus wood burning profile............... 4-9

4-5. Percent of wood smoke contributions to total calculated mass for each wood-burning profile .................................................................................................... 4-10
4-6. Forecast bias (forecast minus obser vation) for various criteria.................................... 4-14
4-7. Carryover impacts estimated by the BlueSky Gateway for northeast Sacramento County....................................................................................................... 4-15
4-8. Relative carryover impacts predicted by Bl ueSky Gateway for Sacramento County .. 4-16

### x LIST OF TABLES

Table Page
A-1. Cases selected for BlueSky modeling analysis.............................................................. A-5
A-2. SMOKE PM2.5 speciation profile................................................................................. A-6

A-3. Modeled benefits of State 1 and St age 2 burn bans estimated by the BlueSky Gateway for northeast Sacramento County ................................................................... A-9
A-4. Relative benefit of Stage 1 and Stage 2 burn bans predicted by BlueSky Gateway for Sacramento County ................................................................................................ A-10

### 1-1 1. INTRODUCTION

On October 25, 2007, the Sacramento Metropol itan Air Quality Management District (SMAQMD) adopted Rule 421, Mandatory Episodic Curtailment of Wood and Other Solid Fuel Burning. The rule established a burning curt ailment program with three messages and/or restrictions that are distributed to the public before a possible curtailment. The message/restriction depends on the “next-day” 24-hr average PM 2.5 1 

### concentration forecast for Sacramento County, which is issued every day by 11 a.m. The messages/restrictions based on the forecasted concentrations follow:

 PM 2.5 

### forecast >25 μ g/m 3 

### to ≤ 35 μ g/m 3 

### : Voluntary curtailment during which burning is discouraged.

 PM 2.5 

### forecast >35 μ g/m 3 

### to ≤ 40 μ g/m 3 

### : Stage 1 mandatory curtailment, during which no burning is allowed except in U.S. Environm ental Protection Agency (EPA)-certified woodstoves and inserts or pellet stoves.

 PM 2.5 

### forecast >40 μ g/m 3 

### : Stage 2 mandatory curtailment during which no burning is allowed in any device.
Rule 421 is in effect each “winter” from the beginning of November through the end of February. The rule has been in place for two “win ters”. However, for th e first “winter” of the program, the rule was in effect from Decem ber 1, 2007 through the end of February 2008.
The primary objective of the work described in this report is to (1) determine the effectiveness of Rule 421 in reducing PM 2.5 

### concentrations in Sacramento County; and (2) to provide information that can be used to dete rmine whether any program changes should be considered to help Sacramento County attain National Ambient Air Qu ality Standards (NAAQS) for PM 2.5 

### . This work was divided into two phases: (1 ) Phase I results are presented in this report and (2) Phase II work will take place during summer 2009 and findings will be reported in September 2009.
Several questions were posed by SMAQMD a nd addressed by Sonoma Technology, Inc. (STI) in Phase I:

 What is the effectiveness of the burning curtailment program toward meeting the NAAQS?

 Does chemical mass balance modeling (CMB) 2 

### confirm the effectiveness findings?

 Was the 2008/2009 wood-burning season representative of normal PM 2.5 

### concentrations?

 What should the no-burn thresh old be to help reduce PM 2.5 

### below the NAAQS?

 Should no-burn days be called on th e day prior to forecasted high-PM 2.5 

### concentration days (days expected to exceed NAAQS ) to help lower pollution during PM 2.5 

### episodes?

1 PM 2.5 includes particles in the air that are less then 2.5 microns in diameter.
2 CMB modeling uses observations of PM 2.5 species and the known abundances of chemical speci es from emission sources (e.g., wood burn ing, automobile exhaust, dust, etc.) to determ ine the contribution from each source type to each measured PM 2.5 daily sample.

### 1-2 To address these questions, STI performed a combination of numerical modeling, CMB modeling, and data analysis to develop weight -of-evidence findings on the effectiveness of Rule 421 in reducing ambient PM 2.5 

### concentrations. This approach provides greate r certainty in the findings compared to those uncertainty findi ngs based on modeling or analysis alone. A similar approach is being taken by the Bay Ar ea Air Quality Manageme nt District (BAAQMD) and was taken by the San Joaquin Valley Unified Air Pollution Control Dist rict (SJVUAPCD) to address the effectiveness of their no-burn programs.
After completing the work presented in the body of this report, STI conducted modeling analyses to provide additional in formation to address the question: “What is the effectiveness of the burning curtailment program toward mee ting the NAAQS?” The findings from the additional analyses are provided in Appendix A and are consistent with the results of the data analysis described in the main body of the report. The addition of Appendix A is the reason this report was updated on August 24, 2009.

### 2-1 2. SUMMARY OF FINDINGS

Answers to the key questions posed by SMAQMD are summarized in this section. Additional details of Phase I findi ngs can be found in Section 4.

Question 1: What is the effe ctiveness of the burning curta ilment program toward meeting NAAQS? Answers : 1. Daily PM 2.5 

### data collected by Beta Attenuation Monitors (BAMs) 3 

### show that the NAAQS threshold of 35.5 μ g/m 3 

### was exceeded on 20 days during the 2008/2009 winter. Analysis of a limited number of days 4 

### indicated that without Rule 421 there would have been about 13 additional days on which the NAAQS was exceeded.
2. Data analysis results of a limited number of days 2 

### show the average re duction of the daily 24-hr average PM 2.5 

### concentration on

a. Stage 1 days was about 4 μ g/m 3 

### or 10% of the total PM 2.5 

### concentration and
b. Stage 2 days was about 12 μ g/m 3 

### or 23% of the total PM 2.5 

### concentration.
3. Data analysis results show the greatest reduction of PM 2.5 

### concentrations resulting from a burn ban occurred during the evening hours. The average reducti on during the evening

on

a. Stage 1 days was 19 μ g/m 3 

### and
b. Stage 2 days was 23 μ g/m 3



### . 4. Data analysis results suggest that, without a burn ban, the change in the concentration from the day prior would have been greater . The average change in the 24-hr daily average concentration from a day prior on

a. Stage 1 days was 5 μ g/m 3 

### greater and
b. Stage 2 days was 17 μ g/m 3 

### greater.

3 Official NAAQS exceedance days for non-attainment designation are based on Federal Reference Monitor (FRM) data. To be NAAQS compliant for PM 2.5 , about two days per year can exceed NAAQS assuming samples taken every third day and about 5 days if the sampling frequency were daily.

4 Eleven day-pairs were analyzed to derive this resu lt. Each day-pair consists of two days with similar meteorology: one day on which a Stage 1 or Stage 2 burn ban was issued and a second day on which no burning restrictions were imposed. Therefore, for each day-pair, differences in PM 2.5 concentrations between days could be primarily attributed to a Stage 1 or Stag e 2 burn ban. Because of the limited number of day-pairs, the results derived from analysis should be viewed as rough approximations.

### 2-2 Question 2: Does CMB modeling co nfirm the effectiveness findings? Answers:
1. Review of CMB output indicates that th e average wood smoke contribution on days when PM 2.5 

### concentrations were high was at least 9.4 μ g/m 3 

### , or 25% of the total PM 2.5 

### concentration, 5 

### and was as high as 19 μ g/m 3 

### , or 40%, on individual days. These results indicate that it is possible to achieve a PM 2.5 

### reduction on the order of 10 to 20 μ g/m 3 

### through the curtailment of wood burning and support Question 1 findings.
2. Review of CMB output indicates that the av erage reduction of the daily 24-hr average PM 2.5 

### concentration for Stage 1 and Stage 2 days combined was 1.5 μ g/m 3 

### , or 3.6% of the total PM 2.5 

### mass. However, because data for th is CMB analysis were only available for a limited number of days, we have less c onfidence in this findi ng than in the data analysis findings for Question 1 above.
Question 3: Was the 2008/2009 wood burning season representative of normal PM 2.5 

### concentrations? Answer:
Data analysis results show that the 2008/2009 winter season represented an average year, with 20 days exceeding the NAAQS. Without curtailment, the 2008/2009 winter season would have had an estimated 33 days exceeding NAAQS.
Question 4: What should the no-burn threshold be to most effectively reduce PM 2.5 

### below the NAAQS? Answer:
Calculations show that to help reduce PM 2.5 

### below the NAAQS, the no-burn thresholds should be set as follows:
1. Single-stage program – 31 μ g/m 3



### . 2. Two-stage program – 31 μ g/m 3 

### for Stage 1 and 35 μ g/m 3 

### for Stage 2.
These thresholds include a margin of safety e qual to the median forecast bias for days on which (a) burning was discouraged and (b) the concentrations were under predicted.
Question 5: Should no-burn days be called on the day prior to forecasted high PM 2.5 

### concentration days to help lower pollution during PM 2.5 

### episodes? Answer:
Modeling analysis results show that the average contributi on of carryover from the prior (build-up) day to the average 24-hr PM 2.5 

### concentration on a “Stage” day was 0.7 μ g/m 3 

### , or 2.2% of the total observed PM 2.5 

### concentration. The range of contributions of carryover for

5 Due to the nature of CMB analysis, it is not possible to attribute secondary PM 2.5 (organic carbon and ammonium nitrate) to wood smoke emissions, even though wood smoke contributes to the formation of secondary PM 2.5 . Therefore, 9.4 μ g/m 3 , or 25%, is likely a low-end estimate of the wood smoke contribution to total PM 2.5 concentrations.

### 2-3 days investigate was 0.0 to 2.7 μ g/m 3 

### , or 0 to 10.6%. Therefore, at this time, there is insufficient evidence that a no-burn da y called on the day prior to high PM 2.5 

### concentration days will significantly lower pollution during PM 2.5 

### episodes.

### 3-1 3. METHODS

STI performed cluster analysis, CMB modeli ng, and numerical modeling to address the questions listed in Section 2. This section disc usses the methods used to perform each of these analyses and is organized according to the question(s) addressed by each analysis.

3.1 PROGRAM EFFECTIVENESS
This subsection describes the methods used to answer the question: What is the overall effectiveness of Rule 421 toward meeting the PM 2.5 

### 24-hr National Ambient Air Quality Standard (NAAQS)?
To address this question, STI compared wintertime PM 2.5 

### concentrations on selected days prior to the adoption of Rule 421 (i.e., days when no restrictions on burning were in effect from November 1, 2004, through November 30, 2007) to da ys when Rule 421 was in effect (i.e., a Stage 1 or Stage 2 burn ban was issued). Only re cent years’ data were used in the analysis to limit the influence of emission changes on PM 2.5 

### concentrations due to non-woodburning emission control measures and population changes. PM 2.5 

### concentrations were only compared on days when meteorological conditions we re similar; thus, the difference in PM 2.5 

### concentrations between days could be primarily 6 

### attributed to a Stage 1 or Stage 2 burn ban.
To identify days with similar meteorology, STI performed cluster analysis. Cluster analysis involves grouping like obje cts (in this case, days) into clus ters based on the similarity of the objects’ characteristics (in th is case, meteorology). For this analysis, the similarity (or variation) between days was determined by calculating the Euclidean distance using the normalized values of meteorologica l variables such as wind speed, temperature, etc. Euclidean distance is the sum of root mean square differenc es of each variable between days or clusters. The statistical technique used in the cluster analysis was “K-mea ns”. K-means clustering splits a set of days into a selected number of cluste rs by maximizing the between-cluster variation and minimizing the within-clust er variation of the mete orological variables.

The data considered for use in the cluster analysis included 19 surface meteorological parameters and 40 upper-air meteorological paramete rs derived from hourly data collected at the Sacramento Executive Airport and twice-daily radiosonde data from Metropolitan Oakland International Airport. Of these 59 meteorological variables, only 12 were determined critical for differentiating day types (see Table 3-1 ). Therefore, data for thes e 12 meteorological variables collected on winter days (November 1 through th e end of February) for 2004 through 2009 were ultimately used in the cluster analysis.

6 Other factors that may cause differences in PM 2.5 concentrations include, but are not limited to, changes in population, changes in emissions from non-woodburning sources such as transportation due to fleet turnover, and changes in the PM 2.5 monitoring techniques.

### 3-2 Table 3-1. The 12 meteorological variab les used in the cluster analysis.

Maximum surface temperature (°C) Minimum surface temperature (°C) Average surface dew point temperature (°C) Average daytime cloud cover Average overnight cloud cover Precipitation (inches) 6 a.m. – 6 p.m. PST vector averaged wind speed (knots) 6 a.m. – 6 p.m. PST vector averaged wind direction (°) 4 a.m. PST 500-mb heights (m-agl) 4 a.m. PST 700-mb relative humidity (%) 4 a.m. PST 850-mb temperature (°C) 4 a.m. PST 925-mb temperature (°C)

Once the cluster analysis was complete, ST I meteorologists reviewed the results to further refine the day matches and ensure that the matched days were very similar meteorologically. This step was critical beca use small differences in meteorology can have a moderate impact on PM 2.5 

### concentrations. In particular, meteorologists reviewed the point measurements of the variables in Table 3-1 for each day and the larg e-scale weather patterns over the western United States at the surface and at the 500-mb pressure level of the atmosphere (~18,000 ft). Reviewing large-scal e weather patterns, the meteorologist looked for and noted the similarity of the following features between matching days:

 location of surface high-pressure systems;

 orientation and strength of th e surface pressure gradient;

 500-mb height patterns (e.g., ridges of high-pressure, trough s of low pressures); and

 any other features of interest (e.g., a thermal trough on the California coast).

Table 3-2 illustrates an example of the meteorol ogical conditions for a matching day pair which includes a “no-restrict ion” day (December 2, 2006) and a “Stage 2” day (January 11, 2009). Figure 3-1 shows the surface and 500-mb weather ch arts for these days. Figure 3-1 and the data in Table 3-2 indicate that these days were meteorologically similar, that is, most all weather parameters important to PM 2.5 

### concentrations were similar on both days. Following are examples of comparisons of meteorological para meters between days: (1) the maximum surface temperatures were 15°C and 16°C on the no-restric tion and Stage 2 days, respectively, while the 6 a.m. to 6 p.m. PST vector-averaged wind speeds were 0 knots on both days and (2) a ridge of high pressure at 500-mb, a thermal trough of lo w pressure on the California coast, and a northwest to southeast-orientated pressure gradient of similar strength (shown in Figure 3-1) occurred on both days. As a result of these meteorological similarities, differences in PM 2.5 

### concentrations between these two days can be attributed primarily to the burn ban.
The meteorologists also evaluate d the prior-day 24-hr average PM 2.5 

### concentrations when selecting days for comparison. Fo r example, if a “no-restriction” day and a “Stage 1” day were

### 3-3 meteorologically very similar, yet the prior-day PM 2.5 

### concentrations were very different (i.e., difference greater than ~15 μ g/m 3 

### ), then the day pair was eliminated from the day matches.
The cluster analysis and subsequent subjectiv e review resulted in 11 matching days (day pairs). The matching days are shown in Table 3-3 . Each day pair consis ted of two days with similar meteorology: one day on which a Stage 1 or Stage 2 burn ban was called and a second day for which there were no burning restrictions. The PM 2.5 

### concentration data were compared for each day pair using PM 2.5 

### data from the site within the urban core of Sacramento County with the highest 24-hr average concentration and with data available for both days in the day-pair. For all day-pairs except one, the data collected at Del Paso Manor (DPM ) were used; T Street data were used for the January 11, 2007/Novemb er 27, 2008 day-pair. For all dates except two, the data used were collected by beta attenua tion monitors (BAM). Because BAM data were missing, Federal Reference Monitor (FRM) data collected at DPM were used for December 2, 2006, and December 11, 2008. The BAM and FRM data were obtained from EPA’s Air Quality System (AQS) ( www.epa.gov/ttn/airs/airsaqs/ ). Because FRM data were not available on January 11, 2009, for the December 2, 2006/January 11, 2009 day-pair, FRM data were compared to BAM data.

Table 3-2. Meteorological conditions for a matching day-pair: December 2, 2006, a no-restriction day, and January 11, 2009, a Stage 2 day

.

Del Paso Manor on Saturday, Dec 02, 2006 – No Restrictions Del Paso Manor on Sunday, Jan 11, 2009 – Stage 2 Meteorologic Cluster 1 1 Location of surface high pressure system Oregon Oregon Orientation of surface pressure gradient Northwest-Southeast Northwest-Southeast Strength of surface pressure gradient Moderate Moderate Surface feature of interest Thermal trough on coast Thermal trough on coast 500-mb pattern Ridge Ridge Maximum surface temperature (°C) 15 16 Minimum surface temperature (°C) -2 -2 Average surface dew point temperature (°C) 1 2 Average daytime cloud cover 0 1 Average overnight cloud cover 0 0 Precipitation (inches) 0 0 6 a.m. – 6 p.m. PST vector averaged wind speed (knots) 0 0

6 a.m. – 6 p.m. PST vector averaged wind direction (°) 0 72

4 a.m. PST 500-mb heights (m-agl) 5766 5887 4 a.m. PST 700-mb relative humidity (%) 8 3 4 a.m. PST 850-mb temperature (°C) 12 14 4 a.m. PST 925-mb temperature (°C) 13 15

### 3-4 Surface Pressure

500-mb Heights January 11, 2009 December 02, 2006 

## Stage 2 No Restrictions

Ridge of high pressure

Thermal trough

Figure 3-1. Example of surface and 500-mb weather patterns us ed to determine matching day-pairs. December 2, 2006, was a no-restriction day and January 11, 2009 was a Stage 2 day.

### 3-5 Table 3-3. Matching days when mete orological conditi ons were similar 7



### .

No Restrictions Dates Burn Ban Dates Stage Value

November 25, 2007 November 28, 2008 2 November 25, 2007 February 17, 2008 1 December 2, 2006 January 11, 2009 2 December 2, 2006 December 11, 2008 2 February 3, 2005 January 30, 2009 2 February 3, 2005 January 14, 2009 2 December 22, 2004 January 29, 2009 1 December 30, 2006 December 14, 2007 1 November 19, 2005 November 30, 2008 1 February 2, 2005 January 13, 2009 2 January 11, 2006 November 27, 2008 2

For the 11 day-pairs, the PM 2.5 

### concentration differences be tween the no-restriction and Stage 1 or Stage 2 days were determ ined for the following averaging times:

 24-hr (12 a.m. to 12 a.m. PST);

 prior-day 24-hr (12 a.m. to 12 a.m. PST);

 morning (12 a.m. to 9 a.m. PST);

 daytime (10 a.m. to 4 p.m. PST); and

 evening (5 p.m. to 11 p.m. PST).
In addition, we compared the daily 24-hr average PM 2.5 

### concentrations on the days listed in Table 3-3 to concentrations on the day pr ior to each of these dates and calculated the concentration change between the consecutive days. We then compared the change in concentration for burn-ban days with the amount of change in concentration for their respective no-restriction dates. The purpose of this analys is was to determine how a burn ban affects the change in concentration from th e day prior to a burn-ban day.
STI used the 24-hr daily average PM 2.5 

### concentration differences between the no-restriction and Stage 1 or Stage 2 days to estimate PM 2.5 

### concentrations in 2008/2009 for a no burn-ban scenario. The results were used to estimate the number of days that would have exceeded NAAQS if there was no “Check Before You Burn” (CBYB) program. This method is detailed in Section 3.3.
The results of this analysis provide an estim ate of benefit from Rule 421. For example, Table 3-4 shows the PM 2.5 

### concentrations for December 22, 2004, and January 29, 2009

. The

7 Different no-burn stages were called on November 28, 2008, and February 17, 2008, yet the days are compared to the same no-restriction day because the no-burn stages are based on forecast ed meteorology; whereas, the day-pairs are based on observed meteorology.

### 3-6 most important column is column 4, “No Restri ction minus Stage 1”, a measure of the reduction in PM 2.5 

### concentrations resulting from the Stage 1 bur n ban. In this example, the total reduction in the 24-hr average PM 2.5 

### concentrations resulting from the Stage 1 burn ban was 11 μ g/m 3



### , with a maximum reduction of 43 μ g/m 3 

### occurring during evening hours. After completing this analysis for each day pair, STI averaged the resu lts for each averaging period listed in bullets on the preceding page.

Table 3-4. PM 2.5 

### concentrations comparisons for a day-pair: December 22, 2004, a no-restrictions day, and January 29, 2009, a Stage 1 day.

Del Paso Manor on Wednesday, Dec 22, 2004 – No Restrictions Del Paso Manor on Thursday, Jan 29, 2009 – Stage 1 No Restrictions Minus Stage 1 24-hr (12 a.m. – 11 p.m. PST) average PM 2.5 concentrations ( μ g/m 3 ) 40 29 11 Morning (12 a.m. – 9 a.m. PST) average PM 2.5 concentrations ( μ g/m 3 ) 26 31 -5 Daytime (10 a.m. – 4 p.m. PST) average PM 2.5 concentrations ( μ g/m 3 ) 21 21 0 Evening (5 p.m. – 11 p.m. PST) average PM 2.5 concentrations ( μ g/m 3 ) 77 34 43 Prior-day 24-hr (12 a.m. – 11 p.m. PST) average PM 2.5 concentrations ( μ g/m 3 ) 16 22 -5 Concentration change from the prior day ( μ g/m 3 ) 23 7 16



### 3.2 CMB MODELING
The methods used to answer the questions that follow are discussed in this subsection. The following question (in boldface) was the main focus of the analyses; important secondary questions related to these analyses are italicized. Does CMB modeling confirm the effect iveness findings from Question 1?
How variable is the wood smoke contri bution based on the various wood burning profiles? How well does CMB predict the wood smoke contribution?
STI applied CMB modeling to speciated PM 2.5 

### data to determine the contribution of residential wood smoke to total PM 2.5 

### concentrations for various groups of days and compared the findings to the “effectiveness” findings de scribed in Section 3.1. The day groups included

 all 2008-2009 “winter” days;

 all Stage 1 and Stage 2 days in 2007-2009;

 Stage 1 days that were matched to days with no burning restrictions because of similar meteorology between matched days;

### 3-7  Stage 2 days that were matched to days with no burning restrictions because of similar meteorology between matched days; and

 no-restriction days (i.e., no burn ban was issued ) that were matched to Stage 1 and Stage 2 days because of similar mete orology between matched days.
Days with similar meteorologi cal conditions (i.e., matching days) were determined using the cluster analysis results desc ribed in Section 3.1. However, the criteria for the selection of meteorologically similar days included in th e CMB modeling were less restrictive than the criteria used in the data analys is described in Section 3.1. We were less restrictive because the number days for which speciated ambient data were available for comparison were limited. The result of less restriction is that the meteorology was only mode rately similar between matching days; thus, differences in wood smoke contribut ions between matching days cannot solely be attributed to a burn ban. The no-restriction days were select ed from winter days during November 2007 through February 2009 when no burn ban was in effect, as well as from the winters prior to the CBYB progr am (November 2003 through Februa ry 2007). The final set of matching no-restriction days and Stage days used to compare the contribution of wood smoke are discussed in Section 4.2.
The data used for CMB modeling were va lidated, speciated 24-hr samples of PM 2.5 

### data collected at Del Paso Manor. The data were co llected every third day. Ambient concentrations and uncertainty values were obtained from SMAQMD for the 2008-2009 “winter” (November 2008–February 2009) and from EPA’s Air Quality System (AQS) for previous “winters” (2003–2008). The source profiles used in CM B modeling were obtained from Katarzyna Turkiewicz from the California Air Resources Board (ARB). Wood-burning source profiles from different types of wood were used in sensi tivity tests to determine the range of wood smoke contributions. Standard CMB performance metric s were used to gauge confidence in results.

3.2.1 CMB Background
CMB is a receptor model used to identify a nd characterize the mixt ure and magnitude of sources contributing to ambient pollutant concentr ations. Known source pr ofiles are linearly fit to ambient data using a least squares solution. Model outputs represent the c ontributions of various emission sources to the observed ambient concentrations. In CMB, only source profiles (i.e., the fraction of each species emitted from each source type), and ambient data collected at the receptor are required as model inputs. U nderlying CMB assumptions include (1) accurate identification of source types and abundances (source profiles); (2) independent source compositions (i.e., abundances are unique to each profile); and (3) cons istent profiles between source and receptor (i.e., no mass removal and constant emissions) and throughout the sampling period. In this analysis, these assumptions we re met to a similar degree as in published literature.

3.2.2 Data Processing and Quality Assurance
Each species was blank-adjusted by subtrac ting the median blank mass of that species from three blanks taken during 2008-2009. Afte r blank-adjusting mass values, total mass was

### 3-8 reconstructed from the speciated data for compar ison with the total measured mass on the filter ( Figures 3-2 and 3-3 ). This comparison helps us understand how well the chemical species represent the total mass on the filter. Organic carbon was adju sted to organic matter (OM) using a factor of 1.4 to account for oxygen and hydrogen associated with the carbon (Malm et al., 2004; Turpin and Lim, 2001). The factor of 1.4 is generally viewed as a low-end factor, but was used in this analysis because it provided the best reconstruction of total mass. The unknown fractions, calculated as the di fference between the r econstructed and measured totals, were typically less than 20% of th e total reconstructed concentration throughout the sampling period, indicating that most of the PM 2.5 

### mass is well explained by the measured chemical species. Additional quality assurance incl uded investigation of ambient da ta values below instrument method detection limits (MDLs) ( Table 3-5 ). Species frequently belo w detection were generally not included in the model; if a species was re latively abundant in sour ce profiles, such as aluminum in the soil profile, it was include d. AQS-reported uncertainties were used for individual species, and an uncerta inty of 5.5% was used for tota l mass, as reported by Research Triangle Institute in the 2008-2009 data submittal to SMAQMD.

1 7 - N o v - 0 8

2 0 -N

o v - 0 8

2 3 -N

o v - 0 8

2 6 - N o v - 0 8

2 9 -N

o v - 0 8

0 2 -D

e c - 0 8

0 5 - D e c - 0 8

0 8 - D e c - 0 8

1 1 -D

e c - 0 8

1 4 - D e c - 0 8

1 7 - D e c - 0 8

2 0 - D e c - 0 8

2 3 - D e c - 0 8

2 6 - D e c -0 8

2 9 - D e c -0 8

0 1 - J a n - 0 9

0 4 - J a n - 0 9

0 7 - J a n - 0 9

1 0 - J a n - 0 9

1 3 - J a n - 0 9

1 6 - J a n - 0 9

1 9 -J a n - 0 9

2 2 -J a n - 0 9

2 5 - J a n - 0 9

2 8 -J a n - 0 9

3 1 -J a n - 0 9 03

- F e b - 0 9

0 6 -F e b - 0 9

0 9 - F e b - 0 9

1 2 - F e b - 0 9 -10

0 10 20 30 40 50 60Reconstructed Concentration (ug/m3) Other Soil Ammonium Nitrate Ammonium Sulfate Elemental Carbon Organic Matter



### Figure 3-2. Reconstructed mass of samp les during winter 2008/2009. “Other” is defined as the difference between the meas ured total filter mass minus the sum of soil, ammonium, nitrate, ammonium su lfate, organic matter, and elemental carbon. A negative value indicates the su m of species was greater than the measured mass. 8

8 Calculations were based on standard Interagency Monitoring of Protected Visual Environments (IMPROVE) methodology (IMPROVE). Soil was calculated as (A luminum*2.2+Silicon*2.49+ Calcium*1.63 +Iron*2.42 +Titanium*1.94); ammonium nitrate was calculated as Nitrate*1.29; ammonium su lfate was calculated as Sulfur*4.125; and organic matter was calculated as Organic Carbon*1.4.

### 3-9 Average Winter 2008-2009 Reconstructed Concentration

Organic Matter 14 μg/m3, 56%

Elemental Carbon 4 μg/m3, 6% Ammonium Sulfate 1.3 μg/m3, 5% A mmonium Nitrate 7.3 μg/m3, 30% Other 0.17 μg/m3, 0.7% Soil 0.33 μg/m3, 1% *Average reconstru c concentration = 24 u



### Figure 3-3. Average reconstructed concentr ations plus the difference between the total reconstructed and measured concen trations (the difference is shown as “other”) for all winter 2008-2009 sample dates.

Table 3-5. Species data below method dete ction limits for each sample date in winter 2008-2009.
Page 1 of 2

Date Al NH 4 Br Ca Cl Cu EC Fe K Pb Mn Na Ni NO 3 OC K (ion) Si Na (ion) SO 4 Ti V Zn

11/17/08 X X X X X 11/20/08 X X X X X X X 11/23/08 X X X X X 11/26/08 X X X X X 11/29/08 X X X X X X X X 12/02/08 X X X X X X X X 12/05/08 X X X X X 12/08/08 X X X X X X X 12/11/08 X X X X X 12/14/08 X X X X X X X X 12/17/08 X X X X X X X X X X 12/20/08 X X X X X X 12/23/08 X X X X X X X X X 12/26/08 X X X X X X X 12/29/08 X X X X X 01/01/09 X X X X X X X X X X 01/04/09 X X X X X X 01/07/09 X X X X X X X X X 01/10/09 X X X X X X

### 3-10 Table 3-5. Species data below method dete ction limits for each sample date in winter 2008-2009.
Page 2 of 2

Date Al NH 4 Br Ca Cl Cu EC Fe K Pb Mn Na Ni NO 3 OC K (ion) Si Na (ion) SO 4 Ti V Zn

01/13/09 X X X X X 01/16/09 X X X X 01/19/09 X X X X X X 01/22/09 X X X X X X X X 01/25/09 X X X X X X X X 01/28/09 X X X X X X X 01/31/09 X X X X X X 02/03/09 X X X X X X 02/06/09 X X X X X X X X X 02/09/09 X X X X X X
02/12/09 X X X X X X



### 3.2.3 Source Profiles and Model Certainty
Source profile abundances and uncertainties were obtained from the ARB for the known major source types. The profiles included four wood smoke varieties (o ak, eucalyptus, almond, and tamarisk), one combination wood smoke prof ile calculated as 50% oak and 50% eucalyptus, one mobile source profile (combined gas and dies el), ammonium sulfate, ammonium nitrate, pure organic carbon (OC), and dust. STI also created an additional wood smoke combination profile, calculated as one-th ird each oak, eucalyptus, and almond. Key species for wood burning, mobile sources, and dust are detailed in Table 3-6. OC, elemental carbon (EC), and potassium were used as markers for wood burning, since levoglucosan data, a more unique tracer for wood burning, were not available. We antic ipate that mobile source emissions and wood burning will account for much of the OC, but there are multiple, smaller sources of OC that are not characterized, so a profile of entirely OC is used to account for these additional small sources. Ammonium sulfate and ammonium nitr ate profiles are used for mass closure; these species are secondary products and so cannot be ascribed to individua l sources with CMB.

The model was run for all the winter 2008-2009 sampling dates. Twenty-one species were used in the CMB model, as shown in Table 3-6. The fourteen fitting species selected in the model were abundant in the sour ce profiles, logical markers for the predominant sources, and typically above method detection limits. The re maining seven floating species were of lesser abundance and served to validate the model a nd satisfy degrees of freedom requirements (Coulter, 2004). Sensitivity tests were performe d to determine the range of results for each of the different wood-burning profiles. Examining these results allowed us to put bounds on the source contribution estimate for wood burning, even if a given wood profile was not wholly representative of the mix of w ood burned in the area. The best performing and most reasonable wood burning profile was selected for further use, measuring model performance by three standard CMB methods:

### 3-11  R square is a measure of the fraction of va riance in measured concentrations that is explained by the variance in calculated concen trations. Higher R s quare values indicate better agreement between measured and calcula ted data sets; a value greater than 0.8 is considered good agreement and wa s required for these analyses.

 Chi square is the weighted sum of squares of the differences between calculated and measured fitting species concentrations. Lower chi square values indicate better agreement between the measured and calculate d data. A chi square value less than 1 indicates very good agreement, and a chi s quare between 1 and 2 indicates acceptable agreement. Chi square values of less than 2 were required for these analyses.

 The total mass calculated by the model should be reasonable when compared to measured mass, falling between 80% and 120% of the measured mass. In addition, the source contribution estimates were evaluate d for significance, and the ratio of calculated/measured concentratio ns for fitting species were compared between profiles.

Table 3-6. Chemical species included in CMB as fitting or floating species. Marker species for emission sources are hi ghlighted in yellow. Under the column heading “Fit”, cells with an “x” indicate species that were used in all CMB runs, and blank cells indicate species that were not always used because of poor fit or data below the MDL.

Species Fit Source Aluminum x Dust Ammonium x Ammonium sulfate, ammonium nitrate Bromine x Gas/diesel Calcium x Dust Chlorine Multiple Copper x Gas/diesel, dust Elemental carbon x Wood burning, gas/diesel Iron x Dust Potassium Multiple Lead Multiple Manganese Multiple Sodium Multiple Nickel Multiple Nitrate x Ammonium nitrate Organic carbon x Wood burning, gas/diesel Potassium (cation) x Wood burning Silicon x Dust Sodium (cation) Multiple Sulfate x Ammonium sulfate Titanium x Multiple Zinc x Multiple

### 3-12 3.3 SEASON REPRESENTATIVENESS
The methods used to answer the questions that follow are discussed in this subsection. The following question (in boldface) was the main fo cus of the analyses; an important secondary question related to these analyses is italicized.
Was the 2008/2009 wood burning season representative of normal PM 2.5 

### concentrations? What would have the 2008/2009 wood burning se ason have been like without the CBYB program?

STI used various metrics to summarize PM 2.5 

### conditions for winter 2008/2009 and compared those metrics to past “w inters” (2004/2005, 2005/2006, 2006/2007, and 2007/2008). Only data from recent years were used in the anal ysis to limit the influence of emission changes on PM 2.5 

### concentrations due to non-woodburning em ission control measures and population changes. For this analysis, we used BAM da ta collected at DPM because (1) there was a complete set of data for this site for the period of interest and (2) it is t ypically the site with the highest daily concentrations in Sacramento Co unty. The metrics calculated for each year included

 number of days in each Air Quality Index ( AQI) category;

 number of days on which the NAAQS was exceeded; and

 average episode length. An episode is de fined as one or more consecutive days exceeding the NAAQS.
STI also compared the number of days in 2008/2009 when meteorological conditions were conducive to high PM 2.5 

### concentrations to the number of su ch days in each of the prior four years. This comparison was made by reviewing the meteorological conditions and observed air quality concentrations on all winter days from November 2004 through February 2009. Conducive conditions include

 surface high pressure north or east of Sacramento;

 aloft high pressure over California;

 relatively warm aloft temperatures and a temperature inversion;

 cool and cloud-free nights; and

 light winds during the day and night.
When comparing these conditions to observed air quality, we determined that observed air quality concentrations were the best metric to define days when meteorology was conducive to poor air quality. In particular , we defined meteorologically c onducive days as days when peak 24-hr average PM 2.5 

### concentrations exceeded 24 μ g/m 3 

### . We also reviewed the number of days above 29 μ g/m 3 

### to confirm that our choice of threshol d did not significantly change the relative difference in days with conducive meteorology between years.
Finally, STI used the burn-benefit results fr om the Question 1 analysis to estimate PM 2.5 

### concentrations in 2008/2009 had no CBYB program b een in place. This estimate was calculated

### 3-13 by adding the average Stage 1 burn-ban benefit to the observed concentrations on Stage 1 days and the average Stage 2 burn-ban benefit to the observed concen trations on Stage 2 days. The observed concentrations were adjusted using da ta collected using BAM monitors. STI then recalculated the metrics for the 2007/2008 and 2008/2009 seasons using the adjusted data. Of particular focus was the reduction in the numbe r of days exceeding NAAQS due to the CBYB program.

3.4 NO-BURN THRESHOLDS
This subsection describes the methods used to answer the question: What should the no-burn threshold be to reduce PM 2.5 

### below NAAQS?
To address this question for the single-stage program, STI estimated the median bias in the daily PM 2.5 

### forecasts and subtracted it from the NAAQS threshold to determine the burn-ban threshold. This calculation was nece ssary because, in practice, if a PM 2.5 

### concentration of 30 μ g/m 3 

### is forecast with a forecast error of 5 μ g/m 3 

### , an observed concentration of 35 μ g/m 3 

### is possible given the forecast error. Therefore, the threshold for a no-burn call should be lowered to 30 μ g/m 3 

### (35 minus 5).
For a two-stage program, STI determined a PM 2.5 

### threshold for which a Stage 1 call will not be sufficient to prevent a violation of the NAAQS. To determine this threshold, STI added the anticipated PM 2.5 

### reduction resulting from a Stage 1 ca ll (obtained from the data analyses performed to address Question 1) to the NAAQS of 35 μ g/m 3 

### and subtracted the forecast error. For example, if the average reduction achieved on a Stage 1 day is 4 μ g/m 3 

### , the no-burn threshold for a Stage 2 day (ignoring forecast error) should be 39 μ g/m 3 

### (35 +4). If forecast error is 5 μ g/m 3 

### , the threshold for a Stage 2 no-burn call day should be lowered to 34 μ g/m 3 

### (39-5).
The forecast error used in the these calculations was determined by calculating the median forecast bias for days during th e 2008/2009 winter season when burning was discouraged. Using the forecast bias from burni ng-discouraged days was determined to be the most appropriate estimate of the actual forecast error for the following reasons: (1) on days when a Stage 1 or Stage 2 burn ban is calle d, the forecast error can be altered by the effectiveness of the burn ban; (2) on clean days, precise forecasts of absolute PM 2.5 

### concentrations tend to be emphasized less compared to days on which a burn ban may be necessary; and (3) on days when burning is disc ouraged, forecast accuracy is emphasized, yet there is no burn ban to alter forecast error.

3.5 BUILD-UP DAY RULE
This subsection describes the methods used to answer the question: Should no-burn days be called on the day prior to forecasted high PM 2.5 

### concentration days to help lower pollution during PM 2.5 

### episodes?
STI used the BlueSky Gateway air quality modeling system. BlueSky Gateway is an operational PM 2.5 

### forecasting system developed for the U.S. Forest Service to predict PM 2.5 

### concentrations resulting from w ildfires and other emission sources on a national scale at coarse

### 3-14 (36 km) resolution. BlueSky Gateway combin es meteorological predictions from the Pennsylvania State University/N ational Center for Atmospheric Research Mesoscale Model (MM5) with air quality predictions from the Comm unity Multiscale Air Quality (CMAQ) model. STI has operated BlueSky Gateway twice daily since its Summer 2007 inception.
Though BlueSky Gateway was designed primarily to track and predict PM 2.5 

### concentrations resulting from w ildfires, the modeling system is run with full gaseous and aerosol chemistry to predict the fate of all types of natural and anthropogenic PM 2.5 

### emission sources, including residential wood burning. Details pertinent to the use of BlueSky Gateway to address the questions posed by SMAQMD are presented below. Additional details can be found in (Craig et al., 2007).
STI performed simulations for several two- consecutive-day periods using the BlueSky Gateway modeling system to evaluate the co ntribution of carryover on multi-day pollution episodes. BlueSky Gateway was run for several days prior to each case date (with residential wood burning allowed) to provide in itialized concentration fields for the sensitivity simulations.
Nine cases from the 2008/2009 winter burning seas on were selected for this analysis, as shown in Table 3-7 . Each case consists of a “build-up” day on which burning was discouraged, followed by a “Stage” day on which a Stage 1 or Stage 2 burn ban was called. In several instances, these cases represented the begi nning of extended periods of elevated PM 2.5 

### concentrations that necessitated burn bans on several consecutive days in Sacramento County.

Table 3-7. Cases selected for modeling analysis.

Case Date Burn Category Observed PM 2.5 

### ( μ g/m 3 

### ) 11/16/2008 Discouraged 30.8 1 11/17/2008 Stage 1 23.8 11/22/2008 Discouraged 49.4 2 11/23/2008 Stage 2 60.7 11/29/2008 Discouraged 35.6 3 11/30/2008 Stage 1 42.3 12/2/2008 Discouraged 33.3 4 12/3/2008 Stage 2 29.3 12/9/2008 Discouraged 34.0 5 12/10/2008 Stage 2 40.5 1/4/2009 Discouraged 31.1 6 1/5/2009 Stage 2 25.7 1/6/2009 Discouraged 25.5 7 1/7/2009 Stage 2 29.2 1/9/2009 Clean 17.8 8 1/10/2009 Stage 2 43.5 1/28/2009 Discouraged 21.6 9 1/29/2009 Stage 1 28.9

### 3-15 For each case, STI ran the two-consecutive-day simulations for two residential wood-burning scenarios:
1. 100% burning curtailment in Sacramento County only on the Stage day.
2. 100% burning curtailment in Sacramento County on both the build-up and the Stage days.

The modeled difference in PM 2.5 

### concentrations between these two emission scenarios on the Stage day yields the relativ e benefit of adding a burn ban on the build-up day. The modeled impacts of carryover were examined at vari ous receptor locations throughout Sacramento County.

Residential wood combustion emission esti mates were taken from the 2002 National Emission Inventory (NEI), processed through versi on 2.3 of the Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE), and projecte d to 2007 using growth factors generated by EPA’s Economic Growth Analysis System (E GAS) version 4.0. The residential wood combustion emission estimates used in BlueSky Gateway are listed in Table 3-8 . These estimates are within approximately 5% of the 2009 California Air Resources Board (ARB) residential wood-burning emission estimates for Sacramento County obtained from the California Emission Forecast System (CEFS) ( http://www.arb.ca.gov/a pp/emsinv/fcemssumcat2005.php ), as shown in Table 3-9 . SMOKE speciates the total PM 2.5 

### mass according to the speciation profile shown in Table 3-10 and applies temporal profiles to convert annualized emissions to episode-specific hourly emissions. The allocation of annual wood-burning emissions by month is shown in Figure 3-4. The default SMOKE seasonal profile applied to residential wood combustion emissions was judged to be reasonable and resulted in average winter day PM 2.5 

### emissions of 10.4 tons for Sacramento County. The default SMOKE diurnal temporal profile for residential wood combustion was replaced with a more appropria te profile developed by ARB.

Table 3-8. 2007 Sacramento County residential wood combustion emission estimates (tons/year) used in BlueSky Gateway simulations.

Source CO NO x 

### PM 10 

### PM 2.5 

### SO 2 

### VOC Fireplaces 11,150.9 114.8 1,428.1 1,374.8 17.5 600.1 Woodstoves 1,789.9 25.3 279.8 269.4 3.8 130.8 Total 12,940.8 140.1 1,708.0 1,644.2 21.3 730.9

Table 3-9. 2009 Sacramento County residential wood combustion emission estimates (tons/year) from the CEFS database.

Source CO NO x 

### PM 10 

### PM 2.5 

### SO 2 

### VOC Fireplaces 11,744.3 120.9 1,504.2 1,448.0 18.5 632.0 Woodstoves 1,788.3 25.5 279.6 269.1 3.7 130.5 Total 13,532.6 146.4 1,783.8 1,717.1 22.1 762.5

### 3-16 Table 3-10. SMOKE PM 2.5 

### speciation profile.

Profile # Model Species Model Species Name Mass Fraction 22061 PEC Primary elemental carbon 0.1077

22061 PMFINE Fine mode PM (metals and other species) 0.3208

22061 PNO 3 

### Primary nitrate aerosol 0.0022 22061 POA Primary organic aerosol 0.5656 22061 PSO 4 

### Primary sulfate aerosol 0.0037

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecPercentage of Annual Emissions



### Figure 3-4. Allocation of annua l wood-burning emissions by month.

Because of the relatively coarse horizontal resolution of BlueSky Gateway (36 km), peak concentrations may be underestimated by the model as localized emissions are artificially diluted into relatively large model grid cells. Figure 3-5 illustrates the size of BlueSky Gateway model grid cells relative to Sacramento County. To address the effect s of model resolution, the use of absolute modeled concentration diffe rences was avoided, and instead the relative difference between the two emission control scenarios was used to determine the impact of carryover.

### 3-17 Figure 3-5. BlueSky Gateway model grid in the Sacramento County region. Grid cells highlighted in red indicate the four cells that encompass most of Sacramento County.

### 4-1 4. FINDINGS

Findings related to each question listed in Sect ion 2 are summarized in this section and are organized according to the main question addressed by each analysis.

4.1 PROGRAM EFFECTIVENESS

Detailed in this subsection are the findings applicable to Question 1: What is the effectiveness of program toward meeting NAAQS?
STI compared wintertime PM 2.5 

### concentrations on days prio r to the adoption of Rule 421 (i.e., no burning restrictions) to days when Rule 421 was in effect (i.e., when a Stage 1 or Stage 2 burn ban was issued). PM 2.5 

### concentrations were only compar ed for days when meteorological conditions were similar; thus, the difference in PM 2.5 

### concentrations between days could be primarily attributed to a Stage 1 or Stage 2 burn ban. Cluster analysis and subsequent subjective review resulted in 11 matching days (day pairs) , which are shown in Tabl e 3-3. Each day pair consists of two days with similar meteorology: one day on which a Stage 1 or Stage 2 burn ban was issued and a second day on which no burning restrictions were imposed. Overall, seven Stage 2 day pairs and four Stage 1 day pairs were identified. Because of the limited number of day pairs, the results derived from analys is should be viewed as rough approximations.

Table 4-1 lists the average reduction in PM 2.5 

### concentrations for various averaging periods (24-hr, morning, daytime, and evening) that resulted from a Stage 1 or Stage 2 burn ban. Figure 4-1a shows time series of the average hourly PM 2.5 

### concentrations for all Stage 2/ no-restriction day pairs. Figure 4-1b shows time series of the average hourly PM 2.5 

### concentrations for all Stage 1/no-restriction day pairs. Table 4-2 lists the 24-hr PM 2.5 

### concentration reduction (benefit) for each of the 11 day-pairs. The tables and figures illustrate the following findings:

 The average reduction resulting from a Stage 1 burn ban was 4 μ g/m 3 

### , or 10% of the total PM 2.5 

### mass that would have occurred wit hout the reduction. For example, if a concentration of 36 μ g/m 3 

### was observed on a day with a Stage 1 burn ban, the concentration would have been about 40 μ g/m 3 

### on average without a burn ban.

 The reductions resulting from a Stage 1 burn ban ranged from -11 μ g/m 3 

### to 10 μ g/m 3 

### . A reduction was observed on three of the four days (Table 4-2 )

.

 The average reduction resulting from a Stage 2 burn ban was 12 μ g/m 3 

### , or 23% reduction of the total PM 2.5 

### mass that would have occurred wit hout the reduction. For example, if a concentration of 40 μ g/m 3 

### was observed on a day with a Stage 2 burn ban, the concentration would have been about 52 μ g/m 3 

### on average without the Stage 2 burn ban.

 The reductions resulting from a Stage 2 burn ban ranged from -6 μ g/m 3 

### to 30 μ g/m 3 

### . A reduction was observed on six of the seven days (Table 4-2 )

.

 The greatest reduction in PM 2.5 

### concentrations resulting from a burn ban occurred during the evening hours. This reduction can be seen in the large separation between the dashed red line (average “No Restriction” days) and solid green lines (avera ge Stage 1 or Stage 2

### 4-2 days) in Figures 4-1a and 4-1b. The aver age reduction during the evening was 23 μ g/m 3 

### and 19 μ g/m 3 

### for Stage 2 and Stage 1 days, respectivel y. This result is consistent with the observation that most people burn in the evening as indicated in the ARB residential wood-burning emission estimates for Sacramento County obtained from the CEFS.

 The average change in the 24-hr daily average concentration from a day prior to a Stage 2 day was 17 μ g/m 3 

### less than the change from a day pr ior to a no-restri ction day (see the last row in Table 4-1). This result suggests that without a Stage 2 burn ban, concentrations would have been, on average, 17 μ g/m 3 

### higher than without a burn ban.

 The average change in the 24-hr daily average concentration from a day prior to a Stage 1 day was 5 μ g/m 3 

### less than the change from a day prio r to a no-restricti on day (see the last row in Table 4-1). This result suggests th at without a Stage 1 burn ban, concentrations would have been on average 5 μ g/m 3 

### higher than without a burn ban.

Table 4-1. Average reduction (benefit) fo r all day-pairs, Stag e 2 day-pairs, and Stage 1 day-pairs.

Benefit Stage 1 and 2 ( μ g/m 3 

### ) Benefit Stage 2 ( μ g/m 3 

### ) Benefit Stage 1 ( μ g/m 3 

### ) 24-hr 9 12 4 Morning 8 11 3 Daytime -7 -4 -11 Evening 21 23 19 Change from the prior day 12 17 5

### 4-3 (a)

(b)



### Figure 4-1. Average hourly PM 2.5 

### time series for the (a) Stage 2 and (b) Stage 1 day pairs. The dashed red line represents the average hourly PM 2.5 

### concentrations for the No Restriction days associated w ith each day pair, and the solid green line represents the average hourly PM 2.5 

### concentrations of the Stage days associated with each day pair. The green box show th e estimated total reduction (benefit) in the 24-hr average PM 2.5 

### concentrations due to the burn ban.

### 4-4 Table 4-2. The 24-hour PM 2.5 

### concentration reduction (ben efit) for each day-pair. Green cells indicate a benefit from the burn ban.

Day Pairs

No Restrictions Date Burn Ban Date Burn Ban Category 24-hr Daily Average PM 2.5 Concentration

Reduction

( μ g/m 3 ) 

### Concentration change from a “prior” day to a no-restriction day minus the concentration change from a prior “day” to a Stage day November 25, 2007 November 28, 2008 Stage 2 15 13 November 25, 2007 February 17, 2008 Stage 1 9 3 December 2, 2006 January 11, 2009 Stage 2 23 26 December 2, 2006 December 11, 2008 Stage 2 30 30 February 3, 2005 January 30, 2009 Stage 2 6 1 February 3, 2005 January 14, 2009 Stage 2 -5 2 December 22, 2004 January 29, 2009 Stage 1 10 16 December 30, 2006 December 14, 2007 Stage 1 6 8 November 19, 2005 November 30, 2008 Stage 1 -11 -7 February 2, 2005 January 13, 2009 Stage 2 -6 12 January 11, 2006 November 27, 2008 Stage 2 18 31



### 4.2 CMB MODELING
The findings applicable to the questions that follow are deta iled in this subsection. The following question (in boldface) was the main focus of the analyses; important secondary questions related to these analyses are italicized. Does CMB modeling confirm the effectiveness findings?
How variable is the wood smoke contri bution based on the various wood-burning profiles? How well does CMB predict the wood smoke contribution?
Figure 4-2a-e shows the average source contributions to total PM 2.5 

### concentration at Del Paso Manor for five day-gr oupings. The wood smoke contri bution and other observations derived from these figures are noted below.
1. Figure 4-2a shows that, on all 2008-2009 winter days, the average wood smoke contribution was 7.1 μg/m 3 

### or 29% of the total PM 2.5 

### mass. Thus, if wood burning were reduced to negligible levels, the burn ban c ould produce concentration reductions of this magnitude. This finding supports the estimated reduction in PM 2.5 

### concentrations resulting from burn bans described in Section 4.1 regarding Question 1.

### 4-5 (a) (b)

(c) (d)

(e)


### Figure 4-2. Source contributi on estimates for each profile averaged across (a) all winter 2008-2009 sample dates, (b) all 2008/2009 Stage dates, (c) Stage 1 days with matching no-restriction days, (d) St age 2 days with matching no-restriction days, (e) no-restriction days with meteorology similar to Stage days.

### 4-6 2. Figure 4-2b shows that, on all 2007/2009 Stage 1 and Stage 2 days, the average wood smoke contribution was 9.4 μg/m 3 

### or 25% of the total PM 2.5 

### mass. 3. Figure 4-2c shows that, on Stag e 1 days that were matched to days with no burning restrictions because of similar meteorol ogy, the average wood smoke contribution was 10.5 μg/m 3 

### or 25% of the total PM 2.5 

### mass. 4. Figure 4-2d shows that, on Stage 2 days that were matched to days with no burning restrictions because of similar meteorol ogy, the average wood smoke contribution was 10.6 μg/m 3 

### or 23% of the total PM 2.5 

### mass.
5. Figure 4-2e shows that, on no-restriction da ys from 2005 – 2007 (i.e., no burn ban was issued) that were matched to Stage 1 and Stage 2 days because of similar meteorology, the average wood smoke contribution was 12.1 μg/m 3 

### or 26% of the total PM 2.5 

### mass. The combination of the findings 3 through 5 suggest that CMB modeling indicates a reduction of PM 2.5 

### from a Stage 1 or Stage 2 burn is about 1.5 μg/m 3 

### (12.1 – 10.5). This amount is much less than that revealed in the data analysis (see Section 4.1) and is probably not a significant enough difference to conclude that th ere was a benefit from burn bans. However, because data for only a limited number of days were available for this CMB analysis, the matching pairs of no-restriction to burn-ban days were not as me teorologically similar as those used in the data analysis. Th erefore, differences in meteorol ogy between days may account for the limited reduction in PM 2.5 

### concentrations resulting from bur n bans identified using CMB.

Table 4-3 shows the reduction in wood smoke co ntributions for each matching pair of no-restriction to burn-ban days. Of note, the only cluster having several meteorologically similar dates for comparison, including five unrestricted dates and four Stage dates, showed a 4.0 μg/m 3 

### reduction for burn-ban days compared to no-re striction days. In addition, the maximum contribution of wood smoke for this cluster of days was 40% or 18.8 μg/m 3 

### of the total PM 2.5 

### concentration and occurred December 4, 2006.
In the Sacramento County emission inve ntory, residential wood burning accounts for approximately 50% of the primary PM 2.5 

### emissions. This percentage is consistent with CMB results. Ammonium nitrate and ammonium sulfate are secondarily produced and are not included in the emission inventory estimate. If these species are ignor ed in Figure 4-2, wood burning would account for approximately 50% of the remaining mass. Secondary Question 1 : How variable is the wood smoke contribution based on the various wood-burning profiles?
Three wood-burning source profiles were eval uated to determine the most appropriate burning profile for Sacramento County. The estimated wood smoke contribution to total calculated concentration was consistent among th e three wood-burning prof iles (oak, eucalyptus, and almond) and the two combination profiles. Source contribution estimates for the almond, oak, eucalyptus, and combined profil es varied between 4% and 8% ( Figure 4-3 ). Variability was the greatest on Stage 2 and unrestricted dates. The consistency among the results using the oak, eucalyptus and almond profiles promotes confidence in the overa ll performance of CMB modeling in quantifying wood burning. As anticip ated, the results when using the Tamarisk profile (tamarisk is a type of grass) were unrealistic, and the model performance was poor. The average wood smoke contribution was 82%, and the average measured mass calculated by the

### 4-7 model was 139%, measurably exceeding the 120% thre shold. This profile represents grassland burning, so if results had been good for wintertime da ta, the data or profiles being used would be suspect, but since the results were poor, we have more confidence in the other burning profiles and results.

Table 4-3. Reduction in wood smoke contri butions for the cluster sample dates.

Wood Smoke Contribution Meteorological Cluster Date (Stage Level) Percent Concentration ( μ g/m 3 )
1 2/3/2005 16 7.8 1 12/4/2006 40 18.8 1 1/9/2007 37 16.7 1 1/24/2007 32 17.3 1 2/2/2007 23 10.9 1 1/19/2008 (1) 27 11.9 1 12/11/2008 (2) 21 10.0 1 1/10/2009 (2) 32 11.9 1 1/13/2009 (2) 20 7.3 Non-Stage 29 14.3 Average Stage (1 or 2) 25 10.3 Reduction 4.0 3 11/21/2005 33 12.3 3 2/5/2007 26 8.6 3 11/23/2008 (2) 29 13.3 Non-Stage 29 10.5 Average Stage (1 or 2) 29 13.3 Reduction -2.8 6 12/15/2005 18 5.3 6 12/5/2008 (1) 30 10.0 Reduction -4.7 7 1/18/2007 35 17.5 7 12/14/2007 (1) 28 12.1 Reduction 5.4 9 1/11/2006 17 5.8 9 11/26/2008 (1) 27 8.1 Reduction -2.3 All Clusters Non-stage 28 12.1 Stage (1) 28 10.5 Stage (2) 25 10.6 Average
Stage (1 or 2) 27 10.6 Reduction 1.5

### 4-8 Figure 4-3. Percent of cont ributions to total calculated concentrations for the eucalyptus, oak, almond, and comb ined wood-burning source profile.

Secondary Question 2: How well is CMB at predicting the contribution?
Three metrics were used to assess the certainty in CMB model predictions: R-square, chi square, and a comparison of the total mass calcula ted by the model and measured mass. Overall, CMB prediction statistics were within toleran ces so comparison of source contributions among days is reasonable ( Table 4-4 ). In addition, using profiles of almond, eucalyptus, oak, oak and eucalyptus combined, and oak, almond, and eucalypt us combined provided a similar estimate of the contribution of wood smoke to total PM 2.5 

### mass ( Table 4-5 ).
The combined oak and eucalyptus profile perf ormed very well in the CMB analysis. On all but two sample dates the R square values were greater than 0.8, indicati ng variance in the data was explained well by the model. The two excep tions were unrestricted burn dates, for which total measured concentrations were low (less than 10 μ g/m 3 

### ), where poorer model performance is expected. Only three samples exceeded the ch i square performance metric on dates with substantial mass (approximately 20 μ 

### g/m 3 

### or greater). The model wa s unable to identify at least 80% of the measured mass on only two dates, November 26 and November 29, which had concentrations in excess of 35 μg/m3. Howe ver, on these dates the total concentrations reconstructed from ambient speciated data were also much less than the measured concentrations, so we expect the model calculat ed mass to be relatively low. Performance was also gauged using the ratio of the calculated to m easured concentration for each fitting species. Ratios substantially greater than 1 were selectiv ely not included as fitt ing species for certain

### 4-9 sample dates. The ratio for EC was typically between 1 and 2. The CMB calculations could be improved by using levoglucosan data to provide a more unique tracer.

Table 4-4. Performance metrics for the combined oak/eucalyptus wood-burning profile. Red indicates Stage 2 days, orange indicates Stage 1 days, clear indicates burning discouraged days, green indicates burn cleanly days, and yellow indicates fitting statistics outside of criteria.

Date Observed Concentration (μg/m 3 ) Reconstructed Concentration (μg/m 3 ) R Square Chi Square Percent of Measured Mass

11/17/2008 18.3 17.9 0.89 1.36 98 11/20/2008 12.3 14.4 0.97 1.10 118 11/23/2008 55.3 46.3 0.98 0.74 84 11/26/2008 37.7 29.9 0.96 1.23 79 11/29/2008 30.3 23.3 0.98 0.62 77 12/2/2008 26.3 23.3 0.97 1.16 89 12/5/2008 40.7 32.9 0.97 1.43 81 12/8/2008 28.1 24.4 0.94 1.76 87 12/11/2008 50.3 48.6 0.96 0.95 97 12/14/2008 6.4 5.2 0.74 6.60 82 12/17/2008 18.5 21.1 0.98 0.79 114 12/20/2008 20.9 20.1 0.96 1.50 96 12/23/2008 8.3 8.8 0.95 1.34 107 12/26/2008 11.3 12.3 0.91 2.17 109 12/29/2008 22.6 21.4 0.96 1.01 95 1/1/2009 19.6 17.2 0.91 3.50 88 1/4/2009 26.3 22.9 0.97 0.96 87 1/7/2009 21.9 20.7 0.93 3.09 94 1/10/2009 40.3 37.3 0.96 1.00 93 1/13/2009 38.1 37.1 0.93 2.76 97 1/16/2009 47.5 45.1 0.94 1.75 95 1/19/2009 24.6 22.8 0.90 1.70 93 1/22/2009 20.9 17.1 0.96 1.30 82 1/25/2009 7.3 5.9 0.78 7.06 80 1/28/2009 15.2 15.5 0.95 1.21 102 1/31/2009 29.8 28.2 0.95 1.36 95 2/3/2009 21.9 22.6 0.92 1.36 103 2/9/2009 12.4 12.9 0.91 1.77 104 2/12/2009 5.1 6.7 0.89 2.77 132

### 4-10 Table 4-5. Percent of wood smoke contribu tions to total calculated mass for each wood-burning profile. Red indi cates Stage 2 days, orange indicates Stage 1 days, clear indicates burning discouraged days, and green indicates burn cleanly days. Burn cleanly days are days with no burni ng restrictions and c oncentrations were less than 25 μ 

### g/m 3 

### . Due to collinearity between sources, some days were unable to be fit by the model (blank percent contribution).

Percent Contribution (%)

Date Total PM 2.5 Concentration (μg/m 3 ) Almond Eucalyptus Oak Oak and Eucalyptus Oak, Almond, and Eucalyptus Tamarisk

11/17/2008 18.3 27 31 26 28 28 11/20/2008 12.3 29 24 26 26 67 11/23/2008 55.3 27 31 26 29 28 73 11/26/2008 37.7 26 30 25 28 27 71 11/29/2008 30.3 39 45 38 42 41 92 12/2/2008 26.3 21 24 20 22 22 58 12/5/2008 40.7 30 35 29 32 31 76 12/8/2008 28.1 32 37 31 34 33 83 12/11/2008 50.3 20 23 19 21 20 50 12/14/2008 6.4 52 57 49 53 53 109 12/17/2008 18.5 34 39 33 36 36 86 12/20/2008 20.9 46 39 42 41 95 12/23/2008 8.3 42 35 38 38 85 12/26/2008 11.3 38 43 36 40 39 91 12/29/2008 22.6 29 33 27 30 30 75 1/1/2009 19.6 31 26 29 28 72 1/4/2009 26.3 47 40 44 42 96 1/7/2009 21.9 28 33 28 30 30 77 1/10/2009 40.3 30 35 29 32 31 78 1/13/2009 38.1 19 22 18 20 20 56 1/16/2009 47.5 21 24 20 22 21 59 1/19/2009 24.6 38 43 36 39 39 91 1/22/2009 20.9 33 39 33 36 36 86 1/25/2009 7.3 32 27 30 30 74 1/28/2009 15.2 38 43 36 40 39 91 1/31/2009 29.8 24 28 23 25 25 67 2/3/2009 21.9 41 46 40 43 42 92 2/6/2009 6.8 47 103 2/9/2009 12.4 48 54 46 50 49 105 2/12/2009 5.1 57 49 53 52 111 Avg. no restriction days 43 44 39 41 41 93

Avg. Stage 1 28 32 27 29 29 73 Avg. Stage 2 28 32 27 29 29 71

### 4-11 4.3 SEASON REPRESENTATIVENESS
The findings applicable to the questions that follow are deta iled in this subsection. The following question (in boldface) was the main fo cus of the analyses; an important secondary question related to these analyses is italicized.
Was the 2008/2009 wood burning season representative of normal PM 2.5 

### concentrations?

What would have the 2008/2009 season been li ke without the “Check Before You Burn” (CBYB) program?
Compared to the preceding four years, the 2008/2009 winter season re presents an average year in terms of the number of days exceed ing the NAAQS. According to the daily BAM measurements from DPM, there were 20 exceedance days 9 

### during the 2008/2009 “winter” season, compared to an average of 16 exceed ances for the preceding four “winters” (2004/2005, 2005/2006, 2006/2007, and 2007/2008) (see Figure 4-4 ). In addition, 2008/2009 represents a polluted year in terms of the number of days wh en meteorological conditions were conducive to poor air quality and was similar to 2006/2007 in that regard (see Figure 4-5 ). When the average burn-ban benefit for Stage 1 and Stage 2 days (f indings discussed in Section 4.1) was added to the observed daily concentrations on Stage 1 and Stage 2 days, the number of exceedance days in 2008/2009 increased from 20 to 33 (see Figure 4-4 ) . Therefore, the CBYB program could have reduced the number of exceedance days duri ng the 2008/2009 winter season by about 40% (or 13 days).
Figure 4-6 shows the distribution of peak daily (a) observed AQI and (b) estimated AQI if there was no CBYB program for the 2008/2009 seas on. As noted in the figure, calculations show that the CBYB program resu lted in a significant reduction in the number of days with Unhealthy for Sensitive Groups and Unhealthy AQI levels.
Figure 4-7 shows the average PM 2.5 

### pollution episode length for the 2004/2005 through 2008/2009 winter seasons; “episode” is defined as the number of consecutive days exceeding the NAAQS. Figure 4-7 show the average episode length in 2008/2009 of about 3.3 days, the longest average episode length among winters ev aluated (along with th e winter of 2005/2006). When the average episode length is recalculated using the estimated number of days that PM 2.5 

### concentrations would have exceeded the NAAQS without the CBYB program, the 2008/2009 average episode length increases to mo re than 4 days (see Figure 4-7).

9 Official NAAQS exceedance days for non-attainment designation are based on FRM data. To be NAAQScompliant for PM 2.5 , the NAAQS can be exceeded about two days a y ear if samples were taken every third day, and about five days if the sampling frequency was daily.

### 4-12 0 5 10 15 20 25 30 35

2 00

4/

2005 200 5/

2006 200 6 /2007 200 7/

2008
20 0 7/2

0 08* 2008/2009
20 0 8/2009*

YearNumber of Days

*Estimate if Stage 1 and Stage 2 days were not called 0 5 10 15 20 25 30 35

2 00

4/

2005 200 5/

2006 200 6 /2007 200 7/

2008
20 0 7/2

0 08* 2008/2009
20 0 8/2009*

YearNumber of Days

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


### Figure 4-4. Number of days that PM 2.5 

### concentrations exceeded the NAAQS from the 2004/2005 through 2008/2009 winter seas ons (solid bars), and estimated number of days PM 2.5 

### concentrations would have exceeded the NAAQS without the CBYB program (striped bars). NAAQS exceedances were determined using the daily BAM data collected at Del Paso Manor.

Figure 4-5. Number of meteorol ogically conducive days for high PM 2.5 

### concentrations for the 2004/ 2005 through 2008/2009 winter seasons.

### 4-13 2008/2009 2008/2009 Adjusted

Figure 4-6. Distributi on of peak daily PM 2.5 

### AQI by category for the 2008/2009 season observed (left) and estimated if there was no CBYB program (right). AQI categories were determined using daily BAM data from all sites within Sacramento County.

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

2004/20 0 5

2 0 0 5/

2 00

6 2006/20 0 7

2 0 0 7/

2 00

8
20 0 7/2008*

2 0 0 8/

2 00

9
20 0 8/2009*

YearNumber of Days



### Figure 4-7. Average PM 2.5 

### pollution episode length for the 2004/2005 through 2008/2009 winter seasons. Solid bars indi cate calculations based on observations; striped bars indicate estimates if Stag e 1 and Stage 2 days were not called. NAAQS exceedances were determined using the daily BAM data collected at Del Paso Manor.

### 4-14 4.4 NO-BURN THRESHOLDS
The findings applicable to Question 4 are detailed in this subsection: What should the no-burn threshold be to reduce PM 2.5 

### below NAAQS?
Based on our calculations, to reduce the num ber of days exceeding NAAQS, the no-burn thresholds should be set as follows:

 A single stage program threshold should be about 31 μ g/m 3 

### . To derive this threshold, the median forecast bias was subtracted from the NAAQS threshold. Because the forecast bias was 4.4 μ g/m 3 

### the threshold for a no-burn call is 35 μ g/m 3 

### minus 4.4 μ g/m 3 

### , or 30.6 μ g/m 3



### .

 A two-stage program threshold should be 31 μ g/m 3 

### for Stage 1 and 35 μ g/m 3 

### for Stage 2

. To derive the Stage 2 thre shold, we determined the PM 2.5 

### concentration for which a Stage 1 call will not be sufficien t to prevent a violation of th e NAAQS. To calculate this threshold, the anticipated PM 2.5 

### reduction resulting from a Stage 1 call was added to NAAQS and forecast error was then subtracte d. Because the average reduction achieved on a Stage 1 day is 4 μ g/m 3 

### , the no-burn threshold for a Stage 2 day (ignoring forecast error) is 39 μ g/m 3 

### (35 μ g/m 3 

### + 4 μ g/m 3 

### ). Subtracting the forecast error of 4.4 μ g/m 3 

### results in a Stage 2 threshold of 34.6 μ g/m 3 

### (39 μ g/m 3 

### minus 4.4 μ g/m 3 

### ).
During the 2008/2009 wood-burning season, thes e new thresholds would have reduced the number of days exceeding the NAAQS from 21 days to 16 days. This conclusion was determined by counting the number of days in 2008/2009 when the following criteria were met: (1) a burn ban was not called, (2) observed co ncentrations exceeded the NAAQS, and (3) the benefit of the Stage 1 or Stag e 2 call would have lowered con centrations below the NAAQS.

The forecast error of 4.4 μ g/m 3 

### cited above was determined by calculating the median forecast bias from days during the 2008/2009 wint er season when burning was discouraged (see Table 4-6 ). The reason for using the forecast bias from the burning-discouraged category was discussed in Section 3.4.
Table 4-6. Forecast bias (forecast minus observation) for various criteria. The red cell indicates the forecast error us ed for the threshold calculations.

Day Type Median Bias ( μ g/m 3 

### ) Median bias for under forecasted days ( μ g/m 3 

### ) Median bias for over forecasted days ( μ g/m 3 

### ) All 1.8 -4.1 4.7 Burn Clearly 1.2 -3.5 3.7 Burning Discouraged 4.1 -4.4 6.4 Burn Clearly and Burning Discouraged 1.4 -3..9 4.2 Stage 1 and Stage 2 5.2 -5.3 12.7 Stage 2 5.1 -4.7 12.9 Stage 1 5.2 -7.1 11.3

### 4-15 4.5 BUILD-UP DAY RULE
The findings applicable to Question 5 are detailed in this subsection: Should no-burn days be called on the day prior to high PM 2.5 

### concentration days to help lower pollution during PM 2.5 

### episodes? Modeling analysis shows that the average contribution of carryover from the preceding build-up day to the average 24-hr PM 2.5 

### concentration on the Stage day was 0.7 μ g/m 3 

### , or 2.2% of the total observed PM 2.5 

### concentration. The range of c ontributions of carryover was 0.0 to 2.7 μ g/m 3 

### , or 0 to 10.6%. Therefore, a no-burn call on the build-up day preceding high PM 2.5 

### concentration days will not significantly lower pollution during PM 2.5 

### episodes. The supporting details associated with this finding follows.For each of the nine selected cases, the benefit of calling an additional burn ban on buildup days was determined by computing the relative difference in model-pr edicted Stage-day 24-hr average PM 2.5 

### concentrations between the two emission scenarios. Two measures of modeled carryover impact were computed: (1) a relati ve percentage impact, and (2) an estimated concentration impact determined by multiplying th e relative percentage impact by the peak 24-hr average PM 2.5 

### concentration observed in Sacramento County on the Stage day.

Modeled PM 2.5 

### values were extracted at several receptors in Sacramento County. At 36-km resolution, most of Sacramento County is c overed by four model grid cells (Figure 3-4). These grid cells divide Sacramento County into approximate quadrants. The northeast quadrant (northeast Sacramento County) covers a signifi cant portion of the count y’s residential population and includes the Del Paso Manor, Folsom, and Sloughhouse PM 2.5 

### monitoring sites. This quadrant also contains the la rgest residential wood-burning em issions and is, therefore, considered representative of resi dential areas of Sacramento County. Modeled carryover impacts for northeast Sacramento County are listed in Table 4-7 . The relative carryover impact (i.e., the additional bene fit of a build-up day burn curtailment) varied from case to case, but ranged be tween 0.1% and 3.6% in eight of the nine cases. The impact was 10.6% on January 5, 2009. The estimated c oncentration impact was less than 1.0 μ g/m 3 

### on seven of the nine cases. The estimated impact was 1.5 μ g/m 3 

### on December 10, 2008, and 2.7 μ g/m 3 

### on January 5, 2009.
Table 4-7. Carryover impacts estimated by the BlueSky Gateway for northeast Sacramento County.

Stage Date Relative Carryover Impact Observed Peak 24-hour Average PM 2.5 Concentration ( μ g/m 3 ) Estimated Carryover Impact ( μ g/m 3 ) 11/17/2008 0.44% 23.8 0.11 11/23/2008 0.90% 60.7 0.55 11/30/2008 0.18% 42.3 0.08 12/3/2008 1.04% 29.3 0.30 12/10/2008 3.62% 40.5 1.46

1/5/2009 10.62% 25.7 2.73 1/7/2009 2.26% 29.2 0.66 1/10/2009 0.07% 43.5 0.03 1/29/2009 0.28% 28.9 0.08

### 4-16 Carryover impacts were determined for a ll four Sacramento County grid cells ( Table 4-8 ). The 10.62% impact in northeast Sacr amento County on January 5, 2009, was the regional peak impact for all cases. Peak da ily regional carryover impacts ranged from 0.39% to 3.62%, except on January 5, 2009. The daily carr yover impact was greatest in northeast Sacramento County on only four of the nine ca ses, indicating that modeled impacts of burn curtailment often occur outside the regi on of peak residential burning emissions.

Table 4-8. Relative carryover impact s predicted by BlueSky Gateway for Sacramento County. Peak daily carryove r impacts are boldfa ced; peak carryover impact for all nine cases is italicized.

Stage Date Northeast Sacramento County Northwest Sacramento County Southwest Sacramento County Southeast Sacramento County 11/17/2008 0.44% 0.86% 1.55% 0.33%
11/23/2008 0.90% 0.86% 0.42% 0.50%
11/30/2008 0.18% 0.24% 0.39% 0.37%
12/3/2008 1.04% -0.12% -0.26% 1.00%
12/10/2008 3.62% 0.86% 0.30% 1.14%
1/5/2009 10.62% 1.52% 2.10% 6.86%
1/7/2009 2.26% -0.04% 0.01% 3.38%
1/10/2009 0.07% 0.31% 1.14% 0.08%
1/29/2009 0.28% 0.20% 0.65% 0.91%

Time series plots show the te mporal behavior of modeled PM 2.5 

### concentrations from both emission scenarios and illustrate how the impact of carryover evolved throughout the simulation. The time series in Figure 4-8 indicates a day with little pred icted Stage-day carryover benefit (0.18 %)

. Significant differences between the two em ission scenarios are apparent during the morning and evening hours of the build-up day as residential wood burning peaks. However, BlueSky Gateway predicted a reduction in PM 2.5 

### concentrations in bot h simulations after the evening PM 2.5 

### peak. This reduction may be a result of winds transporting cleaner air into the grid cell. Benefits of wood-bur ning curtailment achieved during the build-up day were lost as the time series from the two emission scenarios quickly converge. As a result, PM 2.5 

### concentrations during most of the Stage day ar e almost identical, and the Stage-day carryover benefit is small.
Figure 4-9 shows the observed average hourly PM 2.5 

### concentrations using BAM data from Folsom and Del Paso Manor for November 29 through November 30, 2008. As noted in the figure, although the modeled and observed con centrations exhibit a similar diurnal pattern, the observed concentrations are two to three ti mes greater than the modeled concentrations. Causes of the differences between the modeled and observed concentratio ns are discussed in Section 4.5 under “Modeling Caveats”.

### 4-17 A somewhat larger (3.62%) Stage-day carryov er benefit was predicted on December 10, 2008 ( Figure 4-10 ). This simulation differs from th e November 30, 2008 case in that the predicted benefit from burn curt ailment during the build-up day is larger, and an evening cleanout was not predicted; in stead, concentrations rose after the evening PM 2.5 

### concentration peak. As a result, the large carryover benefit genera ted during the build-up da y carried several hours into the Stage day. However, by morning on the Stage day, predicted concentrations from both simulations were nearly the same again, though a small residual benefit remained throughout the Stage day. As a result, though the carryover im pact on December 10, 2008, is larger than on November 30, 2008; the overall impact on the Stage-day 24-hr average PM 2.5 

### concentration is 3.6%. Figure 4-11 shows the observed average hourly PM 2.5 

### concentrations using data from Folsom and Del Paso Manor for December 9, 2008, through December 10, 2008. As noted in the figure, there is a marked difference in the modeled and observed concentrations on December 10, 2008. Causes of the differences between th e modeled and observed concentrations are discussed in Section 4.5 unde r “Modeling Caveats”.

0 5 10 15 20 25

0 3 6 9 12 15 1 8 2 1 0 3 6 9 1 2 15 18 21 Hour (PST)PM2.5 (ug/m3) 2-Day Curtailment 1-Day Curtailment



### Figure 4-8. Time series of BlueSky Gateway PM 2.5 

### concentrations in northeast Sacramento County for November 29 through November 30, 2008, for one-day and two-day residential wood-bur ning curtailment scenarios.

0 5 10 15 20 25 30 35 40 45 50 55 60

0 3 6 9 12 15 18 21 0 3 6 9 1 2 1 5 18 21
Hour (PST)PM2.5 (ug/m3)



### Figure 4-9. Time series of observed average hourly PM 2.5 

### concentrations using data from Folsom and Del Paso Manor for November 29 through November 30, 2008.

### 4-18 0 5 10 15 20 25 30 35 40 45 50

0 3 6 9 12 1 5 18 21 0 3 6 9 12 15 1 8 21 Hour (PST)PM2.5 (ug/m3) 2-Day Curtailment 1-Day Curtailment



### Figure 4-10. Time series of BlueSky Gateway PM 2.5 

### concentrations in northeast Sacramento County for December 9, 2008, through December 10, 2008, for one-day and two-day residential wood-burning curtailment scenarios.

0 10 20 30 40 50 60

0 3 6 9 12 1 5 1 8 21 0 3 6 9 12 15 1 8 21
Hour (PST)PM2.5 (ug/m3)



### Figure 4-11. Time series of observed average hourly PM 2.5 

### concentrations using data from Folsom and Del Paso Manor for December 9, 2008, through December 10, 2008.
Modeling Caveats
The following limitations should be consider ed when interpreting results from the BlueSky Gateway modeling system:

 Because of the relatively coarse (36 km ) horizontal grid resolution, predicted PM 2.5 

### concentrations in Sacramento County are only modeled with 4 grid cells. Emissions from localized sources are diluted into relatively large model grid volumes. This artificially smoothes over localized emissi on patterns, and tends to resu lt in an under prediction of PM 2.5 

### concentrations.

 Wind fields modeled at 36 km resolution are not always representative of the observed localized wind fields that drive dispersion char acteristics at local scales. As a result,

### 4-19 differences between modeled and observed PM 2.5 

### concentrations are to be expected, as PM 2.5 

### concentrations can be highly se nsitivity to the local wind field.

 Predicted PM 2.5 

### concentrations are subject to any biases and errors inherent in the meteorological model used to produce meteor ological data for the air quality model.

 Sensitivity simulations were performed w ith 100% curtailment in residential wood burning, whereas actual curtailm ent is likely less than 100%.

### 5-1 5. REFERENCES

Coulter C.T. (2004) EPA-CMB8.2 users manual. Prepared by the Air Quality Modeling Group, Emissions, Monitoring, and Analysis Divi sion, Office of Air Qu ality Planning and Standards, U.S. Environmental Protecti on Agency, Research Triangle Park, NC, December.
Craig K.J., Wheeler N.J.M., Reid S.B., Gilliland E.K., and Sullivan D.C. (2007) Development and operation of national CMAQ-based PM 2.5 

### forecast system for fire management. Presented at the 6th Annual CMAS Conference, Chapel Hill, NC, October 1-3 , by Sonoma Technology, Inc., Petaluma, CA (STI-3228).

Malm W.C., Schichtel B.A., Pitchford M.L., As hbaugh L.L., and Eldred R.A. (2004) Spatial and monthly trends in speciated fine partic le concentration in the United States. Journal of Geophysical Research (Atmospheres) 109 (D3) (D03306, doi:10.1029/2003JD003739).
Turpin B.J. and Lim H.-J. ( 2001) Species contribution to PM 2.5 

### mass concentrations: revisiting common assumptions for estimating organic mass. Aerosol Sci. Technol. 35 (10), 602- 610.

### A-1 

## APPENDIX A

ADDITIONAL FINDINGS ON THE EFFECTIVENESS OF THE BURNING CURTAILMENT PROGRAM TO WARD MEETING THE NAAQS

### A-3 A. INTRODUCTION
After completing the report entitled “Evaluat ion of Sacramento Metropolitan Air Quality Management District’s ‘Ch eck Before You Burn’ Program” on May 6, 2009, STI conducted additional modeling analyses to provide additional information to address the question: “What is the effectiveness of the burning curtailment program toward meeting the NAAQS?” This appendix reports on the findings from the modeling analyses. The addition of this appendix is the reason for the report update on August 24, 2009.
For these analyses, STI used a three-dime nsional photochemical model to estimate PM 2.5 

### concentrations under three scenar ios for 37 winter days in 2008/2009. The model used estimated emissions from residential wood burning (1) with no burning restricti ons; (2) under a Stage 1 burn ban; and (3) under a Stage 2 bu rn ban. The differences in PM 2.5 

### concentrations between the no-burning restriction scenario a nd each burn ban scenario were then calculated to provide an estimate of the PM 2.5 

### reduction attributable to the Stage 1 and Stage 2 burn bans. Note that the three-dimensional photochemical model includes both secondary and primary PM 2.5 

### ; therefore, the benefits reported in the a ppendix are reductions in total PM 2.5 

### concentrations due to wood-burning restrictions.

A.1 SUMMARY OF FINDINGS
In summary, modeling analyses show that

• the average benefit of a Stage 1 burn ban on the 24-hr average PM 2.5 

### concentration was 5.2 μ g/m 3 

### , or 13.7% of the total PM 2.5 

### concentration;

• the average benefit of a Stage 2 burn ban was 6.4 μ g/m 3 

### , or 16.9% of the total PM 2.5 

### concentration;

• the maximum benefit of a Stage 1 burn ban was 8.7 μ g/m 3 

### , or 18.4%; and

• the maximum benefit of a Stage 2 burn ban was 10.8 μ g/m 3 

### , or 22.7%.
These modeling results are consistent with the results from the data analysis described in the main report.

A.2 METHOD
STI used the BlueSky Gateway air quality modeling system. BlueSky Gateway is an operational PM 2.5 

### forecasting system developed for the U.S. Forest Service to predict PM 2.5 

### concentrations resulting from w ildfires and other emissions sources on a national scale at coarse (36 km) resolution. BlueSky Gateway combin es meteorological predictions from the Pennsylvania State University/N ational Center for Atmospheric Research Mesoscale Model (MM5) (Grell et al., 1994; Dudhi a, 1993) with air quality pr edictions from the Community Multiscale Air Quality (CMAQ) model (ENVIRON International Corporation, 2008). STI has operated BlueSky Gateway twice daily si nce its inception in the summer of 2007 (Craig et al., 2007) .

### A-4 Though BlueSky Gateway was designed primarily to track and predict PM 2.5 

### concentrations from wildfires, the modeling system is run with full gaseous and aerosol chemistry to predict the fate of PM 2.5 

### emissions from all types of natural and anthropogenic sources, including residential wood burning. Details on the use of BlueSky Gateway to address the questions posed by SMAQMD are presented below. Additional details can be found in Development and Operation of National CMAQ-Based PM 2.5 

### Forecast System for Fire Management (Craig et al., 2007).
STI performed numerous BlueSky Gateway simula tions to evaluate the benefit of Stage 1 and Stage 2 burn bans on polluted days. Thir ty-seven cases from the 2008/2009 winter burning season, as shown in Table A-1 , were selected for this analysis. In most cases, PM 2.5 

### concentrations were predicted to be high enough to issue a Stage 1 or Stage 2 burn ban. To provide initialized concentrati on fields for the simulations, BlueSky Gateway models with no burning restrictions were run for several days prio r to each case date un til midnight PST of the case date.

For each case date, three si mulations were performed:
1. Base case simulation with no burning curtailment in Sacramento County.
2. Sensitivity simulation with Stage 1 burn re strictions applied to Sacramento County.
3. Sensitivity simulation with Stage 2 burn re strictions applied to Sacramento County.
Wood combustion emission rates for the Stag e 1 and Stage 2 burn- ban scenarios were developed by applying 57% and 70% across-the- board reduction factors to the Sacramento County 2009 wood smoke emissions estimates. Reduc tion factors were provi ded by the District on the basis of burn ban complian ce rates determined by survey.
Simulations were executed for a 24-hour pe riod starting at midni ght PST on the case date, for consistency with the timing of actual bur n curtailment (midnight to midnight on the day for which a burn ban is issued). The modeled difference in PM 2.5 

### concentrations between the base case simulation and the sensitivity simulations yields the relative be nefit (expressed as a percent reduction in concentration) of a Stage 1 or Stage 2 burn ban. A concentration benefit can be estimated by multiplying the relative percenta ge benefit by the observed peak 24-hr average PM 2.5 

### concentration in Sacramento County.

### A-5 Table A-1. Cases selected for Bl ueSky Gateway modeling analysis.

Case Date Observed Peak 24-hour Average PM 2.5 Concentration ( μ g/m 3 ) Burn Category

11/17/2008 23.8 Stage 1 11/22/2008 49.4 Discouraged 11/23/2008 60.7 Stage 2 11/24/2008 43.0 Stage 2 11/25/2008 36.3 Stage 1 11/26/2008 43.1 Stage 1 11/27/2008 30.5 Stage 2 11/28/2008 30.0 Stage 2 11/30/2008 42.3 Stage 1 12/3/2008 29.3 Stage 2 12/4/2008 30.3 Stage 2 12/5/2008 45.5 Stage 1 12/6/2008 38.9 Stage 2 12/7/2008 32.0 Stage 1 12/10/2008 40.5 Stage 2 12/11/2008 49.3 Stage 2 12/12/2008 69.8 Discouraged

1/5/2009 25.7 Stage 2 1/7/2009 29.2 Stage 2 1/10/2009 43.5 Stage 2 1/11/2009 55.2 Stage 2 1/12/2009 38.0 Stage 2 1/13/2009 43.5 Stage 2 1/14/2009 50.2 Stage 2 1/15/2009 42.5 Stage 2 1/16/2009 50.1 Stage 2 1/17/2009 45.6 Stage 2 1/18/2009 28.1 Stage 2 1/19/2009 28.3 Stage 2 1/20/2009 23.2 Stage 2 1/29/2009 28.9 Stage 1 1/30/2009 39.7 Stage 2 1/31/2009 35.1 Stage 2 2/1/2009 26.7 Stage 2 2/2/2009 29.5 Stage 2 2/3/2009 28.0 Stage 2 2/4/2009 24.7 Stage 1

### A-6 Estimates of residential wood combustion emissions for Sacramento County were developed from 2009 county-level average winter day emissions estimates provided by the District. These emissions were based on the California Air Resources Board (ARB) version 1.06 emissions inventory, which uses a 2002 base ye ar. County-level emissions were spatially allocated according to census data on househol ds with wood heating as the primary heating source. Emissions were allocate d to model grid cells with the Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System (Houyoux et al., 2000).

Residential wood combustion emissions outside of Sacramento County were taken from the 2002 National Emission Inventory (NEI), pr ocessed through SMOKE, and projected to 2007 by using growth factors generated by EPA’ s Economic Growth Analysis System (EGAS) version 4.0 (E.H. Pechan & Associates, 2001). For wood combustion emissions from all counties, the standard SMOKE speciation pr ofile was used to disaggregate total PM 2.5 

### emissions into individual model species ( Table A-2 ), and the default SMOKE diur nal profile for residential wood combustion was replaced with a more a ppropriate profile developed by the ARB.

Table A-2. SMOKE PM 2.5 

### speciation profile

Profile # Model Species Model Species Name Mass Fraction 22061 PEC Primary elemental carbon 0.1077

22061 PMFINE Fine mode PM (metals and other species) 0.3208

22061 PNO 3 

### Primary nitrate aerosol 0.0022 22061 POA Primary organic aerosol 0.5656 22061 PSO 4 

### Primary sulfate aerosol 0.0037

Because of the relatively coarse horizontal resolution of BlueSky Gateway (36 km), peak concentrations may be underestimated by the model as localized emissions are artificially diluted into relatively large model grid cells. Figure A-1 illustrates the size of BlueSky Gateway model grid cells relative to Sacramento County. To address the effects of such coarse resolution, STI avoided using absolute modeled concentr ation differences and instead used the relative difference between the base case (no burn restri ction) scenario and the Stage 1 and Stage 2 sensitivity scenarios to asse ss the benefit of the burn ban.

### A-7 Figure A-1. BlueSky Gateway model grid in the Sacramento County region. Grid cells highlighted in red indicate the four cells that encompass most of Sacramento County.

A.3 MODELING CAVEATS
The following limitations should be consider ed when interpreting results from the modeling analyses:

 Because of the relatively coarse horizontal grid resolution used in the model, PM 2.5 

### concentrations are only estimated in four grid cells, which cover Sacramento County. Emissions from localized sources are diluted in the relatively large model grid volumes. This dilution artificially smoothes over locali zed emission patterns and tends to result in an under-estimation of PM 2.5 

### concentrations.

 Wind fields modeled at 36-km resolution are not always representative of the localized winds that drive transport and dispersion on loca l scales. As a result, differences between modeled and observed PM 2.5 

### concentrations are to be expected, as PM 2.5 

### concentrations can be highly sensitive to the local winds.

 Estimations of PM 2.5 

### concentrations are subject to any biases and errors inherent in the meteorological model used to provid e data to the air quality model.

### A-8 A.4 FINDINGS
Modeling analysis results show that the average benefit of a Stage 1 burn ban on the regional peak 24-hr average PM 2.5 

### concentration was 5.2 μ g/m 3 

### , or 13.7% of the total observed PM 2.5 

### concentration. The average bene fit of a Stage 2 burn ban was 6.4 μ g/m 3 

### , or 16.9% of the total observed PM 2.5 

### concentration. The range of benefits of a Stage 1 burn ban was from 2.2 to 8.7 μ g/m 3 

### , or from 7.8 to 18.4%. The range of benef its of a Stage 2 burn ban was from 2.7 to 10.8 μ g/m 3 

### , or from 9.7 to 22.7%.

Modeled estimates of PM 2.5 

### concentrations were extrac ted at several receptors in Sacramento County. At 36-km resolution, most of Sacramento County is covered by four model grid cells (Figure A-1). Thes e grid cells divide Sacramen to County approximately into quadrants. The northeast quadran t (northeast Sacramento County) covers a significant portion of the county’s residential populat ion and includes the Del Paso Manor, Folsom, and Sloughhouse PM 2.5 

### monitoring sites. This quadrant is respons ible for the largest am ount of residential wood-burning emissions and is, therefore, consider ed representative of residential areas of Sacramento County.
Modeled Stage 1 and Stage 2 burn-ban benef its for northeast Sacramento County are listed in Table A-3 . Modeled benefits varied from case to case, but were never less than 2.2 μ g/m 3 

### for a Stage 1 burn ban, or 2.7 μ g/m 3 

### for a Stage 2 burn ban. Relative benefits of Stage 1 burn bans exceeded 10% for 31 of 37 modeled cas es, while relative benefits of Stage 2 burn bans exceeded 10% for all but one modeled case. The maximum modeled relative benefit occurred on November 17, 2008, with relative be nefits of 18.4% and 22.7% for Stage 1 and Stage 2 burn bans, respectively. The maximu m modeled concentrati on benefit occurred on January 16, 2009, with benefits of 8.7 μ g/m 3 

### and 10.8 μ g/m 3 

### for Stage 1 and Stage 2 burn bans, respectively.

Relative burn-ban benefits were determined fo r all four Sacramento County grid cells ( Table A-4 ). The maximum relative benefit from Stage 1 and Stage 2 burn bans was in northeast Sacramento County, on November 17, 2009. In all cases, benefits were greater in northeast Sacramento County than in other pa rts of Sacramento County, as residential wood-burning emissions were highest in northeast Sacramento County. Southwest Sacramento County had the smallest bene fit for all but one case.

### A-9 Table A-3. Modeled benefits of Stage 1 and Stage 2 burn bans estimated by the BlueSky Gateway for northeast Sacramento County.

Stage Date Observed Peak 24-hour Average PM 2.5 Concentration ( μ g/m 3 ) Relative Stage 1 Benefit Estimated Stage 1 Benefit ( μ g/m 3 ) Relative Stage 2 Benefit Estimated Stage 2 Benefit ( μ g/m 3 )

11/17/2008 23.8 18.4% 4.4 22.7% 5.4 11/22/2008 49.4 13.5% 6.7 16.7% 8.3 11/23/2008 60.7 12.6% 7.6 15.5% 9.4 11/24/2008 43.0 9.9% 4.2 12.2% 5.2 11/25/2008 36.3 11.9% 4.3 14.8% 5.4 11/26/2008 43.1 12.7% 5.5 15.7% 6.8 11/27/2008 30.5 17.4% 5.3 21.4% 6.5 11/28/2008 30.0 12.8% 3.8 15.8% 4.7 11/30/2008 42.3 16.9% 7.1 20.8% 8.8 12/3/2008 29.3 13.0% 3.8 16.0% 4.7 12/4/2008 30.3 8.9% 2.7 10.9% 3.3 12/5/2008 45.5 16.8% 7.7 20.8% 9.5 12/6/2008 38.9 12.4% 4.8 15.3% 6.0 12/7/2008 32.0 13.4% 4.3 16.5% 5.3 12/10/2008 40.5 8.2% 3.3 10.1% 4.1 12/11/2008 49.3 9.7% 4.8 12.0% 5.9 12/12/2008 69.8 7.8% 5.5 9.7% 6.8

1/5/2009 25.7 8.6% 2.2 10.6% 2.7 1/7/2009 29.2 11.8% 3.4 14.5% 4.2 1/10/2009 43.5 16.8% 7.3 20.8% 9.1 1/11/2009 55.2 12.2% 6.7 15.0% 8.3 1/12/2009 38.0 17.5% 6.7 21.6% 8.2 1/13/2009 43.5 16.6% 7.2 20.6% 9.0 1/14/2009 50.2 15.3% 7.7 19.0% 9.6 1/15/2009 42.5 16.6% 7.0 20.5% 8.7 1/16/2009 50.1 17.4% 8.7 21.5% 10.8 1/17/2009 45.6 16.7% 7.6 20.7% 9.4 1/18/2009 28.1 16.0% 4.5 19.8% 5.6 1/19/2009 28.3 15.1% 4.3 18.8% 5.3 1/20/2009 23.2 14.1% 3.3 17.4% 4.0 1/29/2009 28.9 16.6% 4.8 20.5% 5.9 1/30/2009 39.7 14.6% 5.8 18.0% 7.2 1/31/2009 35.1 11.2% 3.9 13.8% 4.8 2/1/2009 26.7 11.9% 3.2 14.8% 3.9 2/2/2009 29.5 11.9% 3.5 14.6% 4.3 2/3/2009 28.0 14.8% 4.2 18.3% 5.1 2/4/2009 24.7 14.9% 3.7 18.2% 4.5

### A-10 Table A-4. Relative benefit (%) of Stag e 1 and Stage 2 burn bans predicted by BlueSky Gateway for Sacramento County. The maximum and minimum relative benefits at each receptor are boldfaced.

Stage Date Northeast Sacramento County Northwest Sacramento County Southwest Sacramento County Southeast Sacramento County Stage 1 Stage 2 Stage 1 Stage 2 Stage 1 Stage 2 Stage 1 Stage 2

11/17/2008 18.4 22.7 8.7 10.8 4.3 5.3 9.1 11.2 11/22/2008 13.5 16.7 4.9 6.0 2.3 2.9 3.6 4.5 11/23/2008 12.6 15.5 3.2 3.9 1.3 1.6 2.9 3.6 11/24/2008 9.9 12.2 3.3 4.1 0.8 1.0 2.0 2.5 11/25/2008 11.9 14.8 4.7 5.8 2.1 2.6 3.9 4.8 11/26/2008 12.7 15.7 2.2 2.6 0.9 1.1 4.0 5.0 11/27/2008 17.4 21.4 4.9 6.1 2.2 2.7 9.9 12.2 11/28/2008 12.8 15.8 2.2 2.7 1.0 1.2 4.5 5.5 11/30/2008 16.9 20.8 6.1 7.5 2.2 2.6 5.6 6.9 12/3/2008 13.0 16.0 4.4 5.4 2.1 2.6 4.9 6.1 12/4/2008 8.9 10.9 1.9 2.4 1.1 1.3 3.8 4.7 12/5/2008 16.8 20.8 5.9 7.3 4.5 5.5 9.8 12.0 12/6/2008 12.4 15.3 3.7 4.5 2.6 3.2 7.9 9.7 12/7/2008 13.4 16.5 3.6 4.4 1.9 2.4 6.9 8.5 12/10/2008 8.2 10.1 1.7 2.0 0.9 1.1 3.9 4.8 12/11/2008 9.7 12.0 2.1 2.5 1.2 1.4 5.2 6.4 12/12/2008 7.8 9.7 2.8 3.4 0.7 0.9 2.1 2.6

1/5/2009 8.6 10.6 5.1 6.2 1.3 1.6 2.3 2.8 1/7/2009 11.8 14.5 3.0 3.6 1.7 2.1 5.2 6.4 1/10/2009 16.8 20.8 6.3 7.7 5.0 6.1 10.7 13.1 1/11/2009 12.2 15.0 3.5 4.3 2.8 3.5 8.6 10.5 1/12/2009 17.5 21.6 6.8 8.4 7.0 8.6 11.7 14.5 1/13/2009 16.6 20.6 6.7 8.3 5.3 6.5 11.4 14.1 1/14/2009 15.3 19.0 5.3 6.5 3.9 4.8 9.4 11.7 1/15/2009 16.6 20.5 7.0 8.6 4.1 5.1 8.8 10.9 1/16/2009 17.4 21.5 7.1 8.7 5.0 6.1 11.8 14.5 1/17/2009 16.7 20.7 5.8 7.2 4.0 4.9 9.8 12.0 1/18/2009 16.0 19.8 6.2 7.7 4.8 5.9 9.9 12.3 1/19/2009 15.1 18.8 6.8 8.4 6.6 8.1 10.4 12.9 1/20/2009 14.1 17.4 6.4 7.9 5.6 6.9 8.5 10.5 1/29/2009 16.6 20.5 5.9 7.3 3.4 4.2 6.0 7.6 1/30/2009 14.6 18.0 4.9 6.1 3.3 4.0 9.2 11.3 1/31/2009 11.2 13.8 3.1 3.8 1.8 2.3 7.2 8.8 2/1/2009 11.9 14.8 4.0 4.9 2.8 3.5 7.8 9.6 2/2/2009 11.9 14.6 5.3 6.6 2.2 2.7 3.5 4.4 2/3/2009 14.8 18.3 7.1 8.8 2.4 2.9 4.6 5.7 2/4/2009 14.9 18.2 8.7 10.9 3.1 3.9 5.0 6.3

### A-11 REFERENCES

Craig K.J., Wheeler N.J.M., Reid S.B., Gilliland E.K., and Sullivan D.C. (2007) Development and operation of national CMAQ-based PM 2.5 

### forecast system for fire management. Presented at the 6th Annual CMAS Conference, Chapel Hill, NC, October 1-3 , by Sonoma Technology, Inc., Petaluma, CA (STI-3228).
Dudhia J. (1993) A non-hydrostatic version of the Penn State/NCAR mesoscale model: validation tests and simulation of an Atlantic cyclone and cold front. MWRv 121 , 1493- 1513.
E.H. Pechan & Associates, Inc. (2001) Economic Growth Analysis System: Version 4.0 Reference Manual. Final draft prepared for Emission Factor and Inventory Group, Emissions, Monitoring, and Analysis Divi sion, Office of Air Qu ality Planning and Standards, U.S. Environmental Protection Ag ency, Research Triangle Park, NC, January.
ENVIRON International Corporat ion (2008) User’s guide - Comp rehensive Air Quality Model with Extensions (CAMx). Version 4. 50. Prepared by ENVIRON International Corporation, Novato, CA, May. Available on the Internet at http://www.camx.com/files/CAMxUsersGuide_v4.5.pdf

. Grell G.A., Dudhia J., and Stauffer D.R. (1994) A description of th e fifth-generation Penn State/NCAR mesoscale model (MM5). Prepar ed by the National Center for Atmospheric Research, Boulder, CO, NCAR Technical Note-398.
Houyoux M., Vukovich J., and Brandmeyer J. (2000) Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE) user manual. Prepared by MCNC-North Carolina Supercomputing Center, Envi ronmental Programs, Research Triangle Park, NC.APPENDIX E

Emissions Calculations
The baseline emissions are based on historical usag e patterns. Emissions are calculated as follows:

Emissions = (Number of Homes) x (fraction of homes w/ device) x (fraction of homes w/ device

that burn) x (Average amount of wood burned per hom e) x (EF burning )

This calculation is repeated for each type of devic e, and the totals are summed. These emissions are assumed to all be emitted during the Check Before You Burn season, which includes 120 days. It is assumed that the percent of fuel burned in November through February is 69.1% of all wood burned.
The reduction from a day of mandatory curtailment i s calculated by taking 1/120 th of the total calculated emissions, and adjusting this amount by the emissions reduction rate determined from the 2009 Aurora survey. The total reduction f or the season for each option is calculated as the daily reduction times the expected additional n umber of days of mandatory curtailment.
Stage 1 and Stage 2 No Burn days are differentiated by allowing all certified and pellet devices to burn on Stage 1 and using stage specific emissio ns reduction rates.

The variables used and the calculations are summari zed in the following tables:2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

26

27

28

29

30 31 32 33 34 35 36 37 38 39 40
41
42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 B C D E

Source

Total Residences in Sacramento County 502095 Units Sta te of CA Finance Dept, 2008

% of Homes w/ fireplace 31% Aurora, 2007 % of Homes w/ fireplace that burn wood 63% Aurora, 20 07 % of Homes that burned that burned cord wood 80% Auro ra, 2007 % of Homes w/fireplace that burn wood for heat 31% Au rora 2007 % of Homes w/fireplace that burn wood for aesthetic 69% Aurora, 2007 % of Homes that used fireplace and burned Manf. Log s 20% Aurora, 2007 % of Homes w/ wood stove 5% Aurora, 2007 % of Homes w/ wood stove that are Phase II certifie d 43% Houck, 2003 % of the Phase II wood stoves that are catalyt ic 17% Houck, 2003 % of Homes w/ stove that burn 63% Aurora, 2007 % of Homes w/ wood insert 16% Aurora, 2007 % of Homes w/ wood insert that are Phase II certifi ed 45% Houck, 2003 % of the Phase II wood inserts that are cataly tic 50% Houck, 2003 % of Homes w/ inserts that burn 63% Aurora, 2007 % of Homes w/ insert that burn wood 87% Aurora, 2007 % of Homes w/pellet stove 3% Aurora, 2007 % of Homes w/pellet stove that burned 63% Aurora, 200 7

Average Wood Usage Per Household that burns wood (heat) 0.656 cord/home Houck, 2001 Average Wood Usage Per Household that burns wood (aesthetic) 0.069 cord/home Houck, 2001 Average Manufactured Log Use Per Household that burns wood 19 Log/yr Houck, 2003 Average Wood Use Per Household that burns wood (stove) 1.5 cord/home Houck, 2003 Average Wood Use Per Household that burns wood (insert) 1.2 cord/home Houck, 2003 Average Pellet Use Per Household that burns wood 4000 lb/yr Houck, 2004

Percent of Wood that is Purchased 51.4% Houck, 2003

Heating Value of Wood 6,050 Btu/lb Representative of Certifed Units Heating Value of Manufactured Log 15,700 Btu/lb Houck, Wax-Sawdust Firelogs

Uncontrolled Fireplace EF 23.6 lb PM 10 /ton of wood Pechan, 2006 Uncontrolled Stove/Insert EF 30.6 lb PM 10 /ton of wood AP 42 1.10 Controlled (non-catalytic) Stove/Insert EF 14.6 lb PM 10 /ton of wood AP 42 1.10 Controlled (catalytic) Stove/Insert EF 20.4 lb PM 10 /ton of wood AP 42 1.10 Manufactured Log EF 29.4 lb PM 10 /ton of log Li, 2006 Pellet Stove EF 3.06 lb PM 10 /ton of wood Houck, 2006 (MARAMA) Wood Smoke PM2.5/PM10 Fraction 0.96 CARB Estimate NG PM10 EF 0.007 lb/MMBtu AP 42 1.4 NOx EF Fireplace 2.6 lb/ton of wood AP 42 1.9 NOx EF Uncontrolled Stove 2.8 lb/ton of wood AP 42 1.9 NOx EF Phase II Stove 2.0 lb/ton of wood AP 42 1.9 NOx EF Pellet Stove 3.8 lb/ton of wood Houck, 2006 (MARAMA) CO EF Fireplace 128.0 lb/ton of wood 2002 NEI, Pechan, 2006 CO EF Uncontrolled Stove 230.8 lb/ton of wood AP 42 1.10 CO EF Phase II Stove 140.8 lb/ton of wood AP 42 1.10 CO EF Pellet Stove 15.9 lb/ton of wood Houck, 2006 (MARAMA) VOC EF Fireplace 229.0 lb/ton of wood AP 42 1.9 VOC EF Uncontrolled Stove 53.0 lb/ton of wood AP 42 1.10 VOC EF Phase II Stove 12.0 lb/ton of wood AP 42 1.10 VOC EF Pellet Stove 0.04 lb/ton of wood Houck, 2006 (MARAMA)

Thermal Efficiency Fireplace 7% Houck, 1998 Thermal Efficiency Certified Stove/Insert 63% Represe ntative of Certified Units Thermal Efficiency NG Fireplace 75% Houck, 1998

Incremental Cost to install Certified Fireplace Ins ert 2,500.00 $ John Crouch, HPBA Incremental Cost to install NG Fireplace Insert 3,00 0.00 $ Staff Survey Incremental Cost to install Gas Log set 300.00 $ Staff Survey

Cost of Wood ($/per Cord) 269.00 ERG, 2009 Cost of Manufactured Log ($/per Log) 5.00 ERG, 2009 Cost of Pellet ($/per Bag) 6.88 ERG, 2009 Cost of NG ($/MMBtu) 10.91 Staff Survey Cost of Electricity ($/kWh) 0.09 Staff Survey

Burn Rate of Wood in Fireplace 6.60 lb/hr Houck, 1998 Burn Rate of Manufactured Log in Fireplace 3.00 hr/lo g Houck, Wax-Sawdust Firelogs Median Burn Rate Certified Fireplace Insert 3.16 lb/h r Representative of Certified Units Heat Input Rate NG Insert 30,000 BTU/hr Staff Survey

Weight of an Average Cord of Wood 1.54 ton/cord Houck & Eagle, 2006 Weight of Manufactured Log 5.00 lb/log Broderick, 2003 % of Fuel Burned in November through February 69.1% CARB Inventory Methodology InformationRecommended Change - Stage 1 Total Housing Units 502095 57%
Threshold 31 μ g/m 3 Stage 1 Days 5

Wood Burning Appliance Types Fireplace Stoves Inserts P ellet Stoves Total

% 31% 5% 16% 3.0% 55% # total 155649 25105 80335 15063 276152 # total that burn 98059 15816 50611 9490 173976 # Phase II certified out of total that burn 6801 22775 # Catalytic out of the Phase II certified 1156 11388

Cords of fuel burned per winter day Nov-Feb 125 137 35 0 Tons of fuel burned Nov thru Feb (heat+asthetic) 230 46 25246 64629 Tons PM2.5 Emissions from cord wood Nov-Feb 271.9 Manufactured logs burned per winter day Nov-Feb 2146 Tons Manufactured logs burned Nov thru Feb 644 Tons PM2.5 Emissions from logs Nov thru Feb 9.5 Total Fireplace Emissions Nov thru Feb 281.4 Cords of fuel burned per winter day (Nov-Feb) nonce rtified 78 192 Cords of fuel burned per winter day (Nov-Feb) certi fied 59 157 Tons of fuel burned Nov-Feb uncertified 14390 35546 Tons of fuel burned Nov-Feb Phase II - non cat 9010 24139 Tons of fuel burned Nov-Feb phase II - cat 1845 4944 Tons PM2.5 Emissions Nov-Feb from uncertified 220.2 543.8 Tons PM2.5 Emissions Nov-Feb from phase II 65.8 176.2 Tons PM2.5 Emissions Nov-Feb from phase II - cataly tic 18.8 50.4 Total Stove Emissions Nov thru Feb 304.8 770.5 Tons fuel burned per winter pellet stove per day 109 Tons PM2.5 Emissions from Pellet Stove Nov thru Feb 20.1

$ amount of fuel used per winter day Nov thru Feb 33 ,635 $ 36,748

$ 94,075

$ 37,597

$ 202,055.77

$ $ amount of reduced fuel used per No Burn day Nov-F eb 19,172 $ 11,939

$ 29,493

$ -

$ 60,604.08

$ PM2.5 Total Uncontrolled Emissions (lb/day) Nov-Feb 4,690 5,079

12,842

334

22,945 PM2.5 Total Reduction (lb/day) Nov-Feb 2,673 2,092

5,167

-

9,931

Percent Reduction per curtailment day 57.0% 41.2% 40.2% 0.0% 43.3% Overall PM2.5 Cost Effectiveness ($/ton) 6,273.10 $ Overall PM2.5 Cost Effectiveness ($/lb) 3.14 $

NOx Uncontrolled (lb/day) 540 517 1,314 415 2,786 NOx Reduction (lb/day) 308 295

749

-

1,351 NOx from NG 10 45

110

-

165 Net NOx Reduction 298 250

639

- 1,186 CO Uncontrolled (lb/day) 25,253 36,229 93,852 1,738 157,071 CO Reduction (lb/day) 14,394 8,749

21,612

-

44,755 CO from NG 8 38

93

-

139 Net CO Reduction 14,386 8,712

21,519

- 44,616 VOC Uncontrolled (lb/day) 44,191 7,487 18,731 4.37 70,410 VOC Reduction (lb/day) 25,189 3,623

8,949

-

37,760 VOC from NG 1 2

6

-

9 Net VOC Reduction 25,189 3,620

8,943

- 37,751 Stage 1 Compliance RateRecommended Change - Stage 2 Total Housing Units 502095 70%
Threshold 35 μ g/m 3 Stage 2 Days 23

Wood Burning Appliance Types Fireplace Stoves Inserts P ellet Stoves Total

% 31% 5% 16% 3.0% 55% # total 155649 25105 80335 15063 276152 # total that burn 98059 15816 50611 9490 173976 # Phase II certified out of total that burn 6801 22775 # Catalytic out of the Phase II certified 1156 11388

Cords of fuel burned per winter day Nov-Feb 125 137 350 Tons of fuel burned Nov thru Feb (heat+asthetic) 230 46 25246 64629 Tons PM2.5 Emissions from cord wood Nov-Feb 271.9 Manufactured logs burned per winter day Nov-Feb 2146 Tons Manufactured logs burned Nov thru Feb 644 Tons PM2.5 Emissions from logs Nov thru Feb 9.5 Total Fireplace Emissions Nov thru Feb 281.4 Cords of fuel burned per winter day (Nov-Feb) nonce rtified 78 192 Cords of fuel burned per winter day (Nov-Feb) certi fied 59 157 Tons of fuel burned Nov-Feb uncertified 14390 35546 Tons of fuel burned Nov-Feb Phase II - non cat 9010 24139 Tons of fuel burned Nov-Feb phase II - cat 1845 4944 Tons PM2.5 Emissions Nov-Feb from uncertified 220.2 543.8 Tons PM2.5 Emissions Nov-Feb from phase II 65.8 176.2 Tons PM2.5 Emissions Nov-Feb from phase II - cataly tic 18.8 50.4 Total Stove Emissions Nov thru Feb 304.8 770.5 Tons fuel burned per winter pellet stove per day 109 Tons PM2.5 Emissions from Pellet Stove Nov thru Feb 20.1

$ amount of fuel used per winter day Nov thru Feb 33 ,635 $ 36,748

$ 94,075

$ 37,597

$ 202,055.77

$ $ amount of reduced fuel used per No Burn day Nov-F eb 23,545 $ 25,724

$ 65,853

$ 26,318

$ 141,439.04

$ PM2.5 Total Uncontrolled Emissions (lb/day) Nov-Feb 4,690 5,079

12,842

334

22,945 PM2.5 Total Reduction (lb/day) Nov-Feb 3,283 3,556

8,989

234

16,062

Percent Reduction per curtailment day 70.0% 70.0% 70.0% 70.0% 70.0% Overall PM2.5 Cost Effectiveness ($/ton) 10,645.16 $ Overall PM2.5 Cost Effectiveness ($/lb) 5.32 $

NOx Uncontrolled (lb/day) 540 517

1,314

415

2,786 NOx Reduction (lb/day) 378 362 920 291 1,950 NOx from NG 12 103

265

58

438 Net NOx Reduction 366 259

655

232 1,512 CO Uncontrolled (lb/day) 25,253 36,229

93,852

1,738

157,071 CO Reduction (lb/day) 17,677 25,360 65,696 1,216 109,949 CO from NG 10 87

222

-

319 Net CO Reduction 17,667 25,273

65,474

1,216 109,630 VOC Uncontrolled (lb/day) 44,191 7,487

18,731

4.37

70,410 VOC Reduction (lb/day) 30,934 5,241 13,112 3 49,287 VOC from NG 1 6

15

3

21 Net VOC Reduction 30,933 5,235

13,097

(0) 49,266 Stage 2 Compliance RateOption B - Stage 1 Total Housing Units 502095 57% Threshold 31 μ g/m 3 Stage 1 Days 5

Wood Burning Appliance Types Fireplace Stoves Inserts P ellet Stoves Total

% 31% 5% 16% 3.0% 55% # total 155649 25105 80335 15063 276152 # total that burn 98059 15816 50611 9490 173976 # Phase II certified out of total that burn 6801 22775 # Catalytic out of the Phase II certified 1156 11388

Cords of fuel burned per winter day Nov-Feb 125 137 350 Tons of fuel burned Nov thru Feb (heat+asthetic) 230 46 25246 64629 Tons PM2.5 Emissions from cord wood Nov-Feb 271.9 Manufactured logs burned per winter day Nov-Feb 2146 Tons Manufactured logs burned Nov thru Feb 644 Tons PM2.5 Emissions from logs Nov thru Feb 9.5 Total Fireplace Emissions Nov thru Feb 281.4 Cords of fuel burned per winter day (Nov-Feb) nonce rtified 78 192 Cords of fuel burned per winter day (Nov-Feb) certi fied 59 157 Tons of fuel burned Nov-Feb uncertified 14390 35546 Tons of fuel burned Nov-Feb Phase II - non cat 9010 24139 Tons of fuel burned Nov-Feb phase II - cat 1845 4944 Tons PM2.5 Emissions Nov-Feb from uncertified 220.2 543.8 Tons PM2.5 Emissions Nov-Feb from phase II 65.8 176.2 Tons PM2.5 Emissions Nov-Feb from phase II - cataly tic 18.8 50.4 Total Stove Emissions Nov thru Feb 304.8 770.5 Tons fuel burned per winter pellet stove per day 109 Tons PM2.5 Emissions from Pellet Stove Nov thru Feb 20.1

$ amount of fuel used per winter day Nov thru Feb 33 ,635 $ 36,748

$ 94,075

$ 37,597

$ 202,055.77

$ $ amount of reduced fuel used per No Burn day Nov-F eb 19,172 $ 11,939

$ 29,493

$ -

$ 60,604.08

$ PM2.5 Total Uncontrolled Emissions (lb/day) Nov-Feb 4,690 5,079

12,842

334

22,945 PM2.5 Total Reduction (lb/day) Nov-Feb 2,673 2,092

5,167

-

9,931

Percent Reduction per curtailment day 57.0% 41.2% 40.2% 0.0% 43.3% Overall PM2.5 Cost Effectiveness ($/ton) 6,273.10 $ Overall PM2.5 Cost Effectiveness ($/lb) 3.14 $

NOx Uncontrolled (lb/day) 540 517

1,314

415

2,786 NOx Reduction (lb/day) 308 295 749

- 1,351 NOx from NG 10 45

110

-

165 Net NOx Reduction 298 250

639

- 1,186 CO Uncontrolled (lb/day) 25,253 36,229

93,852

1,738

157,071 CO Reduction (lb/day) 14,394 8,749 21,612

- 44,755 CO from NG 8 38

93

-

139 Net CO Reduction 14,386 8,712

21,519

- 44,616 VOC Uncontrolled (lb/day) 44,191 7,487

18,731

4.37

70,410 VOC Reduction (lb/day) 25,189 3,623 8,949

- 37,760 VOC from NG 1 2

6

-

9 Net VOC Reduction 25,189 3,620

8,943

- 37,751 Stage 1 Compliance RateOption B - Stage 2 Total Housing Units 502095 70%
Threshold 35 μ g/m 3 Stage 2 Days 23

Wood Burning Appliance Types Fireplace Stoves Inserts P ellet Stoves Total

% 31% 5% 16% 3.0% 55% # total 155649 25105 80335 15063 276152 # total that burn 98059 15816 50611 9490 173976 # Phase II certified out of total that burn 6801 22775 # Catalytic out of the Phase II certified 1156 11388

Cords of fuel burned per winter day Nov-Feb 125 137 35 0 Tons of fuel burned Nov thru Feb (heat+asthetic) 230 46 25246 64629 Tons PM2.5 Emissions from cord wood Nov-Feb 271.9 Manufactured logs burned per winter day Nov-Feb 2146 Tons Manufactured logs burned Nov thru Feb 644 Tons PM2.5 Emissions from logs Nov thru Feb 9.5 Total Fireplace Emissions Nov thru Feb 281.4 Cords of fuel burned per winter day (Nov-Feb) nonce rtified 78 192 Cords of fuel burned per winter day (Nov-Feb) certi fied 59 157 Tons of fuel burned Nov-Feb uncertified 14390 35546 Tons of fuel burned Nov-Feb Phase II - non cat 9010 24139 Tons of fuel burned Nov-Feb phase II - cat 1845 4944 Tons PM2.5 Emissions Nov-Feb from uncertified 220.2 543.8 Tons PM2.5 Emissions Nov-Feb from phase II 65.8 176.2 Tons PM2.5 Emissions Nov-Feb from phase II - cataly tic 18.8 50.4 Total Stove Emissions Nov thru Feb 304.8 770.5 Tons fuel burned per winter pellet stove per day 109 Tons PM2.5 Emissions from Pellet Stove Nov thru Feb 20.1

$ amount of fuel used per winter day Nov thru Feb 33 ,635 $ 36,748

$ 94,075

$ 37,597

$ 202,055.77

$ $ amount of reduced fuel used per No Burn day Nov-F eb 23,545 $ 25,724

$ 65,853

$ 26,318

$ 141,439.04

$ PM2.5 Total Uncontrolled Emissions (lb/day) Nov-Feb 4,690 5,079

12,842

334

22,945 PM2.5 Total Reduction (lb/day) Nov-Feb 3,283 3,556

8,989

234

16,062

Percent Reduction per curtailment day 70.0% 70.0% 70.0% 70.0% 70.0% Overall PM2.5 Cost Effectiveness ($/ton) 10,645.16 $ Overall PM2.5 Cost Effectiveness ($/lb) 5.32 $

NOx Uncontrolled (lb/day) 540 517 1,314 415 2,786 NOx Reduction (lb/day) 378 362

920

291

1,950 NOx from NG 12 103

265

58

438 Net NOx Reduction 366 259

655

232 1,512 CO Uncontrolled (lb/day) 25,253 36,229 93,852 1,738 157,071 CO Reduction (lb/day) 17,677 25,360

65,696

1,216

109,949 CO from NG 10 87

222

-

319 Net CO Reduction 17,667 25,273

65,474

1,216 109,630 VOC Uncontrolled (lb/day) 44,191 7,487 18,731 4.37 70,410 VOC Reduction (lb/day) 30,934 5,241

13,112

3

49,287 VOC from NG 1 6

15

3

21 Net VOC Reduction 30,933 5,235

13,097

(0) 49,266 Stage 2 Compliance RateOption A1 or A2 - Single Stage Total Housing Units 502095 70% Threshold 35 μ g/m 3 Single Stage Days 31

Wood Burning Appliance Types Fireplace Stoves Inserts P ellet Stoves Total

% 31% 5% 16% 3.0% 55% # total 155649 25105 80335 15063 276152 # total that burn 98059 15816 50611 9490 173976 # Phase II certified out of total that burn 6801 22775 # Catalytic out of the Phase II certified 1156 11388

Cords of fuel burned per winter day Nov-Feb 125 137 350 Tons of fuel burned Nov thru Feb (heat+asthetic) 230 46 25246 64629 Tons PM2.5 Emissions from cord wood Nov-Feb 271.9 Manufactured logs burned per winter day Nov-Feb 2146 Tons Manufactured logs burned Nov thru Feb 644 Tons PM2.5 Emissions from logs Nov thru Feb 9.5 Total Fireplace Emissions Nov thru Feb 281.4 Cords of fuel burned per winter day (Nov-Feb) nonce rtified 78 192 Cords of fuel burned per winter day (Nov-Feb) certi fied 59 157 Tons of fuel burned Nov-Feb uncertified 14390 35546 Tons of fuel burned Nov-Feb Phase II - non cat 9010 24139 Tons of fuel burned Nov-Feb phase II - cat 1845 4944 Tons PM2.5 Emissions Nov-Feb from uncertified 220.2 543.8 Tons PM2.5 Emissions Nov-Feb from phase II 65.8 176.2 Tons PM2.5 Emissions Nov-Feb from phase II - cataly tic 18.8 50.4 Total Stove Emissions Nov thru Feb 304.8 770.5 Tons fuel burned per winter pellet stove per day 109 Tons PM2.5 Emissions from Pellet Stove Nov thru Feb 20.1

$ amount of fuel used per winter day Nov thru Feb 33 ,635 $ 36,748

$ 94,075

$ 37,597

$ 202,055.77

$ $ amount of reduced fuel used per No Burn day Nov-F eb 23,545 $ 25,724

$ 65,853

$ 26,318

$ 141,439.04

$ PM2.5 Total Uncontrolled Emissions (lb/day) Nov-Feb 4,690 5,079

12,842

334

22,945 PM2.5 Total Reduction (lb/day) Nov-Feb 3,283 3,556

8,989

234

16,062

Percent Reduction per curtailment day 70.0% 70.0% 70.0% 70.0% 70.0% Overall PM2.5 Cost Effectiveness ($/ton) 10,645.16 $ Overall PM2.5 Cost Effectiveness ($/lb) 5.32 $

NOx Uncontrolled (lb/day) 540 517

1,314

415

2,786 NOx Reduction (lb/day) 378 362 920 291 1,950 NOx from NG 12 103

265

58

438 Net NOx Reduction 366 259

655

232 1,512 CO Uncontrolled (lb/day) 25,253 36,229

93,852

1,738

157,071 CO Reduction (lb/day) 17,677 25,360 65,696 1,216 109,949 CO from NG 10 87

222

-

319 Net CO Reduction 17,667 25,273

65,474

1,216 109,630 VOC Uncontrolled (lb/day) 44,191 7,487

18,731

4.37

70,410 VOC Reduction (lb/day) 30,934 5,241 13,112 3 49,287 VOC from NG 1 6

15

3

21 Net VOC Reduction 30,933 5,235

13,097

(0) 49,266 Stage 2 Compliance RateAPPENDIX G
ERG Final Report Socioeconomic Evaluation

# SOCIOECONOMIC IMPACT ANALYSIS FOR SMAQMD RULE 421 AMENDMENT

Final Report

Sacramento Metropolitan Air Quality Management District Program Coordination Division 777 12 th 

## Street, 3 rd 

## Floor Sacramento, CA 95814

August 21, 2009

## SOCIOECONOMIC IMPACT ANALYSIS FOR SMAQMD RULE 421 AMENDMENT

Final Report

Prepared for:
Sacramento Metropolitan Air Quality Management District Program Coordination Division 777 12 th 

### Street, 3 rd 

### Floor Sacramento, CA 95814

Prepared by:
Eastern Research Group, Inc. (ERG) 8950 Cal Center Drive Suite 348 Sacramento, CA 95826

August 21, 2009Socioeconomic Impact Analysis - Rule 421 Amendment Final, August 21, 2009 i 

## TABLE OF CONTENTS

Section Page

EXECUTIVE SUMMARY .............................................................................................................1

1.0 INTRODUCTION ............................................................................................................... ....3

2.0 OPTIONS AND QUANTITIES AFFECTED.........................................................................4

3.0 SUPPLIER INDUSTRIES ......................................................................................................4

3.1 Industry Characterization........................................................................................... 6 3.1.1 Very Small Independents............................................................................... 7 3.1.2 Small Independents—Primary Business........................................................ 7 3.1.3 Small Independents—Secondary Business.................................................... 8 3.1.4 Retailers—Wood Bundles ............................................................................. 9 3.1.5 Manufactured Logs ...................................................................................... 10 3.1.6 Pellets........................................................................................................... 11 3.2 Potential Impacts...................................................................................................... 12 3.2.1 Small Independents—Primary and Secondary Business ............................. 12 3.2.2 Retail Stores................................................................................................. 15 3.2.3 Manufactured Log Companies..................................................................... 18 3.2.4 Pellet Manufacturer s and Retailers .............................................................. 18

4.0 CONSUMERS.................................................................................................................. .....18

4.1 No Cost Increase Scenario....................................................................................... 18 4.1.1 Residents...................................................................................................... 19 4.2 Price Increase Scenario............................................................................................ 21 4.2.1 Industries and Businesses ............................................................................ 21 4.2.2 Residents...................................................................................................... 21

5.0 CONCLUSIONS ................................................................................................................ ...22

6.0 REFERENCES ................................................................................................................. .....24

Tables Page

ES1 Summary of Impacts on Wood Suppliers ............................................................................2 1 Options and Fuel Qu antities Affected..................................................................................5 2 Small Independent Wood Dealers–Primary Business .........................................................7 3 Small Independent Wood Dealers–Primary Business Revenues and Number of Employees .................................................................................................................. .....7 4 Small Independent Wood Dealers–Secondary Business .....................................................8Socioeconomic Impact Analysis - Rule 421 Amendment Final, August 21, 2009 ii 

## TABLE OF CONTENTS - Continued

5 Small Independent Wood Dealers–Secondary Business Revenues and Number of Employees .................................................................................................................. .....9 6 Retail Stores in Sacramento C ounty Potentially Selling Wood Bundles...........................10 7 Value of Wood, Manufactured Logs , and Pellets not Purchased ......................................13 8 Upper Bound Potential Impacts on Independent Wood Dealers .......................................14 9 Potential Impact on Retail Stores.......................................................................................17 10 Increased Costs as a Percentage of Me dian Household Income for Households that Burn Wood................................................................................................................ ..22 11 Summary of Impacts on Wood Suppliers ..........................................................................23

Figures Page

1 Supplier Types for Wood and Other Solid Fuels.................................................................6 2 Consumers of Wood and Other Solid Fuel: No Cost Increase Scenario ...........................19Socioeconomic Impact Analysis - Rule 421 Amendment Final, August 21, 2009 

### 1 EXECUTIVE SUMMARY Rule 421, Mandatory Episodic Curtailment of Wood and Other Fuel Burning, is being proposed for amendment. SMAQMD is consider ing four options for modifying the rule:

• Recommended Option: Reduce Stage 1 threshold from 35 to 31 μg/m 3 

### and reduce Stage 2 threshold from 40 to 35 μg/m 3



### .

• Option A: Eliminate 2 stages; set no-burn threshold to 30 μg/m 3 

### , and either keep the voluntary threshold at 25 μg/m 3 

### or reduce it to 20 μg/m 3



### .

• Option B: Reduce Stage 1 threshold to 25 μg/m 3 

### , reduce Stage 2 threshold to 30 μg/m 3 

### , and reduce voluntary threshold to 20 μg/m 3



### .

• Contingency Option: Eliminate 2 stages; set no-burn threshold to 20 μg/m 3


### ERG—through information supplied by SMAQMD, Yellow Book searches, and Internet searches—identified independent wood dealer s, manufactured l og producers, pellet manufacturers, and retail stores potentially affected by the rule. ERG searched databases such as
Dun & Bradstreet’s Million Dolla r Directory and manta.com to id entify revenues and the number of employees at each company or at their Sacramento County location(s).

ERG evaluated the four options under two different economic scenarios (i.e., price increase or no price increase) and used wo rst case analyses to avoid the potential to
underestimate costs.

For residential consumers of wood and other solid fuels, SMAQMD has mitigated impacts on low income populations by including a hardship exemption. If suppliers raise pricesto maintain revenues when sales volumes decline, the estimated price increase is less than onequarter of one percent (<0.25 percent) of the 2007 median household income for Sacramento County.

For commercial consumers of wood and other solid fuels (e.g. rest aurants and lodgings), burning is discretionary. If suppliers raise pric es, each business is anticipated to weigh the increased price against the perceived benefit to the business’s ambience to determine whether or not to pay the increased prices. No employment losses are anticipated in this sector as a result of
the rule amendment.Socioeconomic Impact Analysis - Rule 421 Amendment Final, August 21, 2009 

### 2 Impacts to wood and solid fuel suppliers ar e summarized in Table ES1. If the suppliers are able to raise prices to compensate for lowe r sales volumes, there are no impacts under any of the options. If suppliers are unable to raise prices, then small impacts might occur.

Impacts on retailers are negligible.

Table ES1. Summary of Impacts on Wood Suppliers

Scenario and Potential

Impact Option Independent Wood Dealers Manufactured Log Producers Retail Stores Wood Stove and Pellet

Retailer Price Increase Potential Loss in Revenues Recommended Option 0% 0% 0% 0% Option A 0% 0% 0% 0% Option B 0% 0% 0% 0% Contingency Option 0% 0% 0% 0% Potential Loss in Employment Recommended Option 0 0 0 0 Option A 0 0 0 0 Option B 0 0 0 0 Contingency Option 0 0 0 0 No Price Increase Potential Loss in Revenues Recommended Option 4.6% 0.5% 0.01% 0.01% Option A 8.7% 0.8% 0.03% 0.03% Option B 14.0% 1.8% 0.04% 0.04% Contingency Option 34.1% 3.8% 0.10% 0.10% Potential Loss in Employment Recommended Option 2.8 1.2 0 0 Option A 5.3 2.1 0 0 Option B 8.5 4.4 0 0 Contingency Option 20.6 9.4 0 0


### The results reported in Table ES1 are based on worst case analyses, which incorporate such assumptions as:

• No independent wood dealers located outside of Sacramento C ounty sell wood within Sacramento County. If such dealers exist, the impacts would be reduced proportionately by the fraction of wood sold in Sacramento Country by extra-county dealers.Socioeconomic Impact Analysis - Rule 421 Amendment Final, August 21, 2009 

### 3 • All impacts from reduced sale of manuf actured logs are absorbed by the one manufactured log producer that is headquartered in California. In reality, the impacts would reflect that company’s market share of manufactured log sales in Sacramento County.

1.0 INTRODUCTION Rule 421, Mandatory Episodic Curtailment of Wood and Other Fuel Burning, is being proposed for amendment. Under the current re gulation, a Stage 1 “no-burn day” prohibits burning except in EPA certified wood devices or pellet devices. A Stage 2 no-burn day prohibits all burning. The rule does not apply to fireplaces and devices that burn gaseous fuels (natural gas or propane). The rule exempts burni ng that is the sole source of h eat or in situations when not burning would be a financial hardship to a resident.

Sacramento County exceeds state or federal hea lth standards for fine particle air pollution (PM 2.5 

### ). The burning of wood or other solid fuel c onstitutes the single la rgest source of fine particle emissions for this region during the wi ntertime. As a result, SMAQMD is considering four options for modifying the rule:

• Recommended Option: Reduce Stage 1 threshold from 35 to 31 μg/m 3 

### and reduce Stage 2 threshold from 40 to 35 μg/m 3


### .

• Option A: Eliminate 2 stages; set no-burn threshold to 30 μg/m 3 

### , and either keep the voluntary threshold at 25 μg/m 3 

### or reduce it to 20 μg/m 3



### .

• Option B: Reduce Stage 1 threshold to 25 μg/m 3 

### , reduce Stage 2 threshold to 30 μg/m 3 

### , and reduce voluntary threshold to 20 μg/m 3



### .

• Contingency Option: Eliminate 2 stages; set no-burn threshold to 20 μg/m 3


### Section 2.0 of this report describes the quant ities of wood, manufactur ed logs, and pellets that would not be burned pursuan t to a no-burn day, examines the change in the number of Stage 1 and Stage 2 no-burn days under each option, and estimates the annual quantities of fuel not burned for each option.

The proposed amendments have the potential to affect both the businesses which supply
the fuel as well as the residents that consume the fuel. Section 3.0 describes the suppliers, the steps taken to identify the potentially affected industry sectors and prices, and estimates the potential impact of the options.Socioeconomic Impact Analysis - Rule 421 Amendment Final, August 21, 2009 

### 4 Section 4.0 of this report describes the cons umers potentially aff ected by the changes.

Section 5.0 of this report summarizes conclu sions regarding the f our options evaluated.

This socioeconomic analysis uses two boundi ng assumptions to study the suppliers and consumers. The first scenario is that suppliers are not able to raise prices. This scenario maximizes the potential impacts on the suppliers but minimizes them for the consumers. The second scenario is that suppliers are able to maintain their in come by raising prices. The second scenario minimizes impacts on suppliers but maxi mizes them for consumers. What is likely to happen lies in the middle of the findings. There ar e also places where “worst case” assumptions are made in order not to underestimate potentia l impacts. These are noted throughout the report.

2.0 OPTIONS AND QUANTITIES AFFECTED Table 1 summarizes the estimated quantities of fuel not burned and not purchased under each of the three options plus the continge ncy option. The fuel categories are firewood, manufactured logs, and pellets. For each fuel source, SMAQMD has estimated a range in the quantity not burned during a Stage 1 and Stag e 2 no-burn day. For each of the options, SMAQMD calculated the change in the number of Stage 1 and Stage 2 burn days. For example, Option A is considered to result in 15 more Stage 2 days and 7 fewer Stage 1 days for a net increase of 8 no-burn days.

Table 1 also provides estimates of the cha nges by type of wood product. For example, pellets are affected only by the change in th e number of Stage 2 no-burn days, while firewood may or may not be purchased; the economic analysis is structured to address this possibility.

3.0 SUPPLIER INDUSTRIES
Section 3.1 describes the charac teristics of the fuel supplie rs while Section 3.2 examines the potential impacts of the Ru le 421 options on these groups.Socioeconomic Impact Analysis – Rule 421 Amendment 

### 5 Final, August 21, 2009Table 1. Options andFuel Quantities AffectedChange in Number of No Burn DaysProducts Not BurnedProducts NotBurned PerDayRecommendedOptionOption AOption BContingencyOptionProductsUnitsStage1Stage2Stage1Stage2Stage1Stage2Stage1Stage2Stage1Stage2RecommendedOptionOptionAOptionBContingencyOptionWoodcord 225 428 -2 7 -7 15 6 15 -7 48 2,546 4,845 7,770 18,969ManufacturedFire Logslog 1,223 1,502 -2 7 -7 15 6 15 -748 8,068 13,969 29,868 63,535Pellets ton 0 77 -2 7 -7 15 6 15 -7 48 539 1,155 1,155 3,696Quantity Not PurchasedProductsUnitsPercentPurchasedRecommendedOptionOptionAOptionBContingencyOptionWoodcord 51.4% 1,309 2,490 3,994 9,750ManufacturedFire Logslog 100.0% 8,068 13,969 29,868 63,535Pellets ton 100.0%539 1,155 1,155 3,696Sources: SMAQMD, 2009; Williams, 2009; and Houck, 2003.Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 6 3.1 Industry Characterization
ERG identified six types of fuel suppliers that could potentially be affected by the amendments to Rule 421 (See Figure 1). Cons umers can purchase wood from local dealers whether they are individuals (“very small inde pendents”) or small independent companies as a primary or secondary line of business. These thre e types of fuel supplie rs conduct their business directly with the consumer and tend to sell in units of cords or fractions of cords. Thus, any price increase by these suppliers is borne directly by the consumer and, conve rsely, any reduction in fuel demand affects the suppliers’ revenues.

Figure 1. Supplier Types fo r Wood and Other Solid Fuels

Suppliers

Very Small Independents

Small Independents

Non-Local

Manufactured Logs

Pellets Primary Business

Secondary Business

Retailer

Retailer

Retailer Consumers

Consumers

Consumers

Consumers

Consumers

Consumers



### Consumers might purchase wood or other solid fu el from retailers, such as local grocery or hardware stores. The fuel might take the form of wood bundles, manufactu red logs, or pellets. For these supply chains, impacts might fall on ma nufacturers (if located in Sacramento County), retailers, and consumers. Section 3.1 describes the characteristic s of the fuel suppliers while Section 3.2 examines the potential impacts of the Rule 421 options on these groups.Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 7 3.1.1 Very Small Independents

ERG examined craigslist and Penny Saver to locate what we term “very small independents.” These appear to be individuals th at sell small quantities of firewood as a source of secondary income. Addition information (e.g., sales location, financial information, or even primary income source) for this sector could not be located. Thus, a lthough we found about 25 individuals in this sector, we cannot estimate impacts except to say that, as a group they form a very small part of the supply, so it is likely that, as a group, they would incur a proportionally very small part of the impacts.

3.1.2 Small Independents—Primary Business
Based on information provided by SMAQMD, and through local Yellow Pages and Internet searches, ERG identifie d eight businesses that sell firewood as a primary business (See Table 2). Of the eight businesse s, ERG found information on the annual revenues and number of employees for seven of them. Annual revenu es ranged from $58,000 to $800,000 while the number of employees ranged from 1 to 12 (See Table 3).

Table 2. Small Independent Wood Dealers—Primary Business

Company Name Hillerman Farms N.P.C. Firewood Hurd's Firewood Pak & Save Firewood M&M Firewood Tony's Firewood Muschetto's Firewood LLC Turner's Firewood Service


### Table 3. Small Independent Wood D ealers—Primary Business Revenues and Number of Employees

Parameter Annual Revenues Employees Minimum $58,000 1 Maximum $800,000 12 Average $474,321 4 Imputed total $3,794,571 35 

### ERG computed the average revenues and number of employees (roughly $474,300 and 4 employees, respectively) and used the average va lues for the business for which no information could be found. With this imputation, we calcula ted that the small independent wood dealers in Sacramento County represent 35 jobs and a bout $3.8 million in annual revenue. All of the
businesses in Table 2 are small businesses.Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 8 These businesses are listed with one of two NAICS codes: 423990 or 454319. NAICS
423990 is Other Miscellaneous Durable Goods Merchant Wholesaler s. Although firewood merchant wholesalers are part of NAICS 423990, so are merchant dealers for prerecorded DVDs CDs, audio tapes, coin-operated games, musical instruments and accessories, luggage, and other miscellaneous durable goods. Thus, the Census da ta contained under this NAICS code includes information for a wide variety of other goods. NAICS 454319 encompasses only firewood and coal dealers that sell directly to the public. Acco rding to the 2007 County Business Patterns (most current available), 5 establishments fo r NAICS code 454319 and 30 establishments for the wider ranging NAICS 423990 are located in Sacramento County (Census, 2009a).

ERG called these wood dealers for prices pe r cord of wood, and determined that the average price is $301/cord. Wood dealers reported selling alm ond, cedar, cherry, eucalyptus, oak, peach, pine, and walnut. The wood dealers also supplied descriptors such as "semi-dry" and "dry" and generic labels such as "hardwood" and "mixed hardwood."

3.1.3 Small Independents—Secondary Business
Based on information provided by SMAQMD, and through local Yellow Pages and Internet searches, eight businesse s sell firewood as a secondary business (See Table 4). That is, these businesses do landscaping, tree pruning, and similar activities. Rather than disposing of the wood that is cut during the course of their prim ary business, these companies sell the firewood as a secondary income. Of the ei ght businesses, ERG found information on the annual revenues and number of employees for six. Annual reve nues ranged from $48,000 to $820,000 while the number of employees ranged from 1 to 8 (See Table 5).
Table 4. Small Independent Wood Dealers—Secondary Business

Company Name A-1 Rock & Materials Bill's Firewood and Tree Service Bill's Urban Forest Mgmt. Foothill's Firewood & Christmas Trees Jaime Santos Tree Service Longer Decorative Rock & Ready Mix Premier Landscaping Tree Pros IncorporatedSocioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 9 Table 5. Small Independent Wood Dealers—Secondary Business Revenues and Number of Employees

Parameter Annual Revenues Employees Minimum $48,000 1 Maximum $819,000 8 Average $486,167 3 Imputed total $3,889,333 25



### ERG computed the average revenues a nd number of employees (roughly $486,000 and three employees respectively) and used the average values for the business for which no information could be found. With this imputatio n, ERG calculated that the small independent wood dealers in Sacramento County represent 25 jobs and nearly $3.9 million in revenue. All
businesses listed in Table 4 are small businesses.

Consistent with the idea that selling firewood is a secondary business for these companies, most of them are listed w ith a different set of NAICS codes:

• 236210: Industrial Building Construction

• 444220: Nursery and Garden Centers

• 453998: All Other Miscellaneous Store Retailers

• 454319: Other Fuel Dealers

• 541320: Landscape Architectural Services

• 561730: Landscaping Services
Census data, then, would contai n information on a variety of activities other than selling firewood. ERG called these wood dealers for pric es per cord of wood. The average price is $237/cord. The species provided by the dealers are the same as mentioned in Section 3.1.2.

3.1.4 Retailers—Wood Bundles Through local Yellow Pages and Internet searches , ERG compiled a list of retail stores and verified that each retailer operate d a store in Sacramento County. Ta ble 6 is the resultant list of 25 retail stores that might sell wood bundles.Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 10

Table 6. Retail Stores in Sacramen to County Potentially Selling Wood Bundles

Company Ace Hardware 7-Eleven The Home Depot Beverages & More Lowe's Food Maxx

CVS Food Source Costco Foods Co. Safeway Raley's

Kmart Bel Air Michaels Nugget Market Orchard Supply Hardware Pitco Rite Aid Save Mart Supermarkets Sam's Club Smart & Final Target Winco Foods Wal-mart



### ERG called each of the businesse s to ask if they sold firewood and, if so, who supplied the
firewood. Of the 25 businesses:

• 7 of them did not sell firewood

• 9 knew their firewood supplier

• 9 did not know their firewood supplier

All nine businesses that knew their firewood s upplier also knew that the supplier was an outside company and not a local wood dealer. Four brands/companies were specifically mentioned: California Hotwood, CalOak Firewood, L azzari Fuel and Best of the West. The first
three businesses are located in neighboring counties (Stani slaus, San Mateo, and Butte, respectively). Best of the West appears to be a brand sold only by Smart & Final which is headquartered in Los Angeles. Thus, to estima te impacts on Sacramento County, the retailers, but not the companies that supply them, need to be considered.

3.1.5 Manufactured Logs
ERG identified four manufactured log compan ies whose products are sold in Sacramento County (and elsewhere in Ca lifornia and other states):

• Duraflame, Inc., headquartered in Stockton, CA

• Enviro-Log, headquartered in Fitzgerald, GA

• Landmann USA, headquartered in Cartersville, GA

• Jarden Corporation, headquart ered in Daleville, INSocioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 11 Jarden Corporation manufactures Java L og, Northland, Pine Mount ain, and Starter Logg through its Branded Consumables segment. 1 

### For 2008, the Branded Consumables segment reported $805 million in net sales while Jard en Corporation reported $5,383 million (Jarden, 2008).

Duraflame is the only company that has its pr incipal office in California. However, as a company, it reports having approximately 250 empl oyees. This exceeds the state small business size standard of 100 employees.

As shown above, Landmann USA and Enviro-L og are not headquartered in California; therefore, none of the four producers of manuf actured logs meet the definition of a California
small business.

ERG obtained 24 price estimates for manuf actured logs. These ranged from roughly $2.20/log to $7.51/log with an average of $4.91/lo g. For calculating the estimated costs, ERG rounded this value to $5.00/log.

3.1.6 Pellets

ERG searched www.woodpelletprice.com and http://www.pelletheat.org/3/residential/ fuelAvailability.cfm#west to identify North American wood pellet retailers a nd manufacturers. ERG then looked at each supplier’s website to see if they deliv ered to Sacramento County and compiled a list of manufacturers and re tailers. The manufacturers include:

• Bear Mountain Forest Products Inc ., headquartered in Portland, OR

• West Oregon Wood Products, headqu artered in Columbia City, OR

• Rocky Mountain Pellet Company, Inc., headquartered in Walden, CO, and

• Traeger Pellet Grills, LLC., h eadquartered in Silverton, OR.

None of these businesses meet the definition of a California small business because none are headquartered in the state.

1 American Fire Log is co-locat ed with the Pine Mountain Corporation address; thus it is likely to be another brand name

manufactured by Jarden Corporation even though it is not specifi cally mentioned in Jarden Co rporation’s 10-K form for 2008. (Jarden, 2008.)Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 12 ERG identified a wood stove retailer—a Californi a small business—that also sold pellets. ERG located the private company in manta.co m which reported annual company revenues were in the range of $2.5 million to $5 million. In addition, major retailers such as Home Depot and Lowe’s also sell pellets. ER G found pellet prices of about $7.49 per 40-lb bag for local nonchain retailers and $6.27 per 40-lb bag for local chain stores (e.g., Lowe’s). ERG used a midpoint value of $6.88 per bag to calculate a cost of $344/ton.

3.2 Potential Impacts Table 7 lists the quantity of fuel not purchas ed (from Table 1) with the average prices by fuel type as of summ er 2009. ERG could not identify the re lative market share for very small independents, small independents for which se lling wood is a primary business, or small independents for which selling wood is a secondar y business. As a result, Table 7 calculates the value of wood not sold based on a price of $269/ cord—the mid-point between the prices for primary and secondary wood dealers. The cost of the recommended Option is approximately $0.6 million, and the estimated costs for Option A, Option B, and the Contingency Option are $1.1 million, $1.6 million, and $4.2 million, respectively. The potential impacts of these lost sales are examined by industry sector.

3.2.1 Small Independents—Primary and Secondary Business
ERG examined impacts on small independent wood dealers under two scenarios. The
first scenario assumed that wood dealers are not able to raise pr ices to maintain their income while selling a smaller quantity of wood. The second scenario examined the price increase that would offset the smaller quantity of wood sold.

No Price Increase

For the purpose of examining the upper bound of impacts (worst case) on these small businesses, ERG assumed that the total loss in the value of wood sales was borne entirely by independent wood dealers in Sacramento County. In addition, the dealers are assumed to be unable to recoup any income by raising prices . (In this scenario, no impacts are borne by consumers because there are no price increases.)Socioeconomic Impact Analysis – Rule 421 Amendment 

### 13 Final, August 21, 2009Table 7. Value of Wood, Manufactured Logs, and Pellets Not PurchasedQuantity Not PurchasedValue Not PurchasedProductsUnitsRecommendedOptionOptionAOptionBContingencyOptionCostPer UnitRecommendedOptionOption AOption BContingencyOptionWood cord 1,309 2,490 3,994 9,750 $269 $352,025 $669,899 $1,074,327 $2,622,768ManufacturedFire Logs log 8,068 13,969 29,868 63,535 $5 $40,340 $69,845 $149,340 $317,675Pelletston 539 1,155 1,155 3,696 $344 $185,416 $397,320$397,320 $1,271,424AggregateCost $577,781 $1,137,064 $1,620,987 $4,211,867Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 14 Table 8 shows the impacts on wood dealers unde r a no-price increase scenario. The value of wood not purchased is estimated at $0.35 million (Recommended Option), $0.67 million (Option A), $1.07 million (Option B), and $2.62 million (Contingency Option). The total revenue for independent wood dealers in Sacram ento County is approximately $7.68 million (see Tables 3 and 5). Thus, the lost value is about:

• 4.6 percent of revenues fo r the Recommended Option

• 8.7 percent of revenues for Option A

• 14.0 percent of revenues for Option B

• 34.1 percent of revenues for the Contingency Option.

Table 8. Upper Bound Potential Im pacts on Independent Wood Dealers

Value Not Purchased

Parameter Recommended Option Option A Option B Contingency Option Value Not Purchased $352,025 $669,899 $1,074,327 $2,622,768 Group Revenues $7,683,905 $7,683,905 $7,683,905 $7,683,905 Percent of Group Reven ues 4.6% 8.7% 14.0% 34.1% Group Employment 60 60 60 60 Upper Bound Estimate of Employment Loss 2.8 5.3 8.5 20.6 Sacramento County (May 2009) Labor Force 687,758 687,758 687,758 687,758 Employment 610,444 610,444 610,444 610,444 Unemployment rate 11.2% 11.2% 11.2% 11.2% Unemployment rate after option 11.2% 11.2% 11.2% 11.2% Change due to option 0.0% 0.0% 0.0% 0.0%

Source: Tables 3, 5, and 7; BLS, 2009.



### ERG assumed that a small business might r eact by laying off workers in response to
lower revenues and that this reaction would be proportional to the loss in income incurred as a result of an increase in no-burn days. As a group, independent wood dealers employ approximately 60 people in Sacramento County (See Tables 3 and 5). Thus, the potential loss in jobs might be about 3 for the Recommended Op tion, 5 for Option A, 8 to 9 for Option B, and nearly 21 for the Contingency Option.Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 15 ERG examined local area unemployment sta tistics for Sacramento County as of May 2009 (BLS, 2009). The local unemployment rate woul d not change significantly under any of the options considered.

As mentioned above, these impacts are calculated with the assumption that all impacts
fall only on independent wood dealers in Sacramento County. If there are wood dealers in neighboring counties that sell wood in Sacramento County, then the potential loss in the value of wood not sold would be spread across a larger group of companies with a proportional decrease in the impact.

Price Increase

The wood dealers could potentially raise prices to recoup the lost value of wood not sold due to increased no-burn days. Table 8 indicates th at price increases of approximately 5 percent would mitigate any revenue losses from redu ced wood sales under the Recommended Option. For Options A and B, price increases between 9 and 14 percent would offset a lower volume of sales. Under the Contingency Option, a price increase of 34 percent would mitigate impacts on
the wood dealers. In this scen ario, impacts are borne by the consumers and not the wood dealers.

3.2.2 Retail Stores
Retail stores might sell firewood, manufactured logs, and/or pellets. For the analysis of this sector, ERG made the worst case assumption that the entire cost of the option for all fuel

sources is borne by retail stores. (If this we re accurate, there would be no impacts on independent fuel dealers, or manu facturers of pellets and logs.)

For retail stores, we examined the percentage loss in revenue as a measure of impacts because we did not know the profit made on each unit. 2 

### The San Joaquin Valley Unified Air Pollution Control District (SJVUAPCD, 2008) used ne t profit rates in its analysis. But because we would need to assume that the profit ra tes are comparable across all items sold, the percentage of revenues and the percentage of profits lost would be the same.

2 SJVUAPCD (2008), Appendix C, Table 7 compar es the value of lost sales to the estimated net profits by type of retailer. This is akin to saying that firewood sales are all profit (i.e., there are no wholesale costs or operating costs).Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 16 Table 9 summarizes the analysis. First, ERG st arted with the taxable transactions at retail stores in California for fiscal year 2007- 2008 (California, 2009 [T able 19]) for general merchandise stores, drug stores, food stores, and building material retail stores. These types of
retail stores accounted for $112 billion in sales. In order to analyze the potential effects of the options on the wood stove retailers that also sold wood pellets (see Section 3.1.6), ERG added a column for the retailer type, entered the mid-point value for the revenues, and scaled the reported revenues for the other types of retailers to keep the same value of total sales. 3



### Second, ERG multiplied the taxable transactions by the percentage of retail sales in Sacramento County (California, 2009 [Table 20] ) for retail types other than wood stove and pellet retailers. Becau se the revenues represent sales to Sacr amento retailers, 100 percent of the
revenues are from wood stove and pellet retailers.

The distribution of firewood and solid fuel sales among types of retail stores is taken from SJVUAPCD (2008). ERG summed the entries for lumber/building materials and hardware stores because California (2009) did not provide a separate line entry for hardware stores. ERG calculated the percen tage of Sacramento sales represented by wood stove and pellet retailers, and
scaled the SJVUAPCD distributi on of firewood and solid fuel sales to accommodate the additional type of retailer.

The block of entries labeled “Value of Lost Sa les” in Table 9 is the prorated cost of an option for that type of retail store. Note that th e entries listed in the Total column correspond to the estimated option costs in Table 7. The block of entries labeled “Percentage of Lost Sales” is the percentage that these costs comprise of store revenues. Under th e Recommended Option, the
estimated fraction of lost sales does not exceed 0.03 percent for any type of retail store. Under Options A and B, estimated fraction of lost sa les does not exceed 0.08 percent. Even under the Contingency Option, the costs comprise less than 0.25 percent of total revenues for any type of retail store. It is unlikely that such small percenta ges in lost revenue would lead to any job losses.

3 ERG multiplied the revenues for each retailer type by (1- ($3.75 million/$112 billion)) or 0.999967, the fraction of Californi a revenues from these retailer types represente d by the wood stove and pellet dealer.Socioeconomic Impact Analysis – Rule 421 Amendment 

### 17 Final, August 21, 2009Table 9. Potential Impact on Retail StoresParameterGeneralMerchandiseStoresDrug StoresFood StoresLumber/ BuildingMaterials*Stove And PelletRetailers inSacramentoCountyTotalState Sales $52,709,151,410 $6,560,470,618 $22,883,207,787 $29,988,589,184 $3,750,000 $112,145,169,000Sacramento Fractionof State Sales3.63% 3.63% 3.63% 3.63% 100.00%EstimatedSacramento Sales $1,913,342,196 $238,145,083 $830,660,443 $1,088,585,787 $3,750,000 $4,070,733,510Firewood Sales byRetail Store Type29.2% 9.6% 41.0% 20.2% 0.1% 100.00%Value of Lost SalesRecommendedOption $168,557 $55,416 $236,672 $116,604 $532 $577,781Option A $331,717 $109,058 $465,767 $229,475 $1,047 $1,137,064Option B $472,892 $155,471 $663,992 $327,138 $1,493 $1,620,987Contingency Option $1,228,732 $403,967 $1,725,275 $850,013 $3,880 $4,211,867Percentage of Lost SalesRecommendedOption0.01% 0.02% 0.03% 0.01% 0.01% 0.01%Option A 0.02% 0.05% 0.06% 0.02% 0.03% 0.03%Option B 0.02% 0.07% 0.08% 0.03% 0.04% 0.04%Contingency Option 0.06%0.17% 0.21% 0.08% 0.10% 0.10%Source: California, 2009, SJVUAPCD (2008), and manta.com.Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 18 ERG further examined the potential im pact on wood stove and pellet dealers in Sacramento. The estimated losses in pellet sales (rather than all revenues) are less than one-half of one percent (<0.05 percent) for the Recommende d Option, less than 1.25 percent for Options A and B, and approximately 3.2 percent for the Contingency Option. Job losses are not anticipated with these sm all changes in revenue.

3.2.3 Manufactured Log Companies
There are no manufactured log companies wi thin Sacramento County but Duraflame is headquartered in nearby Stockton, CA. Annual revenues for Duraflame are available through
Dun & Bradstreet’s Million Dollar Directory . ERG examined the impacts under the worst-case assumption that Duraflame bore the entire loss of value from manufactured logs (i.e., no impacts to Enviro-Log, Landmann USA, and Jarden Corporation). The estimated losses
represent:

• 0.5 percent of total revenues or less for the Recommended Option

• 0.8 percent of total revenue s or less for Option A,

• 1.8 percent of total revenues or less for Option B, and

• 3.8 percent of total revenues or less for the Contingency Option.
The associated potential loss in employme nt would be 1.2, 2.1, 4.4, or 9.4 jobs under the Recommended Option, Option A, Option B, and the Contingency Option, respectively. The actual loss in revenues or employment would be much less than shown above, and would be prorated by the proportion of Duraflame’s reve nues represented by sales in Sacramento County, compared to the other manufacturers.

3.2.4 Pellet Manufacturers and Retailers
ERG did not identify any pellet manufactur ers within Sacramento County. (See Section 3.2.2 for impacts of lost sales to wood stove and pellet retaile rs in Sacramento County.)

4.0 CONSUMERS

4.1 No Cost Increase Scenario
The economic impact analysis for the consumer s is illustrated in th e logic flowchart in Figure 2. In this scenario, the s uppliers are assumed to be unable to raise prices. Wood and otherSocioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 19 solid fuels might be burned for two reasons: ambi ence (which is discretiona ry), and heat (which is not discretionary).

Figure 2. Consumers of W ood and Other Solid Fuel: No Cost Increase Scenario

Wood-Burning

Ambience Heat

Commercial Residential

Cost savings due to burning less wood Cost savings due to burning less wood Residential

Increased Cost for Alternate Heat Source Exemption

Applies

Alternate Source is Less Expensive

(Cost Savings) Alternate Heating Source? No

Pay for Wood?

Yes No Hardship? No

Yes Yes

Exemption Applies



### 4.1.1 Residents

If residents burn wood or othe r solid fuels only for ambience, then they would realize a cost savings due to reduced wood use.

If residents burn wood or othe r fuels for heat, SMAQMD has st ructured the regulation to mitigate economic impacts, particularly the impacts on low-income residents. For these residents, impacts differ based on whether or not they have an alternate source of heat. 4



### Residents with no alternate sour ce of heat are exempt from Rule 421. Low-income residents may qualify for an exemption based on economic hardship.

4 A telephone survey conducted in April 2009 for SMAQMD foun d no respondents that indicated wood burning as their sole

source of heat (SMAQMD, 2009). Although uncommon, there may be rare cases where burning wood or other solid fuel would be the only heat source.Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 20 For consumers with alternate heat sources, th e potential impacts differ according to the following scenarios:

• Pay for wood, wood used for central heating

• Pay for wood, wood used for zonal heating

• Do not pay for wood
SMAQMD (2009) examined the relative cost s of heating by firepl ace, certified wood stove, electricity, and natural ga s and found that heating with w ood was the most expensive. For consumers that pay for wood and use it to heat th e entire living space (“ce ntral heating”), these consumers will realize cost savings due to switching to a lower cost source of heat.

Wood can be used to heat a room while l eaving other rooms unheated (“zonal heating”). Consumers that fall into this scenario might see an increase in costs due to the change from heating one room with wood to heating all rooms by central hea ting. ERG could not locate data to quantify these relative costs a nd thus could not quantify the potenti al impacts for this scenario. In this scenario, if the consumer incurs a financial hardship, the exemption would apply.

If, however, the consumers did not pay for wood, they would incur the costs of the
alternative heat source for the additional no-burn days. The average cost of heating a home for 30 days is $153 for electricity and $88 for natura l gas. The average per-day costs to heat a home are $5.10 and $2.93 for electricity and natural gas respectively (SMAQMD, 2009). The incremental numbers of no-burn days for which the consumers would have to pay for heat by Option are:

• Recommended Option: 5 additional no-bur n days for a cost between $15 to $25

• Option A: 8 additional no-burn da ys for a cost between $23 to $41

• Option B: 21 additional no-burn da ys for a cost between $62 to $107

• Contingency Option: 41 addi tional no-burn days for a cost between $120 to $209 In 2007, the median household income for Sacramento County was $56,283 (Census, 2009b). The increased costs do not exceed 0.05 percent of household income for the Recommended Option, 0.07 percent for Option A, 0.19 percent for Option B, or 0.37 percent for the Contingency Option regardless of whether the hous ehold heats with electricity or natural gas.Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 21 The typical household would not be disproportionately impacted by the cost for using an
alternative source of heat. If this is not th e case, however, the resident can apply for an exemption based on economic hardship.

4.2 Price Increase Scenario
In this scenario, fuel suppliers are able to raise prices to maintain their income while selling less wood and other solid fuels, and cons umers would bear the in creased costs. Table 8
indicates that lost value to wood dealers is about:

• 4.6 percent of revenues fo r the Recommended Option

• 8.7 percent of revenues for Option A

• 14.0 percent of revenues for Option B

• 34.1 percent of revenues for the Contingency Option
Assuming that wood sales comprise the sole so urce of income, as is likely for independent primary wood dealers, the companies could mainta in their income with price increases of 4.6 percent for the Recommended Option, 8.7 percent under Option, 14 percent under Option B, and 34 percent under the Contingency Option. Given th e findings in Sections 3.2.2, retailer prices for manufactured logs and pellets are unlikely to increase by more than a negligible amount.

4.2.1 Industries and Businesses As shown in Figure 2, industries and businesse s such as restaurants and hotel lobbies might burn wood and other solid fuels for ambien ce. Thus, burning is discretionary for these
commercial entities. If no cost in crease occurs, industries and busin esses might see a cost savings
due to reduced fuel use. If a cost increase occu rs, we anticipate that each location would evaluate the price increase with respect to the (typically non-moneti zed) benefit of “ambience” or increased prices to consumers, and would decide whether to pay the higher prices or reduce the amount burned. ERG anticipates no employment lo sses as a result of increased prices for a discretionary business practice.

4.2.2 Residents Houck (2003) surveyed wood burning pattern fo r households in the Sacramento area. He found that households with fireplaces burned an average of 0.92 cords per season while those with stoves burned an average of 1.5 cords pe r season. ERG calculated the amount a householdSocioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 22 spent on wood, recalculated the amount spent af ter small independent wood dealers increased their prices to maintain income levels under th e new regulation, and then calculated the increased
cost to the consumer.

The calculations are shown in Table 10. The price increases range from $11/season to $138/season depending on the option and whether th e household used a fire place or stove. ERG compared the cost increases to Sacramento County 2007 median household income ($56,283; Census, 2009b). The cost increases do not exceed one-quarter of one percent of median
household income.

Table 10. Increased Costs as a Percenta ge of Median Household Income for Households that Burn Wood

Households that Burn Wood Parameter Fireplace Stove Amount burned per season (cords) 0.92 1.5 Price ($/cord) $269 $269 Cost per Season $247 $404 Estimated Price Increase Recommended Option 4.6% 4.6% Option A 8.7% 8.7% Option B 14.0% 14.0% Contingency Option 34.1% 34.1% New Cost Per Season Recommended Option $259 $422 Option A $269 $439 Option B $282 $460 Contingency Option $332 $541 Incremental Cost Recommended Option $11 $18 Option A $22 $35 Option B $35 $56 Contingency Option $84 $138 Percent of 2007 Median Income Recommended Option 0.02% 0.03% Option A 0.04% 0.06% Option B 0.06% 0.10% Contingency Option 0.15% 0.24%



### 5.0 CONCLUSIONS ERG evaluated four options under two differe nt economic scenarios (price increase or no price increase) and used worst case analyses to avoid the potential to underestimate costs.Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 23 For residential consumers of wood and other solid fuels, SMAQMD has mitigated impacts on low income populations through a hardship exem ption. If suppliers raise prices to maintain revenues when sales volumes decline, the estimated price increased are less th an one-quarter of one percent (<0.25 percent) of the 2007 median household income for Sacramento County.

For commercial consumers of wood and other solid fuels (e.g. rest aurants and lodgings), burning is discretionary. If suppliers raise prices, each business is anticipated to weigh the increased price against the perceived benefit to the business’s ambience to determine whether or not to pay the increased prices. No employment losses are anticipat ed in this sector as a result of the rule.

Impacts to wood and solid fuel suppliers are summarized in Table 11. If the suppliers are able to raise prices to compensate for lower sales volumes, there are no impacts under any of the options. If suppliers are unabl e to raise prices, then small impacts might occur.

Table 11. Summary of Impacts on Wood Suppliers

Scenario and Potential Impact Option Independent Wood Dealers Manufactured Log Producers Retail Stores Wood Stove and Pellet Retailer Price Increase Potential Loss in Revenues Recommended Option 0% 0% 0% 0% Option A 0% 0% 0% 0% Option B 0% 0% 0% 0% Contingency Option 0% 0% 0% 0% Potential Loss in Employment Recommended Option 0 0 0 0 Option A 0 0 0 0 Option B 0 0 0 0 Contingency Option 0 0 0 0 No Price Increase Potential Loss in Revenues Recommended Option 4.6% 0.5% 0.01% 0.01% Option A 8.7% 0.8% 0.03% 0.03% Option B 14.0% 1.8% 0.04% 0.04% Contingency Option 34.1% 3.8% 0.10% 0.10% Potential Loss in Employment Recommended Option 2.8 1.2 0 0 Option A 5.3 2.1 0 0 Option B 8.5 4.4 0 0 Contingency Option 20.6 9.4 0 0



### Impacts on retailers are negligible.Socioeconomic Impact Analysis - Rule 421 Amendment August 21, 2009 

### 24 The results reported in Table 11 are based on wo rst case analyses which incorporate such assumptions as:

• No independent wood dealers located outside of Sacramento C ounty sell wood within Sacramento County. If such dealers exist, the impacts would be reduced proportionately by the fraction of wood sold in Sacram ento Country by extra-county dealers.

• All impacts from reduced sale of manufactured logs are absorbed by the one manufactured log producer that is headquartered in California. In reality, the impacts would reflect that company’s market share of manufacture d log sales in Sacramento County.

6.0 REFERENCES

BLS, 2009. Bureau of Labor Statistics. Lo cal Area Unemployment Statistics (LAUS). Sacramento County, California.

California, 2009. California Board of Equaliz ation. Annual Report for Fiscal Year 2007-2008. Appendix A, Tables 19 and 20. Available at: http://www.boe.ca.gov/annua l/pdf/2008/table19_08.xls and http://www.boe.ca.gov/annua l/pdf/2008/table20_08.xls

Census, 2009a. U.S. Census Bureau. Count y Business Patterns: 2007. Accessed through http://censtats.census.gov/cgi-bin/cbpnaic/c bpdetl.pl for Sacramento County. August 3.

Census, 2009b. U.S. Census Bureau. Quick Facts fo r Sacramento County, California. Available at: http://quickfacts.census .gov/qfd/states/06/06067.html

Houck, 2003. Houck, James. “Results of Wood Burn ing Survey-—Sacramento, San Joaquin, and San Francisco Areas.” University of California Be rkeley/California Air Re sources Board.Jarden, 2008.

Jarden Corporation, 2008. Jarden Corporation Form 10-K for the fiscal year ended December 31, 2008. Available at: http://www.sec.gov

SJVUAPCD, 2008. San Joaquin Valley Unified Ai r Pollution Control District. Proposed Amendments to Rule 4901 (Wood Burning Firepl aces and Wood Burning Heaters). Final Staff Report. October 16.

SMAQMD, 2009. Rule 421, Mandatory Episodic Curt ailment of Wood and Other Solid Fuel Burning. Staff Report. Proposed Amendments . Sacramento Metropolitan Air Quality Management District. July 2.

Williams, 2009. Personal communication betwee n Kevin J. Williams, SMAQMD and Maureen Kaplan, ERG, August 14.