DISCLAIMER

This report presents an analysis of available data to provide information on Bay Area 

PM2.5 sources for the District’s activities to reduce exposure to fine particulate matter. 

Data were obtained from the field program of the California Regional Particulate Air 

Quality Study (CRPAQS) and from routine air monitoring stations. To estimate the 

contributions from various sources, a Chemical Mass Balance (CMB) analysis was 

conducted. Because of limitations in the data and uncertainties in the CMB analysis, 

some of the findings presented in this report should be viewed as preliminary. Certain 

assumptions, as described in the document, were made to aid the analysis. While the 

District continues making additional routine particulate matter measurements, analyses 

similar to those presented in this report will be conducted and the findings of this report 

will be updated as appropriate.
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CONTENTS

CONTENTS........................................................................................................................... 1

FIGURES............................................................................................................................... 2

TABLES................................................................................................................................. 2

SUMMARY........................................................................................................................... 3

Methods.......................................................................................................................... 3

Results and Key Findings................................................................................................. 4

1. INTRODUCTION............................................................................................. 6

2. DATA ............................................................................................................. 6

2.1 Ambient Data ................................................................................................ 7

2.2 Source Profiles .............................................................................................. 7

2.3 Carbon-14 Analysis ....................................................................................... 8

3. SUMMMARY STATISTICS............................................................................... 9

3.1 Total Mass Comparisons............................................................................... 9

3.2 Seasonal Distribution of PM2.5 ...................................................................... 9

3.3 Speciation of PM2.5...................................................................................... 10

3.4 Carbon-14 Analysis Results......................................................................... 14

4. SOURCE APPORTIONMENT METHODOLOGY.............................................. 15

4.1 CMB Modeling ............................................................................................ 15

4.2 Incorporating Carbon-14 measurements................................................... 17

5. SOURCE APPORTIONMENT RESULTS .......................................................... 18

5.1 Annual Summary......................................................................................... 18

5.2 Peak Summary ............................................................................................ 20

5.3 Analysis by Season ...................................................................................... 22

5.4 Comparisons with the 1994 Winter PM10 Field Study................................ 25

6. REFINING SOURCE APPORTIONMENT WITH EMISSIONS INVENTORY 

ESTIMATES .................................................................................................. 27

7. KEY FINDINGS AND ON-GOING WORK........................................................ 34

7.1 Key Findings................................................................................................ 34

7.2 Ongoing work.............................................................................................. 34

8. REFERENCES................................................................................................ 36

Appendix A. Adjustment of OC and EC data ....................................................................A1

Appendix B. Comparison of mass measurements........................................................... B1

Appendix C. Chemical species used in the CMB model................................................... C1

Appendix D. Determining weights applied to the fits......................................................D1

Appendix E. Initial CMB results and source category selection....................................... E1

Appendix F. Modification of CMB results Based on Carbon-14 Analysis ........................ F1
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FIGURES

Figure 3.1. Quarterly averaged PM2.5 for special study sites. ......................................... 10

Figure 3.2. Mean mass contribution of PM2.5 species..................................................... 11

Figure 3.3. Boxplots of most abundant species by site ................................................... 12

Figure 3.4. Annual percentages of contemporary carbon from Bay Area sites for 1998 

and 2004 ....................................................................................................... 13

Figure 5.1. Estimated annual source contributions to Bay Area ambient PM2.5 ............ 17

Figure 5.2. Estimated annual percent contributions from various source categories.... 18

Figure 5.3. Estimated source contributions to peak Bay Area ambient PM2.5 ................ 19

Figure 5.4. Estimated annual percentage contributions to peak PM2.5 .......................... 20

Figure 5.5a. PM2.5 source contributions by season for Bethel Island.............................. 21

Figure 5.5b. PM2.5 source contributions by season for Livermore .................................. 22

Figure 5.5c. PM2.5 source contributions by season for San Francisco ............................. 22

Figure 5.5d. PM2.5 source contributions by season for San Jose 2000............................ 23

Figure 5.5e. PM2.5 source contributions by season for San Jose 2001 ............................ 23

Figure 5.5f. PM2.5 source contributions by season for Point Reyes................................. 24

Figure 5.6. Comparison with percent source attributions from the winter 1993-94 field 

study.............................................................................................................. 26

Figure 6.1. Estimated contributions to annual Bay Area PM2.5 from various sources .... 29

Figure 6.2. Estimated contributions to peak Bay Area PM2.5 from various sources........ 31

Figure A1. Comparisons of EC/TC ratios..........................................................................A1

Figure B1. Comparison of PM2.5 mass measured by CARB and BAAQMD for Livermore, 

2000 .............................................................................................................. B1

Figure E1. Comparison of model uncertainties with total uncertainty ............................ E3

TABLES

Table 2.1. Source profiles used in CMB analysis................................................................ 8

Table 5.1. CMB results for annual PM2.5 samples............................................................. 18

Table 5.2. CMB results for peak PM2.5 samples................................................................ 20

Table 5.3. CMB resultsfor aggregated wintertime high PM10 samples, 11/93-1/94 ....... 25

Table 6.1. Emissions inventory estimates of carbonaceous and secondary precursors. 28

Table 6.2. Percent estimated total annual contribution from various sources............... 29

Table 6.3. Emissions inventory estimates of winter carbonaceous and secondary 

precursors....................................................................................................... 30

Table 6.4. Estimated percentage total peak contribution from various sources............ 30

Table A1. Mean and median EC/TC ratios.......................................................................A2

Table B1. Same-day comparisons of BAAQMD, CARB and EPA PM2.5 measurements.... B2

Table C1. Chemical species – frequency above the limits of detection .......................... C1
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SUMMARY

This study uses Bay Area ambient particulate matter (PM) measurements and emissions 

data to determine the major PM sources and their approximate contributions to Bay 

Area PM concentrations. 

The focus of this study is a chemical mass balance (CMB) analysis where a computer 

model is used to apportion ambient PM collected on filters to a set of source categories. 

The filters contain particles less than or equal to 2.5 microns in diameter (PM2.5) 

collected over 24-hour periods at monitoring sites around the Bay Area. Each filter was 

analyzed for a range of chemical species. The same species were measured in special 

studies of emissions from various sources, such as motor vehicles and wood burning. 

The CMB model finds the mix of these source measurements that best matches the 

ambient sample, chemical species by chemical species.

CMB analysis was limited to identifying source categories, such as fossil fuel 

combustion. In order to make finer distinctions, the CMB results were combined with 

the Bay Area Air Quality Management District's emissions inventory.

Methods

The ambient data were obtained from four different studies or agencies: 1) the 

California Regional Particulate Air Quality Study (CRPAQS), 2) the Speciated Trends 

Network (STN), 3) the IMPROVE network, and 4) Bay Area Air Quality Management 

District (BAAQMD) routine measurements. The source profile data were obtained from 

sample collections of CRPAQS, the California Air Resources Board (ARB) and BAAQMD.

Speciated ambient data were collected from five monitoring sites – Bethel Island, 

Livermore, San Francisco, San Jose, and Point Reyes. The data for the first three sites 

were collected as part of the CRPAQS. They cover a 14 month period, extending from 

December 1999 through January 2001. The Point Reyes (IMPROVE) and San Jose (STN) 

data for 2000 were obtained to supplement the CRPAQS measurements. San Jose data 

from 2001 were also added to the analysis to investigate how source contributions vary 

from year to year.

Source categories used in the CMB analysis initially included fossil fuel combustion, 

vegetative burning, commercial cooking, tire and brake wear, geological dust, marine 

air, ammonium nitrate, and ammonium sulfate. CMB analysis is limited in that the 

contributions from certain sources may occur at levels below the model's ability to 

detect; and certain combinations of sources may be effectively impossible to 

differentiate if their chemical profiles are too similar.

In the initial CMB analysis, it was found that PM from tire and brake wear did not 

exceed the limits of detection, and commercial cooking could not be differentiated from 
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vegetative burning or fossil fuel combustion. These sources were omitted from 

subsequent analyses.

Considerable uncertainty remained in the apportionment of two source categories –

vegetative burning and fossil fuel combustion. To better apportion these categories,

carbon-14 was measured on PM10 filters. This analysis provides a reliable estimate of 

the amount of "new carbon", from vegetative burning and cooking, relative to "fossil 

carbon", from burning fossil fuels. The results of this analysis were used to adjust the 

CMB estimates of the vegetative burning and fossil fuel combustion apportionment.

The CMB analysis produced estimates of the contributions from source categories, but 

could not differentiate between wood smoke and cooking or among on-road, off-road, 

refinery, and power plant emissions. To better differentiate among individual sources, 

the CMB results were combined with emissions inventory estimates for the Bay Area. 

The combination apportions not only directly emitted PM2.5 but also the ammonium 

nitrate and ammonium sulfate with the assumption that these are proportional to the 

emissions of NOx and SO2 respectively.

The California and federal governments each have established two PM standards – a 24-

hour standard to protect against short-term PM exposure, and an annual standard to 

protect against long-term exposure. The CMB results are presented in terms of peak 

and annual average PM to correspond with these standards.

Results and Key Findings

This study found that the three source categories – vegetative burning, fossil fuel 

combustion and ammonium nitrate – are the largest contributors to annual average 

PM2.5. Marine air and ammonium sulfate are substantial contributors. Geological dust 

is a relatively minor contributor. 

The CMB results were also summarized for peak PM2.5 – the average of the 10 highest 

PM2.5 samples for each site. For peak PM2.5, the top three source categories –

vegetative burning, fossil fuel combustion and ammonium nitrate – comprise more 

than 90% of the total. Marine air and ammonium sulfate make up a much smaller 

percentage of the total; and geological dust is negligible.

The combined CMB and emissions inventory analysis showed that vegetative burning

was the largest single source, contributing about 24% of annual PM2.5 and 33% of peak 

PM2.5. On-road vehicles were the second largest source of PM2.5. Off-road vehicles, 

commercial cooking, ships, refining and marine air were also found to be large 

contributors.

Additional findings are listed below:
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 Most anthropogenic PM10 and PM2.5 derive from burning wood or fossil fuels.

 Geological dust is a small contributor to PM10 and a negligible contributor to 

PM2.5. Tire/break wear is also a negligible PM2.5 source.

 Peak PM occurs largely in winter. Reasons include a high rate of ammonium 

nitrate formation and more wood burning, both related to low temperatures. 

Analysis of wintertime meteorological data shows that periods of low winds are 

conducive to the buildup of PM2.5.

 Ammonium nitrate contributes almost 40% to peak PM2.5 and 20% to annual 

PM2.5 on average.

 Carbonaceous PM, that is, PM directly emitted from burning, accounts for 

roughly half of peak PM2.5 and annual PM2.5.

 Ammonium sulfate contributes about 10% to annual PM2.5, but only 5% to peak 

PM, on average.
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1. INTRODUCTION

The Bay Area Air Quality Management District (BAAQMD or District) is responsible for 

assuring clean air in the nine-county San Francisco Bay Area. The Bay Area occasionally 

exceeds health-based ambient air quality standards for particulate matter.

Airborne particulate matter (PM) has serious adverse effects on health. High levels of 

PM, especially PM2.5, are correlated with exacerbations of respiratory problems such as 

asthma, increases in emergency room visits, and increases in respiratory and cardiac 

related deaths. Such effects have been noted in the San Francisco Bay Area. 

In December 2006, the U.S. EPA adopted a new ambient air quality standard for fine 

particulate matter, or PM2.5, of 35 g/m3. Monitoring data indicates that the Bay Area 

may be designated non-attainment for the new standard. The Bay Area does not meet 

the more stringent California PM2.5 and PM10 standards.

In order to reduce the Bay Area's PM levels most effectively, it is necessary to know 

what the major sources are and their approximate contributions to the total PM. Many 

sources contribute to PM. Sources of direct, or primary, emissions include on-road and 

off-road vehicles, power plants, refineries, wood burning, cooking, and dust from roads, 

fields, construction, and farming.

PM also forms from chemical processes in the atmosphere. Earlier studies have shown 

that these chemically formed particulates, or secondary particulates, constitute a 

sizeable fraction of the Bay Area's PM. The major secondary components are 

ammonium nitrate, formed from transformation of NOx and ammonia, and ammonium 

sulfate, formed from transformation of sulfur dioxide and ammonia. There are also 

secondary organic molecules formed from chemical transformation of gaseous organic 

molecules in the atmosphere.

This study analyzes speciated PM2.5 collected at several Bay Area sites using a computer 

model (chemical mass balance, or CMB) to estimate the contributions of various sources 

to total PM. The analysis is summarized both annually and for peak PM to correspond 

with the annual and 24-hour PM standards. The results are studied together with 

BAAQMD emissions inventory estimates to provide a finer breakdown of source 

contributions. Carbon-14 analyses were also used to help distinguish between PM 

contributed by wood burning and fossil fuel combustion. 

2. DATA

In order to conduct CMB analysis, both ambient and source profile data were needed. 

The ambient data were obtained from four different studies or agencies: 1) the 

California Regional Particulate Air Quality Study (CRPAQS), 2) the Speciated Trends 

Network, 3) the IMPROVE network, and 4) Bay Area Air Quality Management District 
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(BAAQMD) routine measurements. The source profile data were obtained from CRPAQS, 

and from ARB and BAAQMD sample collections.

2.1 Ambient Data

CRPAQS included speciated analysis of filters containing PM2.5 samples collected over 

24-hour periods from a variety of central California sites, including three in the Bay 

Area: Bethel Island (BI), Livermore (LI) and San Francisco (SF). The speciated analysis 

included measurement of elements, using X-Ray Fluorescence; ions, using ion 

chromatography; and elemental and organic carbons, using the IMPROVE methodology. 

Samples were collected on a 1-in-6 day schedule from December 1999 through January 

2001.

The Speciated Trends Network (EPA 1999) collected speciated PM2.5 at San Jose – 4

th St. 

on a 1-in-3 day schedule. PM2.5 data were obtained for years 2000 (SJ0) and 2001 (SJ1). 

The abbreviation SJ will be used when referring to both years. The 2001 data are used 

to investigate changes in source contributions from 2000 to 2001 at this site.

The BAAQMD routine monitoring network collected gravimetric PM2.5 data at 

Livermore, San Francisco and San Jose contemporaneously with these other networks.

The IMPROVE network, which collects PM2.5 data from national parks around the United 

States, provided data at Point Reyes (PR). These data were collected on a 1-in-3 day 

schedule. Most of the time, the PR site samples air coming onshore from the Pacific 

Ocean so typically its samples are representative of background PM.

Generally, the species measurements seemed consistent among the labs conducting 

data analyses as confirmed by an inter-lab comparison (EPA/NAREL 2002). One 

exception is the measurement of carbon. There are two distinct approaches for carbon 

measurements: the NIOSH and IMPROVE methods. Although the total carbon 

measurements are similar, the NIOSH method attributes a much higher percentage of 

carbon to OC than the IMPROVE method (Chow et al., 2001). The source profiles as well 

as the CRPAQS and Point Reyes samples used the IMPROVE method, whereas the 

EPA/RTI samples were analyzed by the NIOSH method. In order to make the two sets of 

measurements more comparable, a conversion from NIOSH to IMPROVE was made as 

presented in Appendix A.

2.2 Source Profiles

CRPAQS source profile data included a range of samples: geological, woodsmoke, motor 

vehicle exhaust, commercial cooking, and tire and brake wear. These profiles were 

combined with PM10 source profiles available from a previous District CMB analysis 

conducted in 1994. A subset of these profiles was used for CMB analysis, listed in Table 

2.1.
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Table 2.1. Source profiles used in CMB analysis

Source Abbrev. AgencyaDescription

SJ4PVRD BAAQMD/DRI Paved road dust from San Jose

BYRDC BAAQMD/DRI

Paved road dust from selected sites around the Bay 

Area

ARB_DUST CRPAQS Paved road dust from around central California

AMSUL Ammonium sulfate

AMNIT Ammonium nitrate

NASUL Sodium sulfate

NANIT Sodium nitrate

MARINE Marine air with intermediate aging

MAR0 Fully aged marine air

MAR100 Fresh marine air

GUNPOWDR Gun powder from fireworks

BYTUN4 BAAQMD/DRI Composite motor vehicle exhaust profile

BYWS4 BAAQMD/DRI Composite wood burning

WBOakEuc CRPAQS Oak & Eucalyptus wood burning

BurnWdAg CRPAQS Composite wood burning 

WBOak CRPAQS Oak burning profile

WBEucal CRPAQS Eucalyptus burning

COOK CRPAQS Composite meat cooking 

CAMV CRPAQS CRPACS combined motor vehicle

TireBrke CRPAQS Tire and Brake wear

a

Agency that collected the data: BAAQMD samples were collected in 1993, DRI (Desert Research 

Institute). No agency indicates that the source has a known chemical composition.

2.3 Carbon-14 Analysis

Carbonaceous material mostly from the combustion of fossil fuels and vegetative 

materials represents a large fraction of the Bay Area’s PM2.5. However, the 

contributions of the various carbonaceous sources are difficult to distinguish based on 

the set of measurements made. Carbon-14 analysis offers a way to clearly distinguish 

two categories of these compounds.

Carbon-14 is a naturally occurring radioactive isotope of carbon that is constantly being 

replenished in the atmosphere, representing about one part in a trillion. Living things 

contain this fraction of C-14. On the other hand, C-14 has a half-life of about 5,000 

years, so that fossil fuels contain virtually none. Thus, the fraction of contemporary C14 in the PM allows us to differentiate between sources with contemporary 

concentrations of C-14 (vegetative smoke, cooking emissions, and secondary biogenic 

PM), and sources with fossil concentrations (diesel, gasoline, and natural gas).

Estimates of annual fractions of contemporary and fossil carbon for each site were 

obtained as follows. PM10 filters were used because they are quartz and because they 

contain a much greater mass of material to analyze. For each of the District’s PM10 sites, 
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a representative set of filters from 2004 was selected, and a composite sample created. 

For selected sites, composites of 1998 filters were also selected. The District contracted 

with the University of Arizona for a C-14 analysis of these composite filters. Section 3.4 

contains the results.

3. SUMMMARY STATISTICS

Before conducting the CMB analysis, some of the co-located measurements by 

BAAQMD, the U.S. EPA and CRPAQS at Livermore, San Jose, and San Francisco were 

compared for consistency among different measurement methods. Available ambient 

data were summarized to better understand the seasonal distribution of PM2.5, its 

composition, and the locations of peak concentrations. 

3.1 Total Mass Comparisons

A comparison of CRPAQS, the U.S. EPA and BAAQMD total mass measurements shows a 

high degree of correlation for all 3 sites (LI, SJ and SF), but a substantial difference in 

magnitude for the LI and SF sites. In particular, the CRPAQS measurements average 

about 70-75% of the District measurements. The masses were also compared with the 

sums of the individual chemical species. The sums of species are expected to be smaller 

than the total because not all species are included. Yet the CRPAQS masses were often 

less than the sum of species, whereas the BAAQMD masses were generally larger (see 

Appendix B). Because of the large discrepancies in the LI and SF measurements, their 

total mass measurements, along with measurements at Bethel Island, were adjusted to 

be consistent with BAAQMD measurements. For San Jose, measured for EPA, there was 

a slight difference in masses, with the EPA measurements averaging about 105% of the 

District measurements. These measurements were not adjusted.

3.2 Seasonal Distribution of PM2.5

The seasonal distribution of PM2.5 was studied to better understand when the highest 

concentrations occur in the different regions of the District. The seasons were defined 

to correspond to the PM2.5 season, namely for this study "winter" is defined to include 

November, December, and January. The other quarters follow from the definition of the 

winter quarter: "spring" is February, March and April; "summer" is May, June and July; 

and "autumn" is August, September and October. 
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Figure 3.1. Quarterly averaged PM2.5 for special study sites

0

5

10

15

20

25

30

35

BI LI SF SJ0 SJ1 PR mass (mg/m3)

spring

summer

autumn

winter

quarterly ave

Note: Data from 2000 except for 2001 for SJ1. Quarters are spring = Feb-Apr, summer = May-July, 

autumn=Aug-Oct, winter=Nov-Jan. These quarters were chosen to correspond to the PM season rather 

than the standard definition of seasons. White bars are averages of the 4 quarters. Uncertainty bars 

represent 90% confidence intervals for the annual mean.

Figure 3.1 shows quarterly averages and the average of the four quarters. The figure

shows a clear seasonal pattern, with the winter quarterly averages far exceeding those 

of the other quarters, except at Point Reyes. During the non-winter quarters, the 2000 

average values are not much higher than those of the Point Reyes site. Note the large 

difference between 2000 and 2001 for San Jose, which is likely due to meteorology. In 

particular, the winter quarter decreases from 2000 to 2001 whereas the other quarters 

increase.

The white bars show the means of the 4 quarters. Also shown are 90% confidence 

intervals for the means. Except for the Point Reyes background site, the confidence 

intervals for all the other sites straddle the national annual standard of 15 mg/m3and 

the state annual standard of 12 mg/m3. Thus, the Bay Area is on the borderline for these 

standards.

3.3 Speciation of PM2.5

Many PM2.5 species were measured, but for a number of the elements, the 

concentrations rarely if ever exceed the limits of detection. Appendix C has a list of all 
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species and the number of times the measured concentrations exceeded one and two

standard errors. Those species whose concentrations exceeded one standard error in 

less than 10 samples were eliminated from further analysis because they would likely 

detract from the capacity of the CMB model to differentiate between sources.

Figure 3.2 shows the annual means of the remaining species, averaged across all sites, in 

decreasing order of magnitude. Also shown is the cumulative fraction of total mass. 

Organic carbon (OC) and Nitrate (NO3) account for over 50% of the mass. Total carbon, 

that is, OC plus elemental carbon (EC), and NO3 along with sulfate (SO4) and ammonium 

(NH4), account for almost 90% of the total. Thus, most Bay Area PM is either carbon or 

secondary PM.

Figure 3.2. Mean annual mass contribution of PM2.5 species

0.0001

0.001

0.01

0.1

1

10

OC

NO3

EC

S04

NH4

NA

CL

MG

SI

FE

CA

KP

AL

ZN

NI

PB

CU

BR

MN

SRPH

SE

mass (mg/m3)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Note: Masses averaged across all sites. Bars indicate individual species corresponding to left-hand axis. 

The cumulative amount is shown by the line and corresponds to the right-hand axis. Appendix C provides 

a list of compounds corresponding to the abbreviations on the horizontal axis.

Sodium and chlorine are the next greatest contributors, both present in marine air. 

Sodium is greater than chlorine because some chlorine is replaced by nitrate as the 

marine air mixes with air containing NOx. Elements associated with soil – notably silicon 

and aluminum – are present, but in small quantities, indicating that geological dust is 

not a large component of Bay Area PM2.5.

Figure 3.3 shows boxplots of mass and the major species. Each box represents the range 

from the 25th to 75th percentile, with the horizontal line in the middle representing the 

median. The vertical lines above and below the box extend to the 95th and 5th
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percentiles. Asterisks beyond these lines represent extreme or outlying values of the 

distribution.

The figure shows large contributions of OC and to a lesser extent EC at every site except 

Point Reyes. Chloride and sodium are larger at Point Reyes than other Bay Area sites, 
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Figure 3.3. Boxplots of the most abundant species by site (mg/m3)

SITE

PM2.5 mass (ug/m3)

100

50

0

20

10

0

30

15

0

10

5

0

4

2

0

10

5

0

4

2

0

4

2

0

2

1

0

4

2

0

1.0

0.5

0.0

0.8

0.4

0.0

0.8

0.4

0.0

0.50

0.25

0.00

0.10

0.05

0.00

SJ1SJ0

SFP

BI

LI

R

0.10

0.05

0.00

SJ1SJ0

SFP

BI

LI

R

0.16

0.08

0.00

SJ1SJ0

SFP

BI

LI

R

0.04

0.02

0.00

SJ1SJ0

SFP

BI

LI

R

0.010

0.005

0.000

0.02

0.01

0.00

Mass Organic Carbon Nitrate Elemental Carbon 

Ammonium Sodium Chloride Magnesium 

Iron Calcium Soluble Potassium Aluminum 

Nickel Lead Copper Bromine 
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clearly a result of the site's exposure to the sea breeze. The San Francisco site, which is 

also exposed to air off the ocean and bay, also has elevated levels of these species. The 

patterns for nitrate and sulfate are different, with nitrate occasionally showing very 

large values, whereas sulfate has essentially no outliers. Also, Point Reyes nitrate values 

are much lower than for the other sites, but its sulfate values are comparable, perhaps 

because sulfate is a component of marine air. Aluminum and silicon are elevated on a 

few occasions, but even for these, the concentrations are not that high, indicating that 

occasionally windblown dust contributes somewhat to PM2.5 at these sites, but not in 

very large quantities.

3.4 Carbon-14 Analysis Results

Figure 3.4 shows the fraction of new carbon for each of the samples analyzed by the 

University of Arizona. For 2004, the percentages range from 47% for Fremont to 68% 

for San Rafael. From 1998 to 2004, the percents of new carbon increased approximately 

5%. At 10 of the 14 sites, there was more new carbon than fossil carbon (Fairley, 2005). 

Standard errors of these percentages are all less than 0.5%.

Figure 3.4. Annual percentages of new carbon from Bay Area sites for 1998 and 2004

0%

10%

20%

30%

40%

50%

60%

70% Bethel Island Concord Fremont Livermore Napa Pittsburg Redwood City San Francisco San Jose - Tully San Jose - Jackson San Pablo San Rafael Santa Rosa Vallejo Percentage of new carbon

1998 2004

Old 1st

Rincon
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4. SOURCE APPORTIONMENT METHODOLOGY

Chemical Mass Balance (CMB) modeling is a standard methodology that uses chemical 

measurements of ambient PM on filters from air quality monitors for determining 

categories of PM sources. It is excellent at differentiating among sources that have 

chemical signatures that are clearly defined and measured.1 However, CMB is limited 

when source signatures are too similar, nor can it attribute secondary PM such as 

ammonium nitrate to the original sources. Thus, the CMB analysis results were 

modified using other data sources.

First, with these measurements, CMB could not well differentiate among various 

carbonaceous sources (wood burning, diesel, gasoline, cooking, etc.) To augment the 

CMB analysis, measurements of Carbon-14 were made. This yielded the fraction of 

carbonaceous PM from “new” sources (wood, cooking, secondary organic) and “fossil” 

sources (diesel, gasoline, natural gas).

Second, to further identify sources and incorporate source contributions to secondary 

PM, the results of the CMB/Carbon-14 analysis were merged with information from the 

District’s emissions inventory.

4.1 CMB Modeling

CMB modeling provides a way of estimating the amount that various sources contribute 

to ambient PM concentrations. The CMB model is fit using a computer program whose 

inputs are source profiles and ambient PM samples that have been analyzed for a 

variety of chemical components. For each ambient sample, the CMB model finds the mix 

of sources whose combined amounts of chemical components best approximates those 

on the sample. In other words, the output of the CMB model includes estimates of the 

amounts (concentrations) from the various sources on each ambient filter.

The PM that deposits on an ambient filter sample comes from a wide variety of sources,

only a few for which source profiles have been developed. Even those sources, like 

wood smoke, auto exhaust or geological dust, exhibit infinite variations in the relative 

amounts of various constituents. To some extent, this variation is accounted for in the 

model, which incorporates the variability recorded in the source profiles. Yet, these 

variations in the source profiles can affect the quality of the CMB results.

 

1

 For example, approximately 40% of Bay Area geological dust is aluminum and silicon, whereas other 

potential sources – wood smoke, gasoline and diesel exhaust, cooking, tire and brake wear – have 

virtually none. Thus, the presence of aluminum and silicon indicates geological dust, and their absence 

indicates an absence of geological dust.
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Species whose concentrations were below the limits of detection were not used for the 

CMB fit. Also, several species were measured both as ions and elements: potassium, 

chloride/chlorine, and sodium. Only one of the forms was used to avoid double 

counting. Similarly, sulfur was not fit because it duplicates sulfate. The table in Appendix 

C shows which species were used for fitting. Not all the species were measured at every 

site. For example, ammonium was not measured at Point Reyes and therefore could not 

be used in the CMB analysis for that site. 

CMB version 7 was used in this analysis (Watson et al. 1990). In previous applications of 

the CMB model, the approach used was to find the "best" fit using a variety of criteria –

low chi-squared value, high R2, all positive coefficients, all statistically significant 

coefficients, and lack of identifiability problems.

2

The weakness of this approach is that 

there may be more than one reasonable fit to a set of data. 

The approach taken here was to find a weighted average of fits, weighting by the 

relative likelihood of the fit.3 This process took several iterations to improve the 

likelihood function so that it better matched the actual distribution.4

The method that was ultimately used was to define the likelihood as the product of two 

p-values:

5

the p-value associated with the chi-square statistic for goodness of fit of 

individual chemical species, and the p-value for the difference between estimated and 

measured mass. The p-values were set to zero if any of the estimated source 

coefficients were negative. These likelihoods provided the weights applied to different 

fits. (Appendix D provides the details.)

 

2

A program was written by District staff to automate the application of the CMB model. Specifically, 

CMBRUNS.EXE is a program that generates a file with keystrokes that operate the CMB model, allowing it 

to be run in batch mode and to try a variety of fits. In particular, fits were made for every combination of 

the following sources: 1) marine/mar0/mar100/none, 2) bytun4/camv/none, 3) 

byws4/WBOakEu/WBOak/none, 4) amm. nitrate/none, 5) amm. sulfate/none, 6) cook/none, 7) 

BYRDC/ARB_DUST/none, and 8) TireBreak/none. The "none" option permitted running the model 

without this source, which is desirable because CMB7 estimated contributions can be statistically 

insignificant or even negative. This creates a total of 2,304 fits for each ambient sample.

3

 If the weights were statistical likelihoods, then this approach would have a Bayesian interpretation –

attempting to approximate the mean of a posterior distribution for the model. This approach was tried 

but it had the weakness that the models themselves were uncertain. In particular, this approach led to 

situations where model A might produce estimated species contributions closer to the measured than for 

model B, yet model B would have a higher likelihood because its estimated uncertainties were less.

4

For example, initially, the difference between the measured and calculated mass was assumed to have a 

Gaussian distribution. But it was found that, for some samples, the two values differed dramatically –

many standard deviations apart, i.e., the Gaussian provides a poor fit and leads to unrealistic results. The 

likelihood was modified to minimize the effect of the Gaussian term in these cases. See Appendix D for 

details.

5

p-values can be produced for statistical tests. A p-value represents the probability of seeing something 

at least as extreme as what was recorded given the null hypothesis were true. In this case, the null 

hypothesis is that the model is satisfactory. Small p-values indicate that the model is inadequate.
-17-

April 2008

When the CMB model was applied using all source profiles, the results showed large 

uncertainties for some of the source categories. This indicated that the data should be 

reanalyzed with fewer categories. Details of the analysis with all source profiles and the 

rationale for dropping specific source profiles are explained in Appendix E. The profiles 

included in the analysis were: ammonium sulfate, ammonium nitrate, marine, roaddust, fossil fuel, and wood smoke. Dropped from the analysis were the commercial 

cooking and tire/brake wear profiles. 

4.2 Incorporating Carbon-14 measurements

In theory, Carbon-14 measurements can be incorporated directly into CMB analysis, as 

simply another component in the array of chemical measurements made. But this can 

only be done if the C-14 measurements are made on the same air sample as the other 

measurements. For this study, we have C-14 measurements from PM10 filters for 1998 

and 2004. (The PM2.5 filters analyzed in the rest of the study were unavailable.) Also 

the C-14 measurements represent only an annual average. However, because C-14 

measurements yield a clear-cut division between new and fossil sources, these 

measurements were used to modify the CMB results for the “vegetative burning” and 

“fossil” categories. (See Appendix F for a more detailed rationale for the adjustment.)
-18-

April 2008

5. SOURCE APPORTIONMENT RESULTS

This section summarizes the results of the Carbon-14 adjusted CMB analysis and relates 

these results to the annual and 24-hour PM standards. The annual standard is based on 

the average of the 4 quarterly averages, so the quarterly averaged results are 

presented. The 24-hour standard relates to peak PM2.5 values, so the results for the 

samples with the 10 highest measured masses are presented.

5.1 Annual Summary

Figure 5.1 shows the estimated annual mass contributions from different source 

categories for the various sites. The major categories at the non-background sites are 

direct, combustion-related, largely carbonaceous sources – fossil fuel and wood 

burning; and secondary, combustion-related sources – ammonium nitrate and 

ammonium sulfate.

Figure 5.1. Estimated annual source contributions to Bay Area ambient PM2.5

0

2

4

6

8

10

12

14

16

18

Ammonium

Sulfate

Ammonium

Nitrate

Marine Geological

Dust

Fossil Fuel

Combustion

Vegetative

Burning

Totals PM2.5 (mg/m3)

Bethel Island

Livermore

San Francisco

San Jose 2000

San Jose 2001

Point Reyes

Note: Estimated annual source contributions to Bay Area ambient PM2.5 for 2000 and San Jose 2001. Values 

are quarterly averaged means of individual CMB results, except that the vegetative burning and fossil fuel totals 

have been adjusted based on Carbon-14 measurements. Totals are sums of individual source contributions.
-19-

April 2008

Figure 5.2. Estimated annual percent contributions from various source categories

0

5

10

15

20

25

30

35

40

Ammonium Nitrate Ammonium Sulfate Marine Geologic/Road Dust Fossil Fuel

Combustion

Vegetative Burning Percent

Bethel Island

Livermore

San Francisco

San Jose

San Jose 2001

Point Reyes

Note: Estimated annual percentage contributions to Bay Area ambient PM2.5 for 2000 and San Jose 2001. 

Values are percentages of total quarterly averaged PM2.5 from individual CMB results with fossil fuel 

combustion and vegetative burning adjusted for Carbon-14 results.

Table 5.1. CMB results for annual PM2.5 samplesa

Conc. (mg/m3)

Ammonium

Sulfate

Ammonium

Nitrate

Marine Geological

Dust

Fossil

Fuel

Vegetative

Burning

Gun

Powder

Est.

Total

Bethel Island 1.5 2.9 0.9 0.3 1.9 2.8 0.1 10.3

Livermore 1.3 2.0 1.1 0.1 3.4 4.0 0.1 12.0

San Francisco 1.3 2.3 1.6 0.1 3.4 3.0 0.1 11.8

San Jose 2000 1.5 2.2 1.8 0.2 4.7 4.9 0.1 15.4

San Jose 2001 1.4 1.8 2.6 0.4 4.1 4.5 0.0 14.8

Point Reyes 1.3 0.7 2.0 0.1 0.3 0.7 0.0 5.0

Percentagesb

Bethel Island 14.5 27.7 8.4 2.5 18.8 27.3 0.7 100

Livermore 10.5 16.6 9.2 0.8 28.6 33.3 0.8 100

San Francisco 11.3 19.1 13.8 1.0 29.1 25.1 0.5 100

San Jose 2000 9.8 14.2 11.5 1.5 30.6 32.1 0.4 100

San Jose 2001 9.3 12.1 17.4 2.5 28.1 30.3 0.2 100

Point Reyes 25.2 13.4 38.9 2.2 5.8 14.1 0.4 100

4 site averagec

11.3 18.8 10.9 1.4 27.3 29.7 0.6 100

a

Average of quarterly averages.

b Percentages of estimated mass.

c

BI, SF LI, and SJ 2000.

For the urban sites, Livermore, San Francisco and San Jose, the two carbonaceous

source categories – fossil fuel and vegetative burning – constitute more than half the 

total. The contributions from these two categories is approximately the same. (see 
-20-

April 2008

Figure 5.2 and Table 5.1). For Bethel Island, secondary PM constitutes a similar 

percentage to its carbonaceous fraction, that is, ammonium nitrate plus ammonium 

sulfate constitutes the same percentage as vegetative burning plus fossil fuel 

combustion. For Point Reyes, the largest category is marine air.

Ammonium nitrate is a large source at every site. Ammonium sulfate and marine air are 

also important categories at all sites. The combined direct and secondary combustion6

sources constitute over 80% of the total for all 2000 sites except Point Reyes, and 79.8% 

for San Jose 2001. At no site does geological dust represent more than 4% of the total.

5.2 Peak Summary

Figure 5.3 shows the mass contributions for the 10 days at each site with the highest 

measured PM2.5 masses, and Figure 5.4 shows the percent contributions.

Figure 5.3. Estimated source contributions to peak Bay Area ambient PM2.5

0

5

10

15

20

25

30

35

40

45

Ammonium

Sulfate

Ammonium

Nitrate

Marine Geological Dust Fossil Fuel

Combustion

Vegetative

Burning &

Cooking

Totals pm2.5 (mg/m3)

Bethel Island

Livermore

San Francisco

San Jose 2000

San Jose 2001

Point Reyes

Note: Estimated source contributions to peak Bay Area ambient PM2.5 for 2000 and San Jose 2001. Values are 

averages of CMB and C-14 estimated source contributions from 10 days with highest PM at each site. Totals 

are sums of individual source contributions.

 

6

The District emissions inventory shows that most of the emissions of the precursors of sulfate and 

nitrate, namely SO2 and NOx, derive from combustion. Available evidence suggests that reducing these 

precursors results in reductions in ammonium sulfate and ammonium nitrate concentrations. Thus, it 

seems reasonable to consider these secondary sources as combustion-related.
-21-

April 2008

Figure 5.4. Estimated annual percentage contributions to peak PM2.5

0

10

20

30

40

50

60

Ammonium

Sulfate

Ammonium

Nitrate

Marine Geological Dust Fossil Fuel

Combustion

Vegetative

Burning Percent

Bethel Island

Livermore

San Francisco

San Jose 2000

San Jose 2001

Point Reyes

Note: Estimated annual percentage contributions from various source categories to the 10 highest PM2.5 

days, based on CMB analysis adjusted for Carbon-14. The values shown are the masses from individual 

source categories as a percentage of the total estimated mass.

Table 5.2. CMB results for peak PM2.5 samplesa

Conc. (mg/m3)

Ammonium

Sulfate

Ammonium

Nitrate

Marine Geological

Dust

Fossil

Fuel

Vegetative 

Burning

Gun

Powder

Est.

Total

Bethel Island 1.7 19.1 0.1 0.1 3.4 11.2 0.3 35.7

Livermore 1.6 13.7 0.1 0.2 8.7 15.8 0.4 40.4

San Francisco 2.9 15.0 0.3 0.1 6.6 10.2 0.2 35.3

San Jose 2000 2.1 9.5 1.1 0.3 9.5 18.4 0.2 41.1

San Jose 2001 2.1 9.5 2.9 1.0 7.0 12.6 0.1 35.2

Point Reyes 1.8 2.0 3.2 0.4 0.5 1.7 0.0 9.6

Percentagesb

Bethel Island 4.7 53.4 0.2 0.3 9.5 31.2 0.8 100

Livermore 3.9 33.9 0.2 0.4 21.5 39.0 1.1 100

San Francisco 8.1 42.4 0.9 0.4 18.8 28.8 0.6 100

San Jose 2000 5.2 23.1 2.7 0.8 23.1 44.7 0.4 100

San Jose 2001 6.0 27.0 8.1 2.9 19.9 35.7 0.3 100

Point Reyes 18.9 20.2 33.4 4.0 5.2 17.9 0.4 100

4 site averagec

5.4 38.2 1.0 0.4 18.2 36.0 0.7 100.0

a

Average of results for 10 highest PM2.5 measurements at each site.

b Percentages of estimated mass.

c

BI, SF LI, and SJ 2000.

The pattern of source contributions to peak PM2.5 is different from that of annual PM2.5. 

In particular, ammonium nitrate and vegetative burning represent larger fractions. 
-22-

April 2008

Ammonium nitrate represents over 50% for Bethel Island, over 40% for San Francisco, 

and over 30% for Livermore. Vegetative burning is also a greater factor, representing

over 28% for all other sites except Point Reyes. The percent contribution of fossil fuel is 

smaller for peak PM2.5 than for annual PM2.5, although is still a large contributor.

Ammonium sulfate, marine and geological dust are also smaller contributors to peak 

PM2.5 than to the annual totals. Note that the total peak concentrations are between 35 

- 40 mg/m3at all sites except Point Reyes.

5.3 Analysis by Season

Figures 5.5a-5.5f show source contributions by season for each site. As expected, the 

largest contributions occur in the winter quarter. Ammonium nitrate and either fossil 

fuel combustion or vegetative burning, or both, provide the dominant contribution to 

total PM2.5 at every site, except Point Reyes.

For other seasons, fossil fuel and vegetative burning are large contributors and 

ammonium sulfate forms as large or larger contributor than ammonium nitrate in 

almost every instance. The fossil fuel category is generally as large or larger than 

“vegetative burning,” but often the two are comparable. Note that the “vegetative” 

category includes cooking, secondary organics, planned burns, and accidental fires. This 

may account for the similarity. 

Figure 5.5a. PM2.5 source contributions by season for Bethel Island

0

2

4

6

8

10

12

14

AmSul AmNit Marine Geo Dust Fossil Fuel

Combustion

Vegetative Burning PM2.5 (mg/m3)

spring

summer

fall

winter

Note: Bethel Island PM2.5 source contributions by season. Values represent averages for the 4 pm seasons.
-23-

April 2008

Figure 5.5b. PM2.5 source contributions by season for Livermore

0

2

4

6

8

10

12

14

AmSul AmNit Marine Geo Dust Fossil Fuel

Combustion

Vegetative Burning

PM2.5 (mg/m3)

spring

summer

fall

winter

Note: Livermore PM2.5 source contributions by season. Values represent averages for the 4 pm seasons.

Figure 5.5c. PM2.5 source contributions by season for San Francisco

0

2

4

6

8

10

12

14

AmSul AmNit Marine Geo Dust Fossil Fuel

Combustion

Vegetative Burning PM2.5 (mg/m3)

spring

summer

fall

winter

Note: San Francisco PM2.5 source contributions by season. Values represent averages for the 4 pm seasons.
-24-

April 2008

Figure 5.5d. PM2.5 source contributions by season for San Jose 2000

0

2

4

6

8

10

12

14

AmSul AmNit Marine Geo Dust Fossil Fuel

Combustion

Vegetative Burning PM2.5 (mg/m3)

spring

summer

fall

winter

Note: San Jose 2000 PM2.5 source contributions by season. Values represent averages for the 4 pm seasons.

Figure 5.5e. PM2.5 source contributions by season for San Jose 2001

0

2

4

6

8

10

12

14

AmSul AmNit Marine Geo Dust Fossil Fuel

Combustion

Vegetative Burning PM2.5 (mg/m3)

spring

summer

fall

winter

Note: San Jose 2001 PM2.5 source contributions by season. Values represent averages for the 4 pm seasons.
-25-

April 2008

Figure 5.5f. PM2.5 source contributions by season for Point Reyes

0

2

4

6

8

10

12

14

AmSul AmNit Marine Geo Dust Fossil Fuel

Combustion

Vegetative Burning PM2.5 (mg/m3)

spring

summer

fall

winter

Note: Point Reyes PM2.5 source contributions by season. Values represent averages for the 4 pm seasons.

5.4 Comparisons with the 1994 Winter PM10 Field Study

As an external check on the findings of the current study, comparison was made to the 

results of a previous PM study. During the winter of 1993-94, PM10 samples were 

collected from the four sites analyzed in the present study (BI, LI, SF, and SJ). Samples 

with PM10 greater than 50 mg/m3 were aggregated and analyzed for C-14 for each site. 

Aggregated profiles for wood smoke, auto exhaust, and geological dust were also 

analyzed for C-14. A CMB analysis was then performed on the aggregated samples. 

Table 5.3 shows the results.
-26-

April 2008

Table 5.3. CMB resultsafor aggregated wintertime high PM10 samples, 11/93-1/94.

Source: Fairley (1995)

Conc. (mg/m3)

(std. errors)

Marine Ammonium

Sulfate

Ammonium

Nitrate

Geological

Dustb

Fossil

Fuelc

Wood/plan

t

burningd

Est.

Total

Meas.

Total

Bethel Island 0.5

(0.4)

3.0

(0.2)

30.4

(0.6)

5.6

(0.7)

3.5

(0.9)

18.6

(2.3)

61.7

(2.4)

63.2

(1.1)

San Francisco 2.1

(0.4)

4.3

(0.2)

23.8

(0.5)

10.2

(1.2)

10.7

(1.8)

13.7

(2.0)

64.9

(2.4)

64.5

(1.0)

Livermoree 1.1

(0.4)

2.6

(0.3)

27.3

(0.5)

7.5

(0.7)

6.5

(2.2)

30.0

(2.7)

75.0

(2.8)

74.2

(1.0)

San Josee 2.2

(0.5)

2.3

(0.3)

19.1

(0.6)

11.7

(1.2)

14.9

(2.0)

36.2

(3.5)

86.3

(3.7)

80.9

(1.1)

Percentagesf

Bethel Island 0.9 4.9 49.3 9.1 5.7 30.1 100 102.4

San Francisco 3.2 6.7 36.7 15.8 16.5 21.2 100 99.4

Livermoree 1.4 3.5 36.4 10.0 8.7 40.0 100 98.9

San Josee 2.6 2.6 22.1 13.5 17.3 41.9 100 93.7

4 site average 2.1 4.5 34.9 12.2 12.4 34.2 100 98.2

a CMB estimates based on combined samples for days where PM10 > 50 mg/m3, using data collected 11/93 - 1/94. This analysis 

includes old and new carbon as species based on the University of Arizona C-14 analysis.

b Geological dust profile collected from various sites around the Bay Area, on and off road, mainly near the 4 sites.

c

 Fossil Fuel profile collected in Caldecott Tunnel, adjusted to eliminate other sources. PM10 attributed to this profile is mainly auto 

exhaust, but the CMB model probably attributes PM10 from other fossil fuel burning (such as natural gas burning) to this source 

also.

d Wood/plant burning profile collected in three San Jose back yards of homes burning wood in their fireplaces. PM10 attributed to 

this profile is mainly from woodburning, but there may be some from agricultural burning and other sources also.

e The 2 samples for San Jose and Livermore are averaged.

f

 Percentages of estimated mass.

Figure 5.6 compares the percent of peak PM10 attributed to different source categories 

for the winter of 1993-94 with the percent peak PM2.5 for different source categories for 

the present study.

Certain differences are expected because the comparison is between PM10 in the earlier 

study and PM2.5 in the present study. One key difference is that most airborne 

geological dust is coarse – greater than 2.5 microns in diameter, so that a much higher 

percentage would be expected in PM10 than PM2.5. Indeed, that appears to be the case: 

geological dust represents 12.2% of PM10 on average in the earlier study, and only 0.5% 

of PM2.5 in the present study. Marine air also contains a large coarse fraction. The 

earlier study has somewhat higher marine percentages overall (2.1% vs. 1.0%) and for 

every site except SJ.

For ammonium nitrate and ammonium sulfate, the results seem consistent between 

studies. The overall percentages are somewhat higher in the present study; this is 
-27-

April 2008

expected, because most of this secondary PM is PM2.5. The relationship of the sites is 

reasonably consistent, with BI registering the highest ammonium nitrate in both studies,

SJ the lowest, and SF the highest ammonium sulfate. Note that there is an increase in 

the percent contribution of ammonium sulfate, also secondary PM, at all sites, 

particularly at San Jose.

Figure 5.6 also shows larger contributions from fossil fuel and vegetative combustion in 

the PM2.5 study. The overall percent from fossil fuel has increased at every site, with 

an average increase from 12% to 18%. The greatest increase is at Livermore, which 

experienced significant traffic increases between the two study periods. The vegetative 

burning percentages increased at every site except for Livermore (possibly influenced by 

the relocation of the monitor and increase in traffic between the two study periods). 

The relative order has remained the same, however, with SF having the lowest 

percentages to San Jose having the most.

Figure 5.6. Comparison with percent source attributions from the winter 1993-94 field 

study and the 2000-01 study

Mean of percent

60

45

30

15

0

site bi li sf sj ave

60

45

30

15

0

bi li sf sj ave bi li sf sj ave

ammonium nitrate ammonium sulfate fossil fuel

geological dust marine vegetative burning

study

pm10 93-94

pm2.5 2000

Panel variable: source category

6. REFINING SOURCE APPORTIONMENT WITH EMISSIONS INVENTORY ESTIMATES

CMB analysis with currently available data can only provide accurate distinctions 

between source categories, not specific sources. In an attempt to refine the source 

apportionment, emissions inventory estimates were combined with the CMB results. 

Specifically, the BAAQMD emissions inventory provides estimates of emissions within 
-28-

April 2008

source categories. For the "vegetative burning" category, the emissions inventory 

includes domestic burning (wood burning), commercial cooking, accidental fires, 

tobacco smoke, and waste burning. The major emissions inventory categories within the 

"fossil fuel" category are on-road vehicles, off-road vehicles, aircraft, refineries, and 

power generation.7 The emissions inventory is the product of years of effort by the 

District along with ARB and the U.S. EPA. Emissions estimates are based on carefully 

analyzed methods, and have been tested against emissions measurements and ambient 

concentration data.

CMB analysis and the emissions inventory provide complementary information about 

PM sources. CMB analysis uses measured PM concentrations, so that it reflects what is 

actually in the air. But the measurements are specific to a particular location and can 

only distinguish among a limited number of sources. The emissions inventory is based 

on engineering calculations for a wide range of sources, and represents totals across the 

entire Bay Area, but it doesn't reflect transport, deposition, or chemical transformations 

in the atmosphere.

In this section, CMB is used to provide estimates of the relative contribution of major 

primary and secondary source categories. The emissions inventory is used to apportion 

the PM in these major categories to individual sources.

The emissions inventory provides estimated annual average emissions, appropriate for 

apportioning annual average PM2.5 sources. Peak PM2.5, however, occurs almost 

exclusively in winter, making the winter emissions inventory more appropriate for this 

analysis.

Table 6.1 shows estimated relative contributions of various sources to annual emissions 

based on estimated annual 2000 emissions from the base year 2002 BAAQMD emissions 

inventory. The table presents four components of the emissions inventory: PM2.5 from 

combustion, PM2.5 from geological sources (such as road dust), NOx, and SO2. The latter 

two are precursors to ammonium nitrate and ammonium sulfate, respectively. The 

"vegetative burning" category is broken into cooking and "vegetative fires", which

includes residential wood burning, wildfires, planned burns and cigarette smoke. 

Information in the table was used to apportion the CMB source categories into more 

specific sources.

 

7

In some of these, various subcategories have been combined. For example, "refineries" includes "basic 

refining processes," "flares," "other refining processes," and "oil refineries external combustion."
-29-

April 2008

Table 6.1. Emissions inventory estimates of annual carbonaceous and secondary 

precursors (% of emission inventory total)

PM2.5-

combustion NOx SO2

PM2.5-

geological

Vegetative Fires 44 1 1 0

Commercial Cooking 11 0 0 0

Fossil Fuel Combustors:

On-road 10 57 3 72

Off-road 14 20 0 9

Trains 1 2 1 0

Aircraft 2 3 1 0

Ships 4 3 10 0

Refining 6 4 65 0

Power Generation 2 2 1 0

Domestic fossil heat 2 3 0 0

Other fossil 4 4 4 0

Fossil Fuel Total: 45 99 88 82

Table 6.1 shows that the emissions inventory attributes 44% of combustion PM2.5 to 

vegetative fires, 11% to commercial cooking, and 45% to fossil fuel burning. The table 

also lists specific fossil fuel sources including on-road, off-road, trains, aircraft, ships, 

refining, and power generation. Particles from these sources are likely to be attributed 

to the "fossil" CMB source category.

Note that the percentage of directly emitted PM2.5 from combustion estimated from 

vegetative fires alone is 44%, nearly as large as all fossil fuel emissions. Combined with 

cooking, the total is 55%.

Table 6.2 combines the information in Table 6.1 with the 4-site totals of Table 5.1. The 

percentages in the Table 5.1 are assumed to provide correct totals for each source 

category8, and Table 6.1 is used to apportion PM to specific sources from within each 

source category. For example, Table 5.1 shows that CMB attributes 18.8% of annual 

PM2.5 to ammonium nitrate. This ammonium nitrate is assumed to derive from NOx. 

Table 6.1 shows that 57% of the Bay Area's annual NOx emissions derive from on-road 

vehicles. Therefore, we assume that 57% of the 18.8% or 11% of the Bay Area's annual 

PM2.5 derives from ammonium nitrate originating from on-road motor vehicles.

The numbers in Table 6.2 represent the percent of the total PM attributable to specific 

sources within each category. The numbers in the right-hand column are the percent of 

total PM2.5 attributable to that specific source so that, combining both direct emissions 

and secondary contributions, vegetative fires are estimated to contribute 24% of the 

 

8

In other words, the column totals in Table 6.2 are equal to the 4-site percentages at the bottom of Table 

5.1.
-30-

April 2008

total annual PM2.5, commercial cooking 6%, on-road 18% and so on. Figure 6.1 

summarizes the results.

Table 6.2. Percent estimated total annual contribution from various sources.

Source Category:

Source Marine

Ammonium 

Sulfate

Ammonium 

Nitrate Geological Fossil Vegetative Totals*

Vegetative fires 0 0 0 24 24

Commercial cooking 0 0 0 6 6

On-road 0 11 1 6 18

Off-road 0 4 0 9 12

Trains 0 0 0 0 1

Aircraft 0 1 0 1 2

Ships 1 1 0 2 4

Refining 7 1 0 3 12

Power generation 0 0 0 2 2

Domestic fossil 0 0 0 1 2

Marine 11 11

Other 2 1 0 2 5

Totals* 11 11 19 1 27 30 99

* Totals may differ due to rounding.

Figure 6.1. Estimated contributions to annual Bay Area PM2.5 from various sources

6%

18%

12%

4%

12%

2%

2%

11%

5%

24%

1%

2%

Vegetative fires

Commercial cooking

On-road

Off-road

Trains

Aircraft

Ships

Refining

Power generation

Domestic fossil

Marine

Other

Table 6.3 shows winter emissions inventory percentages for carbonaceous and 

secondary precursors. The inventory attributes 38% of combustion PM2.5 to fossil fuels 

and 62% [56%+6% = 62%] to vegetative fires and cooking. This is close to the 

percentages estimated based on the carbon-14 adjusted CMB analysis shown in Table 

5.2, where fossil fuels were estimated to contribute 18.2% and vegetative burning 

36.0%. Thus, the CMB analysis attributes 34% [100%*18.2/(18.2+36.0) = 34%] of direct 
-31-

April 2008

combustion-related PM2.5 to fossil fuels, and 66% [(100-34)% = 66%] to vegetative 

burning. 
-32-

April 2008

Table 6.3. Emissions inventory estimates of winter carbonaceous and secondary 

precursors (% of emission inventory total)

PM2.5-

combustion NOx SO2

PM2.5-

geological

Vegetative fires 56 1 1 0

Commercial cooking 6 0 0 0

Fossil Fuel Combustors:

On-road 9 60 3 72

Off-road 11 17 0 5

Trains 0 2 1

Aircraft 2 3 1

Ships 3 3 10

Refining 4 4 65

Power generation 2 2 1

Domestic fossil heat 3 4 1 0

Other Fossil 3 4 4 0

Fossil Fuel Total: 38 99 88 77

As with Table 6.2, Table 6.4 contains estimates of contributions from individual sources 

to total PM2.5, but for winter days with high PM2.5. As expected, vegetative fires (almost 

entirely domestic wood burning) constitutes a larger percentage than in Table 6.2, and 

is the largest single source. Interestingly, on-road vehicles also contribute more of the 

total, due to the large amount of ammonium nitrate in wintertime PM2.5. These two 

sources contribute about half of winter peak PM2.5. Other large sources include off-road 

vehicles, ships, and commercial cooking. Figure 6.2 summarizes the results.
-33-

April 2008

Table 6.4. Estimated percentage total peak contribution from various sources.

Source Category:

Source Marine

Ammonium 

Sulfate

Ammonium 

Nitrate Geological Fossil Vegetative Totals*

Vegetative fires 0 0 0 0 33 33

Commercial cooking 0 0 0 0 3 3

On-road 0 23 0 4 0 28

Off-road 0 7 0 5 0 12

Trains 0 1 0 0 0 1

Aircraft 0 1 0 1 0 2

Ships 1 1 0 1 0 3

Refining 4 2 0 2 0 7

Power generation 0 1 0 1 0 2

Domestic fossil 0 1 0 1 0 3

Marine 1 0 0 1

Other 1 2 2 0 5

Totals* 1 5 38 0 19 36 99

* Totals may differ due to rounding.

Figure 6.2. Estimated contributions to peak Bay Area PM2.5 from various sources

33%

3%

28%

12%

1%

2%

3%

7%

1% 5% 3%

2%

Vegetative fires

Commercial cooking

On-road

Off-road

Trains

Aircraft

Ships

Refining

Power generation

Domestic fossil

Marine

Other
-34-

April 2008

7. KEY FINDINGS AND ON-GOING WORK

The currently available data, coupled with the above analysis, makes it possible to draw 

some conclusions about sources of fine PM in the Bay Area.

7.1 Key Findings

 Most anthropogenic PM10 and PM2.5 derive from burning wood or fossil fuels.

 Geological dust is a small contributor to PM10 and a negligible contributor to 

PM2.5. Tire/break wear is also a negligible PM2.5 source.

 Peak PM occurs largely in winter. Reasons include a high rate of ammonium 

nitrate formation and more wood burning, both related to low temperatures. 

Analysis of wintertime meteorological data shows that periods of low winds are 

conducive to the buildup of PM2.5.

 Residential wood burning appears to be the largest single source of wintertime 

PM2.5, a conclusion supported both by ambient data and the emissions 

inventory.

 Ammonium nitrate is a major contributor to peak PM and a large contributor to 

annual PM.

 Ammonium sulfate is a substantial contributor to annual PM2.5, but only a small 

contributor to peak PM.

 Wood burning and on- and off-road vehicles account for approximately 55% of 

annual PM2.5 and 75% of peak PM2.5.

7.2 Ongoing work

The District has a number of projects underway to better understand the sources of 

PM2.5 in the Bay Area and to track progress in reducing PM2.5.

 Performing annual telephone surveys to update and improve the estimates of 

emissions from wood burning. Future surveys can be used to help track progress 

in emissions reduction from wood burning. 

 Implementing a grid-based particulate model. The model will provide 

information on the diffusion and dispersion of primary PM, formation of 

secondary PM and transport of PM from areas outside of the District. 
-35-

April 2008

 Developing an ammonia emissions inventory for the Bay Area that will be used 

for the PM model. The quality of these Bay Area-specific estimates of ammonia 

emissions is expected to be better than those obtained from the use of the EPA 

or ARB methods.

 Performing a study of diesel emissions and concentrations through the District’s 

Community Air Risk Evaluation (CARE) program will help quantify the relative 

contributions to PM2.5 from diesel and other fossil fuel sources.

 Analyzing winter and summer PM10 filters for carbon-14. These measurements 

will add to our understanding of the relative PM contributions of wood smoke 

and cooking.

 Working with UC Davis to investigate the relationship between meteorology and 

PM transport, especially on wintertime high PM days.
-36-

April 2008

8. REFERENCES

Chow, J.C., Watson, J.G., Crow, D., Lowenthal, D.H, Merrifield, T., 2001. Comparison of 

IMPROVE and NIOSH carbon measurements, Aerosol Science and Technology, 34: 23-34. 

EPA/NARAL, 2002. Comparison of CARB and EPA speciated measurements: 

Performance Evaluation Sample audit of the CARB lab conducted by EPA/NAREL. Memo 

(9/12/02) from Michael S. Clark/NARAL to Jim Homolya / OAQPS. Reference found: 

http://www.epa.gov/ttn/amtic/pmspec.html.

EPA, 1999. Particulate Matter (PM2.5) Speciation Guidance, Edition 1, Jim Homolya and 

Joann Rice, US EPA, October 7,1999. Final Draft.

Fairley, D., 1995. Chemical Mass Balance of Bay Area Particulates, draft Technical Report 

TR9501, April 1995.

Fairley, D., 2004. A look at C-14 comparisons, interoffice memo to Gary Kendall, 

8/24/2004.

Fairley, D., 2005. Results from annual carbon-14 sampling, interoffice memo to Gary 

Kendall, et al., October 6, 2005.

Held, T., Ying, Q., Kaduwela, A., Kleeman, M.J., 2004. Modeling particulate matter in the 

San Joaquin Valley with a source-oriented externally mixed three-dimensional 

photochemical grid model. Atmospheric Environment (22): 3689-3711. 

Watson, J.G. et al., 1990. Receptor model technical series, volume III (1989 revision), 

CMB7 user's manual, EPA-450/40-90-004, January 1990.

Watson, J.G. 2008. Personal communication.
-A1-

April 2008

APPENDIX A. ADJUSTMENT OF OC AND EC DATA

Two different approaches have been developed to measure OC and EC on PM filters –

the IMPROVE and NIOSH methods. Chow et al. (2001) showed that the NIOSH method 

attributes considerably more carbon to the OC fraction than the IMPROVE method does. 

CRPAQS and the Speciated Trends Network used the IMPROVE method for ambient 

samples, and CRPAQS and BAAQMD used the IMPROVE method for their source 

profiles. But the EPA has used the NIOSH method for its analysis of San Jose PM2.5

filters. CMB analysis requires consistent source and ambient measurements, so one set 

of measurements had to be adjusted. The choice was made to modify the NIOSH 

measurements, simply because there were fewer datasets to change.

Comparisons showed that the fraction of IMPROVE-measured EC out of total OC+EC 

averaged at least double that of the NIOSH method. Figure A1 shows boxplots of the 

ratios of EC to TC = OC+EC. As can be seen, the NIOSH method EC fraction is 

considerably less than that of the IMPROVE sites. The median and mean ratios for the 

IMPROVE sites are all somewhat more than double the NIOSH site (Table A1). Also 

shown are the EC/TC ratios from the District's Wintertime 1993-94 PM10 Study, where 

all EC/OC measurements were done using the IMPROVE method. Although the ratios 

appear somewhat lower than they do at present, the ratios for SJ are certainly not lower 

than for the other sites. Thus, there is no reason to suspect that the EC fractions at San 

Jose are, in reality, systematically lower than for any of the other sites.

Because the PM2.5 ratios for the IMPROVE sites in Table A1 are at least twice what they 

are for SJ, we adjusted the SJ observations by doubling the EC numbers and subtracting 

off the corresponding amount from the OC, i.e., EC* = 2EC and OC* = OC – EC. The 

boxplot of the ratios of the adjusted EC/OC values is shown in Figure A1.

Figure A1. Comparisons of EC/TC ratios for the CRPAQS and EPA (SJ) sites.
-A2-

April 2008

Site

EC fraction

bi li sf sj sjadj

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Note: SJadj represents the ratios for San Jose after adjustment.

Table A1. Mean and median EC/TC ratios

Site n mean ratio median 

ratio

pm10 

ratio*

SF 71 0.38 0.38 .28

LI 70 0.34 0.35 .24

BI 68 0.34 0.33 .22

SJ 188 0.13 0.14 .27

* From the Winter 1993-94 field study
-B1-

April 2008

APPENDIX B. COMPARISON OF MASS MEASUREMENTS

Various labs measured the data analyzed in this report. Parallel measurements were 

made at some sites. In particular, simultaneous PM2.5 mass measurements were made 

at District sites and two of the three CRPAQS sites, Livermore and San Francisco, and at 

the Speciation Trends Network data from the San Jose site.

We found high correlation between Livermore and San Francisco, and between 

Livermore and San Jose. Figure B1 shows a plot of Livermore PM2.5 mass measured by 

CARB versus mass measured by the District.

Figure B1. Comparison of PM2.5 mass measured by CARB and BAAQMD for Livermore, 

2000

0 50 100

100

50

0

BAAQMD PM2.5 /(μg/m3)

CARB PM2.5 (μg/m3)

Note: 1-to-1 line also shown.

There are discrepancies, however, in the magnitudes of the measured mass for 

Livermore and San Francisco. Table B1 shows comparative statistics between BAAQMD 

measurements and those of CARB. Included are mass measurements and also sums of 

the major individual species, including nitrate, sulfate, EC, OC, aluminum, silicon, sodium 

and chlorine, more precisely, all of the compounds used in CMB fitting listed in 

Appendix C.
-B2-

April 2008

Table B1. Same-day comparisons of BAAQMD, CARB and EPA PM2.5 measurements

Mean Median Ratios: 

Other/BAAQMD

Ratios: Sum of 

Spec./BAAQMD

Site N BAAQ

MD 

mass

Other 

Massa

Sum of 

Spec.b

BAAQ

MD 

mass

Other 

mass

a

Sum of 

Spec. b

Med

-ian

Low 

CI

Upp 

CI

Med

-ian

Low 

CI

Upp 

CI

LI 64 13.6 10.3 11.3 9.0 6.2 9.1 0.70 0.65 0.76 0.93 0.84 1.04

SF 64 14.3 11.5 12.0 9.0 7.2 8.6 0.73 0.67 0.79 0.87 0.81 0.94

SJ 126 14.3 15.4 14.0 11.0 11.8 11.7 1.06 1.03 1.11 1.06 1.03 1.09

a

Other = CARB for LI and SF, and EPA (RTI International) for SJ.

b

Sum of Spec. = Sum of all major components, including nitrate, sulfate, EC, OC, aluminum, silicon, 

sodium and chlorine.

The table shows that CARB PM2.5 averages roughly 70% of BAAQMD. The ratio is 

somewhat higher for higher concentrations. For San Jose, the comparisons are much 

closer, with the EPA measurements averaging slightly higher than the District's. The 

sum of species should be less than the mass, because it omits components like oxygen 

in soil and hydrogen in OC. Yet for LI and SF, the mean and median sum of species is 

larger than the CARB total mass, and the medians are actually close to the BAAQMD 

total mass. Also note that the ratios of the sum of species to BAAQMD mass are closer 

to 1 than the total mass. Therefore, it was assumed that the CARB masses were 

underestimated, and the LI, SF and BI measurements were adjusted by 1.1x + 1.9, where 

x is the CARB measurement. 
-C1-

April 2008

APPENDIX C. CHEMICAL SPECIES USED IN THE CMB MODEL

Table C1 shows the number of times the measured concentrations of various species 

exceeded 1 and 2 standard deviations. Also shown, by asterisks, are those species used 

for fitting by the CMB model.

Table C1. Chemical species – frequency above the limits of detection.

species id species #>1sd #>2sd used for 

CMB* Species id species #>1sd #>2sd used for CMB*

MS mass 194 164 CU copper 138 71 *

CL chloride 139 67 ZN zinc 134 108 *

NO3 nitrate 208 198 * GA gallium 1 0

SO4 sulfate 208 202 * AS arsenic 0 0

NH4 ammonium 187 170 * SE selenium 15 3 *

NA sodium ion 158 140 * BR bromine 124 42 *

KP potassium ion 107 73 RB rubidium 1 0

OC organic carbon 178 139 * SR strontium 16 7 *

EC elemental carbon 189 143 * YT yttrium 0 0

NAX sodium 79 43 ZR zirconium 3 0

MG magnesium 104 33 MO molybdenum 1 0

AL aluminum 34 6 * PD palladium 0 0

SI silicon 24 13 * AG silver 1 0

PH phosphorus 16 1 CD cadmium 0 0

SU sulfur 208 208 IN indium 0 0

CL chlorine 100 78 * SN tin 2 0

KPX potassium 162 82 * SB antimony 1 0

CA calcium 52 13 * BA barium 1 0

TI titanium 0 0 LA lanthanum 0 0

VA vanadium 0 0 AU gold 0 0

CR chromium 0 0 HG mercury 0 0

MN manganese 13 3 * TL thallium 0 0

FE iron 47 24 * PB lead 55 13 *

CO cobalt 0 0 UR uranium 1 0

NI nickel 20 4 *

* Asterisk indicates that the species was used in fitting the CMB model.
-D1-

April 2008

APPENDIX D. DETERMINING WEIGHTS APPLIED TO THE FITS

For each sample, a range of models were fit. As explained in section 4.1, the results 

were summarized as a weighted average, where the weights were based on the product 

of two probabilities – a chi-square for how closely individual calculated species matched 

the measured species, and a Gaussian for the difference between the total measured 

mass and the calculated total.

One limitation with this approach is that the true distribution of the chi-square statistic 

is difficult to estimate because it involves unknown statistical correlations among the 

chemical species. In other words, the chemical species are not statistically independent, 

and the lack of independence between chemical species suggests that the chi-square 

statistic might not have a chi-squared distribution. In fact, the CMB fits produced "chisquare" statistics that averaged about 0.5. If the model were correct, these statistics 

should be averaging about 1.0.9

A second issue is that a range of models is fit to the same sample. Some samples were 

inherently easy to fit, others difficult, so that, for some samples, a large number of chisquare statistics were very small, suggesting good fits, in others few if any chi-square 

statistics were small. Thus, the assumption that the chi-squared statistic has a chisquared distribution yielded unrealistic probabilities.

We minimized this problem by indexing the chi-square fits to the best fit, which was 

arbitrarily given a value of 12 – the 25th percentile of a chi-square with 17 degrees of 

freedom. Each other chi-square statistic, S, was adjusted to 12 times its ratio to the 

minimum: 12*S/Smin. This adjusted value was then assumed to have a chi-squared 

distribution.

For the second p-value, the difference between the measured and calculated masses 

was assumed to have a Gaussian distribution. However, occasionally there were large 

differences between the measured mass and any of the calculated masses – in a 

number of cases, the sum of the measured species was considerably larger than the 

measured mass, indicating that one or the other was substantially mismeasured

resulting in a model that fit badly for individual components, nevertheless gave a better 

second p-value because the sum of the modeled species accidentally got closer to the 

mismeasured total PM.

To account for these anomalies, a "posterior" probability was calculated, for the 

likelihood that a problem had occurred. It was arbitrarily assumed that initially there is 

 

9

Actually, the statistic was had the form of a chi-square statistic divided by its degrees of freedom. Under 

the assumption that the terms in the chi-square come from a set of independent standard normal random 

variables, the expected value of a chi-square equals its degrees of freedom. So dividing by the degrees of 

freedom produces a statistic with mean 1.0.
-D2-

April 2008

a 90% chance that the measurements are not grossly inaccurate. The validity of this 

assumption was checked by comparing the measured mass with the (adjusted) sum of 

the measured species. Specifically, the species were summed, except the OC and EC 

were multiplied by 4/3 to adjust for the fact that for two of the most common sources 

of ambient carbon – wood smoke and motor vehicle exhaust – the measured species 

account for about 75% of the measured mass (the remainder including oxygen and 

hydrogen), and Aluminum and Silicon were multiplied by 1/.6 to account for the fact 

that the measured geological species account for about 60% of the mass (much of the 

rest being oxygen).

A z-statistic was computed as the difference between the measured mass and sum of 

species (as described above) divided by their estimated standard error. A p-value was 

computed as p=2*Phi(|z|), and a posterior probability computed: p1 = .9p/(.9p+.1(1-p)). 

If p is close to 1.0, the posterior is also close to 1.0. If p is equal to 0.1, indicating some 

chance that there are serious measurement discrepancies, then the posterior 

probability is 0.5. If p is small, 0.01 or less, then the posterior is also small.

To adjust the second term for this posterior probability of erroneous measurements, the 

Gaussian was raised to the power of the posterior: [Phi(w)]

p1, where w = |measured 

mass – calculated mass|/(estimated sd of difference). If p1 is near 1, so there is good 

agreement between the measured mass and the sum of individual species, then Phi(w) 

is relatively unchanged. But if there is a large discrepancy, then p1 is small, thereby 

shrinking [Phi(w)]p1 toward 1, so that this term doesn't play much of a role in the overall 

fit.

The weight for an individual fit equals the product of its two p-values, divided by the 

sum of all the 1,100 or so products of p-values (for which all estimated coefficients are 

positive). The coefficients reported for a given sample is the weighted average of the 

coefficients. The coefficients' standard errors are estimated as the square root of the 

weighted average of the individual variances, i.e., the squares of the standard errors 

provided by the model. The definition of the weights, wi, can be summarized as 

follows:

i



i  j w r / r

where ri = P(X > 12Si/Smin)* [Phi(wi)]p1, and X is a chi-squared random variable with 

degrees of freedom = # of fitted species - # of fitted source categories.
-E1-

April 2008

APPENDIX E. INITIAL CMB RESULTS AND SOURCE CATEGORY SELECTION

The initial CMB results showed sizeable uncertainties for some of the source categories. 

This indicated that the data should be reanalyzed with fewer categories (See Section 5). 

This appendix describes how this subset of source categories was chosen.

If several sources have similar relative quantities of chemical species, the CMB model 

will have difficulty distinguishing between them. This leads to an inflation in the 

associated standard errors, i.e. uncertainties. Wood smoke from different tree species, 

for example, may have very similar source profiles, at least on the species measured for 

this analysis.10

In the initial CMB runs, only one source from a class was included at a time, e.g., only 

one woodsmoke profile and only one auto exhaust profile. However, identification 

problems persisted. In particular, the profiles of auto exhaust, wood smoke and 

commercial cooking are similar enough that the CMB model may have difficulty 

distinguishing them. Of course, from the viewpoint of PM controls, the distinction is 

crucial. Thus, for the initial CMB runs, various combinations of these sources were tried.

In order to determine the magnitude of uncertainty, two quantities were estimated. 

One is the total uncertainty – the standard error of the quarterly averaged source 

coefficients. It was computed from the estimated coefficients themselves. If the 

coefficient average were a simple arithmetic mean, then this estimate would be the 

usual sample standard deviation. This standard error includes variation from 3 sources: 

model uncertainty, meteorological variation, and variation in underlying emissions. 

The second quantity was estimated model uncertainty of the quarterly averaged source 

coefficients, based on combining estimated standard errors provided by the model for 

each coefficient for each fit. For a given coefficient and site, the combining formula was 

the square root of:

S

2

ave = 

   

4

1 1

2

2

1

16

1

i

n

j

ij

i

i

s

n

where ni = number of fitted coefficients in quarter i, and sij = model estimated standard 

error for the jth sample in quarter i.

Figure E1 presents a comparison of total and model uncertainties for each source 

category for each site. For the top 3 source categories – ammonium nitrate, ammonium 

sulfate, and marine air – total uncertainty is much greater than modeled. This implies 

 

10 Some differences can be found by speciating the organic carbon. However, this was not done for this 

study. Wood smoke and cooking can be differentiated from fossil fuel PM with Carbon-14 analysis. Some 

of this has been done for Bay Area PM, but not for this study.
-E2-

April 2008

that there is little difficulty in distinguishing these sources and, moreover, there was 

substantial variability in the contributions from that source from sample to sample.

For the three correlated sources – auto exhaust, woodsmoke and commercial cooking –

the modeled uncertainties are sometimes as large or larger than the total. (Of course, 

the actual modeled uncertainty must be less than or equal to the actual total.) This 

indicates that the model has difficulty in distinguishing among them. At some sites, 

however, it does appear the model can distinguish the first two sources – LI and BI for 

woodsmoke and SJ and SF for auto exhaust. But at no site could the commercial 

cooking source be distinguished above the model uncertainty. Therefore, the 

commercial cooking profile was dropped in the final CMB runs.

The final two sources – geological dust and tire/brake wear – also showed modeled 

uncertainties as large as total. This is not due to confounding with other sources but 

instead because they occur at such low concentrations that they are difficult to 

measure, that is, their distinguishing chemical species are not above the limits of 

detection. For example, for geological dust, silicon and aluminum are key species, 

consisting of about 25% and 10% of total mass, respectively. In the samples analyzed, 

these species occurred above the limits of detection in only 24 and 34 out of 208 cases, 

respectively. However, the model was able to distinguish geological dust at some sites. 

Geological dust has a unique signature, so it can be readily differentiated from the other 

sources, even at low concentrations. Because tire/brake wear did not occur above the 

detection limits of the model, it was eliminated from the analysis.
-E3-

April 2008

Figure E1. Model uncertainties vs. total uncertainty for fitted source categories

Note: Comparison of model uncertainties with total uncertainty for fitted source categories. Lined blue 

bars = total uncertainty. Solid red bars = modeled.

geo_dust

0

0.02

0.04

0.06

0.08

0.1

0.12

BTI

LVR1

PORE1

SanJose0SanJose1

SFA

(mg/m3)

woodsmoke

0

0.1

0.2

0.3

0.4

0.5

0.6

BTI

LVR1

PORE1

SanJose0SanJose1

SFA

(mg/m3)

cooking

0

0.05

0.1

0.15

0.2

0.25

0.3

BTI

LVR1

PORE1

SanJose0SanJose1

SFA

(mg/m3)

amm sulfate

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

BTI

LVR1

PORE1

SanJose0SanJose1

SFA

(mg/m3)

amm nitrate

0

0.1

0.2

0.3

0.4

0.5

0.6

BTI

LVR1

PORE1

SanJose0SanJose1

SFA

(mg/m3)

marine

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

BTI

LVR1

PORE1

SanJose0SanJose1

SFA

(mg/m3)

`

auto exhaust

0

0.1

0.2

0.3

0.4

0.5

0.6

BTI

LVR1

PORE1

SanJose0SanJose1

SFA

(mg/m3)
-F1-

April 2008

APPENDIX F. MODIFICATION OF CMB RESULTS BASED ON CARBON-14 ANALYSIS

Carbon-14 analysis combined with results from the previous source apportionment 

study and the fractions in the District’s emissions inventory suggest that the CMB 

analysis of the current data over-estimated the fossil contribution to PM2.5 vis-à-vis the 

vegetative burning component. The current CMB analysis used wood and motor vehicle 

source profiles from CRPAQS (a major PM study in northern California), which were 

developed for the Fresno area. This profile had a much higher soluble potassium 

content than the wood profile from our previous source apportionment study, which 

measured wood smoke from three Bay Area households. An in-house sensitivity 

analysis using wood smoke profiles from various studies showed a strong negative 

correlation between the soluble potassium content of the wood smoke and the 

estimated vegetative burning contribution. Thus, because the Fresno wood profile had 

a soluble potassium content that may be higher than that of Bay Area wood, there is a 

reason to believe that the current CMB analysis underestimates the wood smoke 

contribution vis-à-vis the fossil fuel contribution.

The Carbon-14 analysis provides measurements that can be used to adjust the CMB 

results. The new carbon content from PM10 filters from 2004 and 1998 was measured. 

The filters were composited from a set that included a sample representative of the 4 

quarters in the year and the appropriate percentage of weekends and weekdays. The 

CMB analysis described here was on filters containing PM2.5, not PM10. However, the 

vast majority of ambient carbon (deriving from vegetative burning, fossil fuel burning, 

cooking or secondary organic) is largely in the PM2.5 range. There is no reason to 

suspect that the PM2.5 from the same periods would have a new to old carbon ratio 

different from PM10.

Although the apportionment of carbonaceous PM2.5 into fossil and vegetative sources 

contains large uncertainties, their sum is well-estimated by CMB. The annual mean CMB 

results were adjusted to have the same total carbon, but with the vegetative/fossil ratio 

equal to the new/old carbon ratio found in the University of Arizona analysis.

The San Francisco and Livermore sites have C-14 measurements for both 2004 and 

1998. These were interpolated to provide annual vegetative burning fractions for 2000: 

(2*.452 + .490)/3 = .465 for San Francisco, and (2*.521 + .572)/3 = .538 for Livermore. 

For San Jose and Bethel Island, only the 2004 measurements were available. It was 

assumed that the annual percent change in C-14 fractions at these sites was the average

for the Bay Area sites, namely an increase of 9.5%, yielding .513 for San Jose 2000, .521 

for San Jose 2001, and .592 for Bethel Island.

The source apportionments for peak and seasonal PM2.5 were also adjusted. The goal 

was to adjust the profiles so that new/old carbon ratio of the adjusted average equaled 

the ratio found in the Carbon-14 analysis, yet to remain as faithful as possible to the 
-F2-

April 2008

new/old ranking found in the CMB analysis. A method that fits these constraints is as 

follows.

For a given site, let xi = the CMB vegetative burning estimate for day i, and yi = 

the CMB fossil fuel estimate, and let xi' and yi' be the values modified to correspond to 

the Carbon-14 annual means. That is,

,

' '

'

c

x y

x





(F1)

where c = the new/old carbon ratio, 

x'

mean of the xi', and 

y'

mean of the yi'. We 

used the adjustment xi' = ai*xi, where ai = (1 – k)*ri + k, where ri = xi/(xi+yi). That is, the 

adjustment factor, ai, is a linear function of the initial CMB vegetative fraction, ri, with 

the property that if ri = 1 (i.e., yi = 0), then ai = 1; i.e., there is no adjustment. As ri shrinks 

to zero, aiincreases, provided k>1. We still need ai*ri ≤ 1, which is always satisfied 

provided k≤2.

Applying the formula F1 yields 

x s

c x y s

k



 



( )

, where 

s 

the mean of ri*xi. 

Using this ai formula for Bethel Island and Livermore produced values of k between 1 

and 2. But applying the formula to San Francisco and San Jose produced values of k > 2. 

In these latter cases there were, in fact, a couple of instances where ai*ri was > 1. In 

these cases, ai was set to 1/ri.

To summarize, for a given site, each pair of CMB fits xi = vegetative burning 

concentration on day i and yi = fossil fuel combustion concentration on day i were 

transformed to xi', yi' as follows:

xi' = ai*xi, yi' = xi+yi – xi', where

ai = (1-k)*ri + k (F2)

where ri = xi/(xi+yi), 

x s

c x y s

k



 



( )

, with 

s 

the mean of ri*xi, and c = the vegetative 

fraction for 2000 for that site.