DISCLAIMER

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

PM 2.5 sources for the District’s activities to reduce exposure to fine particulate matter

. Data were obtained from the field program of the California Regi onal 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 measurem ents, analyses similar to those presented in this report will be conducted and the findings of this report will be updated as appropriate.- 1

- April 2008 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 Di stribution of PM 2.5
................................ ................................ ...... 9

3.3 Speciation of PM 2.5
................................ ................................ ...................... 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 Summ ary

................................ ................................ ............................ 20

5.3 Analysis by Season

................................ ................................ ...................... 22

5.4 Comparisons with the 1994 Winter PM 10 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

A ppendix A. Adjustment of OC and EC data

................................ ................................ .... A 1 Appendix B. Comparison of mass measurements

................................ ........................... B 1 Appendix C. Chemical species used i n the CMB model

................................ ................... C 1 Appendix D. Determining weights applied to the fits

................................ ...................... D 1 Appendix E. Initial CMB results and source category selection

................................ ....... E 1 Appendix F. Modification of CMB results Based on Carbon

- 14 Analysis

........................ F 1- 2

- April 2008 FIGURES

Figure 3.1. Quarterly averaged PM 2.5 for special study sites.

................................ ......... 10 Figure 3.2. Mean mass contribution of PM 2.5 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 contr ibutions to Bay Area ambient PM 2.5
............ 17 Figure 5.2

. Estimated annual percent contributions from vari ous source categories

.... 18 Figure 5.3. Estimated source contributions t o peak Bay Area ambient PM 2.5
................ 19 Figure 5.4. Estimated annual percentage contribution s to peak PM 2.5
.......................... 20 Figure 5.5a. PM 2.5 source contributions by season for Bethel Island

.............................. 21 Figure 5.5b. PM 2.5 source contributions by season for Livermore

................................ .. 22 Figure 5.5c. PM 2.5 source contributions by season for San Francisco

............................. 22 Figure 5.5d. PM 2.5 sourc e contribution s by season for San Jose 2000

............................ 23 Figure 5.5e. PM 2.5 source contributions by season fo r San Jose 2001

............................ 23 Figure 5.5f. PM 2.5 source contributions by season for Point Reyes

................................ . 24 Figure 5.6. Comparison with percent source a ttributions from the w inter 1993

- 94 field

study

................................ ................................ ................................ .............. 26 Figure 6.1. Estimated contributions to annual Bay Area PM 2.5 from various sources

.... 29 Figure 6.2. Estimated contributions to peak Bay Area PM 2.5 from various sources

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

................................ ................................ .......... A1 Figure B1. Co mparison of PM 2.5 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 PM 2.5 samples

................................ ............................. 18 Table 5.2. CMB results for peak PM 2.5 samples

................................ ................................ 20
Table 5.3. CMB results f or aggregated wintertime high PM 10 samples, 11/93

- 1/94

....... 25 Table 6.1. Emissions inventory estimates of carbonac eous and secondary precur sors

. 28
Table 6.2. Percent estimated total an nual cont ribution from various sources

............... 29 Table 6.3. Emissions inventory estimates of winter carbonac eous and secondary

precursors

................................ ................................ ................................ ....... 30
Table 6.4 . Estimated percentage total peak cont ribution from various sourc es

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

................................ ................................ ....... A2
Table B1. Same

- day comparisons of BAAQMD, CARB and EPA PM 2.5 measurements

.... B2 Table C1. Chemical species

– frequency above the limits of detection

.......................... C1- 3

- April 2008 SUMMARY

This study us es Bay Area ambient particulate matter ( PM ) measurements and emissions
data to determine the major PM sources and their approx imate 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 filter s contain particles less than or equal to 2.5 microns in diameter (PM 2.5 )
collected over 24

- hour period s at monitoring site s around the Bay Area

. Each filter was analyzed for a range of chemical species. The same speci e s wer e measured in special studies of emissions from various sources, such as motor vehicle s 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 wa s 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 obtaine d from four different studies or agencies: 1) the California Regional P articulate 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 Reye s. The data for the first three sites
were collected as part of the CRPAQS. They c over 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 CRPAQ S measurements. San Jose data
f rom 2001 were also added to the analysis to investigate h ow source contributions vary from year to year.

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

, vegetative burning

, commercial c ooking, 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 t he model's ability to
detect ; and certain combinations of s ources 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 differ entiated from- 4

- April 2008

vegetative burning or fossil fuel combustion . These sources were omitted from subsequent analys e s.

Considerable un c ertainty remained in the apportionment of two source categories

– vegetative burning and fossil fuel combustion. To bette r a pportion these categories,
carbon

- 14 was measured on PM 10 filters. This analysis provides a reliable estima te 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 resul ts were combined with emissions inventory estimates for the Bay Area. The combination apportions not only directly emitted PM 2 .5 but also the ammonium nitrate and ammonium sulfate with the assumption that these are proportional to the
emissions of NOx and SO 2 respectively.

The California and federal governments each have established two PM standards

– a 24

- hour standard to prote ct against short

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

- term exposure. T he 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 t he three source categories

– vegetative burning , fossil fuel
combustion and ammonium nitrate

– are the largest contributors to annual average

PM 2.5 . Marine ai r and ammonium sulfate are substantial contributors. Geological dust

is a relatively minor contribu tor

.

T he CMB results were also summarized for peak PM 2.5 – the average of the 10 highest

PM 2.5 samples for each site . For peak PM 2.5 , the top three source categories

– vegetative burning , fossil fuel combustion and ammonium nitrate

– comp rise more
th an 90% of the total. M arine 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 sing le source, contri buting about 24 % of annual PM 2.5 and 33 % of peak

PM 2.5
. On

- road vehicles were the second largest source of PM 2 .5
. Off

- road vehicles,
commercial cooking, ships, refining and marine air were also found to be large
contributors.

Additional findings are list ed below:- 5

- April 2008

 Most anthropogenic PM 10 and PM 2.5 derive from burning wood or fossil fuels.

 Geological dust is a small contributor to PM 10 and a negligible contributor to

PM 2.5 . Tire/break wear is also a negligible PM 2.5 source.

 Peak PM occurs largely in win ter. 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 PM 2.5
.

 Ammonium nitrate con tributes almost 40% to peak PM 2.5 and 20% to annual

PM 2.5 on average.

 Carbonaceous PM, that is, PM directly emitted from burning, accounts for roughly half of peak PM 2.5 and annual PM 2.5
.

 Ammonium sulfate contributes about 10% to annual PM 2.5 , but only 5 % to peak PM, on average.- 6

- April 2008

1. INTRODUCTION

The Bay A rea 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 quali ty standards for particulate matter.

Airborne particulate matter (PM) has serious adverse effects on health. High levels of PM, especially PM 2.5 , 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 PM 2.5 , of 35 g/m 3 . 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 PM 2.5 and PM 10 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, c ooking, 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 th e 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 che mical transformation of gaseous organic molecules in the atmosphere.

This study analyzes speciated PM 2.5 collected at several Bay Area sites using a computer model (chemical mass balance, or CMB) to estimate the contributions of various sources
to total P M. 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. D ATA

In order to conduct CMB analysis, both ambient and source profile data were needed.
The ambient data were obtain ed from four differen t studies or agencies : 1) t he California Regional P articulate Air Quality Study (CRPAQS), 2) t he Speciated Trends Network, 3) the IMPROVE network, and 4) Bay Area Air Quality Management District- 7

- April 2008

(BAAQMD) routine measurements. Th e source profile data were obtain ed from CRPAQS, and from ARB and BAAQMD sample collections.

2.1 Ambient D ata

CRPAQS included speciated analysis of filters containing PM 2.5 samples collected over

24

- hour periods from a variety of central California sites, including three in the Ba y 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 carbon s , 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 PM 2.5 at San Jose

– 4 th St.

on a 1

- in

- 3 day schedule. PM 2.5 d ata 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 monito ring network collected gravimetric PM 2.5 data at Livermore, San Francisco and San Jose contemporaneously with these other networks.

The IMPROVE network, which collects PM 2.5 data from national parks around the United States, prov id ed data at Point Reyes (PR). These data were collected on a 1

- in

- 3 day
schedule. Mos t 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 in ter

- lab comparison (EPA/NAREL 2002). O ne 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 mu ch 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 cookin g, and tire and brake wear. These profiles were
combined with PM 10 source profiles available from a previous District CMB analysis
conducted in 1994. A subset of these profiles w as used for CM B analysis, listed in Table

2.1.- 8

- April 2008

Table 2.1. Source profiles u sed in CMB analysis

Source Abbrev. Agency a Description

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 Ammon ium 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 ve hicle 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 mostl y from the combustion of fossil fuels and vegetative materials represents a large fraction of the Bay Area’s PM 2.5 . H owever, the
contribution s of the various carbonaceous sources are difficult to disti nguish 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 radioac tive isotope of c arbon that is constantly being replenished in the atmospher e, 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 C

14 in the PM allow s 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 c ontemporary and fossil carbon for each site were obtained as follows

. PM 10 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 PM 10 sites,- 9

- April 2008

a representative set of filte rs 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 th e results.

3. S UMMMARY 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. Avai lable ambient
data were summarized to better understand the seasonal distribution of PM 2.5 , its composition, a nd the locations of peak concentrations.

3.1 Total M ass C omparisons

A comparison of CRPAQ S

, the U.S. EPA and BAAQMD total mass measurements sho ws 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 wit h the sums of the individual chemical spec ies. 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 lar ger (s ee 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 D istribution of PM 2.5

The seasonal distribution of PM 2.5 was studied to better understan d when the highest concentrations occur in the different regions of the District. The seasons were defined to correspond to the PM 2.5 season, namely for this study "winter" is defined to include November, December, and January. The other quarters follow fr om the definition of the winter quarter: "sprin g" is February, March and April; "summer" is May, June and July; and "autumn" is August, September and October.- 10

- April 2008

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

Note: Data from 2000 except for 2001 for SJ1. Quarters are sp ring = Feb

- Apr, su mmer = May

- July,

au tumn =Aug

- Oct, wi nter =Nov

- Jan. T hese quarters were chosen to correspond to the PM season rather
than the standard definition of seasons. White bars are averages of the 4 quarters. Uncertai nty 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 average s far exceed ing those of the other quart ers, 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, th e 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 m g/m 3 and

the state annual standard of 12 m g/m 3 . Thus, the Bay Area is on the borderline for these
standards.

3.3 Speciation of PM 2.5

Many PM 2.5 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- 11

- April 2008

species and the number of times the me asured concentrations exceeded one and two standard errors. Those species whose concentrations exceeded on e 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 acro ss all sites, in decreasing order of magnitude. Also shown is the cumulative fraction of total mass. Organic carbon (OC) and Nitrate (NO 3 ) account for over 50% of the mass. Total carbon, that is, OC plus elemental carbon (EC), and NO 3 along with sulfate ( SO 4 ) and ammonium (NH 4 ), 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 PM 2.5 species

Note: Masses averaged across all sites

. Bars in dicate 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 c hlorine 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 alumi num

– are present, but in small quantities, indicating that geological dust is not a large component of Bay Area PM 2.5
.

Figure 3.3 shows boxplots of mass and the major species. Each box represents the range
from the 25 th to 75 th percentile, with the horiz ontal line in the middle representing the
median. The vertical lines above and below the box extend to the 95 th and 5 th- 12

- April 2008

percentiles. Asterisks beyond these lines represent extreme or outlying values of the
distribution.

The figure shows large contribution s 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,- 13

- April 2008

Figure 3.3. Boxplots of the most abundant species by site ( m g/m 3 )- 14

- April 2008

clearly a resu lt 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
l arge 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 elevat ed on a few occasions, but even for these, the concentrations are not that high, indicating that occasionally windblown dust contributes somewhat to PM 2.5 at these sites, but not in very large quantities.

3.4 Carbon

- 14 Analysis Results

Figure 3.4 shows t he 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 1 4 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 B ay Area sites for 1998 and 2004- 15

- April 2008

4. SOURCE APPORTIONMENT M ETHODOLO GY

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
chem ical signatures that are clearly defined and measured. 1 Howev er, 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

mo dified 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 f raction 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 pr ogram whose inputs are source profiles and ambient PM sample s that have been analyzed for a variety of chemical components. For each ambient sample, t he CMB model finds the mix of sources whose combined amounts of chemical components best approximates thos e 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
mod el, which incorporates the variability recorded in the source profiles. Yet, these

variation s 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.- 16

- April 2008

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 t able in Appendix

C shows wh ic h species were used for fitting. N ot 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 R 2 , all positive coefficients, all statistically significant coefficients, and lack of identifi ability 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 sev eral iterations to improve the
likelihood function so that it better matched the actual distribution. 4

The m ethod that was ultimately used wa s to define the likelihood as the product of two

p

- values : 5 the p

- value associated with the chi

- square statistic f or 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 appli e d 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 p rogram 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 c ontributions 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 approxima te 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 c loser 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 t erm 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 nu ll 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 categ ories. Details of the analysis with all source profiles and the rationale for dropping specific source prof iles are explained in Appendix E . The profiles included in the analysis were : ammonium sulfate, ammonium nitrate, marine, road

dust, 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 th e 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 PM 10 filters for 1998
and 2004

. (The PM 2.5 filters analyz ed in the rest of the study were unavailable. ) Also
the C

- 14 measurements represent only an annual average. However, b ecause C

- 14 measurements yield a clear

- cut division between new and fossil sources, the se measurements were used to modify the CMB resul ts for the “vegetative burning” and
“fossil” cat egories

. (See Appendix F for a more detailed rationale for the adjustment.)- 18

- April 2008

5. S OURCE APPORTIONMENT RESULTS

This section summarizes the results of the Carbon

- 14 adjusted CMB analysis and relates
these res ults 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 PM 2.5 values, so the results for the samples with th e 10 highest measured masses are presented

.

5 .1 Annual S ummary

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

- relate d, 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 PM 2.5- 19

- April 2008

Figure 5.2. Estimated annual percent contributions from various source categories

Table 5 .1. CMB results for annual PM 2.5 samples a

Conc. ( m g/m 3 ) Ammonium

Sulfate Ammonium

Nitrate Marine Geological

Dust Fossil

Fuel Vegetative

B urning Gun

Powder Est.

Total

Bet hel 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

Percentages b

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 2 001 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 average c 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
percenta ge 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 lar gest 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 combustion 6

sources constitute over 80% of the total for all 200 0 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 PM 2.5 masses, a nd Figure 5 .4 shows the percent contributions.

Figure 5.3. Estimated source contributions to peak Bay Area ambient PM 2.5

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 PM 2.5

Table 5 .2. CMB results for peak PM 2.5 samples a

Conc. ( m g/m 3 ) 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

Percentages b

Bethel Island 4.7 53.4 0.2 0.3 9.5 31.2 0.8 100

Livermore 3.9 3 3.9 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 average c 5.4 38.2 1.0 0.4 18.2 36.0 0.7 100.0

a Average of results for 10 highest PM 2.5 measurements at each site.

b Percentages of estimated mass.

c BI, SF LI, and SJ 2000.

The pattern of source contributions to peak PM 2.5 is different from th at of annual PM 2.5
. In part icular, ammonium nitrate and vegetative burning represent l arger fraction s.- 22

- April 2008

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

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

over 28 % for all other sites except Point Reyes

. The percent contribution of fossil fuel is smaller for peak PM 2.5 than for annual PM 2.5 , although is still a large contributor. Ammonium sulfate, marine and geological dust are also smaller contributors to peak

PM 2. 5 than to the annual totals. Note that the total peak concentrations are between 35

- 40 m g/m 3 at all sites except Point Reyes

.

5 .3 Analysis by S eason

Figures 5 .5a

- 5 .5f show source contributions by season for each site . As expected, the largest contribut ions occur in the winter quarter. Ammonium nitrate and either fossil
fuel combustion or vegetative burning , or both, provide the dominant contribution to
total PM 2.5 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 c ontribut or 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 th at the “vegetative” category includes cooking, secondary organics, planned burns, and accidental fires. This may account for the similarity.

Figure 5.5a. PM 2.5 source contributions by season for Bethel Island- 23

- April 2008

Figure 5.5b. PM 2.5 source contribution s by season for Livermore

Figure 5.5c. PM 2.5 source contributions by season for San Francisco- 24

- April 2008

Figure 5.5d. PM 2.5 source contributions by season for San Jose 2000

Figure 5.5e. PM 2.5 source contributions by season for San Jose 2001- 25

- April 2008

Figure 5.5f. PM 2.5 source contributions by season for Point Reyes

5 .4 Comparisons with the 1994 Winter PM 10 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, PM 10 s amples were
collected from the four sites analyzed in the present study (BI, LI, SF, and SJ). Samples

with PM 10 greater than 50 m g/m 3 were aggregated and analyzed for C

- 14 for each site. Aggregated profiles for wood smoke, auto exhaust, and geol ogical 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 results a for aggregated wintertime high PM 10 samples, 11/93

- 1/94. Source: Fairley (1995)

Conc. ( m g/m 3 ) (std. errors) Marine Ammonium
Sulfate Ammonium

Nitrate Geological

Dust b Fossil

Fuel c Wood/plan

t
burning d 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 Franci sco 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)

Livermore e 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 Jose e 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)

Percentages f

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

Livermore e 1.4 3.5 36.4 10.0 8.7 40.0 100 98.9

San Jose e 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 PM 10 > 50 m g/m 3 , 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. PM 10 attr ibuted to this profile is mainly auto

exhaust, but the CMB model probably attributes PM 10 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 burnin g wood in their fireplaces. PM 10 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.

Fig ure 5 .6 compare s the percent of peak PM 10 attributed to different source categories
for the winter of 19 9 3

- 94 with the percent peak PM 2.5 for different source categories for the present study.

Certain differences are expected because the comparison is bet ween PM 10 in the earlier study and PM 2.5 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 PM 10 than PM 2.5 . Indeed, that a ppears to be the case: geological dust represents 12.2% of PM 10 on average in the earlier study, and only 0.5%
of PM 2.5 in the present study. Marine air also contains a large coarse fraction. T he earlier study has somewhat higher marine percentages overa ll (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 PM 2.5 . 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 ever y site, with

an averag e 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 a ttributio ns from the winter 1993

- 94 field study and the 2000

- 01 study

6

. R EFINING SOURCE APPOR TIONMENT WITH E MISSIONS I N VENTORY E STIMATES

CMB analysis with currently available data can only provide accurate distinctions
between sour ce 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), commercia l cooking, accidental fires,
tobacco smok e , and waste burning. T he 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
actuall y in the air. But the measurements are specific to a particul ar 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 th e entir e Bay Area, but it doesn't reflect transport, deposition, or chemical transformations in the atmosphere.

I n 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 PM 2.5 sources. Peak PM 2.5 , however, occurs almost

exc lusively 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 e mi ssions inventory . The table presents four components of the emissions inventory: PM 2.5 from combustion, PM 2.5 from geological sources (such as road dust), NOx, and SO 2 . The latter two are precursors to ammonium nitrate and ammonium sulfate, respectively. The "vegetative burning" category is broken into cooking and "vegetative fires", which
includ es residential wood burning, wildfires, planned burns and cigarette smoke. Information in the table was used to apportion the CMB source categories into more

sp ecific sources.

7 In some of these, various s ubcategories 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

prec ursors (% of emission inventory total)

PM 2.5

- combustion NOx SO 2 PM 2.5

- geo logical
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 PM 2.5 to vegetative fires

, 1 1 % to commercial cooking, and 4 5 % to fossil fuel burning. The table also lists specific fossil fuel sources including on

- road, off

- road, trains, aircraft, ships,

r efining, 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 PM 2.5 from combustion estimated from vegetative fires alone is 44 %, nearly as large a s all fossil fuel emissions. Combin ed with cooking, the total is 55 %

.

Table 6.2 co mbine s 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 e ach source
category 8

, 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

PM 2.5 to ammonium nitrate. This ammonium nitrate is assumed to derive fr om 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

PM 2.5 derives from ammonium nitrate originating from on

- road motor vehicles.

The num bers in Table 6.2 represent the percent of the total PM attributable to specific
sources within each c ategory. The numbers in the right

- hand column are the percent of
total PM 2.5 attri butable to that specific source so that, combining both direct emissions and secondary c ontributions, vegetative fires are estimated to contribute 24 % of th e

8 In other words, the column totals in Table 6.2 are equal to the 4

- site perce ntages at the bottom of Table

5.1.- 30

- April 2008

total annual PM 2.5 , commercial cooking 6 %, on

- road 1 8 % and so on. Figure 6.1
summarizes the results.

Table 6 .2. Percent estimated total annual contribution from various sources.

Sour ce C ategory:

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 Ship s 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 Are a PM 2.5 from various sources

Table 6.3 shows winter emissions inventory percentages for carbonaceous and secondary precurso rs. The inventory attributes 38 % of combustion PM 2.5 to fossil fuels
and 62 % [ 56 %+ 6 % = 6 2 % ] to vegetative fires and cooking. Thi s 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 PM 2.5 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 SO 2 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 contain s estimates of contributions from individual sources
to total PM 2.5 , but for winter days with high PM 2.5 . As expected, vegetative fires (almost

ent irely 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 PM 2.5 . These tw o sources contribute about half of winter peak PM 2.5 . 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.

Sourc e C ategory:

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 PM 2.5 from various sources- 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 F indings

 Most anthropogen ic PM 10 and PM 2.5 derive from burning wood or fossil fuels.

 Geological dust is a small contributor to PM 10 and a negligible contributor to

PM 2.5 . Tire/break wear is also a negligible PM 2.5 source.

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

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

A nalysis of wintertime meteorological data show s that periods of low winds are conducive to the buildup of PM 2.5
.

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

PM 2.5 , 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 annu al PM 2.5 , but only a small contributor to peak PM.

 Wood burning and on

- and off

- road vehic les account for approximately 55 % of
annual PM 2.5 and 7 5 % of peak PM 2.5
.

7. 2 Ongoing work

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

PM 2.5 in the Bay Area and to track progress in reducing PM 2.5
.

 Performing annual telephone surveys to u pdat e and improv e the estimates of emissions from wood burning. Future surveys can be used to help track progress

in emission s reduction fro m wood burning.

 Implementing a grid

- based part iculate 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 am monia emi ssions 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 method s

.

 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 PM 2.5 from diesel and other fossil fuel sources.

 Analyzing winter and summer PM 10 filters for carbon

- 14. The se measurements
will add to our understanding of the relative PM contributions of wood smoke and cooking.

 W orking with UC Davis to investigate the relationship between meteorology and PM transport, especially on wintertime high PM days.- 36

- April 2008

8. REFERENCES

C how, 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 Eva luation Sample audit of the CARB lab conducted by EPA/NAREL. Memo (9/12/02) from Michael S. Clark/NARAL to Jim Homolya / OAQPS. R eference found: http://www.epa.gov/ttn/amtic/pmspec.html.

EPA, 1999. Particulate Matter (PM 2.5 ) 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 Kend all, 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 sou rce

- 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.- A 1

- 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

att ributes considerably more carbon to the OC fraction tha n the IMPROVE method does.
CRPAQS and the Speciated Trends Network u sed the IMPROVE method for ambient samples , and CRPAQS and BAAQMD used the IMPROVE method for their source profiles . But the EPA ha s used the NIOSH method for its analysis of San Jose PM 2.5 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 ther e 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 meth od 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 PM 10 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 no t 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 PM 2.5 ratios for the IMPROVE sites in Table A1 are at least twice what they are for SJ, we adjust ed the SJ observations by doubling the EC numbers an d 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.- A 2

- April 2008

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- B 1

- April 2008 APPENDIX B

. COMPARISON OF M ASS M EASUREMENTS

Various labs measured the data analyzed in this report

. Parallel measurements were
made at some sites. In particular, simultaneous PM 2.5 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 PM 2.5 mass measured by CARB versus mass measured by the District.

Figure B1. Comparison of PM 2.5 mass measured by CARB and BAAQMD for Livermore,

2000

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 spec ies, inclu ding nitrate, sulfate, EC, OC, a luminum, s ilicon, s odium
and c h l orine , more precisely, all of the compounds used in CMB fitting listed in Appendix C

.- B 2

- April 2008

Table B1 . Same

- day comparisons of BAAQMD, CARB and EPA PM 2.5 measurements

Mean Median Rati os: Other/BAAQMD Ratios: Sum of

Spec./BAAQMD

Site N BAAQ

MD

mass Other

Mass a 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 1 2.0 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, E C, OC, aluminum, s ilicon,

s odium and c h l orine.

The table shows that CARB PM 2.5 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 sli ghtly 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 medi ans 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 we re adjusted by 1.1x + 1.9, where
x is the CARB measurement.- C 1

- April 2008 APPENDIX C

. CHEMICAL SPECIES USE D 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 ast erisks, 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* S pecies id species #>1sd #>2sd used for
CMB*

MS mass 194 164 CU copper 138 7 1 *

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

NA X 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 phosphoru s 16 1 CD cadmium 0 0

SU sulfur 208 208 IN indium 0 0

CL chlorine 100 78 * SN tin 2 0

KP X 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.- D 1

- April 2008 APPEND IX D . DETERMINING WEIGH TS APPLIED TO T HE 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 spe cies matched the measured species, and a Gaussian for the difference between the total measured
mass and the calculated total.

O ne 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 "chi

square" statistics that averaged about 0.5 . I f 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 chi

square statistics were very small, suggesting good fits, in others few if any chi

- square statistics were small. Thus, the assumption that the chi

- squar ed statistic has a chi

squared distribution yielded unrealistic probabilities.

We minimized this problem by index ing the chi

- square fits to the best fit, which was arbitrarily given a value of 12

– the 25 th percentile of a chi

- square with 17 degrees of
fr eedom. Each other chi

- square statistic, S, was adjusted to 12 times its ratio to the minimum: 12*S/S min . 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

meas ured 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

mismeas ured 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
variable s, 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.- D 2

- April 2008

a 90% chance that the measurements are not grossly inaccurate. Th e 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 co mmon 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 err or. 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 discrepan cies, 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 discrepan cy, 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 indiv idual variances, i.e., the squares of the standard errors provided by the model. The definition of the weights, w i , can be summarized as
follows:

where r i = P(X > 12S i /S min )* [Phi(w i )] p1 , and X is a chi

- squared random variable w ith degrees of freedom = # of fitted species

- # of fitted source categories.- E 1

- April 2008 APPENDIX E

. INITIAL CMB R ESULTS AND SOURCE CA TEGORY SELECTION

The initial CMB results showed sizeable uncertainties for some of the source categories.
This indicated that the data should be reanalyzed with fewer c ategories (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 distingu ishing 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 th e 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

commercia l 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 av erage w ere 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 s quare root of:

S 2

ave =

where n i = number of fitted coefficients in quarter i, and s ij = model estimated standard error for the jth sample in quarter i.

Figure E 1 presents a comparison of total and model uncertainties for each sou rce 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.- E 2

- April 2008

that the re is little difficulty in distinguishing these sources and, moreover, there was substantial variability in the contribution s 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 source s

– 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 d istinguishing 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 abo ve the limits of detection in only 24 and 34 out of 208 cases, respectively. However, the model was able to distinguish geo logical dust at some sites. Geological dust has a unique signature, so it can be readily differentiated from the other

sources, ev en at low concentrations. Because tire/brake wear did not occur above the
detection limits of the model , it was eliminated from the analysis.- E 3

- 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.- F 1

- April 2008

APPENDIX F. MODIFICATION OF CMB RESULTS BASED ON CAR BON

- 14 ANALYSIS

Carbon

- 14 analysis combine d with results from the previous source apportionment study and the fractions in the Dis trict’s emissions inventory suggest that the CMB analysis of the current data over

- estimated the fossil contribution to PM 2.5 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 ou r 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 underestimate s 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 PM 10 filters from 2004 and 1998 was measured

. The filters were com posited 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 PM 2.5 , not PM 10 . However, the vast majority of ambie nt carbon ( deriv ing from vegetative burning, fossil fuel burning, cooking or secondary organic ) is largely in the PM 2.5 range. There is no reason to suspect that the PM 2.5 from the same periods would have a new to old carbon ratio
different from PM 10
.

Al though the apportionment of carbonaceous PM 2.5 into fossil and vegetative sources contains large uncertainties, their sum is well

- estimated by CMB. T he annual mean CMB
results were adjusted to have the same total carbon, but with the vegetative/fossil rat io 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.

T he source apportionments for peak and seasonal PM 2.5 were also adjusted

. The goal was to adjust the profiles so th at 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- F 2

- April 2008

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

For a given si te, let x i = the CMB vegetative burning estimate for day i, and y i = the CMB fossil fuel estimate, and let x i ' and y i ' be the values modified to correspond to
the Carbon

- 14 annual means. That is,

(F1)

where c = the new/ol d carbon ratio, mean of the x i ' , and mean of the y i '

. We

used the adjustment x i ' = a i *x i , where a i = (1

– k)*r i + k, where r i = x i /(x i +y i ). That is, the adjustment factor , a i , is a linear function of the init ial CMB vegetative fraction , r i , with the property that if r i = 1 (i.e., y i = 0), then a i = 1; i.e., there is no adjustment. As r i shrinks
to zero, a i increases , provided k>1 . We still need a i *r i ≤ 1 , which is always satisfied provided k≤2.

Applying the formula F1 yields , where the mean of r i *x i
.

Using this a i formula for Bethel Island and Livermore produc ed values of k between 1 and 2. But applying the formula to San Francisco and San Jose produced value s of k > 2. In these latter cases there were, in fact, a couple of instances where a i *r i was > 1. In these cases, a i was set to 1/r i
.

To summarize, for a given site, each pair of CMB fits x i = vegetative burning concentration on day i and y i = fossil fuel combustion concentration on day i were
transformed to x i ', y i ' as follows:

x i ' = a i *x i , y i ' = x i +y i – x i ', where

a i = (1

- k)*r i + k (F2)

where r i = x i /(x i +y i ), , with the mean of r i *x i , and c = t he vegetative

fraction for 2000 for that site.