Table of Contents

Summary.......................................................................................................................................................1

1. Introduction...........................................................................................................................................8

2. PM Measurements in the Bay Area ......................................................................................................9

3. PM Standards ......................................................................................................................................14

4. Statistical Methods..............................................................................................................................16

5. PM2.5 Trends .......................................................................................................................................20

6. PM10 Trends........................................................................................................................................28

7. Trends in PM components..................................................................................................................31

8. PM2.5 Trends in Central California Air Basins....................................................................................40

9. Comparisons with the Emissions Inventory........................................................................................44

10. Trends in PM quantiles...................................................................................................................48

11. Trends in PM Health Effects...........................................................................................................52

12. Recommendations...........................................................................................................................53

References...................................................................................................................................................54

Appendix A. Uncertainty of 24-hour design value trend estimates............................................................55

Appendix B Methodology and Meteorological Variables Considered in Wintertime Regression ............58

Appendix C – Numbers of days with PM10 Measurements........................................................................61

Appendix D. Use of Dichotomous PM2.5 Data for Trend Analysis ...........................................................62

Appendix E. Adjustment of nitrate measurements for the impact of refrigeration....................................64
1

Summary

Airborne particles present the greatest estimated health risk of any air pollutant in the San Francisco Bay 

Area. Understanding the trends in ambient concentrations of this particulate matter (PM) is a key to judge 

the effectiveness of past and future air quality regulations. This report analyzes the trends in Bay Area 

PM from 1990-2010.

Background

PM is a complex pollutant, not only deriving from many sources, but also appearing in a wide variety of 

sizes and compositions, and forming via multiple pathways. PM was one of the six pollutants chosen in 

the original 1970 Clean Air Act for regulation and adoption of standards.

The original PM standard was set for total suspended particles (TSP). But over the next 40 years a 

mountain of scientific evidence has shown that the tiniest PM particles are the most dangerous1. Thus, 

the TSP standard was changed to a PM10 standard (particles less than 10 microns in diameter) in 1987,

and a PM2.5 standard (particles less than 2.5 microns in diameter, aka fine PM) was promulgated in 1997. 

As a result, the District began routine, multi-site PM10 monitoring in 1989 and PM2.5 monitoring in 1999.

Historically, PM has been measured on filters. A filter is pre-weighed then placed in a sampler that draws 

air through the filter, typically for 24 hours. Then the filter is post-weighed and the difference in weights 

converted to concentration by dividing by the volume of air drawn. More recently, instruments have been 

developed to measure PM continuously. However, PM standards are still based on 24-hour 

concentrations.

There are two forms of PM standards – annual and 24-hour. The annual standard is designed to cap 

individuals’ total PM exposure; the 24-hour standard, individuals’ peak PM exposure. Corresponding to 

each standard is a design value – a statistic computed from the set of PM measurements that is compared 

with the standard to determine compliance. For the annual standard, the design value is the quarterly 

averaged annual mean averaged over the three most recent years. For the 24-hour PM2.5 standard, the 

design value is the 98th percentile of the PM measurements averaged over the three most recent years. 

Thus, we focus on the annual means and 98th percentiles in the trend analysis below.

Ambient PM is a mixture of many components that transform, coalesce and agglomerate in the 

atmosphere. In contrast to the scientific evidence for an increased risk as particle size decreases there is 

little information for determining which component of PM poses the highest risk; current PM standards

implicitly assume that all fine PM poses a risk irrespective of composition. 

Ambient PM is comprised of two formation categories: primary PM – PM that is directly emitted, and 

secondary PM – PM that forms through atmospheric transformations of gases and liquids. It is also 

comprised of two source categories: anthropogenic PM – PM from human activity; and biogenic, or 

 

1 The USEPA has compiled the relevant studies into a 2000 page science assessment for the latest update to the 

National Ambient Air Quality Standards (EPA 2009).
2

“natural”, PM – PM from natural sources. Table S1 shows the main components in these categories in 

the Bay Area.

Table S1. Main components of Bay Area PM2.5 by category

Anthropogenic Biogenic/“natural”

Primary carbonaceous PM from combustion of fossil 

fuels and wood, some geological dust, tire 

and brake wear

marine air (sea salt and sulfate), some 

geological dust, wildfires

Secondary ammonium nitrate and ammonium sulfate secondary carbonaceous (from vegetative 

emissions)

The District has been making measurements of a number of PM components, including nitrate, sulfate, 

ammonium, chloride (a marker for sea salt) since 1990 and, more recently, elemental and organic carbon. 

This allowed the tracking of the contributions of these individual components to the total PM.

Results

PM2.5 trends: Figure S1 shows the trend in the District’s design value relative to the national annual 

PM2.5 standard. The District’s design value dropped from 14 μg/m3for 1999-2001 to 10 μg/m3for 2008-

2010, a 28% reduction, with a 90% confidence interval of 21% to 33%. As can be seen, the District met 

the national annual PM2.5 standard for the whole period.

Figure S2 shows the District’s design values relative to the national 24-hour PM2.5 standard. For the 

earliest period available, 1999-2001, the design value was 57 μg/m3. By 2008-2010, it was 31 μg/m3, 

which meets the standard. Although there is considerable year-to-year variation in PM2.5 concentrations, 

the District is likely to continue to meet this standard. The reduction from 1999-2001 to 2008-2010 was 

46%, with a 90% confidence interval of 29% to 63%.

0

2

4

6

8

10

12

14

16

2000 2002 2004 2006 2008 2010

PM2.5 (ug/m3)

Figure S1. Bay Area PM2.5 Annual Design Values

1999-2001 through 2008-2010

national standard

The District annual design value is the maximum of design values from individual PM2.5 sites. The annual 

design value for a particular site is the 3-year average of its quarterly averaged annual mean PM2.5 

concentrations. The design values are marked at the third year of three year averages.
3

We also investigated trends in number of days exceeding the 24-hour standard. Figure S3 shows the trend 

in exceedances of the standard across 11 winters from 1999-2000 through 2010-2011. There is 

considerable winter-to-winter weather-driven variation, giving the graph a saw-tooth pattern. But 

overlain is a clear downtrend, from an average of over 24 exceedances for the first 3 winters to under 8 

exceedances in the most recent 3 winters, a 68% decrease. This decrease is statistically significant, and 

becomes more obvious with adjustment for meteorology.

PM10 Trends: Bay Area annual mean PM10 was reduced from 33 μg/m3in 1989-1991 to 18 μg/m3in 

2007-2009, a total reduction of 44% representing an annual reduction of 3% per year. These reductions 

are a result of reductions in each of the anthropogenic components tracked by the District: ammonium 

nitrate, ammonium sulfate, and elemental and organic carbon.

Figure S4 shows PM10 composition by decade. There have been substantial reductions in each of the 

anthropogenic components.

0

10

20

30

40

50

60

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

PM2.5 (ug/m3)

Figure S2. Bay Area 24-Hour Standard Design Values

1999-2001 through 2008-2010

national standard

The District 24-hour design value is the maximum of design values from individual PM2.5 sites. The annual 

design value for a particular site is the 3-year average of the 98th percentiles of its PM2.5 concentrations. 

The design values are marked at the third year of three year averages.
4

PM2.5 – PM10 comparison: Figure S5 shows the annual percent reductions in PM2.5 summary statistics 

from 2000 to 2010 and PM10 components from 1990 to 2010 along with 90% confidence intervals. 

Reductions are shown on an annual basis to make the PM2.5 and PM10 reductions comparable. Reductions 

0

5

10

15

20

25

30

35

40

45

1999-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11

Number of Days

Figure S3. Bay Area PM2.5 Exceedances by Winter

Exceedances of the national 24-hr std, Nov - Feb & regression line

0

5

10

15

20

25

30

35

1989-91 1999-01 2008-10

Concentration (ug/m3)

Figure S4. Bay Area Annual Mean PM10 Composition by Decade

other

marine

organic carbon species

elemental carbon

nss ammonium sulfate

ammonium nitrate (adj)

nss = non-sea salt. nitrate adjusted to correct for volatilization loss. organic carbon divided by 0.7 to 

approximate mass of oc species. marine = chloride/0.55. other includes geological dust, tire and brake wear.
5

in annual mean PM2.5 and PM10 are not statistically different, although PM2.5 may have a larger rate of 

reduction, because it has a lower percentage of components not expected to decrease – marine air and 

geological dust. Note that the rate of decrease in 98th percentile is larger than for the annual average, so 

that peak PM values appear to be particularly affected by anthropogenic emissions reductions.

Among the PM components that the District tracks, the greatest decrease is in elemental carbon (EC), 

which dropped about 7% per year from 1990 through 2010. The largest single source of Bay Area EC is 

diesel exhaust. It appears that diesel exhaust PM may have been reduced more than predicted in the 

emissions inventory, which estimates a decrease of only a couple percent per year. This conclusion is 

strengthened by the observation that organic carbon (OC) has been reduced less than EC. Unlike EC, Bay 

Area OC derives largely from other sources, including wood burning, cooking, and gasoline exhaust. The 

fact that EC was reduced at roughly double the rate of OC suggests that sources with high EC/OC ratios, 

specifically diesel, were reduced more than sources with low EC/OC ratios. Another bit of evidence is 

that the ratio of carbon from fossil fuels to carbon from non-fossil sources (including woodsmoke and 

meat cooking) has decreased, based on Carbon-14 analysis.

Ammonium nitrate, one of the largest components of Bay Area PM2.5, has also been reduced 

substantially, with a rate of about 5% per year. Organic carbon and non-sea salt sulfate have been 

reduced about 3% per year, and other PM10 species (including geological dust, tire and brake wear) have

been reduced about 2% per year. As expected, the marine air component shows no change.

Comparison with other air basins: The Bay Area has shown more consistent progress in reducing 

PM2.5 than its two neighboring air basins to the east – the Sacramento and San Joaquin valleys. Like the 

District, these areas have PM2.5 measurements dating back to 2000. Over the past decade, Sacramento’s 

annual PM2.5 has been reduced by an amount similar to that of the Bay Area, and it meets the national 

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Annual percent reduction

Figure S5. Bay Area Mean Annual Reductions in PM2.5, PM10

and PM10 Components, 1990 to 2010
6

annual PM2.5 standard. But its 24-hour design value hasn’t dropped significantly and it still violates the 

national 24-hour standard. The San Joaquin Valley has seen little progress in reducing its annual mean 

PM2.5 concentrations, and it is still well above the national annual standard. San Joaquin has reduced its 

24-hour design value significantly, but it is still well above the standard.

Comparison with emissions inventory trends: Figure S6 shows a comparison of annual reductions in 

ambient nitrate, sulfate, PM10, and PM2.5 compared with reductions in emissions of NOx, SO2, PM10 and 

PM2.5. The percent decreases in sulfate match those of SO2. Although the point estimate of the decrease 

in nitrate is greater than that of NOx, the two are not statistically different, owing to nitrate’s large yearto-year variability.

Reductions in ambient PM10 and PM2.5 concentrations are much greater than the reductions registered in 

the emissions inventory, even excluding road dust (which the inventory estimates to have been 

increasing). Although ambient PM contains secondary compounds, which have been reduced, there has 

been a substantial reduction in concentrations of PM directly emitted from anthropogenic sources. So 

there is a substantial difference between ambient and emissions inventory PM reductions for some source 

categories.

Health effects: Ambient PM has a major impact on the health of individuals in the Bay Area. It is 

implicated in a variety of serious health effects, including triggering chronic bronchitis, asthma, heart 

attacks, and premature death. The number of deaths affected is on the same order as that of homicide or 

traffic accidents.

0%

1%

2%

3%

4%

5%

6%

7%

8%

NOx vs nitrate SO2 vs sulfate PM10 PM2.5

Reduction (%)

Figure S6. Annual Reductions in Bay Area Emissions and Concentrations

emissions

concentrations

Reductions over 20-year period for all except a 10-year period for PM2.5. PM emissions 

exclude geological dust. Also shown, 90% confidence intervals for concentrations.
7

We estimate that reductions in PM2.5 from 1990 to 2010 have reduced the number of premature deaths in 

the Bay Area by more than 3,000 per year2. Life expectancy in the Bay Area has grown by 5 years in that 

time, from 76 years in 1990 to 81 years in 2010. We estimate that reductions in PM resulted in a 6-month 

increase in life expectancy, so that about a tenth of the total improvement is due to reductions in PM2.5

concentrations.

 

2

Specifically, the number of deaths that occurred in 2010 with 2010 PM2.5 concentrations is estimated to be 3,000 

fewer than would have occurred if PM2.5 concentrations had remained at 1990 levels.
8

1. Introduction

Airborne particulate matter (PM) constitutes the class of ambient air pollutants with the most serious 

impact on the health of Bay Area residents. PM health effects range from lower and upper respiratory 

symptoms including asthma and chronic bronchitis to heart attacks and death. These illnesses result in 

thousands of absences from school, days of lost work, and hospital visits annually. We estimate that the 

number of deaths in the Bay Area from elevated PM levels rivals that of motor vehicle fatalities or 

homicides.

The Bay Area attains the national annual PM2.5 standard, but has continued to violate California’s annual 

standard. The Bay Area is in non-attainment of the national 24-hour PM2.5 standard. It meets the national 

PM10 standard, but violates California’s PM10 standards. Efforts at the regional, state and national levels

have reduced PM concentrations. This analysis estimates the amount of reductions in ambient PM as well 

as in its different chemical and size components.

1.1 History of PM standards and PM measurements

PM was one of the six “criteria” pollutants regulated in the original Clean Air Act of 1970. Standards 

were set for each of the pollutants and a procedure established for periodic review and update based on 

new scientific evidence, a process that continues 40 years later.

The original PM standards were set in terms of total suspended particulates (TSP). Evidence gradually 

accumulated that smaller particles appeared to account for most of the health impacts. Thus, in 1987, the 

EPA replaced the TSP standard with a standard for PM10 (particles less than 10 microns in diameter), and 

in 1997, it added a standard for even smaller particles, PM2.5 (particles less than 2.5 microns in diameter).

(See Lippmann 2011.) Today, there is mounting evidence that ultrafine particles (particles less than about 

0.1 micron in diameter) may even have greater health impacts.

1.2 Time intervals for PM standards and measurements

Until recently, PM was measured by collecting particles on filters over 24 hours, and PM concentrations 

were estimated by weighting the filters before and after collecting the particles. Because this process is 

labor-intensive, measurements have not been made every day at every station. At most stations, they were 

made either on a 1-in-3 or 1-in-6 day schedule. 

Air pollution trends are typically hard to discern over a period of a few years. Day-to-day and year-toyear variation in meteorology can lead to large swings in pollutant concentrations. Therefore, we

conducted analyses of trends over the entire period of PM2.5 measurements: 1999-2010. For PM10, data 

are sufficient to estimate trends only by decade: 1990s and 2000s.

PM also varies in composition including: carbonaceous particles from combustion of fossil fuels and 

wood; geological dust particles; sea salt particles; tire and brake wear particles. Secondary particles are 

those formed by chemical reactions or physical interactions in the atmosphere. Ammonium nitrate and 
9

ammonium sulfate account for the bulk of secondary PM in the Bay Area. Evaluating component trends 

is useful for understanding the overall PM trend, which is the composite of these component trends. The 

District has measured nitrate and sulfate on a 1-in-6 day schedule since 1989. It has also measured 

organic and elemental carbon, two other key PM components, since 2004.

1.3 Seasonality

There are large swings in PM concentrations by season. Almost all the Bay Area’s highest PM 

concentrations occur in the wintertime. The reasons are several: 1. Wood burning is a significant source, 

and occurs mainly in winter. 2. Winter includes periods of atmospheric stagnation and stability, with 

particles trapped close to the surface that build up over period of days. 3. Ammonium nitrate particles 

form in colder weather. 4. Significant transport occurs from the Central Valley, as stagnant periods 

coincide with drainage of air through the Carquinez Strait and various passes out to the Pacific Ocean.

The District defines the Winter Spare the Air season as the months of November through February. 

Analysis of exceedances of the 24-hour PM2.5 standard is restricted to these months.

2. PM measurements in the Bay Area

The set of PM measurements made in the Bay Area has changed over time. This section discusses what 

measurements have been made and where and when.

2.1 PM measurements

The District began measuring PM10 concentrations at a number of sites in 1989 on a 1-in-6 day schedule. 

In addition to total PM10 concentrations, a set of ions has been measured: nitrate, sulfate, ammonium, and 

chloride. Starting in 1995, potassium was added, and elemental and organic carbon (EC and OC) in 2004. 

In recent years, the number of sites measuring PM10 has been substantially reduced.

The District began measuring PM2.5 in 1999. Until recently, the primary measurements were filter-based. 

A filter is put in a sampler that draws a steady and monitored airflow through it for 24 hours. The PM2.5

concentration is estimated from the post-pre weight difference of the filter divided by the total air flow,

yielding a measurement of ambient PM in micrograms per cubic meter (μg/m3). These measurements are 

called Federal Reference Method (FRM) and have been the ones used for determining compliance with 

the national air quality standards.

The PM2.5 measurement schedule was complex, with some sites having daily wintertime measurements

and others once in 3 days. All sites had a 1-in-6 day measurement schedule from April through 

September.

Starting in 2001, the District began operation of BAM units – continuous PM2.5 analyzers at Livermore, 

San Jose and San Francisco. These units collect PM2.5 data on an hourly basis. Initially, the 

measurements were not accepted by EPA for reference to the national PM2.5 standards because of large 

uncertainty in this type of measurements. Recently, the accuracy has been significantly improved and the 
10

District upgraded most of its BAM instruments. Since 2009, the PM2.5 measurements from the upgraded 

BAM instruments have been accepted as a federal equivalent measurement method (FEM) by EPA.

2.1.1 Carbon measurements

The District commenced measurement of elemental and organic carbon (EC and OC, respectively) in 

2004 for PM10 filters. Previously, only sporadic measurements were made.

TSP measurements were discontinued in 1998, but another measurement, Coefficient of Haze (COH),

continued well after the turn of the century. COH measurements had been made by the District dating 

back to the 1950s. COH is a measurement based on the difference in light transmitted through paper 

before and after PM has been deposited on it; it is highly correlated with the elemental carbon in the 

particles. Thus, it can serve as a surrogate measure for EC.

2.1.2 Other measurements

Speciated PM2.5 measurements have been made at San Jose since February, 2000 as part of the Speciation 

Trends Network. Measurements include all higher elements, a set of ions, and organic and elemental 

carbon. A similar set of measurements has been made at Point Reyes, part of the IMPROVE network, 

dating back to before 1989.

In late 2008, the District started a program of speciated PM2.5 measurements at Livermore and Vallejo. 

West Oakland was added in February, 2009.

Extensive measurements were made as part of the California Regional Particulate Air Quality Study 

(CRPAQS), late 1999 through early 2001. These measurements included speciated PM2.5 measurements 

at several Bay Area sites that can serve as a baseline for comparison.

2.1.3 Measurements by site and date

Table 2 presents information about the PM measurements made in the Bay Area. The measurements have 

been made for different lengths of time with changes in the number of sites. Thus, continuous records 

have not always been available. Figure 2.1 shows a map with the sites.
11

Table 2. Measurements and measurement history at Bay Area PM monitoring stations between 1989 and 2010.

Site PM2.5 Filter 

(frequency)

a

PM2.5 FEM PM2.5 Speciesb PM10 Mass+Ions PM10 Carbon COH

Berkeley 2008-2010

Bethel Island 12/1999-1/2001 1989-2010 7/2004-2010 1989 – 3/1992

Concord 3/1999-2010 (1) 1989-2010 10/2005-2010 1989 – 1/2004

Fremont 3/1999-7/2009 (3) 8/2009-2010 3/1989 – 6/2008 1989 – 1/2004

Gilroy 3/2007-10/2009 (3) 11/2009-2010

Livermorec12/1999-9/2008 (3)

10/2008-6/2010 (1)

7/2010-2010 12/1999-1/2001

9/2008-2010

1989 – 6/2008 10/2005-2/2008 1989 – 8/1995

Napa 1989-2010 10/2005-2010 1989 – 1/2004

Oakland (East) 11/2007-9/2009 10/2009-2010

Oakland (West) 2/2009-2010

Point Reyes 1989-2010 (3) 1989-2010

Redwood City 3/1999-7/2008 10/2009–2010 1989 – 6/2008 1989 – 2006

Richmond/San Pablod3/1989 – 2010 1989 – 8/1995

San Francisco 3/1999-9/2009 (1) 10/2009-2010 12/1999-1/2001 1989-2010 7/2004-2010 1989 – 5/2006

San Josee1999-2010 (1)

1990-1997f

2/2000-2010 1989-2010 7/2004-2010 1989 – 4/2002

SJ – Tully Rd 3/1999-9/2006 1989-2006 7/2004-2005

San Rafael 11/2009-2010 1989-2010 11/2008-2010 1989 – 8/1995

Santa Rosa 2/1999-10/2009 (3) 11/2009-2010 7/1994-6/2008 1989 – 1/2004

Vallejo 9/2008-2010 12/1994 – 6/2008 10/2005-2/2008 1989 – 8/1995

a

(frequency): October-March frequency 1 = every day, 3 = every 3rd day; April-September frequency every 6th day at all sites.

b

Species include all elements with atomic weight of sodium or higher; ions including nitrate, sulfate, ammonium; and elemental and organic carbon.

c

Site moved in 2000 from Old Railroad Road to Rincon.

d

Richmond site closed in 5/1997, replaced by San Pablo site at El Portal Shopping Ctr through 8/2002. Replaced by San Pablo site at Rumrill Blvd 9/2002.

e

San Jose site at 4th St through 4/2002, replaced by site at Jackson St in 10/2002 (no San Jose data from 5/2002 through 9/2002).

f

A dichot sampler operated at San Jose from 1990 through 1997.
12

Figure 2.1. Bay Area PM monitoring sites and District boundaries.
13

2.2 Methodology for combining data for trend analysis

As Table 2 shows, the amount of data collected on PM and PM species has varied over time. Several strategies 

were used to yield as long a record as possible and to ensure that the data were comparable over time. Here is a 

summary of the issues and the methodology employed to deal with them.

In general, a subset of sites was chosen that had long data records. A 2-way ANOVA was used to fill in missing 

values, then the average taken across sites for each year. See section 4 for a discussion of the 2-way ANOVA 

technique.

2.2.1 PM10 and PM10 components

PM10 filters were collected from 1989 through 2007 at many sites and analyzed for PM10 mass, nitrate, sulfate, 

and chloride. Measurements were discontinued at about half the sites in 2008. A 2-way ANOVA was used to fill 

in the missing values to extend the record through 2010.

2.2.2 PM10 nitrate

Since 2009, the District has refrigerated PM10 filters after the sample had been collected. Before 2009, PM10

filters were unrefrigerated. The impact of the change could be estimated by comparison with PM2.5 measurements 

made at San Jose for the STN network where the filters have always been refrigerated. There appears to have 

been little impact for other PM10 components, but for nitrate there was a clear difference, with substantial 

volatilization loss from the unrefrigerated filters. A method was developed to adjust the pre-2009 measurements 

to make them comparable to the current ones. (See Appendix E.)

2.2.3 PM2.5 measurements

The District began its program of collecting PM2.5 measurements in 1999. Before that there had been some 

limited PM2.5 measurements made in a special study in San Jose from 1990 to 1997. Comparisons with 

concurrently measured PM10 showed that the special study PM2.5 registered concentrations about 20% lower than 

the PM2.5 measured since 1999. Appendix D presents an analysis and develops an adjustment method.

2.2.4 Carbon measurements

In 2004, the District began measuring EC and OC on PM10 filters. Before 2004, these were only measured in 

special studies, but the District had a very long record of another measurement, Coefficient of Haze (COH). COH 

is an optical measurement that is highly correlated with carbon, EC in particular. A period of overlap in EC and 

COH measurements facilitated the development of a method to estimate EC concentrations from COH 

measurements. This allowed the extension of EC measurements back to 1990.
14

3. PM standards

The set of PM standards is complex. There are both national and California standards for both PM2.5 and PM10 for

both the annual average and for 24 hours (with a couple of exceptions). Here is a table with the level of the 

various standards.

Table 3. Levels of national and State PM standards

PM2.5 PM10

annual 24-hour annual 24-hour

National 15.0 μg/m3

35 μg/m3 N/A 150 μg/m3

California 12.0 μg/m3 N/A 20 μg/m3

50 μg/m3

Differences among the standards and how they are met are explained below.

3.1 National PM standards

The annual PM2.5 national ambient air quality standard (NAAQS) is 15.0 μg/m3. Compliance with the standard is 

determined as follows. The standard is met by the Bay Area if and only if it is met at every Bay Area site 

collecting EPA-approved PM2.5 measurements. For a given site, the standard is met if its annual design value 

does not exceed the standard. The annual design value is the 3-year average of annual averages of the site’s PM2.5

measurements, where each annual average is the mean of the four quarterly averages3. For example, the 2010 

annual design value would be the average of the 2008, 2009 and 2010 annual averages.

The 24-hour PM2.5 NAAQS is 35 μg/m3. Compliance with the standard is determined as follows. The standard is 

met by the Bay Area if and only if it is met at every site collecting EPA-approved PM2.5 measurements. For a 

given site, the standard is met if its 24-hour design value does not exceed the standard. The 24-hour design value

is the 3-year average of annual 98th percentiles of the site’s PM2.5 measurements4.

There is also a 24-hour PM10 NAAQS of 150 μg/m3. This standard is met if the 3-year average of the 99th

percentile values at each site is less than or equal to 150 μg/m3.

Additional details of the standards are given in the Code of Federal Regulations, Title 40, Part 50, Appendix N. 

(CFR 40, 2011)

3.2 California PM standards

California standards are also based on 24-hour PM2.5 and PM10 measurements, but differ from the NAAQS in both 

level and form. Like the PM NAAQS, the California annual standards are based on 3 years of data. But the 

 

3 Each quarter must have measurements for 75% or more of the scheduled samples.

4 The 98th percentile is simply the sample 98th percentile if the sampling schedule has the same frequency year round (for 

example the 8th highest value if sampling is every-day). If the sampling frequency differs by season (as it has with many Bay 

Area sites), the 98th percentile is the determined from the empirical distribution. (See US EPA 1999, page 27).
15

California standards are violated if the annual average exceeds the standard in any of the 3 years. The California 

annual PM2.5 standard is 12 μg/m3. Currently, California has no 24-hour PM2.5 standard.

The California annual PM10 standard is 20 μg/m3. Its 24-hour PM10 standard is 50 μg/m3. Unlike the 24-hour 

PM10 NAAQS, the 24-hour California PM10 standard is violated if any PM10 measurement exceeds it, with certain 

exceptions. Among the exceptions are values that are expected to occur less than once per year on average. 

These are determined by a statistic termed the expected peak day concentration (EPDC), computed from a year’s 

worth of data. Any measured value above a site’s EPDC is excluded. The highest non-excluded value is the 

site’s design value for that year. A site meets the standard if none of its past three year design values exceed the 

standard.
16

4. Statistical methods used for trend analysis

For both the national annual and 24-hour PM2.5 standards, the design value is a 3-year average – the average of 

annual means for the annual standard, the average of 98th percentiles for the 24-hour standard. Thus, the focus of 

this analysis is on methods to estimate trends in these averages, and also to estimate the uncertainty in these trend 

estimates.

Let t represent years from the initial year, so that, for example, for PM2.5 as the years range from 1999, 2000,...,

2010, t ranges from 0, 1,...,11. Let Yt be a summary statistic for year t for a given site. Here Ytis either the 

annual mean or the 98th percentile for year t.

Some assumption about the form of the trend is necessary for statistical inference. For this analysis, we assume

Yt = m0(1-r)tet (4.1)

where m0 is the initial mean [symbolically, E(Y0) ], r is the annual rate of reduction and etis a random error term

with mean 1. 

There is a plethora of ways to estimate trend. These include:

1. Regression vs. year: Y0, Y1, ...Yk against t = 0, 1, ..., k

2. Regression on the log scale: ln(Y0), ln(Y1),...ln(Yk) against t = 0, 1, ..., k. 

3. Comparing the 3 most recent years with the earliest 3 years: the absolute reduction, ̅

 ̅ , or the 

relative reduction, ̅

 ̅ , where ̅ 

 

 

and ̅

 

 

 

4. Using a permutation approach: Let Wt(r) = Yt/(1-r)t. Find the value of r so that ̅ ( ) ̅ 

( ) , or 

 ̅ ̅ 0.

5. A nonparametric approach, e.g., using Kendall’s tau: (Y1, Y2, ..., Yk) = ∑ ∑ ( )

 

 

 

 

, 

where sgn(x) is the sign function, sgn(x) = -1 if x<0, =0 if x=0 and =1 if x>0. Find the value of r so that 

(W1(r), W2(r), ..., Wk(r)) = 0.

All these were used, in different parts of the analysis.

4.1 Focus of trend analysis

This analysis focuses on progress toward meeting the federal PM standards. For PM2.5 and PM10 the statistical 

tests applied used design values from the most recent and earliest years available. For PM components, the tests 

used means for the most recent and earliest years.

4.2 Inference for ratios -- permutations

In light of this discussion, we consider inferences about the design values, ̅

 ̅ . Statistical inference for the 

difference in design value for a given site is straightforward – using a two-sample t-test. But to report progress as 
17

a difference of so many micrograms/cubic meter is difficult for a non-expert to understand and, further, the degree 

of progress will depend on the typical levels, which vary from compound to compound.

The alternative it to use the relative progress, that is, the relative reduction, ̅

 ̅ . The interpretation of the 

relative reduction is straightforward and it makes it possible to compare the reductions of various PM 

components. The statistical inference is not as straightforward, however. A parametric approach is possible, one 

that assumes the sample means are normal. But unlike the inference for the difference in means, inference on the 

ratio involves a distribution with unknown parameters that, because of the small samples, can only be roughly 

approximated.

Here we use a non-parametric approach based on the permutation distribution. Assume that the data fit model 

(4.1) above. Then, if the true reduction rate is r, the set of Wt, where Wt = Yt/(1-r)t, will be identically distributed. 

Therefore, if we compute ( ) ̅ ̅ , with high probability it will be within the range of the ratios 

 

 

( ) ̅ 

 

 ̅ 

 where the Wt* are random permutations of the Wt

, that is, permuting the values among different 

years.

A confidence interval for r can thus be found as the set of r such that D(r) is not unusual among the permuted 

values D*(r). More precisely, the set {r: rl ≤ r ≤ ru} such that P(D*(rl) ≤ D(rl)) = /2, and P(D*(ru) ≥ D(ru)) = /2

is a 100(1-)% confidence interval for r.5

4.3 Inference for District design values

The design values consist of 3-year averages, so the methods discussed above are appropriate for the design 

values for individual sites. But the design value for the District presents a more difficult problem. As the 

maximum of 3-year averages, it does not have the distribution of a simple average.6 One inference method, using 

regression against year, is described in Appendix A.

Here we use a generalization of the ratio-permutation method. Effectively, what that method does is to consider 

permutations of years. This same approach can be applied to the District design value. See Appendix A.

4.4 Methods for trends in PM2.5 exceedances, including meteorological adjustment

We also consider trends in the number of exceedances of the 24-hour PM2.5 standard. Exceedances occur almost 

exclusively in the winter months. Therefore, we limited the analysis to November through February. Because the 

high PM season overlaps years, we considered the number of exceedances by winter, rather than by year.

 

5

For the PM2.5 case, we are using six Wt’s to compute the two means. With 12 years, there are a total of (

 

 

)(

 

 

)=18,480 

distinct ways to pick the six, so there will be at most 18,480 distinct values of D*(r), one of which is, of course, D(r). If we 

chose a 90% confidence interval, then /2 = 0.05. Since 0.05 x 18480 = 924, we want to find rl so that D(rl) is the 924th

smallest of the D*(rl) values, and ru so that D(ru) is the 924th largest of the D(ru) values. We wrote a program that found the 

confidence interval iteratively – guessing values of r and, for each guess, cycling though all the possible permutations and 

computing D*(r) for each – until the value of D(r) was close to the value of the 924th smallest D*(r); then repeating the 

process until the value of D(r) was close to the value of the 924th largest D*(r).

6

In contrast, the 3-year average of yearly maxima would have the distribution of averages.
18

Exceedances are relatively infrequent events, so their distribution can be reasonably modeled by the Poisson 

distribution. Poisson regressions against year were performed.

Exceedances are highly dependent on meteorology, leading to large year-to-year fluctuations. Thus a key 

question is how much of any trend is due to changes in emissions and how much due to a trend in meteorology. 

A measure was developed that provides a rough first estimate of the meteorological potential, namely the number 

of light wind, no rain days. Specifically, we averaged the 24-hour average wind speeds at three sites: Bethel 

Island, Pleasanton and San Carlos. A light wind day was defined as one with an average wind speed less than 5 

mph. We used rainfall from San Jose, and defined a “no rain” day as one with less than 0.02 inches. Because the 

highest PM2.5 tends to occur only after several days of buildup, we defined a PM2.5 conducive day as a day where 

there had been three or more days in a row with both light winds and no rain up to and including that day.

We also performed a more comprehensive analysis using multiple regression to predict daily PM2.5 concentrations 

from meteorological and temporal variables. The goal was to estimate how many exceedances would have 

occurred each year if the meteorology was the same every year. The methodology is explained in Appendix B.

4.5 Annual decreases for comparability

Because the number of years available for trend analysis varied for the different PM species, the annual reduction 

rates are also provided, where the annual rate is defined as r = ( ̅

 ̅ )

 ( )

.

4.6 Estimation of missing values

In looking at trends in annual statistics across sites, ignoring these missing data could result in biases. To reduce

the potential bias, an ANOVA model was fit to the data, namely 

Ytj = μ + t + j + etj

where Ytj is an annual statistic for site j in year t. The missing Ytj, were estimated iteratively 1. computing

 ̅

 

 ̅

 ̅ 

, where the dot indicates which index over which the average is taken, then 2. estimating the missing 

values by ̅

 ̅ ̅ 

. Steps 1 and 2 were repeated until the estimates for missing values converged.

4.7 Statistical inference issues

For all of the above techniques, statistical independence of the Yt’s is required to estimate uncertainty. Although 

PM2.5 values can be highly correlated one day to the next, this correlation decays within a few days. Thus, annual 

means from different years, which have at most a very few measured values within a few days, should be close to 

statistically independent of each other. Successive-year 98th percentiles may be correlated if a stagnant period 

occurs right around January 1, but it is unlikely to affect many years. Thus, day-to-day autocorrelation should not 

invalidate the inferences made.

Some factors do have a potential impact, however. One was a change of instrumentation. In 2009, continuous 

FEM measurements replaced filter measurements. Analysis where parallel measurements were made showed that 
19

the FEM measurements were higher by a random amount up to 3 μg/m3(Fairley 2011). Because of this, FEM 

measurements were not included in the PM2.5 trend analysis.

Another potential factor is the moving of a station. A couple of key stations were moved – the downtown San 

Jose site was moved from 4th Street to Jackson Street in 2002. The Livermore site was moved in 2000. Based on 

comparisons using other sites as controls, it appears these changes didn’t have much impact on measured PM 

concentrations.

The same concerns applied to PM2.5 measurements made by other agencies – the PM2.5 filter measurements made 

for CRPAQS, for the STN instrument at San Jose, and for the DRI measurements at Livermore, Oakland and 

Vallejo. These measurements were also excluded.

The degree of uncertainty will be expressed as 90% confidence intervals. 
20

5. PM2.5 trends

This section presents trends in annual mean and 98th percentile PM2.5 design values, and also in numbers of 

exceedances of the 24-hour PM2.5 standard.

5.1 Annual trends

Figure 5.1 shows the quarterly averaged annual means for District sites and Point Reyes. Also shown is a 3-year 

average of BA urban levels. The average PM2.5 across the District sites shows a steady reduction from 12.2 

μg/m3for 1999-2001 to 8.6 μg/m3for 2008-2010, a reduction of 30%, or a 3.9% per year compounded rate with a 

90% confidence interval of 2.1% to 5.6%.

Figure 5.2 shows the District design values relative to the annual standard from 1999-2001 through 2008-2010. 

There has been an almost monotonic reduction, from 14.2 μg/m3for 1999-2001 to 10.2 μg/m3for 2008-2010, a 

reduction of 28% or 3.7% per year, with a 90% permutation confidence interval 1.7% to 5.1%.

0

2

4

6

8

10

12

14

16

18

1998 2000 2002 2004 2006 2008 2010

PM2.5 (ug/m3)

Figure 5.1. Annual Mean PM2.5: San Francisco Bay Area

Livermore

Fremont

Concord

San Francisco

Redwood City

San Jose

Vallejo

Santa Rosa

3-yr BA urban

mean

Point Reyes
21

5.1.1 Longer-term trends

There were only limited PM2.5 data collected in the Bay Area before 1999. There is a continuous record from 

Point Reyes extending back into 1988, but it is of limited value being a background site. PM2.5 data were 

collected at Fremont from 1988 through 1994 as part of the National Acid Deposition Network, but Fremont is 

not one of the sites where routine PM2.5 measurements have been made. At San Jose, however, PM2.5

measurements were collected with a dichotomous sampler from 1990 through 1997, and also since 1999 as part of 

the District’s routine monitoring network.

Figure 5.3 shows the annual concentrations for San Jose, where the dichot measurements for 1992-97 have been 

divided by 0.78 according to the analysis in Appendix D. Also shown are the PM10 annual averages from the 

District FRM sampler. The relationship between annual mean PM2.5 and PM10 has held fairly constant over time. 

A regression line shows that there has been a consistent trend in PM2.5 over the 20-year period from 1990 through 

2010, a reduction from 17.9 μg/m3in 1990-1992 to 10.1 μg/m3in 2008-2010, representing a 43% decrease.

0

2

4

6

8

10

12

14

16

2000 2002 2004 2006 2008 2010

PM2.5 (ug/m3)

Figure 5.2. Bay Area PM2.5 Annual Design Value

1999-2001 through 2008-2010

national standard
22

5.2 Trends in 24-hour design value

Figure 5.4 shows the annual 98th percentile concentrations at District sites. It also shows a 3-year moving average 

of the average of all sites plotted vs. middle year. The plot shows a sudden dip in 2003, but some of this is

because the meteorology was much less conducive to high PM in 2003 than 2002. The 3-year average dropped 

from 47 μg/m3for 1999-2001 to 27 μg/m3for 2008-2010, a 41% reduction, or 5.7% per year.

0

5

10

15

20

25

30

35

40

45

0

5

10

15

20

25

1985 1990 1995 2000 2005 2010 2015

PM2.5 (ug/m3)

PM10 (ug/m3)

Figure 5.3. Annual PM2.5 Concentrations

San Jose 1990-2010

PM2.5

PM10

Dichot

FRM PM2.5

0

10

20

30

40

50

60

70

1998 2000 2002 2004 2006 2008 2010

PM2.5 (ug/m3)

Figure 5.4. PM2.5 98th Percentiles: San Francisco Bay Area

Livermore

Fremont

Concord

San Francisco

Redwood City

San Jose

SJ-Tully Rd

Vallejo

Santa Rosa

national standard

3-yr BA urban mean
23

Figure 5.5 shows the trend in the Bay Area 24-hour design values. The values fell almost monotonically, from 57 

μg/m3for 1999-2001 to 30 μg/m3for 2008-2010, a reduction of almost 50%. The annual reduction was 6.7% per 

year with a 90% confidence interval of 5.2% to 8.4%.

5.3 Trends in winter PM2.5

As discussed earlier, virtually all of the Bay Area’s highest PM2.5 concentrations occur on winter days. These 

days are distinctive not only in their meteorology, but also their mix of PM2.5 – woodsmoke and ammonium 

nitrate predominating. Thus, we focus on wintertime trends, more precisely the trends from November through 

February. For this purpose, we look at trends in counts of exceedances by winter.

The number of exceedances per winter is highly variable because of meteorological fluctuations. A simple 

method to track the meteorological effect is to compare the number of exceedances with the number of PM 

conducive days. A more sophisticated analysis was done to account for meteorology, where formulas were 

established for each winter that related daily maximum PM2.5 to various weather and time-of-year factors. Such 

formulas represent estimates of the potential for high PM2.5 under given meteorological conditions, so applying 

each of the formulas across the same set of winter conditions allows us to look at the trend in the average 

potential, which we will term Average Exceedance Potential.

5.3.1 Trends in PM2.5 exceedances

Figure 5.6a shows the number of days PM2.5 exceeded the 35 μg/m3standard for each winter 1999-2000 through 

2010-2011. The sawtooth pattern in the numbers of exceedances is mostly due to meteorology rather than 

changes in emissions. A reduction in the numbers of exceedances is clear, and the Poisson regression line shows 

a reduction from a predicted 30.6 exceedances in 1999-2000 to 7.4 exceedances in 2010-2011.

0

10

20

30

40

50

60

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

PM2.5 (ug/m3)

Figure 5.5. Bay Area 24-Hour Standard Design Value Trend

Design values plotted vs. most recent year. E.g. 2008-2010 dv plotted vs. 2010

national standard
24

5.3.2 PM2.5 conducive days

Figure 5.6b shows the number of PM2.5 conducive days along with the number of exceedances. Although not 

perfectly correlated, the PM2.5 conducive days generally follow the same sawtooth pattern as the PM2.5

exceedances. However, unlike the decline in exceedances, there is no long-term trend in conducive days; there 

are actually more conducive days in the winters of 2008-2009 through 2010-2011 than in 1999-2000 through 

2001-2002.

0

5

10

15

20

25

30

35

40

45

1999-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11

Number of Days

Figure 5.6a. Bay Area PM2.5 Exceedances by Winter

Exceedances of the 24-hour NAAQS and Poisson regression fit

Exceedances Poisson regression fit
25

5.3.3 Evaluating exceedance potential

To estimate potential, we began with the District-wide daily maximum PM2.5 concentrations.

The independent variables include a range of meteorological measurements made at various District sites. These 

were reduced to 24-hour summaries like 24-hour averaged wind speed and 24-hour resultant wind direction. We 

also considered the 24-hour rainfall at the City of San Jose meteorological station and measurements from the 

Oakland RAOB sounding7. In addition we considered the time lags of some of these variables. We also included 

temporal variables: day of week; holidays; and time of year, specifically, the number of days from January 1st. 

See Appendix B for methodology.

Results

A set of trial variables was selected. (See Appendix B.) Stepwise regressions were performed and the strong 

predictors – those with small p-values – were selected. The resulting regression formula was

f = ln(dmax) = 1.79 - 0.00819 jd - 0.0263 avews + 1.41 1/avews + 0.202 sjrn<.02 + 3.77 1/3dyws + 0.250 

pbwd<90 - 0.00118 fuws&pb<90

The coefficient values, definitions, uncertainties and t-values are shown in Table 5.1.

 

7 The RAOB soundings measurements made twice a day (4am and 4pm) at Oakland Airport using instruments on a balloon. 

The measurements are recorded as the balloon reaches certain air pressure levels.

0

5

10

15

20

25

30

35

40

45

50

1999-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11

Number of Days

Figure 5.6b. Bay Area PM2.5 Exceedances by Winter

Days conducive to high PM and exceedances of the national 24-hr std, Nov - Feb

Conducive Days Exceedances
26

Table 5.1. Predictors for District maximum PM2.5.

Predictor Description

Estimated 

Coefficient (ai) SE Coef t

pvalue

x0 Constant 1.790 0.110 16.33 0

x1 jd # of days from Jan 1 -0.00819 0.00063 -12.96 0

x2 avews 3-site average of 24-hour ave ws -0.0263 0.0087 -3.04 0.002

x3 1/avews reciprocal of x2 1.409 0.300 4.70 0

x4

sj rain<.02

indicator of san jose rain < 0.02 

inches 0.2017 0.0283 7.12 0

x5

1/3dayavews

reciprocal of 3-day average wind 

speed 3.768 0.221 17.07 0

x6

san pablo wd<90o

indicator of san pablo wind 

direction between 0oand 90o0.2504 0.0391 6.41 0

x7

fuws&pb<90

standard deviation at fort funston 

times x6 -0.00118 0.00037 -3.16 0.002

The adjusted R2 was 65.9%.

A plot of the residuals vs. predicted shows a relatively even scatter, indicating no serious problems with 

heteroskedasticity (non-constant variance). The first order autocorrelation of the residuals was 0.39. This is 

statistically significant, but small enough that the variables in Table 5.1 should all still be statistically non-zero.

Regressions were run for each individual winter using these same 7 variables. The adjusted R2s ranged from 57% 

to 82%. The variables jd and 1/3dyws are consistently significant from winter to winter, the others intermittently

so. The predictions based on all variables were used. So, for each winter,

fw = a0w + a1w*x1 + a2w*x2 + ...+a7w*x7

These formulas were then applied to each of the winters 1999-2000 through 2010-2011 (Steps 3 and 4 above). 

The results are shown in Table B2 in Appendix B. Figure 5.7 compares the predicted number of exceedances in 

winter w with the actual number in winter w. There is a high correlation between the predicted number of 

exceedances and the actual number. The closeness suggests that the Gaussian error assumption works well.
27

Figure 5.8 shows the estimated average exceedance potential for each winter. In most winters, this potential is 

closer to a linear trend line than the observed number of exceedances. Whereas the correlation of actual 

exceedances with year is 0.72, the correlation of exceedance potential with year is 0.86, reinforcing the conclusion 

that the observed trend is real. A regression of log of exceedance potential vs. year has a slope of -0.16, 

indicating an annual rate of decrease in the number of exceedances of 1 – e

-0.16 = 0.15, i.e. 15% per year.

0

5

10

15

20

25

30

35

40

45

1999-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11

Figure 5.7. Actual and Estimated Exceedances

Estimates using prediction formula and meteorology of same year

Exceedances

Predicted Exceeds

0

5

10

15

20

25

30

35

40

45

1999-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11

Number of Days

Winter

Figure 5.8. Average Exceedance Potential

Exceedances

Average Exceedance Potential
28

6. PM10 trends

For this trend analysis, we consider the period from1989 through 2007 for peak PM10 and 1989-2010 for mean 

PM10. In order to establish consistency across years, for the peak PM10 analysis, we only considered days where 

measurements were made at least 8 sites. As the table in Appendix C shows, the number of such days is relatively 

consistent across years.

6.1. Trends in mean PM10

Figure 6.1 shows quarterly averaged annual PM10 concentrations from 1989 through 2010. The solid red line 

shows the average of 9 sites, where missing values were filled in by repeatedly fitting a 2-way ANOVA and 

replacing the missing values with the 2-way ANOVA estimates.

The reductions were approximately 3% per year in the 1990s and 2% per year from 2000 through 2010, both 

statistically significant. The 9-site average dropped from 33 μg/m3in 1989-1991 to 18 μg/m3in 2008-2010, a 

reduction of 44%.

The District is close, but does not meet, the California annual PM10 standard. Its 18 μg/m3design value seems to 

indicate it met the standard, but it hasn’t yet, because first the standard is based on the maximum average over the 

most recent 3 years, and second the California standard is based on PM10 measured locally, which is generally 

somewhat higher. Bethel Island registered the highest annual average of 24 μg/m3in 2008. However, the 

maximum average since then was 20.4 μg/m3for San Jose in 2009.8 

The 2008 maxima were impacted by wildfires. Assuming that these don’t recur or that wildfire-influenced 

concentrations are excluded as exceptional events, then the District should reliably meet the California annual 

standard in a very few years. 

 

8 California PM10 standards are based on local PM10 concentrations, using local temperature and pressure, whereas national 

PM10 uses a standardized temperature and pressure. California values were not available for the whole period but were 

available for the most recent data. The local and measurements are very highly correlated and are 1% to 3% higher than the 

standardized. Both the California PM2.5 standard and the PM2.5 NAAQS are based on local measurements.
29

6.2. Trends in peak PM10

Figure 6.2 shows the number of exceedances of the California 24-hour PM10 standard. It also shows the 

predictions from a Poisson regression. The reduction in exceedances has been considerable. The Poisson 

regression shows a statistically significant decrease from almost 15 days per year in 1989 to 3 days per year in 

2007, a reduction of approximately 8% per year.

Figure 6.3 shows the trend in District design values relative to the California standard. There has been a 

significant reduction over the entire 1989-2007 period. The trend in the maximum design value from 2000 to 

2007 is not statistically significant and the downtrend in the 6-site average is borderline significant. 

The overall drop, from an average maximum design value of 157 μg/m3for 1989-1991 to 78 μg/m3for 2005-2007

was 50% for an annual rate of 4.3% per year (90% confidence interval 2.4% to 6.5%). Nevertheless, the District 

still violates the standard by a considerable margin.

0

5

10

15

20

25

30

35

40

45

1989199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Concentration (ug/m3)

Figure 6.1. Annual Mean PM10

Quarterly averaged, 1989-2010

Fremont

Livermore

Concord

Bethel Island

San Rafael

Napa

San Francisco

Redwood City

San Jose

9-site mean

California standard
30

0

2

4

6

8

10

12

14

16

18

20

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Exceedances

Figure 6.2 Exceedances of the California PM10 Standard, 1989-2007

Days exceeding the 24-hour standard with measurements at at least 8 sites

Exceedances (standard

conditions)

Poisson regression fit

0

20

40

60

80

100

120

140

160

180

1989199019911992199319941995199619971998199920002001200220032004200520062007

Concentration (ug/m3)

Figure 6.3. Bay Area PM10 Design Values 

for the 24-hour California Standard, 1989-2007

maximum

6-site average

standard

California Standard
31

7. Trends in PM components

7.1. Nitrate trends

Figure 7.1 shows the trends in annual mean nitrate concentrations. All sites show a downtrend although there is 

considerable year-to-year variation. The 9-site mean nitrate was reduced from 5.3 μg/m3in 1989-1991 to 2.6

μg/m3in 1999-2010 to 1.8 μg/m3in 2008-2010. These constitute reductions of 52% from 1990 to 2000, 29%

from 2000 to 2009, and 66% from 1990 to 2009, an overall reduction of nearly two-thirds (a 90% confidence 

interval of 48% to 83%). On an annual basis, nitrate was reduced at a compound average rate of 5.2% per year 

from 1990 through 2010 with a confidence interval of 2.4% to 7.0%.

A comparison of trends by decade (Figure 7.2) shows no significant difference between the rates in the 1990s and 

the 2000s.

0

1

2

3

4

5

6

7

8

9

10

1985 1990 1995 2000 2005 2010

NO3 (ug/m3)

Figure 7.1. Annual Mean Nitrate 1989-2010

Values prior to 2009 adjusted for volatilization loss

Fremont

Livermore

Concord

Bethel Island

San Rafael

Napa

San Francisco

Redwood City

San Jose

9-site mean

Point Reyes
32

7.2. Sulfate trends

Sulfate is another large component of Bay Area PM. As with nitrate, sulfate is formed from reactions with 

ammonia, where SO2 converts to sulfuric acid, combining with ammonia to produce ammonium sulfate

(NH4)2SO4. 

There are a couple of differences from nitrate to keep in mind: 1. A sizeable amount of Bay Area sulfate derives 

from direct emissions from ships, and also as a naturally-occurring components of the salts in marine air. 2. 

Sulfate is less volatile, so that its concentration on filters is not affected by whether or not the filter was 

refrigerated.

Figure 7.3 shows the trend in mean sulfate. Although there is considerable variation, largely from meteorology, 

there is a substantial 20-year decline in mean sulfate concentrations. The 9-site mean sulfate dropped from 2.3

μg/m3in 1990 to 1.9 μg/m3in 2000 to 1.3 μg/m3in 2009. The reduction from 1990 to 2000 was 17%; the 

reduction from 2000 to 2009 was 29%, and the overall reduction was 41% (with a 90% confidence interval of 

27% to 51%). On an annual basis, the average reduction rate between 1990 and 2010 was 3.0% per year with a 

90% confidence interval of 2.1% to 4.1%.

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

Annual Reduction

Figure 7.2. Nitrate Trends in 2 Decades: 1989-99 and 1999-06/09

Error bars are 90% confidence intervals based on Kendall's tau

89-99

99-06/8
33

7.3. Chloride trends

Chloride is a major constituent of the Bay Area’s marine air. In fresh marine air, it constitutes about 55% of the 

non-aqueous mass; other key components are sodium 31%, sulfate (8%), magnesium (4%), calcium and 

potassium (1% each).

Chloride has no other significant sources, so its presence is a marker for marine air. But as marine air passes over 

the Bay Area’s urban centers, much of the sodium chloride is replaced by sodium nitrate. Thus, in areas some 

distance from the ocean or Bay, or at times when the air has been stagnant, the chloride concentration tends to

under-represent the marine component.

As Bay Area chloride concentrations derive from marine air, we don’t expect a secular trend. As Figure 7.4 

shows, this is indeed the case. The mean of Bay Area geometric mean chloride was 1.2 μg/m3for 1989-1991, 0.9 

μg/m3for 1999-2001 and 1.1 μg/m3for 2008-2010. The annual rate of reduction for the whole period, from 1990 

through 2010, was 0.5% per year. This reduction is not statistically significant. However, with the reductions in 

nitrate and sulfate, chloride is now as large a contributor to Bay Area PM10 as these.

Among the sites, San Francisco stands out, being surrounded by salt water on three sides. Point Reyes also has 

high levels given that it’s PM2.5 not PM10, and the chloride in marine air is largely in the coarse (> 2.5 micron 

diameter) fraction.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1985 1990 1995 2000 2005 2010

SO4 (ug/m3)

Figure 7.3. Bay Area Geometric Mean Sulfate 1989-2010

Fremont

Livermore

Concord

Bethel Island

San Rafael

Napa

San Francisco

Redwood City

San Jose

Point Reyes

Mean
34

7.5 Potassium trends

The District began regular potassium ion measurements on PM10 filters mid-1995 with the idea that it could serve 

as a marker for wood burning in the Bay Area. Although this might have been effective in some areas, for the 

Bay Area, the presence of substantial amounts of soluble potassium in marine air obscures the woodsmoke signal. 

Figure 7.5 shows mean soluble potassium concentrations from 1996 through 2010 at Bay Area sites. The 1996-

1998 mean was 7.5 μg/m3compared with 8.7 μg/m3for 2008-2010. The 1.1% per year increase is not statistically 

significant. Unlike with chloride in Figure 7.4, San Francisco doesn’t stand out above and Point Reyes does stand 

out below the concentrations at other sites. This indicates that there are substantial non-marine sources. Whereas 

concentrations of nitrate, sulfate and chloride at Napa are all at or below average, it often registers the highest 

potassium concentrations – with woodsmoke being the likely source; the fact that San Francisco potassium levels 

aren’t higher, despite the fact potassium is a component of marine air, suggests that woodsmoke is not a big 

source there.

The District has instituted winter Spare the Air alerts that ban wood burning on certain days. It also has had an 

extensive educational campaign on the negative health effects of woodsmoke. Responses to the District’s 

wintertime surveys have indicated that fewer residents burned wood, with the extent of the reduction being 25% 

to 50% since the winter of 2006-2007. The absence of a downtrend in soluble potassium suggests that reductions 

in woodsmoke emissions may have been more modest than the survey indicates.

0

0.5

1

1.5

2

2.5

3

1985 1990 1995 2000 2005 2010

Chloride (ug/m3)

Figure 7.4. Bay Area Geometric Mean Chloride 1989-2010

Fremont

Livermore

Concord

Bethel Island

San Rafael

Napa

San Francisco

Redwood City

San Jose

Point Reyes

Mean
35

7.6 Trends in elemental and organic carbon

The record of elemental and organic carbon (EC and OC) in the Bay Area is spotty and not totally consistent. 

What follows is an analysis utilizing and sometimes piecing together what is available. It should be considered 

preliminary.

7.6.1. Data

The sources of EC and OC measurements are the following:

1. Starting in 2004, the District began a program of measuring EC and OC on its PM10 filters at some sites: 

Bethel Island, Livermore, Napa, San Francisco, San Jose, San Pablo, and Vallejo. These measurements continue 

to the present except for Livermore and Vallejo, where PM10 sampling was discontinued in 2008.

2. At San Jose, EC and OC measurements have been made from PM2.5 filters since February 2000. But: a) the 

site moved in 2002, so the pre- and post-move measurements are not strictly comparable, and b) the EC and OC 

measurement method was changed9in April 2009, a significant enough change so that those after April 2009 are 

not really comparable.

3. At Point Reyes, consistent EC and OC measurements have been made for the IMPROVE network on PM2.5

filters since 1989.

4. EC and OC measurements were made on PM2.5 filters at Bethel Island, Livermore and San Francisco from late 

1999 to early 2001 for the CRPAQS study.

 

9

From the NIOSH method to the IMPROVE method. All other EC/OC measurements use the IMPROVE method.

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

1996 1998 2000 2002 2004 2006 2008 2010

Potassium (ug/m3)

Figure 7.5. Bay Area Geometric Mean Potassium 1996-2010

Fremont

Livermore

Concord

Bethel Island

San Rafael

Napa

San Francisco

Redwood City

San Jose

Vallejo

Santa Rosa

San Pablo

Point Reyes (pm2.5)

Mean
36

5. EC and OC measurements have been made for the District by DRI on PM2.5 filters at Livermore and Vallejo 

since September 2008 and at West Oakland since February 2009.

6. In addition to filter measurements, the District operated instruments whose measurements are well-correlated 

with carbon particles, especially EC. There has been an aethalometer operating at San Jose-Jackson Street since 

2004. Up until the mid-2000s there had been a set of coefficient of haze (COH) analyzers operating at many sites, 

including six that operated through 2004.

7.6.2 Analysis

Data were averaged by quarter for all quarters with sufficient measurements. Then the quarterly averages were 

averaged over 3-year periods. Table 7.1 shows the earliest and latest averages and the annual rates of decrease.

There is considerable site-to-site variation in trends, especially in OC. Some of it probably comes from piecing 

together data from more than one lab, but there seem to be other factors as well. 

Among filter measurements, the median change in OC is a decrease of about 4% per year. The change in EC is a 

decrease of about 8% per year. The San Jose aethalometer shows a decrease of 5% per year compared with filterbased measurement reduction of 9% per year. The COH instruments show the likely reduction in EC during the 

1990s as 7% to 8% per year, about the same as the filter measurements indicate for the 1st decade of the 21st

century.

Table 7.1. Trends in OC and EC at Bay Area sites.

Site Data Source 1

st

OC

Last 

OC

Annual 

Reduction

1

st EC Last EC Annual 

Reduction

Bethel Island CRPAQS PM2.5 99-01/ District PM10

08-10

2.91 3.36 -1.6% 1.34 0.61 8.4%

Concord District PM10 05-07 / 08-10 3.50 3.35 1.4% 0.86 0.77 3.7%

Livermore CRPAQS PM2.5 99-01/DRI PM2.5 08-

10

4.54 2.41` 6.8% 2.39 0.61 14.1%

Napa District PM10 05-07/08-10 4.57 4.08 3.7% 1.18 0.95 7.0%

Point Reyes IMPROVE 97-99/07-09 0.98 0.48 6.9% 0.14 0.07 7.3%

San Francisco CRPAQS PM2.5 99-01/District PM10

08-10

3.41 2.91 1.7% 1.85 0.76 9.5%

San Jose District 04-06/08-10 4.54 4.52 0.1% 1.48 1.02 8.9%

San Pablo District 04-06/07-09 3.38 3.01 3.8% 0.96 0.84 4.3%

Vallejo District PM10 04-06/DRI PM2.5 08-10 3.75 2.32 14.8% 1.08 0.58 18.8%

Median 3.7% 7.8%

SJ aethalometer District 04-06/08-10 1.08 0.84 4.9%

COH 7-site mean 89-91/99-01 3.16 1.43 7.6%

Figure 7.6.1 shows a plot of EC values from the various sources from 1990 through 2010. Note that Point Reyes 

values have been multiplied by 10 to put them on a comparable scale. The graph shows what appears to be a 

steady reduction in EC. The larger than average reductions from 2008 to 2009 at Livermore and Vallejo may 

represent the change from District PM10 measurements to DRI PM2.5 measurements.
37

The COH mean for 1989-1991 was 2.99 μg/m3. A mean of Concord, Napa and San Francisco EC, the 3 of the 6 

COH sites with EC measurements from PM10 filters had a mean of 0.82 μg/m3for 2008-2010. This represents a 

reduction of 73%. For Point Reyes, EC dropped from 2.4 μg/m3in 1989-1991 to 0.69 μg/m3in 2007-2009, a 

reduction of 70%.

Figure 7.6.2 shows trends in OC concentrations. For most sites, the data record begins in 2004. As discussed 

above, we do see a median downtrend of about 4% a year among these sites. The Point Reyes site has a long and 

consistent record. The figure shows its OC values times 5 to make it comparable with the concentrations at other 

sites. On its original scale, Point Reyes OC averaged 0.99 μg/m3for 1990-1992 and 0.48 μg/m3for 2007-2009, 

representing a reduction of about 50%. This represents an annual rate of decrease of 4.1% per year, in line with 

the reduction rate at District urban sites.

0

0.5

1

1.5

2

2.5

3

3.5

1985 1990 1995 2000 2005 2010

EC (ug/m3)

Figure 7.6.1. Bay Area Elemental Carbon Concentrations

from various measurement sources 1989-2010 Bethel Island

Concord

Livermore

Napa

San Francisco

San Jose

San Pablo

Vallejo

San Jose aeth

Point Reyes x 10

COH 6-site ave.
38

7.6.3 Carbon-14 trends

In addition to EC/OC measurements, there has been a limited amount of measurement of Carbon-14. C-14 is a 

radioactive isotope of carbon with a half-life of 5,730 years. It is constantly renewed in the atmosphere by cosmic 

radiation. C-14 is taken up by plants, and through them animals including humans. Therefore, wood and meat 

have the modern fraction of C-14, whereas fossil fuels, whose carbon was absorbed millions of years ago, have

virtually no C-14. Thus, the amount of C-14 in the carbon of filter samples provides an excellent estimate of the 

fraction of carbon from “new” vs. fossil sources.

The District has had measurements made by the University of Arizona’s Accelerator Mass Spectrometry facility. 

A significant portion of these measurements have been made, not on a single filter, but on a composite of a 

random sample of filters taken around the year. This not only helps gain an understanding of annual average 

fractions, it also saved money in the high per-sample cost. Such samples have been made on a sporadic basis on 

filters dating back to 1998. 

Figure 7.6.3 shows the fraction of modern carbon in samples from various Bay Area sites from 1998-2010. It is 

likely that the sites with the highest modern carbon fractions – Bethel Island, Napa, Santa Rosa and San Rafael –

are impacted by significant woodsmoke, and conversely for the sites with low carbon fractions, including San 

Francisco and Oakland.10 

Also shown is the mean of the 5 sites that had measurements made in at least 4 years: Concord, Livermore, San 

Francisco, San Jose, and Vallejo. Missing site-years were imputed iterating a 2-way ANOVA. The mean value 

 

10 Air quality sites can’t represent a whole city or metropolitan area, even one as small as San Francisco (10 km x 10 km). 

Where a monitoring site is placed can make a substantial difference. The SF site, for example, is located in an industrial area 

with few homes nearby. 

0

1

2

3

4

5

6

7

8

1985 1990 1995 2000 2005 2010

Organic Carbon (ug/m3)

Figure 7.6.2. Bay Area Organic Carbon Concentrations

from various measurement sources 1989-2010

Bethel Island

Concord

Livermore

Napa

San Francisco

San Jose

San Pablo

Vallejo

Point Reyes x 5
39

increases almost monotonically from 0.57 in 1998 to 0.63 in 2010, a 12% increase. This suggests that PM from 

fossil combustion has been reduced faster than PM from wood burning or cooking.

0.3

0.4

0.5

0.6

0.7

0.8

1998 2000 2002 2004 2006 2008 2010

Fraction Modern Carbon

Figure 7.6.3. Annual Mean Modern* Carbon Fractions

for various Bay Area sites, 1998-2010 Bethel Island

Concord

Fremont

Livermore

Oakland-West

San Francisco

Redwood City

SJ -- Jackson St

Santa Rosa

Vallejo

San Jose Tully Rd.

San Pablo

San Rafael

Napa

Pittsburg

5-site mean

*Ambient carbon from "new" sources such as wood burning and meat cooking as a fraction 

of all carbon. The 5 sites in the mean are Concord, Livermore, San Francisco, San Jose, and 

Vallejo.
40

8. PM2.5 trends in Central California air basins

The Sacramento and San Joaquin valleys border the Bay Area and there is considerable air exchange between 

them. In summer, the typical pattern is westerly winds blowing from the Bay Area into these valleys. In the 

winter, during periods of stagnation when PM concentrations are high, the pattern tends to reverse with easterly 

flow draining from the valleys out into the Pacific Ocean.

Thus, there is interest in how the PM concentrations in these Central Valley air basins compare with those of the 

Bay Area. Figures 8.1a and 8.1b show the annual means for the Sacramento and San Joaquin valleys. Comparing 

with the Bay Area means in Figure 5.1, we see that Sacramento means are similar, and San Joaquin urban means 

are higher.

0

2

4

6

8

10

12

14

16

18

2000 2002 2004 2006 2008 2010

PM2.5 (ug/m3)

Figure 8.1a. Annual Mean PM2.5: Sacramento Valley

Chico

Colusa

Sacramento-D

Sacramento-T

Sacramento-HD

Yuba City

Woodland

national standard

0

5

10

15

20

25

30

2000 2002 2004 2006 2008 2010

PM2.5 (ug/m3)

Figure 8.1b. Annual Mean PM2.5: San Joaquin Valley

Fresno-1st

Clovis

Fresno-H

Bakersfield-G

Mojave

Bakersfield-Cal

Ridgecrest

Bakersfield

Corcoran

Merced

Stockton

Modesto

Visalia

national standard
41

Figure 8.2 shows the trend in Bay Area mean PM2.5 compared with trends in the Central Valley. The trends in the 

Bay Area and Sacramento are significant and similar: 2.8% per year (90% confidence interval 1.5% to 4.1%) for 

the Bay Area and 1.9% per year (90% confidence interval 0.5% to 3.2%) for Sacramento. The trend for San 

Joaquin is virtually 0 and not statistically significant.

8.1 98th Percentile comparison

Figures 8.3a and 8.3b show PM2.5 98th percentiles for Sacramento and San Joaquin valleys. These are comparable 

to Figure 5.3 for the Bay Area. Sacramento and the Bay Area are similar – with values above the standard in 

1999 falling below the standard by 2010. The San Joaquin Valley also shows a decrease, but its sites all continue 

to violate the standard.

0

5

10

15

20

25

99-01 00-02 01-03 02-04 03-05 04-06 05-07 06-08 07-09 08-10

PM2.5 (ug/m3)

Figure 8.2. Design Values for Annual PM2.5 Standard

for 3 Central California Air Basins

San Joaquin

Sacramento

Bay Area

national

standard
42

Figure 8.4 shows the 24-hour design values for the three air basins. The Bay Area’s design value has decreased 

5.8% per year from 2000 to 2010 (90% confidence interval 3.6% to 7.8%) and met the standard for 2008-2010. 

The design value in the San Joaquin Valley shows a decrease of 3.8% per year that is borderline statistically 

significant. Sacramento’s design value shows no decrease, partly because of the 2008 wildfires. Excluding the 

wildfire months of June and July, the decrease is 3.3% per year, still not statistically significant.

0

20

40

60

80

100

120

1998 2000 2002 2004 2006 2008 2010

PM2.5 (ug/m3)

Figure 8.3a. PM2.5 98th Percentiles: Sacramento Valley

Chico

Colusa

Roseville

Sacramento-Del

Paso

Sacramento-T St

Sacramento-Health

Dept

Redding

Yuba City

Woodland

national standard

*

*Values influenced by wildfires June & July, 2008.

0

20

40

60

80

100

120

140

1998 2000 2002 2004 2006 2008 2010

PM2.5 (ug/m3)

Figure 8.3b. PM2.5 98th Percentiles: San Joaquin Valley

Fresno-1st St

Clovis

Fresno-H

Bakersfield-GSH

Bakersfield-Cal

Bakersfield-P

Corcoran

Merced

Stockton

Modesto

Visalia

national standard
43

0

20

40

60

80

100

120

2000 2002 2004 2006 2008 2010

PM2.5 (ug/m3)

Figure 8.4 Design Values* for 24-Hour PM2.5 Standard

for 3 Central California Air Basins.

San Joaquin

Sacramento

Bay Area

National Standard

**

* Design values are 3-year averages of 98th percentiles, plotted vs. most recent year.

**Values for 2008-2010 influenced by wildfires in summer 2008.
44

9. Comparisons with the emissions inventory

Trends estimated from ambient measurements and the emissions inventory have somewhat complementary 

strengths and limitations. Comparing these trends can serve as a reality check on both.

Ambient trends have the strength that they are based on directly measured pollutant concentrations. But they are 

limited in the number of locations, and subject to considerable variation from meteorology. Emissions inventory 

trends, on the other hand, are based on a comprehensive set of sources covering the entire Bay Area and are 

unaffected by meteorology. But they are based on engineering calculations that, inevitably, don’t perfectly match 

actual emissions, and don’t account for atmospheric reactions and transport.

Comparing total PM concentrations with emissions is complex, both because PM is a mixture of many 

compounds, and because some PM is not directly emitted. The analysis of ammonium nitrate and sulfate is also 

complex, since although each involves only one compound, each has two precursors and several chemical 

pathways for their formation. 

Figure 9.1 compares trends in nitrate (a 10-site average) and NOx emissions from 1988 to 2010. NOx emission 

estimates are taken from a report, BAAQMD 2008. Reductions in NOx don’t automatically imply reductions in 

nitrate, because nitrate concentrations are a function of concentrations of other compounds especially ammonia. 

But the similar trends (including a correlation of 0.74) suggests that there is a causal link. 

Figure 9.2 shows trends in sulfate concentrations and SO2 emissions. As with nitrate/NOx we don’t expect a 

match, necessarily. But again we see similar trends (correlation 0.83).

0

0.5

1

1.5

2

2.5

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

900.0

1985 1990 1995 2000 2005 2010

NO3 (ug/m3)

NOx emissions (t/d)

Figure 9.1. Mean Nitrate Concentrations vs. NOx emissions 1989-

2008

NOx emissions

10-site Mean

nitrate
45

Figures 9.3 and 9.4 show PM2.5 and PM10, where geological dust is excluded from emissions.

0

0.5

1

1.5

2

2.5

0

20

40

60

80

100

120

140

1985 1990 1995 2000 2005 2010

SO4 (ug/m3)

SO2 emissions (t/d)

Figure 9.2. Mean Sulfate Concentrations vs. SO2 Emissions 1990-

2010

SO2 emissions

10-site mean sulfate

0

2

4

6

8

10

12

14

16

0

10

20

30

40

50

60

70

1998 2000 2002 2004 2006 2008 2010

Emissions (t/d)

PM2.5 (ug/m3)

Figure 9.3. Annual Mean PM2.5 vs. Emissions excluding Dust

Emissions - Dust

8-site Mean (all PM2.5)

8-site Mean (from direct

emissions)
46

Figure 9.5 shows a comparison of percent reductions in emissions and concentrations. The reductions for SO2 vs.

sulfate are quite similar. The trends for NOx vs. nitrate appear different, but are within statistical uncertainty.11 

Some of the difference derives from the choice of summary statistic.12

The PM trends are quite different, however. Even excluding geological dust emissions (which is estimated to 

increase), the trend in PM emissions is relatively slight – a reduction of about 15% for a 20-year period for PM10

and 6% over a 10-year period for PM2.5. In contrast, PM10 concentrations have fallen over 40% in 20 years, and 

PM2.5 has fallen 30% in 10 years. Moreover, excluding nitrate and sulfate, which aren’t emitted directly, and 

chloride, which is from marine air not counted in the inventory, the PM2.5 reductions are greater. 

 

11 The rate of NOx emissions was within a 95% confidence interval for the rate of nitrate emissions.

12 The reductions in geometric mean nitrate was 45%, almost identical with the NOx decrease. The difference with the 

arithmetic mean derives from the difference in rates that different parts of the nitrate distribution have been reduced (see 

section 10).

0

5

10

15

20

25

30

35

40

0

10

20

30

40

50

60

70

80

90

1989199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Concentration (ug/m3)

Emissions (t/d)

Figure 9.4. Annual Mean PM10 vs. Emissions

1989-2010 (Emissions exclude geogical dust)

Emissions

excluding

Dust

9-site mean
47

0%

10%

20%

30%

40%

50%

60%

70%

NOx vs nitrate SO2 vs sulfate PM10 PM2.5

Reduction (%)

9.5. Comparison of reductions in emissions and concentrations

emissions

concentrations

Reductions over 20-year period for all except a 10-year period for PM2.5. PM 

emissions exclude geological dust.
48

10. Trends in PM quantiles

The above analysis shows downtrends in both coarse and fine PM and in key anthropogenic components. But 

downtrends in peak PM generally appeared greater than trends in the mean. This section explores how that might 

occur.

Figure 10.1 shows P-P plots13 from the six Bay Area sites with PM10 measurements stretching from 1989 through 

2010. The plot compares the PM10 distributions from 2000-2010 with 1989-1999. The lines y = x are drawn for 

comparison. In every case, the data fall below the line y = x, indicating that PM10 has been reduced across the 

board. But in every case, the lines of data bend away from y = x for the larger percentiles (starting at about the 

75th percentile), indicating that there has been a greater percentage reduction in the top quarter of the distribution 

than in the rest.

Figure 10.2 shows percentile-percentile plots comparing 2000-2010 nitrate and sulfate percentiles with 1989-1999 

for two Bay Area sites. The sites were chosen to represent the eastern and western parts of the Bay Area. The 

eastern part is affected more by Central Valley PM, which has high nitrate levels but less sulfate. San Francisco 

sulfate is elevated by shipping in the Bay. The plots for the two sites are remarkably similar in shape, suggesting 

that causal factors resulting in lower nitrate are widespread. Both nitrate and sulfate follow the pattern of PM10, 

with across-the-board reductions in the distribution, but greater reductions for the high percentiles. The bend for 

nitrate is more extreme, indicating large reductions in peak nitrate, smaller relative reductions for peak sulfate.

 

13 A Q-Q plot is a method for comparing two distributions. It is a scatterplot showing the quantiles of one distribution against the quantiles of another. It 

pairs the first percentile of one distribution with the first percentile of the other, the 2nd percentile with the second percentile, and so on up to the 99th vs. the 

99th. If the two distributions are equal, the data will lie on the line y = x. If one distribution is a simple shift of the other, then the Q-Q plot will lie on a 

straight line, offset from the line y = x and parallel to it. If the difference is a simple proportion (e.g., all values have been reduced by 10% across the board) 

then again the Q-Q values will lie on a straight line, but this time through the origin. Q-Q plots that are not straight lines indicate a more complex difference 

that affects different parts of the distribution differently.
49 60

45

30

15

0

0 50 100

60

45

30

15

0

0 50 100 0 50 100

Concord

1989-1999 PM10 percentiles (ug/m3)

2000-2010 PM10 percentiles (ug/m3)

San Francisco Bethel Island

Napa San Jose San Rafael

y = x

y = x

Figure 10.1. Percentile-Percentile Plots Comparing PM10 in 2 Decades

2000-2010 vs. 1989-1999 PM10 percentiles from 6 Bay Area sites
50

0 5 10 15 20

10.0

7.5

5.0

2.5

0.0

0 5 10 15 20

8

6

4

2

0

0.0 1.5 3.0 4.5 6.0

4.8

3.6

2.4

1.2

0.0

0 2 4 6 8

6.0

4.5

3.0

1.5

0.0

Concord-Nitrate

1989-1999 Concentration (ug/m3)

2000-2010 Concentration (ug/m3)

San Francisco-Nitrate

Concord-Sulfate San Francisco-Sulfate

Figure 10.2. Percentile-Percentile Plots of Comparing NO3 and SO4 for 2 Decades

2000-2010 vs. 1989-1999 Nitrate and Sulfate Percentiles at 2 Bay Area Sites
51

One consequence of this analysis is to show that peak PM has declined faster than lower percentiles. Thus, the 

percentage reduction in the 98th percentile is greater than the reduction in the mean (or median).

What do these plots imply about the dynamics of PM trends? An approach to answering this question is to find a 

simulated process with a similar P-P plot. One hypothesis is that the hockey-stick pattern derives from reductions 

in the distribution of anthropogenic PM against a constant distribution of background PM. Various simulations 

were performed attempting to mimic the appearance of the Q-Q plots in Figures 10.1 and 10.2. Notable was the 

failure of the rollback model Yj = Xback + cj*Xanthro, in which the trend was modeled as a reduction in cj where j = 

1989-1999 and 2000-2010. This led not to a hockey stick but, rather, roughly to the line y = dx.

It was noted that PM concentrations are much higher in winter than the rest of the year. Our next simulation 

assumed that there was no change in 3⁄4 of the year and a large change in the other 1⁄4. Specifically we assumed 

that X had a lognormal(0, .5) distribution. Y1989-1999 = 12*(Xback + 3*Xanthro), Y2000-2010 = 12*(Xback + Xanthro) for the 

3 winter months, and Y1989-1999 = Xback and Y2000-2010 = Xback for the other 9 months. The factor 12 was selected to 

scale the Y to the PM10 range seen in Figure 10.1. Figure 10.3 is based on a simulation of 10,000 winter values 

and 30,000 non-winter values for Y1989-1999 and Y2000-2010.

Although the simulation is not intended to be a comprehensive model for PM10, its similarity with the patterns in 

Figure 10.1 suggests this combination as an explanation of the behavior of the Q-Q plot.

0 10 20 30 40 50 60 70 80

50

40

30

20

10

0

Simulated 1989-1999 Percentiles

Simulated 2000-2010 Percentiles

Figure 10.3 Q-Q Plot of Simulated PM10 Trend
52

11. Trends in PM health effects

The health effects of breathing ambient PM have been extensively researched both in large-scale epidemiological 

studies and animal experiments. The US EPA has concluded that: “...the epidemiological evidence continues to 

support likely causal associations between PM2.5 and PM10 and both mortality and morbidity from 

cardiovascular and respiratory diseases...” (US EPA 2004, Page 9-48, Paragraph 1).

Dose-response relations have been established in various studies (e.g., Hall et al. 2008, Stratus 2008) for a range 

of health endpoints. We used the values developed for the US EPA in their BenMAP program (US EPA 2008). 

Although a no-effects threshold has not been found, we assumed that effects were limited to PM2.5 concentrations 

above the Bay Area background, which we estimated to be the 5.5 μg/m3 mean measured at the Point Reyes site. 

We estimated the 1990 PM2.5 values by scaling the 2008-2010 values by 17.9/10.2, the ratio estimated for San 

Jose in section 5.1.1. Because we look at the excess above 5.5 μg/m3(i.e., 17.9-5.5 vs. 10.2-5.5) the reduction 

from 1990 to 2010 in concentration above the background is about 2/3. 

Figure 11 shows estimates of the number of annual cases of various health effects caused by elevated PM2.5

concentrations in the Bay Area. Note that the reduction in the number of cases estimated is substantial, changing 

from over 6,000 in 1990 to under 3,000 in 2010.

Over the past 20 years, Bay Area life expectancy has increased by almost 5 years, from 75.7 in 1990 to 80.5 in 

2011, due to a variety of factors. Of the overall increase in life expectancy during this period, we estimate that the 

improvements in air quality can be credited with extending average life expectancy in the Bay Area by 6 months.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

1990 2010 1990 2010 1990 2010 1990 2010 1990 2010 1990 2010

Mortality Respiratory

Hospital

Admissions

Cardiovascular

Hospital

Admissions

Chronic

Bronchitis

Onset

Nonfatal Heart

Attacks

Asthma

Emergency

Room Visits

Annual Number of Cases

Figure 11. Trends in Health Effects from Ambient PM

Annual number of cases of health effects 1990 and 2010 in the Bay Area
53

12. Recommendations

The ability to analyze ambient air quality trends depends crucially on the existence of the appropriate air quality 

data. The ideal is to have a long-term set of measurements made with a constant methodology at a large number 

of sites that represent an area. But the primary driving force behind the District’s monitoring network are national 

and State requirements. New instruments and sites must be added periodically, and this requires tradeoffs to stay 

within a reasonable monitoring budget. Nevertheless, there are some principles that could be followed even in an 

era of tight budgets:

 When one measurement method is replaced by another, as with COH by EC, or filter PM2.5 by continuous 

PM2.5, it is valuable to provide a period of overlap at several sites where both measurements are made

will help to establish how to correlate the two so that their trends can be melded together.14

 From an analytical point of view, a way to economize would be to make some measurements periodically 

rather than continuously. An example is our speciated PM2.5 measurements at Livermore, Vallejo and 

Oakland. Valuable as these are, they could be discontinued for a couple of years and then resumed. 

 Sites with high PM should have monitors that yield official measurements. The District generally 

complies with this principle, but West Oakland is an exception, having no FEM or FRM monitor even 

though it may have (or have had) the highest annual average PM2.5 in the District. A dramatic reduction 

in PM is likely to have occurred at this site, but the lack of official measurements makes it impossible to 

document with certainty.

 There is real value in continued monitoring at fixed monitoring locations. They provide the backbone of 

any trend analysis; without a set of these fixed points, trend analysis becomes difficult or impossible. 

 

14 The EPA has a program to compare FEM measurements with FRM, but the relationship varies by region, so additional analysis for the 

Bay Area can be useful. The District made COH and EC measurements in addition to EPA-required sampling; the COH-EC relationship 

has been studied, but also varies by region.
54

References

BAAQMD (2008). Base Year 2008 Emissions Inventory Summary Report, May 2011. [Available from

BAAQMD, 939 Ellis St., San Francisco, CA 94109]. 

CFR 40 (2011). Code of Federal Regulations, Title 40 Protection of Environment Part 50 National Primary and 

Secondary Air Quality Standards: 50.6, 50.7 and Appendix N. Available at

http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr&tpl=/ecfrbrowse/Title40/40cfr50_main_02.tpl [accessed 

1/3/12]

Fairley D (2011). E-mail to Eric Stevenson & Glen Colwell, 2/7/2011.

Hall JV, Brajer V, Lurmann, FW (2008). The benefits of meeting clean air standards in the South Coast and San 

Joaquin Valley air basins. Institute for Economic and Environmental Studies, Cal State Fullerton, November, 

2008.

Lippmann M (2011). "Total Suspended Particles (TSP)." In Encyclopedia of Public Health. Ed. Lester Breslow. 

Gale Cengage, 2002. Available at: http://www.enotes.com/public-health-encyclopedia/total-suspended-particlestsp [accessed: 9/14/2011]

Stratus (2008). Recommended health benefit assessment methods for the 2007 AQMP socioeconomic assessment 

– Final report, prepared by Stratus Consulting, Leland Deck, for the South Coast AQMD, Diamond Bar, CA, 

July, 2008.

US EPA (1999). Guideline on data handling conventions for the PM NAAQS. [Available from Office of Air 

Quality Planning and standards, EPA-454/R-99-008, April 1999].

US EPA (2004). Air Quality Criteria for Particulate Matter. National Center for Environmental Assessment-RTP 

Office, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, 

NC 27711; report no. EPA/600/P-99/002bF, October 2004. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_cd.html.

US EPA (2008). BENMAP Users Manual Appendices, September, 2008. Prepared for the Office of Air Quality 

Planning and Standards, USEPA by Abt Associates, Inc.

US EPA (2009). Integrated Science Assessment: Final Report.. National Center for Environmental AssessmentRTP Division, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711. report no. 

EPA/600/R-08/139F. December 2009. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html.

Westerling, AL; Hildago HG, Cayan DR, Swetnam TW (2006). “Warming and earlier spring increase U.S. 

forest wildfire activity,” Science 8/18/2006, Vol. 313 no. 5789 pp. 940-943.
55

Appendix A. Uncertainty of 24-hour design value trend estimates

The District design value for either the annual or 24-hour PM2.5 NAAQS is the maximum of 3-year averages. Its 

distribution does not lend itself to off-the-shelf statistical tests. We’ve developed two test methods that should be 

appropriate – one a test based on permuting years, the other a generalized regression.

Permutation test for District design values

Here we use a generalization of the ratio-permutation method described in Section 4.2. Effectively, what that 

method does is to consider permutations of years. This same approach can be applied to the District design value.

Let xt = (x1t, x2t, ..., xkt) be a vector of statistics from k sites for year t (the annual means or 98th percentiles). We 

compute wit = xit/(1-r)t, creating a vector wt = (w1t, w2t, ..., wkt). For each site, we compute its design values for 

the 1st 3 years and last 3 years and, from these, compute the District design value for the 1st 3 years, WE, and the 

last 3 years, WR, and then compute the ratio WE/WR. We then take permutations of the vectors wt, that is, 

permuting years, so that, for example, the vector for 2007 is chosen as the permutation representing 1999 (w1999* 

= w2007), the vector for 2002 is chosen as the permutation representing 2000 (w2000* = w2002), and so on. For each 

of these permutations, we compute WE* and WR*, and WR*/WE*. We find a 90% confidence interval (rl,ru) by 

with the 5th and 95th percentiles. So, for PM2.5, where there are 18,480 permutations, we find rl so that WR/WE

equals the 924th smallest of the WR*/WE*, and ru so that WR/WE equals the 924th largest of the WR*/WE*.

Regression method for District design values

The trends in 24-hour 98th percentiles and design values were made by simple linear regression against year, that 

is, fitting a straight line through the data. Using simple linear regression to fit the line presents no problem 

provided the trends are roughly linear, as they appear to be.

But difficulty arises evaluating the uncertainty in the estimates of the design value. Simple linear regression 

makes several assumptions about the data, one being that the values are statistically independent, or at least 

uncorrelated. But design values are based on running 3-year averages, so that design values less than 3 years 

apart use overlapping measurements and hence are not independent. For example, the 1999-01 design value uses 

the 1999, 2000 and 2001 98th percentiles, the 2000-2002 design value also uses the 2000 and 2001 98th percentiles 

along with the 2002. Design values three or more years apart can be assumed to be statistically independent. 

(Those three years apart share one winter, so their 98th percentiles may be slightly correlated but likely not much.)

Now, if this were the only issue, it could be taken care of relatively simply, since a running 3-year mean is a linear 

transformation that meshes smoothly with the linear regression model. But the design values are means on the 

original scale, not the log scale, and the log of the means doesn’t equal the mean of the logs, in general. 

Furthermore, the District design value is the maximum of the sites’ design values, and the site representing the 

maximum can switch from one 3-year period to the next.
56

Here is one approach to estimating design value uncertainty. This is intended to be applied to the District’s 

design value, but could also be used for individual sites. Let the design value for the years t-2, t-1, and t be Dt, 

and let Vt = ln(Dt). Assume a linear trend in the mean of Vt: 

E(Vt) = 0 + 1(t – t0)

where t0 is chosen as the average value of the years. Assume that the variance of Vtis constant: Var(Vt) = 0

2

. 

Moreover, assume that the 1st and 2nd order autocovariances are Cov(Vt, Vt+1) = 1 and Cov(Vt, Vt+2) = 2, and 

that Cov(Vt, Vt+k) = 0 for k > 2. Let  be the variance covariance matrix of V1,...,Vn, so that  has entries tt = 

0

2

on the diagonal, tt+1 = 1 = t+1t one off the diagonal, tt+2 = 2. = t+2ttwo off the diagonal, and st=0 

elsewhere. The regression coefficients are estimated as

̂ (̂

 

 ̂

 

)

 

 ( 

 ) 

where X is a nx2 matrix with a first column of 1’s, and a second column with t – t0. The variance-covariance 

matrix of ̂ is

(XX)-1XX(XX)-1. (A1)

So, we need to estimate , i.e., 0

2

, 1, and 2. Here we use the regression residuals rt = vt – (̂

 ̂ 

( )). 

The idea is to use

 ∑ 

 

 

 

 

∑ 

 

 

 ∑ 

 

 

 

find the expected values of each of these sums in terms of , 0

2

, 1, and 2, then equate the expected values with 

the sample values and solve for the unknown 0

2

, 1, and 2.

E(SSE0) = E rr = EV[I - X(XX)-1X]V = tr[[I - X(XX)-1X]

=0

2

tr[[I - X(XX)-1X] + 1 tr[[I - X(XX)-1X]K + 1 tr[[I - X(XX)-1X]L (A2)

where tr() is the trace, K = (kij), with kij = 1 if |i-j|=1 and 0 otherwise, L = (lij) with lij = 1 if |i-j|=2, 0 otherwise. 

SSE1 = rM-1M-nr = V[I - X(XX)-1X]M-1M-n [I - X(XX)-1X]V

where r is the vector of residuals, M-1 is an (n-1)xn matrix created by removing the 1st row from the nxn identity 

matrix I, and M-n is the matrix created by removing the last row of I. Then

E(SSE1)= ErM-1M-nr = EV[I - X(XX)-1X]M-1M-n [I - X(XX)-1X]V

= tr[I - X(XX)-1X]M-1M-n[I - X(XX)-1X]

= 0

2

tr[[I - X(XX)-1X]M-1M-n[I - X(XX)-1X] + 1 tr[[I - X(XX)-1X]M-1M-n[I - X(XX)-1X]K + 1

tr[[I - X(XX)-1X]M-1M-n[I - X(XX)-1X]L (A3)

SSE2 = rM-2M-(n-1)r = V[I - X(XX)-1X]M-2M-(n-1) [I - X(XX)-1X]V
57

where M-2 is an (n-2)xn matrix created by removing the 1st 2 rows of I and M-(n-1) is the matrix created by 

removing the last 2 rows of I. Then

E(SSE2)= ErM-2M-(n-1)r = EV[I - X(XX)-1X]M-2M-(n-1) [I - X(XX)-1X]V

= tr[I - X(XX)-1X]M-2M-(n-2)[I - X(XX)-1X]

= 0

2

tr[[I - X(XX)-1X]M-2M-(n-1)[I - X(XX)-1X]

+ 1 tr[[I - X(XX)-1X]M-2M-(n-1)[I - X(XX)-1X]K

+ 1 tr[[I - X(XX)-1X]M-2M-(n-1)[I - X(XX)-1X]L (A4)

Although messy, this is straightforward to compute, leaving us with 3 linear equations in 3 unknowns A2, A3, and 

A4, to estimate 0

2

, 1, and 2:

a00̂ 

 

+ a01̂ + a02̂ = SSE0

a10̂ 

 

+ a11̂ + a12̂ = SSE1

a20̂ 

 

+ a21̂ + a22̂ = SSE2

For n = 10, the table of aij is:

Table of aij for n = 10.

j=0 j=1 j=2

i=0 8 -1.6 -1.2121

i=1 -3.2 5.8666 -2.29818

i=2 -2.4242 -2.29818 5.76514

Note that a00 = 8, i.e., the well-know result that E(SSE0) = (n-2) 0

2

.

The observed values for the SSE’s were: SSE0 = 0.021718, SSE1 = 0.008228, and SSE2 = -0.0007205. Solving

yields: ̂ 

 ̂ ̂ . This allows us to estimate the standard 

deviations of ̂

 

and ̂

 

using term A1: 0.0391 and 0.01205. The fitted value of the slope was ̂

 

= -0.0589, so a 

90% confidence interval is -0.0589 ± t8,.95 (0.01205) = -0.0803 to -0.0365 or, roughly, reductions between 4% and 

8%.
58

Appendix B. Methodology for meteorological variables considered in 

wintertime regression

Methodology for estimating meteorological potential

The key idea of the method is for each winter, w, to find a formula, fw, that predicts what PM2.5 concentrations 

would be expected from a given set of meteorological variables (e.g. wind speeds, wind directions, rainfall). 

Then we cycle through the meteorology of each of the winters, computing what fw predicts for each. This 

provides an estimate of the number of exceedances that would be expected in winter w, averaged across the 

different winter seasons. That is, we are trying to estimate what would have happened in winter w under a variety 

of weather conditions. We can then look at these expected exceedances across winters w = 1999-00, 2000-01,..., 

2010-11, and again look at the trend. Here are the steps in the process:

Step 1. Choose basic model and set of meteorological and other predictors using all winters pooled. For the 

dependent variable we started with the daily maximum PM2.5 concentration. We found that the natural log 

transform produced regression residuals that appear close to Gaussian15. (See Figure B1.)

-4 -3 -2 -1 0 1 2 3 4

4

3

2

1

0

-1

-2

-3

-4

normal scores

residuals

Figure B1. Q-Q Plot of Residuals vs. Normal Scores

Step 2. Fit the model for each winter, w, yielding prediction functions, fw, and regression standard deviations sw. 

 

15 We first tried Weibull regression, assuming that the regression errors were better modeled with a Weibull distribution, as 

demonstrated by Cox and Chu (1993) for ozone data. But Q-Q plots of the sorted residuals rk vs. ln(-ln(k/(n+1))) was not 

straight. Regressions on the original scale have the usual problems of heteroskedasticity and skewness. We found that a log

transform resulted in a remarkably strong correlation with normal scores (see Figure B in Appendix B) – indicating that the 

residual distribution of these PM2.5 data are well-approximated by a log-normal distribution.
59

Step 3. Apply fw to each winter v’s meteorology, and get predictions p1wv, p2wv, ..., pnwv for the ln(PM2.5) where n 

= 120 or 121 depending on whether winter v contained a February 29th.

Step 4. Estimate the expected number of exceedances from each formula fw. That is, estimate the average across 

different winters’ meteorologies of the exceedance potential of winter w: Estimate the probability of exceedance 

for each day j: [(pjwv – ln(35.5))/sw], j = 1, ..., nv, where x is the Gaussian cumulative distribution function. 

The value 35.5 is chosen based on the 35 μg/m3standard; any PM2.5 measurement greater than this exceeds the 

standard. The expected number of exceedances for a given winter v is just the summation of these probabilities: 

∑ (

 ( )

 

 

 

).

Step 5. We define the exceedance potential of a winter, w, as the average across all winters, v, of the expected 

number of exceedances:

Exceedance potential for winter w = 

 

 

∑ ∑ (

 ( )

 

 

 

 

 

)

Table B1. Meteorological Variables Considered

Variable Description

days from Jan 1 number of days between the date and January 1 (e.g., Jan 5 = 4, Dec 29 = 3) 

avews mean of 24-hour average wind speeds from San Carlos, Pleasanton & Bethel Island

1/avews the reciprocal of avews

3dyavews the 3-day average of avews

1/3dyws the reciprocal of 3dyavews

nd<5 the number of consecutive days (up to 3) where the wind speed was < 5 mph

sjrain san jose rainfall in inches

sjrn<.02 =1 if sj rainfall was < 0.02 inches, =0 if sj rainfall ≥ 0.02 inches

pb-result san pablo 24-hour resultant wind direction

pbres<90 =1 if pb-result was < 90o, =0 if pb-result was ≥ 90o.

funwdsd standard deviation of Fort Funston wind direction

fuws&pb<90 = funwdsd if pb-result < 90, =0 otherwise

we+hol = 1 if a weekend or holiday, =0 if weekday

r1day Scott’s r1 day

r2day Scott’s r2 day

r3day Scott’s r3 day

vday Scott’s v day

zday Scott’s z day

950mb height Height in meters of the 950 mb pressure measured during the 4am RAOB sounding

1000mb height Height in meters of the 1000 mb pressure measured during the 4am RAOB sounding
60

Table B2. Exceedance potential by winter formula and meteorology

Formula for winter:

Winter

1999-

2000

2000-

2001

2001-

2002

2002-

2003

2003-

2004

2004-

2005

2005-

2006

2006-

2007

2007-

2008

2008-

2009

2009-

2010

2010-

2011

Condu

-civeness:

Meteorology of winter:

1999-00 20 25 18 17 17 18 9 19 11 10 7 3 14.6

2000-01 28 37 26 29 27 27 15 28 17 16 14 5 22.5

2001-02 16 22 14 13 13 14 6 15 6 6 3 1 10.7

2002-03 24 34 22 25 23 23 12 24 13 11 10 3 18.8

2003-04 15 23 14 16 14 14 6 15 6 6 4 1 11.0

2004-05 21 32 19 22 20 19 9 20 9 8 6 2 15.5

2005-06 19 26 17 20 18 18 10 19 11 9 8 3 14.8

2006-07 18 25 17 17 16 17 8 18 9 9 7 2 13.6

2007-08 13 18 12 10 11 12 5 12 6 5 3 1 9.0

2008-09 27 38 25 28 26 26 13 28 14 13 11 3 21.2

2009-10 23 33 21 24 22 22 10 23 11 10 8 2 17.4

2010-11 22 32 21 22 21 21 9 22 10 9 7 2 16.5

Exceedance 

Potential: 20.5 28.8 18.8 20.3 18.9 19.4 9.5 20.3 10.2 9.3 7.3 2.3
61

Appendix C. Numbers of days with PM10 Measurements

Table C. Numbers of days with PM10 measurements at District monitoring sites

fr li cc bi sr np sf rc sj va st 8+days*

1989 47 60 60 60 60 60 61 60 178 0 0 56

1990 61 61 60 61 61 61 61 61 174 0 0 56

1991 59 60 59 60 61 60 60 60 168 0 0 57

1992 61 61 61 61 61 61 61 61 61 0 0 60

1993 61 61 59 61 61 61 61 61 61 0 0 56

1994 60 61 60 60 61 59 61 61 61 9 31 59

1995 61 61 61 61 61 61 61 61 61 61 61 61

1996 61 61 61 61 61 61 61 61 61 61 61 61

1997 61 61 61 61 61 60 61 61 61 61 61 61

1998 61 61 61 61 61 60 61 61 60 61 61 61

1999 60 60 60 60 60 60 60 60 60 60 59 59

2000 61 62 61 61 61 61 61 61 61 61 60 61

2001 60 61 61 61 61 61 61 61 61 61 61 61

2002 61 61 61 61 61 61 61 61 14 61 61 61

2003 61 61 61 61 61 61 61 61 61 61 62 61

2004 61 61 58 61 61 61 61 63 61 60 61 61

2005 61 61 21 61 61 64 61 63 61 59 61 61

2006 66 61 61 60 61 64 61 63 61 60 61 61

2007 60 60 58 60 60 61 59 61 60 58 60 60

* Number of days with at least 8 sites with PM10 measurements.
62

Appendix D. Use of dichotomous PM2.5 data for trend analysis

Analysis of long-term PM2.5 trends is hampered by the lack of routinely collected PM2.5 before 1999. However, 

some PM2.5 measurements were made at one site, San Jose, from 1990 through 1997. The sampling equipment 

was different, however, and this complicated the analysis.

Although there are no dichot PM2.5 overlapping with routine filter measurements, dichot PM10 measurements 

overlap with FRM PM10. Figure D1 shows a comparison by year for 1990 through 1997. First note that the 

correlation is quite high, over 90%. Next notice that the points lie near the line y = x for 1990 and 1991 but fall 

below it for 1992-1997 A regression through the origin produced a slope of 0.98 for 1990-1991 and 0.78 for 

1992-1997. Table D1 shows a comparison of mean values on days when both instruments made measurements. 

The ratios of the means average 0.99 for 1990-1991 and 0.78 for 1992-1997.

Table D1. FRM and Dichot PM10 annual means on days when both had measurements

sj pm10 dichot pm10 dichot/frm ratio

1990 33.54 33.29 0.99

1991 31.49 30.82 0.98

1992 32.94 26.39 0.80

1993 30.51 24.29 0.80

1994 28.46 23.33 0.82

1995 26.02 18.71 0.72

1996 24.53 19.15 0.78

0 20 40 60 80 100 120 140

160

140

120

100

80

60

40

20

0

sj frm pm10 (ug/m3)

sj dichot pm10 (ug/m3)

1990

1991

1992

1993

1994

1995

1996

1997

year

Figure D1. Dichot PM10 vs. FRM PM10 by year: San Jose 1990-1997

y = x

y = .777x

1992-1997:

regression for
63

1997 25.21 19.32 0.77

Figure D2 shows the relationship between PM2.5 and PM10 for both the dichot and the FRM samplers. The 

patterns are very similar, with a lower PM2.5 /PM10 slope for small PM values and a larger slope for high PM 

values, with a few exceptions. Thus, the PM2.5 /PM10 relationship is roughly the same for the two measurement 

methods, so that the ratio of mean PM2.5 to mean PM10 appears very similar (dichot PM2.5 /PM10  frm 

PM2.5/PM10). Thus, for 1990 and 1991 we assume that the dichot PM2.5 represents what the FRM PM2.5 would 

have measured, and for 1992-1997, it appears reasonable to use the approximation

mean frm PM2.5  mean dichot PM2.5 (frm PM10/dichot PM10 )  mean dichot PM2.5/0.78.

0 20 40 60 80 100 120 140 160

120

100

80

60

40

20

0

PM10 (ug/m3)

PM2.5 (ug/m3)

sj pm2.5 * sj

sj dichot pm2.5 * sj dichot pm10

Variable

Figure D2. Relationship of PM2.5 and PM10 measurements at San Jose

Dichot samples 1990-1997 vs. FRM samples 1999-2010
64

Appendix E. Adjustment of nitrate measurements for the impact of 

refrigeration

In 2009, the District started refrigerating PM10 filters after sample collection. The goal was to prevent nitrate 

from volatilizing before the filters could be weighed. To assess the impact of the change, we compared San Jose 

PM10 nitrate measurements against PM2.5 nitrate measurements, where the filters have always been refrigerated 

after sample collection, as a control. The change caused by refrigeration is substantial and appears to have fixed

the volatilization problem.

Figure E1 shows PM10 nitrate values relative to the corresponding PM2.5 values, the black circles for 

measurements before 2009, the red squares for 2009-2010. Before 2009, many PM10 nitrate values were well 

below the corresponding PM2.5 nitrate value; in 2009-2010, they lie close to the line y = x, i.e., close to the 

corresponding PM2.5 nitrate value.

0 5 10 15 20

12

10

8

6

4

2

0

sj pm2.5 no3 (ug/m3)

sj PM10 no3 (ug/m3)

<2009

>=2009

Period

Figure E1. PM10 nitrate vs PM2.5 nitrate: San Jose

Impact of refrigeration of PM10 filters that started in 2009.

y = x

Figure E2 shows the relationship before 2009 split into winter-season (Nov-Feb) and non-winter. Simple linear 

regression lines for each season are also shown. The effect of non-refrigeration is apparent for both seasons, but 

more pronounced in the winter months (significantly more gradual slope). Also note that the effect appears nonlinear, with PM10 nitrate matching PM2.5 nitrate for small concentrations (≤ 1 μg/m3

).

Figure E3 shows the same plot but for 2009-2010. For both seasons, PM10 nitrate matches PM2.5 nitrate, and there 

is no statistical difference between the seasons. The figure also shows a non-parametric fit – that is, a curve 

representing a best guess as to the true , underlying relation between PM10 nitrate and PM2.5 nitrate. The curve 

starts at (0,0) but goes above the line y = x for smaller concentrations, then descends back to the y=x line. That is, 

PM10 nitrate measurements are somewhat higher than the corresponding PM2.5 nitrate unless the concentrations 
65

are high. Of course, since PM2.5 is a subset of PM10, there’s no reason why they should be equal. It does raise the 

question of the extent that nitrate lies in the coarse fraction of PM10.

0 5 10 15 20

10

8

6

4

2

0

pm2.5 no3 (ug/m3)

pm10 no3 (ug/m3)

non-winter

winter

C1007

Fig. E2. PM10 NO3 vs. PM2.5 NO3: Winter and Non-Winter pre-2009

0 2 4 6 8 10

12

10

8

6

4

2

0

pm2.5 no3 (ug/m3)

pm10 no3 (ug/m3)

non-winter

winter

C999

Fig. E3. PM10 NO3 vs. PM2.5 NO3: Winter and Non-Winter 2009-10

y = x

non-parametric fit
66

Adjusting pre-2009 nitrate measurements

Although the refrigeration of PM10 nitrate filters appear to have solved one problem, it created another, namely a 

discontinuity in measured nitrate trends. To make past measurements closer to being comparable, we can attempt 

to adjust them based on the relationship with PM2.5 nitrate. This would also have the advantage of allowing us to 

get a better estimate of historical nitrate concentrations and what fraction these are of total PM10.

It should be noted that we are making several assumptions – one being that the relationship between PM10 nitrate 

and PM2.5 nitrate at San Jose would carry over to other sites. Also, the finding that PM10 nitrate is scarcely larger 

than PM2.5 nitrate is somewhat questionable; we have been told that a larger percentage was in the coarse fraction. 

Also, if we adjust PM10 nitrate, we presumably want to adjust PM10 concentrations too, since it also is impacted 

by the loss of nitrate.

With these caveats, we plunge ahead. Figure E4 shows the pre-2009 relationship of PM2.5 nitrate to PM10 nitrate 

(just flipping the axes from Figure E2). Notice that the deviations from the line y=x are highly asymmetric, with 

many values below, but close to the line, and a few values far above the line. Also, the variability increases with 

increasing concentration.

0 2 4 6 8 10

20

15

10

5

0

pm10 no3 (ug/m3)

pm2.5 no3 (ug/m3)

Figure E4. PM2.5 Nitrate vs. PM10 Nitrate: pre-2009

Standard statistical techniques like regression are suited to Gaussian variability or, at least, symmetric and shorttailed distributions.

A log transform comes part way to solving this problem. Figure E5 shows the same data but transformed so that 

x = ln(pm10 no3 + 0.3) and y = ln(pm2.5 no3 + 0.3). [Adding 0.3 moderates the influence of the smallest 
67

observations.] As previously discussed the winter and non-winter relationships are different, so separate 

regressions were fit.

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

3

2

1

0

-1

x' = ln( pm10 no3 + 0.3 )

y' = ln (pm2.5 no3 + 0.3)

Figure E5. PM10 Nitrate vs. PM2.5 Nitrate on the Log Scale

y' = .201 x'^2 + .654x' + .171

For the winter, a simple linear regression was fit: y = 1.21 x + .177. For non-winter, a quadratic regression was 

fit: y = .202*x

2

+ .504*x + .177 In both cases, the standard error was .4945. Transforming back to the original 

scale:

adjusted winter PM10 NO3 = e1.21 x + .177– 0.3,

and

adjusted non-winter PM10 NO3 = e.202*x^2 + .504*x + .177– 0.3.