## 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. PM 2.5 Trends

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

6. PM 10 Trends

................................ ................................ ................................ ................................ ........ 28

7. Trends in P M components

................................ ................................ ................................ .................. 31

8. PM 2.5 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
Ap pendix C

– Numbers of days with PM 10 Measurements

................................ ................................ ........ 61
Appendix D. Use of Dichotomous PM 2.5 Data for Trend Analysis

................................ ........................... 62
Appendix E. Adjustment of nitrate measurements for the impact of refrigeration

................................ .... 641



## Summary

Ai rborne 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 fr om 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

.

T he original PM standard was set for total suspended particle s (TSP). But over the next 40 years a mountain of scientific evidence has shown that the tiniest PM particles are the most dangerous 1 . Thus, the TSP standard was changed to a PM 10 standard (particles less than 10 microns in diameter) in 1987

, and a PM 2.5 standard (particles less than 2.5 microns in diameter, aka fine PM) was promulgated in 1997. As a result, the District began r outine, multi

- site PM 10 moni toring in 1989 and PM 2.5 monitoring in 1999.

Historically, PM has been measured on filters. A filt er 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, instrume nts 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 PM 2.5 standard, the design value is the 98 th percentile of the PM measurements averaged over the three most recent years

. Thus, we focus on the annual mean s and 98 th percentile s 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 lit tle information for determining which component of PM pose s the highe st risk; current PM standard s 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 d irectly 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 PM 2.5 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 )

T he District has been making measureme nts 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 contribution s of these individual components to the tota l PM.

Results

PM 2.5 trends : Figure S1 shows the trend in the District’s design value relative to the national annual
PM 2.5 standard. The District’s design value dropped from 14 μ g/m 3 for 1999

- 2001 to 10 μ g/m 3 for 2008

- 20 10, a 28% reduction, with a 90% confidence interval of 21% to 33%. As can be seen

, the District met the national annual PM 2.5 standard for the whole period.

Figure S2 shows the District’s design values relative to the national 24

- hour PM 2.5 standard. For the earliest period available , 1999

- 2001, the design value was 57 μ g/m 3 . By 2008

- 20 10, it was 31 μ g/m 3

, which meets the standard. Although there is considerable year

- to

- year variation in PM 2.5 concentrations, the District is likely to continue to meet this stan dard. The r eduction from 1999

- 20 01 to 2008

- 20 10 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 investigat ed trend s 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.

PM 10 Trends : Bay Area a nnual mean PM 10 was reduced from 33 μ g/m 3 in 1989

- 19 91 to 18 μ g/m 3 in 2007

- 20 09, 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 D istrict: ammonium nitrate, ammonium sulfate, and elemental and organic carbon.

Figure S4 shows PM 10 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 percentile s of its PM2.5 concentrations. The design values are marked at the third year of three year averages.4

PM 2.5 – PM 10 comparison : Figure S5 shows the annual percent reductions in PM 2.5 summary statistics from 2000 to 2010 and PM 10 components from 1990 to 2010 along with 90% confidence intervals. Reductions are shown on an annual basis to make the PM 2.5 and PM 10 reductions comparable. Reduct ions 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 PM 2.5 and PM 10 are not statistically different, although PM 2.5 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 decrea se in 98 th 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 redu ced more than predicted in the emissions i nventory , 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 t o carbon from non

- fossil sources (including wo odsmoke and meat cooking) has de creased, based on Carbon

- 14 analysis.

Ammonium nitrate, one of the largest components of Bay Area PM 2.5 , has also been reduced substantially

, with a rate of about 5% per yea r. Orga nic carbon and non

- sea salt sulfate have been reduced about 3% per year, and other PM 10 species ( including geologica l dust, tire and brake wear) have been reduced about 2% per year. As expected, the marine air component shows no change.

Compariso n with other air basins : The Bay Area has shown more consistent progress in reducing
PM 2.5 than its two neighboring air basins to the east

– the Sacramento and San Joaquin valleys

. Like the District, these areas have PM 2.5 measurements dating back to 2000. Over the past decade, Sacramento’s annual PM 2.5 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 20106

annual PM 2.5 standard. But its 24

- hour design value hasn’t dropped significantly a nd it still violates the national 24

- hour standard. The San Joaquin Valley has seen little progress in reducing its annual mean
PM 2.5 concentrations, and it is still well above the national annual standard. San Joaquin has reduced its
24

- hour design valu e significantly, but it is still well above the standard.

Comparison with e missions i nventory trends : Figure S6 shows a comparison of annual reductions in ambient nitrate, sulfate, PM 10 , and PM 2.5 compared with reductions in emissions of NOx, SO 2 , PM 10 a nd
PM 2.5 . The percent decreases in sulfate match those of SO 2 . 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 year

to

- year variability.

Reductions in ambient PM 10 and PM 2.5 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 hav e 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 e ffects : 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 affecte d 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 PM 2.5 from 1990 to 2010 have reduced the number of premature deaths in the Bay Area by more than 3,000 per year 2 . 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 PM 2.5 concentrations.

2 Specifically, the number of deaths that occurred in 2010 with 2010 PM 2.5 concentrations is es timated to be 3,000 fewer than would have occurred if PM 2.5 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 i n 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 leve ls rivals that of motor vehicl e fatalities or homicides.

The Bay Area attains the national annual PM 2.5 standard

, but has continued to violate California ’s annual standard. The Bay Area is in non

- attainment of the national 24

- hour PM 2.5 standard. It meets the national
PM 10 standard

, but violates California’s PM 10 standards

. E fforts at the regional, state and national level s have reduced PM concentrations. This analysis estimates the amount of re ductions in ambient PM as well

as in its different chemical a nd size components

.

1.1 Hi story of PM s tandards and PM measurements

PM was on e 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 PM 10 (particles less than 10 m icrons in diameter), and in 1997, it added a standard for even smaller particles, PM 2.5 (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 m easurements

Until recently, PM was measur ed by collecting particles on filter s over 24 hours

, and PM concentrations were estimated by weighting the filters before a nd 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

.

A ir pollution trends are typically hard to discern over a period of a few years. Day

- to

- day and year

- to

year variation in meteorology can lead to large swings in pollutant concentrations. Therefore, we conducted analyses of trends over the entire period of PM 2.5 measurements: 1999

- 2010. For PM 10 , 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 part icles; tire and brake wear particles. S econdary particles are those formed by chemical reactions or physical interactions in the atmosphere

. A mmonium nitrate and9

ammonium sulfate account for the bulk of secondary PM in the Bay Area. E valuating 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 ke y 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 particl es 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 a ir 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 PM 2.5 standard is restricted to these months.



## 2. PM m easurements 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 whe re and when

.

2.1 PM m easurements

The District began measuring PM 10 concentration s at a number of sites in 1989 on a 1

- in

- 6 day schedule. In addition to total PM 10 concentration s , 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 PM 10 has been substantially reduced.

T he District began measuring PM 2.5 in 1999. Until recently, t he primary me asurements were filter

- based

. A filter is put in a sampler that draws a steady and monitored airflow through it for 24 hours. The PM 2.5 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 ( μ g/m 3 ) . These measurements are called Federal Reference Method (FRM) and have been the ones used for determining compliance with the national air quality standards.

The PM 2.5 measurement schedule was complex, with some sites having daily wintertime measurements and others once in 3 days . A ll sites had a 1

- in

- 6 day measurement schedule from April through September

.

Starting in 2001

, the District began operation of BAM units

– continuous PM 2.5 an alyzers at Livermore, San Jose and San F rancisco . These units collect PM 2.5 data on an hourly basis. Initially, the

measurements were not accepted by EPA for reference to the national PM 2.5 standards because of large uncertainty in this type of measurements

. Recently, the accuracy has been significantly im proved and the10

District upgraded most of its BAM instruments . Since 2009, the PM 2.5 measurements from the upgraded BAM instruments have been accept ed 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 PM 10 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 di fference 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 a s a surrogate measure for EC

.

2.1.2 Other measurements

Speciated PM 2.5 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 PM 2.5 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 PM 2.5 measurements at several Bay Area sites that can serve as a baseline for c omparison.

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 PM 2.5 Filter (f requency ) a PM 2.5 FEM PM 2.5 Species b PM 10 Mass+Ions PM 10 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 Livermore c 12/1999

- 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 Pablo d 3/1989

– 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 Jose e 1999

- 2010 (1) 1990

- 1997 f 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 3 rd day; April

- September frequency every 6 th 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, r eplaced 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 4 th St through 4/2002, r eplaced by site at Jackson St in 10/2002 (no San J ose 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 me thodology 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 A NOVA technique.

2.2.1 PM 10 and PM 10 components

PM 10 filters were collected from 1989 through 2007 at many sites and analyzed for PM 10 mass, nitra te, sulfate, and chloride. Measurements were discontinued at about half the sites in 2008. A 2

- way ANOVA wa s used to fill in the missing values to extend the record through 2010.

2.2.2 PM 10 n itrate

Since 2009, the District has refrigerated PM 10 filters after the sample had been collected. Before 2009, PM 10 filters were unrefrigerated. The impact of the change could be estimated by comparison with PM 2.5 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 PM 10 components, but for nitrate there was a cl ear 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 PM 2.5 measurements

The District began its program of collecting PM 2.5 measurements in 1999. Before that there had been some limited PM 2.5 measurements made in a special study in San Jose from 1990 to 1997. Comparisons with concurrently measured PM 10 showed that the special study PM 2.5 regist ered concentrations about 20% lower than the PM 2.5 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 PM 10 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 perio d 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 s tandards

The set of PM standards is complex. T here are both national and California standards for both PM 2.5 and PM 10 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 S tate PM standards PM 2.5 PM 10 annual 24

- hour annual 24

- hour National 15.0 μ g/m 3 35 μ g/m 3 N/A 150 μ g/m 3
California 12.0 μ g/m 3 N/A 20 μ g/m 3 50 μ g/m 3

Differences among the standards and h ow they are met are explained below.

3.1 National PM standards

The annual PM 2.5 national ambient air quality standard (NAAQS) is 15 .0 μ g/m 3 . 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 PM 2.5 measurements. For a giv en 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 PM 2.5 measurements , where each annual average is the mean of the four quarterly averages 3

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

The 24

- hour PM 2.5 NAAQS is 35 μ g/m 3 . Compliance with the standard is determined as follows. The standard is met by the Bay Area if and only if i t is met at every site collecting EPA

- approved PM 2.5 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 98 th percentiles of the site’s PM 2 .5 measurements 4

.

There is also a 24

- hour PM 10 NAAQS of 150 μ g/m 3

. This standard is met if the 3

- year average of the 99 th percentile values at each site is less than or equal to 150 μ g/m 3

.

Additional d etails of the standard s 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 PM 2.5 and PM 10 measurements, but differ from the NAAQS in both level and form. Like the PM NAAQS, the Californ ia annual standards are base d on 3 years of data. But the

3 Each quarter must have measurements for 75% or more of the scheduled samples.
4 The 98 th percentile is simply the sample 98 th percentile if the sampli ng schedule has the same frequency year round (for example the 8 th highest value if sampling is every

- day). If the sampling frequency differs by season (as it has with many Bay Area sites), the 98 th percentile is the determined from the empirical distribu tion. (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 PM 2.5 standard is 12 μ g/m 3 . Currently, California has no 24

- hour PM 2.5 st andard.

The California annual PM 10 standard is 20 μ g/m 3 . Its 24

- hour PM 10 standard is 50 μ g/m 3

. Unlike the 24

- hour
PM 10 NAAQS, the 24

- hour California PM 10 standard is violated if any PM 10 measurement exceeds it, with certain exceptions. Among the e xception s 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 exc luded. 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 m ethods used for trend analysis

For both the national annual an d 24

- hour PM 2.5 standards, the design value is a 3

- year average

– the average of annual means for the annual standard, the average of 98 th percentiles for the 24

- hour standard. Thus, the focus of this analysis is on methods to estimate trends in the se ave rage s, and also to estimate the uncertainty in these trend estimates.

L et t represent years from the initial year, so that , for example, for PM 2.5 as the years range from 1999, 2000,..., 2010, t ranges from 0, 1,
..., 11. Let Y t be a summary statistic for year t for a given site. Here Y t is either the annual mean or the 98 th percentile for year t

.

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

Y t = m 0 (1

- r) t e t (4.1)

where m 0 is the initial mean [ symbolically, E(Y 0 ) ], r is the annual rate of reduction and e t is a random error term with mean 1

.

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

1. Regression vs. year : Y 0 , Y 1 , ...Y k against t = 0, 1, ..., k
2. Regression on the log scale: ln(Y 0 ), ln(Y 1 ),...ln(Y k ) 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 W t (r) = Y t /(1

- r) t . Find the value of r so that ̅ ( ) ̅ ( ) , or

̅ ̅ 0.
5. A nonparametric approach, e.g. , u sing Kendall’s tau:  (Y 1 , Y 2 , ..., Y k ) = ∑ ∑ ( )

, 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

 (W 1 (r), W 2 (r),
..., W k (r)) = 0.

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

4.1 F ocus of trend analysis

This analysis focuses on progress toward meeting the federal PM standards. For PM 2.5 and PM 10 the s tatistical

test s appl ied used design values from the most recent an d 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 va lue for a given site is straightforward

– using a two

- sample t

- te st. But to report progress as17

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 u se the relativ e progress, that is, the relative reduction, ̅ ̅

. The interpretation of the relative reduction is straightforward and it makes it possible t o compar e the reductions of various PM components. The statistical inference is not as straigh tforward , 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 sa mples, 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 W t , where W t = Y t /(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 W t * are random permutation s of the W t , 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: r l ≤ r ≤ r u } such that P(D*(r l ) ≤ D(r l )) =  /2, and P(D*(r u ) ≥ D(r
u )) =  /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 t he 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 m ethod. Effectively, what that method does is to consider permutations of years. This same approach can be appli ed to the District design value. See Appendix A.

4.4 Methods for t rends in PM 2.5 exceedances , including meteorological adjustment

We also consider trends in the number of exceedances of the 24

- hour PM 2.5 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 PM 2.5 case, we are using six W t ’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 r l so that D(r l ) is the 924 th smallest of the D*(r l ) values, and r u so that D(r u ) is the 924 th largest of the D(r u ) 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 924 th smallest D*(r); the n repeating the process until the value of D(r) was close to the value of the 924 th 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 avera ge 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 PM 2.5 tends to occur only after several days of buildup, we defined a PM 2.5 conducive day as a day where the re had been three or more day s 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 PM 2.5 concentrations from meteorological and temporal variables. The goal wa s to estimate how many exceedances would have occurred each year if the meteorology was the same every year. Th e 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 acro ss sites, ignoring these missing data could result in biases. T o reduce the potential bias, an ANOVA model was fit to the data

, namely

Y tj = μ +  t +  j + e tj

where Y tj is an annual statistic for site j in year t

. The missing Y tj , we re 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 repeat ed until the estimates for missing values converge d

.

4.7 Statistical i nference issues

For all of the above techniques, statistical independence of the Y t ’s is required to estimate uncertainty. Although
PM 2.5 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 98 th 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 that19

the FEM measurements were higher by a random amount up to 3 μ g/m 3 (Fairley 2011 ). Because of this, FEM measurements were not included in the PM 2.5 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 4 th Street to Jackson S treet in 2002. The Livermor e 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 PM 2.5 measurements made by other agencies

– the PM 2.5 filter measur ements 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

. PM 2.5 

## t rends

This section presents trends in annual mean and 98 th percentile PM 2.5 design values, and also in numbers of exceedances of the 24

- hour PM 2.5 standard.

5 .1 Annual t rends

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 PM 2.5 across the District sites shows a steady reduction f rom 12.2

μ g/m 3 for 1999

- 2001 to 8.6 μ g/m 3 f or 2008

- 20 10, 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

- 20 01 through 2008

- 20 10. There has been an almost monotonic reduction, from 14.2 μ g/m 3 for 1999

- 2001 to 10.2 μ g/m 3 for 20 08

- 20 10, 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 Reyes21

5.1.1 Longer

- term trends

There were only limited PM 2.5 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. PM 2.5 data were collected at Fremont from 1988 through 1994 as part of the Nationa l Acid Deposition Network, but Fremont is not one of the sites where routine PM 2.5 measurements have been made. At San Jose , however, PM 2.5 measurements were collected with a dichotomous sampler from 1990 through 1997 , and also since 1999 as part of the D istrict’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 PM 10 annual averages from the
D istrict FRM sampler. The relationship between annual mean PM 2.5 and PM 10 has held fairly constant over time. A regression line shows that there has been a consistent trend in PM 2.5 over the 20

- year period from 1990 through 2010, a reduction from 17.9 μ g/m 3 in 1990

- 19 92 to 10.1 μ g/m 3 in 2008

- 20 10, 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 standard22

5 .2 Trends in 24

- hour design value

Figure 5.4 shows the annual 98 th percentile concentrations at District sites. It also show s a 3

- year moving average of the average of all sit es plotted vs. middle year. The plot shows a sudden dip in 2003, but some of this is because t he meteorology was much less conducive to high PM in 2003 than 2002

. The 3

- year average dropped from 47 μ g/m 3 for 1999

- 2001 to 27 μ g/m 3 for 2008

- 2010, a 41% red uction, 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 PM10 (ug/m3) PM2.5 (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 mean23

Figure 5.5 shows the trend in the Bay Area 24

- hour design value s . The values fell almost monotonically, from 57

μ g/m 3 for 1999

- 2001 to 30 μ g/m 3 for 2008

- 2010, a reduction of almost 50%. The annual reduction was 6 .7 % per
year wit h a 90% confidence interval of 5.2 % to 8

. 4 %.

5 .3 Trends in winter PM 2.5

As discussed earlier, virtually all of the Bay Area’s highest PM 2.5 concentrations occur on winter days. These days are distinctive not only in their meteorology, but also their mix of PM 2.5 – woodsmoke and ammonium nitrate predominating. Thus, we focus on wintertime trends, more precisely the trends from November throu gh 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 relat e d daily maximum PM 2.5 to various weather and time

- of

- year factors. Such formulas represent estimates of the potential for high PM 2.5 under given meteorologi cal 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 PM 2.5 exceedances

Figure 5.6 a shows the number of days PM 2.5 exceeded the 35 μ g/m 3 standard 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 standard2 4

5 .3.2 PM 2.5 conducive days

Figure 5.6 b shows the number of PM 2.5 conducive days along with the number of exceedances. Although not perfectly correlated, the PM 2.5 conducive days generally follow the same sawtooth pattern as the PM 2.5 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

- 20 09 through 2010

- 20 11 than in 1999

- 20 00 through 2001

- 20 02. 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 fit25

5 .3. 3 Evaluating exceedance potential

To estimate potential , we bega n with the District

- wide daily maximum PM 2.5 concentrations.

The independent variables include a range of meteorological measurements made at various District sites. These were reduced to 24

- hour summaries li ke 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
Oa kland RAOB sounding 7 . 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 1 st

. See Appendix B for methodology.

R esults

A set of trial variables was selected . (S ee 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 Exceedances26

Table 5.1. Predictors for District maximum PM 2.5 .

Predictor Description Estimated Coefficient (a i ) SE Coef t p value

x 0 Constant 1.790 0.110 16.33 0

x 1 jd # of days from Jan 1

- 0.00819 0.00063

- 12.96 0

x 2 avews 3

- site average of 24

- hour ave ws

- 0.0263 0.0087

- 3.04 0.002

x 3 1/avews reciprocal of x 2 1.409 0.300 4.7 0 0

x 4 sj r ai n<.02 indicator of san jose rain < 0.02 inches 0.2017 0.0283 7.12 0

x 5 1/3d a y ave ws reciprocal of 3

- day average wind speed 3.768 0.221 17.07 0

x 6 san pablo wd <90 o indicator of san pablo wind direction between 0 o and 90 o 0.2504 0.0391 6.41 0

x 7 fuws&pb<90 standard deviation at fort funston times x 6 - 0.00118 0.00037

- 3.16 0.002

The adjusted R 2 was 65.9%.

A plot of the residu als 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 R 2 s ranged from 57% to 82%. The variables jd and 1/3dyws are consistently significant from winter to winter, the others intermittently

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



## f w 

## = a 0w 

## + a 1w 

## *x 1 

## + a 2w 

## *x 2 

## + ...+a 7w 

## *x 7

These formulas were then applied to each of the winters 1999

- 2000 through 2010

- 2011 (Steps 3 and 4 above)

. T he 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. T he closeness su ggests 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 c orrelation 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, indicatin g 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 Potential28 

## 6. PM 10 

## t rends

For this trend analysis, we consider the period from1989 through 2007 for peak PM 10 and 1989

- 2010 for mean
PM 10 . In order to establish consistency across years, for the peak PM 10 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 PM 10

Figu re 6.1 shows quarterly averaged annual PM 10 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 ANO VA 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 μ g/m 3 in 1989

- 19 91 to 18 μ g/m 3 in 2008

- 20 10, a reduction of 44%.

The Dist rict is close

, but does not meet

, the California annual PM 10 standard. Its 18 μ g/m 3 design 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 PM 10 measured locally, which is generally

som ewhat higher . Bethel Isl and registered the highest annual average of 24 μ g/m 3 in 2 008. However, the maximum average since then was 20.4 μ g/m 3 for 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 PM 10 standards are based on local PM 10 concen trations, using local temperature and pressure, whereas national PM 10 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 PM 2.5 standard and the PM 2.5 NAAQS are based on local measurements.29

6.2

. Trends in peak PM 10

Figure 6. 2 shows the number of exceedances of the California 24

- hour PM 10 standard. It also shows the predictions from a Poisson regression. The reduction in exceedances has been considerable. The Poisson regression shows a stati stically significant decrease from almost 15 days per yea r 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 red uction over the entire 1989

- 2007 period. The trend in the max imum design value from 2000 to 2007 is not statistically significant and the downtrend in the 6

- site aver age is borderline significant.

The overall drop, f

rom an average maximum design value of 1 57 μ g/m 3 for 19 89

- 19 91 to 7 8 μ g/m 3 for 200 5

- 20 07 was 5 0 % f

or 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

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 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 standard30

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

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 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 Standard31 

## 7. Trends in PM components

7.1. Nitrate t rends

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 r educed from 5.3 μ g/m 3 in 1989

- 19 91 to 2.6

μ g/m 3 in 1999

- 2010 to 1.8 μ g/m 3 in 2008

- 20 10 . T hese 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 annua l basis, nitrate was reduced a t 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 Reyes32

7.2. Sulfate t rends

Sulfate is another large component of Bay Area PM. As with nitrate, sulfate is formed from reactions with ammonia, where SO 2 converts to sulfuric acid, combining with ammonia to produce ammonium sulfate (NH 4 ) 2 SO 4 .

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. S ulfate is less volatile, so that its con centration 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 co ncentrations. The 9

- si te mean sulfate dropped from 2.3

μ g/m 3 in 1 990 to 1.9 μ g/m 3 in 2000 to 1.3 μ g/m 3 in 2009. The re duction from 1990 to 2000 was 17 % ; t he

re duction 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
99-06/833

7.3. Chloride t rends

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 mar ine 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 ten ds 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 μ g/m 3 for 1989

- 19 91, 0.9

μ g/m 3 for 1999

- 20 01 and 1.1 μ g/m 3 for 2008

- 20 10. 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 PM 10 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 PM 2.5 not PM 10 , and the chloride in marine air is large ly 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

Mean34

7.5 Potassium t rends

The District began regular potassium ion measurements on PM 10 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 s ites. The 1996

- 19 98 mean was 7.5 μ g/m 3 compared with 8.7 μ g/m 3 for 2008

- 20 10 . 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 day s. It also has had an extensive educational campaign on the negative health effects of wood smoke. Respon ses 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

- 20 07. The absence of a downtrend in soluble potassium suggests that reductions in woodsmok e 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

Mean35

7.6 Trends in e lemental and o rganic c arbon

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 PM 10 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 PM 10 sampling w as discontinu ed in 2008.

2. At San Jose, EC and O C measurements have been made from PM 2.5 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 changed 9 in April 2009, a significant eno ugh 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 PM 2.5 filters since 1989.

4. EC and OC measurements were made on PM 2.5 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)

Mean36

5. EC and OC measurements have been made for the District by DRI on PM 2.5 filters at Livermore and Vallejo since September 2008 and at West Oakland since February 200 9.

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 St reet since 2004. Up until the mid

- 20 00s 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 we re averaged over 3

- year periods. T able 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 y ear compared with filter

based 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 1 st decade of the 21 st
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 PM 2.5 99

- 01/ District PM 10

08

- 10 2.91 3.36

- 1.6% 1.34 0.61 8.4%

Concord District PM 10 05

- 07 / 08

- 10 3.50 3.35 1.4% 0.86 0.77 3.7% Livermore CRPAQS PM 2.5 99

- 01/DRI PM 2.5 08

-

10 4.54 2.41` 6.8% 2.39 0.61 14.1%

Napa District PM 10 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 PM 2.5 99

- 01/District PM 10

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 PM 10 04

- 06/DRI PM 2.5 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 fr om 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 appea rs to be a steady reduction in E C. The larger than average reductions from 2008 to 2009 at Livermore and Vallejo may

repr esent the change from District PM 10 measurements to DRI PM 2.5 measurements.37

The COH mean for 1989

- 19 91 was 2.99 μ g/m 3 . A mean of Concord, Napa and San Francisco EC , the 3 of the 6 COH sites with EC measurements from PM 10 filters had a mean of 0.82 μ g/m 3 for 2008

- 20 10. This represents a reduction of 73%. For Point Reyes, EC dropped from 2.4 μ g/m 3 in 1989

- 19 91 to 0.69 μ g/m 3 in 2007

- 20 09, 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 ha s a long and consistent record. The figure shows its OC values times 5 to make it comparable with the concentrations at other sites. O n its original scale, Point Reyes OC averaged 0.99 μ g/m 3 for 1990

- 19 92 and 0.48 μ g/m 3 for 2007

- 20 09, 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 site s. 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 t rends

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 abs orbed 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 mad e 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, Sa nta 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
Fra ncisco, 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 a rea wit h 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 539

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. PM 2.5 

## t rends in Central California a ir b asins

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 easterl y flow draining from the valley s out into the Pacific Ocean

.

Thus, there is interest in how the PM concentrations in these Central Valley ai r basins compare with those of the Bay Area. Figures 8.1a and 8.1b show the annual means for the Sacramento and Sa n 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 standard41

Figure 8.2 shows the trend in Bay Area mean PM 2.5 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 98 th Percentile comparison

Figures 8.3a and 8.3b show PM 2.5 98 th 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 viola te 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 standard42

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

- 20 10

. The design valu e 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% p er 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 standard43

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 e missions i nventory

Trends estimated from ambient measurements and the emissions inventory have somewhat co mplementary 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 di rectly emitted. The analysis of ammonium nitrate and sulfate is also complex , sinc e although each i nvolves only one compound, each ha s two precursors and several chemical pathways for their formation.

Figure 9.1 compares trends in nitrate (a 10

- site a verage) 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 compound s especially ammonia. But the similar trends (including a cor r elation 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 nitrate45

Figures 9.3 and 9.4 show PM 2.5 and PM 10 , 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 PM2.5 (ug/m3) Emissions (t/d) 

## 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 SO 2 vs. sulfate are quite similar. Th e 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 PM 10 and 6% over a 10

- year period for PM 2.5 . In contrast, PM 10 concentrations have fallen over 4 0% in 20 years, and
PM 2.5 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 PM 2.5 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 d erives 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

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

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 mean47

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 plots 13 from the six Bay Area sites with PM 10 measurements stretching from 1989 through 2010. The plot compares the PM 10 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 PM 10 has been reduced across the board. But in every case, the lines of data bend awa y from y = x for the larger percen tiles (starting at about the 75 th percentile), indicating that there has been a greater percentage reduction in t he 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

t hat causal factors resulting in lower nitrate are widespread. Both nitrate and sulfate follow the pattern of PM 10 , 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 distribut ions. It is a scatterplot showing the quantiles of one distribution against the qu antiles of another. It pairs the first percentile of one distribution with the first percentile of the other, the 2 nd percentile with the second percentile, and so on up to the 99 th vs. the 99 th . 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 pr oportion (e.g., all values have been reduced by 10% acro ss 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.495051

One consequence of this analysis is to show that peak PM has declined faster than lower percentiles . Thus, the percentage reduction in the 98 th 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 anthr opogenic 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 Y j = X back + c

j *X anthro , in which the trend was modeled as a reduction in c j where j = 1989

- 19 99 and 2000

- 20 10. 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 simu lation 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. Y 1989

- 19 99 = 12*( X back + 3*X anthro ), Y 2000

- 20 10 = 12*( X back + X anthro ) for the 3 winter months, and Y 1989

- 19 99 = X back and Y 2000

- 20 10 = X back for the other 9 months. T he 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

- wi nter values for Y 1989

- 19 99 and Y 2000

- 2010 .

Although the simulation is not intended to be a comprehensive model for PM 10 , its similarity with the patterns in Figure 10.1 suggests this combination as an explanation of the behavior of the Q

- Q plot.52 

## 11. Trends in PM h ealth e ffects

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 estab lished 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 PM 2.5 concentrations above the Bay Area background , which we estimat ed to be the 5.5 μ g/m 3 mean measured at the Point Reyes site. We estimated the 1990 PM 2.5 values by scaling the 2008

- 20 10 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 μ g/m 3 (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 va rious health effects caused by elevated PM 2.5 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 qualit y 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 Area53 

## 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 lo ng

- term set of measurements made with a constant methodology at a large number of sites that represent an are a. 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 repl aced by another, as with COH by EC , or filter PM 2.5 by continuous
PM 2.5 , 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 PM 2.5 measurements at Livermore, Vallejo and Oakland. Valuable as these are, they could be discontinued for a couple o f 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 PM 2.5 in the District. A dramatic reduction
in PM i s 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 f ixed 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. Av ailable 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. A vailable at: http://www.e notes.com/public

- health

- encyclopedia/total

- suspended

- particles

tsp [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 A ssessment : Final Report. . National Center for Environmental Assessment

RTP 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 PM 2.5 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. Effecti vely, what that method does is to consider permutations of years. This same approach can be applied to the District design value

.

L et x t = (x 1t , x 2t , ..., x kt ) be a vector of statistics from k sites for year t (the annual means or 98 th percentiles). We compute w it = x it

/(1

- r) t , creating a vector w t = (w 1t , w 2t , ..., w kt ). For each site, we compute its design values for the 1 st 3 years and last 3 years and, from these, compute the District design value for the 1 st 3 years, W E , and the la st 3 years, W R , and then compute the ratio W E /W R . We then take permutations of the vectors w t , that is, permuting years, so that, for example, the vector for 2007 is chosen as the permutation representing 1999 ( w 1999 *

= w 2007 ), the vector for 2002 is cho sen as the permutation representing 2000 ( w 2000 * = w 2002 ), and so on. For each of these permutations, we compute W E * and W R *, and W R */W E *. W e find a 90% confidence interval (r l ,r u ) by with the 5 th and 95 th percentiles. So, for PM 2.5 , where there are 18,480 permutations , we find r l so that W R /W E equals the 924 th smallest of the W R */W E *, and r u so that W R /W E equals the 924 th largest of the W R */W E *.

Regression method for District design values

The trends in 24

- hour 98 th 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 desi gn values are based on runni ng 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 98 th percentiles, the 2000

- 20 02 de sign value also uses the 2000 and 2001 98 th 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 98 th percentiles may be slightly corr elated 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 t he 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

- y ear 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 D t , and let V t = ln(D t ). Assume a linear trend in the mean of V t :

E(V t ) =  0 +  1 (t

– t 0 )

where t 0 is chosen as the average value of the years. A ssume that the variance of V t is constant: Var(V t ) =  0 2

. Moreover, assume that the 1 st and 2 nd order autocov ariances are Cov(V t , V t+1 ) =  1 and Cov(V t , V t+ 2 ) =  2 , and that Cov(V t , V t+ k ) = 0 for k > 2. Let  be the variance covariance matrix of V 1 ,...,V n , so that  has entries  tt =

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

– t 0 . T he variance

- covariance
matrix of 

̂ is

(X  X) - 1 X   X(X  X) - 1

. (A1)

So, we need to estimate  , i.e.,  0 2

,  1 , and  2 . Here we use the regression residuals r t = v t – (  ̂  ̂ ( ) )

. 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(SSE 0 ) = E r  r = E V  [ I

- X(X  X) - 1 X  ] V = tr[ [ I

- X(X  X) - 1 X  ] 

=  0 2 tr[ [ I

- X(X  X) - 1 X  ] +  1 tr[ [ I

- X(X  X) - 1 X  ] K +  1 tr[ [ I

- X(X  X) - 1 X  ] L (A2)

where tr() is the trace, K = (k ij ), with k ij = 1 if |i

- j|=1 and 0 otherwise, L = (l ij ) with l ij = 1 if |i

- j|=2, 0 otherwise.

SSE 1 = r  M 

- 1 M

- n r = V  [ I

- X(X  X) - 1 X  ]M 

- 1 M

- n [ I

- X(X  X) - 1 X  ] V

where r is the vector of residuals, M

- 1 is an (n

- 1)xn matrix created by removing the 1 st row from the nxn identity matrix I, and M

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

E(SSE 1 )= E r  M 

- 1 M

- n r = E V  [ I

- X(X  X) - 1 X  ]M 

- 1 M

- n [ I

- X(X  X) - 1 X  ] V

= tr [ I

- X(X  X) - 1 X  ]M 

- 1 M

- n [ I

- X(X  X) - 1 X  ] 

=  0 2 tr[ [ I

- X(X  X) - 1 X  ]M 

- 1 M

- n [ I

- X(X  X) - 1 X  ] +  1 tr[ [ I

- X(X  X) - 1 X  ]M 

- 1 M

- n [ I

- X(X  X) - 1 X  ]K +  1 tr[ [ I

- X(X  X) - 1 X  ]M 

- 1 M

- n [ I

- X(X  X) - 1 X  ]L (A3)

SSE 2 = r  M 

- 2 M

- ( n

- 1) r = V  [ I

- X(X  X) - 1 X  ]M 

- 2 M

- ( n

- 1) [ I

- X(X  X) - 1 X  ] V57

where M

- 2 is an (n

- 2)xn matrix created by removing the 1 st 2 rows of I and M

- ( n

- 1) is the matrix created by removing the last 2 rows of I. Then

E(SSE 2 )= E r  M 

- 2 M

- ( n

- 1) r = E V  [ I

- X(X  X) - 1 X  ]M 

- 2 M

- ( n

- 1) [ I

- X(X  X) - 1 X  ] V

= tr [ I

- X(X  X) - 1 X  ]M 

- 2 M

- ( n

- 2) [ I

- X(X  X) - 1 X  ] 

=  0 2 tr[ [ I

- X(X  X) - 1 X  ]M 

- 2 M

- ( n

- 1) [ I

- X(X  X) - 1 X  ]

+  1 tr[ [ I

- X(X  X) - 1 X  ]M 

- 2 M

- ( n

- 1) [ I

- X(X  X) - 1 X  ]K

+  1 tr[ [ I

- X(X  X) - 1 X  ]M 

- 2 M

- ( n

- 1) [ I

- X(X  X) - 1 X  ]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 :

a 00  ̂ + a 01  ̂ + a 02  ̂ = SSE 0

a 10  ̂ + a 1 1  ̂ + a 1 2  ̂ = SSE 1

a 2 0  ̂ + a 2 1  ̂ + a 2 2  ̂ = SSE 2

For n = 10, the table of a ij 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 a 00 = 8, i.e., the well

- know result that E(SSE 0 ) = (n

- 2)  0 2

.

The observed values for the SSE’s were: SSE 0 = 0.021718, SSE 1 = 0.008228, and SSE 2 =
- 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 inter val is

- 0.0589 ± t 8,.95 (0.01205) =

- 0.0803 to

- 0.0365 or, roughly, reductions between 4% and 8%.58 

## Appendix B

. Methodology for m eteorological v ariables c onsidered in

w intertime r egression

Methodology for estimating meteorological potential

The key idea of the method is for each winter, w, to find a formula, f w , that predicts what PM 2.5 concentrations would be expected from a given set of meteorological variables (e.g. wind speed s, wind directions, rainfall)

. Then we cycle through the meteorology of each of the winters, computing what f w 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 meteoro logical and other predictors using all winters pooled. For the dependent variable we started with the daily maximum PM 2.5 concentration. We found that the natural log transform produced regression residuals that appear close to Gaussian 15 . (See Figure B1

. )

Step 2. Fit the model for each winter, w, yielding prediction functions, f w , and regression standard deviations s w .

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 ozo ne data. But Q

- Q plots of the sorted residuals r k 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 correlat ion with normal scores (see Figure B in Appendix B)

– indicating that the residual distribution of these PM 2.5 data are well

- approximated by a log

- normal distribution.59

Step 3. Apply f w to each winter v’s meteorology, and get predictions p 1w v , p 2w v , ..., p nw v for the ln (PM 2.5 ) where n = 120 or 121 depending on whether winter v contained a February 29 th

.

Step 4. Estimate the expected number of exceedances from each formula f w . 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:  [(p jw v – ln(35.5))/s w ], j = 1, ..., n v , where  x  is the Gaussian cumulative distribu tion function. The value 35.5 is chosen based on the 35 μ g/m 3 standard; any PM 2.5 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 B 1

. 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 < 90 o , =0 if pb

- result was ≥ 90 o

. 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 wee k day 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 sounding60

Table B 2. Exceedance potential by winter formula and meteorology

Formula for winter:

Winter 1999

-

20 00 20 00

-

20 01 20 01

-

20 02 20 02

-

20 03 20 03

-

20 04 20 04

-

20 05 20 05

-

20 06 20 06

-

20 07 20 07

-

20 08 20 08

-

20 09 20 09

-

20 10 20 10

-

20 11 Condu

- cive

ness : 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 2.361 

## Appendix C

. Numbers of days with PM 10 

## Measurements

Table C . Numbers of days with PM 10 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 PM 10 measurements.62 

## Appendix D. Use of d ichotomous PM 2.5 

## d ata for t rend a nalysis

Analysis of long

- term PM 2.5 trends is hampered by the lack of routinely collected PM 2.5 before 1999. However,

s ome PM 2.5 measurements were made at one site, San Jose, from 1990 through 1997. The sampling equipment was differ ent, however, and this complicate d the analysis.

Although there are no dichot PM 2.5 overlapping with routine filter measurements, dichot PM 10 measurem ents overlap with FRM PM 10 . 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 regr ession through the origin produced a slo pe of 0.98 for 1990

- 19 91 and 0.78 for 1992

- 19 97

. 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

- 19 91 and 0.78 for 1992

- 1997

.

Table D1. FRM and Dichot PM 10 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 0.7863

1997 25.21 19.32 0.77

Figure D2 shows the relationship between PM 2.5 and PM 10 for both the dichot and the FRM samplers. The patterns are very similar, with a lower PM 2.5 /PM 10 slope for small PM values and a larger slope for high PM values, with a few exceptions. Thus, the PM 2.5 /PM 10 relationship is roughly the same for the two measurement methods, so that the ratio of mean PM 2.5 to mean PM 10 appears very similar (dichot PM 2 .5 /PM 10  frm
PM 2.5 /PM 10 ). Thus, for 1990 and 1991 we assume that the dichot PM 2.5 represents what the FRM PM 2.5 would have measured, and for 1992

- 1997, it appears reasonable to use the approximation
mean frm PM 2.5  mean dichot PM 2.5 (frm PM 10 /dichot PM 10 )  mean dichot PM 2.5 /0.78.64 

## Appendix E. Adjustment of nitrate measurements for the i mpact of refrigeration

In 2009, the District started refrigerating PM 10 filters after sample collection. The goal was to prevent nitrate from volatilizing b efore the filters could be weighed . To assess the impact of the change, we compared San Jose
PM 10 nitrate measurements against PM 2.5 nitrate measurements, where the filters have always been refrigerated after sample collection, as a control. The change c aused by refrigeration is substantial and appears to have fixed the volatilization problem.

Figure E 1 shows PM 10 nitrate values relative to the corresponding PM 2.5 values, the black circles for measurements before 2009, the red squares for 2009

- 20 10. Be fore 2009, many PM 10 nitrate values were well below the corresponding PM 2.5 nitrate value; in 2009

- 20 10, they lie close to the line y = x, i.e., close to the corresponding PM 2.5 nitrate value.

Figure E 2 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 non

linear, with PM 10 nitrate matching PM 2.5 nitrate for small concentrations (≤ 1 μ g/m 3 ).

Figure E 3 shows the same plot but for 2009

- 20 10. For both seasons, PM 10 nitrate matches PM 2 .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 PM 10 nitrate and PM 2.5 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,
PM 10 nitrate measurements are somewhat higher than the corresponding PM 2.5 nitrate unless the concentrations65

are high. Of course, since PM 2.5 is a subset of PM 10 , 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 PM 10 .66

Adjusting pre

- 2009 nitrate mea surements

Although the refrigeration of PM 10 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 PM 2.5 nitrate. T his would also have the advantage of allowing us to get a better estimate of historical nitrate concentrations and what fraction these are of total PM 10 .

It should be noted that we are making several assumptions

– one being that the relationship between P M 10 nitrate and PM 2.5 nitrate at San Jose would carry over to other sites. Also, the finding that PM 10 nitrate is scarcely larger than PM 2.5 nitrate is somewhat questionable; we have been told that a larger percentage was in the coarse fraction. Also, if we adjust PM 10 nitrate, we presumably want to adjust PM 10 concentrations too, since it also is impacted by the loss of nitrate.

With these caveats, we plunge ahead. Figure E 4 shows the pre

- 2009 relationship of PM 2.5 nitrate to PM 10 nitrate (just flipping the axes from Figure E 2). 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 conc entration.

Standard statistical techniques like regression are suited to Gaussian variability or, at least, symmetric and short

tailed distributions.

A log transform comes part way to solving this problem. Figure E 5 sho ws 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 smallest67

observations.] As previously discussed the winter and non

- winter relationships are different, so separate

r egressions were fit.

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 = e 1. 21 x  + .17 7

– 0.3,

and

adjusted non

- winter PM10 NO3 = e .202*x  ^2 + .504*x  + .177

– 0.3.