i

Disclaimer

This report presents results from analyses of measurements and computer modeling 

performed by the District to study PM2.5. The results presented should be viewed as 

preliminary because uncertainties may exist in emissions estimate, modeling and ambient 

measurements. This is the first attempt in analyzing ambient data and modeling PM2.5. The 

District plans to refine and expand upon these preliminary results, and minimize 

uncertainties via its on-going research effort.
ii

Table of contents

Executive summary................................................................................................................ E-1

E.1 Data analysis..........................................................................................................E-1

E.2 Computer modeling ...............................................................................................E-2

E.3 Conclusions............................................................................................................E-6

1. Introduction ....................................................................................................................... 1

1.1 Background Information........................................................................................... 1

1.2 Regulatory history..................................................................................................... 2

1.3 BAAQMD research efforts ........................................................................................ 2

2. Measurements and available data .................................................................................... 4

3. Emissions inventory preparation....................................................................................... 6

4. Data analysis.................................................................................................................... 10

4.1 Analysis of PM measurements................................................................................ 10

4.1.1 Seasonal patterns........................................................................................ 10

4.1.2 Annual trends.............................................................................................. 10

4.1.3 Spatial patterns........................................................................................... 12

4.2 Chemical Mass Balance (CMB) analysis.................................................................. 15

4.2.1 Source contributions to peak PM2.5 levels................................................... 16

4.2.2 Source contributions to annual average PM2.5 levels................................. 17

4.3 Relationship between weather patterns and PM................................................... 20

4.3.1 Comparing and contrasting direct measurements..................................... 20

4.3.2 Cluster analysis for Bay Area weather patterns ......................................... 21

4.3.3 Cluster analysis for inter-regional transport patterns................................ 23

5. Computer simulations...................................................................................................... 25

5.1 Description of models............................................................................................. 25

5.1.1 Meteorological modeling............................................................................ 25

5.1.2 PM modeling............................................................................................... 26

5.1.3 Wood smoke modeling ............................................................................... 27

5.2 Base case PM modeling results............................................................................... 28

5.2.1 Individual PM2.5 components...................................................................... 28

5.2.2 Ammonium nitrate limitation ..................................................................... 30

5.2.3 Spatial distribution of PM2.5........................................................................ 30

5.3 Development of a typical Bay Area PM episode .................................................... 32

5.4 PM sensitivities to Bay Area emissions reductions ................................................ 34

5.4.1 Across-the-board emissions reductions...................................................... 35

5.4.2 Wood smoke impacts.................................................................................. 38

5.5 Transport impacts on Bay Area............................................................................... 42

5.5.1 Bay Area emissions zeroed out ................................................................... 42

5.5.2 Upwind emissions zeroed out ..................................................................... 43

6. Conclusions and further study......................................................................................... 48

6.1 Data analysis................................................................................................................. 48

6.1.1 Analysis of PM measurements.................................................................... 48

6.1.2 Chemical Mass Balance (CMB) for source apportionment ......................... 49
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6.1.3 Relationships between weather patterns and PM...................................... 50

6.2 Computer modeling ................................................................................................ 51

6.2.1 Base-case PM modeling.............................................................................. 51

6.2.2 PM sensitivities to Bay Area emissions reductions..................................... 53

6.2.3 Transport impacts on Bay Area................................................................... 54

6.2.4 Wood smoke modeling ............................................................................... 55

6.3 Overall conclusions................................................................................................. 56

References ............................................................................................................................... 58

Appendix A. PM modeling emissions inventory preparation ...............................................A-1

A.1 BAAQMD emissions .............................................................................................. A-1

A.2 Emissions outside the BAAQMD........................................................................... A-9

Appendix B. Meteorological model validation .....................................................................B-1

B.1 MM5 validation for PM simulation periods.......................................................... B-1

B.2 MM5 validation for wood smoke simulation periods ........................................ B-11

Appendix C. Air quality model validation .............................................................................C-1

C.1 Validation of total PM levels................................................................................. C-2

C.2 Validation of PM component levels...................................................................... C-3
iv

List of figures

Figure 1. Map of Bay Area domain showing locations of key PM monitors, weather stations, and sounding 

launch point.. ................................................................................................................................................5

Figure 2. Emissions modeling domains.. ................................................................................................................7

Figure 3. Emissions inventory for winter wood burning within Bay Area. .............................................................8

Figure 4. Emissions inventory for ammonia within modeling domain. ..................................................................9

Figure 5. Percentage of 24-hour PM2.5 NAAQS exceedances by calendar month................................................11

Figure 6. Number of 24-hour PM2.5 exceedance days and number of rainy days for each winter season.. ........11

Figure 7. Trends for annual average PM10 and PM2.5 levels.................................................................................12

Figure 8. Annual average PM2.5 levels by site for 3-yr period 2006-08. ...............................................................14

Figure 9. Average 98th percentile 24-hour PM2.5 levels (design values) by site for 3-yr period 2006-08 .............14

Figure 10. Levels for PM2.5 major components at 6 Bay Area monitoring locations............................................15

Figure 11. CMB results for CRPAQS period. Results for exceedance days only....................................................18

Figure 12. CMB results for CRPAQS period. Annual results..................................................................................19

Figure 13. CMB results for CRPAQS period. Seasonal results for Bethel Island ...................................................19

Figure 14. Mean observed Bay Area PM2.5 level in response to number of consecutive days with favorable 

conditions.. .................................................................................................................................................21

Figure 15. Pair of episodic transport clusters.......................................................................................................24

Figure 16. Surface plots of simulated 24-hour levels for the six PM2.5 components............................................28

Figure 17. Aerial plots of simulated 24-hour levels for gas phase ammonia and nitric acid................................31

Figure 18. Eight Bay Area subdomains sharing similar air quality characteristics................................................32

Figure 19. Simulated secondary versus primary PM2.5 levels for four selected subdomains...............................33

Figure 20. Simulation results for four consecutive days showing the development of a relatively severe PM 

episode in the Bay Area ..............................................................................................................................34

Figure 21. Surface plots of simulated sensitivities of PM2.5 for five 20% emissions reductions...........................37

Figure 22. Tabulated PM2.5 values for grid cell with highest simulated PM2.5 level for eight subdomains for 

20% direct PM emissions reductions and 20% ammonia reductions.........................................................38

Figure 23. Wood smoke simulation results for Spare the Air days 12/10/2008 and 1/18/2009 .........................40

Figure 24. Simulated effect of wood burning restrictions during 2008-09 winter at Concord and San Jose.......41

Figure 25. Same as Figure 16, except simulated with Bay Area anthropogenic emissions zeroed out................43

Figure 26. Benefit (PM2.5) upon eliminating anthropogenic emissions for the Sacramento area .....................45

Figure 27. Benefit (PM2.5) upon eliminating anthropogenic emissions for the San Joaquin Valley ...................46

Figure 28. PM2.5 values averaged across 41 days with transport from the Sacramento Valley and 5 days with 

transport from the San Joaquin Valley . Transport simulations were conducted with Sacramento area 

anthropogenic emissions zeroed out and with San Joaquin Valley anthropogenic emissions zeroed out 47

List of tables

Table 1. Surface monitoring locations for PM and meteorology ...........................................................................4

Table 2. Summary of emissions input to SMOKE (tons/day). Biogenic emissions are excluded. ...........................7

Table 3. Spare the Air days for 2008-09 winter....................................................................................................40
E-1

Executive summary

This report presents preliminary findings of BAAQMD’s on-going data analysis and modeling 

efforts to better understand fine particulate matter (PM2.5) formation in the Bay Area. PM2.5

is a complex mixture of suspended particles and liquid droplets having aerodynamic 

diameters of 2.5 m or less. For the Bay Area, public health impacts from PM2.5 may well 

exceed the combined impacts of all other currently regulated air pollutants. The body of 

knowledge contained in this report is an enhancement by BAAQMD investigations to 

supplement research performed under state and federal agencies. Research efforts include 

analyses of measurements and computer modeling. This document is the first in a series of 

periodic technical reports describing BAAQMD PM2.5 research efforts. 

E.1 Data analysis

Bay Area 24-hour PM2.5 exceedances, almost entirely confined to winter months, occur 

under increased stable atmospheric conditions that trappe pollutants near the ground. 

Winters with frequent stagnant periods tend to have more 24-hour PM2.5 exceedances than

winters with more periods of windy and stormy conditions. Consecutive stagnant, clear 

winter days are typically requisite for PM2.5 episodes to develop. PM2.5 episodes are regional 

in nature and impact most Bay Area locations. Livermore, Concord, San Jose, and Vallejo 

have 24-hour PM2.5 design values at or near the NAAQS exceedance threshold of 35 g/m3. 

Other populated locations exhibit design values around 25-30 g/m3. Near-background 

PM2.5 levels were observed at Point Reyes, where annual-average PM2.5 levels were about 

half that of other Bay Area monitoring locations. The Bay Area is expected to attain the 

NAAQS for annual-average PM2.5 level.

The Chemical Mass Balance (CMB) analysis was applied for PM2.5 source apportionment, 

using specialized measurements mostly obtained during the years 1999-2001. Primary 

combustion sources (both fossil fuels and biomass) were prevalent PM2.5 contributors for all 

seasons. The biomass combustion contribution to peak PM2.5 levels was about 3-4 times 

higher during winter than for the other seasons. Isotopic carbon (14C) analysis confirmed 

this increased proportion of winter biomass combustion emissions. The increased winter 

biomass combustion sources reflected increased levels of wood burning during the winter 

season. Wood burning may have been the single largest contributing source to PM2.5 under 

episodic conditions. Secondary PM2.5 levels were only elevated during the winter months. 

Wintertime secondary PM2.5 was mostly ammonium nitrate. This semi-volatile PM2.5

component is stable in its solid form only during the cooler winter months. Secondary 

ammonium sulfate PM2.5 levels were generally low (< 1-2 g/m3) but non-negligible. Sea 

salt, geological dust, and tire and brake wear contributed minimally to PM2.5.

Meteorological cluster analysis, a data mining technique, was implemented by UC Davis to 

determine how weather patterns impact PM2.5 levels. Clustering was applied to 

measurements from every winter day across more than 10 years. This method robustly 
E-2

established how the prevailing weather drives the development of PM2.5 episodes. Episodes 

nearly always developed under: stable atmospheric conditions inhibiting vertical dispersion; 

clear and sunny skies favoring enhanced secondary PM2.5 formation; and pronounced 

overnight drainage (downslope) flows off the Central Valley rims, causing low-level air in the 

Central Valley to empty through the Delta and into the Bay Area along its eastern boundary. 

Atmospheric transitions of aloft weather systems profoundly influenced the surface winds 

that determine PM2.5 levels. Surface conditions stagnated whenever an upper-level high 

pressure system moved over Central California. Persisting high pressure conditions allowed 

PM2.5 buildup, and Bay Area 24-hour PM2.5 exceedances generally occurred after 2-4 days. 

A refined cluster analysis further characterized the upwind Central Valley conditions during 

Bay Area episodes. Two distinct inter-regional air flow patterns were associated with 

different types of Bay Area episodes. The majority of exceedance days (around 60 percent) 

were associated with winds from the Sacramento Valley to the northeast entering the Bay 

Area through the Delta. Peak PM2.5 levels typically occurred at San Jose for this type of 

episode. A minority of exceedance days (around 20 percent) were associated with winds 

from the San Joaquin Valley from the southeast entering the Bay Area through the Delta. 

Peak PM2.5 levels typically occurred in the East Bay (at Livermore, Concord, or Vallejo) for 

this type of episode. The remaining relatively moderate episodes (around 20 percent) could 

not be associated with any distinct inter-regional transport pattern linking the Bay Area and 

surrounding air basins.

E.2 Computer modeling

Computer modeling efforts were initially based on data from the California Regional 

Particulate Air Quality Study (CRPAQS) and provided by the California Air Resources Board 

(ARB). BAAQMD staff and its contractors have since contributed to a custom computer 

model developed to address Bay Area PM2.5 exceedances. PM2.5 simulations were 

performed for the core PM season months of December-January for both the 2000-01 and 

2006-07 periods. Wood smoke simulations covered most of the 2008-09 winter.

PM2.5 base case simulations were performed using the most historically accurate winter 

emissions inventory available. This inventory comprised various data compiled by the ARB, 

BAAQMD, and Sonoma Technology, Inc. The base case simulation was validated against 

measurements to adequately represent PM levels in the Bay Area and Delta regions. Figure 

E-1 shows the spatial distribution of simulated primary and secondary PM2.5 components 

averaged across the 52 simulated days for which measured Bay Area 24-hour PM2.5 level 

(FRM or BAM) exceeded 35 g/m3. For these episodic days, light winds flowed through the 

Bay Area from the east, and Central Valley conditions were near calm. Primary PM2.5 levels 

were elevated primarily near centers of commerce around the bay; over the Central Valley 

major cities and to a lesser extent its rural areas; near industrial facilities along Carquinez 

Straight; and somewhat at Travis, Santa Rosa, and Santa Cruz. Secondary PM2.5, present 

mostly as ammonium nitrate, was not localized near the sources of its precursor emissions 
E-3

NOx and ammonia. Rather, secondary PM2.5 was regionally elevated. A sharp gradient 

existed with very high secondary PM2.5 levels in the Central Valley decreasing westward 

through the Bay Area to reach background levels over the Pacific Ocean. The model

suggested that regional ammonium nitrate buildup is limited by nitric acid, a product of NOx

emissions photochemical aging, and not by ammonia. In the Central Valley, PM2.5 levels 

were dominated by secondary components which could alone build to the exceedance 

level. Around San Francisco and San Jose, PM2.5 levels were dominated by primary 

components which could alone build to the exceedance level. For other areas affected by 

PM episodes, such as the eastern and northern Bay Area and the Delta, primary and

secondary PM2.5 levels were comparable. Both primary and secondary buildup was required 

for exceedances to occur in these locations.

Figure E-1. Spatial distribution of simulated primary and secondary PM2.5 levels, averaged across the 52 

simulated days for which measured Bay Area 24-hour PM2.5 level exceeded 35 g/m3. Bay Area counties and 

the California coastline are drawn using thick black lines. City limits for Sacramento and Stockton are drawn 

using thin black lines.

PM2.5 sensitivity simulations were performed by reducing Bay Area emissions relative to the 

base case. Across-the-board Bay Area emissions reductions of 20 percent were simulated 

for the following five classes of chemical species: NOx and VOC combined; gaseous sulfur 

species; ammonia; directly emitted PM; and these four classes combined, comprising all 

anthropogenic emissions. These across-the-board reductions were simulated for one 

episode each from 2000-01 and 2006-07. Reducing the directly emitted PM reduced peak 

PM2.5 levels nearly ten times more efficiently than reducing the secondary PM precursors. 

Reducing primary PM emissions by 20 percent (around 18 tons/day eliminated) typically 

reduced primary PM2.5 levels by 12-20 percent, depending on location, with an average 

around 16 percent. Reductions of directly emitted PM were most effective near the PM 

emissions sources where primary PM2.5 levels were highest (see Figure E-1). The largest 

benefits of around 4-6 g/m3occurred near San Jose. Reducing ammonia emissions by 20

percent (around 15 tons/day) was the most effective of the precursor emissions reductions. 

Secondary PM2.5 levels were typically reduced 0-4 percent, depending on location, with an 
E-4

average around 2 percent. Ammonia emissions reductions were less effective near 

ammonia sources, where the secondary PM forming chemistry was limited by lack of nitric 

acid. Reducing NOx and VOC emissions by 20 percent (around 250 tons/day total) was 

relatively ineffective. Reducing sulfur-containing PM precursor emissions by 20 percent

(around 16 tons/day) typically had a small impact on Bay Area PM2.5 levels under episodic 

conditions. On certain days with the offshore shipping lanes upwind of the Bay Area, 

however, the 20 percent sulfur emissions reductions produced over 1 g/m3benefit.

PM2.5 transport simulations were performed by zeroing out anthropogenic emissions for 

various air basins. Transport impacts were evaluated for 55 days from 2000-01 and 2006-07 

having simulated base case 24-hour PM2.5 levels 35 g/m3or higher. Anthropogenic Bay 

Area emissions were eliminated to estimate the cumulative transport impacts from all 

sources outside of the Bay Area. On average, transported primary PM2.5 levels were around 

2-8 g/m3. Transport impacts were highest through the Carquinez Straight and Altamont 

Pass, which connect the Bay Area with the Central Valley. Transport impacts of secondary 

ammonium nitrate were as high as 13 g/m3along the Bay Area eastern boundary, 8 g/m3

through the Carquinez Strait and Altamont Pass, and 5 g/m3around the bay. Transported 

ammonium sulfate levels of around 1 g/m3 were present uniformly throughout the Bay 

Area. Ammonium sulfate appeared to be transported mostly from regions outside of the 

Central Valley. Central Valley PM2.5 levels were relatively unaffected by eliminating the Bay 

Area emissions.

A second pair of PM2.5 transport simulations was conducted by zeroing out anthropogenic 

emissions for the Sacramento area and San Joaquin Valley separately. Reductions in 

simulated PM2.5 levels relative to the base case reflected transport impacts from the 

respective air basin for which emissions were eliminated. Eliminating the Sacramento area 

emissions reduced primary PM2.5 levels by around 5 g/m3through Carquinez Strait, by less

than 2 g/m3in the northern half of the Bay Area, and had little effect elsewhere in the Bay 

Area. Secondary PM2.5 levels transported from the Sacramento area were 2.5 g/m3

through Carquinez Strait and Altamont Pass and around 1.5 g/m3elsewhere in the Bay 

Area. Eliminating the San Joaquin Valley emissions reduced primary PM2.5 levels by around 4 

g/m3through Carquinez Strait and Altamont Pass, by around 3 g/m3through Pacheco 

Pass and into southern Santa Clara Valley, and had little impact farther into the Bay Area. 

Secondary PM2.5 levels transported from the San Joaquin Valley were up to 8 g/m3through 

Pacheco Pass and into southern Santa Clara Valley, around 6 g/m3through Altamont Pass 

and into eastern Contra Costa County, and around 5 g/m3elsewhere in the Bay Area. 

The above transport simulation results were based on average episodic conditions. During 

the more severe episodes, transport impacts were often greater than for the average 

episodic conditions. Transport impacts were tabulated for the six days each 2000-01 and 

2006-07 winter period with the highest measured PM2.5 levels, for 12 days total. Total 

transported PM2.5 levels averaged 19, 19, 11, and 12 g/m3at the Livermore, Vallejo, San 

Jose, and San Francisco monitoring locations, respectively. Maximum total transported 

PM2.5 levels were 33, 37, 24, and 24 g/m3for these stations, respectively. Thus, 24-hour
E-5

PM2.5 exceedances could have occurred in the Bay Area without any Bay Area 

anthropogenic emissions. Transported secondary PM2.5 levels averaged around 40-80

percent more than transported primary PM2.5 levels, depending upon location. Total 

transported PM2.5 levels from the Sacramento area averaged 4, 5, 2, and 3 g/m3for these 

stations, respectively. At Vallejo, transported PM2.5 from the Sacramento area had a 

somewhat higher proportion of primary components. At the other three locations, 

transported PM2.5 from the Sacramento area had a somewhat higher proportion of 

secondary components. Total transported PM2.5 levels from the San Joaquin Valley 

averaged 13, 10, 7, and 7 g/m3for these stations, respectively. Transported secondary 

PM2.5 from San Joaquin Valley were 2.5-4 times higher than transported primary PM2.5

levels.

Simulated transport impacts were also compared across two different transport scenarios 

identified by the measurements-based meteorological cluster analysis (described above). 

There were 41 episodic days classified as having transport from the Sacramento area and 5 

episodic days classified as having transport from the San Joaquin Valley. Zeroing out the 

Sacramento area emissions provided greater benefit in the Bay Area when transport 

occurred from the Sacramento area. Transported PM2.5 from the Sacramento area was 

about half primary and half secondary. It mostly impacted the central and northern portions 

of the Bay Area. Zeroing out the San Joaquin Valley emissions provided greater benefit in 

the Bay Area when transport occurred from the San Joaquin Valley. Transported PM2.5 from 

the San Joaquin Valley was about one-third primary and two-thirds secondary. It mostly 

impacted the central and southern portions of the Bay Area.

Wood smoke PM2.5 levels were simulated for the 2008-09 winter. The simulation period 

contained 8 of the 11 Spare the Air days during the 2008-09 winter. Two different model 

runs were performed. Bay Area wood smoke levels were simulated without and with wood 

burning restrictions during the Spare the Air periods. Without burning restrictions on the 

Spare the Air days, peak wood smoke levels of up to 10-20 g/m3 were simulated over the 

areas having high wood burning emissions. Calculated wood smoke levels were around 5 

g/m3or more for many of the remaining populated locations within the Bay Area. For the 

run with burning restrictions, wood burning emissions were eliminated from the simulation 

only for noon of each Spare the Air day through noon of the following day. Peak benefits of 

the wood burning restrictions were around 10 g/m3of reduced wood smoke. The 24-hour

wood smoke levels (averaged midnight to midnight) were not reduced to zero because the 

burning restrictions did not begin until noon of the Spare the Air days. Also, carried over 

wood smoke from previous days may have impacted the Bay Area during the Spare the Air 

days. Simulated peak wood smoke levels and maximum benefits of burning restrictions 

sometimes occurred away from the monitoring locations. Modeling results suggested that 

reductions of population exposure to wood smoke were considerably greater than indicated 

by the monitoring data alone. Without the burning restrictions, wood smoke levels for the 

eight simulated Spare the Air days were averaged to be 11, 7, 5, 3, and 3 g/m3for the 

Concord, San Jose, San Francisco, Vallejo, and Livermore monitoring locations, respectively. 

Assuming 100 percent compliance, the burning restrictions were estimated to reduce these 
E-6

wood smoke levels by about 50-75 percent, depending on location. Because the burning 

restrictions reduced carry over, enhanced benefits may be achieved for multiple, 

consecutive Spare the Air calls.

E.3 Conclusions

This report summarizes a wealth of knowledge generated from BAAQMD in-house PM2.5

research efforts that builds on the U.S. EPA (EPA) and ARB efforts. Various analyses of 

measurements were conducted to identify major sources and important weather patterns 

contributing to PM2.5 buildup. Extensive simulations covered the bulk of three winter 

seasons. The custom computer model adequately reproduced the various phenomena 

represented in the measurements. The high degree of corroboration between the 

measurements- and modeling-based results provides a high level of confidence that the 

findings presented herein are both accurate and representative.

Primary and secondary PM2.5 impact the Bay Area differently by location and across a range 

of typical meteorological conditions. The model suggests that reducing direct PM2.5

emissions within the Bay Area is the most effective means of reducing Bay Area primary 

PM2.5 levels. These reductions, however, are most effective near direct PM2.5 emission 

sources. These are the areas in which Bay Area total PM2.5 levels are highest. The model also 

suggests that significant amounts of PM2.5, especially secondary PM2.5, are transported from 

the Central Valley. Secondary PM2.5 exhibits a fairly regionally uniform influence throughout 

the Bay Area. Analysis of measurements identified separate transport patterns occurring on 

different days from either the Sacramento Valley or San Joaquin Valley. During 1999-2007, 

around 60 percent of Bay Area 24-hour PM2.5 exceedance days occurred with transport 

from Sacramento area, and 20 percent with transport from San Joaquin Valley. 

Model results indicated that Bay Area wood smoke levels during 2008-09 episodic 

conditions would have averaged 3-11 g/m3 without wood burning restrictions, depending 

on location. Spare the Air burning restrictions were estimated to have reduced wood smoke 

levels by around 50-75 percent, assuming 100 percent compliance. Spare the Air calls on 

consecutive days had the added benefit of reducing carried over wood smoke, in addition to 

reducing fresh burning emissions.

Further research results and refinements to existing findings will be reported as new 

information becomes available. More measurements, including those from recently 

commissioned monitoring stations, will be added to the analyzed databases. Longer records 

of measurements will be especially useful for evaluating the effectiveness of wood burning 

restrictions. Modeling results will be enhanced by the development of more accurate 

emissions inventories. Additional winter seasons will be selected for both PM2.5 and wood 

smoke simulations. Newer version models having enhanced physics and chemistry will be 

implemented for all simulations.
1

Fine Particulate Matter Data Analysis and Modeling

in the Bay Area

1. Introduction

This report provides technical details of data analysis and modeling efforts to better 

understand fine particulate matter (PM2.5) formation in the Bay Area. This is the first 

significant published report describing Bay Area Air Quality Management District (BAAQMD) 

scientific investigations into PM2.5. The report includes a history of BAAQMD’s activities and 

describes key results from a number of important studies.

1.1 Background Information

PM2.5 is a complex mixture of suspended particles and liquid droplets in the atmosphere

having aerodynamic diameters of 2.5 m or less. An individual particle typically begins as a 

core or nucleus of carbonaceous material, often containing trace metals. These primary

(directly emitted) particles usually originate from incomplete combustion of fossil fuels or 

biomass. Layers of organic and inorganic compounds are then deposited onto a particle, 

causing it to grow in size. These layers are largely comprised of secondary material that is 

not emitted directly. Secondary PM instead forms from chemical reactions of precursor 

gasses released from combustion, agriculture, household activities, industry, vegetation, 

and other sources. As a particle grows larger, gravity eventually causes it to be deposited 

onto a surface. Naturally emitted dust particles mostly have diameters too large to be 

classified as PM2.5.

Numerous studies have demonstrated PM2.5 to be deleterious to human health. Major 

human health outcomes resulting from PM2.5 exposure include: aggravation of asthma, 

bronchitis, and other respiratory problems, leading to increased hospital admissions; 

cardiovascular symptoms, including chronic hardening of arteries and acute triggering of 

heart attacks; and decreased life expectancy, potentially on the order of years. Smaller 

particles have increasingly more severe impacts on human health as compared to larger 

particles. This occurs in part because smaller particles are able to penetrate more deeply 

into the human body. For the Bay Area, public health impacts from PM2.5 may well exceed 

the combined impacts of all other currently regulated air pollutants. 

Bay Area PM2.5 levels are elevated during the winter months. Elevated winter PM2.5 levels 

result from a combination of conducive weather patterns and increased wood burning 

emissions.
2

1.2 Regulatory history

Regulation of airborne particles started with total suspended particulates (TSP) in the 

original 1970 federal Clean Air Act. TSP is effectively a measure of particles with 

aerodynamic diameters of 100 m or less. In 1987, TSP was replaced by PM10, or particles 

with aerodynamic diameters of 10 m or less. In 1997, the federal PM2.5 standard was 

created in addition to the PM10 standard, which was retained. Since then, California has 

established standards for PM2.5 and PM10 that are more stringent than required under 

federal regulations. Both the California and federal standards have tightened as more is 

learned about the consequences of PM exposure. This trend is expected to continue.

Under federally mandated programs, the BAAQMD began measuring ambient PM2.5 levels 

in 1999. PM2.5 is a subset of PM10, measured in the Bay Area since 1985. Prior to that, 

measurements for coefficient of haze reflected ambient levels of carbonaceous particles.

In 2008, the United States Environmental Protection Agency (USEPA) proposed the first 

attainment designations for PM2.5 under the National Ambient Air Quality Standards 

(NAAQS) of the Clean Air Act. Attainment status has two components: daily (24-hour) and 

annual average ambient PM2.5 levels. The Bay Area currently attains the annual standard, 

but does not attain the 24-hour standard. As a result, the District will be required to 

develop a State Implementation Plan (SIP), tentatively scheduled for 2013. This SIP 

development process will largely be guided by scientific information summarized in this 

document and refined as research progresses.

1.3 BAAQMD research efforts

A tremendous effort to advance the scientific understanding of PM2.5 has been carried out 

in recent years at the federal, state, and local levels. Much of the work that will ultimately 

drive the Bay Area SIP process has been performed in-house by the BAAQMD Research and 

Modeling Section. This undertaking has been necessary because PM2.5 is a complex mixture 

of individual pollutants that can vary considerably from one region to the next. As such, 

research performed at the state and federal levels cannot be expected to sufficiently 

address the relevant intricacies of the Bay Area PM2.5 problem.

Research into the Bay Area PM2.5 problem began with a review and evaluation of 

monitoring stations and an analysis of ambient measurements. Simple data analyses were 

first applied to understand when, where, and to what extent elevated PM2.5 levels occurred. 

Increasingly sophisticated statistical analyses were subsequently applied to understand the 

sources of PM2.5 and how this pollutant is affected by the prevailing weather. These 

analyses of measurements have been instructive; however, measurement campaigns alone 

are insufficient to fully characterize the Bay Area PM2.5 problem. As such, computer 

simulations were performed to characterize PM2.5 at times and locations for which 

measurements were not feasible. These modeling efforts were initially based on data 
3

resulting from the California Regional Particulate Air Quality Study (CRPAQS), provided by 

the California Air Resources Board (ARB). BAAQMD staff and contractors have since 

contributed to improve and expand upon these initial simulations. As a result, a custom 

computer model was utilized to explain many of the intricacies of the Bay Area PM2.5

problem.

District efforts for data analysis and computer modeling of PM2.5 are ongoing and will 

continue through the SIP development process and beyond. These efforts include dedicated 

field measurement campaigns, advanced statistical analyses of expanding volumes of 

historical records, development of increasingly accurate and contemporary emissions 

inventories, and development and implementation of meteorological and photochemical 

computer models. These studies are expected to continue to expand and refine the 

scientific understanding of PM2.5 in the Bay Area and its surrounding regions. This scientific 

knowledge is critical for the development of effective regulatory strategies. This document 

will be the first in a series of technical reports that will be periodically published. These 

reports are intended to convey information about PM2.5 that is necessary for bringing the 

Bay Area into attainment for this pollutant.
4

2. Measurements and available data

Measurements of both PM2.5 levels and meteorological conditions are necessary for better 

understanding PM2.5. Networks of instruments are continuously operated to provide data 

for key Bay Area locations. Locations of PM monitors (FRM and BAM) and weather stations 

are shown in Figure 1. A large number of additional instruments are not included for brevity

such as NOx, ozone, precipitation, etc. The map denotes instruments positioned within the 

Bay Area, as well as selected proximal sampling locations in the Central Valley to the east. 

Measurements performed at each of these locations are described in Table 1. The air 

quality monitoring network plan published by BAAQMD (2009) provides additional details 

for the PM monitors. Soundings launched twice daily from Oakland, and Wind 

Table 1. Surface monitoring locations for PM and meteorology. Check marks indicate type of measurements

performed at each location. PM may be monitored using FRM or BAM instrument.

Monitoring Location Agency FRM BAM Meteorology

San Francisco Bay Area 

Bethel Island BAAQMD x

Concord BAAQMD x x

Fort Funston BAAQMD x

Fremont BAAQMD x

Kregor Peak BAAQMD x

Livermore BAAQMD x x

Mt Tamalpais BAAQMD x

NUMMI BAAQMD x

Oakland BAAQMD x

Pleasanton STP BAAQMD x

Pt. San Pablo BAAQMD x

Redwood City BAAQMD x x

Rio Vista BAAQMD x

San Carlos BAAQMD x

San Francisco BAAQMD x x

San Jose BAAQMD x x

San Martin APT BAAQMD x

Santa Rosa BAAQMD x x

Suisun STP BAAQMD x

Vallejo BAAQMD x x

Outside the San Francisco Bay Area

Davis ARB x

Modesto ARB x x x

Roseville ARB x

Sacramento SMAQMD/ARB x x

Stockton ARB x x x

Woodland YSAQMD x x
5

Profiler and RASS systems, operated continuously at Livermore and Burkeville, provide aloft 

weather data.

Data obtained from these monitoring stations undergo rigorous evaluation for quality 

assurance and quality control, and are regularly archived by staff of the BAAQMD before 

they are used for scientific studies.

Figure 1. Map of Bay Area domain showing locations of key PM monitors, weather stations, and sounding

launch point. A large number of sampling locations have been excluded for brevity. Named locations refer to 

PM monitors.
6

3. Emissions inventory preparation

An emissions inventory was prepared to establish source-receptor relationships, identify 

major sources contributing to primary PM and releasing precursors of secondary PM (TOG, 

NOx, SO2, NH3), and provide inputs for modeling. The inventory prepared for this study 

covers an area extending from Redding in the north to Bakersfield in the south and from the 

Pacific Ocean in the west to the Sierra Nevada in the east. This domain is essentially the 

same as the California Regional Particulate Air Quality Study (CRPAQS) domain (Figure 2). 

Initially, the 2000 CRPAQS inventory was utilized for modeling. Subsequently, the Bay Area 

portion of the CRPAQS inventory was replaced with local information, as these local data 

were assumed to be more accurate and up to date. In addition, new inventories were 

developed for Bay Area emissions of ammonia and wood smoke, as it was determined that 

the CRPAQS emissions were not representative of local sources.

Both District and ARB emissions data were manipulated and reformatted by Sonoma 

Technology, Inc. for use in the EPA’s Sparse Matrix Operator Kernel Emissions (SMOKE) 

model. District staff then prepared the CMAQ inputs for both winter 2000-01 and 2006-07 

PM simulations. Several different versions of these inventories were prepared for PM 

sensitivity simulations, transport analyses, future year modeling, and wood smoke analyses.

Below are summarized some notable features and special handling requirements of the 

BAAQMD and ARB emissions data, respectively. BAAQMD emissions associated with some 

non-road diesel sources were halved to reflect staff’s belief that these emissions were 

overestimated by ARB’s OFFROAD model. Also, Bay Area wood burning emissions were 

estimated using information from several winter season surveys conducted by the District. 

For areas outside the Bay Area, ARB provided emissions data that were pre-gridded 

according to the California statewide 4-km grid (Figure 2). SMOKE processing retained the 

pre-existing spatial distribution for these emissions. Temporal distribution information 

embedded in the ARB data were also retained.

Modeling was performed for both the 2000-01 and 2006-07 winter seasons. Therefore, the 

2000 ARB data had to be projected forward to 2006, while the 2005 District data were 

backcasted to 2000. Projection factors for criteria pollutants were developed using 

information in ARB’s Emissions Almanac. Bay Area ammonia emissions were projected using 

activity indicators from ARB’s EMFAC model (for on-road) and EPA’s Economic Growth and 

Analysis System (EGAS 5.0) for the remaining source categories. Ammonia emissions outside 

the Bay Area as well as biogenic emissions were not projected because of insufficient data.

Table 2 summarizes the emissions input to SMOKE as obtained through the processes 

described above. Figure 3 shows the resulting spatial distribution of the model-ready wood 

burning emissions for the Bay Area. Figure 4 shows the spatial distribution of the model-
7

ready ammonia emissions for the entire modeling domain. For further reference, Appendix 

A provides detailed summaries as well as SMOKE-related information. 

Figure 2. Emissions modeling domains. Gray shows ARB 4-km statewide domain. Blue is the CRPAQS domain 

for which emissions were prepared for District PM modeling.

Table 2. Summary of emissions input to SMOKE (tons/day). Biogenic emissions are excluded.

2000 2006

BAAQMDaRemainder of DomainbBAAQMDaRemainder of Domainb

TOG 751 3877 620 3670

NOx 609 1785 508 1595

SO2 89 130 66 129

PM2.5 88 653 87 640

NH3 72 299 82 299

a Annual average daily

b December weekday
8

Figure 3. Emissions inventory for winter wood burning within Bay Area.
9

Figure 4. Emissions inventory for ammonia within modeling domain.
10

4. Data analysis

A variety of data analyses have been performed to better understand Bay Area PM2.5

formation. They can be summarized in three main categories: characteristics of measured 

PM2.5, Chemical Mass Balance (CMB) analysis, and statistical methods including cluster 

analysis to infer how PM2.5 is affected by weather patterns. Details of these analyses and 

findings are reported in various District papers or reports. Key findings are briefly 

summarized in this report.

4.1 Analysis of PM measurements

4.1.1 Seasonal patterns

Analyses were performed to characterize when, where, and to what extent elevated PM2.5

levels occurred. Figure 5 shows the percentage of 24-hour NAAQS exceedance days for 

PM2.5 by calendar month. An exceedance day is defined as any day for which PM2.5 levels at 

one or more sites exceeds 35 g/m3. Most exceedances occur during the winter months 

from November through February. The bulk of the exceedances, however, occur during the 

core winter PM season of December and January. 

Several reasons explain the pronounced seasonality for PM2.5. First, the winter months favor 

the development of stable air masses over the Bay Area. Such conditions exhibit a layer of 

relatively warm air aloft and cool air at surface, creating negative buoyancy. Thus, vertical 

mixing of pollutants are limited and pollutants are trapped near the surface. Second, the 

cooler winter temperatures favor partitioning of ammonium nitrate, a major PM2.5

component, into the solid particulate phase. Higher temperatures for other seasons favor 

the evaporation of particulate ammonium nitrate into its gaseous (non-particle) 

constituents ammonia and nitric acid. Third, household wood burning during the winter 

season is a significant source of PM2.5.

PM2.5 concentrations exceed the federal 24-hour standard from 5 to 40 times a year, 

depending on meteorological conditions. Typical elevated exceedances range from 36 to 55 

ug/m3. 

4.1.2 Annual trends

Attainment status for PM is based on the calendar year; however, it is instructive to 

examine the number of 24-hour PM2.5 exceedances for each winter season. Figure 6 shows 

the number of 24-hour exceedance days by winter season (November 15 through February 

10), and also the number of rainy days for each corresponding season. There is a general 

alternating trend with every other winter having relatively high then moderate numbers of 

exceedances. An opposite trend occurs for the number of rainy days per winter season. 
11

Winters with relatively high numbers of rainy days have fewer PM exceedances. This trend 

occurs because rainy conditions are associated with strong, (vertically) deep, turbulent air 

flow patterns that allow ventilation of emitted PM and its precursors away from the ground 

level. Thus, the relative severity of a given winter PM season is largely governed by global 

circulation patterns determining the proportion of stormy days during a given winter. The 

2000-01 winter had a particularly high proportion of stagnant days. This period exhibited 

extreme PM2.5 levels and had the most exceedances of any winter on record PM2.5 was 

measured.

Percentage of days with PM2.5 exceedance, by month

0%

5%

10%

15%

20%

25%

30%

35%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 5. Percentage of 24-hour PM2.5 NAAQS exceedances by calendar month.

Figure 6. Bar plots showing number of 24-hour PM2.5 exceedance days and number of rainy days for each 

winter season from November 15 through February 10. PM2.5 exceedance days occurred when at least one 

Bay Area monitor recorded 24-hour PM2.5 level of 35 g/m3or greater. Rainy days occurred when at least 0.1 

in. of rain was reported in the Bay Area.
12

Trend analyses have been performed for both PM2.5 and PM10 levels using historical 

measurements. Time series for the annual average levels are shown in Figure 7. PM2.5

measurements are only available since 1999. The data appears to indicate a decreasing 

trend for PM2.5 and PM10 levels. Since the late 1980s, PM10 levels have decreased nearly 50 

percent. Decrease in PM2.5 concentrations is not certain because there are fewer data 

points to confirm this conclusion. Significant year-to-year variability in weather patterns (as 

evidenced in Figure 6) may confound the interpretation of pollutant long-term trends over 

periods of less than about 10 years. 

Figure 7. Trends for annual average PM10 and PM2.5 levels, with annual standards shown for reference. PM2.5

data were not available before 1999. There is no annual component of the federal PM10 standard.

4.1.3 Spatial patterns

PM2.5 is a regional pollutant. Its concentrations within the Bay Area are relatively uniform as 

compared to locations outside of the District. PM2.5 measurements for all Bay Area 

monitoring locations are strongly correlated. This correlation implies that PM2.5 levels 

generally rise and fall with similar timing for all locations throughout the District. PM2.5

episodes develop under conducive meteorological conditions, when levels throughout the 

District increase and may eventually level off well above the background concentration. 

Typically, PM2.5 levels must build for 2-4 days before a 24-hour exceedance occurs. Episodes 

commonly last a few days, but they may persist 1-2 weeks or longer.

Despite its regional nature, there is significant spatial variability for PM2.5 levels within the 

Bay Area. It is important to understand how the spatial distribution of PM impacts 
13

attainment for both the daily and annual components of the PM2.5 standards. These levels 

are calculated using a 3-year period to be consistent with how attainment status is defined. 

The 3-year period was selected as 2006-08.

Annual average PM2.5 levels for 9 Bay Area locations are shown in Figure 8. Point Reyes, 

largely reflecting the background, has annual PM2.5 levels around half that of other sites. 

San Jose and Vallejo have somewhat higher annual average levels than the other sites. 

These two sites appear to be the most important in terms of attainment of annual 

standards. The remaining sites have roughly uniform annual PM2.5 levels around 9-10 

g/m3.

Design values are estimated as a 3-yr average of the quarterly 98th percentiles for measured 

24-hour PM2.5 levels. They are shown in Figure 9 for the period 2006-08. There is 

considerably more site-to-site variability than for the annual averages (Figure 8) because 

the design values are based on peak PM2.5 levels. Four stations are at or near the 

exceedance threshold: Livermore, Concord, San Jose, and Vallejo. The design value for the 

remote Point Reyes location is around half that of the other sites. Design values for the 

remaining sites are around 25-30 g/m3.

The composition of PM2.5 also varies throughout the Bay Area. Chemical speciation of PM2.5

samples was performed at six locations during the 2000-01 winter as part of CRPAQS. 

Results are shown in Figure 10. Levels for sulfate, nitrate, and ammonium are relatively 

uniform throughout the Bay Area. They reflect the secondary particulate compounds 

ammonium nitrate and ammonium sulfate. These secondary PM2.5 components may require 

a few days of air mass aging to reach appreciable levels. During this aging process, the 

secondary PM and its precursors can be transported and dispersed uniformly throughout 

the Bay Area. Elemental carbon (EC), and to a lesser extent organic carbon (OC), reflect 

primary PM2.5. These compounds are more concentrated near their sources around the Bay 

such as San Francisco and San Jose. In terms of percentages, more urban locations around 

the bay have relatively higher proportions of primary PM2.5, whereas more rural locations 

further inland have relatively higher proportions of secondary PM2.5. Sodium and chloride 

levels reflect the presence of sodium chloride from suspended sea salt. This natural PM2.5

component represents a small fraction of the overall particulate mass. As expected, sodium 

chloride levels are much higher at San Francisco and Point Reyes than at the other noncoastal sites.
14

0

2

4

6

8

10

12

14

Livermore Fremont Concord San

Francisco

Redwood

City

San Jose Vallejo Santa Rosa Point Reyes

PM2.5 (ug/m3)

California Standard

Figure 8. Annual average PM2.5 levels by site for 3-yr period 2006-08. Error bars represent 95% confidence 

intervals.

0

5

10

15

20

25

30

35

40

45

Livermore Fremont Concord San

Francisco

Redwood

City

San Jose Vallejo Santa Rosa Point Reyes

PM2.5 (ug/m3)

National Standard

Figure 9. Average 98th percentile 24-hour PM2.5 levels (design values) by site for 3-yr period 2006-08. Error bars 

represent 95% confidence intervals.
15

SITE

PM2.5 mass (ug/m3)

100

50

0

20

10

0

30

15

0

10

5

0

4

2

0

10

5

0

4

2

0

4

2

0

2

1

0

4

2

0

1.0

0.5

0.0

0.8

0.4

0.0

0.8

0.4

0.0

0.50

0.25

0.00

0.10

0.05

0.00

SJ1SJ0

SFP

BI

LI

R

0.10

0.05

0.00

SJ1SJ0

SFP

BI

LI

R

0.16

0.08

0.00

SJ1SJ0

SFP

BI

LI

R

0.04

0.02

0.00

SJ1SJ0

SFP

BI

LI

R

0.010

0.005

0.000

SJ1SJ0

SFP

BI

LI

R

0.02

0.01

0.00

Mass Organic Carbon Nitrate Elemental Carbon Sulfate

Ammonium Sodium Chloride Magnesium Silicon

Iron Calcium Soluble Potassium Aluminum Zinc

Nickel Lead Copper Bromine Manganese

Figure 10. Levels for PM2.5 major components at 6 Bay Area monitoring locations.

4.2 Chemical Mass Balance (CMB) analysis

Chemical Mass Balance (CMB) is a statistical receptor model used for source 

apportionment. It requires speciated PM measurements and also experimentally 

determined source profiles for the source categories of interest. CMB then determines the 

contribution (strength) of each source category that best accounts for the observed PM 

composition on a given day. Detailed results of CMB analysis have been presented in a 

separate report by Fairley et al. (2008).

CMB was applied to speciated PM2.5 measurements from five locations obtained as part of 

CRPAQS. These sampling locations accounted for the spatial variability in PM2.5 composition 

throughout the Bay Area. The San Francisco and San Jose locations represented urban 

conditions. Livermore was suburban. Bethel Island was a rural location between the Bay 

Area and heavily polluted Central Valley. Point Reyes was a remote coastal location, 

presumably sampling near-background pollution levels. Speciated PM2.5 measurements 

were obtained for these locations every third day from late 1999 through early 2001.

Two separate CMB analyses were applied to determine source contributions to annual 

average and peak PM2.5 levels. Annual average contributions were estimated by averaging 

CMB results from year-round records. Peak contributions were estimated by averaging CMB 

results for the ten winter days having the highest 24-hour PM2.5 levels for each monitoring 

location. 
16

For each analysis, the same six known source categories were used: ammonium nitrate; 

ammonium sulfate; marine (sea salt) aerosol; geological and road dust; fossil fuels 

combustion; and biomass combustion. The fossil category included combustion of fuels 

from on-road and off-road vehicles, construction equipment, aircraft, heavy industry, and 

power generation. The biomass category included combustion products of wood burning, 

commercial cooking, fires, tobacco smoke, and waste incineration. Isotopic carbon (C14) 

analysis was used to adjust the raw CMB results to distinguish between combustion of fossil 

and non-fossil (biomass) materials.

4.2.1 Source contributions to peak PM2.5 levels

The ten samples with the highest 24-hour PM2.5 levels were all obtained during winter. 

Most of these measurements occurred on days exceeding the 24-hour PM2.5 standard of 35 

g/m3. Figure 11 shows the percentage contribution of each source category for each 

location. These results do not reflect differences in total PM2.5 levels between the stations.

The urban and suburban locations (San Francisco, San Jose, and Livermore) exhibited

comparable source contributions. Primary combustion sources (fossil and biomass) and 

secondary ammonium nitrate accounted for the bulk of PM2.5. San Jose had a somewhat 

lower contribution of ammonium nitrate than the other heavily populated locations. It was 

more distant from the concentrated ammonia sources in rural North Bay and Central Valley 

locations. San Francisco had a somewhat lower contribution of biomass burning than the 

other urbanized locations. Household wood burning was less common in this densely 

populated city that has many apartments lacking fireplaces. Fossil fuel combustion 

contributions were very comparable for these three locations impacted by vehicular and 

construction emissions related to commerce.

For the rural Bethel Island location, over half of the PM2.5 was ammonium nitrate. This 

location is closest to the Central Valley where intense ammonia emissions and 

meteorologically conducive conditions enhanced ammonium nitrate formation. Due to the 

lack of substantial commercial activity, fossil fuel combustion contribution was about half 

that of the more populated locations. Biomass burning contribution was comparable to the 

more populated locations. Likely, wood burning occurred in a higher proportion of 

households, but population density was considerably lower for this rural location.

Point Reyes was quite different from the other locations. The strongest contributing source 

was naturally occurring marine aerosols at over 30 percent. Ammonium sulfate contribution 

was also significant at nearly 20 percent. This was the only location with an elevated 

proportion of ammonium sulfate. The site was proximal to offshore shipping lanes and 

lacked other local sources. Secondary ammonium nitrate contribution was lowest for this 

location most distant from concentrated ammonia sources. Fossil fuel combustion 

contribution was also low for this remote location with negligible commercial activity.
17

Overall, primary combustion particles and secondary ammonium nitrate were the main 

drivers for Bay Area PM2.5 episodes impacting populated areas. Wood burning may have 

been the single largest PM2.5 source contribution for the Bay Area during episodes. 

Geological dust, tire and break wear, and marine sources contributed near-negligible 

amounts to PM2.5 exceedances. 

4.2.2 Source contributions to annual average PM2.5 levels

Many PM2.5 measurements made were during non-winter months when PM2.5 levels are 

generally low. Annual averages were computed from quarterly averages to avoid overweighting seasons for which more measurements were performed. Figure 12 shows the 

percentage contribution of each source category for each location. These results do not 

reflect differences in total PM2.5 levels between the stations.

The urban and suburban locations exhibited comparable source contributions. Primary 

combustion sources dominated. These locations were impacted by vehicular and 

construction emissions related to commerce and also household activities. As with peak 

PM2.5, biomass contributions were somewhat lower for San Francisco as compared to the 

other heavily populated locations. The contributions of ammonium nitrate, ammonium 

sulfate, and marine particulates were similar and are about half that of the combustion 

sources.

The rural Bethel Island location had higher secondary and lower primary contributions as 

compared with the more populated locations. This location was relatively distant from the 

intense primary PM sources around the bay. It was most strongly impacted by regional 

secondary PM2.5. The marine contribution was relatively weak for this inland location.

The remote coastal location at Point Reyes was affected most strongly by marine 

particulate. Marine percentage contributions were 2-3 times greater than for the other 

locations. Ammonium sulfate percentage contribution was around twice that of the other 

locations because of proximity to offshore shipping lanes. In terms of mass contributions, 

however, marine and ammonium sulfate contributions were not very different from the 

other locations. This is because anthropogenic emissions had little impact at Point Reyes, 

and thus total PM2.5 levels here were low.

Overall, primary combustion particles accounted for the bulk of annual average PM2.5 levels. 

Ammonium nitrate contributed somewhat more than ammonium sulfate and marine 

influences, which were comparable. Geological dust and tire and break wear contributed

negligible amounts. 

Figure 13 shows the quarterly CMB results for Bethel Island. It demonstrates the 

pronounced impacts of ammonium nitrate and biomass burning during the winter season. 

Ammonium nitrate formation was favored by low winter temperatures. This season also
18

exhibited increased wood burning emissions. Similar results were obtained for other 

locations; however, the more urbanized locations may not have been as strongly affected 

by winter wood burning.

0

10

20

30

40

50

60

Ammonium

Sulfate

Ammonium

Nitrate

Marine Geological Dust Fossil Fuel

Combustion

Vegetative

Burning Percent

Bethel Island

Livermore

San Francisco

San Jose 2000

San Jose 2001

Point Reyes

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

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

source categories as a percentage of the total estimated mass.

Figure 11. CMB results for CRPAQS period. Results for exceedance days only.
19

0

5

10

15

20

25

30

35

40

Ammonium Nitrate Ammonium Sulfate Marine Geologic/Road Dust Fossil Fuel

Combustion

Vegetative Burning Percent

Bethel Island

Livermore

San Francisco

San Jose

San Jose 2001

Point Reyes

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

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

combustion and vegetative burning adjusted for Carbon-14 results.

Figure 12. CMB results for CRPAQS period. Annual results.

0

2

4

6

8

10

12

14

AmSul AmNit Marine Geo Dust Fossil Fuel

Combustion

Vegetative Burning PM2.5 (g/m3)

spring

summer

fall

winter

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

Figure 13. CMB results for CRPAQS period. Seasonal results for Bethel Island location used to compute the 

annual averages shown in Figure 12.
20

4.3 Relationship between weather patterns and PM

Air monitoring showed that, along with elevated PM2.5 levels during the winter season, 

there was a considerable day-to-day variability for PM levels. A number of analyses were 

performed to identify weather patterns associated with high PM episodes.

4.3.1 Comparing and contrasting direct measurements

Meteorological characteristics associated with PM episodes were identified by comparing 

weather station observations between episodic and non-episodic winter days. It was 

possible that different types of PM episodes could occur under different weather patterns. 

Pooling meteorological observations according to whether or not an exceedance occurred 

could not possibly resolve such intricacies.

Common meteorological characteristics associated with elevated Bay Area PM levels were 

light winds from the east throughout much of the Bay Area and no rain. Specifically, 24-hour

average winds at San Carlos in the South Bay, Pleasanton in the Livermore Valley, and 

Bethel Island in the East Bay had speeds less then 0.5 mph and an easterly (from the east) 

directional component. Also, no appreciable precipitation was measured at San Jose. Such 

days generally exhibited cool temperatures; however, temperature itself was not a strong 

indicator of elevated PM levels. 

Exceedances of the 24-hour PM2.5 standard in the Bay Area typically occur as PM levels 

build over time upon three or more consecutive days having the above conditions. Thus, the 

exceedances appear to exhibit considerable carryover of PM from previous days. This effect 

is demonstrated in Figure 14 which shows the mean Bay Area PM2.5 level as a function of 

the number of consecutive days having the above described characteristics. The increase in 

PM2.5 levels over time is mathematically consistent with exponential decay. Physically, this 

model corresponds to new PM being added on to existing, carried over PM on each 

additional day, with atmospheric loss of PM proportional to PM level.
21

Figure 1. Mean winter PM2.5 vs.number of consecutive PM-conducive days

Also, curve of cumulative PM2.5 assuming exponential deposition with a 2 day half-life

0

5

10

15

20

25

30

35

40

45

50

0 1 2 3 4 5 6 7

Consecutive conducive days

PM2.5 (ug/m3)

Mean PM2.5 

(± 1 std error)

Exponential curve

(2 day half-life)

Figure 14. Mean observed Bay Area PM2.5 level in response to number of consecutive days with favorable 

conditions. Curve is fit for exponential decay with 2-day time constant.

4.3.2 Cluster analysis for Bay Area weather patterns

Cluster analysis is a data mining technique that can identify recurring patterns among 

historical observations. A rigorous approach to characterizing episodic meteorological 

conditions can be achieved through cluster analysis of weather station measurements. 

Disregarding PM levels, cluster analysis was applied solely to meteorological measurements 

for each winter day of an extended study period. “Clusters,” or homogeneous groups, of 

days are identified that share similar meteorological characteristics. The average 

meteorological conditions associated with each cluster constitute a distinct weather 

pattern. Then, average pollutant levels are tabulated for each cluster (weather pattern) to 

determine its propensity to result in elevated PM levels. This framework allows 

identification of different weather patterns that may result in PM exceedances.

A cluster analysis of Bay Area conditions was performed at the University of California, 

Davis. Detailed results are provided in a report by Palazoglu (2009). This study analyzed 

each winter day (November through March) of the years 1996-2007, for a total of 1754 

days. This large sample size ensured robust identification of weather patterns influencing 

PM in the Bay Area. Also, the decade-long study period allowed characterization of year-toyear variability in global weather patterns affecting the Bay Area. Meteorological 

observations input to the cluster analysis were obtained from 12 weather stations 

positioned at key locations throughout the Bay Area (stations in Figure 1 within the District 

boundaries).

The cluster analysis revealed five dominant winter weather patterns prevailing over the Bay 

Area. Three of the clusters are conducive to elevated Bay Area PM levels. All three exhibit 
22

winds entering the Bay Area from the east. Two of the three clusters exhibit near-calm 

conditions in the Central Valley, where PM levels are considerably higher than in the Bay 

Area. These two clusters account for around 80 percent and 15 percent of all Bay Area 24-

hour PM2.5 exceedances, respectively. The cluster accounting for 80 percent of the Bay Area 

exceedances exhibits persistent easterly winds into the Bay Area throughout the day. The 

cluster accounting for 15 percent of the Bay Area exceedances exhibits easterly winds into 

the Bay Area during the overnight and morning hours. Winds may reverse to enter the Bay 

Area from the west during the afternoon. The third cluster with easterly flow into the Bay 

Area has high winds in the Central Valley and correspondingly increased dispersion rates. 

PM levels are higher than average; however, Bay Area exceedances are uncommon. The 

remaining two clusters exhibit marine winds entering the Bay Area from the west. Both of 

these clusters represent stormy conditions, and PM levels are low. 

The pair of episodic clusters exhibits considerable buildup of secondary PM, particularly 

ammonium nitrate. For these two episodic weather patterns, PM levels in the Central Valley 

can be about 2-3 times higher than in the Bay Area. This is largely because of meteorological 

conditions, especially for the most episode-prone cluster. Conditions are more conducive to 

the transformation of NOx to nitric acid (needed for the formation of ammonium nitrate) in 

the Central Valley than for coastal locations. These conditions include low wind speeds

combined with high amounts of sunlight during the day and high humidity at night. These 

settings enhance daytime and nighttime conversion of NOx to nitric acid. Nitric acid then 

rapidly reacts with ammonia emissions, mostly from dairy activities, which are especially 

concentrated in the northern San Joaquin Valley, to form ammonium nitrate.

The clusters, based solely on surface meteorological conditions, correspond well with aloft 

weather systems influencing much of the West Coast and/or southwestern United States. A 

developing or approaching upper-level high pressure center over Central California causes a 

transition from a high wind pattern into a low wind pattern for the region, resulting in 

increasing PM levels. Typically, PM builds to the exceedance level in 2-4 days upon a 

transition into an episodic weather pattern. Thereafter, PM levels remain approximately 

constant while the high pressure system persists over Central California. A transition from 

an episodic weather pattern into one of the non-episodic patterns marks the onset of high 

surface winds that terminate the episodic conditions. Episodes often terminate abruptly 

when a migrating storm (cyclone) passes over the Bay Area. Strong, deep, turbulent winds 

associated with the storm, but not the precipitation itself, is the key factor resulting in a 

sudden decrease of elevated PM levels. Transitions from an episodic weather pattern into a 

non-episodic pattern lacking rain are often associated with PM levels that decrease 

gradually over a few days.

A single cluster accounted for over 80 percent of all Bay Area exceedances; however, only 

around one in three days belonging to this cluster resulted in an exceedance. Therefore, this 

weather pattern constitutes a necessary but insufficient condition for an exceedance to 

occur. Days belonging to this lone cluster were further explored to distinguish between 

exceedances and non-exceedances. Exceedance days could be defined in terms of a number 
23

of simultaneous meteorological characteristics: a ridge of high pressure aloft moving over 

SFBA and providing a weak surface pressure gradient over Central California; persistent 

easterly flows through SFBA extending vertically from the surface to around the 925-hPa 

pressure level; orographically channeled winds resulting from strongly stable conditions; 

enhanced nocturnal cooling under clear-sky conditions providing for enhanced overnight 

drainage flows off the Central California slopes; and at least two consecutive days of these 

listed conditions.

Year-to-year differences in global weather patterns strongly influence the number of 24-

hour PM2.5 exceedances. Winters with more rainy days tended to have fewer exceedances. 

This is because winters frequently affected by stormy conditions tended to have fewer days 

with the light easterly winds and other above listed conditions that are associated with PM 

episodes.

4.3.3 Cluster analysis for inter-regional transport patterns

The cluster analysis of Bay Area weather patterns described above suggests that transport 

from the Central Valley occurs during many Bay Area exceedances. That study, however, 

was unable to resolve the upwind source regions from which PM and its precursors may 

have originated. It was suspected that sources in the Sacramento Valley and/or San Joaquin 

Valley may contribute to Bay Area exceedances, depending upon prevailing atmospheric 

conditions.

A second set of cluster analysis was conducted at the University of California, Davis to 

investigate these potential transport patterns. This study applied the clustering technique 

described above to an expanded spatial domain. In addition to Bay Area weather stations, 

measurements were also taken from stations in the Delta region (all stations shown in 

Figure 1). In this manner, the clustering reflects Bay Area conditions as well as upwind 

conditions where the Sacramento and San Joaquin Valleys directly connect to the Bay Area. 

High quality weather station observations were not available in the Delta region for the 

entire 1996-2007 period used in the previous Bay Area clustering. Also, the weather stations 

in the Delta region exhibited considerable gaps in their records which resulted in days being 

excluded from the cluster analysis. In total, 1001 days from the years 1999-2007 had 

sufficient observations to be included in this transport cluster analysis. Though smaller than 

the above Bay Area clustering, this sample size was still sufficiently large enough for robust 

identification of inter-regional transport patterns.

The transport cluster analysis reveals six dominant winter weather patterns prevailing over 

the Bay Area. Together, two of these clusters largely correspond with the single pattern 

from the Bay Area clustering that accounted for over 80 percent of all Bay Area 

exceedances. Both of these episodic transport patterns exhibit air flows entering the Bay 

Area from the east. This pair of patterns is distinguished by upwind conditions in the Delta 

and beyond. Average surface air flows for this pair of clusters are shown in Figure 15. One 
24

cluster has winds entering the Bay Area from the Sacramento Valley and accounts for 

around 60 percent of the Bay Area exceedances. The highest Bay Area PM2.5 levels are 

usually observed in San Jose. The other cluster has winds entering the Bay Area from the 

San Joaquin Valley and accounts for around 20 percent of the Bay Area exceedances. The 

highest Bay Area PM2.5 levels are usually observed in East Bay locations such as Concord, 

Livermore, or Vallejo.

These transport patterns will be further used for evaluating transport simulation results in 

section 6.2.3.

Figure 15. Pair of episodic transport clusters in which air flow into the Bay Area occurs from the Sacramento 

Valley (left, 60% of Bay Area exceedances) and from the San Joaquin Valley (right, 20% of Bay Area 

exceedances).
25

5. Computer simulations

A number of different simulations were performed to better understand the formation of 

PM2.5 in the Bay Area. In section 5.1, the computer models and their configurations are 

briefly described. In section 5.2, base case simulation results are presented. The models 

were applied to estimate the sensitivity of PM2.5 levels to reductions of Bay Area emissions 

(section 5.4) and transport impacts on the Bay Area (section 5.5).

Three winter seasons were simulated. All PM and precursor sources were modeled for the 

winters of 2000-01 and 2006-07. The 2000-01 winter was simulated because extensive 

measurements were available from CRPAQS; however, this was a severe PM season with 

the highest PM levels on record. The 2006-07 winter exhibited moderate conditions that 

may be more representative for air quality planning purposes. In total, PM2.5 was simulated 

for 128 days. This large sample size ensured that all representative meteorological 

conditions under which elevated PM levels occurred were modeled. The third modeled 

winter was 2008-09. For this winter, only directly emitted PM from wood burning was 

simulated. This winter was simulated to evaluate the effectiveness of wood burning 

restrictions first enacted for the 2008-09 winter.

5.1 Description of models

Computer modeling of PM required two types of simulations to be performed. First, 

meteorological fields were simulated. These meteorological fields were then used as inputs 

to an air quality model to simulate PM levels.

5.1.1 Meteorological modeling

Meteorological modeling used the Fifth-Generation National Center for Atmospheric 

Research (NCAR)/Pennsylvania State University Mesoscale Model (MM5). Separate MM5 

configurations were used for PM modeling and wood smoke modeling.

For PM modeling, three nested modeling domains were used. The outer domain covered 

the entire western United States with 36-km horizontal grid resolution to capture synoptic 

(large-scale) flow features and the impact of these features on local meteorology. The 

second domain covered California and portions of Nevada with 12-km horizontal resolution 

to capture mesoscale (sub-regional) flow features and their impacts on local meteorology. 

The third domain covered Central California with 4-km resolution to capture localized air 

flow features. The 4-km domain included the Bay Area, San Joaquin Valley, and Sacramento 

Valley, as well as portions of the Pacific Ocean and the Sierra Nevada mountains. This 

innermost domain had 189 grid cells in both the east-west and north-south directions.

All three domains employed 30 vertical layers with thicknesses expanded with height from 

the surface to the top of the modeling domain (about 15 km). Meteorological variables are 
26

estimated at the middle of the layers in MM5. The thickness of the lowest layer nearest the 

surface was about 30 m. Thus, meteorological variables near the surface were estimated 

around 15 m above ground level. 

The model configuration was tested using a number of available physics options. The final 

choice of options that proved to be the best for characterizing meteorology in the domain

were similar to those used by the National Oceanic and Atmospheric Administration (NOAA) 

for the Central California Ozone Study simulations. An exception is that the Pleim-Xiu Land 

Surface Model and PBL Scheme were used to provide additional fields to feed the CMAQ 

model.

Simulation periods were 12/1-2/2 of both 2000-01 and 2006-07. The resulting 

meteorological fields showed reasonable agreement with observations archived at the 

National Center for Atmospheric Research (NCAR). Details of the validation of the simulated 

meteorological fields against measurements are presented in Appendix B.

A different MM5 configuration was used for wood smoke modeling for 11/15/2008-

2/15/2009. The inner 4-km modeling domain had 79 grid cells in both the east-west and

north-south directions. This domain covered the Bay Area and surrounding regions, but not 

the rest of Central California that was included in the PM modeling MM5 configuration. 

There were 50 vertical layers. Physics options were the same as for the PM modeling runs. 

The resulting meteorological fields compared well against observations. Details of the 

validation of the simulated meteorological fields against measurements are presented in 

Appendix B.

5.1.2 PM modeling

PM modeling used the EPA Community Multiscale Air Quality (CMAQ) modeling system 

version 4.6. CMAQ simulates six individual PM2.5 components which are combined to 

estimate total PM2.5 levels: ammonium, sulfate, nitrate, OC, EC, and other unresolved 

components. The model distinguishes between primary and secondary OC, which is not 

possible from measurements. The simulated “other” components account for inert 

particulates including dust, marine aerosol, brake and tire wear, and trace metals.

The PM modeling domain had 185185 horizontal grid cells and 4-km grid resolution. It was 

centered in the innermost meteorological modeling domain. The meteorological fields from 

2 grid cells along each edge of the meteorological modeling domain were not used in order 

to minimize the impact of boundary conditions on PM modeling within the 4-km modeling 

domain. This resulting modeling domain matches the 4-km CRPAQS modeling domain 

shown in Figure 2. Some upper-level meteorological model layers were collapsed while 

preparing meteorological inputs for CMAQ to reduce computational time. This is a common 

practice in air quality modeling, as pollutant levels in layers aloft are relatively low and do 

not significantly impact levels at the surface. The resulting number of vertical layers in 

CMAQ was 16, with layer thicknesses expanding with height from the surface to the top of 
27

the modeling domain (about 16 km). The thickness of the first layer of CMAQ was kept the 

same as in MM5 (about 30 m), estimating pollutant concentrations at around 15 m above 

the surface.

Secondary PM formation was dependent upon ozone photochemistry. The ozone chemistry 

used in CMAQ was the Statewide Air Pollution Research Center version 1999 chemistry 

mechanism (SAPRC99). Secondary PM gas to particle conversion chemistry was simulated 

using in the Models-3 AE3 aerosol module in conjunction with the Regional Acid Deposition 

Model (RADM) aqueous-phase chemistry model.

As described in section 3, base case emissions inventories were developed for one week 

each for the years 2000 and 2006. These one-week inventories were replicated such that 

identical emissions were simulated for each week of the 2000-01 and 2006-07 winters, 

respectively. The replicated weeks were taken from near the middle of each winter 

simulation period. The inventories are believed to be representative of their respective 

winter simulation periods; however, specific emissions events such as increased wood 

burning were not reflected in the replicated inventories. The simple replication also ignored 

differences in plume rise that may have resulted under different meteorological conditions 

occurring during different weeks of a given winter. This approximation is believed to have 

negligible impact on simulated PM levels.

The boundary and initial conditions for the simulations were taken from CRPAQS. The only 

particulates included in the boundary conditions were dust and sulfur species, both set to 

low levels. Boundary conditions were also supplied for nitrogen species, VOCs, and ozone to 

drive the ozone photochemistry upon which PM depends. Initial conditions were set for the 

same set of species as present in the boundary conditions. Initial conditions were uniform in 

the horizontal dimension, but changed vertically. Initial conditions for pollutant levels were 

set to small values to avoid numerical instabilities. Each model run was initialized during a 

period of low PM levels to avoid excessive model spin-up time.

5.1.3 Wood smoke modeling

Wood smoke modeling used the ENVIRON International Corporation Comprehensive Air 

quality Model with extensions (CAMx) version 4.50. CAMx was used to simulated wood 

burning emissions during the winters of 2000-01 and 2008-09. The wood burning emissions 

inventory is described in section 3. The wood smoke modeling used an emissions inventory

for a single day that was replicated over the winters of 2000-01 and 2008-09. As such, the 

emissions inventory represents average levels of winter wood burning. It does not reflect 

specific, intensified wood burning events expected during periods with colder temperatures 

or holidays. The directly emitted, nonreactive wood smoke components were simulated 

without any chemistry to reduce computational burden. Initial and boundary conditions had 

small amounts of wood smoke particles.
28

5.2 Base case PM modeling results

CMAQ was run with the base case emissions inventory for 12/2-2/2 of both 2000-01 and 

2006-07. These base case simulations were intended to reconstruct actual pollutant levels 

that occurred during these periods. The simulated surface PM2.5 fields compared favorably 

against PM2.5 measurements. Certain limitations of the model were noted, however, such as 

a tendency to underestimate very high PM2.5 levels. This shortcoming was primarily due to

the overestimation of winds within the boundary layer by the meteorological model during 

extremely stable atmospheric conditions. When the atmosphere is stable, air aloft 

decouples from the surface and the model tends to generate local low-level jets that 

subsequently cause underestimation of PM in the air quality model. A separate research 

project is considered as part of the CRPAQS program to improve the meteorological model 

performance for such conditions. Details of the validation of the simulated PM2.5 levels 

against measurements are presented in Appendix C.

5.2.1 Individual PM2.5 components

A key benefit of modeling is the ability to estimate levels of the major PM2.5 components 

throughout the Bay Area. This information helps fill the large gaps in the speciated PM2.5

measurements that are only available for a limited number of days at a few locations. 

Simulated episodic 24-hour levels for the PM2.5 components are shown in Figure 16. These 

results are averaged across the 55 days for which simulated Bay Area 24-hour PM2.5

exceeded 35 g/m3. For these episodic days, light winds flowed through the Bay Area from 

the east, and Central Valley conditions were near calm.

Figure 16. Surface plots of simulated 24-hour levels for the six PM2.5 components, averaged across the 55 days 

for which simulated base case Bay Area maximum 24-hour PM2.5 level exceeded 35 g/m3. Bay Area counties 

and the California coastline are drawn using thick black lines. City limits for Sacramento and Stockton are 

drawn using thin black lines.
29

EC and most of the OC result from primary emissions. EC levels are elevated primarily at and

near centers of commerce around the bay; over the Central Valley major cities and, to a 

lesser extent, rural areas. The highest Bay Area EC levels occur over West Oakland, 

reflecting Port of Oakland diesel sources. Aside from the rural Central Valley locations, EC 

accumulates only near its sources. OC levels are elevated for the same locations as EC, as 

well as other moderately populated areas. OC is associated with both commerce and 

household activities. Also, OC may be proportionally higher than EC over populated areas as 

compared to over highways and centers of commerce and industry. Likely, this reflects 

impacts of household wood burning and cooking in residential areas. Accumulated EC and 

OC in the rural Central Valley locations demonstrate the lack of ventilation in the Valley. 

During an episode, a stable, stagnating air mass is trapped between the Valley’s rims. 

The "other" PM2.5 component presented in Figure 16 also results from primary emissions 

only. It is elevated near highways and populated areas. It reflects trace metals from mobile 

and stationary combustion sources, suspended road dust, and also small amounts of tire 

and brake wear particles. As with carbonaceous components, the other primary PM2.5

components are trapped in the Central Valley and accumulate in its rural areas. Geological 

dust would not appear to be a major contributor to elevated PM2.5 levels under relatively 

calm conditions or during the winter rainy season.

Figure 16 shows that ammonium nitrate is the dominant component of secondary PM2.5. 

During episodic conditions, nearly all of the nitrate and most of the ammonium are present 

as the compound ammonium nitrate. (Nitrate with molar mass 62 g/mol and ammonium 

with molar mass 18 g/mol would appear with the mass ratio of 3.4 g nitrate/g ammonium if 

they contributed solely to ammonium nitrate. This is very close to the ratio visually 

apparent for most of the plotted domain in Figure 16.) Notable exceptions include near 

Richmond and Carquinez Strait where excess ammonium is present. The ammonium nitrate 

is not localized near ammonia or NOx sources. It is diffuse. This is because ammonium 

nitrate forms by chemical reactions occurring over the course of several days and nights, as 

fresh emissions are aged in a relatively calm air mass. Ammonium nitrate levels are highest 

in the Central Valley. There is a general gradient with levels decreasing from east to west 

through the Bay Area.

Ammonium sulfate is a minor component of secondary PM2.5 during episodic conditions. 

Presence of this compound is reflected by the sulfate levels shown in Figure 16. Appreciable 

levels of ammonium sulfate in the Bay Area appear only around the ports, industrial plants,

and refineries near Oakland, Richmond, and Carquinez Straight. (Hence the excess ammonia 

here that does not neutralize nitrate, as noted above.) These areas represent the only 

significant land-based sulfur sources in the Bay Area. Ammonium sulfate also forms over the 

offshore shipping lanes from combustion of sulfur-laden bunker fuel. Because winds are 

from the east during episodes, these offshore sulfur sources do not strongly impact the Bay 

Area. 
30

Ammonium sulfate can occasionally account for a moderate proportion of PM2.5. This 

situation may occur under non-episodic conditions when winds are from the west and the 

shipping lanes are upwind of the Bay Area (not shown). Under such conditions, PM2.5 levels 

are generally low, and ammonium sulfate mass loading is still typically not much more than 

a few micrograms per cubic meter..

5.2.2 Ammonium nitrate limitation

Ammonium nitrate particulate forms upon the reaction of gaseous compounds ammonia 

and nitric acid. Ammonia is directly emitted, whereas nitric acid forms through the 

photochemical aging of NOx emissions. Lack of availability of either ammonia or nitric acid 

can be the limiting factor in ammonium nitrate formation. Figure 17 shows levels for these 

gases averaged across the 55 days for which simulated Bay Area 24-hour PM2.5 exceeded 35 

g/m3.

Under episodic conditions, ammonia accumulated in the atmosphere only near intense 

ammonia sources. (Compare ambient ammonia levels of Figure 17 with ammonia emissions 

levels of Figure 4.) Ammonia was especially concentrated in the Central Valley, but its levels 

also increased for some Bay Area source locations. Near the ammonia sources, nitric acid 

was consumed to near-zero levels. Also, ammonium nitrate levels were elevated over the 

ammonia sources (see Figure 16), indicating locations of significant secondary particulate 

formation. Nitric acid was only able to accumulate for remote locations over the Coast 

Range and the Pacific Ocean, and to a lesser extent rural locations near the Central Valley 

rims. There were no major NOx emissions sources near the locations where nitric acid 

accumulated. These findings suggested that ammonium nitrate formation was limited by 

nitric acid, and not by ammonia. It is cautioned, however, that these results could be

misleading because of the preliminary ammonia emissions inventory used for modeling.

Nitric acid limited chemistry means that nitric acid availability ultimately limited the total 

tonnage of secondary ammonium nitrate that formed. It does not imply that reductions of 

nitric acid levels would be the most efficient means for reducing ammonium nitrate levels. 

Ammonia emissions reductions could still reduce ammonium nitrate levels under nitric 

limited chemistry; however, ammonia emissions reductions could not impede ammonium 

nitrate formation for regions in which all nitric acid has been consumed.

5.2.3 Spatial distribution of PM2.5

The base case simulation results demonstrate how primary PM2.5 accumulates near its 

sources, whereas secondary PM2.5 is spread out through the Bay Area. To analyze the spatial 

gradients in finer detail, it is constructive to divide the Bay Area into a number of subregions 

sharing distinct PM2.5 characteristics. To objectively identify the relevant subregions, a 

cluster analysis was performed on the simulated PM2.5 levels. (This is a different type of 

cluster analysis than applied to meteorological measurements in section 4.3. The term 

“cluster analysis” applies to a variety of data mining techniques having different mechanics 
31

and goals.) The cluster analysis was performed to group model grid cells having strongly 

correlated PM2.5 levels. By definition, correlated grid cells exhibit PM2.5 levels that rise and 

fall with the same timings. This type of clustering naturally identifies contiguous domains, 

because adjacent grid cells directly interact via the wind and the terrain. The clustering was 

applied only to days with simulated PM levels exceeding 30 g/m3so that the identified 

subregions reflect episodic conditions. Eight subdomains were identified within the Bay 

Area (Figure 18). Other subdomains of interest in the Central Valley (not shown) include the 

Modesto area and the Delta region immediately east of Carquinez Straight. Plots of 

secondary versus primary PM2.5 levels were constructed for each identified subregion. 

Results for selected subregions are shown in Figure 19.

Figure 17. Aerial plots of simulated 24-hour levels for gas phase ammonia and nitric acid, averaged across the 

55 days for which simulated Bay Area maximum 24-hour PM2.5 level exceeded 35 g/m3. Bay Area counties 

and the California coastline are drawn using thick black lines. City limits for Sacramento, Stockton, Modesto, 

Fresno, and Bakersfield (listed north to south) are drawn using thin black lines.

Around Modesto, PM2.5 is composed of approximately equal portions of primary and 

secondary components, up to about 50 g/m3of total PM2.5. This distribution demonstrates 

the typical buildup of secondary PM2.5 in the Central Valley to levels as high as primary 

PM2.5 around a major urban source area for primary particles. Secondary PM2.5 levels 

occasionally exceed 35 g/m3, indicating that an exceedance could occur in the Central 

Valley even without any primary PM emissions. For high levels of primary PM2.5 (beyond 25 

g/m3), additional buildup of secondary PM2.5 does not occur. Likely, this indicates a limit 

for air mass aging to progress. Conditions in the Central Valley are near-stagnant, yet not 

stagnant enough to allow for indefinite secondary PM accumulation.

In the Delta subregion, PM2.5 characteristics are similar to Modesto; however, there is 

slightly more primary than secondary contribution. Primary levels are similar to Modesto, 

but secondary levels are slightly decreased outside of the Central Valley proper. A similar 

effect is observed through the Altamont Pass and into the Livermore Valley. Here, however, 

total PM2.5 levels are lower than in the Central Valley or Delta. Livermore Valley is buffered 

from the heavily polluted Central Valley by the Coast Range. Significant contributions of 
32

both primary and secondary PM2.5 are required for an exceedance to occur in the eastern 

portion of the Bay Area.

Figure 18. Eight Bay Area subdomains sharing similar air quality characteristics when PM levels are elevated. 

Bay Area counties and the California coastline are drawn using thick black lines. City limits for Sacramento and 

Stockton are drawn using thin black lines.

In the San Francisco Peninsula and San Jose, PM2.5 is dominated by primary PM emissions. 

These urban areas have intense primary sources, and also are distant from the main locus of 

secondary formation in the Central Valley. Secondary levels are around 5 g/m3lower than 

in the Delta or Livermore Valley. In the San Francisco Peninsula and San Jose, PM2.5

exceedances can occur from primary contributions alone.

North of San Francisco, in the Petaluma Valley, PM2.5 is more primary than secondary; 

however, primary components do not dominate as in the urban areas. Secondary PM2.5

levels are similar as in the urban areas, reflecting the regional nature of secondary PM. 

Primary PM2.5 levels are around half that of San Francisco and San Jose, reflecting the 

smaller source strength of Santa Rosa. Significant contributions of both primary and 

secondary PM2.5 are required for an exceedance to occur in the North Bay.

5.3 Development of a typical Bay Area PM episode

Simulations depicted how a typical PM episode developed in the Bay Area over time. Figure 

20 shows simulation results for the four-day period January 21-24, 2007. On January 21, a 

high pressure system was approaching the northern California coast from the west. The 

offshore clockwise rotating high pressure system created strong winds entering the 

Sacramento Valley from the north. The winds blew down through the Sacramento Valley 

and split at its southern end to enter the Bay Area and San Joaquin Valley. The low-level 

winds remained strong through the Bay Area and exited over the Pacific Ocean. PM levels 
33

were low for the Bay Area, Delta region, and Sacramento Valley because of the strong winds 

and associated dispersion. Deeper into the San Joaquin Valley, however, the winds became 

weaker and PM was accumulated to high levels. On the next day, January 22, the center of 

the high pressure system reached land and winds became weaker throughout Central 

California. Light winds entered the Bay Area from the east as the Central Valley air mass 

emptied toward the Pacific Ocean. In the San Joaquin Valley, secondary PM levels were 

regionally elevated. In the Bay Area and around Sacramento, primary PM levels were 

elevated near their sources. On January 23, Central Valley winds were near calm and light 

flow continued through the Bay Area from east to west. The entire Central Valley, the Bay 

Area, and offshore locations immediately west of the Bay Area were impacted by regionally 

accumulated secondary PM. As the high pressure system gained strength and its west to 

east motion was slowed down by the Sierras, PM2.5 levels were especially elevated for 

around the San Francisco bay, through Carquinez Straight, and near the Central Valley cities. 

For these source areas, localized primary PM in addition to regional secondary PM allowed 

for high PM2.5 levels. Similar meteorological conditions persisted through January 24. PM2.5

levels were similarly elevated as in the previous day. The plume of regional PM expanded 

somewhat to more strongly affect outlying areas such as Santa Rosa and Santa Cruz. This 

episode continued for several more days as meteorological conditions persisted (not 

shown).

Figure 19. Simulated secondary versus primary PM2.5 levels for four selected subdomains shown in Figure 18, 

plus Modesto and the Delta. Grayscale indicates relative frequency at which combinations of primary and 

secondary PM2.5 levels occurred. Diagonal 1:1 lines indicate equal proportions of primary and secondary 

components.
34

Figure 20. Simulation results for four consecutive days showing the development of a relatively severe PM 

episode in the Bay Area. Aerial plots of CMAQ-simulated 24-hour PM2.5 levels with superimposed 24-hour

averaged wind field simulated with MM5. Bay Area counties and the California coastline are drawn using thick 

black lines. City limits for Sacramento, Stockton, and Modesto are drawn using thin black lines.

5.4 PM sensitivities to Bay Area emissions reductions

The sensitivities of PM2.5 levels to various reductions of Bay Area emissions were estimated 

by simulation. This approach required implementing a "sensitivity run" of the air quality 

model using a modified base case inventory. Typically, the modified inventory has emissions 

reductions for one or more important source categories relative to the base case. 

Differences in simulated PM levels between the base case and the sensitivity run reflect the 

modeled impact of the reduced emissions. Two types of sensitivity runs were performed: 

across-the-board emissions reductions for classes of chemical species and reductions of 

wood smoke emissions only.

The first type of simulation explored the sensitivity of PM levels to 20 percent across-theboard reductions of classes of chemical species emitted by anthropogenic sources within 

the Bay Area: NOx and VOC combined; gaseous sulfur species; ammonia; directly emitted 

PM; and these four classes combined, comprising all anthropogenic emissions. 

Simultaneous NOx and VOC emissions reductions were simulated because historical controls 

for ozone have targeted both of these emissions classes. Ammonia, sulfur, and direct PM 

emissions reductions were simulated independently. This type of sensitivity run based on 

classes of chemical species did not distinguish between specific source categories (e.g. 

mobile and stationary sources). 
35

Additional sensitivity simulations explored the impact of Bay Area wood burning on PM2.5

levels. Model runs were performed with 100 percent, 50 percent, and 0 percent simulated 

compliance rate with the District wood burning restriction enacted for the 2008-09 winter. 

Reductions of wood smoke emissions were simulated only for actual Spare the Air days 

during which wood burning was restricted. The simulated emissions reductions were 

implemented from noon of the Spare the Air day through noon of the following day. This 

timing was consistent with the no-burn period specified in the regulation. This type of

sensitivity run was intended to estimate how much higher PM2.5 levels would have been 

without wood burning control measures for the 2008-09 winter.

5.4.1 Across-the-board emissions reductions

Across-the-board emission reductions were simulated for 12/18/2000-1/6/2001 and 

12/27/2006-1/11/2007, for a total of 36 days when PM levels were elevated in the Bay 

Area. Differences in PM2.5 levels (PM2.5 equals base case PM2.5 level minus sensitivity run 

PM2.5 level) were computed for each model grid cell. PM2.5 is defined such that positive 

values reflect a decrease in PM levels when emissions are reduced. PM2.5 values at each 

grid cell were averaged across the 20 days in the 36-day study period having simulated base 

case Bay Area PM2.5 levels exceeding 35 g/m3. These sensitivities under episodic conditions 

are shown in Figure 21. 

Reducing 20 percent of the directly emitted PM (around 18 tons/day eliminated, see Table 

2) was approximately an order of magnitude more effective in reducing peak PM2.5 levels 

than reducing 20 percent of the secondary PM precursors. In Figure 21, PM2.5 achieved 

from direct PM emissions reductions is only slightly below than that achieved from reducing 

all emissions. Correspondingly, PM2.5 achieved from 20 percent reductions in precursor 

gas emissions (NOx and VOC; ammonia; and sulfur-containing species) was about 10 times 

lower than for 20 percent reductions of directly emitted PM. Reductions of directly emitted 

PM, however, were most effective near the PM emissions sources.

Reducing 20 percent of the ammonia emissions (around 15 tons/day eliminated, see Table 

2) was the most effective of the precursor emissions reductions simulated. The PM2.5

achieved mostly reflects reduced particulate ammonium nitrate levels. The ammonia 

emissions reductions also reduced ammonium sulfate levels by up to 0.1 g/m3near sulfur 

sources around Richmond and through Carquinez Strait (not shown). The effectiveness of 

the 20 percent ammonia emissions reductions was roughly uniform throughout the Bay 

Area. This contrasts markedly with reductions of primary PM emissions, which were most 

effective near primary PM source areas. Ammonia emissions reductions were less effective 

near ammonia sources, where the secondary PM forming chemistry was limited by lack of 

nitric acid. (See Figure 17 and also the discussion of the implications of nitric acid limited 

chemistry in the last paragraph of section 5.2.2.). This effect was most notable in the Bay 

Area around San Francisco, San Jose, and Santa Rosa.
36

Reducing a combined 20 percent of the NOx and VOC emissions (around 110 tons/day NOx

and 140 tons/day VOC eliminated, see Table 2) was relatively ineffective. These precursors 

contribute primarily to ammonium nitrate formation. Benefits from these emissions 

reductions occurred primarily around Santa Rosa. The benefit here was around 0.25 g/m3. 

For other locations, PM2.5 was small in magnitude. The negative PM2.5 values may be 

mathematical artifacts from the model. 

Reducing 20 percent of the sulfur-containing PM precursor emissions (around 16 tons/day 

eliminated, see Table 2) had a relatively small impact on Bay Area PM2.5 levels. Benefits of 

these emissions reductions occurred primarily near sulfur sources around the bay. 

Maximum PM2.5 of around 0.25 g/m3 was comparable with that of the combined 20

percent NOx and VOC emissions reductions. Tonnage reductions of sulfur (16 tons/day), 

however, were an order of magnitude lower than for the NOx and VOC reductions (around 

250 tons/day) required to achieve a similar effect. 

Under episodic conditions with winds from the east, sulfur emissions from the offshore 

shipping lanes were downwind of the Bay Area. Offshore sulfur emissions reductions under 

winds from the east likely had little impact around the bay where ammonium sulfate 

reductions occurred. Reductions of sulfur emissions may have been more effective under 

non-episodic conditions having winds from the west. Under such conditions, the offshore 

shipping lanes were upwind of the Bay Area. PM2.5 resulting from the 20 percent sulfur 

emissions reductions were over 1 g/m3in some cases with winds from the west (not 

shown).

Emissions reductions benefits were tabulated for the Bay Area subdomains shown in Figure 

18. Benefits were tabulated only for directly emitted PM and ammonia emissions 

reductions, the most effective reductions for controlling primary and secondary PM2.5, 

respectively. For each subdomain, PM2.5 values were tabulated only for the grid cell with 

the highest simulated PM2.5 levels on each episodic day. Pooling the model results in this 

manner reflects how emissions reductions impact attainment of 24-hour PM standards, 

which is likewise based on peak PM levels. Results are shown in Figure 22.

Directly emitted PM reductions of 20 percent impacted ambient PM2.5 levels differently for 

different Bay Area subdomains. The directly emitted PM reductions were most effective 

around San Jose, where primary PM2.5 levels were high over a relatively large area. Benefits 

ranged about 4-6 g/m3. Directly emitted PM reductions were less effective for some days 

at locations distant from PM sources, such as the eastern extremity of the Bay Area, Santa 

Rosa, and Livermore Valley. Otherwise, PM2.5 values were generally in the range 3-5 

g/m3. In terms of percentage reductions of primary PM2.5, there was much less spatial 

variability. This is because reductions of directly emitted PM more strongly impact primary 

PM2.5 levels near PM sources where primary levels are higher. Primary PM2.5 levels were 

typically reduced 12-20 percent, with an average around 16 percent. Secondary PM levels 

were not significantly affected by reductions of directly emitted PM.
37

Ammonia emissions reductions of 20 percent impacted ambient PM2.5 levels relatively 

uniformly across the Bay Area subdomains. An exception was the extreme eastern portion 

of the Bay Area, where PM2.5 levels were somewhat less sensitive to the ammonia 

emissions reductions. This subdomain adjacent to the Delta was mostly affected by 

transported secondary PM under episodic conditions having winds from the east. PM2.5

values were generally in the range 0.1-0.5 g/m3. Secondary PM2.5 levels were typically 

reduced 0-4 percent, with an average around 2 percent. Primary PM levels were not 

significantly affected by reductions of ammonia emissions.

Figure 21. Surface plots of simulated sensitivities of PM2.5 for five 20% emissions reductions, averaged across 

the 20 days for which simulated Bay Area maximum 24-hour PM2.5 level exceeded 35 g/m3. Bay Area 

counties and the California coastline are drawn using thick black lines. City limits for Sacramento and Stockton 

are drawn using thin black lines. Positive PM2.5 values indicate that ambient PM levels decrease with 

emissions reductions. Same scale is used for top and bottom tiles.
38

Figure 22. Tabulated PM2.5 values for grid cell with highest simulated PM2.5 level for eight subdomains 

depicted in Figure 18, for 20 exceedance days only. Top plot shows sensitivity to 20% direct PM emissions 

reductions. Bottom plot shows sensitivity to 20% ammonia reductions. Horizontal lines on boxes indicate 25th, 

50th (median), and 75th percentiles for PM2.5 values. Remaining values are contained within whiskers, except 

for outliers plotted using a plus signs.

5.4.2 Wood smoke impacts

Wood smoke PM2.5 levels were simulated for 11/15/2008-1/31/2009. This was the first 

winter for which BAAQMD wood burning restrictions were in effect. The simulation period 

contained 8 of the 11 Spare the Air days during the 2008-09 winter (Table 3). Two different 

model runs were performed. Bay Area wood smoke levels were simulated with and without 

wood burning restrictions during the Spare the Air periods. For the run without burning 

restrictions, full wood burning emissions were simulated for the entire winter period. For 

the run with burning restrictions, wood burning emissions were eliminated from the 

simulation only for noon of each Spare the Air day through noon of the following day. This 

timing reflects 100 percent compliance with the actual periods of wood burning restriction. 

For all other periods, full wood burning emissions were simulated. The only difference 

between the pair of model runs was the presence or absence of wood burning during the 

eight 24-hour Spare the Air periods.

Examples of the wood smoke simulations are shown in Figure 23 for Spare the Air days

12/10/2008 and 1/18/2009. Without burning restrictions, peak wood smoke levels of 10-20 

g/m3 would have occurred over the areas having high wood burning emissions (see Figure 

3). Wood smoke levels would have been around 5 g/m3or more for many of the remaining 

populated locations within the Bay Area. Without wood burning restrictions on 12/10/2008, 

wood smoke levels would have been comparably high in the South Bay and East Bay. Wood 

smoke levels also would have been elevated around the bay and Santa Rosa. Without wood 

burning restrictions on 1/18/2009, wood smoke levels would have been highest in the 
39

South Bay. Other locations would have been less strongly impacted by wood smoke. With 

burning restrictions on these Spare the Air days, considerable reductions in wood smoke 

levels occurred for most Bay Area locations near concentrated wood burning source areas. 

Peak benefits were around 10 g/m3of reduced wood smoke. On 12/10/2008 greater 

benefit was obtained in the East Bay, whereas on 1/18/2008 greater benefit was obtained 

in the South Bay. Wood smoke levels were not reduced to zero because the burning 

restrictions did not begin until noon of these days. Also, carried over wood smoke from 

previous days may have impacted the Bay Area during the Spare the Air days. These results 

were representative of the 2008-09 winter. The burning restrictions had a significant effect 

which varied by location depending on the prevailing weather.

Figure 24 shows the effect of the wood burning ban for the 2008-09 winter at the Concord 

and San Jose monitoring locations. These data are representative of the other Bay Area 

sites. Blue bars indicate the observed PM2.5 level. It is assumed that measurements during 

these Spare the Air periods reflected the effects of 100 percent compliance with the wood 

burning ban (i.e. no wood burning emissions). Green bars indicate the simulated additional 

concentration of wood smoke that would have resulted without the burning restrictions. 

These simulated wood smoke levels were averaged across the 33 array of model grid cells 

surrounding each respective monitoring location. The sum of the blue and green bars reflect 

the total PM2.5 levels that would have resulted without burning restrictions. Thus, the green 

bars reflect the effect of the restrictions. 

On Spare the Air day 12/10/2008, observed Concord PM2.5 level was 17 g/m3. Without 

burning restrictions, more than 14 g/m3additional wood smoke would have been present, 

for a total PM2.5 level of over 31 g/m3. This was the largest benefit of the wood burning 

restrictions to have occurred at Concord. At San Jose, PM2.5 level was 30 g/m3 with burning 

restrictions, but would have been nearly 34 g/m3 without the restrictions. This benefit at 

the San Jose monitoring location was far smaller than the maximum South Bay benefit (10 

g/m3) indicated in the surface plot (Figure 23). This discrepancy arises because the San 

Jose monitor was not located at the area of peak simulated wood smoke level in the South 

Bay. For Spare the Air day 1/18/2009, benefits of the burning restriction were roughly equal

(3-4 g/m3) for both the Concord and San Jose monitoring locations; however, greater 

reductions of peak wood smoke levels (Figure 23) occurred in the South Bay (10 g/m3)

than in the East Bay (7 g/m3). These modeling results suggest that reductions of 

population exposure to wood smoke were considerably greater than indicated by the 

monitoring data alone.

Without the burning restrictions, wood smoke levels for the eight Spare the Air days would 

have averaged around 11, 7, 5, 3, and 3 g/m3for the Concord, San Jose, San Francisco, 

Vallejo, and Livermore monitoring locations, respectively. Assuming 100 percent

compliance, the burning restrictions were estimated to reduce these wood smoke levels by 

about 50-75 percent, depending on location. The 24-hour (midnight to midnight) wood 

smoke levels were never reduced to zero because of both the timing (noon to noon) of the 

burning restrictions and also carried over wood smoke. The burning restrictions had the 
40

added benefit of reducing carried over wood smoke into the days following burning 

restrictions. Because the burning restrictions reduced carry over, enhanced benefits may be 

achieved for multiple, consecutive Spare the Air calls. For most locations, the two 

consecutive Spare the Air calls on 11/24-25/2008 provided the largest reductions of PM2.5

levels simulated for the 2008-09 winter.

Table 3. Spare the Air days for 2008-09 winter during which wood burning ban was in effect from noon 

through noon the following day.

11/19/2008 12/7/2008 1/15/2009 2/3/2009

11/24/2008 12/10/2008 1/18/2009 2/4/2009

11/25/2008 1/4/2009 1/30/2009

Figure 23. Wood smoke simulation results for Spare the Air days 12/10/2008 (top row) and 1/18/2009 

(bottom row). Simulation with wood burning emissions (left column) shows estimated Bay Area wood smoke 
41

levels assuming full wood burning. Simulation without wood burning emissions (right column) shows 

estimated benefit (reduction) of wood smoke levels resulting from the burning restrictions.

0

5

10

15

20

25

30

35

40

45

50 11/15/2008 11/22/2008 11/29/2008 12/6/2008 12/13/2008 12/20/2008 12/27/2008 1/3/2009 1/10/2009 1/17/2009 1/24/2009 PM2.5 (ug/m3)

0% compliance

observed

Concord

0

5

10

15

20

25

30

35

40

45

50 11/15/2008 11/22/2008 11/29/2008 12/6/2008 12/13/2008 12/20/2008 12/27/2008 1/3/2009 1/10/2009 1/17/2009 1/24/2009 PM2.5 (ug/m3)

0% compliance

observed

San Jose

Figure 24. Simulated effect of wood burning restrictions during 2008-09 winter at Concord and San Jose

monitoring locations. Blue bars are observed PM2.5 levels. For Spare the Air days, the observed PM2.5 levels are 

assumed to result from 100% compliance with the burning restrictions. Green bars indicate simulated wood 

smoke that would have additionally been present without the burning restrictions. They represent the benefit 

of the burning restrictions.
42

5.5 Transport impacts on Bay Area

PM2.5 transport impacts on the Bay Area were explored by simulation. The approach 

implemented “transport runs” of the air quality model similar to the sensitivity runs 

described in section 5.4. Two types of transport runs were performed: zeroing out of Bay 

Area anthropogenic emissions and reducing upwind areas’ anthropogenic emissions. 

Zeroing out the Bay Area emissions provided an estimate of the cumulative transport 

impacts from all sources outside of the Bay Area. Reducing upwind area emissions provided

an estimate of how Bay Area air quality may benefit from reduced upwind emissions 

sources. This latter approach was applied to estimate transport impacts separately from the 

Sacramento area and San Joaquin Valley. The model results indicated differences in 

transported primary and secondary PM2.5 from these Central Valley areas.

The transport analyses in which emissions were zeroed out were unrealistic. Such 

simulations only provided qualitative information regarding the relative contributions of 

sources within and outside of the Bay Area. Moreover, they did not account for potential 

interactions of emissions from different air basins.

5.5.1 Bay Area emissions zeroed out

CMAQ was run with zero Bay Area anthropogenic emissions for 12/2-2/2 of both 2000-01 

and 2006-07. Simulated 24-hour levels for the six PM2.5 components are shown in Figure 25. 

These results are averages across the same 55 days as shown in Figure 16, for which Bay 

Area maximum 24-hour PM2.5 level exceeded 35 g/m3in the base case simulation. PM 

levels shown in Figure 25 resulted from emissions outside of the Bay Area, as well as 

biogenic emissions within the Bay Area. Bay Area biogenic emissions contributed to OC and 

“other” primary PM only. Differences in PM levels between Figure 16 and Figure 25

represent the contribution of Bay Area anthropogenic emissions to PM2.5 levels.

Without Bay Area anthropogenic emissions, primary PM2.5 levels (EC, most of OC, and 

other) in the Bay Area were markedly reduced. This effect was most pronounced near the 

Bay Area sources, where base case levels were locally elevated (see Figure 16). Transported 

EC levels were as high as 1 g/m3in the eastern portion of the Carquinez Straight and 

through the Altamont Pass. These corridors directly connect with the Central Valley, where 

EC levels were higher. For the rest of the Bay Area, EC levels were 0.5 g/m3or lower. 

Similar spatial distributions were observed for OC and other PM2.5. Higher levels were 

observed nearer the mountain passes connecting to the Central Valley. In the Bay Area, OC 

levels ranged about 1-4 g/m3, and other PM2.5 levels ranged about 0.5-3 g/m3.

Transport impacts of secondary ammonium nitrate were generally greater than for the 

primary PM2.5 components combined. Ammonium nitrate levels along the eastern boundary 

of the Bay Area were as high as13 g/m3. Plumes of ammonium nitrate with levels around 8 

g/m3 were present through the Carquinez Strait and Altamont Pass. Areas around the bay 

had about 5 g/m3transported ammonium nitrate.
43

Ammonium sulfate levels in the Bay Area were spatially uniform at around 1 g/m3. This 

regional ammonium sulfate did not appear to be strongly transported from the Central 

Valley.

Central Valley levels for all PM components were nearly the same with and without Bay 

Area emissions. Therefore, it is unlikely that PM and its precursors were transported from 

the Bay Area into the Central Valley when Bay Area episodes occurred.

Figure 25 indicates average transport impacts that occurred under episodic Bay Area 

conditions. During the more severe episodes, transport impacts were often greater. 

Transport impacts were tabulated for the six days with highest measured PM2.5 levels for 

both 2000-01 and 2006-07, for 12 days total. Total transported PM2.5 levels averaged 19, 19, 

11, and 12 g/m3at the Livermore, Vallejo, San Jose, and San Francisco monitoring 

locations, respectively. Maximum total transported PM2.5 levels were 33, 37, 24, and 24 

g/m3for these stations, respectively. Thus, 24-hour PM2.5 exceedances could have 

occurred in the Bay Area without any Bay Area anthropogenic emissions. For these 12 days, 

transported primary PM2.5 averaged 7, 8, 4, and 5 g/m3and transported secondary PM2.5

averaged 12, 11, 7, and 7 g/m3for these four stations, respectively. Transported secondary 

PM2.5 levels averaged around 40-80 percent more than transported primary PM2.5 levels, 

depending upon location.

Figure 25. Same as Figure 16, except simulated with Bay Area anthropogenic emissions zeroed out. Scales are 

same as in Figure 16 to facilitate comparison.

5.5.2 Upwind emissions zeroed out

CMAQ was run separately with zero Sacramento area and San Joaquin Valley anthropogenic 

emissions for 12/2-2/2 of both 2000-01 and 2006-07. Simulated differences in 24-hour
44

PM2.5 component levels between the base case and each transport run (PM2.5, as defined 

in section 5.4.1) indicated how the Bay Area benefits from eliminating emissions in these 

upwind regions. The results were averaged across the same 55 days as shown in Figure 16

for which Bay Area maximum 24-hour PM2.5 level exceeded 35 g/m3in the base case 

simulation. PM2.5 values for eliminating Sacramento area and San Joaquin Valley emissions 

are shown in Figure 26 and Figure 27, respectively.

Eliminating the primary PM emissions from the Sacramento area reduced levels of primary 

PM2.5 (EC, OC, and other) through Carquinez Strait and into the northern portion of the Bay 

Area. Benefits of around 5 g/m3primary PM2.5 were achieved through Carquinez Strait. 

Benefits of reducing Sacramento area EC and OC emissions were primarily localized through 

Carquinez Strait. For the “other” PM2.5 component, benefits additionally extended over the 

northern San Francisco peninsula and Santa Rosa. Benefits of reducing the other PM 

components extended farther into the Bay Area because these emissions occurred in the 

rural southwestern portion of the Sacramento Valley adjacent to the Bay Area. Sacramento 

area EC and OC emissions, on the other hand, were concentrated only around Sacramento. 

EC and OC were less strongly transported into the Bay Area because of the increased 

transport distance relative to the other primary PM components.

Eliminating the PM precursor emissions from the Sacramento area reduced levels of 

secondary ammonium nitrate PM2.5 throughout the Bay Area. The benefits were largest and 

approximately equal in magnitude through both Carquinez Straight and Altamont Pass. The 

Sacramento area precursors contributed to regionally elevated Central Valley secondary 

PM2.5 levels. This regional secondary PM2.5 was then transported into the Bay Area through 

mountain passes connecting with the Central Valley. Through these passes, reductions in 

ammonium nitrate of around 2.5 g/m3resulted from elimination of the Sacramento area 

emissions. Elsewhere throughout the Bay Area, the benefit was around 1.5 g/m3. These 

benefits may have been underestimated because the model sometimes produced overly 

strong winds throughout the Sacramento Valley during Bay Area episodes. This bias resulted 

in an underestimation of secondary PM levels away from the Sacramento source area.

Eliminating sulfur emissions from the Sacramento area had little impact on the Bay Area. 

Benefits of up to 0.1 g/m3occurred for Solano County only.

Figure 26 indicates average transport impacts from the Sacramento area that occurred 

under episodic Bay Area conditions. During the more severe episodes, transport impacts 

were often greater. Transport impacts were tabulated for the six days with highest 

measured PM2.5 levels for both 2000-01 and 2006-07, for 12 days total. Total transported 

PM2.5 levels averaged 4, 5, 2, and 3 g/m3at the Livermore, Vallejo, San Jose, and San 

Francisco monitoring locations, respectively. For these 12 days, transported primary PM2.5

averaged 2, 3, 1, and 1 g/m3and transported secondary PM2.5 averaged 2, 2, 1, and 2 

g/m3for these four stations, respectively. Transported primary PM2.5 levels were 

somewhat higher at Vallejo, and transported secondary PM2.5 levels were somewhat higher 

for the other three monitoring locations.
45

Figure 26. Benefit (PM2.5) upon eliminating anthropogenic emissions for the Sacramento area. Averaged 

across same 55 days as for Figure 16 having base case simulated Bay Area PM levels exceeding 35 g/m3.

Elimination of the primary PM emissions from the San Joaquin Valley reduced levels of 

primary PM2.5 (EC, most of the OC, and other) through Carquinez Strait and Altamont Pass. 

Benefits of around 4 g/m3primary PM2.5 were achieved through these mountain passes. 

Slightly lower benefits of around 3 g/m3primary PM2.5 were achieved through the Pacheco 

Pass and into southernmost Santa Clara County. Transported EC, OC, and other PM2.5

affected the same locations within the Bay Area.

Eliminating the PM precursor emissions from the San Joaquin Valley reduced levels of 

secondary ammonium nitrate PM2.5 throughout the Bay Area. The benefits were largest (up 

to 8 g/m3) through Pacheco Pass and into southernmost Santa Clara County. The benefits 

were enhanced and approximately equal in magnitude (up to 6 g/m3) over easternmost 

Contra Costa County and into Altamont Pass. Transport impacts were increased for the 

more southern mountain passes because secondary PM2.5 levels generally increased further 

south into San Joaquin Valley. Elsewhere throughout the Bay Area, the benefit was around 

5 g/m3.

Eliminating sulfur emissions from the San Joaquin Valley provided less than 0.5 g/m3

benefit through Carquinez Strait and Altamont Pass only.

Figure 27 indicates average transport impacts from the San Joaquin Valley that occurred 

under episodic Bay Area conditions. During the more severe episodes, transport impacts 

were often greater. Transport impacts were tabulated for the six days with highest 

measured PM2.5 levels for both 2000-01 and 2006-07, for 12 days total. Total transported 

PM2.5 levels averaged 13, 10, 7, and 7 g/m3at the Livermore, Vallejo, San Jose, and San 
46

Francisco monitoring locations, respectively. For these 12 days, transported primary PM2.5

averaged 4, 2, 2, and 1 g/m3and transported secondary PM2.5 averaged 9, 7, 6, and 6

g/m3for these four stations, respectively. Transported secondary PM2.5 levels were 2.5-4 

times higher than transported primary PM2.5 levels.

Figure 27. Benefit (PM2.5) upon eliminating anthropogenic emissions for the San Joaquin Valley. Averaged 

across same 55 days as for Figure 16 having base case simulated Bay Area PM levels exceeding 35 g/m3.

The transport cluster analysis of section 4.3.3 identified two distinct weather patterns under 

which transport is believed to occur. One pattern exhibited bulk air flow into the Bay Area 

(through Carquinez Strait) from the Sacramento Valley (Figure 15, left panel), while the 

other pattern exhibited bulk flow from the San Joaquin Valley (Figure 15, right panel). 

Transport impacts were assessed by averaging PM2.5 values across days associated with 

each transport pattern. Only days for which Bay Area maximum 24-hour PM2.5 level 

exceeded the 30 g/m3in the base case simulation were included in the averages. 

Transport impacts under each transport pattern were assessed separately for Sacramento 

area and San Joaquin Valley emissions zeroed out. During the transport simulation period, 

there were 41 days classified as having transport from the Sacramento area and 5 days 

classified as having transport from the San Joaquin Valley. Results are shown in Figure 28.

Zeroing out the Sacramento area emissions provided the greatest benefit to the Bay Area 

under the transport pattern with bulk air flow arriving from the Sacramento Valley. The 

benefit was around 5 g/m3throughout the central portion of the Bay Area. This 

transported PM2.5 was about half primary and half secondary. With bulk air flow arriving 

from the San Joaquin Valley, benefits of eliminating the Sacramento area emissions were 

smaller and limited to adjacent portions of the Bay Area.
47

Zeroing out the San Joaquin Valley emissions provided the greatest benefit to the Bay Area 

under the transport pattern with bulk air flow arriving from the San Joaquin Valley. The 

benefit was around 5-15 g/m3throughout the northern portion of the Bay Area. Transport 

impacts were highest through the Carquinez Strait. This transported PM2.5 was about onethird primary and two-thirds secondary. With bulk air flow arriving from the Sacramento 

Valley, benefits of eliminating the San Joaquin Valley emissions were smaller. Under these 

conditions, the central and southern portions of the Bay Area benefited the most, around 5-

10 g/m3. Transported PM2.5 levels through both the Altamont and Pacheco Passes were 

higher than through Carquinez Strait.

Figure 28. PM2.5 values averaged across 41 days with transport from the Sacramento Valley (top panels) and 

5 days with transport from the San Joaquin Valley (bottom panels). Transport simulations were conducted 

with Sacramento area anthropogenic emissions zeroed out (left panels) and with San Joaquin Valley 

anthropogenic emissions zeroed out (right panels).
48

6. Conclusions and further study

This report provided interim technical details of research efforts to better understand fine 

particulate matter (PM2.5) formation in the Bay Area. The body of knowledge contained in 

this report was an enhancement by BAAQMD investigations to supplement research 

performed under state and federal agencies. This document provided a history of BAAQMD 

PM2.5 research activities and described key results from a number of important studies. 

Research efforts included analyses of measurements as well as computer modeling.

6.1 Data analysis

6.1.1 Analysis of PM measurements

The Bay Area 24-hour PM2.5 exceedances were almost entirely confined to the winter 

months. This season exhibited increased atmospheric stability that trapped pollutants near 

the ground. Consecutive stagnant, clear, and cool winter days were typically requisite for 

PM2.5 episodes to develop. Stagnant conditions prevented dispersion of PM2.5. Clear skies 

favored photochemical production of secondary PM2.5, especially ammonium nitrate. Cool 

temperatures favored secondary ammonium nitrate particulate buildup by decreasing its 

tendency to evaporate. Typically, PM2.5 levels would build for 2-4 days before a 24-hour

exceedance occurred. Episodes commonly lasted a few days, but some persisted 1-2 weeks 

or longer. More stagnant winters tended to have more 24-hour PM2.5 exceedances than 

those with more windy and stormy conditions.

PM2.5 episodes were regional in nature and impacted most Bay Area locations. Livermore, 

Concord, San Jose, and Vallejo had 24-hour PM2.5 design values at or near the NAAQS 

exceedance threshold of 35 g/m3. Other populated locations exhibited design values 

around 25-30 g/m3. Near-background PM2.5 levels were observed at Point Reyes, where 

annual-average PM2.5 levels were about half that of other Bay Area monitoring locations. 

The Bay Area is expected to attain the NAAQS for annual-average PM2.5 level.

The composition of PM2.5 varied throughout the Bay Area. Carbonaceous PM2.5 (EC and OC) 

was mostly concentrated near its sources around the bay. Levels for secondary components 

sulfate, nitrate, and ammonium were relatively spatially uniform throughout the Bay Area. 

The secondary PM2.5 components required a few days of air mass aging to reach 

appreciable levels, during which they were dispersed throughout the Bay Area. Bay Area 

secondary PM2.5 was mostly ammonium nitrate. In terms of percentages, more urban 

locations around the bay had relatively higher proportions of primary PM2.5, whereas more 

rural locations further inland had relatively higher proportions of secondary PM2.5. Natural 

sea salt (sodium and chloride) levels were generally low, and were somewhat higher nearer 

the coast.
49

6.1.2 Chemical Mass Balance (CMB) for source apportionment

The Chemical Mass Balance (CMB) analysis was applied for source apportionment. CMB 

estimates the contributions (strengths) of predefined source categories to best account for 

the observed PM composition on a given day. Six source categories were used in the 

analysis: ammonium nitrate, ammonium sulfate, marine (sea salt) aerosol, geological and 

road dust, fossil fuels combustion, and biomass combustion. CMB analysis was performed 

on speciated PM2.5 measurements from 5 locations mostly obtained during 1999-2001. 

Separate CMB analyses were performed to determine source contributions to peak 24-hour

and annual average PM2.5 levels. Isotopic carbon (14C) analysis was used to adjust the raw 

CMB results to distinguish between combustion of fossil and non-fossil (biomass) materials. 

Source contributions were expressed as percentages to allow comparisons between sites 

having different PM2.5 levels.

Source contributions to peak 24-hour PM2.5 levels were based on measurements during 

episodic winter conditions. The urban and suburban locations (San Francisco, San Jose, and 

Livermore) exhibited comparable source contributions. Primary combustion sources (fossil 

and biomass) and secondary ammonium nitrate accounted for the bulk of PM2.5. San Jose 

had a somewhat lower contribution of ammonium nitrate than the other heavily populated 

locations. San Francisco had a somewhat lower contribution of biomass burning than the 

other urbanized locations. The rural Bethel Island location had increased ammonium nitrate 

contribution and decreased fossil fuel combustion source strength relative to the more 

urbanized locations. Point Reyes was quite different from the other locations. The strongest 

contributing sources were naturally occurring marine aerosols (over 30 percent) and 

ammonium sulfate (20 percent). Ammonium nitrate contribution was lower at Point Reyes 

than for the other Bay Area monitoring locations. Overall, primary combustion particles and 

secondary ammonium nitrate were the main drivers for Bay Area PM2.5 episodes impacting 

populated areas. Wood burning may have been the single largest PM2.5 source contribution 

for the Bay Area during episodes. Geological dust, tire and break wear, and marine sources 

contributed near-negligible amounts to PM2.5 exceedances. 

Source contributions to annual average PM2.5 levels were based on year-round 

measurements. The urban and suburban locations exhibited comparable source 

contributions. Primary combustion sources dominated. As with peak PM2.5, biomass 

contributions were somewhat lower for San Francisco as compared to the other heavily 

populated locations. The rural Bethel Island location had higher secondary and lower 

primary contributions as compared with the more populated locations. It was most strongly 

impacted by regional secondary PM2.5. The remote coastal location at Point Reyes was 

affected most strongly by marine and ammonium sulfate particulate. Overall, primary 

combustion particles accounted for the bulk of annual average PM2.5 levels. Ammonium 

nitrate contributed somewhat more than ammonium sulfate and marine influences, which 

are comparable. Geological dust and tire and break wear contributed negligible amounts. 

Ammonium nitrate and biomass burning sources were much stronger during the winter 

than for the other seasons.
50

6.1.3 Relationships between weather patterns and PM

Meteorological characteristics associated with PM episodes were identified by comparing 

weather station observations between episodic and non-episodic winter days. Common 

meteorological characteristics associated with elevated Bay Area PM levels were light winds 

from the east throughout much of the Bay Area and also no rain. Such days generally 

exhibited cool temperatures; however, temperature itself was not a strong indicator of 

elevated PM levels. Exceedances of the 24-hour PM2.5 standard in the Bay Area typically 

occurred as PM levels built over time upon three or more consecutive days having the 

above conditions.

Clustering of measurements from every winter day across more than 10 years robustly 

established how weather patterns impact Bay Area PM2.5 levels. The cluster analysis 

revealed five dominant winter weather patterns prevailing over the Bay Area. Three of the 

clusters were conducive to elevated Bay Area PM levels. All three exhibited winds entering 

the Bay Area from the east. Two of these three clusters exhibited near-calm conditions in 

the Central Valley, where PM levels were considerably higher than in the Bay Area. These 

two clusters accounted for around 80 percent and 15 percent of all Bay Area 24-hour PM2.5

exceedances, respectively. The cluster accounting for 80 percent of the Bay Area 

exceedances exhibited persistent easterly winds (arriving from the east) into the Bay Area 

throughout the day. The cluster accounting for 15 percent of the Bay Area exceedances 

exhibited easterly winds into the Bay Area during the overnight and morning hours. Winds 

often reversed to enter the Bay Area from the west during the afternoon. The third cluster 

with easterly flow into the Bay Area had high winds in the Central Valley and 

correspondingly increased dispersion rates. PM levels were higher than average; however, 

Bay Area exceedances were uncommon. The remaining two clusters exhibited marine winds 

entering the Bay Area from the west. Both of these clusters represented stormy conditions, 

and PM levels were low.

 

Atmospheric transitions of weather systems aloft profoundly influenced the surface winds 

that determine PM2.5 levels. A developing or approaching upper-level high pressure center 

over Central California caused a transition from a high wind pattern into a low wind pattern 

for the region, resulting in increasing PM levels. Typically, PM would build to the 

exceedance level in 2-4 days upon a transition into an episodic weather pattern. Thereafter, 

PM levels remained approximately constant while the high pressure system persisted over 

Central California. A transition from an episodic weather pattern into one of the nonepisodic patterns marked the onset of high surface winds that terminate the episodic 

conditions. Episodes often terminated abruptly when a migrating storm (cyclone) passed 

over the Bay Area. Transitions from an episodic weather pattern into a non-episodic pattern 

lacking rain were often associated with PM levels that decreased gradually over a few days.

Exceedances nearly always developed under: stable atmospheric conditions inhibiting 

vertical dispersion; clear and sunny skies favoring enhanced secondary PM2.5 formation; and 

pronounced overnight drainage (downslope) flows off the Central Valley rims, causing low-
51

level air in the Central Valley to empty through the Delta and into the Bay Area from its 

eastern boundary. Episodes exhibited considerable buildup of secondary PM, particularly 

ammonium nitrate. Ammonium nitrate levels in the Central Valley could be about 2-3 times 

higher than in the Bay Area. Central Valley conditions were more conducive to the 

transformation of NOx to nitric acid (needed for the formation of ammonium nitrate) in the 

Central Valley than for coastal locations. Nitric acid then rapidly reacted with ammonia 

emissions, which were especially concentrated in the northern San Joaquin Valley, to form 

ammonium nitrate.

A refined cluster analysis further characterized the upwind Central Valley conditions during 

Bay Area episodes. Two distinct inter-regional air flow patterns were associated with 

different types of Bay Area episodes. The majority of exceedance days (around 60 percent) 

were associated with winds from the Sacramento Valley to the north entering the Bay Area 

through the Delta. Peak PM2.5 levels typically occurred at San Jose for this type of episode. A 

minority of exceedance days (around 20 percent) were associated with winds from the San 

Joaquin Valley from the south entering the Bay Area through the Delta. Peak PM2.5 levels 

typically occurred in the East Bay (at Livermore, Concord, or Vallejo) for this type of episode. 

The remaining relatively moderate episodes (around 20 percent) could not be associated 

with any distinct inter-regional transport pattern linking the Bay Area and surrounding air 

basins.

6.2 Computer modeling

A number of different simulations were performed to better understand PM2.5 formation in 

the Bay Area. Three winter seasons were simulated. All PM and precursor sources were 

modeled for the winters of 2000-01 and 2006-07. The 2000-01 winter was simulated 

because extensive measurements were available from CRPAQS; however, this was a severe 

PM season with the highest PM levels on record. The 2006-07 winter exhibited moderate 

conditions that may have been more representative for air quality planning purposes. The 

third modeled winter was 2008-09. For this winter, only directly emitted PM from wood 

burning was simulated. This winter was simulated to evaluate the effectiveness of wood 

burning restrictions first enacted for the 2008-09 winter. 

Meteorology for all three winters was simulated using MM5. PM2.5 was simulated using 

CMAQ for 2000-01 and 2006-07, and wood smoke was simulated using CAMx for 2008-09. 

Emissions inventories comprised various data compiled by the ARB, BAAQMD, and Sonoma 

Technology, Inc. All meteorology and air quality model results were validated against 

measurements to be reasonably accurate.

6.2.1 Base-case PM modeling

CMAQ simulated six individual PM2.5 components which were combined to estimate total 

PM2.5 levels: ammonium, sulfate, nitrate, OC, EC, and other unresolved components. The 
52

simulated “other” component accounted for inert particulates including dust, marine 

aerosol, brake and tire wear, and trace metals.

EC and most of the OC resulted from primary emissions. The highest Bay Area EC levels 

occurred over West Oakland, reflecting Port of Oakland diesel sources. Aside from the rural 

Central Valley locations, EC accumulated only near its sources. OC levels were elevated for 

the same locations as EC, as well as other moderately populated areas. OC was associated 

with both commerce and household activities. OC levels over West Oakland, the EC hot 

spot, were lower than other areas having elevated OC levels. Also, OC may have been 

proportionally higher than EC over populated areas as compared to over highways and 

centers of commerce and industry. Accumulated EC and OC in the rural Central Valley 

locations demonstrated the lack of ventilation for this polluted valley. The "other" PM2.5

component resulted from primary emissions only. It was elevated near highways and 

populated areas. It reflected trace metals from mobile and stationary combustion sources, 

suspended road dust, and small amounts of tire and brake wear particles. Geological dust 

did not appear to be a major contributor to elevated PM2.5 levels under relatively calm 

conditions during the winter rainy season.

Ammonium nitrate was the dominant component of secondary PM2.5. Ammonium nitrate 

levels were highest in the Central Valley. There was a general gradient with levels 

decreasing with distance from the Delta deeper into the Bay Area. The model results 

suggested that ammonium nitrate formation is limited by nitric acid, and not by ammonia. 

Ammonium sulfate was a minor component of secondary PM2.5 during episodic conditions. 

Appreciable levels of ammonium sulfate in the Bay Area appeared only around the ports, 

industrial plants, and refineries near Oakland, Richmond, and Carquinez Straight. 

Ammonium sulfate also formed over the offshore shipping lanes from combustion of sulfurladen bunker fuel. Because winds were from the east during episodes, these offshore sulfur 

sources did not strongly impact the Bay Area. Ammonium sulfate occasionally accounted for 

a moderate proportion of PM2.5. This situation sometimes occurred under non-episodic 

conditions when winds were from the west and the shipping lanes were upwind of the Bay 

Area.

There was a distinct spatial gradient in the fraction of primary versus secondary PM2.5 over 

Central California. In the Central Valley, at Modesto, PM2.5 was composed of approximately 

equal portions of primary and secondary components, up to about 25 g/m3of primary 

PM2.5. Secondary PM2.5 levels occasionally exceeded 35 g/m3, indicating that an 

exceedance could have occurred in the Central Valley even without any primary PM 

emissions. For high levels of primary PM2.5 (beyond 25 g/m3), additional buildup of 

secondary PM2.5 did not occur. In the Delta subregion, PM2.5 characteristics were similar to 

Modesto; however, there was slightly more primary than secondary contribution. A similar 

effect was observed through the Altamont Pass and into the Livermore Valley. Here, 

however, total PM2.5 levels were lower than in the Central Valley or Delta. Significant 

contributions of both primary and secondary PM2.5 were required for an exceedance to 

occur in the eastern portion of the Bay Area. For the San Francisco and San Jose urban 
53

areas, PM2.5 was dominated by primary contribution. Near these cities, PM2.5 exceedances 

could have occurred from primary contributions alone. North of San Francisco, in the 

Petaluma Valley, PM2.5 was more primary than secondary; however, primary components 

did not dominate as in the urban areas. Significant contributions of both primary and 

secondary PM2.5 were required for an exceedance to occur in the North Bay.

6.2.2 PM sensitivities to Bay Area emissions reductions

Across-the-board Bay Area emissions reductions of 20 percent were simulated for the 

following five classes of chemical species: NOx and VOC combined, gaseous sulfur species, 

ammonia, directly emitted PM, and these four classes combined, comprising all 

anthropogenic emissions. These across-the-board reductions were simulated for one 

episode each from 2000-01 and 2006-07. 

Reducing the directly emitted PM reduced peak PM2.5 levels nearly ten times more 

efficiently than reducing the secondary PM precursors. Benefits achieved from direct PM 

emissions reductions were only slightly below than that achieved from reducing all 

emissions. Reducing primary PM emissions by 20 percent (around 18 tons/day eliminated) 

typically reduced primary PM2.5 levels by 12-20 percent, depending on location, with an 

average around 16 percent. Reductions of directly emitted PM were most effective near the 

PM emissions sources where primary PM2.5 levels were highest (see Figure E-1). The largest 

benefits of around 4-6 g/m3occurred near San Jose. Directly emitted PM reductions were 

less effective for some days at locations distant from PM sources, such as the eastern 

extremity of the Bay Area, Santa Rosa, and Livermore Valley.

Reducing ammonia emissions by 20 percent (around 15 tons/day) was the most effective of 

the precursor emissions reductions. The effectiveness of the 20 percent ammonia emissions 

reductions was roughly uniform throughout the Bay Area. Secondary PM2.5 levels were 

typically reduced 0-4 percent, depending on location, with an average around 2 percent. 

Ammonia emissions reductions were less effective near ammonia sources, where the 

secondary PM forming chemistry was limited by lack of nitric acid. Ammonia reductions 

were also relatively ineffective for the extreme eastern portion of the Bay Area. This area 

adjacent to the Delta was mostly affected by transported secondary PM under episodic 

conditions having winds from the east.

Reducing NOx and VOC emissions by 20 percent (around 250 tons/day total) was relatively 

ineffective. Small benefits from these emissions reductions occurred primarily around Santa 

Rosa. 

Reducing sulfur-containing PM precursor emissions by 20 percent (around 16 tons/day) 

typically had a small impact on Bay Area PM2.5 levels under episodic conditions. Benefits 

were similar as for the NOx and VOC reductions. Tonnage reductions of sulfur (16 T/day), 

however, were around 15 times lower than for the combined NOx and VOC reductions (250 

T/day) required to achieve a similar effect. On certain days with the offshore shipping lanes 
54

upwind of the Bay Area, the 20 percent sulfur emissions reductions produced over 1 g/m3

benefit. 

6.2.3 Transport impacts on Bay Area

Transport PM2.5 simulations were performed by zeroing out anthropogenic emissions for 

various air basins. Transport impacts were evaluated for 55 days from 2000-01 and 2006-07 

having simulated base case PM2.5 levels 35 g/m3or higher. Anthropogenic Bay Area 

emissions were eliminated to estimate the cumulative transport impacts from all sources 

outside of the Bay Area. Transported primary PM2.5 levels were around 2-8 g/m3. 

Transport impacts were highest through Carquinez Straight and Altamont Pass which 

connect the Bay Area with the Central Valley. Transport impacts of secondary ammonium 

nitrate were as high as 13 g/m3along the Bay Area eastern boundary, 8 g/m3through the 

Carquinez Strait and Altamont Pass, and 5 g/m3around the bay. Transported ammonium 

sulfate levels of around 1 g/m3 were present uniformly throughout the Bay Area. 

Ammonium sulfate appeared to be transported mostly from regions outside of the Central 

Valley. Central Valley PM2.5 levels were relatively unaffected by eliminating the Bay Area 

emissions.

A second pair of PM2.5 transport simulations was conducted by zeroing out anthropogenic 

emissions for the Sacramento area and San Joaquin Valley separately. Reductions in 

simulated PM2.5 levels relative to the base case reflected transport impacts from the 

respective air basin for which emissions were eliminated. Eliminating the Sacramento area 

emissions reduced primary PM2.5 levels around 5 g/m3through Carquinez Strait, less than 

2 g/m3in the northern half of the Bay Area, and had little effect elsewhere in the Bay Area. 

Secondary PM2.5 levels transported from the Sacramento area were 2.5 g/m3through 

Carquinez Strait and Altamont Pass and around 1.5 g/m3elsewhere in the Bay Area. 

Eliminating the San Joaquin Valley emissions reduced primary PM2.5 levels around 4 g/m3

through Carquinez Strait and Altamont Pass, around 3 g/m3through Pacheco Pass and into 

southern Santa Clara Valley, and had little impact farther into the Bay Area. Secondary PM2.5

levels transported from the San Joaquin Valley were up to 8 g/m3through Pacheco Pass 

and into southern Santa Clara Valley, around 6 g/m3through Altamont Pass and into 

eastern Contra Costa County, and around 5 g/m3elsewhere in the Bay Area. 

During the more severe episodes, transport impacts were often greater than for the 

average episodic conditions. Transport impacts were tabulated for the six days with highest 

measured PM2.5 levels for both 2000-01 and 2006-07, for 12 days total. Total transported 

PM2.5 levels averaged 19, 19, 11, and 12 g/m3at the Livermore, Vallejo, San Jose, and San 

Francisco monitoring locations, respectively. Maximum total transported PM2.5 levels were 

33, 37, 24, and 24 g/m3for these stations, respectively. Thus, 24-hour PM2.5 exceedances 

could have occurred in the Bay Area without any Bay Area anthropogenic emissions. 

Transported secondary PM2.5 levels averaged around 40-80 percent more than transported 

primary PM2.5 levels, depending upon location. Total transported PM2.5 levels from the 
55

Sacramento area averaged 4, 5, 2, and 3 g/m3for these stations, respectively. At Vallejo, 

transported PM2.5 from the Sacramento area had a somewhat higher proportion of primary 

components. At the other three locations, transported PM2.5 from the Sacramento area had 

a somewhat higher proportion of secondary components. Total transported PM2.5 levels 

from the San Joaquin Valley averaged 13, 10, 7, and 7 g/m3for these stations, respectively. 

Transported secondary PM2.5 from San Joaquin Valley were 2.5-4 times higher than 

transported primary PM2.5 levels.

Simulated transport impacts were also compared across two different transport scenarios 

identified by the measurements-based meteorological cluster analysis (described above). 

There were 41 episodic days classified as having transport from the Sacramento area and 5 

episodic days classified as having transport from the San Joaquin Valley. Zeroing out the 

Sacramento area emissions provided greater benefit in the Bay Area when transport 

occurred from the Sacramento area. Transported PM2.5 from the Sacramento area was 

about half primary and half secondary. It mostly impacted the central and northern portions 

of the Bay Area. Zeroing out the San Joaquin Valley emissions provided greater benefit in 

the Bay Area when transport occurred from the San Joaquin Valley. Transported PM2.5 from 

the San Joaquin Valley was about one-third primary and two-thirds secondary. It mostly 

impacted the central and southern portions of the Bay Area.

6.2.4 Wood smoke modeling

Wood smoke PM2.5 levels were simulated for the 2008-09 winter. The simulation period 

contained 8 of the 11 Spare the Air days during the 2008-09 winter. Two different model 

runs were performed. Bay Area wood smoke levels were simulated with and without wood 

burning restrictions during the Spare the Air periods. For the run without burning 

restrictions, full wood burning emissions were simulated for the entire winter period. For 

the run with burning restrictions, wood burning emissions were eliminated from the 

simulation only for noon of each Spare the Air day through noon of the following day. This 

timing reflected 100 percent compliance with the actual periods of wood burning 

restriction. For all other periods, full wood burning emissions were simulated. 

Without burning restrictions on the Spare the Air days, peak wood smoke levels of up to 10-

20 g/m3 would have occurred over the areas having high wood burning emissions. Wood 

smoke levels would have been around 5 g/m3or more for many of the remaining 

populated locations within the Bay Area. Peak benefits of the wood burning restrictions 

were around 10 g/m3of reduced wood smoke. The 24-hour wood smoke levels (averaged 

midnight to midnight) were not reduced to zero because the burning restrictions did not 

begin until noon of the Spare the Air days. Also, carried over wood smoke from previous 

days may have impacted the Bay Area during the Spare the Air days. Simulated peak wood 

smoke levels and maximum benefits of burning restrictions sometimes occurred away from 

the monitoring locations. Modeling results suggested that reductions of population 

exposure to wood smoke were considerably greater than indicated by the monitoring data 

alone.
56

Without the burning restrictions, wood smoke levels for the eight simulated Spare the Air 

days would have averaged around 11, 7, 5, 3, and 3 g/m3for the Concord, San Jose, San 

Francisco, Vallejo, and Livermore monitoring locations, respectively. Assuming 100 percent

compliance, the burning restrictions were estimated to reduce these wood smoke levels by 

about 50-75 percent, depending on location. The burning restrictions had the added benefit 

of reducing carried over wood smoke into the days following burning restrictions. Because 

the burning restrictions reduced carry over, enhanced benefits may be achieved for 

multiple, consecutive Spare the Air calls. Two consecutive Spare the Air calls during 2008-09 

provided the largest reductions of wood smoke levels simulated.

6.3 Overall conclusions

This report summarized a wealth of knowledge generated from BAAQMD in-house PM2.5

research efforts that build on the EPA and ARB efforts. Various analyses of measurements 

were conducted to identify major sources and important weather patterns contributing to 

PM2.5 buildup. Extensive simulations covered the bulk of three winter seasons. This custom 

computer model adequately reproduced the various phenomena represented in the 

measurements. The high degree of corroboration between the measurements- and 

modeling-based results provides a high level of confidence that the findings presented 

herein are both accurate and representative.

Primary and secondary PM2.5 impact the Bay Area differently for both various locations and 

across a range of typical meteorological conditions. The model suggests that reducing direct 

PM2.5 emissions within the Bay Area is the most effective means of reducing Bay Area 

primary PM2.5 levels. These reductions, however, are most effective only near direct PM2.5

emissions sources. These are the areas in which Bay Area total PM2.5 levels are highest. The 

model also suggests that significant amounts of PM2.5, especially secondary PM2.5, are 

transported from the Central Valley. Secondary PM2.5 exhibits a fairly regionally uniform 

influence throughout the Bay Area. Analysis of measurements identified separate transport 

patterns occurring on different days from either the Sacramento Valley or San Joaquin 

Valley. During 1999-2007, around 60 percent of Bay Area 24-hour PM2.5 exceedance days 

occurred with transport from Sacramento area, and 20 percent with transport from San 

Joaquin Valley. 

Model results indicated that Bay Area wood smoke levels during 2008-09 episodic 

conditions would have averaged 3-11 g/m3 without wood burning restrictions, depending 

on location. Spare the Air burning restrictions were estimated to have reduced wood smoke 

levels by around 50-75 percent assuming 100 percent compliance. Spare the Air calls on 

consecutive days had the added benefit of reducing carried over wood smoke, in addition to 

reducing fresh burning emissions. 

Further research results and refinements to existing findings will be reported as new 

information becomes available. More measurements, including those from recently 

commissioned monitoring stations, will be added to the analyzed databases. Longer records 
57

of measurements will be especially useful for evaluating the effectiveness of wood burning 

restrictions. Modeling results will be enhanced by the development of more accurate 

emissions inventories. Additional winter seasons will be selected for both PM2.5 and wood 

smoke simulations. Newer version models having enhanced physics and chemistry will be 

implemented for all simulations.
58

References

BAAQMD, 2009. 2008 Air monitoring network plan. 

http://www.baaqmd.gov/Divisions/Technical-Services/Ambient-AirMonitoring/~/media/2F9844F2D4934AAAB8339552605A570A.ashx

Emery, C.A., Tai, E., and Yarwood, G., 2001. “Enhanced meteorological modeling and 

performance evaluation for two Texas ozone episodes.” Prepared for the Texas Natural 

Resource Conservation Commission by ENVIRON International Corp, Novato, CA.

EPA, 2007. Guidance on the use of models and other analyses for demonstrating attainment 

of air quality goals for ozone, PM2.5, and regional haze. EPA-454/B-07-002, 253 pp.

Fairly, D. et al., 2008. Sources of Bay Area fine particles.

Palazoglu, A., 2009. Cluster analysis of air quality data for Bay Area PM study. Prepared for

BAAQMD by University of California Davis under contract #2007-045.

Reid S.B., and Gilliland E.K., 2008. The development of a SMOKE-based emissions processing 

stream. Prepared for BAAQMD by Sonoma Technology, Inc., Petaluma, CA. STI-908007-

3411-TM.

Sullivan D., Reid S.B., 2008. Development of an Ammonia Emission Inventory for the San 

Francisco Bay Area. Prepared for BAAQMD by Sonoma Technology, Inc., Petaluma, CA. STI907026.01-3187-FMD.
A-1

Appendix A. PM modeling emissions inventory preparation

This appendix provides additional information on the magnitudes as well as the spatial and 

temporal distributions of both directly-emitted PM2.5 and key secondary PM2.5 precursors

(TOG, NOx, SO2, and NH3) used for modeling. Because much of the modeling effort was for

wintertime 24-hour PM, the focus here is on wintertime emission levels rather than annual 

average levels. This will facilitate direct interpretation of PM modeling results as they are 

influenced by emissions. The modeling domain includes much of northern and central 

California. 

The anthropogenic emissions inventory for the Bay Area portion of the modeling domain

was developed from local information and assumptions. For the rest of the domain outside 

the Bay Area, inventories were taken from the ARB. Therefore, emissions are summarized 

below under two subheadings: BAAQMD emissions and emissions outside of BAAQMD.

Modeling was conducted for two separate winters (2000-01 and 2006-07). Emissions were 

summarized for winter 2005 for the Bay Area portion and for winter 2006 for the rest of the 

modeling domain; i.e., tables below show base year emissions for BAAQMD and CARB data 

adjusted from 2000 to 2006.

The ARB provided a biogenic emissions inventory for the entire modeling domain from 

December 16, 2000 to January 7, 2001, the California Regional Particulate Matter Air 

Quality Study modeling period. For winter 2000-01 modeling, ARB’s biogenic emissions 

inventory was used for the period coinciding with the above dates. For 2000-01 modeling 

periods outside of the above dates and for the entire 2006-07 winter, biogenic emissions 

were used from December 19, 2000. This day had an average winter temperature.

A.1 BAAQMD emissions

Table A-1 summarizes BAAQMD 2005 wintertime PM2.5 emissions by county and major 

source category. These emissions were obtained by adjusting annual average daily 

emissions to reflect a winter scenario. The adjustments were based upon seasonal 

adjustment factors used in SMOKE. Overall, area source emissions dominated, with Santa 

Clara and Contra Costa Counties showing the two highest estimates. These two counties 

were followed by Alameda and Sonoma. The remaining counties had appreciably less 

emissions.

Table A-2 shows source contributions to the 2005 area source fine PM emissions shown 

above. Together, these two tables indicate that wood burning was the largest source of 

PM2.5 for all but San Francisco and San Mateo Counties, where non-road sources (such as 

commercial marine and construction equipment) were larger.
A-2

Table A-1. 2005 wintertime PM2.5 emissions by Bay Area county and major source category. (tons/day)

2005

County Area Nonroad Onroad Point Total

Alameda 10.8 2.2 2.3 1.3 16.6

Contra Costa 14.9 0.9 1.1 3.4 20.3

Marin 4.2 0.6 0.2 0.1 5.1

Napa 3.3 0.2 0.2 0.1 3.8

San Francisco 4.0 1.3 0.5 0.2 6.0

San Mateo 5.3 2.1 0.6 0.5 8.5

Santa Clara 15.2 1.0 1.7 1.0 18.9

Solano 3.5 0.5 0.4 0.9 5.3

Sonoma 8.8 0.3 0.4 0.1 9.6

Total 70.0 9.1 7.4 7.6 94.1

Table A-2. 2005 wintertime BAAQMD PM2.5 emissions from area source categories. (tons/day)

Source category Alameda Contra 

Costa

Marin Napa S.F. San 

Mateo

Santa 

Clara

Solano Sonoma Total

Residential Wood Combustion 4.67 10.15 2.57 1.38 0.63 2.07 7.47 1.70 4.95 35.59

All Paved Roads 2.28 1.62 0.39 0.26 0.80 1.20 2.59 0.46 0.66 10.25

Charbroiling 0.92 0.46 0.14 0.09 0.87 0.46 1.05 0.28 0.32 4.59

Res./Com. Natural Gas Comb. 0.60 1.05 0.11 0.05 0.29 0.36 0.77 0.14 0.15 3.52

Misc. Food & Kindred Products 0.69 0.35 0.10 0.07 0.66 0.35 0.80 0.21 0.24 3.47

Poultry Waste Emissions 0.17 0.00 0.28 0.94 0.00 0.00 0.35 0.00 1.54 3.28

Domestic Animal Wastes 0.46 0.41 0.11 0.08 0.26 0.32 0.54 0.13 0.17 2.48

Industrial/Commercial/Institutional 0.22 0.15 0.03 0.07 0.20 0.11 0.28 0.16 0.07 1.29

All Unpaved Roads 0.04 0.06 0.08 0.02 0.00 0.11 0.45 0.03 0.03 0.82

Concrete/Gypsum/Plaster Prods 0.16 0.10 0.04 0.02 0.10 0.09 0.16 0.03 0.05 0.75

All Others 0.61 0.59 0.37 0.27 0.14 0.23 0.70 0.35 0.63 3.89

Total 10.82 14.94 4.22 3.25 3.95 5.30 15.16 3.49 8.81 69.94

Tables A-3 through A-6 show 2005 wintertime county-level emissions of total organic gases 

(TOG), nitrogen oxides (NOx), sulfur dioxide (SO2), and ammonia (NH3), respectively.

Emissions are broken out by major source category. Solano and Sonoma County emissions 

only included those portions which lie inside the District boundaries.

Table A-3. 2005 wintertime TOG emissions by Bay Area county and major source category. (tons/day)

2005

County Area Nonroad Onroad Point Total

Alameda 61.9 15.8 37.4 2.9 118.0

Contra Costa 77.9 10.9 25.2 9.5 123.5

Marin 21.4 5.8 7.0 0.2 34.4

Napa 16.9 3.5 5.5 0.3 26.2

San Francisco 23.7 8.5 13.5 1.0 46.7

San Mateo 25.6 9.3 17.7 1.1 53.7

Santa Clara 86.7 15.0 41.1 13.6 156.4

Solano 14.3 6.7 7.1 2.1 30.2

Sonoma 38.3 4.6 13.6 0.6 57.1

Total 366.8 80.1 168.1 31.3 646.2
A-3

Table A-4. 2005 wintertime NOx emissions by Bay Area county and major source category. (tons/day)

2005

County Area Nonroad Onroad Point Total

Alameda 5.6 45.3 78.5 0.9 130.3

Contra Costa 7.4 17.3 40.1 21.8 86.6

Marin 1.3 5.7 9.8 0.0 16.8

Napa 0.8 2.8 8.0 0.0 11.6

San Francisco 3.0 20.3 19.5 0.1 42.9

San Mateo 3.3 37.4 23.2 0.1 64.0

Santa Clara 7.6 24.2 60.8 1.0 93.6

Solano 1.3 9.5 14.1 17.8 42.7

Sonoma 2.0 5.5 17.3 0.0 24.8

Total 32.3 168.0 271.3 41.7 513.3

Wintertime TOG emissions were dominated by area sources, with solvent utilization leading 

the way, followed by livestock, waste disposal/treatment, and industrial processes.

The largest NOx contribution came from on-road vehicles, with slightly more than half 

attributable to gasoline vehicles and the remainder from heavy-duty diesel trucks. NOx

emissions from commercial marine and construction equipment comprised roughly 70

percent of the non-road source contribution.

Table A-5. 2005 wintertime SO2 emissions by Bay Area county and major source category. (tons/day)

2005

County Area Nonroad Onroad Point Total

Alameda 0.2 2.2 0.6 4.0 7.0

Contra Costa 0.3 1.2 0.3 17.8 19.6

Marin 0.1 1.8 0.1 0.2 2.2

Napa 0.0 0.1 0.0 0.3 0.4

San Francisco 0.1 4.9 0.2 2.0 7.2

San Mateo 0.1 11.7 0.2 0.7 12.7

Santa Clara 0.3 0.4 0.4 7.4 8.5

Solano 0.0 0.7 0.1 5.9 6.7

Sonoma 0.1 0.2 0.1 0.4 0.8

Total 1.2 23.2 2.0 38.7 65.1

Table A-6. 2006 wintertime NH3 emissions by Bay Area county and major source category. (tons/day)

2006

County Area Nonroad Onroad Point Biogenic Total

Alameda 7.49 0.02 2.52 0.62 0.49 11.14

Contra Costa 6.50 0.02 1.76 1.79 0.70 10.77

Marin 3.34 0.01 0.46 0.25 0.62 4.68

Napa 1.09 0.00 0.27 0.28 0.83 2.47

San Francisco 3.81 0.03 0.98 0.13 0.01 4.96

San Mateo 4.14 0.02 1.57 0.67 0.30 6.70

Santa Clara 8.23 0.02 3.13 5.84 0.43 17.65

Solano 2.05 0.00 0.46 1.53 1.20 5.24

Sonoma 6.29 0.01 0.73 1.01 2.47 10.51

Total 42.94 0.13 11.88 12.12 7.05 74.12
A-4

Bay Area ammonia emissions were predominantly from area sources. A breakdown of these 

area sources is shown in Figure A-1. By far, the largest area sources were animal wastes, 

both domestic and livestock. Human perspiration and respiration and residential fuel

combustion rounded out the top 90 percent of area source emissions. For point sources, 

composting facilities/landfills contributed two-thirds of emissions, and the remainder came

from miscellaneous industrial processes including wastewater treatment facilities and 

refineries.

Domestic Animal 

Waste

32%

Agriculture 

Production - 

Livestock

23%

Human 

Respiration and 

Perspiration

23%

Residential Fuel 

Combustion

15%

Agriculture 

Production - 

Crops

5%

Food and 

Kindred Products

1%

Others

1%

Figure A-1. Distribution of ammonia area source emissions for winter 2006.
A-5

Figures A-2 through A-6 illustrate the spatial distributions of BAAQMD wintertime PM2.5, 

TOG, NOx, SO2 and NH3 emissions, respectively. 

Figure A-2. Spatial distribution of PM2.5 emissions.
A-6

Figure A-3. Spatial distribution of TOG emissions.

Figure A-4. Spatial distribution of NOx emissions.
A-7

Figure A-5. Spatial distribution of SO2 emissions.

Figure A-6. Spatial distribution of NH3 emissions.
A-8

With the exception of SO2, emissions of these pollutants were generally found in populated 

places and/or nearby major arterials and highways. SO2 emissions arose from commercial 

ships and stationary sources; therefore, their spatial distribution fell on major shipping lanes 

as well as centers of industry such as northern Contra Costa County.

With regards to temporal distributions of emissions, some source categories were assumed 

to follow a relatively “flat” diurnal distribution. This was particularly true of major stationary 

point sources, animal wastes, human perspiration and respiration, and commercial marine. 

Other categories followed distinctive hourly profiles for which data were available to 

construct the distributions. Some of the more significant of these sources are shown in 

Figure A-7. 

Light-duty on-road vehicles had morning and afternoon peaks on weekdays, corresponding 

to the commute pattern. The morning peak was decidedly sharper than the evening peak. 

Heavy-duty vehicles, on the other hand, showed a single broad peak during the middle of 

the day and a much smaller late-night rise in activity. These profiles were based upon data 

from the ARB’s EMFAC model. Wood burning had a bimodal distribution similar to lightduty vehicles, except that the evening peak was much later in the night, presumably after 

residents returned from work. The profile used to distribute construction equipment 

emissions concentrated activity in the afternoon and early evening hours. This allocation 

was questionable given anecdotal evidence and noise considerations and will undergo 

review. Also under review is the use of weekday profiles for weekend days for which local 

data were lacking. 

Diurnal Profiles

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour of Day

Light-Duty Onroad

Heavy-Duty Onroad

Wood Burning

Construction Equipment

Figure A-7. Weekday diurnal profiles assumed for emissions from select key Bay Area PM2.5 sources.
A-9

A.2 Emissions outside the BAAQMD

Table A-7 shows 2006 wintertime emissions by major source category for California areas 

outside of the BAAQMD but within the CRPAQS modeling domain (see Figure 2 in the main 

report). These emissions included the portions of Solano and Sonoma Counties which lie 

outside the Bay Area District. A total of 45 counties were included, though some only in 

part.

Table A-7. 2006 wintertime weekday emissions for areas of the modeling domain outside the BAAQMD 

(tons/day).

Source Category SO2 NOx TOG PM2.5

Area 18 158 2935 374

Non-road 49 412 124 25

Heavy-duty On-road 3 414 49 20

Light-duty On-road 2 393 223 6

Point 56 207 318 40

Total 128 1584 3649 465

Area source TOG emissions in the table above were dominated by livestock operations and 

landfills. For PM2.5, emissions were from residential fuel combustion, road dust, crops 

production (tilling), and prescribed burning.

The total winter daily average ammonia emissions for portions of the modeling domain 

which lie outside the BAAQMD were about 300 tons. This was about four times the amount 

in the Bay Area.

Biogenic TOG emissions over the entire modeling domain, including the Bay Area, ranged 

from 490 to 668 tons per day during the winter simulation period. These values represented

the sum of methyl butenol, isoprene, monoterpenes and other volatile organic compounds 

(OVOC). Much of these emissions were outside the BAAQMD, as shown in Figure A-8.

Biogenic emissions were strongly influenced by both temperature and photosyntheticallyactive radiation (PAR). These two factors dictated the diurnal distribution of these 

emissions. Due to the day-to-day variation of temperture and PAR, the magnitude and 

temporal profile of biogenic emissions changed each day. In general, biogenic emissions 

peaked during daylight hours and returned to a baseline value for the overnight hours. This 

timing is illustrated in Figure A-9.
A-10

Figure A-8. Representative spatial distribution of biogenic TOG emissions.

0

0.02

0.04

0.06

0.08

0.1

0.12

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour of Day

Figure A-9. Diurnal profile for biogenic TOG emissions throughout the modeling domain on December 19, 

2000.
B-1

Appendix B. Meteorological model validation

The ENVIRON METSTAT program (Emery et al., 2001) was used to compare the MM5 

generated meteorological fields against hourly surface observations archived at NCAR. 

METSTAT is a statistical analysis software package that calculates and graphically presents 

statistics such as mean observation, mean simulation, bias error, gross error, index of 

agreement (IOA), etc. Locations of the weather stations used for the validation are shown in 

Figure 1. 

Hourly time series of observed and simulated surface-layer wind and temperature are 

presented to evaluate the model performance. Statistics are defined as follows:

Mean observation (Mo): calculated from all sites with valid data within a given analysis 

region and for a given time period (hourly or daily):

where 

i Oj

is the individual observed quantity at site i and time j, and the summations are 

over all sites (I) and over time periods (J).

Mean prediction (Mp): calculated from simulation results that are interpolated to each 

observation used to calculate the mean observation (hourly or daily):

where 

i Pj

is the individual simulated quantity at site i and time j. Note that mean observed 

and simulated winds are vector-averaged (for east-west component u and north-south 

component v), from which the mean wind speed and mean resultant direction are derived.

Bias error (B): calculated as the mean difference in prediction-observation pairings with 

valid data within a given analysis region and for a given time period (hourly or daily):

B.1 MM5 validation for PM simulation periods

The hourly time series of regionally averaged observed and simulated surface-layer wind, 

temperature, and humidity for December-January of 2000-01 and 2006-07 are shown in 

Figures B-1 through B-16. The MM5 model results were averaged over the 4-km CRPAQS 

 



J

j

I

i

i

o Oj

IJ

M

1 1

1

 



J

j

I

i

i

p Pj

IJ

M

1 1

1

 

 

 

J

j

I

i

i

j

i Pj O

IJ

B

1 1

1
B-2

MM5 domain. The figures show that the MM5 simulations are in good agreement with the 

observations.
B-3

BAAQMD 4km 2000 MM5 -- December

Figure B-1. Hourly time series of region-average observed and predicted surface-layer wind speed and 

performance statistics in the 4-km MM5 domain for December 2000.

BAAQMD 4km 2000 MM5 – December

Figure B-2. Hourly time series of region-average observed and predicted surface-layer wind direction and 

performance statistics in the 4-km MM5 domain for December 2000.

Observed/Predicted Windspeed

0

2

4

6

8

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

m/s

ObsWndSpd PrdWndSpd Bias Windspeed

- 3

- 2

- 1

0

1

2

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

m/s

BiasWndSpd Observed/Predicted Wind Direction

0

60

120

180

240

300

360

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

deg

ObsWndDir PrdWndDir Bias Wind Direction

-90

-60

-30

0

30

60

90

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

deg

BiasWndDir
B-4

BAAQMD 4km 2000 MM5 – December

Figure B-3. Hourly time series of region-average observed and predicted surface-layer temperature and 

performance statistics in the 4-km MM5 domain for December 2000.

BAAQMD 4km 2000 MM5 – December

Figure B-4. Hourly time series of region-average observed and predicted surface-layer humidity and 

performance statistics in the 4-km MM5 domain for December 2000.

Observed/Predicted Temperature

260

265

270

275

280

285

290

295

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

K

ObsTemp PrdTemp Bias Temperature

- 6

- 4

- 2

0

2

4

6

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

K

BiasTemp Predicted/Observed Humidity

0

2

4

6

8

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

g/kg

ObsHum PrdHum Bias Humidity

- 2

-1.5

- 1

-0.5

0

0.5

1

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

g/kg

BiasHum 
B-5

BAAQMD 4km 2001 MM5 -- January

Figure B-5. Hourly time series of region-average observed and predicted surface-layer wind speed and 

performance statistics in the 4-km MM5 domain for January 2001.

BAAQMD 4km 2001 MM5 – January

Figure B-6. Hourly time series of region-average observed and predicted surface-layer wind direction and 

performance statistics in the 4-km MM5 domain for January 2001.

Observed/Predicted Windspeed

0

2

4

6

8

10

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

m/s

ObsWndSpd PrdWndSpd Bias Windspeed

- 3

- 2

- 1

0

1

2

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

m/s

BiasWndSpd Observed/Predicted Wind Direction

0

60

120

180

240

300

360

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

deg

ObsWndDir PrdWndDir Bias Wind Direction

-90

-60

-30

0

30

60

90

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

deg

BiasWndDir
B-6

BAAQMD 4km 2001 MM5 -- January

Figure B-7. Hourly time series of region-average observed and predicted surface-layer temperature and 

performance statistics in the 4-km MM5 domain for January 2001.

BAAQMD 4km 2001 MM5 – January

Figure B-8. Hourly time series of region-average observed and predicted surface-layer humidity and 

performance statistics in the 4-km MM5 domain for January 2001.

Observed/Predicted Temperature

260

270

280

290

300

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

K

ObsTemp PrdTemp Bias Temperature

- 6

- 4

- 2

0

2

4

6

8

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

K

BiasTemp Predicted/Observed Humidity

0

2

4

6

8

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

g/kg

ObsHum PrdHum Bias Humidity

-1.5

- 1

-0.5

0

0.5

1

1.5

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

g/kg

BiasHum 
B-7

BAAQMD 4km 2006 MM5 – December

Figure B-9. Hourly time series of region-average observed and predicted surface-layer wind speed and 

performance statistics in the 4-km MM5 domain for December 2006.

BAAQMD 4km 2006 MM5 – December

Figure B-10. Hourly time series of region-average observed and predicted surface-layer wind direction and 

performance statistics in the 4-km MM5 domain for December 2006.

Observed/Predicted Windspeed

0

2

4

6

8

10

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

m/s

ObsWndSpd PrdWndSpd Bias Windspeed

- 2

- 1

0

1

2

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

m/s

BiasWndSpd Observed/Predicted Wind Direction

0

60

120

180

240

300

360

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

deg

ObsWndDir PrdWndDir Bias Wind Direction

-90

-60

-30

0

30

60

90

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

deg

BiasWndDir
B-8

BAAQMD 4km 2006 MM5 – December

Figure B-11. Hourly time series of region-average observed and predicted surface-layer temperature and 

performance statistics in the 4-km MM5 domain for December 2006.

BAAQMD 4km 2006 MM5 – December

Figure B-12. Hourly time series of region-average observed and predicted surface-layer humidity and 

performance statistics in the 4-km MM5 domain for December 2006.

Observed/Predicted Temperature

250

260

270

280

290

300

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

K

ObsTemp PrdTemp Bias Temperature

- 4

- 2

0

2

4

6

8

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

K

BiasTemp Predicted/Observed Humidity

0

2

4

6

8

10

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

g/kg

ObsHum PrdHum Bias Humidity

- 2

- 1

0

1

2

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

g/kg

BiasHum 
B-9

BAAQMD 4km 2007 MM5 -- January

Figure B-13. Hourly time series of region-average observed and predicted surface-layer wind speed and 

performance statistics in the 4-km MM5 domain for January 2007.

BAAQMD 4km 2007 MM5 – January

Figure B-14. Hourly time series of region-average observed and predicted surface-layer wind direction and 

performance statistics in the 4-km MM5 domain for January 2007.

Observed/Predicted Windspeed

0

2

4

6

8

10

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

m/s

ObsWndSpd PrdWndSpd Bias Windspeed

- 4

- 2

0

2

4

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

m/s

BiasWndSpd Observed/Predicted Wind Direction

0

60

120

180

240

300

360

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

deg

ObsWndDir PrdWndDir Bias Wind Direction

-90

-60

-30

0

30

60

90

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

deg

BiasWndDir
B-10

BAAQMD 4km 2007 MM5 – January

Figure B-15. Hourly time series of region-average observed and predicted surface-layer temperature and 

performance statistics in the 4-km MM5 domain for January 2007.

BAAQMD 4km 2007 MM5 – January

Figure B-16. Hourly time series of region-average observed and predicted surface-layer humidity and 

performance statistics in the 4-km MM5 domain for January 2007.

Observed/Predicted Temperature

250

260

270

280

290

300

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

K

ObsTemp PrdTemp Bias Temperature

- 4

- 2

0

2

4

6

8

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

K

BiasTemp Predicted/Observed Humidity

0

2

4

6

8

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

g/kg

ObsHum PrdHum Bias Humidity

- 2

-1.5

- 1

-0.5

0

0.5

1

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

g/kg

BiasHum 
B-11

B.2 MM5 validation for wood smoke simulation periods

The hourly time series of regionally averaged observed and predicted surface-layer wind 

and temperature for November-February of 2008-09 are shown in Figures B-17 to B-24. The 

MM5 model results were averaged over the 4-km Bay Area MM5 domain. The MM5 

simulations showed reasonably good agreement with the observations.

BAAQMD 4km 2008 MM5 – November

Figure B-17. Hourly time series of region-average observed and predicted surface-layer wind speed and 

direction and performance statistics in the 4-km MM5 domain for November 2008.

Observed/Predicted Windspeed

0

5

11/15 11/16 11/17 11/18 11/19 11/20 11/21 11/22 11/23 11/24 11/25 11/26 11/27 11/28 11/29 11/30

m/s

ObsWndSpd PrdWndSpd Bias Windspeed

- 2

0

2

11/15 11/16 11/17 11/18 11/19 11/20 11/21 11/22 11/23 11/24 11/25 11/26 11/27 11/28 11/29 11/30

m/s

BiasWndSpd Observed/Predicted Wind Direction

0

60 120 180 240 300 360

11/15 11/16 11/17 11/18 11/19 11/20 11/21 11/22 11/23 11/24 11/25 11/26 11/27 11/28 11/29 11/30

deg

ObsWndDir PrdWndDir Bias Wind Direction

-90 -60 -300

306090

11/15 11/16 11/17 11/18 11/19 11/20 11/21 11/22 11/23 11/24 11/25 11/26 11/27 11/28 11/29 11/30

deg

BiasWndDir
B-12

BAAQMD 4km 2008 MM5 – November

Figure B-18. Hourly time series of region-average observed and predicted surface-layer temperature and 

performance statistics in the 4-km MM5 domain for November 2008.

Observed/Predicted Temperature

260

270

280

290

300

310

11/15 11/16 11/17 11/18 11/19 11/20 11/21 11/22 11/23 11/24 11/25 11/26 11/27 11/28 11/29 11/30

K

ObsTemp PrdTemp Bias Temperature

- 4

- 2

0

2

4

6

11/15 11/16 11/17 11/18 11/19 11/20 11/21 11/22 11/23 11/24 11/25 11/26 11/27 11/28 11/29 11/30

K

BiasTemp 
B-13

BAAQMD 4km 2008 MM5 – December

Figure B-19. Hourly time series of region-average observed and predicted surface-layer wind speed and 

direction and performance statistics in the 4-km MM5 domain for December 2008.

Observed/Predicted Windspeed

0

2

4

6

8

10

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

m/s

ObsWndSpd PrdWndSpd Bias Windspeed

- 4

- 2

0

2

4

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

m/s

BiasWndSpd Observed/Predicted Wind Direction

0

60

120

180

240

300

360

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

deg

ObsWndDir PrdWndDir Bias Wind Direction

-90

-60

-30

0

30

60

90

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

deg

BiasWndDir
B-14

BAAQMD 4km 2008 MM5 – December

Figure B-20. Hourly time series of region-average observed and predicted surface-layer temperature and 

performance statistics in the 4-km MM5 domain for December 2008.

Observed/Predicted Temperature

260

265

270

275

280

285

290

295

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

K

ObsTemp PrdTemp Bias Temperature

- 6

- 4

- 2

0

2

4

6

8

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 9

12/1012/1112/1212/1312/1412/1512/1612/1712/1812/1912/2012/2112/2212/2312/2412/2512/2612/2712/2812/2912/3012/31

K

BiasTemp 
B-15

BAAQMD 4km 2009 MM5 – January

Figure B-21. Hourly time series of region-average observed and predicted surface-layer wind speed and 

direction and performance statistics in the 4-km MM5 domain for January 2009.

Observed/Predicted Windspeed

0

2

4

6

8

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

m/s

ObsWndSpd PrdWndSpd Bias Windspeed

- 4

- 2

0

2

4

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

m/s

BiasWndSpd Observed/Predicted Wind Direction

0

60

120

180

240

300

360

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

deg

ObsWndDir PrdWndDir Bias Wind Direction

-90

-60

-30

0

30

60

90

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

deg

BiasWndDir
B-16

BAAQMD 4km 2009 MM5 – January

Figure B-22. Hourly time series of region-average observed and predicted surface-layer temperature and 

performance statistics in the 4-km MM5 domain for January 2009.

Observed/Predicted Temperature

250

260

270

280

290

300

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

K

ObsTemp PrdTemp Bias Temperature

- 4

- 2

0

2

4

6

8

 1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

 1/10 1/11 1/12 1/13 1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31

K

BiasTemp 
B-17

BAAQMD 4km 2009 MM5 – February

Figure B-23. Hourly time series of region-average observed and predicted surface-layer wind speed and 

direction and performance statistics in the 4-km MM5 domain for February 2009.

Observed/Predicted Windspeed

0

2

4

6

8

10

 2/ 1 2/ 2 2/ 3 2/ 4 2/ 5 2/ 6 2/ 7 2/ 8 2/ 9 2/10 2/11 2/12 2/13 2/14 2/15

m/s

ObsWndSpd PrdWndSpd Bias Windspeed

- 4

- 2

0

2

4

 2/ 1 2/ 2 2/ 3 2/ 4 2/ 5 2/ 6 2/ 7 2/ 8 2/ 9 2/10 2/11 2/12 2/13 2/14 2/15

m/s

BiasWndSpd Observed/Predicted Wind Direction

0

60

120

180

240

300

360

 2/ 1 2/ 2 2/ 3 2/ 4 2/ 5 2/ 6 2/ 7 2/ 8 2/ 9 2/10 2/11 2/12 2/13 2/14 2/15

deg

ObsWndDir PrdWndDir Bias Wind Direction

-90

-60

-30

0

30

60

90

 2/ 1 2/ 2 2/ 3 2/ 4 2/ 5 2/ 6 2/ 7 2/ 8 2/ 9 2/10 2/11 2/12 2/13 2/14 2/15

deg

BiasWndDir
B-18

BAAQMD 4km 2009 MM5 – February

Figure B-24. Hourly time series of region-average observed and predicted surface-layer temperature and 

performance statistics in the 4-km MM5 domain for February 2009.

Observed/Predicted Temperature

260

265

270

275

280

285

290

295

300

 2/ 1 2/ 2 2/ 3 2/ 4 2/ 5 2/ 6 2/ 7 2/ 8 2/ 9 2/10 2/11 2/12 2/13 2/14 2/15

K

ObsTemp PrdTemp Bias Temperature

- 4

- 2

0

2

4

6

 2/ 1 2/ 2 2/ 3 2/ 4 2/ 5 2/ 6 2/ 7 2/ 8 2/ 9 2/10 2/11 2/12 2/13 2/14 2/15

K

BiasTemp 
C-1

Appendix C. Air quality model validation

Air quality model performance was evaluated consistent with guidelines established by EPA

(2007). A number of individual analyses were performed to validate the model outputs 

against available measurements. They included statistical (quantitative) as well as visual 

(qualitative) methods. Measurements were paired against their corresponding simulated 

values extracted from model grid cells nearest the respective monitoring locations. The 

performance evaluation was based on 24-hour averages (midnight to midnight) for 

measured and simulated PM levels.

Statistics are defined follows:

Mean observed PM level (PMo

i

): calculated for a single monitoring location i using all valid 

data available during the simulation period:

where 

i Oj

is the individual observed PM level (g/m3) at site i for day j, and the summations 

are over all days (J). 

i Oj

may reflect total PM levels or individual PM components such as 

EC, OC, nitrate, or sulfate.

Mean model estimated PM level (PMp

i

): calculated from simulation results averaged for the 

model grid cell(s) nearest site i using the entire simulation period:

where 

i Pj

is the individual model estimated PM level (g/m3) at site i for day j, and the 

summations are over all days (J). 

i Pj

may reflect total PM levels or individual PM 

components such as EC, OC, nitrate, or sulfate.

Bias error (B

i

): calculated as the mean difference in simulation-observation pairings for the 

same PM type (total, EC, OC, nitrate, or sulfate) with valid data at site i:

Large, positive values for B

i

indicate that the model is overestimating PM levels. Large, 

negative values for B

i

indicate that the model is underestimating PM levels.





J

j

i

j

i

o O

J

PM

1

1





J

j

i

j

i

p P

J

PM

1

1

i

o

i

p

i B  PM  PM i

o

i

p

i B  PM  PM
C-2

C.1 Validation of total PM levels

Validation of simulated total PM2.5 levels was performed separately for each monitoring 

location. Generally, the model was able to reproduce the observed spatial distribution of 

PM. The model reasonably estimated total PM2.5 levels throughout the Bay Area and, to a 

lesser extent, around the Delta. Realistically, simulated PM levels in the Delta were 

important as this region is immediately upwind of the Bay Area under episodic conditions. 

Mean observed and simulated total PM2.5 levels are shown in Figure C-1. Simulated PM2.5

levels for most Bay Area locations were in good agreement with measurements. PM2.5 levels 

were overestimated at San Francisco. This overestimation is believed to have resulted from 

an over-allocation of emissions for downtown San Francisco. Emissions from trucks and 

construction equipment that did not actually operate within San Francisco may have been 

placed into this city’s inventory because they were linked to business addresses there. PM2.5

levels were underestimated at the inland northern Bay Area sites Napa and Vallejo. 

Underestimations there likely resulted from underestimations around the Delta. PM2.5

levels were also underestimated in Sacramento. This underestimation is believed to have 

resulted from an underestimation of regional secondary PM2.5 levels in the Sacramento 

Valley.

Figure C-2 demonstrates the validity of the model across the range of historical PM levels 

that occurred for the 2006-07 winter. The 1:1 line on the scatter plots of paired observed 

and simulated total PM2.5 levels indicates ideal model performance. For only a single outlier 

day, occurring on Christmas Day, the model performed poorly. This day is believed to have 

exhibited unusual emissions characteristics that were not properly represented in the 

model inventory that reflected average winter emissions. Otherwise, the data points on the 

scatter plots generally fell around the 1:1 line. One exception was San Francisco, where the 

data points fell below the 1:1 line. This reflects the general overestimation of San Francisco 

PM levels also indicated in Figure C-1. Observed and simulated total PM2.5 levels correlated 

well here. This correlation suggests that PM buildup mechanisms were simulated 

realistically, but emissions levels were overestimated. Other exceptions were for Napa, 

Vallejo, and Sacramento. For these locations, total PM2.5 levels were accurately estimated 

when PM levels were low. Higher PM levels, however, were significantly underestimated by 

the model. This discrepancy suggests shortcomings of the simulated chemistry to produce 

sufficient levels of secondary PM2.5. The nature of the PM2.5 level underestimation at 

Modesto was quite different than at Sacramento. At Modesto, simulated and observed total 

PM2.5 levels correlated well, but the data points fell above the 1:1 line. This correlation 

suggests that PM buildup mechanisms were simulated realistically, but emissions levels 

impacting Modesto were underestimated.

Another reason for underestimation of higher PM levels was due to overestimated wind 

speed in MM5. Analysis of meteorological model outputs showed that the model tended to 

overestimate winds when the atmosphere was strongly stable, generating local low level 
C-3

jets. This short coming of the model is being investigated and is planned to be corrected 

soon.

0

5

10

15

20

25

30

35

40 livermore fremont concord san francisco redwood city san jose vallejo santa rosa oakland napa point reyes sacto-del paso sacto-t st modesto PM2.5 (ug/m3)

measured

modeled (on same days)

Figure C-1. Mean observed and model estimated total PM2.5 levels at 11 Bay Area and 2 Delta region 

monitoring locations. Statistics are averaged across both the 2000-01 and 2006-07 winter simulation periods.

C.2 Validation of PM component levels

The model was validated to realistically simulate the major individual PM components EC, 

OC, nitrate, and sulfate. This verification is necessary to ensure that total PM2.5 was 

estimated accurately from the accurate simulations of various PM components. Such 

behavior would indicate that the model produced the right answers for the right reasons 

and would therefore be reliable for planning purposes.

Scatter plots for carbonaceous PM2.5 components EC and OC are shown in Figures C-3 and 

C-4, respectively. Generally, the data points fell along the 1:1 line for most stations. 

Exceptions included Napa and Vallejo for both EC and OC, and also San Jose for EC. 

Carbonaceous PM levels at Napa were particularly underestimated by the model, often by a 

factor of 2-3. However, simulation results did correlate well with the observations. This 

finding suggested that carbonaceous emissions in the Napa Valley, perhaps wood burning, 

may have been strongly underrepresented in the inventory. The underestimation of San 

Jose EC levels may have reflected an underestimation of vehicular and/or construction 

related emissions around this urban area. The degraded model performance at Vallejo was 
C-4

more difficult to attribute to a special cause. This is a complex area, which can be impacted 

by transport from the Central Valley and/or local industrial emissions, depending on the 

prevailing wind conditions.

Scatter plots for PM2.5 component nitrate are shown in Figure C-5. This figure demonstrates 

the model’s ability to simulate secondary PM2.5 ammonium nitrate levels. Nitrate levels 

were overestimated slightly (below the 1:1 line) at the remote Point Reyes location. For 

other locations, especially in the Central Valley, nitrate levels were underestimated. The 

model particularly had difficulty simulating high ammonium nitrate levels in the Sacramento 

Valley.

Scatter plots for PM2.5 component sulfate are shown in Figure C-6. This figure demonstrates 

the model’s ability to simulate secondary PM2.5 ammonium sulfate levels. The model 

simulated ammonium sulfate levels with higher accuracy than for any other PM2.5

component. This may have resulted from the relatively simple emissions patterns for sulfurcontaining substances as compared to other types of emissions. Sulfur emissions were 

highly localized around both the offshore shipping lanes and industrial facilities through 

Carquinez Straight. These results indicated the model's ability to capture source-receptor 

relationships for sulfur compounds under both easterly and westerly air flow patterns.
C-5

Modeled PM2.5 (ug/m3)

Measured PM2.5 (ug/m3)

80

40

0

80

40

0

80

40

0

0 40 80 0 40 80

0 40 80

80

40

0

0 40 80

livermore fremont concord san francisco

redwood city san jose vallejo santa rosa

oakland napa point reyes sacto-del paso

sacto-t st modesto

Ambient vs. Modeled PM2.5 - Winter 2006-07

Figure C-2. Scatter plots for measured versus simulated total PM2.5 levels at 11 Bay Area and 2 Delta region 

monitoring locations. Each data point represents an individual day for the 2006-07 winter simulation period. 

An outlier plotted in red occurred on Christmas Day. Diagonal 1:1 lines indicate ideal model performance.
C-6

Modeled EC (ug/m3)

Measured EC (ug/m3)

10

5

0

10

5

0

0 2 4 0 2 4

0 2 4

10

5

0

0 2 4

livermore san francisco san jose bethel island

san pablo vallejo concord napa

san jose 2.5 point reyes

Ambient vs Modeled Elemental Carbon, Winter 2006-07

Figure C-3. Scatter plots for measured versus simulated PM2.5 EC component levels.
C-7

Modeled OC (ug/m3)

Measured OC (ug/m3)

20

10

0

20

10

0

0 5 10 0 5 10

0 5 10

20

10

0

0 5 10

livermore san francisco san jose bethel island

san pablo vallejo concord napa

san jose 2.5 point reyes

Ambient vs Modeled Organic Carbon, Winter 2006-07

Figure C-4. Scatter plots for measured versus simulated PM2.5 OC component levels.
C-8

Modeled NO3 (ug/m3)

Measured NO3 (ug/m3)

30

20

10

0

0 10 20

0 10 20

30

20

10

0

0 10 20

san jose point reyes sacramento-del paso

sacramento-t st modesto

Ambient vs Modeled NO3, Winter 2006-07

Figure C-5. Scatter plots for measured versus simulated PM2.5 nitrate component levels.
C-9

Measured SO4 (ug/m3)

Measured SO4 (ug/m3)

16

8

0

16

8

0

16

8

0

0 2 4 0 2 4

0 2 4

16

8

0

0 2 4

fremont livermore pittsburg bethel island

san pablo concord san rafael napa

san francisco redwood city san jose vallejo

santa rosa pr_ammso4

Ambient vs Modeled Sulfate, Winter 2006-07

Figure C-6. Scatter plots for measured versus simulated PM2.5 sulfate component levels.