i 

## Disclaimer This report presents results from analyses of measurements and computer modeling performed by the District to study PM 2.5 . The results presented should be viewed as preliminary because uncertainties may exist in emissions estimate, modeling a nd ambient measurements. This is the first attempt in analyzing ambient data and modeling PM 2.5 . The District plans to refine and expand upon these preliminary results , and minimize uncertainties via its on

- going research effort.ii 

## Table of c ontents 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. Measureme nts 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 contri butions to peak PM 2.5 levels

................................ ................... 16 4.2.2 Source contributions to annual average PM 2.5 levels

................................ . 17 4.3 Relationship between weather pa tterns 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 PM 2.5 components
................................ ................................ ...... 28 5.2.2 Ammonium nitrate limitation

................................ ................................ ..... 30 5.2.3 Spatial distribution of PM 2.5 ................................ ................................ ........ 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

......................... 49iii 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 emission s 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 simu lation 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

- 3iv 

## List of f igures
Figure 1. Map of Bay Area domain showing locations of key PM mo nitors, weather stat ions, and sounding launch point

.. ................................ ................................ ................................ ................................ ................ 5 Figure 2. Emissions modeling domains..

................................ ................................ ................................ ................ 7 Figure 3. Emissions inventory f or winter wood burning within Bay Area.

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

................................ ................................ .. 9 Figure 5. Percenta ge of 24

- hour PM 2.5 NAAQS exceedances by calendar month.

................................ ............... 11 Figure 6. N umber of 24

- hour PM 2.5 exceedance days and number of rainy days for each winter season

.. ........ 11 Figure 7. Trends for annual average PM 10 and PM 2.5 levels

................................ ................................ ................. 12 Figure 8. Annual average PM 2.5 levels by site for 3

- yr period 2006

- 08.
................................ ............................... 14 Figure 9. Average 98 th percentile 24

- hour PM 2.5 levels (design values) by site for 3

- yr period 2006

- 08
............. 14 Figure 10. Levels for PM 2.5 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 PM 2.5 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 PM 2.5 components
................................ ............ 28 Figure 17. Aerial plots of simulated 24

- hour levels for gas pha se ammonia and nitric acid

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

................................ ................ 32 Figure 19. Sim ulated secondary versus primary PM 2.5 levels for four selected subdomains

............................... 33 Figure 20. Simulation results for four consecutive days showing the development of a relatively severe PM episode i n the Bay Area

................................ ................................ ................................ .............................. 34 Figure 21. Surface plots of simulated sensitivities of PM 2.5 for five 20% emissions reductions

........................... 37 Figure 22. T abulated  PM 2.5 values for grid cell with highest simulated PM 2.5 level for eight subdomains fo r 20% direct PM emissions reductions and 20% ammonia reductions

................................ ......................... 38 Figure 23. Wood smoke simulati on 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 zero ed out

................ 43 Figure 26. Benefit (  PM 2.5 ) upon eliminating anthropogenic emissions for the Sacramento area

..................... 45 Figure 27. Benefit (  PM 2.5 ) upon eliminating anthropogenic emissions for the San Joaquin Valley

................... 46 Figure 28.  PM 2.5 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 anth ropogenic emissions zeroe d out and with San Joaquin Valley anthropogenic emis sions zeroed out 47


## List of t ables
Table 1. Surface monitoring locations for PM and met eorology

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

........................... 7 Table 3. Spare the Air da ys for 2008

- 09 winter

................................ ................................ ................................ .... 40E

- 1



## Executive s ummary This report presents preliminary findings of BAAQMD’s on

- going data analysis and modeling efforts to better understand fine particulate matter (PM 2.5 ) formation in the Bay Area . PM 2.5 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 PM 2.5 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 res earch 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 PM 2.5 research efforts

. 

### E.1 Data analysis
Ba y Area 24

- hour PM 2.5 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 PM 2.5 exceedances than winters with more periods of windy and stormy conditions

. Consecutive stagnant , clear winter days are typically requisite for PM 2.5 episodes to develop

. PM 2.5 episodes are regional in nature and impact most Bay Area locations

. Livermore, Concord, San Jose , and Vallejo have 24

- hour PM 2.5 design values at or near the NAAQS exceedance threshold of 35  g/m 3

. Other populated locations exhibit design values around 25

- 30  g/m 3

. Near

- background PM 2.5 levels were observed at Point Reyes, where annual

- average PM 2.5 levels were about half that of other Bay Area monitoring locations

. The Bay Area is expected to attain the NAAQS for annual

- average PM 2.5 level. The Chemical Mass Balance (CMB) analysis was applied for PM 2.5 source apportionment, using specialized measurements mostly obtained during the years 1999

- 2001
. Primary combustion sources (both fossil fuels and biomass) were prevalent PM 2.5 contributors for all seasons . The biomass combustion contribution to peak PM 2.5 levels was about 3

- 4 times higher during winter than for the other seasons

. Isotopic carbon ( 14 C) analysis confirmed this increased proportio n 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 PM 2.5 under episodic conditi ons . Secondary PM 2.5 levels were only elevated during the winter months

. Wintertime secondary PM 2.5 was mostly ammonium nitrate

. This semi

- volatile PM 2.5 component is stable in its solid form only during the cooler winter months

. Secondary ammonium sulfate PM 2.5 levels were generally low (< 1

- 2  g/m 3 ) but non

- negligible

. Sea salt, geological dust, and tire and brake wear contributed minimally to PM 2.5 . Meteorological cluster analysis, a data mining technique, was implemented by UC Davis to determine how we ather patterns impact PM 2.5 levels

. Clustering was applied to measurements from every winter day across more than 10 years

. This method robustlyE

- 2

established how the prevailing weather drives the development of PM 2.5 episodes

. Episodes nearly always develop ed under: stable atmospheric conditions inhibiting vertical dispersion; clear and sunny skies favoring enhanced secondary PM 2.5 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 PM 2.5 levels

. Surface conditions stagnated when ever an upper - level high pressure system moved over Central California

. Persisting high pressure conditions allowed PM 2.5 buildup, and Bay Area 24

- hour PM 2.5 exceedances generally occurred after 2

- 4 days

. A refined cluster analysis further characterized the upwind Central V alley 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 east entering the Bay Area through the Delta

. Peak PM 2.5 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 east e ntering the Bay Area through the Delta

. Peak PM 2.5 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 disti nct 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 Cal ifornia Air Resources Board (ARB)

. BAAQMD staff and its contractors have since contributed to a custom computer model developed to address Bay Area PM 2.5 exceedances

. PM 2.5 simulations were performed for the core PM season months of December

- January for bo th the 2000

- 01 and 2006

- 07 periods

. Wood smoke simulations covered most of the 2008

- 09 winter. PM 2.5 base case simulations were performed using the most historically accurate winter emissions inventory available

. This inventory comprised various data comp iled 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 region s . Figure E

- 1 shows the spatial distribution of simulated primary and seconda ry PM 2.5 components averaged across the 52 simulated days for which measured Bay Area 24

- hour PM 2.5 level (FRM or BAM) exceeded 35  g/m 3

. For these episodic days, light winds flowed through the Bay Area from the east, and Central Valley conditions were near calm

. Primary PM 2.5 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 PM 2.5 , present mostly as ammonium nitrate, was not localized near the sources of its precursor emissi onsE

- 3

NO x and ammonia

. Rather, secondary PM 2.5 was regionally elevated

. A sharp gradient existed with very high secondary PM 2.5 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 NO x emissions photochemical aging, and not by ammonia

. In the Central Valley, PM 2.5 levels were dominated by secondary components which could alone build to the excee dance level

. Around San Francisco and San Jose, PM 2.5 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 PM 2.5 levels were comparable

. Both primary and secondary buildup was required for exceedance s to occur in these locations.

Figure E

- 1
. Spatial distribution of simulated primary and secondary PM 2.5 levels, averaged across the 52 simulated days for which measured Bay Area 24

- hour PM 2.5 level exceeded 35  g/m 3

. 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. PM 2.5 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: NO x and VOC combined; gaseous sulfur species; ammonia; directly emitted PM; and these four classes combined, comprisi ng 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 PM 2.5 levels nearly ten times more efficiently than reducing the secondary PM prec ursors . Reducing primary PM emissions by 20 percent (around 18 tons/day eliminated) typically reduced primary PM 2.5 levels by 12

- 20 percent , depending on location, with an average around 16 percent
. Reductions of directly emitted PM were most effective nea r the PM emissions sources where primary PM 2.5 levels were highest (see Figure E

- 1)

. The largest benefits of around 4

- 6  g/m 3 occurred near San Jose

. Reducing ammonia emissions by 20 percent (around 15 tons/day) was the most effective of the precursor emissions reductions

. Secondary PM 2.5 levels were typically reduced 0

- 4 percent , depending on location, with anE

- 4

average arou nd 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 NO x and VOC emissions by 20 percent (around 250 tons/day total) was relatively ineffe ctive
. Reducing sulfur

- containing PM precursor emissions by 20 percent (around 16 tons/day) typically had a small impact on Bay Area PM 2.5 levels under episodic conditions

. On certain days with the offshore shipping lanes upwind of the Bay Area, however, t he 20 percent sulfur emissions reductions produced over 1  g/m 3 benefit. PM 2.5 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 PM 2.5 levels 35  g/m 3 or 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 PM 2.5 levels were around 2

- 8  g/m 3

. 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/m 3 along the Bay Area eastern boundary, 8  g/m 3 through the Carquinez Strait and Altamont Pass, and 5  g/m 3 around the bay

. Transported ammonium sulfate levels of around 1  g/m 3 were present uniformly throughout the Bay Area
. Ammonium sulfate appeared to be transported mostly from regions outside o f the Central Valley

. Central Valley PM 2.5 levels were relatively unaffected by eliminating the Bay Area emissions. A second pair of PM 2.5 transport simulations was conducted by zeroing out anthropogenic emissions for the Sacramento area and San Joaquin V alley separately

. Reductions in simulated PM 2.5 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 PM 2.5 levels by around 5  g/m 3 through Carquinez Strait, by less than 2  g/m 3 in the northern half of the Bay Area, and had little effect elsewhere in the Bay Area
. Secondary PM 2.5 levels transported from the Sacramento area were 2.5  g/m 3 through Carquinez Strait and Altamont Pass and around 1.5  g/m 3 elsewhere in the Bay Area
. Eliminating the San Joaquin Valley emissions reduced primary PM 2.5 levels by around 4  g/m 3 through Carquinez Strait and Altamont Pass, by around 3  g/m 3 through Pacheco Pass and into southern Santa Clar a Valley, and had little impact farther into the Bay Area

. Secondary PM 2.5 levels transported from the San Joaquin Valley were up to 8  g/m 3 through Pacheco Pass and into southern Santa Clara Valley, around 6  g/m 3 through Altamont Pass and into eastern Co ntra Costa County, and around 5  g/m 3 elsewhere 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 conditi ons . Transport impacts were tabulated for the six days each 2000

- 01 and 2006

- 07 winter period with the highest measured PM 2.5 levels, for 12 days total

. Total transported PM 2.5 levels averaged 19, 19, 11, and 12  g/m 3 at the Livermore, Vallejo, San Jose, a nd San Francisco monitoring locations, respectively

. Maximum total transported PM 2.5 levels were 33, 37, 24, and 24  g/m 3 for these stations, respectively

. Thus, 24

- hourE

- 5

PM 2.5 exceedances could have occurred in the Bay Area without any Bay Area anthropogen ic emissions
. Transported secondary PM 2.5 levels averaged around 40

- 80 percent more than transported primary PM 2.5 levels, depending upon location

. Total transported PM 2.5 levels from the Sacramento area averaged 4, 5, 2, and 3  g/m 3 for these stations, re spectively

. At Vallejo, transported PM 2.5 from the Sacramento area had a somewhat higher proportion of primary components

. At t he other three locations, transported PM 2.5 from the Sacramento area had a somewhat higher proportion of secondary components

. To tal transported PM 2.5 levels from the San Joaquin Valley averaged 13, 10, 7, and 7  g/m 3 for these stations, respectively

. Transported secondary PM 2.5 from San Joaquin Valley were 2.5

- 4 times higher than transported primary PM 2.5 levels. Simulated transport impacts were also compared across two different transport scenarios identified b y 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 PM 2.5 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 PM 2.5 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 PM 2.5 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 pe rformed

. Bay Area wood smoke levels were simulated with out 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/m 3 were simulated over the area s having high wood burning emissions

. Calculated wood smoke levels were around 5  g/m 3 or 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 simulat ion 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/m 3 of 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 bu rning 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 restriction s, wood smoke levels for the eight simulated Spare the Air days were averaged to be 11, 7, 5, 3, and 3  g/m 3 for the Concord, San Jose, San Francisco, Vallejo, and Livermore monitoring locations, respectively

. Assuming 100 percent compliance, the burning r estrictions were estimated to reduce theseE

- 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 Conclusion s This report summarizes a wealth of knowledge generated from BAAQMD in

- house PM 2.5 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 pattern s contributing to PM 2.5 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 measurement s - and modeling

- based results provides a high level of confidence that the findings presented herein are both accurate and representative. Primary and secondary PM 2.5 impact the Bay Area differently by location and across a range of typical meteorological conditions

. The model suggests that reducing direct PM 2.5 emissions within the Bay Area is the most effective means of reducing Bay Area primary PM 2.5 levels

. These reductions, however, are most effective near direct PM 2.5 emission sources . These are the areas in which Bay Area total PM 2.5 levels are highest

. The model also suggests that significant amounts of PM 2.5 , especially secondary PM 2.5 , are transported from the Central Valley

. Secondary PM 2.5 exhibits a fairly regionally uniform influence throughou t 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 PM 2.5 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/m 3 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 ad ded 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 PM 2.5 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 (PM 2.5 ) formation in the Bay Area

. This is the first significant published re port describing Bay Area Air Quality Management District (BAAQMD) scientific investigations into PM 2.5 . The report includes a history of BAAQMD ’s activities and describes key results from a number of important studies. 

### 1.1 Background Information
PM 2.5 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 re leased 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 PM 2.5 . Numerous studies have demonstrated PM 2.5 to be deleterious to human health

. Major human health outcomes resulting from PM 2.5 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 PM 2.5 may well exceed the combined impacts of all other currently reg ulated air pollutants

. Bay Area PM 2.5 levels are elevated during the winter months

. Elevated winter PM 2.5 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

. I n 1987, TSP was replaced by PM 10 , or particles with aerodynamic diameters of 10  m or less

. In 1997, the federal PM 2.5 standard was created in addition to the PM 10 standard, which was retained

. Since then, California has established standards for PM 2.5 and PM 10 that are more stringent than required under federal regul ations
. 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 PM 2.5 levels in 1999
. PM 2. 5 is a subset of PM 10 , 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 attai nment designations for PM 2.5 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 PM 2.5 levels

. The Bay Area currently attains the annual standard, b ut 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 th is document and refined as research progresses

. 

### 1.3 BAAQMD research efforts A tremendous effort to advance the scientific understanding of PM 2.5 has been carried out in recent years at the federal, state, and local levels

. Much of th e work that will ultim ately drive the Bay Area SIP process has been performed in

- house by the BAAQMD Research and Modeling S ection
. This undertaking has been necessary because PM 2.5 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 PM 2.5 problem. Research into the Bay Area PM 2.5 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 PM 2.5 levels occurred

. Increasingly sophisticated statistical analyses were subsequently applied to understand the sources of PM 2.5 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 PM 2.5 problem

. As such, comput er simulations were performed to characterize PM 2.5 at times and locations for which measurements were not feasible

. These modeling efforts were initially based on data3

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 PM 2 .5 problem. District efforts for data analysis and computer modeling of PM 2.5 are ongoing and will continue through the SIP development process and beyond

. These efforts include dedicated field measurement campaigns, advanced statistical analyses of expan ding 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 re fine the scientific understanding of PM 2.5 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 PM 2.5 that is necessary for bringing the Bay Area into attainment for this pollutant.4



## 2. Measurements and available data
Measurements of both PM 2.5 levels and meteorologi cal conditions are necessary for better understanding PM 2.5 . Networks of i nstruments 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 numb er of additional instruments are not included for brevity such as NO x , ozone, precipitation, etc
. The map denotes instruments positioned within the Bay Area, as well as selected proximal sampling locations in the Centra l 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 instru ment. 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 BAAQ MD 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 Sui sun 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 x5

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 monit ors.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, NO x , SO 2 , NH 3 ), and provide inputs fo r 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 loca l information , as t hese 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 loca l 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 an d 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 requ irements 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 emi ssions 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 winte r 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 emis sions 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 spa tial 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, Appe ndix A provides detailed summaries as well as SMOKE

- related information

.

Figure 2
. Emissions modeling domains

. Gray shows ARB 4

- km s tatewide domain

. Blue is the CRPAQS domain for which emissions were prepared for District PM mo deling.

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

2000 2006 BAAQMD a Remainder of Domain b BAAQMD a Remainder of Domain b TOG 751 3877 620 3670 NO x 609 1785 508 1595 SO 2 89 130 6 6 129 PM 2.5 88 653 87 640 NH 3 72 299 82 299
a Annual average daily b December weekday8

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

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



## 4. Data analys i s A variety of data analyses have been performed to better understand Bay Area PM 2.5 formation

. They can be summarized in three main categories: characteristic s of measured PM 2.5 , Chemical Mass Balance (CMB) analysis , and statistical methods including cluster analysis to infer how PM 2.5 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
A nalyses were performed to characterize when, where, and to what extent elevated PM 2.5 levels occurred

. Figure 5 shows the percentage of 24

- hour NAAQS exceedance days for PM 2.5 by calendar month

. An exceedance day is defined as any day for which PM 2.5 levels at one or more sites exceeds 35  g/m 3

. 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 PM 2.5 . Fir st, 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 p ollutants are limited and pollut ants are trapped near the surface

. Second, the cooler winter temperatures favor partitioning of ammonium nitrate, a major PM 2.5 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 PM 2.5 . PM 2.5 concentrations exceed the federal 24

- hour standard from 5 to 40 times a year, dep ending 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 PM 2.5 exceedances for ea ch 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 altern ating 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 tre nd 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 lar gely 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 PM 2.5 levels and had the most exceedances of any winter on record PM 2.5 was measured

.

Figure 5
. Percentage of 24

- hour PM 2.5 NAAQS exceedances by calendar month.

Figure 6
. Bar plots showing number of 24

- hour PM 2.5 exceedance days and number of r ainy days for each winter season from November 15 through February 10

. PM 2.5 exceedance days occurred when at least one Bay Area monitor recorded 24

- hour PM 2.5 level of 35  g/m 3 or 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 PM 2.5 and PM 10 levels using historical measurements
. Time series for the annual average levels are shown in Figure 7
. PM 2.5 measurements are only available since 1999

. The data appears to indicate a decreasing trend for PM 2.5 and PM 10 levels. Since the late 1980s, PM 10 levels have decreased nearly 50 percent
. Decrease in PM 2 .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 PM 10 and PM 2.5 levels, with annual standards shown for reference

. PM 2.5 data were not available before 1999

. There is n o annual component of the federal PM 10 standard.

4.1.3 Spatial patterns
PM 2.5 is a regional pollutant

. Its concentrations within the Bay Area are relatively uniform as compared to locations outside of the District

. PM 2.5 measurements for all Bay Area mon itoring locations are strongly correlated

. This correlation implies that PM 2.5 levels generally rise and fall with similar timing for all locations throughout the District

. PM 2.5 episodes develop under conducive meteorological conditions, when levels throu ghout the District increase and may eventually level off well above the background concentration

. Typically, PM 2.5 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 longe r. Despite its regional nature, there is significant spatial variability for PM 2.5 levels within the Bay Area
. It is important to understand how the spatial distribution of PM impacts13

attainment for both the daily and annual components of the PM 2.5 standa rds

. These levels are calculated using a 3

- y ea r period to be consistent with how attainment status is defined

. The 3

- y ea r period was selected as 2006

- 08. Annual average PM 2.5 levels for 9 Bay Area locations are shown in Figure 8
. Point Reyes, largely reflecting the background, has annual PM 2.5 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 PM 2.5 levels around 9

- 10  g/m 3

. Design values are estimated as a 3

- yr average of the quarterly 98 th percentiles for measured 24

- hour PM 2.5 levels

. They are shown in F igure 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 PM 2.5 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/m 3

. The composition of PM 2.5 also varies throughout the Bay Area

. Chemical speciation of PM 2.5 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 sulfat e . These secondary PM 2.5 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. E lemental carbon ( EC ), and to a lesser extent organic carbon ( OC ), reflect primary PM 2.5 . These co mpounds 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 PM 2.5 , whereas more rural locations further inlan d have relatively higher proportions of secondary PM 2.5 . Sodium and chloride levels reflect the presence of sodium chloride from s uspended sea salt

. This natural PM 2.5 component represents a small fraction of the overall particulate mass

. As expected, sodi um chloride levels are much higher at San Francisco and Point Reyes than at the other non

coastal sites.14

Figure 8
. Annual average PM 2.5 levels by site for 3

- yr period 2006

- 08
. Error bars represent 95% confidence intervals.

F igure 9
. Average 98 th percentile 24

- hour PM 2.5 levels (design values) by site for 3

- yr period 2006

- 08
. Error bars represent 95% confidence intervals.15

Figure 10
. Levels for P M 2.5 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 de termined 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 PM 2.5 measurements from five locations obtained as part of CRPAQS
. These sampling locations account ed for the spatial variability in PM 2.5 composition throughout the Bay Area

. The San Francisco and San Jose locations represent ed urban conditions

. Livermore wa s suburban

. Bethel Island wa s a rural location between the Bay Area and heavily polluted Central Valley

. Point Reyes wa s a remote coastal location, presumably sampling near

- bac kground pollution levels

. Speciated PM 2.5 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 PM 2.5 levels

. A nnual 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 PM 2.5 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 combustio n . The fossil category included combustion of fuels from on

- roa d 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 (C 14 ) anal ysis 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 PM 2.5 levels The ten samples with the highest 24

- hour PM 2.5 levels were all obtained during win ter
. Most of these measurements occurred on days exceeding the 24

- hour PM 2.5 standard of 35  g/m 3

. Figure 11 shows the percentage contribution of each source category for each location

. These results do not reflect differences in total PM 2.5 levels between the stations. The urban and suburban locations (San Francisco, San Jose, and Livermore) exhibit ed comparable source contributions

. Primary combustion sources (fossil and biomass) and secondary ammonium nitrate account ed for the bulk of PM 2.5 . San Jose had a somewhat lower contribution of ammonium nitra te than the other he avily populated locations

. It wa s 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 wa s less common in this densely populated city that has many apartments lacking fireplaces

. Fossil f uel 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 PM 2.5 wa s ammonium nitrate
. This location is closest to the Central Valley where intense ammonia emissions and meteorologically conducive conditions enhance d ammonium nitrate formation

. Due to the lack of substantial commercial activity, fossi l fuel combustion contribution wa s about half that of the more populated locations

. Biomass burning contribution wa s comparable to the more populated locatio ns . Likely, wood burning occurred in a hig her proportion of hous eholds, but population density wa s considerably lower for this rural location. Point Reyes wa s quite different from the other locations

. The strongest contributing source wa s naturally occurring marine aerosols at over 30 percent
. Am monium sulfate contribution wa s also s ignificant at nearly 20 percent
. This wa s the only location with an elevated proportion of ammonium sulfate

. The site wa s proximal to offshore shipping lanes and lack ed other local sources

. Secondary ammonium nitrate c ontribution wa s lowest for this location most distant from concentrated ammonia sources

. Fossil fuel combustion contribution wa s also low for this remote location with negligible commercial activity.17

Overall, primary combustion particles an d secondary amm onium nitrate were the main drivers for Bay Area PM 2.5 episodes impacting populated areas

. Wood burning may have be en the single largest PM 2.5 source contribution for the Bay Area during episodes

. Geological dust, tire and break wear, and marine sources co ntributed near

- negligible amounts to PM 2.5 exceedances.

4.2.2 Source contributions to annual average PM 2.5 levels Many PM 2.5 measurements made were during non

- winter months when PM 2.5 levels are generally low

. Annual averages were computed from quarterl y averages to avoid over

weighting 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 PM 2.5 levels between the stations. The urban and suburban locations exhibit ed comparable source contributions

. Primary combustion sources dominate d

. These locations were impacted by vehicular and construction emissions related to commerce and al so household activities

. As with peak PM 2.5 , biomass contributions were somewhat lower for San Francisco as compared to the other heavily populated locations

. The contributions of ammonium nitrate, ammonium sulf ate, and marine particulates were similar an d 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 popul ated locations

. This location wa s relatively distant from the intense primary PM sources around the bay

. It was most strongly impacted by regional secondary PM 2.5 . The m arine contribution wa s relatively weak for this inland location. The remote coastal location at Point Reyes wa s affected most strongly by marine particulate

. Marine percentage contributions were 2

- 3 times greater than for the other locations

. Ammonium sulfate percentage contribution wa s around twice that of the other locations because of proximity to offshore shipping lanes

. In terms of mass contributions, however, marine and a m monium sulfate contributions were not very different from the other locations

. This is because anthropogenic emissions had little impact at Point Reyes , and thus total PM 2.5 levels here were low. Overall, primary combustion particles account ed for the bu lk of annual average PM 2.5 levels

. Ammonium nitr ate contributed somewhat more than ammonium sulfate and marine influences, which were comparable

. Geological dust and tire and break wear contribute d 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 seaso n . Ammonium nitrate formation wa s favored by low winter temperat ures . This season also18

exhibited increased wood burning emissions

. Similar results were obtained for other locations; however, the more urbanized locations may not have be en as strongly affected by winter wood burning.

Figure 11
. CMB results for CRPAQS period

. Results for exceedance days only.19

Figure 12
. CMB results for CRPAQS period

. Annual results.

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 PM 2.5 levels during the winter season , t here was a considerable da y - 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 measur e ments Meteorological characteristics associated with PM episodes were ide ntified 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 n ot 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 aver age 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 exhibit ed cool temperatures; however, temperature itself was not a strong indicator of elevated PM levels

. Exceedances of the 24

- hour PM 2.5 standard in the Bay Area typically occur as PM levels build over time upon three or more cons ecutive 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 PM 2.5 level as a function of the number of consecutive days having the above described characteristics

. The increase in PM 2.5 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 14
. Mean observed Bay Area PM 2.5 level in response to number of consecutive days with favorable conditions

. Curve is fit for e xponential 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 cl uster (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 Unive rsity 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 o f weather patterns influencing PM in the Bay Area

. Also, the decade

- long study period allowed characterization of year

- to
year variability in global weather patterns affecting the Bay Area

. Meteorological observations input to the cluster analysis were obt ained from 12 weather stations positioned at key lo cations throughout the Bay Area ( stations in Figure 1 within the District boundaries ). The cluster analysis revealed five dominant winter weather patterns prevail ing over the Bay Area
. Three of the clusters are conducive to elevated Bay Area PM levels

. All three exhibit22

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 consid erably higher than in the Bay Area
. These two clusters account for around 80 percent and 15 percent of all Bay Area 24

- hour PM 2.5 exceedances , respectively

. The cluster accounting for 80 percent of the Bay Area exceedances exhibits persistent easterly wind s 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 aft ernoon . 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 ex hibit 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 the se two e pisodic 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 transfor mation of NO x to nitric acid (needed for the formation of ammonium nitrate) in the Central Valley than for coastal locations

. These conditions include l ow wind speeds combine d with high amounts of sunlight during the day and high humidity at night

. These s ettings enhance daytime and nighttime conversion of NO x to nitric acid

. Nitric acid then rapidly reacts with ammonia emissions , mostly from da i r y activities , which are especially concentrated in the northern San Joaquin Valley, to form ammonium nitrate. T he 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 Calif ornia 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 t h is lone cluster were further explored to distin guish between exceedance s and non

- exceedance s . Exceedance days could be defined in terms of a number23

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 PM 2.5 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 r esolve 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 applie d the clustering technique described above to an expanded spatial domain

. In addition to Bay Area w eather 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 S acramento 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 s maller 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
. One24

cluster has winds entering t he Bay Area from the Sacramento Valley and accounts for around 60 percent of the Bay Area exceedances

. The highest Bay Area PM 2.5 levels are usually observed in San Jose

. The other cluster has winds entering the Bay Area from the San Joaquin Valley and acc ounts for around 20 percent of the Bay Area exceedances

. The highest Bay Area PM 2.5 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 se ction 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 Joaq uin Valley (right, 20% of Bay Area exceedances).25



## 5. Computer simulations
A number of different simulations were performed to better understand the formation of PM 2.5 in t he B ay Area
. In section 5.1, the computer models and their configurations are brief ly described

. In section 5.2, base case simulation results are presented

. The models were applied to estimate the sensitivity of PM 2.5 levels to reductions of Bay Area emissions (section 5.4 ) and transport impacts on the Bay Area (section 5.5 ). Three wint er 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 P M levels on record

. The 2006

- 07 winter exhibited moderate conditions that may be more representative for air quality planning purposes

. In total, PM 2.5 was simulated for 128 days

. This large sample size ensured that all representative meteorological condit ions under which elevated PM levels occur red were modeled

. The third modeled winter was 2008

- 09
. For this winter, only directly e mitted PM from wood burning was simulated

. T his winter was simulated to evaluat e 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 th e Fifth

- Generation National Center for Atmospheric Research (NCAR)/Pennsylvania State University M esoscale M odel (MM5)

. Separate MM5 configurations were used for PM modeling and wood smoke modeling. For PM modeling, t hree 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 Cal ifornia 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 1 8 9 g rid 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 1 5 km)

. M eteorological variables are26

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 availa ble physics options

. The final choice of options that proved to be the best for characte rizing meteorology in the domain were similar to those used by the National Oceanic and Atmospheric Administration (NOAA) for the Central California Ozone Study simulat ions
. 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 a greement 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 /1 5 /2009

. The i nner 4

- km modeling domain had 79 grid cells in both the east

- west and north

- south direction s . This domain covered the Bay Area and surrounding regions, but not the rest of Central California that was included i n 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 Com munity Multiscale Air Quality (CMAQ) modeling system version 4.6
. CMAQ simulates six individual PM 2.5 components which are combined to estimate total P M 2.5 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” component s account for inert particulates including d ust, 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

. T he meteorological fields from 2 grid cells alo ng each edge of the meteorological modeling domain were not used in order to minimize the impact of boundary conditions on PM modeling with in the 4

- km modeling domain

. Th is resulting modeling domain matches the 4

- km CRPAQS modeling domain shown in Figure 2
. S ome 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 l ayers 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 of27

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 1 5 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) aqueo us - 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 winter s, 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 su ch 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 giv en 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 spec ies, 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 avo id 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 win ters of 2000

- 01 and 2008

- 09
. The wood burning emissions inventory is described in section 3
. T he 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 B ase 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 ba se case simulations were intended to reconstruct actual pollutant levels that occurred during these periods

. The simulated surface PM 2.5 fields compared favorably against PM 2.5 measurements
. Certain limitations of the model were noted, however, such as a t endency to underestimate very high PM 2.5 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 alof t 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 PM 2.5 levels against measurements are presented in Appendix C

. 5.2.1 Individual PM 2.5 components A key benefit of modeling is the ability to estimate levels of the major PM 2 .5 components throughout the Bay Area

. This information helps fill the large gaps in the speciated PM 2.5 measurements that are only available for a limited number of days at a few locations

. Simulated episodic 24

- hour levels for the PM 2.5 components are sh own in Figure 16
. These results are averaged across the 55 days for which simulated Bay Area 24

- hour PM 2.5 exceeded 35  g/m 3

. For these episodic days, light winds flow ed through the Bay Area from the east, and Cen tral Valley conditions were near calm.

Figure 16
. Surface plots of simulated 24

- hour levels for the six PM 2.5 components, averaged across the 55 days for which simulated base case Bay Area maximum 24

- hour PM 2.5 level exceeded 35  g/m 3

. 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 loca tions, 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, O C 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 V alley

. During an episode, a stable, stagnating air mass is trapped between th e V alley’s rims. The "other" PM 2.5 component presented in Figure 16 also results from primary emissions only

. It is elevated near highways and populated are as . 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 PM 2.5 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 PM 2.5 levels u nder relatively calm conditions or during the winter rainy season. Figure 16 shows that a mmonium nitrate is the dominant component of s econdary PM 2.5 . 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 n itrate/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 Carqu inez Strait where excess ammonium is present

. The ammonium nitrate is not localized near ammonia or NO x sources . It is diffuse

. This is because ammonium nitrate forms by chemical reactions occurring over the course of several days and nights , as fresh emis sions 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 PM 2.5 du ring 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 , an d 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 form s 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 c an occasionally account for a moderate p roportion of PM 2.5 . 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, PM 2.5 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 NO x emissions
. Lack of a vailability of either ammonia or nitric acid can be the limiting factor in ammonium nitrate formation

. Figure 17 shows levels for these gases average d across the 55 days for which simulated Bay Area 24

- hour PM 2.5 exceeded 35  g/m 3

. 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 wa s especially concentrated in the Central Valley , but its levels also increased for some Bay Area source locations

. Near th e ammonia sources, nit ric acid wa s consumed to near

- zero levels

. A lso, ammonium nitrate levels were elevated over the ammonia sources (see Figure 16 ), indicating locations of significant secondary particulate formation

. Nitric acid wa s 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 NO x emissions sources near the locatio ns where nitric acid accumulated

. These findings suggest ed that ammonium nitrate formation wa s 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 limi ted 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

. A mmonia emissions reductions could still reduce ammonium nitrate levels under nitric limited chemistry; however, ammonia emissions reductions c ould n o t impede ammonium nitrate formation for regions in which all nitric acid has been consumed. 5.2.3 Spatia l distribution of PM 2.5 The base case simulation results demonstrate how primary PM 2.5 accumulates near its sources, whereas secondary PM 2.5 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 PM 2.5 characteristics

. To objectively identify the relevant subregions, a cluster analysis was performed on the simulated PM 2.5 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 mechanics31

and goals.) The cluster analysis was performed to group model grid cells having strongly correlated PM 2.5 le vels

. By definition, correlated grid cells exhibit PM 2.5 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 clust ering was applied only to days with simulated PM levels exceeding 30  g/m 3 so that the identified subregions reflect episodic conditions

. Eight subdomains were identified within the Bay Area ( Figure 18 )

. Other sub domains 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 PM 2.5 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 max imum 24

- hour PM 2.5 level exceeded 35  g/m 3

. 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, PM 2.5 is composed of approximately equal portions of primary and secondary components, up to about 50  g/m 3 of total PM 2.5 . This distribution demonstrates the typical buildup of secondary PM 2.5 in the Central Valley to levels as high as pri mary PM 2.5 around a major urban source area for primary particles

. Secondary PM 2.5 levels occasionally exceed 35  g/m 3 , indicating that an exceedance could occur in the Central Valley even without any primary PM emissions

. For high levels of primary PM 2.5 (beyond 25  g/m 3 ), additional buildup of secondary PM 2.5 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 accumul ation. In the Delta subregion, PM 2.5 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 Val ley proper

. A similar effect is observed through the Altamont Pass and into the Livermore Valley

. Here, however, total PM 2.5 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 of32

both primary and secondary PM 2.5 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 characte ristics 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, PM 2.5 is dominate d 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/m 3 lower than in the Delta or Livermore Valley

. In the San Fr ancisco Peninsula and San Jose , PM 2.5 exceedances can occur from primary contributions alone. North of San Francisco, in the Petaluma Valley, PM 2.5 is more primary than secondary; however, primary components do not dominate as in the urban areas

. Secondar y PM 2.5 levels are similar as in the urban areas, reflecting the regional nature of secondary PM

. Primary PM 2.5 levels are around half that of San Francisco and San Jose, reflecting the smaller source strength of Santa Rosa

. Significant contributions of bo th primary and secondary PM 2.5 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 w inds 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 P acific Ocean

. PM levels33

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 Joa quin 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, PM 2.5 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 PM 2.5 levels

. Similar meteorological conditions persisted through January 24

. PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 levels with superimposed 24

- hour averaged wind fiel d 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 sensitivi ties of PM 2.5 levels to various redu ctions 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 emis sions 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 wo od smoke emissions only. The first type of simulation explored the sensitivity of PM levels to 20 percent across - the

board reductions of classes of chemi cal species emitted by anthropogenic sources within the Bay Area: NO x and VOC combined; gaseous sulfur species; ammonia; directly emitted PM; and these four classes combine d , comprising all anthropogenic emissions

. Simultaneous NO x and VOC emissions reduct ions were simulated becaus e 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 simulation s explore d the impact of Bay Area wood burning on PM 2.5 levels

. Model runs were pe rformed with 100 percent , 50 percent , and 0 perce nt simulated compliance rate with the District wood burning restriction enacted for th e 2008

- 09 winter
. R eductions of wood smoke emissions were simulated only for actual Spare the Air days during which wood burning was restricted

. The simulated emissions r eductions 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 PM 2.5 lev els would have been without wood burning control me asures for the 2008

- 09 winter. 5.4.1 Across - the - board emissions reductions
Across

- the

- board emission red uctions 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 PM 2.5 levels (  PM 2.5 equals base case PM 2.5 level minus sensitivity run PM 2.5 level) were computed for each model grid cell

.  PM 2.5 is defined such that positive values reflect a decrease in PM level s when emissions are reduced

.  PM 2.5 values at each grid cell were averaged across the 20 days in the 3 6

- day study period having simulated base case Bay Area PM 2.5 levels exceeding 35  g/m 3

. These sensitivities under episodic conditions are shown in Figure 21
. Reducing 20 percent of th e directly emitted PM (around 18 tons /day eliminated , see Table 2 ) was approximately an order of magnitude more effective in reducing peak P M 2.5 levels than reducing 20 percent of the secondary PM precursors

. In Figure 21 ,  PM 2.5 achieved from direct PM emissions red uctions is only slightly below than that achieved from reducing all emissions

. Correspondingly,  PM 2.5 achieved from 20 percent reductions in precursor gas emissions (NO x and VOC; ammonia; and sulfur

- containing s pecies) 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 1 5 tons /day elimin ated , see Table 2 ) was the most effective of the precursor emissions reductions simulated

. The  PM 2.5 achieved mostly reflects reduced particulate ammonium nitrate levels

. The ammonia emissions reductions also reduced ammonium sulfate levels by up to 0.1  g/m 3 near sulfur sources around Richmond and through Carquinez Strait (not shown)

. The effectiv eness 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 les s effective near ammonia sources, where the secondary PM forming chemistry was limited by lack of nitric acid

. ( S ee 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 NO x and VOC emissions (around 110 tons /day NO x and 1 4 0 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/m 3

. For other locations,  PM 2.5 was small in magnitude

. The negative  PM 2.5 values may be mathematical artifacts from the model

. Reducing 20 percent of the sulfur

- containing P M precursor emissions (around 16 tons /day eliminated , see Table 2 ) had a relatively small impact on Bay Area PM 2.5 levels

. Benefits of these emissions reductions occurred primarily near sulfur sources around the bay

. Maximum  PM 2.5 of around 0.25  g/m 3 wa s comparable with that of the combi ned 20 percent NO x and VOC emissions reductions

. Tonnage reductions of sulfur (16 tons /day), however, were an order of magnitude lower than for the NO x and VOC reductions ( around 25 0 tons /day) required to achieve a similar effect

. U nder episodic conditio ns with winds from the east, sulfur emissions from the offshore shipping lanes were downwind of the Bay Area

. O ffshore 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, t he offshore shipping lanes w ere upwind of the Bay Area

.  PM 2.5 resulting from the 20 percent sulfur emissions red uctions were over 1  g/m 3 in 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 PM 2.5 , respectively

. For each subdomain,  PM 2.5 values were tabulated only for the grid cell with the highest simulated PM 2.5 levels on each ep isodic 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 PM 2.5 levels differently for different Bay Area subdomains

. The directly emitted PM reductions were most effective around San Jose, where primary PM 2.5 levels were high over a relatively large area

. Ben efits ranged about 4

- 6  g/m 3

. 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,  PM 2.5 values were generally in the range 3

- 5  g/m 3

. In terms of percentage reductions of primary PM 2.5 , there was much less spatial variability

. This is because reductions of directly emitted PM more strongly impact primary PM 2.5 levels near PM sources where primary levels are higher

. Primary PM 2.5 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 PM 2.5 level s relatively uniformly across the Bay Area subdomains

. An exception was the extreme eastern portion of the Bay Area, where PM 2.5 levels were somewhat less sensitive to the ammonia emissions reductions

. This subdomain adjacent to the Delta was mostly affect ed by transported secondary PM under episodic conditions having winds from the east

.  PM 2.5 values were generally in the range 0.1

- 0.5  g/m 3

. Secondary PM 2.5 levels were typically reduced 0

- 4 percent , with an average around 2 percent
. Primary PM levels wer e not significantly affected by reductions of ammonia emissions.

Figure 21
. Surface plots of simulated sensitivities of PM 2.5 for five 20% emissions reductions , averaged across the 20 days for w hich simulated Bay Area maximum 24

- hour PM 2.5 level exceeded 35  g/m 3

. 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  PM 2.5 values indicate that ambient PM levels decrease with emissions reductions

. Sa me scale is used for top and bottom tiles.38

Figure 22
. Tabulated  PM 2.5 values for grid cell with highest simulated PM 2.5 level for eight subdomains depicted in Figure 18 , for 20 exceedanc e days only

. Top plot shows sensitivity to 20% direct PM emissions reductions

. B ottom plot shows sensitivity to 20% ammonia reductions

. Horizontal lines on boxes indicate 25 th , 50 th (median), and 75 th percentiles for  PM 2.5 values. Remaining values are con tained within whiskers, except for outliers plotted using a plus signs. 5.4.2 Wood smoke impacts Wood smoke PM 2.5 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 simu lation 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 restricti ons 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 throu gh noon of the following day

. This timing reflects 100 percent compliance with the actual perio ds of wood burning restriction

. For all other periods, full wood burning emissions were simulated

. T he only difference be tween the pair of model runs was the presence or absence of wood burning during the eight 24

- hour Spare the Air periods. E xample s of the wo od smoke simulation s are shown in Figure 23 for Spare the Air day s 12/10/ 2008 and 1/18/2009

. Without burning restrictions, peak wood smoke levels of 10

- 20  g/m 3 would have occurred over the areas having high wood burning emissions (see Figure 3 )

. Wood smoke levels would have been around 5  g/m 3 or 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 o n 1/18/200 9, wood smoke levels would have been highest in the39

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 A rea locations near concentrated wood burning source areas . Peak benefits were around 10  g/m 3 of 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 restriction s had a signifi cant 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 Con cord and San Jose monitoring locations

. These data are representative of the other Bay Area sites
. B lue bars indicate the observed PM 2.5 level

. It is assumed that measurements during these Spare the Air periods reflected the effects of 100 percent complian ce 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 PM 2.5 levels that would have resulted without burning restrictions

. Thus, the green bars reflect the effect of the restrictio ns . On Spare the Air day 12/10/2008, observed Concord PM 2.5 level was 17  g/m 3

. Without burning restrictions, more than 14  g/m 3 additional wood smoke would have been present, for a total PM 2.5 level of over 31  g/m 3

. This was the largest benefit of the wood burning restrictions to have occurred at Concord

. At San Jose, PM 2.5 level was 30  g/m 3 with burning restrictions, but would have been nearly 34  g/m 3 without the restrictions

. Th is benefit at the San Jose monitoring location wa s far smaller than the maximum South Bay benefit (10  g/m 3 ) 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/m 3 ) 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/m 3 ) than in the East Bay (7  g/m 3 )

. These modeling results suggest that r eductions of population exposure to wood smoke were considerably greater than indicated by the monitoring data alone. Without the burning res trictions , wood smoke levels for the eight Spare the Air days would have averaged around 11, 7, 5, 3, and 3  g/m 3 for the Concord, San Jose, San Francisco, Vallejo, and Livermore monitoring locations , respectively

. Assuming 100 percent compliance, the burn ing 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 restri ctions and also carried over wood smoke

. The burning restrictions had the40

added benefit of reducing carried over wood smoke into the days following burning restrictions
. Because the burning restrictions reduce d carry over, enhanced benefits may be achieved for multiple, consecutive Spare the Air calls

. For most locations, the t wo consecutive Spare the Air calls on 11/24

- 25/2008 provided the largest reduction s of PM 2.5 levels simulated for the 2008

- 09 winter. Table 3
. Spare the Air d ays 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 day s 12/10/2008 (top row) and 1/18/2009 (bottom row)

. S imulation with wood burning emissions (left column ) show s estimated Bay Area wood smoke41

levels assuming full wood bur ning
. Simulation without wood burning emissions (right column) shows estimated benefit (reduction) of wood smoke levels resulting from the burning restrictions.

Figure 24
. Simu lated effect of wood burning restrictions during 20 08

- 09 winter at Concord and San Jose monitoring locations

. Blue bars are observed PM 2.5 levels

. For Spare the Air days, the observed PM 2.5 levels are assumed to result from 100% compliance with the burning restrictions

. Green bars indicate simulated wood s moke 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
PM 2.5 transport impacts on the Bay Area were explored by simulation

. The approach im plemented “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

. Ze roing 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 provide d an estimate of how Bay Area air quality may benefit from reduced upwind emissions so urces . 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 PM 2.5 from these Central Valley areas . The t rans port analyses in wh ich emissions were zeroed out were unrealistic

. Such simulations only provide d qualitative information regarding the relative contributions of sources within and outside of the Bay Area

. Moreover, they did n ot account for potential inter actions of em issions 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 PM 2.5 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 PM 2.5 level exceeded 35  g/m 3 in 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 contribute d 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 PM 2.5 levels. Without B ay Area anthropogenic emissions, primary PM 2.5 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/m 3 in 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/m 3 or lower

. Similar spatial distributions were observed for OC and other PM 2.5 . Higher levels were observed nearer the mountain passes connecting to the Central Valley

. In the Bay Area, OC levels ranged about 1

- 4  g/m 3 , and other PM 2.5 levels ranged about 0.5

- 3  g/m 3

. Transport impacts of secondary ammonium nitrate were generally greater than for the primary PM 2.5 components combined

. Ammonium nitrate levels along the eastern boundary of the Bay Area were as high as13  g/m 3

. Plumes of ammonium nitrate with levels around 8  g/m 3 were present through the Carquinez Strait and Altamont Pass

. Areas around the bay had a bout 5  g/m 3 transported ammonium nitrate.43

Ammonium sulfate levels in the Bay Area were spatially uniform a t around 1  g/m 3

. 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 th e more severe episodes, transport impacts were often greater

. Transport impacts were tabulated for the six days with highest measured PM 2.5 levels for both 2000

- 01 and 2006

- 07, for 12 days total

. Total transported PM 2.5 levels averaged 19, 19, 11, and 12  g/m 3 at the Livermore, Vallejo, San Jose, and San Francisco monitoring locations, respectively

. Maximum total transported PM 2.5 levels were 33, 37, 24, and 24  g/m 3 for these stations, respectively

. Thus, 24

- hour PM 2.5 exceedances could have occurred in t he Bay Area without any Bay Area anthropogenic emissions

. For the se 12 days, transported primary PM 2.5 averaged 7, 8, 4, and 5  g/m 3 and transported secondary PM 2.5 averaged 12, 11, 7, and 7  g/m 3 for the se four stations, respectively

. Transported secondar y PM 2.5 levels averaged around 40

- 80 percent more than transported primary PM 2.5 levels , depending upon location.

Figure 25
. Same as Figure 16 , except simulated with Bay Area anthropogeni c 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

- hour44

PM 2.5 component levels between the base case and each transport run (  PM 2.5 , as defined in section 5.4.1 ) indicate d how the Bay Area benefits from eliminating emissions in these upw ind regions

. The results were averaged across the same 55 days as shown in Figure 16 for which Bay Area maximum 24

- hour PM 2.5 level exceeded 35  g/m 3 in the base case simulation

.  PM 2.5 values for eliminating Sacr amento 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 leve ls of primary PM 2.5 (EC, OC, and other) through Carquinez Strait and into the northern portion of the Bay Area
. Benefits of around 5  g/m 3 primary PM 2.5 were achieved through Carquinez Strait

. Benefits of reducing Sacramento area EC and OC emissions were primarily localized through Carquinez Strait
. For the “other” PM 2.5 component, benefits additionally extended over the northern San Fr ancisco 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 O C 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 emis sions from the Sacramento area reduced levels of secondary ammonium nitrate PM 2.5 throughout the Bay Area

. The benefits were largest and approximately equal in magnitude through both Carquinez Straight and Altamont Pass

. The Sacramento area precursors cont ributed to regionally elevated Central Valley secondary PM 2.5 levels

. This regional secondary PM 2.5 was then transported into the Bay Area through mountain passes connecting with the Central Valley

. Through these passes, reductions in ammonium nitrate of a round 2.5  g/m 3 resulted from elimination of the Sacramento area emissions
. Elsewhere throughout the Bay Area, the benefit was around 1.5  g/m 3

. These benefits may have been undere stimated because the model sometimes produced overly strong winds throughout the Sacramento Valley dur ing 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

. B enefits of up to 0.1  g/m 3 occurred 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 we re often greater

. Transport impacts were tabulated for the six days with highest measured PM 2.5 levels for both 2000

- 01 and 2006

- 07, for 12 days total

. Total transported PM 2.5 levels averaged 4, 5, 2, and 3  g/m 3 at the Livermore, Vallejo, San Jose, and Sa n Francisco monitoring locations, respectively

. For these 12 days, transported primary PM 2.5 averaged 2, 3, 1, and 1  g/m 3 and transported secondary PM 2.5 averaged 2, 2, 1, and 2  g/m 3 for these four stations, respectively

. Transported primary PM 2.5 levels were somewhat higher at Vallejo, and transported secondary PM 2.5 levels were somewhat higher for the other three monitoring locations.45

Figure 26
. Benefit (  PM 2.5 ) 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/m 3

. Elimination of the primary PM emissions from the San Joaquin Valley reduced levels of primary PM 2.5 (EC, most of the OC, and other) through Carquinez Strait and Altamont Pass

. Benefits of around 4  g/m 3 primary PM 2.5 were achieved through these mountain passes

. Slightly lower benefits of around 3  g/m 3 primary PM 2.5 were achieved through the Pacheco Pass and into southern most Santa Clara County

. Transported EC, OC, and other PM 2.5 a ffect ed the same locations within the Bay Area. Eliminating the PM precursor emissions from the San Joaquin Valley reduced levels of secondary ammonium nitrate PM 2.5 throughou t the Bay Area

. The benefits were largest (up to 8  g/m 3 ) through Pacheco Pass and into southernmost Santa Clara County

. The benefits were enhanced and approximately equal in magnitude (up to 6  g/m 3 ) over easternmost Contra Costa County and into Altamont Pass

. Transport impacts were increased for the more southern mountain passes because secondary PM 2.5 levels generally increase d further south into San Joaquin Valley

. Elsewhere throughout the Bay Area, the benefit was around 5  g/m 3

. Eliminating sulfur emissions from the San Joaquin Valley provided less t han 0.5  g/m 3 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 se vere episodes, transport impacts were often greater

. Transport impacts were tabulated for the six days with highest measured PM 2.5 levels for both 2000

- 01 and 2006

- 07, for 12 days total

. Total transported PM 2.5 levels averaged 13 , 10 , 7 , and 7  g/m 3 at the Livermore, Vallejo, San Jose, and San46

Francisco monitoring locations, respectively

. For these 12 days, transported primary PM 2.5 averaged 4 , 2 , 2 , and 1  g/m 3 and transported secondary PM 2.5 averaged 9 , 7 , 6 , and 6  g/m 3 for these four stations, respectiv ely

. Transported secondary PM 2.5 levels were 2.5

- 4 times higher than transported primary PM 2.5 levels

.

Figure 27
. Benefit (  PM 2.5 ) 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/m 3

. The transport cluster analysis of section 4.3.3 identified two distinct weather patterns under which transport is believed to occ ur . 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  PM 2.5 values across days associated with each transport pattern. Only days for which Bay Area maximum 24

- hour PM 2.5 level exceeded the 30  g/m 3 in the base case simulation were included in the averages

. Transport impacts under each transport pattern were assessed separatel y 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 J oaquin 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 Va lley

. The benefit was around 5  g/m 3 throughout the central portion of the Bay Area

. This transported PM 2.5 was about half primary and half secondary

. With bulk air flow arriving from the San Joaquin Valley, benefits of eliminating the Sacramento area emis sions 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 benefi t was around 5

- 15  g/m 3 throughout the northern portion of the Bay Area

. Transport impacts were highest through the Carquinez Strait

. This transported PM 2.5 was about one

third primary and two

- thirds secondary

. With bulk air flow arriving from the Sacramento Valley, benefit s 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/m 3

. T ransport ed PM 2.5 levels through both the Altamont and Pacheco Passes were high er than through Carquinez Strait.

Figure 28
.  PM 2.5 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 simu lations 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 o f research efforts to better understand fine particulate matter (PM 2.5 ) 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 PM 2.5 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 meas urements The Bay Area 24

- hour PM 2.5 exceedances were almost entirely confined to the winter months. This season exhibited increased a tmospheric stability that trapp ed pollutants near the ground

. Consecutive stagnant, clear, and cool winter days were typic ally requisite for PM 2.5 episodes to develop

. Stagnant conditions prevented dispersion of PM 2.5 . Clear skies favored photochemical production of secondary PM 2.5 , especially ammonium nitrate

. Cool temperatures favored secondary ammonium nitrate particulate buildup by decreasing its tendency to evaporate

. Typically, PM 2.5 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 2 4

- hour PM 2.5 exceedances than those with more windy and stormy conditions. PM 2.5 episodes were regional in nature and impacted most Bay Area locations

. Livermore, Concord, San Jose, and Vallejo had 24

- hour PM 2.5 design values at or near the NAAQS exceedan ce threshold of 35  g/m 3

. Other populated locations exhibited design values around 25

- 30  g/m 3

. Near

- background PM 2.5 levels were observed at Point Reyes, where annual

- average PM 2.5 levels were about half that of other Bay Area monitoring locations

. The Bay Area is expected to attain the NAAQS for annual

- average PM 2.5 level. The composition of PM 2.5 varied throughout the Bay Area

. Carbonaceous PM 2.5 (EC and OC) was mostly concentrated near its sources around the bay

. Levels for secondary components sulfate, nitrate, and ammo nium were relatively spatially uniform throughout the Bay Area

. The secondary PM 2.5 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 PM 2.5 was mostly ammonium nitrate
. In terms of percentages, more urban locations around the bay had relatively higher proportions of primary PM 2.5 , whereas more rural locations further inland had relatively higher proportions of secondary PM 2.5 . Natural sea salt (sodium an d 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 fuel s combustion , and biomass combustion

. CMB analysis was performed on speciated PM 2.5 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 PM 2.5 levels

. Isotopic carbon ( 14 C) 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 d ifferent PM 2.5 levels. Source contributions to peak 24

- hour PM 2.5 levels were based on measurements during episodic winter conditions

. The urban and suburban locations (San Francisco, San Jose, and Livermore) exhibited comparable source contributions

. Pri mary combustion sources (fossil and biomass) and secondary ammonium nitrate accounted for the bulk of PM 2.5 . San Jose had a somewhat lower contribution of ammonium nitrate than the other heavily populated locations

. San Francisco had a somewhat lower contr ibution 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 monitor ing locations

. Overall, primary combustion particles and secondary ammonium nitrate were the main drivers for Bay Area PM 2.5 episodes impacting populated areas

. Wood burning may have been the single largest PM 2.5 source contribution for the Bay Area during episodes

. Geological dust, tire and break wear, and marine sources contributed near

- negligible amounts to PM 2.5 exceedances. Source contributions to annual average PM 2.5 levels were based on year

- round measurements
. The urban and suburban locations exhi bited comparable source contributions

. Primary combustion sources dominated

. As with peak PM 2.5 , biomass contributions were somewhat lower for San Francisco as compared to the other heavily populated locations

. The rural Bethel Island location had higher s econdary and lower primary contributions as compared with the more populated locations

. It was most strongly impacted by regional secondary PM 2.5 . The remote coastal location at Point Reyes was affected most strongly by marine and ammonium sulfate particul ate
. Overall, primary combustion particles accounted for the bulk of annual average PM 2.5 levels

. Ammonium nitrate contributed somewhat more than ammonium sulfate and marine influences, which are comparable

. Geological dust and tire and break wear contribu ted 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 ident ified 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 PM 2.5 standard in the Bay Area typically occurred as PM levels built over time upon three or more co nsecutive 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 PM 2.5 levels

. The cluster analysis revealed five dominant winter weather patte rns 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 PM 2.5 exceedances, respectively

. The cluster accounting for 80 percent of the Bay Area exceedances exhibited pe rsistent 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 exc eedances 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 infl uenced the surface winds that determine PM 2.5 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 level s. 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 a n episodic weather pattern into one of the non

episodic 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 a n 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; cle ar and sunny skies favoring enhanced secondary PM 2.5 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 b oundary

. 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 transf ormation of NO x 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 PM 2.5 levels typically occurred at San Jose for this type of episode

. A minority of exceedance days (ar ound 20 percent ) were associated with winds from the San Joaquin Valley from the south entering the Bay Area through the Delta

. Peak PM 2.5 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 PM 2.5 formation in t he 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; h owever, this was a severe PM season with the highest PM levels on record

. The 2006

- 07 winter exhibited moderate conditions that may have be en 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

. PM 2.5 was simulate d 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 ag ainst measurements to be reasonably accurate. 6.2.1 Base - case PM modeling CMAQ simulated six individual PM 2.5 components which were combined to estimate total PM 2.5 levels: ammonium , sulfate , nitrate , OC , EC , and other unresolved components

. The52

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 househ old 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" PM 2.5 component resulted from primary emissions only

. It was elevated near highways and populated areas

. It reflected t race 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 PM 2.5 levels under relatively calm conditions during th e winter rainy season. Ammonium nitrate was the dominant component of secondary PM 2.5 . 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

. T he model results suggested that ammonium nitrate formation is limited by nitric acid, and not by ammonia

. Ammonium sulfate was a minor component of secondary PM 2.5 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 sulfur

laden bunker fuel

. Because winds were from the east during epis odes, these offshore sulfur sources did not strongly impact the Bay Area

. Ammonium sulfate occasionally accounted for a moderate proportion of PM 2.5 . This situation sometimes occurred under non

- episodic conditions when winds were from the west and the ship ping lanes were upwind of the Bay Area. There was a distinct spatial gradient in the fraction of primary versus secondary PM 2.5 over Central California

. In the Central Valley, at Modesto, PM 2.5 was composed of approximately equal portions of primary and s econdary components, up to about 25  g/m 3 of primary PM 2.5 . Secondary PM 2.5 levels occasionally exceeded 35  g/m 3 , indicating that an exceedance could have occurred in the Central Valley even without any primary PM emissions
. For high levels of primary PM 2.5 (beyond 25  g/m 3 ), additional bui ldup of secondary PM 2.5 did not occur

. In the Delta subregion, PM 2.5 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 Livermo re Valley

. Here, however, total PM 2.5 levels were lower than in the Central Valley or Delta

. Significant contributions of both primary and secondary PM 2.5 were required for an exceedance to occur in the eastern portion of the Bay Area

. For the San Francisc o and San Jose urban53

areas, PM 2.5 was dominated by primary contribution

. Near these cities, PM 2.5 exceedances could have occurred from primary contributions alone

. North of San Francisco, in the Petaluma Valley, PM 2.5 was more primary than secondary; howev er, primary components did not dominate as in the urban areas

. Significant contributions of both primary and secondary PM 2.5 were required for an exceedance to occur in the North Bay. 6.2.2 PM sensitivities to Bay Area emissions reductions
Across

- the

- boar d Bay Area emissions reductions of 20 percent were simulated for the following five classes of chemical species: NO x and VOC combined , gaseous sulfur species , ammonia , directly emitted PM , and these four classes combined, comprising all anthropogenic emiss ions
. These across

- the

- board reductions were simulated for one episode each from 2000

- 01 and 2006

- 07
. Reducing the directly emitted PM reduced peak PM 2.5 levels nearly ten times more efficiently than reducing the secondary PM precursors

. Benefits achieve d 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 PM 2.5 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 PM 2.5 levels were highest (see Figure E

- 1)

. The largest benefits of around 4

- 6  g/m 3 occurred 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 PM 2.5 levels were typically reduced 0

- 4 percent , depe nding 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 NO x and VOC emissions by 20 percent (around 250 tons/day total) wa s 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 PM 2.5 levels unde r episodic conditions

. Benefits were similar as for the NO x and VOC reductions

. Tonnage reductions of sulfur (16 T/day), however, were around 15 times lower than for the combined NO x and VOC reductions (250 T/day) required to achieve a similar effect

. On c ertain days with the offshore shipping lanes54

upwind of the Bay Area, the 20 percent sulfur emissions reductions produced over 1  g/m 3 benefit

. 6.2.3 Transport impacts on Bay Area Transport PM 2.5 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 PM 2.5 levels 35  g/m 3 or higher

. Anthropogenic Bay Area emissions were eliminated to estimate the cumulative transport impacts from all sources outside of the Bay Area

. Transported primary PM 2.5 levels were around 2

- 8  g/m 3

. 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/m 3 along the Bay Area eastern boundary, 8  g/m 3 through the Carquinez Strait and Altamont Pass, and 5  g/m 3 around the bay

. Transported ammonium sulfate levels of around 1  g/m 3 were present uniformly throughout the Bay Area

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

. Central Valley PM 2.5 lev els were relatively unaffected by eliminating the Bay Area emissions. A second pair of PM 2.5 transport simulations was conducted by zeroing out anthropogenic emissions for the Sacramento area and San Joaquin Valley separately

. Reductions in simulated PM 2. 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 PM 2.5 levels around 5  g/m 3 through Carquinez Strait, less than 2  g/m 3 in the northern half of the Bay Area, and had little effect elsewhere in the Bay Area

. Secondary PM 2.5 levels transported from the Sacramento area were 2.5  g/m 3 through Carquinez Strait and Altamont Pass a nd around 1.5  g/m 3 elsewhere in the Bay Area

. Eliminating the San Joaquin Valley emissions reduced primary PM 2.5 levels around 4  g/m 3 through Carquinez Strait and Altamont Pass, around 3  g/m 3 through Pacheco Pass and into southern Santa Clara Valley, an d had little impact farther into the Bay Area

. Secondary PM 2.5 levels transported from the San Joaquin Valley were up to 8  g/m 3 through Pacheco Pass and into southern Santa Clara Valley, around 6  g/m 3 through Altamont Pass and into eastern Contra Costa C ounty, and around 5  g/m 3 elsewhere 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 PM 2.5 levels for both 2000

- 01 and 2006

- 07, for 12 days total

. Total transported PM 2.5 levels averaged 19, 19, 11, and 12  g/m 3 at the Livermore, Vallejo, San Jose, and San Francisco monitoring locations, respectively

. Maximum total transported PM 2.5 levels were 33, 37, 24, and 24  g/m 3 for these stations, respectively

. Thus, 24

- hour PM 2.5 exceedances could have occurred in the Bay Area without any Bay Area anthropogenic emissions

. Transported secondary PM 2.5 levels averaged around 40

- 80 percent more than transported primary PM 2.5 levels, depending upon location

. Total transported PM 2.5 levels from the55

Sacramento area averaged 4, 5, 2, and 3  g/m 3 for these stations, respectively

. At Vallejo, transported PM 2.5 from the Sacramento area had a somewhat higher proportion of prima ry components
. At t he other three locations, transported PM 2.5 from the Sacramento area had a somewhat higher proportion of secondary components

. Total transported PM 2.5 levels from the San Joaquin Valley averaged 13, 10, 7, and 7  g/m 3 for these stations, respectively

. Transported secondary PM 2.5 from San Joaquin Valley were 2.5

- 4 times higher than transported primary PM 2.5 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 PM 2.5 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 PM 2.5 from the San Joaquin Valley was about one

- third primary and two

- thirds secondary

. It mostly impacted the ce ntral and sou thern portions of the Bay Area. 6.2.4 Wood smoke modeling Wood smoke PM 2.5 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 p erformed

. 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 restrictio n . 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/m 3 would have occurred over the areas having high wood burning emissions

. Wood smoke levels would have been around 5  g/m 3 or more for many of the remaining populated locations within the Bay Area

. Peak benefits of the wood burning restrictions were aro und 10  g/m 3 of 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 exp osure 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/m 3 for 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 locat ion
. 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 PM 2.5 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 PM 2.5 buildup

. Extensive simulations covered the bulk of three winter seasons

. This custom computer m odel 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 PM 2.5 impact the Bay Area differently for both various locations and across a range of typical meteorological conditions

. The model suggests that reducing direct PM 2.5 emissions within the Bay Area is the most ef fective means of reducing Bay Area primary PM 2.5 levels

. These reductions, however, are most effective only near direct PM 2.5 emissions sources
. These are the areas in which Bay Area total PM 2.5 levels are highest

. The model also suggests that significant amounts of PM 2.5 , especially secondary PM 2.5 , are transported from the Central Valley

. Secondary PM 2.5 exhibits a fairly regionally uniform influence throughout the Bay Area

. Analysis of measurements identified separate transport patterns occurring on diff erent days from either the Sacramento Valley or San Joaquin Valley

. During 1999

- 2007, around 60 percent of Bay Area 24

- hour PM 2.5 exceedance days occurred with transport from Sacramento area, and 20 percent with transport from San Joaquin Valley

. Model r esults indicated that Bay Area wood smoke levels during 2008

- 09 episodic conditions would have averaged 3

- 11  g/m 3 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 exis ting 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 records57

of measurements will be especially useful for ev aluating 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 PM 2.5 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

- Air

Monitoring/~/media/2F9844F2D4934AAAB8339552605A570 A.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 R esource Conservation Commission by ENVIRON International Corp, Novato, CA. EP A, 2007
. Guidance on the use of models and other analyses for demonstrating attainment of air quality goals for o zone, PM 2.5 , and regional haze

. EPA

- 454/B

- 07

- 002, 253 pp. Fairly, D. et al., 2008

. Sources of Bay Area fine particles. Palazoglu, A., 2009

. C luster analysis of air quality data for Bay Area PM study

. P repared 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
. P repared for BAAQ MD 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
. P repared for BA AQMD by Sonoma Technology, Inc., Petaluma, CA

. STI

907026.01

- 3187

- FM D.A

- 1



## Appen dix 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 PM 2.5 and key secondary PM 2.5 precursor s (TOG, NO x , SO 2 , and NH 3 ) 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 ar e 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

. F or the rest of t he 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). E missions 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 C ARB data adjusted from 2000 to 2006. The ARB provided a biogenic emissio ns 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 coi nciding with the above dates. For 2000

- 01 modeling periods outside of the above dates and for the entire 2006

- 07 winter , b iogenic emissions were used from December 19, 2000

. This day had an average winter temperature. 

### A.1 BAAQMD e mission s Table A

- 1 summar izes BAAQMD 2005 wintertime PM 2.5 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 SMO KE. Overall, area source emissions dominate d , 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 sour ce contributions to the 2005 area source fine PM emissions shown above. Together, these two tables indicate that wood burning was the largest source of PM 2.5 for all but San Francisco and San Mateo Counties, where non

- road sources (such as commercial marin e and construction equipment) were larger.A

- 2

Table A

- 1. 2005 wintertime PM 2.5 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. 200 5 wintertime BAAQMD PM 2.5 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 (NO x ), sulfur dioxide (SO 2 ) , and ammonia (NH 3 ), respectively. Emissions are broken out by major source category . Solano and Sonoma County emissions only include d 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 Al ameda 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 31.3 646.2A

- 3

Table A

- 4. 2005 wintertime NO x 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 NO x contribution came f rom on - road vehicles , with slightly more than half attributable to gasoline vehicles and the remainder from heavy

- duty diesel trucks

. NO x emissions from commercial marine and construction equipment comprise d roughly 70 percent of the non

- road source contri bution.
Table A

- 5. 2005 wintertime SO 2 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 NH 3 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 74.12A

- 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 we re animal wastes, both domestic and livestock

. Human perspiration and respiration and residential fuel combustion round ed out the top 90 percent of area source emissions
. For point sources, composting facilities/landfills contribute d two
- thirds of emissions, and the remainder came from miscellaneous industrial processes including wastewater treatment facil ities and refineries.

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 PM 2.5 , TOG, NO x , SO 2 and NH 3 emissions, respectively.

Figure A

- 2. Spatial distribution of PM 2.5 emissions.A

- 6

Figure A

- 3. Spatial distribution of TOG emissions.

Figure A

- 4. Spatial distribution of NO x emissions.A

- 7

Figure A

- 5. Spatial distribution of SO 2 emissio ns.

Figure A

- 6. Spatial distribution of NH 3 emissions.A

- 8

With the exception of SO 2 , emissions of these pollutants we re generally found in populated places and /or nearby major arterials and highways

. SO 2 emissions aro se 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 relative ly “ flat ” diurnal distribution

. This wa s particularly true of major stationary point sources, animal wastes, human perspiration and respiration, and commercial marine

. Other categories follow ed distinctive hourly profiles for which data we re available to c onstruct 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 peak s on weekdays, corresponding to the commute pattern

. The morning peak wa s decidedly sharper than the evening peak

. Heavy

- duty vehicles, on the other hand, show ed 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 ha d a bimodal dis tribution similar to light

duty vehicles, except that the evening peak wa s much later in the night, presumably after residents return ed from work

. The profile used to distribute construction equipment e missions concentrated activity in the afternoon and ea rly evening hours

. This allocation wa s 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

.

Figure A

- 7
. Weekday diurna l profiles assumed for emissions from select key Bay Area PM 2.5 sources.A

- 9



### A.2 Emissions o utside 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 include d 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 SO 2 NO x TOG PM 2.5

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 PM 2.5 , emissions were from residential fuel combustion, road dust, crops production (tilling) , and prescribed burn ing. The total winter daily average ammonia emissions for portions of the modeling domain which lie outside the BAAQMD were about 300 tons

. This wa s about four times the amount in the Bay Area. Biogenic TOG emissions over the entire modeling domain, incl uding the Bay Area, ranged from 490 to 668 tons per day during the winter simulat ion period

. These values represent ed the sum of methyl butenol, isoprene, monoterpenes and other volatile organic compounds (OVOC)

. Much of these emissions we re outside the BA AQMD, as shown in Figure A

- 8. Biogenic emissions were strongly influenced by both temperature and photosynthetically

active radiation (PAR)

. These two factors dictate d the diurnal distribution of these emissions
. Due to the day

- to
- day variation of tempert ure and PAR , the magnitude and temporal profile of biogenic emissions change d each day

. In general, biogenic emissions peak ed during daylight hours and return ed to a baseline value for the overnight hours

. This timing is illustrated in Figure A

- 9.A

- 10

Figur e A

- 8. Representative spatial distribution of biogenic TOG emissions.

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 progra m (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 obs ervation, 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 sur face

- layer wind and temperature are presented to evaluate the model performance. Statistics are defined as follows: Mean observation ( M o ) : calculated from all sites with valid data within a given analysis region and for a given time period (hourly or dai ly):

where 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 ( M p ): calculated from simulation results that are interpolated to eac h observation used to calculate the mean observation (hourly or daily):

where 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 p eriod (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 CRPAQSB

- 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 obs erved and predicted surface

- layer wind direction and performance statistics in the 4

- km MM5 domain for December 2000.B

- 4 BAAQMD 4km 2000 MM5 – December

Figure B

- 3
. Hourly time series of region

- average observed and predicted s urface

- 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.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.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.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.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 pred icted surface

- layer humidity and performance statistics in the 4

- km MM5 domain for December 2006.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 statistic s in the 4

- km MM5 domain for January 2007.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.B

- 11



### B.2 MM5 validat ion 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.B

- 12 BAAQMD 4km 2008 MM5 – November

Figure B

- 18
. Hourly time series of region

- average observed and predicted surf ace - layer temperature and performance statistics in the 4

- km MM5 domain for November 2008.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.B

- 14 BAAQMD 4km 2008 MM5 – December

Figure B

- 20
. Hourly time series of region

- average observed and predicted surface

- layer temperature and performance s tatistics in the 4

- km MM5 domain for December 2008.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 t he 4

- km MM5 domain for January 2009.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 Janu ary 2009.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.B

- 18 B AAQMD 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.C

- 1



## Appendix C . Air quality mod el 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 (quantit ative) 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 (mid night to midnight) for measured and simulated PM levels. Statistics are defined follows: Mean observed PM level ( PM o i ) : calculated for a single monitoring location i using all valid data available during the simulation period:

where is the individual observed PM level (  g/m 3 ) at site i for day j , and the summations are over all days ( J )

. may reflect total PM levels or individual PM components such as EC, OC, nitrate, or sulfate. Mean model estimated PM level ( PM p i ): calculated from simulation res ults averaged for the model grid cell(s) nearest site i using the entire simulation period:

where is the individual model estimated PM level (  g/m 3 ) at site i for day j , and the summations are over all days ( J )

. 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 d ata 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.C

- 2



### C.1 Validation of total PM levels
Validation of simulated total PM 2.5 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 PM 2.5 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 PM 2.5 levels are shown in Figure C

- 1
. Simulated PM 2.5 levels for most Bay Area locations were in good agreement with measurements

. PM 2.5 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 const ruction 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

. PM 2.5 levels were underestimated at the inland northern Bay Area sites Napa and Valle jo
. Underestimations there likely resulted from underestimations around the Delta

. PM 2.5 levels were also underestimated in Sacramento

. This underestimation is believed to have resulted from an underestimation of regional secondary PM 2.5 levels in the Sacr amento 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 PM 2.5 levels indicates ideal model per formance

. 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 reflect ed average winte r 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 indica ted in Figure C

- 1
. Observed and simulated total PM 2.5 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 Sacr amento
. For these locations, total PM 2.5 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 suffi cient levels of secondary PM 2.5 . The nature of the PM 2.5 level underestimation at Modesto was quite different than at Sacramento

. At Modesto, simulated and observed total PM 2.5 levels correlated well, but the data points fell above the 1:1 line

. This corre lation 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 meteorologi cal model outputs showed that the model tended to overestimate winds when the atmosphere was strongly stable, generating local low levelC

- 3

jets. This short coming of the model is being investigated and is planned to be corrected soon.

Figure C

- 1
. Mean ob served and model estimated total PM 2.5 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 level s 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 PM 2.5 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 PM 2.5 components EC and OC are shown in Figures C

- 3 and C

- 4, respectively. Generally, the data points fel l 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 d id 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 wasC

- 4

more difficult to attribute to a special cause

. This is a complex area, which can be impacted by transport from the Cen tral Valley and/or local industrial emissions, depending on the prevailing wind conditions. Scatter plots for PM 2.5 component nitrate are shown in Figure C

- 5
. This figure demonstrates the model’s ability to simulate secondary PM 2.5 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 PM 2.5 component sulfate are shown in Figure C

- 6
. This figure demonstrates the model’s ability to simulate secondary PM 2.5 ammonium sulfate levels

. The model simulated ammonium sulfate levels with higher accuracy than for any other PM 2.5 component
. This may have resulted from the relatively simple emissions patterns for sulfur

containing substances as compared to other types of emissions

. Sulfur emissions were highly localized around both the offsho re 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

Figure C

- 2
. Scatter plots for measured versus simulated total PM 2.5 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 occur red on Christmas Day

. Diagonal 1:1 lines indicate ideal model performance.C

- 6

Figure C

- 3
. Scatter plots for measured versus simulated PM 2.5 EC component levels.C

- 7

Figure C

- 4
. Scatter plots for meas ured versus simulated PM 2.5 OC component levels.C

- 8

Figure C

- 5
. Scatter plots for measured versus simulated PM 2.5 nitrate component levels.C

- 9

Figure C

- 6
. Scatter plots for measured versus simulated PM 2.5 sulfate component levels.