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Appendix G: PM2.5 Weight of Evidence Analysis 

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2012 PM2.5 Plan

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Appendix G 

PM2.5 Weight of 

Evidence Analysis 

 
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SAN JOAQUIN VALLEY PM2.5 WEIGHT OF EVIDENCE ANALYSIS 

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Appendix G: PM2.5 Weight of Evidence Analysis

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TABLE OF CONTENTS 

EXECUTIVE SUMMARY .................................................................................................. i

1. INTRODUCTION ...................................................................................................... 1

2. PM2.5 STANDARDS AND HEALTH EFFECTS ....................................................... 3

3. MONITORING IN THE SAN JOAQUIN VALLEY ...................................................... 4

a. Established monitoring network ......................................................................... 4

b. Extensive field studies ........................................................................................ 4

4. NATURE AND EXTENT OF THE PM2.5 PROBLEM ............................................... 7

a. Current air quality ............................................................................................... 7

b. Seasonal variability. ........................................................................................... 7

c.      Diurnal variability ................................................................................................ 8

d. Chemical composition ........................................................................................ 9

e. Spatial distribution of the major PM2.5 components; local versus regional ...... 11

f.      Episode development ....................................................................................... 13

5. SECONDARY AMMONIUM NITRATE FORMATION ............................................. 15

a. Chemistry ......................................................................................................... 15

b. Limiting precursor concept ............................................................................... 16

c.      Role of ammonia in ammonium nitrate formation ............................................. 17

d. Role of VOC in ammonium nitrate formation .................................................... 24

6. SECONDARY ORGANIC AEROSOL FORMATION ............................................... 32

7. EMISSION SOURCES OF WINTERTIME PM2.5 ................................................... 34

a. Emission inventory ........................................................................................... 34

b. Chemical markers of source types ................................................................... 36

c.      Source apportionment using source receptor models ...................................... 37

d. Photochemical modeling source apportionment ............................................... 41

8. PM2.5 AIR QUALITY PROGRESS ......................................................................... 42

a. Annual PM2.5 trends ........................................................................................ 42

b. 24-Hour PM2.5 trends ...................................................................................... 43

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c.      Meteorology impacts on air quality ................................................................... 44

d. Annual trends adjusted for meteorology ........................................................... 47

e. 24-hour trends adjusted for meteorology ......................................................... 48

f.      Trends in 24-hour, seasonal, and hourly PM2.5 ............................................... 50

g. Chemical composition trends ........................................................................... 55

h. Emission inventory trends ................................................................................ 57

9. LINKING AIR QUALITY TRENDS TO EMISSION REDUCTIONS ......................... 58

a. NOx control ...................................................................................................... 58

b. Residential wood burning controls ................................................................... 60

10. MODELED ATTAINMENT DEMONSTRATION .................................................. 62

a. Modeling results ............................................................................................... 62

b. Benefits of emission reductions from on-going programs ................................ 63

c.      Evaluation of precursor sensitivity .................................................................... 64

d. Demonstrating attainment at Bakersfield-California ......................................... 70

11. SUMMARY .......................................................................................................... 71

REFERENCES .............................................................................................................. 72

Appendix 1: California Regional PM10/PM2.5 Air Quality Study (CRPAQS) Publications 

Appendix 2: PM2.5 Source Apportionment for the San Joaquin Valley Air Basin Using 

the Chemical Mass Balance Receptor Model 

Appendix 3: Source Apportionment of PM2.5 Measured at the Fresno and Bakersfield 

Chemical Speciation Network Sites in the San Joaquin Valley Using the Positive Matrix 

Factorization Model 

Appendix 4: Methodology for Meteorological Adjustment of PM2.5 Trend Statistics 

Appendix 5: Speciated Linear Rollback Modeling as a Corroborative Analysis to the 

Regional Photochemical Model 

Appendix 6: Unmonitored Areas Analysis

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EXECUTIVE SUMMARY 

The San Joaquin Valley 2012 PM2.5 Plan demonstrates that the San Joaquin Valley will 

attain the PM2.5 24-hour standard of 35 ug/m3 as expeditiously as practicable due to 

adopted and proposed control measures. As part of the attainment demonstration, the 

2012 PM2.5 Plan specifically identifies the: 1) most expeditious date of when the San 

Joaquin Valley (Valley) will attain the standard, 2) attainment plan precursors, 

3) amount of emissions needed to attain, and 4) sources to control. The weight of 

evidence analysis provides a set of complementary analyses that supplement the 

required modeling. Because all methods have strengths and weaknesses, examining 

an air quality problem in a variety of ways offsets the limitations and uncertainty that are 

inherent in air quality modeling. This approach also provides a better understanding of 

the overall problem and the level and mix of emissions controls needed for attainment. 

Analyses conducted by Air Resources Board (ARB) and San Joaquin Valley Air 

Pollution Control District (SJVAPCD or District) staff, along with findings from the 

California Regional Particulate Air Quality Study (CRPAQS) provide the supplemental 

information supporting the attainment demonstration. CRPAQS was a public/private 

partnership designed to advance our understanding of the nature of PM2.5 in the Valley 

and guide development of effective control strategies. The study included monitoring at 

over 100 sites as well as data analysis and modeling, results of which have been 

published in over 60 papers and presented at national and international conferences. 

Studies such as CRPAQS provide valuable information that supports the State 

Implementation Plan (SIP) process in a number of ways. First, these studies provide 

additional observational data that help to provide a more detailed understanding of the 

nature of the PM2.5 problem in the San Joaquin Valley. This data also is used to 

update the fundamental algorithms contained within air quality models, thereby 

enhancing their ability to simulate observed air quality conditions. Finally, they provide 

an improved basis for model applications used in the preparation of SIPs and a more 

robust platform for evaluating the response to emission controls and predicting future air 

quality. 

What is the nature of the 24-hour PM2.5 problem in the Valley? 

The geography of the San Joaquin Valley, along with weather patterns influence the 

accumulation, formation, and dispersion of PM2.5. As a result, PM2.5 concentrations 

are generally higher in the central and southern portions of the Valley, with highest 

values in the urban areas of Fresno and Bakersfield. Concentrations are highest during 

the winter months of November through February. During these months, high-pressure 

weather systems over Northern California can cause the atmosphere to become 

stagnant for extended periods, resulting in PM2.5 episodes that can persist from several 

days up to several weeks. 

Ammonium nitrate and carbonaceous material (organic and elemental carbon) are the 

largest constituents of PM2.5 on exceedance days, comprising 85 to 90 percent of the 

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mass. Geological material (dust), and ammonium sulfate are small contributors. 

Ammonium nitrate is formed in the atmosphere from reactions of gaseous precursors. 

Emissions of nitrogen oxides (NOx) from mobile sources and stationary sources react 

with ammonia which is primarily emitted from livestock operations, fertilizer application, 

and mobile sources. The stagnant, cold, and damp conditions that occur during the 

winter promote the formation and accumulation of ammonium nitrate. Elevated 

concentrations can be found at both urban and rural sites. In contrast, organic carbon is 

highest in urban areas due to emissions from residential wood combustion, commercial 

cooking operations, and mobile source tailpipe emissions which are largest in urban 

areas. Due to the localized urban increment from these activities, which adds to the 

more regional ammonium nitrate concentrations, the highest PM2.5 concentrations in 

the Valley occur at urban sites. 

What progress has been made in reducing PM2.5 concentrations? 

The Valley has experienced progress in reducing both annual average and 24-hour 

PM2.5 concentrations over the last ten years. Between 2001 and 2011, annual average 

design values in the Valley declined between 30 and 40 percent at individual monitoring 

locations. Overall, annual PM2.5 trends adjusted for the effects of meteorology indicate 

that between 1999 and 2010, annual PM2.5 concentrations decreased about 40 to 50 

percent at Bakersfield and Fresno due to emission reductions. With on-going 

implementation of the 2008 PM2.5 Plan, annual average PM2.5 concentrations in the 

Valley are expected to continue to improve and reach attainment in 2014. 

During this same time period, 24-hour PM2.5 design values in the Valley have also 

decreased between approximately 30 and 50 percent. In addition, the number of days 

exceeding the 24-hour standard decreased by about 45 to 50 percent. After adjusting 

for the influence of meteorology, the number of exceedance days has decreased 

between 60 and 65 percent in Bakersfield and Fresno. 

Additional evaluations provide further insight into the annual and 24-hour PM2.5 

progress that has been observed. For example, as the fraction of days recording PM2.5 

levels above the 24-hour standard has decreased, there has been a corresponding 

increase in the fraction of days below the level of the annual standard of 15 ug/m3

. 

Average concentrations during the winter months have decreased, and under similar 

meteorological conditions, peak 24-hour concentrations during episodes are now 

40 percent lower than they were ten years ago. 

What are the attainment plan precursors? 

Ambient PM2.5 is comprised of many different constituents and as a result there are 

multiple precursor pollutants that lead to PM2.5 formation (directly emitted PM2.5, NOx, 

sulfur oxides (SOx), volatile organic compounds (VOCs), and ammonia). The 

U.S. Environmental Protection Agency’s (U.S. EPA) PM2.5 implementation rule 

specifies that a precursor is considered “significant” for control strategy development 

purposes when a significant reduction in the emissions of that precursor pollutant leads 

to a significant decrease in PM2.5 concentrations. Such pollutants are known as 

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“PM2.5 attainment plan precursors” (72 FR 20586). The PM2.5 implementation rule 

also establishes a presumption that PM2.5, NOx, and SOx are attainment plan 

precursors, while VOCs and ammonia are not. For the annual PM2.5 plan, PM2.5, 

NOx, and SOx were identified and approved as the only attainment plan precursors by 

U.S. EPA. 

Given the large contribution of ammonium nitrate on 24-hour PM2.5 exceedance days, 

a number of different studies and analyses were evaluated to understand the role of 

VOCs and ammonia in ammonium nitrate formation in the San Joaquin Valley and to 

determine whether they should be considered attainment plan precursors for the 2012 

24-hour PM2.5 Plan. The amount of ammonium nitrate produced depends upon the 

relative atmospheric abundance of its precursors. It is therefore important to 

understand which precursor controls are most effective in reducing ammonium nitrate 

concentrations. In simple terms, the precursor in shortest supply will limit how much 

ammonium nitrate is produced. This is known as the limiting precursor and controls of 

this precursor will have the most significant benefits in reducing PM2.5 concentrations. 

The precursor assessment for the 24-hour PM2.5 plan included evaluation of emissions 

inventories, monitoring studies, and photochemical modeling analyses of ammonium 

nitrate sensitivity to precursor emission reductions. While emissions inventory and 

monitoring data can indicate the relative abundance of the different precursors, 

photochemical models provide a quantitative approach to simulate the effects that 

emission reductions in each of gaseous precursors would have on the predicted 

ammonium nitrate concentrations. 

Evaluation of both emissions inventory and monitoring data concluded that the 

ammonia-rich conditions throughout the Valley demonstrate that NOx rather than 

ammonia is the limiting precursor during wintertime PM2.5 episodes. In addition, 

photochemical modeling studies found that while large reductions in NOx led to 

commensurate reductions in ammonium nitrate, comparable reductions in ammonia 

were much less effective. Precursor sensitivity modeling conducted for the 2012 PM2.5 

Plan showed that on a per ton basis, reductions in NOx are approximately nine times 

more effective than reductions in ammonia. Finally, evaluation of ambient air quality 

trends show that reductions in NOx emissions, gaseous NOx concentrations, and 

particulate nitrate all track each other well. 

Evaluation of monitoring studies also provided some evidence that VOCs could be 

important at times, however these studies were not conclusive. Therefore 

photochemical modeling studies are more appropriate to assess the overall impact of 

VOC controls. These modeling studies found that at current NOx levels, further VOC 

emission reductions produce essentially no benefit, and in some instances may actually 

lead to an increase in ammonium nitrate concentrations. Findings from these prior 

studies were supported by precursor sensitivity modeling conducted for the 2012 PM2.5 

SIP, which indicated a very small disbenefit from reductions in VOCs. 

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As noted previously, U.S. EPA’s PM2.5 implementation rule directs SIP planning efforts 

and regulation to those pollutants generally known to significantly contribute to PM2.5 

concentrations. Based on the weight of evidence presented from historical studies, 

coupled with the modeled precursor sensitivity analyses conducted as part of the 2012 

PM2.5 Plan, VOCs and ammonia are not considered significant precursors for 24-hour 

PM2.5. Therefore the 2012 24-hour PM2.5 plan attainment precursors are directly 

emitted PM2.5, NOx, and SOx. 

When will the Valley attain the 24-hour PM2.5 standard? 

Consistent with U.S. EPA guidelines, air quality modeling was done to predict future 

PM2.5 concentrations at each monitoring site in the San Joaquin Valley. This modeling 

shows attainment of the 24-hour PM2.5 standard by 2019 in all counties except Kings 

and Kern, based on implementation of the ongoing control program. In these counties, 

additional focused emission reductions are needed to provide for attainment. The 

modeling analysis includes new emission reductions each year between now and 2019 

from implementation of a combination of adopted ARB and District programs. As a 

result, most sites in the northern and central Valley are expected to attain prior to 2019. 

ARB staff then modeled a scenario with an enhanced wood burning curtailment 

program Valley wide, which would be designed to prevent wood burning on days that 

may lead up to a PM2.5 exceedance. The predicted design values for each site from 

this modeling scenario are shown in Table E-1. 

Table E-1.

2019 Modeled 24-hour PM2.5 Design Values with Enhanced Residential Wood 

Burning Curtailment Program. 

Monitoring Site Design Value (μg/m3) 

Bakersfield - California 35.7 

Bakersfield - Planz 32.9 

Corcoran - Patterson 32.1 

Visalia - N. Church 29.4 

Fresno - Hamilton 28.6 

Fresno - First 30.5 

Clovis 28.6 

Merced 22.6 

Modesto 24.7 

Stockton 21.4 

While adoption of a more stringent wood burning curtailment program brings the 

Bakersfield-California site very near attainment, further reductions are still needed and 

will be provided through a measure to achieve additional emission reductions from 

commercial cooking operations. Design values at all other sites are well below 

attainment levels. 

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What is the attainment control strategy? 

In order to determine the emission reductions needed to bring Bakersfield into 

attainment, ARB staff conducted additional modeling sensitivity runs to assess the 

relative efficacy of further reductions of different PM2.5 precursors. The current 24-hour 

PM2.5 standard modeling demonstrates that on a relative basis the greatest benefits 

are achieved from reductions in sources of directly emitted PM2.5, followed by NOx, 

based on U.S. EPA’s relative response factor procedures. Kern County specific model 

sensitivity runs were also conducted to evaluate the benefits of emission reductions 

focused on the Bakersfield area. These runs show that directly emitted PM2.5 emission 

reductions are approximately 8 times more effective than NOx reductions. 

The implementation of new reductions from California’s on-going emission control 

programs will provide the majority of the emission reductions needed to attain the 

24-hour PM2.5 standard throughout the San Joaquin Valley in 2019. The PM2.5 design 

value at the Bakersfield-California site must decrease by approximately 45 percent to 

demonstrate attainment. Between 2007, the base year used in the photochemical 

modeling attainment demonstration and 2019, implementation of these control 

programs will reduce NOx emissions by 55 percent. The weight of evidence analysis 

has demonstrated that prior reductions in NOx have resulted in commensurate 

reductions in ambient concentrations of nitrate. This is consistent with modeled 

predictions that demonstrate a nearly 50 percent reduction in ammonium nitrate 

concentrations. 

In addition, while directly emitted PM2.5 emissions in aggregate are decreasing by 

nearly 30 percent, a major focus of the attainment control strategy is further curtailment 

of residential wood burning, along with implementation of a measure to reduce 

emissions from commercial cooking. District analysis has demonstrated the significant 

benefits of past implementation of wood burning curtailment. Further, examination of 

emission sources surrounding the Bakersfield-California monitor, and a modeling 

sensitivity run support the benefits of reducing emissions from cooking operations. The 

final attainment demonstration for the Bakersfield-California design site is provided in 

Table E-2. 

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Table E-2. 

Attainment Demonstration for the Bakersfield-California Design Value Site. 

2007 Design Value 

(ug/m3) 

2019 Design Value 

with Wood 

Burning Program 

Enhancement 

(ug/m3) 

2019 Final Design 

Value (ug/m3) 

65.6 35.7 ≤35.4 

Note: The benchmark for attainment is a design value that is equal to 

or less than 35.4 μg/m3

. 

 

Consideration of the entirety of information presented in the weight of evidence provides 

a consistent assessment that supports the modeled attainment date of 2019. The 

substantial continuing reductions that will result from implementation of the ongoing 

control program, coupled with new measures addressing residential wood burning and 

cooking, are consistent with the results predicted in the modeled attainment 

demonstration.

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1. INTRODUCTION 

The 2012 PM2.5 Plan demonstrates that the San Joaquin Valley will attain the PM2.5 

24-hour standard as expeditiously as practicable due to adopted and proposed control 

measures. As part of the attainment demonstration, the 2012 PM2.5 Plan specifically 

identifies the: 1) most expeditious date for when the San Joaquin Valley (SJV or Valley) 

will attain the standard, 2) attainment plan precursors, 3) amount of emissions needed 

to attain, and 4) sources to control. 

Following U.S. Environmental Protection Agency (U.S. EPA) guidance and procedures, 

the attainment demonstration was conducted through a modeled attainment test. 

Photochemical modeling was used to identify the most expeditious attainment date, the 

relative benefits of controlling different PM2.5 precursor pollutants, and the magnitude 

of emission reductions needed from each pollutant. The Weight of Evidence (WOE) 

analysis provides a set of complementary analyses that supplement the required 

modeling. 

A WOE approach looks at the entirety of the information at hand to provide a more 

informed basis for the attainment strategy. Because all methods have strengths and 

weaknesses, examining an air quality problem in a variety of ways offsets the limitations 

and uncertainty that are inherent in air quality modeling. This approach also provides a 

better understanding of the overall problem and the level and mix of emissions controls 

needed for attainment. 

The U.S. EPA recognizes the importance of a comprehensive assessment of air quality 

data and modeling and encourages this type of broad assessment for all attainment 

demonstrations. In their modeling guidance, they further note that the results of 

supplementary analyses may be used in a WOE determination to show that attainment 

is likely despite modeled results which may be inconclusive (U.S. EPA 2007). Following 

the U.S. EPA guidance, future year modeled 24-hour design values that fall between 

32 and 37 ug/m3

 need to be accompanied by a WOE demonstration to determine 

whether attainment will occur. This range in modeled design values reflects the 

uncertainty in predicting absolute PM2.5 concentrations that is inherent in air quality 

modeling, and therefore recognizes that an improved assessment of attainment can be 

derived from examining a broader set of analyses. 

U.S. EPA recommends that three basic types of analyses be included to supplement 

the primary modeling analysis in the WOE approach: 1) analyses of trends in ambient 

air quality and emissions, 2) observational models and diagnostic analyses, and 

3) additional modeling evaluations. The scope of the WOE analysis is different for each 

nonattainment area. The level of detail appropriate for each area depends upon the 

complexity of the air quality problem, how far into the future the attainment deadline is, 

and the amount of data and modeling available. For example, less analysis is needed 

for an area that is projecting attainment near-term and by a wide margin, and for which 

recent air quality trends have demonstrated significant progress, than for areas with 

more severe air quality challenges 

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The following sections present the WOE assessment that supports the attainment 

demonstration the 24-hour PM2.5 standard in the San Joaquin Valley. 

 

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2. PM2.5 STANDARDS AND HEALTH EFFECTS 

PM2.5 is a complex mixture of particles and liquid droplets that vary in size and 

chemical composition. As a subset of PM10, particles with diameters up to 

10 micrometers, PM2.5 comprises particles with diameters up to 2.5 micrometers 

(Figure 1). PM2.5 contains a diverse set of substances including elements such as 

carbon and metals, compounds such as nitrates, sulfates, and organic materials, and 

complex mixtures such as diesel exhaust and soil or dust. Some of the particles are 

directly emitted into the atmosphere. Others, referred to as secondary particles, result 

when gases are transformed into particles through physical and chemical processes in 

the atmosphere. 

Figure 1. PM2.5 particle diameter compared to the thickness of a single strand of hair. 

Numerous health effects studies have linked exposure to PM2.5 to increased severity of 

asthma attacks, development of chronic bronchitis, decreased lung function in children, 

increased respiratory and cardiovascular hospitalizations, and even premature death in 

people with existing cardiac or respiratory disease. In addition, California has identified 

particulate exhaust from diesel engines as a toxic air contaminant – suspected to cause 

cancer, other serious illnesses, and premature death. Those most sensitive to PM2.5 

pollution include people with existing respiratory and cardiac problems, children, and 

older adults. 

Ambient air quality standards establish the levels above which PM2.5 may cause 

adverse health effects. In 1997, U.S. EPA adopted the first set of PM2.5 air quality 

standards, an annual standard of 15 μg/m3 and a 24-hour standard of 65 μg/m3

. To 

address the 1997 PM2.5 standards, the San Joaquin Valley Air Pollution Control District 

(SJVAPCD or District) adopted the 2008 PM2.5 Plan. At the time of plan development, 

the San Joaquin Valley already attained the 24-hour standard, thus the 2008 PM2.5 

Plan focused on the annual PM2.5 standard. U.S. EPA approved this Plan in 2011 

(76 FR 41338; 76 FR 69896). In 2006, U.S. EPA tightened the 24-hour standard to 

35 μg/m3. Attainment of this standard is the focus of the SJV 2012 PM2.5 Plan. 

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3. MONITORING IN THE SAN JOAQUIN VALLEY 

a. Established monitoring network 

An extensive network of PM2.5 monitors throughout the SJV provides data to assess 

compliance with ambient air quality standards and to study the nature of ambient 

PM2.5. Currently, the network comprises 21 monitoring sites. Many sites include 

multiple monitoring instruments running in parallel. Seven sites operate Federal 

Reference Monitors (FRMs), which provide regulatory data that are used to assess 

compliance with the federal PM2.5 standards. An additional 20 monitors provide hourly 

PM2.5 measurements. Eleven of these continuous monitors are Federal Equivalent 

Monitors (FEM), which can also be used to assess compliance with the standards. The 

FRM and FEM monitoring sites are shown in Figure 2. The locations of these monitors 

are designed to capture population exposure. In addition, data collected at these 

monitors serve to report air quality conditions to the public, and support forecasting for 

the District’s agricultural and residential burning curtailment programs. Finally, four 

sites have chemical speciation monitors. The speciation monitors collect samples that 

are further analyzed in the laboratory to determine the chemical make-up of PM2.5. 

Figure 2. San Joaquin Valley PM2.5 monitoring network (FRMs and FEMs, 

October 2012). 

b. Extensive field studies 

The San Joaquin Valley is one of the most studied areas in the world with an extensive 

number of publications in peer-reviewed international scientific/technical journals and 

other major reports. Since 1970, close to 20 major field studies have been conducted in 

the Valley and surrounding areas that have elucidated various aspects of the nature and 

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causes of ozone and particulate matter. A comprehensive listing of publications 

(reports and peer-reviewed journal articles) is provided in Appendix 1. 

The first major study specifically focused on particulate matter was the Integrated 

Monitoring Study in 1995 (IMS-95), which was the pilot study for the subsequent 

California Regional Particulates Air Quality Study (CRPAQS) in 2000 (Solomon and 

Magliano, 1998). IMS-95 formed the technical basis for the SJV 2003 PM10 Plan that 

was approved by the U.S. EPA in 2004 (71 FR 63642), and the Valley was 

subsequently re-designated as attainment in 2008 (73 FR 66759). CRPAQS was a key 

component of the technical foundation for the SJV 2008 PM2.5 Plan that U.S. EPA 

approved in 2011 (76 FR 41338; 76 FR 69896). Although conducted more than ten 

years ago, CRPAQS findings remain relevant to the development of the current 24-hour 

PM2.5 Plan. 

CRPAQS was a public/private partnership designed to advance the understanding of 

the nature of PM2.5 in the Valley and guide development of effective control strategies. 

The study included monitoring at over 100 sites (Figure 3) as well as data analysis and 

modeling, results of which have been published in over 60 papers and presented at 

national and international conferences. The field campaign was carried out between 

December 1999 and February 2001. CRPAQS improved our understanding of the 

spatial and temporal distribution of PM2.5 in the Valley, its chemical composition, 

transport and transformation processes, and contributing sources. More details on 

CRPAQS can be found at the following link: http://www.arb.ca.gov/airways/ccaqs.htm. 

Figure 3. CRPAQS monitoring program. 

Findings from CRPAQS and other studies have been integrated into the conceptual 

model of PM2.5 in the San Joaquin Valley. The conceptual model provides the 

scientific foundation for the WOE analysis supporting the 24-hour PM2.5 standard 

attainment demonstration. Specific findings are integrated into the various WOE 

analysis sections of this document. 

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Further field studies relevant to PM2.5 include the California portion of the Arctic 

Research of the Composition of the Troposphere (ARCTAS-CARB) which took place in 

2008 (Jacob, et al., 2010) and Research at the Nexus of Air Quality and Climate 

(CalNex2010) conducted in 2010 (www.esrl.noaa.gov/csd/calnex/). The monitoring 

operations for both studies occurred during the early to mid-summer and extended over 

Southern California and the Central Valley. Some study findings have been published 

(e.g., Kaduwela and Cai, 2009, Cai and Kaduwela, 2011, Kelly et al., 2011), but data 

analysis is still in progress. 

 

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4. NATURE AND EXTENT OF THE PM2.5 PROBLEM 

a. Current air quality 

The geography of the San Joaquin Valley, along with large-scale regional and local 

weather patterns, influence the accumulation, formation and, dispersion of air pollutants. 

Covering nearly 25,000 square miles, the Valley is a lowland area bordered by the 

Sierra Nevada Mountains to the east, the Pacific Coast range to the west, and the 

Tehachapi Mountains to the south. The mountains act as air flow barriers, with the 

resulting stagnant conditions favoring the accumulation of pollutants. To the north, the 

Valley borders the Sacramento Valley and Delta lowland, which allows for some level of 

pollutant dispersion. As a result of geography and meteorology, PM2.5 concentrations 

are generally higher in the southern and central portions of the Valley. 

To determine attainment for the 24-hour standard, the design value at each monitoring 

site must be calculated following strict U.S.EPA protocols. The design value represents 

a three-year average of the 98th percentile of the measured PM2.5 concentrations. 

Depending on a site’s 24-hour PM2.5 data collection schedule, the 98th percentile 

usually corresponds to a value between the 2nd and the 8th highest value. If the design 

value is equal to or below 35.4 μg/m3, the site attains the standard. Figure 4 shows the 

2011 24-hour PM2.5 design values throughout the San Joaquin Valley. All sites 

currently record design values above the standard, although design values are 

generally lower in the northern and central Valley. Urban sites in the Fresno and 

Bakersfield areas register the higher design values. 

Figure 4. 2011 24-hour design values 

b. Seasonal variability 

PM2.5 concentrations in the San Joaquin Valley exhibit a strong seasonal pattern, with 

highest concentrations occurring from November through February (Figure 5). During 

the winter, PM2.5 builds up over several days or weeks. These PM2.5 episodes are 

caused by increased activity in some emission sources and by meteorological 

0

10

20

30

40

50

60

70

PM2.5 Design Value (μg/m3)

2011 24-Hour PM2.5 Design Values

Standard

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conditions that are conducive to the build-up and formation of PM2.5. During the winter, 

high-pressure weather systems over California can cause the atmosphere to become 

stagnant for extended periods leading to temperature inversions. Under normal 

conditions, temperature decreases with altitude, allowing free upward air flow and 

dispersing emissions and pollutants. In contrast, a temperature inversion positions a 

layer of warmer air above cooler air, impeding upward flow of emissions and air 

pollutants. Often the inversion layer is lower than the mountains surrounding the Valley, 

trapping emissions and pollutants. 

Figure 5. Seasonal variation in PM2.5 concentrations at Bakersfield-California. 

c. Diurnal variability 

During the winter, PM2.5 levels in the San Joaquin Valley also vary significantly across 

the 24-hour period. For example, in urban Fresno, the highest PM2.5 concentrations 

occur during the night (Figure 6). Peak evening concentrations generally reflect the 

influence of lowering inversion heights which trap pollutants close to the surface, as well 

as increased activity from evening commute traffic and residential wood combustion. 

The smaller peak of PM2.5 concentrations observed during mid-day is due in part to 

traffic activity, but mostly reflects secondary pollutant formation and PM2.5 formed 

above the inversion layer from previous day’s emissions that mix back to the surface 

during the day. 

0

10

20

30

40

50

1 2 3 4 5 6 7 8 9 10 11 12

Concentration (μg/m3)

Month

2009-2011 Monthly Average PM2.5 

Concentration at Bakersfield-California

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Figure 6. Variation in hourly PM2.5 concentrations during the winter at Fresno-1st. 

d. Chemical composition 

Examination of the chemical make-up of PM2.5 on days exceeding the daily standard 

provides another important element in understanding the nature of PM2.5 in the Valley 

and contributing sources. The pie charts in Figure 7 show the current chemical 

components that contribute to PM2.5 on days that exceed the standard at urban sites in 

the southern (Bakersfield), central (Fresno), and northern (Modesto) regions of the 

Valley. These sites currently record the highest PM2.5 concentrations in their 

corresponding regions. While the relative percentages vary, in all cases the major 

components are ammonium nitrate and organic material (organic carbon). 

Ammonium nitrate is the largest contributor to PM2.5, especially in the southern region. 

At Bakersfield, ammonium nitrate constitutes about 65 percent of PM2.5, while at 

Fresno and Modesto it constitutes about 55 percent. Ammonium nitrate is formed in the 

atmosphere from chemical reactions of NOx and ammonia. Sources emitting NOx 

include motor vehicles and stationary combustion sources. The largest sources of 

ammonia are livestock operations, fertilizer application, and mobile. The stagnant, cold, 

and damp conditions that occur during the winter promote the formation and 

accumulation of ammonium nitrate. Additional information on ammonium nitrate 

formation can be found in section 5. 

The organic matter component of PM2.5 is largest in the central and northern portions 

of the Valley. Organic matter constitutes about 30 percent of PM2.5 at Modesto and 

Fresno compared to less than 20 percent at Bakersfield. Activities such as residential 

wood combustion, cooking, biomass burning, and direct tailpipe emissions from mobile 

sources contribute to the PM2.5 organic matter component. 

Ammonium sulfate and elemental carbon each contribute about five percent at the three 

sites. Ammonium sulfate is also formed in the atmosphere from SOx emitted from 

0

10

20

30

40

50

PM2.5 Concentration (

0 4 8 12 16 20

μg/m3)

Time-of-day

2009-20011 Average Hourly PM2.5 

Variation During Nov-Dec at Fresno-1st

2009-2011

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combustion sources. Elemental carbon results from mobile and stationary combustion 

sources, with significant contributions from diesel sources. 

Geological material contributes to a lesser extent, about five percent at Bakersfield and 

about two percent at Modesto and Fresno. Geological material comes from dust 

suspended into the air by vehicle travel on roads, soil from agricultural activities, and 

other dust producing activities such as construction. 

Figure 7. 2009-2011 average peak day PM2.5 chemical composition at a) Bakersfield, 

b) Fresno, and c) Modesto. 

Ammon.

Nitrate

65%

Ammon. 

Sulfate

7%

Organic 

Matter

17%

Elemental

Carbon

5%

Geologic.

5%

Elements

1%

a) 2009-2011 Peak Day PM2.5 

Composition at Bakersfield

Ammon. 

Nitrate

54%

Ammon.

Sulfate

6%

Organic 

Matter

29%

Elemental 

Carbon

6%

Geologic.

2%

Elements

3%

b) 2009-2011 Peak Day PM2.5 

Composition at Fresno-1st

Ammon.

Nitrate

56%

Ammon.

Sulfate

6%

Organic 

Material

28%

Elemental

Carbon

6%

Geologic.

2%

Elements

2%

c) 2009-2011 Peak Day PM2.5 

Composition at Modesto

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e. Spatial distribution of the major PM2.5 components; local versus 

regional 

As noted previously, high PM2.5 concentrations in the Valley occur almost exclusively 

during multiday pollution episodes under stagnant winter weather conditions. The 

duration and strength of an episode depends on atmospheric stability, but episodes can 

last several weeks. Once the weather conditions conducive to an episode set in, PM2.5 

concentrations increase due to the accumulation of primary pollutants and formation of 

secondary pollutants. 

Each episode has a regional as well as local component (Turkiewicz et al., 2006). High 

concentrations of nitrate can occur over large regions, including both urban and rural 

areas (Figure 8). As shown in Figure 9, ammonia is mostly concentrated in rural areas, 

particularly between Fresno and Bakersfield. On the other hand, high concentrations of 

organic carbon are more localized around urban sites, especially Fresno, with lower 

concentrations at rural sites (Figure 10). 

The differences between the regional and local component can be traced back to the 

emission sources and subsequent formation and transport processes for each chemical 

component. Gaseous precursors of ammonium nitrate (NOx and ammonia) are 

transported much more efficiently than directly emitted organic matter particles (Ying 

and Kleeman, 2009). Although, some of the emitted NOx forms ammonium nitrate in 

urban areas, it is also transported to downwind regions where it reacts with ammonia to 

form particulate ammonium nitrate in the rural areas. While transport does occur, the 

distances are still relatively limited, with transport distances of 50 to 60 kilometers in the 

central and southern Valley. Ying et.al. (2009) found for example that most of the 

PM2.5 nitrate in Bakersfield is produced from sources within the southern Valley. 

In contrast, carbonaceous aerosols are emitted into the atmosphere as particles and 

have a shorter lifetime due to higher deposition rates. Under stagnant conditions they 

can only be transported a short distance and therefore, have the greatest impact locally. 

Transport distances for carbonaceous aerosols during CRPAQS were only 20 to 40 

kilometers. Due to this localized organic carbon increment, which adds to the more 

regional ammonium nitrate concentrations, the highest PM2.5 concentrations occur at 

urban sites. 

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Figure 9. Spatial distribution of annual ammonia (NH3) concentrations (2/1/2000-

1/31/2001) during CRPAQS (Chow et al., 2005). 

Figure 8. Spatial distribution of winter ammonium nitrate concentrations measured during 

CRPAQS (Chow et al., 2005).

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f. Episode development 

The development of PM2.5 episodes in the Valley is strongly controlled by 

meteorological conditions. The rate of concentration buildup depends on the intensity of 

atmospheric stability, with concentrations building up faster at urban sites than at rural 

sites (Turkiewicz et al., 2006). Figure 11 illustrates the differences in the PM2.5 buildup 

rate between an urban (Fresno) and a rural (Selma) site in the Fresno area during 

CRPAQS. Although urban sites reach the highest overall concentrations, at the end of 

an episode rural sites may reach equivalent levels. However, because of the lag in the 

overall buildup rate, rural sites have fewer days above the standard and lower 

episode-average concentrations. 

Figure 11. Atmospheric stability and buildup of PM2.5 concentrations at an urban site 

(Fresno) and a rural site (Selma) in the Fresno area during the December 2000 

CRPAQS episode. 

-5

0

5

10

15

20

0

20

40

60

80

100

120

140

160

12/19/00 12/22/00 12/25/00 12/28/00 12/31/00 1/3/01 1/6/01

Stability (T850-Tmin °C)

PM2.5 Mass (μg/m3)

Episode Day

Fresno PM2.5 Selma PM2.5 Stability

Figure 10. Spatial distribution of winter organic carbon concentration 

measured during CRPAQS (Chow et al., 2005).

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The rate of buildup and the differences between urban and rural sites can be explained 

by the differential contributions of ammonium nitrate and organic carbon. Throughout 

the duration of an episode, ammonium nitrate concentrations tend to build to a plateau 

that is maintained until a weather front breaks the stagnation, causing the levels to 

decrease. Figure 12 illustrates the buildup of ammonium nitrate concentrations 

measured during the 2000/2001 PM2.5 episode in Bakersfield. This ammonium nitrate 

buildup generally begins in urban areas, followed by a buildup in rural areas as urban 

NOx is mixed downwind and reacts with rural ammonia. In contrast, organic carbon is 

largest in urban areas, and tends to be more stable across an episode, although 

individual peaks can occur during periods of enhanced wood burning such as weekends 

and holidays. The combination of early ammonium nitrate buildup along with the urban 

organic carbon increment results in the highest concentrations being observed in urban 

areas. The abrupt decrease in concentrations on January 8th was due to the passage of 

a cold front effectively ending the PM2.5 episode. 

Figure 12. Ammonium nitrate concentrations at Bakersfield during the 2000/2001 

CRPAQS episode. 

 

0

10

20

30

40

50

60

70

80

12/23/0012/24/0012/25/0012/26/0012/27/0012/28/0012/29/0012/30/0012/31/00

1/1/011/2/011/3/011/4/011/5/011/6/011/7/011/8/01

Concentrations (ug/m3)

Ammonium Nitrate Concentrations at Bakersfield during

the 2000/2001 CRPAQS Episode

Hourly

24‐hr Avg.

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5. SECONDARY AMMONIUM NITRATE FORMATION 

a. Chemistry 

As discussed previously, the cooler temperatures and higher humidity of the winter 

months are conducive to ammonium nitrate formation through a complex process 

involving NOx, ammonia, and VOCs. This occurs both at the surface and aloft, via both 

daytime and nighttime chemistry. Understanding the interactions amongst these 

precursors is needed to design an appropriate and effective approach to reduce 

ammonium nitrate. 

During the day, NO2 is oxidized to nitric acid (HNO3). This daytime pathway also 

involves sunlight, VOCs, and background ozone: 

 O3 OH Main oxidant is OH

NO NO2 HNO3 Requires high sunlight, VOC rich environment

During the night, nitric acid is formed through oxidation of NO2 (via N2O5) by 

background ozone: 

 O3 O3 H2O Main Oxidant is Ozone (O3) 

NO NO2 NO3 N2O5 2 HNO3 Favors low sunlight intensity,

 wet conditions

The nitric acid formed from these reactions then combines with ammonia (NH3) to form 

ammonium nitrate (NH4NO3): 

HNO3 + NH3 NH4NO3 

Since the chemistry of NOx to nitric acid formation involves multiple steps and also 

depends on the availability of oxidants, only a portion of the NOx emitted ultimately 

forms ammonium nitrate. An early photochemical modeling study applying a box model 

to a typical winter episode in the San Joaquin Valley found that approximately 

33 percent of the molecules of emitted NOx were converted to ammonium nitrate 

(Stockwell et. al. 2000). A subsequent study that modeled the January 4-6, 1996 

episode in the San Joaquin Valley with the University California Davis/California 

Institute of Technology (UCD-CIT) photochemical transport model found that on 

average, only 13 to 18 percent of the emitted NOx (expressed as NO2) was converted 

to ammonium nitrate (Kleeman et. al. 2005). The fraction of NOx converted varied by 

location, with urban regions converting little NOx to ammonium nitrate, while in remote 

areas up to 70 percent NOx was converted. 

As previously described, NOx emissions mostly originate from urban traffic and 

transportation corridors, while ammonia is primarily generated from livestock operations, 

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fertilizer application, and mobile sources. Analysis of CRPAQS measurements suggest 

that, on average, daytime production of nitric acid in the San Joaquin Valley is relatively 

slow, and that nighttime production is the more dominant pathway (Lurmann et al. 

2006). Although daytime mixing is limited, NOx and ammonia emitted during the day 

can be mixed upward where nighttime interactions can occur more regionally to form 

ammonium nitrate. Based on analyses conducted to characterize the atmospheric 

transport and dispersion processes during the winter CRPAQS episodes, MacDonald 

et al. (2006) found that the ammonium nitrate that is formed aloft during the night is 

subsequently entrained into the daytime boundary layer. This was observed through a 

rapid rise in hourly ammonium nitrate concentrations which coincided with the growth of 

the surface mixed layer (Watson and Chow 2002). These mechanisms help explain the 

more regional distribution of ammonium nitrate that is observed throughout the Valley. 

b. Limiting precursor concept 

The amount of ammonium nitrate produced will depend on the relative atmospheric 

abundance of its precursors – VOCs, NOx, and ammonia (NH3). It is therefore 

important to understand which precursor controls are most effective in reducing 

ammonium nitrate concentrations. In simple terms, the precursor in shortest supply will 

limit how much ammonium nitrate is produced. This is known as the “limiting” 

precursor. The following figures provide an illustration of this concept. As shown in 

Figure 13, each molecule of ammonia pairs with one NOx molecule to produce one 

molecule of ammonium nitrate. In this example, there are more ammonia molecules 

than NOx, and therefore not all of the ammonia participates in forming ammonium 

nitrate, i.e. there is “excess” ammonia. Figure 14 illustrates the impact of reducing NOx. 

Here, a reduction in NOx, the less abundant precursor, leads to a commensurate 

reduction in ammonium nitrate. In contrast, Figure 15 illustrates that a larger reduction 

in the more abundant precursor, ammonia, results in no reduction in ammonium nitrate, 

as the ammonia reduced did not participate in ammonium nitrate production. 

Figure 13. Ammonium nitrate formation. 

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Figure 14. Reducing the less abundant precursor is more effective in reducing 

ammonium nitrate. 

Figure 15. Reducing the more abundant precursor is less effective in reducing 

ammonium nitrate. 

The following sections describe the current state of the science regarding the role of 

ammonia, VOCs, and NOx in ammonium nitrate formation and identify the most 

effective precursors for control. 

c. Role of ammonia in ammonium nitrate formation 

A number of different studies and analyses were evaluated to understand the role of 

ammonia in ammonium nitrate formation in the San Joaquin Valley. These included: 

a) comparison of the magnitude of the NOx and ammonia emissions inventories, 

b) ambient measurements of ammonia, nitric acid, and particulate ammonium; and 

c) photochemical modeling analyses of ammonium nitrate sensitivity to precursor 

emission reductions. While evaluation of emissions inventory and ambient data can 

provide indications of the relative abundance of different precursors, photochemical 

models provide a tool to quantitatively evaluate the impact of reducing precursor 

emissions on resulting ammonium nitrate concentrations. 

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Emission inventory 

As discussed in the limiting precursor section, the precursor in shortest supply limits the 

amount of ammonium nitrate formation. An evaluation of the magnitude of NOx and 

ammonia emissions provides a first level assessment of the relative abundance of these 

two precursors. Table 1 lists NOx and ammonia winter emissions in the current 

inventory for three years (2000, 2011, and 2019). As Figure 13 in the limiting precursor 

section illustrated, in simple terms it takes one molecule of NOx and one molecule of 

ammonia to form one molecule of ammonium nitrate. However, due to differing 

molecular weights, one ton of NOx contains fewer molecules than one ton of ammonia. 

Therefore it is most appropriate to make an emissions inventory comparison after 

normalizing for molecular weight. 

Due to emission source test procedures, most NOx emissions are expressed in terms of 

nitrogen dioxide (NO2). Since one NO2 molecule weighs 46 universal atomic units (u) 

and one NH3 molecule weighs 17 u, one ton of NH3 has 2.7 times (46 u/17 u) the 

number of molecules as one ton of NO2. Dividing the NOx emissions by 2.7 therefore 

provides a common basis for comparison to the ammonia emissions. On this 

normalized comparison basis, ammonia is significantly more abundant than NOx, 

particularly in future years (Table 1). In addition, as noted in the chemistry section, only 

a portion on the NOx is ultimately converted to ammonium nitrate. 

Table 1. Comparison of NOx and ammonia emissions in selected years. 

Year Winter NH3

emissions (tpd) 

Winter NOx 

emissions (tpd) 

Normalized NOx 

emissions (tpd) 

2000 330 550 204 

2011 386 330 122 

2019 360 209 77 

Monitoring studies 

Ambient measurements of precursor concentrations provide another method to 

investigate the relative abundance of each precursor and therefore which is most 

effective for control of ammonium nitrate. Blanchard, et al. (2000) examined two 

metrics using ambient data collected during the IMS-95 field program in the San 

Joaquin Valley. The first parameter was the excess of particulate ammonium plus gasphase ammonia over the sum of nitric acid, particulate nitrate, and particulate sulfate. 

The second was the ratio of particulate to total nitrate concentrations. Both metrics 

indicated an excess of ammonia in most IMS-95 samples and concluded that greater 

reductions in aerosol nitrate would occur when nitric acid was reduced rather than 

ammonia. 

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Lurmann, et al. (2006) also compared ammonia and nitric acid ambient concentrations 

measured in the San Joaquin Valley during the winter of 2000/2001 as part of 

CRPAQS. Figures 16 and 17 show the concentrations of nitric acid and ammonia 

measured at the rural Angiola site and at the urban Fresno site. At both sites ammonia 

concentrations are generally at least an order of magnitude higher than the nitric acid 

concentrations. These ammonia-rich conditions throughout the Valley indicate that, 

during the winter, nitric acid rather than ammonia is the limiting precursor. 

Figure 16. Comparison of ammonia and nitric acid concentrations measured at Angiola 

during the winter of 2000/2001 as part of CRPAQS. 

Comparison of Ammonia and Nitric Acid Concentrations 

Angiola - Winter 2000/2001

-10

0

10

20

30

40

50

60

-2 0 2 4 6 8 10 12 14 16 18

HNO3 Concentration (ug-N/m3)

NH3 Concentration (ug-N/m3)

1:1 Line

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Figure 17. Comparison of ammonia and nitric acid concentrations measured at Fresno 

during the winter of 2000/2001 as part of CRPAQS. 

The amount of gaseous ammonia (NH3) compared to particulate ammonium (NH4) 

provides another indicator of how much of the ammonia is converted to ammonium 

nitrate and therefore whether there is excess ammonia available. These measurements 

were collected at a larger number of sites during CRPAQS. Figure 18 shows the 

concentrations of particulate ammonium and gaseous ammonia at three urban sites 

(Fresno-1st, Bakersfield-California, and Bakersfield-residential), and three rural sites 

(Angiola, Pixley, and Feedlot) measured during the 2000/2001 winter CRPAQS 

episode. Overall, the levels of particulate ammonium at all sites are comparable, 

consistent with a regional formation mechanism of ammonium nitrate. Although 

ammonia concentrations are higher at the rural sites, especially at the Feedlot site, 

there is still a large amount of ammonia at each site beyond the amount that reacted 

with nitric acid to form ammonium nitrate. Again, these ammonia rich conditions 

indicate that nitric acid, rather than ammonia is the limiting precursor. 

Comparison of Ammonia and Nitric Acid Concentrations 

Fresno - Winter 2000/2001

-4

0

4

8

12

16

20

-2 0 2 4 6 8

HNO3 Concentration (ug-N/m3

)

NH3 Concentration (ug-N/m3)

1:1 Line

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Figure 18. Comparison of particulate ammonium and gaseous ammonia 

concentrations measured throughout the SJV during the winter of 2000/2001 as part of 

CRPAQS. 

Photochemical Modeling 

In contrast to the previous analyses, photochemical models provide a quantitative 

approach to simulate the effects that emission reductions in each of the gaseous 

precursors would have on the predicted ammonium nitrate concentrations. A number of 

modeling studies have been conducted by ARB staff and academic researchers to 

evaluate precursor sensitivity. 

An investigation of precursor limitations for the January 4-6, 1996 PM2.5 episode 

measured in San Joaquin Valley as part of the IMS-95 field study used the UCD-CIT 

model. This sensitivity analysis revealed that NOx controls were the most effective 

control strategy to reduce PM2.5 ammonium nitrate concentrations (Kleeman, et al. 

2005). In this study, a 50 percent reduction in NOx emissions resulted in a 25 percent 

reduction in total nitrate, while a 50 percent reduction in ammonia emissions resulted in 

a 10 percent reduction in total nitrate. The results of this analysis are shown graphically 

across the entire San Joaquin Valley in Figure 19. 

Urban Rural 

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Figure 19. Particulate nitrate reductions in response to 50 percent reductions in 

precursor emissions on January 6, 1996. 

In 2006, ARB staff modeled air quality during the three week winter CRPAQS episode 

using U.S. EPA’s Community Multiscale Air Quality (CMAQ) model with Californiaspecific modifications and corrections (Liang et al. 2006). Figure 20 illustrates the 

effects that reducing the emissions of ammonia and NOx have on ammonium nitrate 

levels. This modeling indicated that reducing ammonia emissions by 50 percent 

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reduced ammonium nitrate by less than 5 percent. On the other hand, reducing NOx 

emission by 50 percent reduced ammonium nitrate concentrations by approximately 

35 percent. This analysis, therefore, indicated that reducing NOx emissions was the 

most beneficial control strategy to reduce ammonium nitrate. 

Figure 20. Percent ammonium nitrate reduction in response to 50 percent reduction in 

NOx or ammonia emission reductions at Fresno during the winter of 2000/2001. 

In another study based on sensitivity analyses using CMAQ-Madrid simulations of the 

December 2000 CRPAQS episode, Pun et al. (2009) found that a 50 percent reduction 

in NOx emissions reduced ammonium nitrate by approximately 50 percent at rural sites 

and between 30-45 percent at Bakersfield. As shown in Figure 19, a 50 percent 

reduction in ammonia emissions did not have a significant effect on ammonium nitrate 

concentrations at urban sites. At the rural site of Angiola, ammonium nitrate 

concentrations decreased between 10 and 25 percent. However, such reductions in 

ammonium nitrate occurred only at the end of the episode, when PM2.5 concentrations 

at the rural site reached approximately 80 μg/m3

 and urban concentrations peaked at 

over 110 μg/m3

 (Figure 21). Such high PM2.5 levels are no longer reached in the 

Valley. The authors noted that under wintertime conditions, nitric acid concentrations in 

the SJV were small and therefore ammonium nitrate formation was generally limited by 

the availability of nitric acid rather than ammonia. 

-60%

-50%

-40%

-30%

-20%

-10%

0%

Percent Nitrate Reduced

50% NOx Reduction 50% Ammonia Reduction

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Figure 21. Time series with daily observations, base case simulation results and 

results from the sensitivity cases of (a) nitrate and (b) PM2.5 at Angiola (left) and 

Bakersfield (right). (Source: Pun et al., 2009, excerpt from Figure 2, pg. 406). 

Taken together, the emission inventory, monitoring data, and precursor sensitivity 

analyses all indicate that in the San Joaquin Valley, NOx, rather than ammonia is the 

limiting precursor for ammonium nitrate formation. 

d. Role of VOC in ammonium nitrate formation 

A number of studies have also been examined regarding the role of VOCs in 

ammonium nitrate formation. These include both monitoring studies conducted as part 

of CRPAQS, as well as studies that used differing types of air quality modeling to 

quantitatively assess the expected change in ammonium nitrate to hypothetical VOC 

reductions. 

Monitoring studies 

As previously mentioned, there are two primary pathways through which ammonium 

nitrate can form. During the day, NO2 is oxidized to nitric acid. Nitric acid then reacts 

with ammonia to form ammonium nitrate. This daytime nitric acid formation pathway 

involves sunlight, VOCs, and background ozone. During the night, nitric acid is formed 

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through oxidation of NO2 (via N2O5) by background ozone, which then also reacts with 

ammonia to form ammonium nitrate. Studies by Pun et al. (1998, 2004) suggested that 

the daytime pathway may be important and therefore the formation of ammonium nitrate 

would be sensitive to changes in VOC emissions. However, other studies (Lurmann et 

al., 2006), suggest that on average, daytime production of nitric acid in the San Joaquin 

Valley is relatively slow and that nighttime production of ammonium nitrate aloft, which 

then mixes to the surface after sunrise could explain the observed homogeneous 

patterns of ammonium nitrate in the Valley. Ying et al. (2009) also theorized that the 

ozone concentration aloft in the San Joaquin Valley is predominantly due to the regional 

background and does not vary significantly with surface-level VOC emissions. 

Therefore, nighttime ammonium nitrate formation in the San Joaquin Valley would not 

be sensitive to VOC reductions. 

While the monitoring studies cited above provide evidence that the VOC pathway may 

be important at times, these studies do not provide quantitative information about the 

overall role of and cannot be used to evaluate the benefits of, VOC controls. Rather, 

modeling studies are more appropriate to assess the overall impact of precursor 

controls. 

Photochemical Modeling 

Staff reviewed the results of six modeling studies containing information on the 

significance of VOC controls in reducing ammonium nitrate in the San Joaquin Valley. 

While the results of the earliest studies were mixed, later studies provide generally 

consistent results regarding the role of VOCs. In assessing the potential benefits of 

VOC controls it is important that significance be interpreted in the context of California’s 

overall control program with its strong focus on NOx control to achieve benefits for both 

PM2.5 and ozone. 

Two early studies used simplified box modeling to explore the sensitivity of ammonium 

nitrate to VOC and NOx reductions. One of the two studies simulated a typical winter 

episode (Stockwell et al., 2000) and found that decreases in VOC emissions had little 

effect. The second study (Pun and Seigneur, 2001) simulated winter conditions during 

the 1996 IMS-95 pilot study around the Fresno area. The study found that ammonium 

nitrate formation decreased with VOC emission reductions, but increased with NOx 

reductions. Pun and Seigneur (2001) theorized that reducing NOx could lead to higher 

concentrations of the hydroxyl radical (OH) and increase the overall rate of nitrate 

production, despite the reductions in NOx. However, the box modeling approach used 

had a number of limitations, including lack of transport into/out of the box, robust vertical 

transport, and use of an older chemical mechanism. In addition, the VOC emissions 

were increased by a factor of two to improve model performance. As such, the box 

modeling did not fully represent the complete scope of atmospheric variations and has 

limited usefulness in assessing the responsiveness to VOC controls. 

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Subsequent modeling sensitivity studies for the same winter episode were conducted 

with the UCD-CIT model, an advanced research grade modeling system (Kleeman et 

al., 2005). The authors concluded that NOx emission controls are more effective in 

reducing PM2.5 nitrate concentrations in the San Joaquin Valley. Summary study 

results indicate that on average, large reductions in VOC emissions (on the order of 

50 percent) reduced PM2.5 nitrate concentrations by approximately 17 percent. 

However, to evaluate the significance and effectiveness of VOC controls in the context 

of control strategy design, the study’s isopleths of PM2.5 nitrate response to combined 

NOx/VOC emission reductions provide more in-depth information. 

Figures 22 (a) and 23 (a) show that, based on the shapes of the graphs, NOx controls 

are the most effective approach to reduce PM2.5 nitrate concentrations at Fresno and 

at the location with the highest modeled PM2.5 nitrate concentration (grid location -

85 km Northing, 90 km Easting) respectively. Once NOx controls are taken into 

consideration, VOC emission reductions produce essentially no benefit, and in some 

instances may actually lead to an increase in PM2.5 nitrate concentrations. For 

example, as illustrated in Figure 22 (a) for Fresno, after considering an approximately 

70 percent reduction in NOx emissions resulting from existing and proposed controls, 

reductions in VOC emissions to any level would not decrease PM2.5 nitrate 

concentrations. Furthermore, at grid location -85 km Northing, 90 km Easting 

(Figure 23 (a)), any level of VOC emission reductions would actually cause an increase 

in nitrate concentrations. Nitrogen-containing molecules such as PAN can act as 

temporary sinks for nitrogen dioxide (NO2). When VOCs are controlled, the reduced 

availability of certain radicals, which are generated from VOCs, reduces the amount of 

NO2 that is sequestered, thereby increasing the availability of NO2 and enhancing 

ammonium nitrate formation (Meng et al., 1997). 

 

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27 

Figure 22. 24-hour average NOx/VOC particulate nitrate isopleths at Fresno for (a) all 

sources, (b) diesel engines, (c) catalyst equipped gasoline engines, and (d) upwind 

sources of nitrate. Units are μg/m3. (Source: Kleeman et al., 2005, Figure 3 pg. 5333). 

 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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Figure 23. 24-hour average NOx/VOC particulate nitrate isopleths at grid 

location -85 km Northing, 90 km Easting for (a) all sources, (b) diesel engines, 

(c) catalyst equipped gasoline engines, and (d) upwind sources of nitrate. Units are 

μg/m3. (Source: Kleeman et al., 2005, Figure 5 pg. 5335). 

Three additional modeling studies investigated the more recent two-week winter 

episode of 2000-2001 that occurred during the CRPAQS field study. 

In the first study, preliminary data from modeling of this CRPAQS winter episode 

conducted using the Lagrangian form of the UCD-CIT model qualitatively confirm that 

NOx control is the most efficient method to reduce nitrate concentrations (Kleeman, 

M.J., personal communication, May 2008). Figure 24 illustrates the response of PM2.5 

nitrate concentrations to NOx and VOC emission reductions at a rural (Angiola) and an 

urban (Fresno) site on December 31, 2000. Again, based on their shapes, these 

graphs show that NOx controls are the most effective approach to reduce PM2.5 nitrate 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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concentrations. Once NOx controls are taken into consideration (approximately 

70 percent reduction in NOx emissions), reductions in VOCs of up to 30 percent 

produce basically no benefit (Fresno). Furthermore, at some locations (Angiola) any 

VOC emission reductions may actually lead to an increase in PM2.5 nitrate 

concentrations. 

Figure 24. The isopleths plot of PM2.5 nitrate with emission control of NOx and VOC at 

Angiola (ANG) and Fresno (FEI) after a five-day back trajectory simulation for 

December 31, 2000. Units are in μg/m3. (Source: Kleeman, M.J., personal 

communication, May 2008). 

A second study conducted simulations of the two-week CRPAQS episode with the 

CMAQ photochemical model (Livingston, et al., 2009). The study consisted of two 

simulations. The first was a baseline scenario using a preliminary emissions inventory. 

This simulation showed that 50 percent reductions in anthropogenic VOC and NOx 

emissions had similar effects in reducing ammonium nitrate (about 20 percent each). A 

second simulation was conducted using an updated emission inventory representing a 

more accurate spatial distribution of total ammonia emissions (referred to as “Vehicle 

NH3” scenario, per Livingston, P., personal communication, January 19, 2011). This 

second 50 percent VOC reduction simulation showed a much lower response to VOC 

controls. The response was lowered to a 12 percent reduction in ammonium nitrate, 

with a corresponding increase in responsiveness to NOx control of 38 percent reduction 

in ammonium nitrate. These results are consistent with those found by Kleeman et al., 

2005. 

A third study modeled one week of the CRPAQS episode using a version of CMAQ with 

a more advanced chemical mechanism (CMAQ-Madrid) (Pun et al, 2009). In contrast to 

the earlier Pun study using a simplified box modeling approach, this later work found 

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that on average, nitrate was most sensitive to reductions in NOx emissions. While 

isopleths were not provided, the time evolution of nitrate and PM2.5 mass to VOC 

response illustrated in Figure 25 provides further details regarding the efficacy of VOC 

control. The response of nitrate to a 50 percent reduction in VOC emissions increased 

as PM2.5 levels rose during the episode. In urban areas, a 50 percent reduction in 

anthropogenic VOC emissions caused small reductions in nitrate, on the order of 

10 percent, on the modeled days when 24-hour PM2.5 concentrations measured over 

100 μg/m3 at urban sites and above 65 μg/m3 in rural areas. 

The difference in the VOC response on the days with the higher PM2.5 concentrations 

as compared to those days with lower concentrations may be due to a difference in the 

chemical formation regime for nitrate. In general, there is sufficient background ozone 

to generate enough free radicals to initiate and propagate the chemistry of nitrate 

formation (Ying et. al, 2009). However, on days with high PM2.5 concentrations, the 

daytime photochemistry may have contributed to a rapid increase in nitrate, resulting in 

higher VOC and NOx sensitivity. It does not appear that VOCs contributed significantly 

to the free radical budget on the simulated days mainly because rapid increases in 

ozone were not observed. The effect of VOC levels on nitrate formation may also have 

a diurnal pattern since the hydroxyl and hydroperoxyl radical levels are high during the 

daytime and negligible at night. In addition, more reactive VOCs react quickly during 

the day and there is a minimal carry over to the next day. Therefore, it is reasonable to 

assume that the higher response to VOC and NOx at higher PM2.5 concentrations may 

be due to the nitrate formation mechanism rather than to PM2.5 accumulation due to 

the length of the episode. 

Overall, nitrate was only responsive to a 50 percent reduction in VOCs at PM2.5 

concentration levels that are no longer reached in the San Joaquin Valley. Currently, 

the 24-hour PM2.5 design value in the Valley is 62 μg/m3

 recorded at Bakersfield and 

the rest of the Valley records 24-hour design values between 38 μg/m3 and 58 μg/m3. 

Given the current levels of PM2.5, we believe the Valley is now in a nitrate chemical 

formation regime that is less responsive to VOC controls. 

 

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Figure 25. Time series with daily observations, base case simulation results and 

results from the sensitivity cases of (a) nitrate and (b) PM2.5 at Angiola (left) and 

Bakersfield (right). (Source: Pun et al., 2009, excerpt from Figure 2, pg. 406). 

Taken together, these air quality modeling studies indicate that in the San Joaquin 

Valley, NOx, rather than VOCs, is the limiting precursor for nitric acid, and subsequent 

ammonium nitrate formation. 

 

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32 

6. SECONDARY ORGANIC AEROSOL FORMATION 

VOC emissions also have the potential to contribute to secondary organic aerosols 

(SOA). While these components contribute to observed PM2.5 concentrations in the 

San Joaquin Valley to a small degree, the weight of evidence indicates that 

anthropogenic VOC is not a significant contributor to PM2.5. 

SOA form when intermediate molecular weight VOCs, emitted by anthropogenic and 

biogenic sources, react and condense in the atmosphere to become aerosols. In 

addition, lighter VOCs participate in the formation of atmospheric oxidants which then 

participate in the formation of SOA. The processes of SOA formation are complex and 

have not been fully characterized. The apportionment of PM2.5 organic carbon to 

primary and secondary components is a very active area of current research. 

Using the UCD-CIT model, Chen et al. (2010) investigated the apportionment of PM2.5 

organic carbon for the 2000/2001 CRPAQS episode. From the total predicted PM2.5 

organic carbon in the urban Fresno and Bakersfield areas, six percent and four percent 

were SOA, respectively, while in the rural Angiola area, 37 percent was SOA. The 

major SOA precursors of secondary organic aerosol were long-chain alkanes followed 

by aromatic compounds. The sources of these precursors were solvent use, catalyst 

gasoline engines, wood smoke, non-catalyst gasoline engines, and other anthropogenic 

sources, in that order. 

In contrast, on an annual average basis, secondary organic aerosols derived from 

anthropogenic VOC emissions account for only one to two percent of the annual total 

PM2.5 concentrations throughout the Valley. ARB air quality modeling exercises 

conducted as part of the SJV 2008 PM2.5 Plan attainment demonstration analysis using 

the CMAQ model showed that primary PM2.5 emissions are the main contributor to 

organic aerosols and SOA contribute to only a small extent. Furthermore, as illustrated 

in Figure 26, SOA are mostly formed during the summertime, when total PM2.5 

concentrations are low, and are mainly derived from biogenic emission sources. On an 

annual average basis, SOA derived from anthropogenic VOC emissions are a small part 

of the organic aerosol concentrations (three to five percent). 

 

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Figure 26. Daily contributions to organic aerosol concentrations in Bakersfield in 2000 

modeled with CMAQ: Primary organic aerosols (PA), secondary aerosols formed from 

biogenic VOC emissions (SB) and secondary aerosols formed from anthropogenic 

source VOC emissions (SA). Units are μg/m3

. 

As part of the CRPAQS study, simulations of a wintertime episode conducted using 

CMAQ-Madrid, a model with an enhanced secondary organic aerosol formation 

mechanism, also found that organic aerosol concentrations were dominated by directly 

emitted (primary) emissions. The study found that, because of the dominance of 

primary PM2.5 organic matter, a 50 percent reduction in anthropogenic VOC emissions 

has limited effects on the modeled PM2.5 organic matter (Pun, et al., 2009). 

These study results show that for secondary organic aerosols, further VOC reductions 

would have very limited effectiveness in reducing PM2.5 concentrations. 

 

BAC

0

2

4

6

8

10

12

14

1

163146617691

106121136151166181196211226241256271286301316331346361

SB

SA

PA

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34 

7. EMISSION SOURCES OF WINTERTIME PM2.5 

a. Emission inventory 

Emission inventories provide emission estimates for sources of directly emitted 

(primary) PM2.5 and of each of the gaseous precursors of secondary PM2.5 (NOx, 

SOx, and ammonia). Table 2 lists the main PM2.5 components and links them to their 

largest emission sources based on the 2011 San Joaquin Valley emission inventory 

data. Emission sources are listed in descending order of magnitude. 

As described in section 4d, ammonium nitrate is the main PM2.5 component, 

contributing about 55 to 65 percent of PM2.5. It is formed in the atmosphere from 

reactions of NOx and ammonia. Heavy-duty diesel vehicles (trucks) emit most of the 

NOx, followed by off-road equipment, light-duty vehicles, and trains. Ammonia is 

primarily emitted from livestock husbandry, fertilizer application, and mobile sources. 

Ammonium sulfate, formed in the air from reactions of SOx and ammonia, contributes 

about five percent to PM2.5. SOx is mostly emitted from fuel combustion sources in oil 

and industrial manufacturing processes. Organic carbon, which contributes about 20 to 

30 percent to PM2.5, and elemental carbon, which contributes about five percent of 

PM2.5, are directly emitted, with key sources being residential fuel combustion, 

managed burning, diesel trucks, and commercial cooking operations. Geological, a 

minor component contributing about two to five percent of the PM2.5 mass, is directly 

emitted from activities generating dust, such as farming operations and on-road and offroad vehicle travel, as well as wind-blown dust. It should be noted that while windblown dust may contribute on some winter days, PM2.5 exceedances primarily occur on 

very stagnant days when windblown dust emissions are minimal. 

While emission inventories provide a broad overview of Valley wide and county level 

sources, additional methods using ambient data and source apportionment modeling 

provide supplemental information on the sources directly impacting individual monitoring 

sites. The following sections describe these analyses. 

 

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Table 2. Main emission sources of PM2.5 components. 

PM2.5 Component 

(percent of PM2.5) 

Process Emission Sources 

Ammonium nitrate

(about 55-65 percent) 

Formed in the 

atmosphere from the 

reactions of NOx and 

ammonia emissions 

NOx: 

Heavy duty diesel vehicles account for 

40 percent of the 2011 winter NOx 

emissions. 

Farm equipment, off-road equipment, light 

and medium duty trucks, trains, light duty 

passenger cars, and residential fuel 

combustion account for an additional 

40 percent. 

Ammonia: 

Livestock husbandry, fertilizer application, 

and mobile sources account for over 

90 percent of the 2011 winter ammonia 

emissions. 

Ammonium sulfate 

(about 5 percent) 

Formed in the 

atmosphere from the 

reactions of SOx and 

ammonia emissions 

SOx: 

Fuel combustion in oil production, at electric 

utilities, and in manufacturing and industrial 

boilers, heaters, and engines, 

manufacturing of chemicals and glass 

related products, residential wood 

combustion, and aircraft account for about 

75 percent of the 2011 winter SOx 

emissions. 

Organic Carbon 

(about 20-30 percent) 

Directly emitted from 

motor vehicles and 

combustion processes 

Combustion PM2.5: 

Residential fuel combustion, managed 

burning and disposal, diesel trucks, 

cooking, oil and gas production, and farm 

equipment account for 80 percent of the 

combustion PM2.5 emissions. 

Elemental Carbon 

(about 5 percent) 

Directly emitted from 

motor vehicles and 

combustion processes 

Geological 

(about 2-5 percent) 

Directly emitted from 

dust generating sources 

Dust PM2.5: 

Farming operations, fugitive windblown 

dust, paved and unpaved road dust, mineral 

processes, and construction and demolition 

account for 100 percent of the 2011 dust 

PM2.5 emissions. 

 

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b. Chemical markers of source types 

Selected compounds measured in the atmosphere can serve as chemical markers for 

specific sources. Based on this approach, as part of the extensive monitoring effort 

during CRPAQS, residential wood combustion was identified as the main source of 

PM2.5 organic carbon in the San Joaquin Valley. Measurements of levoglucosan, a 

chemical marker for wood smoke were conducted throughout the San Joaquin Valley. 

Figure 27 illustrates the geographical distribution of the annual averages of these 

levoglucosan measurements (pink circles on the map). Each circle size is proportional 

to the levoglucosan concentration. The largest levoglucosan levels occurred in urban 

areas, most notably the Fresno area (FSF and FSR), as did the largest PM2.5 organic 

carbon levels depicted on the small map to the upper left. The second largest 

levoglucosan levels the San Joaquin Valley were measured in Modesto (M14), 

sequentially followed by Bakersfield (BAC) and then Corcoran (COP). 

Figure 27. Spatial distribution of annual levoglucosan measured throughout the San 

Joaquin Valley during CRPAQS (Watson, J., Roth, P., 2006). 

Additional measurements of levoglucosan collected during the winter of 2003/2004 in 

the Fresno area showed wood smoke was a significant percentage of PM2.5 at all 

locations, ranging from 10 to 40 percent (Figure 28). 

 

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Figure 28. Wood smoke contribution to PM2.5 at Fresno-1st during a number of winter 

days in 2003 and 2004 (Gorin et al., 2005). 

 

c. Source apportionment using source receptor models 

Source receptor models (also known as observational models) can be used to 

determine the relative importance of the different types of PM2.5 emission sources at 

individual monitoring sites. The Chemical Mass Balance (CMB) model statistically 

relates measured chemical species of ambient PM2.5 to the chemical species emitted 

by diverse sources. The Positive Matrix Factorization (PMF) statistical model 

distinguishes correlation patterns among measured PM2.5 species to identify sources. 

Previous studies have applied source apportionment models to IMS-95 and CRPAQS 

data. For the present study, both CMB and PMF were applied to recent PM2.5 data 

collected in the San Joaquin Valley. 

Prior Source Apportionment Studies 

In earlier studies, Schauer and Cass, 2000 estimated source contributions to wintertime 

PM2.5 through CMB modeling of data collected during the IMS-95 field study. Chen et 

al., 2007, applied two types of multivariate statistical models, PMF and UNMIX, to 

identify sources contributing to wintertime PM2.5 during the CRPAQS field study. In 

addition, Chow et al., 2005, applied CMB to the CRPAQS data set. Table 3 

summarizes the source contributions to wintertime PM2.5 estimated through these 

studies. In all cases, ammonium nitrate is the major source, contributing approximately 

50 percent to wintertime PM2.5 throughout the Valley (23-site average); ranging from 

40 and 50 percent at urban sites (Fresno and Bakersfield) to around 65 percent at rural 

sites (Kern Wildlife Refuge and Angiola). The combined biomass burning and cooking 

source, dominated by biomass burning, contributes over 25 percent of PM2.5 

0

5

10

15

20

25

30

35

40

45

Percent Contribution

Wood Smoke Contribution 

to PM2.5 at Fresno-1st

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Table 3. Wintertime PM2.5 source contributions estimates for IMS-95 and CRPAQS. 

 Source Contribution Estimates (% of PM2.5 mass) 

Study and 

Sites Salt Dust Exhaust Biom Burn Cook Amm. Sulfate 

Amm. 

Nitrate 

Sec 

Orga Misc 

 Gas Dies 

IMS-951 

Fresno 

avg. of 

12/26-28/1995 

and 1/4-6/1996 

- 1.0 2.5 9.6 37.8 6.4 4.8 32.6 4.9 0.5 

Bakersfield 

avg. of 

12/26-28/1995 

and 1/4-6/1996 

- 1.5 3.4 9.5 18.6 5.1 7.5 41.6 12.1 0.7 

Kern WR 

avg. of 

12/26-28/1995 

and 1/4-6/1996 

- 0.9 0 5.0 0.5 0.0 7.9 66.8 15.6 3.2 

CRPAQS, 

UNMIX2 

23-site avg. 

Nov. 2000 - 

Jan. 2001 

0 3 15 24 5 51 2 

CRPAQS, 

PMF2 

23-site avg. 

Nov. 2000 - 

Jan. 2001 

0 5 10 23 3 48 11 

CRPAQS, 

CMB3

 

Fresno 

IOPs4 0.2 0.3 1.5 5.8 48.9 3.1 40.1 

Bakersfield 

IOPs4 0.2 1.2 6.4 4.5 30.4 3.9 53.5 

Angiola 

IOPs4 0.3 5.2 7.4 1.9 14.8 4.4 65.9 

Sierra Nevada 

Foothills 

IOPs4

0.2 1.2 4.0 7.4 41.8 4.7 40.6 

1 Schauer and Cass, 2000. 

2 Chen et al., 2007. 

3

 Chow et al., 2005. 

4

 IOPs = Intensive Operating Periods, 12/15-18/200, 12/26-28/2000, 1/4-6/2001, and 

 1/31/2001- 2/3/2001. 

a

 Secondary organic aerosol estimated from organic carbon not accounted for by primary source profiles. 

 

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valley wide, constituting the second major source at urban sites – with larger 

contributions at Fresno than at Bakersfield. In contrast, biomass burning and cooking 

are not a major sources at rural sites. Engine exhaust, dominated by diesel, is the third 

major source of directly emitted PM2.5, contributing approximately 10 to 15 percent to 

PM2.5 valley wide. Since secondary organics are estimated from the organic carbon 

not accounted for by the apportionment of other organic carbon sources, small changes 

in the organic carbon content in the chemical composition profiles for other sources may 

impact the estimate of the secondary organics contribution. 

Recent Source Apportionment Studies 

Chemical Mass Balance Modeling 

Source contributions during high PM2.5 concentration days at Bakersfield-California 

(BAC) and Fresno –1st Street (FSF) were estimated by applying the CMB model version 

8.2 to individual PM2.5 samples using PM2.5 source profiles developed during previous 

studies. The PM2.5 samples were from days measuring concentrations greater than 

30 μg/m3

 between 2007 and 2010. Per U.S. EPA guidance, between 2007 and 2009, 

the carbon collection and analysis method was changed to improve comparability with 

the rural Interagency Monitoring of Protected Visual Environments (IMPROVE) PM2.5 

carbon data. Since the new carbon method started operating in May 2007 at 

Bakersfield and in April 2009 at Fresno, the CMB analysis relied on 2007-2010 data 

from Bakersfield and 2009-2010 data from Fresno. Appendix 2 describes this CMB 

analysis in further detail. 

Figure 29 shows the calculated contributions to ambient PM2.5 from sources included in 

the CMB model. Ammonium nitrate, the most significant source, contributed 67 percent 

at Bakersfield and 54 percent at Fresno-1st. Biomass burning, which included 

residential wood combustion and agricultural, prescribed burning, and likely also 

cooking, contributed nine percent at Bakersfield and 23 percent at Fresno. Motor 

vehicle exhaust (diesel and gasoline combined) accounted for ten percent at Bakersfield 

and 12 percent at Fresno-1st. Ammonium sulfate contributed eight percent at 

Bakersfield and six percent at Fresno-1st. Contributions of the remaining sources were 

minor at both sites. 

 

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Figure 29. CMB model calculated 2007-2010 average PM2.5 source contributions of 

days with PM2.5 concentrations measuring over 30 μg/m3 at a) Bakersfield-California 

(BAC) between 2007 and 2010 and b) Fresno-1st Street (FSF) between 2009 and 2010. 

Positive Matrix Factorization 

The PMF2 model was applied to the chemically speciated PM2.5 data collected at the 

Bakersfield-California and Fresno-1st Street monitoring sites. Bakersfield data from 

2008-2010 and Fresno-1st data from 2009-2010 were used. Appendix 3 describes this 

PMF analysis in further detail. The average source contributions on days with PM2.5 

concentrations measuring over 30 μg/m3 are illustrated in Figure 30. Similar to the CMB 

results, ammonium nitrate contributes the most at both sites, 64 percent at Bakersfield 

and 54 percent at Fresno-1st. Motor vehicle exhaust contributes 13 percent at 

Bakersfield and 23 percent at Fresno-1st, while biomass burning (which includes 

residential wood combustion, agricultural burning, and likely also cooking) contributes 

six percent at Bakersfield and 13 percent at Fresno-1st. Secondary ammonium sulfate 

accounts for eight percent at Bakersfield and seven percent at Fresno-1st. Airborne soil 

and industrial sources are minor contributors. 

 

Ammon. 

nitrate

53.6%

Ammon.

sulfate

6.0%

Sea Salt

0.7%

Motor 

Vehicle

11.5%

Biomass 

burning

22.9%

Organic

Carbon

4.4%

Tire and 

Brake

0.5%

Geologic.

0.3%

b) FSF Average High Day

(New Carbon) (2009-2010)

Ammon.

nitrate

67.4%

Ammon.

sulfate

7.8%

Sea Salt

0.5%

Motor 

Vehicle

9.6%

Biomass 

burning

9.1%

Organic 

Carbon

2.9%

Tire and 

Brake

0.7%

Geologic.

1.7% Oil 

Refineries

0.3%

a) BAC Average High Day

(New Carbon) (2007-2010)

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Figure 30. Average high day source contributions estimated using PMF on days with 

PM2.5 concentrations measuring over 30 μg/m3 at a) Bakersfield-California (BAC) 

between 2008 and 2010 and b) Fresno-1st Street (FSF) between 2009 and 2010. 

While the absolute magnitude of the contributions estimated by the two models vary to 

some extent, taken together, the CMB and PMF source apportionment studies confirm 

the importance of secondary ammonium nitrate contributions to PM2.5 on high 

concentration days. In addition, motor vehicle exhaust and biomass burning were found 

to be significant contributors to primary PM2.5. 

d. Photochemical modeling source apportionment 

While observational models like CMB and PMF are most useful in identifying sources of 

primary PM2.5, photochemical models are needed to identify sources of secondary 

PM2.5. Ying et al. (2008, 2009) simulated the 2000/2001 CRPAQS PM2.5 episode 

using the source-oriented UCD-CIT air quality model. Source apportionment of primary 

PM2.5 in the SJV found elemental and organic carbon (EC and OC) to be the two 

largest components. Wood burning was the major OC source in the Valley, contributing 

approximately 50 percent to the total PM2.5. At Fresno, wood burning accounted for 

approximately 70 to 80 percent of the OC, while meat cooking accounted for 

approximately 10 to 15 percent. Diesel engines were identified as the major EC source. 

These results are generally consistent with those of the receptor modeling discussed 

above. 

Source apportionment of secondary nitrate at Fresno revealed diesel engines were the 

largest contributor to nitrate, accounting for approximately 40 percent of the PM2.5 

nitrate, while catalyst equipped gasoline engines accounted for approximately 

20 percent. Agricultural sources accounted for approximately 80 percent of the PM2.5 

ammonium. 

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42 

8. PM2.5 AIR QUALITY PROGRESS 

a. Annual PM2.5 trends 

On an annual average basis, PM2.5 air quality has improved over the last ten years. As 

shown in Figure 31, annual design values at sites in the northern (e.g., Modesto), 

central (e.g., Fresno-1st) and southern regions (e.g., Bakersfield) in the Valley show 

progress towards attainment of the standard. The design value -- the metric used to 

determine compliance with the standard -- represents the average of three consecutive 

annual averages of the PM2.5 concentrations measured at a specific site (e.g. the 2011 

PM2.5 annual design value is the average of the 2009, 2010, and 2011 annual average 

PM2.5 concentrations). If the annual design value is equal to or below 15.0 μg/m3, the 

site attains the standard. Between 2001 and 2011, annual design values in the Valley 

declined between 30 and 40 percent. The largest decreases occurred in the northern 

and central Valley, where, based on 2011 design values, most sites attain the annual 

PM2.5 standard. While the southern Valley has shown less improvement, sites are 

nearing attainment, with design values about 10 to 20 percent over the standard. With 

on-going implementation of the 2008 PM2.5 Plan, air quality in the Valley is expected to 

continue to improve and reach attainment in 2014. 

Figure 31. Trend in annual PM25 design values (2001-2011) at the BakersfieldCalifornia, Fresno-1st, and Modesto monitoring sites. 

0

5

10

15

20

25

30

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Design Value (μg/m3)

Change in Annual PM2.5 Design Values

Bakersfield-California Fresno-1st Modesto

Standard

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43 

b. 24-Hour PM2.5 trends 

As illustrated in Figure 32, over the long-term, the 24-hour PM2.5 design values also 

show a downward trend. The most pronounced progress occurred between 2001 and 

2003. Extensive wildfires occurred during the summer of 2008 in Northern California. 

These wildfires adversely impacted the 2008, 2009, and 2010 design values throughout 

the Valley, with a greater impact in the northern Valley. Overall, between 2001 and 

2011, the 24-hour PM2.5 design values in the Valley have decreased between 30 and 

55 percent. 

Figure 32. Trend in 24-Hour PM2.5 Design Values (2001-2011) at the BakersfieldCalifornia, Fresno-1st, and Modesto monitoring sites. 

Meeting the PM2.5 24-hour standard poses a significant challenge because the focus is 

on the most severe days, which are strongly influenced by meteorology as well as 

emissions from episodic activities, such as residential wood burning. Thus, evaluating 

multiple PM2.5 air quality parameters and the meteorology effects on air quality trends 

provides a broader picture of progress in the Valley. 

Looking at the number of days with measured PM2.5 concentrations over the 35 μg/m3

standard provides another way to assess PM2.5 trends. Over the long term, between 

1999 and 2011, the number of days exceeding the standard decreased by about 

50 percent at the Bakersfield-California site and by about 45 percent at the Fresno-1st

site (Figure 33). The increase in the number of exceedance days in 2011 compared to 

2010 was due to the very severe meteorological conditions experienced in the Valley 

during the winter of 2011. The Valley experienced similar meteorological conditions 

during the 1999-2000 and 2000-2001 winters. The total number of exeedance days, 

however, was much higher during these earlier years, providing evidence that the 

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

PM2.5 Design Value (μg/m3)

Change in 24-Hour PM2.5 Design Values

Bakersfield-CA Fresno‐1st Modesto

Include wildfire impacts

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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44 

emission reductions achieved in the Valley have resulted in significant PM2.5 air quality 

improvement. 

Figure 33. Trend in measured days over the 24-Hour standard of 35 μg/m3 (1999-

2011) at the Bakersfield-California and Fresno-1st monitoring sites. 

c. Meteorology impacts on air quality 

Although the San Joaquin Valley is large, almost 250 miles long and 80 miles wide, it 

has a reasonably uniform climatology characterized by hot, dry summers and cool, rainy 

winters. Mountains on the eastern, western, and southern edges create a long deep 

basin that can allow pollutants to accumulate under stagnant weather conditions. 

The “Pacific High”, a semi-permanent subtropical high pressure system located off the 

west coast of North America, and the “Great Basin High”, a high pressure region that 

forms in the winter to the area east of the Sierra Nevada Mountains, are major 

influences on Valley weather, particularly in the winter. In turn, the strength and position 

of these high pressure regions are influenced by the strength of the El Nino/Southern 

Oscillation. El Nino years, characterized by warmer than normal temperatures in the 

equatorial Pacific and La Nina years, characterized by colder than normal temperatures, 

can alter the position of the Pacific High, allowing or blocking the passage of frontal 

systems through California and the San Joaquin Valley. A strong La Nina year can 

keep the Pacific High from moving south in the winter, diverting normal winter frontal 

systems northward, and resulting in drier conditions in California, particularly in the 

southern portions of the State. Due to decreases in the number and strength of frontal 

systems passing through the Valley, as well as increases in potential stagnant 

conditions, a strong La Nina year can result in higher than expected PM2.5 

concentrations. 

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1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

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Measured Days over 24-Hour PM2.5 Standard of 35 ug/m3

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Include 

wildfire

impacts

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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45 

In a normal year, when the Pacific High moves south in winter and diminishes in 

strength, storms can penetrate further into the Valley, bringing clouds and rain. In 

between these storms, higher pressure can build, bringing mild, bright, sunny weather. 

A strong Great Basin High can direct winds into the Valley, cleaning out any 

accumulated particulates. When the Great Basin High is weak, cool, damp air can be 

trapped in the Valley, with stagnant conditions and poor ventilation lasting for days. The 

frontal systems which pass through the Valley in winter are weaker than those in the 

summer and the approach of a weak, slow-moving system can bring light surface winds 

with weak vertical mixing. The resulting stagnant conditions can persist for extended 

periods before the frontal system bringing precipitation and stronger winds finally 

passes through the area. The southern portion of the Valley is effectively blocked by 

the Tehachapis and the Coast Ranges to the south and west, leaving it dependent on 

frontal systems from the north for much needed precipitation and winds to scour out any 

accumulated pollutants. Stagnant conditions can lead to temperature inversions. 

Under normal conditions, temperature decreases with height, allowing free upward air 

flow and dispersion of emissions and pollutants. In contrast, a temperature inversion 

positions a layer of warm air above cooler air impeding upward air flow. Often the 

inversion layer is lower than the mountains surrounding the Valley providing a “cap” and 

effectively trapping pollutants. The frequency and intensity of the two high pressure 

systems and the speed and intensity of the periodic storm systems that clean the air are 

expected to cause large variations in year-to-year average wintertime PM2.5 

concentrations. 

Measurements conducted during the CRPAQS winter of 2000/2001 indicated that high 

PM2.5 concentrations usually occur during days dominated by a strong upper-level 

ridge of high pressure located over Central California (Figure 34 ) (MacDonald et al., 

2006). These days are characterized by light winds, low mixing heights, and limited 

pollutant dispersion. These PM2.5 episodes can last weeks, making addressing the 

24-hour PM2.5 standard in the Valley a significant challenge. 

Figure 34. Frequency of high PM2.5 days in different regions of the San Joaquin Valley 

corresponding to different synoptic meteorological conditions during the CRPAQS 

winter of 2000/2001 (Mac Donald et al., 2006). 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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46 

Examples of the impact of La Nina on Valley weather patterns can be seen during the 

winters of 2011/2012 and 2000/2001, the period during CRPAQS. As noted above, the 

winter of 2000/2001 was characterized by the persistence of strong surface high 

pressure that brought light-to-calm winds and stable, stagnant conditions to the Valley. 

Several fairly strong frontal systems crossed through the region, bringing precipitation, 

high wind speeds, and strong vertical mixing, allowing accumulated pollutants to 

disperse. The winter of 2011/2012 was also characterized by a strong surface highpressure system, but frontal passages were weaker and drier with less vertical mixing, 

allowing stagnant conditions to continue for longer periods. 

The graphs in Figures 35 and 36 compare PM2.5 concentrations measured at 

Bakersfield and Fresno, respectively, between November 1, 2011 and 

February 29, 2012 to the PM2.5 concentrations measured during the same four months 

(November through February) in earlier years (1999/2000 and 2000/2001). The 

2011/2012 air quality was much better compared to earlier years for all air quality 

statistics. Peak 24-hour concentrations were over 40 percent lower. The average 

concentration during the four months period was also 40 percent lower. The number of 

days over the 24-hour standard of 35 μg/m3

 was cut by about 35 percent. Even more 

significant was the 70 percent decline in the number of days with very high 

concentrations (over 65 μg/m3

). 

Figure 35. Comparison of the 2011/2012 PM2.5 episode to the CRPAQS episodes of 

1999/2000 and 2000/2001 at Bakersfield-California. 

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Select PM2.5 Episodes at Bakersfield-California

1999-2000

2000-2001

2011-2012

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Figure 36. Comparison of the 2011/2012 PM2.5 episode to the CRPAQS episodes of 

1999/2000 and 2000/2001 at Fresno 1st. 

d. Annual trends adjusted for meteorology 

To understand the effects of emission reductions on ambient PM2.5 concentration 

trends, the effects of meteorology need to be separated out as fully as possible. The 

Classification and Regression Trees (CART) method was used for this purpose in the 

SJV. CART-defined relationships developed for Bakersfield and Fresno accounted for 

most, but not all, of the effects of meteorology on the annual PM2.5 trends. One 

aspect that may not have been fully captured was the role of carryover of PM2.5 during 

extended duration episodes. Further analysis is underway to better address this impact 

within the CART analysis. Appendix 4 describes the current CART analysis in further 

detail. 

The meteorology-adjusted (met-adjusted) trends in the figures below integrate the 

CART-defined meteorology-effects. For example, in years with meteorology conditions 

more conducive to PM2.5 formation, the PM2.5 concentrations were adjusted 

downward. Conversely, the PM2.5 concentrations were adjusted upward in years with 

meteorology conditions less conducive to PM2.5 formation. 

Met-adjusted trends are designed to be better indicators than the observed trends for 

showing the effects of changing emissions. At Bakersfield, the resulting meteorologyadjusted trend between 1999 and 2010 indicates greater decline in PM2.5 

concentrations than the unadjusted trend (Figure 37), while at Fresno the two trends are 

generally similar (Figure 38). Overall, the meteorology-adjusted trends indicate that 

between 1999 and 2010, the annual average PM2.5 concentrations decreased about 40 

to 50 percent at both locations due to emission reductions. 

 

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Select PM2.5 Episodes at Fresno-1st

1999-2000

2000-2001

2011-2012

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48 

Figure 37. Observed and met-adjusted PM2.5 trends in Bakersfield. 

Figure 38. Observed and met-adjusted PM2.5 trends in Fresno. 

e. 24-hour trends adjusted for meteorology 

Similar to annual average trends, the number of exceedance days that occur each year 

can be strongly affected by differences in meteorological conditions from year to year. 

Figure 39 shows observed and met-adjusted trends for PM2.5 exceedance days in the 

Bakersfield and Fresno areas. The observed values each year may differ from those in 

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Observed and Met‐Adjusted PM2.5 Trends in Bakersfield Area

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Met‐Adjusted PM2.5

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Linear (Met‐Adjusted PM2.5)

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Met‐Adjusted PM2.5

Linear (Observed PM2.5 Concentrations)

Linear (Met‐Adjusted PM2.5)

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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49 

Figure 33 for several reasons including a) they are averages of multiple sites in each 

area, b) more days could be included where missing values could be imputed (missing 

values were filled in using relationships in existing data), and c) some days with 

incomplete meteorological data could not be included in the analysis. 

The met-adjusted trend for Bakersfield shows a stronger decline compared to the 

observed trend, while in Fresno the observed and met-adjusted trends are similar. The 

decrease from 1999 through 2010 for the met-adjusted trend is 60 to 65 percent in both 

areas. 

Figure 39. Observed and met-adjusted trends for PM2.5 exceedance days in a) the 

Bakersfield area and b) the Fresno area. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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50 

f. Trends in 24-hour, seasonal, and hourly PM2.5 

Comparing the change in the frequency distribution of 24-hour PM2.5 concentrations 

over the last decade provides another means of looking at air quality changes over the 

years. As illustrated in Figure 40, the fraction of days recording PM2.5 over the 24-hour 

standard of 35 μg/m3 decreased between the three-year periods of 1999-2001 and 

2009-2011 at the three monitoring sites shown. At Bakersfield, the frequency 

decreased from over 15 to less than ten percent, at Fresno from 20 to less than 

ten percent, and at Modesto from about ten percent to less than five percent. In 

contrast, during these same periods, the fraction of days recording concentrations at or 

below the annual standard increased from about 50 up to 70 percent at Bakersfield, 

from 55 up to 70 percent at Fresno, and from about 65 up to 80 percent at Modesto. 

Figure 40. Change in PM2.5 concentration frequency distribution between the 1999-

2001 and 2009-2011 periods at the a) Bakersfield-California, b) Fresno-1st, and 

c) Modesto monitoring sites. 

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Concentration (ug/m3)

a) Decrease in PM2.5 Concentrations 

at Bakersfield-California

1999-2001 2009-2011

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at Fresno-1st 

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c) Decrease in PM2.5 Concentrations 

at Modesto

1999-2001 2009-2011

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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51 

In the San Joaquin Valley, PM2.5 concentrations over the 24-hour standard occur 

during the winter season. Figure 41 illustrates the overall downward trend in the threeyear averages of 1st and 4th quarter (Q1+Q4) PM2.5 concentrations between the 

periods of 1999-2002 and 2009-2011. Over the long-term, Q1+Q4 average PM2.5 

concentrations decreased by 37 percent at Bakersfield and Modesto and 47 percent at 

Fresno. Most recently, between the periods of 2004-2006 and 2009-2011, Q1+Q4 

average PM2.5 concentrations decreased by eight percent at Bakersfield, 11 percent at 

Fresno, and ten percent at Modesto. 

Figure 41. Change in three-year averages of 1st and 4th quarter PM2.5 concentrations 

at the Bakersfield-California, Fresno-1st, and Modesto monitoring sites. 

 

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Change in Three-Year Averages of 1st and 4th Quarter 

PM2.5 Concentrations

BakersfieldCA

Fresno-1st

Modesto

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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52 

Focusing on changes in winter (November through February) average PM2.5 

concentrations in years when meteorological conditions were most conducive to PM2.5 

formation and accumulation provides further insight into PM2.5 air quality progress. 

These years include 2000, 2002, 2007, and 2011, which as illustrated on Figure 33, 

also had the highest numbers of days measuring over the 24-hour PM2.5 standard. 

Figure 42 illustrates the decrease in the winter average PM2.5 concentrations in these 

four years at the Bakersfield-California, Fresno-1st, and Modesto monitoring sites. 

Comparing 2000 to 2011, winter average PM2.5 concentrations decreased by about 

35 percent in Bakersfield, about 40 percent in Fresno, and about 30 percent in Modesto. 

Comparing the more recent years of 2007 and 2011, winter average PM2.5 

concentrations decreased by about 30 percent in Bakersfield, 20 percent in Fresno, and 

ten percent in Modesto. 

Figure 42. Changes in winter-months average (January, February, November, 

December) PM2.5 concentrations at the Bakersfield-California, Fresno-1st and Modesto 

monitoring sites among years with most PM2.5 conducive meteorology. 

 

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2000 2002 2007 2011

Concentration (μg/m3)

Changes in Winter Months Average 

PM2.5 Concentrations

Bakersfield-CA Fresno-1st Modesto

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53 

Progress in PM2.5 is further corroborated by comparing changes in monthly average 

PM2.5 concentrations between 1999-2001 and 2009-2011 (Figure 43). The overall 

PM2.5 seasonal pattern has not changed; however the average monthly concentrations 

have decreased. The most significant improvements in PM2.5 have been achieved 

during the winter months. 

Figure 43. Changes in PM2.5 monthly concentrations between the 1999-2001 and 

2009-2011 three-year periods at the a) Bakersfield-California, b) Fresno-1st, and 

c) Modesto monitoring sites. 

 

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1 2 3 4 5 6 7 8 9 10 11 12

Concentrations (ug/m3)

Month

b) Changes in PM2.5 Seasonal Pattern

Fresno-1st

1999-2001 2009-2011

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1 2 3 4 5 6 7 8 9 10 11 12

Concentrations (ug/m3)

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a) Changes in PM2.5 Seasonal Pattern

Bakersfield-California

1999-2001 2009-2011

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1 2 3 4 5 6 7 8 9 10 11 12

Concentrations (ug/m3)

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c) Changes in PM2.5 Seasonal Pattern

at Modesto

1999-2001 2009-2011

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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54 

Comparing changes in PM2.5 diurnal patterns offers further insights into the progress 

achieved. Figure 44 illustrates changes in the three-year averages of hourly PM2.5 

concentrations recorded during November and December between 2001-2003 and 

2009-2011 at a) Bakersfield-California, b) Fresno-1st, and c) Modesto. The overall 

diurnal patterns have not changed, yet hourly concentrations have decreased 

throughout the day. Peak daytime concentrations decreased approximately 20 percent, 

and peak nighttime concentrations approximately 30 percent. 

Figure 44. Changes in the average November-December PM2.5 hourly concentrations 

between the 1999-2001 and 2009-2011 three-year periods at the a) BakersfieldCalifornia, b) Fresno-1st, and c) Modesto monitoring sites. 

 

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Time-of-day

b) Changes in PM2.5 Diurnal Variation 

(Nov-Dec) at Fresno-1st Street

2001-2003 2009-2011

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PM2.5 Concentration (ug/m

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a) Changes in PM2.5 Diurnal Variation 

(Nov-Dec) at Bakersfield-CA

2001-2003 2009-2011

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PM2.5 Concentration (ug/m3)

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Change in PM2.5 Diurnal Variation

(Nov-Dec) at Modesto

2001-2003 2009-2011

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55 

g. Chemical composition trends 

As previously discussed, PM2.5 concentrations measured at monitoring sites in the SJV 

have decreased from the 1999-2001 to the 2009-2011 three-year periods. Trends in 

individual PM2.5 chemical components, as well as emission inventory trends were 

evaluated to highlight the main chemical components leading to the progress in PM2.5 

air quality and to evaluate the response to State and District control programs. 

Speciation monitors in the SJV collect data on PM2.5 chemical composition. 

Figures 45, 46, and 47 illustrate the trends in the individual PM2.5 components at 

Bakersfield, Fresno, and Modesto. Between 2007 and 2009, the carbon collection and 

analysis method was changed to improve comparability with the rural IMPROVE PM2.5 

carbon data. Since the change was implemented mid-year, there are gaps in carbon 

data for years with a mix of the old and new methods. 

Ammonium nitrate, ammonium sulfate, and carbon compounds are the major 

constituents of PM2.5. On an annual average basis, concentrations of these key 

constituents have all shown significant decreases. Ammonium nitrate concentrations in 

the Valley declined about 40 percent between 2002 and 2011. During the same 

time-frame, concentrations of ammonium sulfate and carbon compounds declined about 

20 to 30 percent. The most significant declines occurred between 2002 and 2003, and 

again between 2007 and 2010. 

Figure 45. Trends in PM2.5 chemical components at Bakersfield. 

 

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Geological

Elements

OM_Old

EC_New

OM_New

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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56 

Figure 46. Trends in PM2.5 chemical components at Fresno-1st. 

Figure 47. Trends in PM2.5 chemical components at Modesto. 

The 2012 SJV PM2.5 Plan’s Appendix A describes further analyses on PM2.5 air quality 

trends. 

 

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Trends in PM2.5 Chemical Components at Modesto

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Elements

EC_Old

OM_Old

EC_New

OM_New

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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57 

h. Emission inventory trends 

 

As specified by U.S. EPA’s PM2.5 implementation rule, required PM2.5 plan precursors 

are directly emitted PM2.5, NOx, and SOx. As discussed in sections 5 and 6, VOCs 

and ammonia are not significant precursors in terms of reducing PM2.5 concentrations. 

Figure 48 illustrates wintertime emission trends in the San Joaquin Valley air basin from 

2000 through 2011 for the three key precursors. 

 NOx emissions have decreased by 219 tons per day (tpd) or 40 percent. 

Major reductions occurred in emissions from heavy-duty diesel trucks, stationary 

combustion sources, and other mobile sources (e.g., farm and off-road 

equipment, trains) 

 Direct PM2.5 emissions decreased by 28 tpd or about 30 percent. 

Major reductions occurred in emissions from residential wood combustion and 

entrained dust. 

 SOx decreased by 15 tpd or about 60 percent. 

Major reductions occurred in emissions from stationary fuel combustion sources 

and industrial processes. 

The combined downward trends in PM2.5 components and emissions of PM2.5, NOx, 

and SOx indicate that the ongoing control program has had substantial benefits in 

improving air quality in the SJV and that further emission reductions in the future are 

expected to provide continuing progress towards attaining the 24-hour PM2.5 standard. 

Figure 48. PM2.5 and PM2.5 precursor winter emission trends in the San Joaquin 

Valley. 

 

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Emissions (tpd)

Winter Emission Inventory Trends

NOx

PM2.5

SOx

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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58 

9. LINKING AIR QUALITY TRENDS TO EMISSION REDUCTIONS 

a. NOx control 

Programs aimed at reducing NOx emissions have played an important role in reducing 

nitrate concentrations and, consequently, overall PM2.5 concentrations in the Valley. 

As discussed in section 5, previous studies have identified NOx as the limiting precursor 

for ammonium nitrate formation. As a result, NOx emissions and PM2.5 nitrate levels 

track each other over the years. Trends in estimated NOx emissions, as well as 

monitored ambient concentrations, are compared with trends in measured PM2.5 nitrate 

concentrations. As illustrated in Figure 49, between 2004 and 2011, Valley NOx 

emissions decreased by about one third, with a commensurate reduction of 30 percent 

in PM2.5 nitrate concentrations. Furthermore, the reductions in NOx emissions were 

also reflected in the corresponding reduction in the ambient gaseous NOx 

concentrations. Figures 50 and 51 show a strong correlation between trends in PM2.5 

nitrate concentrations and ambient NOx concentrations at the Bakersfield and Fresno 

sites. Between 2004 and 2011, concentrations of both PM2.5 nitrate and NOx 

decreased approximately 30 percent. 

Figure 49. Comparison between trends in Valley wide winter average NOx emission 

and PM2.5 nitrate concentrations at Bakersfield and Fresno. Emissions and 

concentrations are presented as three-year winter averages. 

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Winter average PM2.5 nitrate at Bakersfield and Fresno 

compared to basin-wide NOx emissions

NOx

Emissions

BAC NO3-

FSF NO3-

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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Figure 50. Comparison of trends in wintertime PM2.5 nitrate and NOx concentrations 

in Bakersfield. Concentrations are presented as three-year winter averages. 

Figure 51. Comparison of trends in wintertime PM2.5 nitrate and NOx concentrations 

in Fresno. Concentrations are presented as three-year winter averages. 

Because the PM2.5 chemical speciation network is just over ten years old, data from 

the PM10 ion analysis network were also used to assess longer-term trends. Although, 

the earlier data do not meet the strict quality assurance/quality control requirements of 

the PM2.5 chemical speciation network, they do provide a historical perspective. The 

highest PM10 nitrate concentrations were measured in the Valley in early 90’s. Since 

then, concentrations of both PM10 nitrate and NOx have decreased about 60 percent 

(Figure 52). The yearly variability in the ammonium nitrate concentrations reflects the 

effects of the varying meteorology on ammonium nitrate formation. 

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Comparison of winter average concentrations

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San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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Figure 52. Long-term trends in three-year average concentrations of PM10 nitrate and 

NOx in the San Joaquin Valley. 

b. Residential wood burning controls 

As previously discussed, annual average concentrations of PM2.5 carbon components 

have decreased about 20 to 30 percent since 2002. The decrease in the carbon 

component reflects substantial benefits from the implementation of District Rule 4901, 

which prohibits residential wood-burning on days when high concentrations of PM2.5 

are predicted. In addition, as part of the District’s stringent smoke management 

program, agricultural burning is prohibited on those same days. Through a series of 

Rule 4901 amendments, the PM2.5 threshold for calling no-burn days was established 

in 2003 at 65 μg/m3 and subsequently tightened to 30 μg/m3 in 2008. District staff 

analyzed the effect that Rule 4901 has had on PM2.5 in the Fresno area using a 

statistical model developed to quantify PM2.5 reductions attributable to the 2003 and 

2008 rule amendments. Based on PM2.5 concentration relationships to meteorological 

variables before the wood-burning curtailments came into effect, the model predicts 

what the PM2.5 concentrations would have been if the curtailments had not been 

adopted. These expected concentrations are then compared to the measured 

concentrations. This analysis is further described in the 2012 SJV PM2.5 Plan’s 

Appendix A. 

The analysis results indicate that as of the 2011-2012 wood-burning season, 24-hour 

average PM2.5 levels in Fresno have improved by 41 percent (21 μg/m3) since the 2003 

and 2008 amendments to Rule 4901 (Figure 53). This improvement is especially 

marked in PM2.5 concentrations measured during the evening hours of 8:00 p.m. to 

12:00 a.m. The average evening PM2.5 concentrations have improved by 50 percent 

(42 μg/m3) over the same time period. As shown in this analysis, the 2008 amendment 

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1987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011

NOx (pphm)

PM10 Nitrate (ug/m3)

Three-year Average Concentrations

of PM10 Nitrate and NOx in San Joaquin Valley

NO3-

NOx

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to Rule 4901 has approximately doubled the seasonal improvements in PM2.5 

attributable to the 2003 amendments. 

Rule 4901 will continue to play an important role in reducing PM2.5 concentrations 

throughout the San Joaquin Valley both within and beyond the timeframe of this plan. 

Figure 53: Effect of SJVAPCD’s Rule 4901 on PM2.5 concentrations in Fresno. 

 

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10. MODELED ATTAINMENT DEMONSTRATION 

a. Modeling results 

Consistent with U.S. EPA guidelines, air quality modeling was done to predict future 

PM2.5 concentrations at each monitoring site in the San Joaquin Valley. This modeling 

shows attainment of the 24-hour PM2.5 standard by 2019 in all counties except Kings 

and Kern, based on implementation of the ongoing control program. In these counties, 

additional focused emission reductions are needed to provide for attainment. As 

required by U.S. EPA, additional analyses has been done to confirm that attainment is 

predicted throughout each county (i.e. in each modeled grid cell). The “Attainment 

Demonstration” chapter of the District’s plan provides an overview of the photochemical 

modeling performed. Additional information on the periods modeled, the models 

selected, and model application can be found in the Modeling Protocol document 

prepared for this effort. 

The air quality modeling analysis includes new emission reductions each year between 

now and 2019 from implementation of a combination of adopted ARB and District 

programs. As a result, most sites in the northern and central Valley are expected to 

attain by 2019. As required by U.S. EPA, the modeling replicates the base year 2007 

meteorological conditions for each calendar day in the year 2019. The 2007 

meteorological conditions included several periods of time especially conducive to the 

formation of PM2.5. 

Given the past effectiveness of District programs to curtail residential wood burning, 

ARB staff then modeled a scenario with an enhanced curtailment program, which would 

be designed to prevent wood burning on days that may lead up to a PM2.5 exceedance. 

The modeling results for this scenario indicate that only one site (Bakersfield-California) 

would not attain the standard with this additional level of control. The predicted design 

values for each site from this modeling scenario are shown in Table 4. 

Table 4. 2019 Modeled 24-hour PM2.5 Design Values.

Monitoring Site Design Value (μg/m3) 

Bakersfield - California 35.7 

Bakersfield - Planz 32.9 

Corcoran - Patterson 32.1 

Visalia - N. Church 29.4 

Fresno - Hamilton 28.6 

Fresno-1st 30.5 

Clovis 28.6 

Merced 22.6 

Modesto 24.7 

Stockton 21.4 

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b. Benefits of emission reductions from on-going programs 

The implementation of new reductions from California’s on-going emission control 

programs will provide the major portion of the emission reductions needed to attain the 

24-hour PM2.5 standard throughout the San Joaquin Valley in 2019. The PM2.5 design 

value at the Bakersfield-California site must decrease by approximately 45 percent to 

demonstrate attainment. Between 2007, the base year used in the photochemical 

modeling attainment demonstration, and 2019, implementation of these control 

programs will reduce NOx emissions by 55 percent. Previous sections of this WOE 

document have demonstrated that prior reductions in NOx have resulted in 

commensurate reductions in ambient concentrations of nitrate. This is consistent with 

modeled predictions that demonstrate a nearly 45 percent reduction in ammonium 

nitrate concentrations. In addition, while directly emitted PM2.5 emissions in aggregate 

are decreasing by nearly 30 percent, a major focus of the attainment control strategy is 

further curtailment of residential wood burning. Ambient measurements and modeling 

studies have shown the large contribution that residential wood burning has on PM2.5 

exceedance days. In addition, prior District analysis has demonstrated the significant 

benefits of past implementation of wood burning curtailment. Therefore, the substantial 

continuing reductions that will result from implementation of the ongoing control 

program, coupled with an enhanced residential burning curtailment program, are 

consistent with the benefits predicted in the modeled attainment demonstration. 

As a result of the overall control program, coupled with the enhanced wood burning 

curtailment measure, ammonium nitrate concentrations are predicted to decrease by 

nearly 45 percent, organic carbon concentrations by approximately 65 percent, and 

elemental carbon concentrations by nearly 80 percent. A comparison of the 

concentrations of the main chemical constituents in 2007 to that predicted in 2019 at 

three sites (Modesto, Fresno-1st, and Bakersfield-California) illustrates the significant 

reductions in these components (Table 5). 

Table 5. Comparison of the concentration of chemical constituents for 2007 and 2019 

design values at selected sites. 

Component 

(ug/m3) 

Bakersfield – Calif. Fresno-1st Modesto 

 2007 2019 2007 2019 2007 2019 

Ammonium 

Nitrate 

41.1 22.6 32.1 17.0 28.5 15.6

Ammonium 

Sulfate 

4.7 4.4 3.2 2.5 3.1 2.7

Organic 

Carbon 

15.2 6.6 22.9 8.9 19.7 4.6

Elemental 

Carbon 

2.2 0.5 2.8 0.6 1.6 0.3

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c. Evaluation of precursor sensitivity 

Effectiveness of Valley wide emission reductions 

In order to determine where to focus the remaining emission reductions needed to bring 

Bakersfield-California into attainment, as well as identify the attainment plan precursors, 

ARB staff conducted additional modeling sensitivity runs to assess the relative efficacy 

of further reductions of different PM2.5 precursors. U.S. EPA’s PM2.5 implementation 

rule specifies that a precursor is considered “significant” for control strategy 

development purposes when a significant reduction in the emissions of that precursor 

pollutant leads to a significant decrease in PM2.5 concentrations. Such pollutants are 

known as “PM2.5 attainment plan precursors” (72 FR 20586). The U.S. EPA’s 

implementation rule also establishes a presumption that PM2.5, NOx, and SOx are 

attainment plan precursors, while VOCs and ammonia are not. In the past for the 

annual PM2.5 plan, PM2.5, NOx, and SOx were identified and approved as the only 

attainment plan precursors by U.S. EPA. Results of the annual PM2.5 modeling 

showed that of these three pollutants, reductions in directly emitted PM2.5 was the most 

effective. However, because emissions change over time, it is important to continue to 

assess the attainment plan precursors each time a plan is developed. 

Additional photochemical modeling analyses were therefore conducted to understand 

the relative effectiveness of emission reductions for primary PM2.5 and precursors 

throughout the Valley in 2019. In these analyses, the model was run with varying 

combinations of valley wide precursor emission reductions from anthropogenic sources: 

 NOx vs. PM2.5 

 NOx vs. Ammonia 

 NOx vs. VOCs 

 NOx vs. SOx 

Table 6 compares the modeled effect on the 2019 design value obtained at each 

monitoring site from a 25 percent reduction in the specified precursor. Consistently, 

direct PM2.5 productions have the most benefit, followed by NOx reductions. 

Reductions in ammonia and SOx provide much smaller benefits, while reductions in 

VOCs result in very small disbenefits at many sites. Table 7 presents this same 

information, but normalized to reflect the reduction in design value per ton of each 

precursor reduced. On this basis, valley wide reductions in PM2.5 are approximately 

four times as effective as NOx, and approximately five times as effective as SOx. In 

contrast, reductions in ammonia are approximately nine times less effective than NOx, 

and as noted above, reductions in VOCs result in either no impact of very small 

disbenefits. 

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Table 6. Modeled reduction in 2019 PM2.5 design value resulting from 25 percent 

reduction in valley wide precursor emissions. 

Monitoring Site 

PM2.5 Reduction (μg/m3) 

Primary 

PM2.5 

NOx Ammonia SOx VOC 

Bakersfield -California 4.44 3.75 0.55 0.18 - 0.10 

Bakersfield-Planz 3.80 3.64 0.58 0.19 -0.06 

Visalia 3.51 3.10 0.37 0.09 -0.06 

Corcoran 3.34 3.99 0.70 0.08 -0.20 

Fresno-1st 4.12 2.62 0.51 0.09 0.03 

Fresno-Hamilton 3.73 2.57 0.50 0.11 0.05 

Clovis 3.29 3.17 0.55 0.09 0.00 

Modesto 2.49 1.76 0.43 0.17 0.03 

Merced 2.54 2.31 0.34 0.11 -0.01 

Stockton 1.87 1.30 0.48 0.20 0.03 

Table 7. Modeled PM2.5 air quality benefit per ton of valley wide precursor emission 

reductions. 

Monitoring Site 

PM2.5 Benefit (μg/m3 per ton reduction) 

Primary 

PM2.5 NOx Ammonia SOx VOC 

Bakersfield-California 0.34 0.08 0.008 0.08 -0.001 

Bakersfield-Planz 0.29 0.08 0.009 0.08 -0.001 

Visalia 0.27 0.07 0.005 0.04 -0.001 

Corcoran 0.25 0.09 0.010 0.04 -0.003 

Fresno-1st 0.31 0.06 0.008 0.04 0.000 

Fresno-Hamilton 0.28 0.06 0.007 0.05 0.001 

Clovis 0.25 0.07 0.008 0.04 0.000 

Modesto 0.19 0.04 0.006 0.08 0.000 

Merced 0.19 0.05 0.005 0.05 0.000 

Stockton 0.14 0.03 0.007 0.09 0.000 

 

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66 

The results of these modeling sensitivity runs were also plotted on isopleth diagrams 

which reflect the change in the 2019 design value at each level of emission reduction. 

Isopleth diagrams for the Bakersfield-California site are shown in Figures 54 (a) through 

(d) to illustrate the overall nature of the modeled response: 

 While reducing SOx results in less sulfuric acid and subsequent ammonium 

sulfate formation, SOx reductions have only a small effect on the predicted 

design value since ammonium sulfate is a small component of measured PM2.5. 

 Reducing VOCs leads to very small increases in the design value because these 

reductions have the effect of making more NOx available for nitric acid, and 

subsequent ammonium nitrate formation. 

 Because ammonia is much more abundant than NOx, the atmosphere is more 

responsive to reductions in NOx as compared to ammonia. Reductions in NOx in 

turn have significant benefits as ammonium nitrate is a large component of 

measured PM2.5. 

 Reductions in directly emitted PM2.5 result in significant benefits due to the 

reduction in organic carbon which is a large component of measured PM2.5. 

These modeling results, along with the findings from past modeling and monitoring 

studies highlight that reductions in directly emitted PM2.5 and NOx provide the greatest 

benefit in further reducing PM2.5 concentrations and making progress towards 

attainment. Given that significant reductions in VOCs and ammonia do not provide 

significant air quality benefits, per U.S. EPA guidance, the 24-hour PM2.5 attainment 

plan precursors are directly emitted PM2.5, NOx, and SOx. 

 

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Figure 54. Bakersfield–California Isopleth Diagrams.

 

b) NOx vs. VOC

c) NOx vs. Ammonia d) NOx vs. PM2.5

a) NOx vs. SOx

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Effectiveness of localized emission reductions 

The valley wide precursor sensitivity modeling demonstrates that on a relative basis the 

greatest benefits are achieved from reductions in sources of directly emitted PM2.5, 

followed by NOx. Due to the stagnant conditions that occur during wintertime episodes, 

and the local nature of directly emitted PM2.5 carbon sources in particular, Kern County 

specific model sensitivity runs were also conducted to evaluate the benefits of emission 

reductions focused on the nonattainment sub-area. The Kern County sensitivity runs 

demonstrated that: 

 One ton per day of directly emitted PM2.5 reductions provides a 1 μg/m3

improvement in the Bakersfield-California design value; 

 One ton per day of NOx reductions provides for a 0.12 μg/m3 improvement in the 

Bakersfield-California design value; 

 One ton per day of SOx reductions provides for a 0.21 μg/m3

 improvement in the 

Bakersfield-California design value; 

 One ton per day of ammonia reductions provides for a 0.02 μg/m3 improvement 

in the Bakersfield-California design value; and 

 One ton per day of VOC reductions has no effect on the Bakersfield-California 

design value. 

An examination of sources surrounding the Bakersfield-California monitoring site was 

then conducted in order to identify potential PM2.5 and NOx sources for further control. 

The forecasted 2019 PM2.5 and NOx gridded emission inventories were evaluated, 

focusing on the winter months of November through February when the majority of 

PM2.5 exceedances occur. The top five emission sources of PM2.5 and NOx in the 

9 grid cells (3x3 grid cells, each measuring 4 km x4 km) centered on the BakersfieldCalifornia monitoring site are shown in Figures 55 and 56. The main combustion 

sources of PM2.5 are commercial cooking, residential fuel combustion, and on-road 

vehicles. The main NOx source is on-road vehicles, with smaller contributions from offroad equipment, residential fuel combustion, and trains. This analysis suggests that for 

PM2.5, a focused effort to further reduce residential wood burning and limit emissions 

from commercial cooking operations would have significant benefits in reducing PM2.5 

concentrations in the Bakersfield area. Key NOx sources include on- and off-road 

mobile sources which are already the focus of ongoing control programs. 

 

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Figure 55. 2019 top five wintertime PM2.5 emission sources within the BakersfieldCalifornia 9-grid cell area (3x3 grid cells, each measuring 4 km x 4 km with the 

Bakersfield-California monitor located in the center cell). Wintertime emissions 

expressed as an average of January, February, November and December emissions. 

Figure 56. 2019 top five wintertime NOx emission sources within the BakersfieldCalifornia 9-grid cell area (3x3 grid cells, each measuring 4 km x 4 km with the 

Bakersfield-California monitor located in the center cell). Wintertime emissions 

expressed as an average of January, February, November and December emissions. 

0.0

0.1

0.2

0.3

0.4

0.5

PM2.5 Emissions (tpd)

2019 top Five Winter PM2.5 Emission Sources withih the 

Bakersfield-California Nine Grid Cells

0

1

2

3

4

5

6

NOx Emissions (tpd) 

2019 Top Five Winter NOx Emission Sources within 

Bakersfield-California Nine Grid Cells

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d. Demonstrating attainment at Bakersfield-California 

While adoption of a more stringent wood burning curtailment program brings the 

Bakersfield-California site very near attainment, further reductions are needed to meet 

the attainment target of 35.4 μg/m3. Based upon the precursor sensitivity analysis and 

evaluation of the localized inventory discussed in the previous section, further control of 

PM2.5 emissions from commercial cooking operations was identified as the most 

effective approach to provide the emission reductions needed to reach attainment. The 

final attainment demonstration for the Bakersfield-California design site is provided in 

Table 8 below: 

Table 8. Attainment Demonstration for the Bakersfield-California Design Value Site.

2007 Design Value 

(μg/m3) 

2019 Design Value 

with Wood 

Burning Program 

Enhancement 

(μg/m3) 

2019 Final Design 

Value (μg/m3) 

65.6 35.7 ≤35.4 

Note: The benchmark for attainment is a design value that is equal to 

or less than 35.4 μg/m3. 

As noted above, the design value in the center column of the table reflects the 

implementation of ongoing control programs, as well as implementation of an enhanced 

residential wood burning curtailment program. The final design value reflects the 

combined impact of further reductions in commercial cooking, as well as a small 

increase in motor vehicle emissions due to updated vehicle activity data from the San 

Joaquin Valley Metropolitan Planning Organizations (MPOs). Based on a modeling 

sensitivity run, implementation of further controls on commercial cooking is expected to 

result in a 0.6 μg/m3

 reduction in the baseline design value. The revised MPO activity 

data represents approximately one percent of Valley wide NOx emissions. Based on 

modeling sensitivity runs, this is estimated to result in a design value increase of 

0.2 μg/m3

. In aggregate, the modeling demonstrates a design value that meets 

U.S. EPA’s attainment target of 35.4 μg/m3

. 

 

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11. SUMMARY 

Consideration of the entirety of information presented in the weight of evidence provides 

a consistent assessment that supports the modeled attainment date of 2019. The 

substantial continuing reductions that will result from implementation of the ongoing 

control program, coupled with new measures addressing residential wood burning and 

cooking, are consistent with the results predicted in the modeled attainment 

demonstration. This weight of evidence assessment is based upon the following 

factors: 

 Over the last decade significant progress has occurred in reducing 24-hour PM2.5 

concentrations. The 24-hour design value has decreased by over 30 μg/m3

, while 

the number of exceedance days has declined by nearly 50 percent. 

Meteorologically adjusted trends for the Bakersfield area show an even greater 

reduction in exceedance days, with a decline of over 60 percent. 

 Evaluation of the air quality model response to emission reductions, as well as 

model sensitivity runs demonstrates that reductions in directly emitted PM2.5 have 

the greatest impact per ton of emissions, followed by NOx. For example, in Kern 

County, PM2.5 emission reductions are approximately eight times more effective 

than NOx. 

 Both receptor and photochemical grid based modeling have identified residential 

wood burning as a significant contributor to wintertime PM2.5 concentrations. The 

reductions in the organic carbon component of PM2.5 that have occurred can be 

linked to implementation of the District’s residential wood burning curtailment 

program. 

 Evaluation of emissions inventory data, monitoring studies, and photochemical 

modeling indicate that controlling NOx emissions is the most effective strategy to 

reduce ammonium nitrate concentrations. 

 The decrease in ammonium nitrate concentrations observed at Valley monitoring 

sites tracks concurrent reductions in NOx emissions as well as trends in gaseous 

NOx concentrations. 

 Substantial NOx and PM2.5 emission reductions will occur between 2007 and 2019 

due to the implementation of on-going measures and additional new measures. As 

a result of these programs, NOx emissions will decrease by over 50 percent, and 

PM2.5 emissions by nearly 30 percent. 

 The modeled attainment demonstration predicts that all sites in the Valley will attain 

by 2019. This modeling assessment is consistent with the benefits seen from 

previous reductions in the sources and pollutants being addressed as part of the 

attainment strategy. 

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Stockwell, W.R., Watson, J.G., Robinson, N.F., Steiner, W., and Sylte, W.W., 2000, The 

ammonium nitrate particle equivalent of NOx emissions for wintertime conditions in 

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airborne particulate matter, Atmospheric Environment, 43, 419-430. 

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mass response to sulfate reductions in the Eastern United States, J. Air & Waste 

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Watson, J., Roth, P.: Evolving Science in Central California-Why do we need air quality 

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San Joaquin Valley PM2.5 Weight of Evidence Analysis 

Appendix 1 

California Regional PM10/PM2.5 Air Quality Study 

Publications 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
CALIFORNIA REGIONAL PM10/PM2.5 

AIR QUALITY STUDY 

PUBLICATIONS 

1. Roth, P. M. et al., 1993, “A Proposed PM-10 Program for the San Joaquin Valley”, prepared by 

Envair for the San Joaquin Valleywide Air Pollution Study Agency. 

2. Gray, H.A. et al. 1995, “San Joaquin Valley Regional PM10 Study Technical Support Study 5A: 

Characterizing Micrometeorological Phenomena: Mixing and Diffusion in Low Wind 

Speed Stable Conditions”, prepared by Systems Applications International for the San 

Joaquin Valleywide Air Pollution Study Agency. 

3. Gillies, J. et al., 1995, “Literature Review Plan for the San Joaquin Valley Regional Particulate 

Study Technical Support Study No. 5: Particle Resuspension”, prepared by the Desert 

Research Institute for the San Joaquin Valleywide Air Pollution Study Agency. 

4. Gillies, J. et al., 1995, “Resuspension of Particles by the Wind: A Literature Review for the San 

Joaquin Valley Regional Particulate Study Technical Support Study No. 5: Particle 

Resuspension”, prepared by the Desert Research Institute for the San Joaquin 

Valleywide Air Pollution Study Agency. 

5. Gillies, J. et al., 1996, “Resuspension of Particles by the Wind: Research Initiatives for California 

Regional Particulate Study Technical Support Study No. 5: Particle Resuspension”, 

prepared by the Desert Research Institute for the San Joaquin Valleywide Air Pollution 

Study Agency. 

6. Gillies, J. et al., 1996, “Resuspension of Particles by the Wind: Modeling, Data Analysis Initiatives 

for California Regional Particulate Study Technical Support Study No. 5: Particle 

Resuspension”, prepared by the Desert Research Institute for the San Joaquin 

Valleywide Air Pollution Study Agency. 

7. Hagan, C. et al., 1996, “California Regional PM10 Air Quality Study: Emissions Modeling Plan”, 

prepared by Envair for the San Joaquin Valleywide Air Pollution Study Agency. 

8. Roth, P. M. et al., 1996, “Revisions to Cost Estimates for the Central California Fine Particle 

Study”, prepared by Envair for the San Joaquin Valleywide Air Pollution Study Agency. 

9. Watson, J. et al., 1996, “Data Analysis Activities for the 1995 Integrated Monitoring Study (IMS95) 

of the California Regional Particulate Matter Study (CRPAQS)”, prepared by Desert 

Research Institute for the San Joaquin Valleywide Air Pollution Study Agency. 

10. Kumar, N. et al., 1996, “User’s Guide to the UAM-AERO Model”, prepared by Sonoma Technology 

Inc. for the San Joaquin Valleywide Air Pollution Study Agency. 

11. Kumar, N. et al., 1996, “User’s Guide to the Speciated Rollback Model for Particulate Matter”, 

prepared by Sonoma Technology Inc. for the San Joaquin Valleywide Air Pollution 

Study Agency. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
12. Lurmann, F. et al., 1996, “PM10 Air Quality Models for Application in the San Joaquin Valley 

PM10 SIP”, prepared by Sonoma Technology, Inc. for the San Joaquin Valleywide Air 

Pollution Study Agency. 

13. Lehrman, D. E. et al., 1996, “SARMAP II Design: Analysis of the San Joaquin Valley 

Meteorological Environment During High PM10 Loading”, prepared by Technical & 

Business Systems, Inc. for the San Joaquin Valleywide Air Pollution Study Agency. 

14. Rogge, W., 1996, “Feasibility of Using Organic Tracers for Tracking the Fate of Primary and 

Secondary Aerosols from Combustion Sources”, prepared by Florida International 

University for the San Joaquin Valleywide Air Pollution Study Agency. 

15. Chinkin, L. R. et al., 1996, “Final Workplan for Evaluation and Improvement of Methods for 

Determining Ammonia Emissions in the San Joaquin Valley Technical Support Study 

5”, prepared by Sonoma Technology, Inc. for the San Joaquin Valleywide Air Pollution 

Study Agency. 

16. Chinkin, L. R., 1996, “CRPAQS TSS15: Task 3, Initial Ammonia Inventory for the San Joaquin 

Valley”, prepared by Sonoma Technology, Inc. for the San Joaquin Valleywide Air 

Pollution Study Agency. 

17. Chinkin, L. R. et al., 1996, “Review of Current Methodologies for Estimating Ammonia Emissions”, 

prepared by Sonoma Technology, Inc. for the San Joaquin Valleywide Air Pollution 

Study Agency. 

18. Watson, J. G. et al., 1996, “Final Study Plan for: Effectiveness Demonstration of Fugitive Dust 

Control Methods for Public Unpaved Roads and Unpaved Shoulders on Paved Roads”, 

prepared by the Desert Research Institute for the San Joaquin Valleywide Air Pollution 

Study Agency. 

19. Watson, J. G. et al., 1996, “Final Report: Effectiveness Demonstration of Fugitive Dust Control 

Methods for Public Unpaved Roads and Unpaved Shoulders on Paved Roads”, 

prepared by Desert Research Institute for the San Joaquin Valleywide Air Pollution 

Study Agency. 

20. Solomon, P.A. et al., 1996, “1995 Integrated Monitoring Study: Objectives and Design”, presented 

at the 1996 U.S. EPA/A&WMA International Symposium on Measurement of Toxic 

and Related Air Pollutants, Research Triangle Park, NC. 

21. Magliano, K.L. et al., 1996, “1995 Integrated Monitoring Study: Temporal Variability of Particulate 

Matter - Preliminary Results”, presented at the 1996 U.S. EPA/A&WMA International 

Symposium on Measurement of Toxic and Related Air Pollutants, Research Triangle 

Park, NC. 

22. Solomon, P.A. et al., 1996, “1995 Integrated Monitoring Study: Spatial and Temporal Variability of 

PM10 During the Fall Saturation Study - Preliminary Results”, presented at the 1996 

U.S. EPA/A&WMA International Symposium on Measurement of Toxic and Related Air 

Pollutants, Research Triangle Park, NC. 

23. Coe, D. et al., 1996, “Emission Activity Measurement During the Fall andd Winter IMS95 

Components - Preliminary Results”, presented at the 1996 U.S. EPA/A&WMA 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
International Symposium on Measurement of Toxic and Related Air Pollutants, 

Research Triangle Park, NC. 

24. Bush, D.H. et al., 1996, “IMS95 Winter Study - Quality Assurance”, presented at the 1996 U.S. 

EPA/A&WMA International Symposium on Measurement of Toxic and Related Air 

Pollutants, Research Triangle Park, NC. 

25. Collett, J. et al., 1996, “1995 Integrated Monitoring Study: Fog Measurements in the Southern 

Valley - Preliminary Results”, presented at the 1996 U.S. EPA/A&WMA International 

Symposium on Measurement of Toxic and Related Air Pollutants, Research Triangle 

Park, NC. 

26. Collett, J. et al., 1996, “1995 Integrated Monitoring Study: Fog Measurements in the Northern 

Valley - Preliminary Results”, presented at the 1996 U.S. EPA/A&WMA International 

Symposium on Measurement of Toxic and Related Air Pollutants, Research Triangle 

Park, NC. 

27. Gray, H.A., 1996, “1995 Integrated Monitoring Study: Low Wind Speed Study - Preliminary 

Results”, presented at the 1996 U.S. EPA/A&WMA International Symposium on 

Measurement of Toxic and Related Air Pollutants, Research Triangle Park, NC. 

28. Lindsey, C. et al., 1996, “Data Collected by a Network of Radar Profilers, Rawinsonde Sounding 

Stations, and Surface Meteorological Monitoring Sites During IMS95”, prepared by 

Sonoma Technology Inc. for the San Joaquin Valleywide Air Pollution Study Agency. 

29. Barnett, A. et al., 1996, “Integrated Measurements Study - 1995 Audit Program Final Report”, 

prepared by Aerovironment Environmental Services Inc. for the San Joaquin 

Valleywide Air Pollution Study Agency. 

30. Bush, D. et al., 1996, “IMS95 Winter Study Meteorological Measurements Audits”, prepared by 

Aerovironment Environmental Services, Inc. for the San Joaquin Valleywide Air 

Pollution Study Agency. 

31. Ashbaugh, L. A. et al., 1996, “Road Silt Loadings in the San Joaquin Valley During IMS-95”, 

prepared by U.C. Davis for the San Joaquin Valleywide Air Pollution Study Agency. 

32. Wilson, M., 1996, “Traffic Counts in Support of the 1995 Integrated Monitoring Study”, prepared by 

WILTEC for the San Joaquin Valleywide Air Pollution Study Agency. 

33. Wilson, M., 1996, “Traffic Counts in Support of the 1995 Integrated Monitoring Study - Axle 

Classification Counts”, prepared by WILTEC. 

34. Coe, D. et al., 1996, “IMS95 Daily Activity Surveys”, prepared by Sonoma Technology Inc. for the 

San Joaquin Valleywide Air Pollution Study Agency. 

35. Roberts, P. et al., 1996, “Collection of Continuous Gas-Phase Data at Two Sites During IMS95”, 

prepared by Sonoma Technology Inc. for the San Joaquin Valleywide Air Pollution 

Study Agency. 

36. Westerinin, A. et al., 1996, “Final Audit Results for the Integrated Monitoring Study”, prepared by 

the Air Resources Board for the San Joaquin Valleywide Air Pollution Study Agency. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
37. Watson, J. G. et al., 1997, “The Fugitive Dust Characterization Study (FDCS): Potential Methods 

and Research Needs for Receptor Methods to Distinguish Among Fugitive Dust 

Sources”, prepared by Desert Research Institute for the San Joaquin Valleywide Air 

Pollution Study Agency. 

38. Watson, J. G. et al., 1997, “Analysis of Historical PM10 and PM2.5 Measurements in Central 

California”, prepared by Desert Research Institute for the San Joaquin Valleywide Air 

Pollution Study Agency. 

39. Ondov, J., 1997, “Technical Support Study No. 13: Feasibility of Using Rare Earth Isotope and 

Activatable Tracers for Tracking the Fate of Primary and Secondary Aerosols from 

Combustion Sources”, prepared by John Ondov for the San Joaquin Valleywide Air 

Pollution Study Agency. 

40. Pankratz, D., 1997, “Quality Assurance Project Plan for Evaluation and Improvement of Methods 

for Determining Ammonia Emissions in the San Joaquin Valley”, prepared by 

Aerovironment for the San Joaquin Valleywide Air Pollution Study Agency. 

41. Richards, L. W., 1997, “Technical Support Study 11: Investigating the Dynamics and Chemistry of 

Fog Formation and Dissipation - Literature Review”, prepared by Sonoma Technology 

Inc. for the San Joaquin Valleywide Air Pollution Study Agency. 

42. Collett, J. et al., 1997, “Technical Support Study 11: Investigating the Dynamics and Chemistry of 

Fog Formation and Dissipation - San Joaquin Valley Fog Measurements During the 

1995 Integrated Monitoring Study”, prepared by Colorado State University for the San 

Joaquin Valleywide Air Pollution Study Agency. 

43. Pankratz, D. et al., 1997, “Measurements Report for High Resolution Meteorological Monitoring in 

the San Joaquin Valley”, prepared by Aerovironment Environmental Service Inc. for the 

San Joaquin Valleywide Air Pollution Study Agency. 

44. Pandis, S. et al., 1997, “Technical Support Study 11: Investigating the Dynamics and Chemistry of 

Fog Formation and Dissipation Paper Copies of References Cited in the Review of 

Literature on Fogs”, prepared by Carnegie Mellon University for the San Joaquin 

Valleywide Air Pollution Study Agency. 

45. Richards, L. W. et al., 1997, “Technical Support Study 11: Investigating the Dynamics and 

Chemistry of Fog Formation and Dissipation Phase II Report Measurement at the 

Candalabra Tower”, prepared by Sonoma Technology Inc. for the San Joaquin 

Valleywide Air Pollution Study Agency. 

46. Pandis, S. et al., 1997, “Technical Support Study 11: Investigating the Dynamics and Chemistry of 

Fog Formation and Dissipation Fog Modeling”, prepared by Carnegie Mellon University 

for the San Joaquin Valleywide Air Pollution Study Agency. 

47. Chow, J. C. et al., 1997, “San Joaquin Valley 1995 Integrated Monitoring Study: Documentation, 

Evaluation, and Descriptive Data Analysis of PM10, PM2.5 and Precursor Gas 

Measurements Technical Support Studies No. 4 and 8”, prepared by the Desert 

Research Institute for the San Joaquin Valleywide Air Pollution Study Agency. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
48. Chow, J. C. et al., 1997, “Size-resolved Particle and Light Extinction Measurements During 

IMS95”, presented at the A&WMAVisual Air Quality - Aerosols and Global Radiation 

Balance Conference, Bartlett, NH. 

49. Solomon, P. A. et al., 1997. “Operational Field Monitoring Plan for the 1995/1996 Integrated 

Monitoring Study (IMS95) of the California Regional PM10 Air Quality Study 

(CRPAQS), Post Field Version, Draft Report”, prepared by Pacific Gas & Electric 

Company for the San Joaquin Valleywide Air Pollution Study Agency. 

50. Solomon, P. A. et al., 1997, “Site Description Report for the 1995 Integrated Monitoring Study 

(IMS95) of the California Regional PM10 Air Quality Study (CRPAQS), Post Field 

Version, Final Report”, prepared by Pacific Gas & Electric Company for the San 

Joaquin Valleywide Air Pollution Study Agency. 

51. Fitz, D., 1997, “Evaluation and Improvement of Methods for Determining Ammonia Emissions in 

the San Joaquin Valley Field Data Report”, prepared by University of California, 

Riverside CE-CERT for the San Joaquin Valleywide Air Pollution Study Agency. 

52. McDade, C., 1997, “Ims95 Data Analysis Draft Work Plan for Work Elements 7.3, 7.4, 7.5”, 

prepared by ENSR Consulting & Engineering for the San Joaquin Valleywide Air 

Pollution Study Agency. 

53. Magliano, K. L., 1997, “Work Plan for Chemical Mass Balance Modeling of IMS95 Data”, prepared 

by the California Air Resources Board for the San Joaquin Valleywide Air Pollution 

Study Agency. 

54. Blanchard, C., 1997, “California Regional PM10 Air Quality Study 1995 Integrated Monitoring 

Study Data Analysis Combined Workplan for Tasks 4.2.1 and 4.5.6”, prepared by 

Envair for the San Joaquin Valleywide Air Pollution Study Agency. 

55. Chinkin, L. R. et al., 1997, “IMS 95 Data Analysis Workplans for Tasks 4.1.2, 4.5.1, 4.5.3, 4.6.1, 

4.6.2, 4.7.1, and 4.7.2”, prepared by Sonoma Technology, Inc. for the San Joaquin 

Valleywide Air Pollution Study Agency. 

56. Collins, J., 1997, “IMS95 Data Analysis Workplan for Work Element 3.3 Boundaries and 

Backgrounds”, prepared by the University of California, Riverside, CE-CERT for the 

San Joaquin Valleywide Air Pollution Study Agency. 

57. Collet, J., 1997, “Work Plan for Work Element 4.6.7”, prepared by Colorado State University for 

the San Joaquin Valleywide Air Pollution Study Agency. 

58. Collet, J., 1997, “Work Plan for Work Element 4.6.8”, prepared by Colorado State University for 

the San Joaquin Valleywide Air Pollution Study Agency. 

59. Pandis, S., 1997, “Work Plan for Work Element 4.6.4”, prepared by Carnegie Mellon University for 

the San Joaquin Valleywide Air Pollution Study Agency. 

60. “Lehrman, D. L., 1997, “Work Plan for IMS95 Analysis Elements 2.2.2 and 2.2.3”, prepared by 

Technical & Business Systems, Inc. for the San Joaquin Valleywide Air Pollution Study 

Agency. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
61. McDade, C. E., 1997, “Draft Final Report IM95 Data Analysis Work Elements 7.3 Contributions of 

Chemical Constituents to Visibility Reduction, 7.4 Contributions of Chemical 

Constituents to Light Extinction Budget, 7.5 Contributions of Emission Sources to Light 

Extinction Budget”, prepared by ENSR Consulting and Engineering for the San Joaquin 

Valleywide Air Pollution Study Agency. 

62. Chinkin, L. R. et al., 1998, “Use of Ambient Data to Evaluate a Regional Emission Inventory in the 

San Joaquin Valley”, presented at the A&WMA PM2.5: A Fine Particle Standard 

Conference, Long Beach, CA. 

63. Coe, D. L. et al., 1998, “Technical Support Study 15: Evaluation and Improvement of Methods for 

Determining Ammonia Emissions in the San Joaquin Valley Final Report”, prepared by 

Sonoma Technology, Inc. for the San Joaquin Valleywide Air Pollution Study Agency. 

64. Hackney, R. A. et al., 1998, “Meteorological Data Analysis of Surface, Upper Air, and Synoptic 

Conditions during the Integrated Monitoring System 1995 (IMS95) Study Period, from 

November 1995 through January 1996”, presented at the A&WMA PM2.5: A Fine 

Particle Standard Conference, Long Beach, CA. 

65. Hughes, V. et al., 1998, “Development of the Baseline PM-10 Emissions Inventory for Modeling 

and Data Analysis of the IMS-95 Wintertime Field Study”, presented at the A&WMA 

PM2.5: A Fine Particle Standard Conference, Long Beach, CA. 

66. Hoag, K. et al., 1998, “Integrated Monitoring Study 1995: Variations in Fog Chemistry as a 

Function of Drop Size and Their Effects on Aerosol Processing”, presented at the 

A&WMA PM2.5: A Fine Particle Standard Conference, Long Beach, CA. 

67. Collett, J. L. et al., 1998, “Integrated Monitoring Study 1995: The Effect of Internal Acid Buffering 

on Aerosol Formation in San Joaquin Valley Fogs”, presented at the A&WMA PM2.5: A 

Fine Particle Standard Conference, Long Beach, CA. 

68. Schauer, J. J. et al., 1998, “Source Apportionment of Wintertime Gas-Phase and Particle-Phase 

Air Pollutants Using Organic Compounds as Tracers”, presented at the A&WMA 

PM2.5: A Fine Particle Standard Conference, Long Beach, CA. 

69. Kaduwela, A. P. et al., 1998, “Numerical Simulation of Particulate Matter in the San Joaquin 

Valley: Application of UAM-AERO to the January 4-6, 1996 Winter Episode”, presented 

at the A&WMA PM2.5: A Fine Particle Standard Conference, Long Beach, CA. 

70. Magliano, K. L. et al., 1998, “Chemical Mass Balance Modeling of the 1995 Integrated Monitoring 

Study Database”, presented at the A&WMA PM2.5: A Fine Particle Standard 

Conference, Long Beach, CA. 

71. Coe, D. L. et al., 1998, “The Use of a Day-Specific Source Activity Database to Augment CMB 

Source Apportionment Modeling”, presented at the A&WMA PM2.5: A Fine Particle 

Standard Conference; Long Beach, CA. 

72. Kumar, N. et al., 1998, “Evaluation of Gas-Aerosol Phase Distribution of Nitrogen and Sulfur in the 

IMS95 Winter Study”, presented at the A&WMA PM2.5: A Fine Particle Standard 

Conference, Long Beach, CA. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
73. Richards, L. W. et al., 1998, “1995 Integrated Monitoring Study: Comparison of Light Scattering 

Measurements During Winter in the San Joaquin Valley”, presented at the A&WMA 

PM2.5: A Fine Particle Standard Conference, Long Beach, CA. 

74. Dye, T. S. et al., 1998, “Mixing Depth Structure and Evolution During the Wintertime in the San 

Joaquin Valley as Diagnosed from Upper-Air Meteorological Data”, presented at the 

A&WMA PM2.5: A Fine Particle Standard Conference, Long Beach, CA. 

75. McDade, C. et al., 1998, “Optical Properties of the San Joaquin Valley Aerosol Collected During 

the 1995 Integrated Monitoring Study”, presented at the A&WMA PM2.5 : A Fine 

Particle Standard Conference, Long Beach, CA. 

76. Solomon, P.A. et al., 1998, “Overview of the 1995 Integrated Monitoring Study: Field Study, Data 

Analysis, and Modeling”, presented at the A&WMA PM2.5: A Fine Particle Standard 

Conference,; Long Beach, CA. 

77. Collett, J. L. et al., 1998, “1995 Integrated Monitoring Study: Measurements at Several Heights on 

a TV Tower During Fog Events in the San Joaquin Valley”, presented at the A&WMA 

PM2.5: A Fine Particle Standard Conference, Long Beach, CA. 

78. Blanchard, C. L. et al., 1998, “Spatial and Temporal Variability of Particulate Concentrations and 

Composition as Measured in Four Saturation Monitoring Networks During the 1995 

Integrated Monitoring Study in the San Joaquin Valley, California”, presented at the 

A&WMA PM2.5: A Fine Particle Standard Conference, Long Beach, CA. 

79. Collett, J. L. et al., 1998, “Final Report: IMS95 Data Analysis Work Element 4.6.8 How Does 

Acidification of Drops Due to Aqueous Phase Acid Production Limit Aerosol Formation 

in Fog Drops”, prepared by Colorado State University for the San Joaquin Valleywide 

Air Pollution Study Agency. 

80. Collett, J. L. et al., 1998, “Final Report: IMS95 Data Analysis Work Element 4.6.7 The Influence of 

Drop Size-Dependent Fog Chemistry on Aerosol Production and Deposition in San 

Joaquin Valley Fogs”, prepared by Colorado State University for the San Joaquin 

Valleywide Air Pollution Study Agency. 

81. Lehrman, D. E. et al., 1998, “Final Report California Regional PM10/PM2.5 Air Quality Study 

(CRPAQS) 1995 Integrated Monitoring Study Data Analysis Work Element 2.2.2 - 

Meteorological Representativeness and Work Element 2.2.3 - Fog And Low Clouds 

Characteristics”, prepared by Technical & Business Systems, Inc. for the San Joaquin 

Valleywide Air Pollution Study Agency. 

82. Strader, R. et al., 1998, “Final Report 1995 Integrated Monitoring Study Data Analysis 6.5 

Evaluation of Secondary Organic Aerosol Formation in Winter”, prepared by Carnegie 

Mellon University for the San Joaquin Valleywide Air Pollution Study Agency. 

83. Pandis, S. et al., 1998, “Final Report 1995 Integrated Monitoring Study Data Analysis Fog Effects 

on PM Concentration and Composition in the SJV”, prepared by Carnegie Mellon 

University for the San Joaquin Valleywide Air Pollution Study Agency. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
84. Blanchard, C. L. et al., 1998, “Final Report for Tasks 4.2.1 and 4.5.6 Spatial Representativeness 

of Monitoring Sites and Zones of Influence of Emission Sources”, prepared by Envair 

for the San Joaquin Valleywide Air Pollution Study Agency. 

85. Magliano, K. L. et al., 1998, “Final Report Chemical Mass Balance Modeling of Data from the 1995 

Integrated Monitoring Study”, prepared by the California Air Resources Board for the 

San Joaquin Valleywide Air Pollution Study Agency. 

86. Carr, E. L. et al., 1998, “Final Report San Joaquin Valley Regional PM-10 Study Characterizing 

Micrometeorological Phenomena: Mixing and Diffusion in Low Wind Speed Conditions 

Phase III: Monitoring and Data Analysis”, prepared by Systems Applications 

International for the San Joaquin Valleywide Air Pollution Study Agency. 

87. Dye, T. S. et al., 1998, “Measurement Methods Validation: Adequacy and Validation of 

Meteorological Measurements Aloft During IMS95”, prepared by Sonoma Technology, 

Inc. for the San Joaquin Valleywide Air Pollution Study Agency. 

88. Coe, D. L. et al., 1998, “Emission Source Activity Detection: Analysis of CMB Model Results and 

Daily Activity Data for IMS95”, prepared by Sonoma Technology, Inc. for the San 

Joaquin Valleywide Air Pollution Study Agency. 

89. Main, H. H. et al., 1998, “Characterization of the Spatial and Temporal Patterns of Visibility in the 

San Joaquin Valley During IMS95, prepared by Sonoma Technology, Inc. for the San 

Joaquin Valleywide Air Pollution Study Agency. 

90. Carr, E. L. et al., 1998, “1995 Integrated Monitoring Study Data Analysis Work Element 4.6.3: 

Time and Length Scales for Mixing of Secondary Aerosols During Stagnation Periods”, 

prepared by Systems Applications International for the San Joaquin Valleywide Air 

Pollution Study Agency. 

91. Collins, J. F., 1998, “Characterization of Boundaries and Backgrounds in the San Joaquin Valley 

Integrated Monitoring Study 1995”, prepared by University of California Riverside CECERT for the San Joaquin Valleywide Air Pollution Study Agency. 

92. Haste, T. L. et al., 1998, “Use of Ambient Data Collected During IMS95 to Evaluate a Regional 

Emissions Inventory for the San Joaquin Valley”, prepared by Sonoma Technology Inc. 

for the San Joaquin Valleywide Air Pollution Study Agency. 

93. Kumar, N., et al., 1998, “Analysis of Atmospheric Chemistry During 1995 Integrated Monitoring 

Study”, prepared by Sonoma Technology, Inc. for the San Joaquin Valleywide Air 

Pollution Study Agency. 

94. Magliano, K. L., et al., 1998, “Draft Modeling Protocol for the California Regional PM10/PM2.5 Air 

Quality Study: Preliminary Approaches and Data Requirements”, prepared by the 

California Air Resources Board for the San Joaquin Valleywide Air Pollution Study 

Agency. 

95. Pun, B. et al., 1998, “Comments on Field Program Plan for the California Regional PM10/PM2.5 

Air Quality Study”, prepared by Atmospheric and Environmental Research for Pacific 

Gas & Electric. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
96. Pun, B. et al., 1998, “Phase II Reports Integrated Monitoring Study 95: Assessment of 

Mathematical Models and Associated Data Needs”, prepared by Atmospheric and 

Environmental Research for Pacific Gas & Electric. 

97. Pun, B. et al., 1998, “Conceptual Model of Particulate Matter Pollution in the California San 

Joaquin Valley”, prepared by Atmospheric and Environmental Research for Pacific Gas 

& Electric. 

98. Richards, W. L. et al., 1998, “Characterization of the Validity of Light Scattering Measurements 

During the 1995 Integrated Monitoring Study”, prepared by Sonoma Technology Inc. 

for the San Joaquin Valleywide Air Pollution Study Agency. 

99. Schauer, J. J. et al., 1998, “Source Contributions to Airborne Particles in the San Joaquin Valley 

During the IMS95 Study”, prepared by California Institute of Technology for the San 

Joaquin Valleywide Air Pollution Study Agency. 

100. Seinfeld, J. H. et al., 1998, “Grid-Based Aerosol Modeling: A Tutorial”, prepared by California 

Institute of Technology for the San Joaquin Valleywide Air Pollution Study Agency. 

101. Watson, J. G. et al., 1998, “Aerometric Field Program Plan for the California Regional 

PM2.5/PM10 Air Quality Study”, prepared by Desert Research Institute for the San 

Joaquin Valleywide Air Pollution Study Agency. 

102. Wilkinson, J. G., et al., 1998, “A Model to Estimate Temporally Resolved Ammonia Emissions 

at a Dairy”, presented at the A&WMA 91st Annual Meeting, San Diego, CA. 

103. Richards, W. L. et al., 1999, “Effects of Atmospheric Dynamics and Chemical Concentrations 

on Aerosol Formation in Fog ” prepared by Sonoma Technology, Inc. for the San 

Joaquin Valleywide Air Pollution Study Agency. 

104. Magliano, K. L. et al., 1999, “California Regional PM10/PM2.5 Air Quality Study: Objectives 

and Associated Data Analysis and Modeling Approaches”, prepared by the California 

Air Resources Board for the San Joaquin Valleywide Air Pollution Study Agency. 

105. Chow, J.C. et al., 1999, “Temporal Variations of PM2.5, PM10, and Gaseous Precursors 

During the 1995 Integrated Monitoring Study in Central California”, Atmospheric 

Environment (33).

106. Collett, J. L. et al., 1999, “Spatial and Temporal Variations in San Joaquin Valley Fog 

Chemistry”, Atmospheric Environment (33). 

107. Magliano, K. L. et al., 1999, “Recommendations for Emissions Work in Support of the 

California Regional PM10/PM2.5 Air Quality Study”, prepared by the California Air 

Resources Board for the San Joaquin Valleywide Air Pollution Study Agency. 

108. Solomon, P. A. et al., 1999, “The 1995 Integrated Monitoring Study (IMS95) of the California 

Regional PM10/PM2.5 Air Quality Study (CRPAQS): Study Overview”, Atmospheric 

Environment (33).

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
109. Solomon, P. A. et al., 1999, “Objectives and Design of Central California’s 1995 Integrated 

Monitoring Study of the California Regional PM10/PM2.5 Air Quality Study”, Journal 

A&WMA.

110. Magliano, K. L. et al., 1999, “Spatial and Temporal Variations in PM10 and PM2.5 Source 

Contributions and Comparison to Emissions During the 1995 Integrated Monitoring 

Study”, Atmospheric Environment (33). 

111. Lehrman, D. E., et al., 1999, “Assessment of the Methods and Adequacy of Meteorology 

Measurements for IMS95”, Atmospheric Environment (33). 

112. McDade, C. et al., 1999, “Optical Properties of the San Joaquin Valley Aerosol Collected 

During the 1995 Integrated Monitoring Study”, Atmospheric Environment (33). 

113. Blanchard, C. L. et al., 1999, “Spatial Scales of Transport and Influence During the 1995 

Integrated Monitoring Study in California’s San Joaquin Valley”, Atmospheric 

Environment (33). 

114. Lillis, D. et al., 1999, “Production and Removal of Aerosol in a Polluted Fog Layer - Part I 

Model Development and Evaluation”, Atmospheric Environment (33). 

115. Lillis, D. et al., 1999, “Production and Removal of Aerosol in a Polluted Fog Layer - Part II 

Effects of Fogs on PM in the San Joaquin Valley”, Atmospheric Environment (33). 

116. Collett, J. L. et al., 1999, “Internal Acid Buffering in San Joaquin Valley Fog Drops and its 

Influence on Aerosol Processing”, Atmospheric Environment (33). 

117. Hoag, K, J., et al., 1999, “The Influence of Drop Size-Dependent Fog Chemistry on Aerosol 

Processing by San Joaquin Valley Fogs”, Atmospheric Environment (33). 

118. Strader, R. et al., 1999, “Evaluation of Secondary Organic Aerosol Formation in Winter”, 

Atmospheric Environment (33). 

119. Pun, B. et al., 1999, “Understanding Particulate Matter Formation in the California San Joaquin 

Valley: Conceptual Model and Data Needs”, Atmospheric Environment (33). 

120. Magliano, K. L. et al., 1999, “Field Program Plans for the California Regional PM10/PM2.5 Air 

Quality Study”, in Proceedings of the A&WMA 92nd Annual Meeting. 

121. Wittig, A. E. et al., 1999, “Health and Safety Plan for the California Regional PM10/PM2.5 Air 

Quality Study (CRPAQS)”, prepared by Sonoma Technology Inc. for the San Joaquin 

Valleywide Air Pollution Study Agency, STI-999215-1894-HASP. 

122. Magliano, K.L. et al, 2000, “Coordinated Efforts of the California Regional PM10/PM2.5 Air 

Quality Study and the U.S. Environmental Protection Agency Supersite in Fresno, 

California”, in Proceedings of the 93rd Annual A&WMA Conference, Salt Lake City, UT, 

June 19-24, 2000. 

123. Magliano, K. L. et al., 2000, “Light Scattering Measurements in the California Regional 

PM10/PM2.5 Air Quality Study”, In Proceedings of the 93rd Annual A&WMA 

Conference, Salt Lake City, UT, June 19-24, 2000. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
124. Schauer, J. J. et al., 2000, “Source Apportionment of Wintertime Gas-Phase and ParticlePhase Air Pollutants Using Organic Compounds as Tracers”, Environmental Science 

and Technology (34), 1821-1832. 

125. Blanchard, C. L. et al. 2000, “The Use of Ambient Measurements to Identify Which Precursor 

Species Limit Aerosol Nitrate Formation”, Journal of the Air and Waste Management 

Association (50), 2073-2084. 

126. Wittig, A. E. et al., 2000, “Quality Integrated Workplan for the Continuous and Filter Air Quality 

Measurements in the California Reqional PM10/PM2.5 Air Quality Study (CRPAQS)”, 

prepared by Sonoma Technology Inc. for the San Joaquin Valleywide Air Pollution 

Study Agency, STI-999214-1921-QIWP. 

127. Flocchini, R. G. et al., 2001, “Sources and Sinks of PM10 in the San Joaquin Valley”, Interim 

Report to U.S. Department of Agriculture, Contract Nos. 94-33825-0383 and 98-38825-

6063, prepared by University of California Davis. 

128. Main, H. et al. 2001, “Managing Data from the California Regional PM10/PM2.5 Air Quality 

Study”, in Proceedings of the A&WMA/AGU Regional Haze and Global Radiation 

Balance Conference, Bend, OR, October 2-5, 2001. 

129. Magliano, K.L. et al., 2001, “The California Regional PM10/PM2.5 Air Quality Study 

(CRPAQS): Field Study Description and Initial Findings”, in Proceedings of the 

A&WMA/AGU Regional Haze and Global Radiation Balance Conference, Bend, OR, 

October 2-5, 2001. 

130. Richards, L.W. et al., 2001, “Light Scattering Measurements During the California Regional 

PM10/PM2.5 Air Quality Study”, in Proceedings of the A&WMA/AGU Regional Haze 

and Global Radiation Balance Conference, Bend, OR, October 2-5, 2001. 

131. Watson, J. G. et al., 2001, “Comparison of Nephelometer Light Scattering Measurements in 

Central California as a Function of Meteorological Variables Particle Size, and Particle 

Composition”, in Proceedings of the A&WMA/AGU Regional Haze and Global 

Radiation Balance Conference, Bend, OR, October 2-5, 2001. 

132. Chow, J. C. et al., 2001, “Particle Nitrate Measurements at the Fresno Supersite", in 

Proceedings of the A&WMA/AGU Regional Haze and Global Radiation Balance 

Conference, Bend, OR, October 2-5, 2001. 

133. Lowenthal, D. H. et al., 2001, “Particle Size Relationships at the Fresno Supersite”, in 

Proceedings of the A&WMA/AGU Regional Haze and Global Radiation Balance 

Conference, Bend, OR, October 2-5, 2001. 

134. Pun B. K. et al., 2001; “Sensitivity of Particulate Matter Nitrate Formation to Precursor 

Emissions in the California San Joaquin Valley”; Environmental Science and 

Technology; (35), 2979-2987. 

135. Watson, J.G. et al., 2001, “Comparison and Evaluation of In-Situ And Filter Carbon 

Measurements at the Fresno Supersite”, Journal of Geophysical Research – 

Atmospheres”. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
136. Rappolt, T.J. et al., 2001, “Mapping Air Quality Plumes from Fossil Energy Sources to Assess 

the Impacts of Secondary Aerosol Development”, presented at the SPE/EPA/DOE 

Exploration and Production Environmental Conference, San Antonio, TX, February 

2001. 

137. Funk, T. et. al., 2001, “Development of Gridded Spatial Allocation Factors for the State of 

California”, prepared by Sonoma Technology Inc. for the San Joaquin Valleywide Air 

Pollution Study Agency. 

138. Knuth, W. et al., 2001, “Fall Saturation Network – An Element of the Satellite Network 

Operations for the CRPAQS”, prepared by Technical & Business Systems Inc. for the 

San Joaquin Valleywide Air Pollution Study Agency. 

139. Knoderer, C, et. al., 2001, “Data Collected by a 915-MHz Radar Wind Profiler, RASS, and 

Surface Meteorological Station at San Martin, California During Fall 2000 and Winter 

2001 for the California Regional PM10/PM2.5 Air Quality Study (CRPAQS)”, prepared 

by Sonoma Technology Inc. for the San Joaquin Valleywide Air Pollution Study 

Agency. 

140. Chung, A. et. al, 2001, “Detection of Alkaline Ultrafine Atmospheric Particles at Bakersfield, 

California, Environmental Science Technology, (35), 2184-2190. 

141. Bush, D. et al., 2002, “An Evaluation of Portable Nephelometer Performance During 

CRPAQS”, presented at A&WMA Symposium on Air Quality Measurement Methods 

and Technology, November 13-15, 2002. 

142. Watson, J. G. et al., 2002, “Air Quality Measurements from the Fresno Supersite”, Journal of 

the Air and Waste Management Association, (50), 1321-1334. 

143. Watson, J. G. et al., 2002, “A Wintertime PM2.5 Episode at the Fresno, CA, Supersite”, 

Atmospheric Environment (36), 465-475. 

144. Watson, J.G., 2002, “Particle Size Relationships at the Fresno Supersite”, Journal of the Air 

and Waste Management Association, (52), 822-827 

145. Herckes, P. et al., 2002, “Organic Matter in Central California Radiation Fogs”, Environmental 

Science & Technology, (36), 4777-4782. 

146. Hyslop, N. et. al., 2002, “Continuous PM Measurements in a Rural Town in the San Joaquin 

Valley”, presented at A&WMA Symposium on Air Quality Measurement Methods and 

Technology, November 13-15, 2002. 

147. Holmen B. A. et al., 2002, “Lidar-Assisted Measurement of PM10 Emissions from Agricultural 

Tilling in California’s San Joaquin Valley – Part I: Lidar”, Atmospheric Environment 

(35), 3251-3264. 

148. Holmen B. A. et al., 2002, “Lidar-Assisted Measurement of PM10 Emissions from Agricultural 

Tilling in California’s San Joaquin Valley – Part II: Emission Factors”, Atmospheric 

Environment, (35), 3265-3277. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
149. Technical & Business Systems Inc., 2002, “Satellite Network Operations for the California 

Regional PM10/PM2.5 Air Quality Study”, prepared by Technical & Business Systems 

Inc. for the San Joaquin Valleywide Air Pollution Study Agency. 

150. Bush, D. et. al., 2002, “Final Quality Assurance Audit Report California Regional PM10/PM2.5 

Air Quality Study”, prepared by Parsons Engineering Science for the San Joaquin 

Valleywide Air Pollution Study Agency. 

151. Stiefer, P. et al., 2002, “Atlas of Ground Truth Surveys”, prepared by Sonoma Technology Inc. 

for the San Joaquin Valleywide Air Pollution Study Agency. 

152. Engelbrecht, J. P., 2002, “Laboratory Measurement Systems Audit”, prepared by Desert 

Research Institute for the San Joaquin Valleywide Air Pollution Study Agency. 

153. Countess Environmental, 2002, “Quantifying the Contribution of Fugitive Geological Dust to 

Ambient PM10 and PM2.5 Concentrations in the San Joaquin Valley”, prepared for the 

San Joaquin Valley Air Pollution Control District. 

154. Whiteaker, J.R., 2002, “Effects of Meteorological Conditions on Aerosol Composition and 

Mixing State in Bakersfield CA”, Environmental Science and Technology, (36), 2345-

2353. 

155. Ashbaugh, L.L. et al., 2003, “Soil Sample Collection and Analysis for the Fugitive Dust 

Characterization Study”, Atmospheric Environment, (37), 1163-1173. 

156. Chow, J.C. et al., 2003, “Similarities and Differences in PM10 Chemical Source Profiles for 

Geological Dust From the San Joaquin Valley, California”, Atmospheric Environment, 

(37), 1317-1340. 

157. Rinehart, L.R. et al., 2003, “Characterization of PM2.5 Associated Organic Compounds of 

Emission Sources Collected During the California Regional PM10/PM2.5 Air Quality 

Study”, in Proceedings of the 96th Annual A&WMA Conference, San Diego, CA, June 

2003. 

158. Najita, T. et al., 2003, “Relationship Between PM2.5 and PM10 During the California Reqional 

Particulate Air Quality Study”, presented at the 2003 Annual Conference of the 

American Association for Aerosol Research, Anaheim, CA, October 20-24, 2003. 

159. Velasco, P. et al., 2003, “Diurnal Variations in PM2.5, PM10, Chemical Components, Gaseous 

Precursors, and Co-Pollutants during the California Regional Particulate Air Quality 

Study”, presented at the 2003 Annual Conference of the American Association for 

Aerosol Research, Anaheim, CA, October 20-24, 2003. 

160. Turkiewicz, K. et al., 2003, “Spatial and Temporal Variations of PM2.5, PM10, and their 

Chemical Components during the California Regional Particulate Air Quality Study”, 

presented at the 2003 Annual Conference of the American Association for Aerosol 

Research, Anaheim, CA, October 20-24, 2003. 

161. Casuccio, G. et al., 2003, “Characterization of San Joaquin Valley Samples Using Scanning 

Electron Microscopy (SEM) Techniques”, presented at the 2003 Annual Conference of 

the American Association for Aerosol Research, Anaheim, CA, October 20-24, 2003. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
162. Chow, J. C. et al., 2003, “Spatial and Temporal Variations in PM2.5 Chemical Composition 

during Winter, 2000/2001 Episodes in Central California”, presented at the 2003 

Annual Conference of the American Association for Aerosol Research, Anaheim, CA, 

October 20-24, 2003. 

163. Watson, J., et al., 2003, “Equivalence and Comparability Among Different PM2.5 Mass 

Measurements in Central California”, presented at the 2003 Annual Conference of the 

American Association for Aerosol Research, Anaheim, CA, October 20-24, 2003. 

164. Countess, R. et. al. 2003, “Fugitive Dust Concentrations in the San Joaquin Valley”, presented 

at the 2003 Annual Conference of the American Association for Aerosol Research,

Anaheim, CA, October 20-24, 2003. 

165. Herckes, P. et. al., 2003, “Fog Processing of Atmospheric Organic Compounds During the 

California Regional PM10/PM2.5 Air Quality Study (CRPAQS)”, presented at the 2003 

Annual Conference of the American Association for Aerosol Research, Anaheim, CA, 

October 20-24, 2003. 

166. McDonald, C. et. al., 2003, “Evaluation of Transport of Particulate Matter and Its Precursors 

Into and Out of the San Joaquin Valley, California, Using Two Approaches presented 

at the 2003 Annual Conference of the American Association for Aerosol Research,

Anaheim, CA, October 20-24, 2003. 

167. Kleeman, M. et. al., 2003, “Characterization of Atmospheric Nano-Fine Particles at Bakersfield 

California”, presented at the 2003 Annual Conference of the American Association for 

Aerosol Research, Anaheim, CA, October 20-24, 2003. 

168. Kleeman, M. et. al., 2003, “Simulating Air Quality in the San Joaquin Valley with a SourceOriented Externally Mixed Airshed Model; Evaluation of Model Performance and 

Control Strategy Effectiveness”, presented at the 2003 Annual Conference of the 

American Association for Aerosol Research, Anaheim, CA, October 20-24, 2003. 

169. Heissler, S. 2003, “Contributions of Chemical Constituents to Visibility Reduction during the 

California Regional PM10/PM2.5 Air Quality Study”, presented at the 2003 Annual 

Conference of the American Association for Aerosol Research, Anaheim, CA, October 

20-24, 2003. 

170. Countess, R. et. al., 2003, “Contribution of Vegetative Burning to PM2.5 Concentrations in the 

San Joaquin Valley”, presented at the 2003 Annual Conference of the American 

Association for Aerosol Research, Anaheim, CA, October 20-24, 2003. 

171. Rappolt, T.J. et. al., 2003, “Near-Term and Low Wind Dispersion Measurements of Plumes 

from Steam Generators during the CRPAQS presented at the 2003 Annual Conference 

of the American Association for Aerosol Research, Anaheim, CA, October 20-24, 2003. 

172. Alcorn, S. et. at., 2003, “Comparisons Between California Regional PM10/PM2.5 Air Quality 

Study (CRPAQS) Particle Light Scattering and Fine Particle Mass Data”, presented at 

the 2003 Annual Conference of the American Association for Aerosol Research,

Anaheim, CA, October 20-24, 2003. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
173. Chen, A. et. al., 2003, “Evaluating Similarities of Source Profiles Using Pair Slope Distribution 

(PSD) Method: Applications to PM2.5 Source Profiles from the BRAVO and CRPAQS 

Studies”, presented at the 2003 Annual Conference of the American Association for 

Aerosol Research, Anaheim, CA, October 20-24, 2003. 

174. Hyslop, N. et. al., 2003, “Vertical Variations in PM and PM Precursor Concentrations in the 

San Joaquin Valley, California”, presented at the 2003 Annual Conference of the 

American Association for Aerosol Research, Anaheim, CA, October 20-24, 2003. 

175. Chang, et. al., 2003, “Aerosol Processing and Removal by Fogs: Observations in Radiation 

Fogs During the CRPAQS Study”, presented at the 2003 Annual Conference of the 

American Association for Aerosol Research, Anaheim, CA, October 20-24, 2003. 

176. Chin, et. al., 2003, “Comparison Between Particulate Matter Sampled at Urban and Rural 

Locations in the San Joaquin Valley: Continuous Measurement of Single Particle Size 

and Composition”, presented at the 2003 Annual Conference of the American 

Association for Aerosol Research, Anaheim, CA, October 20-24, 2003. 

177. Rinehart, L. et. al., 2003, “Semi-volatile Organic Compound Characterization and Evaluation of 

PM2.5 Emission Sources Collected During the California Regional PM10/PM2.5 Air 

Quality Study”, presented at the 2003 Annual Conference of the American Association 

for Aerosol Research, Anaheim, CA, October 20-24, 2003. 

178. Hering, S. et. al., 2003, “Temporal and Spatial Distribution of Fine Particle Nitrate and Black 

Carbon in the San Joaquin Valley of California, USA”, presented at the 2003 Annual 

Conference of the American Association for Aerosol Research, Anaheim, CA, October 

20-24, 2003. 

179. Scow, K. M. et al., 2003, “DNA Profiling Analysis of Dust and Source Material Collected in the 

CRPAQS Fall 2000 Sampling Campaign”, prepared by the University of California, 

Davis for the San Joaquin Valleywide Air Pollution Study Agency. 

180. Hafner, H. R., et. al., 2003, “California Reqional PM10/PM2.5 Air Quality Study (CRPAQS) 

Management of Anchor Site Data”, prepared by Sonoma Technology Inc for the San 

Joaquin Valleywide Air Pollution Study Agency, STI-999242-2087-FR. 

181. Wittig, A.E., et. al., 2003, “California Regional PM10/PM2.5 Air Quality Study (CRPAQS) 

Anchor Site Measurements and Operations”, prepared by Sonoma Technology Inc for 

the San Joaquin Valleywide Air Pollution Study Agency, STI-999231-2332-FR. 

182. Hyslop, N. P., et. al., 2003, “California Regional PM10/PM2.5 Air Quality Study (CRPAQS) 

Data Quality Summary Reports”, prepared by Sonoma Technology Inc for the San 

Joaquin Valleywide Air Pollution Study Agency, STI-999242-2310-FR. 

183. Neff, W.D., et. al., 2003, “Final Report Contract 99-4PM A Study of Mesoscale Transport and 

Boundary Layer Processes in the Central Valley of California”, prepared by the 

National Oceanic and Atmospheric Administration for the San Joaquin Valleywide Air 

Pollution Study Agency. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
184. Zielinska, B. et. al., 2003, “California Regional PM10/PM2.5 Air Quality Study – Organic 

Compound Measurements”, prepared by Desert Research Institute for the San Joaquin 

Valleywide Air Pollution Study Agency. 

185. Ervens, B. et. al., 2003, “On the Drop-Size Dependence of Organic Acid and Formaldehyde 

Concentrations in Fog”, Journal of Atmospheric Chemistry, (46), 239-269. 

186. Whiteaker, J.R., 2003, “Hydroxymethanesulfonate as a Tracer for Fog Processing of Individual 

Aerosol Particles”, Atmospheric Environment, (37), 1033-1043. 

187. Fitz, D. et. al., 2004, “Development of a Gas and Particulate Matter Organic Speciation Profile 

Database”, prepared by the University of California, Riverside Center for 

Environmental Engineering and Technology for the San Joaquin Valleywide Air 

Pollution Study Agency. 

188. Prather, K., 2004, ATOFMS Field Study Operations Final Report, prepared by the University of 

California, San Diego, Prather Research Group for the San Joaquin Valleywide Air 

Pollution Study Agency. 

189. Chow, J.C. et. al., 2004, “Comparison of PM2.5 Chemical Composition from the Fresno 

Supersite During Wintertime Pollution Episodes in Central California”, presented at the 

13th World Clean Air and Environmental Protection Congress and Exhibition, London 

England, August 2004. 

190. Watson, J.G. et. al., 2004, “Particle Nitrate Sampling Artifacts in California’s San Joaquin 

Valley”, presented at the 13th World Clean Air and Environmental Protection Congress 

and Exhibition, London England, August 2004. 

191. DeYoung, R.J. et. al., 2004, “Aerosol Transport in the California Central Valley Observed by 

Airborne Lidar” 

192. Collett, J.L. et. al., 2004, “The Organic Composition of California Fogs”, submitted to the 3rd

International Conference on Fog and Fog Collection, Capetown, South Africa, October 

2004. 

193. Carillo, J.H. et. al., 2004, “An Economical Optical Fog Detector”, submitted to the 3rd

International Conference on Fog and Fog Collection, Capetown, South Africa, October 

2004. 

194. Hyslop, N.P., et. al., 2004, “Exploratory Data Analysis of Particulate Matter Concentrations in 

Corcoran, California”, prepared by Sonoma Technology, Inc, for the San Joaquin 

Valleywide Air Pollution Study Agency, STI-999300-2485-FR. 

195. Bahm, K.E. et. al., 2004, “Detecting Source Activities and Reconciling Ambient Measurement 

Variations with Field Observations – California Regional PM10 and PM2.5 Air Quality 

Study (CRPAQS) Data Analysis Task 4.3”, prepared for the San Joaquin Valleywide 

Air Pollution Study Agency by Sonoma Technology Inc., STI-902328-2579-TM. 

196. Baxter, B. et. al., 2004, “Task 1.3 Adequacy and Validity of Meteorological Measurements”, 

prepared by Technical & Business Systems for the San Joaquin Valleywide Air 

Pollution Study Agency. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
197. Lehrman D. et. al., 2004, “Task 3.3 How Well Do Measurements Characterize Critical 

Meteorological Features”, prepared by Technical & Business Systems for the San 

Joaquin Valleywide Air Pollution Study Agency. 

198. Knuth, B., 2004, “Task 5.1 CRPAQS Synoptic Conditions Weather Typing”, prepared by 

Technical & Business Systems for the San Joaquin Valleywide Air Pollution Study 

Agency. 

199. Held, T. et. al., 2004, “Modeling Particulate Matter in the San Joaquin Valley with a SourceOriented Externally Mixed Three-Dimensional Photochemical Grid Model: Atmospheric 

Environment, 38(22), 3689-3711. 

200. Heisler, S. L., 2004, “Contributions of Chemical Constituents to Visibility Reduction During the 

California Regional PM10/PM2.5 Air Quality Study”, prepared by ENSR Corporation for 

the San Joaquin Valleywide Air Pollution Study Agency. 

201. Tracer ES&T, 2004, “California Regional PM10/PM2.5 Air Quality Study (CRPAQS) Initial Data 

Analysis of Clean Airship 1 Measurements (Task 6.4)”, prepared by Tracer 

Environmental Sciences & Technologies, Inc., for the San Joaquin Valleywide Air 

Pollution Study Agency. 

202. Buhr, M.P. et. al., 2004, Examination of Reactive Nitrogen Partitioning at the Bakersfield and 

Angiola Field Sites”, prepared by Sonoma Technology Inc. for the San Joaquin 

Valleywide Air Pollution Study Agency. 

203. Buhr, M.P. et. al., 2004, “Comparison and Descriptive Evaluation of the Airborne and GroundBased Measurements Collected During the CCOS Field Experiment”, prepared by 

Sonoma Technology Inc. for the San Joaquin Valleywide Air Pollution Study Agency. 

204. Yoho, D. et. al., 2004, “How is Visibility Affected by Atmospheric Constituents, Sources, and 

Meteorology – Sources of Light Extinction in the Mojave Desert”, prepared by 

Technical & Business Systems for the San Joaquin Valleywide Air Pollution Study 

Agency. 

205. Alcorn, S.A. et. al., 2004, “Comparisons Between Light Scattering and Fine-Particle Mass 

Data”, prepared by Sonoma Technology Inc. for the San Joaquin Valleywide Air 

Pollution Study Agency. 

206. Pun, B., 2004, “CRPAQS Task 2.7 When and Where Does High O3 Correspond to High 

PM2.5? How Much PM2.5 Corresponds to Photochemical End Products?”, prepared 

by Atmospheric and Environmental Research, Inc. for the San Joaquin Valleywide Air 

Pollution Study Agency. 

207. Collett, J. L. et. al., 2005, “Examination of the Influence of California Regional Particulate Air 

Quality Study Fog Episodes on Aerosol Formation and Removal”, prepared by 

Colorado State University for the San Joaquin Valleywide Air Pollution Study Agency. 

208. Raffuse, S. M. et. al., 2005, “Emission Inventory Reconciliation in the San Joaquin Valley”, 

prepared by Sonoma Technology Inc. for the San Joaquin Valleywide Air Pollution 

Study Agency. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
209. Held, T. et. al., 2005, “A Comparison of the UCD/CIT Air Quality Model and the CMB SourceReceptor Model for Primary Airborne Particulate Matter”, Atmospheric Environment,

(39), 2281-2297. 

210. Kleeman, M.J., Ying, Q., Kaduwela, A. 2005. “Control Strategies for the Reduction of Airborne 

Particulate Nitrate in California’s San Joaquin Valley”. Atmospheric Environment, (39): 

5325-5341. 

211. Fahey, K.M., Pandis, S.N., Collett, Jr. J.L., Herckes, P. 2005. “The influence of sizedependent droplet composition on pollutant processing by fogs”. Atmospheric 

Environment, (39): 4561-4574. 

212. Blanchard, C., 2005, “Comparison of Particulate Nitrate Formation in Different Chemical 

Regimes”, presented at the American Association for Aerosol Research (AAAR), 

Supersites Conference, February 2005, Atlanta, GA.

213. Cahill, T., 2005, “Continuous Analysis of Fresno Aerosols by Size, Time, and Elemental 

Concentrations, March – December 2001”, presented at the American Association for 

Aerosol Research (AAAR), Supersites Conference, February 2005, Atlanta, GA.

214. Collett, J., 2005, “Pollution Processing by Radiation Fogs During the California Regional 

PM10/PM2.5 Air Quality Study (CRPAQS)”, presented at the American Association for 

Aerosol Research (AAAR), Supersites Conference, February 2005, Atlanta, GA.

215. Goohs, K., 2005, “Function and Performance of a New Real-Time Black Carbon Monitor 

During the 2004 Fresno Study”, presented at the American Association for Aerosol 

Research (AAAR), Supersites Conference, February 2005, Atlanta, GA.

216. Herckes, P., 2005, “Fog Processing of Atmospheric Organic Matter”, presented at the 

American Association for Aerosol Research (AAAR), Supersites Conference, February 

2005, Atlanta, GA.

217. Lurmann, F., 2005, “Spatial Representativeness of the Fresno Supersite PM and Gaseous CoPollutant Measurements”, presented at the American Association for Aerosol Research 

(AAAR), Supersites Conference, February 2005, Atlanta, GA.

218. Magliano, K., 2005, “Comparison of Air Quality During Two Winter Episodes”, presented at the 

American Association for Aerosol Research (AAAR), Supersites Conference, February 

2005, Atlanta, GA.

219. Magliano, K., 2005, “PM2.5 Concentrations in Urban and Rural Areas”, presented at the 

American Association for Aerosol Research (AAAR), Supersites Conference, February 

2005, Atlanta, GA.

220. McCarthy, M., 2005, “The Role of Nighttime Chemistry in Winter Ammonium Nitrate Formation 

in the San Joaquin Valley”, presented at the American Association for Aerosol 

Research (AAAR), Supersites Conference, February 2005, Atlanta, GA.

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
221. Pun, B., 2005, “Relationships Between Ozone and PM During CRPAQS”, presented at the 

American Association for Aerosol Research (AAAR), Supersites Conference, February 

2005, Atlanta, GA.

222. Qin, X., 2005, “ATOFMS Measurements at Urban and Rural Locations: Comparison of Single 

Particle Size and Composition”, presented at the American Association for Aerosol 

Research (AAAR), Supersites Conference, February 2005, Atlanta, GA.

223. Watson, J., 2005, “Spatial Variability of PM2.5 Species in the California Central Valley”, 

presented at the American Association for Aerosol Research (AAAR), Supersites 

Conference, February 2005, Atlanta, GA.

224. Zielinska, B., 2005, “Semi-volatile Organic Compounds: Characterization at the Fresno 

Supersite During Wintertime Stagnation Episodes”, presented at the American 

Association for Aerosol Research (AAAR), Supersites Conference, February 2005, 

Atlanta, GA.

225. Cassucio, G., 2005, “Corcoran Fall 2000 study: Apportionment of ambient PM10 using 

scanning electron microscopy”, presented at the American Association for Aerosol 

Research (AAAR), Supersites Conference, February 2005, Atlanta, GA.

226. Chow, J., 2005, “Particulate carbon measurements in California's San Joaquin Valley”, 

presented at the American Association for Aerosol Research (AAAR), Supersites 

Conference, February 2005, Atlanta, GA.

227. Chow, J.C., 2005, “Loss of PM2.5 Nitrate from Filter Samples in Central California”, Journal of 

the Air and Waste Management Association, (55) 1158-1168. 

228. Watson, J.G., et. al., 2005, “Nanoparticle and Ultrafine Particle Events at the Fresno 

Supersite”. Journal of the Air & Waste Management Association, (56) 417-430. 

229. Gorin, C.A., et. al., 2005, “Wood Smoke Contributions to Ambient Aerosol in Fresno During 

Winter 2003-2004”. Prepared for the San Joaquin Valley Air Pollution Control District. 

Fresno, California. 

230. Park, K., et. al., 2005, “Comparison of continuous and filter-based carbon measurements at 

the Fresno Supersite”. Journal of the Air & Waste Management Association, (56) 474-

491. 

231. Herner, J.D., et. al., 2005, “Size and Composition Distribution of Airborne Particulate Matter in 

Northern California 1. Particulate Mass, Carbon, and Water Soluble Ions”, Journal of 

the Air & Waste Management Association, (55), 30-51.

232. Turkiewicz, K., et. al., 2006, “Comparison of Two Winter Air Quality Episodes During the 

California Regional Particulate Air Quality Study”. Journal of the Air & Waste 

Management Association (56): 467-473. 

233. Rinehart, L.R., et. al., 2006, “Spatial Distribution of PM2.5 Associated Organic Compounds in 

Central California”. Atmospheric Environment (40): 290-303. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
234. Chow, J.C., Watson, J.G., Park, K., Lowenthal, D.H., Robinson, N.F., Magliano, K.L., 2006. 

“Comparison of Particle Light Scattering and PM2.5 Mass in Central California”. 

Journal of the Air & Waste Management Association (56): 398-410. 

235. Chow, J.C., Watson, J.G., Lowenthal, D.H., Chen, A.L-W., Magliano, K.L., 2006. “Particulate 

Carbon Measurements in California’s San Joaquin Valley”. Chemosphere (62): 337-

348.

236. Chow, J.C., Chen, A.L-W., Watson, J.G., Lowenthal, D.H., Magliano, K.L., Turkiewicz, K., 

Lehrman, D., 2006. “PM2.5 Chemical Composition and Spatiotemporal Variability 

During the California Regional PM10/PM2.5 Air Quality Study (CRPAQS)”. Journal of 

Geophysical Research, (111): D10S04, doi:10.1029/2005JD006457. 

237. Ying, Q., Kleeman, M.J., 2006. “Source Contributions to the Regional Distribution of 

Secondary Particulate Matter in California”. Atmospheric Environment (40): 736-752. 

238. Herner, J.D., Green, P.G., Kleeman, M.J. 2006. “Measuring the Trace Elemental Composition 

of Size-Resolved Airborne Particles”. Environmental Science and Technology (40): 

1925-1933. 

239. Chow, J.C., Watson, J.G., Lowenthal, D.H., Chen, A.L-W., Tropp, R., Park, K., Magliano, K.L., 

2006. “PM2.5 and PM10 Mass Measurements in California’s San Joaquin Valley”. 

Aerosol Science & Technology (40): 796-810. 

240. Lurmann, F. et. al., 2006, “Processes Influencing Secondary Aerosol Formation in the San 

Joaquin Valley During Winter”, Journal of the Air & Waste Management Association, 

(56): 1679-1693. 

241. Brown, S.G. et. al., 2006, “Wintertime Vertical Variations in Particulate Matter (PM) and 

Precursor Concentrations in the San Joaquin Valley During the California Regional 

Coarse PM/Fine PM Air Quality Study”, Journal of the Air & Waste Management 

Association, (56): 1267-1277. 

242. MacDonald, C.P. et. al., 2006, “Transport and Dispersion During Wintertime Particulate Matter 

Episodes in the San Joaquin Valley, California, Journal of the Air & Waste 

Management Association, (56): 961-976. 

243. Gorin, C.A. et. al., 2006, “Wood Smoke Contribution to Winter Aerosol in Fresno, CA”, Journal 

of the Air & Waste Management Association, (56): 1584-1590. 

244. Chow, J.C. et. al., 2006, “Evaluation of Organic markers for Chemical Mass Balance Source 

Apportionment at the Fresno Supersite”, Atmos. Chem. Phys. Discuss. (6) 10341-

10372 

245. Grover, B.D. et. al., 2006, “Measurement of Both Volatile and Semi-Volatile Fractions of Fine 

Particulate Matter in Fresno, CA”, Aerosol Science & Technology, (40): 811-826. 

246. Liang, J. et. al., 2006, “Off-line Diagnostic Analyses of a Three-dimensional PM Model Using 

Two Matrix Factorization Methods”, Atmospheric Environment, (40): 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

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247. Qin, X. et. al., 2006, “Impact of Biomass Emissions on Particle Chemistry During the California 

Regional Particulate Air Quality Study”, International Journal of Mass Spectrometry, 

(258): 142-150. 

248. Qin, X. et. al., 2006, “Comparison of Two Methods for Obtaining Quantitative Mass 

Concentrations from Aerosol Time-of-Flight Mass Spectrometry Measurements”, Anal. 

Chem., (78): 6169-6178. 

249. Chen, A.L-W., et. al., 2007, “Quantifying PM2.5 Source Contributions for the San Joaquin 

Valley with Multivariate Receptor Models”. Environmental Science and Technology, 

(41), 2818-2826. 

250. Hering, et. al., 2007, “Comparison of Particle Size Distributions at Urban and Agricultural Sites 

in California’s San Joaquin Valley”. Aerosol Science and Technology, (41:1), 86-96. 

251. Watson, J.G. et. al., 2008, “Estimating Aerosol Light Scattering at the Fresno Supersite”, 

Atmospheric Environment, (42), 1186-1196. 

252. Chow, J.C. et.al., 2008, “Size Resolved Aerosol Chemical Concentrations at Rural and Urban 

Sites in Central California, USA”, Atmospheric Research, (90), 243-252. 

253. Ying, Q. et. al., 2008, “Modeling Air Quality During the California Regional PM10/PM2.5 Air 

Quality Study (CRPAQS) Using the UCD/CIT Source Oriented Air Quality Model – Part 

I. Base Case Model Results”, Atmospheric Environment, (42), 8954-8966. 

254. Ying, Q. et. al., 2008, “Modeling Air Quality During the California Regional PM10/PM2.5 Air 

Quality Study (CRPAQS) Using the UCD/CIT Source Oriented Air Quality Model – Part 

II. Regional Source Apportionment of Primary Airborne Particulate Matter”, 

Atmospheric Environment, (42), 8967-8978. 

255. Kleeman, M.J. et.al., 2008, “Size Distribution of Particle-Phase Molecular Markers During a 

Severe Winter Pollution Episode”, Environmental Science & Technology, (42), 6469-

6475. 

256. Ying, Q. et. al., 2008, “Modeling Air Quality During the California Regional PM10/PM2.5 Air 

Quality Study (CRPAQS) Using the UCD/CIT Source Oriented Air Quality Model – Part 

III. Regional Source Apportionment of Secondary and Total Airborne PM2.5 and 

PM0.1”, Atmospheric Environment, (43), 419-430. 

257. Chen, J. et. al., 2009, “Source Apportionment of Visual Impairment During the California 

Regional PM10/PM2.5 Air Quality Study, Atmospheric Environment, (43), 6136-6144. 

258. Kleeman, M.J., et. al., 2009, “Source Apportionment of Fine (PM1.8) and Ultrafine (PM0.1) 

Airborne Particulate Matter During a Severe Winter Pollution Episode, Environmental 

Science & Technology, (43), 272-279. 

259. Livingstone, P.L., et.al., 2009, “Simulating PM Concentrations During a Winter Episode in a 

Subtropical Valley and Sensitivity Simulations and Evaluation methods”, Atmospheric 

Environment, (43), 5971-5977. 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
260. Ying, Q., et. al., 2009, “Regional Contributions to Airborne Particulate Matter in Central 

California During a Severe Pollution Episode”, Atmospheric Environment, (43), 1218-

1228. 

261. Pun, B., et. al., 2009, “Modeling Wintertime Particulate Matter Formation in Central California”, 

Atmospheric Environment, (43) 402-409. 

262. Chen, J., et. al., 2010, “Source Apportionment of Wintertime Secondary Organic Aerosol 

During the California Regional PM10/PM2.5 Air Quality Study”, Atmospheric 

Environment, (44), 1331-1340. 

263. Hu, J., et. al., 2010, “Particulate Air Quality Model Predictions Using Prognostic Versus 

Diagnostic Meteorology in Central California”, Atmospheric Environment, (44), 215-

226. 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
San Joaquin Valley PM2.5 Weight of Evidence Analysis 

Appendix 2 

PM2.5 Source Apportionment for the San Joaquin Valley Air Basin 

Using the Chemical Mass Balance Receptor Model 

 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
PM2.5 Source Apportionment for the San Joaquin Valley Air Basin Using the 

Chemical Mass Balance Receptor Model 

1) Data Collection and Screening 

PM2.5 chemical composition data collected at the Bakersfield-California and 

Fresno-1st Street sites were used for the Chemical Mass Balance (CMB) analysis. The 

two sites are part of the Chemical Speciation Network (CSN) and use the SASS (Spiral 

Aerosol Speciation Sampler, Met One, Grants Pass, OR.) for data collection. The 

Bakersfield-California and Fresno-1st samplers are configured with several channels, 

each channel containing one 47mmfilter with a 6.7 L/min flow rate. One channel 

contains a Whatman Teflon®-membrane filter for mass by gravimetry and elements by 

XRF. Another channel includes a magnesium oxide-coated aluminum (Al) honeycomb 

after the cyclone followed by a Nylasorb nylon-membrane filter for water-soluble anions 

i.e., NO3

−

 and SO4

=

) and cations (i.e., ammonium [NH4

+

] and water-soluble sodium [Na+] 

and potassium [K+]) by IC. In the past, another channel containing a Whatman QMA 

quartz-fiber filter was used for OC and EC analysis by the STN thermal/optical 

transmittance (TOT) protocol. In recent years changes were made to the carbon 

sampling and analysis method. The collection method changed from the MetOne SASS 

to the URG3000N sampler, which is very similar to the IMPROVE module C sampler. 

The analytical method was changed from the NIOSH-like thermal optical transmittance 

(TOT) method to IMPROVE_A thermal optical reflectance (TOR). A new backup quartz 

filter is also collected using the URG3000N to help assess artifacts. The backup filter is 

placed behind the routine quartz sampler filter. This change took place on May 3, 2007 

at Bakersfield and April 1, 2009 at Fresno. 

Due to the change in carbon collection and analysis method, several data sets were 

generated for CMB modeling to allow separate analysis of old and new carbon data. 

Throughout this document we will refer to ‘old carbon’ data and ‘new carbon’ data. Old 

carbon data were collected using the SASS sampler and analyzed using the NIOSH-like 

thermal optical transmittance (TOT) method. New carbon data were collected using the 

modified IMPROVE version II Module C sampler, the URG3000N, and analyzed using 

the IMPROVE-A thermal optical reflectance (TOR) method. Both old and new carbon 

data were corrected for sampling artifacts prior to running CMB. 

 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
2) Data Preparation 

Organic carbon (OC) data were corrected for sampling artifacts prior to running CMB. 

Old carbon data, collected using the SASS sampler and analyzed using the NIOSH-like 

thermal optical transmittance (TOT) method, were corrected by subtracting a California 

network-wide average organic carbon blank of 1 ug/m3 from the measured OC 

concentration. New carbon data were adjusted by subtracting network-wide monthly 

average concentrations measured on a backup filter from daily measurements of 

organic carbon [88370]. The monthly average backup concentrations are shown in 

Table 1. 

Table 1. Organic Carbon Monthly Average Concentrations on Backup Filter 

Month 1 2 3 4 5 6 7 8 9 10 11 12 

Avg Blank Value (ug/m3) 0.66 0.54 0.48 0.43 0.43 0.48 0.54 0.49 0.53 0.50 0.60 0.57

3) Source Profiles 

The major source types which have been found to contribute to primary PM2.5 in the 

San Joaquin Valley are motor vehicle exhaust, vegetative burning, geological material, 

marine-derived aerosols, residual or crude oil combustion, and tire and brake wear. 

Most of the source profiles applicable to the San Joaquin Valley were determined during 

the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) or earlier. Therefore, 

the profiles used in this analysis, listed in Table 4, are the same profiles that were used 

in the previous analysis for the 2008 San Joaquin Valley PM2.5 Plan. 

Motor vehicle profiles for diesel (DIES) and gasoline (GAS) (Fujita et al., 2005) were 

used in modeling PM2.5 concentrations. Since more specific organic markers for 

gasoline and diesel were not available at the receptor site, the two profiles were 

collinear and had to be combined into a single profile representing motor vehicle 

emissions. Diesel and gasoline vehicle emissions source profiles were combined in 

proportions equivalent to their county-level contributions to the PM2.5 emissions to 

produce a single emission-weighted overall source profile. Table 2 lists PM2.5 

emissions (EMFAC 2011, July 2011) that were used as a basis for creating 

county-based composite profiles for Bakersfield and Fresno. 

Table 2. Average 2004-2010 PM2.5 Exhaust Emissions (tons per day) 

County Gasoline Vehicles Diesel Vehicles 

Kern County 2004-2006 (K6GASDIE) 0.12 2.54 

2008-2010 (K9GASDIE) 0.09 1.88 

Fresno County 2004-2006 (F6GASDIE) 0.12 1.33 

2008-2010 (F9GASDIE) 0.08 0.97 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Table 3. Source Profiles (as Percent of the PM2.5 Mass) Used in the CMB Modeling 

PNO 38 35 13 18 41 54 32

SOURCE AMNIT AMSUL WBOakEuc AgBWheat OC MARINE75 TireBrke

N3IC 77.50 ± 7.75 0.00 ± 0.00 0.57 ± 0.07 0.16 ± 0.02 0.00 ± 0.00 22.88 ± 2.60 0.19 ± 1.14

S4IC 0.00 ± 0.00 72.70 ± 7.27 1.30 ± 0.83 0.44 ± 0.04 0.00 ± 0.00 7.20 ± 0.82 0.78 ± 2.10

N4CC 22.55 ± 2.26 27.30 ± 2.73 0.58 ± 0.47 0.59 ± 0.04 0.00 ± 0.00 0.00 ± 0.10 0.16 ± 0.73

NAAC 0.00 ± 0.00 0.00 ± 0.00 0.38 ± 0.15 0.54 ± 0.04 0.00 ± 0.00 28.80 ± 3.27 0.10 ± 0.42

KPAC 0.00 ± 0.00 0.00 ± 0.00 2.89 ± 0.45 6.79 ± 0.50 0.00 ± 0.00 1.07 ± 0.12 0.05 ± 0.17

OCTC 0.00 ± 0.00 0.00 ± 0.00 59.58 ± 4.75 57.03 ± 4.54 100.00 ± 10.00 0.00 ± 0.10 18.81 ± 24.53

ECTC 0.00 ± 0.00 0.00 ± 0.00 5.20 ± 1.12 10.31 ± 0.85 0.00 ± 0.00 0.00 ± 0.10 4.55 ± 5.99

ALXC 0.00 ± 0.00 0.00 ± 0.00 0.07 ± 0.05 0.07 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 0.32 ± 1.89

SIXC 0.00 ± 0.00 0.00 ± 0.00 0.22 ± 0.10 0.13 ± 0.01 0.00 ± 0.00 0.01 ± 0.00 0.69 ± 1.81

PHXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.01 0.00 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.05

CLXC 0.00 ± 0.00 0.00 ± 0.00 1.72 ± 2.02 6.16 ± 0.44 0.00 ± 0.00 38.74 ± 4.40 0.04 ± 0.08

KPXC 0.00 ± 0.00 0.00 ± 0.00 2.86 ± 0.93 5.50 ± 0.39 0.00 ± 0.00 1.07 ± 0.12 0.10 ± 0.36

CAXC 0.00 ± 0.00 0.00 ± 0.00 0.15 ± 0.10 0.10 ± 0.03 0.00 ± 0.00 1.10 ± 0.12 0.28 ± 1.04

TIXC 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.02 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.38

MNXC 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.48 ± 0.29

FEXC 0.00 ± 0.00 0.00 ± 0.00 0.09 ± 0.06 0.07 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 58.11 ± 31.26

CUXC 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.00 1.02 ± 0.07 0.00 ± 0.00 0.00 ± 0.00 0.16 ± 0.69

ZNXC 0.00 ± 0.00 0.00 ± 0.00 0.04 ± 0.02 0.01 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.35 ± 2.37

BRXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.00 0.00 ± 0.00 0.18 ± 0.02 0.00 ± 0.01

RBXC 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.01

SRXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.02 ± 0.00 0.23 ± 0.66

PBXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.06 ± 0.03

VAXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

NIXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

San Joaquin Valley Unified Air Pollution Control District 

Appendix G: PM2.5 We 
Table 3, continued. 

PNO 79 80 83 84 66 67 85 

SOURCE F6GASDIE K6GASDIE F9GASDIE K9GASDIE FDFREANN FDKERANN CHCRUC N3IC 0.22 ± 1.24 0.16 ± 1.24 0.21 ± 1.24 0.16 ± 1.24 0.02 ± 0.28 0.05 ± 0.16 0.00 ± 0.05 

S4IC 2.77 ± 7.25 2.60 ± 7.25 2.74 ± 7.25 2.61 ± 7.25 0.56 ± 0.72 0.47 ± 0.29 14.72 ± 6.24 

N4CC 0.98 ± 3.24 0.89 ± 3.24 0.96 ± 3.24 0.89 ± 3.24 0.04 ± 0.18 0.13 ± 0.20 0.76 ± 0.08 

NAAC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.17 ± 0.07 0.26 ± 0.24 0.25 ± 0.06 

KPAC 0.10 ± 0.08 0.10 ± 0.08 0.10 ± 0.08 0.10 ± 0.08 0.27 ± 0.12 0.80 ± 1.25 0.01 ± 0.01 

OCTC 43.05 ± 27.33 42.40 ± 27.33 42.93 ± 27.33 42.41 ± 27.33 14.34 ± 8.66 10.29 ± 5.32 1.99 ± 1.33 

ECTC 50.59 ± 17.73 51.50 ± 17.73 50.75 ± 17.73 51.49 ± 17.73 1.92 ± 1.29 0.69 ± 0.72 3.01 ± 1.12 

ALXC 0.11 ± 0.14 0.11 ± 0.14 0.11 ± 0.14 0.11 ± 0.14 9.97 ± 2.95 7.67 ± 2.53 0.00 ± 0.05 

SIXC 1.14 ± 4.12 0.99 ± 4.12 1.11 ± 4.12 0.99 ± 4.12 26.77 ± 9.63 22.05 ± 5.29 0.00 ± 0.08 

PHXC 0.14 ± 0.51 0.13 ± 0.51 0.14 ± 0.51 0.13 ± 0.51 0.33 ± 0.91 0.33 ± 0.91 0.00 ± 0.57 

CLXC 0.07 ± 0.30 0.06 ± 0.30 0.07 ± 0.30 0.06 ± 0.30 0.11 ± 0.08 0.46 ± 0.48 0.05 ± 0.01 

KPXC 0.07 ± 0.08 0.07 ± 0.08 0.07 ± 0.08 0.07 ± 0.08 2.30 ± 0.92 3.26 ± 1.59 0.00 ± 0.00 

CAXC 0.50 ± 1.42 0.49 ± 1.42 0.50 ± 1.42 0.49 ± 1.42 3.01 ± 0.67 5.54 ± 3.03 0.00 ± 0.03 

TIXC 0.01 ± 0.08 0.01 ± 0.08 0.01 ± 0.08 0.01 ± 0.08 0.48 ± 0.05 0.44 ± 0.24 0.01 ± 0.00 

MNXC 0.00 ± 0.01 0.00 ± 0.01 0.00 ± 0.01 0.00 ± 0.01 0.11 ± 0.02 0.11 ± 0.08 0.00 ± 0.00 

FEXC 0.44 ± 0.44 0.44 ± 0.44 0.44 ± 0.44 0.44 ± 0.44 5.30 ± 0.58 5.09 ± 2.84 0.71 ± 0.09 

CUXC 0.01 ± 0.05 0.01 ± 0.05 0.01 ± 0.05 0.01 ± 0.05 0.02 ± 0.00 0.01 ± 0.01 0.01 ± 0.01 

ZNXC 0.27 ± 0.41 0.26 ± 0.41 0.27 ± 0.41 0.26 ± 0.41 0.14 ± 0.08 0.07 ± 0.05 0.01 ± 0.00 

BRXC 0.03 ± 0.06 0.03 ± 0.06 0.03 ± 0.06 0.03 ± 0.06 0.01 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 

RBXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.00 ± 0.00 

SRXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.01 0.03 ± 0.01 0.00 ± 0.00 

PBXC 0.01 ± 0.03 0.01 ± 0.03 0.01 ± 0.03 0.01 ± 0.03 0.18 ± 0.13 0.09 ± 0.32 0.00 ± 0.00 

VAXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.42 ± 0.04 

NIXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 2.48 ± 0.25 

San Joaquin Valley Unified Air Pollution Control District 

Appendix G: PM2.5 We 
Biomass burning was represented using an agricultural burning profile (AgBWheat) from 

June through October and a composite residential wood burning profile (WBOakEuc) 

the rest of the year. The agricultural burning profile (AgBWheat) was based on burning 

of wheat stubble (Fitz et al., 2000). The residential wood burning profile (WBOakEuc) 

was used to represent residential wood combustion during colder months and was 

calculated as an average of oak and eucalyptus. 

Geological material in the San Joaquin Valley comes from a variety of sources, 

including roads (paved and unpaved), agricultural operations such as land preparation 

and harvesting, construction, and soil erosion. The Central California Fugitive Dust 

Characterization Study acquired 47 samples from 37 areas (Chow et al., 2003). These 

included: 1) paved road dust from urban and rural areas, 2) unpaved road dust, 

3) agricultural soil from five crop fields (almond, cotton, grape, safflower, and tomato), 

4) dairy and feedlot soil, 5) salt buildup deposits from irrigation canal drainages, and 

6) building construction/earthmoving soil. 

In addition to these latest profiles, some older soil profiles collected in the Valley in the 

late 80's were also used to create composite profiles that best represent fugitive dust 

sources at each site in the San Joaquin Valley. Information on the relative fractions of 

paved and unpaved road dust, as well as agricultural dust, along with information on the 

seasonality of agricultural operations and predominant crop types were used to 

determine which source profiles to include in each composite. Site specific composite 

profiles were then used in the CMB analysis. Table 3 lists geological profiles included in 

the composites created for modeling PM2.5 concentrations. Appendix A includes 

additional information about geological profiles. 

Table 4. Geological Composite Source Profiles 

Composite Profile ID Sample 

ID

% Weight Applicable Area 

FDKERANN 

SOIL31 25 

Bakersfield FDPVR1 25 

FDCTF 25 

SOIL13 25 

FDFREANN 

SOIL03 70 

Fresno FDALM 10 

FDGRA1 10 

FDTOM1 10 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Sea salt was represented using a reacted sea salt profile, MARINE75, in which 25 

percent of the Cl was replaced by nitrate on a molar basis (Chow et al., 1996a). 

Tire and brake samples were collected as part of the ‘Development of a Gas and 

Particulate Matter Organic Speciation Profile Database’ conducted by CE-CERT (Fitz et 

al., 2000). Tire and brake samples were composited into a single weighted average 

profile. The two profiles were weighted based on EMFAC 2011 emissions, which 

estimate a 9 to 1 ratio of brake emissions to tire emissions. 

Secondary nitrate and sulfate were represented by pure ammonium nitrate (NH4NO3) 

and ammonium sulfate (NH4)2SO4. A “pure” OC profile was used to represent other 

unidentified primary sources, contributions from secondary OC, and the possible 

positive OC sampling artifacts. 

Crude-oil combustion profiles were included to help explain ambient concentrations of 

vanadium (V) and nickel (Ni). The profile representing the Santa Fe crude-oil broiler at 

the Westside Kern County oil field helped to explain vanadium and nickel 

concentrations at Bakersfield, while the crude-oil profile representing the Chevron 

Racetrack boiler at the Kern River oil field provided a better fit at Fresno. 

4) Fitting Species 

Table 5 lists fitting species used in CMB runs. 

Table 5. CMB Fitting Species 

Nitrate Silicon Zinc 

Sulfate Chlorine Bromine 

Ammonium Potassium Rubidium 

Soluble Sodium Calcium Strontium 

Soluble Potassium Titanium Lead 

Organic Carbon Manganese Vanadium 

Elemental Carbon Iron Nickel 

Aluminum Copper 

5) Runs 

PM2.5 chemical composition data were collected on a one in three days schedule at 

each site. Table 6 shows the number of samples included under each scenario. 

Table 6. Number of Samples included in the CMB Runs 

Site Old Carbon New Carbon 

BAC 157 267 

FSF 390 200 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Data for each sampling day were run individually under several scenarios. Each run 

included the following profiles: ammonium nitrate, ammonium sulfate, motor vehicle, 

fugitive dust, tire and brake wear, marine, and in the case of Bakersfield, crude oil 

combustion. In the case of Fresno, the crude oil combustion profile was included only 

when it was necessary to explain the vanadium and nickel contributions. Biomass 

burning and ‘other OC’ profiles were included as needed. First, all data were run with a 

biomass burning profile (AgBWheat from June through October, WBoakEuc the rest of 

the year) and the ‘other OC’. The results were examined to determine if all source 

contributions were positive and performance parameters were within acceptable ranges. 

If using the biomass burning profile along with the ‘other OC’ gave unsatisfactory 

results, the data were run again using just one of the two profiles, as described below. 

1. Run 1 included a biomass burning profile and ‘other OC’. 

2. Run 2 included a biomass burning profile but not the ‘other OC’ profile. It was 

geared towards days when primary sources of organic carbon (biomass burning, 

motor vehicle exhaust, and geological material) sufficiently accounted for the 

ambient organic carbon. 

3. Run 3 included ‘other OC’ but no biomass burning profile. It was geared towards 

days with no biomass burning and applied only when soluble potassium 

concentration was reported as zero. 

Data from several runs were combined into a single data file to best represent source 

contributions. Data were combined as follows: 

1. Days with estimated positive contributions from wood burning and ‘other OC’ 

were included in the composite file. 

2. Days with estimated negative contributions from ‘Other OC’ were treated as 

follows: 

a. If there should have been no burning on that day because the soluble 

potassium concentration was zero, run 3 which includes the ‘other OC’ 

and no wood burning was utilized. 

b. If there could have been wood burning because the soluble potassium 

concentration was greater than zero, run 2 which includes biomass 

burning was used. 

3. Occasionally, the results were still unsatisfactory and profiles were adjusted 

individually for a particular day. 

Composite files were used for subsequent analysis. Table 7 shows the number of data 

points from each run included in the composite file. 

 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Table 7. Data included in the composite file*. 

Site Carbon Run 1 Run2 Run 3 Special Run

   Type WBOakEuc AgBWheat         

BAC Old 68 41 4 16 5

BAC New 80 113 28 12 0

FSF Old 176 105 7 40 9

FSF New 52 66 21 56 6

* Data with performance measures far exceeding the acceptable criteria were not included in 

the composite 

6) Comparison of CMB Estimates using Old and New Carbon 

The Bakersfield-California site has 14 days with parallel old and new carbon data. 

Since these data were collected during the low season, the average PM2.5 

concentration was only 13.6 ug/m3. The CMB model was applied to the old and new 

carbon data to evaluate the impact of changing carbon collection and analysis on 

source contribution. Using the old carbon, 10 percent more of the mass was 

apportioned to sources. Regardless of what carbon data were used, the model 

apportioned almost the same concentration to each source, except ‘other OC’. Using 

the old carbon data, on average, 2.5 ug/m3 was assigned to the ‘other OC’. Switching 

to the new carbon data reduced the ‘other OC’ estimate to 1 ug/m3. There were also 

several days when the motor vehicle contribution estimate differed slightly depending on 

which carbon data were used. Even though, on average there is no difference in 

measured EC between the old and new carbon method, on these particular days the 

measurements differed and that difference was reflected in the motor vehicle 

contribution. The new OC measurement is, on average, about 50 percent lower 

compared to the old one. This impacts how much mass gets apportioned to the ‘other 

OC’ but has no impact on motor vehicle or biomass burning contributions. Figures 1 

through 4 compare percent of mass and contribution from major carbon sources using 

old and new carbon data. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
 

 

7) Results 

The CMB model was applied to 424 samples at BAC (157 with old carbon and 267 with 

new carbon) and 590 samples at FSF (390 with old carbon and 200 with new). Source 

contribution estimates were averaged to determine a typical contribution. Separately, 

days with concentrations greater than 30 ug/m3 were averaged to determine the typical 

contribution on a high PM2.5 day. 

Performance measures and statistics used to evaluate the validity of CMB source 

apportionments include chi-square, r-square, and percent of mass accounted for by the 

estimated source contributions. The target values for these performance measures are 

chi-square less than 4, r-square greater than 0.8, and percent of mass accounted for by 

0

20

40

60

80

100

120

140

5/3/075/6/07

5/12/075/15/07

6/2/076/5/07

6/11/076/14/076/17/076/20/076/23/076/26/076/29/07

7/5/07

Percent

Figure 1.  Compare Percent Mass

Old_% New%

0

0.5

1

1.5

2

2.5

3

5/3/075/6/07

5/12/075/15/07

6/2/076/5/07

6/11/076/14/076/17/076/20/076/23/076/26/076/29/07

7/5/07

Concentration (ug/m3)

Figure 3.  Compare Motor Vehicle Estimate

Old_MV New_MV

0

1

2

3

4

5

6

7

8

5/3/075/6/07

5/12/075/15/07

6/2/076/5/07

6/11/076/14/076/17/076/20/076/23/076/26/076/29/07

7/5/07

Concentration (ug/m3)

Figure 2. Compare Woodburning Estimate

Old_WB New_WB

0

1

2

3

4

5

5/3/075/6/07

5/12/075/15/07

6/2/076/5/07

6/11/076/14/076/17/076/20/076/23/076/26/076/29/07

7/5/07

Concentration (ug/m3)

Figure 4.  Compare 'Other OC' Estimate

Old_OC New_OC

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
the estimated source contributions between 80% and 120%. The average performance 

measures for both sites were within the acceptable limits as shown in Tables 8 and 10. 

The results are discussed separately for each site for two reasons. First of all, each site 

switched to the new method at a different time. Second of all, 2009 had to be excluded 

from the annual average calculation at Bakersfield due to missing data. 

a) Bakersfield (BAC) 

The average PM2.5 concentration based on old carbon data for 2006 was 20.5 ug/m3. 

Based on the new carbon data, the 2008 and 2010 average PM2.5 concentration was 

18.6 ug/m3. Between 2006 and 2007 (old carbon data) there were 29 high days with 

chemical composition data. The average PM2.5 concentration on these days was 

47 ug/m3,. Between 2007 and 2010 (new carbon data), there were 36 high days with 

chemical composition data, with the average PM2.5 concentration of 46.7 ug/m3. 

Sources identified by the CMB accounted for 79 to 94 percent on annual basis and 94 

to 95 percent on high days. 

i) Annual 

Ammonium nitrate dominated the PM2.5 mass contributing 42 to 47 percent of mass. 

Ammonium sulfate and biomass burning were the next most important sources 

contributing 10 to 12 percent of mass. Biomass burning contributed 9 to 10 to percent 

of the mass. The ‘Other OC’ contribution depended on the carbon data method; using 

old carbon apportioned 16 percent of mass to the ‘other OC’ while using new carbon 

reduced that contribution to 8 percent. Geological material comprised 7 to 10 percent of 

the mass. Each of the remaining sources (tire and brake wear, sea salt, and oil 

combustion) contributed no more than 1 percent of the mass. 

ii) High Days 

The ammonium nitrate contribution was even more significant on high days, ranging 

from 59 to 67 percent. Biomass burning and motor vehicles each contributed 

9 to 13 percent. The ‘Other OC’ contribution ranged from 3 percent using new carbon 

to 10 percent using old carbon. Geological material contributed about 2 percent. Each 

of the remaining sources, tire and brake wear, sea salt, and oil combustion contributed 

less than 1 percent of the mass. 

 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
b) Fresno (FSF) 

The average PM2.5 concentration based on old carbon data for 2006 to 2008 was 

20.3 ug/m3. Based on the new carbon data, the 2010 average PM2.5 concentration 

was 14.2 ug/m3. Between 2006 and 2009 (old carbon data) there were 67 high days 

with chemical composition data. The average PM2.5 concentration on these days was 

46.3 ug/m3. Between 2009 and 2010 (new carbon data), there were 22 high days with 

chemical composition data, with the average concentration of 40.6 ug/m3. Sources 

identified by the CMB accounted for 74 to 97 percent of the mass on an annual basis 

and 82 to 93 percent on high days. 

i) Annual 

Ammonium nitrate dominated the PM2.5 mass contributing 40 to 43 percent of the 

mass. Biomass burning contributed about 16 percent of the mass. Motor vehicles 

contributed slightly less, 11 to 13 percent. The ammonium sulfate contribution was 

9 to 11 percent. The ‘Other OC’ contribution, once again, depended on carbon data; 

using old carbon apportioned 18 percent of mass to the ‘other OC’ while using new 

carbon reduced that contribution to 9 percent. Geological material comprised 4 to 6 

percent of the mass. Each of the remaining sources contributed no more than 1 percent 

of the mass. 

ii) High Days 

The ammonium nitrate contribution was even more significant on high days when 

52 to 54 percent of the mass was ammonium nitrate. Biomass burning was the second 

most significant source, contributing 19 to 23 percent. The motor vehicle contribution 

ranged from 9 to 12 percent. The ‘Other OC’ ranged from 4 percent using new carbon 

data to 13 percent using old carbon data. The remaining sources contributed less than 

1 percent of the mass. 

 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Table 8. BAC Source Contribution (ug/m3) 

Source Profile Annual Average High Days (>=30 ug/m3)

Name 2006 2008 and 2010 2006‐2007 2007‐2010

# of samples Obs Count 90 138 29 36

Mconc Mconc 20.5 ± 1.1 18.6 ± 1.0 47.0 ± 2.4 46.7 ± 2.4

Cconc Cconc 19.4 ± 1.5 15.6 ± 1.2 45.0 ± 3.1 43.9 ± 3.1

Rsquare Rsquare 0.9 0.9 0.9 0.9

CHIsquare CHIsquare 3.1 2.7 1.8 1.4

%MASS %MASS 94.3 79.3 95.5 93.8

AMNIT AMNIT 8.2 ± 0.8 7.4 ± 0.7 26.7 ± 2.5 29.6 ± 2.5

AMSUL AMSUL 2.0 ± 0.6 1.9 ± 0.5 2.5 ± 1.2 3.4 ± 1.3

Biomass burning Seasonal* 1.9 ± 0.4 1.5 ± 0.3 5.7 ± 1.0 4.0 ± 0.7

Motor Vehicle K9GASDI** 2.4 ± 1.0 1.9 ± 0.6 4.2 ± 1.6 4.2 ± 1.4

OC OC 3.2 ± 1.1 1.2 ± 0.7 4.3 ± 1.7 1.3 ± 1.2

Tire and Brake TireBrk 0.2 ± 0.1 0.2 ± 0.1 0.3 ± 0.2 0.3 ± 0.2

Sea Salt MARINE75 0.1 ± 0.1 0.1 ± 0.1 0.3 ± 0.1 0.2 ± 0.1

Geological FDKERANN 1.3 ± 0.3 1.6 ± 0.3 0.7 ± 0.3 0.8 ± 0.3

Oil Refinery SFCRUC 0.3 ± 0.1 0.1 ± 0.1 0.3 ± 0.3 0.2 ± 0.3

Table 9. BAC Source Contribution (%) 

Source Profile Annual Average High Days (>=30 ug/m3)

Name 2006 2008 and 2010 2006‐2007 2007‐2010

# of samples Obs Count 90 138 29 36

AMNIT AMNIT 42.2 46.8 59.3 67.4

AMSUL AMSUL 10.2 11.9 5.6 7.8

Biomass burning Seasonal* 9.6 9.3 12.8 9.1

Motor Vehicle KGASDI** 12.3 11.8 9.3 9.6

OC OC 16.2 7.7 9.5 2.9

Tire and Brake TireBrk 1.0 1.3 0.7 0.7

Sea Salt MARINE75 0.4 0.5 0.6 0.5

Geological FDKERANN 6.7 10.3 1.6 1.7

Oil Refinery SFCRUC 1.3 0.5 0.6 0.3

* AgBWheat from June through October, WBoakEuc the rest of the year 

** K6GASDIE for old carbon and K9GASDIE for new carbon 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
 

AMNIT

42.2%

AMSUL

10.2%

Sea Salt

0.4%

Motor 

Vehicle

12.3%

Biomass 

burning

9.6%

OC

16.2%

Tire and 

Brake

1.0%

Geological

6.7%

Oil 

Refineries

1.3%

Figure 5. BAC 2006 Average Old Carbon

AMNIT

59.3%

AMSUL

5.6%

Sea Salt

0.6%

Motor 

Vehicle

9.3%

Biomass 

burning

12.8%

OC

9.5%

Tire and 

Brake

0.7%

Geological

1.6%

Oil 

Refineries

0.6%

Figure 7. BAC Average High Day Old Carbon 

(2006-2007)

AMNIT

46.8%

AMSUL

11.9%

Sea Salt

0.5%

Motor 

Vehicle

11.8%

Biomass 

burning

9.3%

OC

7.7%

Tire and 

Brake

1.3%

Geological

10.3%

Oil 

Refineries

0.5%

Figure 6. BAC 2008 and 2010 Average New Carbon

AMNIT

67.4%

AMSUL

7.8%

Sea Salt

0.5%

Motor 

Vehicle

9.6%

Biomass 

burning

9.1%

OC

2.9%

Tire and 

Brake

0.7%

Geological

1.7%

Oil 

Refineries

0.3%

Figure 8. BAC Average High Day

New Carbon (2007-2010)

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Table 10. FSF Source Contribution (ug/m3) 

Source Profile Annual Average High Days (>=30 ug/m3)

Name 2006‐2008 2010 2006‐2009 2009‐2010

# of samples Obs Count 275 105 67 22

Mconc Mconc 20.3 ± 1.1 14.2 ± 0.8 46.3 ± 2.3 40.6 ± 2.1

Cconc Cconc 19.0 ± 1.5 10.8 ± 0.8 43.0 ± 2.9 33.1 ± 2.3

Rsquare Rsquare 0.8 0.9 0.9 0.9

CHIsquare CHIsquare 4.3 3.5 1.7 1.4

%MASS %MASS 96.7 74.3 92.9 81.5

AMNIT AMNIT 7.5 ± 0.7 4.7 ± 0.5 22.4 ± 2.1 17.8 ± 1.7

AMSUL AMSUL 1.7 ± 0.5 1.2 ± 0.4 2.1 ± 1.1 2.0 ± 1.0

Biomass burning Seasonal* 3.1 ± 0.5 1.7 ± 0.3 8.0 ± 1.2 7.6 ± 1.1

Motor Vehicle FGASDI** 2.1 ± 0.9 1.4 ± 0.5 4.0 ± 1.5 3.8 ± 1.3

OC OC 3.3 ± 1.1 1.0 ± 0.5 5.7 ± 2.0 1.5 ± 1.3

Tire and Brake TireBrk 0.1 ± 0.1 0.1 ± 0.1 0.2 ± 0.1 0.2 ± 0.1

Sea Salt MARINE75 0.1 ± 0.1 0.1 ± 0.1 0.2 ± 0.2 0.2 ± 0.2

Geological FDFREANN 0.8 ± 0.2 0.6 ± 0.1 0.2 ± 0.2 0.1 ± 0.1

Oil Combustion CHCRUC 0.01 ± 0.0 0.1 ± 0.0 0.0 ± 0.0

Table 11. FSF Source Contribution (%) 

Source Profile Annual Average High Days (>=30 ug/m3)

Name 2006‐2008 2010 2006‐2009 2009‐2010

# of samples Obs Count 275 105 67 22

AMNIT AMNIT 40.0 43.2 52.0 53.6

AMSUL AMSUL 9.1 11.4 4.9 6.0

Biomass burning WBOakEuc 16.5 15.7 18.7 22.9

Motor Vehicle F9GASDI 11.1 13.3 9.3 11.5

OC OC 17.5 9.2 13.4 4.4

Tire and Brake TireBrk 0.8 0.8 0.5 0.5

Sea Salt MARINE75 0.6 0.9 0.5 0.7

Geological FDFREANN 4.2 5.6 0.5 0.3

Oil Combustion CHCRUC 0.1 0.0 0.1 0.0

* AgBWheat from June through October, WBoakEuc the rest of the year 

** F6GASDIE for old carbon and F9GASDIE for new carbon 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
 

Contributions on individual exceedance days are illustrated in Figures 13 through 16. 

The highest contribution from each source is also summarized in Tables 10 and 11. 

AMNIT

40.0%

AMSUL

Sea Salt 9.1%

0.6%

Motor 

Vehicle

11.1%

Biomass 

burning

16.5%

OC

17.6%

Tire and 

Brake

0.8%

Geological

4.2%

Figure 9. FSF 2006-2008 Average Old Carbon

AMNIT

52.7%

AMSUL

4.9%

Sea Salt

0.5%

Motor 

Vehicle

11.8%

Biomass 

burning

24.5%

OC

4.6%

Tire and 

Brake

0.5%

Geological

0.4% Oil 

Refineries

0.1%

Figure 11. FSF Average High Day Old Carbon 

(2006-2009)

AMNIT

43.2%

AMSUL Sea Salt 11.4% 0.9%

Motor 

Vehicle

13.3%

Biomass 

burning

15.7%

OC

9.2%

Tire and 

Brake

0.8%

Geological

5.6%

Figure 10. FSF 2010 Average New Carbon

AMNIT

53.6%

AMSUL

6.0%

Sea Salt

0.7%

Motor 

Vehicle

11.5%

Biomass 

burning

22.9%

OC

4.4%

Tire and 

Brake

0.5%

Geological

0.3%

Figure 12. FSF Average High Day New Carbon 

(2009-2010)

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
0

10

20

30

40

50

60

70

80

90

100

1/5/061/8/06

1/11/061/23/06

2/4/062/7/06

2/10/062/13/0610/2/0611/1/0611/7/06

11/16/0611/19/06

12/4/0612/7/06

12/13/0612/25/0612/31/06

1/3/071/9/07

1/18/071/21/071/24/071/27/071/30/07

2/2/072/5/072/8/07

2/17/07

Concentrations (ug/m3)

Figure 13. PM2.5 Source Contribution on High Days 2006-2007

Bakersfield, Old Carbon

Oil Refining

Other OC

Biomass Burning

Motor Vehicle

Ammonium Sulfate

Ammonium Nitrate

Airborne Soil

Tires&Brakes

Sea Salt

PM2.5 Mass

0

10

20

30

40

50

60

70

80

10/27/07

11/2/0711/8/07

11/14/0711/26/0711/29/0712/11/0712/14/0712/23/07

1/10/081/13/081/16/081/19/08

2/9/08

2/12/082/18/082/27/08

3/7/08

10/27/08

11/8/08

11/17/0811/20/0811/26/0812/11/08

11/3/09

11/24/0911/27/09

12/3/09

12/21/0912/27/09

1/5/102/4/10

11/4/1012/1/1012/4/10

12/10/10

Concentrations (ug/m3)

Figure 14. PM2.5 Source Contribution on High Days 2007-2010

Bakersfield, New Carbon

Oil Refining

Other OC

Biomass Burning

Motor Vehicle

Ammonium Sulfate

Ammonium Nitrate

Airborne Soil

Tires&Brakes

Sea Salt

PM2.5 Mass

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
 

0

10

20

30

40

50

60

70

80

90

100

1/5/06

1/11/061/17/061/23/06

2/1/06

2/10/062/13/062/22/0610/2/0611/1/06

11/16/0611/22/0611/25/06

12/1/0612/4/0612/7/06

12/13/0612/25/0612/31/06

1/3/071/9/07

1/15/071/18/071/21/071/24/071/27/071/30/07

2/2/072/8/07

2/17/0711/2/0711/5/0711/8/07

11/14/0711/23/0711/26/0711/30/0712/11/0712/14/0712/23/0712/29/07

1/1/08

1/10/081/13/081/16/081/19/08

2/9/08

2/12/082/18/08

3/7/08

11/8/08

11/14/0811/26/0811/29/08

12/5/0812/8/08

12/11/0812/29/08

1/1/091/4/09

1/10/091/13/091/16/091/19/091/22/091/28/091/31/09

Concentrations (ug/m3)

Figure 15. PM2.5 Source Contribution on High Days 2006-2009

Fresno, Old Carbon

Other OC

Biomass Burning

Motor Vehicle

Ammonium Sulfate

Ammonium Nitrate

Airborn Soil

Tires&Brakes

Sea Salt

PM2.5 Mass

0

10

20

30

40

50

60

70

11/12/0911/15/0911/24/09

12/3/0912/6/0912/9/09

12/21/0912/24/0912/30/09

1/2/101/8/10

1/11/101/15/10

2/4/10

2/19/10

11/13/1011/16/1011/25/10

12/4/1012/7/10

12/10/1012/13/10

Concentrations (ug/m3)

Figure 16. PM2.5 Source Contribution on High Days 2009-2010

Fresno, New Carbon

Other OC

Biomass Burning

Motor Vehicle

Ammonium Sulfate

Ammonium Nitrate

Airborn Soil

Tires&Brakes

Sea Salt

PM2.5 Mass

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Table 12. BAC Highest Contribution by Source 

Source Old Carbon New Carbon 

Contribution (ug/m3) Date Contribution (ug/m3) Date 

Ammonium Nitrate 60 2/5/07 50 2/12/08 

Ammonium Sulfate 5 10/2/06 11 1/5/10 

Biomass Burning 18 12/25/06 12 11/29/07

Motor Vehicle 12 12/25/06 7 1/19/08 

Other OC 9 2/5/07 5 2/12/08 

Tire & Brake 0.6 1/24/07 0.8 12/4/10 

Sea Salt 1 11/16/06 0.7 11/26/08

Geological 2.5 11/24/07 2.5 11/8/07 

Oil Combustion 1 11/24/07 1 11/8/07 

Table 13. FSF Highest Contribution by Source 

Source Old Carbon New Carbon 

Contribution (ug/m3) Date Contribution (ug/m3) Date 

Ammonium Nitrate 50 12/14/07 38 12/4/10 

Ammonium Sulfate 5 12/23/07 7 1/8/10 

Biomass Burning 21 1/1/08 19 11/25/10

Motor Vehicle 8 12/4/06 8 11/25/10

Other OC 13 1/19/08 6 12/24/09

Tire & Brake 0.6 12/7/06 0.4 11/24/09

Sea Salt 0.6 2/9/08 0.4 12/10/10

Geological 1.4 11/8/07 0.6 12/4/10 

Oil Combustion 2 2/8/07 

References 

Chow, J.C., Watson, J. G., Lowenthal, D. H., and Countess, R. J.: Sources and 

chemistry of PM10 aerosol in Santa Barbara County, CA, Atmospheric Environment, 30, 

1489-1499, 1996. 

Chow, J.C., Watson, J.G., Crow, D., Lowenthal, D. H., and Merrifield, T.: Comparison 

of IMPROVE and NIOSH carbon measurements. Aerosol Science and Technology. 

34:23-34, 2001. 

Chow, J.C., Watson, J.G., Ashbaugh, L. L., and Magliano, K. L.: Similarities and 

differences in PM10 chemical source profiles for geological dust from the San Joaquin 

Valley, California. Atmospheric Environment. 37 (9-10):1317-1340, 2003. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Chow, J. C., Chen, L.-W. A., Lowenthal, D. H., Doriswamy, P., Park, K., Kohl, S., 

Trimble, D. L., and Watson, J. G.: California Regional PM10/PM2.5 Air Quality Study 

(CRPAQS) – Initial data analysis of field program measurements, California Air 

Resources Board, Sacramento, CA, by Desert Research Institute, Reno, NV, 2005. 

Fitz, D. R. and Chow, J. C.: Development of gas and particulate matter organic profile 

database – draft work plan. Prepared for the San Joaquin Valleywide Air Pollution 

Study Agency, California Air Resourced Board. Sacramento, CA, by the University of 

California CE-CERT, Riverside, CA, 2001. 

Fujita. E. M., Campbell, D., Zielinska, B., and Chow, J. C.: Kansas City PM 

Characterization Study, Round 1 Testing Report – Addendum A, Chemical Speciation, 

prepared for the U.S. Environmental Protection Agency, Desert Research Institute, 

Reno, NV, 2005. 

 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Appendix A 

Summary of Geological Profiles Used in CMB Modeling 

Source Type Subtype County Sample ID Source 

FDKERANN 

Agricultural Soil Kern Soil 31 Houck, et al, 1989 

Paved Road Urban Kern FDPVR1 Central California Fugitive Dust 

Study Animal 

Husbandry 

Feedlot 

(Composite) 

Kern&Fresno FDCTF 

Unpaved Road 

Dust 

Unpaved 

Parking lot 

Kern Soil 13 Houck, et al, 1989 

FDFREANN 

Paved Road Fresno Soil 03 Houck, et al, 1989 

Agricultural Soil Almonds 

(Composite) 

Kern, Fresno, 

King, and 

Madera 

FDALM Central California Fugitive Dust 

Study 

Agricultural Soil Grapes Fresno FDGRA1 

Agricultural Soil Tomato 

(Composite) 

Fresno FDTOM1 

 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
San Joaquin Valley PM2.5 Weight of Evidence Analysis 

Appendix 3 

Source Apportionment of PM2.5 Measured at the Fresno and 

Bakersfield Chemical Speciation Network Sites in San Joaquin Valley 

Using the Positive Matrix Factorization Model 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Source Apportionment of PM2.5 Measured at the Fresno and 

Bakersfield Chemical Speciation Network Sites in San Joaquin Valley 

Using the Positive Matrix Factorization Model 

Sample Collection and Data Screening

 PM2.5 chemical speciation samples were collected on a one-in-three day 

schedule at the Fresno-First St. and Bakersfield-California Ave. Chemical Speciation 

Network (CSN) monitoring sites located in the San Joaquin Valley (SJV). There were 

good agreements between PM2.5 data collected by the speciation samplers and the 

collocated Federal Reference Method (FRM) samplers in matched Fresno data 

(340 samples, slope = 1.00, Intercept = 1.08, r

2

 = 0.97) and Bakersfield (175 samples, 

slope = 0.94, Intercept = 0.92, r

2

 = 0.94) between 2008 and 2010. 

 The Thermal Optical Transmittance (TOT) protocol had been used to analyze 

carbon mass collected on the quartz filters. This method was changed to the Thermal 

Optical Reflectance (TOR) protocol and TOR organic carbon (OC) and elemental 

carbon (EC) concentrations were available starting from January 2008 and April 2009 at 

the Bakersfield and Fresno monitoring sites, respectively. Only the speciation data for 

which TOR OC and EC concentrations were available were considered in this source 

apportionment study. 

 Since a carbon denuder that minimizes the positive sampling artifact caused by 

adsorption of gaseous organic materials was not included upstream of quartz filter in the 

CSN samplers, and none of the reported CSN data were blank corrected, an integrated 

OC artifact concentration that includes OC adsorption and desorption was estimated 

utilizing the intercept of the regression of OC concentrations against PM2.5 mass 

concentrations (Tolocka et al. 2001, Kim et al. 2005). Samples for which PM2.5 or OC 

concentrations had an error flag and samples for which the PM2.5 or OC data were not 

available were excluded from the regression analysis between PM2.5 and OC 

concentrations. Comparing co-located PM2.5 data measured by CSN and FRM 

samplers, and comparing PM2.5 and Sulfur (S) concentrations, outliers were censored 

for the two data sets. Using 189 data points out of 353 data points between 2009 and 

2010 at Fresno and 187 data points out of 192 data points at Bakersfield between 2008 

and 2010, the intercept s of 0.576 μg/m3

 and 1.480 μg/m3 in PM2.5 regression against 

OC concentrations are considered to be the integrated OC artifact concentrations at 

Fresno and Bakersfield, respectively (Figure 1). The OC concentrations analyzed in 

this study were corrected by subtracting the integrated OC artifact concentrations. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Fresno-First St. 

Bakersfield-California Ave. 

Figure 1. OC artifact estimations: PM2.5 concentrations versus OC concentrations. 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
 The Positive matrix factorization model version 2 (PMF2) model was used for the 

source apportionment of PM2.5 at the Fresno and Bakersfield monitoring sites. Samples 

were excluded from the data set for which the PM2.5, artifact corrected OC, or EC data 

were not available or below zero, or for which PM2.5 artifact corrected OC, or EC had an 

error flag. Samples for which the sum of all measured species were larger than twice 

the PM2.5 concentrations or the sum of all measured species were less than 50% of 

PM2.5 concentrations were also excluded. Finally, samples that contain fireworks 

particles collected on Independence Day and New Year’s Day were excluded since they 

had unusually high concentrations of OC, EC, K+, Na+ and metals. Overall, 10.3% of 

the Fresno data and 16.5% of the Bakersfield data were excluded in this study. 

 For the chemical species screening, X-Ray Fluorescence (XRF) S was excluded 

from the analyses to prevent double counting of mass concentrations. Due to the 

higher analytical precision compared to XRF Na and XRF K, IC Na+ and IC K+ were 

included in the analyses. Chemical species below MDL values more than 90% were 

excluded. As recommended by Paatero and Hopke (2003), the species that had a 

Signal-to-Noise (S/N) ratio below 0.2 were excluded. Thus, a total of 174 samples and 

21 species including PM2.5 mass concentrations collected between April 2009 and 

December 2010 were used for the Fresno site. For the Bakersfield site, a total of 147 

samples and 24 species including PM2.5 mass concentrations collected between 

January 2008 and December 2010 were used. Since new TOR OC and EC 

concentrations were not accompanied by detection limit and uncertainty values, a 

comprehensive set of uncertainty structure (i.e., 7% of measured concentration) 

estimated by Kim et al. (2005) and 0.1 μg/m3

 of detection limit value estimated from the 

State and Local Air Monitoring Stations (SLAM) speciation data were used in this study. 

Summaries of PM2.5 speciation data are provided in Tables A1 and A2 in the Appendix. 

 The procedure of Polissar et al. (1998) was used to assign input data for PMF2. 

The measurement values are used for the input concentration data, and the sum of the 

analytical uncertainty and one-third of the detection limit value is used as the input 

uncertainty data assigned to each measured value. Concentration values below the 

detection limit are replaced by half of the detection limit values, and their input 

uncertainties are set at five-sixth of the detection limit values. Missing values are 

replaced by the geometric mean of the measured values for each species. To downweight these replaced data and then to reduce their influence on the solution, their 

accompanying uncertainties are set at four times the geometric mean value. The 

conditional probability function (CPF) analysis was used to estimate the possible 

directions of the local source impacts (Kim and Hopke, 2004). The CPF was calculated 

for each source using the PMF2 source contributions coupled with wind data. As 

recommended by Paatero and Hopke (2003), which is to down-weight the variable in 

the analysis so that the noise does not compromise the solution, it was found necessary 

to increase the input uncertainties of OC, EC, and Cl by a factor of 3 for the Fresno data 

and OC and Na+

 by a factor of 3 for the Bakersfield data to obtain physically 

interpretable PMF2 results. 

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PMF Results

 Seven major sources were resolved from PMF2 analyses for both sites (matrix 

rotational parameter: Fresno FPEAK = 0.1; Bakersfield FPEAK = 0). The comparison of 

the reconstructed PM2.5 contributions (sum of contributions from all sources) with 

measured PM2.5 concentrations shown in Figure 2 indicates that the resolved sources 

effectively reproduce the measured values and account for most of the variation in the 

PM2.5 concentrations (slope = 0.88, r

2

 = 0.95 for Fresno data; slope = 0.93, r

2

 = 0.91 for 

Bakersfield data). 

Average Source Contributions 

 As shown in Figure 3 and Table A3 which present average source contributions, 

secondary nitrate contributed the most at both sites (35% at the Fresno site, 41% at the 

Bakersfield site). The pie charts indicate that three major sources (i.e., secondary 

nitrate, secondary sulfate, and motor vehicle) contributed 74% of PM2.5 concentrations 

at both sites. Figure 4 shows monthly average source contributions. Secondary nitrate, 

motor vehicle, and biomass smoke contributed the most in winter. The source profiles, 

corresponding source contributions, weekday/weekend variations, monthly averaged 

source contributions, and potential source directions are presented in Figures A1 

through A10 in the Appendix. 

 The secondary nitrate factor was identified by its high concentration of NO3

-

 and 

NH4

+

. It consisted of NH4NO3 and several minor species such as secondary OC and 

EC that transport together. It contributed the most at both sites, accounting for 35% and 

41% of the PM2.5 mass concentrations at Fresno and Bakersfield, respectively. 

Bakersfield showed higher secondary nitrate concentrations than Fresno. Secondary 

nitrate particles had winter- high trends at both sites. Secondary sulfate was identified 

by its high concentration of SO4

2- and NH4+

 and accounted for 27% and 20% of the 

PM2.5 mass concentration at Fresno and Bakersfield, respectively. Secondary nitrate 

and secondary sulfate did not show clear weekday/weekend variations. Secondary 

sulfate showed seasonal variations with higher concentrations in summer when the 

photochemical activity was highest at both sites. The CPF plots for secondary nitrate 

pointed S and NE at both sites. The CPF plots for secondary sulfate pointed SE at the 

Fresno site and SW at the Bakersfield site. 

 The motor vehicle factor was identified by its high concentration of OC, EC, NO3

-

, 

and minor species such as Fe (Watson et al., 1994). Motor vehicle emissions 

contributed 12% and 13% of the PM2.5 mass concentrations at Fresno and Bakersfield, 

respectively. Motor vehicle emissions did not show clear weekday/weekend variations 

at either site, however there was a winter-high seasonal trend. 

 

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The biomass smoke factor was characterized by OC, EC, and K+

 (Watson et al., 2001) 

and contributed 11% and 10% to the PM2.5 mass concentrations at Fresno and 

Bakersfield, respectively. The biomass smoke category reflects contributions from 

residential wood burning and smoke from commercial cooking. The biomass smoke did 

not show weekday/weekend variations. The biomass smoke did show winter-high 

trends suggesting that it was mostly contributed by residential wood burning. The CPF 

plots for the biomass smoke pointed to high contributions from NE and S at both sites. 

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 Fresno-First St. 

 Bakersfield-California Ave. 

Figure 2. Measured versus PMF predicted PM2.5 mass concentrations. 

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Figure 3. Average source contributions. 

Figure 4. Monthly average source contributions. 

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The airborne soil factor was identified by its high concentrations of Si, Al, Ca and 

Fe. It contributed 7% and 10% to the PM2.5 mass concentration at Fresno and 

Bakersfield, respectively. Airborne soil reflects wind-blown dust as well as resuspended crustal materials by road traffic as indicated by the presence of OC or EC in 

the source profiles. Airborne soil did not show clear weekday/weekend variation. Both 

sites exhibited autumn-high seasonal trends. The CPF plots for airborne soil suggested 

high contributions from SW and S at both sites. 

 The aged sea salt factor was represented by its high concentrations of NO3

-

, 

SO4

2-, and Na+

, accounting for 6% the PM2.5 mass concentration at Fresno and 4% at 

Bakersfield. Aged sea salt reflects particles in which Cl- in the fresh sea salt is partially 

displaced by acidic gases during the transport and collected along with NO3

-

 and SO4

2-

(Song and Carmichael, 1999). Aged sea salt did not show weekday/weekend variation 

at either site. Aged sea salt had high contributions in summer at the Fresno site. 

Interestingly, it had a high contribution in winter at the Bakersfield site. The CPF plot for 

aged sea salt at Fresno site pointed towards NE. The CPF plot for aged sea salt at 

Bakersfield site suggested high contributions from NE and S. 

 A possible industrial source such as metal processing that was characterized by 

OC, EC, Fe, and Zn was identified at both sites. This source accounted for 2% of the 

PM2.5 mass concentrations at both sites. It showed weak weekday-high variations at 

the Bakersfield site. The industrial source showed winter-high variations at the Fresno 

site. The CPF plot suggested high contributions from NE and SW at both sites. 

 

Figure 5. Average source contributions in Fresno – First St. and Bakersfield – California 

Ave. when PM2.5 concentrations were higher than 30 μg/m3 in the high PM2.5 season 

(Nov. - Feb.). 

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Higher PM2.5 Day Contributions 

 The average source contributions when PM2.5 concentrations were higher than 

30 μg/m3

 in the high PM2.5 season (Nov. - Feb.) are shown in Figure 5 for percentiles 

and in Table A4 for mass concentrations. The contributions from secondary nitrate and 

motor vehicle were increased from 35% up to 54% and from 12% up to 23%, 

respectively, at the Fresno site. The biomass burning contributions also increased 

slightly from 11% up to 13% at Fresno site. At the Bakersfield site, the contributions 

from secondary nitrate increased from 41% up to 64% and aged sea salt from 4% up to 

6%. 

Conclusions 

 PM2.5 speciation and related meteorological data collected at the Fresno-First St. 

and Bakersfield-California Ave. CSN monitoring sites between 2008 and 2010 were 

analyzed by PMF2. Seven major PM2.5 sources were identified at both monitoring sites: 

secondary nitrate, secondary sulfate, motor vehicle, biomass smoke, airborne soil, aged 

sea salt, and industrial. Annual average and high day source contributions showed that 

secondary nitrate, secondary sulfate, motor vehicles, and biomass burning were the 

largest contributors to PM2.5 concentrations. 

References 

Kim, E., Hopke, P.K. Comparison between conditional probability function and 

nonparametric regression for fine particle source directions, Atmospheric 

Environment 38, 11, 4667-4673, 2004. 

Kim, E., Hopke, P.K. and Qin, Y. Estimation of organic carbon blank values and error 

structures of the speciation trends network data for source apportionments, 

Journal of Air and Waste Management Association 55, 1190–1199, 2005. 

Paatero, P., Hopke, P.K. Discarding or downweighting high-noise variables in factor 

analytic models, Analytica Chimica Acta, 490, 277-289, 2003. 

Polissar, A.V., Hopke, P.K., Paatero, P., Malm, W.C. and Sisler, J.F. Atmospheric 

Aerosol over Alaska 2. Elemental Composition and Sources, Journal of 

Geophysical Research, 103, 19045-19057, 1998. 

Song, C.H., Carmichael, G.R. The aging process of naturally emitted aerosol (sea salt 

and mineral aerosol) during long-range transport, Atmospheric Environment 33, 

2203-2218, 1999. 

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Appendix G: PM2.5 Weight of Evidence Analysis

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Tolocka, M.P., Solomon, P.A., Mitchell, W., Norris, G.A., Gemmill, D.B., Wiener, R.W., 

Vanderpool, R.W., Homolya, J.B. and Rice, J.East versus west in the US: 

Chemical characteristics of PM2.5 during the winter of 1999, Aerosol Science 

and Technology 34, 88-96, 1999. 

Watson, J.G., Chow, J.C., Lowenthal, D.H., Pritchett, L.C. and Frazier, C.A. Differences 

in the carbon composition of source profiles for diesel and gasoline powered 

vehicles, Atmospheric Environment 28(15), 2493-2505, 1994. 

Watson, J.G., J.C. Chow, and J.E. Houck, PM2.5 chemical source profiles for vehicle 

exhaust, vegetative burning, geological material, and coal burning in 

northwestern Colorado during 1995, Chemosphere, 43, 1141-1151, 2001. 

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APPENDIX 

Table A1. Summary of PM2.5 species mass concentrations at Fresno. 

Species 

Arithmetic 

mean 

(μg/m3

) 

Geometric 

mean 

(μg/m3) 

Minimum 

(μg/m3) 

Maximum 

(μg/m3) 

Number of 

below MDL 

values (%) 

PM2.5 14.5649 11.2762 0.3000 66.6000 0.6

OC 2.7861 1.9428 0.1600 17.4240 0.0

EC 0.7934 0.5764 0.0769 5.0400 0.6

SO4 1.2507 1.1155 0.2440 5.3900 0.6

NO3- 3.6499 1.9445 0.0445 29.1000 0

NH4

+

 1.3964 0.8936 0.1380 9.3500 0.6

Al 0.0646 0.0418 0.0013 0.6330 25.3

Br 0.0042 0.0032 0.0001 0.0338 15.5

Ca 0.0420 0.0310 0.0027 0.2860 4.0

Cl 0.0563 0.0156 0.0001 0.5130 40.2

Cr 0.0026 0.0018 0.0000 0.0387 75.3

Cu 0.0044 0.0032 0.0001 0.0163 28.7

Fe 0.1007 0.0843 0.0129 0.6890 0

K+ 0.1072 0.0770 0.0169 0.6460 32.8

Mg 0.0200 0.0138 0.0002 0.1140 68.4

Mn 0.0019 0.0015 0.0000 0.0126 62.1

Na+ 0.1373 0.0972 0.0176 0.8720 3.4

Ni 0.0074 0.0017 0.0000 0.1850 65.5

Si 0.1682 0.1100 0.0006 1.6400 1.7

Ti 0.0057 0.0043 0.0001 0.0448 61.5

Zn 0.0067 0.0045 0.0004 0.0296 25.3

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Table A2. Summary of PM2.5 species mass concentrations at Bakersfield. 

Species 

Arithmetic 

mean 

(μg/m3) 

Geometric 

mean 

(μg/m3) 

Minimum 

(μg/m3

) 

Maximum 

(μg/m3) 

Number of 

below MDL 

values (%) 

PM2.5 20.9253 17.1631 2.9000 73.3000 0

OC 2.4981 1.7591 0.0400 18.2200 1.3

EC 1.1390 0.9764 0.2160 3.0900 0

SO4 1.6927 1.4718 0.1200 8.0600 0

NO3- 6.4908 3.2626 0.3520 35.7000 0

NH4

+

 2.5874 1.5187 0.3160 14.8000 0.7

Al 0.1276 0.0818 0.0013 1.0800 12.7

As 0.0015 0.0013 0.0001 0.0056 73.3

Br 0.0058 0.0048 0.0001 0.0299 4.7

Ca 0.1096 0.0791 0.0065 0.6770 1.3

Cl 0.0436 0.0192 0.0002 0.3270 26.0

Co 0.0013 0.0010 0.0000 0.0047 76.0

Cr 0.0021 0.0016 0.0001 0.0156 80.0

Cu 0.0089 0.0064 0.0002 0.0570 10.0

Fe 0.1923 0.1555 0.0020 1.0900 0

K+ 0.1091 0.0931 0.0183 0.5280 16.0

Mg 0.0238 0.0160 0.0002 0.2310 60.7

Mn 0.0034 0.0025 0.0003 0.0276 32.7

Na+ 0.1556 0.1229 0.0168 0.6980 1.3

Ni 0.0010 0.0009 0.0000 0.0042 84.7

Si 0.3586 0.2322 0.0217 3.4300 0.7

Sr 0.0019 0.0017 0.0001 0.0120 84.0

Ti 0.0103 0.0068 0.0001 0.0818 46.7

Zn 0.0127 0.0084 0.0006 0.1300 8.7

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Table A3. Average source contributions (μg/m3

) to PM2.5 mass concentration. 

Sources 

Average source contribution (± 95 % distribution) 

Fresno Bakersfield 

Secondary nitrate 4.89 (1.09) 8.07 (1.85) 

Secondary sulfate 1.72 (0.16) 2.60 (0.39) 

Motor vehicle 3.70 (0.44) 4.01 (0.39) 

Biomass smoke 1.47 (0.28) 2.02 (0.28) 

Airborne soil 0.83 (0.14) 1.97 (0.37) 

Aged sea salt 1.04 (0.16) 0.79 (0.22) 

Industrial 0.22 (0.03) 0.40 (0.11) 

Estimated PM2.5 (μg/m3) 13.86 (1.57) 19.85 (2.27) 

Measured PM2.5 (μg/m3) 14.56 (1.74) 20.48 (2.33) 

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Table A4. Average source contributions (μg/m3

) to PM2.5 mass concentration at FresnoFirst St. and Bakersfield-California Ave. when PM2.5 mass concentrations were higher 

than 30 μg/m3

 between Nov. and Feb. 

Sources 

Average source contribution (Nov. – Feb.) 

Fresno Bakersfield 

Secondary nitrate 20.14 28.83 

Secondary sulfate 2.42 3.80 

Motor vehicle 8.58 5.69 

Biomass smoke 4.87 2.83 

Airborne soil 0.18 0.71 

Aged sea salt 0.47 2.52 

Industrial 0.35 0.38 

No. of days 21 25 

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Figure A1. Source profiles deduced from PM2.5 samples measured at Fresno-First St. 

(prediction ± standard deviation).

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Figure A2. Source profiles deduced from PM2.5 samples measured at BakersfieldCalifornia Ave. (prediction ± standard deviation).

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Figure A3. Source contributions deduced from PM2.5 samples measured at Fresno-First 

St. 

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Figure A4. Source contributions deduced from PM2.5 samples measured at BakersfieldCalifornia Ave. (missing data: Jan. - Jun. 2009) 

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Figure A5. Weekday/weekend variations at Fresno-First St. (mean ± 95 % distribution).

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Figure A6. Weekday/weekend variations at Bakersfield-California Ave. (mean ± 95 % 

distribution). 

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Figure A7. The monthly variations of source contributions to PM2.5 mass concentration 

at Fresno-First St. (mean ± 95 % distribution). 

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Figure A8. The monthly variations of source contributions to PM2.5 mass concentration 

at Bakersfield-California Ave. (mean ± 95 % distribution). 

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Secondary nitrate 

Figure A9. Conditional probability function plots for the highest 25% of the mass 

contributions at Fresno-First St. 

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Secondary nitrate 

Figure A10. Conditional probability function plots for the highest 25% of the mass 

contributions at Bakersfield-California Ave. 

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San Joaquin Valley PM2.5 Weight of Evidence Analysis 

Appendix 4 

Methodology for Meteorological Adjustment of 

PM2.5 Trend Statistics 

 

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Introduction 

Air quality trends can help reveal the effects of emission control strategies and 

regulations on ambient air pollution levels. However, meteorological conditions also 

affect pollutant levels and can obscure the effects of changing emissions on ambient air 

pollution levels over time. If the met-effects can be identified, quantified, and removed, 

the met-adjusted trends may reveal the emissions-induced trends with greater clarity. 

For the San Joaquin Valley PM2.5 SIP, met-adjusted trends were prepared for 

annual average PM2.5 and for PM2.5 exceedance days. This Technical Appendix 

presents the methodology used to construct the met-adjusted trends. 

1. Data Acquisition and Preparation 

PM2.5 mass concentrations from the air quality monitoring sites in two major 

urban centers of the SJV (Bakersfield and Fresno) were collected. Meteorological data 

for factors that may impact the PM2.5 concentrations were also acquired from various 

meteorological monitoring networks. Monitors at ground level provided temperature, 

relatively humidity, pressure, wind speed, wind direction, precipitation, and solar 

radiation data. For various reasons, surface pressure, wind direction, precipitation, and 

solar radiation were not used in the final analysis. Routine rawinsondes (weather 

balloons) at Oakland provided data for 500 millibar heights and 850 millibar 

temperatures. These surface and upper air factors are consistent with studies of 

meteorological conditions associated with daily PM2.5 levels [Dye et al., 2003]. 

Table 1 lists the air quality and meteorological monitoring sites that provided data 

used in this analysis. The PM2.5 and meteorological data presented are daily regional 

averages of the data collected from the sites in Table 1. 

Table 1. Air quality and meteorological monitoring sites 

Region Air Quality Sites Meteorological Sites 

Bakersfield 

Area 

Bakersfield-Golden State Highway, 

Bakersfield-5558 California Avenue, 

Bakersfield-410 E Planz Road 

Oakland (Upper Air), Mercury/Desert Rock 

(Upper Air), Vandenberg AFB (Upper Air), 

Bakersfield-Golden State Highway, 

Bakersfield-5558 California Avenue, Oildale3311 Manor Street, Shafter-Walker Street, 

Arvin-Edison, Belridge 

Fresno Area Fresno-1st Street, 

Clovis-N Villa Avenue 

Fresno-Hamilton and Winery 

Oakland (Upper Air) 

Fresno-1st Street 

Clovis-N Villa Avenue, 

A consistent analysis of met-effects on daily PM2.5 will benefit from and may 

require the presence of all PM2.5 and meteorological data for each daily record used in 

the analysis. If any values are missing, the entire day might be excluded from further 

consideration. Therefore, data completeness is very desirable for the analysis to be as 

meaningful as possible. To minimize instances of missing PM2.5 and meteorological 

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data, imputed values were calculated based on relationships for measured data at sites 

nearby. The imputed values were used when appropriate. Details concerning the 

imputation method (called “I-Bot”) are available from the Air Quality and Statistical 

Studies Section of the ARB. 

2. Analytical method: Classification and Regression Trees 

Classification and Regression Trees (CART) is a statistical exploratory technique 

for uncovering structures in the data, which is sometimes called “data mining” [Breiman 

et al., 1984; Thompson et al., 2001; Slini, et al., 2007]. CART is a non-parametric 

decision tree learning technique that produces a classification tree if the dependent 

(target) variable is categorical or a regression tree if the dependent variable is numeric. 

At each step of the tree building process, CART finds the best possible independent 

variable (or linear combination of independent variables) to split the values of the target 

variable into two groups for which the means are as different as possible (subject to 

certain constraints). Each of the new groups is called a “child” node. The process of 

node splitting is repeated for each child node and continued recursively until a stopping 

criterion is satisfied and a set of terminal nodes is reached [Breiman et al., 1984; Xu et 

al., 2005]. In this way, the nodes of the final CART tree explain the values of the 

dependent variable in terms of the independent variables used to make splits. 

In this analysis of PM2.5 and meteorology, the final CART tree explains daily 

PM2.5 in terms of the meteorological variables (parameters) used to make the splits. 

Table 2 lists all the parameters used in this particular analysis. The parameters used 

are much the same as those listed in U.S. EPA Guidelines for Developing an Air Quality 

(Ozone and PM2.5) Forecasting Program [Dye et al., 2003]. 

Table 2. Meteorological parameters used in CART analysis 

Target: Average PM2.5 Concentrations 

Predictor Type 

Season Categorical 

Weekday / Weekend Categorical 

Holiday or Not Categorical 

Temperature Surface 

Wind Speed Surface 

Relative Humidity Surface 

500 mbar Height Upper Air 

850 mbar Temperature Upper Air 

Difference between Surface and 850 mbar 

Temperature (Surrogate for Stability) Derived 

Difference of Maximum and Minimum 

Temperature (Diurnal Variability) Derived 

To prepare a CART tree, we selected the years 2004 – 2006 as base years, 

assuming that the relevant emissions did not change greatly during these few years. 

When emissions are reasonably stable, day-to-day differences in PM2.5 concentrations 

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are mostly due to differences in meteorology. We then applied CART analysis to the 

base years to define a relationship (“tree”) between daily PM2.5 and daily 

meteorological conditions. 

First, we forced the tree to be split by season so that an independent sub-tree 

was generated for each season. Each sub-tree consisted of one or more terminal 

nodes representing different meteorological classes. The CART system makes the 

differences in PM2.5 between the met-classes as large as possible and the differences 

in PM2.5 within the met-classes as small as possible. The PM2.5 concentration 

representing each met-class (terminal node) is the average concentration of all the days 

assigned to that met-class in the base years. For each day assigned to a met-class, the 

average PM2.5 for the met-class serves as a “predicted PM2.5” for that day. Days with 

high predicted values have met-conditions that are more conducive to PM2.5 formation 

compared to days with low predicted values. 

The CART-defined relationships between meteorology and PM2.5 in the base 

years were then used to assign days in the other years to their appropriate met-classes 

based on their day-specific meteorological data. The predicted PM2.5 values for all the 

days are then used to adjust PM2.5 trends up or down to compensate for each year’s 

PM2.5-conduciveness relative to “normal”.

3. Results and Discussion 

Based on daily air quality and meteorological data in 2004-2006, a CART tree 

with 17 met-classes (terminal nodes) was constructed for the Bakersfield area 

(Figure 1a) and a CART tree with 22 met-classes was constructed for the Fresno area 

(Figure 1b). Figure 2 indicates that ~75 – 80 percent of the variation in daily PM2.5 

during the base years is accounted for by each of the CART trees. Table 3 shows that 

three met-factors – wind speed, stability (difference surface 850mb temperatures), and 

minimum surface temperature – affected daily PM2.5 concentrations strongly in both 

Bakersfield and Fresno, while relative humidity (RH) was more important for PM2.5 

production in the Bakersfield area than in the Fresno area. In general, high PM2.5

concentrations in the Bakersfield were associated with relatively high stability, low wind 

speed and high RH. In Fresno, high PM2.5 was generally associated with cold 

mornings (low minimum surface temperature), high stability, and low wind speed. 

It is worth mentioning that this CART model treats each day independently and 

does not directly characterize met-conditions over a sequence of days that may result in 

long-term buildup and transport of PM2.5. 

A sensitivity analysis was, also, done to explore the impact of the selected base 

years on the CART results for the Bakersfield area. For this purpose, different sets of 

base years (2003-2005, 2004-2006, and 2006-2008) were used with CART to develop 

relationships between meteorology and PM2.5. The met-adjusted annual average 

PM2.5 concentrations proved to be quite similar regardless of the base years used in 

the CART analysis. 

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(a) 

(b) 

Figure 1. CART terminal nodes sorted by target variable predictions (PM2.5) in (a) 

Bakersfield area and (b) Fresno area. 

 

 

20

40

60

80

BAKERSFIELD_PM25_AVERAGE

Terminal Nodes Sorted By Target Variable Prediction

20

40

60

80

FRESNO_AVERAGE

Terminal Nodes Sorted By Target Variable Prediction

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(a) 

(b) 

Figure 2. Observations vs. CART predictions during the base years in the (a) 

Bakersfield and (b) Fresno areas 

 

y = 1x + 5E‐06

R2 = 0.7523

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

Observed PM2.5 at Bakersfield

CART Predicted PM2.5 at Bakersfield Area

y = 1x ‐ 5E‐08

R2 = 0.789

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70

Observed PM2.5 at Fresno Area

CART Predicted PM2.5 at Fresno Area

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Table 3. Relative importance of met-factors in forming the CART trees on a 

0 to 100 scale. 

Annual average PM2.5 trends for observed data and for CART-predicted values 

(2004-2006 used as base years) were compared in the Bakersfield and Fresno areas. 

In both areas, observed PM2.5 levels decreased significantly from 1999 to 2003, were 

relatively flat from 2003 to 2008, then decreased in 2009 and 2010. CART-predicted 

trends represent meteorological conditions that affect PM2.5 concentrations. For the 

Bakersfield area, the CART-predicted trend indicates that met-conditions favored lower 

than normal PM2.5 in 1999 – 2000, normal PM2.5 from 2001 – 2006, and higher than 

normal PM2.5 from 2007 – 2010. The CART-predicted trend for the Fresno area 

indicates that met-conditions have been more stable and have had relatively small 

impacts on the observed PM2.5 trends from 1999 – 2010. 

The CART-predicted trend information was merged with the observed trends to 

produce met-adjusted trends for annual average PM2.5. Figure 3 shows the observed 

and met-adjusted trends for (a) the Bakersfield area and (b) the Fresno area. Linear 

trend lines are shown for the observed and the met-adjusted trends in each area. 

Figure 3 indicates that the met-adjusted trend shows a greater decrease than the 

observed trend in the Bakersfield area, while the met-adjusted trend is similar to the 

observed trend in the Fresno area. In both areas, met-adjusted PM2.5 decreased by 

~ 0.8 g/m3

 per year from 1999 – 2010. Overall, the met-adjusted trends indicate that 

average PM2.5 decreased 40 – 50 percent in the Bakersfield and Fresno areas from 

1999 – 2010 as a consequence of ongoing emission reductions. 

Meteorological Factor Variable Importance in Bakersfield Variable Importance in Fresno

Average Wind Speed 100 100

Aveage Surface T ‐ 850 mbar T 69.02 56.24

Minimum Surface Temperature 56.11 60.89

Season 52.52 51.48

Minium Surface T ‐ 850 mbar T 47.74 36.37

Maximum Surface T ‐ 850 mbar T 45.59 96.49

Average Relatively Humidity 40.12 10.11

Average Surface Temperature 39.84 59.65

Maximum Surface Temperature 28.51 37.02

Afternoon 850 mbar Temperature 26.4 6

Average 850 mbar Temperature 20.88 5.89

Average 500 mbar Height 12.42 22.91

Afternoon 500 mbar Height 12.14 7.07

Maximum Relative Humidity 10.45 9.67

Morning 850 mabr Temperature 6.01 5.85

Maximum Surfact T ‐ Minimum Surface T 4.15 3.76

Minimum Relative Humidity 3.54 2.84

Morning 500 mbar Height 0.07 5.43

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 2012 PM2.5 Plan
 

(a) 

(b) 

Figure 3. Trends of observed and meteorologically adjusted PM2.5 

concentrations in (a) Bakersfield and (b) Fresno areas of the San Joaquin Valley 

y = ‐0.56x + 1139.6

R2 = 0.62

y = ‐0.8x + 1623.7

R2 = 0.77

0

10

20

30

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

PM2.5 (g/m3)

Year

Observed and Met‐Adjusted Annual Average PM2.5 Trends in Bakersfield Area

Observed PM2.5

Met‐Adjusted PM2.5  in Bakersfield Area

Linear (Observed PM2.5)

Linear (Met‐Adjusted PM2.5  in Bakersfield Area)

y = ‐0.9x + 1820.5

R2 = 0.81

y = ‐0.8x + 1614.4

R2 = 0.74

0

10

20

30

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

PM2.5 (g/m3)

Year

Observed and Met‐Adjusted Annual Average PM2.5 Trends in Fresno Area

Observed PM2.5

Met‐Adjusted PM2.5

Linear (Observed PM2.5)

Linear (Met‐Adjusted PM2.5)

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Trends for exceedance days were prepared for the Bakersfield and Fresno subregions. For this work, an exceedance day meant that the sub-regional average daily 

PM2.5 concentration was greater than or equal to 35 ug/m3. Trends for the observed 

PM2.5 data and for the CART-predicted PM2.5 data (representing meteorological 

effects) were prepared. 

In the Bakersfield area, similar to the annual averages, the CART-predicted 

exceedance days increased from the earlier years to recent years, indicating an 

increase in meteorological conduciveness for PM2.5. In the Fresno area, the impact of 

meteorology on PM2.5 exceedance days was relatively small, again similar to the 

annual averages. In both areas, the observed PM2.5 exceedance days were greater 

than the CART-predicted PM2.5 exceedance days from 1999 through 2002. The two 

trends were similar from 2003 through 2008. Finally, for 2009 and 2010, observed 

PM2.5 exceedance days decreased significantly and dipped below the CART-predicted 

exceedance days. The implication of these results is that emission reductions played a 

significant role in decreasing the PM2.5 exceedance days from 1999 – 2010, especially 

in the Bakersfield area. 

The CART-predicted trend information was merged with the observed trends to produce 

met-adjusted trends for PM2.5 exceedance days. Figure 4 shows that after adjusting 

for meteorology, PM2.5 exceedance days decreased about 60 – 70 percent from 1999 

to 2010, with decreases of ~ 3.5 days per year in the Fresno area and ~ 4.5 days per 

year in the Bakersfield area. 

 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
(a) 

(b) 

Figure 4. Trends of observed and meteorologically adjusted PM2.5 exceedance 

days in (a) the Bakersfield area and (b) the Fresno area. 

y = ‐2.5x + 5117

R2 = 0.37

y = ‐4.5x + 8976

R2 = 0.73

0

30

60

90

1998 2000 2002 2004 2006 2008 2010 2012

PM2.5 Exceedance Days

Year

Observed and Met‐Adjusted PM2.5 Exceedance Days in Bakersfield Area

Observed PM2.5 Exceedance Days in Bakersfield Area

Met‐Adjusted PM2.5 Exceedance Days in Bakersfield Area

Linear (Observed PM2.5 Exceedance Days in Bakersfield Area)

Linear (Met‐Adjusted PM2.5 Exceedance Days in Bakersfield Area)

y = ‐4.2x + 8449

R2 = 0.58

y = ‐3.5x + 7138

R2 = 0.50

0

30

60

90

1998 2000 2002 2004 2006 2008 2010 2012

PM2.5 Exceedance Days

Year

Observed and Met‐Adjusted PM2.5 Exceedance Days in Fresno Area

Observed PM2.5 Exceedance Days in Fresno Area

Met‐Adjusted PM2.5 Exceedance Days in Fresno Area

Linear (Observed PM2.5 Exceedance Days in Fresno Area)

Linear (Met‐Adjusted PM2.5 Exceedance Days in Fresno Area)

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
4. Summary 

Overall, CART analysis can help us to define the relationship between PM2.5 

mass concentrations and meteorological conditions and to calculate meteorologically 

adjusted trends. Such trends can help reveal the impact of emission changes on air 

pollutant levels, and promote the development of effective air pollution control strategies 

and regulations. Of course, as with any statistical analysis, there are uncertainties and 

limitations in CART analysis. Therefore, caution is needed when interpreting the 

resulting air quality trends, especially when small differences occur within short time 

periods. 

The annual average PM2.5 concentrations and the number of exceedances of 

the 24-hour PM2.5 standard followed similar trends in the Bakersfield and Fresno areas 

from 1999-2010. In the Fresno area, the meteorological conditions seem to have been 

relatively stable, so met-adjusted trends were similar to the observed trends. In the 

Bakersfield area, however, meteorological conditions were relatively less PM2.5

conducive in the earlier years (i.e. 1999-2000) and more conducive in recent years (i.e. 

2007-2010), with more normal years in between. Accordingly, the met-adjusted trends 

for the Bakersfield area show a greater decrease in PM2.5 levels compared to the 

observed trends. 

Based on the differences between the predicted PM2.5 levels under the 

observed meteorological conditions and under “normal” meteorological conditions, the 

PM2.5 observations are adjusted to derive met-adjusted PM2.5 trends. The analyses 

indicate that the met-adjusted annual average PM2.5 concentrations decreased at a 

rate of ~0.8 g/m3 per year between 1999 and 2010 for a total of ~40-50 percent 

decrease in met-adjusted PM2.5 in the Bakersfield and Fresno areas as a result of 

emission reductions during this period. Met-adjusted trends for PM2.5 exceedance 

days indicate ~60-70 percent progress from 1999 – 2010, with decreases of ~ 3.5 days 

per year in the Fresno area and ~ 4.5 days per year in the Bakersfield area. 

5. References 

Breiman, L., J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and regression 

trees, Wadsworth and Brooks/Cole, Monterey, 1984. 

Dye, T.S., MacDonald, C.P., Anderson, C.B., Hafner, H.R., Wheeler, N.J.M., Chan, 

A.C., Guidelines for developing an air quality (ozone and PM2.5) forecasting 

program, EPA-456/R-03-002, Environmental Protection Agency, NC, U.S.A., 2003. 

Slini, T., A. Kaprara, K. Karatzas, N. Moussiopoulos, PM10 forecasting for 

Thessaloniki, Greece, Environmental Modelling and Software, Volume 21, Issue 4, 

April 2006, Pages 559-565, ISSN 1364-8152, 10.1016/j.envsoft.2004.06.011, 2006. 

 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Thompson, M. L., J. Reynolds, L. H. Cox, P. Guttorp, P. D. Sampson, A review of 

statistical methods for the meteorological adjustment of tropospheric ozone, 

Atmospheric Environment, Volume 35, Issue 3, Pages 617-630, ISSN 1352-2310, 

10.1016/S1352-2310(00)00261-2, 2001. 

Xu, M., P. Watanachaturaporn, P.K. Varshney, M.K. Arora, Decision tree regression 

for soft classification of remote sensing data. Remote Sensing of Environment 97, 

322-336, 2005. 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
San Joaquin Valley PM2.5 Weight of Evidence Analysis 

Appendix 5 

Speciated Linear Rollback Modeling as a Corroborative Analysis to 

the Regional Photochemical Model 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Speciated Linear Rollback Modeling as a Corroborative Analysis to 

the Regional Photochemical Model 

Background 

To add to the weight-of-evidence analysis to determine that the control strategy 

adopted as a part of this Plan will be successful, the District conducted a “Speciated 

Linear Rollback” analysis to corroborate the results of the regional photochemical 

model. Different from the photochemical model, which simulates the complex 

interaction between meteorology, chemical mechanisms, and other physical 

phenomena, speciated linear rollback is an approach where the change in the 

emissions inventory from the base year to the future year is applied to measured base 

year concentrations analyzed into major component sources by the Chemical Mass 

Balance (CMB) 1 model to project an estimated future year concentration. The CMB 

model accounts for the end products of the atmospheric process without the 

requirement to simulation the chemical interactions. The linear component of the 

process assumes that the rate of reduction in the emissions inventory is related linearly 

to the reduction in ambient concentrations. Through the combination of source 

identification and relative linear emissions projection, the measured concentrations can 

be projected in connection to the inventory changes between the base year and 

projected future year. 

Analysis Procedure 

To begin this analysis, the District collected PM2.5 speciation data from the 

Environmental Protection Agency (EPA) Air Quality System (AQS) database for the 

years 2009-2011 at the sites of Fresno-First and Bakersfield-California. The data was 

then organized and limited to only days in the 1st and 4th quarters where the total 

concentration was above 35.4 ug/m3. The data was then formatted into a structure 

acceptable as an input into the CMB model, which connects observed PM2.5 

concentrations to likely pollution sources though the commonality between the 

speciation of concentrations and source profiles in the area. The CMB model then 

outputs a source contribution estimate from each pollution source (in μg/m3

) for each 

selected PM2.5 sample. 

To improve the quality of the CMB results dataset, specific criteria were used to 

filter out any poor results where the source profiles did not adequately explain the mass 

from the speciation sample. In order for a CMB result to be kept, the R2 value had to be 

greater than 0.8, the chi- square value had to be less than 4, and the percent of mass 

explained had to be at least 80%. From here, the final daily results were then averaged 

 

1

 The Environmental Protection Agency’s (EPA) Chemical Mass Balance (CMB) model is available at 

http://www.epa.gov/scram001/receptor_cmb.htm 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
for each site to create a generalized source contribution profile for both Fresno and 

Bakersfield. The source profiles used for the CMB analysis consisted of ammonium 

nitrate, ammonium sulfate, fugitive dust, mobile exhaust, marine influence, organic 

carbon, tire/brake wear, and vegetative burning. Contributions from other sources are 

not excluded, but rather are not separable from the identified major components either 

due to chemical similarity or lack of a characteristic signature to isolate the emissions 

from other groupings. 

Connecting CMB Results to the Emissions Inventory 

Next, with the average CMB results in hand, an assignment was made between 

the emissions sources identified through CMB and appropriate subsets of the emissions 

inventory developed for this plan, e.g. ammonium nitrate from CMB was connected to 

NOx from the emissions inventory, ammonium sulfate was connected to SOx, fugitive 

dust was connected to direct PM2.5 from sources than generate dust, etc. Since the 

selected base year in the air quality modeling for this plan was 2007, and with the future 

year being 2019, the change in the emissions inventory from 2007 to 2019 was 

analyzed. The specific change in the emissions inventory subsets assigned to the CMB 

sources were calculated in detail, and these changes were applied directly to the mass 

from each source specific subset in the average PM2.5 concentration as estimated by 

CMB. Through this procedure, each source specific subset of the total PM2.5 mass of 

the average sample value were rolled back at the same rate of their respective 

representative emissions inventory subsets, assuming a linear relationship between the 

current and future emissions levels in connection to the particulate matter that the 

emissions will produce . For example: the ammonium nitrate particulate mass was 

reduced at the same relative rate that NOx in the inventory is projected to change, the 

fugitive dust portion of the total mass in the design value was reduced at the same rate 

of reduction of direct PM2.5 in the emissions inventory, and so forth for all of the 

categories. 

It should be noted that in this analysis all PM2.5 mass sources were reduced 

linearly at the same rate that their respective emissions inventory subsets were being 

reduced toward 2019. No adjustments were made for non-linearity among any of the 

trends. In addition, specific background levels of PM2.5 were assumed for each source 

category. These background amounts were not reduced at the same rate of their 

respective emissions inventory subsets, but were rather reduced at the rate of reduction 

in the California statewide emissions inventory. This approach was taken to account for 

emissions reductions happening in other parts of the State that would effectively reduce 

any background/transport emissions coming into the San Joaquin Valley. 

Analysis Results 

Based on the average CMB results, the ratio of the source specific subsets to the 

total PM2.5 mass value were applied to the 2007 24-hour design value to create a CMB 

breakdown of the design value itself. Next, the speciated linear rollback produced 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
percentage concentration change from 2007 to 2019 in each of these emissions 

inventory subsets were applied to the source specific subsets of the 2007 design value, 

therefore driving down the concentration from each of these categories. Note that the 

small contribution from tire and brake wear that is projected to increase slightly with the 

growth in vehicle travel is reflected in the projection as well as an unchanged 

contribution from natural sea salt particulate (marine). When these projected reduction 

calculations were complete, the source specific subsets of the design value were added 

back together to provide an estimated future year 2019 design value. The results of this 

analysis for the Fresno and Bakersfield areas are summarized in Table 1 below. 

Table 1. Summary of Results of Speciated Linear Rollback Analysis (in μg/m3

) 

Region 2007 24-hr PM2.5 

Design Value 

Projected 2019 24-hr 

PM2.5 Design Value 

Fresno 63 31.8 

Bakersfield 66 35.2 

As the summary in Table 1 indicates, the speciated linear rollback analysis 

estimates that both the Fresno and Bakersfield areas are projected to attain the 24-hour 

PM2.5 standard of 35.4 ug/m3 by 2019. Therefore the application of the reduction in 

the emissions inventory (from 2007 to 2019) to the respective source specific subsets of 

the 2007 design value concentration drive the total mass below the attainment target by 

the deadline. 

To compare these results against those of the regional photochemical modeling 

analysis, Table 2 below shows both the speciated linear rollback and regional model 

final figures for Fresno and Bakersfield. 

Table 2. Comparison of Results between Speciated Linear Rollback Analysis and 

Regional Photochemical Model (in μg/m3

) 

Region 

Speciated Linear Rollback 

Projected 2019 24-hr PM2.5 

Design Value 

Regional Model Projected 2019 

24-hr PM2.5 Design Value 

Fresno 31.8 30.5 

Bakersfield 35.2 35.4 

As can be observed, the results of the speciated linear rollback analysis drive the 

2019 design value down to a value comparable to the projected design value from the 

regional model. Since these two approaches arrive at a similar conclusion, this builds 

confidence into the control strategy and technical analysis developed for this plan. In 

summary, the speciated linear rollback analysis provides a corroborative piece to the 

regional model, and adds to the weight-of-evidence that the District’s current control 

strategy and the further reductions committed to in this plan will be successful in 

attaining the current federal 24-hour PM2.5 standard by 2019. 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
San Joaquin Valley PM2.5 Weight of Evidence Analysis 

Appendix 6 

Unmonitored Areas Analysis 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Unmonitored Area Analyses for the 2012 24-Hour PM2.5 State 

Implementation Plan for the San Joaquin Valley 

1. Introduction: 

The U.S. EPA guidance for PM2.5 modeling (U.S. EPA, 2007) recommends an 

analysis known as the “Unmonitored Area Analysis” that requires testing all grid cells in 

the modeling domain for their potential to violate the NAAQS. The guidance (U.S. EPA, 

2007) also provides a procedure for this analysis and recommends using a software 

package known as the Model Attainment Test Software (MATS) (Abt Associates, Inc., 

2010). However, in 2011 U.S. EPA issued an addendum to the modeling guidance 

which changed the procedure for the 24-hour PM2.5 attainment test (U.S. EPA, 2011). 

This addendum did not contain guidance on a modified Unmonitored Area Analysis that 

matched the new attainment test. Therefore, the staff of the Air Resources Board has 

constructed the following Unmonitored Area Analysis approach. In brief, this analysis 

has following steps. 

 For each of the three years and for the first and fourth quarters that are used for 

the attainment test, identify the top 8 days with high PM2.5 measured with the 

Federal Reference Method (FRM) anywhere in the San Joaquin Valley (SJV). 

This results in a total of 48 days over three years. 

 Interpolate speciated FRM values for each of these top 48 days using inverse 

distance weights. 

 For each day, multiply nitrate, sulfate, organic carbon, elemental carbon, salt, 

and geologic fractions in each grid cell by the corresponding simulated Relative 

Response Factor (RRF) for that chemical component. Obtain the ammonium ion 

field by ion balancing nitrate and sulfate. Then, calculate particle bound water 

explicitly. 

 Add the chemical components for each day for each grid cell. 

 For each year and each grid cell, sort the 16 days for that year and select the 3rd

highest value. This is the 98th percentile for that year for that grid cell. 

 Average the 98th percentile value for each grid cell for each of the three years to 

obtain a relative design value for that grid cell. 

 Examine the relative design value field to determine if there are peak values 

higher than those at the monitors that could cause violations of the NAAQS. 

More details on each step are provided in the subsequent sections. 

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 2012 PM2.5 Plan
2. Methodology and Discussion: 

2.1. Identification of the Top 8 Days per Quarter: 

We have taken all available FRM measurements for the years 2005 through 2007 

and selected those days that fall on a regular one-in-three-day schedule. While some 

monitors collect data more frequently, this allowed us to select the days for which nearly 

all stations had data. This was important for subsequent interpolations. The days 

selected for each year and quarter are shown in Table 1. 

Table 1: The top 8 days selected for each year and quarter. 

Year

Quarter

ID

Sampling Day

SJV Max

Bakersfield

–

410 E Planz

Bakersfield

–

5558 California

Corcoran‐

Patterson

Visalia‐N Church

Fresno‐Hamilton

and Winery

Fresno‐1st Street

Clovis‐N Villa

Avenue

Merced‐2334

M

Modesto‐14th

Stockton

–

Hazelton

2005 1 1 3/11 53 51.6 50.8 48.1 50 45.7 53 12.1 34 23 19

2005 1 2 2/3 53 26 31.1 41 29 36 36 27.5 48.6 53 44

2005 1 3 2/6 50.5 41.8 50.5 34.5 46 41.3 40 41.3 19 9 7

2005 1 4 2/12 47.3 40.2 47.3 24.3 23.7 23 26.2 18.3 31 22

2005 1 5 1/16 47 26.6 43.4 35 47 39 37.8 31.5 42 27

2005 1 6 2/9 38.7 33.4 38.7 31.4 36 30.1 27 23.6 29.4 21 16

2005 1 7 1/25 37.9 27.3 32.2 32 33.9 35 32.1 37.9 30 32

2005 1 8 3/8 34.9 13.3 34.9 33.4 30 18.2 18 15.8 24.8 29 23

2005 4 13 12/12 92.5 92.5 84 79 86 77 80 63

2005 4 14 11/24 85.7 85.7 77.8 79 74.1 71 67.1 48.3 37 31

2005 4 15 12/15 80.1 77.5 80.1 74.5 54 71.2 62 47.2 38.3 45 43

2005 4 16 11/21 71.7 71.7 64.6 47 43.9 51 37.6 46 61 46

2005 4 17 12/6 67 47.6 54.1 50.7 54 57 67 56.9 46.8 55 37

2005 4 18 12/9 66 57.7 65 61 66 63 53.9 39 28

2005 4 19 11/15 56 41.3 39.7 36.7 49 49 56 49.8 44 13 11

2005 4 20 10/22 55.5 55.5 43.2 34 33.9 32 22 17 12

2006 1 26 2/13 71 62.5 32.1 65 65 71 65.8 34.7 27 22

2006 1 27 2/10 63.8 33 60.5 63.8 49 52.7 56 41.8 43.8 52 42

2006 1 28 2/7 45.7 43.8 45.7 30.4 32 27.3 26 19.9 28.5 22 22

2006 1 29 1/23 43 32.5 32.8 23.3 34 35.5 43 37.2 28.5 21 17

2006 1 30 2/25 42 38.7 29 28.2 28 22.6 23.9 42 35

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Year

Quarter

ID

Sampling Day

SJV Max

Bakersfield

–

410 E Planz

Bakersfield

–

5558 California

Corcoran‐

Patterson

Visalia‐N Church

Fresno‐Hamilton

and Winery

Fresno‐1st Street

Clovis‐N Villa

Avenue

Merced‐2334

M

Modesto‐14th

Stockton

–

Hazelton

2006 1 31 2/1 41 17.3 15.6 23.9 31 36.7 41 33.3 17.6 21 17

2006 1 32 1/11 38 36.3 35.1 29.2 31 30.1 32 28.7 27.5 38 38

2006 1 33 1/5 37 29.8 24.4 29 26.1 30 20.3 26.5 37 30

2006 4 40 12/7 87 78.6 77.7 74.2 87 60.5 55.8 71 47

2006 4 41 12/25 71.2 71.2 33.3 42 46.2 58 22 42.5 39 36

2006 4 42 11/19 64.7 64.7 22.5 27 30.3 31 28.6 27.1 23 22

2006 4 43 12/31 57 49.6 44 45 53.5 57 51.3 52.5 54 47

2006 4 44 11/22 57 50.2 53.1 42 57 40.6 49 39.6 32.8 22 21

2006 4 45 12/4 55 47.5 45.7 28 55 40.5 43 45 36

2006 4 46 11/7 53 53 50.1 47 33 35 19 19 14

2006 4 47 12/19 51 18.7 23.6 30.6 30 36.7 51 34.6 32.2 44 36

2007 1 57 2/5 90.7 90.7 85.8 75 71 52.5 51 34.8 52.7 64 50

2007 1 58 1/9 69.8 67 69.8 56.1 56 62 47.9 46.7 50 48

2007 1 59 1/21 65.1 27 44 33 65.1 46 61.2 53 17 14

2007 1 60 1/3 60 45.5 53.9 52 55.3 60 42.2 42.2 44 32

2007 1 61 1/24 59.5 51.1 59.5 51 48.7 48 40.4 50.7 56 52

2007 1 62 2/2 55.2 55.2 49.3 34.5 39 32.9 35 31.7 36.8 37 41

2007 1 63 1/15 55 23 28.7 30.7 35 54.2 55 41.8 42 34

2007 1 64 2/8 53 39.8 33.2 53 43.5 44 36.4 36.8 49 42

2007 4 74 11/29 81.6 43.8 42 45.5 59.7 57.9 64.4 64.7 81.6 59.7 47.5

2007 4 75 11/2 72.2 72.2 71.5 53.3 58.4 35.3 40.8 30.5 29.3 23.4 24.5

2007 4 76 12/14 70.7 66 70.7 57.7 60.4 55.2 66.5 60.9 49.7 53.9 45.5

2007 4 77 12/11 59.7 40.2 42 54 39.8 57.4 59.7 58.2 35.7 6.8

2007 4 78 11/8 57.9 53.1 56.2 57.9 53.3 55.3 50 34.2 25.7 22.3

2007 4 79 11/26 57.4 48.7 52.1 40 49 47.2 50.5 46.6 57.4 43

2007 4 80 12/23 54.6 52.5 37.3 43.8 44.8 54.6 52.5 44.5 26.2 19.8

2007 4 81 11/14 52.7 43.4 48.7 52.7 42.9 32.8 35.5 32.1 26.5 21.3 12.8

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
2.2. The Interpolation of Speciated FRM Measurements: 

Table 1 shows 10 monitoring stations with either direct or inferred chemical 

speciation information. First, FRM measurements for each day were speciated with the 

speciation profiles appropriate for the quarter that day was in. Those speciated 

fractions were then interpolated using an inverse distance weight. 

The speciation fractions we used during the interpolation are shown in Tables 2 and 

3. These speciation fractions were derived following the Sulfate, Adjusted Nitrate, 

Derived Water, Inferred Carbonaceous Material Balance Approach (SANDWICH) 

described in the peer-reviewed literature (Frank, 2006) and in the guidance (U.S. EPA, 

2007). 

Table 2: Speciation fractions used for the first quarter of each year. The monitors in 

the header are located from south to north in the San Joaquin Valley and their long 

names are listed in the header of Table 1 from left to right.

BEP BAK COP VCS FSH FSF CLO MRM M14 SOH

NH4 0.1293 0.1293 0.1424 0.1390 0.1198 0.1198 0.1384 0.1306 0.1307 0.1306

NO3 0.3992 0.3992 0.4456 0.4010 0.3704 0.3704 0.4509 0.3883 0.4232 0.4186

SO4 0.0358 0.0358 0.0692 0.0602 0.0328 0.0328 0.0399 0.0476 0.0418 0.0485

OC 0.2849 0.2849 0.2312 0.2912 0.3454 0.3454 0.2211 0.3122 0.2898 0.2586

EC 0.0361 0.0361 0.0086 0.0099 0.0416 0.0416 0.0400 0.0169 0.0136 0.0172

Salt 0.0063 0.0063 0.0051 0.0043 0.0056 0.0056 0.0046 0.0070 0.0049 0.0071

Geologic 0.0317 0.0317 0.0189 0.0154 0.0130 0.0130 0.0192 0.0217 0.0152 0.0392

PBWater 0.0768 0.0768 0.0790 0.0790 0.0713 0.0713 0.0859 0.0756 0.0807 0.0803

Total 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

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 2012 PM2.5 Plan
Table 3: Speciation fractions used for the fourth quarter of each year. The monitors 

in the header are located from south to north in the San Joaquin Valley and their long 

names are listed in the header of Table 1 from left to right.

BEP BAK COP VCS FSH FSF CLO MRM M14 SOH

NH4 0.1482 0.1482 0.1485 0.1388 0.1106 0.1106 0.1290 0.1110 0.1152 0.1146

NO3 0.4546 0.4546 0.4901 0.4410 0.3494 0.3494 0.4253 0.3516 0.3833 0.3790

SO4 0.0431 0.0431 0.0331 0.0288 0.0244 0.0244 0.0297 0.0238 0.0209 0.0242

OC 0.2106 0.2106 0.1818 0.2546 0.3809 0.3809 0.2634 0.3870 0.3631 0.3371

EC 0.0318 0.0318 0.0172 0.0199 0.0473 0.0473 0.0454 0.0350 0.0281 0.0356

Salt 0.0037 0.0037 0.0051 0.0043 0.0062 0.0062 0.0051 0.0078 0.0055 0.0079

Geologic 0.0204 0.0204 0.0348 0.0283 0.0144 0.0144 0.0212 0.0165 0.0116 0.0298

PB Water 0.0876 0.0876 0.0893 0.0842 0.0668 0.0668 0.0807 0.0672 0.0724 0.0718

Total 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

However, the positioning of the FRM monitoring stations in the San Joaquin Valley 

poses a special challenge during the interpolation. They are nearly lined up on a 

northwest to southeast diagonal of the modeling domain. When the FRM specie 

fractions are interpolated, they form bands perpendicular to that diagonal. These bands 

extend from the coastal mountains to the Sierras and register high PM2.5 values outside 

of the nonattainment area. To reduce the effect of this geometrical artifact on the 

current analysis, during the interpolation we have applied a mild decay function in the 

form of (1/distance)a, where a=0.25. 

Also note in Table 1 that not all monitors have FRM measurements for all days. 

Thus, we had to use only the available measured values for the interpolation for a given 

day. This has the effect of mixing measured and interpolated values at a given monitor 

for a given quarter. 

2.3. Application of RRF to Interpolated Speciated FRM Fields: 

The speciated FRM fields for each day were then multiplied by the simulated 

quarterly RRF fields appropriate for that day to obtain the 2019 concentrations. 

The simulation of PM2.5 loadings and chemical component specific RRFs are 

described in detail in the modeling protocol document (ARB, 2012) in this package. In 

short, we have simulated the first and fourth quarters of 2007 as the base case. We 

used the Mesoscale Meteorological Model version 5 (MM5; Grell et al., 1994) to 

simulate gridded hourly meteorological fields for 2007, which were in turn used to drive 

the Community Multi-scale Air Quality (CMAQ) Model v4.7.1 (Byun and Schere, 2006; 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Foley et al., 2010), with the SAPRC99 chemical mechanism (Carter, 2000) and the 

aero5 aerosol module. 

Simulations were conducted at a 12-km resolution for the entire state and nested 

down to 4-km for the SJV. Chemical boundary conditions for the 12-km domain were 

provided by downscaled MOZART global chemistry model (Emmons et al., 2010) output 

for the year 2007 (http://www.acd.ucar.edu/wrf-chem/mozart.shtml). 

The simulated future year is 2019. We have used the same inputs for that 

simulation except for the anthropogenic emissions that were projected to 2019. The top 

10% of the simulated chemical specie concentrations were averaged for each quarter 

for both 2007 and 2019. RRFs were calculated for each chemical species as the ratio 

between 2019 and 2007 for each grid cell for each quarter. 

The 2019 species concentrations for each grid cell for each day were summed to 

obtain the total PM2.5. 

2.4. Determination of the 98th Percentile and the relative design value for each 

Grid Cell: 

As mentioned previously, the data in Table 1 were selected on a one-in-three-day 

schedule so that nearly all stations would have measured data. This makes the 98th

percentile the third highest value. Therefore, we sorted the total PM2.5 concentration for 

each grid cell for a given year (16 days) and selected the third highest value as the 98th

percentile for that grid cell for that year. The average of the 98th percentile value for a 

given grid cell over three years was the relative design value for that grid cell. This 

relative design value is used only on a relative basis for the identification of unmonitored 

peaks that may violate the NAAQS. 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
2.5. Identification of Unmonitored Peaks: 

Figure 1: The 3-year average of the 98th percentile values for 2017-2019. The red 

dots are the FRM monitoring stations from south to north in the order listed in Table 1 

from left to right.

Figure 1 shows the final result of this analysis. The highest value occurs at grid cell 

(57,12) which is the location of the Bakersfield - California Street FRM station. 

Therefore, there is no higher peak value in an unmonitored area in the domain. 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
3. Conclusions: 

There is no guidance on how to perform the unmonitored area analysis for the 

24-hour PM2.5 standard within the framework of the new attainment test (U.S. EPA, 

2011). Therefore, we have designed a test to do so. We included a mild decay function 

that remedies a geometric artifact due to the near-linear positioning of the FRM stations 

in the San Joaquin Valley. 

The highest three-year average value calculated with this method occurs at the 

Bakersfield – California Street FRM monitor. This monitor is projected to be in 

attainment of the 24-hour PM2.5 NAAQS pursuant to the current plan. Therefore, this 

analysis projects that there will not be an unmonitored peak anywhere in the modeling 

domain that would violate the 24-hour PM2.5 NAAQS. 

References: 

Abt Associates, Inc., 2010, “Modeled Attainment Test Software User's Manual,” pp. 103 

(Found at http://www.epa.gov/ttn/scram/guidance/guide/MATS-2-3-1_manual.pdf). 

Air Resource Board, 2012, Photochemical Modeling for the 24-Hour PM2.5 State 

Implementation Plan in the San Joaquin Valley. 

Byun, D., and K. L. Schere (2006). Review of the governing equations, computational 

algorithms, and other components for the Models-3 Community Multiscale Air Quality 

(CMAQ) modeling system, Appl. Mech. Rev., 59, 51-77. 

Carter, W. L. (2000). Implementation of the SAPRC-99 Chemical Mechanism into the 

Models-3 Framework, Report to the United States Environmental Protection Agency. 

http://www.engr.ucr.edu/~carter/pubs/s99mod3.pdf 

Emmons, L. K., S. Walters, P. G. Hess, J.-F.Lamarque, G. G. Pfister, D. Fillmore, C. 

Granier, A. Guenther, D. Kinnison, T. Laepple, J. Orlando, X. Tie, G. Tyndall, C. 

Wiedinmyer, S. L. Baughcum, and S. Kloster (2010). Description and evaluation of the 

Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model 

Dev., 3, 43-67. doi:10.5194/gmd-3-43-2010 

Foley, K. M., S. J. Roselle, K. W. Apel, P. V. Bhave, J. E. Pleim, T. L. Otte, R. Mathur, 

G. Sarwar, J. O. Young, R. C. Gilliam, C. G. Nolte, J. T. Kelly, A. B. Gilliland, and J. O. 

Bash (2010). Incremental testing of the Community Multiscale Air Quality (CMAQ) 

modeling system version 4.7, Geosci. Model Dev., 3, 205-226. 

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Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan
Frank, N.H., 2006, Retained nitrate, hydrated sulfates, and carbonaceous mass in 

federal reference method fine particulate matter for six eastern U.S. cities, Journal of Air 

& Waste Management Association, 56, 500-511. 

Grell, G.A., J. Dudhia, and D.R. Stauffer (1994).A Description of the Fifth-Generation 

Penn State/NCAR Mesoscale Model (MM5). Boulder, CO: National Center for 

Atmospheric Research. NCAR/TN-398+STR. 

South Coast Air Quality Management District, Draft Final 2012 Air Quality Management 

Plan, Appendix V, pp V-5-46 - 48. (available at 

http://www.aqmd.gov/aqmp/2012aqmp/DraftFinal/index.html) 

U.S. EPA, 2007, Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, EPA-454/B07-

002. 

U.S. EPA, 2011, Memorandum on Update to the 24 hour PM2.5 NAAQS Modeled 

Attainment Test (available at 

http://www.epa.gov/ttn/scram/guidance/guide/Update_to_the_24-

hour_PM25_Modeled_Attainment_Test.pdf) 

San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

 2012 PM2.5 Plan