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Appendix G: PM2.5 Weight of Evidence Analysis

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### 2012 PM2.5 Plan

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## SAN JOAQUIN VALLEY PM2.5 WE IGHT OF EVIDENCE ANALYSIS

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

Appendix G: PM2.5 Weight of Evidence Analysis

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### TABLE OF CONTENTS

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

1. INTRODUCT ION ...................................................................................................... 1

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

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

a. Established monito ring netwo rk ......................................................................... 4

b. Extensive fiel d studies ........................................................................................ 4

4. NATURE AND EXTENT OF THE PM2.5 PR OBLEM ............................................... 7

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

b. Seasonal vari ability. ........................................................................................... 7

c. Diurnal vari ability ................................................................................................ 8

d. Chemical com position ........................................................................................ 9

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

f. Episode devel opment ....................................................................................... 13

5. SECONDARY AMMONIUM NI TRATE FORM ATION ............................................. 15

a. Chemistr y ......................................................................................................... 15

b. Limiting precurso r concept ............................................................................... 16

c. Role of ammonia in ammoni um nitrate fo rmati on ............................................. 17

d. Role of VOC in ammonium nitrate fo rmation .................................................... 24

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

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

a. Emission inv entory ........................................................................................... 34

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

c. Source apportionment using s ource receptor models ...................................... 37

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

8. PM2.5 AIR QUALIT Y PROGRES S ......................................................................... 42

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

b. 24-Hour PM2. 5 trends ...................................................................................... 43 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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c. Meteorology impacts on air qua lity ................................................................... 44

d. Annual trends adjusted for meteor ology ........................................................... 47

e. 24-hour trends adjusted for meteor ology ......................................................... 48

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

g. Chemical compos ition tr ends ........................................................................... 55

h. Emission inventor y trends ................................................................................ 57

9. LINKING AIR QUALITY TRENDS TO EMISSION REDUCTION S ......................... 58

a. NOx cont rol ...................................................................................................... 58

b. Residential wood bur ning contro ls ................................................................... 60

10. MODELED ATTAINMENT DEMONSTRAT ION .................................................. 62

a. Modeling re sults ............................................................................................... 62

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

c. Evaluation of precur sor sensit ivity .................................................................... 64

d. Demonstrating attainment at Bakersfield- Californi a ......................................... 70

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

REFERENC ES .............................................................................................................. 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 Apportionm ent of PM2.5 Measured at the Fresno and Bakersfield Chemical Speciation Network Sites in the S an Joaquin Valley Using the Positive Matrix

Factorization Model

Appendix 4: Methodology for Meteorological Adjustment of PM 2.5 Trend Statistics

Appendix 5: Speciated Linear Rollback Model ing as a Corroborative Analysis to the Regional Photochemical Model

Appendix 6: Unmonitored Areas Analysis San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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i EXECUTIVE SUMMARY

The San Joaquin Valley 2012 PM2.5 Plan demons trates that the S an Joaquin Valley will attain the PM2.5 24-hour standard of 35 ug/m 3 

### as expeditiously as practicable due to adopted and proposed control measures. As par t 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 st andard, 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 comp lementary analyses t hat supplement the required modeling. Because all methods have strengths and weaknesses, examining an air quality problem in a variety of ways offs ets the limitations and uncertainty that are inherent in air quality modeling. This approach also provi des a better understanding of the overall problem and the le vel and mix of emissions controls needed for attainment.

Analyses conducted by Air Resources B oard (ARB) and San Joaquin Valley Air Pollution Control District (SJVAPCD or Distr ict) staff, along with findings from the California Regional Particulate Air Quality Study (CRPAQS) provide the supplemental information supporting the attainment demon stration. CRPAQS was a public/private partnership designed to advance our under standing of the nature of PM2.5 in the Valley and guide development of effective control stra tegies. 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 nati onal and international conferences.

Studies such as CRPAQS provide valuab le 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 detail ed understanding of the nature of the PM2.5 problem in the San Joaq uin Valley. This data also is used to update the fundamental algorit hms contained within air quality models, thereby enhancing their ability to simulate observed ai r 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 probl em in the Valley?

The geography of the San J oaquin Valley, along with weat her patterns influence the accumulation, formation, and dispersion of PM2. 5. As a result, PM2.5 concentrations are generally higher in the c entral and southern portions of the Valley, with highest

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

the winter months of Novem ber 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, comprisi ng 85 to 90 percent of the San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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ii mass. Geological material (dust), and ammo nium sulfate are small contributors. Ammonium nitrate is formed in the atmospher e from reactions of gaseous precursors. Emissions of nitrogen oxides (NOx) from m obile sources and stationary sources react with ammonia which is primarily emitted from livestock operat ions, fertilizer application,

and mobile sources. The st agnant, cold, and damp conditions that occur during the winter promote the formation and accumulati on of ammonium nitrate. Elevated

concentrations can be found at both urban and rura l sites. In contrast, organic carbon is highest in urban areas due to emissions from residential wood combustion, commercial cooking operations, and mobile source tailp ipe emissions which are largest in urban

areas. Due to the localized urban increment from these activities , which adds to the more regional ammonium nitrate concentra tions, 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 adjust ed for the effects of meteorology indicate that between 1999 and 2010, annual PM2.5 conc entrations 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 im prove and reach attainment in 2014.

During this same time period, 24-hour PM2. 5 design values in the Valley have also decreased between approx imately 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 pr ovide 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 dec reased, there has been a corresponding increase in the fraction of days below the level of the annual standard of 15 ug/m 3



### . Average concentrations during the winter m onths 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 differ ent constituents and as a result there are multiple precursor pollutants that lead to PM 2.5 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 “sig nificant” for control strategy development purposes when a significant reduction in the em issions of that precursor pollutant leads to a significant decrease in PM2.5 concent rations. Such pollutants are known as San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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iii “PM2.5 attainment plan precursors” (72 FR 20586). The PM2.5 implementation rule also establishes a presumption that PM 2.5, NOx, and SOx ar e attainment plan precursors, while VOCs and ammonia are not. For the annual PM 2.5 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 PM 2.5 exceedance days, a number of different studies and analyses we re evaluated to understand the role of

VOCs and ammonia in ammonium nitrate fo rmation 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 ammo nium nitrate produced depends upon the relative atmospheric abundance of its precur sors. It is ther efore important to understand which precursor controls are most effective in reducing ammonium nitrate concentrations. In simple te rms, the precursor in shorte st supply will limit how much ammonium nitrate is produced. This is know n 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 eval uation of emissions inventories, monitoring studies, and photoche mical 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 pr ecursors would have on the predicted

ammonium nitrate concentrations.

Evaluation of both emissi ons inventory and monitori ng data concluded that the ammonia-rich conditions throughout the Va lley demonstrate that NOx rather than ammonia is the limiting prec ursor during wintertime PM2. 5 episodes. In addition, photochemical modeling studies found that while large reductions in NOx led to

commensurate reductions in ammonium nitr ate, comparable reductions in ammonia

were much less effective. Precursor s ensitivity modeling conduc ted 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 pr ovided some evidence that VOCs could be important at times, however these studi es were not conclusive. Therefore photochemical modeling studies are more appr opriate to assess the overall impact of VOC controls. These modeling studies found that at current NOx levels, further VOC emission reductions produce esse ntially no benefit, and in some instances may actually

lead to an increase in ammonium nitrate co ncentrations. Findings from these prior studies were supported by precursor sens itivity modeling condu cted for the 2012 PM2.5 SIP, which indicated a very small di sbenefit from reductions in VOCs. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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iv As noted previously, U.S. EPA’s PM2.5 implem entation rule directs SIP planning efforts and regulation to those pollutants generally k nown to significantly contribute to PM2.5 concentrations. Based on the weight of ev idence presented from historical studies, coupled with the modeled precursor sensit ivity analyses conducted as part of the 2012 PM2.5 Plan, VOCs and ammonia are not consi dered significant precursors for 24-hour

PM2.5. Therefore t he 2012 24-hour PM2.5 plan attainm ent 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 implementati on of the ongoing cont rol program. In these counties,

additional focused emission reductions ar e needed to provide for attainment. The modeling analysis includes new emission re ductions each year between now and 2019 from implementation of a combination of adopted ARB and District programs. As a result, most sites in the nort hern and central Valley are expected to attain prior to 2019.

ARB staff then modeled a scenario wit h an enhanced wood burning curtailment program Valley wide, which would be desig ned to prevent wood burning on days that may lead up to a PM2.5 exceedance. The pr edicted 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/m 3 

### )

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 attain ment, further reductions are still needed and will be provided through a measure to achi eve additional emission reductions from commercial cooking operations. Design valu es at all other sites are well below

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

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v What is the attainme nt control strategy?

In order to determine the emission r eductions needed to bring Bakersfield into attainment, ARB staff conducted additional modeling sensitivity runs to assess the relative efficacy of further reductions of di fferent 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 need ed 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, t he base year used in the photochemical modeling attainment demons tration and 2019, implement ation of these control programs will reduce NOx emissions by 55 percent. T he weight of evidence analysis has demonstrated that prior reductions in NOx have resulted in commensurate

reductions in ambient concentrations of nitr ate. This is cons istent with modeled predictions that demonstrat e a nearly 50 percent reduc tion 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 attainm ent control strategy is further curtailment of residential wood burning, along with im plementation of a measure to reduce emissions from commercial cooking. Distric t analysis has demonstrated the significant benefits of past implem entation of wood burning curtailm ent. Further, examination of emission sources surrounding the Bakersfiel d-California monitor, and a modeling sensitivity run support the benef its of reducing emissions from cooking operations. The

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

Table E-2. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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vi Table E-2.

Attainment Demonstration for the Baker sfield-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/m 3

.



### Consideration of the entirety of information presented in the weight of evidence provides a consistent assessment that supports t he modeled attainment date of 2019. The substantial continuing reducti ons that will result from im plementation of the ongoing control program, coupled wit h new measures addressing residential wood burning and cooking, are consistent with the resu lts predicted in the modeled attainment

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

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### 1 1. INTRODUCTION

The 2012 PM2.5 Plan demonstrates that the San Joaquin Vall ey will attain the PM2.5 24-hour standard as expeditiously as prac ticable due to adopted and proposed control measures. As part of the attainment dem onstration, the 2012 PM2. 5 Plan specifically identifies the: 1) most exp editious date for when the San J oaquin Valley (SJV or Valley) will attain the standard, 2) attainment pl an precursors, 3) am ount 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 conduct ed 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 pollut ants, and the magnitude of emission reductions needed from each pollu tant. The Weight of Evidence (WOE) analysis provides a set of complementar y analyses that suppl ement 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 qualit y modeling. This approach also provides a better understanding of the overall problem and t he level and mix of emissions controls

needed for attainment.

The U.S. EPA recognizes the importance of a comprehensiv e 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 t hat 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/m 3 

### need to be accompanied by a WOE demonstration to determine

whether attainment will occur. This r ange in modeled design values reflects the uncertainty in predicting absolute PM2.5 conc entrations that is inherent in air quality modeling, and therefore recogni zes that an improved assessment of attainment can be derived from examining a br oader set of analyses.

U.S. EPA recommends that three basic ty pes of analyses be included to supplement the primary modeling analysis in the WOE approach: 1) anal yses of trends in ambient air quality and emissions, 2) observati onal models and diagnostic analyses, and 3) additional modeling evaluations. The scope of the WOE analysis is different for each

nonattainment area. The level of deta il 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 mode ling 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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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2 The following sections present the WOE as sessment that supports the attainment

demonstration the 24-hour PM2.5 st andard in the San Joaquin Valley. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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3 2. PM2.5 STANDARDS AND HEALTH EFFECTS

PM2.5 is a complex mixture of particles and liquid droplet s 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 nitr ates, sulfates, and organic materials, and complex mixtures such as diesel exhaust and soil or dust. Some of the particles are directly emitted into the atmo sphere. 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 co mpared to the thickness of a single strand of hair.

Numerous health effects studies have linked ex posure to PM2.5 to increased severity of asthma attacks, development of chronic bronchitis, decreased lung function in children, increased respiratory and cardiovascular hosp italizations, and even premature death in people with existing cardiac or respiratory diseas e. In addition, California has identified particulate exhaust from diesel engines as a toxic air contaminant – suspected to cause cancer, other serious illnesses, and premat ure death. Those most sensitive to PM2.5 pollution include people with existing respirat ory and cardiac problems, children, and

older adults.

Ambient air quality standards establish t he 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 st andard of 15 μg/m 3 

### and a 24-hour standard of 65 μg/m 3 

### . To address the 1997 PM2.5 standards, the San Joaqui n 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/m 3 

### . Attainment of this standard is t he focus of the SJV 2012 PM2.5 Plan. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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4 3. MONITORING IN TH E SAN JOAQUIN VALLEY

a. Established monitoring network

An extensive network of PM2.5 monitors throughout the SJV prov ides data to assess compliance with ambient air quality standards and to study the nature of ambient PM2.5. Currently, t he 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 Figur e 2. The locations of these monitors are designed to capture population exposure . In addition, data collected at these monitors serve to report air quality conditi ons to the public, and support forecasting for the District’s agricultural and residential burni ng curtailment programs. Finally, four sites have chemical speciation monitors. Th e speciation monitors collect samples that are further analyzed in the l aboratory to determine the chem ical make-up of PM2.5.

Figure 2. San Joaquin Valley PM2.5 m onitoring 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 inte rnational scientific/te chnical journals and other major reports. Since 1970, close to 20 major field studies have been conducted in the Valley and surrounding areas t hat have elucidated various aspects of the nature and San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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5 causes of ozone and particulate matter. A comprehensive listin g of publications (reports and peer-reviewed journal arti cles) is provided in Appendix 1.

The first major study specifically focus ed on particulate matter was the Integrated Monitoring Study in 1995 (IMS-95), which was the pilot study for the subsequent California Regional Particulates Air Q uality Study (CRPAQS) in 2000 (Solomon and Magliano, 1998). IMS-95 formed the techni cal 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 fo r 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 si tes (Figure 3) as well as data analysis and modeling, results of which have been publ ished in over 60 papers and presented at

national and international c onferences. The field ca mpaign was carried out between December 1999 and February 2001. CRPAQ S improved our understanding of the spatial and temporal di stribution 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 moni toring 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 t he WOE analysis supporting the 24-hour PM2.5 standard attainment demonstration. Specific findi ngs are integrated into the various WOE analysis sections of this document. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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6 Further field studies relevant to PM2.5 in clude the California portion of the Arctic Research of the Composition of the Tropos phere (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-su mmer and extended over

Southern California and the C entral Valley. Some study findings have been published (e.g., Kaduwela and Cai, 2009, Cai and Ka duwela, 2011, Kelly et al., 2011), but data analysis is still in progress. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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

a. Current air quality

The geography of the San J oaquin Valley, along with lar ge-scale regional and local weather patterns, influence the accumulation, fo rmation and, dispersion of air pollutants. Covering nearly 25,000 squar e 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 mo untains act as air flow barriers, with the resulting stagnant conditions favoring the accumu lation 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 t he southern and central porti ons of the Valley.

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

site must be calculated follo wing strict U.S.EPA protocols. The design value represents a three-year average of the 98 th 

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

### percentile usually corresponds to a value between the 2 nd 

### and the 8 th 

### highest value. If the design value is equal to or below 35.4 μ g/m 3 

### , 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 norther n 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 Va lley exhibit a strong seasonal pattern, with highest concentrations occurring from Novem ber through February (Figure 5). During the winter, PM2.5 builds up ov er 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 70PM2.5DesignValue(μg/m3) 2011 24-Hour PM2.5 Design Values

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

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8 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 te mperature inversions. Under normal conditions, temperature decreases with alti tude, allowing free upward air flow and dispersing emissions and polluta nts. In contrast, a temper ature inversion positions a layer of warmer air above cooler air, impeding upward flow of emissions and air pollutants. Often the inversi on layer is lower than the mountains surrounding the Valley, trapping emissions and pollutants.

Figure 5. Seasonal variation in PM2.5 concent rations 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 ur ban Fresno, the highest PM2.5 concentrations occur during the night (Figure 6). Peak ev ening concentrations generally reflect the influence of lowering inversion heights which tr ap pollutants close to the surface, as well as increased activity from evening commu te traffic and resident ial 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 12Concentration (μg/m3)

Month 2009-2011 Monthly Average PM2.5

Concentration at Bakersfield-California San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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



### .

d. Chemical composition

Examination of the chemical make-up of PM2.5 on days exceed ing the daily standard provides another important el ement 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 t hat exceed the standard at urban sites in the southern (Bakersfield), central (Fre sno), and northern (Modes to) 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 organi c material (organic carbon).

Ammonium nitrate is the larges t contributor to PM2.5, especia lly in the southern region. At Bakersfield, ammonium nitrate consti tutes about 65 percent of PM2.5, while at Fresno and Modesto it constitutes about 55 perc ent. Ammonium nitrate is formed in the atmosphere from chemical reactions of NOx and ammonia. Sources emitting NOx include motor vehicles and stationary combus tion sources. The largest sources of ammonia are livestock operations, fertilizer application, and mobile. The stagnant, cold, and damp conditions that occur during t he winter promote the formation and

accumulation of ammonium ni trate. Additional info rmation on ammonium nitrate

formation can be found in section 5.

The organic matter component of PM2.5 is lar gest in the central and northern portions of the Valley. Organic matter constitute s about 30 percent of PM2.5 at Modesto and Fresno compared to less than 20 percent at Bakers field. 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 per cent at the three

sites. Ammonium sulfate is also formed in the atmosphere from SOx emitted from 0 10 20 30 40 50

0 4 8 12 16 20PM2.5 Concentration (μg/m3)

Time-of-day 2009-20011 Average Hourly PM2.5 Variation During Nov-Dec at Fresno-1 st

2009-2011 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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10 combustion sources. Element al carbon results from mobile and stationary combustion sources, with significant contri butions from diesel sources.

Geological material contributes to a lesser extent, about five percent at Bakersfield and about two percent at Modesto and Fresno. Geological ma terial 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 PM 2.5 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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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

As noted previously, high PM2.5 concentration s in the Valley occur almost exclusively during multiday pollution episodes under st agnant winter weather conditions. The duration and strength of an episode depends on atmospheric stability, but episodes can

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

concentrations increase due to the accumulati on of primary pollutant s and formation of secondary pollutants.

Each episode has a regional as well as loca l 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, a mmonia is mostly concentrated in rural areas, particularly between Fresno and Bakersfield. On the other hand, hi gh 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 lo cal component can be traced back to the emission sources and subsequent formation and transport processes for each chemical component. Gaseous precursors of amm onium nitrate (NOx and ammonia) are transported much more efficiently than direct ly emitted organic matter particles (Ying and Kleeman, 2009). Although, some of the emitted NOx fo rms ammonium nitrate in urban areas, it is also transported to downwi nd regions where it r eacts with ammonia to form particulate ammonium nitrate in the ru ral areas. While transport does occur, the distances are still relatively limited, with trans port 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 fr om sources within the southern Valley.

In contrast, carbonaceous aerosols are emit ted into the atmosphere as particles and have a shorter lifetime due to higher depositi on rates. Under stagnant conditions they can only be transported a short distance and t herefore, have the great est impact locally. Transport distances for carbonaceous aeros ols during CRPAQS were only 20 to 40 kilometers. Due to this localized organic carbon increment, which adds to the more regional ammonium nitrate c oncentrations, the highest PM2.5 concentrations occur at

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

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12 Figure 9. Spatial distributi on of annual ammonia (NH 3 

### ) concentrations (2/1/2000- 1/31/2001) during CRPAQS (C how et al., 2005).

Figure 8. Spatial distribution of winter ammonium nitrate concentrations measured during CRPAQS (Chow et al., 2005). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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13 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 conc entrations building up faster at urban sites than at rural sites (Turkiewicz et al., 2006). Figure 11 illust rates the differences in the PM2.5 buildup rate between an urban (Fresno) and a rural (S elma) 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 1/6/01Stability (T850-Tmin°C)PM2.5 Mass (μg/m3)

Episode Day Fresno PM2.5 Selma PM2.5 Stability 

### Figure 10. Spatial distribution of win ter organic carbon concentration measured during CRPAQS (Chow et al., 2005). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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14 The rate of buildup and the differences bet ween urban and rural sites can be explained by the differential contributions of amm onium nitrate and organi c carbon. Throughout the duration of an episode, amm onium nitrate concentrations tend to build to a plateau

that is maintained until a w eather front breaks t he stagnation, causing the levels to decrease. Figure 12 illustrates the build up of ammonium nitrate concentrations measured during the 2000/ 2001 PM2.5 episode in Bakersfield. This ammonium nitrate buildup generally begins in ur ban areas, followed by a buildup in rural areas as urban

NOx is mixed downwind and reacts with rural a mmonia. 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 amm onium nitrate buildup along with the urban organic carbon increment results in the highe st concentrations being observed in urban areas. The abrupt decrease in concentrations on January 8 th 

### 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 8012/23/0012/24/0012/25/0012/26/0012/27/0012/28/0012/29/0012/30/0012/31/001/1/011/2/011/3/011/4/011/5/011/6/011/7/011/8/01Concentrations(ug/m3) 

### Ammonium Nitrate Concentrations at Bakersfield during

the 2000/2001 CRPAQS Episode

Hourly

24

‐ hr Avg. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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15 5. SECONDARY AMMONIUM NITRATE FORMATION

a. Chemistry

As discussed previously, the cooler tem peratures and higher hum idity of the winter

months are conducive to ammonium nitr ate formation through a complex process involving NOx, ammonia, and VOCs. This o ccurs both at the surface and aloft, via both daytime and nighttime chemistry. Under standing the interactions amongst these precursors is needed to design an appropr iate and effective approach to reduce

ammonium nitrate.

During the day, NO 2 

### is oxidized to nitric acid (HNO 3 

### ). This daytime pathway also involves sunlight, VOCs , and background ozone:

O 3 OH 

### Main oxidant is OH

NO NO 2 

### HNO 3 

### Requires high sunlight, VOC rich environment

During the night, nitric acid is formed through oxidation of NO 2 

### (via N 2 

### O 5 

### ) by background ozone:

O 3 O 3 H 2 O 

### Main Oxidant is Ozone (O 3 

### )

NO NO 2 

### NO 3 

### N 2 

### O 5 

### 2 HNO 3 

### Favors low sunlight intensity,

wet conditions

The nitric acid formed from these r eactions then combines with ammonia (NH 3 

### ) to form ammonium nitrate (NH 4 

### NO 3 

### ):

HNO 3 

### + NH 3 

### NH 4 

### NO 3



### Since the chemistry of NOx to nitric acid formation involves multiple steps and also depends on the availability of oxidants, onl y a portion of the NOx emitted ultimately forms ammonium nitrate. An early photoc hemical 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 NO x were converted to ammonium nitrate (Stockwell et. al. 2000). A subsequent study that modeled t he January 4-6, 1996 episode in the San Joaquin Valley with the University California Davis/California Institute of Technology ( UCD-CIT) photochemical trans port model found that on average, only 13 to 18 percent of the emitted NOx (expressed as NO 2 

### ) was converted to ammonium nitrate (Kleeman et. al. 2005). The fraction of NOx converted varied by location, with urban regions converting little NO x to ammonium nitrate, while in remote areas up to 70 percent NO x was converted.

As previously described, NOx emissions mostly originate from urban traffic and transportation corridors, while ammonia is pr imarily generated from livestock operations, San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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16 fertilizer application, and mobile sources. Analysis of CRPAQS measurements suggest that, on average, daytime production of nitric ac id 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 interact ions 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 boundar y layer. This was observed through a rapid rise in hourly ammonium nitrate concent rations which coincided with the growth of the surface mixed layer (W atson and Chow 2002). These mechanisms help explain the more regional distribution of ammonium nitrat e that is observed throughout the Valley.

b. Limiting precursor concept

The amount of ammonium ni trate produced will depend on the relative atmospheric abundance of its precursors – VOCs, NOx, and ammonia (NH 3 

### ). It is therefore important to understand which precursor cont rols are most effective in reducing ammonium nitrate concentrations. In simple terms, the precursor in shortest supply will limit how much ammonium nitrate is produc ed. This is known as the “limiting” precursor. The following figur es provide an illustration of this concept. As shown in Figure 13, each molecule of ammonia pair s with one NOx molecule to produce one

molecule of ammonium nitrate. In this example, there are more ammonia molecules than NOx, and therefore not a ll 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 partici pate in ammonium nitrate production.

Figure 13. Ammonium nitr ate formation. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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

ammonium nitrate.

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

ammonium nitrate.

The following sections describe the current state of the scien ce regarding the role of ammonia, VOCs, and NOx in ammonium ni trate formation and identify the most effective precursors for control.

c. Role of ammonia in a mmonium nitrate formation

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

ammonia in ammonium nitrat e formation in the San Joaqui n Valley. These included: a) comparison of the magnit ude of the NOx and ammonia em issions inventories, b) ambient measurements of ammonia, nitric acid, and particulate ammonium; and c) photochemical modeling analyses of amm onium nitrate sensitivity to precursor

emission reductions. While evaluation of emissions inventory and ambient data can provide indications of the relative abundanc e of different precursors, photochemical models provide a tool to quantitatively ev aluate the impact of reducing precursor emissions on resulting ammoni um nitrate concentrations. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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18 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 a ssessment of the relative abundance of these two precursors. Table 1 lists NOx and a mmonia winter emissions in the current inventory for three years ( 2000, 2011, and 2019). As Figure 13 in the limiting precursor section illustrated, in simp le terms it takes one molecule of NOx and one molecule of

ammonia to form one molecule of ammonium nitrate. Howeve r, 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 (NO 2 

### ). Since one NO 2 

### molecule weighs 46 universal atomic units (u)

and one NH 3 

### molecule weighs 17 u, one ton of NH 3 

### has 2.7 times (46 u/17 u) the

number of molecules as one ton of NO 2 

### . Dividing the NOx emi ssions 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 not ed in the chemistry section, only a portion on the NOx is ultimately c onverted to ammonium nitrate.

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

Year Winter NH 3 

### 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 c oncentrations provide another method to investigate the relative abundance of each pr ecursor and therefore which is most

effective for control of ammonium nitrat e. Blanchard, et al . (2000) examined two metrics using ambient data collected duri ng the IMS-95 field pr ogram in the San Joaquin Valley. The first parameter was the excess of particulate ammonium plus gasphase ammonia over the sum of nitric acid, par ticulate 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. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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19 Lurmann, et al. (2006) also compared ammoni a and nitric acid ambient concentrations measured in the San Joaquin Valley duri ng the winter of 2000/2001 as part of CRPAQS. Figures 16 and 17 show the conc entrations of nitric acid and ammonia measured at the rural Angiola site and at th e urban Fresno site. At both sites ammonia concentrations are generally at least an order of magnitude higher t han the nitric acid

concentrations. These ammonia-rich condi tions throughout the Valley indicate that, during the winter, nitric acid rather t han ammonia is the limit ing precursor.

Figure 16. Comparison of ammonia and nitric ac id 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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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20 Figure 17. Comparison of ammonia and nitric ac id concentrations measured at Fresno during the winter of 2000/2001 as part of CRPAQS.

The amount of gaseous ammonia (NH3) com pared to particulate ammonium (NH4) provides another indicator of how much of the ammonia is converted to ammonium

nitrate and therefore whether t here is excess ammonia available. These measurements were collected at a larger number of si tes during CRPAQS. Figure 18 shows the concentrations of particulate ammonium and gaseous ammonia at three urban sites (Fresno-1 st 

### , Bakersfield-California, and Bakersfiel d-residential), and three rural sites (Angiola, Pixley, and Feedlot) measur ed during the 2000/2001 winter CRPAQS episode. Overall, the levels of particula te ammonium at all sites are comparable, consistent with a regional formation mec hanism of ammonium nitrate. Although ammonia concentrations are high er at the rural sites, espec ially 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 nitrat e. Again, these ammonia rich conditions indicate that nitric acid, rather t han 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/m 3 )NH3 Concentration (ug-N/m3)

1:1 Line San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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21 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, phot ochemical models provide a quantitative approach to simulate the effects that em ission reductions in each of the gaseous precursors would have on the pr edicted ammonium nitrate c oncentrations. A number of modeling studies have been c onducted by ARB staff and academic researchers to evaluate precursor sensitivity.

An investigation of prec ursor 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 Joaqui n Valley in Figure 19. Rural Urban San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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22 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 duri ng the three week winter CRPAQS episode using U.S. EPA’s Community Multiscale Ai r Quality (CMAQ) model with Californiaspecific modifications and corrections (Li ang 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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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23 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, indi cated that reducing NOx emissions was the most beneficial control strategy to reduce ammonium nitrate.

Figure 20. Percent ammonium nitr ate 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 t hat a 50 percent reduction

in NOx emissions reduced amm onium nitrate by approximately 50 percent at rural sites and between 30-45 per cent 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 ru ral site of Angiola, ammonium nitrate concentrations decreased between 10 and 25 perc ent. 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/m 3 

### and urban concentrations peaked at over 110 μg/m 3 

### (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 theref ore ammonium nitrate format ion 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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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24 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, mo nitoring data, and precursor sensitivity analyses all indicate that in the San Joaquin Valley, NOx, rather than ammonia is the limiting precursor for ammoni um nitrate formation.

d. Role of VOC in ammonium nitrate formation

A number of studies have also been exam ined 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 di ffering 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, NO 2 

### is oxidized to nitric acid. Nitric acid then reacts

with ammonia to form ammonium nitrate. This daytime ni tric acid formation pathway involves sunlight, VOCs, and background ozone. During the night, nitric acid is formed San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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25 through oxidation of NO 2 

### (via N 2 

### O 5 

### ) by background ozone, which then also reacts with

ammonia to form ammonium nitrate. Studi es by Pun et al. (1998, 2004) suggested that the daytime pathway may be important and ther efore 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 producti on 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 sunris e could explain the observed homogeneous patterns of ammonium nitrate in the Valley. Ying et al. (2009) al so 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 VO C emissions. Therefore, nighttime ammonium nitrate formation in the San Joaquin Valley would not

be sensitive to VOC reductions.

While the monitoring studies cited above prov ide evidence that the VOC pathway may be important at times, thes e studies do not provide quant itative information about the

overall role of and cannot be used to evaluat e 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 modelin g studies containing information on the significance of VOC controls in reducing amm onium nitrate in the San Joaquin Valley.

While the results of the earliest studies we re mixed, later studi es provide generally consistent results regarding the role of VO Cs. In assessing the potential benefits of VOC controls it is important t hat 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 simplif ied box modeling to explore t he sensitivity of ammonium

nitrate to VOC and NOx reduction s. 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 emi ssion 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 trans port into/out of the box, robust vertical transport, and use of an older chemical me chanism. In addition , the VOC emissions were increased by a factor of two to impr ove 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. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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26 Subsequent modeling sensitivity studies fo r the same winter episode were conducted with the UCD-CIT model, an advanced research grade modeling system (Kleeman et al., 2005). The authors conclude d 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 r eductions in VOC emissions (on the order of 50 percent) reduced PM2.5 nitrate concent rations by approximately 17 percent. However, to evaluate the significance and effe ctiveness of VOC controls in the context of control strategy design, t he study’s isopleths of PM2.5 nitrate response to combined NOx/VOC emission reductions prov ide 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 approac h 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) respective ly. Once NOx controls are taken into consideration, VOC emission reductions pr oduce essentially no benefit, and in some instances may actually lead to an increas e 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 resu lting from existing and proposed controls, reductions in VOC emissions to any le vel would not decrease PM2.5 nitrate concentrations. Furthermore, at grid lo cation -85 km Northing, 90 km Easting (Figure 23 (a)), any level of VOC emission reductions would actually cause an increase in nitrate concentrations. Nitrogen-contai ning molecules such as PAN can act as temporary sinks for nitrogen dioxide (NO 2 

### ). When VOCs are c ontrolled, the reduced availability of certain radica ls, which are generated from VO Cs, reduces the amount of

NO 2 

### that is sequestered, thereby in creasing the availability of NO 2 

### and enhancing ammonium nitrate formation (Me ng et al., 1997). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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27 Figure 22. 24-hour average NOx/VOC par ticulate nitrate isopleths at Fresno for (a) all sources, (b) diesel engines, (c) catal yst equipped gasoline engines, and (d) upwind

sources of nitrate. Units are μg/m 3 

### . (Source: Kleeman et al., 2005, Figure 3 pg. 5333). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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28 Figure 23. 24-hour average NOx/VO C 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/m 3 

### . (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 dur ing 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 qualitativ ely confirm that

NOx control is the most efficient method to reduce nitrate concentrations (Kleeman, M.J., personal communication, May 2008). Figure 24 illustra tes the response of PM2.5

nitrate concentrations to NOx and VOC emi ssion 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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29 concentrations. Once NOx controls are taken into consideration (approximately 70 percent reduction in NOx emissions), re ductions in VOCs of up to 30 percent produce basically no benefit (Fresno). Furthe rmore, at some locations (Angiola) any VOC emission reductions may actually le ad 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 fi ve-day back trajectory simulation for December 31, 2000. Units are in μg/m 3 

### . (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 prelim inary emissions inventory. This simulation showed that 50 percent reductions in anthropogenic VOC and NOx emissions had similar effects in reducing amm onium nitrate (about 20 percent each). A second simulation was conducted using an updated emission invent ory representing a more accurate spatial distribution of tota l ammonia emissions (referred to as “Vehicle

NH 3 

### ” 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 responsivene ss to NOx control of 38 percent reduction

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

2005.

A third study modeled one week of the CRPAQ S episode using a version of CMAQ with a more advanced chemical mechanism (CMAQ-Madr id) (Pun et al, 2009). In contrast to the earlier Pun study using a simplified box mode ling approach, this later work found San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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30 that on average, nitrate was most sensitive to reductions in NOx emissions. While isopleths were not provided, the time ev olution of nitrate and PM2.5 mass to VOC response illustrated in Figure 25 provides fu rther 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 c aused small reductions in ni trate, on the order of 10 percent, on the modeled days when 24-hour PM2.5 concentrations measured over 100 μg/m 3 

### at urban sites and above 65 μg/m 3 

### 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 concen trations 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 chem istry 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 hy droperoxyl 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 t he next day. Therefore, it is reasonable to assume that the higher response to VOC a nd NOx at higher PM2.5 concentrations may

be due to the nitrate formation mechanism ra ther 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 reac hed in the San Joaquin Valley. Currently, the 24-hour PM2.5 design valu e in the Valley is 62 μg/m 3 

### recorded at Bakersfield and the rest of the Valley records 24-hour design values between 38 μg/m 3 

### and 58 μg/m 3



### . Given the current levels of PM2.5, we belie ve the Valley is now in a nitrate chemical formation regime that is less responsive to VOC controls. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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31 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 qualit y modeling studies indicate that in the San Joaquin Valley, NOx, rather than VOCs, is the limit ing precursor for nitric acid, and subsequent

ammonium nitrate formation. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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32 6. SECONDARY ORGANI C AEROSOL FORMATION

VOC emissions also have the potential to contribute to secondary organic aerosols (SOA). While these component s contribute to observed PM2.5 concentrations in the San Joaquin Valley to a small degree, t he weight of evidence indicates that anthropogenic VOC is not a signifi cant contributor to PM2.5.

SOA form when intermediate molecular we ight VOCs, emitted by anthropogenic and biogenic sources, react and condense in t he atmosphere to become aerosols. In addition, lighter VOCs partici pate in the formation of at mospheric oxidants which then participate in the formation of SOA. The processes of SOA formation are complex and have not been fully characterized. The appor tionment 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. (20 10) investigated the apportionment of PM2.5 organic carbon for the 2000/2001 CRPAQS episode. From t he total predicted PM2.5 organic carbon in the urban Fresno and Bakersfi eld areas, six percent and four percent were SOA, respectively, while in the rura l Angiola area, 37 perc ent was SOA. The major SOA precursors of secondary organic aerosol were long-chain alkanes followed by aromatic compounds. The sources of thes e precursors were solvent use, catalyst gasoline engines, wood smoke, non-catal yst gasoline engines, and other anthropogenic sources, in that order.

In contrast, on an annual average basis, secondary organic aerosols derived from anthropogenic VOC emissions acc ount for only one to two perc ent of the annual total PM2.5 concentrations throughout the Valley. ARB air quality modeling exercises conducted as part of the SJV 2008 PM2.5 Pl an attainment demonstration analysis using
the CMAQ model showed that primary PM2.5 emi ssions are the main contributor to organic aerosols and SOA contribute to only a sm all extent. Furthermore, as illustrated in Figure 26, SOA are mostly formed dur ing the summertime, when total PM2.5 concentrations are low, and are mainly deriv ed from biogenic emission sources. On an annual average basis, SOA derived from anthropogenic VOC emi ssions are a small part of the organic aerosol concentrations (three to five percent). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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33 Figure 26. Daily contributions to organic aerosol concentrations in Bakersfield in 2000
modeled with CMAQ: Pr imary organic aerosols (PA), secondary aerosols formed from biogenic VOC emissions (SB) and sec ondary aerosols form ed from anthropogenic

source VOC emissions (SA). Units are μg/m 3



### .

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 c oncentrations 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 anthropoge nic VOC emissions

has limited effects on the modeled PM 2.5 organic matter (Pun, et al., 2009).

These study results show that for secondar y organic aerosols, further VOC reductions would have very limited effectiveness in reducing PM2.5 concentrations. BAC

0 2 4 6 8 10 12 141163146617691106121136151166181196211226241256271286301316331346361 SB

SA

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

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34 7. EMISSION SOURCES OF WINTERTIME PM2.5

a. Emission inventory

Emission inventories provide emission esti mates 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 component s 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 ni trate is the main PM2.5 component, contributing about 55 to 65 perc ent of PM2.5. It is fo rmed 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, fert ilizer application, and mobile sources. Ammonium sulfate, formed in the air from reactions of SO x 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 emitt ed, with key sources being resi dential fuel combustion, managed burning, diesel trucks, and commerc ial cooking operations . Geological, a minor component contributing about two to five percent of t he 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 da ys, PM2.5 exceedances primarily occur on very stagnant days when windblown dus t emissions are minimal.

While emission inventories pr ovide a broad overview of Valley wide and county level sources, additional methods using ambi ent data and source appo rtionment modeling provide supplemental information on the source s directly impacting individual monitoring sites. The following sections describe these analyses. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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35 Table 2. Main emission sour ces 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. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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36 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 Va lley. Measurements of levoglucosan, a chemical marker for wood smoke were conducted throughout the San Joaquin Valley. Figure 27 illustrates the geographical dist ribution of the annual averages of these levoglucosan measurements (pink circles on the map). Each circle size is proportional to the levoglucosan concentration. The la rgest levoglucosan levels occurred in urban areas, most notably the Fresno area (FSF and FSR), as did the la rgest PM2.5 organic carbon levels depicted on the small map to the upper left. The second largest levoglucosan levels the San Joaquin Vall ey were measured in Modesto (M14), sequentially followed by Bakersfiel d (BAC) and then Corcoran (COP).

Figure 27. Spatial distribution of annual levo glucosan measured throughout the San
Joaquin Valley during CRPAQS (W atson, J., Roth, P., 2006).

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

the Fresno area showed wood smoke was a si gnificant percentage of PM2.5 at all locations, ranging from 10 to 40 percent (Figure 28). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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37 Figure 28. Wood smoke contributi on to PM2.5 at Fresno-1 st 

### 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 diffe rent 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 Matr ix Factorization (PMF) statistical model distinguishes correlation patterns among measur ed 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 estima ted 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 CRPAQ S field study. In addition, Chow et al., 2005, applied CM B to the CRPAQS data set. Table 3

summarizes the source contributions to wintertime PM2.5 estimated through these studies. In all cases, ammoni um nitrate is the major sour ce, contributing approximately 50 percent to wintertime PM2.5 throughout t he Valley (23-site average); ranging from 40 and 50 percent at urban sites (Fresno and Ba kersfield) to around 65 percent at rural sites (Kern Wildlife Refuge and Angiola). The combined biomass burning and cooking source, dominated by biomass burning, co ntributes over 25 percent of PM2.5 0 5 10 15 20 25 30 35 40 45Percent Contribution Wood S moke Contribution

to PM2.5 at Fresno-1 st San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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38 Table 3. Wintertime PM2.5 sour ce 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 Org a Misc

Gas Dies

IMS-95 1

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,

UNMIX 2
23-site avg.

Nov. 2000 -

Jan. 2001 0 3 15 24 5 51 2

CRPAQS,

PMF 2
23-site avg.

Nov. 2000 -

Jan. 2001 0 5 10 23 3 48 11

CRPAQS,

CMB 3

Fresno

IOPs 4 0.2 0.3 1.5 5.8 48.9 3.1 40.1

Bakersfield

IOPs 4 0.2 1.2 6.4 4.5 30.4 3.9 53.5

Angiola

IOPs 4 0.3 5.2 7.4 1.9 14.8 4.4 65.9

Sierra Nevada

Foothills

IOPs 4 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 carb on not accounted for by primary source profiles. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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39 valley wide, constituting the second majo r source at urban sites – with larger

contributions at Fresno than at Bakersfield. In contrast, biomass burning and cooking are not a major sources at rura l sites. Engine exhaust, domi nated by diesel, is the third major source of directly emitted PM2.5, c ontributing approximately 10 to 15 percent to PM2.5 valley wide. Since secondary organics are estimat ed from the organic carbon not accounted for by the apportionment of ot her 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 c oncentration days at Bakersfield-California (BAC) and Fresno –1 st 

### Street (FSF) were estimated by applying the CMB model version 8.2 to individual PM2.5 samples using PM2. 5 source profiles devel oped during previous studies. The PM2.5 samples were from da ys measuring concentrations greater than 30 μg/m 3 

### between 2007 and 2010. Per U.S. EPA guidance, between 2007 and 2009, the carbon collection and analysis method wa s changed to improve comparability with the rural Interagency Monitoring of Protec ted Visual Environm ents (IMPROVE) PM2.5

carbon data. Since the new carbon me thod 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 nitr ate, the most significant source, contributed 67 percent

at Bakersfield and 54 percent at Fresno-1 st 

### . Biomass burning, which included residential wood combustion and agricultura l, prescribed burning, and likely also cooking, contributed nine perc ent at Bakersfield and 23 per cent at Fresno. Motor vehicle exhaust (diesel and gasoline combined) accounted for ten percent at Bakersfield and 12 percent at Fresno-1 st 

### . Ammonium sulfate cont ributed eight percent at Bakersfield and six percent at Fresno-1 st 

### . Contributions of the remaining sources were

minor at both sites. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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40 Figure 29. CMB model calculated 2007-2010 aver age PM2.5 source contributions of days with PM2.5 concentrations measuring over 30 μg/m 3 

### at a) Bakersfield-California (BAC) between 2007 and 2010 and b) Fresno-1 st 

### Street (FSF) between 2009 and 2010.

Positive Matrix Factorization

The PMF2 model was applied to the chemica lly speciated PM2.5 dat a collected at the

Bakersfield-California and Fresno-1 st 

### Street monitoring sites. Bakersfield data from

2008-2010 and Fresno-1 st 

### data from 2009-2010 were used. Appendix 3 describes this PMF analysis in further detail. The aver age source contributions on days with PM2.5 concentrations measuring over 30 μg/m 3 

### are illustrated in Figure 30. Similar to the CMB results, ammonium nitrate contributes the mo st at both sites, 64 per cent at Bakersfield and 54 percent at Fresno-1 st 

### . Motor vehicle exhaust contributes 13 percent at Bakersfield and 23 percent at Fresno-1 st 

### , while biomass burning (which includes residential wood combustion, agricultural burni ng, and likely also cooking) contributes six percent at Bakersfiel d and 13 percent at Fresno-1 st 

### . Secondary ammonium sulfate accounts for eight percent at Bakers field and seven percent at Fresno-1 st 

### . Airborne soil

and industrial sources are mi nor 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) San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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41 Figure 30. Average high day source contributi ons estimated using PMF on days with PM2.5 concentrations measuring over 30 μg/m 3 

### at a) Bakersfield-California (BAC) between 2008 and 2010 and b) Fresno-1 st 

### 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 appor tionment studies confirm the importance of secondary ammonium ni trate contributions to PM2.5 on high concentration days. In addition, motor v ehicle exhaust and biomass burning were found to be significant contributor s 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) simu lated the 2000/2001 CRP AQS PM2.5 episode using the source-oriented UCD-CIT air quality model. Source apport ionment of primary

PM2.5 in the SJV found elemental and or ganic carbon (EC and OC) to be the two largest components. Wood burni ng was the major OC source in the Valley, contributing approximately 50 percent to t he 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 sec ondary nitrate at Fresno reveal ed diesel engines were the largest contributor to nitrate, accounti ng for approximately 40 per cent of the PM2.5 nitrate, while catalyst equipped gasoli ne engines accounted for approximately 20 percent. Agricultural sources accounted for approximately 80 percent of the PM2.5

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

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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-1 st 

### ) and southern regions (e.g., Bake rsfield) in the Valley show progress towards attainment of the standard. The design value -- the metric used to determine compliance with the standard -- repres ents the average of three consecutive annual averages of the PM2.5 concentrations m easured 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 μ g/m 3 

### , the

site attains the standard. Between 2001 and 2011, annual design values in the Valley declined between 30 and 40 percent. The larges t decreases occurred in the northern and central Valley, where, based on 2011 desi gn values, most sites attain the annual

PM2.5 standard. While the southern Valle y 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 val ues (2001-2011) at the BakersfieldCalifornia, Fresno-1 st 

### , and Modesto monitoring sites.

0 5 10 15 20 25 30

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Design Value (μg/m3) 

### Change in Annual PM2.5 Design Values

Bakersfield-California Fresno-1st Modesto Standard San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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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 t he 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 Va lues (2001-2011) at the BakersfieldCalifornia, Fresno-1 st 

### , and Modesto monitoring sites.

Meeting the PM2.5 24-hour standar d poses a significant challenge because the focus is on the most severe days, which are strongl y influenced by meteorology as well as emissions from episodic activities, such as residential wood burning. Thus, evaluating multiple PM2.5 air quality parameters and t he meteorology effects on air quality trends provides a broader picture of progress in the Valley.

Looking at the number of da ys with measured PM2.5 concent rations over the 35 μg/m 3


### standard provides another way to assess PM2. 5 trends. Over the long term, between 1999 and 2011, the number of days exceed ing the standard decreased by about 50 percent at the Bakersfield-California si te and by about 45 percent at the Fresno-1 st


### site (Figure 33). The increase in the num ber of exceedance days in 2011 compared to 2010 was due to the very severe meteorolog ical conditions experienced in the Valley during the winter of 2011. The Valley expe rienced similar meteorological conditions during the 1999-2000 and 2000-2001 winters. T he total number of exeedance days, however, was much higher during these earlie r 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 2011PM2.5 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 hav e resulted in significant PM2.5 air quality improvement.

Figure 33. Trend in measured days over the 24-Hour standard of 35 μg/m 3 

### (1999- 2011) at the Bakersfield-California and Fresno-1 st 

### 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 characte rized 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 accu mulate under stagnant weather conditions.

The “Pacific High”, a semi-permanent subtr opical high pressure sys tem 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 t he winter. In turn, t he strength and position of these high pressure regions are influenced by the strength of t he El Nino/Southern Oscillation. El Nino years, characterized by warmer than normal temperatures in the equatorial Pacific and La Nina ye ars, characterized by colder than normal temperatures, can alter the position of the Pacific High, allowing or blocking t he passage of frontal systems through California and t he San Joaquin Valley. A strong La Nina year can keep the Pacific High from mo ving south in the winter, diverting normal winter frontal systems northward, and resulting in drier condi tions in California, particularly in the southern portions of the State. Due to decreases in the num ber and strength of frontal systems passing through the Valley, as we ll as increases in potential stagnant conditions, a strong La Nina year can result in higher than expected PM2.5

concentrations. 0 10 20 30 40 50 60 70 80

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Number of days Measured Days over 24-Hour PM2.5 Standard of 35 ug/m 3

Bakersfield-CA Fresno-1st 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 bui ld, 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-mov ing system can bring light surface winds with weak vertical mixing. The resulti ng stagnant conditions can persist for extended periods before the frontal syst em bringing precipitatio n 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 t he 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 condition s can lead to temperature inversions. Under normal conditions, temperature decreas es 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 frequen cy and intensity of the two high pressure systems and the speed and intensity of the per iodic storm systems t hat 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 duri ng 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 li ght 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 Va lley a significant challenge.

Figure 34. Frequency of high PM2.5 days in different regions of the San Joaquin Valley corresponding to different synoptic met eorological conditions during the CRPAQS winter of 2000/2001 (Mac D onald et al., 2006). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

46 Examples of the impact of La Nina on Valley weather patterns can be seen during the

winters of 2011/2012 and 2000/20 01, the period during CRPAQ S. 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, bri nging precipitation, high wind speeds, and strong vertical mixi ng, 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 compar e PM2.5 concentrations measured at Bakersfield and Fresno, respective ly, between November 1, 2011 and February 29, 2012 to the PM2.5 concentration s measured during the same four months (November through February) in ear lier years (1999/2000 and 2000/2001). The 2011/2012 air quality was much better compar ed 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 wa s also 40 percent lower. The number of days over the 24-hour standard of 35 μg/m 3 

### 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/m 3 

### ).

Figure 35. Comparison of the 2011/2012 PM2. 5 episode to the CRPAQS episodes of

1999/2000 and 2000/2001 at Bakersfield-California.

0 20 40 60 80 100 120 140 160 18011/111/711/1311/1911/2512/112/712/1312/1912/2512/311/61/121/181/241/302/52/112/172/232/29PM2.5 (ug/m3) Select PM2.5 Episodes at Bakersfield-California

1999-2000

2000-2001

2011-2012 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

47 Figure 36. Comparison of the 2011/2012 PM2. 5 episode to the CRPAQS episodes of

1999/2000 and 2000/2001 at Fresno 1 st



### .

d. Annual trends adjusted for meteorology

To understand the effects of emission r eductions on ambient PM2.5 concentration trends, the effects of meteor ology 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 develope d for Bakersfield and Fresno accounted for most, but not all, of the effects of me teorology on the annual PM2.5 trends. One aspect that may not have been fully captured wa s 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 fi gures below integrate the CART-defined meteorology-effect s. For example, in year s with meteorology conditions more conducive to PM2.5 formation, t he PM2.5 concentrations were adjusted downward. Conversely, the PM2.5 concentra tions were adjusted upward in years with meteorology conditions less c onducive 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 indi cates 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-adjus ted 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. 0 20 40 60 80 100 120 140 160 18011/111/711/1311/1911/2512/112/712/1312/1912/2512/311/61/121/181/241/302/52/112/172/232/29PM2.5 (ug/m3) Select PM2.5 Episodes at Fresno-1st

1999-2000

2000-2001

2011-2012 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

48 Figure 37. Observed and met-adjusted PM 2.5 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 diffe rences in meteorological c onditions 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 val ues each year may differ from those in 0 10 20 30

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011PM2.5(g/m3)

Year 

### Observed and Met

‐ Adjusted PM 2.5 

### Trends in Bakersfield Area

Observed PM2.5 Concentrations

Met

‐ Adjusted PM2.5

Linear (Observed PM2.5 Concentrations)

Linear (Met

‐ Adjusted PM2.5)

0 10 20 30

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011PM2.5(g/m3)

Year Observed and Met

‐ Adjusted PM 2.5 Trends in Fresno Area

Observed PM2.5 Concentrations

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

49 Figure 33 for several reasons including a) they are averages of multiple sites in each area, b) more days could be included wher e missing values could be imputed (missing values were filled in using relationship s in existing data), and c) some days with incomplete meteorological data co uld not be included in the analysis.

The met-adjusted trend for Bakersfield s hows 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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

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

Comparing the change in the frequency distri bution 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 frac tion of days recording PM2.5 over the 24-hour

standard of 35 μ g/m 3 

### decreased between the three-y ear periods of 1999-2001 and 2009-2011 at the three monitoring sites s hown. 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 fr om about ten percent to le ss than five percent. In contrast, during these same periods, the frac tion of days recording concentrations at or

below the annual standard increa sed 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 fr equency distribution between the 1999- 2001 and 2009-2011 periods at the a) Bake rsfield-California, b) Fresno-1 st 

### , and c) Modesto monitoring sites.

0 10 20 30 40 50 60 70 80

0-15 >15-25 >25-35 >35-50 >50-65 >65-90 > 90% of Samples

Concentration (ug/m 3 ) a) Decrease in PM2.5 Concentrations

at Bakersfield-California

1999-2001 2009-2011 0 10 20 30 40 50 60 70 80

0-15 >15-25 >25-35 >35-50 >50-65 >65-90 > 90% of Samples

Concentration (μg/m 3 ) b ) Decrease in PM2.5 Concentrations

at Fresno-1st

1999-2001 2009-2011

0 10 20 30 40 50 60 70 80 90

0-15 >15-25 >25-35 >35-50 >50-65 >65-90 > 90% of Samples

Concentration (ug/m 3 ) c) Decrease in PM2.5 Concentrations

at Modesto

1999-2001 2009-2011 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

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 1 st 

### and 4 th 

### 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 Ba kersfield 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 perc ent at Modesto.

Figure 41. Change in three-y ear averages of 1 st 

### and 4 th 

### quarter PM2.5 concentrations at the Bakersfield-California, Fresno-1 st 

### , and Modesto monitoring sites.

0 5 10 15 20 25 30 35 40 45

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Concentration (ug/m3) 

### Change in Three-Year Averages of 1 st 

### and 4 th 

### Quarter

PM2.5 Concentrations

BakersfieldCA

Fresno-1st

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

52 Focusing on changes in winter (Novem ber 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 measur ing over the 24-hour PM2.5 standard. Figure 42 illustrates the decrease in the win ter average PM2.5 concentrations in these four years at the Bakers field-California, Fresno-1 st 

### , 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, wint er 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 aver age (January, February, November, December) PM2.5 concentrations at t he Bakersfield-California, Fresno-1 st 

### and Modesto monitoring sites among years with mo st PM2.5 conducive meteorology.

0 5 10 15 20 25 30 35 40 45 50

2000 2002 2007 2011Concentration (μg/m3) 

### Changes in Winter Months Average

PM2.5 Concentrations

Bakersfield-CA Fresno-1st Modesto San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

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; ho wever the average monthly concentrations have decreased. The most significant im provements in PM2.5 have been achieved during the winter months.

Figure 43 . Changes in PM2.5 monthly c oncentrations between the 1999-2001 and 2009-2011 three-year periods at the a) Bakersfield-California, b) Fresno-1 st 

### , and c) Modesto monitoring sites.

0 10 20 30 40 50 60

1 2 3 4 5 6 7 8 9 10 11 12Concentrations (ug/m3)

Month b) Changes in PM2.5 Seasonal Pattern

Fresno-1st

1999-2001 2009-2011 0 10 20 30 40 50 60

1 2 3 4 5 6 7 8 9 10 11 12Concentrations (ug/m3)

Month a) Changes in PM2.5 Seasonal Pattern

Bakersfield-California

1999-2001 2009-2011

0 10 20 30 40 50

1 2 3 4 5 6 7 8 9 10 11 12Concentrations (ug/m3)

Month c) Changes in PM2.5 Seasonal Pattern

at Modesto

1999-2001 2009-2011 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

54 Comparing changes in PM2.5 diurnal patterns offers further insights into the progress achieved. Figure 44 illustrates changes in the three-year aver ages of hourly PM2.5 concentrations recorded during November and December between 2001-2003 and 2009-2011 at a) Bakersfield- California, b) Fresno-1 st 

### , and c) Modesto. The overall diurnal patterns have not changed, yet hourly concentrations have decreased throughout the day. Peak daytime concent rations decreased approximately 20 percent, and peak nighttime concentration s approximately 30 percent.

Figure 44. Changes in the average November-Dec ember PM2.5 hourly concentrations

between the 1999-2001 and 2009-2011 three-year periods at the a) BakersfieldCalifornia, b) Fresno-1 st 

### , and c) Modesto monitoring sites.

0 10 20 30 40 50 60 70

0 4 8 12 16 20PM2.5 Concentration (ug/m3)

Time-of-day b) Changes in PM2.5 Diurnal Variation (Nov-Dec) at Fresno-1st Street

2001-2003 2009-2011 0 10 20 30 40 50 60

0 4 8 12 16 20PM2.5 Concentration (ug/m3)

Time-of-day a) Changes in PM2.5 Diurnal Variation (Nov-Dec) at Bakersfield-CA

2001-2003 2009-2011

0 10 20 30 40 50 60

0 4 8 12 16 20PM2.5 Concentration (ug/m3)

Time-of-day Change in PM2.5 Diurnal Variation (Nov-Dec) at Modesto

2001-2003 2009-2011 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

55 g. Chemical composition trends

As previously discussed, PM2.5 concentrations measured at monitoring sites in the SJV

have decreased from the 1999-20 01 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 t he 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. Betw een 2007 and 2009, the carbon collection and analysis method was changed to improve com parability with the rural IMPROVE PM2.5 carbon data. Since the change was implemen ted mid-year, there are gaps in carbon data for years with a mix of the old and new methods.

Ammonium nitrate, ammonium sulfat e, and carbon compounds are the major

constituents of PM2.5. On an annual aver age basis, concentrations of these key constituents have all shown significant decreas es. Ammonium nitrate concentrations in the Valley declined about 40 percent between 2002 and 2011. During the same time-frame, concentrations of ammonium su lfate 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.

0 2 4 6 8 10 12

2002200320042005200620072008200920102011Concentrations (ug/m3) 

### Trends in PM2.5 Chemical Components at Bakersfield

Ammonium Nitrate

Ammonium Sulfate

Geological

Elements

OM_Old

EC_New

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

56 Figure 46. Trends in PM2.5 chemic al components at Fresno-1 st



### .

Figure 47. Trends in PM2.5 chemical components at Modesto.

The 2012 SJV PM2.5 Plan’s App endix A describes further a nalyses on PM2.5 air quality

trends. 0 2 4 6 8 10 12 14

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Concentrations (ug/m3) 

### Trends in PM2.5 Chemical Components at Fresno-1st

Ammonium Nitrate

Ammonium Sulfate

Geological

Elements

EC_Old

OM_Old

EC_New

OM_New

0 2 4 6 8 10 12

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Concentrations (ug/m3) 

### Trends in PM2.5 Chemical Components at Modesto

Ammonium Nitrate

Ammonium Sulfate

Geological

Elements

EC_Old

OM_Old

EC_New

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

57 h. Emission inventory trends

As specified by U.S. EPA’s PM2.5 implemen tation rule, required PM 2.5 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 fr om heavy-duty diesel trucks, stationary combustion sources, and other mobi le 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 co mponents and emissions of PM2.5, NOx,

and SOx indicate that the ongoing control program has had substantial benefits in improving air quality in the SJ V and that further emission reductions in the future are expected to provide continuing progress toward s attaining the 24-hou r PM2.5 standard.

Figure 48. PM2.5 and PM2.5 precursor winter em ission trends in the San Joaquin Valley.

0 100 200 300 400 500 600Emissions (tpd) 

### Winter Emission Inventory Trends

NOx

PM2.5

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

Appendix G: PM2.5 Weight of Evidence Analysis

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### 11/8/12

58 9. LINKING AIR QUALITY TREN DS TO EMISSION REDUCTIONS

a. NOx control

Programs aimed at reducing NO x emissions have played an im portant role in reducing nitrate concentrations and, consequently, overa ll 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 emissi ons and PM2.5 nitrate levels track each other over the y ears. Trends in estimated NOx emissions, as well as monitored ambient concentrations, are com pared with trends in measured PM2.5 nitrate

concentrations. As illustrated in Fi gure 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. Furthermo re, the reductions in NOx emissions were also reflected in the corresponding r eduction in the ambient gaseous NOx concentrations. Figures 50 and 51 show a st rong correlation between trends in PM2.5

nitrate concentrations and ambient NOx concent rations at the Bakersfield and Fresno sites. Between 2004 and 2011, concentrati ons of both PM2.5 nitrate and NOx decreased approximately 30 percent.

Figure 49. Comparison between trends in Valle y wide winter average NOx emission

and PM2.5 nitrate concentrations at Ba kersfield and Fresno. Emissions and concentrations are presented as three-year winter averages.

0 100 200 300 400 500 600

0 2 4 6 8 10 12 1420042005200620072008200920102011NOxEmissions (tpd)PM2.5 Nitrate (ug/m3) Winter average PM2.5 nitrate at Bakersfield and Fresno compared to basin-wide NO

x emissions

NOx

Emissions

BAC NO3-

FSF NO3- San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

59 Figure 50. Comparison of trends in wintertim e PM2.5 nitrate and NOx concentrations

in Bakersfield. Concentrations are presented as three-year winter averages.

Figure 51. Comparison of trends in wintertim e PM2.5 nitrate and NOx concentrations in Fresno. Concentrations are pres ented 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 us ed to assess longer-term trends. Although,

the earlier data do not meet t he strict quality assurance/qual ity control requirements of the PM2.5 chemical speciation network, they do provide a historical perspective. The highest PM10 nitrate concentrations were meas ured in the Valley in early 90’s. Since then, concentrations of both PM10 nitrat e 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 nitrat e formation. 0 1 2 3 4 5 6

0 2 4 6 8 10 12 1420042005200620072008200920102011NOx(pphm)PM2.5 Nitrate (ug/m3) Comparison of winter average concentrations

of PM2.5 nitrate and NO

x in Bakersfield

NOx

NO3-

0 1 2 3 4 5 6

0 2 4 6 8 10 1220042005200620072008200920102011NOx(pphm)PM2.5 Nitrate (ug/m3) Comparison of winter average concentrations

of PM2.5 nitrate and NO

x in Fresno

NOx

NO3- San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

60 Figure 52. Long-term trends in three-year aver age concentrations of PM10 nitrate and NOx in the San Joaquin Valley.

b. Residential wood burning controls

As previously discussed, annual average co ncentrations 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 da ys. Through a series of Rule 4901 amendments, the PM2.5 threshol d for calling no-burn days was established

in 2003 at 65 μ g/m 3 

### and subsequently tightened to 30 μ g/m 3 

### 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 concent ration relationships to meteorological variables before the wood-burning curtailm ents came into effec t, 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 furt her described in the 2012 SJV PM2.5 Plan’s Appendix A.

The analysis results indicate that as of the 2011-2012 wood-bur ning season, 24-hour average PM2.5 levels in Fresno have improved by 41 percent (21 μ g/m3) since the 2003 and 2008 amendments to Rule 4901 (Figure 53). This improvement is especially

marked in PM2.5 concentrations measured dur ing the evening hour s of 8:00 p.m. to 12:00 a.m. The average evening PM2.5 conc entrations have improved by 50 percent (42 μ g/m3) over the same time period. As shown in th is analysis, the 2008 amendment 0 1 2 3 4 5

0 1 2 3 4 5 6 7 8 9 101987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011NOx(pphm)PM10 Nitrate (ug/m3) 

### Three-year Average Concentrations

of PM10 Nitrate and NO

x 

### in San Joaquin Valley

NO3-

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

61 to Rule 4901 has approximately doubled the seasonal improvements in PM2.5

attributable to the 2003 amendments.

Rule 4901 will continue to play an import ant 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. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

62 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 implementati on of the ongoing cont rol program. In these counties,

additional focused emission reductions ar e needed to provide for attainment. As required by U.S. EPA, additional analyses ha s been done to confirm that attainment is predicted throughout each county (i.e. in each modeled grid cell). The “Attainment Demonstration” chapter of t he District’s plan provides an overview of the photochemical modeling performed. Additional informa tion on the periods modeled, the models selected, and model application can be f ound 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 co mbination of adopted ARB and District programs. As a result, most sites in the northern and ce ntral Valley are expected to attain by 2019. As required by U.S. EPA, th e modeling replicates the base year 2007 meteorological conditions for each ca lendar 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 progr ams to curtail residential wood burning,

ARB staff then modeled a scenario with an enhanc ed curtailment pr ogram, 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 (Bak ersfield-California)

would not attain the standard with this additional level of control. The predicted design

values for each site from this model ing scenario are shown in Table 4.

Table 4. 2019 Modeled 24-hour PM 2.5 Design Values.

Monitoring Site Design Value (μg/m 3 

### )

Bakersfield - California 35.7

Bakersfield - Planz 32.9

Corcoran - Patterson 32.1

Visalia - N. Church 29.4

Fresno - Hamilton 28.6

Fresno-1 s t 

### 30.5

Clovis 28.6

Merced 22.6

Modesto 24.7

Stockton 21.4 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 11/8/12

63 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 majo r portion of the emission r eductions 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, t he base year used in the photochemical modeling attainment demons tration, and 2019, implement ation 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 near ly 45 percent reduction in ammonium nitrate concentrations. In addition, while di rectly emitted PM2.5 emissions in aggregate are decreasing by nearly 30 percent, a major focu s of the attainment control strategy is

further curtailment of residential wood bur ning. Ambient meas urements and modeling studies have shown the large contribution t hat residential wood burning has on PM2.5 exceedance days. In addition, prior Distric t analysis has demonstrated the significant benefits of past implement ation of wood burning curtailment. Therefore, t he substantial continuing reductions that will result from implementation of the ongoing control program, coupled with an enhanced residentia l burning curtailm ent program, are

consistent with the benefits predicted in the modeled at tainment demonstration.

As a result of the overall control pr ogram, coupled with the enhanced wood burning curtailment measure, ammoni um nitrate concentrations are predicted to decrease by nearly 45 percent, organic carbon concentrations by approximately 65 percent, and elemental carbon concentrations by near ly 80 percent. A comparison of the

concentrations of the main chemical consti tuents in 2007 to that predicted in 2019 at three sites (Modesto, Fresno-1 st 

### , and Bakersfield-California) illustrates the significant reductions in these components (Table 5).

Table 5. Comparison of the concentration of c hemical constituents for 2007 and 2019 design values at selected sites.

Component (ug/m 3 

### ) Bakersfield – Calif. Fresno-1 st 

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

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64 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 sensit ivity runs to assess the relative efficacy of further reductions of different PM2.5 pr ecursors. U.S. EPA’s PM2.5 implementation rule specifies that a precursor is cons idered “significant” for control strategy development purposes when a significant reduc tion 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 prec ursors” (72 FR 20586). The U.S. EPA’s implementation rule also establishes a pr esumption 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, NO x, 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, reducti ons 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 precurso rs each time a plan is developed.

Additional photochemical modeling analyse s were therefore conducted to understand

the relative effectiveness of emission reduc tions 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 emi ssion 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 re duction 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 approximat ely five times as effective as SOx. In contrast, reductions in ammoni a are approximately nine time s less effective than NOx, and as noted above, reductions in VOCs resu lt in either no impact of very small

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

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65 Table 6 . Modeled reduction in 2019 PM2.5 des ign value resulting from 25 percent reduction in valley wide precursor emissions.

Monitoring Site PM2.5 Reduction (μg/m 3 

### ) 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-1 s t 

### 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 qualit y benefit per ton of valley wide precursor emission

reductions.

Monitoring Site PM2.5 Benefit (μg/m 3 

### 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-1 st 

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

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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 val ue at each level of emission reduction. Isopleth diagrams for the Bakersfield-Californi a 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 sma ll effect on the predicted design value since ammonium sulfate is a small component of me asured PM2.5.

 Reducing VOCs leads to very small in creases in the design value because these reductions have the effect of making mo re NOx available for nitric acid, and subsequent ammonium ni trate formation.

 Because ammonia is much more abundant than NOx, the atmosphere is more responsive to reductions in NOx as compar ed to ammonia. Reductions in NOx in turn have significant benefits as ammoni um nitrate is a la rge component of

measured PM2.5.

 Reductions in directly emitted PM2.5 re sult in significant benefits due to the reduction in organic carbon which is a la rge component of m easured PM2.5.

These modeling results, along with the fi ndings from past modeling and monitoring studies highlight that reductions in direct ly emitted PM2.5 and NO x provide the greatest benefit in further reducing PM2.5 conc entrations and making progress towards attainment. Given that significant reduc tions 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. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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67 Figure 54. Bakersfield–Californi a Isopleth Diagrams.

b ) NOx 

### vs. VOC

d) NOx vs. PM2.5 c ) N O x v s . Amm o ni a a) NOx vs. SOx San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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68 Effectiveness of localized emission reductions

The valley wide precursor sensit ivity modeling demonstrates t hat on a relative basis the greatest benefits are achieved from reductions in sources of directly emitted PM2.5, followed by NOx. Due to t he stagnant conditions that occu r during wintertime episodes, and the local nature of directly emitted PM2. 5 carbon sources in particular, Kern County specific model sensitivity runs were also c onducted to evaluate the benefits of emission

reductions focused on the nonattainment sub-ar ea. The Kern County sensitivity runs

demonstrated that:

 One ton per day of directly emitte d PM2.5 reductions provides a 1 μg/m 3


### improvement in the Bakersfiel d-California design value;

 One ton per day of NOx reducti ons provides for a 0.12 μg/m 3 

### improvement in the

Bakersfield-Californi a design value;

 One ton per day of SOx reducti ons provides for a 0.21 μg/m 3 

### improvement in the

Bakersfield-Californi a design value;

 One ton per day of ammonia redu ctions provides for a 0.02 μg/m 3 

### improvement

in the Bakersfield-Californ ia 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 ident ify potential PM2.5 and NOx sour ces for further control. The forecasted 2019 PM2.5 and NOx gridded em ission inventories were evaluated, focusing on the winter months of Novem ber through February when the majority of PM2.5 exceedances occur. The top five emi ssion sources of PM2.5 and NOx in the 9 grid cells (3x3 grid cells, each measuri ng 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 comme rcial cooking, residential fuel combustion, and on-road

vehicles. The main NOx source is on-road v ehicles, 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 cont rol programs. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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69 Figure 55. 2019 top five wintertim e PM2.5 emission sources within the BakersfieldCalifornia 9-grid cell area (3x3 grid ce lls, each measuring 4 km x 4 km with the

Bakersfield-California monitor located in the center cell). Wi ntertime emissions expressed as an average of January, Febr uary, November and December emissions.

Figure 56. 2019 top five wintertime NOx emi ssion sources within the BakersfieldCalifornia 9-grid cell area (3x3 grid ce lls, each measuring 4 km x 4 km with the

Bakersfield-California monitor located in the center cell). Wi ntertime emissions expressed as an average of January, Febr uary, November and December emissions. 0.0 0.1 0.2 0.3 0.4 0.5PM2.5 Emissions (tpd) 2019 top Five Winter PM2.5 Emission Sources withih the

Bakersfield-California Nine Grid Cells

0 1 2 3 4 5 6NOx Emissions (tpd) 2019 Top Five Winter NOx Emission Sources within

Bakersfield-California Nine Grid Cells San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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70 d. Demonstrating attainment at Bakersfield-California

While adoption of a more stringent wood burning curtailment program brings the Bakersfield-California site very near attain ment, further reducti ons are needed to meet the attainment target of 35.4 μg/m 3 

### . Based upon the precursor sensitivity analysis and

evaluation of the localized inv entory discussed in the previous section, further control of PM2.5 emissions from commercial cooking operations was identif ied as the most effective approach to provide the emission re ductions needed to reach attainment. The

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

Table 8 below:

Table 8. Attainment Demonstration for the Bake rsfield-California Design Value Site.

2007 Design Value (μg/m 3 

### ) 2019 Design Value

with Wood Burning Program

Enhancement (μg/m 3 

### ) 2019 Final Design Value (μg/m 3 

### )

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/m 3

.


### As noted above, the design value in the cent er column of the table reflects the implementation of ongoing cont rol programs, as well as implementation of an enhanced residential wood burning curta ilment 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 Pl anning Organizations (MPOs ). Based on a modeling sensitivity run, implementati on of further controls on commercial cooking is expected to result in a 0.6 μg/m 3 

### reduction in the baseline design va lue. The revised MPO activity data represents approximately one percent of Valley wide NOx emissions. Based on modeling sensitivity runs, this is estimat ed to result in a des ign value increase of 0.2 μg/m 3 

### . In aggregate, the mo deling demonstrates a des ign value that meets U.S. EPA’s attainment target of 35.4 μg/m 3



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

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71 11. SUMMARY

Consideration of the entirety of information presented in the weight of evidence provides a consistent assessment that supports t he modeled attainment date of 2019. The substantial continuing reducti ons that will result from im plementation of the ongoing control program, coupled wit h new measures addressing residential wood burning and cooking, are consistent with the resu lts predicted in the modeled attainment demonstration. This weight of evidenc e assessment is based upon the following

factors:

 Over the last decade significant progre ss has occurred in reducing 24-hour PM2.5 concentrations. The 24-hour design va lue has decreased by over 30 μg/m 3 

### , while the number of exceedance days has declined by nearly 50 percent. Meteorologically adjusted trends for t he Bakersfield area show an even greater reduction in exceedance days, wit h a decline of over 60 percent.

 Evaluation of the air qua lity model response to emission reductions, as well as model sensitivity runs demonstrates that re ductions in directly emitted PM2.5 have the greatest impact per ton of emissions, fo llowed by NOx. For example, in Kern County, PM2.5 emission reduc tions are approximately eight times more effective

than NOx.

 Both receptor and photochemical grid based modeling have ident ified 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, monitori ng studies, and photochemical modeling indicate that contro lling NOx emissions is the mo st effective strategy to

reduce ammonium nitrate concentrations.

 The decrease in ammonium nitrate conc entrations 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 meas ures 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 demonstr ation 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 po llutants being addressed as part of the attainment strategy. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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72 REFERENCES

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Chen, L.-W. A., Watson, J.G.; Chow, J.C. ; Magliano, K.L., 2007, Quantifying PM2.5 source contributions for the San Joaquin Va lley with multivariate receptor models,

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Chow, J.C.; Chen, L.-W.A.; Lowenthal, D.H .; Doraiswamy, P.; Park, K.; Kohl, S.D.; Trimble, D.L.; Watson, J.G., 2005, Califor nia Regional PM10/PM2. 5 Air Quality Study (CRPAQS) - Initial data analysis of field program measurements. Report Number 2497; prepared by Desert Research Institute, Reno, NV, for California Air Resources Board, Sacramento, CA.

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.

Jacob, D.J., Crawford, J.H., Maring, H., Clark e, A.D., Dibb, J.E., Emmons, L.K., Ferrare, R.A., Hostetler, C.A., Russell, P.B., Singh, H.B., Thompson, A.M., Shaw, G.E., McCauley, E., Pederson, J.R., and Fisher, J.A., 2010, The Arctic Research of the Composition of the Tropospher e from Aircraft and Satellites (ARCTAS) Mission: Design, Execution, and First Results, At mospheric Chemistry and Physics, 10(11),

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Kaduwela A. and Cai, C., 2009, CARB Simu lation of ARCTAS-CA Measurements with MM5/CMAQ Modeling System, Presentatio n made at the A RCTAS-California Preliminary Data Analysis Workshop, June 30 – July 1, 2009, University of California at Davis, CA ( http://www.arb.ca.gov/r esearch/arctas/agenda.htm ).

Kelly, J.T., Baker, K.R., Cai, C., Avis e, J., Jackson, B., and Kaduwela, A., 2011, Preliminary Inter-comparison of Photochem ical Modeling by EPA and CARB for the CalNex 2010 Study, Poster presented at the 10th Annual CMAS Conference, Chapel Hill, NC, October 24-26. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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73 Kleeman, M.J., Ying, Q., and Kaduwela, A., Control strategies for the reduction of airborne particulate nitrate in California's San Joaquin Valley, Atmospheric Environment, 2005, 39, 5325-5341.

Lurmann, F.W., Brown, S.G., McCarthy, M.C., and Roberts, P.T., December 2006, Processes Influencing Secondary Aerosol Fo rmation in the San Joaquin Valley during Winter, Journal of Air and Waste Management Association, 56, 1679-1693.

Liang, J., Gürer, K., Allen, P.D., Zhang, K. M., Ying, Q., Kleeman, M., Wexler, A., and Kaduwela, A.,2006, A photochemical model in vestigation of an ex tended winter PM episode observed in Central California: Mo del Performance Evaluation, Proceedings of
the 5th Annual CMAQ Models-3 User ’s Conference, Chapel Hill, NC.

Livingstone, P.L., et. al., 2009, Simulating PM concentrations during a winter episode in a subtropical valley: Sensitivity simula tions and evaluation methods, Atmospheric Environment, 43, 5971-5977.

MacDonald, C.P.; McCarthy, M.C.; Dye, T.S .; Wheeler, N.J.M.; Ha fner, H.R.; Roberts, P.T., 2006, Transport and dispersion during win tertime particulate matter episodes in the San Joaquin Valley, California. J. Air Waste Manage. Assoc., 56(7):961-976.

Meng, Z., Dabdub, D., and Seinfeld, J.H ., July 1997, Chemical coupling between atmospheric ozone and particulate matter, Science,

DOI:10.1126/science.277. 5322.116, 116-119.

Pun, B.K., and Seigneur, C., 1998, Conceptual model of parti culate matter pollution in the California San Joaquin Valley, prepared for Pacific Gas & Electric, Document

CP045-1-98.

Pun, B.K., and Seigneur, C.,2001 , Sensitivity of particulate matter nitrate formation to precursor emissions in the California S an Joaquin Valley, Environmental Science and Technology, 35, 2979-2987.

Pun, B.K., 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 for the San Joaquin Valleywide Air Pollution Study Agency.

Pun, B.K., Balmori R.T.F, and Seigneur, C., 2009, Modeling wintertime particulate matter formation in Central California, Atmospheric Environment, 43, 402-409.

Schauer, J.J., Cass, G.R., 2000, Source app ortionment of wintertime gas-phase and particle-phase air pollutants using organic com pounds as tracers, Environ. Sci. Technol. 34(9): 1821-1832. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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74 Stockwell, W.R., Watson, J.G. , Robinson, N.F., Steiner, W. , and Sylte, W.W., 2000, The ammonium nitrate particle equ ivalent of NOx emissions fo r wintertime conditions in Central California’s San Joaquin Valley , Atmospheric Environment, 34, 4711-4717.

Turkiewicz, K., Magliano, K., Najita, T. , 2006, Comparison of two winter air quality episodes during the California Regional Particulate Air Qua lity Study. J. Air & Waste Manage. Assoc., 56, 467-473.

U.S. EPA, 2007, Guidance on the Use of M odels and Other Analyses for Demonstrating Attainment of Air Quality Goals for Oz one, PM2.5, and Regional Haze, EPA-454/B07-

002.

Ying, Q., Lu, J., Kaduwela, A., and Kleeman, M., 2008, Modeling air quality during the California Regional PM 10 

### /PM 2.5 

### Air Quality Study (CPRAQS) using the UCD/CIT Source Oriented Air Quality Model - Part II. Regi onal source apportionment of primary airborne particulate matter, Atmospheric Environment, 42(39), 8967-8978.

Ying, Q., Kleeman, M., 2009, Regional contribut ion to airborne particulate matter in central California during a severe polluti on episode. Atmos. Environ., 43, 1218-1228.

Ying, Q., Lu, J., and Kleeman, M., 2009, Mo deling air quality dur ing the California Regional PM10/PM2.5 Air Qua lity Study (CRPAQS) using the UCD/CIT source-oriented air quality model – Part III. Regional source apportionment of secondary and total airborne particulate matter, Atmo spheric Environment, 43, 419-430.

West, J.J., Ansari, A.S., and P andis, S.N., 1999, Marginal PM2.5: Nonlinear aerosol mass response to sulfate reductions in t he Eastern United Stat es, J. Air & Waste Manage. Assoc., 49, 1415-1424.

Watson, J.G.; Chow, J.C., 2002, A wintert ime PM2.5 episode at the Fresno, CA, supersite. Atmos. Environ., 36(3):465-475.

Watson, J., Roth, P.: Evolving Science in Central California-Why do we need air quality studies? Air Quality Symposium, Fresno, CA, May 17, 2006. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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75 This page intentionally blank. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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## San Joaquin Valley PM2.5 Weight of Evidence Analysis

Appendix 1

California Regional PM10/ PM2.5 Air Quality Study

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

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 Plan12. 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 PlanInternational 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 Monitori ng 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 Plan37. 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 Projec t 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”, pr epared 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 Plan48. Chow, J. C. et al., 1997, “Size-resolved Pa rticle 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 Calif ornia 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 Plan61. McDade, C. E., 1997, “Draft Final Report IM95 Data Analysis Work Elements 7.3 Contributions of Chemical Constituents to Visibility R eduction, 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 St udy 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 Plan73. 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 Plan84. 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 Duri ng 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 t he 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 Internat ional 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 Pr ogram 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 Plan96. 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 91 st 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 St udy (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 Plan109. 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 92 nd 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 S onoma 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 93 rd

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 93 rd

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 Plan124. 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 Plan136. 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 , ( 3 5), 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 M easurements 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 Plan149. 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 Assu rance 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 Meas urement 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 Diffe rences 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 Calif ornia Regional PM10/PM2.5 Air Quality Study”, in Proceedings of the 96 th 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 Plan162. 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 Plan173. 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 R egional 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 Plan184. Zielinska, B. et. al., 2003, “California Regional PM 10 /PM 2.5 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 Operati ons 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 PM 2.5 Chemical Composition from the Fresno Supersite During Wintertime Pollution Episodes in Central California”, presented at the

13 th 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 13 th 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 3 rd
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 3 rd
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 S onoma 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 Plan197. 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 Synopt ic 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 Chemic al 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/PM 2.5 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 t he 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 A ffected 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 Plan209. 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”. A tmospheric 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 Plan221. 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 Plan234. 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., Low enthal, 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

2012 PM2.5 Plan247. 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-F light 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. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan260. 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. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

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

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 th e 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 colle cted at the Bakersfield-California and

Fresno-1 st 

### Street sites were used for the Chem ical Mass Balance (CMB) analysis. The
two sites are part of the Chemical Specia tion Network (CSN) and use the SASS (Spiral
Aerosol Speciation Sampler, Met One, Grants Pass, OR.) for data collection. The
Bakersfield-California and Fresno-1st sample rs are configured with several channels,
each channel containing one 47 mmfilter with a 6.7 L/min flow rate. One channel

contains a Whatman Teflon®-me mbrane filter for mass by gravimetry and elements by
XRF. Another channel includes a magnesiu m oxide-coated aluminum (Al) honeycomb
after the cyclone followed by a Nylasorb nyl on-membrane filter for water-soluble anions

i.e., NO 3 − 

### and SO 4 = 

### ) and cations (i.e., ammonium [NH 4 + 

### ] 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 co llection method changed from the MetOne SASS
to the URG3000N sampler, which is very si milar to the IMPROVE module C sampler.
The analytical method was c hanged from the NIOSH-like t hermal 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 separat e 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 sa mpler, the URG3000N, and analyzed using
the IMPROVE-A thermal optical reflectance (TOR) method. Both old and new carbon
data were corrected for sampling arti facts prior to running CMB. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 2) Data Preparation

Organic carbon (OC) data were corrected for sa mpling 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 adjus ted by subtracting network-wide monthly
average concentrations measured on a backup filter from daily measurements of
organic carbon [88370]. The monthly aver age backup concentrations are shown in

Table 1.

Table 1. Organic Carbon Monthly Av erage 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 bur ning, geological material,

marine-derived aerosols, residual or crude o il combustion, and tire and brake wear.
Most of the source profiles applicable to t he 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 Ta ble 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. Si nce 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 si ngle profile representing motor vehicle
emissions. Diesel and gasoline vehicle emi ssions source profiles were combined in
proportions equivalent to their county-level contributions to the PM2.5 emissions to
produce a single emission-wei ghted 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 Ex haust 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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 PlanTable 3. Source Profiles (as Percent ofthe PM2.5 Mass) Used inthe CMB ModelingPNO38351318415432SOURCEAMNITAMSULWBOakEucAgBWheatOCMARINE75TireBrkeN3IC77.50±7.750.00±0.000.57±0.070.16±0.020.00±0.0022.88±2.600.19±1.14S4IC0.00±0.0072.70±7.271.30±0.830.44±0.040.00±0.007.20±0.820.78±2.10N4CC22.55±2.2627.30±2.730.58±0.470.59±0.040.00±0.000.00±0.100.16±0.73NAAC0.00±0.000.00±0.000.38±0.150.54±0.040.00±0.0028.80±3.270.10±0.42KPAC0.00±0.000.00±0.002.89±0.456.79±0.500.00±0.001.07±0.120.05±0.17OCTC0.00±0.000.00±0.0059.58±4.7557.03±4.54100.00±10.000.00±0.1018.81±24.53ECTC0.00±0.000.00±0.005.20±1.1210.31±0.850.00±0.000.00±0.104.55±5.99ALXC0.00±0.000.00±0.000.07±0.050.07±0.010.00±0.000.00±0.000.32±1.89SIXC0.00±0.000.00±0.000.22±0.100.13±0.010.00±0.000.01±0.000.69±1.81PHXC0.00±0.000.00±0.000.00±0.010.00±0.010.00±0.000.00±0.000.00±0.05CLXC0.00±0.000.00±0.001.72±2.026.16±0.440.00±0.0038.74±4.400.04±0.08KPXC0.00±0.000.00±0.002.86±0.935.50±0.390.00±0.001.07±0.120.10±0.36CAXC0.00±0.000.00±0.000.15±0.100.10±0.030.00±0.001.10±0.120.28±1.04TIXC0.00±0.000.00±0.000.01±0.020.01±0.010.00±0.000.00±0.000.03±0.38MNXC0.00±0.000.00±0.000.01±0.000.00±0.000.00±0.000.00±0.000.48±0.29FEXC0.00±0.000.00±0.000.09±0.060.07±0.000.00±0.000.00±0.0058.11±31.26CUXC0.00±0.000.00±0.000.01±0.001.02±0.070.00±0.000.00±0.000.16±0.69ZNXC0.00±0.000.00±0.000.04±0.020.01±0.000.00±0.000.00±0.000.35±2.37BRXC0.00±0.000.00±0.000.00±0.000.03±0.000.00±0.000.18±0.020.00±0.01RBXC0.00±0.000.00±0.000.01±0.000.00±0.000.00±0.000.00±0.000.01±0.01SRXC0.00±0.000.00±0.000.00±0.000.00±0.000.00±0.000.02±0.000.23±0.66PBXC0.00±0.000.00±0.000.00±0.000.00±0.000.00±0.000.00±0.000.06±0.03VAXC0.00±0.000.00±0.000.00±0.000.00±0.000.00±0.000.00±0.000.00±0.00NIXC0.00±0.000.00±0.000.00±0.000.00±0.000.00±0.000.00±0.000.00±0.00San Joaquin Valley Unified Air Pollution Control DistrictDecember 20, 2012Appendix G: PM2.5 Weight of Evidence Analysis2012 PM2.5 PlanTable 3, continued.PNO7980838466678586SOURCEF6GASDIEK6GASDIEF9GASDIEK9GASDIEFDFREANNFDKERANNCHCRUCSFCRUCN3IC0.22±1.240.16±1.240.21±1.240.16±1.240.02±0.280.05±0.160.00±0.050.00±0.01S4IC2.77±7.252.60±7.252.74±7.252.61±7.250.56±0.720.47±0.2914.72±6.2420.32±4.24N4CC0.98±3.240.89±3.240.96±3.240.89±3.240.04±0.180.13±0.200.76±0.080.01±0.01NAAC0.00±0.000.00±0.000.00±0.000.00±0.000.17±0.070.26±0.240.25±0.060.76±0.40KPAC0.10±0.080.10±0.080.10±0.080.10±0.080.27±0.120.80±1.250.01±0.010.06±0.01OCTC43.05±27.3342.40±27.3342.93±27.3342.41±27.3314.34±8.6610.29±5.321.99±1.330.09±0.12ECTC50.59±17.7351.50±17.7350.75±17.7351.49±17.731.92±1.290.69±0.723.01±1.120.00±0.07ALXC0.11±0.140.11±0.140.11±0.140.11±0.149.97±2.957.67±2.530.00±0.050.00±0.01SIXC1.14±4.120.99±4.121.11±4.120.99±4.1226.77±9.6322.05±5.290.00±0.080.01±0.02PHXC0.14±0.510.13±0.510.14±0.510.13±0.510.33±0.910.33±0.910.00±0.570.00±0.17CLXC0.07±0.300.06±0.300.07±0.300.06±0.300.11±0.080.46±0.480.05±0.010.02±0.00KPXC0.07±0.080.07±0.080.07±0.080.07±0.082.30±0.923.26±1.590.00±0.000.04±0.01CAXC0.50±1.420.49±1.420.50±1.420.49±1.423.01±0.675.54±3.030.00±0.030.06±0.00TIXC0.01±0.080.01±0.080.01±0.080.01±0.080.48±0.050.44±0.240.01±0.000.01±0.00MNXC0.00±0.010.00±0.010.00±0.010.00±0.010.11±0.020.11±0.080.00±0.000.01±0.00FEXC0.44±0.440.44±0.440.44±0.440.44±0.445.30±0.585.09±2.840.71±0.090.21±0.02CUXC0.01±0.050.01±0.050.01±0.050.01±0.050.02±0.000.01±0.010.01±0.010.00±0.00ZNXC0.27±0.410.26±0.410.27±0.410.26±0.410.14±0.080.07±0.050.01±0.000.26±0.03BRXC0.03±0.060.03±0.060.03±0.060.03±0.060.01±0.000.00±0.000.00±0.000.00±0.00RBXC0.00±0.000.00±0.000.00±0.000.00±0.000.01±0.000.01±0.000.00±0.000.00±0.00SRXC0.00±0.000.00±0.000.00±0.000.00±0.000.03±0.010.03±0.010.00±0.000.00±0.00PBXC0.01±0.030.01±0.030.01±0.030.01±0.030.18±0.130.09±0.320.00±0.000.00±0.00VAXC0.00±0.000.00±0.000.00±0.000.00±0.000.00±0.000.00±0.000.42±0.040.82±0.06NIXC0.00±0.000.00±0.000.00±0.000.00±0.000.00±0.000.00±0.002.48±0.250.79±0.09San Joaquin Valley Unified Air Pollution Control DistrictDecember 20, 2012Appendix G: PM2.5 Weight of Evidence Analysis2012 PM2.5 Plan

### Biomass burning was represented using an agr icultural burning profile (AgBWheat) from
June through October and a composite resid ential wood burning pr ofile (WBOakEuc)
the rest of the year. The agricultural bur ning profile (AgBWheat) was based on burning
of wheat stubble (Fitz et al., 2000). The re sidential wood burning profile (WBOakEuc)
was used to represent residential wood co mbustion during colder months and was
calculated as an average of oak and eucalyptus.

Geological material in the San Joaquin Vall ey comes from a variety of sources,
including roads (paved and unpaved), agricultu ral 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 (a lmond, cotton, grape, safflower, and tomato),
4) dairy and feedlot soil, 5) salt buildup dep osits 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 composit e 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 agricultu ral 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 ge ological profiles included in
the composites created for modeling PM2.5 concentrati ons. 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 nitr ate 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 co mposited into a single weighted average
profile. The two profiles were weighted based on EMFAC 2011 emissions, which

estimate a 9 to 1 ratio of brak e emissions to tire emissions.

Secondary nitrate and sulfate were repr esented by pure ammonium nitrate (NH 4 

### NO 3 

### )
and ammonium sulfate (NH 4 

### ) 2 

### SO 4 

### . A “pure” OC profile was used to represent other
unidentified primary source s, contributions from se condary 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 repr esenting 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 repr esenting the Chevron

Racetrack boiler at the Kern River oil fi eld provided a better fit at Fresno.

4) Fitting Species

Table 5 lists fitting species used in CMB runs.

Table 5. CMB Fitting Species

Nitrate Si licon Zinc

Sulfate Chlorine Bromine

Ammonium Potassium R ubidium

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 co llected 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 indivi dually 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 cr ude oil combustion profile was included only
when it was necessary to explain the v anadium and nickel contributions. Biomass
burning and ‘other OC’ profiles were included as needed. Firs t, all data were run with a
biomass burning profile (AgBWheat from Ju ne through October, WBoakEuc the rest of
the year) and the ‘other OC’. The result s 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 burni ng profile and ‘other OC’.
2. Run 2 included a biomass burning profile but not the ‘other O C’ profile. It was
geared towards days when primary sources of organic carbon (biomass burning,
motor vehicle exhaust, and geological ma terial) sufficiently accounted for the
ambient organic carbon.

3. Run 3 included ‘other O C’ but no biomass burning prof ile. It was geared towards
days with no biomass burning and appli ed 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 contributi ons from wood burning and ‘other OC’
were included in the composite file.
2. Days with estimated negativ e contributions from ‘Ot her OC’ were treated as

follows:

a. If there should have been no burni ng on that day because the soluble
potassium concentration was zero, r un 3 which includes the ‘other OC’
and no wood burning was utilized.

b. If there could have been wood burn ing 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 anal ysis. Table 7 shows the number of data
points from each run include d 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 Estima tes using Old and New Carbon

The Bakersfield-California site has 14 da ys with parallel old and new carbon data.

Since these data were collected duri ng the low season, the average PM2.5
concentration was only 13.6 ug/m3. The CM B 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 ca rbon, 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 t he ‘other OC’. Switching

to the new carbon data reduced t he ‘other OC’ estimate to 1 ug/m3. There were also
several days when the motor vehicle contributi on 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 car bon 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 contri bution 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 sample s at BAC (157 with old carbon and 267 with
new carbon) and 590 samples at FSF (390 wit h old carbon and 200 with new). Source
contribution estimates were averaged to dete rmine a typical contribution. Separately,
days with concentrations greater than 30 ug/m3 were averaged to determine the typical

contribution on a hi gh 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 1405/3/075/6/075/12/075/15/076/2/076/5/076/11/076/14/076/17/076/20/076/23/076/26/076/29/077/5/07Percent Figure 1. Compare Percent Mass

Old_% New%

0 0.5

1 1.5

2 2.5

35/3/075/6/075/12/075/15/076/2/076/5/076/11/076/14/076/17/076/20/076/23/076/26/076/29/077/5/07Concentration(ug/m3) Figure 3. Compare Motor Vehicle Estimate

Old_MV New_MV 0 1 2 3 4 5 6 7 85/3/075/6/075/12/075/15/076/2/076/5/076/11/076/14/076/17/076/20/076/23/076/26/076/29/077/5/07Concentration(ug/m3) Figure 2. Compare Woodburning Estimate

Old_WB New_WB

0 1 2 3 4 55/3/075/6/075/12/075/15/076/2/076/5/076/11/076/14/076/17/076/20/076/23/076/26/076/29/077/5/07Concentration(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 betw een 80% and 120%. The average performance

measures for both sites were within the acc eptable 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 ca lculation at Bakersfiel d due to missing data.

a) Bakersfield (BAC)

The average PM2.5 concentration based on ol d carbon data for 2006 was 20.5 ug/m3.

Based on the new carbon data, the 2008 an d 2010 average PM2.5 concentration was
18.6 ug/m3. Between 2006 and 2007 (old car bon 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 car bon 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 we re the next most important sources
contributing 10 to 12 percent of mass. Biomass burning cont ributed 9 to 10 to percent

of the mass. The ‘Other OC’ contribut ion depended on the car bon data method; using
old carbon apportioned 16 percent of mass to the ‘other OC’ while using new carbon
reduced that contribution to 8 per cent. Geological material co mprised 7 to 10 percent of
the mass. Each of the remaining sour ces (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’ contribut ion 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 ol d carbon data for 2006 to 2008 was
20.3 ug/m3. Based on the new carbon dat a, 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 dat a. The average PM2.5 conc entration on these days was
46.3 ug/m3. Between 2009 and 2010 (new car bon data), there were 22 high days with
chemical composition data, with the average concentrati on of 40.6 ug/m3. Sources
identified by the CMB acc ounted 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 percen t. The ammonium sulf ate contribution was
9 to 11 percent. The ‘Other OC’ cont ribution, once again, depended on carbon data;
using old carbon apportioned 18 percent of mass to the ‘o ther OC’ while using new

carbon reduced that contributi on to 9 percent. Geological material comprised 4 to 6
percent of the mass. Each of the remaini ng 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. Bi omass 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 ca rbon data. The remaining s ources 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 Sour ce 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 PlanAMNIT
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

9.1% Sea Salt

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

11.4% Sea Salt

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 Plan0 10 20 30 40 50 60 70 80 90 1001/5/061/8/061/11/061/23/062/4/062/7/062/10/062/13/0610/2/0611/1/0611/7/0611/16/0611/19/0612/4/0612/7/0612/13/0612/25/0612/31/061/3/071/9/071/18/071/21/071/24/071/27/071/30/072/2/072/5/072/8/072/17/07Concentrations (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 8010/27/0711/2/0711/8/0711/14/0711/26/0711/29/0712/11/0712/14/0712/23/071/10/081/13/081/16/081/19/082/9/082/12/082/18/082/27/083/7/0810/27/0811/8/0811/17/0811/20/0811/26/0812/11/0811/3/0911/24/0911/27/0912/3/0912/21/0912/27/091/5/102/4/1011/4/1012/1/1012/4/1012/10/10Concentrations (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 Plan0 10 20 30 40 50 60 70 80 90 1001/5/061/11/061/17/061/23/062/1/062/10/062/13/062/22/0610/2/0611/1/0611/16/0611/22/0611/25/0612/1/0612/4/0612/7/0612/13/0612/25/0612/31/061/3/071/9/071/15/071/18/071/21/071/24/071/27/071/30/072/2/072/8/072/17/0711/2/0711/5/0711/8/0711/14/0711/23/0711/26/0711/30/0712/11/0712/14/0712/23/0712/29/071/1/081/10/081/13/081/16/081/19/082/9/082/12/082/18/083/7/0811/8/0811/14/0811/26/0811/29/0812/5/0812/8/0812/11/0812/29/081/1/091/4/091/10/091/13/091/16/091/19/091/22/091/28/091/31/09Concentrations (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 7011/12/0911/15/0911/24/0912/3/0912/6/0912/9/0912/21/0912/24/0912/30/091/2/101/8/101/11/101/15/102/4/102/19/1011/13/1011/16/1011/25/1012/4/1012/7/1012/10/1012/13/10Concentrations (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 Barbar a County, CA, Atmospheric Environment, 30,

1489-1499, 1996.

Chow, J.C., Watson, J.G., Cr ow, D., Lowenthal, D. H., and Me rrifield, 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 prof iles for geological dust from the San Joaquin
Valley, California. Atmospheric En vironment. 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.: Calif ornia Regional PM10/PM2. 5 Air Quality Study
(CRPAQS) – Initial data analysis of field program measurements, California Air
Resources Board, Sacramento, CA, by Dese rt Research Instit ute, 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 Re port – Addendum A, Chemical Speciation,
prepared for the U.S. Environm ental 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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### This page intentionally blank.

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

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 Netw ork 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 PM 2.5 

## Measured at the Fresno and Bakersfield Chemical Speciation Netw ork Sites in San Joaquin Valley Using the Positive Matrix Factorization Model

Sample Collection and Data Screening

PM 2.5 

### chemical speciation samples were collected on a one-in-three day
schedule at the Fresno-First St. and Bakersfi eld-California Ave. Chemical Speciation Network (CSN) monitoring sites located in t he San Joaquin Valley (SJV). There were good agreements between PM 2.5 

### data collected by the speciation samplers and the collocated Federal Reference Method (FRM) samp lers 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 av ailable were considered in this source apportionment study.

Since a carbon denuder that minimizes t he positive sampling artifact caused by adsorption of gaseous organic mate rials 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 regre ssion of OC concentrations against PM 2.5 

### mass concentrations (Tolocka et al. 2001, Kim et al. 2005). Samples for which PM 2.5 

### or OC concentrations had an error flag and samples for which the PM 2.5 

### or OC data were not

available were excluded from the regression analysis between PM 2.5 

### and OC concentrations. Comparing co-located PM 2.5 

### data measured by CSN and FRM samplers, and comparing PM 2.5 

### and Sulfur (S) concentrations, outliers were censored

for the two data sets. Us ing 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/m 3 

### and 1.480 μg/m 3 

### in PM 2.5 

### regression against
OC concentrations are considered to be the integrated OC artifact concentrations at Fresno and Bakersfield, respectively (Fi gure 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: PM 2.5 

### 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 PM 2.5 

### at the Fresno and Bakersfield monitoring sites. Samples

were excluded from the data set for which the PM 2.5 

### , artifact corrected OC, or EC data were not available or below zero, or for which PM 2.5 

### artifact corrected OC, or EC had an error flag. Samples for which the sum of all measured species we re larger than twice

the PM 2.5 

### concentrations or the sum of all m easured species were less than 50% of

PM 2.5 

### concentrations were also excluded. Fi nally, samples that contain fireworks particles collected on Independence Day and Ne w 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 conc entrations. Due to the higher analytical precision compared to XRF Na and XRF K, IC Na + 

### and IC K + 

### were included in the analyses. C hemical species below MDL va lues 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 PM 2.5 

### 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 PM 2.5 

### 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 structur e (i.e., 7% of measured concentration) estimated by Kim et al. (2005) and 0.1 μg/m 3 

### of detection limit value estimated from the State and Local Air Monitoring Stations (SLAM) speciation data were used in this study.
Summaries of PM 2.5 

### speciation data are provided in Ta bles 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 i nput concentration data, and the sum of the analytical uncertainty and one-th ird of the detection limit va lue is used as the input uncertainty data assigned to each measured value. Concentration values below the detection limit are replaced by half of t he 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 ar e set at four times the ge ometric mean value. The conditional probability function (CPF) analysi s 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 c ontributions coupled with wind data. As recommended by Paatero and Hopke (2003), whic h is to down-weight the variable in the analysis so that the noise does not comp romise 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 Bake rsfield data to obtain physically interpretable PMF2 results. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### PMF Results

Seven major sources were resolved from PMF2 analyses for both sites (matrix rotational parameter: Fresno FPEAK = 0.1; Ba kersfield FPEAK = 0). The comparison of

the reconstructed PM 2.5 

### contributions (sum of contri butions from all sources) with

measured PM 2.5 

### concentrations shown in Figure 2 i ndicates that the resolved sources effectively reproduce the measured values and account for most of the variation in the

PM 2.5 

### 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 whic h 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 moto r vehicle) contributed 74% of PM 2.5 

### concentrations at both sites. Figure 4 show s monthly average source contri butions. Secondary nitrate, motor vehicle, and biomass smoke contributed t he most in winter. The source profiles, corresponding source contributions, week day/weekend variati ons, monthly averaged source contributions, and potential source directions are presented in Figures A1 through A10 in the Appendix.

The secondary nitrate factor was ident ified by its high concentration of NO 3 - 

### and

NH 4 + 

### . It consisted of NH 4 

### NO 3 

### 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 PM 2.5 

### mass concentrations at Fresno and Bakersfield, respectively. Bakersfield showed higher secondary nitrat e concentrations than Fresno. Secondary nitrate particles had winter- high trends at bot h sites. Secondary sulfate was identified by its high concentration of SO 4 2- 

### and NH 4 + 

### and accounted for 27% and 20% of the

PM 2.5 

### mass concentration at Fresno and Bakersfield, respectively. Secondary nitrate and secondary sulfate did not show clear weekday/weekend variations. Secondary

sulfate showed seasonal vari ations with higher concentr ations in summer when the photochemical activity was highest at both si tes. The CPF plots for secondary nitrate pointed S and NE at both sites. The CPF plots for secondar y 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, NO 3 -



### , and minor species such as Fe (Watson et al., 1994). Motor vehicle emissions

contributed 12% and 13% of the PM 2.5 

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### The biomass smoke factor was characterized by OC, EC, and K + 

### (Watson et al., 2001)

and contributed 11% and 10% to the PM 2.5 

### mass concentrations at Fresno and Bakersfield, respectively. The biomass sm oke category reflects contributions from residential wood burning and smoke from commerc ial cooking. The biomass smoke did not show weekday/weekend variations. T he biomass smoke did show winter-high trends suggesting that it was mostly contri buted by residential wood burning. The CPF plots for the biomass smoke pointed to high cont ributions from NE and S at both sites. 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 2. Measured ve rsus PMF predicted PM 2.5 

### mass concentrations. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Figure 3. Average source contributions.

Figure 4. Monthly average source contributions. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### The airborne soil factor was identified by its high concentrations of Si, Al, Ca and

Fe. It contributed 7% and 10% to the PM 2.5 

### mass concentration at Fresno and Bakersfield, respectively. Airborne soil reflects wind-blown dust as well as resuspended crustal materials by r oad traffic as indicated by t he presence of OC or EC in the source profiles. Airborne soil did not show clear we ekday/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 repres ented by its high concentrations of NO 3 -



### , SO 4 2- 

### , and Na + 

### , accounting for 6% the PM 2.5 

### 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 NO 3 - 

### and SO 4 2-


### (Song and Carmichael, 1999). Aged sea salt did not show weekday/weekend variation at either site. Aged sea salt had high cont ributions in summer at the Fresno site. Interestingly, it had a high contribution in winte r at the Bakersfield si te. 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 me tal processing that was characterized by OC, EC, Fe, and Zn was identified at both site s. This source accounted for 2% of the

PM 2.5 

### 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 contri butions from NE and SW at both sites.

Figure 5. Average source contributions in Fr esno – First St. and Bakersfield – California

Ave. when PM 2.5 

### concentrations were higher than 30 μg/m 3 

### in the high PM 2.5 

### season (Nov. - Feb.). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Higher PM 2.5 

### Day Contributions

The average source contributions when PM 2.5 

### concentrations were higher than 30 μg/m 3 

### in the high PM 2.5 

### season (Nov. - Feb.) are shown in Figure 5 for percentiles

and in Table A4 for mass concentrations. T he 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 bi omass 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

PM 2.5 

### speciation and related meteorological data collected at the Fresno-First St.

and Bakersfield-California Ave. CSN m onitoring sites between 2008 and 2010 were analyzed by PMF2. Seven major PM 2.5 

### sources were identified at both monitoring sites: secondary nitrate, secondary sulf ate, motor vehicle, biomass smoke, airborne soil, aged sea salt, and industrial. Annual average and hi gh day source contributions showed that secondary nitrate, secondary sulfate, moto r vehicles, and biomass burning were the largest contributors to PM 2.5 

### concentrations.

References

Kim, E., Hopke, P.K. Comparison betw een conditional probability function and nonparametric regression for fine particl e source directions, Atmospheric Environment 38, 11, 4667-4673, 2004.

Kim, E., Hopke, P.K. and Qi n, Y. Estimation of organic ca rbon blank values and error structures of the speciation trends net work data for source apportionments, Journal of Air and Waste Management Association 55, 1190–1199, 2005.

Paatero, P., Hopke, P.K. Discarding or dow nweighting high-noise va riables in factor analytic models, Analytica Ch imica 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 tr ansport, Atmospheric Environment 33, 2203-2218, 1999. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 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 dur ing the winter of 1999, Aerosol Science and Technology 34, 88-96, 1999.

Watson, J.G., Chow, J.C., Lowenthal, D.H., Prit chett, L.C. and Frazier, C.A. Differences in the carbon composition of source profiles for diesel and gasoline powered vehicles, Atmospheric Envi ronment 28(15), 2493-2505, 1994.

Watson, J.G., J.C. Chow, and J.E. Houck, PM 2.5 

### chemical source profiles for vehicle exhaust, vegetative burning, geologica l material, and coal burning in northwestern Colorado during 1995, Chemosphere, 43, 1141-1151, 2001. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### APPENDIX

Table A1. Summary of PM 2.5 

### species mass concentrations at Fresno.

Species Arithmetic

mean (μg/m 3 

### ) Geometric

mean (μg/m 3 

### ) Minimum (μg/m 3 

### ) Maximum (μg/m 3 

### ) Number of

below MDL values (%)

PM 2.5 

### 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

NO 3- 

### 3.6499 1.9445 0.0445 29.1000 0

NH 4 + 

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Table A2. Summary of PM 2.5 

### species mass concentrations at Bakersfield.

Species Arithmetic

mean (μg/m 3 

### ) Geometric

mean (μg/m 3 

### ) Minimum (μg/m 3 

### ) Maximum (μg/m 3 

### ) Number of

below MDL values (%)

PM 2.5 

### 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

NO 3- 

### 6.4908 3.2626 0.3520 35.7000 0

NH 4 + 

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Table A3. Average source contributions (μg/m 3 

### ) to PM 2.5 

### mass concentration.

Sources Average source contributi on (± 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 PM 2.5 

### (μg/m 3 

### ) 13.86 (1.57) 19.85 (2.27)

Measured PM 2.5 

### (μg/m 3 

### ) 14.56 (1.74) 20.48 (2.33) San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Table A4. Average source contributions (μg/m 3 

### ) to PM 2.5 

### mass concentration at FresnoFirst St. and Bakersfield-California Ave. when PM 2.5 

### mass concentrations were higher than 30 μg/m 3 

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Figure A1. Source profiles deduced from PM 2.5 

### samples measured at Fresno-First St. (prediction ± standard deviation). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Figure A2. Source profiles deduced from PM 2.5 

### samples measured at BakersfieldCalifornia Ave. (prediction ± standard deviation). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Figure A3. Source contributions deduced from PM 2.5 

### samples measured at Fresno-First
St. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Figure A4. Source contributions deduced from PM 2.5 

### samples measured at BakersfieldCalifornia Ave. (missing data: Jan. - Jun. 2009) San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Figure A5. Weekday/weekend variations at Fr esno-First St. (mean ± 95 % distribution).

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Figure A6. Weekday/weekend variations at Bakersfield-California Ave. (mean ± 95 % distribution). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Figure A7. The monthly variations of source contributions to PM 2.5 

### mass concentration at Fresno-First St. (mean ± 95 % distribution). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Figure A8. The monthly variations of source contributions to PM 2.5 

### mass concentration
at Bakersfield-California Av e. (mean ± 95 % distribution). San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Secondary nitrate

Figure A9. Conditional probability function plots for the highest 25% of the mass

contributions at Fresno-First St. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Secondary nitrate

Figure A10. Conditional probability functi on plots for the highes t 25% of the mass

contributions at Bakers field-California Ave. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### This page intentionally blank.

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2012 PM2.5 Plan

## San Joaquin Valley PM2.5 Weight of Evidence Analysis

Appendix 4

Methodology for Meteorological Adjustment of
PM2.5 Trend Statistics San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Introduction

Air quality trends can help reveal the effe cts 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 ident ified, quantified, and removed, the met-adjusted trends may reveal the emi ssions-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 c onstruct the met-adjusted trends.

1. Data Acquisition and Preparation

PM2.5 mass concentrations from the air qua lity monitoring sites in two major urban centers of the SJV (Baker sfield and Fresno) were collected. Meteorological data for factors that may impact the PM2.5 concent rations were also acquired from various meteorological monitoring net works. Monitors at ground level provided temperature, relatively humidity, pressure, wind speed, wind direction, prec ipitation, and solar radiation data. For various reasons, surface pressure, wind directi on, precipitation, and

solar radiation were not used in the final analysis. Routine rawinsondes (weather balloons) at Oakland provided data fo r 500 millibar heights and 850 millibar temperatures. These surface and upper air factors are consist ent with studies of meteorological conditions a ssociated with daily PM2.5 leve ls [Dye et al., 2003].

Table 1 lists the air quality and meteorol ogical monitoring sites that provided data used in this analysis. The PM2.5 and meteorological data pres ented are daily regional averages of the data collected fr om the sites in Table 1.

Table 1. Air quality and me teorological 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, t he entire day might be excluded from further consideration. Therefore, dat a completeness is very desirable for the analysis to be as meaningful as possible. To minimize inst ances of missing PM2.5 and meteorological San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### data, imputed values were calc ulated 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 availa ble from the Air Quality and Statistical

Studies Section of the ARB.

2. Analytical method: Classi fication 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 techni que that produces a classification tree if the dependent (target) variable is categorical or a regressi on tree if the dependent va riable is numeric. At each step of the tree bu ilding process, CART finds the best possible independent variable (or linear combination of independent vari ables) 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 independe nt 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 meteorol ogical 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 Pr ogram [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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### are mostly due to differences in meteorolog y. 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-tr ee consisted of one or more terminal nodes representing different meteorological classes. The CART system makes the

differences in PM2.5 between the met-classe s 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 (t erminal node) is the average c oncentration 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 betw een meteorology and PM2.5 in the base years were then used to assign days in the ot her 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 meteorologic al 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-cla sses was constructed for the Fresno area (Figure 1b). Figure 2 indicates that ~75 – 80 percent of the vari ation 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 (di fference surface 850mb temperatures), and minimum surface temperature – affected da ily PM2.5 concentrations strongly in both Bakersfield and Fresno, while relative humid ity (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 asso ciated with relatively high stability, low wind speed and high RH. In Fresno, high PM2.5 was generally associated with cold mornings (low minimum surface temperatur e), high stability, and low wind speed.

It is worth mentioning t hat this CART model treat s each day independently and does not directly characterize met-conditions over a sequence of days that may result in long-term buildup and trans port of PM2.5.

A sensitivity analysis was, also, done to ex plore 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) we re used with CART to develop relationships between meteorology and PM 2.5. The met-adjusted annual average PM2.5 concentrations proved to be quite sim ilar regardless of the base years used in the CART analysis. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### (a)

(b)

Figure 1. CART terminal nodes sorted by ta rget variable predicti ons (PM2.5 ) in (a) Bakersfield area and (b) Fresno area. 20 40 60 80BAKERSFIELD_PM25_AVERAGE Terminal Nodes Sorted By Target Variable Prediction

20 40 60 80FRESNO_AVERAGE Terminal Nodes Sorted By Target Variable Prediction San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### (a)

(b)

Figure 2. Observations vs. CART predic tions 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 100ObservedPM2.5atBakersfield

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 70ObservedPM2.5atFresnoArea

CART Predicted PM2.5 at 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

### 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 dec reased in 2009 and 2010. CART-predicted trends represent meteorological conditions t hat affect PM2.5 concentrations. For the Bakersfield area, the CART-predicted trend i ndicates 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 fo r annual average PM2.5. Fi gure 3 shows the observed and met-adjusted trends for (a) the Bakersfi eld area and (b) the Fresno area. Linear

trend lines are shown for the observed and t he met-adjusted trends in each area. Figure 3 indicates that the met-adjust ed 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 bot h areas, met-adjusted PM2.5 decreased by

~ 0.8  g/m 3 

### per year from 1999 – 2010. Overall, the met-adjusted trends indicate that average PM2.5 decreased 40 – 50 percent in t he 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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### (a)

(b)

Figure 3. Trends of observed a nd meteorologically adjusted PM2.5 concentrations in (a) Baker sfield 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 2011PM2.5(g/m3)

Year 

### Observed and Met

‐ Adjusted Annual Average PM 2.5 

### 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 2011PM2.5(g/m3)

Year 

### Observed and Met

‐ Adjusted Annual Average PM 2.5 

### 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 fo r 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 (repr esenting meteorological effects) were prepared.

In the Bakersfield area, similar to the annual averages, the CART-predicted exceedance days increased from the earlier y ears to recent years, indicating an increase in meteorological c onduciveness for PM2.5. In t he Fresno area, the impact of meteorology on PM2.5 exceedance days was relatively small, again similar to the annual averages. In both ar eas, the observed PM2.5 exceedance days were greater than the CART-predicted PM 2.5 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 implicatio n 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 me rged with the observed trends to produce met-adjusted trends for PM2.5 exceedance days. Figure 4 s hows 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. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### (a)

(b)

Figure 4. Trends of observed and me teorologically 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 2012PM2.5ExceedanceDays

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 2012PM2.5ExceedanceDays

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 condi tions and to calculate meteorologically adjusted trends. Such trends can help reve al the impact of emission changes on air pollutant levels, and promote t he development of effective ai r pollution control strategies and regulations. Of course, as with any stat istical analysis, there are uncertainties and limitations in CART analysis. Therefor e, 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 meteor ological conditions seem to have been relatively stable, so met-adjusted trends were similar to the obser ved 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 dec rease in PM2.5 levels compared to the

observed trends.

Based on the differences between t he predicted PM2.5 levels under the observed meteorological condit ions and under “normal” meteorological conditions, the

PM2.5 observations are adjusted to derive met-adjusted PM2.5 trends. The analyses

indicate that the met-ad justed annual average PM2.5 concentrations decreased at a

rate of ~0.8  g/m 3 

### per year between 1999 and 2010 fo r a total of ~40-50 percent decrease in met-adjusted PM2.5 in the Bake rsfield 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 ar ea 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, Envi ronmental Protection A gency, NC, U.S.A., 2003.

Slini, T., A. Kaprara, K. Karatzas , N. Moussiopoulos, PM10 forecasting for Thessaloniki, Greece, Envi ronmental Modelling and Softwar e, Volume 21, Issue 4, April 2006, Pages 559-565, ISSN 1364-8152, 10.1016/j.envsoft.2004.06.011, 2006. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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

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

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 succe ssful, the District conducted a “Speciated Linear Rollback” analysis to corroborate t he results of the regional photochemical model. Different from the photochemica l model, which simulates the complex interaction between meteorology, chemic al mechanisms, and other physical phenomena, speciated linear rollback is an approach where the change in the emissions inventory from the base year to t he 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 futu re year concentration. The CMB model accounts for the end products of the atmospheric process without the requirement to simulation the chemical in teractions. The linear component of the process assumes that the rate of reduction in the emissions inventory is related linearly

to the reduction in ambient concentrati ons. Through the comb ination of source

identification and relative linear emissions projection, the measured concentrations can be projected in connection to the inv entory changes between the base year and projected future year.

Analysis Procedure

To begin this analysis, the District colle cted PM2.5 speciation data from the Environmental Protection Agency (EPA) Ai r 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 1 st 

### and 4 th 

### quarters where the total concentration was above 35.4 ug/m3. The da ta was then formatted into a structure acceptable as an input into the CMB model, which connects observed PM2.5 concentrations to likely pollution s ources though the commonality between the speciation of concentrations and source pr ofiles in the area. The CMB model then outputs a source contribution estimate from each pollution source (in μg/m 3 

### ) 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 R 2 

### 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 Agen cy’s (EPA) Chemical Mass Balance (CMB) model is available at http://www.epa.gov/scram001/receptor_cmb.htm San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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 fo r the CMB analysis consisted of ammonium nitrate, ammonium sulfate, fugitive dust, mobile exhaus t, marine influence, organic carbon, tire/brake wear, and vegetative burni ng. Contributions from other sources are not excluded, but rather are not separable from the identif ied major components either due to chemical similarity or lack of a char acteristic 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 CM B and appropriate subsets of the emissions inventory developed for this plan, e.g. amm onium 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 dus t, etc. Since the selected base year in the air quality modelin g for this plan was 2007, and with the future year being 2019, the change in the emissi ons 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 thes e 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 s pecific subset of t he 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 conne ction to the particula te matter that the emissions will produce . For example: t he 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 t he 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-linearit y 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 sa me rate of their respective emissions inventory subsets, but we re rather reduced at the rate of reduction

in the California statewide emis sions inventory. This appr oach was taken to account for emissions reductions happening in other parts of the State that w ould effectively reduce any background/transport emissions comi ng into the San Joaquin Valley.

Analysis Results

Based on the average CMB result s, the ratio of t he source specific subsets to the total PM2.5 mass value were applied to t he 2007 24-hour design value to create a CMB breakdown of the design value itself. Ne xt, the speciated linear rollback produced San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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 conc entration 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 spec ific subsets of the design value were added back together to provide an estimated future y ear 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/m 3 

### )

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. Theref ore 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 tota l 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 s peciated 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/m 3 

### )

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 s peciated 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 arri ve at a similar conclusion, this builds confidence into the control strategy and tec hnical analysis developed for this plan. In summary, the speciated linear rollback analysis provides a corroborative piece to the regional model, and adds to t he weight-of-evidence that t he District’s current control strategy and the further reductions committe d to in this plan will be successful in attaining the current federal 24-hour PM2.5 standard by 2019. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

## Unmonitored Area Analyses for the 2012 24-Hour PM 2.5 

## State
Implementation Plan for the San Joaquin Valley

1. Introduction:

The U.S. EPA guidance for PM 2.5 

### 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 vi olate 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 iss ued an addendum to the modeling guidance
which changed the procedure for the 24-hour PM 2.5 

### 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 Ai r Resources Board has
constructed the following Unmonitored Area Anal ysis approach. In brief, this analysis
has following steps.

 For each of the three years and for the fi rst and fourth quarters that are used for
the attainment test, identify the top 8 days with high PM 2.5 

### measured with the Federal Reference Method (FRM) anywher e 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 t he 16 days for that year and select the 3 rd



### highest value. This is the 98 th 

### percentile for that year for that grid cell.

 Average the 98 th 

### percentile value for each grid cell for each of the three years to
obtain a relative design va lue for that grid cell.

 Examine the relative design value field to determine if there are peak values
higher than those at the m onitors that could cause vi olations of the NAAQS.

More details on each step are provi ded in the subsequent sections. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 2. Methodology and Discussion:

2.1. Identification of the Top 8 Days per Quarter:

We have taken all available FRM m easurements for the years 2005 through 2007
and selected those days that fall on a regul ar one-in-three-day schedule. While some
monitors collect data more frequently, this allo wed 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.YearQuarterIDSamplingDaySJVMaxBakersfield–410EPlanzBakersfield–5558CaliforniaCorcoran‐PattersonVisalia‐NChurchFresno‐HamiltonandWineryFresno‐1stStreetClovis‐NVillaAvenueMerced‐2334MModesto‐14thStockton–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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 PlanYearQuarterIDSamplingDaySJVMaxBakersfield–410EPlanzBakersfield–5558CaliforniaCorcoran‐PattersonVisalia‐NChurchFresno‐HamiltonandWineryFresno‐1stStreetClovis‐NVillaAvenueMerced‐2334MModesto‐14thStockton–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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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 measur ements for each day were speciated with the
speciation profiles appropriate for the quar ter that day was in. Those speciated
fractions were then interpolated using an inverse distance weight.

The speciation fractions we used during t he interpolation are shown in Tables 2 and
3. These speciation fractions were deriv ed following the Sulfate, Adjusted Nitrate,
Derived Water, Inferred Carbonaceous Ma terial 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 San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

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 nor th 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 monito ring stations in the San Joaquin Valley
poses a special challenge duri ng the interpolation. They are nearly lined up on a
northwest to southeast diagonal of the mo deling domain. W hen the FRM specie
fractions are interpolated, they form bands perpendicular to that di agonal. These bands

extend from the coastal mountains to the Sierras and register high PM 2.5 

### 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 monito rs have FRM measurements for all days.
Thus, we had to use only the available measur ed values for the interpolation for a given
day. This has the effect of mixing measur ed and interpolated values at a given monitor
for a given quarter.

2.3. Application of RRF to Inte rpolated Speciated FRM Fields:

The speciated FRM fields for each day were then multiplied by the simulated
quarterly RRF fields appropriate for t hat day to obtain the 2019 concentrations.

The simulation of PM 2.5 

### 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 ve rsion 5 (MM5; Grell et al., 1994) to
simulate gridded hourly meteorological fields fo r 2007, which were in turn used to drive the Community Multi-scale Air Quality (CMA Q) 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 chemic al 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 boun dary conditions for the 12-km domain were provided by downscaled MOZART global chemis try 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 ant hropogenic emissions that were projected to 2019. The top
10% of the simulated chemical specie c oncentrations 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 PM 2.5



### .

2.4. Determination of the 98 th 

### Percentile and the rela tive design value for each

Grid Cell:

As mentioned previously, the data in Tabl e 1 were selected on a one-in-three-day
schedule so that nearly all stations would have measured data. This makes the 98 th



### percentile the third highest value. Therefore, we sorted the total PM 2.5 

### concentration for each grid cell for a given year (16 days) and selected the third highest value as the 98 th



### percentile for that grid cell for that year. The average of the 98 th 

### percentile value for a
given grid cell over three years was the relati ve 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 viol ate the NAAQS. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

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 stat ions from south to north in the order listed in Table 1 from left to right.

Figure 1 shows the final result of this analysi s. The highest value occurs at grid cell
(57,12) which is the location of the Bakers field - California Street FRM station.

Therefore, there is no hi gher peak value in an unmoni tored area in the domain. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### 3. Conclusions:

There is no guidance on how to perform the unm onitored area analysis for the

24-hour PM 2.5 

### standard within the framew ork of the new attainment test (U.S. EPA,
2011). Therefore, we have desig ned a test to do so. We included a mild decay function
that remedies a geometric arti fact due to the near-linear posit ioning of the FRM stations in the San Joaquin Valley.

The highest three-year average value calc ulated with this method occurs at the

Bakersfield – California Street FRM monitor. This monitor is projected to be in

attainment of the 24-hour PM 2.5 

### 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 PM 2.5 

### NAAQS.

References:

Abt Associates, Inc., 2010, “M odeled Attainment Test Softwar e User's Manual,” pp. 103
(Found at http://www.epa.gov/ttn/scram/gui dance/guide/MATS-2-3-1_manual.pdf ).

Air Resource Board, 2012, Photochem ical Modeling for the 24-Hour PM 2.5 

### State
Implementation Plan in the San Joaquin Valley.

Byun, D., and K. L. Schere (2006). Review of the governing equat ions, computational
algorithms, and other components for the Models-3 Communi ty Multiscale Air Quality
(CMAQ) modeling system, A ppl. Mech. Rev., 59, 51-77.

Carter, W. L. (2000). Implem entation of the SAP RC-99 Chemical Mechanism into the
Models-3 Framework, Report to the United States Environm ental 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. Kinni son, T. Laepple, J. Orlando, X. Tie, G. Tyndall, C.
Wiedinmyer, S. L. Baughcum, and S. Kloste r (2010). Description and evaluation of the

Model for Ozone and Related chemical Tracer s, version 4 (MOZAR T-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 te sting of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7, Geosci. Model Dev., 3, 205-226. San Joaquin Valley Unified Air Pollution Control District December 20, 2012

Appendix G: PM2.5 Weight of Evidence Analysis

2012 PM2.5 Plan

### Frank, N.H., 2006, Retained nitrate, hydr ated sulfates, and carbonaceous mass in
federal reference method fine parti culate 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 Distric t, Draft Final 2012 Air Quality Management
Plan, Appendix V, pp V- 5-46 - 48. (available at http://www.aqmd.gov/aqmp/2012 aqmp/DraftFinal/index.html )

U.S. EPA, 2007, Guidance on the Use of M odels and Other Analyses for Demonstrating
Attainment of Air Quality Goals for Oz one, 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