## State of California

Staff Report



# Proposed Revision to the PM2.5 State Implementation Plan (SIP) for the San Joaquin Valley

Release Date: January 11, 2013

Schedule d for Consideration: January 24 , 20 13 California Environmental Protection Agency



# AIR RESOURCES BOARDThis document has be en reviewed by the staff of the California Air Resources Board and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the Air Resources Board, nor does the mention of trade

names or commerci al products constitute endorsement or recommendation for use.

Electronic copies from this document are available for download from the Air Resources

Board’s Internet site at: http://ww w.arb.ca.gov/planning/sip/sjvpm25/sjvpm25.htm

. In addition, written copies may be obtained from the Public Information Office, Air Resources Board, 1001 I Street, 1 st Floor, Visitors and Environmental Services Center, Sacramento, California 95814, (916) 3 22

- 2990.

For individuals with sensory disabilities, this document is available in Braille, large print, audiocassette or computer disk. Please contact ARB's Disability Coordinator at (916) 323

- 4916 by voice or through the California Relay Services at 711 , to place your request for disability services. If you are a person with limited English and would like to request interpreter services, please contact ARB's Bilingual Manager at (916) 323

- 7053.

For q uestions

, contact:

Ms

. Sylvia Vanderspek or Ms. Patricia Velasco, Ph.D. Manager, PM Analysis Section Project Lead Phone: (916) 324

- 7163 Phone: (916) 323

- 7560

Email: svanders@arb.ca.gov Email: pvelasco@arb. ca.gov

P.O. Box 2815 Sacramento, Calif ornia 95814TABLE OF CONTENTS

I. INTRODUCTION

................................ ................................ ................................ .... 2

II

. NATURE OF THE SAN JOAQUIN VALLEY PM2.5 AIR QUALITY PROBLEM

.... 3

III. DEMONSTRATING ATTAINMENT

................................ ................................ ........ 6

A. Attainment Demonstration

................................ ................................ .......... 6

1. Grid

- Based Modeling

................................ ................................ ................... 6

2. PM2.5 Attainment Plan Precursors

................................ ............................ 8

3. Attainment Date

................................ ................................ ........................... 9

4. Weight of Evidence

................................ ................................ .................... 10

B. Control Strategy

................................ ................................ ........................ 10

IV. OTHER CLEAN AIR ACT REQUIREMENTS

................................ ....................... 17

A. Emissions Inventory

................................ ................................ .................. 18

B. Reasonably Available Control Measure Analysis

................................ ... 18
C. Reasonable Further Progress

................................ ................................ .. 18

D. Contingency Measures

................................ ................................ ............. 19

E. Tr ansportation Conformity Budgets

................................ ........................ 19

V. ENVIRONMENTAL IMPACTS

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

VI. STAFF RECOMMENDATION

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

A PPENDICES

Appendix A: District Submittal Letter and Governing Board Resolution

Appendix B: Weight of Evidence Analysis

Appendix C: Link to District 2012 PM2.5 Plan

Appendix D: Link to U.S. EPA Approval of 2007 State Strategy2 I. INTRODUCTION

The San Joaquin Valley Air Pollution Control District (District) adopted on

December 20, 2012 a revision to the S tate I mplementation P lan (SIP or 2012 PM2.5

Plan ) to address the 24

- hour fine particulate matter (PM2.5) ambient air quality standard

of 35 ug/m3

. The SIP demonstrates that the San Joaquin V alley ( Valley ) will attain the

24

- hour PM2.5 standard by 2019

, with many areas of the Valley reach ing attainment

well before 2019. The SIP shows that by 2017, approximately 90 percent of Valley

residents will be liv ing in communities meet ing t his air quality standard.

The A ir Resources Board’s (ARB) mobile source control program will be providing major

new emission reductions of oxides of nitrogen (NOx) and particulate matter needed for

attainment of the PM2.5 sta ndard. Mobile source PM2.5 and NOx are expected to decrease by almost 50 and 60 percent, respectively, betwe en 2007 and 2019. T he

District w ood burning curtailment program is also an essential measure provid ing

reductions predicted to bring a ll but one Valley location into attainment p rior to 2019. To address this remaining localized area of nonattainment in Bakersfield, the District will develop a rule to f urther reduc e emissions from commercial cooking operations . This

measure will provide the fina l increment of reductions needed to demonstrate

attainment at this location by the deadline of 2019.

SIP technical analyses, including air quality modeling, establish and quantify the relative effectiveness of reducing each PM2.5 precursor pollutant. This s cien tific information is specific to the Valley and i ndicates that the most important pollutants to reduce for the

24

- hour PM2.5 standard a re direct PM2.5 and NOx emissions. A mmonia reductions

are an order of magnitude less effective than NOx emiss ion reductions

, providing only a slight benefit

. Nevertheless, since a small reduction in PM2.5 could occur from further

reduct ions in ammonia emission s, the District has included a measure to assess the feasibility of further ammonia controls. The Distr ict will also conduct further research on

ammonia emissions and implement any feasible and cost

- effective mitigation measures identified through this process

.

The federal Clean Air Act establishes the SIP requirements for areas that are designated nonat tainment for an air quality standard. U.S. EPA first set standards for PM2.5 in 1997, adopting a 24

- hour PM2.5 standard of 65 μg/m3 and an annual standard of 15 μg/m3. The key elements of SIPs are a demonstrati on of attainment of the standard, including identification of the most expeditious date for attainment, determination of the amount of emission reductions needed, and design of the control strategy 1

. In 2006, the 24

- hour PM2.5 standard was lowered to 35 μg/m3. This report

discusses how the District ’s 2012 PM2.5 SIP demonstrates attainment of th is standard

. The District’s adopted SIP can be found at : www.valleyair.org/Air_Quality_Plans/PM25Plans2012.htm

.

1 In 2008, ARB and the District submitted a plan to U.S. EPA demonstrating how the San Joaquin Valley would attain the 1997 PM2.5 standards by 2014. U.S. EPA ap proved this plan in 2011. Although U.S. EPA further revised the annual standard in December 2012, a separate, future planning process will

address this new standard.3 II. N ATURE OF THE SAN JOAQUIN VALLEY PM2.5 AIR QUALITY P ROBLEM

PM2.5 is a complex mixture of many different species generated from a wide array of

sources. Some PM2.5 is emitted directly into the air (primary particles) in the form of

soot

, smoke or dust. PM2.5 can also be f o rmed in the atmosphere (secondary particles) from the reactions of precursor gases , forming compounds such as

a mmonium nitrate and ammonium sulfate

. The relative mixture of these constituents in a region drives the nature of the needed control strategy.

The San Joaquin Valley is one of the most intensively studied locations in the world, providing a wealth of information regarding the nature of the Valley’s PM2.5 problem. ARB and the District operate a comprehensive monitoring network that provides ongoing measurement of PM2.5 concentrations and chemical composition. In addition, numerous special studies have been conducted. The largest of these, the California Regional Particulate Air Quality Study (CRPAQS), was conducted in 1999 through

2001. Th e study included monitoring at over 100 locations, with results published in peer reviewed publications and presented at national and international conferences. CRPAQS findings continue to provide a strong scientific foundation for planning efforts

. The Valley also continues to be a focus of intensive study, with more recent programs including CalNex 2010, and the upcoming DISCOVER

- AQ study to be conducted in

2013.

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

concentrations over the 24

- hour standard occurring primarily during the winter months. Cold temperatures, fog, stagnant airflow, and extended periods without rainfall result in episodes of elevated PM2.5 that can persist for a week or more. E pisodic activities

such as seasonal wood burning also add to the pollution burden during the winter. As shown in Fig ure 1, PM2.5 concentrations are generally higher in the central and southern portions of the Valley, with highest values recorded in the urban areas of

Fresno and Ba kersfield. The high values used for SIP planning purposes are called design values , and are calculated on a three year average.

Figure 1. 2011 24

- hour PM2.5 design values

0 10 20 30 40 50 60 70PM2.5 Design Value (ug/m3)4 The Valley is making progress in reducing PM2.5 po llution, both annual average and

24

- hour PM2.5 concentrations. Between 2001 and 2011, annual average design values have decreased between 30 and 40 percent. The largest decreases occurred in the

northern and central portions of the Valley, where most sit es now meet the annual standard. While the southern Valley has shown a slower rate of improvement, sites are nearing attainment, with 2011 design values 10 to 20 percent over the annual standard.

With respect to the 24

- hour standard, t he most pronounce d progres s occurred between

2001 and 2004

. Over this period, t he three year average design values show a consistent downward trend. During this timeframe

, ammonium nitrate levels were decreasing consistent with implementation of NOx emission reduction st rategies in the Valley.

Characterizing the overall design value trend in more recent years is complex. Although the design value is the planning metric used to assess compliance with the standard, evaluation of a variety of air quality indicators provi des a broader understanding of the nature of air q uality progress. It is also necessary to a c count for exceptional events and yearly variability in weather conditions

, which can strongly affect PM2.5 concentrations

. All these indicator s and factors must b e considered when interpreting air quality trends

and the effects of the ongoing control program.

Figure 2 illustrates the year to year variability i n the number of days over the 24

- hour

PM2.5 standard in Bakersfield and Fresno

. T he large wildfires tha t occurred in 2008 throughout central and northern California affected the number of exceedance days in that year

. In addition, some years have meteorological conditions that are much more

conducive to the formation of PM2.5 than others. Analyses of mete orological data show that the years 2000, 2002, 2007, and 2011 were especially conducive to periods of

PM2.5 formation and multi

- day pollution episodes. These factors can mask the benefit of declining emissions if not accounted for in the analysis of the air quality trend.

Figure 2: Trend in m easured days over the 24

- h our standard of 35 μ g/m 3

0 10 20 30 40 50 60 70 80Number of days

Bakersfield-CA Fresno-1st5 To help understand the interplay between declining emissions and year to year meteorological variability, the years 2000, 2002, 2007, and 2011 were compared for both winter average 24

- hour PM2.5 concentrations and the number of days over the standard (Figures 3 and 4).

Despite the adverse meteorology in these four years , there is a general downward trend

in both PM2.5 levels a nd the frequency of exposure as a result of the continuing decline in emissions. In addition, t he number of days with the highest concentrations (over 65 ug/m3) has also decreased, dropping from 22 days in Bakersfield in 2000, to 14 days in 2007, and 6 d ays in 2011

.

Figure 3 . Change in winter average

24

- hour PM2.5 concentrations in years

with PM 2.5 conducive weather Figure 4. Change in days over the

24

- hour PM2.5 standard in years with

PM 2.5 conducive weather

This air quality progress is driven by decreases in the key chemical constituents that make up the PM2.5 mass and the associated emission reductions from sources that

contribute to those constituents . During the winter, ammonium nitrate is the largest contributor to PM2.5, especially in the central and southern portions of the Valley, where it comprises approximately 60 percent of the PM2.5 mass. Carbonaceous compounds (organic and elemental carbon) are the next largest contributor. Together, ammonium nitrate and carbon comprise 85 to 90 pe rcent of the PM2.5 mass

.

Routine measurements of these chemical constituents have been collected at four locations in the Valley since 2002. Between 2002 and 2011, winter

- average 24

- hour

concentrations of organic carbon have decreased by approximately 50 percent and ammonium nitrate by approximately 40 percent. The major sources of winter

- time organic carbon in the Valley are mobile sources

, residential wood burning, and commercial cooking. During that same time period, PM2.5 emissions from mobile

sourc es decreased by nearly 25 percent due to ongoing implementation of ARB’s mobile source control program. In addition, the decrease in organic carbon reflects substantial benefits from the implementation of the District rule prohibiting residential

wood

- bur ning on high PM2.5 days . Through a series of rule amendments, the threshold6 for curtailing burning was tightened to 30 μg/m 3 in 2008 . A s a result, PM2.5 emissions from residential wood burning decreased 60 percent. Although a District rule controls emissions from certain commercial cooking operations, as a result of population growth,

PM2.5 emissions from commercial c ooking have decreased only modestly (approximately 10 percent).

Ammonium nitrate is formed in the atmosphere from chemical reactions of NOx, volatile organic compounds (VOCs), and ammonia. It is therefore important to understand which precursor controls have been most effective in reducing ammonium nitrate

concentrations. A number of different methods can be used to a ssess this, including evaluation of emissions inventories, review of monitoring data, and air quality modeling.

Evaluation of both emissi ons inventory and monitoring data suggest that in the Valley’s

ammonia

- rich conditions, NOx, rather than ammonia controls are more effective in reducing wintertime PM2.5 concentrations. In addition, air quality modeling has shown that while large reductio ns in NOx lead to commensurate reductions in ammonium nitrate, comparable reductions in ammonia are much less effective. On a per ton basis, reductions in NOx are approximately an order of magnitude more effective than reductions in ammonia. Finally, air quality modeling has indicated that VOC emission reductions produce no PM2.5 benefit, and in some instances may actually lead to a very

small disbenefit.

As a result, programs aimed at reducing NOx emissions have played an important role in reducing ammo nium nitrate concentrations. Between 2002 and 2011, Valley NOx emissions decreased by about 38 percent , with a commensurate reduction in both gaseous NOx concentrations and ammonium nitrate concentrations. Major NOx reductions during this time period hav e resulted from the ongoing implementation of both new vehicle standards for passenger and heavy

- duty diesel vehicles and equipment, as well as rules accelerating the turnover of the legacy diesel fleet. District rules have also resulted in ongoing reduct ions in stationary source NOx emissions over this time period.

III. DEMONSTRATING ATTAINMENT

A. Attainment Demonstration

1. Grid

- Based Modeling

Consistent with U.S. EPA guidelines, ARB staff conducted air quality modeling to predict future PM2.5 concentrat ions at each monitoring site in the San Joaquin Valley. This modeling was used to identify the most expeditious attainment date, the relative benefits of controlling different PM2.5 precursor pollutants, and the magnitude of

emission reductions needed fro m each pollutant. Following U.S. EPA’s guidance , the

m odeling addressed the most severe air quality days, which relies on characterization of episodic daily emissions.

For the 2012 PM2.5 Plan , 2007 was chosen as the base year for the air quality

modeli ng. The design values recorded in 2007 were some of the highest in recent years7 and analysis indicate s that the meteorology was one of the most conducive to PM2.5

formation

. Thus, the selection of 2007 represents a health protective approach to the

attain ment demonstration modeling.

The attainment demonstration incorporates modeling that includes the benefits of all adopted regulations plus a dditional scenario and sensitivity runs. Continued implementation of ARB and District ongoing control programs pro vide new emission reductions each year , resulting in a forecasted 55 percent decrease in NOx emissions,

and about a 30 percent decrease in PM2.5 emissions between 2007 and 2019.

M odeling these reductions show ed attainment of the 24

- hour PM2.5 standard by 2019 in all counties except Kings and Kern

.

ARB staff then modeled a new scenario reflecting the ongoing control program coupled

with an enhanced wood burning curtailment program designed to prevent burning on days that may lead up to a PM2.5 exceedan ce

. The modeling results for this scenario

indicate that this regional control would bring almost the entire Valley into attainment by

2019 , with only the B akersfield

- California Street site a remaining localized area of

nonattainment

. The predicted desig n values from this modeling are shown in Table 1.

The substantial improvement i n design values reflect ammonium nitrate concentrations

that are predicted to decrease by nearly 45 percent, organic carbon concentrations by approximately 65 percent, and elem ental carbon concentrations by nearly 80 percent.

T able 1

. 2019 modeled 24

- hour PM2.5 design v alues with ongoing programs and
enhanced wood burning curtailment

Monitoring Site 2007 Design Value 2019 Modeled Design Value ( μ g/m 3 )

Bakersfield

- Californi a 66 35.7

Bakersfield

- Planz 68 32.9

Corcoran

- Patterson 61 32.1

Visalia

- N. Church 58 29.4

Fresno

- Hamilton 61 28.6

Fresno

- 1 st 63 30.5

Clovis 58 28.6

Merced 48 22.6

Modesto 55 24.7

Stockton 45 21.4

In order to determine the emission reduct ions needed to bring the final site into attainment, ARB staff conducted additional modeling sensitivity runs to assess the relative efficacy of further reductions in different PM2.5 precursors in Kern County . This modeling demonstrated that on a relative basis, the greatest benefits are achieved from reductions in directly emitted PM2.5, followed by NOx , with PM2.5 emission reductions approximately eight times more effective than NOx

.
Because the remaining nonattainment problem is very localized, ARB st aff examined the emissions inventory in the area surrounding the Bakersfield

- California Street8 monitoring site. This analysis identified commercial cooking, residential fuel combustion, and on

- road motor vehicles as the top three sources of directly emitt ed

PM2.5

. Although a District rule controls emissions from some commercial cooking operations, emissions from this category are forecasted to increase slightly into the future due to population growth. Therefore, further control of PM2.5 emissions from

c ommercial cooking operations was identified as the most effective approach to meet

the standard at the remaining nonattainment location

. The final attainment

demonstration for Bakersfield

- California, the Valley design site is shown in Table 2.

T able 2

. Attainment d emonstration for the Bakersfield

- California design value s ite.

2007 Design Value ( μ g/m 3 ) 2019 Design Value

with Wood Burning Program

Enhancement ( μ g/m 3 ) 2019 Final Design Value ( μ g/m 3 )

65.6 35.7 35. 3

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

or less than 35.4 μ g/m 3

.

The final attainment demonstration for Bakersfield

- California includ es two new District measures, the enhanced wood burning program and commercial cooking controls

. The forecasted 2019 design va lue for Bakersfield

- California with implementation of ongoing control programs and an enhanced residential wood burning cur tailment

program is 35.7 ug/m3. Model sensitivity analysis of commercial cooking contr ols

shows a 0.6 ug/m3 reduction

, b ringing the predicted design value to 35.1 ug/m3.

Ho wever, due to a small increase in motor vehicle emissions resulting from use of updated vehicle activity data , the projected de s ign value was adjusted upward by 0.2 ug/m3

. The new data from the San Joaquin V alley Metropolitan Planning Organizations (MPOs) represents about a one percent increase in Vall eywide NOx

emissions . A modeling sensitivity ru n served as the basis for this adjustment to the design value. In total, with reductions from existing programs

, further wood burning

restrictions , new controls on commercial cooking, and updated MPO data, the predicted design value is 35.3 ug/m3 . As a result, the attainment demonstration meets U.S. EPA’s requirement for an attainment design value of n o greater than 35.4 ug/m3.

2. PM2.5 Attainment Plan Precursors

As discussed earlier, a mbient PM2.5 is comprised of many different constituents and as a result there are multiple precursor pollutants that lead to PM2.5 formation (directly emitted PM2.5, NOx, SOx, VOCs, and ammonia). U.S. EPA ’s PM2.5 implementation rule (Rule) specifies that a precursor is considered “significant” for control strategy development purposes when a significant reduction in the emissions of that precursor pollutant leads to a sig nificant decrease in PM2.5 concentrations. Such pollutants are9 known as “PM2.5 attainment plan precursors”

. 2 The Rule also establishes a presumption that PM2.5, SOx and NOx are attainment plan precursors, while VOCs and

ammonia a re not significant precurs ors. U.S. EPA noted in the Rule that the

uncertainties in ammonia emission inventories and efficacy of ammonia control measures did not provide enough evidence to consider ammonia as a mandatory PM2.5 attainment plan precursor.

The U.S. EPA Rule does not define significance nor define a quantitative test for determining significance. When it approved the Valley’s annual PM2.5 plan in 2011

, U.S. EPA determined that while ammonia reductions provided a small benefit, they were not significant when compared to the benefits of reducing NOx

. T he only PM2.5 attainment plan precursors identified and approved by U.S. EPA for the annual standard

are PM2.5, NOx, and S O x

.

In developing the PM2.5 SIP for the 24

- h ou r standard, precursor sensitivity modeling

was ag ain done based on the most recent data. This modeling showed that a 25 percent reduction in NOx and PM2.5 would reduce the 2019 design value at Bakersfield by 10 to 12 percent respectively

, while a similar reduction in ammonia would reduce the design val ue by only one percent. On a per ton basis, re ductions in the required attainment precursors

- NOx, SOx , and PM2.5

- are ten to forty times more

beneficial than ammonia reductions

. T h is modeling also indicate s that reductions in VOCs have no benefit, and may in some cases cause a very slight increase in PM2.5

. Based on the current science showing the relative effectiveness of precursor reductions, the appropriate attainment plan precursors for PM2.5 remain PM2.5, NOx, and SOx.

W hile ammonia has not bee n shown to be a significant precursor for PM2.5

, ARB staff supports the District ammonia feasibility measure to conduct further research on

ammonia emissions and implementation of any feasible and cost

- effective mitigation

measures identified through this process

. This approach is consistent with U.S. EPA’s

Rule which enable states to focus on the most effective control strategies by distinguishing among PM2.5 precursors on the basis of significant contribution to

attainment

.

3. Attainment Date

N onattainm ent areas must attain the PM2.5 standards as expeditiously as practicable

, but no later than 2019. Looking at the modeled design values and rates of emission reductions, b y 2017

, approximately 90 percent of the population in the Valley is predicted to liv e in areas that meet the standard , and the 2012 PM2.5 Plan demonstrates full attainment in 2019 at the last remaining site in Bakersfield. The District assessed whether attainment at this last site could be accelerated, and

calculated that emission reduct ions sufficient to achieve a bout a 1 ug/m3 decrease in design value would be needed in order to accelerate attainment at this location by one

year.

As discussed, reductions in PM2.5 and NOx emissions are the most effective in reducing PM2.5 concentrations . ARB regulations and California’s incentive programs

2 (72 FR 20586).10 are reducing mobile source NOx and PM2.5 emissions as expeditiously as possible,

with increasingly stringent new engine standards and the early turn o ver of legacy fleet s. In addition, new District con trol measures addressing residential wood burning and commercial cooking will be implemented in advance of 2019 to provide early emission

reductions.

In looking for other potential ways to accelerate attainment by one year at the remaining

nonattainment site, modeling sensitivity runs were evaluated. The modeling showed that to achieve a 1 ug/m3 decrease in the design value at the Bakersfield site all

ammonia emissions in Kern County would need to be eliminated. On a Valleywide

basis a 3 4 percent reduc tion in ammonia emissions would be needed. Reductions on

this scale in this timeframe were not found to be feasible. Ammonia is currently being controlled through the District Confined Animal Feeding Operations rule, and research is currently underway to provide an improved characterization of ammonia emissions and the potential for further mitigation

. Given the need for further research

, completion of the ammonia feasibility study is needed prior to determination of whether further

ammonia emission cont rol could accelerate attainment.

4. Weight of Evidence

U.S. EPA’s modeling guidance requires that the modeled attainment demonstration be accompanied by a w eight of e vidence analysis to provide a set of complementary analyses

. E xamining an air quality pr oblem in a variety of ways provides a more

inform ed basis for the attainment strategy as well as a better understanding of the overall problem and the level and mix of emissions controls needed for attainment.

Appendix G of the 2012 PM2.5 Plan provides th e weight of evidence analysis conducted by ARB staff

. Following U.S. EPA guidance, t his includes assessment of trends in air quality and emissions, source

- receptor models and other diagnostic analyses, additional modeling evaluations, and description of a conceptual model of PM2.5 in the Valley

. The weight of evidence analysis draws upon the wealth of data collected in the Valley over the years, both from the routine monitoring network, as well as special studies. The substantial continuing reductions th at will result from implementation of the ongoing control program, coupled with new measures addressing residential wood burning and cooking are consistent with past progress and the results predicted in the modeled attainment demonstration and support the selected control approach and the associated attainment date of 2019

.

B. Control Strategy

Attainment of the 24

- hour PM2.5 standard in the Valley will require the combined efforts

of ARB and District control programs . As a result of these programs, NOx e missions

are forecasted to decrease by 55 percent and PM2.5 emissions by about 30 percent by 2019. The following sections highlight ARB’s ongoing control programs and new District

measures that provide the emission reductions included in the attainment

de monstration

.

The 2012 PM2.5 Plan is built upon the District’s adopted strategy that is the foundation

for the 8

- hour ozone and annual PM2.5 plans approved by U.S. EPA in 2011 . In11 addition, the District has committed to pursue five new regulatory control measures to
further reduce PM2.5 and NOx emissions as outlined in Table 3.

Table 3 . District regulatory control measures.

Rule Amendment

Date Compliance

Date Emission

Reductions Rule 4308 Boilers, Steam Generators, and

Process Heaters 0.075 to <2 MMB tu/hr 2013 2015 TBD Rule 4692 Commercial Charbroiling 2016 2017 0.4 tpd

PM2.5 Rule 4901 Wood Burning Fireplaces and Wood Burning Heaters 2016 2016 1.5 tpd of

PM2.5

Rule 4905 Natural Gas

- Fired, Fan

- Type

Residential Central Furnaces 2014 2015 TBD

Rule 9610 SIP

- Creditability of Incentives 2013 2013 TBD

Two rule amendments focus on reducing directly emitted PM2.5 and are key elements of the attainment control strategy. This i ncludes expanding the scope of Rule 4692 for commercial charbroiling to include under

- fired charbroilers , similar to the commitment made by the South Coast Air Quality Mana gement District (South Coast). The South

Coast and the District continue to partner in efforts to evaluate and advance technologies for this source sector. In addition, the District has committed to further strengthen ing the successful residential wood burning curtailment program under

Rule 4901. Compliance dates for these measures are intended to provide early

emission reductions.

O ther rules will r educ e NOx emissions from residential central furnaces, and boilers, steam generators, and process heaters , with benefits to be quantified as part of the rulemaking. No reductions were included in the attainment demonstration. Working with U.S. EPA, the District also plans to pursue a new rule to allow SIP credit for

emission reductions from incentives.

The 2012 PM2.5 Plan also identifies seven further study measures which upon completion could result in opportunities for additional emission reduction s. These study measures seek to explore where and how addition reductions may be achieved in the future. The District is committing to release study reports by the dates listed in Table 4.12 Table 4. District further study measures.

Control M easure Description Completion Date

Rule 4103 Open Burning Evaluate the feasibility of postponed burning activities every 5 years, as outlined in the

current rule. 2015

Rule 4106 Prescribed Burning Examine the feasibility of implementing a

biomass remo val program similar to the Placer County program. 2013

Rule 4311

Flares Review flare minimization plans and annual reports for further emission reduction opportunities. 2013

Rule 4550

CMPs Analyze existing studies and support new

studies to establish a more accurate inventory of PM2.5 emissions and identify potential addition emission reduction opportunities. 2014

Rule 4570

Confined Animal

Facilities Analyze existing studies on ammonia at dairies and evaluate potential ammonia

controls for their effectiveness in reducing PM2.5 concentrations in the Valley. 2017

SC 001

Lawn Care Equipment Evaluate emissions inventory and technology demonstration efforts to identify potential emission reduction opportunities. 2013

SC 005 Asphalt/Concrete Oper ations Examine feasibility of warm

- mix asphalt as a potential emission reduction opportunity.

2013

Over the past five years , ARB adopted a number of regulations aimed at reducing emissions of diesel particulate matter and oxides of nitrogen from freigh t transport sources like heavy

- duty diesel trucks, and off

- road sources like large construction equipment. Phased implementation of these regulations will produce increasing emission reduction benefits over time, as the regulated fleets are retrofitted, a nd as older and dirtier portions of the fleets are replaced with newer and cleaner models at an accelerated pace.

ARB’s longstanding program s to reduce emissions from passenger vehicles along with the smog check program provide continuing benefits neede d for attainment of the

24

- hour PM2.5 standard. Implementation of the ARB 2007 State Strategy approved by

U.S. EPA 3 is providing new reductions included in the PM2.5 SIP for the San Joaquin Valley. Since development of the 2007 State Strategy, the ARB measures listed in

Table 5 have been adopted and improvements to California’s smog check and vehicle retirement programs have been made.

3 Approval and Promulgation of Implementations Plans; California ; 2008 San Joaquin Valley PM2.5 Plan and 2007 State Strategy

- Federal Register Vol. 76, No. 217, Page 6989613 Table 5

. Measures in the 2007 State Strategy

Passenger Vehicles

Smog Check Improvements

Expanded Vehicle Retirement (AB 118)

Modifications to Reformulated Gasoline Program

Trucks

Cleaner In

- Use Heavy

- Duty Trucks

Goods Movement Sources

Auxiliary Ship Engine Cold Ironing & Other Clean Tech

Cleaner Main Ship Engines and Fuel

Port Truck Modernization

Cle an Up Existing Harbor Craft

Off

- Road Equipment

Cleaner In

- Use Off

- Road Equipment

Other Off

- Road Sources

Enhanced Vapor Recovery for Above

- Ground Storage

Tanks

Additional Evaporative Emission Standards

Areawide Sources

Consumer Products Program

Pest icide Regulation ( adopted by Department of Pesticide Regulation)

**Clean New and In** 

**- Use Heavy** 

**- Duty Trucks** 

A central control element of ARB’s 2007 State Strategy is increasingly stringent

standards for new truck s as shown in the Table 5 . N ew heavy

- dut y trucks sold since 2010 must emit 98 percent less NOx and PM2.5 than new trucks sold in 1986.

Table 5 : Phase

- in of truck engine s tandards

.

Model Year Applicable Standard g/bhp

- hr

NOx PM

1986 and older 10.7 0.60

1987

- 2006 From 6.0 to 2.0 From 0.6 to 0.10

2007

- 2009 1.1 0.01

2010 0.2 0.0114 However, older, higher

- emitting trucks with long service lives would stay on the road for many years to come. With attainment of the PM2.5 standards required soon after the cleanest trucks were introduced, the typically slow replacement of older trucks on the road with the latest models would not provide emission reduction benefits soon enough.

To address this, ARB developed t he Cleaner In

- use Heavy

- duty Truck SIP measure. This measure leverages the benefits provided by new truck emission standards by accelerating introduction of the cleanest trucks. The Truck and Bus Regulation was adopted in December 2008

, and amended in December 2010 to account for the reduced emissions resulting from the economic effects of the recession

. This rule represents a multi

- year effort to turn over the legacy fleet of engines and replace them with the cleanest technology available.

Starting in 2012, the Truck and Bus Regulation phases in requirements so that by 2023 nearly al l vehicles will meet 2010 model year engine emissions levels. T he regulation applies to nearly all diesel fueled trucks and buses with a gross vehicle weight rating greater than 14,000 pounds that are privately or federally owned, including on

- road and

of f

- road agricultural yard goats, and privately and publicly owned school buses. Moreover, the regulation applies to any person, business, school district, or federal government agency that owns, operates, leases or rents affected vehicles. The regulation also establishes requirements for any in

- state or out

- of

- state motor carrier,

California

- based broker, or any California resident who directs or dispatches vehicles subject to the regulation. Finally, California sellers of a vehicle subject to the regulat ion would have to disclose the regulation’s potential applicability to buyers of the vehicles.

F igure 5 below portrays reductions in NOx from in

- u se trucks within the San Joaquin Valley , and shows the benefits of ARB’s mobile strategy.

Figure 5. Truc k and b us r egulation implementation.15 In addition to the Truck and Bus Regulation, separate regulations reduce emissions from other public fleets, solid waste collection trucks and transit buses. Trucks that transport marine containers must comply with the drayage truck regulation.

**Cleaner In**

**- Use Off** 

**- Road**  **Equipment**

As with trucks, the control strategy for off

- road equipment is based on increasingly stringent new off

- road diesel engines. As a result, new construction, mining, industrial

and oil drillin g equipment will become progressively cleaner. The requirements vary according to the power rating of engines. Table 6 shows the schedule for phasing in tiered requirements for new off

- road engines with a power rating between 175 and 300 horsepower ( hp). Beginning in 2014, new Tier 4 construction equipment with the power rating shown below must emit about 96 percent less NOx and PM than new Tier 1 equipment sold in the year 2000.

Table 6 : Phase

- in of o ff

- road e ngine s tandards

Model year Level of Control Applicable Emission Standard for

New Off

- road Engines 175<hp<300 g/bhp

- hr

NOx PM

1996

- 2002 Tier 1 6.9 0.4

2003

- 2005 Tier 2 4.9* 0.15

2006

- 2010 Tier 3 3.0* 0.15

2011

- 2013 Tier 4 interim 1.5 0.015

2014+ Tier 4 final 0.3 0.015

* Reflects co mbined limit for non

- methane hydrocarbons and NOx

However, large diesel off

- road equipment typically remains in use for long periods of time. As with heavy

- duty trucks, this long life means that newer, lower

- emitting engines

would be introduced into flee ts relatively slowly. The impact of this is that emission reductions and associated health benefits from these cleaner engines would also be fairly slow to materialize. To address this, the 2007 SIP included the Cleaner In

- use Off

- road Equipment measure.

First approved in 2007, the Off

- Road Regulation was amended in 2010 in light of the impacts of the economic recession. Affected off

- road equipment is used in construction, manufacturing, the rental industry, road maintenance, airport ground support and landscaping. In December 2011, the Off

- Road Regulation was modified to include on

road trucks with two diesel engines.

Figure 6 portrays reductions in NOx emissions from off

- road equipment within the San Joaquin Valley , and shows the benefits of ARB’s mo bile strategy.16 Figure 6

. Off

- Road Regulation NOx emission reductions.

The Off

- Road Regulation will significantly reduce emissions of diesel PM and NOx from the over 150,000 in

- use off

- road diesel vehicles that operate in California by requiring

t heir owners to modernize their fleets and install exhaust retrofits. The regulation affects dozens of vehicle types used in thousands of fleets by requiring owners to modernize their fleets by replacing older engines or vehicles with newer, cleaner

models , retiring older vehicles or using them less often, or by applying retrofit exhaust

controls.

The Off

- Road Regulation imposes idling limits on off

- road diesel vehicles, requires a written idling policy, and requires a disclosure when selling vehicles. T he regulation also requires that all vehicles be reported to ARB and labeled, restricts the addition of older vehicles into fleets, and requires fleets to reduce their emissions by retiring, replacing, or repowering older engines, or installing verified ex haust retrofits. The requirements and compliance dates of the Off

- Road Regulation vary by fleet size.

**Passenger Cars** 

The Board established California’s Low Emission Vehicle (LEV) program in 1990, and the LEV2 program in 1998. Additionally, ARB’s Zero E mission Vehicle (ZEV) regulation which affects passenger cars and light

- duty trucks, has spurred movement towards commercialization of advanced clean cars and light

- duty trucks. As a result, many new gasoli ne engines now emit extremely low emission levels of smog forming emissions. Conventional hybrid electric vehicles have been commercialized, and the number of models offered for sale is quickly expanding. Recently, battery electric vehicles and plug

- in hybrid electric vehicles have been introduced for sale, and fuel cell electric vehicles are expected to follow.17 ARB’s Advanced Clean Cars (ACC) Program, approved in January 2012, is a pioneering approach of a ‘package’ of regulations, that although separate in construction, are related in terms of the synergy developed to address both ambient air quality needs and climate change. The ACC program combines the control of smog, soot causing pollutants and greenhouse gas emissions into a single coordinated package of requirements for model years 2015 throu gh 2025. The program assures the development of environmentally superior cars that will continue to deliver the performance

, utility, and safety vehicle owners have come to expect.

The ACC program approved by ARB in January 2012 included amendments aff ecting the current ZEV regulation through the 2017 model year in order to enable manufacturers to successfully meet 2018 and subsequent model year requirements. The ZEV amendments for 2018 and subsequent model years in the ACC program approved by ARB in J anuary 2012 are intended to achieve commercialization through simplifying the regulation and pushing technology to higher volume production in order

to achieve cost reductions.

The ACC Program will produce increasing benefits over time as new cleaner cars enter the fleet displacing older and dirtier vehicles. In this manner, the benefits in 2023 will be realized through the cumulative reduction in emissions achieved by new cars entering the fleet in 2017 through 2023. This program will continue to provid e benefits well after 2025 as vehicles meeting the new standards replace older, higher

- emitting vehicles.

Many additional programs are currently in place to reduce emissions from the passenger car legacy fleets and accelerate fleet turn over . The Smog Ch eck Program ensures that passenger vehicles stay clean as they age and on

- board diagnostic systems identify smog control problems. The Smog Check Program is continuously being improved to provide addition al emission reductions such as requiring stricter

i nspection standards and annual inspection o f older vehicles. ARB is also active in encouraging consumers with older dirtier vehicles to retire them early. Replac ing older

dirtier vehicles with cleaner new vehicles provides permanent emission reductions

.

IV. OTHER CLEAN AIR ACT REQUIREMENTS

In addition to the elements related to the attainment demonstration, the Clean Air Act also requires that SIPs contain the following information:

• B ase year emission inventories and future year forecasts for manmade sou rces of air pollution in the nonattainment area;

• D emonstration that all reasonably available control technology/reasonably available control measures (RACM/RACT) have been applied to existing

sources;

• Reasonable Further Progress (RFP) towards attainment;

• C ontingency measures in the event the controls fall short of achieving needed reductions; and

• T ransportation conformity emission budgets to ensure transportation plans and projects are consistent with the SIP

.18 A. Emissions Inventory

SIP s must contain bas e

- year inventories for PM2.5 and all precursor emissions as well

as future year forecasts for all pollutants identified as PM2.5 attainment plan precursor s

. An emission inventory consists of a systematic list ing of the sources of air pollutants

with an es timate of the amount of pollutants from each source or source category over a given period of time.

ARB and District staff worked jointly to prepare an updated emission inventory for the

2012 PM2.5 Plan

. This included a category

- by

- category review and update to the growth profile s using sector specific forecasts based on the most recent economic

information available

. Extensive effort also went into developing an inventory for use in the air quality modeling that appropriately represented the nature of episodic emissions during the winter months. Additional information on the emission inventory

m ethodologies and resulting base and future year emissions can be found in Appendix B of the 2012 PM2.5 Plan.

B. Reasonabl y Available Control Measure Analysis

As specified in the Clean Air Act

, SIPs shall provide for the implementation of all RACM as expeditiously as practicable , including at minimum RACT . U.S. EPA has interpret ed

RACM as those measures that are technologically and economically feasible and w hen considered in aggregate, would advance the attainment date by at least one year

.

The District RACM /RACT demonstration includes a comparison of stationary source measures the District has implemented or plans to implement with U.S. EPA’s list of

sugg ested PM2.5 control measures along with other local district measures and comments received during the public process

. Based on this, t he District determined that four rules could be strengthened to provide reductions needed to attain the PM2.5

standard i n the Valley

. No additional feasible measures were identified that could in aggregate accelerate attainment by one year, as discussed previously in the attainment

date section.

California’s comprehensive mobile source program continues to be RACM as it expands

and further reduces emissions. Given the significant emission reductions needed for attainment in California, ARB has adopted the most stringent control measures

nationwide for on

- road and off

- road mobile sources and the fuels that power them.

T hese measures provide a significant amount of emission reductions needed for the Valley to attain the PM2.5 standard. The complete RACM and RACT assessment is provided in Chapter 9 and Appendix D of the 2012 PM2.5 Plan.

C. Reasonable Further Progress

SIPs must also provide for steady progress in reducing emissions during the years leading to attainment. The pace of ARB and District control programs, as well as the early introduction of the District’s wood burning and commercial cooking measures fully

meets the RFP requirements. Figure 7 shows projected NOx emission levels along with19 the RFP linear target . As shown in the figure, NOx emission levels are projected to be well below the level of required linear progress, ensuring early emission reductions

tow ards attainment.

Figure 7

. NOx emission reductions compared to RFP target

.

D. Contingency Measures

C ontingency measures provide additional emission reductions in the event a nonattainment area fails to achieve RFP targets o r attain the PM2.5 standard by its attainment date. These contingency measures are to take effect without further ARB or

District action. As shown above in the example in Figure 7

, early reduc tions from the control strategy beyond those needed for RFP pr ovide for interim year contingency . In addition, reductions that accrue between 2019 and 2020 provide the majority of the attainment contingency reductions

. Additional Valleywide reductions from the

commercial cooking and enhanced wood burning curtailme nt rules beyond those

needed for localized attainment

, as well as a contingency trigger in the residential wood burning rule provide the remaining contingency reductions. Additional discussion of contingency measures is provided in Chapter 9 of the 2012 P M2.5 Plan.

E. Transportation Conformity Budgets

Under section 176(c) of the Clean Air Act, transportation activities that receive federal

fund ing or approval must be fully consistent with the SIP. U.S. EPA’s transportati on conformity rule 4 details requir ements for establishing motor vehicle emission budgets (budgets) in SIPs for the purpose of ensuring the conformity of transportation plans and programs with the SIP

.

4 U.S. EPA maintains online information on its transportation conformity program, including access to relevant rulemakings, policy guidance, and repo rts at: http://www.epa.gov/otaq/transp/traqconf.htm20 The 2012 PM2.5 Plan establishes county

- level on

- road motor vehicle emission budgets

fo r each RFP milestone year, as well as for the attainment year

. Emission budgets for

direct PM2.5 and NOx were calculated using EMFAC2011 and reflect winter average emissions

. The emission bud gets established in the 2012 PM2.5 Plan fulfill the requirement s of the Clean Air Act and U.S. EPA regulations to ensure that transportation projects will not interfere with progress and attainment of the annual

PM2.5 standard. Additional detail on the on

- road motor vehicle emission budgets can

be found in Appendix C of the 2012 PM2.5 Plan.

V. ENVIRONMENTAL IMPACTS

To meet the requirements of the California Environmental Quality Act (CEQA), the District prepared a Draft Negative Declaration because the Initial Study showed there is no substantial evidence, in light of the whole record, that the 2012 PM2.5 Plan may have a significant effect on the environment. On November 8, 2012, the District issued a Notice of Intent to Adopt a Negative Declaration along with the Initial Study 5

. Comments on the Initial Study and Draft Negative Declaration were accepted from November 9, 2012 to December 8, 2012. The written comments received were reviewed and considered by the District Governing Board prior their adoption of the Final Negative Declaration and approval of the 2012 PM2.5 Plan at a public meeting held on December 20, 2012.

V I

. STAFF RECOMMENDATION

ARB staff recommends that the Board:

1. A dopt the San Joaquin Valley 2012 PM2.5 Plan

, including the emission inventory, local control strategy, attainment demonstration, ide ntification of PM2.5 attainment plan precursors, attainment deadline request, reasonable further progress plan, contingency measures, RACT/RACM demonstration, and transportation conformity emission budgets , as revision s to the California SIP

.

2. Direct the E xecutive Officer to s ubmit the S an Joaquin Valley 2012 PM2.5 Plan

to U.S. EPA for approval.

5 http://www.valleyair.org/notices/Docs/2012/11

- 18

- 12PM25/Notice_of_Intent

- NegativeDeclaration.pdfAppendix A

District Submittal Letter and Governing Board ResolutionThis page intentionally left blankAppendix B

Weight of Evidence AnalysisThis page intentionally left blank

### SAN JOAQUIN VALLEY PM2.5 WEIGHT OF EVIDENCE ANALYSISi TABLE OF CONTENTS

EXECUTIVE SUMMARY

................................ ................................ ................................ . 1

1. INTRODUCTION

................................ ................................ ................................ ...... 1

2. PM2.5 STANDARDS AND HEALTH EFFECTS

................................ ....................... 3

3. MONITORING IN THE SAN JOAQUIN VALLEY

................................ ...................... 4

a. Established monitoring network

................................ ................................ ......... 4

b. Extensive field studies

................................ ................................ ........................ 4

4. NATURE AND EXTENT OF THE PM2.5 PROBLEM

................................ ............... 7

a. Current air quality

................................ ................................ ............................... 7

b. Seasonal variability.

................................ ................................ ........................... 7

c. Diurnal variability

................................ ................................ ................................ 8

d. Chemical composition

................................ ................................ ........................ 9

e. Spa tial distribution of the major PM2.5 components; local versus regional

...... 11

f. Episode development

................................ ................................ ....................... 13

5. SECONDARY AMMONIUM NITRATE FORMATION

................................ ............. 15

a. Chemistry

................................ ................................ ................................ ......... 15

b. Limiting prec ursor concept

................................ ................................ ............... 16

c. Role of ammonia in ammonium nitrate formation

................................ ............. 17

d. Role of VOC in ammonium nitrate formation

................................ .................... 24

6. SECONDARY ORGANIC AEROSOL FORMATION

................................ ............... 32

7. EMISSION SOURCES OF WINTERTIME PM2.5

................................ ................... 34

a. Emission inventory

................................ ................................ ........................... 34

b. Chemical markers of source types

................................ ................................ ... 36

c. Source apportionment using source receptor models

................................ ...... 37

d. Photochemical modeling source apportionment

................................ ............... 41

8. PM2.5 AIR QUALITY PROGRESS

................................ ................................ ......... 42

a. Annual PM2.5 trends

................................ ................................ ........................ 42

b. 24

- Hour PM2.5 trends

................................ ................................ ...................... 43ii c. Meteorology impacts on air quality

................................ ................................ ... 44

d. Annual trends adjusted for meteorology

................................ ........................... 47

e. 24

- hour trends adjusted for meteorology

................................ ......................... 48

f. Trends in 24

- hour, seasonal, and hourly PM2.5

................................ ............... 50

g. Chemical composition trends

................................ ................................ ........... 55

h. Emission inventory trends

................................ ................................ ................ 57

9. LINKING AIR QUALITY TRENDS TO EMISSION REDUCTIONS

......................... 58

a. NOx control

................................ ................................ ................................ ...... 58

b. Residential wood burning controls

................................ ................................ ... 60

10. MODELED ATTAINMENT DEMONSTRATION

................................ .................. 62

a. Modeling results

................................ ................................ ............................... 62

b. Benefits of emission reductions from on

- going programs

................................ 63

c. Evaluation of precursor sensitivity

................................ ................................ .... 64

d. Demonstrating attainment at Bakersfield

- California

................................ ......... 70

11. SUMMARY

................................ ................................ ................................ .......... 71

REFERENCES

................................ ................................ ................................ .............. 72

APPENDIX 1

APPENDIX 2

APPENDIX 3

APPENDIX 4E

- 1 EXECUTIVE SUMMARY

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

attain the PM2.5 24

- hour standard of 35 ug/m 3 as expeditiously as practicable due to adopted and proposed control measures. As part of the attainment demonstration, the

2012 PM2.5 Plan specifically identifies the: 1) most expeditious date of when the San Joaquin Valley (Valley) will attain the stand ard, 2) attainment plan precursors, 3) amount of emissions needed to attain, and 4) sources to control. The weight of evidence analysis provides a set of complementary analyses that supplement the required modeling. Because all methods have strengths and weaknesses, examining an air quality problem in a variety of ways offsets the limitations and uncertainty that are inherent in air quality modeling. This approach also provides a better understanding of the overall problem and the level and mix of emissi ons controls needed for attainment.

A nalyses conducted by Air Resources Board ( ARB ) and San Joaquin Valley Air Pollution Control District ( SJVAPCD or District ) staff, along with findings from the California Regional Particulate Air Quality Study ( CRPAQS ) provide the supplemental information supporting the attainment demonstration. C RPAQS was a public/private partnership designed to advance our understanding of the nature of PM2.5 in the Valley and guide development of effective control strategies. The s tudy included monitoring at over 100 sites as well as data analysis and modeling, results of which have been published in over 60 papers and presented at national and international conferences

.

Studies such as CRPAQS provide valuable information that supports the State Implementation Plan (SIP) process in a number of ways. First, these studies provide additional observational data that help to provide a more detailed understanding of the nature of the PM2.5 problem in the San Joaquin Valley. This dat a also is used to update the fundamental algorithms contained within air quality models, thereby enhancing their ability to simulate observed air quality conditions. Finally, they provide an improved basis for model applications used in the preparation of SIPs and a more robust platform for evaluating the response to emission controls and predicting future air quality

.

What is the nature of the 24

- hour PM2.5 problem in the Valley?

The geography of the San Joaquin Valley, along with weather pattern s influence the accumulation, formation, and dispersion of PM2.5. As a result, PM2.5 concentrations are generally higher in the central and southern portions of the Valley, with highest

values in the urban areas of Fresno and Bakersfield. Concentrations are highest during the winter months of November through February. During these months, high

- pressure weather systems over Northern California can cause the atmosphere to become stagnant for extended periods, resulting in PM2 .5 episodes that can persist f rom several days up to several weeks.

Ammonium nitrate and carbonaceous material (organic and elemental carbon) are the largest constituents of PM2.5 on exceedance days, comprising 85 to 90 percent of theE

- 2 mass. Geological material (dust), and ammonium sulfate are small contributors. Ammonium nitrate is formed in the atmosphere from reactions of gaseous precursors. Emissions of nitrogen oxides (NOx) from mobile sources and stationary sources react with ammonia which is primarily emitted from livestock operations

, fertilizer application

, and mobile sources . The stagnant, cold, and damp conditions that occur during th e winter promote the formation and accumulation of ammonium nitrate. Elevated

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

areas. Due to the localized urban increment from these activities , which adds to the more regional ammonium nitrate concentrations, the highest PM2.5 concentrations in the Valley occur at urban sites.

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

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

- hour PM2.5 concentrations over the last ten years. Between 2001 and 2011, annual average design values in the Valley declined between 30 and 40 percent at individual monitoring

locations . O verall, annual PM2.5 trends adjusted for the effects of meteorology indicate that between 1999 and 2010, annual PM2.5 concentrations decreased about 40 to 50 percent at Bakersfield and Fresno due to emission reductions. With on

- going implementation of the 2008 PM2.5 Plan, annual average PM2.5 concentrations in the Valley are expected to continue to improve and reach attainment in 2014.

During this same time period , 24

- hour PM2.5 design values in the Valley have also

decreased between approximately 30 and 5 0 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 Fr esno

.
Additional evaluations provide further insight into the annual and 24

- hour PM2.5

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

levels above the 24

- hour standard has decreased, there has been a corresponding

i ncrease in the fraction of days below the level of the annual standard of 15 ug/m 3

. Average concentrations during the winter months have decreased, and under similar meteorological conditions, peak 24

- hour concentrat ions during episodes are now

40 percent lower than they were ten years ago.

What are the attainment plan precursors?

Ambient PM2.5 is comprised of many different constituents and as a result there are multiple precursor pollutants that lead to PM2.5 formation (directly emitted PM2.5, NOx,

s ulfur oxides ( SOx )

, volatile organic compounds ( VOCs ) , and ammonia). The U.S. Environmental Protection Agency’s (U.S. EPA) PM2.5 implementation rule specifies that a precursor is considered “significant” for control strategy development

purposes when a significant reduction in the emissions of that precursor pollutant leads to a significant decrease in PM2.5 concentrations. Such pollutants are known asE

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

also establis hes a presumption that PM2.5, NOx, and SOx are attainment plan precursors, while VOCs and ammonia are not. For the annual PM2.5 plan, PM2.5, NOx, and SOx were identified and approved as the only attainment plan precursors by

U.S. EPA.

Given the large contribution of ammonium nitrate on 24

- hour PM2.5 exceedance days,

a number of different studies and analyses were evaluated to understand the role of VOCs and ammonia in ammonium nitrate formation in the San Joaquin Valley and to

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

24

- hour PM2.5 Plan. The amount of ammonium nitrate produced depends upon the relative atmospheric abundance of its precursors. It is therefore important to understand which precursor controls a re most effective in reducing ammonium nitrate concentrations. In simple terms, the precursor in shortest supply will limit how much ammonium nitrate is produced. This is known as the limiting precursor and controls of this precursor will have the most s ignificant benefits in reducing PM2.5 concentrations

.

The precursor assessment for the 24

- hour PM2.5 plan included evaluation of emissions inventories, monitoring studies, and photochemical modeling analyses of ammonium nitrate sensitivity to precursor emission reductions. While emissions inventory and monitoring data can indicate the relative abundance of the different precursors, photochemical models provide a quantitative approach to simulate the effects that emission reductions in each of gaseous pr ecursors would have on the predicted

ammonium nitrate concentrations

.

Evaluation of both emissions inventory and monitoring data concluded that the

ammonia

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

, comparable reductions in ammonia

were much less effective . Precursor sensitiv ity modeling conducted for the 2012 PM2.5 Plan showed that on a per ton basis, reduct ions in NOx are approximately nine times

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

trends show that reductions in NOx emission s, gaseous NOx concentrations, and particulate nitrate all track each other well.

Evaluation of monitoring studies also provided some evidence that VOCs could be important at times, however these studies were not conclusive. Therefore photochemical mode ling studies are more appropriate to assess the overall impact of VOC controls. These modeling studies found that at current NOx levels, further VOC emission reductions produce essentially no benefit, and in some instances may actually

lead to an increase in ammonium nitrate concentrations. Findings from these prior studies were supported by precursor sensitivity modeling conducted for the 2012 PM2.5 SIP, which indicated a very small disbenefit from reductions in VOCs.E

- 4 As noted previously, U.S. EPA’s PM 2.5 implementation rule directs SIP planning efforts and regulation to those pollutants generally known to significantly contribute to PM2.5 concentrations. Based on the weight of evidence presented from historical studies, coupled with the modeled precur sor sensitivity analyses conducted as part of the 2012

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

- hour

PM2.5 . Therefore the 2012 24

- hour PM2.5 plan attainment precursors are directly emitted PM2.5, NOx, and SOx.

When will the Valley attain the 24

- hour PM2.5 standard?

Consistent with U.S. EPA guidelines, air quality modeling was done to predict future PM2.5 concentrations at each monitoring site in the San Joaquin Valley. This modeling

shows attainment of the 24

- hour PM2.5 standard by 2019 in all counties except Kings

and Kern

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

, additional focused emission reductions are needed to provide for attainment. The modeling analysis includes new emiss ion reductions each year between now and 2019

from implementation of a combination of adopted ARB and District programs. As a result, most sites in the northern and c entral Valley are expected to attain prior to 2019.

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

program Valley wide , which would be designed to prevent wood burning on days that may lead up to a PM2.5 exceedance. The predicted design values for each site from this modeling scenario are shown in Table E

- 1.

Table E

- 1.

2019 Modeled 24

- hour PM2.5 Design Values with Enhanced Residential Wood
Burning Curtailment Program

.

Monitoring Site Design Value ( μ g/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 attainment, further reductions are still needed and will be provided through a measure to achieve additional emission reductions from

comme rcial cooking operations. Design value s at all other sites are well below

attainment levels.E

- 5 What is the attainment control strategy?

In order to determine the emission reductions needed to bring Bakersfield into attainment, ARB staff conducted additional modeling sensitivity runs to assess the relative efficacy of further reductions of different PM2.5 precursors. The current 24

- hour

PM 2.5 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

sensi tivity 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 implement ation of new reductions from California’s on

- going emission control programs will provide the majority of the emission reductions needed to attain the

24

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

value at the Ba kersfield

- California site must decrease by approximately 45 percent to demonstrate attainment. Between 2007, the base year used in the photochemical modeling attainment demonstration and 2019, implementation of these control programs will reduce NOx emiss ions by 55 percent. The weight of evidence analysis has demonstrated that prior reductions in NOx have resulted in commensurate

reductions in ambient concentrations of nitrate. This is consistent with modeled predictions that demonstrate a nearly 50 perc ent reduction in ammonium nitrate

concentrations.

In addition, while directly emitted PM2.5 emissions in aggregate are decreasing by nearly 30 percent, a major focus of the attainment control strategy is further curtailment of residential wood burning, along with implementation of a measure to reduce emissions from commercial cooking. District analysis has demonstrated the significant benefits of past implementation of wood burning curtailment. Further, examination of emission sources surrounding the B akersfield

- California monitor, and a modeling sensitivity run support the benefits of reducing emissions from cooking operation s

. The

final attainment demonstration for the Bakersfield

- California design site is provided in

Table E

- 2.E

- 6 Table E

- 2.

At tainment Demonstration for the Bakersfield

- California Design Value Site

.

2007 Design Value (ug/m3) 2019 Design Value

with Wood Burning Program

Enhancement (ug/m3) 2019 Final Design Value (ug/m3)

65.6 35.7 ≤ 35.4

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

or less than 35.4 μ g/m 3

.

Consideration of the entirety of information presented in the weight of evidence provides a consistent assessment that supports the modeled attainment date of 2019. The substantial continuing reductions that will result from implementation of the ongoing control program, coupled with new measures addressing residential wood burning and cooking, are consistent with the results predicted in the modeled attainment

demonstration.1 1. INTRODUCTION

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

2 4

- hour standard as expeditiously as practicable due to adopted and proposed control measures. As part of the attainment demonstration, the 2012 PM2.5 Plan specifically

identifies the : 1) most expeditious date for when the San Joaquin Va lle y (SJV or Valley) will attain the standard, 2) attainment plan precursors, 3) amount of emissions needed to attain, and 4) sources to control.

Following U.S. Environmental Protection Agency (U.S. EPA ) guidance and procedures, the attainment demonstration was conducted through a modeled attainment test. Photochemical modeling was used to identify the most expeditious attainment date, the relative benefits of controlling different PM2.5 precursor pollut ants, and the magnitude

of emission reductions needed from each pollutant. The Weight of Evidence (WOE) analysis provides a set of complementary a nalyses that supplement the req uired modeling.

A WOE approach looks at the entirety of the information at h and to provide a more informed basis for the attainment strategy. Because all methods have strengths and weaknesses, examining an air quality problem in a variety of ways offsets the limitations and uncertainty that are inherent in air quality modeling. This approach also provides a better understanding of the overall problem and the level and mix of emissions controls

needed for attainment.

The U.S. EPA recognizes the importance of a comprehensive assessment of air quality data and modeling and encourag es this type of broad assessment for all attainment demonstrations. In their modeling guidance, they further note that the results of supplementary analyses may be used in a WOE determination to show that attainment is likely despite modeled results which may be inco nclusive (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 range in mo deled design values reflects the uncertainty in predicting absolute PM2.5 concentrations that is inherent in air quality modeling, and therefore recognizes that an improved assessment of attainment can be derived from examining a broader set of analyses.

U.S. EPA recommends that three basic types of analyses be included to supplement the primary modeling analysis in the WOE approach: 1) analyses of trends in ambient air quality and emissions, 2) observational models and diagnostic analyses, and 3) addi tional modeling evaluations

. The scope of the WOE analysis is different for each nonattainment area. The level of detail appropriate for each area depends upon the complexity of the air quality problem, how far into the future the attainment deadline is, and the amount of data and modeling available. For example, less analysis is needed for an area that is projecting attainment near

- term and by a wide margin, and for which recent air quality trends have demonstrated significant progress, than for areas w ith more severe air quality challenges2 The following sections present the WOE assessment that supports the attainment

demonstration the 24

- hour PM2.5 standard in the San Joaquin Valley

.3 2. PM2.5 STANDARDS AND HEALTH EFFECTS

PM2.5 is a complex mixture of particles and liquid droplets that vary in size and chemical composition. As a subset of PM10, particles with diameters up to

10 micrometers

, PM2.5 comprises particles with diameter s up to 2.5 micrometers ( Figure 1 ). PM2.5 c ontains a diverse set of substances including elements such as carbon and metals, compounds such as nitrates, sulfates, and organic materials , and complex mixtures such as diesel exhaust and soil or dust

. Some of the particles are directly emitted into th e atmosphere. Others, referred to as secondary particles, result when gases are transformed into particles through physical and chemical processes in the atmosphere

.

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

.

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

asthma attack s, development of chronic bronchitis, decreased lung function in children, increased respiratory and cardiovascular hospitalizations, and eve n prematur e death in people with existing cardiac or respiratory disease. In addition, California has identified particulate exhaust from diesel engines as a toxic air contaminant

– suspected to cause

cancer, other serious illnesses , and premature death

. Those mos t sensitive to PM2.5 pollution include people with existing respiratory and cardiac problems, children

, and

older adults

.

A mbient air quality standards establish the levels above which PM2.5 may cause adverse health effects. In 1997, U.S. EPA adopted th e first set of PM2.5 air quality standards, an annual standard of 15 μ g/m 3 and a 24

- hour standard of 65 μ g/m 3

. To address the 1997 PM2.5 standards, the San Joaquin Valley Air Pollution Control District (SJVAPCD or District ) adopted the 2008 PM2.5 Plan. At the time of plan development,

the San Joaquin V alley 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 20 11 (76 FR 41338; 76 FR 69896). In 2006, U.S. EPA tightened the 24

- hour standard to

35 μ g/m 3 . Attainment of this standard is the focus of the SJV 2012 PM2.5 Plan

.4 3. MONITORING IN THE SAN JOAQUIN VALLEY

a. Established monitoring network

An extensive network of PM2.5 monitors throughout the SJV provides data to assess compliance with ambient air quality standards and to study the natu re of ambient PM2.5. Currently, the network comprises 21 monitoring sites . Many sites include multiple monitoring in struments running in parallel. S even sites operate Federal

Reference M onitors (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 the se continuous monitors are Federal Equivalent Monitors (FEM), whi ch can also be used to assess compliance with the standards. The FRM and FEM monitoring sites are shown in Figure 2. The location s of these monitors are designed to capture population exposure. In addition, data collected at these monitors serve to repo rt air quality conditions to the public, and support forecasting for the District’s agricultural and residential burning curtailment programs. Finally, four sites have chemical speciation monitors. The speciation monitors collect samples that

are further analyzed in the laboratory to determine the chemical make

- up of PM2.5.

Figure 2

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

, October 2012)

.

b. Extensive field studies

T he San Joaquin Valley is one of the most studied areas in the world with an extensive number of publications in peer

- reviewed international scientific/technical jou rnals and other major reports. Since 1970, close to 20 m ajor field studies have been conducted in

the Valley and surrounding areas that have elucidated various aspects of the nature and5 causes of ozone and particulate matter

. A comprehensive listing of publications (reports and peer

- reviewed journal articles) is provided in Appendix 1

.

The first major study specifically focused on particulat e matter was the Integrated Monitoring Study in 1995 (IMS

- 95), which was the pilot study for the subsequent California Regional Particulates Air Quality Study (CRPAQS) in 2000 (Solomon and Magliano, 1998). IMS

- 95 formed the technical basis for the SJV 2003 PM 10 Plan that

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

- designated as attainment in 2008 (73 FR 66759). CRPAQS was a key component of the technical foundation for the SJV 2008 PM2.5 Plan that U.S. EPA

app roved 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

.

C RPAQS was a public/private par tnership designed to advance the understanding of

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

The s tudy included monitoring at over 100 sites (Figure 3) as well as data analysis and modeling, results of which have been published in over 60 papers and presented at

national and international conferences

. The field campaign was carried out between December 1999 and February 2001. CRPAQS improved our understanding of the spatial and temporal distribution of PM2.5 in the Valley, its chemical composition, transport and transformation processes , and contributing sources. More details on
CRPAQS can be found at the following link: http://www.arb.ca.gov/airways/ccaqs.htm.

Figure 3. CRPAQS monitoring p rogram

.

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

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

scientific founda tion for the WOE analysis supporting the 24

- hour PM2.5 standard

attainment demonstration

. S pecific findings are integrated into the various WOE analysis sections of this document

.6 Fu rther fiel d studies relevant to PM2.5 include the California portion of the Ar c tic Research of the Composition of the Troposphere (ARC TAS

- CARB) w hich took place in

2008 (Jacob, et al., 2010) and Research at the Nexus of Air Qu ality and Climate (CalNex2010 ) conducted in 2010 ( www.esrl.noaa.gov/csd/calnex/ ). The monitoring operations fo r both studies occurred during the early to mid

- summer and extended over Southern California and the Central Valley

. Some study findings have been published (e.g., Kaduwela and Cai, 2009, Cai and K aduwela, 2011, Kelly et al., 2011) , but d ata analys i s is s till in progress.7 4. NATURE AND EXTENT OF THE PM2.5 PROBLEM

a. Current a ir q uality

The geography of the San Joaquin Valley

, along with large

- scale regional and local weather patterns, influence the accumulation, formation and, dispersion of air pollutants. Covering nearly 25,000 square miles , t he Valley is a lowland area bordered by the Sierra Nevada Mountains to the east, the Pacific Coast range to the west, and the Tehachapi Mountain s to the south . The mountains act as air flow barriers , with the resulting stagnant conditions favoring the accumulation of pollutants

. To the north, the Valley borders the Sacramento Valley and Delta lowland, which allows for some level of pollutant dis persion. As a result of geography and meteorology , PM2.5 concentrations are generally higher in the southern and central portions of the Valley.

To determine attainment for the 24

- hour standard, the design value at each monitoring

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

a three

- year average of the 98 th percentile of the measured 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 μg/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 desi gn values above the standard, although design values are generally lower in the northern and central Valley. Urban sites in the Fresno and

Bakersfield areas register t he higher design values.

F igure 4

. 2011 24

- hour design values

b. Seasonal v ariability

PM2.5 concentrations in the San Joaquin Valley exhibit a strong seasonal pattern, with highest concentrations occurring from November through February (Figure 5 ). During the winter, PM2.5 builds up over several days or weeks. These PM2.5 episodes are

c aused by increased activity in some emission sources and by meteorological 0 10 20 30 40 50 60 70PM2.5 Design Value (μg/m3) 2011 24

- Hour PM2.5 Design Values

Standard8 conditions that are conducive to the build

- up and formation of PM2.5. During the winter, high

- pressur e weather systems over California can cause the atmosphere to become
stagnant f or extended periods lead ing to temperature inversions. Under normal

conditions

, temperature decreases with altitude, allowing free upward air flow and dispersing emissions and pollutants. In contrast, a temperature inversion positions a layer of warm er a ir above cooler air

, impeding upward flow of emissions and air pollutants. Often the inversion layer is lower than the mountains surrounding the Valley, trapping emission s and pollutants.

F igure 5

. Seasonal variation in PM2.5 concentrations at Bakersfield

- California

.

c. Diurnal variability

During the winter, PM 2.5 levels in the San Jo aquin V alley also vary significantly across the 24

- hour period

. For example, in urban Fresno

, the highest PM2.5 concentrations occur during the night ( Figure 6 ) . Peak evening concentrations generally reflect the influence of lowering inversion heights which trap pollutants close to the surface, as well

as increased activit y from evening commute traffic a nd residential wood combustion.

The smaller peak of PM2.5 concentrations observed during mid

- day is due in part to traffic activity

, but mostly reflects secondary pollutant formation and PM2.5 formed above the inversion layer from previous day ’s emissions that mix back to the surface during the day

. 0 10 20 30 40 50

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

Month 2009

- 2011 Monthly Average PM2.5

Concentration at Bakersfield

- California9 Figur e 6

. Variation in hourly PM2.5 concentrat ions during the winter at Fresno

- 1 st

.

d. C hemical c omposition

Examination of the chemical make

- up of PM2.5 on days exceeding the daily standard provides a nother important element in understanding the nature of PM2.5 in the Valley

and contributing sources

. The pie charts in Figure 7 show the current chemical components that contribute to PM2.5 on days that exceed the standard at urban sites in

the southern ( Bakersfield )

, central ( Fresno )

, and northern ( Modesto ) regions of the Valley. These sites currently record the highest PM2.5 concentrations in their corresponding region s

. While the relative percentages vary, i n all cases the major
components are ammoni um nitrate and or ganic material (organic carbon).

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

. A t Bakersfield, a mmonium nitrate constitutes about 65 percent of PM2.5, while at

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

atm osphere from chemical reactions of NOx and ammonia. Sources emitting NOx

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

ammonia are livestock operations, fe rtilizer application, and mobile

. The s tagnant, cold

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

accumulation of ammonium nitrate. Additional information on ammonium nitrate

fo r mation can be found in section 5

.

The organic matter component of PM2.5 is largest in the central and northern portions of the Valley . Organic matter constitutes about 30 percent of PM2.5 at Modesto and

Fresno compared to less than 20 percent at Bakersfield. A ctivities such as residential wood combustion, c ooking, 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 pe rcent 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

2009-201110 combustion sources. Elemental carbon results from mobile and stationary combustion

sources , with significant contributions from diesel sources

.

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

about two per cent at Modesto and Fresno . Geological material comes from dust suspended into the air by vehicle travel on roads , soil from agricultural activities, and

other dus t producing activities such as construction

.

Figure 7

. 2009

- 2011 average peak day PM2.5 chemical composition at a) Bakersfield

, b) Fresno, and c) Modesto

.

Ammon.

Nitrate

65% Ammon.
Sulfate

7% Organic

Matter

17% Elemental

Carbon

5% Geologic.

5% Elements

1% a) 2009

- 2011 Peak Day PM2.5

Composition at Bakersfield

Ammon.

Nitrate

54%

Ammon.
Sulfate

6% Organic

Matter

29% Elemental

Carbon

6% Geologic.

2% Elements

3% b) 2009

- 2011 Peak Day PM2.5

Composition at Fresno

- 1st

Ammon.

Nitrate

56%

Ammon.
Sulfate

6% Organic

Material

28%

Elemental

Carbon

6% Geologic.

2% Elements

2% c) 2009

- 2011 Peak Day PM2.5 Composition at Modesto11 e. Spatial distribution of the major PM2.5 components; local versus regional

As noted previously, h igh PM2.5 concentrations in the Valley occur almost exclusively during multiday pollution episodes under stagnant winter weather conditions

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

last several weeks. Once the weather conditions conducive to an episode set in, PM2.5 concentrations increase due to the accumulation of primary pollutants and forma tion of secondary pollutants.

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

concentrations of nitrate can occur over large regions, including both urban and rural areas ( Figure 8 ). As shown in Figure 9, ammonia i s mostly concentrated in rural areas, particularly between Fresno and Bakersfield. On the other hand, high concentrations of organic carbon are more localized around urban sites, especially Fresno, with lower concentrations at rural sites ( Figure 10 ).

T he differences between the regional and local component can be traced back to the emission sources and subsequent formation and transport processes for each chemical component. Gaseous precursors of ammonium nitrate (NOx and ammonia) are transported much more efficiently than directly emitted organic matter particles (Ying and Kleeman, 20 09). Although, some of the emitted NOx forms ammo nium nitrate in urban areas, it is also transported to downwind regions where it reacts with ammonia to form particulate ammonium nitrate in the rural areas

. While transport does occur, the distances are still relatively limited, with tr ansport distances of 50 to 60 kilometers in the

central and southern Valley. Ying et.al. (2009) found for example that most of the PM2.5 nitrate in Bakersfield is produced from sources within the southern Valley.

In contrast, c arbonaceous aerosols are emitted into the atmosphere as particles and have a shorter lifetime due to higher deposition rat e s. U nder stag nant conditions they can only be transported a short distance and therefore, have the greatest impact locally. Transport distances for carbonaceous aerosols dur ing CRPAQS were only 20 to 40

kilometers

. Due to this localized organic carbon increment, which adds to the more regional ammonium nitrate concentrations, the highe st PM2.5 conce n trations occur at

urban sites.12 Figure 9

. Spatial distribution of annual ammonia ( NH 3 ) concentrations (2/1/2000

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

.

Figure 8. Spatial distribution of winter a m monium nitrate concentrations measured during CRPAQS ( Chow et al., 2005 ).13 f. Episode d evelopment

The development of PM2.5 episodes in the Valley is strongly controlled by meteorological conditions. The rate of concentration buildup depends on the intensity of atmospheric stability, with concentrations building up faster at urban sites than at rural

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

rate between an urban (Fresno) and a rural (Selma) site in the Fresno area during CRPAQS. Although urban sites reach the highest overall c oncentrations, 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 winter organic carbon concentration measured during CRPAQS (Chow et al., 2005).14 The rate of buildup and the differences between urban and rural sites can be explained by the differential contributions of ammonium nitrate and organic carbon. T hrough out the duration of an episode, ammonium nitrate concentrations tend to build to a plateau

that is maintained until a weather fron t breaks the stagnation

, causing the levels to

decrease

. Figure 12 illustrat es the buildup of ammonium nitrate concentrations measured during the 2000/2001 PM2.5 episode in Bakersfield. This ammonium nitrate buildup generally begins in urban areas, followed by a buildup in rural areas as urban

NOx is mixed downwind and reacts wit h rural ammonia. In contrast, organic carbon is largest in urban areas, and tends to be more stable across an episode, although individual peaks can occur during periods of enhanced wood burning such as weekends and holidays. The combination of early amm onium nitrate buildup along with the urban organic carbon increment results in the highest concentrations being observed in urban

areas. The abrupt decrease in concentrations on January 8 th was due to the passage of a cold front effectively ending the PM2 .5 episode.

Figure 1 2

. 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.15 5. SECONDARY AMMONIUM NITRATE FORMATION

a. Chemistry

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

months are conducive to ammonium nitrate formation through a complex process involving NOx, ammonia, and VOCs. This occurs both at the surface and aloft, via both daytime and nighttime chemistry. U nderstanding the interactions amongst these precursors is needed to design an appropriate 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

ba ckground 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 reactions 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 NO x to nitric acid formation involves multiple steps and also depends on the availability of o xidants, only a portion of the NO x emitted ultimately forms ammonium nitrate. An early photochemical modeling study applying a box model to a typical winter episode in the San Joaquin Valley found that approximately

33 percent of the molecules of emitted NOx were converted to ammonium nitrate (Stockwell et. al. 2000). A subsequent study that modeled the January 4

- 6, 1996 episode in the San Joaquin Valley with the University California Davis/California Institute of Technology ( UCD

- CIT ) photochemical transport model found that on average, only 13 to 18 percent of the emitted NOx (expressed as NO 2 ) was converted to ammonium nitrate (Kleeman et. al. 2005). The fraction of NOx converted varied by location, with urban regions converting lit tle NOx to ammonium nitrate, while in remote areas up to 70 percent NOx was converted.

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

, while ammonia is primarily generated from livestock operation s,16 fertilizer application, and mobile sources . Analysis of CRPAQS measurements suggest

that

, on average, daytime production of nitric acid in the San Joaquin Valley is relatively slow, and that nighttime production is the more dominant pathway (Lurmann et al. 2006). Although daytime mixing is limited, NOx and ammonia emitted during the day can be mixed upward where nighttime interactions can occur more regionally to form

ammonium nitrate. Based on analyses conducted to characterize the atmospheric
transp ort and dispersion processes during the winte r CRPAQS episodes, MacDonald

et al. (2006) found that the ammonium nitrate that is formed aloft during the night is subsequently entrained into the daytime boundary layer. This was observed through a rapid rise in hourly ammonium nitrate concentrations which coincided with the growth of the surface mixed layer (Watson and Chow 2002). These mechanisms help explain the more regional distribution of ammonium nitrate that is observed throughout the Valley.

b. Limit ing precursor concept

The amount of ammonium nitrate 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 controls are most effective in reduc ing ammonium nitrate concentrations. In simple terms

, the precursor in shortest supply will limit how much ammonium nitrate is produced. This is known as the “limiting” precursor. The following figures provide an illustration of this concept. As shown in Figure 13 , each molecule of ammonia pairs with one NOx molecule to produce one

molecule of ammonium nitrate. In this example, there are more ammonia molecules than NOx, and therefore not all of the ammonia partic i pates 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 ni trate. In contrast, Figure 15 illustrates that a larger reduction

in the more abundant precursor, ammonia, results in no reduction in ammonium nitrate, as the ammonia reduced did not participate in ammonium nitrate production.

Figure 13. Ammonium nitrate formation.17 Figure 14. Reducing the less abundant precursor is more effective in reducing

ammonium nitrate

.

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

ammonium nitrate.

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

c. Role of ammonia in ammonium nitrate formation

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

ammonia in ammonium nitrate form ation in the San Joaquin Valley. These included: a) comparison of the magnitude of the NOx and ammonia emissions inventories, b) ambient measurements of ammonia, nitric acid, and particulate ammonium; and c) photochemical modeling analyses of ammonium nitrate sensitivity to precursor emission reductions. While evaluation of emissions inventory and ambient data can provide indications of the relative abundance of different precursors, photochemical models provide a tool to quantitatively evaluate the i mpact of reducing precursor emissions on resulting ammonium nitrate concentrations.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 assessment of the relative abundance of th ese two precursors. Table 1 lists NOx and ammonia winter emissions in the current inventory for three years (2000, 2011, and 2019). As Figure 13 in the limiting precursor section illustrated, in simple terms it takes one molecule of NOx and one molecule of ammonia to form one molecule of ammonium nitrate. However, due to differing molecular weights, one ton of NOx contains fewer molecules than one ton of ammonia. Therefore it is most appropriate to make an emissions inventory comparison after

normalizin g 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 tim es (46 u/17 u) the

number of molecules as one ton of NO 2 . Dividing the NOx emissions by 2.7 therefore provides a common basis for comparison to the ammonia emissions. On this normalized comparison basis, ammonia is significantly more abundant than NOx, particularly in future years (Table 1). In addition, as noted in the chemistry section, only a portion on the NOx is ultimately converted to ammonium nitrate.

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

Year Winter 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 concentrations provide another method to investigate the relative abundance of each precursor and therefore which is most

effective for control of ammonium nitrate

. Blancha rd, et al. (2000) examined two metrics using ambient data collected during the IMS

- 95 field program in the San Joaquin Valley. The first parameter wa s the excess of particulate ammonium plus gas

phase ammonia over the sum of nitric acid, particulate nitrate, and particulate sulfate. The second was the ratio of particulate to total nitrate concentrations. Both metrics

indicated an excess of ammonia in most IMS

- 95 samples and concluded that greater

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

ammonia.19 Lurmann, et al. (2006) also compared ammonia and nitric acid ambient concentrations

measured in the San Joaquin V alley during the winter of 2000/2001 as part of
CRPAQS

. Figures 16 and 17 show the concentrations of nitric acid and ammonia measured at the rural Angiola site and at the urban Fresno site. At both sites ammonia concentrations are generally at least an o rder of magnitude higher than the nitric acid

concentrations. These ammonia

- rich conditions throughout the Valley indicate that, during the winter, nitric acid rather than ammonia is the limiting precursor.

Figure 16. Comparison of ammonia and nitric acid concentrations measured at Angiola during the winter of 2000/2001 as p art 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 Line20 Figure 17. Comparison of ammonia and nitric acid concentrations measured at Fresno during the winter of 2000/2001 as p art of CRPAQS

.

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

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

were collected at a larger number of sites during CRPAQS. Figure 18 shows the concentrations of particulate ammonium and gaseous ammonia at three urban sites (Fresno

- 1 st , Bakersfield

- California, and Bakersfield

- residential), and three rural sites (Angiola, Pixley, and Fee dlot) measured during the 2000/2001 winter CRPAQS episode. Overall, the levels of particulate ammonium at all sites are comparable, consistent with a regional formation mechanism of ammonium nitrate. Although ammonia concentrations are higher at the rura l sites, especially at the Feedlot site, there is still a large amount of ammonia at each site beyond the amount that reacted with nitric acid to form ammonium nitrate. Again, these ammonia rich conditions indicate that nitric acid, rather than ammonia is the limiting precursor. Comparison of Ammonia and Nitric Acid Concentrations

Fresno - Winter 2000/2001

-4 0 4 8 12 16 20

-2 0 2 4 6 8

HNO3 Concentration (ug-N/m 3 )NH3 Concentration (ug-N/m3)

1:1 Line21 Figure 18. Comparison of particulate ammonium and gaseous a mmonia concentrations measured throughout the SJV during the winter of 2000/2001 as p art of
CRPAQS.

Photochemical Modeling

In contrast to the previous analyses, photochemical models provide a quantitative approach to simulate the effects that emission reductions in each of the gaseous precursors would have on the predicted ammonium nitrate concentrations. A number of

modelin g studies have been conducted by ARB staff and academic researchers to evaluate precursor sensitivity.

An investigation of precursor limitations for the January 4

- 6, 1996 PM2.5 episode measured in San Joaquin Valley as part of the IMS

- 95 field study use d the UCD

- CIT model. This sensitivity analysis revealed that NOx controls were the most effective control strategy to reduce PM2.5 ammonium nitr ate concentrations (Kleeman, et al. 2005). In this study, a 50 percent reduction in NOx emissions resulted in a 25 percent reduction in total nitrate, while a 50 percent reduction in ammonia emissions resulted in

a 10 percent reduction in total nitrate. The results of this analysis are shown graphically across the entire San Joaquin Valley in Figure 19. Rural Urban22 Fi gure 19

. Particulate nitra te reductions in response to 50 percent reductions in precursor emissions on January 6, 1996.

In 2006, ARB staff modeled air quality during the thr ee week winter CRPAQS episode using U.S. EPA’s Community Multiscale Air Quality (CMAQ) model with California

specific modifications and corrections ( Liang et al. 2006 ). Figure 20 illustrates the effects that reducing the emissions of ammonia and NOx ha ve on ammonium nitrate levels. This modeling indicated that reducing ammonia emissions by 50 percent23 red uced ammonium nitrate by less than 5 percent. On the other hand, reducing NOx emission by 50 percent reduce d ammonium nitrate concentrations by approximately

35 percent. This analysis

, therefore

, indicate d that reducing NOx emissions was the most beneficial control strategy to reduce ammonium nitrate.

Figure 20. Percent ammonium nitrate reduction in response to 50 percent reduction in NOx or ammonia emission reductions at Fresno during the winter of 2000 /2001

.

In another study based on sensitivity analyses using CMAQ

- Madrid simulations of the

Decem ber 2000 CRPAQS episode, Pun et al. (2009) found that a 50 percent reduction in NOx emissions reduced ammonium nitrate by approximately 50 per cent at rural sites

and between 30

- 45 percent at Ba kersfield. As shown in Figure 19 , a 50 percent reduction in ammonia emissions did not have a significant effect on ammonium nitrate

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

concentrations decreased between 10 and 25 percent. However, such reductions in ammonium nitrate occurred only at the end of the episode, when PM2.5 concentrations at the rural site reached approximately 80 μg/m 3 and urban co ncentrations peaked at

over 11 0 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 therefore ammonium nitrate formation was generally limited b y the availability of nitric acid rather than ammonia. -60% -50% -40% -30% -20% -10%

0%Percent Nitrate Reduced

50% NOx Reduction 50% Ammonia Reduction24 Figure 21. Time series with daily observations, base case simulation results and results from the sensitivity cases of (a) nitrate and (b) PM2.5 at Angiola (left) and Bakersfield (right). (Source: Pun et al., 2009, excerpt from Figure 2, pg

. 406)

.

Taken together, the emission inventory, monitoring data, and precursor sensitivity analyses all indicate that in the San Joaquin Valley

, NOx, rather than ammonia is the limiting precursor fo r ammonium nitrate formation.

d. Role of VOC in ammonium nitrate formation

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

ammonium nitrate formation. These include both monitoring studies conducted as part
of CRPAQS , as well as studies that used differing types of air quality modeling to quantitatively assess the expected change in ammonium nitrate to hypothetical VOC

reductions.

Monitoring studies

As previously mentioned, t here 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 nitric acid formation pathway involves sunlight, VOCs, and background ozone. During the night, nitri c acid is formed25 through oxidation of NO 2 (via N 2 O 5 ) by background ozone, which then also reacts with ammonia to form ammonium nitrate. Studies by Pun et al. (1998, 2004) suggested that the daytime pathway may be important and therefore the formation of a mmonium nitrate

would be sensitive to changes in VOC emissions. However, other studies (Lurmann et al., 2006), suggest that on average, daytime production of nitric acid in the San Joaquin Valley is relatively slow and that nighttime production of ammoniu m nitrate aloft, which then mixes to the surface after sunrise could explain the observed homogeneous patterns of ammonium nitrate in the Valley. Ying et al. (2009) also theorized that the ozone concentration aloft in the San Joaquin Valley is predominan tly due to the regional background and does not vary significantly with surface

- level VOC emissions. Therefore, nighttime ammonium nitrate formation in the San Joaquin Valley would not

be sensitive to VOC reductions.

W hile the monitoring studies cited a bove provide evidence that the VOC pathway may be important at times, these studies do not provide quantitative information about the

overall role of and cannot be used to evaluate the benefits of

, VOC controls. Rather, modeling studies are more appropria te to assess the overall impact of precursor

controls.

Photochemical Modeling

Sta ff reviewed the results of six modeling studies containing information on the significance of VOC controls in reducing ammonium nitrate in the San Joaquin Valley

. While the results of the earliest studies were mixed, later studies provide generally consistent results regarding the role of VOCs. In assessing the potential benefits of VOC controls it is important that significance be interpreted in the context of California’s overall control program with its strong focus on NOx control to achieve benefits for both

PM2.5 and ozone.

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

nitrate to VOC and NOx reductions. One of the two studies simulated a typical winter episode (Stockwell et al., 2000) and found that decreases in VOC emissions had little effect. The second study (Pun and Seigneur, 2001) simulated winter conditions during

the 1996 IMS

- 95 pilot study around the Fresno area. The study found that ammonium nitrate formation decreased with VOC emission reductions, but increased with NOx reductions. Pun and Seigneur (2001) theorized that reducing NOx could lead to higher

concentrations of the hydroxyl radical ( OH ) and inc rease the overall rate of nitrate production, despite the reductions in NOx. However, the box modeling approach used

ha d a number of limitations, including lack of transport into/out of the box, robust vertical transport, and use of an older chemical mech anism. In addition, the VOC emissions were increased by a factor of two to improve model performance. As such, the box modeling did not fully represent the complete scope of atmospheric variations and has limited usefulness in assessing the responsivenes s to VOC controls.26 Subsequent modeling sensitivity studies for the same winter episode were conducted

with the UCD

- CIT model, an advanced research grade modeling system (Kleeman et al., 2005). The authors concluded that NOx emission controls are more ef fective in reducing PM2.5 nitrate concentrations in the San Joaquin Valley. Summary study results indicate that on average, large reductions in VO C emissions (on the order of

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

H owever, to evaluate the significance and effectiveness of VOC controls in the context of control strategy design, the study’s isopleths of PM2.5 nitrate response to combined NOx/VOC emission reductions provide more in

- depth information.

Figures 22 (a) and 23 (a) show that, based on the shapes of the graphs, NOx controls are the most effective approach to reduce PM2.5 nitrate concentrations at Fresno and at the location with the highest modeled PM2.5 nitrate concentration (grid location

- 85 km Northing, 90 km Easting) respectively. Once NOx controls are taken into consideration, VOC emission reductions produce essentially no benefit , and in some

instances may actually lead to an increase in PM2.5 nitrate concentrations. For example, as illustrated in Figure 22 (a) for Fresno, after considering an approximately

70 percent reduction in NOx emissions resulting from existing and proposed controls, reductions in VOC emissions to any level would not decrease PM2.5 nitrate

concentrations. Furthermore, at gri d location

- 85 km Northing, 90 km Easting (Figure 23 (a)) , any level of VOC emission reductions would actually cause an increase in nitrate concentrations. Nitrogen

- containing molecules such as PAN can act as temporary sinks for nitrogen dioxide ( NO 2 ) . W hen VOCs are controlled, the reduced availability of certain radicals

, which are generated from VOCs

, reduces the amount of

NO 2 that is sequestered, thereby increasing the availability of NO 2 and enhancing ammonium nitrate formation (Meng et al., 1997)

.27 Figure 22. 24

- h our average NOx/VOC particulate nitrate isopleths at Fresno for (a) all sources, (b) diesel engines, (c) catalyst equipped gasoline engines, and (d) upwind sources of nitrate. Units are μg/m 3 . (Source: Kleeman et al., 2005, Figure 3 pg

. 5333).28 Figure 23

. 24

- h our average NOx/VOC particulate nitrate isopleths at grid

location

- 85 km Northing, 90 km Easting for (a) all s ources, (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 during the CRPAQS field study.

In the first study, preliminary dat a from modeling of this CRPAQS winter episode conducted using the Lagrangian form of the UCD

- CIT model qualitatively confirm that NOx control is the most efficient method to reduce nitrate concentrations (Kleeman, M.J., personal communication, May 2008). Figure 24 illustrates the response of PM2.5 nitrate concentrations to NOx and VOC emission reductions at a rural (Angiola) and an urban (Fresno) site on December 31, 2000. Again, based on their shapes, these graphs show that NOx controls are the most effe ctive approach to reduce PM2.5 nitrate29 concentrations. Once NOx controls are taken into consideration (approximately

7 0 percent reduction in NOx emissions), reductions in VOCs of up to 30 percent produce basically no benefit (Fresno). Furthermore , at som e locations (Angiola) any VOC emission reductions may actually lead to an increase in PM2.5 nitrate

concentrations.

Figure 24. The isopleths plot of PM2.5 nitrate with emission control of NOx and VOC at Angiola (ANG) and Fresno (FEI) after a five

- day bac k tra jectory 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 preliminary emissions inventory.

This sim ulation showed that 50 percent reductions in anthropogenic VOC and NOx emissions had similar effects in reducing ammonium nitrate (about 20 percent each). A second simulation was conducted using an updated emission inventory representing a

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

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. T he respo nse was lowered to a 12 percent reduction in ammonium nitrate, with a corresponding increase in responsiveness to NOx control of 38 percent reduction

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

2005.

A third stud y modeled one week of the CRPAQS episode using a version of CMAQ with a more advanced chemical mechanism (CMAQ

- Madrid) (Pun et al, 2009). In contrast to the earlier Pun study using a simplified box modeling approach, this later work found30 that on average, nitrate was most sensitive to reductions in NOx emissions. While isopleths were not provided, the time evolution of nitrate and PM2.5 mass to VOC response illustrated in Figure 25 provides further detail s regarding the efficacy of VOC control. The respo nse of nitrate to a 50 percent reduction in VOC emissions increased as PM2.5 levels rose during the episode. In urban areas, a 50 percent reduction in anthropogenic VOC emissions caused small reductions in nitrate, on the order of

10 percent

, on the model ed 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 concentrations may be due to a difference in the chemical formation regime for nitrate. In general, there is sufficient background ozone to generate enough free radicals to initiate and propagate the chemistry of nitrate formation (Ying et. al, 2009). However, on days wit h 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 da ys mainly because rapid increases in ozone were not observed. The effect of VOC levels on nitrate formation may also have a diurnal pattern since the hydroxyl and hydroperoxyl radical levels are high during the daytime and negligible at night. In additio n, more reactive VOCs react quickly during the day and there is a minimal carry over to the next day. Therefore, it is reasonable to assume that the higher response to VOC and NOx at higher PM2.5 concentrations may

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

Overall, nitrate was only responsive to a 50 percent reduction in VOCs at PM2.5 concentration levels that are no longer reache d in the San Joaquin Valley

. Currently,

the 24

- hour PM2.5 design value 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 believe the Valley is now in a nit rate chemical formation regime that is less responsive to VOC controls.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 quality modeling studies indicate that in the San Joaquin Valley

, NOx, rather than VOCs

, is the limiting precursor for nitric acid, and subsequent

ammonium nitrate formation.32 6. SECONDARY ORGANIC AEROSOL FORMATION

VOC emissions also have the potential to contribute to secondary organic aerosols (SOA). While these components contribute to o bserved PM2.5 concentrations in the San Joaquin Valley to a small degree, the weight of evidence indicates that anthropogenic VOC is not a significant contributor to PM2.5.

SOA form when intermediate molecular weight VOCs

, emitted by anthropogenic and biog enic sources

, react and condense in the atmosphere to become aerosols. In addition, lighter VOCs participate in the formation of atmospheric oxidants which then participate in the formation of SOA. T he processes of SOA formation are complex and

have not been fully characterized

. The apportionment of PM2.5 organic carbon to primary and secondary components is a very active area of current research.

Us ing the UCD

- CIT model, Chen et al. (2010) investigated the apportionment of PM 2.5 organic carbon for the 2000/2001 CRPAQS episode. From the total predicted PM 2.5 organic carbon in the urban Fresno and Bakersfield areas, six percent and four percent

were SOA, respectively, while in the rural Angiola area, 37 percent was SOA. The major SOA precursors of secon dary organic aerosol were long

- chain alkanes followed by aromatic compounds. The sources of these precursors were solvent use, catalyst gasoline engines, wood s moke, non

- catalyst gasoline engines, and other anthropogenic sources, in that order.

In contra st, o n an annual average basis, secondary organic aerosols derived from anthropogenic VOC emissions account for only one to two percent of the annual total

PM2.5 concent rations throughout the Valley. ARB air quality modeling exercises

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

the CMAQ model showed that primary PM2.5 emissions are the main contributor to organic aerosols and SOA contribute to only a small extent. Further more, as illustrated

in Figure 26

, SOA are mostly formed during the summertime, when total PM2.5 concentrations are low, and are mainly derived fr om biogenic emission sources. On an annual average basis, SOA derived from anthropogenic VOC emissions are a small part

of the o rganic aerosol concentrations (three to five percent).33 Figure 26

. Daily contributions to organic aerosol concentrations in Bakersfield in 2000 modeled with CMAQ: Primary organic aerosols (PA), secondary aerosols formed from biogenic VOC emissions (SB) and secondary aerosols formed from anthropogenic

source VOC emissio ns (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 concentrations were dominate d by directly

emit ted (primary ) emissions. The study found that, because of the dominance of prima ry PM2.5 organic matter , a 50 percent reduction in anthropogenic VOC emissions has limited effects on the modeled PM2.5 organic matter (Pun, et al., 2009).

T hese study results show that for secondary organic aerosols, further VOC reductions would have very limited effectiveness in reducing PM2.5 concentrations. BAC

0 2 4 6 8 10 12 141163146617691106121136151166181196211226241256271286301316331346361 SB

SA

PA34 7. EMISSION SOURCES OF WINTERTIME PM2.5

a. Emission i nventory

Emission inventories provide emission estimates for sources of di rectly emitted (primary) PM2.5 and of each of the gaseous precursors of secondary PM2.5 (NOx, SOx, and ammonia)

. Table 2 lists the main PM2.5 components and links them to their largest emission sources based on the 2011 San Joaquin Valley emission inventory

data. Emission sources are lis ted in descending order of magnitude

.

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

, contributing about 55 to 65 percent of PM2.5. It is formed in the atmosphere from reactions of NOx and ammonia. Heavy

- duty diesel vehicles (trucks) emit most of the NOx, followed by off

- road equipment, light

- duty vehicles

, and trains. Ammonia is primarily emitted from livestock husbandry

, f ertilizer application , and mobile sources

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

about five percent to PM2.5. SOx is mostly emitted from fuel combustion sources in oil and industrial manufacturing processes. Organic carbon, which contributes about 20 to

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

PM2.5

, are directly emitted , with key sources being residential fuel combustion, managed burning, diesel trucks, and commercial cooking operations . Geological, a minor component contributing about two to five percent of the PM2.5 mass , is directly emitted from activities generating dust, such as farming operations and on

- road and off

road vehicle travel, as well as wind

- blown dus t. It should be noted that while wind

blown dust may contribute on some winter days, PM2.5 exceedances primarily occur on very stagnant days when windblown dust emissions are minimal.

While emission inventories provide a broad overview of Valley wide an d county level sources, additional methods using ambient data and source apportionm ent modeling provide supplemental information on the sources directly impacting individual monitoring sites. The following sections describe these analyses.35 Table 2. Main emission sources of PM2.5 components.

PM2.5 Component ( percent of PM2.5) Process Emission Sources

Ammonium nitrate

(about 5 5

- 65 percent) Formed in the atmosphere from the

reactio ns 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 : L ivestock husbandry

, fertilizer application

, and mobile sources account for over
90 percent of the 2011 winter ammonia

emissions.

Ammonium sulfate

(about 5 percent) F ormed in the atmosphere from the

reaction s of SOx and

ammonia emissions SOx : Fuel combustion in oil production, at electric utilities, and in manufacturing and industria l boilers, heaters, and engines, manufacturing of chemi cals and glass related products, residential wood combustion, and aircraft account for about

75 percent of the 201 1 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.36 b. C hemical markers of source types

Selected compounds measured in the atmosphere can serve as chemical markers fo r specific sources. Based on this approach , a s part of the extensive monitoring effort during CRPAQS

, residential wood com bustion was identified as the main source of

PM 2.5 organic carb on in the San Joaquin Vall ey

. M easurements of levoglucosan, a chemical marker for wood smoke were conducted throughout the San Joaquin Valley. Figure 27 illustrates the geographical distribution of the annual averages of these levoglucosan measurement s (pink ci r cles on the map). Each circle size is proportional

to the levoglucosan concentration. The largest levoglucosan levels occur red in urban

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

carbon levels depicted on the smal l map to the upper left . The second largest levog lucosan levels the San Joaquin Valley were measured in Modesto (M14), sequentially followed by Bakersfield (BAC) and then Corcoran (COP).

Figure 27

. Spatial distribution of annual levoglucosan measured th roughout the San
Joaquin Val ley during CRPAQS ( Watson, J., Roth, P.

, 2006)

.

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

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

locations

, ranging from 10 to 40 percent (Figure 28 )

.37 Figure 28

. Wood smoke contribution to PM2.5 at Fresno

- 1 st during a number of winter days in 2003 and 2004 (Gorin et al.

, 2005)

.

c. Source a pportionment u sing s ource r eceptor m odels

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

determine the relative importance of the different types of PM2.5 emission sources at individual monitoring sites . The Chemical Mass Balance (CMB) model statistically

relates measured chemical species of ambient PM2.5 to the chemical species emitted by diverse sources. The Positive Matrix Factorization (PMF) statistical model distinguishes correlation patterns among measured PM2 .5 species to identify sources.

Previous studies have applied source apportionment models to IMS

- 95 and CRPAQS data. For the present study , b oth CMB and PMF were applied to recent PM2.5 data collected in the San Joaquin Valley.

**Prio r Source Apportionment Studies** 

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

PM2.5 through CMB modeling of data collected during the IMS

- 95 field study. Chen et al., 2007, applied two types of multivariate statistical models, PMF and UNMIX, to identify sources contributi ng to wintertime PM2.5 during the CRPAQS field study. In addition, Chow et al. , 2005, applied CMB to the CRPAQS data set. Table 3

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

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

50 percent to wintertime PM2.5 throughout the Valle y (23

- site average); ranging from

40 and 50 percent at urban sites (Fresno and Bakersfield) to ar ound 65 percent at rural sites (Kern Wildlife Refuge and Angiola). The combined biomass burning and cooking source, dominated by biomass burning, contribut e s 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 st38 Table 3. Wintertime PM2.5 source contributions estimates for IMS

- 95 and CRPAQS.

Source Contribution Estimates (% of PM2.5 mass) Study and

Sites Salt Dust Exhaust Biom

Burn Cook Amm.
Sulfate Amm.

Nitrate Sec 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

1 2/26

- 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

1 2/26

- 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 Seconda ry organic aerosol estimated fro m organic carbon not accounted for by primary source profiles

.39 valley wide, constituting the second major source at urban sites

– with larger

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

are not a m ajor sources at rural sites. Engine exhaust, dominated by diesel, is the third major source of directly emitted PM2.5, contributing approximately 1 0 to 15 percent to PM2.5 valley wide. Since secondary organics are estimated from the organic carbon

not ac counted for by the apportionment of other organic carbon sources, small changes in the organic carbon content in the chemical composition profiles for other sources may impact the estimate of the secondary organics contribution.

**Recent Source Apportionmen t Studies** 

Chemical Mass Balance Modeling

Source contributions during high PM2.5 concentration days at Bakersfield

- California (BAC) and Fresno

– 1 st Street ( FSF) were es timated by applying the CMB model version

8.2 to individual PM2.5 samples using PM2.5 source profiles developed during previous

studies. The PM2.5 samples were from days measuring concentrations greater than

30 μ g/m 3 between 2007 and 2010. Per U.S. EPA guidance, b etween 2007 and 2009

, the carbon collection and analysis method was changed to improve comparability with

the rural Interagency Monitoring of Protected Visual Environments (IMPROVE) PM2.5

carbon data. Since t he new carbon method started operating in May 2007 at

Bakersfield an d 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 incl uded in

the CMB model. Ammonium nitrate, the most significant source

, contributed 67 percent
at Bakersfield and 54 percent at Fresno

- 1 st . Biomass burning, which included

residential wood combustion and agricultural, prescribed burning , and likely also cooking , contributed nine percent at Bakersfield and 23 percent at Fresno. Motor vehicle exhaust (diesel and gasoline combined) accounted for ten percent at Bakersfield

and 12 percent at Fresno

- 1 st

. Ammonium sulfate contributed eight percent at
Bakersfie ld and six percent at Fresno

- 1 st . Contributions of the remaining sources were minor at both sites.40 Figure 29

. CMB model calculated 2007

- 2010 average 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 PMF 2 model was applied to the chemically speciated PM2.5 data collected at the

Ba kersfield

- California and Fresno

- 1 st Street monitoring sites

. Bakersfield data from

2008

- 201 0 and Fresno

- 1 st data from 2009

- 2010 were used. Appendix 3 describes th is

PMF analysis in further detail. The average 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 most at both sites, 64 percent 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 burning, and likely also cooking) contributes

six percent at Bakersfield and 13 percent at Fre sno

- 1 st . Secondary ammonium sulfate accounts for eight percent at Ba kersfield and seven percent at Fresno

- 1 st . Airborne soil

and industrial sources are minor contributors. Ammon.

nitrate

53.6%

Ammon.

sulfate

6.0% Sea Salt

0.7% Motor

Vehicle

11.5% Biomass burning

22.9% Organic

Carbon

4.4% Tire and

Brake

0.5% Geologic.

0.3% b) FSF Average High Day (New Carbon) (2009

- 2010)

Ammon.

nitrate

67.4% Ammon.

sulfate

7.8% Sea Salt

0.5%

Motor

Vehicle

9.6% Biomass burning

9.1% Organic

Carbon

2.9% Tire and

Brake

0.7% Geologic.

1.7% Oil

Refineries

0.3% a) BAC Average High Day (New Carbon) (2007

- 2010)41 Figure 30

. Average high day source c ontributions 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 s t Street (FSF) between 2009 and 2010.

While the absolute magnitude of the contributions estimated by the two models vary to some extent, taken together, the CMB and PMF source apportionment studies confirm the importance of secondary ammonium nitrate contributions to PM2.5 on high concentration days. In addition, motor vehicle exhaust and biomass burning were found to be significant contributors to primary PM2.5.

d. Photochemi cal 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

P M2.5. Ying et al. (2008 , 2009 ) simulated the 2000/2001 CRPAQS PM2.5 episod e using the source

- oriented UCD

- CIT air quality model . S ource apportionment of primary

PM 2.5 in the SJV found elemental and organic carbon (EC and OC) t o be the two largest components. Wood burning was the major OC source in the Valley , contributing approximately 50 percent to the total PM2.5. At Fresno, wood burning accounted for approximately 70 to 80 percent of the OC, while meat cooking accounted for approximately 10 to 15 percent

. Diesel engines were identified as the m ajor EC source. These results are generally consistent with those of the receptor modeling discussed

above.

S ource apportionment of secondary nitrate at Fresno revealed diesel engines were the largest contributor to nitrate , accounting for approximately 40 percent of the PM2.5 nitrate, while catalyst equipped gasoline engines accounted for approximately

20 pe rcent

. Agricultural sources accounted for approximately 8 0 percent of the PM2.5

ammonium.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., Bakersfield) in the Valley show progress towards attainment of the standard. The design value

-- the metric used to determine compliance with the standard

-- represents the average of three consecutive annual averages of the PM2.5 concentrations measured at a specific site ( e.g. the 2011 PM2.5 annual design value is the average of the 2009, 2010

, and 2011 annual average PM2.5 concentrations). If the annual design value is equal to or below 15 .0 μg/m 3 , the

site attains the standard. Between 2001 and 2011, annual design value s in the Valley declined between 30 and 40 percent. The largest decreases occurred in the northern and central Valley, where, based on 2011 design values, most sites attain the annual PM2.5 standard. While the southern Valley has shown less improvement, sites are nearing attainment

, with design values about 10 to 20 percent over the standard. With

on

- going implementation of the 2008 PM2.5 Plan, air quality in the Valley is expected to continue to improve and reach attainment in 2014.

Figure 31

. Trend in annual PM25 design v alues (2001

- 2011) at the Bake rsfield

California, 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 Standard43 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 b etween 2001 and

2003. E xtensive wild fires occurred during the summer of 2008 in Northern California.

These wildfires adversely impacted the 2008, 2009

, and 2010 design values throughout the Valley , with a greater impact in the n orth ern Valley . Overall

, between 2001 and 2011, the 24

- hour PM2.5 design values in the Valley h ave decreased between 30 and

55 percent.

Figure 32

. Trend in 24

- Hour PM2.5 Design Values (2001

- 2011) at the Bakersfield

Califo rnia, Fresno

- 1 st

, and Modesto monitoring sites.

Meeting the PM2.5 24

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

Lo oking at the number of days with measured PM2.5 concentrations ov er the 35 μ g/m 3

standard provides another way to assess PM2.5 t rend s. 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 site and by about 45 percent at the Fres no

- 1 st

site (Figure 33 )

. The increase in the number of exceedance days in 2011 compared to 2010 was due to the very severe meteorological conditions experienced in the Va lley during the winter of 2011. T he Valley experienced similar meteorological conditions during the 1999

- 200 0 and 2000

- 2001 winters. T he total number of exeedance days

, however, was much higher during these earlier years , providing evidence that the 0 10 20 30 40 50 60 70 80 90 100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011PM2.5 Design Value (μg/m3) Change in 24

- Hour PM2.5 Design Values

Bakersfield-CA Fresno-1st Modesto Include wildfire impacts44 emission reductions achieved in the Valley have resulted in significant PM2.5 air q uality improvement.

Figure 33

. Trend in measured days over the 24

- Hour standard of 35 μ g/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 characterized by hot, dry summers and cool, rainy

winters. Mountains on the eastern, western, and southern edges create a long deep basin that can allow pollutants to accumul ate under stagnant weather conditions.

The “Pacific High”, a semi

- permanent subtropical high pressure system located off the west coast of North America, and the “Great Basin High”, a high pressure region that

forms in the winter to the area east of the Si erra Nevada Mountains, are major influences on Valley weather , particularly in the winter

. In turn, the strength and position of these high pressure regions are influenced by the strength of the El Nino/Southern Oscillation. El Nino years, characterized by warmer than normal temperatures in the equatorial Pacific and La Nina years, characterized by colder than normal temperatures, can alter the position of the Pacific High, allowing or blocking the passage of frontal systems through California and the San Joaquin Valley. A strong La Nina year can keep the Pacific High from moving south in the winter, diverting normal winter frontal systems northward, and resulting in drier conditions in California, particularly in the southern portions of the S tate. Due to decreases in the number and strength of frontal systems passing through the Valley, as well as increases in potential stagnant conditions, a strong La Nina year can result in higher than expected PM2.5

concentrations. 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 impacts45 In a normal year, w hen 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 storm s

, higher pressure can build, bringing m ild, bright, sunny weather. A strong Great Basin High can direct winds into the Valley, clean ing out any

accumulated particulates. When the Great Basin Hi gh 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 V alley in winter are weake r than those in the summer and the approach of a weak, slow

- moving system can bring light surface winds

with weak vertical mixing . The resulting stagnant conditions can persist for extended periods before the frontal system bringi ng precipitation and stronger winds finally passes through the area

. The southern portion of the Valley is effectively block ed by the Tehachapis and the Coast Ranges to the south and west , leaving it dependent on frontal systems from the north for much ne eded precipitation and winds to scour out any accumulated pollutants

. Stagnant conditions can lead to temperature inversions.

Under normal conditions

, temperature decreases with height , allowing free upward air

flow and dispersion of emissions and pollutants. In contrast, a temperature inversion positions a layer of warm air above cooler air impeding upward air flow. Often the inversion layer is lower than the mo untains surrounding the Valley providing a “cap” and effectively trappin g pollutants. The frequency and intensity of the two high pressure systems and the speed and intensity of the periodic storm systems that clean the air are expected to cause large variations in year

- to

- year average wintertime PM2.5

concentrations.

Meas urements conducted during the CRPAQS winter of 2000/2001 indicated that high PM2.5 concentrations usually occur during days dominated by a strong upper

- level ridge of high pressure located over Central California (Figure 3 4 ) (MacDonald et al.

, 2006). The se days are characterized by light winds, low mixing heights, and limited pollutant dispersion. These PM2.5 episodes can last weeks, making addressing the

24

- hour PM2.5 standard in the Valley a significant challenge

.

Figure 3 4. Frequency of high PM2.5 days in different regions of the San Joaquin Valley corresponding to different synoptic meteorological conditions during the CRPAQS winter of 2000/2001 (Mac Donald et al.

, 2006).46 Examples of the impact of La Nina on Valley weather patterns can be seen during the

winters of 201 1 /201 2 and 2000/2001, the period during CRPAQS. As noted above, the

w inter 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, br inging precipitation, high wind speeds, and strong vertical mixing, allowing accumulated pollutants to disperse. The w inter of 201 1 /201 2 was also characterized by a strong su rface high

pressure system, but frontal passages were weaker and drier with less vertical mixing, allowing stagnant conditions to continue for longer periods.

The graphs in Figure s 3 5 and 36 compare PM2.5 concentrations measured at

Bakersfield and Fresno, respectively, between No vember 1, 2011 and February 29, 2012 to the PM2.5 concent rations measured during the same four months (November through February) in earlier years (1999/2000 and 2000/2001). The 2011/2012 air quality was much better com pared to earlier years for all air quality

statistics. Peak 24

- hour concentrations were over 40 percent lower. The average concentration during the four months period was also 40 percent lower. The number of days over the 24

- hour standard of 35 μ g/m 3 wa s cut by about 35 percent. Even more significant was the 70 percent decline in the number of days with very high concentrations (over 65 μ g/m 3 ).

Figure 3 5. Comparison of the 2011/2012 PM2.5 episode to the CRPAQS episodes o f

1999/2000 and 2000/2001 at B akersfield

- 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

2011-201247 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 t r ends adjusted for meteorology

To understand the effects of emission reductions on ambient PM2.5 concentration

trends , the effects of meteorology need to be separated out as fully as possible. The Classification and Regression Trees (CART) method was use d for this purpose in the

SJV. CART

- defined relationships developed for Bakersf ield and Fresno accounted for most, but not all, of the effects of meteorology on the annual PM2.5 trends. One aspect that may not have been fully captured was the role of carryover of PM2.5 during extended duration episodes. Further analysis is underwa y to better address this impact within the CART analysis. Appendix 4 describes th e current CART analysis in further

detail.

The met eorology

- adjusted (met

- adjusted) trends in the figures below integrate the

CART

- defined met eorology

- effects. For example, in years with m eteorology conditions more conducive to PM2.5 formation, the PM2.5 concentrations were adjusted downward. Conversely, the PM2.5 concentrations were adjusted upward in years with

m eteorology conditions less conducive to PM2.5 formation.

Met

- adjusted trends are designed to be better indicators than the observed trends for showing the effects of changing emissions. At Bakersfield, the resulting meteorology

adjusted trend between 1999 and 2010 indicates greater decline in PM2.5

concentrations than the unadjusted trend (Figure 37 ), while at Fresno the two trends are generally similar (Figure 38 ). Overall , the meteorology

- adjusted trends indicate that between 1999 and 2010, the annual average PM2.5 concentrations decreased about 40

to 50 percent at both locations due to emission reductions. 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

2011-201248 Figure 37

. Observed and met

- adjusted PM2.5 trends in Bakersfield

.

Figure 38

. Observed and met

- adjusted PM2.5 trends in Fresno

.

e. 24

- hour trends adjusted for m et eorology

Similar to annual average trends, t he number of exceedance days that occur each year can be strongly affected by differences in meteorological conditions from year to year. Figure 39 shows observed and met

- adjusted tr ends for PM2.5 exceedance days i n the

Bakersfield and Fresno areas. The observed values 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(μg/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(μg/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)49 Figure 33 for several reasons including a) they are averages of multiple sites in each

area , b) more days could be included where missing values could be imputed (m issing values were filled in using relationships in existing data )

, and c) some days with incomplete meteorological data could not be included in the analysis.

The met

- adjusted trend for Bakersfield shows a stronger decline compared to the observed trend, while in Fresno the observed and met

- adjusted trends are similar. The decrease from 1999 through 2010 for the met

- adjusted trend is 60 to 65 percent in both

areas.

Figure 39

. Observed and met

- adjusted trends for PM2.5 exceedance days in a) the Bakersfield area and b) the Fresno area.50 f. T rends in 2 4

- hour, seasonal , and hourly PM2.5

Comparing the change in the frequency distribution of 24

- hour PM2.5 concentrations over the last decade provides another means of looking at air quality changes over the

years . As illustrated i n Figure 40

, the fraction of days recording PM2.5 over the 24

- hour

standard of 35 μg/m 3 decreased between the three

- ye a r periods of 1999

- 2001 and

2009

- 2011 at the three monitoring site s shown . At Bakersfield, the frequency

decreased from over 15 to less than ten percent, at Fresno from 20 to less than

ten percent, and at Modesto from about ten percent to less than five percent. In contrast, during the se same periods

, the fraction of days rec ording concentrations at or

below the annual standard increased from about 50 up to 70 percent at Bakersfield,

from 55 up to 70 percent at Fresno, and from about 65 up to 80 percent at Modesto.

Figure 40

. Change in PM2.5 concentration frequency distrib ution between the 1999

- 2001 and 2009

- 2011 periods at the a) Ba kersfield

- 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 2009-201151 In the San Joaquin Valley, PM2.5 concentrations over the 24

- hour standard occur

duri ng the winter season. Figure 41 illustrates the overall downward trend in the three

year 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 percen t at Bakersfield and Modesto and 47 percent at

Fresno

. Most recently

, between the periods of 2004

- 2006 and 2009

- 2011, Q1 + Q4 average PM2.5 concentrations decreased by eight percent at Bakersfield , 11 percent at Fresno, and ten percent at Modesto

.

Figure 41

. Change in three

- year averages of 1 s t 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

Modesto52 Focusing on chan ges in winter (November through February) average PM2.5

concentrations in years when meteorological conditions were most conducive to PM2.5

formation and accumulation provides further insight in to PM2.5 air quality progress. These years include 2000, 2002, 2007, and 2011, which as illustrated on Figure 33, also had the highest numbers of days measuring over the 24

- hour PM2.5 standard. Figure 42 illustrates the decrease in the winter average PM2.5 concentrations in t hese four years at the Bakersfield

- 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, winter average PM2.5

concentrations decreased by about 30 percent in Bakersfield, 20 pe rcent in Fresno, and

ten percent in Modesto.

Figure 42 . Changes in winter

- month s average (January, February, November, December) PM2.5 concentrations at the Bakersfield

- California, Fresno

- 1 st and Modesto monitoring sites among years with most 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 Modesto53 Progress in PM2.5 is further corroborated by comparing changes in monthly average

PM2.5 concentrations between 1999

- 2001 and 2009

- 2011 (Figure 43 ). T he overall

PM2.5 seasonal pattern has not changed ; however the average monthly concentrations have decreased. The most significant improvements in PM2.5 have been achieved during the winter months.

Figure 43

. Chan ges in PM2.5 monthly concentrations between the 1999

- 2001 and

20 09

- 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 2009-201154 Comparing changes in PM2.5 diurnal patterns offers further insights into the progress

achieved. Figure 44 illustrates chan ges in the three

- year averages of hourly PM2.5 concentrations recorded during November and December between 2001

- 2003 and

2009

- 2011 at a) Bakersfield

- California, b) Fresno

- 1 st , and c) Modesto. The overall diurnal patterns have not changed, yet hourly conc entrations have decreased throughout the day. Peak day time concentrations decreased approximately 20 percent, and peak night time concentrations approximately 30 percent.

Figure 44

. Changes in the average November

- December PM2.5 hourly concentrations

between the 1999

- 2001 and 2009

- 2011 three

- year periods at the a) Bakersfield

California, 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 2009-201155 g. Chemical c omposition t rends

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

have decreased from the 1999

- 2001 to the 2009

- 2011 three

- year periods. Trends in individual PM2.5 chemical components, as well as emission inventory trends were

evaluated to highlight the main chemical components leading to the progress in PM2.5 air qua l ity and to evaluate the response to State and District control programs.

Speciation monitors in the SJV collect data on PM2.5 chemical composition

. Figures 45, 46, and 47 illustrate the trends in the individual PM2.5 components at Bakersfield, Fresno, and Modesto. Between 2007 and 2009, the carbon collection and analysis method was changed to improve comparability with the rural IMPROVE PM2.5

carbon data. Since the chang e was implemented mid

- year, there are gaps in carbon data for years with a mix of the old and new method s

.

Ammonium nitrate, ammonium sulfate, and carbon compounds are the major constituents of PM2.5. On an annual average basis, concentrations of these key constituents have all shown significant decreases. Ammonium nit rate concentrations in the Valley declined about 40 percent between 2002 and 2011. During the same

tim e

- frame, concentrations of ammonium sulfate and carbon compounds declined about

20 to 30 percent. The most significant declines occurred between 2002 and 2003, and again between 2007 and 2010.

Figure 45

. Trends in PM2.5 chemi cal components at Bakersfield.

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 Bakersfield

Ammonium Nitrate

Ammonium Sulfate

Geological

Elements

OM_Old

EC_New

OM_New56 Figure 46

. Trends in PM2.5 chemical components at Fresno

- 1 st

.

Figure 47

. Trends in PM2.5 chemical components at Modesto.

The 2012 SJV PM2.5 Plan’s Appendix A describes further analyses on PM2.5 air quality

trends. 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_New5 7 h. Emission i nventory trends

As specified by U.S. EPA’s PM2.5 implementation rule, required PM2.5 plan precursors are directly emitted PM2.5, NOx, and SOx. As discussed in sections 5 and 6, VOCs and ammonia are not significant precursors in terms of reducing PM2.5 concentrations. Figure 48 illustrates wintertime emission trends in the San Joaqu in Valley air basin from 2000 through 2011 for the three key precursors

.

• NOx emissions have decreased by 219 tons per day (tpd) or 40 percent. Major reductions occurred in emissions from heavy

- duty diesel trucks , s tationary

combustion sources , and o ther mobile sources (e.g., farm and off

- road equipment, trains)

• Direct PM2.5 emissions decreased by 28 tpd or about 30 percent. Major reductions occurred in emissions from r esidential wood combustion and

entrained dust.

• SOx decreased by 15 tpd or about 60 percent. Major redu ctions occurred in emissions fro m s tationary fuel combustion sources

and industrial processes.

The combined downward trends in PM2.5 components and emissions of PM2.5, NOx,

and SOx indicate that the ongoing control program has had s ubstantial benefits in improving air quality in the SJV and that further emission reductions in the future are expected to provide continuing progress towards attaining the 24

- h our PM2.5 standard.

Figure 48

. PM2.5 and PM2.5 p recursor winter emission trends in the San Joaquin Valley.

0 100 200 300 400 500 600Emissions (tpd) Winter Emission Inventory Trends

NOx

PM2.5

SOx58 9. LINKING AIR QUALITY TRENDS TO EMISSION REDUCTIONS

a. NOx c ontrol

Programs aimed at reducing NOx emissions have played an important role in reducing

nitrate c oncentrations and, consequently

, overall PM2.5 concentrations in the Valley

. As discussed in section 5, previous studies have identified NOx as the limiting precursor

for ammonium nitrate formation. As a result, NOx emissions and PM2.5 nitrate levels track each other over the years. Trends in estimated NOx emissions

, as well as

monitored ambient concentrations

, are compared with trends in measured PM2.5 nitrate

concentratio ns. As illustrated in Figure 49

, between 2004 and 2011, Valley NOx

emissions decreased by about one third, with a commens urate reduction of 30 percent

in PM2.5 nitrate concentrations. Furthermore, the reductions in NOx emissions were also reflected in the corresponding reduction in the ambient gaseous NOx

concentrations. Figures 50 and 51 show a strong correlation between trends in PM2.5

nitrate concentrations and ambient NOx concentrations at the Bakersfield and Fresno

sites. Between 2004 an d 2011, concentrations of both PM2.5 nitrate and NOx

dec reased approximately 30 percent.

Figure 49

. Comp arison between trends in Valley wide winter average NOx emission

and PM2.5 nitrate concentrations at Bakersfield and Fresno. Emissions and concentrations are presented as three

- year winter averages.

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 NO3-59 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 wintertime PM2.5 nitrate and NOx concentrations in Fresno. Concentrations are presented as three

- y ear winter averages.

Because the PM2.5 chemical sp eciation network is just over ten years old, data from

the PM10 ion analysis network were also used to assess longe r

- term trends. Although, the earlier data do not meet the strict quality assurance/quality control requirements of the PM2.5 chemical speciation network, they do provide a historical perspective. The highest PM10 nitrate concentrations were measured in the Valley in early 90’s. Since

then

, concentrations of bot h PM10 nitrate and NOx have decreased about 60 percent (Figur e 52 ). The yearly variability in the ammonium nitrate concentrations reflect s the effects of the varying meteorology on ammonium nitrate 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

NO3-60 Figure 52

. Long

- term trends in three

- year average concentrations of PM10 nitrate and NOx in the San Joaquin Valley.

b. Residential wood burning controls

As previously discussed, annual average concentrations of PM2.5 carbon components

have decreased about 20 to 30 percent since 2002. The decrease in t he carbon
component reflects substantial benefits from the implementation of District Rule 4901,

which prohibits residential wood

- burning on days when high concentratio ns of PM2.5 are predicted. In addition, as part of the District’s stringent smoke management program, agricultural burning is prohibited on those same days. Through a series of Rule 4901 amendments, the PM2.5 threshold for calling no

- burn days was establ ished

in 2003 at 65 μg/m 3 and subsequently tightened to 30 μg/m 3 in 2008. District staff analyzed the effect that Rule 4901 has had on PM2.5 in the Fresno area using a

statistical mode l developed to quantify PM2.5 reductions attributable to the 2003 and

2 008 rule amendments. Based on PM2.5 concentration relationships to meteorological

variables before the wood

- burning curtailments came into effect, the model predicts

what the PM2.5 concentrations would have been if the curtailments had not been adopted. These expected concentrations are then compared to the measured

concentrations. Th is analysis is further described in the 2012 SJV PM2.5 Plan’s Appendix A.

The analysis results indicate that as of the 2011

- 2012 wood

- burning season, 24

- hour

average PM2.5 levels in Fresno have improved by 41 percent (21 μg/m3) since the 2003

and 2008 amendments to Rule 4901 (Figure 53 ). This improvement is especially

marked in PM2.5 concentrations measured during the evening hours of 8:00 p.m. to 12:00 a.m. The average evening PM2.5 concentrations have improved by 50 percent (42 μg/m3) over the same time period. As shown in this analysis, the 2008 amendment 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-

NOx61 to Rule 4901 ha s approximately doubled the seasonal improvements in PM2.5

attributable to the 2003 amendments.

Rule 4901 will continue to play an important role in reducing PM2.5 concentrations throughout the San Joaquin Valley both within and beyond the timeframe of th is plan.

Figure 53 : Effect of SJVAPCD’s Rule 4901 on PM2.5 concentrations in Fresno

.62 10. MODELED ATTAINMENT DEMONSTRATION

a. Modeling r esults

Consistent with U.S. EPA guidelines, air quality modeling was done to predict future PM2.5 concentrations at each monitoring site in the San Joaquin Valley. This modeling

shows attainment of the 24

- hour PM2.5 standard by 2019 in all counties except Kings

and Kern , based on implementation of the ongoing control program

. In these counties

, additional focused emission reductions are needed to provide for attainment. As required by U.S. EPA, additional analyses has been done to confirm that attainment is

pre dicted throughout each county (i.e. in each modeled grid cell). The “Attainment

Demonstration” chapter of the District’s plan provide s a n overview of the p hotochemical modeling performed. Additional information on the periods modeled, the models

selected , and model application can be found in the Modeling Protocol document prepared for this effort.

The air quality modeling analysis includes new emission reductions each year between

now and 2019 from implementation of a combination of adopted ARB and Dis trict programs. As a result, m ost sites in the northern and c entral Valley are expected to attain by 2019

. As required by U.S. EPA, the modeling replicates the base year 2007 meteorological conditions for each calendar day in the year 2019. The 2007 meteorological conditions included several periods of time especially conducive to the

formation of PM2.5.

Given the past effectiveness of District programs to curtail residential wood burning,

ARB staff then mode led a scenario with an enhanced curtailm ent program, which would be designed to prevent wood burning on days that may lead up to a PM2.5 exceedance. The modeling results for this scenario indicate that only one site (Bakersfield

- California)

would not attain the standard with this additional le vel of control

. The predicted design

values for each site from this modeli ng scenario are shown in Table 4

.

Table 4

. 2019 Modeled 24

- hour PM2.5 Design Values

.

Monitoring Site Design Value ( μ g/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 st 30.5

Clovis 28.6

Merced 22.6

Modesto 24.7

Stockton 21.463 b. Benefits of emission reductions from on

- going programs

The implementation of new reductions from California’s on

- going emission control programs will provide the major portion of the emission reductions needed to attain the

24

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

val ue at the Bakersfield

- California site must decrease by approximately 45 percent to demonstrate attainment. Between 2007, the base year used in the photochemical modeling attainment demonstration

, and 2019, implementation of these control programs will red uce 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 predicti ons that demonst rate a nearly 45 percent reduction in ammonium nitrate concentrations. In addition, while directly emitted PM2.5 emissions in aggregate are decreasing by nearly 30 percent, a major focus of the attainment control strategy is

further curtailment of residen tial wood burning. Ambient measurements and modeling studies have shown the large contribution that residential wood burning has on PM2.5 exceedance days. In addition, prior District analysis has demonstrated the significant benefits of past implementati on of wood burning curtailment. Therefore, the substantial continuing reductions that will result from implementation of the ongoing control program, coupled with an enhanced residential burning curtailment program, are consistent with the benefits predic ted in the modeled attainment demonstration.

As a result of the overall control program, coupled with the enhanced wood burning curtailment measure, ammonium nitrate concentrations are predicted to decrease by nearly 45 percent, organic carbon concentrati ons by approximately 65 percent , and elemental carbon concentrations by nearly 80 percent

. A comparison of the

concentrations of the main chemical constituents in 2007 to that predicted in 2019 at

th ree sites (Modesto, Fresno

- 1 st , and Bakersfield

- Californ ia) illustrates the significant

reductions in these components ( Table 5 ).

Table 5

. Comparison of the concentration of chemical constituents for 2007 and 2019 design values at selected sites

.

Component (ug/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 0.364 c. Evaluation of precursor sensitivity

Effectiveness of Valley wide emission reductions

In order to determine where to focus the remaining emission reductions needed to bring

Bakersfield

- California into attainment

, as well as identify the attainment plan precursors, ARB staff conducted additional modeling sensitivity runs to assess the relative efficacy of further reductions of different PM2.5 precursors. U.S. EPA’s PM2.5 implementation rule specifies that a precur sor is considered “significant” for control strategy development purposes when a significant reduction in the emissions of that precursor pollutant leads to a significant decrease in PM2.5 concentrations. Such pollutants are known as “PM2.5 attainment pla n precursors” (72 FR 20586). The U.S. EPA’s implementation rule also establishes a presumption that PM2.5, NOx, and SOx are attainment plan precursors, wh ile VOCs and ammonia are not. In the past f or the annual PM2.5 plan, PM2.5, NOx, and SOx were identi fied and approved as the only attainment plan precursors by U.S. EPA. Results of the annual PM2.5 modeling showed that of these three pollutants, reductions in directly emitted PM2.5 was the most effective. However, because emissions change over time, it is important to continue to

assess the attainment plan precursors each time a plan is developed.

Additional photochemical modeling analyses were therefore conducted to understand the relative effectiveness of emission reductions for primary PM 2.5 and prec ursors throughout the Valley in 2019 . In these analyses, the model was run with varying

combinations of valley wide precursor emission reductions from anthropogenic sources :

• NOx vs. PM2.5

• NOx vs. Ammonia

• NOx vs. VOCs

• NOx vs. SOx

Table 6 compares the modeled effect on the 2019 design value obtained at each monitoring site from a 25 percent reduction in the specified precursor. Consistently, direct PM2.5 productions have the most benefit, followed by NOx reductions.

Reductions in ammonia and SOx provide much smaller benefits, while reductions in VOCs result in very small disbenefits at many sites. Table 7 presents this same information, but normalized to reflect the reduction in design value per ton of each precursor reduced. On this b asis, valley wide reductions in PM2.5 are approximately

four times as effective as NOx, and approximately five times as effective as SOx. In contrast, reductions in ammonia are approximately nine times less effective than NOx, and as noted above, red uctions in VOCs result in either no impact of very small

disbenefits.65 Table 6

. Modeled r eduction in 2019 PM2.5 design value resulting from 25 percent

reduction in valley wide precursor emissions

.

Monitoring Site PM2.5 Reduction ( μ g/m 3 ) Primary

PM2.5 NOx Ammonia SOx VOC

Bakersfield

- C alifornia 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 st 4.12 2.62 0.51 0.09 0.03

Fresno

- Hamilton 3.73 2.57 0.50 0.11 0.05

Clovis 3.29 3.17 0.55 0.09 0.00

Modesto 2.49 1.76 0.43 0.17 0.03

Merced 2.54 2.31 0.34 0.11

- 0.01

Stockton 1.87 1.30 0.48 0.20 0.03

Table 7 . Modeled PM2.5 air quality benefit per ton of valley wide precursor emission

reductions

.

Monitoring Site PM2.5 Benefit (μg/m 3 per ton reduction)

Primary

PM2.5 NOx Ammonia SOx VOC

Bakersfield

- C alifornia 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 0.00066 The results of these modeling sensitivity runs were also plotted on isopleth diagrams which reflect the change in the 2019 design value at each level of emission reduction.

Is opleth diagrams for the Bakersfield

- California site are shown in Figures 5 4 ( a ) through ( d ) to illustrate the overall nature of the modeled response :

• While reducing SOx results in less sulfuric acid and subsequent ammonium sulfate formation, SOx reductions have only a small effect on the predicted design value since ammonium sulfate is a small component of measured PM2.5

.

• Reducing VOCs leads to very small increases in the design value because these

reductions have the effect of making more NOx available for nitric acid, and subsequent ammonium nitrate formation.

• Because ammonia is much more abundant than NOx, the atmosphere is more responsive to reductions in NOx as compared to ammonia. Reductions in NOx in

turn have significant benefits as ammonium nitrate is a large component of

measured PM2.5.

• R eductions in directly emitted PM2.5 result in significant benefits due to the reduction in organic carbon which is a large component of measured PM2.5

.

These modeling results , along with the findings from past modeling and monitoring

studies highlight that reductions in directly emit ted PM2.5 and NOx provide the greatest benefit in further reducing PM2.5 concentrations and making progress towards attainment. Given that significant reductions in VOCs and ammonia do not provide significant air quality benefits, per U.S. EPA guidance, t he 24

- hour PM2.5 attainment plan precursors are directly emitted PM2.5, NOx, and SOx.67 F igure 5 4

. Bakersfield

– California Isopleth Diagrams

.

b) NOx vs. VOC

d ) NOx vs. PM2.5 c ) NOx vs. Ammonia a ) NOx vs. SOx68 Effectiveness of localized emission reductions

The valley wide precursor sensitivity modeling demonstrates that on a relative basis the greatest benefits are achieved from reductions in sources of directly emitted PM2.5, followed by NOx. Due to the stagnant condition s that occur during wintertime episodes, and the local nature of directly emitted PM2.5 carbon sources in particular, Kern County specific model sensitivity runs were also conducted to evaluate the benefits of emission

reductions focused on the nonattainme nt sub

- area. The Kern County sensitivity runs

demonstrated that:

• One ton per day of directly emitted PM2.5 reductions provides a 1 μ g/m 3
improvement in the Bake rsfield

- California design value;

• One ton per day of NOx reductions provide s for a 0.1 2 μ g/m 3 improvement in the

Bakersfield

- Ca lifornia design value;

• One ton per day of SOx reductions provides for a 0.21 μ g/m 3 improvement in the

Bakersfield

- California design value ;

• One ton per day of ammonia reductions provides for a 0.02 μ g/m 3 improvement

in t he Bakersfield

- California design value; and

• One ton per day of VOC reductions has no effect on the Bakersfield

- California design value.

A n examination of sources surrounding the Bakersfield

- California monitoring site was

then conducted in order to iden tify potential PM2.5 and NOx sources for further control. The forecasted 2019 PM2.5 and NOx gridded emission inventories were evaluated, focusing on the winter months of November through February when the majority of PM2.5 exceedances occur. The top five emission sources of PM2.5 and NOx in the 9 grid cells (3x3 grid cells, each measuring 4 km x4 km) centered on the Bakersfield

California monitoring site are shown in Figures 5 5 and 5 6

. The main combustion sources of PM2.5 are commercial cooking, residential fuel combustion, and on

- road

vehicles. The main NOx source is on

- road vehicles, with smaller contributions from off

road equipment, residential fuel combustion, and trains. This a nalysis suggests that for PM2.5, a focused effort to further reduce residential wood burning and limit emissions from commercial cooking operations would have significant benefit s in reducing PM2.5 concentrations in the Bakersfield area. Key NOx sources i nclude on

- and of f

- road

mobile sources which are already the focus of ongoing control programs

.69 F igure 5 5

. 2019 top five wintertime PM2.5 emission sources with in the Bakersfield

California 9

- grid cell area (3x3 grid cells, each measuring 4 km x 4 km with the

Bakersfield

- California monitor located in the center cell). Wintertime emissions expressed as an average of January, February, November and December emissions.

Figure 5 6

. 2019 top five wintertime NOx emission sources with in the Bakersfield

California 9

- grid cell area (3x3 grid cells, each measuring 4 km x 4 km with the

Bakersfield

- California monitor located in the center cell). Wintertime emissions expressed as an average of January, February, November and December emissions. 0.0 0.1 0.2 0.3 0.4 0.5PM2.5 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 Cells70 d. Demonstrat ing attainment at Bakersfield

- California

W hile adoption of a more stringent wood burning curtailment program brings the

Bakersfield

- California site very near attainment, further reductions are needed to meet the attainment target of 35.4 μ g /m 3 . Based upon the precursor sensitivity analysis and evaluation of the localized inventory discussed in the previous section, further control of

PM2.5 emissions from c ommercial cooking operations was identified as the most effective approach to provide the emission reductions needed to reach attainment . The

final attainment demonstration for the Bakersfield

- California design site is provided in

Table 8 below:

Table 8

. Attainment Demonstration for the Bakersfield

- California Design Value Site

.

2007 De sign Value ( μ g /m 3 ) 2019 Design Value

with Wood Burning Program

Enhancement ( μ g /m 3 ) 2019 Final D esign

Value ( μ g /m 3 )

6 5.6 35.7 ≤ 35.4
Note: The benchmark for attainment is a design value that is equal to

or less than 35.4 μ g/m 3

.
As noted above, the design value in the center column of the table reflects the implementation of ongoing control programs, as well as implementation of an enhanced residential wood burning curtailment program. The final design value reflects the

combined impact of further reductions in commercial cooking, as well as a small increase in motor vehicle emissions due to updated vehicle activity data from the San Joaquin Valley Metropolitan Planning Organizations (MPOs). Based on a modeling sensitivity run, i mplementation of further controls on commercial cooking is expected to

result in a 0.6 μ g /m 3 reduction in the baseline design value. The revised MPO activity data represents approximately one percent of Valley wide NOx emissions. Based on modeling sensitivity runs, this is estimated to result in a design value increase of

0.2 μ g /m 3 . In aggregate, the modeling demonstrates a design value that meets

U.S. EPA’s attainment target of 35.4 μ g /m 3

.71 11. SUMMARY

Consideration of the entirety of information presented in the weight of evidence provides a consistent assessment that supports the modeled attainment date of 2019. The substantial continuing reductions that will result from implementation of the ongoing control program, coupled with new measures addressing residential wood burning and cooking, are consistent with the results predicted in the modeled attainment

demonstration. This weight of evidence assessment is based upon the following

factors:

• Over the last decade significant progress has occurred in reducing 24

- hour PM2.5

concentrations. The 24

- hour design value has decreased by over 30 μg/m 3

, while the number of exceedance days has declined by nearly 50 percent. Meteorologically adjusted trends for the Bakersfield area show an even greater reduction in exceedance days

, with a decline of over 60 percent.

• Evaluation of the air quality model response to emission reductions, as well as model sensitivity runs demonstrates that reductions in directly emitted PM2.5 have the greatest impact per ton of emissions, followed by NOx. For example, in Kern County, PM2.5 emissio n reductions are approximately eight times more effective

than NOx.

• Both receptor and photochemical grid based modeling have identifie d residential wood burning as a significant contributor to wintertime PM2.5 concentrations. The

r eductions in the organic carbon component of PM2.5 that have occurred can be

linked to implementation of the District’s residential wood burning curtailment

p rogram.

• Evaluation of emissions inventory data , monitoring studies, and photochemical modeling indicate that controlling NOx emissions is the most effective strategy to

reduce ammonium nitrate concentrations.

• The decrease in ammonium nitrate concentrat ions observed at Valley monitoring

sites tracks concurrent reductions in NOx emissions as well as trends in gaseous

NOx concentrations.

• S ubstantial NOx and PM2.5 emission reductions will occur between 2007 and 2019 due to the implementation of on

- going measures and additional new measures . As a result of these programs, NOx emissions will decrease by over 50 percent, and PM2.5 emissions by nearly 30 percent.

• The modeled attainment demonstration pred icts that all sites in the Valley will attain by 2019

. This modeling assessment is consistent with the benefits seen from previous reductions in the sources and pollutants being addressed as part of the attainment strategy.72 REFERENCES

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.1 

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

Appendix 1

California Regional PM10/PM2.5 Air Quality Study

Publications2 

## 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 Diffus ion 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 Te chnical 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

Joa quin 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, “Res uspension 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 Cent ral 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 M atter 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 Pol lution 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.3 12. Lurmann, F. et al., 1996, “PM10 Air Quality Models for Application in the San Joaquin Valley PM10 SIP”, prepared by Sonoma Technology, Inc. for the San Joaquin Valleywide Air Pollution Study Agency.

13. Lehrman, D. E. et al., 1996, “SARMAP II Design: Analysis of the San Joaquin Valley Meteorological En vironment 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 f rom 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 th e 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 Pol lution 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 Joaq uin 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 th e 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 Rela ted 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 Internati onal 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&WMA4 International Symposium on Measurement of Toxic and Related Air Pollutants

, Research Triangle Park, NC.

24. Bush, D.H. et al., 1996, “IMS95 Winter Study

- Quality Assurance”, presented at the 1996 U.S. EPA/A&WMA International Symposium on Measurement of Toxi c 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 Measur ement 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 Sympos ium on Measurement of Toxic and Related Air Pollutants, Research Triangle Park, NC.

27. Gray, H.A., 1996, “1995 Integrated Monitoring Study: Low Wind Speed Study

- Preliminary Results”, presented at the 1996 U.S. EPA/A&WMA International Symposium on

Measurem ent 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 Tec hnology 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 P ollution 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 S ilt 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 Sonom a 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

Stu dy 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.5 37. Watson, J. G. et al., 1997, “The Fugitive Dust Characterizat ion 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, “Analysi s of Historical PM10 and PM2.5 Measurements in Central California”, prepared by Desert Research Institute for the San Joaquin Valleywide Air Pollution Study Agency.

39. Ondov, J., 1997, “Technical Support Study No. 13: Feasibility of Using Rare Earth Isotope and Activatable Tracers for Tracking the Fate of Primary and Secondary Aerosols from Combustion Sources”, prepared by John Ondov for the San Joaquin Valleywide Air Pollution Study Agency.

40. Pankratz, D., 1997, “Quality Assurance Project Plan for Evaluation and Improvement of Methods for Determining Ammonia Emissions in the San Joaquin Valley”, prepared by Aerovironment for the San Joaquin Valleywide Air Pollution Study Agency.

41. Richards, L. W., 1997, “Technical Support Study 11: Investigating the Dynamics an d 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 Chemistr y 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, “Measurement s 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: Invest igating 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 A gency.

46. Pandis, S. et al., 1997, “Technical Support Study 11: Investigating the Dynamics and Chemistry of Fog Formation and Dissipation Fog Modeling”, prepared by Carnegie Mellon University for the San Joaquin Valleywide Air Pollution Study Agency.

47. Chow, J. C. et al., 1997, “San Joaquin Valley 1995 Integrated Monitoring Study: Documentation, Evaluation, and Descriptive Data Analysis of PM10, PM2.5 and Precursor Gas Measurements Technical Support Studies No. 4 and 8”, prepared by the Desert

Research Institu te for the San Joaquin Valleywide Air Pollution Study Agency.6 48. Chow, J. C. et al., 1997, “Size

- resolved Particle and Light Extinction Measurements During IMS95”, presented at the A&WMAVisual Air Quality

- Aerosols and Global Radiation Balance Conference, B artlett, NH.

49. Solomon, P. A. et al., 1997. “Operational Field Monitoring Plan for the 1995/1996 Integrated Monitoring Study (IMS95) of the California Regional PM10 Air Quality Study (CRPAQS), Post Field Version, Draft Report”, prepared by Pacific Gas & Ele ctric 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”, p repared 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 Sa n 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.7 61. McDade, C. E., 1997, “Draft Final Report IM95 Data Analysis Work Elements 7.3 Contributions of Chemical Constituents to Visibility Reduction, 7.4 Contributions of Chemical Constituents to Light Extinction Budget, 7.5 Contribut ions of Emission Sources to Light Extinction Budget”, prepared by ENSR Consulting and Engineering for the San Joaquin Valleywide Air Pollution Study Agency.

62. Chinkin, L. R. et al., 1998, “Use of Ambient Data to Evaluate a Regional Emission Inventory in the San Joaquin Valley”, presented at the A&WMA PM2.5: A Fine Particle Standard Conference, Long Beach, CA.

63. Coe, D. L. et al., 1998, “Technical Support Study 15: Evaluation and Improvement of Methods for Determining Ammonia Emissions in the San Joaquin Valle y 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 Syst em 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 Dat a 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 V alley 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 Ga s

- 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.8 73. Richards, L. W. et al., 1998, “1995 Integrated Monitoring Study: Comparison of Light Scatter ing 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 Monitori ng 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 Standa rd 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 Productio n 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

- Depende nt 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 A ir 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 Val leywide 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 Ai r Pollution Study Agency.9 84. Blanchard, C. L. et al., 1998, “Final Report for Tasks 4.2.1 and 4.5.6 Spatial Representativeness of Monitoring Sites and Zones of Influence of Emission Sources”, prepared by Envair for the San Joaquin Valleywide Air Pollution St udy Agency.

85. Magliano, K. L. et al., 1998, “Final Report Chemical Mass Balance Modeling of Data from the 1995 Integrated Monitoring Study”, prepared by the California Air Resources Board for the San Joaquin Valleywide Air Pollution Study Agency.

86. Carr, E. L. et al., 1998, “Final Report San Joaquin Valley Regional PM

- 10 Study Characterizing Micrometeorological Phenomena: Mixing and Diffusion in Low Wind Speed Conditions Phase III: Monitoring and Data Analysis”, prepared by Systems Applications

International for the San Joaquin Valleywide Air Pollution Study Agency.

87. Dye, T. S. et al., 1998, “Measurement Methods Validation: Adequacy and Validation of Meteorological Measurements Aloft During IMS95”, prepared by Sonoma Technology, Inc. for the San Joaquin Valley wide Air Pollution Study Agency.

88. Coe, D. L. et al., 1998, “Emission Source Activity Detection: Analysis of CMB Model Results and Daily Activity Data for IMS95”, prepared by Sonoma Technology, Inc. for the San Joaquin Valleywide Air Pollution Study Agency.

89. Main, H. H. et al., 1998, “Characterization of the Spatial and Temporal Patterns of Visibility in the San Joaquin Valley During IMS95, prepared by Sonoma Technology, Inc. for the San Joaquin Valleywide Air Pollution Study Agency.

90. Carr, E. L. et al., 199 8, “1995 Integrated Monitoring Study Data Analysis Work Element 4.6.3: Time and Length Scales for Mixing of Secondary Aerosols During Stagnation Periods”, prepared by Systems Applications International for the San Joaquin Valleywide Air Pollution Study Age ncy.

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 CE

CERT for the San Joaquin Valleywide Air Pollution Study Agency.

92. Hast e, 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., 199 8, “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 Regio nal PM10/PM2.5 Air Quality Study: Preliminary Approaches and Data Requirements”, prepared by the California Air Resources Board for the San Joaquin Valleywide Air Pollution Study Agency.

95. Pun, B. et al., 1998, “Comments on Field Program Plan for the Califo rnia Regional PM10/PM2.5 Air Quality Study”, prepared by Atmospheric and Environmental Research for Pacific

Gas & Electric.10 96. Pun, B. et al., 1998, “Phase II Reports Integrated Monitoring Study 95: Assessment of

Mathematical Models and Associated Data Needs ”, prepared by Atmospheric and

Environmental Research for Pacific Gas & Electric.

97. Pun, B. et al., 1998, “Conceptual Model of Particulate Matter Pollution in the California San Joaquin Valley”, prepared by Atmospheric and Environmental Research for Pacific Gas

& Electric.

98. Richards, W. L. et al., 1998, “Characterization of the Validity of Light Scattering Measurements During the 1995 Integrated Monitoring Study”, prepared by Sonoma Technology Inc. for the San Joaquin Valleywide Air Pollution Study Agency.

99. Schauer, J. J. et al., 1998, “Source Contributions to Airborne Particles in the San Joaquin Valley During the IMS95 Study”, prepared by California Institute of Technology for the San Joaquin Valleywide Air Pollution Study Agency.

100. Seinfeld, J. H. et al., 1 998, “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 Me eting , 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

Ch emistry”, 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 A ir Pollution Study Agency.

108. Solomon, P. A. et al., 1999, “The 1995 Integrated Monitoring Study (IMS95) of the California Regional PM10/PM2.5 Air Quality Study (CRPAQS): Study Overview”, Atmospheric Environment (33).11 109. Solomon, P. A. et al., 1999, “Objective s 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 Compar ison to Emissions During the 1995 Integrated Monitoring Study”, Atmospheric Environment (33)

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111. Lehrman, D. E., et al., 1999, “Assessment of the Methods and Adequacy of Meteorology Measurements for IMS95”, Atmospheric Environment (33)

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112. McDade, C. et al., 1 999, “Optical Properties of the San Joaquin Valley Aerosol Collected During the 1995 Integrated Monitoring Study”, Atmospheric Environment (33)

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113. Blanchard, C. L. et al., 1999, “Spatial Scales of Transport and Influence During the 1995 Integrated Monitorin g Study in California’s San Joaquin Valley”, Atmospheric Environment (33)

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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)

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115. Lillis, D. et al., 19 99, “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)

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116. Collett, J. L. et al., 1999, “Internal Acid Buffering in San Joaquin Valley Fog Drops and its

Influenc e on Aerosol Processing”, Atmospheric Environment (33)

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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)

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118. Strader, R. et al., 1999, “Evaluation of Secondary Organic Aerosol Formation in Winter”, Atmospheric Environment (33)

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119. Pun, B. et al., 1999, “Understanding Particulate Matter Formation in the California San Joaquin Valley: Conceptual Model and Data Needs”, Atmospheric Environment (33)

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120. Magli ano, 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

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121. Wittig, A. E. et al., 1999, “Health and Safety Plan for the California Regional PM10/PM2.5 Air
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122. Magliano, K.L. et al, 2000, “Coordinated Efforts of the California Regional PM10/PM2.5 Air Quality Study and the U.S. E nvironmental Protection Agency Supersite in Fresno, California”, in Proceedings of the 93 rd Annual A&WMA Conference , Salt Lake City, UT,

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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
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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

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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 Pol lution Study Agency, STI

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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

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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

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129. Magliano, K.L. et al., 20 01, “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,

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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

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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

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133. Lowenthal, D. H. et al., 2001, “Particle Si ze Relationships at the Fresno Supersite”, in Proceedings of the A&WMA/AGU Regional Haze and Global Radiation Balance

Conference , Bend, OR, October 2

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134. Pun B. K. et al., 2001; “Sensitivity of Particulate Matter Nitrate Formation to Precursor

Emissi ons in the California San Joaquin Valley”; Environmental Science and Technology; (35), 2979

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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

– A tmospheres”.13 136. Rappolt, T.J. et al., 2001, “Mapping Air Quality Plumes from Fossil Energy Sources to Assess the Impacts of Secondary Aerosol Development”, presented at the SPE/EPA/DOE Exploration and Production Environmental Conference , San Antonio, TX, Feb ruary
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 Ne twork

– 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 Ag ency.

140. Chung, A. et. al, 2001, “Detection of Alkaline Ultrafine Atmospheric Particles at Bakersfield, California, Environmental Science Technology , ( 3 5), 2184

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141. Bush, D. et al., 2002, “An Evaluation of Portable Nephelometer Performance During
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142. Watson, J. G. et al., 2002, “Air Quality Measurements from the Fresno Supersite”, Journal of the Air and Waste Management Association, (50) , 1321

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143. Watson, J. G. et al., 2002, “A Wintertime PM2.5 Episode at the Fresno, CA, Supersite”, Atmospheric Environment (36), 465

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144. Watson, J.G., 2002, “Particle Size Relationships at the Fresno Supersite”, Journal of the Air and Waste Management Associati on, (52) , 822

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145. Herckes, P. et al., 2002, “Organic Matter in Central California Radiation Fogs”, Environmental Science & Technology, (36), 4777

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146. Hyslop, N. et. al., 2002, “Continuous PM Measurements in a Rural Town in the San Joaquin Valley”, pres ented at A&WMA Symposium on Air Quality Measurement Methods and Technology, November 13

- 15, 2002.

147. Holmen B. A. et al., 2002, “Lidar

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148. Holmen B. A. et al., 2002, “Lidar

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– Part II: Emission Factors”, Atmospheric Environment, (35), 3265

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150. Bush, D. et. al., 2002, “Final Q uality Assurance Audit Report California Regional PM10/PM2.5 Air Quality Study”, prepared by Parsons Engineering Science for the San Joaquin Valleywide Air Pollution Study Agency.

151. Stiefer, P. et al., 2002, “Atlas of Ground Truth Surveys”, prepared by Sono ma Technology Inc. for the San Joaquin Valleywide Air Pollution Study Agency.

152. Engelbrecht, J. P., 2002, “Laboratory Measurement Systems Audit”, prepared by Desert Research Institute for the San Joaquin Valleywide Air Pollution Study Agency.

153. Countess Envi ronmental, 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 Meteorol ogical Conditions on Aerosol Composition and Mixing State in Bakersfield CA”, Environmental Science and Technology , (36), 2345

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155. Ashbaugh, L.L. et al., 2003, “Soil Sample Collection and Analysis for the Fugitive Dust Characterization Study”, Atmospher ic Environment, (37), 1163

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156. Chow, J.C. et al., 2003, “Similarities and Differences in PM10 Chemical Source Profiles for Geological Dust From the San Joaquin Valley, California”, Atmospheric Environment, (37), 1317

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157. Rinehart, L.R. et al., 2003, “Characterization of PM2.5 Associated Organic Compounds of Emission Sources Collected During the California Regional PM10/PM2.5 Air Quality Study”, in Proceedings of the 96 th Annual A&WMA Conference, San Diego, CA, June

2003.

158. Najita, T. et al., 2003, “Rel ationship 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

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159. Velasco, P. et al., 2003, “Diurna l Variations in PM2.5, PM10, Chemical Components, Gaseous Precursors, and Co

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- 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 Associat ion 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 Associatio n for Aerosol Research, Anaheim, CA, October 20

- 24, 2003.15 162. Chow, J. C. et al., 2003, “Spatial and Temporal Variations in PM2.5 Chemical Composition during Winter, 2000/2001 Episodes in Central California”, presented at the 2003
Annual Conference of the Ame rican 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 Asso ciation for Aerosol Research, Anaheim, CA, October 20

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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, Oct ober 20

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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

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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 America n Association for Aerosol Research, Anaheim, CA, October 20

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167. Kleeman, M. et. al., 2003, “Characterization of Atmospheric Nano

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168. Kleeman, M. et. al., 2003, “Simulating Air Quality in the San Joaquin Valley with a Source

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169. Heissler, S. 2003, “Contributions of Chemical Constituents to Visibility Reduction during the California Regional PM10/PM2.5 Air Quality S tudy”, presented at the 2003 Annual Conference of the American Association for Aerosol Research, Anaheim, CA, October

20

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170. Countess, R. et. al., 2003, “Contribution of Vegetative Burning to PM2.5 Concentrations in the San Joaquin Valley”, presente d at the 2003 Annual Conference of the American Association for Aerosol Research, Anaheim, CA, October 20

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171. Rappolt, T.J. et. al., 2003, “Near

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172. Alcorn, S. et. at., 2003, “Comparisons Between California Regional PM10/PM2.5 Air Quality Study (CRPAQS) Particle Light Scattering and Fine Par ticle Mass Data”, presented at

the 2003 Annual Conference of the American Association for Aerosol Research, Anaheim, CA, October 20

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174. Hyslop, N. et. al., 2003, “Vertical Variations in PM and P M 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

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175. Chang, et. al., 2003, “Aerosol Processing and Removal by Fogs: O bservations in Radiation Fogs During the CRPAQS Study”, presented at the 2003 Annual Conference of the American Association for Aerosol Research, Anaheim, CA, October 20

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176. Chin, et. al., 2003, “Comparison Between Particulate Matter Sampled at Urba n 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

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177. Rinehart, L. et. al., 2003, “Semi

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for Aerosol Res earch, Anaheim, CA, October 20

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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
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20

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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,

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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 Agen cy, STI

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181. Wittig, A.E., et. al., 2003, “California Regional PM10/PM2.5 Air Quality Study (CRPAQS) Anchor Site Measurements and Operations”, prepared by Sonoma Technology Inc for the San Joaquin Valleywide Air Pollution Study Agency, STI

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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

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183. Neff, W. D., et. al., 2003, “Final Report Contract 99

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Pollution Study Agency.17 184. 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

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186. Whiteaker, J.R., 2003, “Hydroxymethanesulfonate as a Tracer for Fog Processing of Individual Aerosol Particles”, Atmospheric Environment , (37), 1033

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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 J oaquin Valleywide Air Pollution Study Agency.

188. Prather, K., 2004, ATOFMS Field Study Operations Final Report, prepared by the University of California, San Diego, Prather Research Group for the San Joaquin Valleywide Air Pollution Study Agency.

189. Chow, J.C. et. al., 2004, “Comparison of PM2.5 Chemical Composition from the Fresno Supersite During Wintertime Pollution Episodes in Central California”, presented at the

13 th World Clean Air and Environmental Protection Congress and Exhibition , London England, Aug ust 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

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191. DeYoung, R.J. et. al ., 2004, “Aerosol Transport in the California Central Valley Observed by

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192. Collett, J.L. et. al., 2004, “The Organic Composition of California Fogs”, submitted to the 3 rd
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193. Carillo, J.H. et. al., 2004, “An Economical Optical Fog Detector”, submitted to the 3 rd
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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

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195. Bahm, K.E. et. al., 2004, “Detecting Source Activities and Reconciling Ambien t Measurement
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196. Ba xter, 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.18 197. Lehrman D. et. al., 2004, “Task 3.3 How Well Do Measurements Chara cterize Critical Meteorological Features”, prepared by Technical & Business Systems for the San Joaquin Valleywide Air Pollution Study Agency.

198. Knuth, B., 2004, “Task 5.1 CRPAQS Synoptic Conditions Weather Typing”, prepared by Technical & Business Systems for the San Joaquin Valleywide Air Pollution Study Agency.

199. Held, T. et. al., 2004, “Modeling Particulate Matter in the San Joaquin Valley with a Source

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200. Heisler, S. L., 2004, “Contributions of Chemical Constituents to Visibility Reduction During the California Regional PM10/PM2.5 Air Quality Study”, prepared by ENSR Corporation for the San Joaquin Valleywide Air Pollution Study Agency.

201. Tracer ES&T, 2004, “California Regional PM10/PM2.5 Air Quality Study (CRPAQS) Initial Data Analysis of Clean Airship 1 Measurements (Task 6.4)”, prepared by Tracer Environmental Sciences & Technologies, Inc., for the San Joaquin Valleywide Air Pollution Study Age ncy.

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 Ground

Based Measurements Collected During the CCOS Field Experiment”, prepared by Sonoma Technology Inc. for the San Joaquin Valleywide Air Pollution Study Agency.

204. Yoho, D. et. al., 2004, “How is Visibility Affected by Atmospheric Constituents, Sources, and Meteorology

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205. Alcorn, S.A. et. al., 2004, “Comparison s 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 Co rresponds 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 A ir 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.19 209. Held, T. et. al., 2005, “A Comparison of the UCD/CIT Air Quality Model and the CMB Source

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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

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211. Fahey, K.M., Pandis, S.N., Col lett, Jr. J.L., Herckes, P. 2005. “ The influence of size

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212. Blanchard, C., 2005, “Comparison of Particulate Nitrate Formation in Different Chemical

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213. Cahill, T., 2005, “Continuous Analysis of Fresno Aerosols by Size, Time, and Elemental Concentrations, March

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214. Collett, J., 2005, “Pollution Processing by Radiation Fogs During the California Regional PM10/PM2.5 Air Quality Study (CRPAQS)”, present ed at the American Association for Aerosol Research (AAAR), Supersites Conference

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215. Goohs, K., 2005, “Function and Performance of a New Real

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216. Herckes, P., 2005, “Fog Processing of Atmospheric Organic Matter”, presented at the American Association for Aerosol Research (AAAR), Supersites Conference

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217. Lurmann, F., 2005, “Spatial Representativeness of the Fresno Supersite PM and Gaseous Co

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218. Magl iano, K., 2005, “Comparison of Air Quality During Two Winter Episod e s”, presented at the American Association for Aerosol Research (AAAR), Supersites Conference

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219. Magliano, K., 2005, “PM2.5 Concentrations in Urban and Rural Area s”, presented at the American Association for Aerosol Research (AAAR), Supersites Conference

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220. McCarthy, M., 2005, “The Role of Nighttime Chemistry in Winter Ammonium Nitrate Formation in the San Joaquin Valley”, presented at th e American Association for Aerosol Research (AAAR), Supersites Conference

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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

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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

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224. Zielinska, B., 2005, “Semi

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225. Cassucio, G., 2005, “Corcoran F all 2000 study: Apportionment of ambient PM10 using scanning electron microscopy”, presented at the American Association for Aerosol Research (AAAR), Supersites Conference

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226. Chow, J., 2005, “Particulate carbon measurements in Ca lifornia's San Joaquin Valley”, presented at the American Association for Aerosol Research (AAAR), Supersites

Conference

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227. Chow, J.C., 2005

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229. Gorin, C.A., et. al., 2005

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230. Park, K., et. al., 20 05

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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”

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232. Turkiewicz, K., et. al., 2006

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233. Rinehart, L.R., et. al., 2006

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Central California ”

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. Journal of the Air & Waste Management Association (56): 398

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235. Chow, J.C., Watson, J.G., Lowenthal, D.H., Chen, A.L

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236. Chow, J.C., Chen, A.L

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237. Ying, Q., Kleeman, M.J., 2006. “Source Contributions to the Regional Distribution of Secondary Particulate Matter in California”. Atmospheric Environment (40): 736

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238. Herner, J.D., Green, P.G., Kleeman, M.J. 2006. “Measuring the Trace Elemental Composition

of Size

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1925

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239. Chow, J.C., Watso n, J.G., Lowenthal, D.H., Chen, A.L

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240. Lurmann, F. et. al., 2006, “Processes Influencing Secon dary Aerosol Formation in the San Joaquin Valley During Winter”, Journal of the Air & Waste Management Association

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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

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242. MacDonald, C.P. et. al., 2006, “Transport and Dispersion During Wintertime Particulate Matter Episo des in the San Joaquin Valley, California, J ournal of the Air & Waste Management Association

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243. Gorin, C.A. et. al., 2006, “Wood Smoke Contribution to Winter Aerosol in Fresno, CA”, Journal of the Air & Waste Management Association , (56): 158 4

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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

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245. Grover, B.D. et. al., 2006, “Measurement of Both Volatile and Semi

- Volat ile Fractions of Fine Particulate Matter in Fresno, CA”, Aerosol Science & Technology , (40): 811

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246. Liang, J. et. al., 2006, “Off

- line Diagnostic Analyses of a Three

- dimensional PM Model Using Two Matrix Factorization Methods”, Atmospheric Environment

, (40):22 247. Qin, X. et. al., 2006, “Impact of Biomass Emissions on Particle Chemistry During the California Regional Particulate Air Quality Study”, International Journal of Mass Spectrometry

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248. Qin, X. et. al., 2006, “Comparison of Two Methods for Obtaining Quantitative Mass

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249. Chen, A.L

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250. Hering, et. al., 200 7

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251. Watson, J.G. et. al., 200 8 , “Estimating Aerosol Light Scattering at the Fresno Supersite”, Atmospheric Environment

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252. Chow, J.C. et.al., 2008, “Size Resolved Aerosol Chemical Concentrations at Rural and Urban Sites in Central California, U SA”, Atmospheric Research , (90), 243

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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

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255. Kleeman, M.J. et.al., 2008, “Size Distribution of Particle

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258. Kleeman, M.J., et. al., 2009, “Source Apport ionment of Fine (PM1.8) and Ultrafine (PM0.1) Airborne Particulate Matter During a Severe Winter Pollution Episode, Environmental Science & Technology , (43), 272

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- 226.1 

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

Appendix 2

PM2.5 Source Apportionment for the San Joaquin Valley Air Basin
Using the Chemical Mass Balance Receptor Model2 PM2.5 Source Apportionment for the San Joaquin Valley Air Basin Using the
Chemical Mass Balance Receptor Model

1) Data Collection and Screening

PM2.5 chemical composition data collected at the Bakersfield

- California and

Fresno

- 1 st Street sites were used for t he Chemical Mass Balance (CMB) analysis. The
two sites are part of the Chemical Speciation Network (CSN) and use the SASS (Spiral
Aerosol Speciation Sampler, Met One, Grants Pass, OR .) for data collection. The

Bakersfield

- California and Fresno

- 1st sample rs are configured with several channels

, each channel containing one 47mmfilter with a 6.7 L/min flow rate. One channel

contains a Whatman Teflon®

- membrane filter for mass by gravimetry and elements by

XRF . Another channel includes a magnesium oxide

- coated aluminum (Al) honeycomb
after the cyclone followed by a Nylasorb nylon

- membrane filter for water

- soluble anions
i.e., 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, ano ther channel containing a Whatman QMA

quartz

- fiber filter was used for OC and EC analysis by the STN thermal/optical
transmittance (TOT) protocol

. In recent years changes were made to the carbon
sampling and analysis method. The collection method changed from the MetOne SASS

to the URG3000N sampler, which is very similar to the IMPROVE module C sampler.
The analytical method was changed from the NIOSH

- like thermal optical transmittance
(TOT) method to IMPROVE_A thermal optical reflectance (TOR). A new b ackup quartz

filter is also collected using the URG3000N to help assess artifacts. The backup filter is
placed behind the routine quartz sampler filter. This change took place on May 3, 2007
at Bakersfield and April 1, 2009 at Fresno.

Due to the change in carbon collection and analysis method, several data sets were
generated for CMB modeling to allow separate analysis of old and new carbon data.
Throughout this document we will refer to ‘old carbon’ data and ‘new carbon’ data. Old

carbon data were co llected using the SASS sampler and analyzed using the NIOSH

- like thermal optical transmittance (TOT) method. New carbon data were collected using the
modified IMPROVE version II Module C sampler, the URG3000N

, and analyzed using

the IMPROVE

- A thermal optical reflectance (TOR) method. Both old and new carbon
data were corrected for sampling artifacts prior to running CMB.3 2) Data Preparation

Organic carbon (OC) data were corrected for sampling artifacts prior to running CMB.

Old carb on 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 o rganic carbon blank of 1 ug/m3 from the measured OC

concentration. New carbon data were adjusted by subtracting network

- wide monthly

average concentrations measured on a backup filter from daily measurements of
organic carbon [88370]. The monthly average backup concentrations are shown in

Table 1.

Table 1. Organic Carbon Monthly Average Concentrations on Backup Filter

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

Avg Blank Value (ug/m3) 0.66 0.54 0.48 0.43 0.43 0.48 0.54 0.49 0.53 0.50 0.60 0.57

3) Source Profiles

The major source types which have been found to contribute to primary PM2.5 in the San Joaquin Valley are motor vehicle exhaust, vegetative burning , geological material,

marine

- derived aerosols, residual or crude oil combustion, and tire and brake wear.
Most of the source profiles applicable to the San Joaquin Valley were d etermined during
the California Regional PM10/PM2.5 Air Quality Study ( CRPAQS ) or earlier. Therefore,
the profiles used in this analysis, listed in Table 4, are the same profiles that were used

in the previous analysis for the 2008 San Joaquin Valley PM2.5 Plan.

Motor vehicle profiles for diesel (DIES) and gasoline (GAS) (Fujita et al., 2005) were used in modeling PM2.5 concentrations. Since more specific organic markers for
gasoline and diesel were not available at th e receptor site, the two profiles were
collinear and had to be combined into a single profile representing motor vehicle

emissions. D iesel and gasoline vehicle emissions source profiles were combined in
proportions equivalent to their county

- level contrib utions to the PM2.5 emissions to
produce a single emission

- weighted overall source profile. Table 2 lists PM2.5

e missions (EMFAC 20 1 1 , July 2011 ) that were used as a bas is for creating

county

- based composite profile s for Bakersfield and Fresno

.

Table 2

. Average 200 4

- 20 10 PM2.5 Exhaust Emissions (tons per day)

County Gasoline Vehicles Diesel Vehicles Kern County 2004

- 2006 ( K6GASDIE ) 0.12 2.54

2008

- 2010 ( K9GASDIE ) 0.09 1.88 Fresno County 2004

- 2006 ( F6GASDIE ) 0.12 1.33

2008

- 2010 ( F9GASDIE ) 0.08 0.974 Table 4. Source Profiles (as Percent of the PM2.5 Mass) Used in the CMB Modeling

PNO 38 35 13 18 41 54 32

SOURCE AMNIT AMSUL WBOakEuc AgBWheat OC MARINE75 TireBrke

N3IC 77.50 ± 7.75 0.00 ± 0.00 0.57 ± 0.07 0.16 ± 0.02 0.00 ± 0.00 22.88 ± 2.60 0.19 ± 1.14

S4IC 0.00 ± 0.00 72.70 ± 7.27 1.30 ± 0.83 0.44 ± 0.04 0.00 ± 0.00 7.20 ± 0.82 0.78 ± 2.10

N4CC 22.55 ± 2.26 27.30 ± 2.73 0.58 ± 0.47 0.59 ± 0.04 0.00 ± 0.00 0.00 ± 0.10 0.16 ± 0.73

NAAC 0.00 ± 0.00 0.00 ± 0.00 0.38 ± 0.15 0.54 ± 0.04 0.00 ± 0.00 28.80 ± 3.27 0.10 ± 0.42

KPAC 0.00 ± 0.00 0.00 ± 0.00 2.89 ± 0.45 6.79 ± 0.50 0.00 ± 0.00 1.07 ± 0.12 0.05 ± 0.17

OCTC 0.00 ± 0.00 0.00 ± 0.00 59.58 ± 4.75 57.03 ± 4.54 100.00 ± 10.00 0.00 ± 0.10 18.81 ± 24.53

ECTC 0.00 ± 0.00 0.00 ± 0.00 5.20 ± 1.12 10.31 ± 0.85 0.00 ± 0.00 0.00 ± 0.10 4.55 ± 5.99

ALXC 0.00 ± 0.00 0.00 ± 0.00 0.07 ± 0.05 0.07 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 0.32 ± 1.89

SIXC 0.00 ± 0.00 0.00 ± 0.00 0.22 ± 0.10 0.13 ± 0.01 0.00 ± 0.00 0.01 ± 0.00 0.69 ± 1.81

PHXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.01 0.00 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.05

CLXC 0.00 ± 0.00 0.00 ± 0.00 1.72 ± 2.02 6.16 ± 0.44 0.00 ± 0.00 38.74 ± 4.40 0.04 ± 0.08

KPXC 0.00 ± 0.00 0.00 ± 0.00 2.86 ± 0.93 5.50 ± 0.39 0.00 ± 0.00 1.07 ± 0.12 0.10 ± 0.36

CAXC 0.00 ± 0.00 0.00 ± 0.00 0.15 ± 0.10 0.10 ± 0.03 0.00 ± 0.00 1.10 ± 0.12 0.28 ± 1.04

TIXC 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.02 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.38

MNXC 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.48 ± 0.29

FEXC 0.00 ± 0.00 0.00 ± 0.00 0.09 ± 0.06 0.07 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 58.11 ± 31.26

CUXC 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.00 1.02 ± 0.07 0.00 ± 0.00 0.00 ± 0.00 0.16 ± 0.69

ZNXC 0.00 ± 0.00 0.00 ± 0.00 0.04 ± 0.02 0.01 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.35 ± 2.37

BRXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.00 0.00 ± 0.00 0.18 ± 0.02 0.00 ± 0.01

RBXC 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.01

SRXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.02 ± 0.00 0.23 ± 0.66

PBXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.06 ± 0.03

VAXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

NIXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.005 Table 4, continued.

PNO 79 80 83 84 66 67 85 86

SOURCE F6GASDIE K6GASDIE F9GASDIE K9GASDIE FDFREANN FDKERANN CHCRUC SFCRUC

N3IC 0.22 ± 1.24 0.16 ± 1.24 0.21 ± 1.24 0.16 ± 1.24 0.02 ± 0.28 0.05 ± 0.16 0.00 ± 0.05 0.00 ± 0.01

S4IC 2.77 ± 7.25 2.60 ± 7.25 2.74 ± 7.25 2.61 ± 7.25 0.56 ± 0.72 0.47 ± 0.29 14.72 ± 6.24 20.32 ± 4.24

N4CC 0.98 ± 3.24 0.89 ± 3.24 0.96 ± 3.24 0.89 ± 3.24 0.04 ± 0.18 0.13 ± 0.20 0.76 ± 0.08 0.01 ± 0.01

NAAC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.17 ± 0.07 0.26 ± 0.24 0.25 ± 0.06 0.76 ± 0.40

KPAC 0.10 ± 0.08 0.10 ± 0.08 0.10 ± 0.08 0.10 ± 0.08 0.27 ± 0.12 0.80 ± 1.25 0.01 ± 0.01 0.06 ± 0.01

OCTC 43.05 ± 27.33 42.40 ± 27.33 42.93 ± 27.33 42.41 ± 27.33 14.34 ± 8.66 10.29 ± 5.32 1.99 ± 1.33 0.09 ± 0.12

ECTC 50.59 ± 17.73 51.50 ± 17.73 50.75 ± 17.73 51.49 ± 17.73 1.92 ± 1.29 0.69 ± 0.72 3.01 ± 1.12 0.00 ± 0.07

ALXC 0.11 ± 0.14 0.11 ± 0.14 0.11 ± 0.14 0.11 ± 0.14 9.97 ± 2.95 7.67 ± 2.53 0.00 ± 0.05 0.00 ± 0.01

SIXC 1.14 ± 4.12 0.99 ± 4.12 1.11 ± 4.12 0.99 ± 4.12 26.77 ± 9.63 22.05 ± 5.29 0.00 ± 0.08 0.01 ± 0.02

PHXC 0.14 ± 0.51 0.13 ± 0.51 0.14 ± 0.51 0.13 ± 0.51 0.33 ± 0.91 0.33 ± 0.91 0.00 ± 0.57 0.00 ± 0.17

CLXC 0.07 ± 0.30 0.06 ± 0.30 0.07 ± 0.30 0.06 ± 0.30 0.11 ± 0.08 0.46 ± 0.48 0.05 ± 0.01 0.02 ± 0.00

KPXC 0.07 ± 0.08 0.07 ± 0.08 0.07 ± 0.08 0.07 ± 0.08 2.30 ± 0.92 3.26 ± 1.59 0.00 ± 0.00 0.04 ± 0.01

CAXC 0.50 ± 1.42 0.49 ± 1.42 0.50 ± 1.42 0.49 ± 1.42 3.01 ± 0.67 5.54 ± 3.03 0.00 ± 0.03 0.06 ± 0.00

TIXC 0.01 ± 0.08 0.01 ± 0.08 0.01 ± 0.08 0.01 ± 0.08 0.48 ± 0.05 0.44 ± 0.24 0.01 ± 0.00 0.01 ± 0.00

MNXC 0.00 ± 0.01 0.00 ± 0.01 0.00 ± 0.01 0.00 ± 0.01 0.11 ± 0.02 0.11 ± 0.08 0.00 ± 0.00 0.01 ± 0.00

FEXC 0.44 ± 0.44 0.44 ± 0.44 0.44 ± 0.44 0.44 ± 0.44 5.30 ± 0.58 5.09 ± 2.84 0.71 ± 0.09 0.21 ± 0.02

CUXC 0.01 ± 0.05 0.01 ± 0.05 0.01 ± 0.05 0.01 ± 0.05 0.02 ± 0.00 0.01 ± 0.01 0.01 ± 0.01 0.00 ± 0.00

ZNXC 0.27 ± 0.41 0.26 ± 0.41 0.27 ± 0.41 0.26 ± 0.41 0.14 ± 0.08 0.07 ± 0.05 0.01 ± 0.00 0.26 ± 0.03

BRXC 0.03 ± 0.06 0.03 ± 0.06 0.03 ± 0.06 0.03 ± 0.06 0.01 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

RBXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

SRXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.01 0.03 ± 0.01 0.00 ± 0.00 0.00 ± 0.00

PBXC 0.01 ± 0.03 0.01 ± 0.03 0.01 ± 0.03 0.01 ± 0.03 0.18 ± 0.13 0.09 ± 0.32 0.00 ± 0.00 0.00 ± 0.00

VAXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.42 ± 0.04 0.82 ± 0.06

NIXC 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 2.48 ± 0.25 0.79 ± 0.096 Biomass burning was represented using an agricultural burning profile (AgBWheat) from
June through October and a composite res idential wood burning profile (WBOakEuc)
the rest of the year. The agricultural burning profile (AgBWheat) was based on burning
of wheat stubble (Fitz et al., 2000). The residential wood burning profile (WBOakEuc)
was used to represent residential wood c ombustion during colder months and was
calculated as an average of oak and eucalyptus.

Geological material in the San Joaquin Valley comes from a variety of sources,
including roads (paved and unpaved), agricultural operations such as land preparation

an d 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) agric ultural soil from five crop fields (almond, cotton, grape, safflower, and tomato),
4) dairy and feedlot soil, 5) salt buildup deposits from irrigation canal drainages, and
6) building construction/earthmoving soil.

In addition to these latest profiles, s ome older soil profiles collected in the Valley in the
late 80's were also used to create composite profiles that best represent fugitive dust
sources at each site in the San Joaquin Valley. Information on the relative fractions of
paved and unpaved road dust, as well as agricultural dust, along with information on the
seasonality of agricultural operations and predominant crop types were used to
determine which source profiles to include in each composite. Site specific composite
profiles were then used in the CMB analysis

. Table 3 lists geological profiles included in
the composites created for modeling PM2.5 concentrations . Appendix A includes
additional information about geological profiles.

Table 3 . 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 107 Sea salt was represented using a reacted sea salt profile, MARINE75

, in which 25 percent of the Cl was replaced by nitrate on a molar basis (Chow et al., 1996a)

.

Tire and brake samples were collected as part of the ‘Development of a Gas and
Particulate Matter Organic Speciation Profile Database’ conducted by CE

- CERT (Fitz et al., 2000). Tire and brake samples were composited into a single weighted average
profile. The two profiles were weighted based on EMFAC 201 1 emissions, which

estimate a 9 to 1 ratio of brake emissions to tire emissions.

Secondary nitrate and sulfate were represented 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 sources, contributions from secondary OC, and the possible
positive OC sampling artifacts.

Crude

- oil combustion profiles were included to help explain ambient concentrations of
vanadium (V ) and nickel (Ni). The profile representing the Santa Fe crude

- oil broiler at

the Westside Kern Count y oil field helped to explain vanadium and nickel

concentrations at Bakersfield, while the crude

- oil profile representing the Chevron
Racetrack boiler at the Kern River oil field provided a better fit at Fresno.

4) Fitting Species

Table 5 lists fitting species used in CMB runs.

Table 5 . CMB Fitting Species

Nitrate Silicon Zinc

Sulfate Chlorine Bromine

Ammonium Potassium Rubidium

Soluble Sodium Calcium Strontium

Soluble Potassium Titanium Lead Organic Carbon Manganese Vanadium

Elemental Carbon Iron Nickel

Aluminum Copper

5) Runs

PM2.5 chemical composition data were collected on a one in three days schedule at

each site

. Table 6 shows the number of samples included under each scenario.

Table 6. Number of Samples included in the CMB Runs

Site Old Carbon New Carbon

BAC 157 267

FSF 390 2008 Data for each sampling day were run individually under several scenarios . Each run
included the following profiles: ammonium nitrate, ammonium sulfate, motor vehicle,
fugitive dust, tire and brake wear, marine, and in the case of Bakers field, crude oil

combustion. In the case of Fresno, the crude oil combustion profile was included only
when it was necessary to explain the vanadium and nickel contribution s . Biomass burning and ‘other OC’ profiles were included as needed. First

, all da ta were run with a biomass burning profile (AgBWheat from June through October, WBoakEuc the rest of
the year) and the ‘other OC’. The results were examined to determine if all source

contributions were positive and performance parameters were within acce ptable ranges

. If using the biomass burning profile along with the ‘other OC’ gave unsatisfactory
results, the data were run again using just one of the two profiles , as described below

.

1. Run 1 included a biomass burning profile and ‘other OC’

. 2. Run 2 inclu ded a biomass burning profile but not the ‘other OC’ profile. It was geared towards days when primary sources of organic carbon (biomass burning,
motor vehicle exhaust, and geological material) sufficiently accounted for the
ambient organic carbon.

3. Run 3 included ‘other OC’ but no biomass burning profile. It was geared towards
days with no biomass burning and applied only when soluble potassium

concentration was reported as zero.

Data from several runs were combined into a single data file to best rep resent source

contrib ution s . Data were combined as follows:

1. Days with estimated positive contribution s from wood burning and ‘other OC’
were included in the composite file.

2. Days with estimated negative contribution s from ‘Other OC’ were treated as

follows:

a. I f there should have been no burning on that day because the soluble
potassium concentration was zero, run 3 which includes the ‘other OC’
and no wood burning was utilized.

b. If there could have been wood burning because the soluble potassium
concentration was greater than zero, run 2 which includes biomass
burning was used.

3. Occasionally, the results were still unsatisfactory and profiles were adjusted
individually for a particular day.

Composite files were used for subsequent analysis. Table 7 shows the number of data
points from each run included in the composite file

.9 Table 7. Data included in the composite file *

.

Site Carbon Run 1 Run2 Run 3 Special Run

Type WBOakEuc AgBWheat

BAC Old 68 41 4 16 5

BAC New 80 113 28 12 0

FSF Old 176 105 7 40 9

FSF New 52 66 21 56 6

* Data with performance measures far exceeding the acceptable criteria were not included in
the composite

6) Comparison of CMB Estimates using Old and New Carbon

The Bakersfield

- California site has 14 days with parallel old and new carbon data.
Since these data were collected during the low season, the average PM2.5
concentration was only 13.6 ug/m3. The CMB model was applied to the old and new

carbon data to evaluate the impact of changing carbon collection and analysis on
source contribution. Using the old carbon, 10 percent more of the mass was
apportioned to sources. Regardless of what carbon data were used, the model
apportioned almost the same conce ntration to each source

, except ‘other OC’. Using
the old carbon data, on average, 2.5 ug/m3 was assigned to the ‘other OC’. Switching
to the new carbon data reduced the ‘other OC’ estimate to 1 ug/m3. There were also
several days when the motor vehicle contribution estimate differed slightly depending on

which carbon data were used. Even though, on average there is no difference in
measured EC between the old and new carbon method, on these particular days the

measurements differed and that difference was reflected in the motor vehicle

contribution. T he 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 contribution s

. Figures 3
through 6 compare percent of mass and contribution from major carbon sources using

old and new carbon data.10 7) Results

The CMB model was applied to 4 24 samples at BAC (157 with old carbon and 267 with
new carbon) and 590 samples at FSF (390 with old carbon and 200 with new)

. Source
contribution estimates were averaged to determine a typical contribution. Sep arately,
days with concentrations greater than 3 0 ug/m3 were averaged to determine the typical

contribution on a high PM2.5 day.

Performance measures and statistics used to evaluate the validity of CMB source
apportionments include chi

- square, r

- square, and percent of mass accounted for by the
estimated source contributions. The target values for these performance measures are

chi

- square less than 4, r

- square greater than 0.8, and percent of mass accounted for by 0 20 40 60 80 100 120 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 3. 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 5. 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 4. 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 6. Compare 'Other OC' Estimate

Old_OC New_OC11 the estimated source contributions betwee n 80% and 120%. The average performance
measures for both sites were within the acceptable limits as shown in Table s 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 dif ferent time. Second of all, 2009 had to be excluded

from the annual average calculation at Bakersfield due to missing data.

a) Bakersfield ( BAC )

The average PM2.5 concentration based on old carbon data for 2006 was 20.5 ug/m3.
Based on the new carbon data, the 2008 and 2010 average PM2.5 concentration was 18.6 ug/m3. Between 2006 and 2007 (old carbon data) there were 2 9 high days with
chemical composition data. T he average PM2.5 concentration on these days was

47 ug/m3,. Between 2007 and 2010 (new carbon data), there were 36 high days with
chemical composition data, with the average PM2.5 concentration of 46.7 ug/m3.
Sources identified by the CMB accounted for 79 to 94 percent on annual basis and 9 4

to 9 5 percent on high days.

i) Annual

Ammonium nitrate dominated the PM2.5 mass contributing 42 to 47 percent of mass.
Ammonium sulfate and biomass burning were the next most important sources
contributing 10 to 12 percent of mass. Biomass burning contributed 9 to 10 to percent

of the mass. The ‘ Other OC ’ contribution depended on the carbon data method ; using

old carbon apportioned 16 percent of mass to the ‘other OC’ while using new carbon

reduced that contrib ution to 8 percent. Geological material comprised 7 to 10 percent of
the mass. Each of the remaining sources (tire and brake wear, sea salt, and oil
combustion) contributed no more than 1 percent of the mass.

ii) High Days

The a mmonium nitrate contribution w as even more significant on high days, ranging

from 59 to 6 7 percent. Biomass burning and motor vehicle s each contributed

9 to 1 3 percent. The ‘Other OC’ contribution ranged from 3 percent using new carbon

to 10 percent using old carbon . Geological mate rial 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.12 b) Fresno ( FSF )

The average PM2.5 concentration based on old carbon data for 2006 to 2008 was
20.3 ug/m3. Based on the new carbon data, the 2010 average PM2.5 concentration was 14.2 ug/m3. Between 2006 and 2009 (old carbon data) there were 67 high days
with chemical composition data. The average PM2.5 concentration on these days was

46.3 ug/m3. Between 2009 and 2010 (new carbon data), there were 22 high days with
chemical composition data, with the average concentration of 4 0 .6 ug/m3. Sources
identified by the CMB accounted for 74 to 97 percent of the mass on an annual basis
and 82 to 93 percen t 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 vehicle s contributed slightly less, 11 to 13 percent. The a mmonium sulfate contribution was

9 to 11 percent. The ‘Other OC’ contribution , once again, depended on carbon data;
using old carbon apportioned 18 percent of mass to the ‘other OC’ while using new

carbon reduced that contribution to 9 percent. Geological material compr ised 4 to 6
percent of the mass. Each of the remaining sources contributed no more than 1 percent

of the mass.

ii) High Days

The a mmonium nitrate contribution was even more significant on high days when

5 2 to 54 percent of the mass was ammonium nitrate

. Biom ass burning was the second
most significant source, contributing 19 to 2 3 percent. The m otor vehicle contribution
ranged from 9 to 12 percent. The ‘Other OC’ ranged from 4 percent using new carbon
data to 13 percent using old carbon data. The remaining sources contributed less than

1 percent of the mass.13 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 2 9 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 9 5

. 5 9 3 .8

AMNIT AMNIT 8.2 ± 0.8 7.4 ± 0.7 26.7 ± 2. 5 29.6 ± 2.5

AMSUL AMSUL 2.0 ± 0.6 1.9 ± 0.5 2. 5 ± 1. 2 3. 4 ± 1. 3

Biomass burning Seasonal* 1.9 ± 0.4 1.5 ± 0.3 5.7 ± 1. 0 4.0 ± 0.7

Motor Vehicle K9GASDI ** 2.4 ± 1.0 1.9 ± 0.6 4. 2 ± 1. 6 4. 2 ± 1. 4

OC OC 3.2 ± 1.1 1.2 ± 0.7 4. 3 ± 1. 7 1.3 ± 1.2

Tire and Brake TireBrk 0.2 ± 0.1 0.2 ± 0.1 0.3 ± 0.2 0.3 ± 0.2

Sea Salt MARINE75 0.1 ± 0.1 0.1 ± 0.1 0.3 ± 0.1 0.2 ± 0.1

Geological FDKERANN 1.3 ± 0.3 1.6 ± 0.3 0. 7 ± 0.3 0. 8 ± 0.3

Oil Refinery SFCRUC 0.3 ± 0.1 0.1 ± 0.1 0.3 ± 0.3 0.2 ± 0.3

Table 9. BAC Source Contribution (%)

Source Profile Annual Average High Days (>= 30 ug/m3)

Name 2006 2008 and 2010 2006

- 2007 2007

- 2010

# of samples Obs Count 90 138 29 36

AMNIT AMNIT 42.2 46.8 59.3 67.4

AMSUL AMSUL 10.2 11.9 5. 6 7.8

Biomass burning Seasonal* 9.6 9.3 12. 8 9.1

Motor Vehicle KGASDI ** 12.3 11.8 9. 3 9. 6

OC OC 16.2 7.7 9. 5 2. 9

Tire and Brake TireBrk 1.0 1.3 0.7 0.7

Sea Salt MARINE75 0.4 0.5 0.6 0.5

Geological FDKERANN 6.7 10.3 1. 6 1. 7

Oil Refinery SFCRUC 1.3 0.5 0. 6 0. 3

* AgBWheat from June through October, WBoakEuc the rest of the year

* * K6GASDIE for old carbon and K9GASDIE for new carbon14 AMNIT

42.2%

AMSUL

10.2% Sea Salt

0.4% Motor

Vehicle

12.3% Biomass burning

9.6%

OC

16.2% Tire and

Brake

1.0% Geological

6.7% Oil

Refineries

1.3% Figure 7. 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 9. 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 8. 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 10. BAC Average High Day New Carbon (2007

- 2010)15 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 1 7 .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 carbon16 Contribution s on individual exceedance days are illustrated in Figure s 15 through 18

. The highest contribution from each source is also summarized in Table s 12 and 13. 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 11. 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 13. 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 12. 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 14. FSF Average High Day New Carbon (2009

- 2010)17 0 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 15. 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 16. 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 Mass18 0 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 17. 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 18. 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 Mass19 Table 12. BAC Highest Contribution by Source

Source Old Carbon New Carbon Contribution (ug/m3) Date Contribution (ug/m3) Date

Ammonium Nitrate 60 2/5/07 50 2/12/08

Ammonium Sulfate 5 10/2/06 11 1/5/10 Biomass Burning 18 12/25/06 12 11/29/07

Motor Vehicle 12 12/25/06 7 1/19/08

Other OC 9 2/5/07 5 2/12/08

Tire & Brake 0.6 1/24/07 0.8 12/4/10

Sea Salt 1 11/16/06 0.7 11/26/08 Geological 2.5 11/24/07 2.5 11/8/07

Oil Combustion 1 11/24/07 1 11/8/07

Table 13. FSF Highest Contribution by Source

Source Old Carbon New Carbon Contribution (ug/m3) Date Contribution (ug/m3) Date

Ammonium Nitrate 50 12/14/07 38 12/4/10

Ammonium Sulfate 5 12/23/07 7 1/8/10 Biomass Burning 21 1/1/08 19 11/25/10

Motor Vehicle 8 12/4/06 8 11/25/10

Other OC 13 1/19/08 6 12/24/09

Tire & Brake 0.6 12/7/06 0.4 11/24/09

Sea Salt 0.6 2/9/08 0.4 12/10/10 Geological 1.4 11/8/07 0.6 12/4/10

Oil Combustion 2 2/8/07

References

Chow, J.C., Watson, J. G., Lowenthal, D. H., and Countess, R. J.: Sources and
chemistry of PM10 aerosol in Santa Barbara County, CA, Atmospheric Environment, 30,

1489

- 1499, 1996.

Chow, J.C., Watson, J.G., Crow, D., Lowenthal, D. H., and Merrifield, T.: Comparison
of IMPROVE and NIOSH carbon measurements. Aerosol Science and Technology.

34:23

- 34, 2001.

Chow, J.C., W atson, J.G., Ashbaugh, L. L., and Magliano, K. L.: Similarities and
differences in PM10 chemical source profiles for geological dust from the San Joaquin
Valley, California. Atmospheric Environment. 37 (9

- 10):1317

- 1340, 2003.20 Chow, J. C., Chen, L.

- W. A. , Lowenthal, D. H., Doriswamy, P., Park, K., Kohl, S.,
Trimble, D. L., and Watson, J. G.: California Regional PM10/PM2.5 Air Quality Study
(CRPAQS)

– Initial data analysis of field program measurements, California Air
Resources Board, Sacramento, CA, by D esert Research Institute, Reno, NV, 2005.

Fitz, D. R. and Chow, J. C.: Development of gas and particulate matter organic profile

database

– draft work plan. Prepared for the San Joaquin Valleywide Air Pollution
Study Agency, California Air Resourced Boar d. Sacramento, CA, by the University of

California CE

- CERT, Riverside, CA, 2001.

Fujita. E. M., Campbell, D., Zielinska, B., and Chow, J. C.: Kansas City PM
Characterization Study, Round 1 Testing Report

– Addendum A, Chemical Speciation,
prepared for the U.S. Environmental Protection Agency, Desert Research Institute,

Reno, NV, 2005.21 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 FDTOM11 

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

Appendix 3

Source Apportionment of PM2.5 Measured at the Fresno and

Bakersfield Chemical Speciation Network Sites in San Joaquin Valley Using the Positive Matrix Factorization M odel2 

### Source Apportionment of PM 2.5 

### Measured at the Fresno and

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

Sample Collection and Data Screening

PM 2.5 chemical speciation samples were collected on a one

- in

- three day

schedule at the Fresno

- First St. and Bakersfield

- California Ave. Chemical Speciation

Network ( CS N) monitoring site s located in the San Joaquin Valley (SJV) . There were good agreement s between PM 2.5 d ata collected by the speciation sampler s and the

collocated Federal Reference Method (FRM) sampler s in matched Fresno data ( 340 samples, slope = 1.00

, Intercept = 1. 0 8, r 2 = 0. 97 ) and Bakersfield ( 175 samples, slope = 0.94, Intercept = 0.92, r 2

= 0.94) between 2008 and 2010.

The Thermal Optica l Transmittance (TOT) protocol had been used to analyze carbon mass collected on the quartz filter s . 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 t he speciation data for

which TOR OC and EC concentrations were available were considered in th is source apportionment stud y

. Since a carbon denuder that minimizes the positive sampling artifact caused by adsorption of gaseous organic materials was not included upstream of quartz filter in the

CSN samplers, and none of the reported CSN data were blank corrected, an integrated OC artifact concentration that includes OC adsorption and desorption was estimated utilizing the intercept of the regression of OC concentrations against PM 2.5 mass concentrations (Tolocka et al. 2001, Kim et al. 2005). Samp les for which PM 2.5 or OC

concentration s 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

concentration s

. Comparing co

- located PM 2.5 data measured by CSN and FR M samplers, and comparing PM 2.5 and S ulfur (S) concentrations, outliers were censored

for the two data sets. Using 189 data points out of 353 data points between 2009 and

2010 at Fresno and 187 data points out of 192 data points at Bakersfield between 200 8

and 20 10 , the intercept s of 0.576 μg/m 3 and 1.480 μg/m 3 in PM 2.5 regression against

OC concentration s are considered to be the integrated OC artifact concentration s at Fresno and Bakersfield, respectively (Figure 1 ). The OC concentrations analyzed in

t h is stud y were corrected by subtracting the integrated OC artifact concentration s.3 Fresno

- First St.

Bakersfield

- California Ave.

Figure 1. OC artifact estimations: PM 2.5 concentrations versus OC c oncentrations.4 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 site s . 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 were larger than twice

the PM 2.5 concentrations or the sum of all measured species were l ess than 50% of

PM 2.5 concentrations were also excluded. Finally, samples that contain fireworks particles collected on Independence Day and New Year’s Day were excluded since they had unusually high concentrations of OC, EC, K + , Na + and metals. Overall, 10.3 % of

the Fresno data and 16.5 % of the Bakersfield data were excluded in this study

. For the chemical species screening, X

- Ray Fluorescence (XRF) S was excluded from the analyses to prevent double counting of mass concentrations

. Due to the higher an alytical precision compared to XRF Na and XRF K, IC Na + and IC K + were included in the analyses. Chemical species below MDL values more than 90% were excluded. As recommended by Paatero and Hopk e (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 2 4 species includ ing 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 structure (i.e., 7% of meas ured 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 Tables A 1 and A 2 in the Appendix

. The procedure of Polissar et al. (1998) was used to assign input data for PMF2. The measurement values are used for the input concentration data, and the sum of the analytical uncertainty and one

- third of the detection limit 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 the detection limit values, and their input

uncertainties are set at five

- sixth of the detection limit values . Missing values are replaced by the geometric mean of the measured values for each species. To down

weight these replaced data and then to reduce their influence on the solution, their accompanying uncertainties are set at four times the geometric mean value. The conditional probability function (CPF) analysis was used to estimate the possible directions of the local source impacts (Kim and Hopke, 2004). The CPF was calculated for each source using the PMF2 source contributions coupled with wind data. As recommended by Paatero and Hopke (2003), which is to down

- weight the variable in the analysis so that the noise does not compromise the solution, it was found necessary to increase the input uncertainties of OC, EC, and Cl by a factor of 3 for the Fres no data

and OC and Na + by a factor of 3 for the Bakersfield data to obtain physically interpretable PMF2 results.5 PMF Results

Seven major sources were resolved from PMF2 analyses for both sites (matrix rotational parameter: Fresno FPEAK = 0.1; Bakersfield FPEAK = 0)

. The comparison of

the reconstructed PM 2.5 contributions (sum of contributions from all sources) with

measured PM 2.5 concentrations shown in Figure 2 indicates that the resolved sources effectively reproduce the measured values and account for most of the variation in the

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 A 3 which present average source contributions,

seconda ry nitrate contributed the most at both sites (35% at the Fresno site, 41% at the Bakersfield site). The pie charts indicate that three major sources (i.e., secondary nitrate, secondary sulfate, and motor vehicle) contributed 74% of PM 2.5 concentrations

a t both sites. Figure 4 shows monthly average source contributions. Secondary nitrate, motor vehicle, and biomass smoke contributed the most in winter. The source profiles, corresponding source contributions, weekday/weekend variations, monthly averaged source contributions, and potential source directions are presented in Figures A1 through A10 in the Appendix

.
The s econdary nitrate factor was identified by its high concentration of NO 3 - and

NH 4 + . It consisted of NH 4 NO 3 and several minor species such a s 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 nitrate concentration s than Fre sno. Secondary nitrate particle s had winter

- high trends at both 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. S econdary nitrate and secondary sulfate did not show clear weekday/weekend variations. Secondary

sulfate showed seasonal variation s with higher concentrations in summer when the photochemical activity was highest at both sites. The CPF plots for secondary nitrate pointed S and NE at both sites . The CPF plots for secondary sulfate pointed SE at the

Fresno site and SW at the Bakersfield site.

The m otor vehicle factor was identified by its high concentration of OC, EC, NO 3 -

, and minor species such as Fe (Wa tson et al., 1994). Motor vehicle emission s contributed 12% and 13% of the PM 2.5 mass concentrations at Fresno and Bakersfield, respectively. Motor vehicle emission s did not show clear weekday/weekend variations

at either site , however there was a winter

- high seasonal trend.6 The b iomass smoke factor was characterized by OC, EC, and K + (Watson et al., 2001)

and contributed 11% and 10% to the PM 2.5 mass concentration s at Fresno and Bakersfield, respectively . The b iomass smoke category reflects contribut ions from residential wood burning and smoke from commercial cooking. The b iomass smoke did not show weekday/weekend variations. The b iomass smoke did show winter

- high trends suggesting that it was mostly contributed by residential wood burning. The CPF plots for the biomass smoke pointed to high contributions from NE and S at both sites.7 Fresno

- First St.

Bakersfield

- California Ave.

Figure 2 . Measured versus PMF predicted P M 2.5 mass concentrations.8 Figure 3 . Average source contributions

.

Figure 4 . Monthly average source contributions.9 The a irborne soil fac tor was identified by its high concentration s 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 re

suspended crustal materials by roa d traffic as indicated by the presence of OC or EC in the source profile s

. Airborne soil did not show clear weekday/weekend variation. Both

sites exhibited autumn

- high seasonal trend s . The CPF plot s for airborne soil suggest ed high contributions from SW and S at both sites.

The a ged sea salt factor was represented 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 ha d high contribution s in summer at the Fresno site. Interestingly, it ha d a high contribution in winter at the Bakersfield site

. The CPF plot for aged sea salt at Fresno site point ed towards NE. The CPF plot for aged sea salt at Bakersfield site suggest ed high contributions from NE an d S.

A possible i ndustrial source such as metal processing that w a s characterized by OC, EC, Fe, and Zn was identified at both sites

. This source account ed for 2 % of the

PM 2.5 mass concentrations at both sites

. It show ed wea k weekday

- high variation s at

the Bakersfield site. The i ndustrial source show ed winter

- high variation s at the Fresno

site. The CPF plot suggest ed high contributions from NE and SW at both sites.

Figure 5

. A verage source contributions in Fresno

– 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.).10 **Higher PM 2.5**  **Day Contributions**

The average source contributions when PM 2.5 concentrations we re 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. The contributions from secondary nitrate and

motor vehicle were increased from 35% up to 54% and from 12% up to 2 3%, respectively, at the Fresno site. The biomass burning contributions also increased slightly from 11% up to 13% at Fresno site. At the Bakersfield site, the contributions from secondary nitrate increased from 41% up to 64% and aged sea salt from 4% up to

6%.

Conclusions

PM 2.5 speciation and related meteorological data collected at the Fresno

- First St.

and Bakersfield

- California Ave. CSN monitoring sites between 200 8 and 20 10 were analyzed by PMF2. Seven major PM 2.5 sources were identified at both mo nitoring sites : secondary nitrate, secondary sulfate, motor vehicle, biomass smok e, airborne soil, aged sea salt, and industrial

. Annual average and high day source contributions showed that

s econdary nitrate, secondary sulfate, motor vehicle s, and biomas s burning were the largest contributors to PM 2.5 concentration s.

References

Kim, E., Hopke, P.K. Comparison between conditional probability function and nonparametric regression for fine particle source directions, Atmospheric Environment 38, 11, 4667

- 4673, 2004.

Kim, E., Hopke, P.K. and Qin, Y. Estimation of organic carbon blank values and error structures of the speciation trends network data for source apportionments, Journal of Air and Waste Management Association 55, 1190

– 1199, 2005.

Paatero, P., H opke, P.K. Discarding or downweighting high

- noise variables in factor analytic models, Analytica Chimica Acta, 490, 277

- 289, 2003.

Polissar, A.V., Hopke, P.K., Paatero, P., Malm, W.C. and Sisler, J.F. Atmospheric Aerosol over Alaska 2. Elemental Compositio n and Sources, Journal of Geophysical Research, 103, 19045

- 19057, 1998.

Song, C.H., Carmichael, G.R. The aging process of naturally emitted aerosol (sea salt and mineral aerosol) during long

- range transport, Atmospheric Environment 33,

2203

- 2218, 1999.11 T olocka, M.P., Solomon, P.A., Mitchell, W., Norris, G.A., Gemmill, D.B., Wiener, R.W., Vanderpool, R.W., Homolya, J.B. and Rice, J.East versus west in the US: Chemical characteristics of PM2.5 during the winter of 1999, Aerosol Science and Technology 34, 88

- 96, 1999.

Watson, J.G., Chow, J.C., Lowenthal, D.H., Pritchett, L.C. and Frazier, C.A. Differences in the carbon composition of source profiles for diesel and gasoline powered vehicles, Atmospheric Environment 28(15), 2493

- 2505, 1994.

Watson, J.G., J.C. C how, and J.E. Houck, PM 2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological material, and coal burning in northwestern Colorado during 1995, Chemosphere, 43, 1141

- 1151, 2001.12 APPENDIX

Table A1. Summary of PM 2.5 species mass c oncentrations 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 25.313 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 8.714 Table A 3 . Average source contributions ( μ g/m 3 ) to PM 2.5 ma ss concentration.

Sources Average source contribution ( ± 95 % distribution )

Fresno Bakersfield

Secondary nitrate 4.89 (1.09) 8.07 (1.85)

Secondary sulfate 1.72 (0.16) 2.60 (0.39)

Motor vehicle 3.70 (0.44) 4.01 (0.39)

Biomass smoke 1.47 (0.28 ) 2.02 (0.28)

Airborne soil 0.83 (0.14) 1.97 (0.37)

Aged sea salt 1.04 (0.16) 0.79 (0.22)

Industrial 0.22 (0.03) 0.40 (0.11)

Estimated 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)15 Table A4. Average sourc e contributions (μg/m 3 ) to PM 2.5 mass concentration at Fresno

First 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 2516 Figure A1 . Source profiles deduced from PM 2.5 sam ples measured at Fresno

- First St. (prediction ± standard deviation).17 Figure A2 . Source profiles deduced from PM 2.5 samples measured at Bakersfield

California Ave. (prediction ± standard deviation).18 Figure A3 . Source contributions deduced from PM 2. 5 samples measured at Fresno

- First

St.19 Figure A4 . Source contributions deduced from PM 2.5 samples measured at Bakersfield

California Ave. ( missing data : Jan.

- Jun. 2009 )20 Figure A5 . Weekday/weekend variations at Fresno

- First St. (mean ± 95 % distri bution).21 Figure A6 . Weekday/weekend variations at Bakersfield

- California Ave. (mean ± 95 % distribution).22 Figure A7 . The monthly variations of source contributions to PM 2.5 mass concentration

at Fresno

- First St. (mean ± 95 % distribution).23 Fi gure A8 . The monthly variations of source contributions to PM 2.5 mass concentration

at Bakersfield

- California Ave. (mean ± 95 % distribution).24 Secondary nitrate

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

contributions at Fresno

- First St.25 Secondary nitrate

Figure A10 . Conditional probability function plots for the highest 25% of the mass

contributions at Bakersfield

- California Ave.1 

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

Appendix 4

Methodology for Meteorological Adjustment of

PM2.5 Trend Statistics2 Introduction

A ir quality trends can help reveal th e effects of emission control s trategies and regulations on ambient air pollution levels

. However, meteorological conditions also

affect pollutant levels and can obscure the effects of changing emission s on ambient air pollution levels ov er time

. If the met

- effects can be identified, quantified, and removed

, the met

- adjusted trends may reveal th e emissions

- induced trends with greater clarity

.
For the San Joaquin Valley PM2.5 SIP, met

- adjusted trends were prepared for annual average PM2.5 and for PM2.5 exceedance days . This Technical Appendix presents the method ology used to con struct the met

- adjusted trends.

1. Data Acquisition and Preparation

PM2.5 mass concentrations from the air quality monitoring sites in two major

urban centers of the SJV (Bakersfield and Fresno) were collected

. Meteorological data

for factor s that may impact the PM2.5 concentrations were also acquired from various meteorological monitoring networks

. Monitors at ground level provided temperature, relatively humidity, pressure, wind speed, wind direction, precipitation, and solar

radiation data. For various reasons, surface pressure, wind direction, precipitation, and

solar radiation were not used in the final analysis

. Routine rawinsondes (weather balloons) at Oakland provided data for 500 millibar height s and 850 millibar
temperature s

. These surface and upper air factors are consistent with studies of meteorological conditions associated with daily PM2.5 levels [Dye et al., 2003]

.
Table 1 lists the air quality and meteorological monitoring sites that provided data used in this analysis

. The PM2.5 and meteorological data presented are daily regional averages of the data collected from the sites in T able 1

.

Table 1

. Air quality and meteorological monitoring sites

Region Air Quality Sites Meteorological Sites

Bakersfield

Area B akersfield

- Golden State Highway,

Bakersfield

- 5558 California Avenue

, Bakersfield

- 410 E Planz Road Oakland (Upper Air), M ercury /D esert Rock (Upper Air), Vandenberg AFB (Upper Air),

B akersfield

- Golden State Highway,

Bakersfield

- 5558 California Avenue

, Oildal e

3311 Manor Street

, Shafter

- Walker Street

, Arvin

- Edison

, Belridge

Fresno Area Fresno

- 1st Street

, Clovis

- N Villa Avenue

Fresno

- Hamilton and Winery Oakland (Upper Air)

Fresno

- 1st Street

Clovis

- N Villa Avenue

,

A consistent analysis of met

- effects on daily PM2.5 will benefit from and may require the presence of all PM2.5 and meteorological data for each daily record used in the analysis. If any values are missing, the entire day might be excluded from further

co nsideration. Therefore, data completeness is very desirable for the analysis to be as meaningful as possible

. To minimize instances of missing PM2.5 and meteorological3 data, imputed values were calculated based on relationships for measured data at sites nearby

. The imputed values were used when appropriate

. D etails concerning the imputation method (called “I

- Bot”) are available from the Air Quality and Statistical

Studies Section of the ARB

.

2. Analytical method: Classification and Regression Trees

Classification and Regression Trees ( CART ) is a statistical exploratory technique for uncovering structures in the data , which is sometimes called “data mining” [ Breiman et al., 1984; Thompson et al., 2001; Slini , et al., 2007]

. CART is a non

- parametric

d ecision tree learning technique that produces a classification tree if the dependent (target) variable is categorical or a regression tree if the dependent variable is numeric

. A t each step of the tree building process, CART finds the best possible indepe ndent

variable ( or linear combination of independent variables ) to split the values of the target

variable into two groups for which the means are as different as possible (subject to certain constraints)

. Each of the new groups is called a “child” node. The process of node splitting is repeated for each child node and continued recursively until a stopping

criteri on is satisfied and a set of terminal nodes is reached [ Breiman et al., 1984 ; Xu et al., 2005 ]

. In this way, the nodes of the final CART tree explain the values of the dependent variable in terms of the independent variables used to make splits

.
In this analysis of PM2.5 and meteorology, the final CART tree explains daily

PM2.5 in terms of the me teorological variables ( paramete rs) used to make the splits

. T able 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 ) For ecasting Program [ Dye et al., 2003]

.

Table 2

. Meteorological p arameters 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 S urface 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 concentrations4 are mostly due to differences in meteorology

. We then applied CART analysis to the base years to define a relationship (“tree”) between daily PM2.5 and daily meteorological conditions.

First, we forced the tree to be split by season so that an independent sub

- tree was generated for each season

. Each sub

- tree consisted of one or more terminal nodes representing different meteorological classes. The CART system makes the

differences in PM2.5 between the met

- class es as large as possible a nd the differences

in PM2.5 within the met

- class es as small as possible. The PM2.5 concentration representing each met

- class (terminal node) is the average concentration of all the days assigned to that met

- class in the base years

. For each day assigned to a met

- class, the

average PM2.5 for the met

- class serves as a “predicted PM2.5 ” for that day. Days with high predicted values have met

- conditions that are more conducive to PM2.5 formation compared to days with low predicted values.

The CART

- defined rel ationships between meteorology and PM2.5 in the base years were then used to assign days in the other years to their appropriate met

- classes

based on their day

- specific meteorological data

. T he predicted PM2.5 values for all the days are then used to adju st PM2.5 t rends up or down to compensate for each year’s

PM2.5

- conduciveness relative to “normal”

.

3. Results and Discussion

Based on daily air quality and meteorological data in 2004

- 2006, a CART tree

with 17 met

- classes (terminal nodes) was constructed for the Bakersfield area (Figure 1a) and a CART tree with 22 met

- classes w as constructed for the Fresno area (Figure 1b)

. Figure 2 indicates that ~75

– 80 percent of the variation in daily PM2.5 during the base years is accounted for by each of the CART trees

. Table 3 shows that

three met

- factors

– wind speed, stability ( difference surface 850 m b temperatures), and

minimum surface temperature

– affect e d daily PM2.5 concentration s strongly in both

Bakersfield and Fresno

, while relative humidity (RH) wa s more important for PM2.5 production in the Bakersfield area than in the Fresno area

. I n general, high PM2.5

concentrations in the Bakersfield we re associated with relatively high stability , low wind speed and high RH

. In Fresno, high PM2.5 was g enerally associated with cold mornings (low minimum surface temperature) , high stability, and low wind speed.

It is worth mentioning that this CART model treats each day independently and does not directly characterize met

- conditions over a sequen ce of days that may result in long

- term buildup and transport of PM2.5.

A sensitivity analysis was, also, done to explore the impact of the selected base years on the CART results for the Bakersfield area

. For this purpose, diff erent sets of base years (2003

- 2005, 2004

- 2006, and 2006

- 2008) were used with CART to develop relationships between met eorology and PM2.5. The met

- adjusted annual average

PM2.5 concentrations proved to be quite similar regardless of the base years used in the CART analysis

.5 (a)

(b)

Figure 1

. CART terminal nodes sorted by target variable predictions ( PM2.5 ) in (a)

Bakersfield area and (b) Fresno area

. 20 40 60 80BAKERSFIELD_PM25_AVERAGE Terminal Nodes Sorted By Target Variable Prediction

20 40 60 80FRESNO_AVERAGE Terminal Nodes Sorted By Target Variable Prediction6 (a)

(b)

Figure 2

. Observations vs. CART predictions during the base years in the (a)

Bakersfield and (b) Fresno a rea s y = 1x + 5E

- 06

R2 = 0.7523

0 10 20 30 40 50 60 70 80 90 100

0 20 40 60 80 100Observed PM2.5 at Bakersfield

CART Predicted PM2.5 at Bakersfield Area

y = 1x

- 5E

- 08

R2 = 0.789

0 10 20 30 40 50 60 70 80 90

0 10 20 30 40 50 60 70Observed PM2.5 at Fresno Area

CART Predicted PM2.5 at Fresno Area7 Table 3

. R elative importance of met

- factor s in form ing the CART tree s on a
0 to 100 scale.

A nnual average PM2.5 trends for observed data and for CART

- predict ed values (2004

- 2006 used as base years) were compared in the Bakersfield and Fresno areas. In both areas, observed PM2.5 levels decreased significantly from 1999 to 2003, were relatively flat from 2003 to 2008 , then decreased in 2009 and 2010. CART

- predicted trends represent meteorological conditions that affe ct PM2.5 concentrations. For the Bakersfield area, the C ART

- predicted trend indicates that met

- conditions favored lower

than normal PM2.5 in 1999

– 2000, normal PM2.5 from 2001

– 2006, and higher than

normal PM2.5 from 2007

– 2010. The CART

- predicted tren d for the Fresno area

indicates that met

- conditions have been more stable and have had relatively small impacts on the observed PM2.5 trends from 1999

– 2010

.

The CART

- predicted trend information was merged with the observed trends to produce met

- adjusted trends for annual average PM2.5. Figure 3 shows the observed

and met

- adjusted trends for (a) the Bakersfield area and (b) the Fresno area. Linear

trend lines are shown for the observed and the met

- adjusted trends in each area. Figure 3 indicates that t he met

- adjusted trend shows a greater decrease than the observed trend in the Bakersfield area, while the met

- adjusted trend is similar to the observed trend in the Fresno area. In both areas, met

- adjusted PM2.5 decreased by

~ 0.8 μ g/m 3 per year from 1999

– 2010. Overall , the met

- adjusted trends indicate that average PM2.5 decreased 40

– 50 percent in the Bakersfield and Fresno areas from

1999

– 2010 as a consequence of ongoing emission reductions. Meteorological Factor Variable Importance in Bakersfield Variable Importance in Fresno

Average Wind Speed 100 100

Aveage Surface T - 850 mbar T 69.02 56.24

Minimum Surface Temperature 56.11 60.89

Season 52.52 51.48

Minium Surface T - 850 mbar T 47.74 36.37

Maximum Surface T - 850 mbar T 45.59 96.49

Average Relatively Humidity 40.12 10.11

Average Surface Temperature 39.84 59.65

Maximum Surface Temperature 28.51 37.02

Afternoon 850 mbar Temperature 26.4 6

Average 850 mbar Temperature 20.88 5.89

Average 500 mbar Height 12.42 22.91

Afternoon 500 mbar Height 12.14 7.07

Maximum Relative Humidity 10.45 9.67

Morning 850 mabr Temperature 6.01 5.85

Maximum Surfact T - Minimum Surface T 4.15 3.76

Minimum Relative Humidity 3.54 2.84

Morning 500 mbar Height 0.07 5.438 (a)

(b)

Figure 3

. Trends of o bserved and meteorologically adjusted PM2.5

concentrations in (a) Bakersfield and (b) Fresno areas of the San Joaquin Valley y =

- 0.56x + 1139.6

R2 = 0.62

y =

- 0.8x + 1623.7

R2 = 0.77

0 10 20 30

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011PM2.5(μg/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(μg/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)9 Trends for exceedance days were prepared for the Bakersfield and Fresno sub

regions. For this work, an exceedance day meant th at the sub

- regional average daily PM2.5 concentration was greater than or equal to 35 ug/m3. Trends for the observed

PM2.5 data and for the CART

- predicted PM2.5 data (representing meteorological effects) were prepared.

In the Bakersfield area, s imilar to the annual avera ges, the CART

- predicted exceedance days increased from the earlier years to recent years, indicating an increase in meteorological conduciveness for PM2.5. In the Fresno area, the impact of meteorology on PM2.5 exceedance days was relative ly small, again similar to the annual averages. In both areas, the observed PM2.5 exceedance days were greater

than the CART

- predicted PM2.5 exceedance days from 1999 through 2002. The two trends were similar from 2003 through 2008. Finally, for 2009 and 2010, observed

PM2.5 exceedance days decr eased significantly and dipped below the CART

- predicted exceedance days. The implication of these results is that emission reduction s played a significant role in decreas ing the PM2.5 exceedance days from 1999

– 2010, especially

in the Bakersfield area.

The CART

- predicted trend information was merged with the observed trends to produce

met

- adjusted trends for PM2.5 exceedance days. Figure 4 shows that after adjusting for meteorology, PM2.5 exceedance days decreased about 60

– 70 percent from 1999 to 2010, with decreases of ~ 3.5 days per year in the Fresno area and ~ 4.5 days per year in the Bakersfield area.10 (a)

(b)

Figure 4

. Trends of observed and meteorologically adjusted PM2.5 exceedance days in (a) the Bakersfield area and (b) the Fresno area

. y =

- 2.5x + 5117

R2 = 0.37

y =

- 4.5x + 8976

R2 = 0.73

0 30 60 90

1998 2000 2002 2004 2006 2008 2010 2012PM2.5Exceedance Days

Year Observed and Met

- Adjusted PM2.5 Exceedance Days in Bakersfield Area

Observed PM2.5 Exceedance Days in Bakersfield Area

Met-Adjusted PM2.5 Exceedance Days in Bakersfield Area

Linear (Observed PM2.5 Exceedance Days in Bakersfield Area)

Linear (Met-Adjusted PM2.5 Exceedance Days in Bakersfield Area)

y =

- 4.2x + 8449

R2 = 0.58

y =

- 3.5x + 7138

R2 = 0.50

0 30 60 90

1998 2000 2002 2004 2006 2008 2010 2012PM2.5Exceedance Days

Year Observed and Met

- Adjusted PM2.5 Exceedance Days in Fresno Area

Observed PM2.5 Exceedance Days in Fresno Area

Met-Adjusted PM2.5 Exceedance Days in Fresno Area

Linear (Observed PM2.5 Exceedance Days in Fresno Area)

Linear (Met-Adjusted PM2.5 Exceedance Days in Fresno Area)11 4. Summary

Overall, CART analysis can help us to define the relationship between PM2.5 mass concentrations and meteorological conditions and to calculate meteorologically adjusted trends. Such trends can help reveal the impact of emission changes on air pollutant levels, and promote the development of effective air pollution control strategies and regulations. Of course, as with any statistical analysis, there are uncertainties and

limita tions in CART analysis. Therefore, caution is needed when interpreting the resulting air quality trends, especially when small differences occur within short time periods.

The annual average PM2.5 concentrations and the number of exceedances of

the 24

- hou r PM2.5 standard follow ed similar trends in the Bakersfield and Fresno areas

from 1999

- 2010 . In the Fresno area, the meteorological conditions seem to have been relatively stable, so met

- adjusted trends were similar to the observed trends. In the

Bakersf ield 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 Ba kersfield area show a greater decrease in PM2.5 levels compared to the

observed trends.

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

PM2.5 obser vations are adjusted to derive met

- adjusted PM2.5 trends. The analyses

indicate that the met

- adjusted annual average PM2.5 concentrations decreased at a

rate of ~0.8 μ g/m 3 per year between 1999 and 2010 for a total of ~40

- 50 percent

decrease in met

- adjust ed PM2.5 in the Bakersfield and Fresno areas as a result of emission reductions during this period. Met

- adjusted trends for PM2.5 exceedance days indicate ~60

- 70 percent progress from 1999

– 2010, with decreases of ~ 3.5 days per year in the Fresno area a nd ~ 4.5 days per year in the Bakersfield area.

5. References

Breiman, L., J.H. Friedman

, R.A. Olshen, C.J. Stone, Clas sification 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 ) foreca sting program, EPA

- 456/R

- 03

- 002, Environmental Protection Agency, NC, U.S.A.

, 2003.

Slini, T.

, A. Kaprar a, K. Karatzas, N. Moussiopoulos, PM10 forecasting for Thessaloniki, Greece, Environmental Modelling and Software, Volume 21, Issue 4, April 2006, Pages 559

- 565, ISSN 1364

- 8152, 10.1016/j.envsoft.2004.06.011 , 2006

.12 Thompson, M

. L

., J

. Reynolds, L

. H

. Cox , P

. Guttorp, P

. D

. Sampson, A review of statistical methods for the meteorological adjustment of tropospheric ozone, Atmospheric Environment, Volume 35, Issue 3, Pages 617

- 630, ISSN 1352

- 2310

, 10.1016/S1352

- 2310(00)00261

- 2, 2001.

Xu, M., P. Watanachaturaporn, P.K. Varshney, M.K. Arora, Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment 97,

322

- 336 , 2005

.Appendix C

Link to District 2012 PM2.5 Plan

www.valleyair.org/Air_Quality_Plans/PM25Plans2012.htmThis page intentionally left blankAppendix D

Link to U.S. EPA Approval of 2007 State Strategy

www.arb.ca.gov/planning/sip/sjvpm25/Final_FR_2008_SJV_PM25.pdf