Chapter 4

Scientific 

Foundation and 

PM2.5 Modeling 

Results

2012 PM2.5 Plan

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

Chapter 4: Scientific Foundation and PM2.5 Modeling Results

2012 PM2.5 Plan 

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4-1 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

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Chapter 4: Scientific Foundation and PM2.5 Modeling Results

The nature of PM2.5 formation in the San Joaquin Valley is highly complex, and 

attainment of the 2006 National Ambient Air Quality Standard (NAAQS) PM2.5 standard 

is not a one-size-fits-all effort. Even though ultimate attainment of the 2006 NAAQS is 

determined by PM2.5 concentrations on the days with the highest concentrations—the 

worst air quality days—significant differences in regional natural environments and the 

relative contribution of precursor emissions requires regionally specific modeling and 

regionally specific control strategies. Also, differences within PM2.5 itself, directlyemitted PM2.5 versus secondary PM2.5 forming in the atmosphere through series of 

chemical reactions, adds to the complexity inherent in modeling and planning efforts. 

Within this complexity, the District’s modeling efforts must be able to project PM2.5 

concentrations for each square kilometer of the San Joaquin Valley (Valley), not just the 

areas that currently have air quality monitoring stations.

This complexity is accounted for in the modeling and other scientific analyses 

conducted for this plan. The District, ARB, and researchers have developed and refined 

these analytical tools, including regional modeling, over many years. The District’s 

regional modeling protocol, included as Appendix G to this plan, is over 150 pages and 

has been reviewed by other air agencies and by atmospheric chemists, atmospheric 

scientists and other air quality experts. This protocol notes that the Valley is one of the 

most studied airsheds in the world in terms of the number of publications in peerreviewed scientific journals and other major reports. Such scientific analyses, and the 

field studies providing data for these analyses, are the foundation of the modeling 

efforts for this plan. Public and private sector partnership through the San Joaquin 

Valleywide Air Pollution Study Agency (Study Agency) provided funding and 

coordination for many of these studies. 

This chapter provides an overview of the scientific foundation for this PM2.5 Plan. It 

describes Study Agency and other research efforts related to this plan, the nature of 

PM2.5 in the Valley, and PM2.5 species. It also summarizes the regional modeling 

effort and the results of the analyses conducted for this plan. These results show which 

emissions control strategies will most effectively assure attainment of the 2006 PM2.5 

NAAQS and improved PM2.5 air quality throughout the Valley.

4.1 AIR QUALITY RESEARCH

The Study Agency has developed and funded extensive research on particulate matter 

in the Valley. The Study Agency was established in 1985 under a joint-powers 

agreement between local counties and includes input from districts, the State, EPA, 

public and private industry representatives, and other governmental agencies to create 

a cooperative and unbiased research program. The Study Agency’s main purpose is to

further the scientific understanding of regional air quality issues to assist regulatory 

agencies in the development of strategies to attain air quality standards by providing the 

best available science for policy decisions. In 1992, Study Agency expanded particulate 

matter research to include PM2.5. 
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4-2 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

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The initial field program for PM10 and PM2.5 was the Integrated Monitoring Study 

(IMS95), which was a two month pilot study that started in December 1995. This short

but informative study provided the foundation for successful PM10 and PM2.5 

monitoring and analysis including the following:

 Evaluating which air monitoring techniques worked best for air quality research 

projects

 Investigating and improving innovative techniques for identifying airborne 

particulate matter with atmospheric tracers

 Developing a model for data collection

 Developing a day-specific emission inventory development

While the entire Valley was studied, special attention was given to Corcoran, Fresno, 

and Bakersfield, which had the highest levels of particulates.

This pilot program led to the Study Agency’s largest PM2.5 sampling and research 

effort. The California Regional Particulate Air Quality Study (CRPAQS) commenced in 

December 1999 and continued through February 2001. The objectives of CRPAQS 

were to provide an improved understanding of PM emissions, composition, and the 

dynamic atmospheric processes surrounding them; establish a strong scientific 

foundation for informed decision making; and develop methods to identify the most 

efficient and cost-effective emission control strategies to achieve the PM10 and PM2.5 

standards in Central California. This robust study collected PM2.5 data from both within 

the Valley and from areas outside the Valley that impact Valley air quality. The resulting 

dataset has been studied by many researchers using Study Agency funding totaling

over $27.5 million. CRPAQS continues to support research and improve the

understanding of PM2.5 in the Valley.

Results from IMS95 and CRPAQS have helped the District and other agencies 

understand differences in urban and rural PM episode development, the mechanisms 

for how nitric acid forms aloft and its contribution to regional ammonium nitrate, the role 

of NOx as the limiting precursor for ammonium nitrate formation, and the role of fog on 

secondary pollutant formation and removal.

This information greatly informed the development of effective PM10 and PM2.5 

attainment plans for the District. Data collected from these studies have allowed the 

District, CARB, EPA, and others understand PM2.5 creation and movement in the 

Valley, model future year PM2.5 levels, and source apportion PM2.5 concentrations to 

the correct contributing sources. Study Agency projects and results have also been 

used to improve air pollutant emissions inventories. 

The District’s 2003 PM10 Plan, 2006 PM10 Plan, and 2008 PM2.5 Plan included data 

and analyses from CRPAQS within the photochemical modeling performed for each

plan. Similarly, research conducted through CRPAQS is being incorporated into 

photochemical modeling, PM2.5 trends evaluation, and weight-of-evidence analysis for 

this plan. Implementation of the plans that use CRPAQS information will result in 

significantly better visibility, improved health and well-being of the citizens of the Valley, 
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4-3 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

2012 PM2.5 Plan 

and eventual attainment of the 2006 PM2.5 NAAQS. The District will continue to build 

on this foundational research and seek additional opportunities to support future 

research. 

4.2 THE NATURE AND FORMATION OF PM2.5

Unlike ozone, which is a fairly simple molecule of three oxygen atoms, PM2.5 can be 

composed of any material that has a diameter of 2.5 microns or less. PM2.5 can be 

emitted directly as primary PM2.5 from various sources, or it can form secondarily 

through chemical reactions in the atmosphere. Among the chemical precursors that can 

form secondary PM2.5 are nitrogen oxides (NOx), volatile organic compounds (VOCs), 

sulfur dioxide (SO2), and ammonia (NH3). In addition, naturally occurring emissions from 

biogenic sources, such as plants, can also add to the formation of PM2.5.

The resulting ambient PM2.5 mixture can include aerosols (fine airborne solid particles 

and liquid droplets) consisting of components of nitrates, sulfates, elemental carbon, 

organic carbon compounds, acid aerosols, trace metals, geological materials, and more. 

The complex formation and composition of PM2.5 requires a robust planning effort, 

where various components of the mass can be targeted for reduction. A control 

strategy that targets reductions among the precursors of PM2.5 has been shown to 

have a positive impact in reducing the total formed mass. Both direct PM2.5 and its 

precursors are tracked and projected within the emissions inventory (see Chapter 3).

The role of each potential precursor varies by region, and research and photochemical 

modeling determine which precursor reductions will contribute to a region’s attainment.

As discussed in Chapter 2, there is considerable variation in the relative toxicity of the 

chemical species found in PM2.5. While attainment of the NAAQS is demonstrated 

through total PM2.5 mass reduction (which is the primary goal of District and ARB 

control strategies), the relative differences in toxicity of species within the total mass 

provides a justification for new source control measures that specifically target the more 

toxic species of PM2.5, especially primary PM2.5 generated by combustion.

4.3 PM2.5 SPECIES IN THE SAN JOAQUIN VALLEY

PM2.5 in the Valley is comprised of many species that contribute to the total PM2.5 

mass. This complex mixture is attributable to stationary, mobile, and area-wide

sources, as well as naturally occurring emissions. Although the list of species 

contributing to PM2.5 in the Valley is lengthy, it can be grouped into larger 

representative categories. The following is a brief description of how each of these 

larger species categories are formed and emitted into the atmosphere.

Organic Carbon—As one of the major constituents of PM2.5 mass in the Valley, 

organic carbon is directly linked to emissions sources such as residential wood 

burning, agricultural burning, cooking, and direct tailpipe emissions from mobile 

sources. Smaller sources of organic carbon are attributable to road dust and 

natural biogenic sources.
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4-4 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

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Elemental Carbon—Incomplete combustion processes from diesel engines and 

other sources create elemental carbon, which is also called soot or black carbon.

Ammonium Nitrate—A portion of nitrogen oxide (NOx) emissions from motor 

vehicles and stationary combustion sources react through photochemical 

processes during the day, or react with ozone at night, to form nitric acid. When 

ammonia emissions react with the nitric acid, ammonium nitrate is created.

Ammonium nitrate is commonly the largest contributor to PM2.5 mass in the 

Valley, especially in the southern region of the Valley. Stagnant, cool, and damp 

conditions promote the formation and accumulation of ammonium nitrate. As 

such, ammonium nitrate is found mostly during winter conditions, transforming 

from gases to particles and back to gases during periods of higher temperature. 

Ammonium Sulfate—Sulfur oxide (SOx) emissions from combustion sources 

undergo photochemical reactions in the atmosphere, sulfuric acid is formed. 

Similar to the ammonium nitrate photochemical reaction, sulfuric acid reacts with 

ammonia emissions to form ammonium sulfate.

Unlike ammonium nitrate, the peak season for ammonium sulfate is the summer. 

As a comparison, there is much less ammonium sulfate in the Valley atmosphere 

than ammonium nitrate.

Geologic Material—Geologic material consists primarily of road dust that is lifted 

into the air from passing vehicles as well as soil dust that is entrained into the 

atmosphere through farming and other activities and high-wind events. This 

category of species tends to be on the coarse side of the particulate matter 

spectrum, as opposed to the fine, and represents a small percentage of the 

overall PM2.5 mass.

Trace metals—Trace metals are present in soil dust and are emitted from mobile 

sources as part of combustion, engine wear, brake wear, and similar processes. 

Fireworks emissions have also been identified as a source of metals that impact

health.

Sea Salt—Sodium chloride within sea spray can contribute to PM2.5 mass under 

meteorological conditions that allow transport of ocean air into the Valley. This 

represents a small portion of the overall mass and is only a contributor under 

specific transport conditions.

Secondary Organic Aerosol—As organic carbon is released into the 

atmosphere, photochemical reactions can occur to create organic aerosol, called 

secondary organic aerosol (SOA).

Combined Water—If a water molecule attaches itself to any of the above 

species, a combined water species is formed, adding to the total PM2.5 mass.
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Figure 4-1 shows the relative chemical composition (major species components) of the 

average annual PM2.5 in Bakersfield and Fresno based on speciation data from 2004–

2006.

Figure 4-1 Comparison of 2004–2006 Average Annual PM2.5 Chemical 

Composition

Note: “Elements” includes trace metals, chlorine, and silicon.

4.4 SCIENTIFIC FOUNDATION FOR THE 2012 PM2.5 PLAN

As described in Chapter 3, the Valley’s climate, topography, and emissions have 

contributed to a unique set of wintertime PM2.5 formation processes resulting in a low 

regional carrying capacity for PM2.5 and secondary precursors. The actual formation 

and makeup of PM2.5 includes a complex mixture of species that have different 

reaction capabilities and human toxicity. 

Meeting the substantial attainment challenge created by the low regional carrying 

capacity requires new control measures that are feasible and effective based on the 

combined weight of scientific evidence surrounding the various PM2.5 species. For this 

plan, the development of new control measures is guided by two sources of scientific 

knowledge: prior research examining the key sources and processes governing PM2.5 

species formation and substantiated evidence of reductions in ambient concentrations 

and key species resulting from existing control measures. In combination, this body of 

knowledge about key sources, their changing inventories, and the unique atmospheric 

processes found in the region is incorporated into the regional modeling developed for 

this plan, the results of which are discussed at the conclusion of this chapter.

40%

12%

35%

5% 6% 2%

Comparison of 2004-2006 

Average Annual PM2.5 

Chemical Composition 

Bakersfield

Ammonium Nitrate Ammonium Sulfate

Organic Carbon Elemental Carbon

Geological Elements

37%

11%

41%

5% 4% 2%

Comparison of 2004-2006 

Average Annual PM2.5 

Chemical Composition 

Fresno

Ammonium Nitrate Ammonium Sulfate

Organic Carbon Elemental Carbon

Geological Elements
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4-6 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

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The following is a review of each of these bodies of evidence with a focus on the 

predominant PM2.5 species of concern: organic carbon compounds (OC) and 

ammonium nitrate (NH4NO3). Prospective controls on these species lie at the heart of 

the regional modeling exercise discussed at the conclusion of this chapter and hold the 

key to attaining the 2006 PM2.5 NAAQS. Species of lesser importance, including 

geologic PM2.5, and VOCs are reviewed as well.

4.4.1 Ammonia Contribution to PM2.5 Concentrations

Ammonium nitrate contribution to total PM2.5 concentrations

Early air quality research in the Valley identified ammonium nitrate (nitrate) as a 

predominant secondary PM2.5 species in the region, with high concentrations forming 

during the winter months.1 Studies have continued to show that ammonium nitrate is a 

primary component of wintertime PM2.5 in the Valley, followed by other species, such 

as organic carbon, ammonium sulfate, and geologic material.2 In addition, PM2.5 

speciation data, collected for many years at four Valley urban monitoring locations, also 

shows nitrate’s substantial contribution to the Valley’s total PM2.5 concentrations, 

especially on days when the 24-hour average concentration exceeds the 35 μg/m324-

hour PM2.5 standard (see Appendix G). 

Ammonium nitrate formation and precursors

Formation of ammonium nitrate is described by Kleeman et al. (2005, pp. 5326-7):3

Particulate ammonium nitrate (NH4NO3) forms when the concentration 

product of gas-phase ammonia (NH3) and nitric acid (HNO3) exceeds a 

saturation point dependent on temperature, relative humidity, and the 

composition of the pre-existing particles that act as condensation 

substrate (Wexler and Seinfeld, 1991).4 Ammonia is a relatively stable 

compound directly emitted to the atmosphere that does not undergo 

significant chemical reaction on the time scale of interest to regional air 

quality problems. Nitric acid is an end product of the photochemical 

transformation of NOx (NO + NO2). The majority of the NOx in the SJV is 

emitted as NO that is then transformed into various species including NO2, 

NO3, HNO2, HNO3, HNO4, Peroxy Acetyl Nitrate (PAN), Particulate Protein 

Nitrogen (PPN), particulate nitrate, etc. The sum of NOx and the entire 

family of NOx reaction products is called “reactive nitrogen” (NOy). The 

fraction of reactive nitrogen that forms HNO3 and/or nitrate depends on the 

 

1

Smith, T.B.; Lehrman, D.E.; Reible, D.D.; and Shair, F.H. (1981). The origin and fate of airborne pollutants within 

the San Joaquin Valley: Extended summary and special analysis topics. Report No. 2. Prepared for the California Air 

Resources Board, and by the California Institute of Technology, Pasadena, CA.

2

Ying, Q. & Kleeman, M.J., (2009). Regional Contributions to Airborne Particulate Matter in Central California during 

a Severe Pollution Episode. Atmospheric Environment, 43, 1218–1228.

3

Kleeman, M.J., Ying, Q., & Kaduwela, A. (2005). Control Strategies for the Reduction of Airborne Particulate Nitrate 

in California's San Joaquin Valley. Atmospheric Environment, 39, 5325–5341.

4 Wexler, A.S., Seinfeld, J.H. (1991). 2nd-Generation inorganic aerosol model. Atmospheric Environment Part aGeneral Topics 25 (12), 2731–2748.
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concentration of NOx and VOC as well on meteorological conditions such 

as temperature, relative humidity, and solar intensity (Aw and Kleeman, 

2003; Nguyen and Dabdub, 2002).5 6 Measurements taken at the remote 

Kern Wildlife Station in the San Joaquin Valley show that approximately 

22% of the reactive nitrogen exists as particulate (ammonium) nitrate 

during typical winter conditions (Chow and Egami, 1997).7 

Nitrate buildup is a signature outcome of multi-day stagnation periods during the winter

(similar buildup is not observed during warmer seasons). The modeled regional 

variation of nitrate concentrations is shown in Figure 4-2. Higher concentrations of 

nitrate occur in the southernmost Valley as a result of slower wind speeds and higher 

levels of reactive nitrogen and ammonia. 

Figure 4-2 Modeled Regional Distribution of Ammonium Nitrate8

Both nitric acid and ammonia are needed to form ammonium nitrate. The extensive 

research conducted through CRPAQS and subsequent studies, as well as ongoing 

evaluation and modeling demonstrates that there is a relative abundance of ammonia 

 

5

Aw, J., Kleeman, M.J. (2003). Evaluating the First-Order Effect of Intra-Annual Temperature Variability on Urban Air

Pollution. Journal of Geophysical Research-Atmospheres 108 (D12).

6 Nguyen, K. & Dabdub, D. (2002). NOx and VOC Control and Its Effects on the Formation of Aerosols. Aerosol 

Science and Technology 36 (5), 560–572.

7 Chow, J.C. & Egami, R.T. (1997). San Joaquin Valley Integrated Monitoring Study: Documentation, Evaluation, and 

Descriptive Analysis of PM10 and PM2.5, and Precursor Gas Measurements. Technical support studies No. 4 and 

No. 8. Final Report prepared for the California Air Resources Board, Sacramento, CA. Desert Research Institute,

Reno, NV.

8 Chow, J.C., Chen, L.-W.A., Lowenthal, D.H., Doraiswamy, P., Park, K., Kohl, S., Trimble, D.L., & Watson, J.G. 

(2005). California Regional PM10/PM2.5 Air Quality Study (CRPAQS) – Initial Data Analysis of Field Program

Measurements. Report No. 2497. Prepared for California Air Resources Board, Sacramento, CA, by Desert Research 

Institute, Reno, NV.
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4-8 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

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(NH3) compared to nitric acid (HNO3), and that the amount of nitric acid (resulting from 

NOx emissions) drives the ultimate formation of ammonium nitrate. Figure 4-3

illustrates this ammonia abundance at the rural Angiola (Fresno County) air monitoring 

site in the Valley during the CRPAQS field study. Ammonia concentrations are 

considerably higher than nitric acid concentrations throughout the Valley, including 

urban areas with concentrated NOx emissions.9 See Appendix G for more information.

Figure 4-3 Ammonia versus Nitric Acid Measurements at Angiola10

Reducing ammonium nitrate

Because of the regional surplus in ammonia, even substantial ammonia emissions 

reductions yield a relatively small reduction in nitrate. Figures 4-4 and 4-5 provide a 

simplified illustration of this situation. As seen in Figure 4-6, a comparable modeling 

analysis based on CRPAQS observational data found a higher disparity between the 

efficiency of NOx versus ammonia controls. Reductions in nitrate concentrations of 

30% to 50% were realized through a 50% reduction in NOx, while a 50% reduction in 

ammonia only realized less than 5% reductions in nitrate concentrations. Finally, Figure 

4-7 provides clear correlative evidence from observed data that NOx controls are 

effectively reducing ammonium nitrate, despite an increase in the regional ammonia 

inventory over the same time period.

 

9 Magliano, K. L. (2009) Science-Based Policies for Particulate Matter Air Quality Management in California. 

International Aerosol Modeling Algorithms Conference. Davis CA.

10 McCarthy, M. (2005) The Role of Nighttime Chemistry in Winter Ammonium Nitrate Formation in the San Joaquin 

Valley. American Association for Aerosol Research (AAAR), Supersites Conference, February 2005, Atlanta, GA.
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Figure 4-4 Abundance of Ammonia in the San Joaquin Valley11 

Figure 4-5 NOx Control Reduces Ammonium Nitrate Most Efficiently

 

11 Ibid. 9
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Figure 4-6 Modeled Ammonium Nitrate Response to Ammonia vs. NOx Controls12

Figure 4-7 Correlation Between NOx Reductions and Observed Ammonium 

Nitrate in Fresno13

 

12 Ibid. 9

13 Ibid. 9

-60%

-50%

-40%

-30%

-20%

-10%

0%

50% NOx Reduction 50% Ammonia Reduction
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Due to this extensive body of science that clearly shows the much greater efficacy of 

reducing NOx emissions relative to ammonia, ammonia reductions have not historically 

been considered a significant precursor to PM2.5 formation in the San Joaquin Valley. 

However, the District and ARB have continued to examine the potential role of ammonia 

with regard to PM2.5 formation (see Appendices F and G).

The modeling sensitivity analysis conducted for this plan shows that reductions in 

ammonia emissions achieve insignificant reductions in the 2019 PM2.5 design values

compared to reductions of direct PM2.5 and NOx emissions. As Appendix G details:

 A 1 ton reduction in the San Joaquin Valley’s total direct PM2.5 emissions 

reduces the Bakersfield-California PM2.5 design value by 0.34 μg/m3

 A 1 ton reduction in the San Joaquin Valley’s total NOx emissions reduces the 

Bakersfield-California PM2.5 design value by a 0.08 μg/m3

 A 1 ton reduction in in the San Joaquin Valley’s total ammonia emissions

reduces the Bakersfield-California PM2.5 design value by a mere 0.008 μg/m3 

Relative to the other pollutants, ammonia reductions at the Bakersfield-California site 

are only 2.3% as effective as direct PM2.5 reductions, and only 10% as effective as 

NOx reductions. 

Furthermore, it would therefore take an unreasonable tonnage of ammonia reductions 

to reduce PM2.5 mass a significant amount. Since, as noted above, 1 ton of reduction 

in ammonia achieves a 0.008 μg/m3reduction in the PM2.5 design value, it would take 

a total ammonia reduction of 125 tons per day for the San Joaquin Valley to achieve 

only a 1 μg/m3reduction in the PM2.5 design value. Based on the total ammonia 

emissions inventory for the San Joaquin Valley in the year 2019, this would equate to a 

34% reduction of the total tonnage. Thus, current technical analyses demonstrate that 

ammonia reductions would not significantly contribute to the Valley’s attainment of the 

2006 PM2.5 standard, so ammonia emissions do not need to be reduced under this 

plan.14 

In the Reasonably Available Control Measures (RACM) analysis in Chapter 9, the 

District analyzed the amount of emissions reductions necessary to advance attainment 

by one year, from 2019 to 2018. The 2018 design value for Bakersfield-California is at 

least 1 μg/m3 higher than the attainment level. As discussed in Chapter 5, the amount 

of ammonia reductions that could advance attainment by one year is an infeasible 

amount, since there are no control strategies that exist or have been identified which 

could achieve such large reductions. 

In contrast, each ton of NOx reduced yields a summer ozone benefit and a winter 

reduction in nitrate. Additionally, NOx controls on mobile sources are largely based on 

new generation Tier IV engines (diesel) that generate OC at much lower rates. Given 

that the OC fraction of Valley PM2.5 is a primary driver of respiratory and cardiovascular 

 

14 Clean Air Fine Particle Implementation Rule [PM2.5 Implementation Rule]. 72 Fed. Reg. 79, pp. 20586–20667 at p. 

20590 (2007, April 25). Retrieved from http://www.gpo.gov/fdsys/pkg/FR-2007-04-25/pdf/E7-6347.pdf#page=1
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effects of PM2.5 exposure, the health benefits of mobile source NOx controls are 

considerable. 

4.4.2 VOC Contribution to PM2.5 Concentrations

Volatile organic compound15 (VOC) emissions have the potential to contribute to the 

formation of two different PM2.5 components: secondary organic aerosols (SOAs) and 

ammonium nitrate (nitrate). While these components contribute to observed PM2.5 

concentrations in the Valley, their contribution is minimal. The anthropogenic VOC 

contribution (those not from biogenic sources) to both components is so minimal, that 

invoking a VOC-centric control strategy is much less effective that primary PM2.5 

controls or NOx controls, as shown through the recent research and modeling.

VOC Contribution to SOA Formation. Secondary organic aerosols form when 

intermediate molecular weight VOCs emitted by anthropogenic and biogenic sources 

react and condense in the atmosphere to become aerosols. Lighter VOCs also 

participate in the formation of atmospheric oxidants, which then participate in the 

formation of SOA. SOAs derived from anthropogenic VOC emissions account for only 

1% to 2% of the annual total PM2.5 concentrations throughout the Valley. 

As part of the attainment demonstration for the District’s 2008 PM2.5 Plan, ARB used 

the Community Multiscale Air Quality (CMAQ) model to show that primary PM2.5 

emissions are the main contributor to organic aerosols, with SOAs being a small fraction 

of the total organic aerosol concentration. Furthermore, SOAs are mostly formed during 

the summer and from predominantly biogenic sources, when total PM2.5 concentrations 

are low. As such, SOAs derived from anthropogenic VOC emission make up only 3% to 

5% of the annual average organic aerosol concentrations. 

Related to this finding, the California Regional Particulate Air Quality Study (CRPAQS) 

also found that because of the dominance of primary PM2.5 organic matter, overall, a 

50% reduction in anthropogenic VOC emissions has limited effect on the modeled 

PM2.5 organic matter.16 Together, these study results show that for SOAs, further VOC 

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

VOC Contribution to Nitrate Formation. Nitrate forms by means of two primary 

chemical pathways: during the day, NO2 is oxidized to nitric acid, some of which then 

reacts with ammonia to form nitrate through interactions with sunlight, VOCs, and 

background ozone; and during the night, when nitric acid is formed through oxidation of 

NO2 (via N2O5) by background ozone, which then reacts with ammonia to form nitrate. 

 

15 EPA defines VOCs as any compound of carbon, excluding carbon monoxide, carbon dioxide, carbonic acid,

metallic carbides or carbonates, and ammonium carbonate, that participates in atmospheric photochemical reactions 

to form ozone or particulates. A subset of non-reactive VOCs does not contribute to ozone or particulates and are 

exempt from regulatory controls. Many VOCs are human-made chemicals used and produced in the manufacture of 

paints, adhesives, petroleum products, pharmaceuticals. The full EPA definition is available at 

http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr&rgn=div8&view=text&node=40:2.0.1.1.2.3.8.1&idno=40

16 Pun, B.K., Balmori R.T.F, & Seigneur, C. (1998). Modeling Wintertime Particulate Matter Formation in Central 

California, Atmospheric Environment, 43, 402-409.
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Several modeling studies17,18,19,20 have investigated the relative veracity of these two 

mechanisms within the Valley and attempted to determine the specific role and 

contribution of VOCs on Valley nitrate concentrations. While the specific conclusions 

were mixed, there was general agreement that the nighttime formation of nitrate in the 

Valley would not be sensitive to VOC reductions.

Further modeling studies21,22,23,24,25,26 evaluated the significance of VOC controls in 

reducing nitrate concentrations in the Valley. ARB evaluated each of these studies in 

the context of two key considerations: whether further VOC reduction would provide 

significant benefits to expedite attainment beyond the District’s existing NOx control 

program, and what would be the feasible magnitude of any potential VOC reductions 

beyond the existing and already rigorous VOC control program. Nitrate was only 

responsive to a 50% reduction in VOCs at very high PM2.5 concentrations, 

concentrations that are no longer reached in the Valley. In contrast, a 50% reduction in 

NOx can reduce significantly more nitrate at current PM2.5 concentrations, one study27

reporting a 38% reduction in nitrate.

Despite the insignificance of VOC emissions with regard to PM2.5 concentrations in the 

Valley, VOC emissions have been reduced and will continue to be reduced through 

implementation of the 2007 Ozone Plan.

4.4.3 Geologic Contribution to PM2.5 Concentrations

Geologic dust is not a major PM2.5 emission source in the Valley. As shown in Figure 

4-1, based on speciation date from 2004–2006, geologic dust accounts for between 4% 

and 5% in Fresno and Bakersfield, respectively. In addition, studies have shown that 

geologic dust, by itself, has relatively low toxicity. 

 

17 Pun, B.K., & Seigneur, C. (1998) Conceptual Model of Particulate Matter Pollution in the California San Joaquin 

Valley. Prepared for Pacific Gas & Electric, Document CP045-1-98.

18 Pun, B.K. (2004). CRPAQS Task 2.7 when and where does high O3 correspond to high PM2.5? How much PM2.5 

corresponds to photochemical end products? Prepared for the San Joaquin Valleywide Air Pollution Study Agency.

19 Lurmann, F.W., Brown, S.G., McCarthy, M.C., & Roberts, P.T. (2006). Processes Influencing Secondary Aerosol 

Formation in the San Joaquin Valley during Winter. Journal of Air and Waste Management Association, 56, 1679-

1693.

20 Ying, Q., Lu, J., & Kleeman, M. (2009). Modeling Air Quality during the California Regional PM10/PM2.5 Air Quality 

Study (CRPAQS) Using the UCD/CIT Source-Oriented Air Quality Model – Part III Regional Source Apportionment of 

Secondary and Total Airborne Particulate Matter. Atmospheric Environment, 43, 419-430.

21 Stockwell, W.R., Watson, J.G., Robinson, N.F., Steiner, W., & Sylte, W.W. (2000). The Ammonium Nitrate Particle 

Equivalent of NOx Emissions for Wintertime Conditions in Central California’s San Joaquin Valley, Atmospheric 

Environment, 34, 4711-4717.

22 Pun, B.K., & Seigneur, C. (2001). Sensitivity of Particulate Matter Nitrate Formation to Precursor Emissions in the 

California San Joaquin Valley. Environmental Science and Technology, 35, 2979-2987.

23 Kleeman, M.J., Ying, Q., & Kaduwela, A. (2005). Control Strategies for the Reduction of Airborne Particulate Nitrate 

in California's San Joaquin Valley. Atmospheric Environment, 39, 5325-5341.

24 Meng, Z., Dabdub, D., & Seinfeld, J.H. (1997) Chemical Coupling Between Atmospheric Ozone and Particulate 

Matter. Science, 277, 116-119. DOI:10.1126/science.277.5322.116

25 Livingstone, P.L., Magliano, K., Gürer, K., Allen, P.D., Zhang, K.M., Ying, Q., ... Byun, D. (2009). Simulating PM 

Concentrations during a Winter Episode in a Subtropical Valley: Sensitivity Simulations and Evaluation Methods.

Atmospheric Environment, 43, 5971-5977.

26 Pun, B.K., Balmori R.T.F, & Seigneur, C. (2009). Modeling Wintertime Particulate Matter Formation in Central 

California. Atmospheric Environment, 43, 402-409.

27 Ibid. 25
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4-14 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

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Fugitive dust rules were critical in the District’s attainment of the PM10 NAAQS. 

However, PM2.5 from geologic dust makes up a very small portion of overall geologic 

dust fraction. The California Emission Inventory and Reporting System (CEIDARS) 

published particulate matter speciation profiles in 2009. As shown in Figure 4-8, for 

windblown dust from agricultural land, there was only a 4% geologic source contribution 

of PM2.5.

Figure 4-8 PM Size Fraction for Windblown Dust from Agricultural Land

Geologic dust emissions are lowest in the winter when the majority of the District’s 

PM2.5 24-hour exceedances occur. The Valley receives the majority of its precipitation 

during the winter, and the emissions inventory methodologies assume that as little as

0.01 inches of rainfall on any day suppresses geologic dust for 24 hours. Based on 

precipitation data from airports throughout the Valley, 71% of the days with at least 0.01 

inches of precipitation occur during the winter months. Additionally, U.S. Forest Service 

and National Park unpaved roads, another source of geologic dust, are inaccessible or 

snow packed, during the winter, thus reducing emissions during those months. 

Not only is the geologic dust contribution to the total PM2.5 mass extremely low, the 

overall toxicity of geologic dust, which consists mainly of oxides of aluminum, silicon, 

calcium, titanium, iron, and other metals, is relatively low. As discussed in Chapter 2, 

geologic PM2.5 by itself is not a major threat to human health.

4.4.4 Organic Carbon Formation: Summary of Research Findings

Carbon-based aerosols contained within the PM2.5 fraction include organic carbon 

(OC), elemental carbon (EC), and carbonate minerals; however, it is the OC species 

that are particularly relevant to reducing the most negative health effects of PM2.5. 

Chapter 2 of this plan discusses the chemical complexity and resultant capacity of 

organic carbon species to impact cardiovascular and respiratory functioning. This 

section provides a brief overview of organic carbon aerosol (OA) atmospheric 

54%

42%

4%

PM Size Fraction for Windblown 

Dust from Agricultural Land

Larger than PM10 PM10 - PM2.5 PM2.5 and smaller

CEIDARS -

Particulate Matter 

Speciation Profiles
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4-15 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

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chemistry, a review of its implications for human exposure and health effects within the 

context of the District’s Risk-based Strategy, and the primary sources of OA, including 

hydrocarbon combustion, residential wood combustion (RWC), motor vehicles

operation, and meat charbroiling.

Organic Aerosol in the Environment. Organic aerosols (OA) and ammonium nitrate 

are the two largest contributors to PM2.5 mass in the Valley; however, the chemical 

complexity is very different between these two species. Many anthropogenic and 

biogenic sources of OA produce hundreds of primary organic aerosol (POA) species 

and hundreds of secondary organic aerosol species (SOA), which emerge from 

chemical aging and photolysis (photo-oxidation). The basic sources and processes that 

govern the dynamic relationship between POA and SOA are shown in Figure 4-9.

Figure 4-9 POA and SOA Formation Processes and Atmospheric Interactions28

Primary Organic Aerosol Formation, Aging, and Photolysis. As opposed to SOAs, 

which appear in the atmosphere as a result of VOC chemical reactions, primary organic 

aerosols (POA) are released directly into the atmosphere by their sources. Most 

anthropogenic POA originates from the combustion of hydrocarbons (primarily coal, 

petroleum fuels, natural gas, and biomass) and the charbroiling of meat. Such particles 

are typically coated by an outer layer of fairly hydrophobic organic material. Over 

several hours or days, this layer is slowly oxidized by hydroxides (OH), ozone, and 

nitrogen oxides in a process known as chemical aging. As shown in Figure 4-10, these 

 

28 U.S. Department of Energy, Pacific Northwest National Laboratory: A Chemistry Tale of Two Carbons. (2012, 

August). Retrieved from http://www.pnl.gov/science/highlights/highlight.asp?id=1193
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particles are subsequently subject to photolysis (chemical decomposition induced by 

light energy), which results in the creation of a breakdown product, HCOOH--formic 

acid, with a yield dependence on the reacting energy wavelength.

Figure 4-10 POA Aging and Photolysis29

Volatility of POA and Impact on SOA Formation. Volatility is a key construct 

necessary for understanding the chemical transport and fate of OA. A traditional 

assumption in chemical transport models (CTMs) has been that POA is emitted to the 

atmosphere as non-volatile compounds and remains in a non-reactive state. In fact, a 

substantial fraction of POA is semi-volatile, including one of the most important 

categories from a public health perspective, polycyclic aromatic hydrocarbon (PAH). In 

this case, PAH species range from the totally condensed coronene to completely 

volatile naphthalene. As such, all OA can be categorized along a volatility continuum 

according to their partitioning coefficient, i.e. that mass fraction that is condensed in a 

given air volume under standard atmospheric conditions.30 As POA species evolve 

through oxidation, their partitioning coefficient changes as well, leading to movement 

into the aerosol state or back to gas, depending on current chemical composition, 

relative dilution, and atmospheric conditions. These new findings have made it clear 

that POA, once volatilized, can and does contribute to subsequent SOA species.

VOCs and SOA Formation. Until the 1950s, scientists assumed that smoke dominated 

by carbon black was the primary source of organic aerosol into the environment. By 

 

29 University of California, Irvine, Department of Chemistry: Photochemistry of Organic Aerosols. (2012). Retrieved 

September 17, 2012 from http://aerosol.chem.uci.edu/research/photochemistry.htm

30 Donahue, N.M., Robinson, A.L. & Pandis, S.N. (2009). Atmospheric Organic Particulate Matter: From Smoke to 

Secondary Organic Aerosol. Atmospheric Environment 43, 94-106.
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1952, however, Hagen-Smit had discovered, while studying Los Angeles smog, that 

non-volatile SOA was being formed from the oxidation of VOCs generated by motor 

vehicles.31 Further laboratory studies conducted two decades later proved that 

oxidation of organic vapors can create products with low volatility that ultimately 

condense to form a fraction of tropospheric OA.32 Just as VOCs react in the presence of 

NOx and sunlight to form ozone, VOC reactions with oxidants, i.e. ozone, OH, and NO3, 

often lead to partitioning from gas to a particle phase forming secondary organic 

aerosols. Many biogenic and anthropogenic volatile compounds yield less-volatile 

products, in effect driving the process of SOA formation.33

Despite exponential growth of global fossil fuel combustion since the late nineteenth 

century, SOAs resulting from the oxidation and condensation of VOCs emitted naturally 

from vegetation still exceeds production from anthropogenic sources.34 In fact, 

Zimmerman et al. have estimated that global terpenes (the primary category of biogenic 

VOC) released by plants are approximately eight times the volume of total 

anthropogenic emissions of non-methane hydrocarbons.35 Recent studies have 

quantified the contribution that VOC emissions from natural vegetation and crops is 

making to the formation of ozone and SOAs, with natural vegetation being the largest 

contributor.36 This suggests that a substantial fraction of the haze found in Valley 

foothills is the result of SOA formed by the oxidation and photolysis of biogenic VOCs. 

On an annual average basis, secondary organic aerosols derived from anthropogenic 

VOC emissions account for only 1% to 2% of the annual total PM2.5 concentrations 

throughout the Valley. ARB air quality modeling exercises conducted as part of the 

District’s 2008 PM2.5 Plan attainment demonstration analysis using the CMAQ model 

showed that primary PM2.5 emissions are the main contributor to organic aerosols and 

SOA contribute to only a small extent. Furthermore, SOA are mostly formed during the 

summertime, when total PM2.5 concentrations are low, and are mainly derived from 

biogenic emission sources. As a result, annual average SOA derived from 

anthropogenic VOC emissions are a small part of the organic aerosol concentrations 

(3% to 5%).

Key POA Sources and Species of Concern in the Valley. Within the Valley and 

globally, biomass and fossil fuel combustion are the most important sources of POA.37 

 

31 Haagen-Smit, A.J. (1952). Chemistry and Physiology of Los Angeles Smog. Analytical Chemistry 44, 1342–1346.

32 McMurry, P.H. & Grosjean, D. (1985). Photochemical Formation of Organic Aerosols: Growth, Laws, and 

Mechanisms. Atmospheric Environment 19, 1445–1451.

33 Donahue, N.M., Robinson, A.L., Stanier, C.O., & Pandis, S.N. (2006). Coupled Partitioning, Dilution, and Chemical 

Aging of Semivolatile Organics. Environmental Science & Technology 40(8), 2635–2643.

34 Duce, R.A., Mohnen, V.A., Zimmerman, R., Grosjean, D. Grosjean, Cautreels, W., ...& Wallace G.T. (1983).

Organic Material in the Global Troposphere, Reviews of Geophysics, 21(4), 921–952.

35 Zimmerman, P.R., Chatfield, R.B., Fishman, J, Crutzen, P., & Hanst, P.L. (1978). Estimates of the Production of 

CO and H2 from the Oxidation of Hydrocarbon Emissions from Vegetation. Geophysical Research Letters, 5(8), 679–

682.

36 Goldstein, A.H. & Karlik, J.F. (2011). Flux Measurements of Biogenic Precursors to Ozone and Particulate Matter in 

the Central Valley. Final Report, Contract No. 06-329. Prepared for the California Air Resources Board and the 

California Environmental Protection Agency. August 16, 2011. Available at 

http://www.arb.ca.gov/research/apr/past/06-329.pdf

37 Liousse, C., Penner, J.E., Chuang, C., Walton, J.J., Eddleman, H., & Cachier, H. (1996). A Global ThreeDimensional Model Study of Carbonaceous Aerosols. Journal of Geophysical Research, 101(D14), 19,411–19,432.
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4-18 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

2012 PM2.5 Plan 

Unlike SOA, which tends to be more evenly distributed between source and non-source 

areas, POA is concentrated in areas near emissions sources, e.g. urban areas, 

freeways, railways, and shipping ports. In the case of fossil fuels, motor vehicles are 

the primary source of POA in urban areas. Rogge et al. conducted a series of 

speciation studies in the Los Angeles basin and found that motor vehicle exhaust 

contributed 21% of the POA in that area, characterized by a variety of cyclic and 

polycyclic compounds, with PAH being the most significant in terms mass contribution 

and potential public health impacts.38 In addition, meat charbroiling was also found to 

contribute 21%, with residential wood combustion contributing up to 30% in winter.39,40 

Finally, 16% was contributed from road dust, tires, and brake dust.41 

The source contribution percentages from the Los Angeles research are comparable to 

POA source contributions in the Valley, with the exception of POA from wood smoke. In 

the case of residential wood combustion (RWC), a Fresno PM2.5 speciation study 

based on the use of levoglucosan as a tracer for wood smoke collected during the 

CRPAQS winter-intensive study in 2000–2001 concluded as follows: “Combined, the 

emissions from wood smoke, meat charbroiling, and motor vehicles appear to contribute

65–80% to measured OC, with wood smoke, on average, accounting for approximately 

41% of OC and approximately18% of PM2.5 mass.”42 

The contribution of OC as a fraction of the total PM2.5 mass has decreased over the 

last nine years as shown in Figure 4-11. This provides evidence that more stringent 

controls on POA from motor vehicles and RWC control measures put in place by ARB 

and the District over that time period have been effective in reducing emissions. The 

District’s stringent RWC controls were implemented just prior to the 2003–2004 winter 

season and were further strengthened prior to the 2008–2009 winter season. Overall, 

the total winter PM2.5 concentration has decreased since the 1999–2000 winter season 

(Figure 4-12). Reductions in potassium levels, a key indicator species for wood 

combustion, provide further evidence of reductions in winter season wood combustion 

(Figure 4-13).

 

38 Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., & Simoneit, B.R.T. (1993). Sources of Fine Organic 

Aerosol, 2, Noncatalyst and Catalyst-Equipped Automobiles and Heavy-Duty Diesel Trucks. Environmental Science & 

Technology, 27(4), 636–651.

39 Rogge, W. F., L. M. Hildemann, M. A. Mazurek, G. R. Cass, and B. R. T. Simoneit. (1991) Sources of fine organic 

aerosol, 1, Charbroilers and meat cooking operations, Environ. Sci. Technol., 25, 1112–1125.

40 Rogge, W. F., L. M. Hildemann, M. A. Mazurek, G. R. Cass,and B. R. T. Simoneit. (1998) Sources of fine organic 

aerosol, 9, Pine, oak and synthetic log combustion in residential fireplaces, Environ. Sci. Technol., 32, 13–22.

41 Rogge, W. F., L. M. Hildemann, M. A. Mazurek, G. R. Cass, and B. R. T. Simoneit. (1993) Sources of fine organic 

aerosol, 3, Road dust, tire debris, and organometallic brake lining dust—Roads as sources and sinks, Environ. Sci. 

Technol., 27, 1892–1904.

42 Gorin, C, J. Collett, and P. Herckes. (2006) Wood Smoke Contribution to Winter Aerosol in Fresno, CA. Journal of 

the Air and Waste Management Association 56: 1584-1590 (quote on p. 1584).
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4-19 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

2012 PM2.5 Plan 

Figure 4-11 Valley Trend of OC Mass as a Fraction of PM2.5 Mass

Figure 4-12 Winter Season Trend of Valley Average Daily PM2.5 Mass

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

02-03 03-04 04-05 05-06 06-07 07-08* 08-09 09-10* 10-11 11-12

% Fraction OC to PM2.5

Winter Season

Modesto Fresno Visalia Bakersfield

*Carbon method changed from TOT to TOR in 2009 for Fresno, and in 2007 for all other sites.

Winter season defined as 

Jan, Feb, Nov, and Dec.

SOURCE: ARB Speciation Data

39 40

33 32

26 25

30 31

28 27

25

22

27

0

5

10

15

20

25

30

35

40

45

Average PM2.5 Concentration (μg/m3)

Winter Season

Winter season defined as 

Jan, Feb, Nov, and Dec.

SOURCE: ARB & District Data
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4-20 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

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Figure 4-13 Average Winter Season Potassium Concentration in the San Joaquin 

Valley

4.4.5 Controlling Organic Aerosol under the Risk-Based Strategy

While reducing ammonium nitrate concentrations through NOx reductions is important 

for attainment, strategies that reduce organic aerosols provide opportunities for not only 

addressing the standard, but also providing much greater relative health benefits. 

Relevance to attainment. New controls on the creation of primary organic aerosol via 

combustion processes hold the promise of a win-win outcome—providing substantial 

reductions in health risk as well as making a significant reduction in PM2.5 mass. The 

attainment strategy discussed below, therefore, is centered on POA reduction based on 

scheduled emission controls by ARB on motor vehicles, especially heavy duty trucks,

combined with further restrictions on RWC, new controls on commercial charbroiling, 

and incentives for zero-emission lawn care equipment.

Species toxicity of POA and SOA. As discussed on Chapter 2, both combustion 

POA, such as PAH, and downstream SOA species have been implicated in a wide 

range of health effects, including cardiovascular and respiratory inflammation and 

carcinogenesis. Unlike other aerosol precursors such as NOx and VOCs that are 

currently being reduced through ongoing ozone control measures, reductions in POA 

(and the subsequent SOA that is produced by downstream oxidation and photolysis of 

POA) necessitates direct controls on primary emission sources. From a public health 

perspective, reductions in POA mass will result in a compounding of risk reduction that 

significantly exceeds the proportional contribution of those controls to PM2.5 mass 

reduction. This is because the chemical aging of SOA is responsible for some of the 

most toxic OA species such as quinones. 

0

0.05

0.1

0.15

0.2

0.25

0.3

02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11 11-12

Potassium Cocentration (μg/m3) 

Winter Season

Modesto Fresno Visalia Bakersfield

Winter season defined as 

Jan, Feb, Nov, and Dec.

SOURCE: ARB Speciation Data
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4-21 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

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Particle size and deposition. POA generated by combustion as well as the SOA that 

emerges from the chemical processing of POA and VOC condensation are largely 

concentrated in the ultrafine (PM0.1) and fine (PM2.5) particle size categories. 

Inhalation of these particles results in deposition in the alveolar and thoracic zones of 

the lungs with potential impacts spanning the cardiovascular, immune, nervous, and 

respiratory systems. Individuals with pre-existing health conditions and those subject to 

high concentrations in urban areas or near high-volume roadways are especially 

vulnerable to inhalation of concentrated POA and SOA species that result from POA 

processing.

Proximity to ultrafine particles. POA is highly concentrated in freshly emitted ultrafine particles (UFP), or those particles less than 0.1 microns (PM0.1). Because of their 

ability to pass through protective epithelium in the lung,

43 enter the brain via the 

olfactory bulb,

44 and enter the bloodstream in the alveolar region,45 UFP-induced health 

effects are disproportionately high relative to their mass. In the case of POA, however, 

their ability to penetrate past the body’s normal particle defense mechanisms is 

magnified by the relative toxicity of POA species, PAH in particular. In the case of 

combustion POA, its chemical toxicity has been shown to be magnified by the presence 

of metals, including iron, vanadium, and nickel.46

Population intake fraction. POA emitted by internal combustion engines, residential 

wood combustion, and meat charbroiling is highly concentrated in urban areas where 

population density is greatest. Whether inhaled as freshly-emitted UFP or as larger fine 

particles, the net effect is a much larger intake fraction for these sources relative to 

comparable emission sources located in less populated, rural areas. Furthermore, 

heavy concentrations of vehicular POA from roadways located near urban 

neighborhoods may result in intake fractions that are an order of magnitude greater then 

outlying suburban areas within the same metro area. This elevated health risk from 

elevated POA exposure is especially relevant to health impacts from RWC, particularly 

given that UFP from wood combustion results in sustained high levels of indoor UFP 

during nocturnal inversions.47 This high level of sustained exposure to PAH-laden UFP 

places the exposed individuals at risk to immune system sensitization and heightened 

vulnerability to subsequent exposure.48

 

43 Donaldson, K., V. Stone, A. Clouter, L. Renwick, and W. MacNee. (2001) Ultrafine Particles. Occupational 

Environmental Medicine 58: 211–216.

44 Oberdorster, G.,Z. Sharp, V. Atudorei, A. Elder, R. Gelein, and W. Kreyling. (2004) Translocation of Inhaled 

Ultrafine Particles to the Brain. Inhalation Toxicology 16:437–445.

45 Delfino, R.J, S. Constantinos, and S. Malik. (2005) Potential Role of Ultrafine Particles in Associations between 

Airborne Particle Mass and Cardiovascular Health. Environmental Health Perspectives 113 (8): 934-946.

46 Chen, L., and M. Lippmann (2009) Effects of Metals within Ambient Air Particulate Matter (PM) on Human Health. 

Inhalation Toxicology 21: 1–31.

47 Thatcher, T., and T. Kirchstetter (2011) Assessing Near-Field Exposures from Distributed Residential Wood Smoke 

Combustion Sources. Report prepared for the California Air Resources Board. California State Polytechnic 

University; Lawrence Berkeley National Laboratory. September.

48 Li, N., Harkema, J.R., Lewandowski, R.P., Wang, M., Bramble, L.A., Gookin, G.R., Ning, Z., Kleinman, M.T., 

Sioutas, C., and Nel, A.A. (2010) Ambient Ultrafine Particles Provide a Strong Adjuvant Effect in the Secondary 

Immune Response: Implication for Traffic-Related Asthma Flares. American Journal of Physiology: Lung and Cell 

Molecular Physiology 299 (3): L374-83.
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4.4.6 Condensable Particulates

Certain high-temperature processes emit gaseous pollutants that rapidly condense into 

particle form in the ambient air. After January 1, 2011, PM2.5 nonattainment areas are 

to consider these condensable particulates for purposes of establishing the emissions 

limits for Reasonable Further Progress (RFP), Reasonably available control technology 

(RACT), and reasonably available control measures (RACM).49 In December 2010, EPA 

revised its “Method 202” stationary source test method to measure condensable 

particulate matter.

50

While this issue may be new and more relevant to other regions, the District has 

historically included condensable particulate emissions in its definition of total 

particulate emissions, well ahead of federal and other states’ efforts to address this 

issue. This has included instituting permit requirements for various emissions sources 

that include condensable particulates as part of total particulate emissions limitations, 

and associated emissions testing requiring that condensable particulates be measured 

(including utilizing an EPA-approved modified test method ahead of EPA’s official test 

method, Method 202). Condensable particulates are thus a part of the total PM2.5 

inventory, and reductions in condensable particulate matter emissions were included in 

the District’s evaluation of various emission reduction opportunities for directly emitted 

PM2.5. 

4.5 PROJECTED FUTURE AIR QUALITY AND IDENTIFYING THE GOAL

[This section was prepared by the California Air Resources Board (ARB)]

Consistent with EPA guidelines, ARB modeled air quality to predict future PM2.5 

concentrations at each monitoring site in the Valley. This modeling shows attainment of 

the 24-hour PM2.5 standard by 2019 based on implementation of the ongoing control 

program in all counties except Kern and Kings. In Kern and Kings Counties, additional 

focused emission reductions are needed to provide for attainment. Additional analyses 

confirmed that attainment is predicted throughout each county (i.e. in each modeled grid 

cell; see Appendix G - Appendix 6). This section summarizes these efforts and results. 

Additional information is available in Appendices F and G. Additional technical 

information can be found on ARB’s website:

http://www.arb.ca.gov/planning/sip/sjvpm25/24hrsjvpm25.htm

4.5.1 Modeling Overview

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

2019 from implementation of a combination of adopted ARB and District programs. As 

a result, most sites in the northern and central Valley are expected to attain before 

 

49

 40 CFR 51.1002(c)

50 http://www.epa.gov/ttn/emc/methods/method202.html
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4-23 Chapter 4: Scientific Foundation and PM2.5 Modeling Results

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2019. As required by 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. 

However, that modeling indicated that only two areas (Corcoran and Bakersfield) would 

not attain with the new emission reductions from adopted measures with 

implementation between now and 2019. 

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

program, 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 4-1. 

Table 4-1 2019 Modeled 24-hour PM2.5 Design Values with Enhanced Residential 

Wood-Burning Curtailment Program

Monitoring Site Design Value 

(μg/m3)

Bakersfield - California 35.7

Bakersfield - Planz 32.9

Corcoran - Patterson 32.1

Visalia - N. Church 29.4

Fresno - Hamilton 28.6

Fresno - First 30.5

Clovis 28.6

Merced 22.6

Modesto 24.7

Stockton 21.4

Additional emissions reductions from Rule 4901 bring Corcoran (Kings County) into 

attainment, and brings Bakersfield (Kern County) closer to attainment. As a result of 

implementation of the ongoing control program, coupled with the enhanced woodburning curtailment measure, ammonium nitrate concentrations are predicted to 

decrease by over 45%, and combustion-related carbon concentrations by approximately 

65%. Like the rest of the Valley, PM2.5 concentrations at the Bakersfield-California site 

are also projected to significantly decrease as emissions are further reduced. While 

adoption of a more stringent wood-burning curtailment program brings the BakersfieldCalifornia site very near attainment, further reductions are still needed and will be 

provided through a measure to achieve additional emission reductions from commercial 

cooking operations. The final attainment demonstration for the Bakersfield-California 

design site is provided in Table 4-2 below:
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Table 4-2 Attainment Demonstration—Bakersfield-California Design Value Site

2007 

Design 

Value 

(μg/m3)

2019 Design 

Value with Wood 

Burning Program 

Enhancement 

(μg/m3)

2019 Final 

Design Value 

(μg/m3)

66 35.7 35.4

Note: The benchmark for attainment is a design value that is equal to or less than 35.4 μg/m3

As noted above, the design value in column 2 of Table 4-2 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 resulting from updated vehicle activity data from Valley Metropolitan Planning 

Organizations (MPOs). Based on a modeling sensitivity run, implementation of further 

controls on commercial cooking in Kern County is expected to result in a 0.6 μg/m3

reduction in the baseline design value. The revised MPO activity data represents 

approximately 1% of Valley-wide NOx emissions. Based on modeling sensitivity runs, 

this is estimated to result in a design value increase of 0.2 μg/m3. In aggregate, the 

modeling demonstrates that with the total plan control strategy, including adopted 

control measures as well as new control measures in Kings and Kern Counties, the 

modeled Bakersfield-California design value meets EPA’s attainment target of less than 

or equal 35.4 μg/m3.

4.5.2 Measuring the Benefit of Control Measure Reductions

In order to determine where to focus the remaining emission reductions needed to bring 

Bakersfield-California into attainment, ARB staff conducted additional modeling 

sensitivity runs to assess the relative efficacy of further reductions of different PM2.5 

precursors. The current 24-hour PM2.5 standard modeling demonstrates that on a 

relative basis the greatest benefits are achieved from reductions in sources of directly 

emitted PM2.5, followed by NOx, based on EPA’s relative response factor procedure. 

ARB also conducted Kern County-specific model sensitivity runs for NOx and PM2.5 to 

evaluate the specific benefits of emission reductions focused on the nonattainment subarea. The Kern County sensitivity runs demonstrated that

 One ton per day of directly emitted PM2.5 reductions provides a 1.0 μg/m3

improvement in the Bakersfield-California design value; and 

 One ton per day of NOx reductions provides for a 0.12 μg/m3improvement in the 

Bakersfield-California design value. 

Therefore, to bring Bakersfield-California into attainment, this plan’s control strategy

focuses on opportunities to further reduce direct PM2.5 and NOx emissions. As 

discussed above, ARB and the District are proposing two control measures with a 

potential emphasis on Kern County that would provide for attainment in 2019. In 
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addition, benefits from incentive programs will be used for contingency purposes. 

Incentive programs provide an effective means to accelerate fleet turnover and the 

conversion to cleaner engine technologies. Both ARB and the District are committed to 

pursuing the needed funding, as well as targeting the incentive programs to those areas 

and sources that will provide for expeditious attainment. The role of incentive funding 

also highlights the need for continued legislative reauthorization of incentive funding 

mechanisms such as the Carl Moyer program. See Chapter 9 for more information.

4.5.3 Modeling Requirements

Following EPA guidance and procedures, the attainment demonstration was conducted 

through a modeled attainment test. Photochemical modeling relates measured PM2.5 

levels to modeled PM2.5 concentrations, using precursor emissions and meteorology in

the region to simulate future PM2.5 levels based on changes in emissions. This 

modeling is used to identify the most expeditious attainment date, the relative benefits 

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

reductions needed from each pollutant. 

This is the first time that modeling has been applied to demonstrate attainment for the 

24-hour PM2.5 standard using revised procedures recently issued by EPA51. This 

requires modeling of the most severe air quality days, which relies on the proper 

characterization of episodic daily emissions. Developing day-specific emission 

estimates and forecasting what those emissions will be on episodic days in the future is 

a complex process that is further described in Appendix F of this plan. The following 

sections summarize the photochemical modeling performed and results.

Under the Clean Air Act, areas are presumed to have five years from the date of 

nonattainment designation to attain the standard, with a potential five-year attainment 

date extension. In the case of the 24-hour PM2.5 standard, this corresponds to 

attainment dates of 2014 and 2019. The benchmark for attainment is a design value 

that is equal to or less than 35.4 μg/m3. 

4.5.4 Base and Future Years for Modeling

As required by EPA, ARB conducted a speciated modeled attainment test (SMAT). The 

SMAT is necessary because of the multiple constituents that form PM2.5 and their 

different relative responses to emission reductions. This test provides a method to link 

PM2.5 constituents with the appropriate emission sources. SMAT requires base and 

future years for the modeling. The base year is used for two purposes: to validate that 

the model is working properly and is able to replicate observed air quality and 

meteorological data, and as the starting year for projections of emissions and air quality 

predictions. For this effort, 2007 was chosen as the base year and 2019 as the 

attainment year for the modeling. The design values recorded in 2007 were some of 

the highest in recent years. In addition, analysis of the impacts of meteorology on 

PM2.5 levels in the Valley over the last ten years indicate that the 2007 meteorology 

 

51 U.S. EPA. (2011, June 28). Memorandum: Update to the 24 Hour PM2.5 NAAQS Modeled Attainment Test. Air 

Quality Modeling Group, U.S. EPA, Research Triangle Park, North Carolina.
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was one of the most conducive to PM2.5 formation. Thus, the selection of 2007 

represents a health-protective approach to the attainment demonstration modeling.

4.5.5 Air Quality and Meteorological Models

The Mesoscale Meteorological Model version 5 (MM5)52 was used to generate the 

three-dimensional meteorological fields used for this effort. MM5 is a mesoscale, 

limited-area, non-hydrostatic numerical model developed by Penn State and the 

National Center for Atmospheric Research (NCAR). It uses a terrain-following, Lambert 

Conformal, sigma coordinate system. MM5 has been improved on an ongoing basis 

over the last two decades by contributions from a broad scientific community and has 

been maintained by NCAR along with necessary meteorological and geographical input 

data. Based on the complexity of terrain in northern and central California, the MM5

model represents an appropriate tool for resolving dynamics and thermodynamics using 

nesting capabilities. ARB has also been using the MM5 model over the last two 

decades, since it has been widely used and tested for various meteorological regimes 

over the world and has been supported by NCAR.

The Community Multiscale Air Quality (CMAQ) Modeling System was used for the air 

quality modeling. The CMAQ model, a state-of-the-science “one-atmosphere” modeling 

system developed by EPA, was designed for applications ranging from regulatory and 

policy analysis to understanding of the atmospheric chemistry and physics. It is a threedimensional Eulerian modeling system that simulates ozone, particulate matter, toxic air 

pollutants, visibility, and acidic pollutant species throughout the troposphere53. The 

CMAQ model has undergone peer review every few years and was found to be state-ofthe-science54. The CMAQ model is regularly updated to incorporate new mechanisms, 

algorithms, and data as they become available in the scientific literature55. In addition, 

the CMAQ model is well documented in terms of its underlying scientific algorithms as 

well as guidance on operational uses.

56

 

52 Grell, G. A., Dundhia, J., and Stauffer, D. R., 1994, A description of the Fifth-Generation Penn State/NCAR 

Mesoscale Model (MM5), National Center for Atmospheric Research, Boulder, CO, USA, NCAR/TN-398+STR, 

122pp.

53 UNC, 2010, Operational Guidance for the Community Multiscale Air Quality (CMAQ) Modeling System Version 

4.7.1., available at http://www.cmascenter.org/help/model_docs/cmaq/4.7.1/CMAQ_4.7.1_OGD_28june10.pdf

54 Aiyyer, A., Cohan, D., Russell, A., Stockwell, W., Tanrikulu, S., Vizuete, W., and Wilczak, J., 2007, Final Report: 

Third Peer Review of the CMAQ Model, submitted to the Community Modeling and Analysis System Center, 

University of North Carolina, Chapel Hill.

55 For example, Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, G., 

Young, J.O., Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., and Bash, J.O., 2010, Incremental testing of the 

Community Multiscale Air Quality (CMAQ) modeling system version 4.7, Geoscientific Model Development, 3, 205-

226.

56 For example:

Binkowski, F.S. and Roselle, S.J., 2003, Models-3 Community Multiscale Air Quality (CMAQ) model aerosol 

component, 2. Model description, Journal of Geophysical Research, 108, D6, doi:10.1029/2001jd001409.

Byun, D.W. and Ching, J.K.S., 1999, Science Algorithms of the EPA Models-3 Community Multiscale Air Quality 

(CMAQ) Modeling System, EPA/600/R-99/030, available at http://www.epa.gov/AMD/CMAQ/CMAQscienceDoc.html

Byun, D.W. and Schere, K.L., 2006, Review of the governing equations, computational algorithms, and other 

components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system, Applied Mechanics 

Review, 59, 51-77.
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4.5.6 Modeling Domains

Meteorological Modeling Domains: The MM5 meteorological modeling domain 

consists of three nested grids of 36 km, 12 km, and 4 km with uniform, horizontal-grid 

spacing. The coarse 36 km grid (D01) provides synoptic-scale conditions to the 12 km 

grid (D02), which in turn provides the same to the 4 km grid (D03). The two coarse 

grids were run simultaneously, and the D03 grid was run independently using the output 

of its coarser, parent D02 grid as input. The D03 grid is intended to resolve the fine 

details of atmospheric motion and is used to feed the air quality modeling simulations. 

The vertical layer structure consists of 30 layers.

Air Quality Modeling Domains: The principle determinants of the extent of the air 

quality modeling domain are the nature of the PM2.5 problem and the scale of the 

emissions that impact the nonattainment area. The modeling domain used for this plan 

is shown in Figure 4-14. This is the same modeling domain used for the previous 

annual PM2.5 plan, which has been approved by the EPA.

57

 This domain provides the 

high-resolution modeling results that are used in SMAT. This domain fully 

encompasses the Valley 24-hour PM2.5 non-attainment area. Consistent with EPA

guidance58, the modeling also includes a larger outer domain to provide for boundary 

conditions and initial inputs to the nonattainment area domain. The coarse domain 

extends from the Pacific Ocean in the west to Eastern Nevada in the east and runs from 

the U.S.-Mexico border in the south to the California-Oregon border in the north. 

 

Carlton, A.G., Bhave, P., Napelenok, S.L., Edney, E.O., Sarwar, G., Pinder, R.W., Pouliot, G.A., and Houyoux, M., 

2010, Model representation of secondary organic aerosol in CMAQv4.7, Environmental Science Technology, 44, 

8553-8560.

Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O., 

Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., and Bash, J.O., 2010, Incremental testing of the Community 

Multiscale Air Quality (CMAQ) modeling system version 4.7, Geoscientific Model Development, 3, 205-226.

Kelly, J.T., Bhave, P., Nolte, C.G., Shankar, U., and Foley, K.M., 2010a, Simulating emission and chemical evolution 

of coarse sea-salt particles in the Community Multiscale Air Quality (CMAQ) model, Geoscientific Model 

Development, 3, 257-273.

UNC, 2010, Operational Guidance for the Community Multiscale Air Quality (CMAQ) Modeling System Version 4.7.1., 

available at http://www.cmascenter.org/help/model_docs/cmaq/4.7.1/CMAQ_4.7.1_OGD_28june10.pdf

57 Approval and Promulgation of Implementation Plans; California; 2008 San Joaquin Valley PM2.5 Plan and 2007 

State Strategy, 76 Fed. Reg. 134, pp. 41337–41363. (2011, July 13). (to be codified at 40 CFR Part 52) 

AND Approval and Promulgation of Implementation Plans; California; 2008 San Joaquin Valley PM2.5 Plan and 2007 

State Strategy; Final Rule. 76 Fed. Reg. 217, pp. 49896–49926. (2011, November 9). (to be codified at 40 CFR Part 

52) 

58 U.S. EPA, 2007, Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality 

Goals for Ozone, PM2.5, and Regional Haze, EPA-454/B07-002.
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Figure 4-14 Air Quality Modeling Domain 

For the coarse portions of nested regional grids, EPA guidance suggests a grid cell size 

of 12 km if feasible, but not larger than 36 km. For the fine scale portions of nested 

regional grids, it is desirable to use grid cells about 4 km.

59

 The defined modeling 

domains for the 24-hour PM2.5 plan are consistent with the guidance. 

EPA guidance does not require a minimum number of vertical layers for an attainment 

demonstration, although typical applications of “one- atmosphere” models (with the 

model top at 100 mb) employ 12 to 21 vertical layers. For the present plan, 15 vertical 

layers are used in the CMAQ model, extending from the surface to 100 mb, consistent 

with the number of vertical layers used for the previous annual PM2.5 plan. The 

majority of the layers are in the planetary boundary layer.

Model Performance Evaluation: As recommended by EPA, the model performance 

evaluation consists of a number of metrics to evaluate performance for PM2.5 mass as 

well as PM2.5 components. These metrics include mean fractional bias (MFB), mean 

fractional error (MFE), normalized mean bias (NMB), and normalized mean error 

(NME). In addition, the evaluation includes other statistics such as mean bias, mean 

error, and the correlation coefficient whenever they provide meaningful information. 

Various forms of graphics are created to visually examine comparison of the model 

predictions to observations. Model performance goals are based on EPA guidance as 

well as performance recommendations proposed in peer-reviewed journal articles.

60

Emission Inventory Preparation and Gridding: The process of preparing base-year 

emission inventories and future-year emission forecasts is described in appendices to 

 

59 Ibid.

60 For example, Boylan, J.W. and Russell, A.G., 2006, PM and light extinction model performance metrics, goals, and 

criteria for three-dimensional air quality models, Atmospheric Environment, 40, 4946-4959. AND U.S. EPA, 2007, 

Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, 

PM2.5, and Regional Haze, EPA-454/B07-002.
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this plan. The gridding process, in which county-level emissions are allocated spatially 

and temporally, as well as chemically- and size-resolved, is described in the Modeling 

Protocol prepared for this effort (Appendix F). Development of the base-year emission 

inventories and the future-year forecasts involved a comprehensive review of the 

methodologies used to estimate key emission categories. As noted earlier, modeling of 

the 24-hour PM2.5 standard requires detailed information on the timing and locations of 

emission sources on the most severe air quality days. This poses a unique challenge to 

translate regional, annual emission estimates into the temporal and spatial resolution 

needed for 24-hour modeling. Special attention was paid to determining the sources 

and pollutants that had the largest effect on PM2.5 concentrations in the portions of the 

Valley with the highest concentrations, and focusing emissions improvement efforts on 

these areas. An iterative process was used as a means to refine the spatial, temporal, 

and chemical characteristics of modeling emission inputs to better reflect observed 

conditions expected at a local, 24-hour scale. Model-simulated concentrations were 

compared with chemical species present in the ambient monitoring data, maps of 

emission sources known to surround the monitoring stations, and temporal trends in the 

monitoring data. This led to updates in the spatial, temporal, and chemical allocation of 

the gridded emissions used in the modeling. 

Attainment Demonstration Modeling Results: The SMAT process produces future 

year design values by monitoring site for comparison to the applicable ambient air 

quality standard. The first step in the future year modeling is to model the 2019 

forecasted emissions. This modeling run includes all adopted regulations plus an 

enhanced wood burning curtailment program along with estimated population and 

economic growth. The modeled, future year projected design values are presented in 

Table 4-1. 

Precursor Sensitivity Modeling Runs: 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, SOx, VOCs, and ammonia). EPA’s PM2.5 

implementation rule specifies that a precursor is considered “significant” for control 

strategy development purposes when a significant reduction in the emissions of that 

precursor pollutant leads to a significant decrease in PM2.5 concentrations. Such 

pollutants are known as PM2.5 attainment plan precursors.

61

 The EPA’s 

implementation rule also establishes a presumption that PM2.5, NOx, and SOx are 

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

plan, PM2.5, NOx, and SOx were identified and approved as the only attainment plan 

precursors by EPA. Results of the annual PM2.5 modeling showed that of these three 

pollutants, reductions in directly emitted PM2.5 was the most effective—reducing one 

ton of directly emitted PM2.5 was approximately nine times more effective than reducing 

one ton of NOx. The evaluation of the 24-hour PM2.5 attainment plan precursor focus 

is on the two winter quarters when peak PM2.5 concentrations occur in the Valley. 

Because annual average concentrations are heavily influenced by winter time levels, 

results for the 24-hour PM2.5 standard are expected to be similar to those for the 

annual standard. 

 

61 Clean Air Fine Particle Implementation Rule; Final Rule. 72 Fed. Reg. 79, pp. 20586–20667. (2007, April 25). (to 

be codified at 40 CFR Part 51)
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In order to identify the PM2.5 attainment plan precursors and better understand the 

effectiveness of emission reductions for primary PM2.5 and precursors, the model is 

typically exercised with varying combinations of precursor reductions from 

anthropogenic sources. The results of these runs are plotted on isopleth diagrams, also 

referred to as carrying capacity diagrams. These carrying capacity diagrams shows the 

level of emissions that the atmosphere can “carry” and still demonstrate attainment. 

These diagrams help show what combinations of precursor emissions reductions

(including which precursors are most effective to reduce as well as the magnitude of 

reductions needed) might lead to attainment, informing the development of a 

corresponding control strategy. The carrying capacity diagrams presented in Figures 4-

15 through 4-24 show that NOx and directly-emitted PM2.5 are the most effective 

precursors to reduce to improve 24-hour PM2.5 design values. 

Assessment of Grid Cell Concentrations: Modeling must demonstrate that the 

standard is attained in every modeling grid cell. In the Valley, the extensive monitoring 

network captures population exposure. Demonstration of attainment at these monitors, 

combined with the modeling grid cell evaluation will ensure that all regions in the Valley 

are in compliance with the 24-hour PM2.5 standard. EPA recommends combining 

interpolated spatial fields and modeled gradients to generate the gradient adjusted 

spatial fields for PM2.5. Future year estimates for all grid cells are created by applying 

the grid specific relative reduction factors to the gradient adjusted spatial fields. This 

analysis confirms that all areas of the Valley will reach attainment with this plan’s control 

strategy.
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Figure 4-15 Bakersfield-California Carrying Capacity Diagrams
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Figure 4-16 Bakersfield-Planz Carrying Capacity Diagrams
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Figure 4-17 Fresno-1st Carrying Capacity Diagrams
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Figure 4-18 Fresno-HW Carrying Capacity Diagrams
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Figure 4-19 Clovis Carrying Capacity Diagrams
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Figure 4-20 Modesto Carrying Capacity Diagrams
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Figure 4-21 Merced Carrying Capacity Diagrams
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Figure 4-22 Stockton Carrying Capacity Diagrams
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Figure 4-23 Visalia Carrying Capacity Diagrams
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Figure 4-24 Corcoran Carrying Capacity Diagrams