2008 PM2.5 Plan

Appendix F 

 

SJV PM2.5 SIP Modeling Protocol 
II 2008 PM2.5 Plan

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I 2008 PM2.5 Plan

San Joaquin Valley Air Pollution Control District 

State Implementation Plan PM2.5 Modeling Protocol 

A revision of the PM10 Modeling Protocol 

to focus on the fine fraction PM2.5 particles 

and address requirements of new guidance. 

Contents 

Introduction: 

SJVAPCD PM2.5 SIP Protocol Review Process F-1 

SJVAPCD PM2.5 SIP Protocol Contents F-2 

Determination of Attainment F-6 

Attachments to the Protocol F-6 

Protocol: F-7

Goal of the Protocol F-7

Regulatory Requirements for Protocol Contents F-7 

Revision of PM10 receptor methods to address PM2.5 F-9 

Data: Meteorological F-13 

Data: Atmospheric Chemistry and Sources F-21 

Data: Ambient Monitoring F-22

Data: Speciation Adjustments “SANDWICH” F-27

Data: Design Value Determinations F-29 

Data: Justification for Use of Off-Site Data F-30 

Model: Selection Justification F-33 

Model: Modes Used F-38 

Model: Analysis and Use Description F-39 

Model: Application Assumptions F-44 

Model: Input Data F-57 

Model: Output Data F-59 

Model: Analysis of Results F-60 

Determining Attainment for All Cases F-68
II 2008 PM2.5 Plan

Attachments: 

A1 SJV PM Conceptual Model 1998 (64 pages) – Conceptual 

description of PM10 and PM2.5 episode dynamics. Performed for 

the CRPAQS study as part of an integrated monitoring study (IMS95) designed to evaluate conceptual issues. Based on 1995 data 

collected in the San Joaquin Valley. 

A2 Modeling Domain –Domain map annotated with model basic 

operating parameters 

A3 Modeling Boundary Conditions – Average annual surface boundary 

conditions 

A4 PMF procedure (5 pages) – Methodology for the Positive Matrix 

Factorization approach to identify key contributing sources 

A5 Receptor Modeling Analysis Layout (4 pages) – Documentation of 

receptor rollback revisions and conversion from analysis of PM10 to 

PM2.5 

A6 Regional Air Quality Modeling Methodology – Model selection, 

emissions and meteorology for PM2.5 annual modeling 

Tables 

Number Title Page 

Table 1 SJVAPCD Meteorological Monitoring F-18 

Table 2 PM2.5 Monitoring Stations in the SJVAPCD F-24 

Table 3 PM 2.5 Monitoring Summary F-25 

Table 4 PM2.5 Annual Average Federal Design Values F-29 

Table 5 Ambient Fine Particles F-49 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

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Introduction 

The PM2.5 State Implementation Plan (SIP) Modeling Protocol is a document that 

describes air quality modeling analyses performed to demonstrate that proposed control 

strategies are sufficient to achieve compliance with the PM2.5 National Ambient Air 

Quality Standards (NAAQS) for protection of public health. The PM2.5 SIP Modeling 

Protocol is a descriptive framework for the analysis to be conducted to determine the 

amount of reductions needed to achieve compliance with the standards. The Protocol 

is utilized as the plan of approach, but is considered to be a working document, subject 

to revision during the SIP development process in response to unexpected findings or 

technical issues. The modeling protocol and SIP submittal must include a description of 

how the analysis was conducted by providing information on the ambient monitoring 

data and meteorological data used, identification of the models used, the justification for 

model selection, assumptions involved in model application, and model input and output 

data. The U. S. Environmental Protection Agency (EPA) has final approval authority for 

the protocol, especially for any variations from standard guidance that are necessary for 

special circumstances that affect our selection of models and analysis approach. EPA 

guidance was established to provide a best general fit for the entire country but there 

are technical issues for San Joaquin Valley PM2.5 identified by EPA, ARB and the 

District that vary from the national average and require special analysis or evaluation. 

Adjustments to the recommendations of EPA guidance are contained in the Protocol. 

SJVAPCD PM2.5 SIP Protocol Review Process 

The Protocol is released as a working document open to revision during the preparation 

of the PM2.5 Plan. The California Air Resources Board (ARB) is performing some of the 

tasks within the protocol but will also act as a review agency. EPA has authority for 

approval of modifications from general guidance as well as review for technical aspects 

and completeness. The protocol is released for review by the Study Agency Policy 

Committee and CRPAQS (particulate studies) Technical Committee due to their specific 

expertise. Stakeholder and public comment should be directed to the District for 

attention at: 

San Joaquin Valley Air Pollution Control District 

1990 E. Gettysburg Avenue 

Fresno, CA 93726 

Attn: James Sweet, PM2.5 Protocol 

Or by electronic message to : james.sweet@valleyair.org 

The protocol will be released for public review no later than the release of the Draft 

Plan, currently scheduled for November 20, 2007. Comments on major issues that 
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Appendix F: SJV PM2.5 SIP Modeling Protocol 

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would modify the approach extensively are requested within thirty days of release of the 

Protocol to accommodate modifications that may be recommended. However, the 

protocol will remain an open working document until the PM2.5 Plan is submitted for 

receive and file. Improvements to methodology, minor modifications to procedures or 

assumptions and corrections to text may be considered until modeling is finalized to 

prepare the receive and file version of the plan. 

SJVAPCD PM2.5 SIP Protocol Contents 

The SJVAPCD PM2.5 SIP Modeling Protocol describes the selection of general 

approach, methods of analysis and identification of data pertinent to support analysis. 

The protocol proposes comprehensive analysis combining the results of evaluations, 

correlated and reconciled to establish by preponderance of results (weight of evidence) 

that all non-attainment areas will be adequately addressed. EPA guidance 

recommends that models be used in a relative sense due to their uncertainties and the 

differences that exist between model input data and design values applicable to 

determine attainment. EPA directs that model results and other analysis be combined 

to establish a confirmed finding of attainment in a “weight of evidence” determination; 

particularly if the predicted future values are close to the standard. New approaches 

recommended by EPA guidance also require special processing of speciated data and 

a Speciated Modeled Attainment Test (SMAT). 

The PM2.5 modeling Protocol updates the PM10 modeling Protocol with revisions for 

PM2.5 air quality observations, revised and updated rollback and regional modeling and 

additional analysis and discussion to meet requirements of revised modeling guidance. 

Extensive evaluation of fine (PM2.5) particles completed as part of the PM10 SIP for 

coarse and fine particles provides the basis for PM2.5 Protocol. PM2.5 evaluation will 

be supplemented by analysis of more recent air quality data and new regional modeling. 

The PM2.5 protocol includes the following component evaluations that will be combined 

to provide the attainment demonstration: 

• Meteorological Evaluation The District has performed meteorological analysis of 

time periods related to episode conditions and the entire monitoring period of the 

California Regional PM10/PM2.5 Air Quality Study (CRPAQS). ARB and the District 

evaluated the meteorological factors and influences related to PM10 and PM2.5 

episodes. This analysis is essential to identify contributing sources and factors. 

Supplemental analysis for additional episodes for PM2.5 may be required to 

evaluate compliance with the revised daily standard of 35 micrograms, but the 

requirements for this plan are to meet the previous 65 micrograms per cubic meter 

standard. All sites in the District currently comply with the 65 micrograms standard. 

• Receptor Modeling Receptor modeling is performed by the District to establish a 

link between observed particulates and contributing emission sources using the 

combined outputs and techniques of several models. Receptor analysis of observed 
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Appendix F: SJV PM2.5 SIP Modeling Protocol 

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events and annual average particulate levels was performed by ARB in consultation 

with the District using the chemical mass balance (CMB) model. The output of this 

model is used in a receptor modeling speciated rollback model developed by the 

District to calculate the effect predicted emission trends and adopted and proposed 

control measure reductions. The District developed receptor speciated rollback as 

one of its earliest improvements to standard rollback modeling, with documented 

assumptions for the isolation of natural emissions and transport of emissions that 

would not be affected by the control program. The effort to remove sampling artifact 

and non-reactive species has been incorporated into EPA SMAT methods to correct 

similar issues for the regional modeling. The District added a spatial area-ofinfluence element to reflect the effect of controls on contributing sources more 

accurately. Receptor modeling methods used for the PM10 SIP have been revised 

to evaluate PM2.5 by modifying assumptions to address fine particle mechanisms 

and with methodology revisions to reduce uncertainties for the PM2.5 annual 

standard. Further revision may occur to incorporate new findings of current regional 

modeling of secondary particle formation. 

This receptor modeling method works well for analysis of directly emitted particles, 

but is less certain in predicting the effect of reductions of secondary precursors 

(gases that form particles in the air that may not produce particles in amounts 

directly proportional to the amount of emissions). The formation of secondary 

particulates has been examined with regional modeling to determine appropriate 

adjustments to predictions of secondary particulates. The receptor modeling 

approach uses direct (linear) assumptions for the connection of emissions changes 

to projected future contributions. This is adjusted to reflect the nonlinear chemistry 

associated with nitrate formation as determined by the regional model. Sulfate 

formation approaches linear relationship and is not adjusted for nonlinear chemistry 

in the receptor modeling. The expected result of linear projection is an overprediction of future concentration. The method anticipates uncertainty in the inputs 

and uses the over-prediction as a safety margin for attainment modeling. The PM10 

receptor modeling resulted in a sizable margin of safety, projecting attainment 

several years after actual compliance was achieved. Comparing the results of 

PM2.5 receptor modeling with regional modeling results and other pertinent 

information is important to develop a weight of evidence approach to determining 

when attainment will be achieved. 

• Regional Modeling EPA guidance expects regional modeling to play a larger role in 

the PM2.5 modeled attainment test. The primary purpose of the regional modeling 

for the PM2.5 SIP is to provide an independent analysis of the reductions needed to 

achieve attainment. Regional photochemical modeling has limitations for depicting 

the dispersion and removal processes that occur in scales finer than the grid 

resolution but provide equilibrium modeling for the most significant contributions to 

observed mass. Regional modeling uses the known emissions inventory in a 

bottom-up analysis that calculates the amount of mass that should be present due to 

the emissions. Using the model to predict mass from a future emissions inventory 

provides the best assessment of nitrate and sulfate levels that can be provided. The 
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use of the model results in relationship to the design value provides a reasonable 

estimate of future PM2.5 nitrate and sulfate mass components. 

Regional modeling of secondary particulates has been conducted by ARB using 

several different data sets and models. Results of regional modeling improve 

understanding of particle formation rates and ratios of precursors to particle 

formation, particularly for nitrate particulates. These results have been used in 

conjunction with receptor modeling to enhance the accuracy and reliability of 

predicted effects of emission trends and adopted and proposed control measure 

reductions of secondary precursors. The first regional assessment used the Urban 

Airshed Model, modified to address aerosol chemistry (UAM Aero). This used the 

IMS-95 dataset (an early component of CRPAQS) to evaluate a monitored event of 

nitrate particulate formation. Regional modeling was also conducted for the later 

2000-2001 CRPAQS data, providing an update to the PM10 receptor modeling 

projections in 2006. A third round of regional modeling with the CMAQ model is 

currently in progress by ARB. If this provides a different regional photochemistry 

analysis for nitrate formation, the receptor modeling estimates for nitrates will be 

reviewed. 

• Statistical Analysis PM2.5 monitoring is a relatively new program; therefore the 

historical record of data does not provide a sufficient history to evaluate long-term 

trends and patterns. The District and ARB will continue evaluation of observed data 

using a variety of statistical methods to identify potential key factors. Results of 

these evaluations will be used to improve our conceptual understanding of events 

and may be considered to provide substantive data in the process of establishing a 

weight of evidence finding for attainment. 

• Technical Issues The PM2.5 receptor modeling examines the 15 microgram per 

cubic meter annual federal standard. When evaluating the 50-microgram annual 

standard for PM10, an uncertainty of one-microgram represented only a two percent 

variation; however, a one-microgram uncertainty in the PM2.5 analysis represents a 

variation that is almost seven percent of the total allowed. This sets a very difficult 

benchmark for accuracy. Furthermore, PM2.5 is not a single material but a host of 

different materials with both separate behaviors and dependent interactions. 

Although there is a solid foundation of conceptual understanding, there are many 

technical issues that remain open questions for continued study. The PM2.5 SIP will 

utilize all completed analyses available at this time; however, we expect emerging 

technical information to enhance our understanding of observed events and 

subsequently improve our ability to predict future PM2.5 concentrations. 

Annual meteorology has seasonal components, reflected in EPA guidance 

recommending at least quarterly evaluation and analysis. Regional modeling is 

being performed for an entire year on a daily basis that can be processed to monthly 

or quarterly analysis as needed. Receptor modeling, with a speciated rollback 

approach used for the PM10 SIP with monthly meteorological analyses and source 

identification combined into an annual composite, is being updated for the PM2.5 
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analysis. Prior analysis determined that fall and winter seasons are most important 

for PM10 in the San Joaquin Valley and the winter remains the most important for 

PM2.5 due to increased carbon and nitrate particulates. 

EPA guidance acknowledges the difficulty in obtaining a data set for modeling that 

exactly matches the current design values and has developed a methodology to 

break down the modeling into the component constituents and use the model 

response in a relative sense to predict the reductions needed to achieve attainment. 

The Speciated Modeled Attainment Test (SMAT) methodology is generally 

applicable throughout the country but conflicts with certain important considerations 

critical to San Joaquin Valley Air Basin (SJVAB) assessment. Specific adjustments 

will be made to address technical issues specific to the SJVAB and to provide the 

most accurate means to reflect the effects of emission reductions. 

• The SMAT methodology separates out trapped water and ammonium, sulfate 

and nitrate ions. This is necessary in portions of the country that are dominated 

by sulfate emissions. Decreases in sulfate emissions can actually cause 

increase in nitrate formation in areas dominated by sulfates and the associated 

water content of sulfates is much different than for nitrates. The SJVAB is 

dominated by ammonium nitrate particulates with low sulfate particulate 

concentrations; therefore this process is of minimal effect. ARB will determine 

appropriate methodology for retained water for the regional modeling. The 

receptor modeling will use the alternate linear estimations for trapped water. The 

linear approximation method for receptor modeling allows the water to remain 

incorporated with the ammonium sulfate and ammonium nitrate mass in the 

rollback method because the trapped water is assumed to be proportional to the 

ammonium nitrate and ammonium sulfate mass. The linear assumption avoids 

the need to handle trapped water as a separate calculation. 

• The SMAT methodology calculates remaining mass other than the nitrates and 

water as organic carbon. This approach is too coarse for use for our District. 

The basis for this method is that most areas of the country do not have extensive 

control programs for geologic material (fugitive dust) or extensive emissions from 

carbon sources subject to direct control (residential wood combustion and 

agricultural burning). SMAT methodology for regional modeling will be adjusted 

to isolate the geologic material portion so that the effect of our control program 

can be reflected for its benefit in this analysis. The SMAT method may be further 

assessed for its adequacy for carbon. Carbon particle size growth and trapped 

carbon particles within nitrate and sulfate particulates that are not measured in 

the analysis methods are further losses not addressed by SMAT. CRPAQS 

modeling for particle size growth is under development but not available at this 

time. Metals are also not isolated by SMAT methods. For any elements not well 

supported by the regional model results, receptor modeling or other substantive 

data will be reviewed to establish a weight of evidence finding for attainment. 
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Determination of Attainment 

The Protocol provides a program of analysis to demonstrate attainment by assessing 

exceedance of the PM2.5 standards detected by the monitoring network. The entire 

District is classified as nonattainment if any monitoring site is classified as 

nonattainment. Receptor modeling will examine all four counties that are not in 

compliance with the annual standard. Regional modeling will assess the entire SJVAB 

and will allow review of areas without monitors. Reliable conclusions are established by 

knowledgeable evaluation to resolve any contradictory indications that arise from the 

uncertainties and limitations inherent in the various methods of evaluation. Analysis 

procedures were selected to establish objective and reliable conclusions with the 

highest confidence that can be established from the best available data. The SIP 

control plan establishes demonstration of attainment by determining emission 

reductions requirements, if any, needed to achieve attainment for all counties in the 

District nonattainment area. 

Attachments to the Protocol 

Attachments are provided with the Protocol to identify results of research efforts that 

provide analysis of key technical issues or provide supporting documentation for the 

protocol or the SIP. 
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Appendix F: SJV PM2.5 SIP Modeling Protocol 

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San Joaquin Valley Air Pollution Control District 

State Implementation Plan PM2.5 Modeling Protocol 

The San Joaquin Valley Air Pollution Control District State Implementation Plan PM2.5 

Modeling/Analysis Protocol (Protocol) outlines the plans of the California Air Resources 

Board (ARB) and the San Joaquin Valley Unified Air Pollution Control District 

(SJVAPCD) to develop analysis and modeling evaluation of San Joaquin Valley PM2.5 

to support development of control plans to achieve compliance of the federal annual 

and 24-hour PM2.5 standards in accordance with EPA guidance for the State 

Implementation Plan (SIP). The Protocol is utilized as the plan of approach, but is 

considered to be a working document subject to revision during the SIP development 

process in response to unexpected findings or technical issues. The Protocol outlines 

the procedures and technical considerations involved in the modeling analysis for the 

SIP for submission to EPA Region IX for review and comment. The ARB, District and 

Valley transportation planning agencies jointly prepare data analyses, emissions 

inventories and modeling analyses to address modeling requirements for the SIP. 

Modeling is conducted with jointly developed input files and mutually accepted modeling 

assumptions. 

Goal of the Protocol

The goal of the Protocol is to determine an effective program of emission control, 

establishing the amount and types of emission reduction that must be implemented to 

achieve compliance with the federal annual and 24-hour PM2.5 National Ambient Air 

Quality Standards (NAAQS). It should be understood that a protocol is a working 

document that is subject to amendment based on comments received or technical 

modifications developed during the analysis process. As specified in guidance: “Major 

steps to implement the protocol should be discussed with the appropriate U.S. EPA 

Regional Office(s) as they are being decided. States/Tribes may choose to update the 

protocol as major decisions are made concerning forthcoming analyses.” 

Regulatory Requirements for Protocol Contents 

“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/B-07-002, April 2007, 

provides information on the expected contents of the Model/Analysis Protocol. 

“The protocol should detail and formalize the procedures for conducting all 

phases of the modeling study, such as describing the background and objectives 

for the study, creating a schedule and organizational structure for the study, 

developing the input data, conducting model performance evaluations, 

interpreting modeling results, describing procedures for using the model to 

demonstrate whether proposed strategies are sufficient to attain the NAAQS 
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Appendix F: SJV PM2.5 SIP Modeling Protocol 

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F-8 

and/or reasonable progress goals, and producing documentation to be submitted 

for EPA Regional Office review and approval.” 

Guidance recommends that the protocol should: 

• Identify who will help the local air quality agency undertake and evaluate the 

analyses needed to support a defensible demonstration (i.e., the stakeholders). 

The District is in consultation with ARB and EPA and will involve the Study Agency 

Policy Committee and particulate study CRPAQS Technical Committee due to their 

expertise. The Study Agency Policy Committee has been involved in guiding 

particulate research for central California for more than a decade and includes 

representatives of several federal agencies, ARB, adjacent air Districts, stakeholders 

and environmental representatives. Public release will seek input from stakeholders 

and the general public. The District may also distribute the Protocol to additional 

experts identified during the review process to solicit their review or assistance. 

• Identify how communication will occur to develop consensus on various issues. 

Meetings with ARB on PM2.5 Plan development and modeling began in March 

2007. Interagency discussions involving EPA staff began in June 2007. The District 

intends to continue these discussions by conference calls and meetings with the 

agencies having approval authority for the protocol. The District will conduct 

conference calls or meetings as needed to facilitate comment on the Protocol and/or 

the Plan to advance consensus on the modeling approach. District communication 

pathways include mail, electronic mail, meetings and videoconference meetings, 

teleconferences, workshops and Governing Board meetings. 

• Describe the review process applied to key steps in the demonstration 

The District will request ARB, EPA, the Study Agency Policy Committee and the 

particulate study CRPAQS Technical Committee to provide technical review and 

recommendations to the District during the SIP development process. The public 

process will include posting of the Protocol on the District website. Comments 

received in writing or at workshops will be reviewed to determine if modification of 

the Protocol is warranted. The District will consult with ARB and EPA regarding any 

suggestions that propose procedures that are alternatives to recommendations of 

guidance prior to accepting a suggested modification. The District will review 

comments received and requests for meetings to determine the scope, method and 

frequency of discussions necessary to advance consensus on critical issues. 

• Describe how changes in methods and procedures or in the protocol itself will be 

agreed upon and communicated with stakeholders and the appropriate U.S. EPA 

Regional Office. 
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The District will evaluate recommended revisions to the methodology of the PM2.5 

modeling protocol in consultation with ARB and EPA staff. The Air Resources Board 

and EPA Region IX have final approval of modifications. Workshops on the PM2.5 

Plan will notify the public if substantive changes to the Protocol are accepted. If 

there are major revisions to the Protocol, a revised version will be posted to the 

District web site. 

The guidance document stresses that the name of the protocol has been revised in their 

terminology to “Modeling/Analysis Protocol” to emphasize that the protocol needs to 

address modeling as well as other supplemental analyses. The difficulties and 

uncertainty in modeling PM2.5 call for careful interpretation of results in a “weight of 

evidence” approach to establish reliable findings. 

The San Joaquin Valley Air Pollution Control District State Implementation Plan PM2.5 

Modeling Protocol (Protocol) contains the required elements, presenting data 

discussions first because the analysis of the data guides model selection and 

application. Because the Valley is a large geographic area with emissions that vary 

extensively by location and season, the Protocol proposes a comprehensive analysis by 

combining, correlating and reconciling results of a series of evaluations to establish by 

preponderance of results (referred to as weight of evidence) that the SIP adequately 

addresses all nonattainment areas. The Protocol proposes a series of evaluation 

elements that include: meteorological evaluation of the influence of factors affecting 

PM2.5 concentrations, statistical analysis of representativeness and 

comprehensiveness of episodes evaluated, receptor modeling to establish the 

contributions of major source types and predicted control effects, and regional modeling 

to enhance predictions of secondary particulates in the receptor modeling and to 

provide a separate assessment of reductions needed to achieve attainment in 

accordance with EPA guidance for a Speciated Modeled Attainment Test (SMAT). The 

Protocol also discusses how the results of the modeling will be processed to develop 

reliable findings and how the results will be utilized to determine the effectiveness of 

proposed additional controls to achieve attainment of the federal PM2.5 standards. 

The following portions of this Protocol and its attachments provide the basis and 

justification for the approach selected for PM2.5 modeling analysis. Descriptions for 

each element of the analysis are provided. The conceptual model for PM2.5 source 

contributions is provided by a referenced attachment. The conceptual model was 

developed from extensive research efforts conducted for the specific purpose of 

understanding the dynamics of PM10 and PM2.5 in the Valley. 

Revision of PM10 Receptor Methods to address PM2.5 

The PM2.5 receptor modeling analysis requires a minimal set of revisions from the 

methods used for the PM10 SIP. New guidance issued by EPA requires additional 

analysis and documentation. The District and ARB will ensure that all analysis and 

documentation requirements are met prior to final submittal. EPA Guidance also 
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reflects that prior modeling methodology guidance remains effective with additional 

clarification provided by the new Guidance. “Much of the information in U.S. EPA 

(1991a) regarding modeling protocols remains applicable. States/Tribes should review 

the 1991 guidance on protocols.” The Protocol for the PM10 SIP will serve as the 

foundation for PM2.5 receptor modeling analysis with the following updates: 

• The annual rollback analysis for PM10 events will be updated with PM2.5 speciation, 

using linear and aerosol modeling conducted with IMS95 and CRPAQS data for the 

PM10 SIP and 2006 PM10 update. The analysis will investigate the emissions 

reductions predicted for future years to quantify improvement. Additional reductions 

for new control measures will be evaluated. 

• Additional regional aerosol modeling, to adjust rollback assumptions and provide 

supporting weight of evidence evaluation, is being conducted by ARB and is 

expected to provide results for evaluation after the first workshop. This modeling is 

being conducted with the CMAQ model. 

• Newer years of data have been evaluated with CMB to examine changes in 

observed species after the year 2000 to evaluate progress and to provide 

information that might indicate revision to species mass allocation in the rollback. 

This effort was conducted by ARB with speciation profiles identified in consultation 

with the District. 

• CART analysis performed for the PM10 SIP will be replaced by PMF evaluation for 

weight of evidence comparison. This analysis is being conducted by ARB. 

CMB speciation for PM2.5 for Annual Rollback Modeling

The speciation data and CMB modeling results for PM10 contain a combination of 

coarse and fine particles. The relationship between PM10 and PM2.5 is not 

proportional; therefore special evaluation is required to develop an appropriate 

speciation set for PM2.5 rollback analysis from the CMB modeling performed for PM10. 

To convert the PM10 annual CMB modeling and rollback analysis to PM2.5, the mass 

distribution must be replaced by PM2.5 speciation data or be calculated by other 

methods from the mass determined by PM10 CMB modeling. 

The methodology includes the following components and considerations: 

• Fresno, Kern, Kings and Tulare counties have 2005 PM2.5 design values that 

exceed the 15 microgram annual standard. Analysis is required for each of these 

counties to determine emission reductions required to achieve compliance. PM2.5 

speciation measurements are collected at Bakersfield, Fresno, Modesto and Visalia. 

Fresno speciation data for PM2.5 covers the period used for the rollback foundation 

annual PM10 CMB analysis (2000-2001). Complete data for Bakersfield and Visalia 

is available for 2002 and thereafter, but is not available for 2000-2001. No PM2.5 
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speciation data is collected for Kings County; however, prior PM10 speciation 

analysis and CMB modeling determined that Tulare and Kings County conditions are 

quite similar, allowing use of Tulare County speciation data with minor adjustments. 

The speciation data provides total mass for annual evaluation, nitrates, sulfates, 

geologic material and metals 

• Natural and regional background for PM2.5 cannot be directly measured and is 

easily identifiable or separable from other measured local contributions. Background 

concentrations have been established in the same manner as was used to establish 

background values for the PM10 rollback analysis. The significance of background 

levels is that such material will not be affected by the District’s control efforts. Since 

rollback provides a linear projection of affect for reducing emissions, failure to 

exclude background will over-predict the effect of control efforts. All assumptions for 

background are now expressed as percentages of material. The background values 

for geologic material, nitrate and sulfate particulates have been set at 10% of the 

observed concentration instead of a fixed concentration. Dynamic background 

contributions of 10% were too high for use with PM10 due to high deposition rate 

and short average distance of travel; however, PM2.5 has greater travel distance 

and persistence in the atmosphere. 

• Mobile exhaust and tire and brake wear were determined by CMB annual analysis 

for PM10. From the emissions inventory we are able to establish that slightly over 

90% of the mobile exhaust emissions are PM2.5 and approximately half of the tire 

and brake wear material is PM2.5. The mass determined for PM10 rollback has 

been adjusted accordingly to establish a reasonable mass attribution for these 

sources. 

• Fresno annual rollback uses a design value derived from the annual speciation data 

set for the years 2000 and 2001. Fresno annual rollback uses limited elements of 

the annual PM10 CMB analysis, performed for the year 2000 with additional data 

from the first quarter of 2001, to provide mass estimates for motor vehicles and tire 

and brake wear. The modeled contribution for motor vehicles and tire and brake 

wear has been adjusted by factors determined from the emissions inventory to be 

representative of PM2.5 material. The average of 2000 and 2001 PM2.5 speciation 

data is used to provide PM2.5 species for nitrates, sulfates, geologic material and 

unassigned (elements). Organic carbon and vegetative burning is composed 

organic and elemental carbon similar to the motor vehicle emissions but contains 

both large and small particles. The mass for organic carbon and vegetative burning 

has been calculated as the remainder of the annual PM2.5 speciated mass not 

assigned to the other categories. 

• Bakersfield uses the same elements from the annual PM10 analysis but is required 

to use 2002 PM2.5 speciation data as the earliest complete speciation available for 

this purpose. Modeling may be revised or adjusted in other ways if improvements to 

the methodology are identified by technical evaluation. 
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• Visalia uses the same elements from the annual PM10 analysis but is required to 

use 2002 PM2.5 speciation data as the earliest complete speciation available for this 

purpose. Modeling may be revised or adjusted in other ways if improvements to the 

methodology are identified by technical evaluation. 

• Kings County uses Visalia speciation data for the year 2002 to establish PM2.5 

mass with adjustment to motor vehicle and tire and brake wear. The PM10 

speciation data was very similar for Kings and Tulare counties in previous years, 

with the exception of fugitive PM10 emissions that are large particles. Motor vehicle 

contributions were similar to Visalia in Hanford but much less in Corcoran. Corcoran 

is the site that sets the design value for Kings County. Prior CMB modeling of the 

same event at both Visalia and Corcoran establishes an approximate ratio of the 

motor vehicle and tire and brake wear contributions at the two sites. Based on this 

evaluation, motor vehicle emissions and tire and brake wear are adjusted to 55% of 

the Visalia concentration to reflect the smaller urban contribution found in Corcoran. 

• CMB results for Fresno and Kern provided by ARB were adjusted by 0.6 micrograms 

reassigned from OC to unassigned mass as found with earlier CMB modeling. Inert 

material is common in SJV samples and the method of assigning all unaccounted 

mass to OC overestimates that profile. 

Other Revisions to the PM10 methodology for PM2.5 for Annual Receptor 

Rollback Modeling

• Cooking emissions, previously grouped with vegetative burning, are included in 

organic carbon associated with industrial and commercial emissions. 

• The speciated rollback analysis developed by the District provides spatial 

projection of the area of influence of contributions. The PM10 methodology 

utilized different assumptions for contributions dominated by large particles and 

contributions dominated by fine particles PM2.5 and smaller. The PM10 travel 

distance assumptions for geologic and construction, tire and brake wear and 

unassigned mass utilized large particle assumptions which are modified for the 

PM2.5 analysis to be consistent with travel distance assumptions for other 

contributions dominated by fine particles. Methodology for the defined 

apportionment of mass to local and regional intrabasin transport has been 

adjusted from a fixed ratio to a mass weighted calculation to improve accuracy 

and correlation to the conceptual model. 

• For categories that are comprised of directly emitted particulates and secondary 

aerosol (SOA) formation of organic carbon compounds a default split was used 

for earlier receptor modeling assuming an even split for the two pathways. 

CRPAQS results indicate a maximum of secondary aerosol formation of fifteen 

percent of the observed carbon. The rollback analysis has been revised for this 

updated information. The emissions linkage to SOA is ROG instead of TOG. 
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• Nitrate mass will be reevaluated to determine if any adjustments to linear 

projection are required. The 2003 analysis included nonlinearity adjustments 

obtained from analysis of UAM-AERO regional modeling of CRPAQS IMS-95 

data. The variation of nitrate chemistry is represented by the monthly variations 

of CMB modeling for the annual, which inherently incorporates the variation in 

nitrate chemistry throughout the year. Regional modeling nitrate nonlinearity 

adjustments are appropriate for episode evaluation but potentially introduce a 

redundant penalty in the annual speciated rollback modeling. The linear 

projection of annualized rollback already includes assessment of the variation of 

nitrate formation; therefore further adjustment with modeling response may 

constitute a redundant adjustment. The comparison of linear model estimation to 

the projections with regional modeling penalties for nonlinear nitrate response will 

be preserved until technical discussions on this matter are complete. 

• Receptor modeling will use the alternate linear estimation assumptions for 

trapped water. Water bonded to ammonium nitrate is approximated as 

equivalent to twelve percent of the mass and water bonded to ammonium sulfate 

is approximated as 26 percent of that mass. This method allows the water to 

remain incorporated in the proportional rollback because the trapped water is 

assumed to be proportional to the ammonium nitrate and ammonium sulfate 

mass. The linear assumption avoids the need to handle trapped water as a 

separate calculation. 

Data: Meteorological 

Collecting meteorological data is as important as obtaining reliable pollutant data to 

predict PM2.5 formation, concentration and response to change. Meteorological data is 

used with pollutant data to improve day-to-day meteorological and air quality 

forecasting, characterize the nature and extent of pollutant problems, and prepare air 

quality trend analyses. Evaluation of air quality model performance relies on 

meteorological as well as pollutant data to support modeling processes to make longterm control strategy assessments and decisions as part of the continuing air quality 

management process of SIP planning 

Meteorology and climate play important roles in determining the levels of air pollution in 

the Valley. Some meteorological patterns cause higher levels of air pollution by 

preventing the dispersion of pollutants. Pertinent meteorological parameters include 

wind speed and direction, ambient atmospheric temperature and inversion layers (i.e., 

layers where the air temperature increases with height), and precipitation. Substantial 

temporal and spatial variations in PM2.5 speciation profiles occur in the District in part 

because the types and quantities of source emissions are different in each major city 

and county, but meteorological factors also affect PM2.5 levels, affecting emissions, 

secondary particle formation and dispersion. Meteorology should not be considered as 
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merely an input in the modeling, but rather as a key parameter to understanding and 

predicting PM2.5 formation and concentrations. 

The Protocol includes evaluation elements distinct from data selection for modeling. 

The evaluation and interpretation of meteorological influences is essential to reliable 

control strategy and trend evaluations. 

• Meteorological Evaluation The District performed meteorological analysis of time 

periods related to episode conditions and the entire CRPAQS monitoring period. 

This information was reviewed by ARB and the District to evaluate the 

meteorological factors and influences related to PM10 and PM2.5 episodes. The 

analysis also establishes the representativeness of transport and formation of PM 

observed in historical episodes. It is essential to evaluate episodes with this 

perspective to identify contributing sources and factors. 

Meteorological evaluation also assesses whether monitoring captured the peak 

values. In accordance with EPA guidelines for PM2.5 monitoring, monitoring is not 

conducted on a daily basis at all sites. Where monitoring does not provide a daily 

record it may not detect the maximum value. Evaluation of meteorological factors on 

days that were not monitored has been compared to historical monitored days to 

evaluate the representativeness and comprehensiveness of monitored exceedances 

by examining the relative severity of the associated factors. The control program 

must consider conditions identified by meteorological analysis that were not 

captured by monitoring. The design of the control plan is sufficient to provide 

attainment if monitoring captures a representative sampling of the most severe days. 

• Statistical Analysis Evaluation using a variety of accepted statistical methods will 

be conducted with available air monitoring data and other data identified as related 

to high concentrations of particulates. The process will evaluate the factors related 

to known and observed episodes and identify other combinations, patterns and 

factors not captured by monitoring that are potentially capable of causing PM2.5 

episodes. Results of this process provide evaluation of: sufficiency of available data 

to establish reliable conclusions and reasonableness of results of other methods. 

Results also provide verification of the completeness of the meteorological 

assessment to establish sufficiency of the control program to provide attainment of 

the annual and 24-hour standard for all predictable conditions. 

Analysis of long-term trends is difficult for PM2.5 due to the recent deployment of the 

sampling network. The mass sampling values and speciated data have been 

examined for implied trends and factors but the number of years of sampling 

reduces the ability to apply normal procedures, such as three year averaging to 

reduce the influence of meteorological variation. The District implemented several 

major programs that reduced emissions during this time period and there was a 

noted national and international trend of declining PM2.5 and PM10 values. The 

limited record of monitoring data makes it difficult to isolate changes resulting from 

controls from regional or national trends without supplemental evaluation through 
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receptor or photochemical modeling. The ARB is also evaluating the positive matrix 

factorization (PMF) model to determine if it can provide useful analysis of 

contributing sources. The data available is believed sufficient for the PMF approach. 

Justification for Meteorological Evaluations 

Justification for including the meteorological evaluation elements as well as using 

appropriate meteorological data for modeling derives from prior analysis of 

meteorological influences on PM10 concentrations conducted for development of daily 

forecasting of air quality and prior SIP development efforts. The chemical mechanisms 

involved in secondary particle formation and inversion patterns affecting retention and 

build up of PM2.5 concentrations must be effectively interpreted to support trend 

evaluation, atmospheric modeling, receptor modeling and consequent control strategy 

evaluations and decisions. 

Meteorological data are used to assess the potential for air pollution to accumulate in 

certain locations. Weather factors that may restrict horizontal and vertical air movement 

of air masses are important factors in air quality. Vertical movement of air disperses 

pollutants vertically while horizontal movement spreads the pollutants over a wider 

geographic area. 

Extensive seasonal variation has been established for sources contributing to PM2.5 

concentrations and atmospheric processes contributing to particle formation and 

retention. Analysis of filters reveals that different meteorological conditions and sources 

contribute to increased PM2.5 formation in the fall and winter. During the October to 

January period the PM2.5 concentrations undergo a shift from dominance by primary 

particles to dominance by secondary particles. Secondary particles are a major fraction 

in colder, wetter periods, but are generally less important before mid-November. 

Colder, frequently stagnant conditions occurring in December and January favor 

formation of ammonium nitrate. 

Inversion Layers 

Inversion layers exist when the air temperature increases with elevation above the 

ground. The strength, altitude of, and duration of inversions determine the amount of 

vertical atmospheric mixing which occurs, which subsequently contributes to PM2.5 

concentrations in the District. Temperature inversions occur in a stable atmosphere of 

warm air over cooler air hindering the upward dispersion of pollutants. Mixing ceases at 

the base of the inversion, which is also known as the mixing height. The Valley 

experiences two common types of inversions; radiation inversions and subsidence 

inversions. 

Nocturnal cooling of an air layer near the Valley surface causes radiation inversions. It 

extends upward several hundred feet and occurs during the evening and early morning 

hours. During a radiation inversion, little vertical mixing occurs near the surface. The 

inversion dissipates when solar radiation warms the ground, which in turn heats the 
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lower layers of the atmosphere. This heating causes the surface-based inversion to 

weaken, and finally dissipate, which allows vertical mixing through a greater depth in the 

atmosphere. Inversions are more persistent (stable) during the winter months, when 

inversions occur from 50 to 1,000 feet above the Valley floor. Studies in the southern 

part of the Valley indicate more frequent and persistent early morning radiation 

inversions than in the northern part of the Valley due to the lack of marine air intrusion. 

Subsidence inversions are caused by downward vertical motion in the atmosphere. 

This is common when the semi-permanent Pacific High pressure system is located off 

the west coast. As air descends, it warms due to compression, and as a result 

becomes warmer than the air beneath it. Daytime temperature inversions during the 

summer are usually encountered 2,000 to 2,500 feet above the Valley floor. During the 

summer months, the Pacific High also protects the Valley Air Basin from weather fronts, 

which could otherwise bring cleansing rains and help reduce PM2.5 concentrations. 

Horizontal Mixing and Dispersion 

In addition to vertical mixing, horizontal mixing, or transport, is also important in the 

dispersal of air pollutants. The greater the velocity of wind in the mixing layer, the 

greater the amount of mixing (dispersion) and transport of pollutants. Analysis of wind 

flow shows that during the winter months, the mean flow is through the Valley from the 

southeast. By mid-spring, coastal breezes enter the Valley from the northwest, which 

reverses the airflow pattern. By summer, the northwest to southeast airflow is at its 

strongest point. During the spring and summer, average wind speeds reach 6-10 mph. 

The frequency of very light winds (0-3 mph) does not exceed 23 percent of all spring 

and summer wind speeds. In the fall and winter, average wind speeds range between 

5-9 mph; however, very light winds occur from 20 to 40 percent of the time. 

PM2.5 originating from or going to other air basins, referred to as pollutant transport, 

has not been definitively quantified. PM2.5 readings in the SJVAB are most severe 

during the fall and winter periods when wind speed and direction are not conducive to 

interregional transport. Monitoring and speciation techniques currently available are not 

able to identify the origin of PM2.5 sources with sufficient detail to indicate if the SJVAB 

is experiencing transport from outside the air basin or contributing transport of PM2.5 to 

other air basins. Transport of some PM2.5 precursors has been studied as part of 

ozone transport evaluation, identifying transport of ozone and ozone precursors from 

and to other air basins surrounding the SJVAB. The transport of ozone was 

documented during the summer when the highest ozone readings are more likely to 

occur. This transport includes precursors of ozone and PM2.5; however, the amount of 

PM2.5 that could be generated in the SJVAB or other air basins from such transport has 

not been quantified. Pollution from areas outside of the Valley may or may not 

contribute to high PM2.5 levels within the Valley. 
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Air Basin Topography 

Air pollution within the SJVAB is intensified by topographical and meteorological 

conditions, which hinder the movement of air, thereby reducing the dispersion and 

dilution of emissions. The surrounding mountain ranges block dispersion, minimizing 

wind flows into and out of the basin. Meteorological conditions contributing to poor air 

quality also includes transport of pollutants into the SJVAB from upwind sources. 

The San Joaquin Valley is a major geographic, population, and agricultural subregion of 

California. The District, and the corresponding air basin, includes the counties of San 

Joaquin, Stanislaus, Merced, Madera, Fresno, Kings, Tulare, and the Valley portion of 

Kern County. Comprising nearly 25,000 square miles, it represents approximately 16% 

of the geographic area of California. The Valley has a population of over 3 million 

people, with major urban centers in Bakersfield, Fresno, Modesto and Stockton. 

The SJVAB consists of a continuous inter-mountain valley approximately 250 miles long 

and averaging 80 miles wide. On the western edge is the Coast Mountain range, with 

peaks reaching 5,020 feet, and on the east side of the Valley is the Sierra Nevada 

range with some peaks exceeding 14,000 feet. The Tehachapi Mountains form the 

southern boundary of the Valley. This mountain range includes peaks over 6,000 feet, 

and contains mountain passes to the Los Angeles basin and the Mojave Desert. 

Wind Speed and Direction 

Wind speed and direction play an important role in dispersion and transport of air 

pollutants. Wind at the surface and aloft can disperse pollution by vertical mixing and 

by transporting it to other locations. Wind speed and direction data indicate that during 

the summer the light and variable winds usually result from an influx of air from the 

Pacific Ocean through the Bay Area delta region, entering the north end of the Valley. 

The wind generally flows in a south-southeasterly direction through the Valley, through 

the Tehachapi Pass, and into the Southeast Desert Air Basin portion of Kern County. 

During the winter, wind speed and direction data indicate that wind occasionally varies 

from the south-southeasterly direction, and originates from the south end of the Valley, 

flowing in a north-northwesterly direction. Also during the winter months, the Valley 

experiences light, variable winds of less than 10 mph. Low wind speeds, combined with 

low lying inversion layers in the winter, create a climate conducive to the formation of 

high PM2.5 concentrations. 

Temperature 

The San Joaquin Valley floor is characterized by warm to hot, dry summers and cooler 

winters. The average mean temperature over a 30-year period is 65°F. High daily 

temperature readings in summer average 95°F in the Valley. The Valley also 

experiences mild winters; the winter average daily low temperature is 45°F. Over the 

last 30 years, the Valley averaged 106 days per year 90°F or hotter, and 40 days a year 
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100°F or hotter. The daily summer temperature variation can exceed 30°F. The Valley 

has an "inland Mediterranean" climate averaging over 260 sunny days per year. 

Precipitation 

Precipitation in the Air Basin is confined primarily to the winter months with some 

occurring in late fall and early spring. Nearly 90% of the annual precipitation in the 

Valley falls between the months of November through April. Average annual rainfall for 

the entire Valley is about 10 inches on the Valley floor. There are north-south and eastwest regional differences, with higher rainfall occurring in the northern and eastern parts 

of the Valley. Historical evaluations have correlated increased annual rainfall to 

decreased PM10 concentrations, reducing the fugitive emission of geological material. 

The impact of rainfall on PM2.5 concentrations has not been well established and will 

continue to be studied. 

Table 1 

SJVAPCD Meteorological Monitoring 

Meteorological Parameters

Station Name Wind 

Speed 

Wind 

Direction 

Outdoor 

Temperature 

Relative 

Humidity 

Barometric 

Pressure 

Solar 

Radiation 

Arvin X X X X X X 

Bakersfield-California X X X X X X 

Bakersfield-Golden St X X X X X X 

Clovis-Villa X X X X X X 

Corcoran-Patterson X X X X 

Edison-Johnson X X X 

Fresno-Drummond X X X X 

Fresno-First Street X X X X X 

Fresno-Sky Park X X X 

Hanford-Irwin X 

Madera-Pump Yard X X X X X X 

Maricopa-Stanislaus X X X X 

Merced-Coffee X X X 

Modesto-14th Street X X X X 

Oildale-Manor X X X 

Parlier X X X X X X 

Sequoia National 

Park-Ash Mountain 

X X X X X 

Sequoia National 

Park-Lower Kaweah 

X X X X X 

Shafter-Walker Street X X X X X X 

Stockton-Hazelton X X X X 

Tracy-Airport X X X X 

Turlock-Minaret X X 

Visalia-Church X X X X 

Visalia-Airport X X X X X X 
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General Weather Types and Seasons 

Additional description of the general patterns and influences affecting the SJVAB is 

provided from excerpts from “Climate of the San Joaquin Valley Air Basin,” C. D. Unger, 

State of California Air Resources Board, December 1974. 

The climate of the San Joaquin Valley is characterized by hot, dry summers and cool, 

rainy winters. The most significant single control of the weather pattern is the semipermanent subtropical high-pressure belt, often referred to as the “Pacific High”. It is 

located off the west coast of North America and is a cell in which air descends almost 

continuously. The descending air is compressed, thereby raising its temperature and 

lowering the relative humidity. Major storms and region-wide precipitation are not 

typical when this pressure cell is dominant. This belt of high pressure migrates north 

and south seasonally. The SJVAB is under its influence almost continuously during 

summer months. In winter, the influence of the Pacific High is intermittent, giving rise to 

alternate periods of stormy, unsettled weather and periods of stable, rainless conditions. 

Annual rainfall totals vary from north to south, with northern counties experiencing as 

much as eleven inches of rainfall and southern counties experiencing as little as four 

inches per year. Air pollutants are generally transported from the north to the south and 

in a reverse flow in the winter due to these influences. Strong temperature inversions 

occur throughout the Valley in the summer, fall and winter. 

Summer 

During summer months the Pacific High pressure cell is positioned over the ocean to 

the west, off the northern California coast. The clockwise flow of air around the high 

results in persistent northwest winds over most offshore areas and enhances 

northwesterly flow through the interior valleys of California. The orientation of this 

trough and the pressure gradient between coastal and inland stations determines the 

variability in the summer weather pattern. Strong onshore pressure gradients occur 

with deep penetration of marine air through the Carquinez Strait into the Central Valley. 

Cooler temperatures and stronger northwest-to-southeast winds result from this 

pressure distribution. 

Summertime relative humidity is quite low on the Valley floor, thereby causing large 

diurnal temperature variations. Daytime temperatures often exceed 100°F, and 

nighttime temperatures can often drop into the upper 50’s. Daytime temperatures are 

generally warmer in the southern than in the northern end of the Valley because of the 

persistent influx of marine air under the influence of the thermal trough. Air over 

Bakersfield in the southern portion of the basin comes in through the Carquinez Strait 

where it was originally about 65°F, but is warmed in its journey down the Valley, 

reaching Bakersfield with an average maximum air temperature of 98°F at the surface. 

When the pressure differential between the coast and the interior is weak, stagnant 

conditions result and dispersion is relatively poor. This may occur when a highpressure cell aloft is located to the east of the Valley during the summer, thereby 
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offsetting the westerly wind component. The thermal trough is weaker on days when 

Valley surface heating is minimal which also brings about poor ventilation conditions. 

On a few days each summer, tropical air will be advected into the area at mid and high 

levels and may result in thunderstorms. The thunderstorms are most prevalent over the 

mountains east of the Valley floor; occasionally a vigorous thunderstorm occurs over the 

floor of the Valley, preceded by gusty winds and blowing dust. 

Fall 

During the transition season of autumn, the storm belt and zone of strong Westerlies 

shifts southward through California, and passing frontal systems may produce showers 

and rain. With the approach of winter, the subtropical high shrinks and frontal passages 

become more vigorous. The advent of a high-pressure ridge over the area causes the 

formation of morning fog. As the intensity of solar radiation steadily diminishes through 

the fall months, daytime surface temperatures in the Valley decrease. This brings about 

a weakening in the thermal trough in the Valley, and the influx of marine air becomes 

negligible. Significant air stagnation occurs under these conditions. 

Winter 

As the Pacific High shifts southward, it diminishes in strength, allowing storms, which 

develop in the Gulf of Alaska to penetrate further south. This can bring clouds and rain 

into the San Joaquin Valley. Occasionally, these storms will stall and deepen off the 

coast, and rainy weather will continue for several days. In between these periods, warm 

ridges may influence the Valley and there is a buildup of pressure through the interior of 

California. Mild, bright, sunny weather usually accompanies these synoptic types. 

Another high-pressure cell that affects the San Joaquin Valley is the Great Basin High, 

which develops during the winter months in the area east of the Sierra Nevada. When 

this high is very strong, the descending winds will scour out the Valley, and dry, bright 

winter days result. When the high is weaker, a layer of cool, damp air is trapped in the 

basin and may last for a week or longer. Climatic records for Bakersfield show that 

heavy fog occurs on the average of 20 days each winter, with December and January 

having the most frequent fog. The top of the low stratus and fog is usually below 3,000 

feet; therefore, higher elevations are usually clear under these conditions. Ventilation 

conditions below the inversion base are usually poor. 

Many of the frontal systems, which pass through the San Joaquin Valley, are 

considerably weakened by the time they have reached this latitude. During the period 

when a weak, slow-moving frontal system is approaching the Valley, surface winds and 

vertical mixing may be light. This often results in stagnant conditions, which may 

persist, from 12 to 48 hours in advance of the front. 

The Southern San Joaquin Valley is generally in the rain shadow of the Tehachapi and 

the Coast Ranges to the south and west. For this reason, the southern Valley depends 
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on cold, unstable, northwesterly flow for its precipitation, which produces showers 

following frontal passages. The northwesterly flow up the Valley is almost unobstructed 

by terrain barriers. The prefrontal southerly winds result in relatively little precipitation in 

the extreme southern end of the Valley. Winter temperatures in the San Joaquin Valley 

are generally mild. Temperatures will drop below freezing occasionally, but throughout 

the Valley, winter daytime highs are around 55°F, with lows around 35°F. Despite the 

latitudinal extent of the Valley, the variation of temperature in winter is small. The 

average January temperature is about 44°F, with little difference between the northern 

and southern portions of the Valley. Surface temperatures are dependent on elevation, 

with colder temperatures on the mountain ridges both east and west of the Valley floor. 

Spring 

This is a transitional period in the San Joaquin Valley Air Basin. Cold pools of Gulf of 

Alaska air frequently move across the region, bringing instability and associated 

showery weather. In addition, the increased surface heating induces further instability. 

Frontal passages become steadily weaker and less frequent as summer approaches. 

Occasionally, in late spring, when Valley surface temperatures near the 90°F mark, 

vigorous updrafts along the Sierra range trigger the development of intense 

thunderstorms in the mountains in the afternoon and evening. Surface winds assume 

more of an up-valley component as the temperatures become warmer. 

On infrequent occasions, a high-pressure system will stagnate over the Pacific 

Northwest, resulting in strong northerly winds, which may persist for several days. 

These are dry, desiccating winds that may cause severe crop damage, but bring 

generally favorable ventilation. 

Data: Atmospheric Chemistry and Sources 

Secondary PM2.5 species, such as ammonium nitrate, ammonium sulfate, and organic 

particles are formed through chemical interactions from directly emitted SOx, NOx, VOC 

and ammonia. Particulate sulfate and nitrate can form via both gas and aqueous phase 

pathways. In the aqueous phase, which is the main pathway during winter fog and cloud 

conditions, secondary ammonium nitrate and ammonium sulfate form when nitric acid 

and S02 dissolve in water droplets and then react with dissolved ammonia. Since the 

sulfate and nitrate ions compete with each other for the available ammonia, SOx, NOx, 

and ammonia must be treated as a coupled system in order to adequately understand 

the interactions and subsequent formation of nitrate and sulfate particles. 

Source apportionment receptor modeling has identified the major contributing sources 

to PM2.5 during the summer as motor vehicle emissions, secondary sulfate, and 

primary geological material from the fine particle fraction of airborne soil entrained by a 

variety of sources and mechanisms. Winter and spring are heavily dominated by 

secondary ammonium nitrate with moderate contributions of secondary sulfate, motor 

vehicle emissions, primary geological material and direct emission or secondary organic 
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aerosol identified as biomass burning from one or more source types including 

residential wood combustion, wild fires or agricultural burning. The secondary organic 

aerosol will also contain small contributions from biogenic VOC emissions. 

Data: Ambient Monitoring 

The federal Clean Air Act set the health-based National Ambient Air Quality Standards 

(NAAQS) specifying maximum acceptable levels of pollutants and authorized the EPA 

to revise and enforce the standards. Primary NAAQS protect public health with an 

adequate margin of safety, and secondary NAAQS are established to protect public 

welfare (i.e. soil, crops, vegetation, animals, visibility, building materials, etc.) from 

known or anticipated harmful effects. The levels for the primary NAAQS for PM2.5 were 

established as 15 μg/m3 (micrograms per cubic meter) for an annual arithmetic mean 

averaged at each site over a three year period and 65 μg/m3 for the 24-hour PM2.5 

standard maximum 24-hour concentration not to be exceeded more than three times in 

three years as determined by calculating the expected number of exceedances when 

monitoring at less than daily frequency. The District exceeds the federal annual PM2.5 

standard but meets the applicable 65 μg/m3

 24-hour PM2.5 standard. The 24-hour 

standard has been revised to 35 μg/m3

 but that revised standard is not applicable to this 

required SIP. A separate calendar has been established for preparing a plan to attain 

the new 24-hour standard. 

To quantify our understanding of the PM2.5 problem in the Valley we have evaluated 

PM2.5 data collected by the NAMS/SLAMS network. This includes federal reference 

method (FRM) samplers and speciation samplers. The monitoring frequency and years 

of available data vary from site to site. Only four sites have speciation sampler data, 

provided by sampler technology that does not provide an identical match with FRM 

monitoring data due to differences between the sampler technologies. The differences 

in data availability, sampling frequency, and availability of speciation data make it 

difficult to establish site or District patterns and trends. The use of supplemental data 

collected for special studies is valuable to enrich this data for a better understanding of 

sources and trends. 

Data collected at locations other than District and ARB operated NAMS/SLAMS 

continuously operated sites (referred to as off site data) is used to support analysis. 

Supplementary data is also used from extra monitoring equipment at continuously 

operated sites or off site temporary monitoring. The supplemental monitoring includes 

additional instruments that comply with federal reference methods (FRM), devices that 

provide additional readings but may not be FRM equivalent and additional equipment 

that measures other atmospheric parameters to support evaluation and modeling. Data 

from equipment or sites which do not meet SLAMS criteria will not be used to 

demonstrate attainment or compute the design value; however, data from additional 

monitoring devices and sites is used to analyze the sources and components of ambient 

PM samples and assess regional and local variations. This data is used to analyze 
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episodes of high ambient PM and determine effective controls to achieve and maintain 

attainment of the PM2.5 NAAQS. 

Monitoring Network Representativeness 

The EPA requires that the state and the District measure the ambient levels of air 

pollution to determine compliance with the NAAQS. The District and state operate the 

ambient monitoring network in order to comply with this mandate. Monitoring for PM2.5 

occurs at fourteen sites within the San Joaquin Valley Air Basin. Eleven of the fourteen 

sites use filter-based units and three contain a real-time particulate monitor. Of the 

eleven sites seven of them also contain real time particulate monitors in addition to the 

filter-based units. Twelve of the sites are neighborhood scale and the remaining two 

are urban scale. All of the PM2.5 sites measure representative concentration. 

Air quality monitoring for PM2.5 is performed at State and Local Air Monitoring Stations 

(SLAMS) within the District, including National Air Monitoring Stations (NAMS) and 

Photochemical Assessment Monitoring Stations (PAMS). The EPA uses data from 

NAMS sites to develop national air quality trends. 

Federal regulations require SLAMS networks to meet four basic monitoring objectives, 

which include: 

1) Monitoring the highest concentration of a pollutant, 

2) Monitoring representative concentrations in areas of high population density, 

3) Monitoring the impact of major pollutant sources, and 

4) Monitoring pollutant background concentrations. 

The physical siting of an air monitoring station must achieve a spatial scale of 

representativeness that is consistent with the monitoring objective. Spatial scales of 

representativeness are categories of sampling exposure. The spatial scale for each site 

results from the physical location of the site with respect to the pollutant sources and the 

population or area, which is to be represented, by the monitoring site. The categories 

are classified by the size of the area surrounding the monitoring site which experiences 

uniform pollutant concentrations. The categories of spatial scale are: 

1) Microscale - An area of uniform pollutant concentrations with a radius ranging 

from several meters up to 100 meters. 

2) Middle Scale - Uniform pollutant concentrations in an area with a radius of 

approximately 100 meters to 0.5 kilometers. 

3) Neighborhood Scale - Uniform pollutant concentrations in an area with a radius 

of approximately 0.5 to 4.0 kilometers. 

4) Urban Scale - Citywide pollutant concentrations in an area with a radius ranging 

from 4 to 50 kilometers. 

5) Regional Scale - Uniform pollutant concentrations that would be characteristic of 

a very large (for example, rural) area that has a radius from tens to hundreds of 

kilometers. 
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The relevant spatial scale for each site is determined from the physical location of the 

site with respect to the pollutant sources and the population or area represented by the 

monitoring site. The middle, neighborhood, and urban scales typically are used for 

meeting the objective of monitoring in high-density populated areas. 

Table 2 

PM2.5 Monitoring Stations in the San Joaquin Valley APCD 

Site Name Sampling 

Interval/Frequency 

Scale Monitoring 

Objective 

Type Agency 

Bakersfield-California 

* 

24 Hour/Daily 

1 Hour/Continuous 

BAM 

Neighborhood Representative 

Concentration 

SLAMS CARB 

Bakersfield-Golden St 

* 

24 Hour/X 

1 Hour/Continuous 

BAM 

Neighborhood Representative 

Concentration 

SLAMS SJVAPCD 

Bakersfield-Planz 24 Hour / 3rd day Neighborhood Representative 

Concentration 

SLAMS CARB 

Clovis-Villa * 24 Hour/X 

1 Hour/Continuous 

BAM 

Neighborhood Representative 

Concentration 

SLAMS SJVAPCD 

Corcoran-Patterson * 24 Hour/X 

1 Hour/Continuous 

BAM 

Neighborhood Representative 

Concentration SLAMS SJVAPCD 

Fresno-First Street * 24 Hour/Daily 

1 Hour/Continuous 

BAM 

Neighborhood Representative 

Concentration NAMS CARB 

FresnoHamilton/Winery 

24 Hour/X Neighborhood Representative 

Concentration 

SLAMS SJVAPCD 

Huron 1 Hour/Continuous 

EBAM Neighborhood Representative 

Concentration

Special 

Purpose 

SJVAPCD 

Merced-2334 M Street 24 Hour/X Neighborhood Representative 

Concentration 

SLAMS SJVAPCD 

Modesto-14th Street * 24 Hour/ 3rd day 

1 Hour/Continuous 

BAM 

Neighborhood Representative 

Concentration 

SLAMS CARB 

 Stockton-Hazelton * 24 Hour/ 3rd day 

1 Hour/Continuous 

BAM 

Neighborhood Representative 

Concentration SLAMS CARB 

Tracy Airport * 1 Hour/Continuous 

BAM Urban Representative 

Concentration SLAMS SJVAPCD 

Turlock-Minaret * 1 Hour/Continuous 

BAM Urban Representative 

Concentration SLAMS SJVAPCD 

Visalia-Church * 24 Hour/ 3rd day 

1 Hour/Continuous 

BAM 

Neighborhood Representative 

Concentration SLAMS CARB 

* In addition to any filter-based sampling that may be present, the site also contains a real time continuous 

particulate matter monitor. 

X Sampling frequency for Apr-Sep is every six days, sampling every three days for the months of Oct-Mar. 
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PM2.5 Monitoring Frequency and Network 

PM2.5 scheduling is varied according to season. Sampling frequency for AprilSeptember is every six days and increases to every third day for the months of OctoberMarch. There are eleven PM2.5 Federal Reference Method (FRM) monitors located 

throughout the San Joaquin Valley. Federal equivalent method (FEM) sites are 

operated to provide hourly real time measurement of PM2.5. To supplement the PM 

FRM network the District monitors real-time hourly PM2.5 monitors at BakersfieldGolden, Corcoran-Patterson, Clovis, Tracy-Airport, Huron, and Turlock. The CARB has 

real-time PM2.5 monitors at Bakersfield-California, Fresno-First, Modesto-14th Street, 

Stockton-Hazelton and Visalia-Church. The data gathered by these units is being used 

to document diurnal variations in particulate matter concentrations, and to document 

PM2.5 concentrations for Air Quality Index (AQI) reporting and forecasting. 

Table 3 

PM 2.5 Monitoring Summary 

MSA/County Pop. 

(2006) FRM Monitors FEM Realtime Monitors 

San Joaquin 668,265 1 2 

Stanislaus 514,370 1 2 

Merced 210,554 1 0 

Madera 144,396 0 0 

Fresno 799,407 3 2 

Kings 129,461 1 1 

Tulare 420,619 1 1 

Kern 661,653 3 2 

Variations in Ambient Data 

Ambient air quality samples suggest different causes of elevated PM2.5 levels at 

different times during the year. Ambient monitoring samples are analyzed to determine 

the chemical make-up of the PM2.5 collected on the filter. Air monitoring data indicates 

that during cooler parts of the year when meteorological conditions produce little or no 

air movement, secondary particulate levels (largely ammonium nitrate) are elevated in 

the entire Valley. Meteorological conditions have a direct influence. Extended periods 

of stagnant air interspersed with cold, damp, foggy conditions are conducive to the 

formation of particulate nitrate in amounts that are frequently the dominant component 

of PM2.5. Nitrates start increasing beginning in mid November to mid December and 

extending through February. The samples during this period are dominated by 
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secondary particulates. Secondary particulates are particles that are the end products 

of many chemical reactions that occur in the atmosphere. Precursors, the chemicals 

that are involved in the chemical reactions, are NOx, VOC, SOx and ammonia. 

Diurnal Variations 

Hourly monitors allow the examination of variation throughout the day. Prior monitoring 

with 24-hour samples required additional evaluation to detect diurnal patterns. During a 

special study as part of the California Regional PM10/PM2.5 Air Quality Study, 

Integrated Monitoring Study-1995 (IMS95), special monitors were run on a daily basis 

for approximately one month with the filters being changed every three hours. This 

sampling process provided data analyzed to provide information about the variability of 

PM10 and PM2.5 throughout the day. IMS95 data showed that in urban areas, the 

greatest concentrations of PM10 during December and January are measured in the 

evening hours after most people arrive home from work. This data suggest that PM10 

could be emitted and forming at increased rates during the evening hours (6:00 PM 

through midnight). Other findings from CRPAQS have already been incorporated in our 

discussions of our understanding of seasons, episode development and patterns. 

New monitoring technologies are in place to allow data to be collected in real time for 

the mass of PM2.5 on an hourly basis at a limited number of locations. Data from 

federal reference monitors (FRM) does not provide a breakdown of the source material. 

Speciation samplers provide source identification information but the measurements are 

not entirely consistent with the FRM samplers due to differences between technologies. 

Transport 

PM2.5 or precursors originating from or going to other air basins, referred to as pollutant 

transport, has not been definitively quantified. Readings in the SJVAB are most severe 

during the fall and winter periods when wind speed and direction are not conducive to 

interregional transport. Monitoring and speciation techniques currently available are not 

able to identify the origin of PM2.5 sources with sufficient detail to indicate if the SJVAB 

is experiencing transport from outside the air basin or contributing transport of PM2.5 to 

other air basins. Transport of some PM precursors has been studied as part of ozone 

transport evaluation, identifying transport of ozone and ozone precursors from and to 

other air basins surrounding the SJVAB. The transport of ozone was documented 

during the summer when the highest ozone readings are more likely to occur. This 

transport includes precursors of ozone and PM; however, the amount of PM that could 

be generated in the SJVAB or other air basins from such transport has not been 

quantified. Pollution from areas outside of the Valley may or may not contribute to high 

PM2.5 levels within the Valley. Regional modeling may be utilized to examine this issue 

in the future. 
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Trend Analysis 

The San Joaquin Valley Air Basin has followed the national trend of declining particulate 

levels since the 1980s. The national long-term trend of declining particulate values 

affects District PM2.5 trends. Complex meteorological phenomena make it challenging 

to isolate the effect of national trends to be able to clearly document the effect of 

improvements made in ambient air quality due to regulatory actions and voluntary 

emission reduction projects. 

Statistical Analysis 

Evaluation using a variety of accepted statistical methods will be conducted with air 

monitoring data and other data identified as related to high concentrations of 

particulates. The evaluation will examine the factors related to known and observed 

episodes and assess the representativeness and comprehensiveness of monitored 

exceedances. Evaluation of components of the mass on PM10 and PM2.5 filters has 

been conducted to establish an analysis of episode sources to compare and contrast to 

receptor modeling with the Chemical Mass Balance (CMB) model. Results of this 

analysis provide evaluation of: sufficiency of available data to establish reliable 

conclusions to determine control program sufficiency and reasonableness of results of 

other methods. 

Data: Speciation Adjustments “SANDWICH” 

Attainment status is dependent upon FRM measurements and concentrations. No 

revision to the standard is intended or implied in the following discussion of adjustments 

to utilize data from different instruments and models. Adjustments are made to allow 

the information to be handled in a compatible manner that adjusts for inherent 

limitations and artifacts and allows a more accurate assessment of the effect of 

reductions for specific chemical species. For compliance with the air quality standards, 

the culmination of this process must return to a comparison with the PM2.5 mass as 

measured by the FRM sampler. 

The various physical and chemical components that contribute to the PM2.5 mass must 

be evaluated as separate species to determine efficient reduction methods to attain the 

air quality standards. However, federal reference sampler (FRM) measurements of 

PM2.5 are subjected to a number of known positive and negative artifacts due to the 

sampler construction and operational characteristics. FRM measurements do not 

necessarily capture the PM2.5 concentrations precisely as they exist in the atmosphere. 

Nitrate and semi-volatile organics can be lost from the filter during the equilibration 

process, and particle bound water associated with hygroscopic species like sulfate 

provides a positive artifact. FRM measurements may differ substantially from what is 

measured by speciation monitors, which have artifacts and operational characteristics of 

their own. Differences between samplers require careful consideration when speciated 
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measurements are used to apportion the bulk FRM mass to individual species and are 

subsequently used to assess the affect of reductions. 

The FRM is the basis for attainment status; therefore, reconstructed PM2.5 mass used 

for modeling or analysis should be based on, or be made consistent with, the 

composition of the mass measured by the FRM. This is accomplished by evaluation 

and adjustment for the differences in methods and artifacts. The method developed to 

speciate FRM PM2.5 mass with known FRM limitations in mind is referred to as 

“SANDWICH” the measured sulfate, adjusted nitrate, derived water, inferred 

carbonaceous mass and estimated aerosol acidity (H+). The approach serves to 

provide the basis for a connection between observations, modeled PM2.5 

concentrations, and the air quality standard. The “Sandwich“ mathematical procedures 

are documented in the Model: Analysis of Results section of the Protocol 

“SANDWICH” Analysis 

Confidence is high in determining the concentrations of the specific ions (sulfates, 

ammonium, sodium and chloride) and the measurements of directly emitted elemental 

and crustal components. Components represented on the FRM filter include elemental 

carbon, crustal material, sea salt, and passively collected mass. Nitrate filter mass loss 

is expected. Primary and secondary organic compounds express greater monitoring 

and analytical variability and the “SANDWICH” method proposes to minimize this 

uncertainty. Previously, analysis of speciated particulate data used measurements of 

ammonium, sulfate, nitrate, organic carbon and elemental carbon. The difference 

between the total filter mass and the sum of the five components was categorized as 

the “other” or unassigned mass. Other mass included the crustal [metals] components, 

sea salts and was used to account for any particle-bonded water, filter blank 

contamination and uncertainties in the data monitoring or laboratory analysis. 

The “SANDWICH” method for data analysis (Frank, 2006) calculates the PM2.5 organic 

carbon mass from the difference between the total mass of the particulate sample and 

the other component species. The “SANDWICH” method for PM2.5 accounts for the 

calculation of bonded water adds a filter blank contamination term estimation of 0.5 

μg/m3 and substitutes organic carbon as the “others” component to be estimated from 

the mass difference. The “SANDWICH” method estimates ammonium (if not directly 

measured) and uses either a linear or polynomial empirical equation to approximate the 

mass of bonded water in the sample. The linear equation approximates bonded water 

assuming that the water content bonded to ammonium nitrate is equivalent to 12 

percent of the mass and that the water bonded to ammonium sulfate is approximately 

equal to 26 percent of that mass. The “SANDWICH” methodology does not exclude the 

use of directly measured ammonium or organic carbon. Estimates of ammonium 

calculated using a empirical relationships (0.29 X nitrate and 0.375 X sulfate) closely 

matched the measured ammonium. 
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Speciated measurements of carbonaceous PM are considered highly uncertain. This is 

due to the large number of carbon compounds in the atmosphere and the measurement 

uncertainties associated with samplers of different configurations. In the “SANDWICH” 

approach, organic carbonaceous mass is calculated by difference. The sum of all nonorganic carbon components may be subtracted from the FRM PM2.5 mass as an 

estimate of organic carbon. This method has a higher uncertainty where there are 

known to be substantial “other” mass contributions. This is the case for the San 

Joaquin Valley with documented substantial levels of mass not quantified by the 

analysis methods. The contributions from soil, metals, plant material and other 

inorganic mass that are not assessed by ion measurement require that we give careful 

review to use of methodology that may falsely assign remaining mass to organic carbon 

as the contributing source. Adjusted speciated data, particularly for the quantification of 

organic carbon, will be carefully reviewed for its correlation to supporting information 

prior to accepting findings for control significance. 

Data: Design Value Determinations 

Table 4 

PM2.5 Design Values and Attainment Test 

(Based on 2004-2006 monitoring data) 

Attainment Tests 

(Sites must pass both tests) 

24-hour PM2.5 

NAAQS (Test 1) 

Annual PM2.5 

Monitoring Site NAAQS (Test 2) 

Design 

Value 

(μg / m3)

Within the 

NAAQS? 

Design 

Value 

(μg / m3)

Within the 

NAAQS? 

Attainment?

Stockton 41 Yes 12.9 Yes Yes 

Modesto 51 Yes 14.1 Yes Yes 

Merced 45 Yes 14.7 Yes Yes 

Fresno-1st 58 Yes 16.7 No No

Fresno-Winery 59 Yes 17.2 No No

Clovis 57 Yes 16.2 No No

Corcoran 58 Yes 17.2 No No

Visalia 56 Yes 18.2 No No

Bakersfield-Golden 64 Yes 18.5 No No

Bakersfield-California 62 Yes 18.5 No No

Bakersfield-Planz 65 Yes 18.9 No No

San Joaquin Valley Air Basin No 

Applicable EPA guidance defines the design concentration as the ambient PM2.5 level 

for a particular site that must be reduced to the level of the NAAQS. The PM2.5 annual 

and 24-hour NAAQS require two separate design concentrations, one for each standard 

per site. The District consulted with ARB and EPA staff to identify methodology for 
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calculating PM2.5 design values. The need for consultation stems from decisions on 

including or excluding data that has been marked with quality assurance concerns and 

procedures for data substitution for missing data or data rejected due to quality 

assurance screening criteria established by EPA. 

Data: Justification for Use of Off-Site Data 

Data additional to the NAMS and SLAMS monitoring data have been utilized to enhance 

understanding of PM sources and characteristic patterns. This information was used to 

assist in the development of effective control strategies for the SIP but does not qualify 

for use to establish design values or demonstrate attainment. Data from equipment or 

sites that do not meet SLAMS criteria is considered as supplementary data to be used 

to analyze the sources and components of ambient PM samples and to analyze 

episodes of high ambient PM to increase the understanding of significant factors and 

determine effectiveness of control strategies to achieve and maintain attainment. 

Evaluation using a variety of accepted statistical methods will be conducted with air 

monitoring data and other data identified as related to high concentrations of 

particulates. Supplemental field program sample collection at District sites and other 

temporary monitoring locations will be included. The evaluation will examine the factors 

related to known and observed episodes and assess the representativeness and 

comprehensiveness of monitored exceedances. Evaluation of components of the mass 

on PM10 and PM2.5 filters was conducted to establish an analysis of episode sources 

to compare and contrast to receptor modeling with the Chemical Mass Balance (CMB) 

model. Results of this analysis provide evaluation of: sufficiency of available data to 

establish reliable conclusions, determine control program sufficiency and 

reasonableness of results of other evaluations. 

California Regional PM10/PM2.5 Air Quality Study 

The California Regional PM10/PM2.5 Air Quality Study is a comprehensive 

public/private sector collaborative program whose goals are to provide an improved 

understanding of particulate matter and visibility in central California and to provide 

decision-makers with the tools needed to identify equitable and efficient control 

methods. The study is intended to evaluate both the national and State air quality 

standards for particulate matter smaller than 10 microns in diameter (PM10) and the 

new national standards for particulate matter smaller than 2.5 microns (PM2.5), which 

are consistently exceeded in central California. This adverse air quality compromises 

the health of the more than 10 million people living in the region, reduces visibility, 

affects crop yields, causes materials damage, and adversely impacts quality of life. 

The information developed by the study allows apportionment of high PM10 and PM2.5 

concentrations to contributing sources, thereby avoiding burdens on the regulated 

community from excess or ineffective control requirements. Implementation of the 
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control plans improved by analysis of study data will result in significant improvements 

in visibility, and the health and well being of the citizens of central California. 

The study has involved extensive planning and preparatory research. Early projects 

included literature review of previous studies, development of conceptual models for 

episode behavior, a multi-year assessment of agricultural practice contributions to 

particulate emissions, a preliminary field monitoring program known as the 1995 

Integrated Monitoring Study (IMS95) that evaluated our conceptual understanding of 

particulate episodes and evaluated monitoring equipment and data collection needs, 

analysis and modeling of historical and IMS95 data to improve our understanding 

through analysis of collected data, and emission inventory improvements. 

As part of the California Regional PM10/PM2.5 Air Quality Study, an integrated field 

monitoring study (IMS) was conducted during the winter of 1995. The objectives of the 

IMS were to: 1) support the objectives of several planning studies comprising the core 

of the IMS, 2) develop a refined conceptual model of winter-like exceedances, and 3) 

provide a database for preliminary modeling and data analysis. Daily PM10 and PM2.5 

monitoring with three-hour resolution were conducted at four core air quality sites 

located in the region between Fresno and Bakersfield. Samples were analyzed for 

mass, ions, elemental and organic carbon, and elemental constituents. These data were 

supplemented with collection of fog measurements, surface and aloft meteorological 

data, measurement of gaseous precursors, and saturation sampling. 

Based upon extensive analysis of data from the 1995 Integrated Monitoring Study and 

the improved understanding gained from this and other early elements, a major field 

program was designed and undertaken to address fall and winter particulate matter 

episodic conditions as well as annual average particulate matter assessment. Input to 

the design of the field program was solicited from regulatory agencies, data analysts 

and modelers, and the research/contracting community. The objective of the field 

program, as with the earlier efforts, was to obtain a documented data set, with 

appropriate data qualification statements, suitable for characterizing the nature and 

causes of particulate concentrations and visibility impairment in central California by 

supporting modeling and data analysis activities. 

The field program commenced in December of 1999 and continued through February of 

2001. The monitoring consisted of 14 months of data collection throughout the San 

Joaquin Valley (SJV) and surrounding regions, as well as intensive, shorter-term 

monitoring during fall and winter-like episodic conditions when PM10 and PM2.5 

concentrations are highest. The program established an array of monitoring throughout 

the period, enhanced during summer months along with a companion ozone study 

(Central California Ozone Study) and considerably expanded during the fall and winter 

with intensive data gathering and monitoring operations. 

Air quality sampling locations for the annual monitoring program built upon and took 

advantage of, the extensive existing PM10 network, as well as the new PM2.5 

monitoring networks established by the Air Resources Board and local air pollution 
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F-32 

control districts. More than 70 PM10 sites and 50 PM2.5 sites comprised this backbone 

network. Study enhancements to these networks included full scale “anchor” monitoring 

sites measuring gaseous and aerosol species, through both filter-based and continuous 

species specific methods. In addition, “satellite” monitoring sites measured aerosol 

species using portable PM monitors and nephelometers. Surface and aloft 

meteorological measurements were collected utilizing a network of surface 

meteorological sites, radar profilers, and sodars. A special 100-meter tower collected 

data at several elevations on meteorological and air quality parameters. 

The fall episodic program took place in October and November of 2000 in the central 

portion of the San Joaquin Valley. This monitoring window corresponds to periods of 

historically high PM10 concentrations that are dominated by geological material. 

Specific issues addressed in the fall monitoring program included identification of the 

sources of geological material, determination of the zone of influence of these sources, 

and development of improved data on dust suspension and deposition. The fall 

measurement program included neighborhood scale saturation monitoring and 

measurement of organic species and particle morphology. 

The winter episodic field study took place during December 2000 through February 

2001. PM2.5 concentrations have been historically highest during the winter months, 

with secondary ammonium nitrate and carbonaceous material the dominant 

constituents. Specific issues addressed in the winter monitoring program included 

identification of the sources of carbonaceous material, determination of the limiting 

precursors for secondary PM species, surface and aloft transport and mixing 

mechanisms under low wind speed conditions, and the zone of influence of both 

primary and secondary sources of PM. The winter measurement program included an 

expanded set of anchor sites, and an enhanced upper-air monitoring network. On days 

forecasted to have the highest PM concentrations, additional special measurements 

were collected including organic species tracers, fog chemistry, time-of-flight mass 

spectrometry, and measurement of wet deposition. Special emphasis was placed on 

collection of continuous and species-specific particulate measurements to support both 

receptor and grid-based modeling approaches. Methods for collecting information on 

air quality aloft included use of the 100-meter tower, an elevated site in the Sierra 

Nevada mountains, and a remotely piloted blimp, which has been specially designed to 

fly under low visibility, stagnant conditions. 

Episode specific and enhanced emission inventory estimation data was also collected. 

Several projects were targeted at collecting improved information for transportation 

sources, including development of updated chemical speciation profiles, and vehicle 

traffic counts. Other emissions projects include development of a GIS-based ammonia 

inventory, and collection of day-specific emissions. A comprehensive emissions 

inventory for the region is being developed to complement the field measurements. 

The CRPAQS main field program collected extensive data during the period of 

December 1999 to February 2001. The data has been processed into a centralized 

database. Quality assurance evaluation of the data is complete. Data analysis 
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contracts have been completed and are in final report approval. Advanced modeling of 

the data collected during the field program is being conducted with experimental 

modeling methods. Extensive analysis of the results can be found in the report 

“California Regional PM10/PM2.5 Air Quality Study (CRPAQS) Initial Data Analysis of 

Field Program Measurements,” Chow, et al July 29, 2005. For additional reports and 

documents go to: http://www.arb.ca.gov/airways/crpaqs/publications.htm

Model: Selection Justification 

It is important to select a modeling and analysis methodology appropriate for the San 

Joaquin Valley (SJV) that considers and compensates for the strengths and 

weaknesses of available approaches. Data requirements and availability for emissions, 

meteorology, and air quality as well as the validity of the representation for 24-hour and 

annual average PM2.5 concentrations must be considered in selecting the appropriate 

approaches. Based upon availability of emission estimates, meteorology, and air 

quality data in the Valley, there are two fundamental methods available and appropriate 

to evaluate current and future emissions for the SIP: 

• Receptor analysis with speciated-rollback using receptor chemical mass balance 

analysis of observed particulate levels 

• Regional grid-based modeling with episode specific chemistry, photochemistry, 

aerosol chemistry and meteorology. 

Supplemental evaluation with other modeling tools, analysis of trends and other 

supporting evidence are permitted to establish a weight of evidence synthesis. 

If a single source is contributing to PM NAAQS violations with a significant coarse 

fraction and identification and emission strength of that source is known, then dispersion 

model, such as AEROMOD, can be used to evaluate control strategies for that source. 

The method can be extended to include a few major well-defined sources, but Chemical 

Mass Balance (CMB) modeling and wind trajectory analysis to define the probable area 

of contributing sources to determine the available emission source types is more 

informative as the number of sources increases and source strengths decrease and is 

therefore more appropriate for the San Joaquin Valley. Multiple sources with low to 

medium source strengths are the predominant cause of PM2.5 NAAQS violations in the 

Valley. The grid-based photochemical methods provide a stronger analysis of 

atmospheric chemistry for nitrate and sulfate particulates, but have difficulty with 

representation of organic carbon and geologic materials that are important contributing 

elements in the San Joaquin Valley. The receptor method developed for the SJV is 

able to divide the organic carbon into separate segments to assess motor vehicle, 

stationary source and vegetative carbon separately. This is more effective for analysis 

of these control programs. Receptor modeling with CMB uses linear assumptions for all 

portions of particulate matter. Although the CMB model can determine the total amount 

of a secondary aerosol species, it cannot estimate specific source contributions to 

secondary aerosols. Photochemical modeling incorporating aerosol chemistry to 

determine appropriate formation ratios and relationships will be used to assess 
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F-34 

modeling of the secondary fraction. The regional modeling is expected to provide a 

more accurate assessment of sulfate and nitrate particulate controls. The combination 

of the two modeling packages provides the best path for the SJVAPCD. 

Receptor chemical mass balance (CMB) modeling is an emission source reconciliation 

approach that relates ambient air quality measurements with emission sources that 

contribute to the air quality of a region. CMB modeling requires chemically speciated 

ambient data and emission source profiles. This data is available through CRPAQS 

field programs. Some of the chemical speciation data is collected using methods, 

samplers and chemical analysis supplemental to NAAQS monitoring. Although CMB 

modeling cannot identify the sources contributing to secondary particulate matter 

formation, the method can provide insight into the major contributors to primary PM2.5 

as well as a bounding estimate of total secondary contributions. It is important that the 

CMB model use current information on source profiles because changes in fuels and 

control technology may make older data inappropriate. Evaluation of source profile 

selection including temporal and spatial evaluation of available emission sources 

improves expected accuracy of the method. Response to control projections and trends 

can be estimated using projections of future emissions to provide an equivalent 

projection of future concentration with a technique referred to as rollback analysis. This 

technique assumes linear chemistry and assumes predominance by local emissions; an 

assumption that may not be appropriate for secondary particulate formation which is not 

believed to be linear in chemistry and is known to typically have larger source zones of 

influence. It is therefore desirable to supplement this approach with improved physical 

assessment parameters and assessment of aerosol chemistry involved in formation of 

secondary particulates. 

To improve the physical representation, the receptor modeling developed by the District 

incorporates elements of a speciated-rollback approach. To apply this method baseline 

and future years must be established and emission estimates generated for various 

precursor gases (reactive organic gases (ROG), S0x, and NOx)) and directly emitted 

particulate matter (soil related components, mineral dust, primary organic carbon, 

elemental carbon) for each episode and area to be evaluated. The inventory is 

developed for the region over which emissions will affect the local air quality. This 

requires an evaluation of the area of influence of local sources for each episode and 

determination of regional contributions to the observed concentrations. The District 

established zones of influence for local sources by meteorological analysis to enhance 

profile selection in receptor modeling, to identify contributing emissions that would affect 

the observed concentrations, to calculate intra-basin transport contributions, and to 

quantify the effect of controls. 

Regional grid-based dispersion modeling with chemistry, photochemistry and aerosol 

chemistry is the most sophisticated and comprehensive approach. Regional grid-based 

models are theoretically the best type of model for SIP applications. These models 

have a fundamental advantage over speciated-rollback and CMB modeling because of 

their ability to simulate the integrated effects of emissions, meteorology, and air quality 

in time and three-dimensional space over large spatial areas. Grid models furthermore 
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undergo a model performance evaluation by comparing model estimates of historical 

concentrations with observational data. This allows an assessment of the model's 

reliability to estimate ambient air concentrations. However, grid-based models require 

extensive input data for emissions, meteorology, and air quality for each episode to be 

evaluated. For each episode this requires generation of an hourly-gridded-emission 

inventory as well as three-dimensional meteorological data, such as winds and 

temperatures, and hourly boundary conditions. Guidance adopted by EPA guides the 

use and interpretation of regional modeling results for PM2.5 to address the strengths 

and weaknesses of the approach and the difficulties in comparing FRM monitoring data 

to species predicted by the model. 

EPA Guidance on Selection of Modeling Technique 

New guidance from EPA encourages reliance on regional models, used in a relative 

sense in part due to uncertainties but primarily because the modeled concentrations 

and events are not usually the same as the design values. ARB is performing regional 

modeling with the specifications consistent with EPA guidance. Further adjustment and 

application of model results is to be performed using a procedure that breaks the results 

into component constituents. This processing is called the Speciated Modeled 

Attainment Test (SMAT). The results of this analysis are to be compared to other 

analyses to establish a weight of evidence determination for attainment. 

Provisions of EPA guidance allow reliance on receptor modeling when it is determined 

to be the most suitable approach. "The chemical mass balance (CMB) model is 

considered to be the most advanced of the available receptor models and its use is the 

most acceptable for attainment demonstration purposes." “Under certain conditions, 

there may be no recommended dispersion model, or the recommended model may not 

be applicable. For example, if area sources are the dominant contributors to ambient 

PM10 concentration, an attainment demonstration might be based on rollback of the 

apportionment derived from two reconciled receptor models. In such instances, the 

modeling approach must be approved by the appropriate Regional Office on a case-bycase basis." (EPA Guideline Document, pp 6-8, 1993). Previous efforts with receptor 

modeling identified 55 to 64 percent contribution in urban areas from "area" sources. 

Contributions from receptor modeling, attributed majority contribution from "soil," 

construction, and vegetative burning. This circumstance for the SJV supports selection 

of CMB modeling as the appropriate modeling technique in accordance with the 

recommendations of EPA guidance. 

Proposed SIP Modeling Approach 

The Protocol proposes using regional modeling supported by receptor CMB modeling, 

PMF analysis and statistical data evaluations combined to establish weight of evidence 

findings. 
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The regional modeling will be conducted by ARB and be used to support a SMAT 

process. Regional aerosol modeling to evaluate secondary formation ratios has been 

and will be used to enhance receptor modeling, with profile selection for CMB modeling 

enhanced by assessment of local temporal and spatial emissions. The complementary 

suite of modeling methods we propose to employ for this is based on relying on CMB to 

analyze the episode specific primary fraction of PM2.5 and regional modeling with 

aerosol chemistry to provide general formation ratios for secondary particulates for 

nitrates. CMB modeling will identify emission source contributors by chemical species, 

which will be used with rollback modeling to estimate reductions of emissions and 

emission controls needed to attain ambient standards. Rollback projection to forecast 

future concentrations will rely on CMB episode evaluation for primary particles and 

episode application of formation rate dynamics for the secondary fraction. 

As previously referenced, EPA guidance allows for the attainment demonstration to be 

derived from two reconciled receptor models. The CMB receptor model, with 

modifications described in the preceding paragraph, provides the primary receptor 

model analysis. Reconciliation with alternative receptor evaluation will be accomplished 

by modeling separate species configurations for 2000 and 2005 and by PMF analysis 

and comparison with the regional modeling. The process of comparison will have to 

compare the relative abilities of the models to capture fine features and explain 

atmospheric dynamics. The receptor modeling represents a “top down” analysis 

evaluating observations to determine components. The regional modeling represents a 

“bottom up” method of using emission inventory information with a photochemical model 

to simulate the observed events. Both methods have uncertainties. The comparison is 

a valuable method to establish a weight of evidence finding. 

Technical Justification for Regional Modeling Approach 

Secondary particulate matter is formed in the atmosphere from gaseous precursors. It 

is well established through the vast experience of ozone modeling that atmospheric 

chemistry of secondary particulates may be non-linear and the concentrations of nitric 

acid (HNO3) and ammonium nitrate (NH4NO3), may not be linearly proportional to the 

concentrations of either NOx or VOC. Therefore, an essential component of developing 

emissions control strategies involves gas and particle phase photochemical modeling. 

References: 

Primary Reference: 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/B-07-002, April 2007. 
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Specifically cited guidance: 

1

 51.112 of 40 CFR Demonstration of adequacy. As also cited by PM10 SIP 

Development Guideline, EPA-450/2-86-001, June 1987, section 4.1 “Section 51.12 of 

40 CFR requires that the adequacy of a control strategy for attainment and maintenance 

of NAAQS be demonstrated by means of a dispersion model or other appropriate 

procedure which is shown to be adequate and appropriate for this purpose.” 

2

 Based upon review of several guidance documents. Applicable citations include: 

 Appendix W 40 CFR Part 51, sections 3.2, 7.2.2, 7.2.2d, 8.2.10, 11.1.b, 11.2.2.b 

approval for use of alternative models, guidance on PM modeling, limits of dispersion 

models, stagnation concerns, cases dispersion modeling is not acceptable, areas 

dominated by nontraditional sources; 

 PM-10 SIP Development Guideline, Supplement, June 1988, page 9 and 

10.discusses treating area sources as background in dispersion modeling; 

 PM10 SIP Development Guideline, EPA-450/2-86-001, June 1987, page 4-10 

dispersion modeling less reliable for 24 hour standard; 

 Example Modeling to Illustrate SIP Development for the PM10 NAAQS, EPA450/4-87-012, May 1987, page 10 through 19 especially section 2.3.1 and section 6.4.1 

small sources are added to area sources and assumed to not dominate local air quality, 

dispersion modeling assumes point sources to be the primary cause of Nonattainment; 

 Protocol for Reconciling Differences Among Receptor and Dispersion Models, 

EPA-450/4-87-008, March 1987, page 2 and 7, CMB is better for categories, DM for 

known individual sources, grouped to mimic and compare to CMB; and 

 EPA PM-10 Guideline Document and Appendix A & B, EPA-452/R-93-008, April 

1993, especially sections 6.3, 6.5.3, and 6.7.5 predominant area sources call for 

rollback on receptor modeling, area sources for dispersion modeling to be based on 

average annual emissions – not a method intended to identify area source hot spots, 

reference to inappropriateness of dispersion modeling for nontraditional source in 

GAQM. 

3

 PM10 SIP Development Guideline, EPA-450/2-86-001, June 1987, section 4.3.2 page 

4-10. “Dispersion models are more reliable for estimating longer time-averaged 

concentrations (e.g., annual average) than for estimating short-term concentrations 

(e.g., 24-hour) at specific locations.11” Reference 11 cited by this passage is Rhoads, R. 

G., “Accuracy of Air Quality Models,” Memorandum to Air and Hazardous Division 

Directors, U. S. Environmental Protection Agency, Research Triangle Park, NC, July 22, 

1981. 

List of applicable guidance reviewed: 

• 40 CFR Appendix V 2.2 e. (7-1-92 edition, currently in force) 

• 40 CFR Appendix W (7-1-99 edition, currently in force although newer editions are 

under review) 

• EPA PM-10 Guideline Document and Appendix A & B, EPA-452/R-93-008, April 

1993 

• PM-10 Serious Area SIP guidance: Final Staff Work Product, September 24, 1993 
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• EPA Receptor Model Technical Series, Volume III (1989 Revision), CMB7 User's 

Manual, EPA-450/4-90-004, January 1990 

• PM-10 SIP Development Guideline, Supplement, June 1988 (OAQPS - no number) 

• Response to Questions Regarding PM10 State Implementation Plan (SIP) 

Development, June 1988 

• Chemical Mass Balance Receptor Model diagnostics, EPA-450/4-88-005, April 1988 

• PM10 SIP Development Guideline, EPA-450/2-86-001, June 1987 

• Example Modeling to Illustrate SIP Development for the PM10 NAAQS, EPA-450/4-

87-012, May 1987 

• Protocol for Applying and Validating the CMB Model, EPA-450/4-87-010, May 1987 

• Protocol for Reconciling Differences Among Receptor and Dispersion Models, EPA450/4-87-008, March 1987 

Model: Modes Used 

Receptor Modeling 

The EPA Chemical Mass Balance Model version 8.0 will be used for receptor 

modeling. Validation procedures for use of the model outlined in the EPA “Protocol 

for Applying and Validating the CMB Model” will be followed. The validation process 

includes the following steps: 

• Determine the general applicability of the CMB model to the application at hand. 

• Setup the model by identifying and assembling the source types, source profiles, 

and receptor concentrations needed for model input. Make a preliminary 

application of the model to these data. 

• Examine the model’s statistics and diagnostics to identify potential deviations 

from the model assumptions. 

• Evaluate problems that might result from problems with model input data. 

• Make model input changes which can be justified to resolve the identified 

problems and re-run the model. 

• Assess the stability of the model results and their consistency with the 

preliminary analyses. 

Regional Modeling 

New modeling is underway conducted by ARB in accordance with Guidance published 

by EPA. For the SIP modeling, the plan is to use boundary conditions extracted from 

the MOZART (Model for Ozone and Related Chemical Tracers) global chemical 

transport model. ARB is conducting modeling of the entire CRPAQS 2000-2001 dataset 

to evaluate annual PM2.5 secondary formation. Modeling and performance analysis will 

be released when complete. 
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Previously for PM10 Plans ARB modeling has included CMAQ and UAM-Aero to 

evaluate secondary formation of nitrate and sulfate particulates. This information was 

used to improve the receptor modeling representation of secondary particulates. CMAQ 

evaluated the CRPAQS 2000-2001 winter episode. UAM-Aero was applied to the IMS95 winter database. 

The modeling domain covers the entire SJV in current modeling. Prior CMAQ modeling 

covered the entire domain, but the available dataset for IMS-95 covers approximately 

215 km east to west and 290 km north to south and extends from the Coastal Range to 

the crest of the Sierra Nevada and from the Tehachapi Mountains to Merced. The grid 

resolution used will be twelve kilometer or four-kilometer squares depending on 

performance evaluation. 

Selecting a chemical mechanism that represents both NOx and VOC chemistry in detail 

is important because HNO3 is a secondary product whose precursors are NOx and VOC 

and HNO3 is one of the gaseous precursors to NH4NO3. Almost all chemical 

mechanisms available have robust NOx chemistry but the sophistication of VOC 

chemistry varies widely. Of the mechanisms available to ARB, those generated at the 

State-wide Air Pollution Research Center (SAPRC) of University of Riverside have the 

best representation of VOC chemistry. 

Model: Analysis and Use Description 

The Protocol establishes an approach that combines the best information that can be 

developed at this time by technical and statistical analysis of meteorology and other 

parametric data, receptor modeling and regional modeling. PM2.5 concentrations in the 

San Joaquin Valley vary between sites and seasons with regard to sample speciation 

and contributions from specific source types. Achieving attainment requires an 

understanding of the seasonal variations as well as average contributions. Analysis of 

PM2.5 concentrations, chemical composition and meteorology has provided information 

of the temporal and spatial behavior of PM2.5 in the Valley. 

Based on the attainment status, model evaluation is required for the annual PM2.5 

standard for the southern four counties of the District, Fresno, Kern, Kings and Tulare. 

The regional model will evaluate the entire District for a full year of data. Receptor 

analysis will evaluate the required four counties for the annual standard. The annual 

standard is considered to be the controlling standard at this time for the PM2.5 

standards in effect for this Plan requirement, because the 65 microgram per cubic meter 

24-hour standard has been achieved at all sites but the annual standard has not been 

met. Receptor modeling will not be conducted for the 24-hour standard at this time due 

to two key considerations: 

• No speciation profile for a 24-hour episode can be identified that would be useful for 

receptor analysis because the starting point for the analysis will already be below the 

standard and emissions are decreasing. Speciated rollback receptor analysis would 
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not provide useful information to determine emission reductions needed to achieve 

this standard because the standard has already been met. 

• The 65 microgram per cubic meter standard has been superceded by a new 

standard that has a different schedule for analysis and compliance. 

Regional Modeling 

Regional modeling of secondary particulates is being conducted by ARB. Results of 

regional modeling improve understanding of particle formation rates and ratios of 

precursors to particle formation, particularly for nitrate particulates. Previously these 

results have been used in conjunction with receptor modeling to enhance the accuracy 

and reliability of predicted effects of emission trends and adopted and proposed control 

measure reductions of secondary precursors. While use of regional modeling results 

will continue to be used to support receptor modeling evaluation for this Plan, EPA 

guidance expects regional modeling to play a larger role in the PM2.5 speciated 

modeled attainment test. The additional purpose of the regional modeling for the PM2.5 

SIP is to provide an independent analysis of the reductions needed to achieve 

attainment. However, EPA recognizes the uncertainties associated with all modeling 

methods and recommends comparing the results of the regional model to other 

evaluations to establish a weight of evidence finding that attainment will be achieved, 

particularly if the projected future value is close to the required standard. 

Receptor Modeling 

Receptor modeling using the chemical mass balance model (version CMB 8) will be 

conducted for sites that currently do not comply with the federal annual PM2.5 air 

quality standard. Analysis of collected air monitoring samples and information about the 

chemical composition of contributing sources are used to evaluate the link between 

observed conditions and emission sources. The District uses the results of the CMB 

analysis with a modified rollback approach to calculate the effect of predicted aggregate 

adopted and proposed control measure reductions and other predicted emission trends 

to establish attainment at sites noncompliant with the standard. This method works well 

for analysis of directly emitted particles but is less certain in predicting the effect of 

reductions of secondary precursors (gases that form particles in the air that may not 

produce particles in amounts directly proportional to the amount of emissions). Findings 

developed by regional modeling of secondary particulate formation rates typical to the 

Valley are used to account for the nonlinear secondary particle formation. 

Receptor Simulation of Observed Particulate Concentrations 

CMB receptor modeling is an analysis method used to link observed levels of 

particulates to the sources of emissions grouped into source categories. The CMB 

model links the speciated chemical composition of the filter sample at the site to 

emissions inventories that represent the emissions at the time of the 24-hour 

observation, or represent seasonal or annual average values as appropriate. A 
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representative CMB analysis for the annual design value establishes the base case 

situation at sites that need emissions reductions to achieve attainment. Evaluation of 

annual concentrations by receptor modeling to determine probable source contributions 

must include appropriate consideration of, and adjustments for, seasonal differences in 

sources and seasonal differences in atmospheric conditions that affect particle origin, 

formation and atmospheric residence time. The relative merits of running the overall 

average, quarterly averages, monthly averages, or every day have been reviewed and 

the protocol reflects the determination that the monthly average approach is the best 

approach with available data for the receptor analysis. 

Receptor Simulation of Future Particulate Concentrations 

From the CMB receptor modeling identification of emissions source contributions by 

chemical species, future source contributions are estimated using baseline and 

projected inventories with speciated rollback techniques to evaluate the effects of trends 

and proposed emissions reductions in future years. The speciated annual average 

concentrations are modeled at each site where concentrations were measured that 

exceeded the federal PM2.5 standards and where adequate data is available to support 

a valid analysis. 

Rollback techniques assume a proportional relationship between source categories 

(separable by CMB analysis) and types of emissions. Rollback projection can be 

applied based on either a regional (county or multi-county) emission inventory or a local 

emission inventory of sources located around a monitoring site to the extent that the 

meteorological analysis of the episode can determine an appropriate zone of influence 

for the local emissions and determine appropriate "background concentrations" for each 

chemical component. 

The appropriate areal extent of sources to include in the inventory for rollback 

calculation is determined from evaluation of back trajectories, atmospheric residence 

time, pollutant deposition rates, and emission distributions and activity data. Back 

trajectories were calculated from two-dimensional wind fields constructed from available 

surface wind data. Potential sources within the area indicated by back trajectories were 

examined in detail and included in the rollback calculation. 

Baseline and projected future year seasonal and annual emissions inventories are 

compared with a rollback analysis that assumes linear relationships between emissions 

and concentrations. To improve rollback projection of future secondary particulate 

levels in correspondence to future emission levels, regional modeling of formation ratios 

is used to establish secondary particulate nonlinear relationships. 

The receptor model uses annual speciation data available for the analysis of the 

required counties based upon their attainment status. Two different sets of data from 

different years are being used which will provide a dual check of the receptor 

evaluation. Data from the intensive CRPAQS database for the year 2000 is used for all 
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four counties (Fresno, Kern, Kings, Tulare). Speciation data for 2005 will also be used 

for annual evaluation of Fresno and Kern. The 2000 data is modeled forward to the 

year 2005 to provide an equivalent starting point for all of the receptor evaluations. The 

2005 species are modeled to the required future year and the relative change from 2005 

to the future year is used with the applicable design value for the location in a relative 

reduction calculation to determine attainment or the need for additional reductions. 

Utilization of Regional Modeling Reaction Rates for Proportional Rollback 

Receptor Modeling Analysis 

Results of the regional modeling performed by ARB are used to adjust the receptor 

modeling analysis nitrate rollback relationship. The grid-based photochemical model will 

provide the conversion factors of precursors into secondary particles that can be used 

to correct the proportional rollback analysis of secondary particulates. The conversion 

factors will be used in the receptor analysis of control strategies projections. 

Evaluating the complexity of the relationships involved in particle formation provides 

information to determine whether an assumption of linear response is acceptable or 

whether specific ratios and factors must be used to predict the secondary particle 

formation. The atmospheric chemistry of secondary particle formation is complex. 

Particle formation from gaseous precursors (such as NOx, VOC, and NH3) is being 

evaluated with the best information available about atmospheric chemistry and 

formation rates. Particle formation rates may vary due to influences of meteorology and 

precursor ratios. Temperature, relative humidity, photochemical energy flux, wind 

speed and atmospheric mixing affect the formation rates of secondary particulates. The 

balance of precursors and concentrations of ozone and carbon dioxide also influence 

particle formation. 

Demonstration of Attainment 

Attainment is demonstrated for each site that is projected to have future concentrations 

at or below the federal standards. The predicted PM2.5 concentration may also be 

achieved with different equivalent reductions in individual sources or source categories. 

EPA guidance acknowledges the difficulty in obtaining a data set for modeling that 

exactly matches the current design values and has developed a methodology to use the 

modeled response for each contributing type of PM2.5. This is necessary because the 

different types of materials come from different types of sources and the emissions 

inventories of these sources vary at different rates and have different relationships for 

the contribution of PM2.5 mass. Therefore, the constituent materials that contribute to 

PM2.5 are not directly additive and must be calculated separately and be added 

together as a final step to determine the total PM2.5 mass. 
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The model response is used in a relative sense to predict the reductions needed to 

achieve attainment. Speciated Modeled Attainment Test (SMAT) calculations 

determine the amount of reductions needed from the major source categories to 

achieve compliance with federal PM2.5 standards at all monitoring sites. The results of 

this process predict future PM2.5 concentrations that would result from trends and 

current and proposed control programs. EPA guidance identifies how these 

calculations should be performed for a regional model. The District has determined 

applicability to the receptor analysis. 

The Speciated Modeled Attainment Test (SMAT) methodology is generally applicable 

throughout the country but conflicts with certain important considerations critical to San 

Joaquin Valley Air Basin (SJVAB) assessment. Specific adjustments will be made to 

address technical issues specific to the SJVAB and to provide the most accurate means 

to reflect the effects of emission reductions. 

• The receptor analysis exceeds SMAT requirements by providing extensive 

separation of contributing direct and secondary carbon sources. Secondary 

formation rate information developed by the CRPAQS program is used to quantify 

the secondary aerosol partition. By means of CMB model evaluation, carbon mass, 

along with associated trace metals and other contributions to the source signature, is 

divided between: 

o Motor vehicle emissions, 

o Tire and brake wear, 

o Stationary and area sources carbon mass, and 

o Vegetative burning. 

• The receptor analysis exceeds SMAT requirements that recommend quarterly 

evaluation by performing monthly evaluation and determining that CMB performance 

meets standards on a monthly basis. The annual CMB modeling adjusts speciation 

selections for wood burning to reflect seasonal changes but otherwise uses a 

consistent set of speciation signatures. Performance verification demonstrates that 

the selected profiles are acceptable for identifying and representing the contributing 

sources throughout the year. 

• The receptor analysis exceeds SMAT requirements by separating the contributions 

of geologic material from other sampling artifacts and unknowns. This is of 

particular importance to the SJV due to the District regulations for control of fugitive 

dust. The benefit of the District programs must be modeled separately from artifacts 

to quantify reductions appropriately. 

• Receptor modeling addresses the SMAT requirement regarding trapped water in 

ammonium nitrates and sulfates using approved alternate linear assumptions. The 

linear assumption avoids the need to consider trapped water as a separate 

calculation. According to the linear assumption, water bonded to ammonium nitrate 

is approximated as equivalent to twelve percent of the ammonium nitrate mass and 

is proportional to the amount of ammonium nitrate particulate mass present. Water 

bonded to ammonium sulfate is approximated as 26 percent of that mass and is 

proportional to the amount of ammonium sulfate particulate mass present. Because 

the amount of trapped water is established by the linear assumption method as 
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proportional to the amount of the ammonium related mass contributions, the 

speciated rollback analysis processes the trapped water as an incorporated linear 

component of the ammonium nitrate and ammonium sulfate particulate masses and 

does not establish a separate calculation column. 

Technical note: The SMAT methodology separates out trapped water and 

ammonium, sulfate and nitrate ions. This is necessary in portions of the country 

that are dominated by sulfate emissions. Decreases in sulfate emissions can 

actually cause increase in nitrate formation in areas dominated by sulfates. The 

associated water content of sulfates is much different than for nitrates and can 

require mass recalculation for areas with high sulfates. The SJVAB is dominated 

by ammonium nitrate particulates with low sulfate particulate concentrations; 

therefore this process is of minimal effect on the Valley PM2.5 mass calculation. 

• The SMAT methodology calculates remaining mass other than the nitrates and 

water as organic carbon. This approach is too coarse for use for our District. The 

basis for this method is that most areas of the country do not have extensive control 

programs for geologic material (fugitive dust) or extensive emissions from carbon 

sources subject to direct control (residential wood combustion and agricultural 

burning). SMAT methodology for regional modeling will be adjusted to isolate the 

geologic material portion so that the effect of our control program can be reflected 

for its benefit in this analysis. The SMAT method may be further assessed for its 

adequacy for carbon. Carbon particle size growth and trapped carbon particles 

within nitrate and sulfate particulates that are not measured in the analysis methods 

are further losses not addressed by SMAT. CRPAQS modeling for particle size 

growth is under development but not available at this time. Metals are also not 

isolated by SMAT methods. For any elements not well supported by the regional 

model results, receptor modeling or other substantive data will be reviewed to 

establish a weight of evidence finding for attainment. 

Model: Application Assumptions 

Regional and Receptor Modeling General Assumptions 

Establishing modeling assumptions and background values requires discussion of 

particle size, formation, composition and chemistry in accordance with current scientific 

understanding of particulate matter in the air. This information provides a basis for 

addressing issues such as natural background and regional and local contribution. 

Assumptions involved in model application are based on data analysis and an 

understanding of the physical and chemical properties, sources and behavior of 

particulate matter. Developing an understanding of the principle factors and influences 

of PM2.5 concentrations provides a greater degree of certainty that proposed control 

strategy reductions will have the desired and expected results and that a projection of 

attainment has the highest degree of reliability achievable with current information. 
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Receptor Modeling Assumptions 

Receptor-oriented air quality models for particulate matter in the air infer source 

contributions by searching for patterns formed by the chemical elements present in the 

ambient samples. The CMB model consists of a set of linear equations that express the 

ambient concentrations of chemical species as the sum of products of source 

compositions and source contributions. The basic model equations represent the 

source receptor relationship. The effective variance weighting and the error 

propagation are all based on physical principals. The fundamental CMB model 

equations have been subjected to verification and evaluation using both real and 

simulated data as part of the Quail Roost II Conference (Stevens and Pace, 1984). 

Additional verification and evaluation efforts have been undertaken by several 

investigators, including, but not limited to: Watson et al. (1984), DeCesar and Cooper 

(1982), Dzubay et al. (1984), Gerlach et al. (1982), Currie et al. (1984), Watson and 

Robinson (1984), Javitz and Watson (1986), Watson and Chow (1986), Henry and Kim 

(1986). Detailed citations for these studies are available through the ARB or from 

“Protocol for Applying and Validating the CMB Model,” EPA-450/4-87-010, 1987. 

The fundamental assumptions of the CMB model are: 1) Compositions of source 

emissions are constant over the period of ambient and source sampling; 2) Chemical 

species do not react with each other; (i.e. they add linearly) 3) All sources with a 

potential for significantly contributing to the receptor have been identified and have had 

their emissions characterized; 4) The number of sources or source categories is less 

than the number of species; 5) The source compositions are linearly independent of 

each other; 6) Measurement uncertainties are random, uncorrelated, and normally 

distributed. Minor deviation from the assumptions does not invalidate the results. 

“Model evaluation studies based on synthetic data sets (Stevens and Pace,1984; Javitz 

et al., 1988) show that modest departures from the above assumptions can be accepted 

within the context of actual applications.” (Appendix A to Sonoma Technology, Inc. “PM10 Air Quality Models for Application in the San Joaquin Valley PM-10 SIP,” page 11, 

Cass, 1996). 

CMB was designed to determine sources of primary particulate emissions from analysis 

of observed samples. The CMB model cannot estimate specific source contributions to 

secondary aerosols, but it can be used to determine the total amount of a secondary 

aerosol species such as ammonium nitrate or ammonium sulfate. The following 

conditions must be met for the CMB modeling to be applicable: 

• A sufficient number of receptor samples of particulate matter have been taken with 

accepted sampling methods to evaluate compliance with the federal annual and 24-

hour PM2.5 standard. 

• These samples are amenable to and have been analyzed for a variety of chemical 

species. Minimal analyses include concentrations of aluminum, bromine, calcium, 

chlorides, copper, lead, manganese, nickel, potassium, silicon, titanium, vanadium, 
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and zinc. Preferable additional analyses would include other elements such as 

arsenic, chromium, selenium, trace elements, cations, anions, elemental carbon and 

organic carbon. 

• From the identified species, the potential source contributions can be identified and 

grouped into source categories of distinct chemical compositions 

• From the identified species and source categories, compositions for the source 

categories are obtainable which represent the source profile as it is perceived at the 

receptor. 

• The number of source types in a single application of the CMB must be fewer than 

the number of chemical species at the receptor measured at concentrations greater 

than the lower quantifiable limits. In other words, to model the source types, a 

sufficient number of chemical species must be collected in the sample that are 

measured in amounts greater than the minimum reliable detection limits for the 

analysis techniques used. 

CMB modeling is well established as a good technique for the San Joaquin Valley and 

meets these requirements. “Chemical mass balance methods are known to be 

applicable to modeling long-term average air quality relationships for particulate matter 

in the San Joaquin Valley because a study of that kind has been conducted by Chow et 

al. (1992). Existing emissions inventories for the Valley provide a basis for identifying 

the most important sources that should be considered for inclusion in the model. 

Extensive libraries of source composition profiles exist based on source measurements 

made in the Valley (Houck et al., 1989) and elsewhere.” (Appendix A to Sonoma 

Technology, Inc. “PM-10 Air Quality Models for Application in the San Joaquin Valley 

PM-10 SIP,” Cass, 1996). 

CMB Analysis with Linear Rollback 

CMB analysis with linear rollback can be applied to short and long term data. However, 

the lack of treatment of meteorology in the model affects seasonal and annual average 

modeling less than modeling of a 24-hour episode. If the meteorology and source 

activity of a specific exceedance day used for modeling is not representative of other 

exceedance days, the resulting analysis may not be generally representative. “If a 

receptor model study were undertaken for a short time period, it must be shown that the 

period covered was generally representative of the type of source activity and 

meteorology associated with exceedances observed in other receptor model studies.” 

(EPA Guideline Document, page 6-3, 1993). 

In the rollback projection, ambient pollutant concentrations are linked to CMB receptor 

analysis of source contributions. “The CMB model is believed to be the most reliable 

model for determining the source contributions of primary particles, especially for 

fugitive dust sources, because it does not rely on emissions rate estimates or 

characterization of atmospheric transport... The principle limitations of the CMB model 
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are that 1) it can only separate the source contributions of a small number of sources... 

and 2) it cannot estimate source contributions to secondary aerosol species.” (Sonoma 

Technology, Inc. “PM-10 Air Quality Models for Application in the San Joaquin Valley 

PM-10 SIP”, page 7-1, 1996). The output of the CMB receptor model will be used with 

speciated linear rollback; utilizing the most accurate source identification available with 

a reliable technique for assessing control programs. 

The future year source contributions, excess of regional background concentrations, are 

assumed to respond in a linear relationship to controls. This means the change in 

concentration is expected to be in direct proportion to changes in area-wide emissions. 

For an inert pollutant or source contribution, such as geologic material, the rollback 

projection is a valid predictor of future pollutant concentrations provided that the relative 

spatial and temporal emissions distribution is the same before and after emissions 

controls have been implemented. When the rollback projection is used to predict the 

effects of reductions in gaseous precursor emissions on secondary aerosol 

constituents, chemical transformation processes are assumed to be linear. Significant 

departures from linearity in the atmosphere could undermine the accuracy of results; 

however, “Despite the clearly nonlinear nature of the fundamental chemical 

transformation mechanisms, there is considerable evidence from nonlinear models and 

observations that the conversions are approximately linear for many of the practical 

emissions control situations studied to date.” (Sonoma Technology, Inc. “PM-10 Air 

Quality Models for Application in the San Joaquin Valley PM-10 SIP”, page 4-7, 1996). 

Factors Essential for Modeling Analysis 

Particulate matter represents a broad class of chemically and physically diverse 

substances. In addition to characterizations by size, particles can be described by their 

formation mechanism or origin, chemical composition, physical properties, and in terms 

of what is measured by a particular sampling technique. To be able to prepare to 

conduct modeling the principal factors for input data must be established. To be able to 

interpret the model output, a clear understanding of the influences of the principal 

factors must also be established. The EPA document “Air Quality Criteria for Particulate 

Matter” contains an extensive analysis of PM10 scientific information. Particulate matter 

concentrations are composed of materials from a variety of sources including: 

• Secondary particle formation of ammonium nitrate, 

• Elemental and organic carbon from a variety of sources including motor vehicles, 

wood smoke from wildfires, residential fireplaces and agricultural burning and direct 

and secondary carbon from fuel combustion (from vehicles, power generation, and 

industrial facilities), 

• Secondary formation of ammonium sulfate, 

• Fugitive dust including wind blown dust and road dust 

• Other primary inorganic particulate matter including metals 

• Particle bound water included with the sulfates and nitrates and other sampling 

artifacts 
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The properties, sources and behavior of PM2.5 must be clearly understood to perform 

modeling. Modeling requires an understanding of the spatial influence of emissions 

sources and sufficient information to estimate background PM2.5 levels. Apportionment 

of secondary PM2.5 is difficult because it requires consideration of atmospheric reaction 

processes and rates. Current scientific knowledge is used to select and evaluate 

modeling methods and results. 

Factors essential for modeling analysis include: 

• Origin of particles, sources and properties – emissions inventories and model 

formulations must be comprehensive 

• Chemistry and physics of atmospheric particles – modeling methods must 

include key technical factors to produce valid results 

• Atmospheric behavior, transport and fate of airborne particles – modeling 

methods must include key technical factors to produce valid results 

• Background concentrations to support modeling – background contributions 

are not responsive to District control efforts and must be modeled as an 

unaffected contribution to the observed mass 

• Key Findings from Scientific Studies and SJV Evaluations – All relevant 

technical information should be considered including findings of scientific 

studies and measurements evaluated in the SJV 

Origin of Particles, Sources and Properties 

The chemical complexity of airborne particles requires that the composition and sources 

of a large number of primary and secondary components be considered. Airborne 

particulate matter is not a single pollutant, but a mixture of many subclasses of 

pollutants with each subclass containing many different chemical species. 

Fine particulate matter is produced mainly by the condensation of gases in the high 

temperature environment of combustion chambers; the condensation of atmospheric 

precursor gases, some of which may undergo further reactions in particles; and the 

condensation of low vapor pressure photochemical reaction products. Major sources of 

these fine mode substances are fossil fuel combustion by electric utilities, industry and 

motor vehicles; vegetation burning; and the smelting or other processing of metals. 

Major components of fine particles are: sulfate, strong acid, ammonium, nitrate, organic 

compounds, trace elements (including metals), elemental carbon, and water. 

Primary (directly emitted) fine particles are formed from condensation of high 

temperature vapors during combustion. Particles formed as a result of chemical 

reaction of gases in the atmosphere are termed secondary particles because the direct 

emissions from a source are a gas that is subsequently converted to form a particle. 

Fine particles, both directly emitted primary particles and secondary particles formed 

later in the atmosphere are usually formed from gases in three ways: (1) nucleation 

(gas molecules coming together to form a new particle), (2) condensation of gases onto 

existing particles, and (3) by liquid phase reactions. Gases may dissolve in a liquid, 
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react with another dissolved gas, and when fog and cloud droplets evaporate, 

particulate matter remains, usually in the fine particle mode. Particles formed from 

nucleation also coagulate to form relatively larger particles. Although directly emitted 

particles are found in the fine fraction, particles formed secondarily from gases 

dominate the fine fraction. 

Table 5 

Ambient Fine Particles 

Property Material 

Formed from: Gases 

Formed by: Chemical reaction 

Nucleation 

Condensation 

Coagulation 

Evaporation of fog and cloud droplets in which gases have 

dissolved and reacted 

Composed of: Sulfate, SO4

=

Nitrate, NO3

-

Ammonium, NH4

+

Hydrogen ion, H+

Elemental carbon, 

Organic compounds (e.g., PAHs, PNAs) 

Metals, (e.g., Pb, Cd, V, Ni, Cu, Zn, Mn, Fe) 

Particle-bound water 

Solubility: Largely soluble, hygroscopic and deliquescent 

Sources: Combustion of coal, oil, gasoline, diesel, wood 

Atmospheric transformation products of NOx, SO2, and 

organic compounds including biogenic organic species, 

e.g., terpenes 

High temperature processes, smelters, steel mills, etc. 

Atmospheric half-life: Days to weeks 

Travel distance: 100s to 1000s of km 

Excerpt from source: USEPA 1996 Criteria Document, page 3-145, adapted from Wilson and Suh (1996)

Chemistry and Physics of Atmospheric Particles 

The major chemical constituents of PM2.5 are sulfates, nitrates, carbonaceous 

compounds (both elemental and organic carbon compounds), acids, ammonium ions, 

metal compounds, water, and crustal materials. The amounts of these components 

vary from place to place and over time. Fine particulate matter is composed of sulfates, 
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acids, nitrates, elemental carbon, volatile organic carbon compounds, water and trace 

elements such as metals. 

In the ambient atmosphere, fine particulate matter is mainly composed of varying 

proportions of six major components: sulfates, acids, nitrates, elemental carbon, organic 

carbon, and trace elements such as metals. A variety of transition metals and nonmetals are volatilized during the combustion of fossil fuels, smelting of ores, and 

incineration of wastes and are emitted as fine particles or vapors that rapidly form fine 

particles. Varying amounts of water may also be present. Sulfates, nitrates, and some 

organic compounds are hygroscopic, that is, they absorb water and form solution 

droplets. A variety of atmospheric pollutant gases can dissolve in the water component 

of the particle. Some types of higher molecular weight organic compounds react with 

hydroxyl (OH) radicals, and olefinic compounds also react with ozone, to form 

oxygenated organic compounds that can condense onto existing particles. Chain 

agglomerates of very small elemental carbon (EC) particles are formed during 

combustion, such as in open-hearth fireplaces, wood stoves and diesel engines. 

Several categories of organic carbon (OC) compounds are also often found in ambient 

air: primary anthropogenic - incomplete combustion forms hundreds of organic 

compounds with low enough vapor pressure to be present in the atmosphere as 

particles including polyaromatic hydrocarbons (PAHs); secondary anthropogenic - some 

organic compounds, including aromatics larger than benzene, cyclic olefins and 

diolefins, and other C7 (compounds with seven carbon atoms in the chain) or higher 

hydrocarbons react with O3 or OH to form polar, oxygenated compounds with vapor 

pressures low enough to form particles; primary biogenic - viruses, some bacteria, and 

plant and/or animal cell fragments may be found in the fine mode; secondary biogenic - 

terpenes, C10 cyclic olefins released by plants, also react in the atmosphere to yield 

organic particulate matter. 

Particles are designated as secondary if they form following a chemical reaction in the 

atmosphere which converts a gaseous precursor to a product which either has a low 

enough saturation vapor pressure to form a particle or reacts further to form a low 

saturation vapor pressure product. Fine particulate matter is derived from combustion 

material that has volatilized and then condensed to form primary particulate matter or 

from precursor gases reacting in the atmosphere to form secondary particulate matter. 

New fine particles are formed by the nucleation of gas phase species, and grow by 

coagulation (existing particles combining) or condensation (gases condensing on 

existing particles). Fine particles are composed of freshly generated particles, in an 

ultrafine or nuclei mode, and an accumulation mode, so called because particles grow 

into and remain in that mode. 

SO2, NOx, and certain volatile organic compounds are major precursors of fine 

secondary particulate matter. SO2 and nitrogen dioxide (NO2) react with hydroxy radical 

(OH) during the daytime to form sulfuric acid (H2SO4) and nitric acid (HNO3). Both VOC 

and NOx are important in the formation of free radicals that contribute to the formation 

of nitric acid (HNO3). 
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NOx is formed during combustion or any high temperature process involving air. The 

NO is converted to NO2 by ozone (O3) or other atmospheric oxidants. During the 

daytime, NO2 reacts with the hydroxyl radical (OH) to form HNO3. During the nighttime 

NO2 reacts with O3 and forms HNO3 through a sequence of reactions involving the 

nitrate radical (NO3). Ammonia (NH3) reacts preferentially with H2SO4, but if sufficient 

NH3 is available, particulate ammonium nitrate (NH4NO3) will form. An example of this 

process is the conversion of NO2 to HNO3 that may react further with NH3 to form 

particulate ammonium nitrate (NH4NO3). 

SO2, mainly from combustion of fossil fuel, is oxidized in the atmosphere to form H2SO4

particles that nucleate or condense on existing particles. The H2SO4 may be partially or 

completely neutralized by reaction with NH3. Since the particles usually contain water, 

the actual species present are H+, HSO, SO, and NH, in varying proportions depending 

on the amount of NH3 available to neutralize the H2SO4. Strong acidity of particles is 

due to free H+ or H+ available from HSO or H2SO4. SO2 also dissolves in cloud and fog 

droplets where it may react with dissolved O3, H2O2, or, if catalyzed by certain metals, 

with O2, yielding sulfuric acid or sulfates, that lead to particulate matter when the droplet 

evaporates. 

Atmospheric Behavior, Transport and Fate of Airborne Particles 

Fine particles are small enough that the random forces from collisions with gas 

molecules largely overcome gravitational forces. Fine particles tend to follow air 

streams and are typically not removed by impaction. As a result, they are not easily 

traced back to their individual sources. Fine particles formed from accumulation 

processes are significantly larger than gas molecules and their diffusion velocity is low. 

These particles have very long half-lives in the atmosphere, travel long distances, and 

tend to be more uniformly distributed over large geographic areas than coarse-mode 

particles. Removal by dry deposition is inefficient since they do not readily diffuse 

through the boundary layer of still air next to surfaces. The atmospheric half-life of fine 

accumulation particles with respect to dry deposition is on the order of weeks; however, 

removal occurs when the particles absorb water, grow into cloud droplets, grow further 

to raindrops, and fall out as rain. This process reduces the typical atmospheric half-life 

to a few days. Ultrafine or nuclei-mode particles, formed by nucleation of low saturation 

vapor pressure substances, tend to exist as disaggregated individual particles for very 

short periods of time (less than minutes) in the ambient atmosphere and tend to age 

rapidly into larger accumulation particles that may be dispersed more widely over long 

distances. Secondary fine particles are formed by atmospheric transformation of gases 

to particles. Atmospheric transformation can take place locally during stagnations or 

during transport over long distances. Fine particles have very low dry deposition 

velocities, which contribute to their uniformity throughout the air mass. Aerosol effects 

on visibility and climate, through light scattering and changes in cloud microphysics, 

primarily arise from fine particles. 
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F-52 

Background Concentrations to Support Modeling 

Background concentrations are an input for the speciated rollback modeling. 

Background estimates for each identifiable chemical species were determined based 

upon available data. The appropriate definition of background for speciated-rollback is 

the air quality concentrations at the influxes boundaries for the sites to be modeled. 

Existing SJV monitoring sites are intended to represent concentrations in populated 

areas; therefore none of the SJV criteria pollutant sites are intended to provide an ideal 

background site for determining natural or interregional contributions to the local air 

quality. Evaluation of CRPAQS sites in remote areas assisted in determination of 

background, with support from technical literature, especially the compilation of 

technical information in the EPA Criteria Documents. 

Natural sources contribute to both fine and coarse particles in the atmosphere. For 

modeling purposes, background particulate matter includes the distribution of particulate 

matter from natural sources as well as anthropogenic emissions of particulate matter 

and precursor emissions of VOCs, NOx, and SOx from areas outside of the SJVAB. To 

discriminate the portion of the measured PM10 affected by control strategies, emissions 

from outside of the SJVAB must be treated as background. If these emissions are not 

treated as background during the modeling process and are attributed to local 

emissions, control effects would be overestimated in the modeling process. This 

definition of background is different than would be used for health assessment studies, 

where background is limited to natural sources and all anthropogenic emissions are 

evaluated for their cumulative health impact. 

Background levels of particulate matter vary by geographic location and season. The 

natural component of the background arises from physical processes of the atmosphere 

that entrain fine particles of crustal material (i.e., soil) as well as emissions of organic 

particles resulting from natural combustion sources such as wildfires. In addition, 

certain vegetation can emit fine organic aerosols as well as their precursors. The exact 

magnitude of the natural portion of particulate matter for a given geographic location 

cannot be precisely determined because they are difficult to separate from the long 

range transport of anthropogenic particles or precursors. Only broad estimates for 

longer averaging times can be developed at this time. Regional annual average natural 

background levels are estimated as 1-4 μg/m3

 PM2.5 for the western US. 

CRPAQS scientific findings on natural background assist in further review of 

assumptions and quantification for receptor analysis. “Non-Anthropogenic Background 

Collins (1998) showed that non-anthropogenic background concentrations were not a 

significant contributor to SJV PM2.5 concentrations. Ammonium nitrate, ammonium 

sulfate, OM, and EC originate from anthropogenic sources in the SJV. The crustal 

component of PM2.5 was usually less than one μg/m3 during winter episodes. 

Moreover, geological material in the SJV during winter stagnation episodes was at least 

partially due to agricultural activity and/or road dust. Typical annual average PM2.5 

concentrations at remote sites in California were on the order of 1.5 to 3.5 μg/m3 
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(Motallebi et al., 2003; IMPROVE, 2003). Wintertime PM2.5 concentrations at the 

cleanest remote sites (i.e., Lassen National Park, Redwood North Coast National Park, 

and Yosemite National Park) in California were 1.0 to 2.0 μg/m3. Assuming these 

concentrations are solely due to nonanthropogenic sources, 1.0 to 2.0 μg/m3 is likely 

the best estimate for typical nonanthropogenic background concentrations in the SJV.” 

Source: BACKGROUND AND BOUNDARY CONDITIONS FOR PARTICULATE 

MATTER AND PRECURSORS IN THE SAN JOAQUIN VALLEY IN WINTER 

TECHNICAL MEMORANDUM STI-902325-2779-TM. By: Michael C. McCarthy, Hilary 

R. Hafner, Steven G. Brown, Fredrick W. Lurmann, Paul T. Roberts, Sonoma 

Technology, Inc., 1360 Redwood Way, Suite C, Petaluma, CA 94954-1169, July 29, 

2005 

Natural Background Emissions: Bioaerosols As part of establishing background 

concentrations, the mass contributed by living organic matter and biogenic emissions 

must also be considered. Ambient bioaerosols include fungal spores, pollen, bacteria, 

viruses, endotoxin, and animal and plant debris. Bacteria, viruses and endotoxin are 

mainly found attached to aerosol particles, while entities in the other categories are 

found as separate particles. Data for characterizing ambient concentrations and size 

distributions of bioaerosols are sparse. 

Most fungal spores are 2 to 4 μm in size, but range in size from 1.5 μm to over 100 μm. 

Spores form the largest and most consistently present component of biological aerosols 

in ambient air. Levels vary seasonally, usually being lowest when and where snow is 

on the ground. Fungal spores often reach levels of 1,000 to 10,000 spores/m3

 during 

the summer months (Lacey and Dutkiewicz, 1994; Madelin, 1994) and may be as high 

as 100,000/m3 near some anthropogenic sources (agriculture activities, compost, etc.). 

Bacterial aerosol counts may range as high as 30,000 bacteria/m3 downwind of sewage 

treatment facilities, composting areas, waterfalls from polluted rivers, or certain 

agricultural activities. Typical levels in urban areas range from several hundred to 

several thousand bacteria/m3

 (Lighthart and Mohr, 1994). Levels of bioaerosols (fungi 

and bacteria) are generally higher in urban than in rural areas (Lighthart and 

Stetzenbach, 1994). 

Gaseous hydrocarbon emissions from plants also form secondary particulates. These 

particles are not identified as bioaerosols and cannot be distinguished from secondary 

particles from anthropogenic activities with information available at this time. 

Key Findings from Scientific Studies and SJV Evaluations 

The CRPAQS research program has provided a wealth of scientific analysis specifically 

targeted to answer key questions that inform decision-making for effective reduction of 

particulate concentrations in the San Joaquin Valley. The findings cover a wide range 

of technical topics. The information may have value to other areas of the country, but in 

some cases the information discloses how California circumstances are different. For 

example: EPA methods consider a strong relationship between sulfate decrease 
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causing a potential and nitrate formation increase. That issue was studied for the SJV 

and found not to be a concern. 

Ammonium Nitrate Sensitivity to Sulfate Levels 

Ammonium nitrate levels are sensitive to changes in sulfate under ammonia 

limited conditions. Sulfate and nitrate compete for ammonia when its supply is 

limited, and the thermodynamics indicates that ammonium preferentially 

associates with sulfate (forming ammonium sulfate or ammonium bisulfate) 

rather than nitrate under ambient conditions. Under ammonia limited conditions, 

reductions in sulfate may not be an effective means to reduce PM mass because 

ammonium sulfate removed from the aerosol is replaced with ammonium nitrate, 

thereby inhibiting significant reduction in PM mass. 

The effects of changes in sulfate levels on ammonium nitrate in the SJV were 

investigated using the eight cases for which isopleth diagrams were constructed. 

These were cases with relative high sulfate levels for the SJV (3 to 4 mg/m3). 

The effects of 50 and 100 percent reduction in sulfates on ammonium nitrate 

concentrations are listed in Table 3-10. The results indicate ammonium nitrate 

concentrations are not sensitive to large changes in sulfate levels. The lack of 

ammonium nitrate sensitivity to sulfate in the SJV is due to the ammonia-rich 

conditions and the low amounts of sulfate compared to nitrate. 

ANALYSIS OF ATMOSPHERIC CHEMISTRY DURING 1995 INTEGRATED 

MONITORING STUDY, STI-997214-1791-FR , Authors: Naresh Kumar, 

Frederick W. Lurmann, Sonoma Technology, Inc., Petaluma, CA; Spyros Pandis, 

Asif Ansari, Carnegie Mellon University, Pittsburgh, PA, July 1998 

The issue of limiting precursors and secondary formation products has been extensively 

evaluated for the CRPAQS research program. 

• Particulate NH4NO3 concentrations are limited by the rate of HNO3 formation, 

rather than by the availability of NH3 

• HNO3 is formed via both daytime photochemistry and aloft nighttime chemistry 

• Secondary organic aerosol formation from VOC emissions may account for 15% 

to 25% of the total OC 

• Relatively low NMOC/NOx ratios indicate the daytime photochemistry is VOC-, 

sunlight-, and background –ozone-limited in winter. This is a nonlinear regime for 

the gas-phase chemistry. 

Presented to: CRPAQS Data Analysis Workshop, Sacramento, CA, March 9-10, 

2004, Tasks 6.1 and 6.2: Phase Distributions Tasks 6.1 and 6.2: Phase Distributions 

and Secondary Formation During Winter and Secondary Formation During Winter in 

the San Joaquin Valley in the San Joaquin Valley, Presented by: Fred Lurmann, 

Siana Alcorn, Manidipa Ghosh, Sonoma Technology, Inc., Petaluma, CA 

“....Comparisons of ammonia and nitric acid concentrations indicate that ammonium 

nitrate formation is limited by the availability of nitric acid, rather than ammonia. Timeresolved aerosol nitrate data at the surface and on a 90-m tower suggest both the 

daytime and nighttime nitric acid formation pathways are active, and that entrainment of 
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aerosol nitrate formed aloft at night may explain the spatial homogeneity of nitrate in the 

San Joaquin Valley.....This study's analyses suggest that reductions in NOx emissions 

will be more effective in reducing secondary ammonium nitrate aerosol concentrations 

than reductions in ammonia emissions. Reductions in VOC emissions will reduce 

secondary organic aerosol concentrations and may reduce ammonium nitrate. 

...Comparisons of ammonia and nitric acid concentrations show that ammonia is far 

more abundant than nitric acid, which indicates that ammonium nitrate formation is 

limited by the availability of nitric acid, rather than ammonia....The results indicate 

ammonium nitrate formation is ultimately controlled by NOx emission rates and the 

other species, including VOCs and background ozone, which control the rate of NOx 

oxidation in winter, rather than by ammonia emissions.” Processes Influencing 

Secondary Aerosol Formation in the San Joaquin Valley During Winter, Frederick W. 

Lurmann, Steven G. Brown, Michael C. McCarthy, and Paul T. Roberts, Sonoma 

Technology, Inc., 1360 Redwood Way, Suite C., Petaluma, CA 94954 

The contributing sources to PM2.5 have properties that are different. This makes 

analysis of reductions more difficult consequently complicating the development of 

effective control programs. Nitrates and carbon are the two largest components and 

their atmospheric behavior is entirely different. 

“High concentrations of PM organic carbon (OC) were spatially limited to core 

urban sites while high concentrations of PM ammonium nitrate were regionally 

distributed throughout the SJV. Concentrations of PM and its precursors were 

typically lower at the elevated sites surrounding the SJV than at monitoring sites 

located on the SJV floor....At distances more than 50 km from the urban areas, 

OM concentrations typically declined by a factor of three or more. Emissions of 

OM at the urban core are either not rapidly transported to the rural sites or are 

diluted too much to substantially impact rural sites. Concentrations of OM at 

elevated sites were comparable to concentrations at rural sites on the Valley 

floor....Overall, these spatial patterns of OM suggest that the impact of emissions 

was largely confined to the local area and OM concentrations were unevenly 

distributed over the duration of the episode. 

The contrast in spatial variability between the ammonium nitrate and OM 

components of PM2.5 in the SJV winter episodes provides information on the 

spatial extent of the production of ammonium nitrate. PM2.5 OM and ammonium 

nitrate are both subject to the same meteorological transport conditions, yet 

ammonium nitrate concentrations are relatively homogeneous and OM 

concentrations are much higher in the urban source areas. In addition, OM and 

ammonium nitrate components are expected to have the majority of their mass in 

a similar size fraction (PM0.1 to PM1) (Lighty et al., 2000; Hughes et al., 1999; 

Bench et al., 2002) and, therefore, the rates of removal should be approximately 

the same. In summary, the likely explanation for the difference in spatial 

variability is the spatial distribution of the emissions or precursors. Primary OM 

emissions occur predominantly from mobile sources and wood smoke located in 

urban areas. The formation of ammonium nitrate from NOx precursors (Lurmann 
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et al., 2004) must occur throughout the SJV to account for its spatial 

homogeneity. 

High concentrations of PM organic carbon were spatially limited to core urban 

sites while high concentrations of PM ammonium nitrate were regionally 

distributed throughout the SJV. The regional homogeneity of ammonium nitrate 

concentrations coupled with the stagnant wind conditions provides evidence that 

production of ammonium nitrate occurs at similar rates throughout the valley. In 

contrast, the OC component of PM indicates that production rates were much 

higher in the urban areas than at rural sites.” 

BACKGROUND AND BOUNDARY CONDITIONS FOR PARTICULATE 

MATTER AND PRECURSORS IN THE SAN JOAQUIN VALLEY IN WINTER, 

TECHNICAL MEMORANDUM STI-902325-2779-TM, Michael C. McCarthy, 

Hilary R. Hafner, Steven G. Brown, Fredrick W. Lurmann, Paul T. Roberts, 

Sonoma Technology, Inc., 1360 Redwood Way, Suite C, Petaluma, CA 94954-

1169, July 29, 2005 

Annual PM2.5 is dominated by winter concentrations and urban sites have more carbon 

“For most of the sites within the SJV, 50 – 75% of the annual average PM2.5 

concentration could be attributed to a high PM2.5 period occurring from 

November to January. At non-urban sites, the elevated PM2.5 was driven by 

secondary NH4NO3. The temperature, RH, and stability of the valley boundary 

layer in winter are all favorable for the formation of NH4NO3 from its NH3 and 

NOx, and VOC precursors. Elevated OM (organic matter, organic carbon 

compounds) exacerbates air quality mostly at urban sites. This is consistent with 

the winter use of wood fuel for home heating, as well as with traffic. This distinct 

spatial distribution also reflects the difference between primary and secondary 

aerosols.” 

“The urban sites experienced much higher BC (black carbon) concentration than 

non-urban sites. This suggested that BC particles were closely related to urban 

sources such as traffic, RWC, and cooking. Multiple spikes of hourly BC were 

frequently observed especially at urban sites, suggesting that those BC 

originated from nearby urban sources. At all urban sites, the morning BC peak 

was found around 0600 to 0800 PST and the large evening peak around 2000 

PST prolonged through the midnight and early morning next day. Non-urban 

sites did not exhibit any distinct diurnal patterns.” 

Initial Data Analysis of Field Program Measurements, DRI Document No. 2497, 

July 29, 2005 Judith C. Chow, L.-W. Antony Chen, Douglas H. Lowenthal, 

Prakash Doraiswamy, Kihong Park, Steven D. Kohl, Dana L. Trimble, John G. 

Watson, DESERT RESEARCH INSTITUTE, Division of Atmospheric Sciences, 

2215 Raggio Parkway, Reno, NV 89512 
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F-57 

Utilization of Application Assumptions 

Based upon review of the preceding information, default estimates of regional and 

background contributions have been established for use with the speciated receptor 

rollback modeling. Particle dynamics, physics, atmospheric behavior and fate establish 

background and regional contribution estimates. These estimates are assumptions 

considered in speciated rollback calculations to prevent overstating the probable effect 

of controls. If such assumptions were not utilized, the analysis would link all of the 

observed particulate to local quantified sources. This would overstate the predicted 

effectiveness of controls. The assumptions for background and regional components 

should not be set to the lowest possible level, which would overstate control 

effectiveness and potentially lead to a failure to attain or to the highest possible 

contribution, which would underestimate control effectiveness and require 

implementation of excessive control measures. 

Model: Input Data 

Emissions Estimates to Support Modeling 

The District and ARB maintain annual emission inventories of permitted emissions and 

estimations of mobile source, area source and naturally occurring emissions. To 

achieve the greatest possible reliability and accuracy of evaluations and predictions, the 

emission inventories must be adjusted for modeling to examine specific episodes and 

seasons for current and future years. The modeling inventory is used as a tool to 

evaluate control measures, the impact of rulemaking, receptor modeling reconciliation, 

and projection to future years. The SIP emission limits should be based on the NAAQS 

(annual or 24-hour) that results in the most stringent control requirements. Specific 

control measures designed to achieve the required emission limitation must then be 

implemented in the SIP. The emissions inventories prepared to correlate with observed 

values for modeling are called baseline inventories. Projections of future year 

conditions with additional controls are referred to as future year inventories. Modeling is 

used to determine the amount of additional reductions, if any, required to achieve 

attainment for the projected future year. 

Regional Modeling 

ARB is providing regional modeling for annual PM2.5 by modeling every day of the year 

for the extensive data available from CRPAQS. The results of this process will establish 

model performance that will then be used for projection of base and future year 

projections to be used for a Speciated Modeled Attainment Test (SMAT). A regional air 

quality modeling methodology summary of model preparation will be included as an 

attachment to this Protocol. 
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Receptor Modeling Source Profiles

Source profiles will be derived from the EPA source library, local geological and burning 

profiles collected during the 1988-89 Valley Air Quality Study, recent motor vehicle 

profiles, wood burning, and meat cooking source profiles. These are supplemented by soil 

profiles collected by U. C. Davis as part of their agricultural emission factor work for 

Technical Support Study 12 for CRPAQS. Profiles appropriate to regional and sitespecific activities, as well as the season of the year were considered. Composite 

profiles were generated to reflect the contribution of sources that cannot be 

distinguished individually, such as paved versus unpaved roads. 

Receptor Modeling Chemical Species

The chemical species to be used in the CMB analysis are listed below. If the presence 

or absence of a specific species causes a large difference in source contribution 

estimates, the species will be retained if all performance measures are within target 

ranges. If the performance measures are not within target ranges, the species will be 

evaluated for possible errors and may be removed from the input file. Additional 

species and carbon fraction profiles will be added as they become available. This may 

include additional trace elements measured in newer source profiles, as well as 

elemental and organic carbon fractions. 

Abbreviation Name 

TMAC PM10 Mass 

N3IC Nitrate 

S4IC Sulfate 

N4TC Ammonium 

ALXC Aluminum 

SIXC Silicon 

CLXC Chlorine 

KPXC Potassium 

CAXC Calcium 

TIXC Titanium 

VAXC Vanadium 

CRXC Chromium 

 

Abbreviation Name 

MNXC Manganese 

FEXC Iron 

NIXC Nickel 

CUXC Copper 

ZNXC Zinc 

BRXC Bromium 

PBXC Lead 

PHXC Phosphorus 

ECTC Elemental Carbon 

OCTC Organic Carbon 

TCTC Total Carbon 

Receptor Modeling Ambient Data 

The receptor modeling use two independent ambient data sets for evaluation. 
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CRPAQS data for the year 2000 is used for evaluation of source receptor relationships 

as previously conducted for the PM10 SIP. This data is available from the ARB website 

http://www.arb.ca.gov/airways/. This website provides access to the collected air quality 

data, surface and aloft meteorological measurements, and measurements from a 

variety of special and research instruments operated during the study. Also available 

from this website are documents reporting on and analyzing the collected data. The 

use of 2000 data remains valuable due to the extensive nature of the dataset and the 

technical reviews and analysis of the SJV during that time period. This robust dataset 

provides greater capability to examine the accuracy of model performance for 

simulating the observed conditions than is provided by the routine network 

measurements collected each year. The purpose of the data use is to provide a high 

quality, verified model construct that is then used to model future years. The regional 

modeling being conducted by ARB uses the same dataset, for the same reason. PM2.5 

mass was determined by analysis of prior PM10 annual CMB modeling and rollback 

analysis with substitution of available PM2.5 speciation data and calculation of species 

mass proportions for segments provided in the pM10 evaluation but missing from the 

PM2.5 speciation data set. The 2000 data is modeled forward to the year 2005 to 

project species comparable to the observed 2005 speciation data. 

Speciation data for 2005 for Fresno and Kern annual PM2.5 evaluation was provided by 

ARB via download from the EPA AIRS system. This data is used as an input for new 

CMB evaluations and subsequent use in the receptor speciated rollback analysis. 

Since this provides an independent set of data, it allows comparison to the model 

formulation initiated from 2000 data as well as providing a more current starting point of 

foundation data. 

Model: Output Data 

EPA has recognized the difficulty faced by California for use of existing modeling 

techniques to adequately quantify the full effect o controls. “It is well-recognized that 

California faces a set of unique and exceptionally difficult challenges in meeting national 

air quality standards, including those for fine particulates.... Key uncertainties exist with 

regard to both emissions inventories and air quality modeling in the West, which may 

understate the effectiveness of certain controls....The magnitude of projected nonattainment is larger than any other state, making the task of simulating attainment much 

more challenging than elsewhere in the nation.” The modeling systems also have 

inherent technical limitations to fully quantify reductions, particularly for evaluation of 

controls that would reduce secondary organic PM from VOC reductions. 

“There is considerable uncertainty and lack of understanding of formation, fate, 

and properties of organic particles. It is estimated that only 10 to 20 percent of 

the PM organic compounds have been quantified using existing methodologies. 

Work is underway at EPA and elsewhere to improve our understanding or 

secondary organic aerosols and our ability to characterize these compounds and 

their precursors in air quality models. In view of these limitations and 

uncertainties, current air quality models, including CMAQ, may understate the 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

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F-60 

reduction in secondary organic PM from controls on particle-forming VOCs, 

including aromatic compounds and higher carbon alkanes and olefins. 

Source: Clean Air Interstate Rule (CAIR), EPA Projection Methods for Air Quality 

Concentrations. 

Regional Modeling 

Regional modeling will output gridded projections of secondary particulate matter. 

Additionally, time series analysis of grid cells of interest will also be possible. Other 

parameters may also be plotted in grid or time series for evaluation. Valid results of 

interest will be included in final modeling documentation. 

The model evaluates ammonium nitrate (NH4NO3) formed from the gaseous precursors 

ammonia and nitric acid (HNO3). Ammonia is mainly emitted into the atmosphere from 

various sources. HNO3 is a secondary gaseous product of the reactions between 

oxides of nitrogen (NOx) and volatile organic compounds (VOC) in the presence of 

sunlight. The regional model will also simulate the formation of secondary organic 

aerosols (SOA) and ammonium sulfate ((NH4)2SO4). The representation of (NH4)2SO4

is robust but only minor amounts (~5 μg/m3) of that pollutant is present in the San 

Joaquin Valley in observed episodes. The representation of SOA in the modeling tools 

currently available is not very robust; therefore, EPA developed a methodology for 

estimation of SOA by adjusting FRM and modeling outputs to incorporate calculations of 

the sampling artifacts and losses, assigning the remainder of mass as SOA after these 

adjustments. However, the methodology may not be fully suitable for the SJV as it does 

not have a strong method for determination of geologic material and metals. This may 

not be a problem for much of the nation but the San Joaquin Valley has extensive 

programs to address fugitive dust that may not be well represented by the EPA 

approach. Furthermore, the District has documented events where carbon emissions 

have increased observed levels of nitrates by mechanisms not incorporated into the 

regional model. 

Receptor Modeling 

Outputs from the CMB 8 model follow standard formats. The primary output of interest 

is the source contribution estimate (SCE) for each episode and source category. Other 

outputs provide performance statistics discussed in the next section of the Protocol 

Model: Analysis of Results. 

Model: Analysis of Results 

Excerpts from presentations provided by EPA staff help clarify what is intended in the 

analysis of model results. 
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F-61 

Modeled Attainment Tests 

All O3/PM2.5/RH modeled attainment tests use model estimates in a “relative” sense 

• Premise: models are better at predicting relative changes in concentrations than 

absolute concentrations 

• Relative Response Factors (RRF) are calculated by taking the ratio of the model’s 

future to current predictions of PM2.5 or ozone 

• RRFs are calculated for ozone and for each component of PM2.5 and regional haze

Weight of Evidence/Supplemental Analyses 

All attainment demonstrations should include “supplemental” analyses to corroborate 

the modeling results. Three main categories of supplemental analyses 

• Modeling 

• Trends 

• Diagnostic analyses 

Weight of evidence applies when future design values are “close to” NAAQS (either 

above or below). Recommended WOE range: 

• Annual PM2.5 14.5-15.5 ug/m3 

• 24-hour PM2.5 62-67 ug/m3 

Source: “Guidance on the use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze”, Final 

version- April 2007, Brian Timin April 17, 2007 

The following sections discuss performance analysis of model results, but we recognize 

and concur that analysis of PM2.5 modeling is a difficult process. “It is difficult to 

establish generally applicable numerical performance goals” Model performance is not 

particularly important for components with small observed concentrations relative to 

other components. In a relative attainment test, a small observed component cannot 

have a large influence. Source: PM2.5 Model Performance Evaluation- Purpose and 

Goals, Brian Timin EPA/OAQPS, PM Model Evaluation Workshop, February 10, 2004, 

Chapel Hill, NC. 

Regional Modeling 

Once the base-case simulation is performed, the estimated emissions, meteorology, 

and air quality need to be compared with observations to assure that the modeling 

system (emissions, meteorology, and air quality) is a satisfactory representation of the 

episode modeled. Only with such an assurance one can use the modeling system to 

evaluate control strategies. The rigorous comparison of model estimates with 

observations is termed model performance evaluation. Emission model evaluation and 

meteorology model evaluation is limited to quality assurance testing. Rigorous 

evaluation of the air quality model performance is proposed. The statistical metrics 

used for the comparison of estimates are in accordance with guidance. For gaseous 

pollutants the comparison is straightforward. But, for particulate matter that has several 
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F-62 

components, the comparison will be done for the total mass as well as for the mass of 

individual components. 

Statistical Measures for Regional Modeling Performance Analysis

Peak Estimation Accuracy: 

Paired Peak Estimation Where, Co(x,t) is the peak concentration observed at location x 

at time t and Ce(x,t) is the model estimated concentration at the same location at the 

same time. Then Ats, is a measure of the extent to which the observed and estimated 

peaks are paired in space and time. 

Equation A1: Paired Peak Estimation

100%

( , )

( ( , ) ( , )) × − =

c x t

c x t c x t A

o

e o

ts

Temporally Paired Peak Estimation: In the above expression if we relax the 

requirement that the modeled estimate should be in the same grid cell as the 

observation, we measure the models ability to reproduce the observed peak at the 

correct hour in the vicinity of the observed peak (e.g. 25 km radius). 

Spatially Paired Peak Estimation: In equation (A1), if we relax the requirement that the 

model estimate should be at the same time as the peak observation, we measure the 

model’s ability to reproduce the observed peak at the correct location at a reasonably 

close hour (e.g. within 3 hours). 

Unpaired Peak Estimation: In equation (A1), if we relax the requirement that model 

estimate should be at the same location and hour as the observation, we measure the 

model’s ability to reproduce the peak in the vicinity (say, 25 km radius) of the monitor at 

a reasonably close hour (e.g. within 3 hours). 

Bias: 

Equation A2: Mean Bias Error where N is the number of valid hourly 

observation-estimation pairs drawn from all valid monitoring data on the 

simulation day of interest. 

∑=

= −

N

i

e i o i c x t c x t

N

MBE

1

( ( , ) ( , )) 1

Equation A3: Mean Normalized Bias Error similar to equation (A-2) but 

normalized with respect to the observation. 
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F-63 

∑=

× − =

N

i o i

e i o i

c x t

c x t c x t

N

MNBE

1

100%

( , )

1 ( ( , ) ( , ))

Error: 

Equation A4: Mean Gross Error where, N is the number of valid hourly 

observation-estimation pairs drawn from all valid monitoring data on the 

simulation day of interest. Summing the absolute values of the difference 

prevents cancellation of errors. 

∑=

= −

N

i

e i o i c x t c x t

N

MAGE

1

( ( , ) ( , )) 1

Equation A5: Mean Normalized Gross Error, similar to equation (A-4) but 

normalized with respect to the observation. 

∑=

× − =

N

i o i

e i o i

c x t

c x t c x t

N

MNBE

1

100%

( , )

1 ( ( , ) ( , ))

Variance 

Equation A6: Variance is measure of spread of data. If the variance is low, then 

the difference between observations and estimation is small and vise versa. 

∑=

= −

N

i

e i o i c x t c x t

N 1

2 2 ( ( , ) ( , )) 1

σ

Receptor Modeling 

Model output and performance measures will be analyzed both individually and in 

aggregate to determine the applicability of the source profiles. Preliminary sets of 

source profiles will consist of at least one source profile from each source category. 

Final profile selection results from interactive application of the CMB with evaluation of 

the performance measures. Calculated contributions will be compared to measured 

ambient concentrations. Reasonable agreement between calculated contributions and 

measured ambient concentrations would indicate that all major source categories were 

included in the calculations, ambient and source profile measurements were fairly 

accurate, and source profiles were reasonably representative of actual emissions. 
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F-64 

Sensitivity matrices were constructed to determine the source profiles with the most 

influence on species apportionment. Final analysis includes comparison to regional 

activities and current emission inventories, and is done in conjunction with 

meteorological and climatological analysis. 

The CMB8 model provides a statistical diagnostic evaluation of correspondence of the 

CMB source identification to the observed sample. The basic statistical performance 

evaluations include R2 - R2

 error analysis, Chi2 - Chi2 distribution, and Pmass - percent 

of mass accounted for by the sum of masses attributed to identified sources. The 

following performance measures will be used to assess the adequacy of the CMB 

model runs: 

Source Contribution Estimate (SCE) This is the contribution of each source type 

to the PM mass. Each SCE should be greater than its standard error. 

Standard Error (STDERR) This is an indicator of the uncertainty of each SCE. 

The STDERR should be much less than the SCE. 

T-Statistic (TSTAT) This is the ratio of the SCE to the STDERR. A value greater 

than 2.0 indicates the precision of the SCE is high and that the source is a significant 

contributor. 

R-Square (R SQUARE) This measures the variance in the sample 

concentrations, which is explained by the model calculated species concentrations. A 

value less than 0.8 indicates that the selected sources do not account for the variance 

in the sample. 

Chi Square (CHI SQUARE) This is a measure of goodness of fit, which is 

inversely proportional to the squares of the uncertainties in the source profiles and the 

receptor data. A CHI SQUARE greater than 4.0 indicates that one of the calculated 

species concentrations differs from the measured value by several uncertainty intervals. 

Percent of Mass Accounted For (PERCENT MASS) This is the ratio of the sum of 

the SCEs to the measured mass of the sample. PERCENT MASS should be within 

80% to 120%. 

Uncertainty/Similarity Clusters (U/S CLUSTERS) This is an indicator of groups of 

source profiles which are either collinear or which have very high uncertainties. 

Sum of Combined Sources (SUM OF CLUSTER SOURCES) This represents the 

sum of the SCEs in the U/S CLUSTER with the standard error of the sum. When the 

standard error is low, it suggests that a composite profile can be created to represent 

the cluster. 

Ratio of Residual to its Standard Error (RATIO R/U) This indicates the difference 

between the calculated and measured species concentration (residual) divided by the 
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F-65 

uncertainty of the residual. An R/U value greater than +/- 2.0 indicates that one or more 

source profiles are contributing too much or too little to the species concentration. 

Ratio of Calculated to Measured Species (RATIO C/M) This is the ratio of the 

calculated species concentration to the measured species concentration along with the 

standard error of the ratio. The ratio should be near 1.0. 

SANDWICH Calculation Procedure 

The measured Sulfate, Adjusted Nitrate, Derived Water, and Inferred Carbonaceous 

mass approach (SANDWICH) was developed to attribute portions of the bulk FRM 

mass to different components. Nitrates are often lost from the FRM filter along with 

semivolatile organics, and water bound to the inorganics also comprises a portion of the 

FRM mass. 

The main steps in estimating the PM2.5 composition are as follows: 

(1) Calculate the nitrate retained on the FRM filter using hourly relative humidity and 

temperature alongside STN nitrate measurements, 

(2) Calculate quarterly averages for retained nitrate, sulfate, elemental carbon, 

ammonium (or sulfate degree of neutralization, [NH4

+

]/[SO4

2-], if not using ammonium 

measurements directly), 

(3) Calculate particle bound water using the concentrations of ammonium, sulfate, and 

nitrate, using an equilibrium model like the Aerosol Inorganic Model (or a polynomial 

equation derived from model output), and 

(4) Calculate organic carbon mass (OCMmb) by difference, subtracting all inorganic 

species (including blank mass) from the PM2.5 mass. 

Total PM2.5 mass is given by 

 PM2.5 FRM = [SO4

2-] + [NO3-

FRM] + [NH4

+

FRM] + [EC] + [Other ] + [OCMmb] + 

 [H2O] + [blank mass = 0.5 μg/m3] 

Where all concentrations have units of μg/m3

 and 

• [SO4 2-] = measured sulfate 

• [NO3 -FRM] = nitrate retained on the FRM filter 

• [NH4+FRM] = ammonium associated with FRM filter nitrate and sulfate 

• [EC] = measured Elemental Carbon 

• Other = other inorganic mass; e.g., crustal material or sea salt 

• [OCMmb] = organic carbon mass calculated by difference by adding all inorganic 

species and subtracting from the FRM PM2.5 mass. Organic carbon 
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F-66 

measurements may also be used if it seems that the OC by mass balance is 

clearly under or overestimated. 

• [H2O] = water bound to the hygroscopic species, dependent on the 

concentrations of sulfate, nitrate, and ammonium. Calculated using a polynomial 

equation dependent on the concentrations of sulfate, nitrate, and ammonium or 

using an inorganic aerosol equilibrium model or use alternative linear estimation 

where water bonded to ammonium nitrate is approximated as equivalent to 

twelve percent of the mass and water bonded to ammonium sulfate is 

approximated as 26 percent of that mass. 

• Blank mass represents the mass passively collected on the filter, represented in 

the equation by an average value of 0.5 mg/m3 (assumed constant) 

District evaluation of filter blank mass for SANDWICH 

The SANDWICH procedure recommended by EPA guidance calls for setting aside 0.5 

ug/m3 as a filter blank artifact, to be kept as a constant and not be reduced for future 

year considerations. EPA identified the source of the artifact as sample handling 

contamination by inference but not by demonstrated measurement or detection. The 

District has identified through review of CRPAQS research that the sampling artifact has 

a different cause than suggested by EPA and will process the artifact accordingly. 

CRPAQS research provides analysis and documentation of the source of the artifact as 

the difference between VOC carbon adsorbed on the filter, partially offset by carbon 

particulate desorbed from the filter. This material is not a handling contaminant and is 

related to ambient precursors and pollutants. Therefore, the mass set aside for this 

calculation should not be considered to be static in future years. Current receptor 

modeling has a very small mass identified as unassigned and mapped against total 

PM2.5 inventory change. Prior discussion indicates that in addition to the VOC 

adsorbed artifact there are other inorganic filter artifacts identified by prior research and 

technical literature. The analysis for CRPAQS determined the source of the artifact but 

qualified the determination of exact mass magnitude. EPA has set a default value of 

0.5 micrograms for the filter blank hypothesis, but this value is too low for the identified 

adsorbed carbon and inorganic artifacts. The District will review establishing appropriate 

mass consideration for both artifacts and will revise receptor speciated rollback methods 

and assumptions accordingly. 

Documentation of the filter artifact analysis performed for CRPAQS can be found in 

sections of the document: 

Initial Data Analysis of Field Program Measurements 

DRI Document No. 2497, July 29, 2005 

Judith C. Chow, L.-W. Antony Chen, Douglas H. Lowenthal, Prakash 

Doraiswamy, Kihong Park, Steven D. Kohl, Dana L. Trimble, John G. Watson, 

DESERT RESEARCH INSTITUTE, Division of Atmospheric Sciences, 2215 

Raggio Parkway, Reno, NV 89512 

Organic Carbon Artifacts (Task 1.1.4) 
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F-67 

Several denuding and backup filter sampling systems were applied to evaluate 

the OC artifacts. Based the analyses, the following were concluded: 

• The OC concentration on the quartz-fiber backup filter to a quartz-fiber front 

filter (QQ2) in an undenuded stream was concluded to be the most appropriate 

estimate of positive artifact (i.e., adsorption of organic gases onto the filters). The 

adsorption of organic gases onto the filters was, on average, 14.4% (i.e., 

QQ2/QPOC) of the particle OC concentration during CRPAQS. The positive 

artifact was lower during winter than the summer season (12.3% in winter versus 

22.8% in summer). 

• The OC on backup quartz-fiber filter to a front quartz-fiber filter in a carbondenuded stream (DNQQ2) may be used as an indication of the amount of carbon 

that was desorbed from the particles on the front quartz-fiber filter (i.e., as an 

estimate of the negative artifact). The desorption of organic vapors from the 

particles on the filter was, on average, 4.9% (i.e., DNQQ2/DNPOC) of the particle 

OC concentration and ranged from 2.3% in winter to 11% in summer. 

• The accuracy of using QQ2 as an estimate of the positive artifact depends on 

whether equilibrium was reached between the VOC in the sample stream and 

both filters in the QQ pair, which is highly questionable, given the difference in 

sampling periods and the diurnal variations in VOC concentrations. More 

research is needed to resolve these issues, especially the specific 

characterization of compounds responsible for the positive and negative artifacts 

and the conditions under which equilibrium is reached under different sampling 

conditions. 

Spatial scales of influence on compliance (backbone) (Task 4.4.3) 

• The comparison between measured and CMB-predicted PM2.5 and PM10 

mass indicates that the CMB reached a good mass closure for the most part. 

Although the slope was within 10% of unity, large intercepts were found for both 

PM2.5 (3.2 μg/m3) and PM10 (1.24 μg/m3). This intercept most likely resulted 

from the organic sampling artifact. 

SMAT Calculation Procedure 

The SMAT procedures for receptor and regional modeling will be slightly different due to 

the differences between the modeling methods. The general procedure to establish a 

Speciated Modeled Attainment Test (SMAT) is to perform calculations that consider the 

differences between FRM samples and the processes and quantities predicted by the 

model. 

 Step 1. Calculate the observed quarterly mean bulk PM2.5 concentration and 

composition for each measurement site. The quarterly mean species concentrations 

can be calculated by multiplying the observed percentage contribution of each species 

against the quarterly mean bulk PM2.5 design value. This design value is calculated 

from the bulk FRM concentrations averaged over a number of years (generally three but 

can be a weighted average spanning 5 years), one of which should be the modeled 
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F-68 

base year. The procedure to speciate this bulk design value is described in the 

“SANDWICH” discussion. 

 Step 2. Calculate the RRFs for each quarter and measurement site. Relative 

response factors are calculated using model results. For species i, site j, and quarter k 

is given by the following equation: 

 RRFijk = ([Ci, quarter k of future year]/[Ci, quarter k of the base year])j

 Ci represents the quarterly modeled concentration (often averaged over a 

number of model cells near the location of the measurement site) for the base year and 

the future year attainment target. The number of model cells recommended for use in 

the average depends on the size of the grid cell and is justified by the long PM2.5

sampling times, the representative spatial scale of the monitors, and the desire to offset 

any potential errors stemming from the geometry of the superimposed grid system. For 

a 12-km cell, it is recommended that RRFs be calculated from a 3x3 cell array. 

 Step 3. Multiply the quarterly, site-specific model-based RRFs of step 2 and 

speciated observations of step 1 to estimate future quarterly species concentrations. 

 Step 4. Sum the future quarterly species concentrations estimates of step 3 to 

estimate a future quarterly PM2.5 estimate at each monitoring site and then average 

these for a projected future year annual PM2.5 concentration for each monitoring site. 

 Step 5. Compare the future year annual average PM2.5 concentrations of step 4 

to the annual PM2.5 standard of 15.0 g/m3

. If all sites have projected PM2.5

concentrations below the standard, the attainment test is passed. 

Determining Attainment for All Cases 

The program of analysis specified in the Protocol will provide a complete attainment 

demonstration by assessment of all exceedances to the PM2.5 annual standard that 

have been detected by the monitoring network and establish the San Joaquin Valley to 

be classified as nonattainment. The demonstration will show that the proposed 

emission control program will successfully lower PM2.5 emissions to the extent that 

exceedances of the federal PM2.5 standards will be eliminated and that such conditions 

will be achieved at the earliest practical date consistent with the requirements of the 

Clean Air Act. The Protocol addresses the annual standard as the primary focus for 

evaluation due to compliance with the 24-hour standard in effect for this SIP. The 

revision of the 24-hour standard will be addressed in subsequent evaluation according 

to schedules promulgated by EPA. 

Procedures for analysis have been selected to establish objective and reliable 

conclusions that have the highest confidence that can be established to establish the 

SIP as comprehensive and sufficient for the entire Valley in all cases. Although the best 
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F-69 

available data will be utilized, uncertainties and limitations of the data affect results of 

analyses and may produce confirming and contradictory indications that must be 

reconciled by knowledgeable evaluation to establish reliable conclusions. The SIP 

control plan will establish an attainment demonstration of the PM2.5 annual and 24-hour 

standards by successfully addressing all identifiable exceedances that classify the 

District as nonattainment. The Protocol includes receptor-modeling evaluation of 

monitoring sites that do not comply with the annual standard as well as regional 

modeling. Regional modeling will assess the entire SJVAB and will allow review of 

areas without monitors. 

Supporting analyses to confirm the projection of attainment by weight of evidence 

include: 

• Examination of historical monitoring data, 

• Evaluation of source zone of influence, 

• Demonstration of spatial representativeness of monitored episodes 

• Assessment of control strategy impact via rollback analysis (chemical mass 

balance modeling with speciated rollback and enhanced analysis of secondary 

particulates for appropriate episodes) with application to the design value by 

SMAT, and 

• Regional modeling used in a relative sense to establish a speciated projection of 

attainment (SMAT). 

The combination and comparison of results provides a weight of evidence analysis to 

establish the adequacy of the SIP to achieve attainment. Examining historical data 

provides context for design value observations and an assessment of whether the 

design values are consistent with previous experience. Evaluation of source zone of 

influence is necessary for prediction of effects of control strategies with receptor and 

rollback techniques. Evaluation of the spatial representativeness of monitored episodes 

is important to determine which episodes are dominated by local sources and which 

have significant contributions from larger portions of the region. Response observed in 

the monitoring data and design values should be consistent with the technical analyses 

and modeling results. Examining this correlation is important to determine if modeling 

or other analysis provides an accurate representation of expected change, to determine 

that the control program is sufficient to achieve attainment of the PM2.5 standards and 

is not excessive in its requirements 

Attachments

The following documents are attached and incorporated as elements of the Protocol, 

providing additional discussion of the analysis and modeling process: 

A1 SJV PM Conceptual Model 1998 (64 pages) – Conceptual 

description of PM10 and PM2.5 episode dynamics. Performed for 

the CRPAQS study as part of an integrated monitoring study (IMS-
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95) designed to evaluate conceptual issues. Based on 1995 data 

collected in the San Joaquin Valley. 

 A2 Modeling Domain –Domain map annotated with model basic 

operating parameters 

A3 Modeling Boundary Conditions – Average annual surface boundary 

conditions 

A4 PMF procedure (5 pages) – Methodology for the Positive Matrix 

Factorization approach to identify key contributing sources 

A5 Receptor Modeling Analysis Layout (4 pages) – Documentation of 

receptor rollback revisions and conversion from analysis of PM10 to 

PM2.5 

A6 Regional Air Quality Modeling Methodology – Model selection, 

emissions and meteorology for PM2.5 annual modeling 

Additional documentation will accompany the submittal of the PM2.5 Plan but is not 

included as part of the Protocol. The District and ARB will retain modeling 

documentation at their respective offices for public access. Materials to be submitted as 

a part of the Plan approval process, either with the Plan submittal or by separate 

transmittal, are expected to include: 

• The PM2.5 Plan, resolution of adoption and other required transmittal 

documents 

• The PM2.5 Modeling Protocol and Attachments 

• Modeling analysis results summary documentation 

• Other appendices to provide supporting information for the plan 

• Other documentation identified as required by EPA 
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F-71 

ATTACHMENT A1 

To the 

San Joaquin Valley Air Pollution Control District 

State Implementation Plan PM2.5 Modeling Protocol 

Reference Document Title: 

“Conceptual Model of Particulate Matter Pollution in the California San Joaquin 

Valley,” Document Number CP045-1-98, 8 September 1998. Prepared by: Betty Pun & 

Christian Seigneur, Atmospheric and Environmental Research, Inc., 2682 Bishop Drive, 

Suite 120, San Ramon, CA 94583. Prepared for: Mr. Eugene Shelar, Jr., Technical and 

Ecological Services, Pacific Gas and Electric Company, 3400 Crow Canyon Road, San 

Ramon, CA 94583. 

Referred to in the Protocol as:

A1 SJV PM Conceptual Model 1998 (64 pages) 

Conceptual description of PM10 and PM2.5 episode dynamics. Performed for the CRPAQS study as part of an integrated 

monitoring study (IMS-95) designed to evaluate conceptual issues. Based on 1995 data collected in the San Joaquin 

Valley. 

For printing purposes this attachment is included by reference rather than being attached 

in its entirety. A copy of this document is available for review and/or purchase from the 

San Joaquin Valley Unified Air Pollution Control District. An electronic copy may be 

obtained at:: http://www.arb.ca.gov/airways/Documents/reports/sjvpmc~1.pdf
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F-72 

ATTACHMENT A2 

Modeling Domain 

Model Performance: 2000 

Reference Year: 2005 

Future Year: 2014 

Monthly 

Boundary/Initial 

Conditions for gas 

and aerosol 

species from the 

global CTM, 

MOZART 

Statewide MM5 meteorology (Dec.99 – 

Jan01)

15 vertical layers up to 100 mb 

ARB emissions 

Anthropogenic/biogenic 

Gaseous/Particulates 

 Monthly 

 Weekday/weekend 

12-km2

grid 

cells 
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F-73 

ATTACHMENT A3 

To the 

San Joaquin Valley Air Pollution Control District 

State Implementation Plan PM2.5 Modeling Protocol 

Average Annual Surface Boundary 

Conditions 

North East South West 

VOC (ppb) 6.6 8.0 5.7 2.9 

NOx (ppb) 0.7 1.5 3.6 0.1 

O3 (ppb) 38 42 39 36 

SO2 (ppb) 0.1 0.3 0.5 0.04 

NH3 (ppb) 0.2 0.2 0.2 0.01 

HNO3 (ppb) 0.2 0.5 0.8 0.1 

NH4NO3 (μg/m3

) 0.2 0.2 0.2 0.02 

(NH4)2SO4 (μg/m3

) 1.3 1.5 1.6 1.1 

OC (μg/m3

) 1.2 1.7 1.3 0.2 

EC (μg/m3

) 0.2 0.3 0.4 0.0 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

2008 PM2.5 Plan

F-74 

ATTACHMENT A4 

To the 

San Joaquin Valley Air Pollution Control District 

State Implementation Plan PM2.5 Modeling Protocol 

Summary of PM2.5 Source Apportionment Procedure using PMF2 

Positive matrix factorization (PMF) is a statistical method used for deducing factors. 

Program PMF2 is used for the source apportionment of airborne particulate matter 

using PMF method. PMF2 decomposes measured data matrix into source profile and 

source contribution matrices simultaneously. By analyzing measured data at the 

receptor, PMF2 provides source profiles and source contributions at the receptor side. 

EPA Speciation Trends Network data measured at Fresno and Bakersfield will be 

analyzed separately using PMF2. 

Prepare Input Data 

Since a carbon denuder that minimizes positive sampling artifact caused by adsorption 

of gaseous organic materials was not included upstream of quartz filter in the Speciation 

Trends Network (STN) samplers, and none of the reported STN data were blank 

collected, an integrated organic carbon (OC) artifact is estimated utilizing the intercept 

of the regression of OC concentrations against PM2.5 mass concentrations suggested 

by Tolocka et al. (2001). Samples for which the PM2.5 or OC data were missing are 

excluded from the regression analysis between PM2.5 and OC concentration. 

Comparing co-located PM2.5 data measured by STN and Federal Reference Method 

(FRM), outliers are censored. As shown in Figure 1, the intercept in PM2.5 regression 

against OC concentration is considered to be the OC artifact concentration. The STN 

OC concentrations are corrected by subtracting the OC artifact concentration. 

Figure 1. PM2.5 mass concentrations vs. OC concentrations 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

2008 PM2.5 Plan

F-75 

For the PMF2 analysis, samples for which the PM2.5 or OC data were not available or 

below zero, or for which PM2.5 or OC mass concentration had an error flag are excluded 

from the data set. Outliers in comparing co-located STN PM2.5 data and FRM data are 

excluded. The samples that contain fireworks particles which have unusually high 

concentrations of OC, K, As, Ba, Cu, and Pb are excluded. 

X-Ray Fluorescence (XRF) S is excluded from the analyses to prevent double counting 

of mass concentrations since XRF S and Ion Chromatography (IC) SO4

2- are highly 

correlated in STN data. Due to the higher analytical precision compared to XRF Na and 

XRF K, IC Na+ and IC K+ are included. Chemical species below MDL values more than 

90% are excluded. As recommended by Paatero and Hopke (2003), the species that 

have Signal-to-Noise (S/N) ratio below 0.2 (bad variable) are excluded. 

PMF2 analysis requires two input data matrices: concentration data matrix and 

uncertainty matrix. To assign input data, the procedure of Polissar et al. (1998) is used. 

The measurement values are used for the input concentration data, and the sum of the 

analytical uncertainty and one-third of the detection limit value is used as the input 

uncertainty data assigned to each measured value. Concentration values below the 

detection limit are replaced by half of the detection limit values, and their input 

uncertainties are set at five-sixth of the detection limit values. Missing values are 

replaced by the geometric mean of the measured values for each species, and to downweight these replaced data and then to reduce their influence on the solution, their 

accompanying uncertainties are set at four times of this geometric mean value. The 

input uncertainties are increased by a factor of thirty for the data which have error flags. 

The measured STN PM2.5 mass concentration is included in the input data as an 

independent variable in the PMF2 analysis to directly obtain the mass apportionment 

(Kim et al, 2003). 

Run Model 

PMF2 requires users to decide number of sources. To determine the appropriate 

number of sources, it is useful to look at the changes in source profiles as a function of 

the number of sources since after an appropriate number of sources are included in the 

fit, additional sources will not be physically interpretable. Because rotational ambiguity 

exists in factor analysis, the parameter FPEAK is used for the PMF2 to control the 

rotations. PMF2 is run with different FPEAK values to determine the range within which 

the modeling residual remains relatively constant. The optimal solution should lie in this 

FPEAK range. 

The profiles of real pollution sources vary because of changing feed stocks, 

atmospheric processing, etc. Thus, PMF2 analysis needs to find an appropriate 

average source profile, but it will not be as tight a fit as would occur if the profiles were 

fixed. Thus, for various species involved in more variable composition profiles, the 

uncertainty space is needed to expand to obtain sensible solutions. As recommended 

by Paatero and Hopke (2003), which is to down-weight the variable in the analysis so 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

2008 PM2.5 Plan

F-76 

that the noise does not compromise the solution, it is possible to increase the input 

uncertainties of several species to obtain physically interpretable PMF2 results (i.e., 

source profiles and contributions) The final PMF2 solutions are determined by 

experiments with different numbers of sources, different FPEAK values, and different 

species down-weight with the final choice based on the evaluation of the resulting 

source profiles as well as the quality of the species fits. For example, Figure 2, a 

comparison of the reconstructed PM2.5 mass contributions resolved by PMF2 with 

measured PM2.5 mass concentrations shows that the resolved sources well reproduce 

the measured values and account for most of the variation in the PM2.5 mass 

concentrations. Figures 3 and 4 show source profile and contribution, respectively, of 

PMF2 resolved secondary nitrate at LA-North main site. 

Figure 2. Measured versus PMF reconstructed PM2.5 mass concentrations. 

Figure 3. Source profile deduced from PM2.5 samples measured at LA-North main 

(prediction  standard deviation). 

Figure 4. Source contribution deduced from PM2.5 samples measured at LA-North 

main. 

Interpret Sources 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

2008 PM2.5 Plan

F-77 

To interpret PMF2 results, the identified source profiles are compared with published 

measurements: For example, Lowenthal et al. (1994) and Watson et al. (1994, 2001a, 

2001b) for the diesel exhaust; Small et al. (1984) and EPA SPECIATE version 4.0 

(2007) for industrial sources. Also, source contributions extracted by PMF2 are 

analyzed by temporal analyses to decide whether or not the sources are physically 

interpretable. The temporal variations shown in Figures 5 and 6 indicate the PMF2 

deduced secondary nitrate has winter-high trend and no weekday/weekend variation 

that physically make sense. 

Figure 5. The monthly variations of source contributions to PM2.5 mass concentration at 

LA-North main (mean  95 % distribution). 

Figure 6. Weekday/weekend variations at LA-North main (mean  95 % distribution). 

References 

Kim, E., Hopke, P.K., Edgerton, E. Source identification of Atlanta aerosol by Positive 

Matrix Factorization, Journal of Air and Waste Management Association. 53, 731-739. 

2003. 

Lowenthal, D.H., Zielinska, B., Chow, J.C., Watson, J.G. Characterization of heavy duty 

diesel vehicle emissions. Atmospheric Environment 28 (4), 731–743, 1994. 

Paatero, P., Hopke, P.K. Discarding or downweighting high-noise variables in factor 

analytic models, Analytica Chimica Acta, 490, 277-289, 2003. 

Polissar, A.V., Hopke, P.K., Paatero, P., Malm, W.C., Sisler, J.F. AtmosphericAerosol 

over Alaska 2. Elemental Composition and Sources, Geophys. Res. 1998, 103 (D15), 

19045-19057. 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

2008 PM2.5 Plan

F-78 

Small, M., Germani, M.S., Small, A.M., Zoller, W.H., Moyers, J.L. Airborne plume study 

of emissions from the processing of copper ores in southeastern Arizona. 

Environmental Science and Technology 15, 293–299, 1981. 

Tolocka, M.P., Solomon, P.A., Mitchell, W., Norris, G.A., Gemmill, D.B., Wiener, R.W., 

Vanderpool, R.W., Homolya, J.B. and Rice, J. East versus west in the US: Chemical 

characteristics of PM2.5 during the winter of 1999, Aerosol Science and Technology 34, 

88-96. 1999. 

US EPA SPECIATE version 4.0. US Environmental Protection Agency, Research 

Triangle Park, NC, 2007. http://www.epa.gov/ttn/chief/software/speciate/index.html 

Watson, J.G., Chow, J.C. Source characterization of major emission sources in the 

Imperial and Mexicali Valleys along the US/Mexico border. The Science of the Total 

Environment 276, 33–47, 2001. 

Watson, J.G., Chow, J.C., Lowenthal, D.H., Pritchett, L.C., 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., Chow, J.C., Houck, J.E.. PM2.5 chemical source profiles for vehicle 

exhaust, vegetative burning, geological material, and coal burning in northwestern 

Colorado during 1995. Chemosphere 43, 1141–1151, 2001. 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

2008 PM2.5 Plan

F-79 

ATTACHMENT A5 

To the 

San Joaquin Valley Air Pollution Control District 

State Implementation Plan PM2.5 Modeling Protocol 

Receptor Modeling Analysis Layout
San Joaquin Valley Unified Air Pollution Control District February 14, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

2008 PM2.5 Plan

F-80 

ATTACHMENT A6 

To the 

San Joaquin Valley Air Pollution Control District 

State Implementation Plan PM2.5 Modeling Protocol 

Regional Air Quality Modeling Methodology 

Referred to in the Protocol as:

A6 Regional Air Quality Modeling Methodology 

Model selection, emissions and meteorology for PM2.5 annual modeling 

The Selection of Air Quality Models: As stipulated in the EPA Modeling Guidance, a 

grid-based photochemical model is necessary to perform the modeled attainment test 

for PM2.5 (EPA, 2007). Such models offer the best available representation of important 

atmospheric processes and are an essential tool in analyzing the impacts of proposed 

emissions controls on pollutant concentrations. The EPA recommends guidelines for 

choosing a model for use in the attainment test. For example, the model source code 

should be free or low cost, modeling elements should have undergone rigorous 

scientific peer-review, and it should have been shown to perform well in the past for 

similar applications. 

The Community Multiscale Air Quality Modeling System (CMAQ) has been selected for 

use in the PM2.5 modeled attainment demonstration for the San Joaquin Valley Air 

Pollution Control District. CMAQ is a state-of-the-science “one-atmosphere” system 

that treats major atmospheric and land processes (e.g., advection, diffusion, gas phase 

chemistry, gas-particle mass transfer, nucleation, coagulation, wet and dry deposition, 

aqueous phase chemistry, etc.) and a range of species (e.g., anthropogenic and 

biogenic, primary and secondary, gaseous and particulate) in a comprehensive 

framework (EPA, 1999; CMAS, 2007). 

CMAQ has been extensively peer-reviewed, is well-documented, and is regularly 

updated to reflect the latest changes in scientific understanding. CMAQ has been 

applied successfully in a range of environments and on many spatial and temporal 

scales. Given that CMAQ has also been applied successfully to episodic modeling in 

Central California, this modeling system has been selected for use in support of the 

PM2.5 modeled attainment demonstration. 

Choice of Chemical Mechanism: There are a number of gas-phase chemical 

mechanisms readily available for application in CMAQ (e.g., CB-IV, CB-V, SAPRC-99). 

The user has the additional option of whether to couple the chosen gas phase 

mechanism with aerosol and/or aqueous phase chemical processes. In order to 

simulate the complex mixture of PM2.5 species in the San Joaquin Valley, SAPRC99 

coupled with CMAQ aerosol code version 4 and aqueous phase chemistry has been 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

2008 PM2.5 Plan

F-81 

chosen for this application. SAPRC-99, a complete update of SAPRC-90, is a detailed 

mechanism describing the gas-phase reactions of volatile organic compounds (VOCs) 

and oxides of nitrogen (NOx) (Carter, 2000). AE4-AQ, the fourth-generation CMAQ 

aerosol code with aqueous phase chemistry, when coupled with a gas phase 

mechanism, represents such phenomena as gas–aerosol/aqueous phase mass 

transfer, chemical transformation of particulate species and their gas phase precursors, 

and the evolution of the aerosol size distribution. 

Model Inputs and Setup: 

Domain Structure: The modeling domain covers the Central Valley and its surroundings 

with nearly 60,000 12x12 km2 grid cells. The domain extends from the Pacific Ocean in 

the west to the Mojave Desert and western Nevada in the east and runs from the 

northern Sacramento Valley to the Tehachapi Mountains in the south. The vertical 

structure is composed of 15 layers of varying thickness up to 100 mb. The finest 

resolution belongs to those layers closest to the surface and is determined largely by 

the vertical structure of the meteorological inputs. The surface layer is approximately 30 

meters thick. 

Initial and Boundary Conditions: Boundary conditions were taken from the global 

chemical transport Model for Ozone And Related chemical Tracers (MOZART). Model 

boundary conditions for major species were extracted for the Central California 

modeling domain from MOZART results representative of the year 2000. In addition to 

VOCs and inorganic gases, boundary conditions were extracted for ammonium, nitrate, 

sulfate, and organic and elemental carbon. Initial conditions were estimated as an 

average of the extracted boundary conditions for each species. The impact of initial 

conditions will be minimized by 8-day spin up periods preceding the simulation of each 

month of the year 2000. 

Emissions: A spatially, temporally, and chemically resolved emissions inventory of 

combined area, mobile, and point sources was generated using the California 

Emissions Forecasting System (CEFS) version 1.06 with offline adjustments. The 

inventory includes emissions estimates for gaseous and particulate species of 

anthropogenic and biogenic origin. Gridded hourly emissions were developed for the 

CMAQ modeling domain for December 1999 through January 2001. 

Meteorological Inputs: The meteorological input fields to CMAQ were generated with the 

Meteorology-Chemistry Interface Processor (MCIP) version 3.0. MCIP serves as a link 

between meteorological models like MM5 or WRF with CMAQ and generates modelready meteorological inputs like the wind and temperature fields necessary to drive the 

transport and chemistry calculations in CMAQ. Inputs to MCIP were generated using 

the mesoscale meteorological model (MM5) (Grell et al., 1994). PBL and radiation 

characteristics were calculated in MCIP, and the Models-3 dry deposition routine (Pleim 

- with chlorine and mercury species) was chosen to represent dry deposition. 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

2008 PM2.5 Plan

F-82 

Model Years: CMAQ will be run from December 1999 – January 2001 to provide the 

basis for the model performance evaluation. Two or more subsequent years will also be 

simulated. It is necessary to execute simulations for a model reference year and a 

future year to perform the recommended modeled attainment demonstration. These 

attainment runs will use meteorological inputs for 2000 and emissions for the model 

reference year (2005) and future year (e.g., 2014). 

Model Performance Evaluation: To assure that the modeling system (emissions, 

meteorology, and air quality) is a satisfactory representation of the period modeled, the 

estimated emissions, meteorology, and air quality of the base-case simulation need to 

be compared with observations. Satisfactory performance of the model in simulating 

observed conditions and responses is a prerequisite for use of the modeling system to 

evaluate control strategies. 

The rigorous comparison of model estimates with observations is termed model 

performance evaluation. Such a rigorous model performance evaluation for a long-term 

simulation requires the availability of a temporally and spatially extensive dataset. The 

California Regional PM10/ PM2.5 Air Quality Study (CRPAQS) was an extensive and 

intensive measurement campaign designed to characterize the important chemical and 

physical processes involved in the formation and evolution of particulate matter in 

Central California (Chow et al. 2006). The CRPAQS measurement campaign extended 

from December 1999 through January 2001 and provided a wealth of data from diverse 

areas for model evaluation. 

Following the quality assurance testing and technical review of the emissions and 

meteorological models, air quality model evaluation will be performed using CRPAQS 

data. Typical statistics for use in quantifying the differences between modeled and 

observed concentrations include metrics like Normalized Mean Error (NME) and 

Normalized Mean Bias (NMB) (Boylan and Russell, 2006): 

= ∑ − (∑ ) MNE Cm Co Co

= ∑( − ) (∑ ) MNB Cm Co Co

where Cm refers to modeled concentrations, Co refers to observed concentrations, and 

the summations are over all model-observation pairs of a given species at stations/timeperiods of interest. Additional statistical metrics typically used for model evaluation are 

further documented in the Protocol. 

There are hourly and daily concentration data for a range of gaseous and particulate 

species at numerous sites available for comparison with CMAQ modeled 

concentrations. These data will be used to assess model performance temporally and 

spatially, with a focus on monthly average performance, as recommended for long-term 

model simulations. Appropriate goals for model performance will be based on the EPA 

Modeling Guidance and recommendations in the scientific literature on appropriate 

measures of model performance for long term PM simulations (EPA, 2007; Boylan and 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 

2008 PM2.5 Plan

F-83 

Russell, 2006). In addition to calculating monthly error statistics for a range of species 

(e.g., total PM2.5 mass and its components such as sulfate, nitrate, ammonium, EC, and 

OC) at various sites in the modeling domain, time series plots will be reviewed 

qualitatively to determine how well the model performs spatially and on a shorter time 

scales. 

References: 

 

Boylan, J.W. and A.G. Russell (2006) PM and light extinction model performance 

metrics, goals, and criteria for three-dimensional air quality models. Atmospheric 

Environment, 40:4946-4959. doi:10.1016/j.atmosenv.2005.09.087 

Carter, W. (2000) Documentation of the SAPRC-99 chemical mechanism for VOC 

reactivity assessment. Final Report to California Air Resources Board. Contract 

92-329 and Contract 95-308, 

http://pah.cert.ucr.edu/ftp/pub/carter/pubs/s99txt.pdf 

Chow, J.C., Chen, L.W.A., Watson, J.G., Lowenthal, D.G., Magliano, K.A., Turkiewicz, 

K., and D.E. Lehrman (2006) PM2.5 chemical composition and spatiotemporal 

variability during the California Regional PM10/PM2.5 Air Quality Study 

(CRPAQS). J. Geophys. Res., 111, D10S04, doi:10.1029/2005JD006457. 

Community Modeling and Analysis System (2007) CMAQ v4.6 Operational Guidance 

 Document. http://www.cmaq-model.org/op_guidance_4.6/manual.pdf

Grell, A.G., J. Dudhia, and D.R. Stauffer (1994) A description of the fifth-generation 

Penn State/NCAR mesoscale model (MM5). NCAR Technical Note NCAR/TN 

398+STR. 

U.S. Environmental Protection Agency (1999) Science Algorithms of the EPA Models-3 

 Community Multiscale Air Quality (CMAQ) Modeling System. EPA/600/R-

 99/030. http://www.epa.gov/asmdnerl/CMAQ/CMAQscienceDoc.html

U.S. Environmental Protection Agency (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/B-07-002. 
San Joaquin Valley Unified Air Pollution Control District April 30, 2008

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A B C DE F G H I J K L M N

Draft Fresno analysis provided as template for receptor modeling methodology documentation.

Methodology uses PM10 receptor analysis, modified to adjust species to information available for PM2.5.

Receptor model analysis method has been reviewed for technical modifications appropriate for PM2.5, see Revision worksheet.

Fresno - Annual 

2000-2001 species mass = 

21.5

2005 Design value = 17.2

General Note Geologic and Construction Tire and Brake Wear Vegetative Burning Ammonium

Nitrate

 including associated water 

Ammonium

Sulfate

Marine Unassigned

Line1 Source Contribution from Analysis From CMB monthly analysis Feb 2000 to Dec 2000, 

adding January 2001 episode for chemistry 

equivalent to annual design value

From CMB From CMB From CMB minus estimated Organic 

Carbon from other sources

From CMB From CMB From CMB, if present Unaccounted mass 

from CMB, if any.

LINE 1 22.45 0.95 0.35 4.73 7.48 2.20 0.00 0.581

Line2 Natural and Transport Contribution, see 

"Background" sheet

Portion not included in rollback analysis, removed 

prior to rollback as not subject to local control, 

added back to projected future concentrations

see background sheet for 

numerical estimate and episode 

adjustment. Removed prior to 

rollback as not subject to local 

control, added back to projected 

future concentrations

0, no natural 

background, transport 

estimated at 0

see background sheet for numerical 

estimate and episode adjustment. 

Removed prior to rollback as not 

subject to local control, added back to 

projected future concentrations. 

Includes wildfires and biogenic.

=20% + 10%

see background sheet for numerical 

estimate and episode adjustment. 

Removed prior to rollback as not 

subject to local control, added back to 

projected future concentrations

see background sheet for 

numerical estimate and episode 

adjustment. Removed prior to 

rollback as not subject to local 

control, added back to 

projected future concentrations

100% because marine 

salts are a natural 

emission

0, background estimate 

at maximum, no 

additional background 

estimate for 

unexplained mass

LINE 2 3.92 0.1 0.0 1.4 1.0 1.0

Line 3 Net for Rollback Net for Rollback, default percentages adjustable for 

episode characteristics, applicable to all columns 

except as indicated.

Net for non-linear rollback, default 

percentages adjustable for episode 

characteristics

Removed entirely from 

rollback, added back to 

result

LINE 3 18.53 0.9 0.4 3.3 6.5 1.2 0.0 0.6

Line4 Local Contribution PM2.5-PM10 Area of Influence Source contribution from smallest area of influence, 

representative of large particle primary source area, 

includes all PM size emissions in the area - Rolled 

back against local area of influence emission 

estimates

50%PM2.5

 of net

50%PM2.5

 of net

50%PM2.5

 of net

50%PM2.5 of net, non-linear rollback 50%PM2.5

 of net

50%PM2.5

 of net

LINE 4 7.03 0.234 0.169 0.892 2.662 0.514 0.119

Line5 Local Contribution Area of Influence of PM2.5 Rolled back against local PM2.5 area of influence 

emission estimates - episode specific adjustments 

based on meteorology and episode duration

30%PM2.5 30%PM2.5 30%PM2.5 30%PM2.5 non-linear rollback 30%PM2.5 30%PM2.5

LINE 5 3.84 0.179 0.071 0.815 1.320 0.254 0.076

Line6 Sub regional Contribution Rolled back against specified County(ies) emission 

estimates - episode specific adjustments based on 

meteorology and episode duration

15%PM2.5 15%PM2.5 15%PM2.5 15%PM2.5 non-linear rollback 15%PM2.5 15%PM2.5

LINE 6 3.56 0.214 0.048 0.954 1.063 0.203 0.193

Line7 Regional Contribution Rolled back against Valleywide emission estimates - 

episode specific adjustments based on meteorology 

and episode duration

5%PM2.5 5%PM2.5 5%PM2.5 5%PM2.5 non-linear rollback 5%PM2.5 5%PM2.5

LINE 7 4.09 0.226 0.062 0.646 1.435 0.229 0.194

Associated Emissions Categories Based upon appropriate seasonal or annual 

inventory

PM10 paved roads+

PM10 unpaved roads+

PM10 off road mobile+

PM10 farm operations+

PM10 construction+ 

PM10 windblown

Tire and brake wear as 

predicted by EMFAC2002

PM10 & CO residential burning

PM10 & CO waste burning and 

disposal

PM10 cooking

PM10 & CO fires

CO presumed to add minimal mass

Total E.I. NOx (+ bacterial soil NOx 

estimate removed as natural 

background) 

Total SOx None, natural emission 

from the ocean, bay 

and delta waters

Total PM2.5

2 5 6 7 8 9 10 14 15 N/A 16

2000 Emissions Inventory (area of influence emissions inventory, each on a separate line for automated calculations) INDEX

PM10 L1= Area 3 2.110 1.300 0.102 1.544 2.136 3.410 3-Fr

L2= Areas 3,4 5.388 1.900 0.143 2.780 6.506 7.288 Sum 3,4

Sr= Fresno, Madera 12.880 2.737 0.195 5.325 15.240 36.970 Fresno + Madera

R= SJV 40.682 12.272 0.746 22.401 30.963 111.525 SJV Total

NOx L1= Area 3 62.380 3-Fr

L2= Areas 3,4 103.071 Sum 3,4

Sr= Fresno, Madera 166.006 Fresno + Madera

R= SJV 672.579 SJV Total

ROG L1= Area 3 20.834 19.683 3-Fr

L2= Areas 3,4 30.979 39.591 Sum 3,4

Sr= Fresno, Madera 54.505 63.131 Fresno + Madera

R= SJV 198.230 235.186 SJV Total

SOx L1= Area 3 4.053 3-Fr

L2= Areas 3,4 6.670 Sum 3,4

Sr= Fresno, Madera 10.689 Fresno + Madera

R= SJV 36.064 SJV Total

2014 Emissions Inventory PM2.5 SIP EI V1.0

PM10 without new controls L1= Area 3 1.882 0.799 0.150 1.291 1.665 2.682 3-Fr

L2= Areas 3,4 4.885 1.225 0.219 2.432 5.566 6.109 Sum 3,4

Sr= Fresno, Madera 11.672 1.771 0.300 4.637 13.608 32.614 Fresno + Madera

R= SJV 37.776 7.748 1.115 18.373 26.911 96.803 SJV Total

PM10 with new controls L1= Area 3 1.882 0.799 0.150 1.291 1.665 2.682 3-Fr

L2= Areas 3,4 4.885 1.225 0.219 2.432 5.566 6.109 Sum 3,4

Sr= Fresno, Madera 11.672 1.771 0.300 4.637 13.608 32.614 Fresno + Madera

R= SJV 37.776 7.748 1.115 18.373 26.911 96.803 SJV Total

NOx without new controls L1= Area 3 38.170 3-Fr

L2= Areas 3,4 67.461 Sum 3,4

Sr= Fresno, Madera 104.709 Fresno + Madera

R= SJV 427.211 SJV Total

NOx with new controls L1= Area 3 38.170 3-Fr

L2= Areas 3,4 67.461 Sum 3,4

Sr= Fresno, Madera 104.709 Fresno + Madera

R= SJV 427.211 SJV Total

ROG without new controls L1= Area 3 10.391 19.845 3-Fr

L2= Areas 3,4 15.979 40.943 Sum 3,4

Sr= Fresno, Madera 29.771 66.916 Fresno + Madera

R= SJV 104.437 236.701 SJV Total

ROG with new controls L1= Area 3 10.391 19.845 3-Fr

L2= Areas 3,4 15.979 40.943 Sum 3,4

Sr= Fresno, Madera 29.771 66.916 Fresno + Madera

R= SJV 104.437 236.701 SJV Total

SOx without new controls L1= Area 3 4.396 3-Fr

L2= Areas 3,4 7.201 Sum 3,4

Sr= Fresno, Madera 11.419 Fresno + Madera

R= SJV 26.491 SJV Total

SOx with new controls L1= Area 3 4.396 3-Fr

L2= Areas 3,4 7.201 Sum 3,4

Sr= Fresno, Madera 11.419 Fresno + Madera

R= SJV 26.491 SJV Total

2014 Rollback Projection IMS95

Local Contribution PM2.5-PM10 Area of Influence =(2014 L1/2000 L1) * LINE 4 0.2 1.0 0.1 0.2 0.4 0.1 0.7 2.0 0.6 0.1

Local Contribution Area of Influence of PM2.5 =(2014 L2/2000 L2) * LINE 5 0.2 0.4 0.1 0.1 0.2 0.0 0.7 1.0 0.3 0.1

Sub regional Contribution =(2014 Sr1/2000 Sr2) * LINE 6 0.2 0.3 0.0 0.1 0.2 0.0 0.9 0.8 0.2 0.2

Regional Contribution =(2014 R/2000 R) * LINE 7 0.2 0.5 0.1 0.1 0.3 0.1 0.6 1.1 0.2 0.2

+ Natural Background contribution = LINE 2 0.1 0.0 0.0 0.4 1.4 1.0 1.0 0.0 0.0

2014 projected Annual Result 18.56 0.9 2.2 0.3 0.5 1.6 0.2 4.2 5.9 2.2 0.0 0.5

2014 Rollback Projection with additional controls IMS95

Local Contribution PM2.5-PM10 Area of Influence =(2014 L1/2000 L1) * LINE 4 0.2 1.0 0.1 0.2 0.4 0.1 0.7 2.0 0.6 0.1

Local Contribution Area of Influence of PM2.5 =(2014 L2/2000 L2) * LINE 5 0.2 0.4 0.1 0.1 0.2 0.0 0.7 1.0 0.3 0.1

Sub regional Contribution =(2014 Sr1/2000 Sr2) * LINE 6 0.2 0.3 0.0 0.1 0.2 0.0 0.9 0.8 0.2 0.2

Regional Contribution =(2014 R/2000 R) * LINE 7 0.2 0.5 0.1 0.1 0.3 0.1 0.6 1.1 0.2 0.2

+ Natural Background contribution = LINE 2 0.1 0.0 0.0 0.4 1.4 1.0 1.0 0.0 0.0

2014 projected Annual Result 18.56 0.9 2.2 0.3 0.5 1.6 0.2 4.2 5.9 2.2 0.0 0.5

Linear

2014 projected Annual Result 17.75 linear nitrate projection 1.6

Modeling comparisons 18.56 IMS95 nitrate modeling 0.9

Current 2005 Design value = 17.2 18.47 CMAQ nitrate modeling 0.7

18.26 Average of all three 0.9

18.51 Average of CMAQ and IMS95 4.1

CMAQ

1.9

1.0

0.8

1.1

4.8

end

Mobile Exhaust

From CMB

4.14

0, no natural background, 

transport estimated at 0

0.0

4.1

50%PM2.5

 of net

1.9

30%PM2.5

0.82

15%PM2.5

Organic Carbon

Estimated portion of mass included in 

Vegetative Burning =30%

2.03

see background sheet for numerical 

estimate and episode adjustment. 

Removed prior to rollback as not 

subject to local control, added back 

to projected future concentrations. 

Includes biogenic emissions.

 = 20%

0.4

1.6

50%PM2.5

 of net

0.6

30%PM2.5

0.31

15%PM2.5

What Row to use for Lookup Function ------------------------------------------->

Total ROG minus motor vehicle, OC 

may also include a small portion of 

otherwise unassigned elemental 

carbon

PM10 & CO Area, Stationary 

CO presumed to add minimal mass

5%PM2.5

0.30

0.42

0.59

5%PM2.5

0.88

PM10, ROG & CO onroad 

mobile+

PM10, ROG & CO 860 

offroad equipment

PM10, ROG & CO 870 

farm equipment

CO presumed to add 

minimal mass

84

Appendix F: SJV PM2.5 SIP Modeling Protocol

2008 PM2.5 Plan
20b Appendix F Attachment 5.xls

Revisions to receptor modeling rollback methodDesign value and species for Fresno and Kern provided from both 2000 PM10/PM2.5 species and 2005 PM2.5 species2005 PM species processed with CMB receptor analysisAdjustments: 0.6 microgram for unassigned subtracted from organic carbon estimate2000 Design value and species determined from evaluation of PM10 receptor analysis and review of PM2.5 dataFrom this process, an approximate match was made to calculate species for the observed concentration of PM2.5 Used Species data for PM2.5 for columns C, J, K and M MV CMB adjustment PM10 to PM2.5 by EI ratio 90% MV Kings adjusted to 55% of Tulare T&BW CMB adjustment PM10 to PM2.5 by EI ratio 50% T&BW CMB Kings adjusted to 55% of Tulare Organic carbon- vegetative burning - adjustment ratio PM10 to PM2.5 75% Kern, Kings, Tulare Organic carbon- vegetative burning - adjustment ratio PM10 to PM2.5 ratio 90% Fresno -representing 2000 OC/EC Fresno uses 2001 PM2.5 nitrates from PM2.5 species data, 2000 not available Kern uses 2002 speciation data for PM2.5 nitrates, 200 and 2001 not available Tulare and Kings both use the Tulare 2002 speciation data for PM2.5 nitrates, 2000 and 2001 not availableMethodology Improvements and correctionsRevise background estimates for geologic , nitrate and sulfate to dynamic 10% rather than set value used for PM10Correct background percent for Organic Carbon to %20 Revision of TOG to ROG for technical basis linking particle formation to reactive species, columns D-E and G-HRevised primary secondary ratio to 85% 15% based on CRPAQS results, columns D-E and G-HRevised intrabasin transport calculations based on mass relationshipUpdated emissions inventory used for base and future yearsAdded model comparison rows and headings Added RRF SMAT calculation fields on appropriate worksheets

Revision