2008 PM2.5 Plan 

## Appendix F

SJV PM2.5 SIP Modeling Protocol2008 PM2.5 Plan II This page intentionally blank.2008 PM2.5 Plan I 

### San Joaquin Valley Air Po llution 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 Prot ocol 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-682008 PM2.5 Plan II Attachments: A1 SJV PM Conceptual Model 1998 (64 pages) – Conceptual description of PM10 and PM2.5 epis ode dynamics. Performed for the CRPAQS study as part of an in tegrated monitoring study (IMS95) designed to evaluate conceptual issues. Based on 1995 data collected in the San Joaquin Valley. A2 Modeling Domain –Domai n map annotated with model basic operating parameters A3 Modeling Boundary Conditions – Average annual surface boundary conditions A4 PMF procedure (5 pages) – Methodo logy for the Positive Matrix Factorization approach to ident ify key contributing sources A5 Receptor Modeling Analysis Lay out (4 pages) – Documentation of receptor rollback revisions and conv ersion 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 Meteorologic al 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-49San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-1 

### Introduction

The PM2.5 State Implementation Plan (SIP) Modeling Protocol is a document that describes air quality modeling analyses perform ed to demonstrate t hat proposed control strategies are sufficient to achieve comp liance 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 comp liance 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 proc ess in response to unexpected findings or technical issues. The modelin g protocol and SIP submittal must include a description of how the analysis was conducted by providin g 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. Environment al 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 se lection of models and analysis approach. EPA guidance was established to provide a best gener al 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 Prot ocol Review Process

The Protocol is released as a working doc ument open to revision during the preparation of the PM2.5 Plan. The California Air Resour ces 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 releas ed 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 thatSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-2 would modify the approach extensively are reques ted within thirty days of release of the Protocol to accommodate modifications that may be recommended. However, the protocol will remain an open working documen t 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 unt il modeling is finalized to prepare the receive and file version of the plan.

SJVAPCD PM2.5 SIP Pr otocol Contents

The SJVAPCD PM2.5 SIP Mode ling Protocol describes the selection of general approach, methods of analysis a nd identification of data perti nent to support analysis. The protocol proposes comprehensive analysis combining the results of evaluations, correlated and reconciled to establish by pr eponderance of results (w eight 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 in put 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 standar d. 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 Pr otocol 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 Qualit y Study (CRPAQS). ARB and the District evaluated the meteorological factors and in fluences related to PM10 and PM2.5 episodes. This analysis is essential to identify contributing sources and factors. Supplemental analysis for additional epi sodes for PM2.5 may be required to evaluate compliance with the revised da ily standard of 35 mi crograms, but the requirements for this plan are to meet t he previous 65 micrograms per cubic meter standard. All sites in the District current ly comply with the 65 micrograms standard.

• Receptor Modeling Receptor modeling is performed by the District to establish a link between observed particulates and c ontributing emission sources using the combined outputs and techniques of several models. Receptor analysis of observedSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-3 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 s peciated rollback model developed by the District to calculate the effect predict ed emission trends and adopted and proposed control measure reductions. The District developed receptor speciated rollback as one of its earliest improvements to st andard rollback modeling, with documented assumptions for the isolation of natural emissions and transport of emissions that would not be affected by the c ontrol 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 modeli ng. 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 assump tions to address fine particle mechanisms and with methodology revisions to reduce uncertainties for the PM2.5 annual standard. Further revision may occur to inco rporate new findings of current regional modeling of secondary particle formation.

This receptor modeling met hod works well for analysis of di rectly emitted particles, but is less certain in predicting the effe ct 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 region al 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 contribut ions. This is adjusted to reflect the nonlinear chemistry associated with nitrate formation as determi ned by the regional model. Sulfate formation approaches linear rela tionship and is not adjusted for nonlinear chemistry in the receptor modeling. The expected result of linear projection is an overprediction of future concentra tion. The method anticipates uncertainty in the inputs and uses the over-prediction as a safety ma rgin for attainment modeling. The PM10 receptor modeling resulted in a sizable ma rgin of safety, projecting attainment several years after actual compliance wa s achieved. Comparing the results of PM2.5 receptor modeling with regional modeling resu lts and other pertinent information is important to develop a wei ght 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 i ndependent analysis of t he reductions needed to achieve attainment. Regional photochemic al modeling has limitations for depicting the dispersion and removal processes that occur in scales finer than the grid resolution but provide equilibrium modeling fo r the most significant contributions to observed mass. Regional modeling uses the known emissions inventory in a bottom-up analysis that calculates the amou nt 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 t hat can be provided. TheSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-4 use of the model results in relationship to the design value provides a reasonable estimate of future PM2.5 ni trate and sulfate mass components.

Regional modeling of secondary particulat es 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 particu lates. 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 asse ssment used the Urban Airshed Model, modified to address aerosol c hemistry (UAM Aero). This used the IMS-95 dataset (an early component of CRPAQ S) to evaluate a monitored event of nitrate particulate formation. Regional modeling was also conducted for the later 2000-2001 CRPAQS data, pr oviding an update to the PM10 receptor modeling projections in 2006. A third round of r egional modeling with the CMAQ model is currently in progress by ARB. If this provides a different regional photochemistry analysis for nitrate formation, the recept or modeling estimates for nitrates will be reviewed.

• Statistical Analysis PM2.5 monitoring is a relative ly new program; therefore the historical record of data does not provi de a sufficient history to evaluate long-term trends and patterns. The District and ARB w ill continue evaluation of observed data using a variety of statistical methods to identify potential key factors. Results of these evaluations will be used to improv e 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 ex amines 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 uncerta inty 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 separat e behaviors and dependent interactions. Although there is a solid foundation of conceptual underst anding, there are many technical issues that remain open questions for continued study. The PM2.5 SIP will utilize all completed analyses available at th is time; however, we expect emerging technical information to enhance our understanding of observed events and subsequently improve our ability to pr edict future PM2.5 concentrations.

Annual meteorology has seasonal com ponents, reflected in EPA guidance recommending at least quarterly evaluati on and analysis. Regional modeling is being performed for an entire year on a daily bas is that can be processed to monthly or quarterly analysis as needed. Receptor modeling, with a speciated rollback approach used for the PM10 SI P with monthly meteorologi cal analyses and source identification combined into an annual com posite, is being updated for the PM2.5San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-5 analysis. Prior analysis determined that fa ll 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 va lues and has developed a methodology to break down the modeling into the co mponent constituents and use the model response in a relative sense to predict t he reductions needed to achieve attainment. The Speciated Modeled Attainment Te st (SMAT) methodol ogy is generally applicable throughout the country but conflicts with certain important considerations critical to San Joaquin Valley Air Basin (S JVAB) assessment. Specific adjustments will be made to address technical issues spec ific to the SJVAB and to provide the most accurate means to reflect th e effects of emission reductions.

• The SMAT methodology separates out trapped water and ammonium, sulfate and nitrate ions. This is necessary in por tions 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 di fferent than for nitrates. The SJVAB is dominated by ammonium ni trate particulates with lo w sulfate particulate concentrations; therefore this process is of minimal effect. ARB will determine appropriate methodology for retained wate r 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 a mmonium sulfate and ammoni um 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 rema ining 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 (fugi tive 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 anal ysis. 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 losse s 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 wei ght of evidence finding for attainment.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-6 Determination of Attainment

The Protocol provides a pr ogram of analysis to demonstrat e attainment by assessing exceedance of the PM2.5 standar ds detected by the monitoring network. The entire District is classified as nonattainment if any monitoring site is classified as nonattainment. Receptor modeling will exam ine all four countie s that are not in compliance with the annual standard. Regi onal modeling will asse ss the entire SJVAB and will allow review of areas without monitors . Reliable conclusions are established by knowledgeable evaluation to resolve any contr adictory indications that arise from the uncertainties and limitations i nherent in the various methods of evaluation. Analysis procedures were selected to establish obj ective and reliable conclusions with the highest confidence that can be established from the best availa ble data. The SIP control plan establishes demonstration of attainment by determining emission reductions requirements, if any, needed to achi eve attainment for all counties in the District nonattainment area.

Attachments to the Protocol

Attachments are provided with the Protocol to identify results of re search efforts that provide analysis of key technical issues or provide supporting documentation for the protocol or the SIP.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-7 

### San Joaquin Valley Air Po llution 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 Un ified 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 accor dance 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 subj ect 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 re view and comment. The ARB, District and Valley transportation planning agencies jointly prepare data analyses, emissions inventories and modeling analyse s to address modeling requirements for the SIP. Modeling is conducted with jointly developed i nput 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 emissi on reduction that must be implemented to achieve compliance with the federal annual and 24-hour PM2.5 Na tional 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 proc ess. As specified in guidance: “Major steps to implement the protocol shoul d be discussed with the appropriate U.S. EPA Regional Office(s) as they are being decid ed. States/Tribes may choose to update the protocol as major decisions are m ade concerning forthcoming analyses.”



### Regulatory Requirements for Protocol Contents

“Guidance on the Use of Model s and Other Analyses for Dem onstrating Attainment of Air Quality Goals for Ozone, PM 2.5 and Regional Haze,” EPA –454/B-07-002, April 2007, provides information on the expected cont ents of the Model/ Analysis Protocol. “The protocol should detail and formaliz e the procedures for conducting all phases of the modeling study, such as describing the background and objectives for the study, creating a schedule and organi zational structure for the study, developing the input data, conducti ng model performance evaluations, interpreting modeling results, describ ing procedures for using the model to demonstrate whether proposed strategies are sufficient to attain the NAAQSSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-8 and/or reasonable progress goals, and produc ing documentation to be submitted for EPA Regional Office review and approval.”

Guidance recommends that the protocol should:

• Identify who will help the local ai r quality agency undertake and evaluate the analyses needed to support a defensible demons tration (i.e., the stakeholders).

The District is in consultation with ARB and EPA and will involve the Study Agency Policy Committee and particulate study CRP AQS Technical Committee due to their expertise. The Study Agency Policy Committee has been involved in guiding particulate research for central Califor nia for more than a decade and includes representatives of several federal agencies, ARB, adjacent air Districts, stakeholders and environmental representatives. Public re lease 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 revi ew or assistance.

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

Meetings with ARB on PM2.5 Plan dev elopment and modelin g 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 fa cilitate comment on the Protocol and/or the Plan to advance consensus on the m odeling approach. District communication pathways include mail, electronic mail, m eetings 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 St udy Agency Policy Committee and the particulate study CRPAQS Technical Commi ttee to provide technical review and recommendations to the District during the SIP development process. The public process will include posti ng of the Protocol on the District website. Comments received in writing or at workshops will be re viewed to determine if modification of the Protocol is warranted. The Distric t will consult with ARB and EPA regarding any suggestions that propose proc edures that are alternatives to recommendations of guidance prior to accepting a suggested modification. The District will review comments received and requests for meeti ngs 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 protoc ol itself will be agreed upon and communicated with stak eholders and the appropriate U.S. EPA Regional Office.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-9 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 suppl emental 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 element s, presenting data discussions first because the analysis of the data guides model selection and application. Because the Valley is a la rge geographic area with emissions that vary extensively by location and season, the Prot ocol proposes a comprehensive analysis by combining, correlating and reconciling results of a series of evaluations to establish by preponderance of results (referred to as wei ght of evidence) that the SIP adequately addresses all nonattainment areas. The Pr otocol proposes a series of evaluation elements that include: meteorological evalua tion of the influence of factors affecting PM2.5 concentrations, statistical analysis of repr esentativeness and comprehensiveness of episodes evaluated, receptor modeling to establish the contributions of major source types and predi cted control effects, and regional modeling to enhance predictions of secondary particula tes in the receptor modeling and to provide a separate assessment of reduc tions 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 a ttainment of the feder al 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 ar e provided. The conceptual model for PM2.5 source contributions is provided by a referenced attachment. The conceptual model was developed from extensive research effort s conducted for the specific purpose of understanding the dynamics of PM 10 and PM2.5 in the Valley.



### Revision of PM10 Receptor Methods to address PM2.5

The PM2.5 receptor modeling analysis requires a minimal se t of revisions from the methods used for the PM10 SI P. 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 alsoSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-10 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 PM 10 SIP will serve as the foundation for PM2.5 rec eptor modeling analysis wit h the following updates:

• The annual rollback analysis for PM10 events will be updated with PM2.5 speciation, using linear and aerosol modeling conduct ed with IMS95 and CRPAQS data for the PM10 SIP and 2006 PM10 update. The analysi s will investigat e the emissions reductions predicted for future years to quant ify improvement. Ad ditional reductions for new control measures will be evaluated.

• Additional regional aerosol modeling, to adjust rollback assumptions and provide supporting weight of evidenc e evaluation, is being conducted by ARB and is expected to provide results for evaluation a fter the first workshop. This modeling is being conducted with the CMAQ model.

• Newer years of data have been evaluat ed 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 spec iation profiles identified in consultation with the District.

• CART analysis performed fo r the PM10 SIP will be replaced by PMF evaluation for weight of evidence compar ison. This analysis is being conducted by ARB.

CMB speciation for PM2.5 for Annual Rollback Modeling

The speciation data and CMB modeling result s for PM10 contain a combination of coarse and fine particles. The rela tionship between PM10 and PM2.5 is not proportional; therefor e special evaluation is requi red to develop an appropriate speciation set for PM2.5 rollback analysis fr om the CMB modeling per formed 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 determi ned by PM10 CMB modeling.

The methodology includes the follo wing components and considerations:

• Fresno, Kern, Kings and Tulare count ies have 2005 PM2.5 design values that exceed the 15 microgram annual standard. Analysis is r equired for each of these counties to determine emission reductions r equired to achieve compliance. PM2.5 speciation measurements are collected at Ba kersfield, Fresno, Modesto and Visalia. Fresno speciation data for PM2.5 covers t he 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 PM2.5San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-11 speciation data is collected for Kings County; however, prior PM10 speciation analysis and CMB modeling det ermined that Tulare and Kings County conditions are quite similar, allowing use of Tulare Count y speciation data with minor adjustments. The speciation data provides total mass fo r annual evaluation, nitrates, sulfates, geologic material and metals

• Natural and regional background for PM2. 5 cannot be directly measured and is easily identifiable or separabl e 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 affe cted by the District’s control efforts. Since rollback provides a linear projection of a ffect for reducing emi ssions, 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 par ticulates have been set at 10% of the observed concentration instead of a fix ed concentration. Dynamic background contributions of 10% were too high for use with PM10 due to high deposition rate and short average distance of travel; howev er, 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 em issions inventory we are able to establish that slightly over 90% of the mobile exhaust emissions ar e PM2.5 and approximatel y 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 va lue 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 fo r the year 2000 with additional data from the first quarter of 2001, to provide mass estimates fo r motor vehicles and tire and brake wear. The modeled contribution fo r motor vehicles and tire and brake wear has been adjusted by factors determin ed 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 car bon and vegetative burning is composed organic and elemental carbon similar to th e motor vehicle emissions but contains both large and small particles. The ma ss 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 earlie st 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.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-12 • Visalia uses the same elements from t he 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 revi sed or adjusted in other wa ys 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 vehicl e 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 emission s that are large parti cles. Motor vehicle contributions were similar to Visalia in H anford but much less in Corcoran. Corcoran is the site that sets the design value for Kings County. Prio r CMB modeling of the same event at both Visalia and Corcoran establishes an approximate ratio of the motor vehicle and tire and brake wear contri butions 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 sm aller 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 assign ing all unaccounted mass to OC overestimates that profile.

Other Revisions to the PM10 methodol ogy for PM2.5 for Annual Receptor Rollback Modeling

• Cooking emissions, previously grouped wi th vegetative burning, are included in organic carbon associated with industr ial and commercial emissions.

• The speciated rollback analysis develop ed by the District provides spatial projection of the area of influence of contributions. The PM10 methodology utilized different assumptions for contri butions dominated by large particles and contributions dominated by fine particles PM2.5 and smaller. The PM10 travel distance assumptions for geologic and c onstruction, tire and brake wear and unassigned mass utilized large particle assumptions which are modified for the PM2.5 analysis to be consistent with tr avel distance assumptions for other contributions dominated by fine parti cles. Methodology for the defined apportionment of mass to local and r egional intrabasin transport has been adjusted from a fixed ratio to a mass we ighted calculation to improve accuracy and correlation to the conceptual model.

• For categories that are comprised of di rectly emitted particulates and secondary aerosol (SOA) formation of organic ca rbon 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 ca rbon. The rollback analysis has been revised for this updated information. The emissions linka ge to SOA is ROG instead of TOG.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-13 • Nitrate mass will be reevaluated to determine if any adjustments to linear projection are required. The 2003 anal ysis included nonlin earity adjustments obtained from analysis of UAM-AERO regi onal modeling of CRPAQS IMS-95 data. The variation of nitr ate chemistry is r epresented by the monthly variations of CMB modeling for the annual, which inher ently 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 speciat ed rollback modeling. The linear projection of annualized rollback already incl udes assessment of the variation of nitrate formation; therefore further adjustment with modeling response may constitute a redundant adjustment. The com parison of linear model estimation to the projections with regiona l modeling penalties for nonli near nitrate response will be preserved until technical discussi ons on this matter are complete.

• Receptor modeling will use the alter nate linear estimation assumptions for trapped water. Water bonded to ammoni um nitrate is approximated as equivalent to twelve percent of the ma ss and water bonded to ammonium sulfate is approximated as 26 percent of that ma ss. This method allows the water to remain incorporated in the proportional rollback because the trapped water is assumed to be proportional to the amm onium 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 obtaini ng reliable pollutant data to predict PM2.5 formation, concentration and res ponse to change. Meteorological data is used with pollutant data to improve day -to-day meteorological and air quality forecasting, characterize the nature and ex tent 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 decisi ons as part of the continuing air quality management process of SIP planning

Meteorology and climate play im portant roles in determining the levels of air pollution in the Valley. Some meteorological patterns cause higher levels of air pollution by preventing the dispersi on of pollutants. Pertinent met eorological parameters include wind speed and direction, ambient atmospheric temperature and inversion layers (i.e., layers where the air temperature increases with height), and precipit ation. Substantial temporal and spatial variations in PM2.5 speciation profiles o ccur in the District in part because the types and quantities of source emissions are different in each major city and county, but meteorol ogical factors also affect PM2.5 levels, affecting emissions, secondary particle formation and dispersion. Meteorology should not be considered asSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-14 merely an input in the modeling, but rat her as a key parameter to understanding and predicting PM2.5 formati on and concentrations.

The Protocol includes evaluati on elements distinct from data selecti on for modeling. The evaluation and interpretation of meteorologi cal influences is essential to reliable control strategy and trend evaluations.

• Meteorological Evaluation The District performed meteorol ogical 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 re lated to PM10 and PM2.5 episodes. The analysis also establishes the representat iveness of transport and formation of PM observed in historical episodes. It is essential to evaluate episodes with this perspective to identify cont ributing sources and factors.

Meteorological evaluation also assesse s 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 com pared to historical monitored days to evaluate the representativeness and compr ehensiveness of monitored exceedances by examining the relative severity of t he associated factors. The control program must consider conditions identified by meteorological analysis that were not captured by monitoring. The design of t he control plan is sufficient to provide attainment if monitoring capt ures 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 da ta and other data identified as related to high concentrations of particulates. T he process will evaluate the factors related to known and observed episodes and ident ify other combinations, patterns and factors not captured by moni toring that are potentially capable of causing PM2.5 episodes. Results of this process provi de evaluation of: sufficiency of available data to establish reliable conclusions and reas onableness of results of other methods. Results also provide verification of t he completeness of the meteorological assessment to establish sufficiency of the c ontrol 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 sampli ng 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 procedur es, such as three year averaging to reduce the influence of meteorological vari ation. The District implemented several major programs that reduced emissions dur ing this time period and there was a noted national and international trend of declining PM2.5 and PM10 values. The limited record of monitoring dat a makes it difficult to isolate changes resulting from controls from regional or national trends without supplem ental evaluation throughSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-15 receptor or photochemical mode ling. The ARB is also ev aluating the positive matrix factorization (PMF) model to determine if it can provide useful analysis of contributing sources. The dat a available is believed suffi cient for the PMF approach.

Justification for Mete orological Evaluations

Justification for including t he meteorological evaluation elements as well as using appropriate meteorological data for mode ling derives from prior analysis of meteorological influences on PM10 concentra tions conducted for development of daily forecasting of air quality and prior SIP develop ment efforts. The chemical mechanisms involved in secondary particle formation and in version patterns affecting retention and build up of PM2.5 concentrations must be effectively interpreted to support trend evaluation, atmospheric modeling, receptor modeling and consequen t control strategy evaluations and decisions.

Meteorological data are used to assess the pot ential for air pollution to accumulate in certain locations. Weather fact ors that may restri ct horizontal and vertical air movement of air masses are important factors in air qua lity. Vertical movement of air disperses pollutants vertically while horizontal move ment spreads the pollu tants over a wider geographic area.

Extensive seasonal variation has been establis hed for sources contributing to PM2.5 concentrations and atmospheric processes contributing to particle formation and retention. Analysis of filt ers reveals that different mete orological conditions and sources contribute to increased PM2.5 formation in the fall and winter. During the October to January period the PM2.5 concentrations under go 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 cond itions occurring in December and January favor formation of ammonium nitrate.

Inversion Layers

Inversion layers exist when the air temper ature increases with elevation above the ground. The strength, altit ude of, and duration of inversio ns 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 di spersion 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 inversio ns; radiation inversions and subsidence inversions.

Nocturnal cooling of an air layer near the Valle y surface causes radiation inversions. It extends upward several hundred feet and occu rs during the evening and early morning hours. During a radiation inversion, little ve rtical mixing occurs near the surface. The inversion dissipates when solar radiation wa rms the ground, which in turn heats theSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-16 lower layers of the atmosphere. This heat ing 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 Va lley due to the lack of marine air intrusion.

Subsidence inversions are caused by downwar d vertical motion in the atmosphere. This is common when the semi-permanent Paci fic 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. Dayt ime 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 Ai r Basin from weather fronts, which could otherwise bring cleansing ra ins and help reduce PM2.5 concentrations.

Horizontal Mixing and Dispersion

In addition to vertical mixing, horizontal mixi ng, 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, th e 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, th e northwest to southeas t 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 wi nter, average wind speeds range between 5-9 mph; however, very light winds occu r from 20 to 40 percent of the time.

PM2.5 originating from or going to other ai r basins, referred to as pollutant transport, has not been definitively quantifi ed. PM2.5 readings in t he SJVAB are most severe during the fall and winter periods when wi nd speed and direction are not conducive to interregional transport. Monitoring and specia tion 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 bas in or contributing transport of PM2.5 to other air basins. Transport of some PM 2.5 precursors has been studied as part of ozone transport evaluation, identifying transpor t 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 highes t ozone readings are more likely to occur. This transport includes precursors of ozone and PM2.5; howe ver, the amount of PM2.5 that could be g enerated in the SJVAB or other ai r basins from such transport has not been quantified. Polluti on from areas outside of the Valley may or may not contribute to high PM2.5 levels within the Valley.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-17 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 mounta in ranges block dispersion, minimizing wind flows into and out of the basin. Meteorol ogical conditions contributing to poor air quality also includes transport of pollutant s 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 populati on of over 3 million people, with major urban cent ers in Bakersfield, Fres no, Modesto and Stockton.

The SJVAB consists of a continuous inter-m ountain valley approximat ely 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 fee t. The Tehachapi Mountains form the southern boundary of the Valley. This mount ain range includes peaks over 6,000 feet, and contains mountain passes to the Los A ngeles 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 c an disperse pollution by vertical mixing and by transporting it to other locations. Wi nd speed and direction data indicate that during the summer the light and variabl e 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-southeas terly direction through the Valley, through the Tehachapi Pass, and into the Southeast De sert Air Basin portion of Kern County. During the winter, wind speed and direction data indicate that wind occasionally varies from the south-southeasterly di rection, 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 wi nds of less than 10 mph. Low wind speeds, combined with low lying inversion layers in the winter, cr eate a climate conducive to the formation of high PM2.5 concentrations.

Temperature

The San Joaquin Valley floor is characteriz ed 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 su mmer average 95°F in the Valley. The Valley also experiences mild winters; the winter average da ily low temperature is 45°F. Over the last 30 years, the Valley av eraged 106 days per year 90°F or hotter, and 40 days a yearSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-18 100°F or hotter. The daily summer temperatur e variation can exceed 30°F. The Valley has an "inland Mediterranean" climate aver aging over 260 sunny days per year.

Precipitation

Precipitation in the Air Basi n is confined primarily to the winter months with some occurring in late fall and early spring. Near ly 90% of the annual pr ecipitation in the Valley falls between the months of November through April. Average annual rainfall for the entire Valley is about 10 inches on the Va lley floor. There are north-south and eastwest regional differences, with higher rainfa ll occurring in the northern and eastern parts of the Valley. Historical evaluations hav e correlated increased annual rainfall to decreased PM10 concentrations, r educing the fugitive emission of geological material. The impact of rainfall on PM2.5 concentra tions 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 XSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-19 General Weather Types and Seasons

Additional description of the general patte rns 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 Res ources Board, December 1974.

The climate of the San Joaquin Valley is characterized by hot, dry summers and cool, rainy winters. The most significant single co ntrol of the weather pattern is the semipermanent subtropical high-pressure belt, often referred to as t he “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 compre ssed, thereby raising its temperature and lowering the relative humidity. Major st orms 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 s outh, with northern count ies experiencing as much as eleven inches of rainfall and southe rn counties experiencing as little as four inches per year. Air pollutants are generally transported from the nor th 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 pre ssure 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 coas tal and inland stations determines the variability in the summer weather pattern. Strong onshore pressure gradients occur with deep penetration of marine air through the Car quinez Strait into the Central Valley. Cooler temperatures and st ronger 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. Daytim e 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 in fluence of the thermal trough. Air over Bakersfield in the southern portion of the ba sin 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 ai r 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 relative ly poor. This may occur when a highpressure cell aloft is located to the east of the Valley during the summer, therebySan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-20 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. T he thunderstorms are most prevalent over the mountains east of the Valley floor; occasiona lly a vigorous thunderstorm occurs over the floor of the Valley, preceded by gusty winds and blowing dust.

Fall

During the transition season of autumn, t he 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 hi gh shrinks and frontal passages become more vigorous. The advent of a hi gh-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 Va lley, and the influx of marine air becomes negligible. Significant air stagnati on occurs under these conditions.

Winter

As the Pacific High shifts southward, it di minishes in strength, allowing storms, which develop in the Gulf of Alaska to penetrate fu rther south. This c an 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 seve ral days. In between these periods, warm ridges may influence the Valley a nd there is a buildup of pressu re 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 t he area east of the Sierra Nevada. When this high is very strong, the descending wind s will scour out the Valley, and dry, bright winter days result. When the hi gh is weaker, a layer of c ool, damp air is trapped in the basin and may last for a week or longer. Clim atic records for Bakersfield show that heavy fog occurs on the average of 20 day s 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 elevat ions are usually clear under t hese 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 dependsSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-21 on cold, unstable, northwesterly flow for it s precipitation, whic h produces showers following frontal passages. The northwesterly flow up the Valley is almost unobstructed by terrain barriers. The prefront al 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. Temperat ures 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 variati on of temperature in winter is small. The average January temperature is about 44°F, with little di fference between the northern and southern portions of the Va lley. 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 regi on, bringing instability and associated showery weather. In addition, th e increased surface heating in duces 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 r ange trigger the development of intense thunderstorms in the mountains in the a fternoon and evening. Surface winds assume more of an up-valley component as the temperatures become warmer.

On infrequent occasions, a high-pressu re system will stagnate over the Pacific Northwest, resulting in str ong northerly winds, which may persist for several days. These are dry, desiccating winds that may cause seve re 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 interacti ons from directly emitted SOx, NOx, VOC and ammonia. Particulate sulfate and nitr ate can form via both gas and aqueous phase pathways. In the aqueous phase, which is t he main pathway during winter fog and cloud conditions, secondary ammonium nitrate and ammonium sulfate form when nitric acid and S0 2 dissolve in water droplets and then react with dissolved ammonia. Since the sulfate and nitrate ions compet e with each other for the avai lable ammonia, SOx, NOx, and ammonia must be treated as a coupled syst em in order to adequately understand the interactions and subsequent formati on of nitrate and sulfate particles.

Source apportionment receptor modeling has identified the major contributing sources to PM2.5 during the summer as motor v ehicle emissions, secondary sulfate, and primary geological material fr om the fine particle fraction of airborne soil entrained by a variety of sources and mechanisms. Wint er and spring are heavily dominated by secondary ammonium nitrate wit h moderate contributions of secondary sulfate, motor vehicle emissions, primary geological material and direct emission or secondary organicSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-22 aerosol identified as biomass burning from one or more source types including residential wood combustion, wild fires or agric ultural burning. The secondary organic aerosol will also contain small cont ributions from biogenic VOC emissions.



### Data: Ambient Monitoring

The federal Clean Air Act set the health-based National Ambi ent Air Quality Standards (NAAQS) specifying maximum acceptable le vels of pollutants and authorized the EPA to revise and enforce the standards. Pr imary NAAQS protect public health with an adequate margin of safety, and secondary NAAQ S are established to protect public welfare (i.e. soil, crops, vegetation, animals, visibility, building materials, etc.) from known or anticipated harmful e ffects. The levels for the primary NAAQS for PM2.5 were established as 15 μg/m 3 (micrograms per cubic meter) for an annual arithmetic mean averaged at each site over a three year period and 65 μg/m 3 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 th e applicable 65 μg/m 3 24-hour PM2.5 standar d. The 24-hour standard has been revised to 35 μg/m 3 but that revised standard is not applicable to this required SIP. A separate calendar has been es tablished 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 net work. This includes federal reference method (FRM) samplers and speciation samp lers. The monitoring frequency and years of available data vary from si te to site. Only four site s have speciation sampler data, provided by sampler technology that does not provide an identical match with FRM monitoring data due to differences between t he sampler technologies. The differences in data availability, sampling frequency, and av ailability of speciation data make it difficult to establish site or District patte rns and trends. The use of supplemental data collected for special studies is valuable to en rich this data for a better understanding of sources and trends.

Data collected at locations other t han 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 ex tra monitoring equipment at continuously operated sites or off site temporary monitori ng. The supplemental monitoring includes additional instruments that co mply with federal reference me thods (FRM), devices that provide additional readings but may not be FRM equivalent and additional equipment that measures other atmos pheric parameters to support ev aluation and modeling. Data from equipment or sites which do not m eet SLAMS criteria will not be used to demonstrate attainment or compute the des ign value; however, data from additional monitoring devices and sites is used to anal yze the sources and components of ambient PM samples and assess regional and local vari ations. This data is used to analyzeSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-23 episodes of high ambient PM and determine effe ctive controls to achieve and maintain attainment of the PM2.5 NAAQS.

Monitoring Network Representativeness

The EPA requires that the state and the Distr ict measure the ambient levels of air pollution to determine compliance with the N AAQS. 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 Joaqui n Valley Air Basin. Eleven of the fourteen sites use filter-based units and three contain a real-time particulat e monitor. Of the eleven sites seven of them also contain real time particulate monitors in addition to the filter-based units. Twelve of the site s are neighborhood scale and the remaining two are urban scale. All of the PM2.5 sites measure representativ e concentration.

Air quality monitoring for PM2.5 is performed at State and Local Air Monitoring Stations (SLAMS) within the District, including Nati onal Air Monitoring Stations (NAMS) and Photochemical Assessment Monitoring Stat ions (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 concentrati ons 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 st ation must achieve a spatial scale of representativeness that is consistent with the monitoring objective . Spatial scales of representativeness are categories of sampling ex posure. 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 surr ounding 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 concentra tions in an area with a radius ranging from 4 to 50 kilometers. 5) Regional Scale - Uniform pollutant concentra tions that would be characteristic of a very large (for example, rural) area that has a radius from tens to hundreds of kilometers.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-24 The relevant spatial scale for each site is determined from the physical location of the site with respect to the pollutant source s and the population or area represented by the monitoring site. The middle, neighborhood, and urban scales typically are used for meeting the objective of monitori ng 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 / 3 rd 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
Pur p ose SJVAPCD

Merced-2334 M Street 24 Hour/X Neighborhood Representative Concentration SLAMS SJVAPCD

Modesto-14 th Street * 24 Hour/ 3 rd day 1 Hour/Continuous BAM Neighborhood Representative Concentration SLAMS CARB

Stockton-Hazelton * 24 Hour/ 3 rd 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/ 3 rd 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.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-25 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 PM 2.5 Federal Reference Method (FRM) monitors located throughout the San Joaquin Valley. Federal equivalent method (FEM) sites are operated to provide hourly real time measur ement of PM2.5. To supplement the PM FRM network the District monitors real-tim e hourly PM2.5 monitors at BakersfieldGolden, Corcoran-Patterson, Clo vis, Tracy-Airport, Huron, and Turlock. The CARB has real-time PM2.5 monitors at Bakersfield-Ca lifornia, Fresno-First, Modesto-14th Street, Stockton-Hazelton and Visalia- Church. The data gathered by these units is being used to document diurnal variations in particu late 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 y ear. Ambient monitoring samp les are analyzed to determine the chemical make-up of the PM2.5 collected on t he filter. Air monitoring data indicates that during cooler parts of the year when meteorological c onditions produce little or no air movement, secondary particulate levels (l argely ammonium nitrat e) are elevated in the entire Valley. Meteorological conditions hav e 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 in creasing beginning in mid Nove mber to mid December and extending through February. The samples during this period are dominated bySan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-26 secondary particulates. Secondary particulate s are particles that are the end products of many chemical reactions that occur in the atmosphere. Prec ursors, the chemicals that are involved in the chemical reac tions, are NOx, VOC, SOx and ammonia.

Diurnal Variations

Hourly monitors allow the examination of va riation throughout the day. Prior monitoring with 24-hour samples required addi tional evaluation to detect di urnal patterns. During a special study as part of the California Regional PM 10/PM2.5 Air Quality Study, Integrated Monitoring Study-1995 (IMS95), special monitors were run on a daily basis for approximately one month wit h the filters being changed ev ery three hours. This sampling process provided data analyzed to pr ovide 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 Januar y are measured in the evening hours after most people arrive home fr om work. This data suggest that PM10 could be emitted and forming at increased ra tes during the evening hours (6:00 PM through midnight). Other findings from CR PAQS have already been incorporated in our discussions of our understanding of s easons, 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 prov ide a breakdown of the source material. Speciation samplers provide source identif ication information but the measurements are not entirely consistent with the FRM samplers due to differences between technologies.

Transport

PM2.5 or precursors originatin g 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 wi nd speed and direction are not conducive to interregional transport. Monitoring and specia tion 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 bas in or contributing tr ansport of PM2.5 to other air basins. Transport of some PM pr ecursors has been studied as part of ozone transport evaluation, identif ying 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 readi ngs 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 basin s 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 m odeling may be utilized to examine this issue in the future.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-27 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 me teorological phenomena make it challenging to isolate the effect of nat ional trends to be able to clear ly document the effect of improvements made in ambient air quality d ue 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 representativenes s 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 epi sode 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 m easurements and concentrations. No revision to the standard is intended or implie d in the following discussion of adjustments to utilize data from different instruments and models. Adjustment s are made to allow the information to be handled in a compatib le 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 retu rn to a comparison with the PM2.5 mass as measured by the FRM sampler.

The various physical and chemic al components that contribut e to the PM2.5 mass must be evaluated as separate species to determine efficient reduction methods to attain the air quality standards. However, federal re ference sampler (FRM) measurements of PM2.5 are subjected to a number of known positive and negative artifacts due to the sampler construction and operational charac teristics. FRM m easurements do not necessarily capture the PM2.5 c oncentrations precisely as they exist in the atmosphere. Nitrate and semi-volatile organics can be lost from the filter duri ng the equilibration process, and particle bound water associated with hygroscopic species like sulfate provides a positive artifact. FRM measurements may differ s ubstantially from what is measured by speciation monitors, which have artifacts and operational characteristics of their own. Differences between samplers r equire careful consideration when speciatedSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-28 measurements are used to apportion the bulk FR M mass to individual species and are subsequently used to assess the affect of reductions.

The FRM is the basis for attainment status ; therefore, reconstr ucted PM2.5 mass used for modeling or analysis should be based on, or be made consistent with, the composition of the mass measur ed 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 lim itations in mind is referred to as “SANDWICH” the measured s ulfate, a djusted n itrate, d erived w ater, i nferred

c arbonaceous 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 concent rations of the specific ions (sulfates, ammonium, sodium and chloride) and the measur ements of directly emitted elemental and crustal components. Component s represented on the FRM f ilter include elemental carbon, crustal material, sea salt, and passively collected mass. Nit rate filter mass loss is expected. Primary and secondary organic compounds ex press greater monitoring and analytical variability and the “SANDWI CH” method proposes to minimize this uncertainty. Previously, analysis of specia ted particulate data used measurements of ammonium, sulfate, nitrate, organic ca rbon 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 crus tal [metals] components, sea salts and was used to account for any particle-bonded wate r, filter blank contamination and uncertainties in the data monitoring or laboratory analysis.

The “SANDWICH” method for data analysis (F rank, 2006) calculates the PM2.5 organic carbon mass from the difference between the to tal 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

μ g/m3 and substitutes organic carbon as the “others” component to be estimated from the mass difference. The “ SANDWICH” method estimates a mmonium (if not directly measured) and uses either a linear or polynomial empirica l equation to approximate the mass of bonded water in the sample. T he linear equation approx imates bonded water assuming that the water content bonded to a mmonium 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” me thodology 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.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-29 Speciated measurements of ca rbonaceous PM are considered highly uncertain. This is due to the large number of carbon compoun ds in the atmosphere and the measurement uncertainties associated with samplers of diffe rent configurations. In the “SANDWICH” approach, organic carbonaceous mass is calcul ated by difference. The sum of all nonorganic carbon components may be subtra cted 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 contri butions. 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 as sessed by ion measurement require that we give careful review to use of methodology that may falsely assign remain ing mass to organic carbon as the contributing source. Adjusted speciat ed data, particularly fo r the quantification of organic carbon, will be carefully reviewed fo r 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 NAAQS (Test 2) Monitoring Site Design Value ( μ g / m 3 ) Within the NAAQS? Design Value ( μ g / m 3 ) 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 mu st be reduced to the level of the NAAQS. The PM2.5 annual and 24-hour NAAQS require two separate desi gn concentrations, one for each standard per site. The District consulted with ARB and EPA staff to identify methodology forSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-30 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 mi ssing 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 m onitoring 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 demons trate attainment. Da ta from equipment or sites that do not meet SLAM S 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 strategi es 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 sa mple 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 monito red exceedances. Evaluation of components of the mass on PM10 and PM2.5 filters was conducted to es tablish an analysis of episode sources to compare and contrast to receptor mode ling with the Chemical Mass Balance (CMB) model. Results of this analysis provide eval uation of: sufficiency of available data to establish reliable conclusions, dete rmine control program sufficiency and reasonableness of results of other evaluations.

California Regional PM10/PM 2.5 Air Quality Study

The California Regional PM10/PM2.5 Air Quality Study is a comprehensive public/private sector collabor ative program whose goals ar e to provide an improved understanding of particulate matter and visib ility in central California and to provide decision-makers with the tools needed to identify equitable and efficient control methods. The study is intended to evaluat e both the national and State air quality standards for particulate matter smaller t han 10 microns in diameter (PM10) and the new national standards for parti culate 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 allo ws apportionment of high PM10 and PM2.5 concentrations to contributing sources, thereby avoiding burdens on the regulated community from excess or ineffective cont rol requirements. Im plementation of theSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-31 control plans improved by analysis of study dat a 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 a nd preparatory research. Early projects included literature review of previous st udies, development of conceptual models for episode behavior, a multi-year assessment of agricultural practice contributions to particulate emissions, a preliminary fi eld 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 em ission inventory improvements.

As part of the California R egional PM10/PM2.5 Ai r Quality Study, an integrated field monitoring study (IMS) was conducted during t he winter of 1995. The objectives of the IMS were to: 1) support the objectives of se veral 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 modeli ng and data analysis. Da ily PM10 and PM2.5 monitoring with three-hour resolution were conducted at four core air quality sites located in the region between Fresno and Ba kersfield. Samples were analyzed for mass, ions, elemental and organic carbon, and elemental constituents. These data were supplemented with collection of fog measurements, surf ace and aloft meteorological data, measurement of gas eous 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 a ddress fall and winter particulate matter episodic conditions as well as annual averag e particulate matter assessment. Input to the design of the field progr am was solicited from r egulatory agencies, data analysts and modelers, and the research/contracting co mmunity. The objective of the field program, as with the earlier efforts, wa s to obtain a documented data set, with appropriate data qualification st atements, suitable for characterizing the nature and causes of particulate concentrations and visi bility 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 mont hs of data collection throughout the San Joaquin Valley (SJV) and surrounding regions, as well as intensive, shorter-term monitoring during fall and winter-like ep isodic conditions when PM10 and PM2.5 concentrations are highest. The program es tablished an array of monitoring throughout the period, enhanced during summer mont hs along with a companion ozone study (Central California Ozone Study) and consid erably expanded during the fall and winter with intensive data gatheri ng 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 Ai r Resources Board and local air pollutionSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-32 control districts. More than 70 PM10 site s and 50 PM2.5 sites comprised this backbone network. Study enhancements to these networ ks included full scale “anchor” monitoring sites measuring gaseous and aerosol species, through both filter-bas ed and continuous species specific methods. In addition, “s atellite” monitoring sites measured aerosol species using portable PM monitors and nephelometers. Surface and aloft meteorological measurements were colle cted utilizing a network of surface meteorological sites, radar profilers, and sodars. A special 100-meter tower collected data at several elevations on meteor ological and air qua lity parameters.

The fall episodic program took place in Oc tober and November of 2000 in the central portion of the San Joaquin Valley. This monitoring window corresponds to periods of historically high PM10 concent rations that are dominated by geological material. Specific issues addressed in the fall monito ring program included id entification of the sources of geological material , determination of the zone of influence of these sources, and development of improved data on dus t suspension and deposition. The fall measurement program in cluded neighborhood scale sa turation monitoring and measurement of organic spec ies and particle morphology.

The winter episodic field study took pl ace during December 2000 through February 2001. PM2.5 concentrations have been historic ally highest during the winter months, with secondary ammonium nitrate and carbonaceous material the dominant constituents. Specific issues address ed in the winter monitoring program included identification of the source s of carbonaceous material, det ermination of the limiting precursors for secondary PM species, surface and aloft transport and mixing mechanisms under low wind speed condition s, 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 enhanc ed upper-air monitoring network. On days forecasted to have the highest PM concent rations, additional special measurements were collected including organic species trac ers, fog chemistry, time-of-flight mass spectrometry, and measurement of wet deposit ion. 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-mete r tower, an elevated site in the Sierra Nevada mountains, and a remotely piloted b limp, 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 collect ing improved information for transportation sources, including development of updated chem ical 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 collect ed extensive data dur ing 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 analysisSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-33 contracts have been completed and are in final report approval. Advanced modeling of the data collected during the field program is being co nducted 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/air ways/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 r equirements and availability for emissions, meteorology, and air quality as well as the va lidity of the representation for 24-hour and annual average PM2.5 concentrations must be c onsidered in selecting the appropriate approaches. Based upon availability of em ission estimates, meteorology, and air quality data in the Valley, there are two f undamental methods available and appropriate to evaluate current and futu re emissions for the SIP:

• Receptor analysis with speciated-rollba ck 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 wit h other modeling tools, anal ysis of trends and other supporting evidence are permitted to estab lish a weight of evidence synthesis.

If a single source is contributing to PM NA AQS violations with a significant coarse fraction and identification and emi ssion strength of that source is known, then dispersion model, such as AEROMOD, can be used to evaluat e 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 av ailable emission source types is more informative as the number of sources in creases 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 ni trate and sulfate particulate s, but have difficulty with representation of organic car bon 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 separ ate segments to assess motor vehicle, stationary source and vegetative carbon separate ly. This is more effective for analysis of these control progr ams. Receptor mode ling with CMB uses linear assumptions for all portions of particulate matter. Although t he CMB model can determine the total amount of a secondary aerosol species, it cannot es timate specific source contributions to secondary aerosols. Photochemical modeli ng incorporating aerosol chemistry to determine appropriate formation ratios and relationships will be used to assessSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-34 modeling of the secondary fraction. The regional modeling is expected to provide a more accurate assessment of sulfate and nitr ate particulate controls. The combination of the two modeling packages provi des the best path for the SJVAPCD.

Receptor chemical mass balance (CMB) modelin g is an emission source reconciliation approach that relates ambient air quality m easurements with emissi on 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 cont ributing 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 secondar y contributions. It is important that the CMB model use current information on sour ce profiles because changes in fuels and control technology may make older data inappr opriate. Evaluation of source profile selection including temporal and spatial ev aluation of available emission sources improves expected accuracy of the method. Response to control projections and trends can be estimated using projections of futu re emissions to provide an equivalent projection of future concentration with a tec hnique referred to as rollback analysis. This technique assumes linear chemistry and assu mes 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 s upplement this approach with improved physical assessment parameters and asse ssment of aerosol chemistr y involved in formation of secondary particulates.

To improve the physical representation, t he receptor modeling developed by the District incorporates elements of a speciated-rollbac k approach. To apply this method baseline and future years must be established and em ission estimates generated for various precursor gases (reactive organic gases (R OG), S0x, and NOx)) and directly emitted particulate matter (soil related components, mineral dust, prim ary 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 t he 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 sour ces by meteorological analysis to enhance profile selection in receptor m odeling, to identify contributi ng emissions that would affect the observed concentrations, to calculate in tra-basin transport contributions, and to quantify the effect of controls.

Regional grid-based dispersion modeling wit h chemistry, photochem istry and aerosol chemistry is the most sophisticated and comp rehensive approach. Regional grid-based models are theoretically the best type of model for SIP applications. These models have a fundamental advantage over speciat ed-rollback and CMB modeling because of their ability to simulate the integrated effe cts of emissions, meteorology, and air quality in time and three-dimensional space over la rge spatial areas. Grid models furthermoreSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-35 undergo a model performance evaluation by com paring model estimates of historical concentrations with observati onal data. This allows an assessment of the model's reliability to estimate ambient air concentra tions. However, grid-based models require extensive input data for emi ssions, meteorology, and air quality for each episode to be evaluated. For each episode this require s generation of an hour ly-gridded-emission inventory as well as three-dimensional meteorological data, such as winds and temperatures, and hourly boundary condition s. Guidance adopted by EPA guides the use and interpretation of regional modeling re sults for PM2.5 to address the strengths and weaknesses of the approach and the difficu lties 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 pr imarily because the modeled concentrations and events are not usually the sa me as the design values. ARB is performing regional modeling with the specifications consist ent with EPA guidance. Further adjustment and application of model results is to be perfo rmed using a procedure that breaks the results into component constituents. This proc essing 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 ev idence determination for attainment.

Provisions of EPA guidance allo w reliance on receptor mode ling when it is determined to be the most suitable approach. "The chemical mass balance (CMB) model is considered to be the most adv anced of the available receptor models and its use is the most acceptable for attainment demonstrati on purposes." “Under certain conditions, there may be no recommended dispersion m odel, or the recommended model may not be applicable. For example, if area source s are the dominant cont ributors to ambient PM10 concentration, an attainment demonstr ation might be based on rollback of the apportionment derived from two reconciled rec eptor models. In such instances, the modeling approach must be approved by the appr opriate Regional Office on a case-bycase basis." (EPA Guideline Document, pp 6- 8, 1993). Previous e fforts with receptor modeling identified 55 to 64 perc ent contribution in urban area s from "area" sources. Contributions from receptor modeling, a ttributed majority contribution from "soil," construction, and vegetative burning. This circumstance for the SJV supports selection of CMB modeling as the appr opriate modeling techniqu e in accordance with the recommendations of EPA guidance.

Proposed SIP Modeling Approach

The Protocol proposes using regional mode ling supported by rec eptor CMB modeling, PMF analysis and statistical data evaluations co mbined to establish weight of evidence findings.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-36 The regional modeling will be conducted by ARB and be used to support a SMAT process. Regional aerosol modeling to ev aluate secondary formation ratios has been and will be used to enhance recept or 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 frac tion of PM2.5 and r egional modeling with aerosol chemistry to provi de general formation ratios fo r 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 epi sode 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 m odels. The CMB receptor model, with modifications described in the preceding paragraph, provides t he primary receptor model analysis. Reconciliation with alternat ive receptor evaluation will be accomplished by modeling separate specie s 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 abiliti es of the models to capture fine features and explain atmospheric dynamics. The receptor modeling represents a “top down” analysis evaluating observations to determine component s. The regional m odeling represents a “bottom up” method of using emission invent ory information with a photochemical model to simulate the observed events. Both meth ods 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 th e atmosphere from gaseous precursors. It is well established through the vast exper ience of ozone modeling that atmospheric chemistry of secondary particulates may be non- linear and the concentrations of nitric acid (HNO 3 ) and ammonium nitrate (NH 4 NO 3 ), may not be linearly proportional to the concentrations of either NOx or VOC. Therefore, an ess ential 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 Ai r Quality Goals for Ozone, PM 2.5 and Regional Haze,” EPA –454/B-07-002, April 2007.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-37 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 docu ments. 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, guid ance on PM modeling, lim its of dispersion models, stagnation concerns, cases disper sion modeling is not acceptable, areas dominated by nontradi tional sources; PM-10 SIP Development Guide line, 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 re liable for 24 hour standard; Example Modeling to Illustrate SI P Development for the PM10 NAAQS, EPA450/4-87-012, May 1987, page 10 through 19 especia lly section 2.3.1 and section 6.4.1 small sources are added to area sources and assu med 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, CM B is better for categories, DM for known individual sources, grouped to mimic and compare to CMB; and EPA PM-10 Guideline Document and A ppendix A & B, EPA-452/R-93-008, April 1993, especially sections 6.3, 6.5.3, and 6.7.5 predom inant area sources call for rollback on receptor modeling, area source s for dispersion modeling to be based on average annual emissions – not a method int ended to identify area so urce hot spots, reference to inappropriateness of dispersi on 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 reli able 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 Qualit y Models,” Memorandum to Air and Hazardous Division Directors, U. S. Environmental Protection A gency, 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 A ppendix A & B, EPA-452/R-93-008, April 1993

• PM-10 Serious Area SIP guidance: Final St aff Work Product, September 24, 1993San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-38 • EPA Receptor Model Technical Series, Vo lume 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 M odel diagnostics, EPA-450/4-88-005, April 1988

• PM10 SIP Development Guide line, EPA-450/2-86-001, June 1987

• Example Modeling to Illustrate SIP De velopment 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 ve rsion 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 t he CMB model to the application at hand.

• Setup the model by identifying and assemb ling the source types, source profiles, and receptor concentrations needed for model input. Make a preliminary application of the mo del to these data.

• Examine the model’s statistics and di agnostics 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 thei r consistency with the preliminary analyses.

Regional Modeling

New modeling is underway conducted by AR B 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 modelin g of the entire CRPAQS 2000-2001 dataset to evaluate annual PM2.5 secondary formati on. Modeling and performance analysis will be released when complete.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-39 Previously for PM10 Plans ARB modelin g 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 se condary particulates. CMAQ evaluated the CRPAQS 2000-2001 winter episode. UAM-Aero was applied to the IMS95 winter database.

The modeling domain covers t he entire SJV in current model ing. 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 so uth and extends from the Coastal Range to the crest of the Sierra Nev ada and from the Tehachapi Mount ains to Merced. The grid resolution used will be twelve kilomete r or four-kilometer squares depending on performance evaluation.

Selecting a chemical mechani sm that represent s both NOx and VOC chemistry in detail is important because HNO 3 is a secondary product whose precursors are NOx and VOC and HNO 3 is one of the gaseous precursors to NH 4 NO 3 . Almost all chemical mechanisms available have robust NOx chem istry but the sophistication of VOC chemistry varies widely. Of the mechanism s available to ARB, those generated at the State-wide Air Pollution Resear ch Center (SAPRC) of Univer sity of Riverside have the best representation of VOC chemistry.



### Model: Analysis and Use Description

The Protocol establishes an approach that co mbines the best information that can be developed at this time by technical and st atistical analysis of meteorology and other parametric data, receptor mode ling and regional modeling. PM2.5 concentrations in the San Joaquin Valley vary between sites and s easons with regard to sample speciation and contributions from specific source types. Achieving attainment requires an understanding of the seasonal vari ations as well as average contributions. Analysis of PM2.5 concentrations, chemical compositio n and meteorology has provided information of the temporal and spatial behavio r of PM2.5 in the Valley.

Based on the attainment st atus, model evaluation is required for the annual PM2.5 standard for the southern four counties of th e District, Fresno, Kern, Kings and Tulare. The regional model will evaluat e the entire District for a full year of data. Receptor analysis will evaluate the required four count ies for the annual standard. The annual standard is considered to be the controllin g standard at this time for the PM2.5 standards in effect for this Plan requirem ent, because the 65 microgram per cubic meter 24-hour standard has been achieved at all si tes but the annual st andard 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. Sp eciated rollback receptor analysis wouldSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-40 not provide useful information to determi ne 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 sch edule for analysis and compliance.

Regional Modeling

Regional modeling of secondary particulates is being conducted by ARB. Results of regional modeling improve under standing 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 em ission trends and adopted and proposed control measure reductions of secondary precursors. While use of regional modeling results will continue to be used to support recept or 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 resu lts of the regional model to other evaluations to establish a weight of evi dence finding that attainment will be achieved, particularly if the projected future va lue 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 m onitoring samples and information about the chemical composition of contributing s ources 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 ca lculate the effect of predicted aggregate adopted and proposed control me asure reductions and other predicted emission trends to establish attainment at sites noncomplia nt 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 dire ctly 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 me thod used to link observed levels of particulates to the sources of emissions grouped into source ca tegories. The CMB model links the speciated chemical compositi on of the filter samp le at the site to emissions inventories that represent the emissions at the time of the 24-hour observation, or represent seasonal or annua l average values as appropriate. ASan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-41 representative CMB analysis for the annual des ign value establishes the base case situation at sites that need emissions reducti ons 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 resid ence time. The relative meri ts of running the overall average, quarterly averages, monthly aver ages, or every day have been reviewed and the protocol reflects the determination t hat the monthly average approach is the best approach with available data fo r the receptor analysis.

Receptor Simulation of Future Particulate Concentrations

From the CMB receptor modeli ng identification of emissions source contributions by chemical species, future source contri butions are estimated using baseline and projected inventories with specia ted rollback techniques to eval uate 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 st andards 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 mu lti-county) emission in ventory 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 appropri ate "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 trajecto ries, atmospheric residence time, pollutant deposition rates, and emission distributions and ac tivity data. Back trajectories were calculated fr om two-dimensional wind fields constructed from available surface wind data. Potential sources within t he area indicated by back trajectories were examined in detail and included in the rollback calculation.

Baseline and projected future year seas onal and annual emissions inventories are compared with a rollback analysis that assume s linear relationships between emissions and concentrations. To improve rollback pr ojection of future secondary particulate levels in correspondence to futu re emission levels, regional m odeling of formation ratios is used to establish secondary pa rticulate nonlinear relationships.

The receptor model uses annual speciation data available for the analysis of the required counties based upon their attainment st atus. 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 allSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-42 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 fo r the location in a relative reduction calculation to determine attainm ent or the need for addi tional reductions.

Utilization of Regional Modeling React ion Rates for Proportional Rollback Receptor Modeling Analysis

Results of the regional mode ling performed by ARB are used to adjust the receptor modeling analysis nitrate rollback relationshi p. The grid-based phot ochemical model will provide the conversion factors of precursors into secondar y particles that can be used to correct the proportional rollback analysis of secondary particulates. The conversion factors will be used in the receptor analysi s of control strategies projections.

Evaluating the complexity of the relationshi ps involved in particle formation provides information to determine whether an assumpti on 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 avail able about atmospheric chemistry and formation rates. Particle formation rates ma y vary due to influences of meteorology and precursor ratios. Temperature, relative humidity, photochemical energy flux, wind speed and atmospheric mixing affect the format ion 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 differ ent rates and have different relationships for the contribution of PM2.5 mass. Therefore, the c onstituent materials that contribute to PM2.5 are not directly additive and mu st be calculated s eparately and be added together as a final step to det ermine the total PM2.5 mass.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-43 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 concent rations that would result from trends and current and proposed control programs. EPA guidance identifies how these calculations should be performed for a r egional model. The District has determined applicability to the receptor analysis.

The Speciated Modeled Attainment Test (SMAT) methodology is generally applicable throughout the country but conflic ts with certain important cons iderations critical to San Joaquin Valley Air Basin (SJVAB) assessment. Specific adjustments will be made to address technical issues specific to the SJ VAB and to provide the most accurate means to reflect the effects of emission reductions.

• The receptor analysis exceeds SMAT requirements by pr oviding 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 c ontributions to the source signature, is divided between:

o Motor vehicle emissions,

o Tire and brake wear,

o Stationary and area s ources carbon mass, and

o Vegetative burning.

• The receptor analysis exceeds SMAT requirements that recommend quarterly evaluation by performing monthly evaluati on and determining that CMB performance meets standards on a monthly basis. T he annual CMB modeling adjusts speciation selections for wood burning to reflect seasonal changes but otherwise uses a consistent set of speciation signatures. Pe rformance verification demonstrates that the selected profiles are acceptable for i dentifying and represent ing the contributing sources throughout the year.

• The receptor analysis exceeds SMAT requi rements by separati ng the contributions of geologic material from other sampling artifacts and unknowns. This is of particular importance to the SJ V due to the District regulations for control of fugitive dust. The benefit of the Distr ict 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 appr oved alternate linear assumptions. The linear assumption avoids the need to c onsider trapped water as a separate calculation. According to the linear a ssumption, water bonded to ammonium nitrate is approximated as equivalent to twelve per cent of the ammonium nitrate mass and is proportional to the amount of ammonium nitrate particulate mass present. Water bonded to ammonium sulfate is approximat ed as 26 percent of that mass and is proportional to the amount of ammonium su lfate particulate mass present. Because the amount of trapped water is established by the linear assumption method asSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-44 proportional to the amount of the ammonium relat ed mass contributions, the speciated rollback analysis processes t he trapped water as an incorporated linear component of the ammonium nitrate and ammonium sulf ate 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 formati on in areas dominated by sulfates. The associated water content of sulfates is much different than fo r nitrates and can require mass recalculation for areas wit h high sulfates. The SJVAB is dominated by ammonium nitrate particulates with low sulfate particulate concentrations; therefore this process is of minimal effe ct on the Valley PM2.5 mass calculation.

• The SMAT methodology calculates rema ining mass other than the nitrates and water as organic carbon. This approach is t oo 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 dus t) or extensive emissions from carbon sources subject to direct control (resi dential 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 SM AT 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 availabl e at this time. Metals are also not isolated by SMAT methods. For any elem ents not well supported by the regional model results, receptor modeling or other substantive data will be reviewed to establish a weight of evi dence finding for attainment.



### Model: Application Assumptions

Regional and Receptor Mode ling General Assumptions

Establishing modeling assumptions and ba ckground values requires discussion of particle size, formation, composition and chem istry in accordance with current scientific understanding of particulate matter in the air. This information provides a basis for addressing issues such as natural backg round and regional and lo cal contribution. Assumptions involved in model application are based on data analysis and an understanding of the physical and chemic al properties, sources and behavior of particulate matter. Developing an understandi ng of the principle factors and influences of PM2.5 concentrations provides a greater degree of certainty t hat proposed control strategy reductions will have the desired and expected results and that a projection of attainment has the highest degree of reliabilit y achievable with current information.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-45 Receptor Modeling Assumptions

Receptor-oriented air quality models for particu late 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 equati ons that express the ambient concentrations of chemical spec ies as the sum of products of source compositions and source contributions. The basic model equations represent the source receptor relationship. The e ffective variance weighting and the error propagation are all based on physical principals. The fundamental CMB model equations have been subjected to verifica tion and evaluation using both real and simulated data as part of the Quail Roos t 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 ar e: 1) Compositions of source emissions are constant over the period of ambient and sour ce 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 numbe r 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 dat a sets (Stevens and Pace,1984; Javitz et al., 1988) show that modest departures fr om 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 particula te 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 nitrat e 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 concent rations of aluminum, bromine, calcium, chlorides, copper, lead, manganese, nickel, potassium, silicon, titanium, vanadium,San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-46 and zinc. Preferable additional analyses would include other elements such as arsenic, chromium, selenium, trace elem ents, cations, anions, elemental carbon and organic carbon.

• From the identified species, the potential source contri butions can be identified and grouped into source categories of di stinct chemical compositions

• From the identified species and source ca tegories, 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 rec eptor 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 qua lity 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 fo r 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 t he model affects seasonal and annual average modeling less than modeling of a 24-hour episode. If th e meteorology and source activity of a specific exceedance day used fo r modeling is not repr esentative of other exceedance days, the resulting analysis may not be generally representative. “If a receptor model study were undertaken for a shor t time period, it must be shown that the period covered was generally representative of the type of source activity and meteorology associated with exceedances obse rved 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 contributi ons. “The CMB model is belie ved to be the most reliable model for determining the source contributio ns of primary particles, especially for fugitive dust sources, because it does not rely on emissions rate estimates or characterization of atmospheric transport... The principle limitatio ns of the CMB modelSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-47 are that 1) it can only separate the source contributions of a small number of sources... and 2) it cannot estimate source contributio ns 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 a ccurate source identification available with a reliable technique for assessing control programs.

The future year source contributions, exce ss 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 pr oportion to changes in area-wide emissions. For an inert pollutant or source contributi on, such as geologic material, the rollback projection is a valid predictor of future pollu tant concentrations provided that the relative spatial and temporal emissions distribution is the same before and after emissions controls have been implemented. When the ro llback projection is used to predict the effects of reductions in gaseous precursor emissions on secondary aerosol constituents, chemical transformation proce sses are assumed to be linear. Significant departures from linearity in the atmosphere could undermine the accuracy of results; however, “Despite the clearly nonlinea r nature of the f undamental chemical transformation mechanisms, there is consid erable evidence from nonlinear models and observations that the conversi ons are approximately linear for many of the practical emissions control situations studied to date.” (Sonoma Te chnology, Inc. “PM-10 Air Quality Models for Applicati on in the San Joaquin Valley PM-10 SIP”, page 4-7, 1996).

Factors Essential fo r Modeling Analysis

Particulate matter represents a broad cla ss of chemically and physically diverse substances. In addition to characterizations by size, particles can be described by their formation mechanism or origin, chemical composition, physi cal properties, and in terms of what is measured by a particular samp ling 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 underst anding of the influences of the principal factors must also be established. The EPA docu ment “Air Quality Crit eria for Particulate Matter” contains an extensive analysis of PM 10 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 fr om a variety of sources including motor vehicles, wood smoke from wildfires, residential firepl aces and agricultural burning and direct and secondary carbon from f uel combustion (from vehi cles, power generation, and industrial facilities),

• Secondary formation of ammonium sulfate,

• Fugitive dust including wind blown dust and road dust

• Other primary inorganic parti culate matter including metals

• Particle bound water included with the su lfates and nitrates and other sampling artifactsSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-48 The properties, sources and behavior of PM2. 5 must be clearly understood to perform modeling. Modeling requires an understanding of the spatial infl uence of emissions sources and sufficient information to estimate background PM2.5 levels. Apportionment of secondary PM2.5 is difficult because it r equires consideration of atmospheric reaction processes and rates. Current scientific k nowledge is used to select and evaluate modeling methods and results.

Factors essential for modeling analysis include:

• Origin of particles, sources and properti es – 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 suppor t modeling – background contributions are not responsive to District contro l 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 consider ed 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 sec ondary components be considered. Airborne particulate matter is not a single pollutan t, but a mixture of many subclasses of pollutants with each subclass containing m any different chemical species.

Fine particulate matter is produced mainly by the condensation of gases in the high temperature environment of combustion ch ambers; the condensati on of atmospheric precursor gases, some of which may undergo further reactions in particles; and the condensation of low vapor pressure photochemic al reaction products. Major sources of these fine mode substances are fossil fuel com bustion by electric utilities, industry and motor vehicles; vegetation burning; and the sm elting or other processing of metals. Major components of fine particles are: sulfat e, strong acid, ammonium, nitrate, organic compounds, trace elements (including me tals), elemental carbon, and water.

Primary (directly emitted) fine particl es are formed from condensation of high temperature vapors during combustion. Part icles formed as a result of chemical reaction of gases in the atmosphere are te rmed 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 primar y particles and secondary particles formed later in the atmosphere are usually formed fr om 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 reac tions. Gases may dissolve in a liquid,San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-49 react with another dissolved gas, and w hen fog and cloud dr oplets evaporate, particulate matter remains, usually in the fine particle mode. Particles formed from nucleation also coagulate to fo rm 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, SO 4 = Nitrate, NO 3 - Ammonium, NH 4 + 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 NO x , SO 2 , 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 Docum ent, 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 materi als. The amounts of these components vary from place to place and over time. Fi ne particulate matter is composed of sulfates,San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-50 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 combusti on of fossil fuels, smelting of ores, and incineration of wastes and are emitted as fi ne 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 atmo spheric 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 co mpounds also react with ozone, to form oxygenated organic compounds t hat can condense onto existing particles. Chain agglomerates of very small elemental ca rbon (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 of ten 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 polyaroma tic hydrocarbons (PAHs); secondary anthropogenic - some organic compounds, including aromatics larger than benz ene, cyclic olefins and diolefins, and other C7 (compounds with seven carbon atoms in the chain) or higher hydrocarbons react with O 3 or OH to form polar, ox ygenated compounds with vapor pressures low enough to form particles; prim ary 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 precur sor to a product which either has a low enough saturation vapor pressure to form a particle or reac ts further to form a low saturation vapor pressure product. Fine parti culate matter is derived from combustion material that has volatilized and then condens ed to form primary particulate matter or from precursor gases reacting in the atmos phere to form secondary particulate matter. New fine particles are formed by the nucleat ion of gas phase species, and grow by coagulation (existing particles combini ng) or condensation (gases condensing on existing particles). Fine particles are co mposed of freshly generated particles, in an ultrafine or nuclei m ode, and an accumulation mode, so called because particles grow into and remain in that mode.

SO 2 , NOx, and certain volatile organic co mpounds are major precursors of fine secondary particulate matter. SO 2 and nitrogen dioxide (NO 2 ) react with hydroxy radical (OH) during the daytime to form sulfuric acid (H 2 SO 4 ) and nitric acid (HNO 3 ). Both VOC and NOx are important in the formation of free radicals that contribute to the formation of nitric acid (HNO 3 ).San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-51 NOx is formed during combustion or any high temperature process involving air. The NO is converted to NO 2 by ozone (O 3 ) or other atmospheric oxidants. During the daytime, NO 2 reacts with the hydroxyl radical (OH) to form HNO 3 . During the nighttime NO 2 reacts with O 3 and forms HNO 3 through a sequence of reactions involving the nitrate radical (NO 3 ). Ammonia (NH 3 ) reacts preferentially with H 2 SO 4 , but if sufficient NH 3 is available, particulate ammonium nitrate (NH 4 NO 3 ) will form. An example of this process is the conversion of NO 2 to HNO 3 that may react further with NH 3 to form particulate ammonium nitrate (NH 4 NO 3 ).

SO 2 , mainly from combustion of fossil fuel, is oxidized in the atmosphere to form H 2 SO 4 particles that nucleate or condens e on existing particles. The H 2 SO 4 may be partially or completely neutralized by reaction with NH 3 . Since the particles usually contain water, the actual species present are H+, HSO, SO , and NH, in varying proportions depending on the amount of NH 3 available to neutralize the H 2 SO 4 . Strong acidity of particles is due to free H+ or H+ av ailable from HSO or H 2 SO 4 . SO 2 also dissolves in cloud and fog droplets where it may react with dissolved O 3 , H 2 O 2 , or, if catalyzed by certain metals, with O 2 , yielding sulfuric acid or sulfates, that lead to particulate matter when the droplet evaporates.

Atmospheric Behavior, Transport a nd Fate of Airborne Particles

Fine particles are small enough that the ra ndom forces from collisions with gas molecules largely overcome gravitational forces. Fine particles tend to follow air streams and are typically not removed by impac tion. As a result, they are not easily traced back to their individ ual sources. Fine particles formed from accumulation processes are significantly lar ger than gas molecules and their di ffusion 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 ine fficient since they do not readily diffuse through the boundary layer of still air next to surf aces. The atmospheric half-life of fine accumulation particles with respect to dry de position is on the order of weeks; however, removal occurs when the particles absorb wate r, grow into cloud droplets, grow further to raindrops, and fall out as rain. This proc ess reduces the typical atmospheric half-life to a few days. Ultrafine or nuclei-mode parti cles, 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 t hat may be dispersed more widely over long distances. Secondary fine particles are form ed by atmospheric transformation of gases to particles. Atmospheric transformation c an take place locally during stagnations or during transport over long distances. Fine particles have very low dry deposition velocities, which contribute to their uniformi ty throughout the air mass. Aerosol effects on visibility and climate, through light sca ttering and changes in cloud microphysics, primarily arise from fine particles.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan 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 appropr iate definition of background for speciated-rollback is the air quality concentrations at the influx es 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 in terregional 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 c oarse particles in the atmosphere. For modeling purposes, background particulate matter includes the distribution of particulate matter from natural sources as well as ant hropogenic emissions of particulate matter and precursor emissions of VO Cs, NOx, and SOx from areas outside of the SJVAB. To discriminate the portion of t he measured PM10 affected by c ontrol strategies, emissions from outside of the SJVAB mu st be treated as background. If these emissions are not treated as background duri ng the modeling process and are attributed to local emissions, control effects would be overesti mated in the modeling process. This definition of background is di fferent than would be used for health assessment studies, where background is limited to natural s ources and all anthropogenic emissions are evaluated for their cumulative health impact.

Background levels of particulate matter va ry by geographic location and season. The natural component of the background arises fr om physical processe s 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 aerosol s as well as their precursors. The exact magnitude of the natural por tion of particulate matter for a given geographic location cannot be precisely determined because they ar e difficult to separate from the long range transport of anthropogenic par ticles or precursors. Only broad estimates for longer averaging times can be developed at th is time. Regional annual average natural background levels are estimated as 1-4 μ g/m 3 PM2.5 for the western US.

CRPAQS scientific findings on natural ba ckground assist in further review of assumptions and quantification for receptor analysis. “Non-Anthropogenic Background Collins (1998) showed t hat non-anthropogenic background c oncentrations were not a significant contributor to SJV PM2.5 concentrations. Amm onium nitrate, ammonium sulfate, OM, and EC origin ate from anthropogenic sources in the SJV. The crustal component of PM2.5 was usually less than one μ g/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 μ g/m3San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-53 (Motallebi et al., 2003; IMPROVE, 2003). Wi ntertime PM2.5 conc entrations 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 μ g/m3. Assuming these concentrations are solely due to nonanthropogenic source s, 1.0 to 2.0 μ g/m3 is likely the best estimate for typica l nonanthropogenic background c oncentrations in the SJV.” Source: BACKGROUND AND BOUNDAR Y CONDITIONS FOR PARTICULATE MATTER AND PRECURSORS IN THE SA N JOAQUIN VALLEY IN WINTER TECHNICAL MEMORANDUM ST I-902325-2779-TM. By: Michael C. McCarthy, Hilary R. Hafner, Steven G. Brow n, Fredrick W. Lurmann, Paul T. Roberts, Sonoma Technology, Inc., 1360 Redw ood Way, Suite C, Petaluma, CA 94954-1169, July 29, 2005

Natural Background Emissions: Bioaerosols As part of establishing background concentrations, the mass contributed by liv ing organic matter and biogenic emissions must also be considered. Ambient bioaeros ols include fungal spores, pollen, bacteria, viruses, endotoxin, and animal and plant debr is. Bacteria, viruses and endotoxin are mainly found attached to aerosol particles, while entities in the other categories are found as separate particles. Data for char acterizing ambient concentrations and size distributions of bioaerosols are sparse.

Most fungal spores are 2 to 4 μ m in size, but range in size from 1.5 μ m to over 100 μ m. 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 r each levels of 1,000 to 10,000 spores/m 3 during the summer months (Lacey and Dutkiewicz, 1994; Madelin, 1994) and may be as high as 100,000/m 3 near some anthropogenic sources (agricul ture activities, compost, etc.).

Bacterial aerosol counts may range as high as 30,000 bacteria/m 3 downwind of sewage treatment facilities, composting areas, wate rfalls from polluted rivers, or certain agricultural activities. Typical levels in urban areas range from several hundred to several thousand bacteria/m 3 (Lighthart and Mohr, 1994). Lev els 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 bioaerosol s and cannot be distingu ished from secondary particles from anthropogenic activities wit h information available at this time.

Key Findings from Scientific Studies and SJV Evaluations

The CRPAQS research program has provided a wealth of scient ific 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 Ca lifornia circumstances are different. For example: EPA methods consider a strong relationship between sulfate decreaseSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-54 causing a potential and nitrat e formation increase. That issue was studied for the SJV and found not to be a concern.

Ammonium Nitrate Sensit ivity to Sulfate Levels Ammonium nitrate levels are sensitiv e to changes in sulfate under ammonia limited conditions. Sulfate and nitrate co mpete for ammonia when its supply is limited, and the thermody namics indicates that a mmonium preferentially associates with sulfate (forming ammoni um sulfate or amm onium bisulfate) rather than nitrate under ambi ent conditions. Under ammonia limited conditions, reductions in sulfate may not be an effe ctive means to reduce PM mass because ammonium sulfate removed fr om the aerosol is replaced with ammonium nitrate, thereby inhibiting significant reduction in PM mass. The effects of changes in sulfate levels on ammonium nitrat e in the SJV were investigated using the eight cases for whic h isopleth diagrams were constructed. These were cases with relative high sulfat e levels for the SJV (3 to 4 mg/m3). The effects of 50 and 100 percent reducti on 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 CHEMI STRY 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 Univer sity, Pittsburgh, PA, July 1998

The issue of limiting precursors and sec ondary 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 photoche mistry 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 indi cate the daytime photochemistry is VOC-, sunlight-, and background –ozon e-limited in winter. This is a nonlinear regime for the gas-phase chemistry. Presented to: CRPAQS Data Analysis Wor kshop, 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 Joaqui n Valley, Presented by: Fred Lurmann, Siana Alcorn, Manidipa Ghosh, Sonom a Technology, Inc., Petaluma, CA

“....Comparisons of ammonia and nitric acid concentrations indicate that ammonium nitrate formation is limited by t he availability of nitric acid, rather than ammonia. Timeresolved aerosol nitrate data at the su rface and on a 90-m tower suggest both the daytime and nighttime nitric acid formation pat hways are active, and that entrainment ofSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-55 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 c oncentrations show that ammonia is far more abundant than nitric acid, which indicate s 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 backgrou nd ozone, which contro l the rate of NOx oxidation in winter, rather than by ammonia emissions.” Processes Influencing Secondary Aerosol Formation in t he San Joaquin Valley During Winter, Frederick W. Lurmann, Steven G. Brown, Michael C. Mc Carthy, and Paul T. Roberts, Sonoma Technology, Inc., 1360 Redwood Wa y, Suite C., Petaluma, CA 94954

The contributing sources to PM2.5 have proper ties that are diffe rent. This makes analysis of reductions more difficult cons equently 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 nitrat e were regionally distributed throughout the SJV. Concentrati ons of PM and its precursors were typically lower at the elevated sites su rrounding the SJV than at monitoring sites located on the SJV floor....At distances more than 50 km fr om the urban areas, OM concentrations typically declined by a fa ctor of three or more. Emissions of OM at the urban core are ei ther 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 concent rations 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 ar ea 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 epi sodes provides information on the spatial extent of the produc tion of ammonium nitrate. PM2.5 OM and ammonium nitrate are both subject to the same me teorological transport conditions, yet ammonium nitrate concentrations are relatively homogeneous and OM concentrations are much higher in the ur ban 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, t herefore, the rates of re moval should be approximately the same. In summary, the likely explanation for t he difference in spatial variability is the spatial di stribution of the emissions or precursors. Primary OM emissions occur predominantly from mob ile sources and wood smoke located in urban areas. The formation of ammonium ni trate from NOx precursors (LurmannSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-56 et al., 2004) must occur throughout t he SJV to account for its spatial homogeneity.

High concentrations of PM organic car bon were spatially limited to core urban sites while high concentrations of PM ammonium nitrate were regionally distributed throughout the SJV. The regi onal homogeneity of ammonium nitrate concentrations coupled with the stagnant win d conditions provides evidence that production of ammonium nitrate occurs at similar rates throughout the valley. In contrast, the OC component of PM indicates that pr oduction rates were much higher in the urban areas than at rural sites.”

BACKGROUND AND BOUND ARY CONDITIONS FOR PARTICULATE MATTER AND PRECURSORS IN THE SA N JOAQUIN VALLEY IN WINTER, TECHNICAL MEMORANDUM STI-902325- 2779-TM, Michael C. McCarthy, Hilary R. Hafner, Steven G. Brown, Fredr ick W. Lurmann, Paul T. Roberts, Sonoma Technology, Inc., 1360 Redwood Wa y, 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 SJ V, 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 site s, the elevated PM2.5 was driven by secondary NH4NO3. The tem perature, 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 par ticles were closely related to urban sources such as traffic, RWC, and cooki ng. 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 t he large evening peak around 2000 PST prolonged through the midnight and early morning next day. Non-urban sites did not exhibit any di stinct 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 89512San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-57 Utilization of Application Assumptions

Based upon review of the preceding informa tion, default estimates of regional and background contributions have been establis hed for use with the speciated receptor rollback modeling. Particle dynamics, ph ysics, atmospheric behavior and fate establish background and regional contribution estima tes. 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 sour ces. This would ov erstate the predicted effectiveness of controls. The assump tions 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 underestima te 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 exam ine 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, recept or modeling reconciliation, and projection to future years. The SI P 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 inve ntories prepared to correlate with observed values for modeling are called baseline inv entories. 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 m odeling for annual PM2.5 by mode ling 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 fo r projection of base and future year projections to be used for a Speciated Modeled Attainment Test (SMA T). A regional air quality modeling methodology summary of m odel preparation will be included as an attachment to this Protocol.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-58 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 St udy, recent motor vehicle profiles, wood burning, and meat cooking sour ce profiles. These ar e supplemented by soil profiles collected by U. C. Da vis as part of their agricultu ral 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 c ontribution of source s that cannot be distinguished individually, such as paved versus unpaved roads.

Receptor Modeling Chemical Species

The chemical species to be used in the CM B 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 a ll 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 t he input file. Additional species and carbon fraction pr ofiles will be added as they become available. This may include additional trace elem ents measured in newer sour ce profiles, as well as elemental and organic carbon fractions.

Abbreviation Name
TMAC PM 10 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.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-59 CRPAQS data for the year 2000 is used for evaluation of sour ce receptor relationships as previously conducted for t he PM10 SIP. This data is av ailable from the ARB website http://www.arb.ca.gov/airways/ . This website provides a ccess to the collected air quality data, surface and aloft meteorological measurements, and m easurements from a variety of special and research instrument s operated during the study. Also available from this website are doc uments reporting on and analyzing the collected data. The use of 2000 data remains valuable due to t he extensive nature of the dataset and the technical reviews and analysis of the SJV during that time per iod. This robust dataset provides greater capability to exami ne the accuracy of model performance for simulating the observed conditions than is provided by the routine network measurements collected each year. The purpos e 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 sa me dataset, for the same reason. PM2.5 mass was determined by analysis of pr ior 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 t he 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 t he full effect o controls. “It is well-recognized that California faces a set of unique and exceptionally difficult ch allenges in meeting national air quality standards, including those for fine particulates.... Key uncertainties exist with regard to both emissions inventories and ai r quality modeling in the West, which may understate the effectiveness of certain c ontrols....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 secondar y 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 quant ified using existing methodologies. Work is underway at EPA and elsewher e 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 model s, including CMAQ, may understate theSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan 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 analysi s 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 a mmonium nitrate (NH 4 NO 3 ) formed from the gaseous precursors ammonia and nitric acid (HNO 3 ). Ammonia is mainly emi tted into the atmosphere from various sources. HNO 3 is a secondary gaseous product of the reactions between oxides of nitrogen (NOx) and volatile or ganic compounds (VOC) in the presence of sunlight. The regional model will also si mulate the formation of secondary organic aerosols (SOA) and ammonium sulfate ((NH 4 )2SO 4 ). The representation of (NH 4 )2SO 4 is robust but only minor amounts (~5 μ g/m3) of that pollutant is present in the San Joaquin Valley in observed episodes. The repr esentation 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 modeli ng outputs to incorporate calculations of the sampling artifacts and losses, assigning the remainder of mass as SOA after these adjustments. However, the me thodology 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 discuss ed in the next section of the Protocol Model: Analysis of Results.



### Model: Analysis of Results

Excerpts from presentations provided by EP A staff help clarify what is intended in the analysis of model results.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-61 Modeled Attainment Tests All O3/PM2.5/RH modeled attain ment 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 calc ulated by taking the ratio of the model’s future to current predictions of PM2.5 or ozone

• RRFs are calculated for ozone and for eac h component of PM2. 5 and regional haze

Weight of Evidence/Supplemental Analyses All attainment demonstrations should in clude “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). Re commended 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 Oz one, PM2.5, and Regional Haze”, Final version- April 2007, Brian Timin April 17, 2007

The following sections discuss performance analys is 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 co mponents with small observed concentrations relative to other components. In a rela tive attainment test, a small observed component cannot have a large influence. Source: PM2.5 Mode l Performance Evaluation- Purpose and Goals, Brian Timin EPA/OA QPS, 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 observa tions to assure that the modeling system (emissions, meteorology, and air quality) is a satisfactory representation of the episode modeled. Only with such an assu rance one can use the modeling system to evaluate control strategies. The rigor ous comparison of model estimates with observations is termed model performance ev aluation. Emission model evaluation and meteorology model evaluation is limited to quality assurance testing. Rigorous evaluation of the air qualit y model performance is proposed. The statistical metrics used for the comparison of estimates are in accordance with gui dance. For gaseous pollutants the comparison is straightforward. But, for particulate ma tter that has severalSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-62 components, the comparison will be done for t he total mass as well as for the mass of individual components.

Statistical Measures for Regiona l Modeling Performance Analysis

Peak Estimation Accuracy:

Paired Peak Estimation Where, Co(x,t) is the peak conc entration 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 A ts , 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 )

, ( ))

, ( )

, ( (

× −

= t x c t x c t x c A

o o e
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 re lax 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 co rrect location at a reasonably close hour (e.g. within 3 hours).

Unpaired Peak Estimation: In equation (A1), if we rela x the requirement that model estimate should be at the same location and hour as the observation, we measure the model’s ability to reproduce t he 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 fr om all valid monitoring data on the simulation day of interest.


# ∑

= 

## − = N

i i o i e 

## t x c t x c N MBE

1 

## ))

, ( )

, ( ( 1

Equation A3: Mean Normalized Bias Error similar to equation (A-2) but normalized with respect to the observation.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-63 

# ∑

= 

## × −

= N

i i o i o i e 

## t x c t x c t x c
N MNBE

1 

## % 100 )

, ( ))

, ( )

, ( ( 1

Error:

Equation A4: Mean Gross Error where, N is the number of valid hourly observation-estimation pairs drawn fr om all valid monitoring data on the simulation day of interest. Summing the absolute values of the difference prevents cancellation of errors.


# ∑

= 

## − = N

i i o i e 

## t x c t x c N MAGE

1 

## ))

, ( )

, ( ( 1

Equation A5: Mean Normalized Gross Error, similar to equation (A-4) but normalized with respect to the observation.


# ∑

= 

## × −

= N

i i o i o i e


## t x c t x c t x c

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 i o i e 

## t x c t x c N 1 2 2 

## ))

, ( )

, ( ( 1

σ

Receptor Modeling

Model output and performance measures wil l 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 s ource profile from eac h source category. Final profile selection results from interact ive application of the CMB with evaluation of the performance measures. Calculated c ontributions will be compared to measured ambient concentrations. Reasonable agree ment between calculated contributions and measured ambient concentrations would indicate that all major source categories were included in the calculations, ambient and s ource profile measurements were fairly accurate, and source profiles were reasonably representative of actual emissions.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-64 Sensitivity matrices were constructed to determine the source profiles with the most influence on species apportionment. Final a nalysis includes comparison to regional activities and current em ission inventories, and is done in conjunction with meteorological and climatological analysis.

The CMB8 model provides a statistical dia gnostic evaluation of correspondence of the CMB source identification to the observed sample. The basic statistical performance evaluations include R2 - R 2 error analysis, Chi2 - Chi 2 distribution, and Pmass - percent of mass accounted for by the su m of masses attributed to identified sources. The following performance meas ures will be used to a ssess 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 it s 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 m odel calculated species concentrations. A value less than 0.8 indicates that the select ed 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 se veral 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 sa mple. 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 ei ther collinear or which have very high uncertainties.

Sum of Combined Sources (S UM 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 co mposite 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 specie s concentration (residual) divided by theSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-65 uncertainty of the residual. An R/U value gr eater than +/- 2.0 indica tes 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 concentrati on 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 i norganics also comprises a portion of the FRM mass.

The main steps in estimating the PM 2.5 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 retai ned nitrate, sulfate, elemental carbon, ammonium (or sulfate degr ee of neutralization, [NH 4 + ]/[SO 4 2- ], if not using ammonium measurements directly),

(3) Calculate particle bound water using the c oncentrations 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 (OCM mb ) by difference, subt racting all inorganic species (including blank mass) from the PM 2.5 mass.

Total PM 2.5 mass is given by

PM 2.5 FRM = [SO 4 2- ] + [NO 3 - FRM ] + [NH 4 + FRM ] + [EC] + [Other ] + [OCM mb ] + [H 2 O] + [blank mass = 0.5 μg/m 3 ]

Where all concentrations have units of μg/m 3 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 t he FRM PM2.5 mass. Organic carbonSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-66 measurements may also be used if it s eems 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. Ca lculated using a polynomial equation dependent on the concent rations of sulfate, ni trate, 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 wate r bonded to ammonium sulfate is approximated as 26 perc ent of that mass.

• Blank mass represents the mass passively collected on the filt er, represented in the equation by an average value of 0.5 mg/m3 (assumed constant)

District evaluation of filt er blank mass for SANDWICH

The SANDWICH procedure recommended by EPA guidance calls for setting aside 0.5 ug/m3 as a filter blank artifa ct, to be kept as a constant and not be reduced for future year considerations. EPA identified the sour ce of the artifact as sample handling contamination by inference but not by dem onstrated measuremen t or detection. The District has identified through review of CRPAQ S research that the sampling artifact has a different cause than suggest ed by EPA and will process the artifact accordingly.

CRPAQS research provides anal ysis and documentation of the so urce of the artifact as the difference between VOC ca rbon adsorbed on the filter, par tially offset by carbon particulate desorbed from the f ilter. 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 stat ic in future years. Current receptor modeling has a very small mass identif ied as unassigned and mapped against total PM2.5 inventory change. Prior discussion indicates that in addition to the VOC adsorbed artifact there ar e other inorganic filter artifacts identified by prior research and technical literature. The analysis for CRPAQ S determined the source of the artifact but qualified the determination of exact mass m agnitude. EPA has set a default value of 0.5 micrograms for the filter bl ank hypothesis, but this value is too low for the identified adsorbed carbon and inorganic artifacts. The Distr ict will review establishing appropriate mass consideration for both artifacts and will re vise receptor speciated rollback methods and assumptions accordingly.

Documentation of the filter artifact anal ysis 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 Ch en, Douglas H. Lowenthal, Prakash Doraiswamy, Kihong Park, Steven D. Kohl, Dana L. Trimble, John G. Watson, DESERT RESEARCH INSTITUTE, Divis ion of Atmospheric Sciences, 2215 Raggio Parkway, Reno, NV 89512

Organic Carbon Artifacts (Task 1.1.4)San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-67 Several denuding and backup filter sa mpling 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., adsorp tion of organic gases onto the filters). The adsorption of organic gases onto the f ilters was, on average, 14.4% (i.e., QQ2/QPOC) of the particle OC conc entration during CRPAQ S. 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 i ndication of the amount of carbon that was desorbed from the particles on t he front quartz-fiber filter (i.e., as an estimate of the negative artifact). T he 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 estima te of the positive artifact depends on whether equilibrium was reached betw een the VOC in the sample stream and both filters in the QQ pair, which is hi ghly questionable, given the difference in sampling periods and the diurnal variat ions 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 equilibr ium is reached under different sampling conditions.

Spatial scales of influence on compliance (backbone) (Task 4.4.3) • The comparison between measur ed 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 μ g/m3) and PM10 (1.24 μ g/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 method s. The general procedure to establish a Speciated Modeled Attainment Test (SMAT) is to perform calculations that consider the differences between FRM samples and the pr ocesses and quantities predicted by the model.

Step 1. Calculate the obs erved quarterly mean bulk PM 2.5 concentration and composition for each measurem ent site. The quarterly mean species concentrations can be calculated by multiplying the obser ved percentage contribution of each species against the quarterly mean bulk PM 2.5 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 year s), one of which should be the modeledSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-68 base year. The procedure to speciate th is bulk design value is described in the “SANDWICH” discussion.

Step 2. Calculate the RRFs for each quar ter and measurement site. Relative response factors are calculated using model result s. For species i, si te j, and quarter k is given by the following equation:

RRF ijk = ([C i, quarter k of future year ]/[C i, quarter k of the base year ]) j

C i represents the quarterly modeled c oncentration (often averaged over a number of model cells near t he 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 PM 2.5 sampling times, the representativ e spatial scale of the monito rs, and the desire to offset any potential errors stemming from the geome try of the superimpos ed grid system. For a 12-km cell, it is recommended that RRFs be calculated from a 3x3 cell array.

Step 3. Multiply the qu arterly, 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 specie s concentrations estimates of step 3 to estimate a future quarterly PM 2.5 estimate at each monitoring site and then average these for a projected future year annual PM 2.5 concentration for each monitoring site.

Step 5. Compare the fu ture year annual average PM 2.5 concentrations of step 4 to the annual PM 2.5 standard of 15.0 g/m 3 . If all sites have projected PM 2.5 concentrations below the standard, t he attainment test is passed.



### Determining Attainment for All Cases

The program of analysis specified in the Prot ocol will provide a complete attainment demonstration by assessment of all exceedances to th e PM2.5 annual standard that have been detected by the monitoring network and establish the San Joaquin Valley to be classified as nonattainment. The dem onstration will show that the proposed emission control program will successfully lo wer PM2.5 emissions to the extent that exceedances of the federal PM 2.5 standards will be eliminated and that such conditions will be achieved at the earliest practical date consistent with the require ments of the Clean Air Act. The Protocol addresses t he annual standard as t he 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 addre ssed 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 a ll cases. Although the bestSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-69 available data will be utilized, uncertainties and limitations of the dat a affect results of analyses and may produce confirming and cont radictory 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 iden tifiable exceedances that classify the District as nonattainment. The Protocol includes receptor-modeling evaluation of monitoring sites that do not comply wit h 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 t he projection of attainment by weight of evidence include:

• Examination of histor ical monitoring data,

• Evaluation of source zone of influence,

• Demonstration of spatial repres entativeness of monitored episodes

• Assessment of control strategy impac t via rollback analysis (chemical mass balance modeling with speciated roll back and enhanced analysis of secondary particulates for appropriate episodes) wit h application to the design value by SMAT, and

• Regional modeling used in a relative sens e 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 SI P to achieve attainment. Examining historical data provides context for design value obser vations and an assessment of whether the design values are consistent with previous experience. Ev aluation of source zone of influence is necessary for prediction of effe cts of control strat egies with receptor and rollback techniques. Evaluation of the spat ial representativeness of monitored episodes is important to determine which episodes ar e dominated by local sources and which have significant contributions from larger po rtions 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 im portant to determine if modeling or other analysis provides an accurate repr esentation of expected change, to determine that the control program is sufficient to ac hieve attainment of t he PM2.5 standards and is not excessive in its requirements



### Attachments

The following documents are attached and inco rporated 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 epis ode dynamics. Performed for the CRPAQS study as part of an in tegrated monitoring study (IMS-San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-70 95) designed to evaluate conceptual issues. Based on 1995 data collected in the San Joaquin Valley. A2 Modeling Domain –Domai n map annotated with model basic operating parameters A3 Modeling Boundary Conditions – Average annual surface boundary conditions A4 PMF procedure (5 pages) – Methodo logy for the Positive Matrix Factorization approach to ident ify key contributing sources A5 Receptor Modeling Analysis Lay out (4 pages) – Documentation of receptor rollback revisions and conv ersion 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. T he District and ARB will retain modeling documentation at their respective offices for publ ic 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 adopt ion and other required transmittal documents

• The PM2.5 Modeling Prot ocol and Attachments

• Modeling analysis results summary documentation

• Other appendices to provide sup porting information for the plan

• Other documentation ident ified as required by EPASan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan 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 Sept ember 1998. Prepared by: Betty Pun & Christian Seigneur, Atmospheric and Environm ental Research, Inc., 2682 Bishop Drive, Suite 120, San Ramon, CA 9458 3. Prepared for: Mr. Eugene Shelar, Jr., Technical and Ecological Services, Pacific Gas and Elec tric 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 incl uded 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 Cont rol District. An electronic copy may be obtained at:: http://www.arb.ca.gov/airways/ Documents/reports/sjvpmc~1.pdfSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan 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 la y ers u p to 100 mb

ARB emissions Anthropogenic/biogenic Gaseous/Particulates Monthly Weekday/weekend 12-km 2 grid cellsSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan 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

O

3 (ppb) 38 42 39 36 SO 2 (ppb) 0.1 0.3 0.5 0.04
NH 3 (ppb) 0.2 0.2 0.2 0.01 HNO 3 (ppb) 0.2 0.5 0.8 0.1
NH 4 NO 3 (μg/m 3 ) 0.2 0.2 0.2 0.02 (NH 4 ) 2 SO 4 (μg/m 3 ) 1.3 1.5 1.6 1.1 OC (μg/m 3 ) 1.2 1.7 1.3 0.2 EC (μg/m 3 ) 0.2 0.3 0.4 0.0San Joaquin Valley Unified Air Polluti on 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 PM 2.5 Source Apportionment Procedure using PMF2

Positive matrix factorization (PMF) is a statistical method used for deducing factors. Program PMF2 is used for the source appor tionment of airborne particulate matter using PMF method. PMF2 decomposes measur ed data matrix into source profile and source contribution matrices simultaneous ly. By analyzing m easured data at the receptor, PMF2 provides source profiles and s ource contributions at the receptor side. EPA Speciation Trends Network data meas ured at Fresno and Bakersfield will be analyzed separately using PMF2.

Prepare Input Data

Since a carbon denuder that mi nimizes positive sampling artifact caused by adsorption of gaseous organic materials was not included ups tream 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) ar tifact is estimated utilizing the intercept of the regression of OC concentrations against PM 2.5 mass concentrations suggested by Tolocka et al. (2001). Samples for which the PM 2.5 or OC data were missing are excluded from the regr ession analysis between PM 2.5 and OC concentration. Comparing co-located PM 2.5 data measured by STN and Federal Reference Method (FRM), outliers are censored. As s hown in Figure 1, the intercept in PM 2.5 regression against OC concentration is considered to be the OC artifact concentration. The STN OC concentrations are corrected by subtra cting the OC artifact concentration.

Figure 1. PM2.5 mass concentrations vs. OC concentrationsSan Joaquin Valley Unified Air Polluti on 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 PM 2.5 or OC data were not available or below zero, or for which PM 2.5 or OC mass concentration had an error flag are excluded from the data set. Outliers in comparing co-located STN PM 2.5 data and FRM data are excluded. The samples that contain fire works particles which have unusually high concentrations of OC, K, As, Ba, Cu, and Pb are excluded.

X-Ray Fluorescence (XRF) S is excluded fr om the analyses to prevent double counting of mass concentrations since XRF S and Ion Chromatography (IC) SO 4 2- are highly correlated in STN data. Due to the higher an alytical precision compared to XRF Na and XRF K, IC Na + and IC K + are included. Chemical specie s below MDL values more than 90% are excluded. As recommended by Paat ero 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 ma trices: concentration data matrix and uncertainty matrix. To assign input data, the procedure of Poli ssar et al. (1998) is used. The measurement values are used for the i nput concentration data, and the sum of the analytical uncertainty and one-th ird of the detection limit va lue is used as the input uncertainty data assigned to each measured va lue. Concentration values below the detection limit are replaced by half of t he detection limit values, and their input uncertainties are set at five -sixth of the detection limit values. Missing values are replaced by the geometric mean of the measured values for each species, 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 wh ich have error flags. The measured STN PM 2.5 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 det ermine 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 appropria te number of sources are included in the fit, additional sources will not be physically inte rpretable. 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 constan t. The optimal solution should lie in this FPEAK range.

The profiles of real pollution source s 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 com position profiles, the uncertainty space is needed to expand to obtai n sensible solutions. As recommended by Paatero and Hopke (2003), which is to dow n-weight the variable in the analysis soSan Joaquin Valley Unified Air Polluti on 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 solu tion, it is possible to increase the input uncertainties of several species to obtain ph ysically interpretable PMF2 results (i.e., source profiles and contributions) The final PMF2 solutions are determined by experiments with different numbers of source s, 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 t he species fits. For example, Figure 2, a comparison of the reconstr ucted PM2.5 mass contributi ons resolved by PMF2 with measured PM2.5 mass concentrations shows that the resolved so urces 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 nitrat e at LA-North main site.

Figure 2. Measured versus PMF rec onstructed PM2.5 mass concentrations.

Figure 3. Source profile deduced from PM2.5 samples m easured at LA-North main (prediction

 standard deviation).

Figure 4. Source contribution deduced from PM2.5 samples measured at LA-North main.

Interpret SourcesSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-77 To interpret PMF2 results, the identifi ed 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, sour ce 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 variat ions 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 Associ ation. 53, 731-739. 2003.

Lowenthal, D.H., Zielinska, B. , Chow, J.C., Watson, J.G. C haracterization of heavy duty diesel vehicle emissions. Atmosp heric Environment 28 (4), 731–743, 1994.

Paatero, P., Hopke, P.K. Discarding or dow nweighting high-noise va riables in factor analytic models, Analytica Ch imica Acta, 490, 277-289, 2003.

Polissar, A.V., Hopke, P.K., Paatero, P., Malm, W.C., Sisler, J.F. AtmosphericAerosol over Alaska 2. Elemental Composition and Sources, Geophys. Res. 1998, 103 (D15), 19045-19057.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-78 Small, M., Germani, M.S., Sma ll, 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 Envi ronmental Protection Agency, Research Triangle Park, NC, 2007. http://www.epa.gov /ttn/chief/software/speciate/index.html Watson, J.G., Chow, J.C. Source characte rization 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., Pritc hett, L.C., Frazier, C.A. Differences in the carbon composition of s ource profiles for diesel and gasoline powered vehicles. Atmospheric Environment 28 (15), 2493–2505, 1994.

Watson, J.G., Chow, J.C., H ouck, J.E.. PM2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological mate rial, and coal burning in northwestern Colorado during 1995. Chem osphere 43, 1141–1151, 2001.San Joaquin Valley Unified Air Polluti on 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 LayoutSan 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 me teorology for PM 2.5 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 model ed attainment test for PM 2.5 (EPA, 2007). Such models offer the best available r epresentation of important atmospheric processes and are an essential tool in analyzing the impacts of proposed emissions controls on pollutant concentrati ons. The EPA recommends guidelines for choosing a model for use in the attainment te st. For example, t he model source code should be free or low cost, modeling el ements 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 Modeli ng System (CMAQ) has been selected for use in the PM 2.5 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, coagulat ion, wet and dry deposition, aqueous phase chemistry, etc.) and a range of species (e.g., anthropogenic and biogenic, primary and sec ondary, 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 m any spatial and temporal scales. Given that CMAQ has also been a pplied successfully to episodic modeling in Central California, this modeling system has been selected for use in support of the PM 2.5 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 w hether to couple the chosen gas phase mechanism with aerosol and/or aqueous phase chemical processes. In order to simulate the complex mixture of PM 2.5 species in the San Joaquin Valley, SAPRC99 coupled with CMAQ aerosol code vers ion 4 and aqueous phase chemistry has beenSan Joaquin Valley Unified Air Polluti on 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 co mplete 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 chemis try, when coupled with a gas phase mechanism, represents such phenomena as gas–aerosol/aqueous phase mass transfer, chemical transformati on of particulate species and their gas phase precursors, and the evolution of the aer osol size distribution.

Model Inputs and Setup:

Domain Structure: The modeling domain covers the Central Valley and its surroundings with nearly 60,000 12x12 km 2 grid cells. The domain extends from the Pacific Ocean in the west to the Mojave Desert and west ern 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 va rying 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 su rface layer is approximately 30 meters thick.

Initial and Boundar y Conditions: Boundary conditions were taken from the global chemical transport Model for Ozone And Rela ted chemical Tracers (MOZART). Model boundary conditions for major species were extracted for the Central California modeling domain from MOZART re sults representative of the year 2000. In addition to VOCs and inorganic gases, boundary conditions we re extracted for ammonium, nitrate, sulfate, and organic and element al carbon. Initial condit ions 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 chemical ly resolved emissions inventory of combined area, mobile, and point sour ces was generated using the California Emissions Forecasting System (CEFS) vers ion 1.06 with offline adjustments. The inventory includes emissions estimates fo r gaseous and particulate species of anthropogenic and biogenic origin. Gridded hour ly emissions were developed for the CMAQ modeling domain for Dece mber 1999 through January 2001.

Meteorological Inputs: The meteorological input fields to CMAQ were generated with the Meteorology-Chemistry Interfac e 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 te mperature fields necessary to drive the transport and chemistry calculations in CM AQ. 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 depos ition routine (Pleim - with chlorine and mercury species) was c hosen to represent dry deposition.San Joaquin Valley Unified Air Polluti on 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 si mulations for a model reference year and a future year to perform the recommended mo deled attainment demonstration. These attainment runs will use meteorological inputs for 2000 and emissions for the model reference year (2005) and fu ture year (e.g., 2014).

Model Performance Evaluation: To assure that the modeling system (emissions, meteorology, and air quality) is a satisfacto ry representation of t he period modeled, the estimated emissions, meteorology, and air q uality of the base-case simulation need to be compared with observations. Satisfactory performance of the model in simulating observed conditions and responses is a prerequi site for use of the modeling system to evaluate control strategies.

The rigorous comparison of model estima tes with observations is termed model performance evaluation. Such a rigorous m odel performance evaluation for a long-term simulation requires the availabi lity of a temporally and spat ially extensive dataset. The California Regional PM 10 / PM 2.5 Air Quality Study (CR PAQS) 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). T he CRPAQS measurement campaign extended from December 1999 through J anuary 2001 and provided a weal th of data from diverse areas for model evaluation.

Following the quality assuranc e testing and technical review of the emissions and meteorological models, air quality model evaluation will be per formed using CRPAQS data. Typical statistics for use in quant ifying the differences between modeled and observed concentrations include metri cs like Normalized Mean Error (NME) and Normalized Mean Bias (NMB) (Boylan and Russell, 2006): 

# ( ) ∑ ∑

− = o o m C C C MNE 

## ( ) 

# ( ) ∑ ∑

− = o o m C C C MNB

where C m refers to modeled concentrations, C o refers to observed concentrations, and the summations are over all model-observation pairs of a given species at stations/timeperiods of interest. Additi onal statistical metrics typica lly 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 availabl e for comparison with CMAQ modeled concentrations. These data will be used to assess model performance temporally and spatially, with a focus on monthly aver age 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 liter ature on appropriate measures of model performance for long term PM simulations (EPA, 2007; Boylan andSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix F: SJV PM2.5 SIP Modeling Protocol 2008 PM2.5 Plan F-83 Russell, 2006). In addi tion to calculating monthly error statistics for a range of species (e.g., total PM 2.5 mass and its components such as sulf ate, nitrate, ammonium, EC, and OC) at various sites in the modeling domai n, time series plots will be reviewed qualitatively to determine how well the model performs spatia lly 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-di mensional air quality models. Atmospheric Environment, 40:4946-4959. doi:10. 1016/j.atmosenv.2005.09.087

Carter, W. (2000) Docum entation of the SAPRC-99 chem ical mechanism for VOC reactivity assessment. Final Report to California Air Resource s 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., Low enthal, D.G., Magliano, K.A., Turkiewicz, K., and D.E. Lehrman (2006) PM 2.5 chemical composition and spatiotemporal variability during the Calif ornia 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.or g/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 Technica l Note NCAR/TN 398+STR.

U.S. Environmental Protec tion Agency (1999) Science Al gorithms of t he EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System

. EPA/600/R- 99/030. http://www.epa.gov/asmdnerl/CM AQ/CMAQscienceDoc.html

U.S. Environmental Protection Agency ( 2007). Guidance on t he Use of Models and Other Analyses for Demonstrating Attain ment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze. EPA -454/B-07-002.San Joaquin Valley Unified Air Pollution Control District April 30, 2008

1 2 3

4

5 6

7 8

9 10

11 12

13 14

15 16

17 18

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
91
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95
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98 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 Plan20b Appendix F Attachment 5.xlsRevisions 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 PM2.5Used Species data for PM2.5 for columns C, J, K and MMV CMB adjustment PM10 to PM2.5 by EI ratio 90%MV Kings adjusted to 55% of TulareT&BW CMB adjustment PM10 to PM2.5 by EI ratio 50%T&BW CMB Kings adjusted to 55% of TulareOrganic carbon- vegetative burning - adjustment ratio PM10 to PM2.5 75% Kern, Kings, TulareOrganic carbon- vegetative burning - adjustment ratio PM10 to PM2.5 ratio 90% Fresno -representing 2000 OC/ECFresno uses 2001 PM2.5 nitrates from PM2.5 species data, 2000 not availableKern uses 2002 speciation data for PM2.5 nitrates, 200 and 2001 not availableTulare 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 %20Revision 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 headingsAdded RRF SMAT calculation fields on appropriate worksheetsRevision Page 85