A-1 APPENDIX A

REGIONAL AIR QUALITY MODELINGA-2 Regional Air Quality Modeling and PM 2.5 Attainment Demonstration

Introduction

The San Joaquin Valley (SJV) is cu rrently designated as nonattainment
for PM 2.5 . Addressing this issue properly requires rigorous long-term chemical transport modeling to support the development of a plan to attain the National Ambient Air Quality Standard (NAAQS) for PM 2.5 . These modeling exercises serve to determine future year attainm ent status for the SJV given projected
emissions scenarios and also the most e ffective emissions reduction pathways to
control PM concentrations for different seasons and regions of the domain.

A complex interplay between meteorology and chemistry shapes the aerosol size and composition distribution. Atmospheric particulate matter is made up of both directly emitted particl es, like road dust or soot, as well as secondary pollutants formed via chemical reactions with gas-phase compounds. Both primary particles and secondary par ticles (e.g., amm onium nitrate (NH 4 NO 3 ) and ammonium sulfate ((NH 4 ) 2 SO 4 )) make up significant portions of the total
PM 2.5 levels in the SJV. Strong seasonal va riations and many different sources, pathways, and components make PM 2.5 in the region impossible to characterize simply. Effectively understanding the PM 2.5 problem in the SJV therefore requires comprehensive modeling with well-characterized emissions, meteorology, and chemistry in addition to a thorough knowledge of the observed
concentrations of bulk PM 2.5 and its individual PM components. Model predictions combined with observed parti culate concentrations provide the
foundation for the EPA-recommended a ttainment demonstration for PM 2.5

.

While model predictions represent a significant contribution to the attainment demonstration, it is recomm ended that models be used in a “relative” sense in conjunction with observations . Given that models may perform differently for each PM component, it ma y be generally assumed that models will be more successful at predicting concentration changes than absolute concentrations. To dampen the effect s of varying degrees of performance amongst modeled species and the potential model bias in predicting absolute species concentrations, the EPA recommends that the models provide “relative response factors” (RRF) to quantify the effects of emissions changes between base and future years. These relative response factors in conjunction with speciated PM 2.5 measurements form the basis of the Speciated Modeled Attainment Test (SMAT).

In the following sections , the regional air quality modeling performed for use in the Speciated Modeled Attain ment Test is outlined (EPA, 2007). Following a description of the modeling methodology and inputs, the steps of the EPA-recommended Speciated Modeled Attainment Test are described in detail. Finally the modeling result s are presented alongside observational data in order
to determine the future year attainment status for the San Joaquin Valley.A-3 Regional Air Quality Methodology

As stipulated in the EPA Modeling Guidance, a grid-based photochemical model is necessary to perform t he modeled attainment test for PM 2.5 (EPA, 2007). Such models offer the best ava ilable representati on of important atmospheric processes and are an essent ial tool in analyzing the impacts of proposed emissions controls on pollutant concentrations. The EPA recommends guidelines for choosing a model for use in the attainment test. For example, the
model source code should be free or lo w cost, modeling elements should have undergone rigorous scientific peer-review , and it should have been shown to perform well in the past for similar applications.

The Community Multiscale Air Qualit y Modeling System (CMAQ) has been
selected for use in the PM 2.5 modeled attainment demonstration for the San Joaquin Valley Air Pollution C ontrol District. CMAQ is a state-of-the-science “one-atmosphere” system t hat treats major atmospheric and land processes (e.g., advection, diffusion, gas phase c hemistry, gas-particle mass transfer, nucleation, coagulation, wet and dry depos ition, aqueous phase chemistry, etc.) and a range of species (e.g., anthr opogenic and biogenic, primary and secondary, gaseous and particulate) in a comprehensive framework (EPA, 1999; CMAS, 2007).

CMAQ has been extensively peer-revi ewed, is well-documented, and is regularly updated to reflect the latest changes in scientific understanding. CMAQ has been applied successfully in a r ange of environments and on many spatial and temporal scales. Given that CMAQ has also been applied successfully to episodic modeling in Central California , the CMAQ modeling system version 4.6 with California-specific updates, as descri bed in Liang and Kadu wela (2005), was selected for use in support of the PM 2.5 modeled attainment demonstration.

Chemical Mechanism

There are a number of gas-phase chem ical mechanisms readily available for application in CMAQ (e.g., CB-IV, CB-V, SAPRC-99). The user has the additional option of whether to coupl e the chosen gas phase mechanism with aerosol and/or aqueous phase chemical proc esses. In order to simulate the complex mixture of PM 2.5 species in the San Joaquin Valley, SAPRC99 coupled with CMAQ aerosol code version 4 and aqueous phase chemistry has been chosen for this application. SAPRC-99, a complete update of SAPRC-90, is a detailed mechanism describing the gas- phase reactions of volatile organic compounds (VOCs) and oxides of nitr ogen (NOx) (Carter, 2000). AE4-AQ, the fourth-generation CMAQ aerosol code with aqueous phase chemistry, when coupled with a gas phase mechanism, represents such phenomena as gas– aerosol/aqueous phase mass transfer, ch emical transformation of particulateA-4 species and their gas phase precursors, and the evolution of the aerosol size
distribution.

Model Inputs and Setup:

Domain Structure:

Two modeling domains were used for this work. The first modeling domain (“CCAQS”) covers the Central Valley and its surroundings with 63x63
lateral 12x12 km 2 grid cells for each vertical la yer. The CCAQS domain extends
from the Pacific Ocean in the west to t he Mojave Desert and western Nevada in
the east and runs from the northern Sacramento Valley to the Tehachapi
Mountains in the south. The second domain (“SJV”) is nest ed within the CCAQS domain and covers the San Joaquin Valley with 80x89 lateral 4x4 km 2 grid cells for each vertical layer (Figure 1). The vertical structure for both domains is composed of 15 layers of varying thickne ss up to the top of the meteorological domain (100 mb). The finest resolution belongs to those layers closest to the surface and is determined largely by the ve rtical structure of the meteorological inputs. The surface layer is approximately 30 meters thick.

Initial and Boundar y Conditions:

Boundary conditions for the CCAQS dom ain were taken from the global chemical transport Model for Ozone And Re lated chemical Tracers (MOZART). Model boundary conditions for major spec ies were extracted for the Central California modeling domain from MOZART results representative of the year
2000. In addition to VOCs and inor ganic gases, boundary conditions were extracted for ammonium, nitrate, sulfat e, and organic and elemental carbon.
Initial conditions were estimated as an average of the extracted boundary conditions for each species. The CCAQS domain provided the initial andboundary conditions for the SJV domain. All species reported in the threedimensional output fields for the simulation over the CCAQS domain were
included in the initial and boundary conditions for t he SJV domain. While boundary conditions for the CCAQS domain we re held constant for each month, boundary conditions for the SJV domain varied for each hour. The impact of
initial conditions was minimized for eac h domain by simulating 8-day spin up periods prior to the simulati on of each month of the year.

Emissions:

A spatially, temporally, and chemically resolved emissions inventory of combined area, mobile, and point sour ces was generated using the California Emissions Forecasting Syst em (CEFS) version 1.06 with offline adjustments. The inventory includes emissions estimates for gaseous and particulate species of anthropogenic and biogenic origin. Gr idded hourly emissions were developed for the CMAQ modeling domain for the years 2000, 2005, and 2014 (baseline).A-5 Quality assurance checks of domain emi ssions totals and spatial distribution were performed at various steps in emissi ons processing in order to ensure that the CMAQ emissions i nput files were sound.

In order to better esti mate future air quality, a second 2014 emissions sensitivity scenario was gener ated incorporating expected reductions from future state and local controls beyond the baselin e. These were applied uniformly over the domain on a percentage basis. The per centage difference between baseline
and “controlled” emissions was taken from the information in Table 1.

Meteorological Inputs:

The meteorological input fields to CMAQ were generated with the Meteorology-Chemistry Interf ace Processor (MCIP) version 3.0. MCIP serves as a link between meteorological model s like MM5 or WRF with CMAQ and generates model-ready met eorological inputs like t he wind and temperature fields necessary to drive the transpor t and chemistry calcul ations in CMAQ. Inputs to MCIP were generated using the PSU/NCAR Mesoscale Model (MM5) (version 3.6) (Grell et al, 1995). Planetary boundary la yer and radiation
characteristics were calculated in MCIP , and the Models-3 dr y deposition routine (Pleim - with chlorine and mercury spec ies) was chosen to represent dry deposition.

MM5 Simulation

MM5 is a limited area, terrain-fo llowing sigma coordinate model on Lambert Conformal projection that wa s developed by Penn State and NCAR as a community mesoscale model. The model is based on non-hydrostatic, fully compressible motions that allow users to study the atmospheric motions at small scales by explicitly treating the effect s of convective motions on atmospheric
circulations. The MM5 model has been improved over more than two decades by contributions from a broad scientific community.

MM5 was set up for a 14 month simu lation (December 1999 – January 2001) with three nested grids using (70 x 70), (133 x 133), and (94 x 85) grid points in (x, y) or (south-north, west -east) direction with 36, 12, and 4 km horizontal resolution in each grid, respecti vely (Figure 2). The vertical structure of the domain was defined with 30 layers extending to 100 mb at the top of the
domain

. The first two coarse grids defined t he atmospheric initial and boundary conditions for the area at large scale, while the innermost grid with 4 km
horizontal resolution resolved the fine det ails of atmospheric motions within the SJV modeling domain. MM5 has several opt ions to calculate the components of internal and external forces acting on a volu me of air, such as those for radiation, convection, cloud microphysics, soil fl uxes, and boundary layer physics. While many sensitivity studies were conducted using various model options to find the best agreement with observations, an effort was also made to use the sameA-6 model options from one simulation to the next. The Grell (1995) cumulus parameterization scheme for coarse gr ids was used along with the Blackadar boundary layer scheme for calculation of fl uxes (Blackadar, 1979, Grell, 1995). The Dudhia simple ice scheme was used for the treatment of excess moisture (Dudhia, 1989) and the Dudhia cloud radi ation scheme (Dudhia, 1993) was used for radiational heating and cooling of the at mosphere. The Blackadar multi-layer, force-restore method soil model (Blackada r, 1976) was used for soil physics in all grids.

Initial and boundary conditions were prepared using the analyses of observations prepared by the National Ce nter for Environmental Prediction (NCEP) archived at NCAR. The 14 -month period (December 1999 through January 2001) was first simulated using initial and boundary conditions (IC/BCs) with the analysis nudging option on the two c oarse grids. Then, IC/BCs were prepared from the 12 km grid output for the initialization of the 4 km grid. The three-dimensional (3-D) wind and temper ature values simulated by the model were compared against surface observations obtained from the f our SJV stations (Fresno, Bakersfield, Arvin, and Parlier) to study the temporal and 3-D spatial structure of atmospheric motions as we ll as to evaluate the model performance within SJV. Figure 2 shows the MM5 domains employed to generate meteorological fields for the CMAQ simulation.

Model Years:

CMAQ was run for the year 2000 to provide the basis for the model performance evaluation. It was duri ng 2000 that the Ca lifornia Regional
PM 10 /PM 2.5 Air Quality Study (CRPAQS) took place. The study resulted in a
wealth of data with which to evaluate model performance. As it is necessary to
execute simulations for a model reference year and a future year to perform the
recommended modeled attainment demonstr ation, 2005 and 2014 were also simulated. Simulations for all years were driven by the meteor ological inputs for 2000, while emissions vari ed from year to year.

Model Performance Evaluation:

To assure that the modeling system (emissions, meteorology, and air quality) is a satisfactory representati on of the period modeled, the estimated emissions, meteorology, and air quality of the base-case simulation need to be compared with observations. Satisfac tory performance of the model in simulating observed conditions and responses is a prerequisite for use of the modeling system to evaluate control strategies.

CRPAQS was an extensive and int ensive measurement campaign designed to characterize the important chemical and physical processes involved in the formation and evolution of particula te matter in Central California (Chow et al. 2006). The CRPAQS measurement camp aign extended from December 1999A-7 through January 2001 and provided a wealth of data from diverse areas for
model evaluation.

There are hourly and daily concentrat ion data for a range of gaseous and particulate species at numerous site s available for comparison with CMAQ
modeled concentrations. These data wil l be used to assess model performance temporally and spatially, with a focu s on monthly average performance, as recommended for long-term model simulati ons. Appropriate goals for model performance will be based on the EPA Modeling Guidance and
recommendations in the scientific literat ure on appropriate measures of model performance for long term PM simulations (EPA, 2007; Boylan and Russell, 2006). A model performance analysis is fort hcoming which will be provided at a later date as an appendix to this work.

Speciated Modeled Attainment Test

Regional air quality modeling only repr esents a portion of the attainment test. In order to perform the EPA-re commended Speciated Modeled Attainment Test, or SMAT, the relative response between the modeled reference and future years must be considered in conjunction with observations. This approach minimizes the uncertainties in predicting fu ture year attainment that result from potential model bias in pr edicting absolute species concentrations. In the following sections, the Speciated Modeled At tainment Test, as described in the
EPA “Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze”, is outlined in general terms. Here the recommended procedure for combining model results (relative response factors (RRFs)) wit h speciated and bulk observations (STN and FRM measurements) in order to determi ne future year attainment status is explained. This is followed by a descr iption of the application of the SMAT procedure to the San Joaquin Valley to determine future year PM 2.5 attainment status for the region.

General Procedure of SMAT

Step 1. Calculate the obs erved quarterly mean bulk PM 2.5 concentration and composition for each measurement site. The quarterly mean species concentrations can be calculated by multiplying the observed percentage contribution of each species aga inst 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 years), one of which should be the model ed reference year. The procedure to speciate this bulk design value is descr ibed in the “SANDWICH” section below.

Step 2. Calculate the RRFs for each quarter and measurement site. Relative response factors are calculated usi ng model results. For species i, site j, and quarter k, the RRF is gi ven by the follo wing equation:A-8 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 the locati on of the measurement site) for the reference year and the future year attain ment 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 represent ative spatial scale of the monitors, and the desire to offset any potential errors stemming from the geometry of the superimposed grid system.

Step 3. Multiply the qu arterly, site-specific m odel-based RRFs from step 2 and speciated observations from step 1 to estimate future quarterly species
concentrations.

Step 4. Sum the future quarterly s pecies concentration estimates from 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 from step 4 to the annual PM 2.5 standard of 15.0 μ g/m 3 . If all sites have projected PM 2.5 concentrations below the standard, the attainment test is passed.

These are the basic steps of SMAT. The following sections examine the attainment test in more detail, starti ng with the calculation of the base year design values against which the model-based RRFs are multiplied.

Speciated Base Year Design Values

Federal Reference Method

Federal Reference Method (FRM) PM 2.5 mass measurements provide the basis for nonattainment designations. For this reason it is recommended that the FRM data also be used to project fu ture air quality and progress towards
attainment of the health-based National Ambient Air Quality Standard (NAAQS)
for PM 2.5 . However, given the comple x physicochemical nature of PM 2.5 , it is necessary to consider individual specie s as well. While the FRM measurements give the mass of the bulk sample, a me thod for apportioning this bulk mass to
individual PM 2.5 components is a first step towards determining the best targets
for emissions controls in order to r each NAAQS levels in a timely manner.

The FRM measurement protocol finds it s roots in the past epidemiological
studies of health effects associated with PM 2.5 exposure. It is upon these studies that the NAAQS is based. The protocol is sufficiently detailed so that results might be easily reproducible and involves the measurement of filter mass beforeA-9 and after sampling after equilibrating at nar rowly defined conditions. Filters are equilibrated for more than 24 hours at a standard relative humidity between 30 and 40% and temperature between 20 and 23 oC. Due to the sampler construction and a lengthy filter e quilibration period, however, FRM measurements are subjec ted to a number of k nown positive and negative
artifacts. FRM measurements do not necessarily capture the PM 2.5 concentrations in the atmosphere and can differ substantially from what is measured by speciation monitors (e.g., STN). Nitrate and se mi-volatile organics
can be lost from the filt er during the equilibrati on process, and particle bound water associated with hygroscopic species like sulfate provides a positive artifact. These differences present an area for careful consideration when one attempts to utilize speciated measurements to appor tion the bulk FRM mass to individual species.

Given that (1) attainment stat us is currently dependent upon FRM measurements and (2) concent rations of individual PM 2.5 species need to be
considered in order to under stand the nature of and effi cient ways to ameliorate
the PM 2.5 problem in a given region, a method has been developed to speciate
bulk FRM PM 2.5 mass with known FRM limitations in mind. This method is
referred to as the measured S ulfate, A djusted N itrate, D erived W ater, I nferred
C arbonaceous material balance approac h or “SANDWICH”. SANDWICH is based on speciated measuremen ts from other (often co-located) samplers, such as those from the Speciated Trends Ne twork (STN), and the known sampling environment of the FRM. The approac h serves to provide mass closure, reconciliation between speciated and bulk mass concentration measurements, and the basis for a connection between observations, modeled PM 2.5 concentrations, and the air quality standard.

SANDWICH

Given that the FRM is the basis for attainment status, the reconstructed
PM 2.5 mass should be based on the composit ion of the mass measured by the FRM. SANDWICH was developed in order to attribute portions of the bulk FRM mass to different components using STN measurements while also considering
the limitations of the FRM m onitor. Nitrates are often lost from t he FRM filter along with semivolatile organics, and water bound to the inorganics also comprises a portion of the FRM mass. The main steps in estimating the PM 2.5 composition are as follows:

(1) calculate the nitrate retained on the FR M filter using hourly relative humidity and temperature alongside ST N 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),A-10 (3) calculate particle bound water using the concentrations of ammonium, sulfate, and nitrate, using an equilibrium model like the Aerosol Inorganic Model or a polynomial equation derived from model output, and

(4) calculate organic carbon mass (OCM mb ) by difference, subtracting all inorganic species (includi ng 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 μ g/m 3 ]

where all concentrations have units of μ g/m 3 and

[SO 4 2- ] = measured sulfate

[NO 3 - FRM ] = nitrate retained on the FRM filter

[NH 4 + FRM ] = ammonium associated with the ni trate and sulfate on the FRM filter

[EC] = measured EC

Other = other inorganic mass; e.g., crustal material or sea salt

[OCM mb ] = organic carbon mass calculated by difference by adding all inorganic species and subtract ing from the FRM PM 2.5 mass. Organic carbon measurements may also be used if it s eems that the OC by mass balance is clearly under or overestimated.

[H 2 O] = water bound to the hygroscopic species; here, calculated using a polynomial equation dependent on the concentrations of sulfate, nitrate, and ammonium or using an inorgani c aerosol equilibrium model.

Blank mass represents the mass passively collected on the filter and is assumed

to be held constant at a value of 0.5 μ g/m 3

.

SANDWICH may be applied directly in areas where FRM and speciation
monitors are collocated. When that is not the case, the fo llowing calculations and adjustments may still be applied using s patially interpolated speciation data
or some other means to estimate the s peciation. The speciation concentrations referred to in the following paragraphs can be represented, therefore, by measurements from a spec iation monitor collocated with the FRM or by interpolated values for the species measured by speciation monitors in the region.A-11 The first step in estimating the speciati on of the bulk sample is to assume that, due to the stability and nonvolatility of sulfate, sulfate measured by the speciation samplers is similar to the sulfate captured in the FRM sampler. The same is assumed for [EC]. As menti oned above, a passively collected mass of

0.5 μ g/m 3 is assumed. Estimation of other species’ contributions to the FRM mass is described in the following sections.

Adjusted Nitrate

The FRM does not retain all of the semi-volatile PM 2.5 mass, and at warmer temperatures, loss of particulate nitrate from filters has been widely observed (Chow et al., 2005). In order to estimate how much nitrate is retained on the FRM filter, simple thermodynamic equilibrium relations may be used. Necessary inputs include 24-hour aver age nitrate measurements and hourly temperature and relative humidity data. Frank (2006) suggests the following methodology for estimati ng retained nitrate:

(1) For each hour (i) of the day, calc ulate the dissociat ion constant, K i , from ambient temperature and relative humidity.

For RH < 61%: ln(K) = 118.87 – (24084/T) – 6.025 ln(T), where T is temperature
in kelvins and K is in nanobars.

For RH >= 61%, K is replaced by: K’ = [P 1 - P 2 (1-a) + P 3 (1-a) 2 ] x (1-a) 1.75 x K,
where a is “fractional” relative humidity and

ln(P 1 ) = -135.94 + 8763/T + 19.12ln(T)

ln(P 2 ) = -122.65 + 9969/T + 16.22ln(T)

ln(P 3 ) = -182.61 + 13875/T + 24.46ln(T)

(2) From that, calculate the nitrate retained on the filter:

Retained Nitrate = ST N nitrate – [745.7/T R x ( κ

- γ ) x 1/24 x Σ (i=1 to 24) (K i ) 0.5 ]

where T R is the daily average temperature fo r the sampled air volume (K), K i is

the dissociation constant for NH 4 NO 3 at ambient temperat ure for hour i, and ( κ

- γ ) relates to the temperature rise of the filter and vapor depletion from the inlet surface and is assumed to have a value eq ual to 1 (Hering and Cass, 1999). For further details, please refer to Frank (2006).

Ammonium

All nitrate on the FRM filter is assu med to be neutralized by ammonium.
Ammonium associated with nitrate on the FRM filter, therefore, is given byA-12 [NH4] NO3,FRM = 0.29 x [NO3] FRM

where [NO 3 ] FRM is the nitrate remaining on the FRM filter and 0.29 is the mass
ratio of NH 4 to NO 3 if fully neutralized.

The form of (NH 4 ) 2 SO 4 , however, can vary depending on location and season. Sulfate may or may not be fully neutralized. In order to determine the
amount of ammonium asso ciated with the sulfate aerosol, the ammonium
difference between the STN and FRM filt er needs to be determined. Some studies point to a loss of STN NH 4 during sampling conditions where nitrate
volatilization is favored. While the volatilized NO 3 will be recaptured on the basic nylon filter and reported as nitrate, the associated ammonium, not sharing the same affinity for the basic filter, may pass through. In some cases, an adjustment assuming a loss of STN NH 4 corresponds more closely with the NH 4 on the FRM filter; in other cases, the unadjusted meas urement agrees better with
FRM NH 4 . For the calculations for the SJV, an area rich in NH 3 , NH 4 on the filter
is assumed to be the measured STN NH 4 minus any losses associated with fully neutralized nitrate (as NH 4 NO 3 ) that may have volatili zed off the FRM filter.

[NH4] FRM = [NH4] STN - 0.29 x ([NO3] STN -[NO3] FRM )

Here, ([NO3] STN -[NO3] FRM ) represents the amount of nitrate mass lost due to
volatilization.

Particle Bound Water

At the FRM filter equilibrat ion conditions, hygroscopic aerosol will retain its particle bound water (PBW) and be in cluded in the observed FRM PM 2.5 mass. PBW can be calculated using an equilibri um model like the Aerosol Inorganics Model (AIM). AIM requires the concentrati ons of ammonium, nitrate, sulfate, and
estimated H + as inputs. In addition to inorgani c concentrations, the equilibration conditions are also necessary model inpu ts. In this case, a temperature of
294.15 K and 35% RH is recommended. For simplification, a polynomial regression equation may be constructed by fitting the calculated water concentration from an equilibrium model and the concentrations of nitrate, ammonium, and sulfate.

Other Species

Other components that may be repres ented on the FRM filter include elemental carbon, crustal material, s ea salt, and passively collected mass. Depending on location, for example, cert ain species may be neglected (like sea salt for inland areas).A-13 Carbonaceous Mass

While carbonaceous aerosol may make up a large portion of airborne aerosol, speciated measurements of ca rbonaceous PM are considered highly
uncertain. This is due to the lar ge number of carbon compounds in the atmosphere and the measurement uncertain ties associated with samplers of different configurations. In the SANDWICH approach, or ganic carbonaceous mass is calculated by difference. T he sum of all non-organi c carbon components may be subtracted from the FRM PM 2.5 mass as an estimate of organic carbon.

After having calculated the species concentrations as outlined above, one can calculate the percentage contribution of each species to the measured FRM mass (minus the blank concentration of 0.5 μ g/m 3 ) for each quarter of the years represented by the speciat ed data. One can then apply those percentages to the base year FRM design value, with the a ssumption that the years for which the speciation was calculated are representat ive of all years over which the design value is averaged. For the annual PM 2.5 attainment demonstration, the speciated reference year FRM design value for each quarter is multiplied by the RRF for each species at each site, averaged toget her, and summed to get a future year

PM 2.5 concentration. Note that blank mass is kept constant at 0.5 μ g/m 3 between the base and future years, and future year particle bound water needs to be calculated for the future year values of nitrate, ammonium, and sulfate. A numerical example of t he SMAT for the annual PM 2.5 standard is given in the EPA modeling guidance on pp. 53-56 (U.S. EPA, 2007).

24-hour Attainment Test

The 24-hour attainment test is similar to the attainment test for the annual
PM 2.5 standard. In the case of the 24-hour test, however, the goal is to have the

future 24-hour PM 2.5 design value at or below 65 μ g/m 3 for all sites. The test has the following steps:

Step 1. Identify the observed 98 th percentile 24-hour PM 2.5 average concentrations for each year (e.g., of the three year period that makes up the reference year design value) and the nex t highest concentrations for the other quarters.

Step 2. Using the SANDWICH methodology outlined above, calculate the species percentages of the bulk FRM samp les for each site, quarter, and year.
These fractions should be calculat ed using days at the high end of the distribution (i.e., days ar ound or above the standard).

Step 3. Calculate RRFs for each species at each site for each quarter from modeled days at t he high end of the PM 2.5 distribution (i .e., modeled days having PM 2.5 concentrations around or above 65 μ g/m 3 ). As in the case of theA-14 annual test, for species i, site j, and quarte r k, the RRF is given by the following equation:

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

Step 4. Multiply the qu arterly, site-specific m odel-based RRFs from step 3 and speciated observations from step 2 to estimate future quarterly species
concentrations.

Step 5. Sum the future quarterly s pecies concentration estimates from step 4 to estimate a quarterly “potential” 98 th percentile PM 2.5 estimate at each monitoring site.

Step 6. Average the highest quarterly values for each year and then compare the future year design val ues at each site to the 24-hour PM 2.5 standard

of 65 μ g/m 3 . If all sites are at or below the standard, the 24-hour PM 2.5 attainment test is passed.

A numerical example of an application of the 24-hour PM 2.5 attainment test is given on pp. 60-63 of the EPA Model Guidance (U.S. EPA, 2007).

SJV Attainment Demonstration for the Annual PM 2.5 Standard

The SMAT procedure as outlined abov e was applied for FRM monitors operating in the San Joaquin Valley with minimal deviations from the recommended procedure. The 2006 design value was used as a basis from which to project forward the future year design value. Speciation data for four STN (speciation) sites was used to specia te the FRM mass for all FRM sites.
For those sites not collocated with STN monitors, “surrogate” speciation sites were determined based on analysis of CRPAQS data to determine which sites had similar speciation profiles. The com position was assumed to be the same at all three Bakersfield sites (BAC, BG S, and BEP). Similarly, the percent composition at the two Fresno sites (F SF and FSH) was assumed to be the same. In addition, Sto ckton (SOH), Clovis (CLO), Corcoran (COP), and Modesto (MRM), were assumed to have the same spec iation as one of the four speciation sites based on CRPAQS data analysis. The results of that analysis are given in
Table 2. For a list of all FRM sites and t heir associated speciation site, see Table
3.

The steps followed in order to determi ne future year design values for the
FRM sites of the SJV were similar to those outlined above in the generalized description of the SMAT/SANDW ICH procedure. Specific points to note are that in the speciation calculations, only days with all the necessary hourly temperature and relative humidity data in addition to values for all major components (ammonium ion, nitrate ion, su lfate ion, and elemental carbon) plus FRM mass were included in the species average calculations. Temperature andA-15 relative humidity data were obtai ned from the ARB’s Air Quality and Meteorological Information System (AQMIS, www.arb.ca.gov/aqd/aqinfo.htm ) database for each monitoring station (colloca ted with the STN sites or nearby if the STN site was not available) for each available hour of the years 2004-2006.

Quarterly average species concentrations were calculated at each STN site using the SANDWICH procedure descr ibed above. In addition, a blank mass average of 0.5 μg/m 3 was assumed for each quarter and held constant into the
future. Modeled concentrations for the reference year (2005) and future year (2014) for each component were extract ed for the FRM sites as a nine-cell average. The relative response factors (as defined above) were calculated for each component for each quarter. These calculations were performed using all modeled days, as we assumed that the selected FRM measurements provided a stable quarterly average value. Finally , particle bound water was calculated using a polynomial algorithm provided by Dr. Bong-Mann Kim of the South Coast Air Quality Management District. Organi c mass was calculated by difference between the average total FRM mass and the sum of all other species and blank.

These quarterly species percentages were then multiplied against the reference year design value for 2006 (the average FRM PM 2.5 concentrations for 2004, 2005, and 2006). The quarterly obser ved species concentrations were then multiplied by the RRFs and summed and averaged to get a future year
PM 2.5 design value at each FRM site. See Table 3 for the predicted baseline and
controlled 2014 PM 2.5 design values. For the “controlled” 2014 emissions sensitivity scenario descri bed above, future annual PM 2.5 concentrations at all

FRM sites are below the annual PM 2.5 NAAQS of 15.0 μ g/m 3 , and, therefore, the San Joaquin Valley has passed the speciated modeled attainment test for the
annual PM 2.5 NAAQS.

SJV Attainment Demonstration for the 24-Hour PM 2.5 Standard

A similar procedure to the attain ment demonstration for the annual PM 2.5 standard was followed for the 24-hour PM 2.5 standard attainment demonstration. The exception was that only the top 25% of the measured and modeled days for each quarter were used instead of all avai lable days. The top 25% of the days are expected to be more representative of the 24-hour desi gn value than would all available days for a given quarter.

Table 4 shows the project ed future year 24-hour PM 2.5 design values for the top five 2006 design value sites for the controlled emissions sensitivity case. As shown, all sites in the SJV attained the 24-hour standard in 2006, and further emissions controls do not cause any monitors to become non-attainment.A-16 Unmonitored peaks

EPA’s modeling guidance (EPA, 2007) describes the “Unmonitored Area Analysis” as an analysis used to ensure t hat a proposed contro l strategy will be effective in reducing PM 2.5 at locations without air quality monitors so that attainment is shown throughout a nonatta inment area. The purpose of the analysis is to use a combination of m odel output and ambient data to identify areas that might exceed the NAAQS if monitors were located there.

This analysis has four major steps. They are:

1. Interpolation of the base year design values to create a set of spatial fields 2. Adjustment of the spat ial design value fields using gridded model output gradients (base year values) 3. Application of the gr idded model RRFs to the gradi ent adjusted spatial design
value fields 4. Determination if any unmonitored areas are predicted to exceed the NAAQS
in the future

We will present a complete analysis of unmonitored areas in a future version of this document. A screening analysis that is designed to assess the possibility of unmonitored vi olations of the annual PM 2.5 NAAQS is presented
here.

First, an annual-averaged modeled PM 2.5 field is generated for the entire modeling domain as shown in figure 3. Th is field is then scrutinized to see if there will be gradients in the field that woul d give rise to higher values away from
monitors if this field were to be us ed to adjust the interpolated annual-averaged design value field. As s een from figure 3, t here are no areas with steep gradients that would result in higher design values than those measured at monitors. That is, for the central and southern part of t he Valley where design values are high, the highest modeled concentrations occur at monitors so that there can not be higher values away from monitors.

As mentioned before, this is a si mple screening analysis and a complete analysis of the unmonitored areas will be presented in the future.

References

Blackadar, A. K. (1976) Modeling the Nocturnal Bou ndary Layer. Preprints of Third Symposium on Atmospheric Tur bulence and Air Quality, Raleigh, NC. 19-22, October 1976, Amer. Meteor. Soc ., Boston, 46-49.

Blackadar, A. K. (1979) Advances in En vironmental Science and Engineering, 1. No. 1. Pfaflin and Ziegler, Eds. Gordon and Breach Publishers, 50-85.A-17 Boylan, J.W. and A.G. Russell (2006) PM and light extinction model performance metrics, goals, and criteria for three- dimensional air quality
models. Atmospheric Environment , 40:4946-4959. doi:10.1016/j.atmo senv.2005.09.087

Carter, W. (2000) Docu mentation of the SAPRC-99 chemical mechanism for VOC reactivity assessment. Final Report to California Air Resources
Board. Contract 92-3 29 and Contract 95-308, http://pah.cert.ucr.edu/ftp/ pub/carter/pubs/s99txt.pdf

Chow, J.C., Chen, L.W.A., Watson, J. G., Lowenthal, D.G., Magliano, K.A., Turkiewicz, K., and D.E. Lehrman (2006) PM 2.5 chemical composition and spatiotemporal variability during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS). J. Geophys. Res ., 111, D10S04,
doi:10.1029/2005JD006457.

Chow, J.C., Watson, J.G., Lowenthal, D. H., Magliano, K. (2005) Loss of PM 2.5 Nitrate from Filter Sample s in Central California. J. Air & Waste Manage.
Assoc . 55:1158-1168.

Community Modeling and Analysis Syst em (2007) CMAQ v4.6 OperationalGuidance Document. http://www.cmaqmodel.org/op_guidan ce_4.6/manual.pdf

Dudhia, J. (1989) Numerical Study of Convection Observed During the Winter Monsoon Experiment Using a Two-Dimensional Model. J. Atmos. Sci ., 46,
3077-3107.

Dudhia, J. (1993) Radiation Studies with a High-Resolution Mesoscale Model. Proceedings of the Thir d Atmospheric Radiation Measurement (ARM) Science Team Meeting. March 1- 4, 1993, Norman, OK. pp. 363-366.

Frank, Neil H. (2006) Retained Nit rate, Hydrated Sulfates, and Carbonaceous
Mass in Federal Reference Method Fine Particulate Matter for Six Eastern
U.S. Cities . J. Air & Waste Manage. Assoc. 56:500-511.

Grell, G. A., J. Dudhia and D. R. Stau ffer. (1995) A Description of the FifthGeneration Penn State/NCAR Mesoscale Model (MM5). NCAR Technical
Note , NCAR/TN-398+STR.

Hering, S.V. and Cass, G.R. (1999) The M agnitude of Bias in the Measurement
of PM 2.5 Arising from Volatilization of Particulate Nitrate from Teflon Filters. J. Air & Waste Manage. Assoc . 49:725-733.A-18 Liang, J. and A. Kaduwela, 2005: Microdev elopment of CMAQ for California Regional Particulate-Matter Air Qua lity Study. Proceedings of the 4th
Annual CMAS Models-3 Users’ Conf erence; September 26-28, 2005, Chapel-Hill, NC.

U.S. Environmental Protection Agency (1999) Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System

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

U.S. Environmental Protecti on Agency. (2007) Guidance on the Use of Models and Other Analyses for Demonstrating A ttainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze. EPA-454/B-07-002A-19 Figure 1. The CCAQS modeling domain with the SJV modeling domain inset.A-20 Figure 2. The location of the three nest ed grids adopted for the numerical modeling of SJV using MM5.A-21 Figure 3. The annua l-averaged modeled PM 2.5 concentrations in the domain.A-22 Table 1. Percentage reduction between 2014 baseline and controlled emissions

NOx PM2.5 SOx ROG Defined State Measures (Strategy 4/26/07) 20 5 0 6 District Rules (Dft Plan 12/11/07) 4 8 4 0

Total Reduction 24 13 4 6

Table 2. Percent Composition Ratio Based on 2000 Average CRPAQS Data

Site AmmNitrate AmmSulfate EC OC Geological Elements

CLO % 30.78 9.09 10.49 43.22 4.14 2.27

FSF % 26.79 10.15 7.07 49.31 4.16 2.52

CLO to FSF Ratio 1.15 0.90 1.48 0.88 1.00 0.90

COP % 36.44 9.86 8.37 37.87 5.06 2.39

VCS % 35.38 9.53 9.04 39.53 4.37 2.14

COP to VCS Ratio 1.03 1.03 0.93 0.96 1.16 1.12

MRM % 29.33 9.08 10.92 45.50 2.82 2.35

M14 % 26.39 9.84 11.43 46.35 2.71 3.27

MRM to M14 Ratio 0.90 1.08 1.05 1.02 0.96 1.39

SOH % 24.80 11.76 13.13 44.07 2.93 3.31

M14 % 26.39 9.84 11.43 46.35 2.71 3.27

SOH to M14 Ratio 0.94 1.19 1.15 0.95 1.08 1.01

Table 3. Reference and future year annual design values for SJV FRM sites

Site Code Speciation 2006
DV 2014
Baseline
DV 2014
"Controlled"
DV

Bakersfield - 5558 California BAC BAC 18.51 15.86 14.28

Bakersfield - 410 E Planz
Road BEP BAC 18.86 16.26 14.70

Bakersfield - Golden State BGS BAC 18.64 15.98 14.39

Clovis - N Villa Avenue CLO FSF 16.39 14.10 12.72

Corcoran - Patterson
Avenue COP VCS 17.24 14.75 13.27

Fresno - 1st Street FSF FSF 16.68 14.43 13.01

Fresno - Hamilton and Winery FSH FSF 17.16 14.93 13.47

Merced - 2334 M Street MRM M14 14.69 12.85 11.76

Modesto - 14th Street M14 M14 14.10 12.52 11.44

Stockton - Hazelton Street SOH M14 12.93 11.77 10.87

Visalia - N. Church Street VCS VCS 18.20 16.05 14.47A-23 Table 4. Reference and future year 24-hour design values for SJV FRM sites

Site Code Speciation 2006
DV 2014
"Controlled"
DV

Bakersfield - 5558 California BAC BAC 62.4 46.2

Bakersfield - 410 E Planz
Road BEP BAC 65.2 45.9

Bakersfield - Golden State BGS BAC 64.4 45.3

Fresno - 1st Street FSF FSF 58.0 41.2

Fresno - Hamilton and Winery FSH FSF 58.5 41.7