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APPENDIX A 

REGIONAL AIR QUALITY MODELING 
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Regional Air Quality Modeling and PM2.5 Attainment Demonstration 

Introduction 

The San Joaquin Valley (SJV) is currently designated as nonattainment 

for PM2.5. 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 PM2.5. These modeling exercises 

serve to determine future year attainment status for the SJV given projected 

emissions scenarios and also the most effective 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 particles, like road dust or soot, as well as 

secondary pollutants formed via chemical reactions with gas-phase compounds. 

Both primary particles and secondary particles (e.g., ammonium nitrate (NH4NO3) 

and ammonium sulfate ((NH4)2SO4)) make up significant portions of the total 

PM2.5 levels in the SJV. Strong seasonal variations and many different sources, 

pathways, and components make PM2.5 in the region impossible to characterize 

simply. Effectively understanding the PM2.5 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 PM2.5 and its individual PM components. Model 

predictions combined with observed particulate concentrations provide the 

foundation for the EPA-recommended attainment demonstration for PM2.5. 

While model predictions represent a significant contribution to the 

attainment demonstration, it is recommended that models be used in a “relative” 

sense in conjunction with observations. Given that models may perform 

differently for each PM component, it may be generally assumed that models will 

be more successful at predicting concentration changes than absolute 

concentrations. To dampen the effects 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 PM2.5 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 Attainment 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 results are presented alongside observational data in order 

to determine the future year attainment status for the San Joaquin Valley. 
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Regional Air Quality Methodology 

As stipulated in the EPA Modeling Guidance, a grid-based photochemical 

model is necessary to perform the modeled attainment test for PM2.5 (EPA, 

2007). Such models offer the best available representation of important 

atmospheric processes and are an essential tool in analyzing the impacts of 

proposed emissions controls on pollutant concentrations. The EPA recommends 

guidelines for choosing a model for use in the attainment test. For example, the 

model source code should be free or low cost, modeling elements should have 

undergone rigorous scientific peer-review, and it should have been shown to 

perform well in the past for similar applications. 

The Community Multiscale Air Quality Modeling System (CMAQ) has been 

selected for use in the PM2.5 modeled attainment demonstration for the San 

Joaquin Valley Air Pollution Control District. CMAQ is a state-of-the-science 

“one-atmosphere” system that treats major atmospheric and land processes 

(e.g., advection, diffusion, gas phase chemistry, gas-particle mass transfer, 

nucleation, coagulation, wet and dry deposition, aqueous phase chemistry, etc.) 

and a range of species (e.g., anthropogenic and biogenic, primary and 

secondary, gaseous and particulate) in a comprehensive framework (EPA, 1999; 

CMAS, 2007). 

CMAQ has been extensively peer-reviewed, is well-documented, and is 

regularly updated to reflect the latest changes in scientific understanding. CMAQ 

has been applied successfully in a range of environments and on many spatial 

and temporal scales. Given that CMAQ has also been applied successfully to 

episodic modeling in Central California, the CMAQ modeling system version 4.6 

with California-specific updates, as described in Liang and Kaduwela (2005), was 

selected for use in support of the PM2.5 modeled attainment demonstration. 

Chemical Mechanism 

There are a number of gas-phase chemical mechanisms readily available 

for application in CMAQ (e.g., CB-IV, CB-V, SAPRC-99). The user has the 

additional option of whether to couple the chosen gas phase mechanism with 

aerosol and/or aqueous phase chemical processes. In order to simulate the 

complex mixture of PM2.5 species in the San Joaquin Valley, SAPRC99 coupled 

with CMAQ aerosol code version 4 and aqueous phase chemistry has been 

chosen for this application. SAPRC-99, a complete update of SAPRC-90, is a 

detailed mechanism describing the gas-phase reactions of volatile organic 

compounds (VOCs) and oxides of nitrogen (NOx) (Carter, 2000). AE4-AQ, the 

fourth-generation CMAQ aerosol code with aqueous phase chemistry, when 

coupled with a gas phase mechanism, represents such phenomena as gas–

aerosol/aqueous phase mass transfer, chemical transformation of particulate 
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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 km2 grid cells for each vertical layer. The CCAQS domain extends 

from the Pacific Ocean in the west to the Mojave Desert and western Nevada in 

the east and runs from the northern Sacramento Valley to the Tehachapi 

Mountains in the south. The second domain (“SJV”) is nested within the CCAQS 

domain and covers the San Joaquin Valley with 80x89 lateral 4x4 km2 grid cells 

for each vertical layer (Figure 1). The vertical structure for both domains is 

composed of 15 layers of varying thickness 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 vertical structure of the meteorological 

inputs. The surface layer is approximately 30 meters thick. 

Initial and Boundary Conditions:

Boundary conditions for the CCAQS domain were taken from the global 

chemical transport Model for Ozone And Related chemical Tracers (MOZART). 

Model boundary conditions for major species were extracted for the Central 

California modeling domain from MOZART results representative of the year 

2000. In addition to VOCs and inorganic gases, boundary conditions were 

extracted for ammonium, nitrate, sulfate, and organic and elemental carbon. 

Initial conditions were estimated as an average of the extracted boundary 

conditions for each species. The CCAQS domain provided the initial and 

boundary 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 the SJV domain. While 

boundary conditions for the CCAQS domain were held constant for each month, 

boundary conditions for the SJV domain varied for each hour. The impact of 

initial conditions was minimized for each domain by simulating 8-day spin up 

periods prior to the simulation of each month of the year. 

Emissions:

A spatially, temporally, and chemically resolved emissions inventory of 

combined area, mobile, and point sources was generated using the California 

Emissions Forecasting System (CEFS) version 1.06 with offline adjustments. 

The inventory includes emissions estimates for gaseous and particulate species 

of anthropogenic and biogenic origin. Gridded hourly emissions were developed 

for the CMAQ modeling domain for the years 2000, 2005, and 2014 (baseline). 
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Quality assurance checks of domain emissions totals and spatial distribution 

were performed at various steps in emissions processing in order to ensure that 

the CMAQ emissions input files were sound. 

In order to better estimate future air quality, a second 2014 emissions 

sensitivity scenario was generated incorporating expected reductions from future 

state and local controls beyond the baseline. These were applied uniformly over 

the domain on a percentage basis. The percentage 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 Interface Processor (MCIP) version 3.0. MCIP serves as 

a link between meteorological models like MM5 or WRF with CMAQ and 

generates model-ready meteorological inputs like the wind and temperature 

fields necessary to drive the transport and chemistry calculations in CMAQ. 

Inputs to MCIP were generated using the PSU/NCAR Mesoscale Model (MM5) 

(version 3.6) (Grell et al, 1995). Planetary boundary layer and radiation 

characteristics were calculated in MCIP, and the Models-3 dry deposition routine 

(Pleim - with chlorine and mercury species) was chosen to represent dry 

deposition. 

MM5 Simulation

MM5 is a limited area, terrain-following sigma coordinate model on 

Lambert Conformal projection that was 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 effects 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 simulation (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, respectively (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 the atmospheric initial and boundary 

conditions for the area at large scale, while the innermost grid with 4 km 

horizontal resolution resolved the fine details of atmospheric motions within the 

SJV modeling domain. MM5 has several options to calculate the components of 

internal and external forces acting on a volume of air, such as those for radiation, 

convection, cloud microphysics, soil fluxes, 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 same 
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model options from one simulation to the next. The Grell (1995) cumulus 

parameterization scheme for coarse grids was used along with the Blackadar 

boundary layer scheme for calculation of fluxes (Blackadar, 1979, Grell, 1995). 

The Dudhia simple ice scheme was used for the treatment of excess moisture 

(Dudhia, 1989) and the Dudhia cloud radiation scheme (Dudhia, 1993) was used 

for radiational heating and cooling of the atmosphere. The Blackadar multi-layer, 

force-restore method soil model (Blackadar, 1976) was used for soil physics in all 

grids. 

Initial and boundary conditions were prepared using the analyses of 

observations prepared by the National Center 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 coarse 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 temperature values simulated by the model 

were compared against surface observations obtained from the four SJV stations 

(Fresno, Bakersfield, Arvin, and Parlier) to study the temporal and 3-D spatial 

structure of atmospheric motions as well 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 during 2000 that the California Regional 

PM10/PM2.5 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 demonstration, 2005 and 2014 were also 

simulated. Simulations for all years were driven by the meteorological inputs for 

2000, while emissions varied from year to year. 

Model Performance Evaluation: 

To assure that the modeling system (emissions, meteorology, and air 

quality) is a satisfactory representation of the period modeled, the estimated 

emissions, meteorology, and air quality of the base-case simulation need to be 

compared with observations. Satisfactory performance of the model in 

simulating observed conditions and responses is a prerequisite for use of the 

modeling system to evaluate control strategies. 

CRPAQS was an extensive and intensive measurement campaign 

designed to characterize the important chemical and physical processes involved 

in the formation and evolution of particulate matter in Central California (Chow et 

al. 2006). The CRPAQS measurement campaign extended from December 1999 
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through January 2001 and provided a wealth of data from diverse areas for 

model evaluation. 

There are hourly and daily concentration data for a range of gaseous and 

particulate species at numerous sites available for comparison with CMAQ 

modeled concentrations. These data will be used to assess model performance 

temporally and spatially, with a focus on monthly average performance, as 

recommended for long-term model simulations. Appropriate goals for model 

performance will be based on the EPA Modeling Guidance and 

recommendations in the scientific literature on appropriate measures of model 

performance for long term PM simulations (EPA, 2007; Boylan and Russell, 

2006). A model performance analysis is forthcoming which will be provided at a 

later date as an appendix to this work. 

Speciated Modeled Attainment Test 

Regional air quality modeling only represents a portion of the attainment 

test. In order to perform the EPA-recommended 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 future year attainment that result from 

potential model bias in predicting absolute species concentrations. In the 

following sections, the Speciated Modeled Attainment Test, as described in the 

EPA “Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze”, is outlined 

in general terms. Here the recommended procedure for combining model results 

(relative response factors (RRFs)) with speciated and bulk observations (STN 

and FRM measurements) in order to determine future year attainment status is 

explained. This is followed by a description of the application of the SMAT 

procedure to the San Joaquin Valley to determine future year PM2.5 attainment 

status for the region. 

General Procedure of SMAT 

Step 1. Calculate the observed quarterly mean bulk PM2.5 concentration 

and composition for each measurement site. The quarterly mean species 

concentrations can be calculated by multiplying the observed percentage 

contribution of each species against the quarterly mean bulk PM2.5 design value. 

This design value is calculated from the bulk FRM concentrations averaged over 

a number of years (generally three but can be a weighted average spanning 5 

years), one of which should be the modeled reference year. The procedure to 

speciate this bulk design value is described in the “SANDWICH” section below. 

Step 2. Calculate the RRFs for each quarter and measurement site. 

Relative response factors are calculated using model results. For species i, site 

j, and quarter k, the RRF is given by the following equation: 
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 RRFijk = ([Ci, quarter k of future year]/[Ci, quarter k of the base year])j

Ci represents the quarterly modeled concentration (often averaged over a 

number of model cells near the location of the measurement site) for the 

reference year and the future year attainment target. The number of model cells 

recommended for use in the average depends on the size of the grid cell and is 

justified by the long PM2.5 sampling times, the representative spatial scale of the 

monitors, and the desire to offset any potential errors stemming from the 

geometry of the superimposed grid system. 

Step 3. Multiply the quarterly, site-specific model-based RRFs from step 2 

and speciated observations from step 1 to estimate future quarterly species 

concentrations. 

Step 4. Sum the future quarterly species concentration estimates from 

step 3 to estimate a future quarterly PM2.5 estimate at each monitoring site and 

then average these for a projected future year annual PM2.5 concentration for 

each monitoring site. 

Step 5. Compare the future year annual average PM2.5 concentrations 

from step 4 to the annual PM2.5 standard of 15.0 μg/m3

. If all sites have 

projected PM2.5 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, starting 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) PM2.5 mass measurements provide the 

basis for nonattainment designations. For this reason it is recommended that the 

FRM data also be used to project future air quality and progress towards 

attainment of the health-based National Ambient Air Quality Standard (NAAQS) 

for PM2.5. However, given the complex physicochemical nature of PM2.5, it is 

necessary to consider individual species as well. While the FRM measurements 

give the mass of the bulk sample, a method for apportioning this bulk mass to 

individual PM2.5 components is a first step towards determining the best targets 

for emissions controls in order to reach NAAQS levels in a timely manner. 

The FRM measurement protocol finds its roots in the past epidemiological 

studies of health effects associated with PM2.5 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 before 
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and after sampling after equilibrating at narrowly 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 equilibration period, however, FRM 

measurements are subjected to a number of known positive and negative 

artifacts. FRM measurements do not necessarily capture the PM2.5

concentrations in the atmosphere and can differ substantially from what is 

measured by speciation monitors (e.g., STN). Nitrate and semi-volatile organics 

can be lost from the filter during the equilibration process, and particle bound 

water associated with hygroscopic species like sulfate provides a positive artifact. 

These differences present an area for careful consideration when one attempts 

to utilize speciated measurements to apportion the bulk FRM mass to individual 

species. 

Given that (1) attainment status is currently dependent upon FRM 

measurements and (2) concentrations of individual PM2.5 species need to be 

considered in order to understand the nature of and efficient ways to ameliorate 

the PM2.5 problem in a given region, a method has been developed to speciate 

bulk FRM PM2.5 mass with known FRM limitations in mind. This method is 

referred to as the measured Sulfate, Adjusted Nitrate, Derived Water, Inferred 

Carbonaceous material balance approach or “SANDWICH”. SANDWICH is 

based on speciated measurements from other (often co-located) samplers, such 

as those from the Speciated Trends Network (STN), and the known sampling 

environment of the FRM. The approach serves to provide mass closure, 

reconciliation between speciated and bulk mass concentration measurements, 

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

concentrations, and the air quality standard. 

SANDWICH 

Given that the FRM is the basis for attainment status, the reconstructed 

PM2.5 mass should be based on the composition 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 monitor. Nitrates are often lost from the FRM filter 

along with semivolatile organics, and water bound to the inorganics also 

comprises a portion of the FRM mass. The main steps in estimating the PM2.5

composition are as follows: 

(1) calculate the nitrate retained on the FRM filter using hourly relative humidity 

and temperature alongside STN nitrate measurements, 

(2) calculate quarterly averages for retained nitrate, sulfate, elemental carbon, 

ammonium (or sulfate degree of neutralization, [NH4

+

]/[SO4

2-], if not using 

ammonium measurements directly), 
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(3) calculate particle bound water using the concentrations of ammonium, sulfate, 

and nitrate, using an equilibrium model like the Aerosol Inorganic Model or a 

polynomial equation derived from model output, and 

(4) calculate organic carbon mass (OCMmb) by difference, subtracting all 

inorganic species (including blank mass) from the PM2.5 mass. 

Total PM2.5 mass is given by 

 PM2.5 FRM = [SO4

2-] + [NO3-

FRM] + [NH4

+

FRM] + [EC] + [Other ] + [OCMmb] + 

 [H2O] + [blank mass = 0.5 μg/m3] 

where all concentrations have units of μg/m3

 and 

[SO4

2-] = measured sulfate 

[NO3

-

FRM] = nitrate retained on the FRM filter 

[NH4

+

FRM] = ammonium associated with the nitrate and sulfate on the FRM filter 

[EC] = measured EC 

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

[OCMmb] = organic carbon mass calculated by difference by adding all inorganic 

species and subtracting from the FRM PM2.5 mass. Organic carbon 

measurements may also be used if it seems that the OC by mass balance is 

clearly under or overestimated. 

[H2O] = water bound to the hygroscopic species; here, calculated using a 

polynomial equation dependent on the concentrations of sulfate, nitrate, and 

ammonium or using an inorganic 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 μg/m3. 

SANDWICH may be applied directly in areas where FRM and speciation 

monitors are collocated. When that is not the case, the following calculations 

and adjustments may still be applied using spatially interpolated speciation data 

or some other means to estimate the speciation. The speciation concentrations 

referred to in the following paragraphs can be represented, therefore, by 

measurements from a speciation monitor collocated with the FRM or by 

interpolated values for the species measured by speciation monitors in the 

region. 
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The first step in estimating the speciation 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 mentioned above, a passively collected mass of 

0.5 μg/m3

 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 PM2.5 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 average nitrate measurements and hourly 

temperature and relative humidity data. Frank (2006) suggests the following 

methodology for estimating retained nitrate: 

(1) For each hour (i) of the day, calculate the dissociation constant, Ki, 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’ = [P1 - P2 (1-a) + P3 (1-a)2

] x (1-a)1.75 x K, 

where a is “fractional” relative humidity and 

 ln(P1) = -135.94 + 8763/T + 19.12ln(T) 

 ln(P2) = -122.65 + 9969/T + 16.22ln(T) 

 ln(P3) = -182.61 + 13875/T + 24.46ln(T) 

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

Retained Nitrate = STN nitrate – [745.7/TR x (κ-γ) x 1/24 x Σ (i=1 to 24) (Ki)

0.5] 

where TR is the daily average temperature for the sampled air volume (K), Ki is 

the dissociation constant for NH4NO3 at ambient temperature 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 equal to 1 (Hering and Cass, 1999). For 

further details, please refer to Frank (2006). 

Ammonium 

All nitrate on the FRM filter is assumed to be neutralized by ammonium. 

Ammonium associated with nitrate on the FRM filter, therefore, is given by 
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 [NH4]NO3,FRM = 0.29 x [NO3]FRM

where [NO3]FRM is the nitrate remaining on the FRM filter and 0.29 is the mass 

ratio of NH4 to NO3 if fully neutralized. 

The form of (NH4)2SO4, however, can vary depending on location and 

season. Sulfate may or may not be fully neutralized. In order to determine the 

amount of ammonium associated with the sulfate aerosol, the ammonium 

difference between the STN and FRM filter needs to be determined. Some 

studies point to a loss of STN NH4 during sampling conditions where nitrate 

volatilization is favored. While the volatilized NO3 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 NH4 corresponds more closely with the NH4

on the FRM filter; in other cases, the unadjusted measurement agrees better with 

FRM NH4. For the calculations for the SJV, an area rich in NH3, NH4 on the filter 

is assumed to be the measured STN NH4 minus any losses associated with fully 

neutralized nitrate (as NH4NO3) that may have volatilized 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 equilibration conditions, hygroscopic aerosol will retain its 

particle bound water (PBW) and be included in the observed FRM PM2.5 mass. 

PBW can be calculated using an equilibrium model like the Aerosol Inorganics 

Model (AIM). AIM requires the concentrations of ammonium, nitrate, sulfate, and 

estimated H+

 as inputs. In addition to inorganic concentrations, the equilibration 

conditions are also necessary model inputs. 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 represented on the FRM filter include 

elemental carbon, crustal material, sea salt, and passively collected mass. 

Depending on location, for example, certain species may be neglected (like sea 

salt for inland areas). 
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Carbonaceous Mass 

While carbonaceous aerosol may make up a large portion of airborne 

aerosol, speciated measurements of carbonaceous PM are considered highly 

uncertain. This is due to the large number of carbon compounds in the 

atmosphere and the measurement uncertainties associated with samplers of 

different configurations. In the SANDWICH approach, organic carbonaceous 

mass is calculated by difference. The sum of all non-organic carbon components 

may be subtracted from the FRM PM2.5 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 μg/m3

) for each quarter of the years 

represented by the speciated data. One can then apply those percentages to the 

base year FRM design value, with the assumption that the years for which the 

speciation was calculated are representative of all years over which the design 

value is averaged. For the annual PM2.5 attainment demonstration, the speciated 

reference year FRM design value for each quarter is multiplied by the RRF for 

each species at each site, averaged together, and summed to get a future year 

PM2.5 concentration. Note that blank mass is kept constant at 0.5 μg/m3

 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 the SMAT for the annual PM2.5 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 

PM2.5 standard. In the case of the 24-hour test, however, the goal is to have the 

future 24-hour PM2.5 design value at or below 65 μg/m3 for all sites. The test has 

the following steps: 

Step 1. Identify the observed 98th percentile 24-hour PM2.5 average 

concentrations for each year (e.g., of the three year period that makes up the 

reference year design value) and the next highest concentrations for the other 

quarters. 

Step 2. Using the SANDWICH methodology outlined above, calculate the 

species percentages of the bulk FRM samples for each site, quarter, and year. 

These fractions should be calculated using days at the high end of the 

distribution (i.e., days around or above the standard). 

Step 3. Calculate RRFs for each species at each site for each quarter 

from modeled days at the high end of the PM2.5 distribution (i.e., modeled days 

having PM2.5 concentrations around or above 65 μg/m3). As in the case of the 
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annual test, for species i, site j, and quarter k, the RRF is given by the following 

equation: 

 RRFijk = ([Ci, quarter k of future year]/[Ci, quarter k of the base year])j

Step 4. Multiply the quarterly, site-specific model-based RRFs from step 3 

and speciated observations from step 2 to estimate future quarterly species 

concentrations. 

Step 5. Sum the future quarterly species concentration estimates from 

step 4 to estimate a quarterly “potential” 98th percentile PM2.5 estimate at each 

monitoring site. 

Step 6. Average the highest quarterly values for each year and then 

compare the future year design values at each site to the 24-hour PM2.5 standard 

of 65 μg/m3. If all sites are at or below the standard, the 24-hour PM2.5

attainment test is passed. 

A numerical example of an application of the 24-hour PM2.5 attainment test 

is given on pp. 60-63 of the EPA Model Guidance (U.S. EPA, 2007). 

SJV Attainment Demonstration for the Annual PM2.5 Standard 

The SMAT procedure as outlined above 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 speciate 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 composition was assumed to be the same at 

all three Bakersfield sites (BAC, BGS, and BEP). Similarly, the percent 

composition at the two Fresno sites (FSF and FSH) was assumed to be the 

same. In addition, Stockton (SOH), Clovis (CLO), Corcoran (COP), and Modesto 

(MRM), were assumed to have the same speciation 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 their associated speciation site, see Table 

3. 

The steps followed in order to determine future year design values for the 

FRM sites of the SJV were similar to those outlined above in the generalized 

description of the SMAT/SANDWICH 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, sulfate ion, and elemental carbon) plus 

FRM mass were included in the species average calculations. Temperature and 
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relative humidity data were obtained from the ARB’s Air Quality and 

Meteorological Information System (AQMIS, www.arb.ca.gov/aqd/aqinfo.htm) 

database for each monitoring station (collocated 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 described above. In addition, a blank mass 

average of 0.5 μg/m3

 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 extracted 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. Organic 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 PM2.5 concentrations for 

2004, 2005, and 2006). The quarterly observed species concentrations were 

then multiplied by the RRFs and summed and averaged to get a future year 

PM2.5 design value at each FRM site. See Table 3 for the predicted baseline and 

controlled 2014 PM2.5 design values. For the “controlled” 2014 emissions 

sensitivity scenario described above, future annual PM2.5 concentrations at all 

FRM sites are below the annual PM2.5 NAAQS of 15.0 μg/m3, and, therefore, the 

San Joaquin Valley has passed the speciated modeled attainment test for the 

annual PM2.5 NAAQS. 

SJV Attainment Demonstration for the 24-Hour PM2.5 Standard 

A similar procedure to the attainment demonstration for the annual PM2.5

standard was followed for the 24-hour PM2.5 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 available days. The top 25% of the days 

are expected to be more representative of the 24-hour design value than would 

all available days for a given quarter. 

Table 4 shows the projected future year 24-hour PM2.5 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. 
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Unmonitored peaks 

EPA’s modeling guidance (EPA, 2007) describes the “Unmonitored Area 

Analysis” as an analysis used to ensure that a proposed control strategy will be 

effective in reducing PM2.5 at locations without air quality monitors so that 

attainment is shown throughout a nonattainment area. The purpose of the 

analysis is to use a combination of model 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 spatial design value fields using gridded model output 

gradients (base year values) 

3. Application of the gridded model RRFs to the gradient 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 violations of the annual PM2.5 NAAQS is presented 

here. 

First, an annual-averaged modeled PM2.5 field is generated for the entire 

modeling domain as shown in figure 3. This field is then scrutinized to see if 

there will be gradients in the field that would give rise to higher values away from 

monitors if this field were to be used to adjust the interpolated annual-averaged 

design value field. As seen from figure 3, there 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 the 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 simple screening analysis and a complete 

analysis of the unmonitored areas will be presented in the future. 

References 

Blackadar, A. K. (1976) Modeling the Nocturnal Boundary Layer. Preprints of 

Third Symposium on Atmospheric Turbulence and Air Quality, Raleigh, 

NC. 19-22, October 1976, Amer. Meteor. Soc., Boston, 46-49. 

Blackadar, A. K. (1979) Advances in Environmental Science and Engineering, 1. 

No. 1. Pfaflin and Ziegler, Eds. Gordon and Breach Publishers, 50-85. 
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Boylan, J.W. and A.G. Russell (2006) PM and light extinction model 

performance metrics, goals, and criteria for three-dimensional air quality 

models. Atmospheric Environment, 40:4946-4959. 

doi:10.1016/j.atmosenv.2005.09.087 

Carter, W. (2000) Documentation of the SAPRC-99 chemical mechanism for 

VOC reactivity assessment. Final Report to California Air Resources 

Board. Contract 92-329 and Contract 95-308, 

http://pah.cert.ucr.edu/ftp/pub/carter/pubs/s99txt.pdf 

Chow, J.C., Chen, L.W.A., Watson, J.G., Lowenthal, D.G., Magliano, K.A., 

Turkiewicz, K., and D.E. Lehrman (2006) PM2.5 chemical composition and 

spatiotemporal variability during the California Regional PM10/PM2.5 Air 

Quality Study (CRPAQS). J. Geophys. Res., 111, D10S04, 

doi:10.1029/2005JD006457. 

Chow, J.C., Watson, J.G., Lowenthal, D.H., Magliano, K. (2005) Loss of PM2.5

Nitrate from Filter Samples in Central California. J. Air & Waste Manage. 

Assoc. 55:1158-1168. 

Community Modeling and Analysis System (2007) CMAQ v4.6 Operational 

Guidance Document. http://www.cmaqmodel.org/op_guidance_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 Third Atmospheric Radiation Measurement (ARM) 

Science Team Meeting. March 1-4, 1993, Norman, OK. pp. 363-366. 

Frank, Neil H. (2006) Retained Nitrate, 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. Stauffer. (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 Magnitude of Bias in the Measurement 

 of PM2.5 Arising from Volatilization of Particulate Nitrate from Teflon Filters. 

J. Air & Waste Manage. Assoc. 49:725-733. 
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Liang, J. and A. Kaduwela, 2005: Microdevelopment of CMAQ for California 

Regional Particulate-Matter Air Quality Study. Proceedings of the 4th 

Annual CMAS Models-3 Users’ Conference; 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 Protection Agency. (2007) Guidance on the Use of Models 

 and Other Analyses for Demonstrating Attainment of Air Quality Goals for 

 Ozone, PM2.5, and Regional Haze. EPA-454/B-07-002 
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Figure 1. The CCAQS modeling domain with the SJV modeling domain inset. 
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Figure 2. The location of the three nested grids adopted for the numerical 

modeling of SJV using MM5. 
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Figure 3. The annual-averaged modeled PM2.5 concentrations in the domain.
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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 14.47 
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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