2008 PM2.5 Plan This appendix has been provided by the California Air Resources Board (ARB). For more information on modeling, please see Chapter 3 and Appendix F.



## Appendix G

Regional Air Quality Modeling2008 PM2.5 Plan This page intentionally blank.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-1 

## Appendix G: Regional Air Quality Modeling

Regional Air Quality Modeling and PM 2.5 Attainment Demonstration

Introduction

The San Joaquin Valley (SJV) is curr ently designated as nonattainment for

PM 2.5 . Addressing this issue properly requi res 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 se rve to determine future year attainment status for the SJV given proj ected emissions scenarios and also the most effective emissions reduction pathways to control PM concentra tions for different seasons and regions of the domain.

A complex interplay between meteorology and chemistry shapes the aerosol size and composition distribution. Atmospheric parti culate 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., a mmonium 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 variations 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 mode ling 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 particulate concent rations provide the foundation for the EPArecommended attainment demonstration for PM 2.5

.

While model predictions represent a signi ficant 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 t hat 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 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 Attainment Test is outlined (EPA, 2007). Following a description of the modeling methodolog y and inputs, the steps of the EPArecommended Speciated Modeled Attainment Test are described in detail. Finally theSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-2 modeling results are presented alongside obse rvational data in order to determine the future year attainment status for the San Joaquin Valley.

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 PM 2.5 (EPA, 2007). Such

models offer the best availabl e representation of important atmospheric processes and are an essential tool in analyzing the im pacts of proposed emissions controls on pollutant concentrations. The EPA recomme nds guidelines for choosing a model for

use in the attainment test. For example, the model sour ce code should be free or low cost, modeling elements should have underg one 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 demonst ration 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 transfe r, nucleation, coagulation, wet and dry deposition, aqueous phase chemistry, etc.) a nd a range of species (e.g., anthropogenic and biogenic, primary and seco ndary, gaseous and particulat e) 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 appl ied successfully to episodic modeling in Central California, the CMAQ modeling syst em version 4.6 with California-specific updates, as described in Liang and Kaduwela (2005), was selected for use in support of

the PM 2.5 modeled attainment demonstration.

Chemical Mechanism

There are a number of gas-phase chemic al 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 PM 2.5 species in the San Joaquin Valley, SAPRC 99 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 deta iled mechanism describing the gas-phase reactions of volatile organic compounds (VOC s) and oxides of nitr ogen (NOx) (Carter, 2000). AE4-AQ, the fourth-generation CM AQ aerosol code with aqueous phase chemistry, when coupled with a gas phase mechanism, represents such phenomena as gas–aerosol/aqueous phase mass transfer, c hemical transformation of particulate species and their gas phase precursors, and the evolution of the aerosol size

distribution.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-3 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 layer. The CCA QS 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 second domain (“SJV”) is nested within the CCAQS dom ain 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 co mposed of 15 layers of varying thickness up to the top of the meteorological domain ( 100 mb). The finest resoluti on belongs to those layers

closest to the surface and is determined lar gely by the vertical structure of the meteorological inputs. The surface layer is approximately 30 meters thick.

Initial and Boundar y Conditions:

Boundary conditions for the CCAQS domain were taken from the global chemical transport Model for Ozone And Related chemic al Tracers (MOZART). Model boundary conditions for major species were extracted for the Cent ral California modeling domain from MOZART results repres entative of the year 2000. In addition to VOCs and inorganic gases, boundary conditions were extr acted for ammonium, nitrate, sulfate, and organic and elemental carbon. Initial co nditions were estimated as an average of the extracted boundary conditions for each spec ies. The CCAQS domain provided the initial and boundary conditions fo r 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 condition s for the SJV domain. While boundary conditions for
the CCAQS domain were held constant fo r 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 re solved emissions inventory of combined area, mobile, and point sources was gener ated using the California Emissions Forecasting System (CEFS) version 1.06 wi th 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 (bas eline). Quality a ssurance checks of domain emissions totals and spatial distribut ion were performed at various steps in emissions processing in order to ensure that the CMAQ emissions input files were

sound.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-4 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 we re applied uniformly over the domain on a percentage basis. The percentage differ ence between baseline and “controlled”

emissions was taken from the information in Table 1.

Meteorological Inputs:

The meteorological input fields to CMAQ were generated wit h the MeteorologyChemistry Interface Processor (MCIP) vers ion 3.0. MCIP serves as a link between meteorological models like MM5 or WRF with CMAQ and generates model-ready meteorological inputs like t he wind and temperature fields necessary to drive the transport and chemistry calculations in CM AQ. Inputs to MCIP were generated using the PSU/NCAR Mesoscale Model (MM5) (versi on 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 chlori ne and mercury species) was chosen to represent dry deposition.

MM5 Simulation

MM5 is a limited area, terrain-followin g 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 mo tions at small scales by explicitly treating

the effects of convective motions on atmo spheric 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) direct ion 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 bound ary conditions for the area at large scale, while the innermost grid with 4 km horizontal resoluti on resolved the fine details of atmospheric motions within the SJV modeling domain. MM 5 has several options to calculate the components of internal and external forces acti ng on a volume of air, such as those for radiation, convection, cloud microphysi cs, soil fluxes, and boundary layer physics. While many sensitivity studies were conduct ed using various model options to find the best agreement with observations, an effort was also made to use the same 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, Gre ll, 1995). The Dudhia simple ice scheme was

used for the treatment of excess moistu re (Dudhia, 1989) and the Dudhia cloud radiation scheme (Dudhia, 1993) was used fo r radiational heating and cooling of theSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-5 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 prepar ed using the analyses of observations prepared by the National Center for Envir onmental Prediction (N CEP) 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/B Cs were prepared from the 12 km grid output for the initialization of the 4 km grid. T he three-dimensional (3-D) wind and temperature values simulated by the model were com pared against surface observations obtained from the four SJV stations (Fresno, Bakersfi eld, Arvin, and Parlier) to study the temporal and 3-D spatial structure of atmospheric mo tions 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 provi de the basis for the model performance evaluation. It was during 2000 t hat the California 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 re commended modeled attainment demonstration, 2005 and 2014 were also simulated. Simulations for all years were driven by the meteorological inputs for 2000, while em issions varied from year to year.

Model Performance Evaluation:

To assure that the modeling system (emissi ons, meteorology, and air quality) is a satisfactory representati on of the period modeled, the estimated emissions, meteorology, and air quality of the bas e-case simulation need to be compared with observations. Satisfactory performance of t he 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 intensiv e measurement campaign designed to characterize the important chemical and physi cal processes involved in the formation and evolution of particulate matter in C entral California (Chow et al. 2006). The CRPAQS measurement campaign extended from December 1999 through January 2001 and provided a wealth of data from diverse areas for model evaluation.

There are hourly and daily concentrat ion data for a range of gaseous and particulate species at numerous sites av ailable 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 literat ure on appropriateSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-6 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 Specia ted 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 attain ment that result from potenti al model bias in predicting absolute species concentrations. In the following sections, the Speciated Modeled Attainment Test, as described in the EPA “G uidance 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 te rms. 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 fo llowed by a description of the application of the SMAT procedure to the San Joaquin Va lley 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 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 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 quar ter and measurement site. Relative response factors are calculated using model result s. For species i, si te j, and quarter 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

C i represents the quarterly modeled conc entration (often averaged over a

number of model cells near the location of the measurement si te) for the reference year and the future year attainment target. T he number of model cells recommended for use in the average depends on the size of the gr id 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 geometry of the superimposed grid system.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-7 Step 3. Multiply the quar terly, site-specific model- based RRFs from step 2 and speciated observations from step 1 to estima te future quarterly species concentrations.

Step 4. Sum the future quarterly species 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, t he attainment test is passed.

These are the basic steps of SMAT. The following sections examine the

attainment test in more det ail, starting with the calculat ion of the base year design values against which the m odel-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 re ason it is recommended that the FRM data also be used to project future air quality and progress towards attainment of the healthbased National Ambient Air Q uality Standard (NAAQS) for PM 2.5 . However, given the complex physicochemical nature of PM 2.5 , 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 PM 2.5 components is a first step towards determining the best targets for emissions cont rols in order to reach 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 sufficient ly detailed so that results might be easily reproducible and involves the measurement of filter mass before and after sampling after equilibrating at narrowly defined conditions . Filters are equilibrated for more than 24 hours at a standard relative humidity bet ween 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 subjected to a number of known positive and negative artifacts. FRM measurement s do not necessarily capture the PM 2.5 concentrations in the atmosphere and can differ substantially from w hat 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 a ttempts to utilize speciated measurements to apportion the bulk FRM mass to individual species.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-8 Given that (1) attainment status is currently dependent upon FRM measurements and (2) concentrations of individual PM 2.5 species need to be considered in order to

understand the nature of and efficient 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 me thod is referred to as the measured S ulfate, A djusted

N itrate, D erived W ater, I nferred C arbonaceous material balance approach or “SANDWICH”. SANDWICH is based on specia ted measurements from other (often colocated) samplers, such as those from the Speciated Trends Ne twork (STN), and the known sampling environment of the FRM. The approach serves to provide mass closure, reconciliation between speciat ed 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 a ttainment status, the reconstructed PM 2.5 mass should be based on the composition of the mass measur ed by the FRM. SANDWICH was developed in order to attri bute portions of the bulk FRM mass to different components using STN measurements while also cons idering 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 PM 2.5 composition are as follows:

(1) calculate the nitrate retained on the FR M 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 μ g/m 3 ]

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

[SO 4 2- ] = measured sulfateSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-9 [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 diffe rence by adding all inorganic species and subtracting from the FRM PM 2.5 mass. Organic carbon measurements may also be used if it seems that the OC by mass bal ance is clearly under or overestimated.

[H 2 O] = water bound to the hygroscopic spec ies; here, calculated using a polynomial equation dependent on the concentra tions of sulfate, nitrat e, and ammonium or using an inorganic aerosol equilibrium model.

Blank mass represents the mass passively colle cted 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 follo wing 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 m easurements from a speciation monitor collocated with the FRM or by interpolated values for the species measured by speciation monitors in the region.

The first step in estimating the speciation of the bulk sample is to assume that, due to the stability and nonvolatility of sulf ate, sulfate measur ed 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 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 fr om filters has been widel y observed (Chow et al., 2005). In order to estima te how much nitrate is reta ined on the FRM filter, simple thermodynamic equilibrium relations may be used. Necessary inputs include 24-hour average nitrate measurements and hourly te mperature and relative humidity data. Frank (2006) suggests the following method ology for estimating retained nitrate:

(1) For each hour (i) of the day, calc ulate the dissociation constant, K i , from ambient temperature and relative humidity.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-10 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 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 (Her ing 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 by

[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 associated with t he sulfate aerosol, the amm onium difference between the

STN and FRM filter needs to be determined. Some studies point to a loss of STN NH 4 during sampling conditions where nitrate volatiliz ation 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, t he unadjusted measurement 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 )San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-11 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 equilibrium model like the Aerosol Inor ganics Model (AIM). AIM requires the concentrations of ammoni um, nitrate, sulfate, and estimated H + as inputs. In addition to inorganic concentrati ons, 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 polynom ial regression equation may be constructed by fitting the calculated water concent ration 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 passive ly collected mass. Depending on location, for example, certain species may be neglec ted (like sea salt for inland areas).San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-12 Carbonaceous Mass

While carbonaceous aerosol may make up a large portion of airborne aerosol, speciated measurements of car bonaceous 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 di fferent configurations. In the SANDWICH approach, organic carbonaceous mass is calculat ed by difference. The sum of all nonorganic 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 cont ribution 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 speciated data. One can then appl y those percentages to the base year FRM design value, with the assumpti on that the years for which the speciation was calculated are representative of all year s over which the design valu e is averaged. For the annual

PM 2.5 attainment demonstration, the speciat ed 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 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 ni trate, ammonium, and

sulfate. A numerical example of the 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 t he 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 th ree 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 sample s 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 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 the annual test, for species i, site j, and quarter 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 ]) jSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-13 Step 4. Multiply the quar terly, site-specific model- based RRFs from step 3 and speciated observations from step 2 to estima te future quarterly species concentrations.

Step 5. Sum the future quarterly species 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 va lues for each year and then compare the future year design values 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 above was applied for FRM monitors operating in the San Joaquin Valley with minimal dev iations 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 thos e sites not collocated with STN monitors, “surrogate” speciation sites were dete rmined based on analysis of CRPAQS data to determine which sites had similar speciation pr ofiles. The composition was assumed to be the same at all three Bakersfield sites ( BAC, BGS, and BEP). Si milarly, the percent composition at the two Fresno sites (FSF and FSH) was a ssumed 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 result s 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 out lined above in the generalized description of the SMAT/SANDWICH proc edure. 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 comp onents (ammonium ion, nitrate ion, sulfate ion, and elemental carbon) plus FRM ma ss were included in the species average calculations. Temperature and relative humid ity 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 additi on, a blank mass average of 0.5 μg/m 3 was assumed for each quarter and held c onstant into the future. Modeled concentrations for the reference year (2005) and future year (2014) for each componentSan Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-14 were extracted for the FRM si tes as a nine-cell average. T he 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 algo rithm provided by Dr. Bong-Mann Kim of the South Coast Air Quality Management Dis trict. Organic mass was calculated by difference between the average total FRM ma ss and the sum of all other species and

blank.

These quarterly species percentages were then multiplied against the reference year design value fo r 2006 (the average FRM PM 2.5 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 PM 2.5 design value at each FRM site. See Table 3 for the predi cted baseline and controlled 2014 PM 2.5 design values.

For the “controlled” 2014 emissions sensit ivity scenario described 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 Joaqui n Valley has passed the s peciated 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 t he top 25% of the measur ed and modeled days for each quarter were used instead of all available da ys. 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 project ed future year 24-hour PM 2.5 design values for the top five 2006 design value sites for the controlled em issions sensitivity case. As shown, all

sites in the SJV attained the 24-hour standar d in 2006, and further emissions controls do not cause any monitors to become non-attainment.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-15 Unmonitored peaks

EPA’s modeling guidance (EPA, 2007) describes the “Unmonitored Area Analysis” as an analysis used to ensure that a proposed control stra tegy will be effective in reducing PM 2.5 at locations without air quality monito rs 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 identif y 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 spatia l design value fields using gr idded model output gradients (base year values) 3. Application of the gri dded model RRFs to the gradient adjusted spatial design value

fields 4. Determination if any unmonitored areas ar e 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 violatio ns 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. 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 inter polated annual-averaged design value field. As seen from figure 3, t here are no areas with steep gradients that would result in higher design values than those meas ured at monitors. That is, for the central and southern part of the Valley where des ign 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) M odeling the Nocturnal Boundary Layer. Preprints of Third Symposium on Atmospheric Turbulence an d Air Quality, Raleigh, NC. 19-22, October 1976, Amer. Meteor. Soc ., Boston, 46-49.

Blackadar, A. K. (1979) Advances in Enviro nmental Science and Engineering, 1. No. 1. Pfaflin and Ziegler, Eds. Gor don and Breach Publishers, 50-85.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-16 Boylan, J.W. and A.G. Russell (2006) PM and light extinction model performance metrics, goals, and criteria for th ree-dimensional 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.

Chow, J.C., Watson, J.G., Lowenthal, D. H., Magliano, K. (2005) Loss of PM 2.5 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.cmaq-model.or g/op_guidance_4.6/manual.pdf

Dudhia, J. (1989) Numerical Study of Conv ection Observed During the Winter Monsoon Experiment Using a Tw o-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) Re tained Nitrate, Hydrated Sulfat es, and Carbonaceous Mass in

Federal Reference Method Fine Particulat e Matter for Six Ea stern 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 Fifth-Generation Penn State/NCAR Mesoscale Model (MM5). NCAR Technical Note , NCAR/TN398+STR.

Hering, S.V. and Cass, G.R. (1999) The Magni tude of Bias in t he Measurement of

PM 2.5 Arising from Volatilizati on of Particulate Nitrate fr om Teflon Filters. J. Air & Waste Manage. Assoc . 49:725-733.

Liang, J. and A. Kaduwela, 2005: Microdevelopm ent of CMAQ for California Regional Particulate-Matter Air Quality Study. Proceedings of the 4th Annual CMAS Models-3 Users’ Conference; Sept ember 26-28, 2005, Chapel-Hill, NC.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-17 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 the Use of Models and Other Analyses for Demonstrating Attainm ent of Air Quality Goals for Ozone,

PM 2.5 , and Regional Haze. EPA-454/B-07-002San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-18 Figure 1. The CCAQS modeling domain with the SJV modeling domain inset.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-19 Figure 2. The location of the three nested grid s adopted for the numer ical modeling of SJV using MM5.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-20 Figure 3. The annua l-averaged modeled PM 2.5 concentrations in the domain.San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-21 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 14.47San Joaquin Valley Unified Air Polluti on Control District April 30, 2008

Appendix G: Regional Air Quality Modeling

2008 PM2.5 Plan G-22 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