**AIR QUALITY MODELING** 

**Overview** Visibility impairment occurs when fine particulate matter (PM 2.5 ) in the atmosphere scatters and absorbs light, thereby

creating haze. PM 2.5 can be emitted into the atmosphere directly as primary particulates, or it can be pro duced in the atmosphere from photochemical reactions of gas

- phase precursors and subsequent condensation to form secondary particulates. Examples of primary PM 2.5 include crustal materials and elemental carbon; examples of secondary PM include ammo

nium ni trate, ammonium sulfates, and secondary organic aerosols (SOA). Secondary PM 2.5 is generally smaller than primary

PM 2.5 , and because the ability of PM 2.5 to scatter light depends on particle size, with light scattering for fine particles being greater than for coarse particles, secondary PM 2.5 play s an especially important role in visibility impairment. Moreover, the smaller secondary

PM 2.5 can remain suspended in the atmosphere for longer periods and is transported long distances, thereby contributing to r egional

- scale impacts of pollutant emissions on visibility

. The sources of PM 2.5 are difficult to quantify because of the complex nature of their formation, transport, and removal from the atmosphere. This makes it difficult to simply use emissions data t o determine which pollutants should be controlled to most effectively improve visibility

. Photochemical air quality models offer opportunity to better understand the sources of PM 2.5 by simulating the emissions of pollutants and the formation, transport, a nd deposition of PM 2.5 . I f an air quality

model performs well for a historical episode, the model may then be useful for identify ing the sources of PM 2.5 and helping to select the most effective emissions reduction strategies for attaining visibility goals . Although several ty pes of air quality

modeling sy stems are available, the gridded, three

- dimensional, Eulerian models provide the most complete spatial representation and the most comprehensive representation of processes affecting PM 2.5 , especially for situations in which multiple pollutant sources interact to form PM 2.5 . For less complex situations in which a few large point sources of emissions are the dominant source of PM 2.5 , trajectory

models (such as the California Puff Model [CAL PUFF]) may also be useful for simulating PM 2.5 . Air Quality

Models The WRAP RMC utilized two regulatory air quality

modeling sy stems to conduct all regional haze modeling. A brief discussion of each of these models is provided below. Community Multi

- Scale Air Quality Mo del EPA initially

developed the Community Multi

- Scale Air Quality

(CMAQ) modeling sy stem in the late 1990s. The model source code and supporting data can be downloaded from the Community

Modeling and Analy sis Sy stem (CMAS) Center ( http://www.cmascenter.org/ ), which is funded by

EPA to distribute and provide limited support for CMAQ users. CMAQ was designed as a “one atmosphere” modeling sy stem to encompass modeling of multiple pollutants and issues, including ozo ne, PM, visibility , and air toxics. This is in contrast to many earlier air quality models that focused on single

- pollutant issues (e.g., ozone modeling by the UrbanAirshed Model). CMAQ is an Eulerian model

— that is, it is a grid

- based model in which the

f rame of reference is a fixed, three

- dimensional (3

- D) grid with uniformly sized horizontal grid cells and variable vertical lay er thicknesses. The number and size of grid cells and the number and thicknesses of lay ers are defined by the user, based in part on the size of the modeling domain to be used for each modeling project. The key science processes included in CMAQ are emissions, advection and dispersion, photochemical transformation, aerosol thermody namics and phase transfer, aqueous chemistry , and we t and dry deposition of trace species. CMAQ offers a variety of choices in the numerical algorithms for treating many of these processes, and it is designed so that new algorithms can be included in the model. CMAQ offers a choice of three photochemical me chanisms for solving gas

- phase chemistry : the Regional Acid Deposition Mechanism version 2 (RADM2), a fixed coefficient version of the SAPRC90 mechanism, and the Carbon Bond I V mechanism (CB

- IV ). Comprehensive Air Quality Model with Extensions The Compre hensive Air Quality Model with extensions (CAMx) model was initially developed by ENVI RON in the late 1990s as a nested

- grid, gas

- phase, Eulerian photochemical grid model. ENVI RON later revised CAMx to treat PM, visibility , and air toxics. While there are many similarities between the CMAQ and CAMx sy stems, there are also some significant differences in their treatment of advection, dispersion, aerosol formation, and dry and wet deposition. Model Versions Both EPA and ENVI RON periodically update and revise their models as new science or other improvements to the models are developed. For CMAQ, EPA ty pically provides a new release about once per y ear. The initial 2002 MPE for WRAP used CMAQ version 4.4, which was released in October 2004. I n October 2005 EPA released CMAQ version 4.5, which includes the following updates and improvements to the modeling sy stem:

 A new vertical advection algorithm with improved mass conservation
 Changes in deposition velocities for some PM species
 A new sea

- salt emissions mode l and inclusion of sea salt in the aerosol thermody namics
 An option to make vertical mixing parameters vary as a function of land use ty pe The RMC completed the initial CMAQ MPE using CMAQ v.4.4. When version 4.5 was released in October, the modeling was revised and a comparison of the model performance using the two versions was compared. Note that some of the new features in CMAQ v4.5 (e.g., sea salt in the AE4 aerosol dy

namics module, and percent urban minimum vertical diffusivity ) require the reproces sing of the MM5 data using the new version of MCI P (MCI P v3.0). However, because such reprocessing could potentially jeopardize the WRAP modeling schedule, WRAP elected to operate CMAQ v4.5 using the MM5 data processed using a previous MCI P version, MCI P v 2.3, and the AE3 aerosol module that does not include active sea salt chemistry

. ENVI RON releases updated versions of CAMx approximately every two y ears, or as new features become available. The version used for the comparison of CMAQ and CAMx was CAMx v4 .3. There are many similarities between CMAQ and CAMx regarding the science algorithms and chemical mechanisms used, including the CB

- IV gas - phase and RADM aqueous

-phase chemistries, I SORROPI A aerosol thermody namics, and PPM horizontal advection scheme. I n the past, the treatment of vertical advection was a major difference between the two models; however, the incorporation of the new mass conservation scheme in CMAQ v4.5 makes its vertical advection algorithm much more similar to that of CAMx. Major dif ferences between the two models that still exist are in the basic model code, in the treatment of horizontal diffusion SOA formation mechanisms, and in grid nesting (CAMx supports one
- way

and two

- way

nesting, whereas CMAQ supports just one

- way grid nesting ). Both models include process analy sis for the gas

- phase portions of the model. The publicly released version of CAMx supports ozone and PM source apportionment through its Ozone and PM Source Apportionment Technology (OSAT/PSAT) probing tools, while for CMAQ there are research versions of the model that include Tagged Species Source Apportionment (TSSA) for some PM species (e.g., sulfate and nitrate). There are also research versions of CMAQ and CAMx that support the Decoupled Direct Method (DDM) sensitiv ity tool for PM and ozone. The CAMx model is computationally more efficient than CMAQ. However, CAMx is currently supported for use on only a single central processing unit (CPU) and can perform multiprocessing using Open Multi

- Processing (OMP) paralleli zation (i.e., shared memory multiprocessors). CMAQ parallelization, on the other hand, is implemented using Message Passing I nterface (MPI ) multiprocessing and therefore can be run using any number of CPUs. Depending on the number of model simulations to b e performed and the manner in which they are set up, there can be a slight advantage either to CAMx or to CMAQ in regard to computational efficiency

. Model Simulations In support of the WRAP Regional Haze air quality modeling efforts, the RMC developed ai r quality modeling inputs including annual meteorology and emissions inventories for a 2002 actual emissions base case, a planning case to represent the 2000

- 04 regional haze baseline period using averages for key emissions categories, and a 2018 base case of projected emissions determined using factors known at the end of 2005. All emission inventories were developed using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling sy stem. Each of these inventories has undergone a number of revisions thro ughout the development process to arrive at the final versions used in CMAQ and CAMx air quality modeling. The development of each of these emission scenarios is documented under the emissions inventory sections of the TSS. I n addition to various sensiti vities scenarios, the WRAP performed air quality model simulations for each of the emissions scenarios as follows:

 The 2002 base case emissions scenario, referred to as “ 2002 Base Case” or “Base02”. The purpose of the Base02 inventory is to represent th e actual conditions in calendar y ear 2002 with respect to ambient air quality and the associated sources of criteria and particulate matter air pollutants. The Base02 emissions inventories are used to validate the air quality

model and associated database s and to demonstrate acceptable model performance with respect to replicating observed particulate matter air quality

.  The 2000

- 04 baseline period planning case emissions scenario is referred to as “ Plan02”. The purpose of the Plan02 inventory is to repre sent baseline emission patterns based onaverage, or “ty pical”, conditions. This inventory provides a basis for comparison with the future y ear 2018 projected emissions, as well as to gauge reasonable progress with respect to future y ear visibility

.  The 2018 future

- ye ar base case emissions scenario, referred to as “ 2018 Base Case” or “Base18”. These emissions are used to represent conditions in future y ear 2018 with respect to sources of criteria and particulate matter air pollutants, taking into consid eration growth and controls. Modeling results based on this emission inventory are used to define the future y ear ambient air quality and visibility

metrics. Data Sources The CMAQ model requires inputs of three

- dimensional gridded wind, temperature, humi dity , cloud/precipitation, and boundary lay er parameters. The current version of CMAQ can only utilize output fields from the PSU/NCAR MM5 meteorological model. MM5 is a state

- of

- the

science atmosphere model that has proven useful for air quality applic ations and has been used extensively

in past local, state, regional, and national modeling efforts. MM5 has undergone extensive peer

- review, with all of its components continually undergoing development and scrutiny by the modeling community . I n - depth de scriptions of MM5 can be found in Dudhia (1993) and Grell et al. (1994), and at http://www.mmm.ucar.edu/mm5 . All meteorological data used for the WRAP air quality modeling efforts are derived from MM5 model si mulations. The development of these data is documented in ( Kemball

- Cook, S. et al., 2005) Emission inventories for all WRAP air quality simulations were developed using the Matrix Operator Kernel Emissions (SMOKE) modeling sy stem . The development of the se data has been discussed and documented elsewhere ( T onnesen, G. et al., 2006 ) In itial conditions (I Cs) are specified by the user for the first day

of a model simulation. For continental

- scale modeling using the RPO Unified 36

- km domain , the I Cs can affec t model results for as many

as 15 day s, although the effect ty pically becomes very

small after about 7 day s. A model spin

- up period is included in each simulation to eliminate any effects from the IC s. For the WRAP modeling, the annual simulation is divide d into four quarters, and included a 15

- day spin

- up period for the quarters beginning in April, July , and October. For the quarter beginning in January

2002, a spin

- up period covering December 16

- 31, 2001, using meteorology and emissions data developed for CENRAP were used

.. Boundary conditions (BCs) specify the concentrations of gas and PM species at the four lateral boundaries of the model domain. BCs determine the amounts of gas and PM species that are transported into the model domain when winds flow is into the domain. Boundary conditions have a much larger effect on model simulations than do I Cs. For some areas in the WRAP region and for clean conditions, the BCs can be a substantial contributor to visibility impairment. For this study

BC data generat ed in an annual simulation of the global

- scale GEOS

- Chem model that was completed by

Jacob et al. ( http://www

- as.harvard.edu/chemistry /trop/geos/ ) for calendar ye ar 2002 were applied . Addition al data processing of the GEOS

- Chem data was required before using them in CMAQ and CAMx. The data first had to be mapped to the boundaries of the WRAP domain, and the gas and PM species had to be remapped to a set of species used in theCMAQ and CAMx mode ls. This work was completed by By un and coworkers ( http://www

as.harvard.edu/chemistry

/trop/geos/meetings/2005/ppt/Expan ding_Model_Capabilities/GEOS

CMAQ_april_4_By

un.ppt The CMAQ model options and configuration used for the WRAP 36

- km model simulations are described in Tonnesen, G. et al., 200 6. Model Run Specification Sheets
In order to provide documentation for each of the CMAQ and CAMx air quality model simulations conducted by the WRAP RMC during Calendar y ear 2006, a series of Model Run Specification Sheets were developed. These “Spec Sheets” provide a description of each simulation, the various air quality model options and configurations used and detailed listing and description of the meteorological data and emission inventories for each scenario. These Spec Sheets also provide a means for the RMC to track the development of each of the input data sets and defi ned the modeling schedule. The purpose of each simulation, and expected results, including their im plications, are also included. A link to each of the individual Specification Sheets for the model simulations can be found on the RMC web site at: http://pah.cert.ucr.edu/aqm/308/cmaq.shtml

. **2002 Base Case Modeling** Base02 Sensitivity

Simulations The purpose of the 2002 Base Case modeling efforts was to evaluate air quality /visibility modeling sy s tems for a historical episode

— in this case, for calendar y ear 2002

— to demonstrate the suitability

of the modeling sy stems for subsequent planning, sensitivity , and emissions control strategy

modeling. Model performance evaluation is performed by comparing output from model simulations with ambient air quality data for the same time period. After creating emissions and meteorology inputs for the two air quality models, CMAQ and CAMx, the next step was to perform the visibility modeling and the model performa nce evaluations, which are described below. A detailed discussion of the results of the CMAQ and CAMx model simulations can be found in Tonnesen, G. et al., 200 6. Also documented in Tonnesen, G. et al., 200 6 are the results of the model performance evalua tion, a model inter

- comparison and discussion of various sensitivity simulations. T his information was used a s the basis for recommending the selection of CMAQ and/or CAMx to complete the remaining modeling efforts in RMC’s support of WRAP. Model Performa nce Evaluation The objective of a model performance evaluation (MPE) is to compare model

- simulated concentrations with observed data to determine whether the model’s performance is sufficiently accurate to justify

using the model for simulating future cond itions. There are a number of challenges in completing an annual MPE for regional haze. The model must be compared toambient data from several different monitoring networks for both PM and gaseous species, for an

annual time period, and for a large number of sites. The model must be evaluated for both the worst visibility

conditions and for very

clean conditions. Finally , final guidance on how to perform an MPE for fine

- particulate models is not y et available from EPA. Therefore, the RMC experimented with many

different approaches for showing model performance results. The plot ty pes that were found to be the most useful are the following:

 Time

- series plots comparing the measured and model

- predicted species concentrations

 Scatter plots showing model predict ions on the y - axis and ambient data on the x - axis

 Spatial analy sis plots with ambient data overlaid on model predictions

 Bar plots comparing the mean fractional bias (MFB) or mean fractional error (MFE) performance metrics

 “Bugle plots” showing how model performance varies as a function of the PM species concentration

 Stacked

- bar plots of contributions to light extinction for the average of the best

- 20% visibility day s or the worst

- 20% visibility day s at each site; the higher the light extinction, the lowe r the visibility Examples of each of these MPE metrics and analy sis products can be found in Tonnesen, G. et al., 200 6 . The results of the MPE are available from the WRAP RMC website ( http://pah.cert.ucr.edu/aqm/308/eval.shtml )

**2002 Plan ni**  **ng Scenario**
T he 2000

- 04 baseline period planning case scenario is referred to as “ Plan02”. The purpose of the Plan02 scenario is to simulation the air quality representative of baseline emission patterns based on average, or “ty

pical”, conditions. This scenario provid es a basis for comparison with the future y ear 2018 scenario based on projected emissions, as well as to gauge reasonable progress with respect to future y ear visibility

. Plan02 Simulations In put Data In put data used for the 2002 Planning model simulati ons consisted of the same meteorology as for the 2002 Base Case and the Plan02 emission inventories described under the Emissions Modeling section of the TSS. The setup of the CMAQ model (including science options, run scripts, simulation periods, and an cillary data) for the Plan02 cases was identical to that used in the Base02 modeling, as described in the 2002 MPE report (Tonnesen et al., 2006). I n summary , CMAQ v4.5 (released by EPA in October 2005) was used on the RPO Unified 36

- km domain . The Carbon Bond Mechanism version 4 (CB4) with RADM aqueous chemistry , the SORGAM organic aerosol algorithm, and all other science algorithms detailed in Tonnesen et al., 2006 were used . I nitial condition (I C) data for January 1, 2002, were developed using a 15

- day spin

- up period(December 16

- 31, 200 1 ). Boundary condition (BC) data were generated in an annual simulation of the global

- scale GEOS

- Chem model that was completed by Jacob et al. ( http://www

as. harvard.edu/chemistry

/trop/geos/ ) for calendar y ear 2002.

Comparison With Base02 Simulations For each of the three Plan02 emissions datasets, annual visibility modeling was performed using the CMAQ model. This was a key aspect of the QA procedure, sinc e errors in the emis sions inventories that might not be apparent during the emissions QA steps might be more readily detected in the results from the CMAQ modeling. In our initial analy sis of the Plan02 scenario, plots were prepared for QA p urposes that compared the Plan02a CMAQ results with the Base02a CMAQ results for daily and monthly

averages. After revising Plan02a to create Plan02b and Plan02c, additional QA plots were prepared to compar e the CMAQ results of each revised Plan02 case to the previous iteration. These were prepared as Program for the Analy sis and Visualization of Environmental data (PAVE) spatial plots showing the change in individual PM 2.5 species concentrations as daily , monthly , and annual averages. The final set of analy sis products , available on the RMC web site , include PAVE difference plots comparing the CMAQ

- predicted annual average species concentrations from th e Plan02c case with those from the Base02b case. Note that these plots are not usefu l for visibility plannin g purposes , but are being provided to show the magnitudes of changes when moving from the 2002 Base Case to the 2002 Planning Case

— in other words, from the actual e missions for the y ear 2002 to the “ty pical

- ye ar” emissions created for the final Plan02 scenario. The primary

analy sis “ product ” from the Plan02 CMAQ modeling is the use of its output in combination with the CMAQ output from the 2018 modeling to develop the visibility progress calculations and glide path plots , described below

.

**20 18 Model Simulations** 
Th e 2018 future

- ye ar base case scenario is referred to as “ 2018 Base Case” or “Base18”. The purpose of the Base18 scenario is to simulation the air quality representative of conditions in future y ear 2018 with respect to sources of criteria and particulate matter air pollutants, taking into consideration growth and controls. Modeling results based on this emission inventory are used to define the future y ear ambient air quality and visibility

metrics. Base18 Simulation In put Data In put data used for the 201 8 Base Case model simulations consisted of the same meteorology as for the 2002 Base Case and the Base18 emission inventories described under the Emissions Modeling section of the TSS.The setup of the CMAQ model (including science options, run scripts, simulation periods, and

ancillary data) for the Base18 cases was identical to that used in the Base02 modeling, as described in the 2002 MPE report (Tonnesen et al., 2006). I n summary , CMAQ v4.5 (released by EPA in October 2005) was used on the RPO Unifie d 36

- km domain . The Carbon Bond Mechanism version 4 (CB4) with RADM aqueous chemistry , the SORGAM organic aerosol algorithm, and all other science algorithms detailed in Tonnesen et al., 2006 were used. I nitial condition (I C) data for January 1, 2002, were developed using a 15

- day spin

- up period (December 16

- 31, 200 1 ). Boundary condition (BC) data were generated in an annual simulation of the global

- scale GEOS

- Chem model that was completed by Jacob et al. ( http://www

as.harvard.edu/chemistry

/trop/geos/ ) for calendar y ear 2002. Base18 Simulation Results The purpose of modeling 2018 visibility is to compare the 2018 visibility predictions to the 2002 ty pical

- ye ar visibility

modeling results, as d iscussed below. S ome improvements in visibility by 2018 are expected because of reductions in emissions due to currently planned regulations and technology

improvements. A brief summar y is provided here of the comparison between the 2018 and 2002 results u sing annual average PAVE spatial plots. The goal of this summary is to convey the scale and spatial extent of changes in key PM 2.5 species from 2002 to 2018. F or planning purposes, on the other hand, states and tribes should focus on the visibility project ions and glide path calculations at individual Class I Areas

. Figures 1 through 4 show the annual average concentration s for sulfate, nitrate, PM 2.5 and model

reconstructed visibility

( in deciviews), respectively . I n each figure, the bottom two plots show the modeled concentration or deciviews for the Plan02b and B ase18b case s , while the top plot shows the change in visibility

calculated as Base18b minus Plan02b. T he Plan02b results are presented here instead of Plan02c results because these plots had prev iously been prepared with version B. As t he differences between Plan02b and Plan02c are extremely small, new plots prepared using Plan02c would be essentially identical to the results in Figure 1 through 4. In each of the top plots in the four f igures, coo l colors indicate areas in which model

- predicted visibility improved from 2002 to 2018, while warm colors indicate areas where modeled visibility became worse over that period . Figure 1 shows that reductions in sulfate were largest in the southwest corner of the WRAP region and in Texas and Oklahoma. This results from planned SO x emissions reductions in the CENRAP region. There were smaller reduction s in sulfate in the Lo s Angeles area, western Washington state , and southern Nevada. There were small increas es of sulfate , mostly

in Wy oming , due to growth in SO x emissions. Most regions of the WRAP domain had low concentrations of sulfate in 2002 and little change in sulfate by 2018. Figure 2 shows the result s for nitrate. I n the both 2002 and 2018 , the modeled nitrate was greatest in California, and there were reduction in nitrate in that state in 2018 because of reductions in mobile

- source NO x emissions. There were small reductions in the Phoenix area as well, also from reductions in mobile

- source NO x emission s. Figure 3 shows the comparison of PM 2.5 for 2002 and 2018. I n most areas of the WRAP region , changes in PM 2.5 were less than 1  g/m 3

. Lo cations with increases in PM 2.5 correspond to areas of increased sulfate (see Figure 3

- 1 ) . Areas with the largest red uctions in PM 2.5 were the areas in California that had large reductions in modeled nitrate in 2018 (see Figure 3

- 2) . Results for otherspecies that contribute to PM 2.5 are available on the RMC web site at

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#base18bvsplan02b

. Figure 4 compares model

- reconstructed visibility for 2002 and 2018. N ote that these results are calculated using the modeled relative humidity (RH), so they differ from the results that use site

specific mo nthly

average RH. Nonetheless, the results in Figure 4 are indicative of the direction and magnitude of visibility change s in from 2002 to 2018. Although t he largest improvements are in California and the Pacific Northw est, there were improvements throughout the WRAP region. The change in deciviews is more dramatic than the change in PM 2.5 mass (Figure 3) because the visibility

in deciviews is a relative metric, so small mass changes in PM 2.5 in good visibility areas can result in large relative improvements in visibility

.**Figure 1. Annual average**  **aerosol sulfate (ASO4) concentration comparisons between**

**B ase18b and**  **P lan02b**  **. T**  **op plot :**  **difference between the two (B**  **ase18b**

**– P**  **lan02b );**  **b ottom left**  **plot: P**  **lan02b results**  **; bottom right**  **plot: B**  **ase18b results**  **. Figure 2.**  **Annual average aerosol nitrate (ANO3) concentration comparisons between** 

**B ase18b and**  **P lan02b**  **. T**  **op plot :**  **difference between the two (B**  **ase18b**

**– P**  **lan02b );**  **b ottom left**  **plot: P**  **lan02b results;**  **bottom right p**  **lot: B**  **ase18b results**  **. Figure 3.**  **Annual average PM**  **2.5 concentration comparisons**  **between B**  **ase18b and**  **P lan02b**  **. T**  **op plot :**  **difference between the two (B**  **ase18b**

**– P**  **lan02b );**  **b ottom left**  **plot: P**  **lan02b results;**  **bottom right plot:**  **B ase18b**  **results .** **Fig ure 4.**  **Annual average deciview comparisons between**  **B ase18b and**  **P lan02b**  **. T**  **op plot :**  **difference between the two (B**  **ase18b**

**– P**  **lan02b ); b**  **ottom left plot:**  **P lan02b**  **results; bottom right**  **plot: B**  **ase18b results**  **. Visibility Projections** 

T he Regional Haze Rule ( RHR ) go als include achieving natural visibility conditions at 156 Federally mandated Class I

areas by 2064. I n more specific terms, that RHR goal is defined as (1) visibility

improvement toward natural conditions for the 20% of day s that have the worst visibility (termed “20% worst,” or W20%, visibility day s) and (2) no worsening in visibility for the 20% of day s that have the best visibility (“20% best,” or B20%, visibility day s). One compo

nent of the states’ demonstration to EPA that they are making reasonable progress toward this 2064 goal is the comparison of modeled visibility projections for the first milestone y ear of 2018 with what is termed a uniform rate of progress (URP) goal. As explained in detail below, the 2018 URP goal is obtained by constructing a “linear glide path” (in deciviews) that has at one end the observed visibility conditions during the mandated five

- ye ar (2000

- 2004) baseline period and at the other end natural visibility conditions in 2064; the visibility value that occurs on the glide p ath at y ear 2018 is the URP goal. P reliminary

WRAP 2018 visibility

projections have been made using the Plan02c and Base18b CMAQ 36

- km modeling results, following EPA guidance that recommends apply ing the modeling results in a relative sense to project fu ture
- ye ar visibility conditions (U.S. EPA, 2001, 2003a, 2006 **).** Projections are made using relative response factors (RRFs), which are defined as the ratio of the future

- ye ar modeling results to the current

- ye ar modeling results. The calculated RRFs are app lied to the baseline observed visibility conditions to project future

- ye ar observed visibility . These projections can then be used to assess the effectiveness of the simulated emission control strategies that were included in the future

- ye ar modeling. The major features of EPA’s recommended visibility projections are as follows (U.S. EPA, 2003a,b, 2006):

 Monitoring data should be used to define current air quality

.
 Monitored concentrations of PM 10 are divided into six major components; the first five are as sumed to be PM 2.5 and the sixth is PM 2.5

- 10 .  SO 4 (sulfate)
 NO 3 (particulate nitrate)
 OC (organic carbon)
 EC (elemental carbon)
 OF (other fine particulate or soil)
 CM (coarse matter).

 Models are used in a relative sense to develop RRFs between future and cu rrent predicted concentrations of each component.

 Component

- specific RRFs are multiplied by current monitored values to estimate future component concentrations.

 Estimates of future component concentrations are consolidated to provide an estimate of future air quality

. Future estimated air quality is compared with the goal for regional haze to see whether the simulated control strategy would result in the goal being met.

 It is acceptable to assume that all measured sulfate is in the form of ammonium sulfate [(NH 4 ) 2 SO 4 ] and all particulate nitrate is in the form of ammonium nitrate [NH 4 NO 3 ]. To facilitate tracking the progress toward visibility goals, two important visibility parameters are required for each Class I area:

 Baseline Conditions: “Baseline Condit ions” represent visibility for the B20% and W20% day s for the initial five

- ye ar baseline period of the regional haze program. Baseline Conditions are calculated using monitoring data collected during the 2000

- 2004 five

- ye ar period and are the starting poin t in 2004 for the uniform rate of progress (URP) glide path to Natural Conditions in 2064 (U.S. EPA, 2003a).

 Natural Conditions: “Natural Conditions,” the RHR goal for 2064 for the Federally mandated Class I

areas, represent estimates of natural visibility condi tions for the B20% and W20% day s at a given Class I area.

Baseline Conditions Baseline Conditions for Class I areas are calculated using fine and coarse PM concentrations measured at I nteragency

Monitoring of Protected Visual Environments (I MPROVE) monitors (Malm et al., 2000). Each Class I area in the WRAP domain has an associated I MPROVE PM monitor. The I MPROVE monitors do not measure visibility directly , but instead measure speciated fine particulate (PM 2.5 ) and total PM 2.5 and PM 10 mass concentr ations from which visibility is calculated using the I MPROVE aerosol extinction equation, discussed later. Visibility conditions are estimated starting with the I MPROVE 24

- h average PM mass measurements related to six PM components of light extinction :

 Su lfate [(NH 4 ) 2 SO 4 ]
 Particulate nitrate [(NH 4 NO 3 ]
 Organic matter [OMC]

 Li ght

- absorbing carbon [LAC] or elemental carbon [EC]

 Soil

 Coarse matter [CM] The I MPROVE monitors do not directly measure some of these species , so assumptions are made as to how the I MP ROVE measurements can be adjusted and combined to obtain these six components. For example, sulfate and particulate nitrate are assumed to be completely neutralized by

ammonium and only

the fine mode (PM 2.5 ) is speciated to obtain sulfate and nitrate measu rements (that is , any coarse - mode sulfate and nitrate in the real atmosphere may be present in the I MPROVE CM measurement). Concentrations for the above six components oflight extinction in the I MPROVE aerosol extinction equation are obtained from the I MP ROVE

measured species using the formulas shown in Table 1.

**Table 1**  **. Definition of IMPROVE components from measured species.**  **IMPROVE Component**  **Calculation of Component from IMPROVE Measured Species**  Sulfate 1.375 x (3 x S) Nitrate 1.29 x NO 3 - OMC 1.4 x OC LAC EC Soil ( 2.2 x A l) + ( 2.49 x S i) + ( 1.63 x C a) + ( 2.42 x F e) + ( 1.94 x Ti ) CM MT

– MF

where

 S is elemental sulfur as determined from proton

- induced x

- ray emissions (PI XE) analy sis of the I MPROVE Module A. To estimate the mass of the sulfate ion ( SO 4 = ), S is multiplied by

3 to account for the presence of oxy gen. I f S is missing then the sulfate (SO 4 ) measured by

ion chromatography

analy sis of Module B is used to replace (3 x S). For the I MPROVE aerosol extinction calculation, sulfate is assumed to be completely neutralized by

ammonium (1.375 x SO 4 ).

 NO 3 - is the particulate nitrate measured by ion chromatography

analy sis of Module B. For the I MPROVE aerosol extinction calculation, it is assumed to be completely neutralized by ammonium (1.29 x NO 3 ).

 T he I MPROVE organic carbon (OC) measurements are multiplied by 1.4 to obtain organic matter (OMC), which adjusts the OC mass for other elements assumed to be associated with OC.

 Elemental carbon (EC) is also referred to as light

- absorbing carbon (LAC).

 Soil is determined as a sum of the masses of those elements (measured by PI XE) predominantly

associated with soil (Al, Si, Ca, Fe, K, and Ti), adjusted to account for oxy gen associated with the common oxide forms. Because K is also a product of the combustion of vegetation, it is represented in the formula by 0.6 x Fe and is not shown explicitly

.
 MT and MF are total PM 10 and PM 2.5 mass, respectively

. Associated with each PM species is an extinction efficiency that converts concentrations (in  g/m 3 ) to light ex tinction (in inverse megameters, Mm - 1 ), as listed below. Sulfate and nitrate are hy groscopic, so relative humidity (RH) adjustment factors, f(RH), are used to increase theparticles’ extinction efficiency with increasing RH; this accounts for the particles ’ taking on water and having greater light scattering. Note that some organic matter (OMC) compounds may also have hy groscopic properties, but the I MPROVE aerosol extinction equation assumes OMC is nonhy groscopic. β Sulfate = 3 x f(RH) x [sulfate] β Nitrate = 3 x f(RH) x [nitrate] β OM = 4 x [OMC] β EC = 10 x [EC] β Soil = 1 x [soil] β CM = 0.6 x [CM] The total light extinction (β ext ) is assumed to be the sum of the light extinctions due to the six PM species listed above plus Ray leigh (blue sky ) background extin ction (β Ray ) , which is assumed to be 10 Mm - 1 . This is reflected in the I MPROVE extinction equation: β ext = β Ray + b Sulfate + β Nitrate + β EC +β OMC + β Soil + β CM The total light extinction (β ext ) in Mm - 1 is related to visual range (VR) in kilometers using t he following relationship: VR = 3912 / β ext The RHR requires that visibility be expressed in terms of a haze index (HI ) in units of deciview (dv), which is calculated as follows: HI = 10 ln(β ext /10) The equations above, with measurements from the associate d I MPROVE monitor, are used to estimate the daily

average visibility

at each Class Iarea for each I MPROVE monitored day . For each y ear from the 2000

- 2004 baseline period, these daily average visibility

values are then ranked from highest to lowest. The “w orst day s” visibility for each of the five y ears in the baseline period is defined as the average visibility across the 20% worst

- visibility day s (highest deciview values); similarly , the “best day s” visibility is defined as the average visibility across t he 20% best

- visibility

day s (lowest deciview values) for each y ear. The Baseline Conditions for the best and worst day s are defined as the five

- ye ar average of the B20% visibility day s and of the W20% visibility

day s, respectively , across the five

- ye ar bas eline period. The set of equations given above for relating measured PM species to visibility (light extinction) are referred to as the “Old I MPROVE” equation. The I MPROVE Steering Committee has developed a “New I MPROVE” equation that they believe better represents the fit between measured PM species concentrations and visibility impairment. Although conceptually similar to the Old I MPROVE equation, the New I MPROVE equation includes updates to many of the parameters and the addition of extinctions due to N O 2 absorption and sea salt. 2018 visibility projections and comparisons with the URP glide path goals were performed using both the New and Old I MPROVE equations. The reader is referred elsewhere for details on the New IM PROVE extinction equation (e.g., EP A, 2006a,b).Mappin g Model Results to I MPROVE Measurements

As noted above, future

- ye ar visibility at Class I areas is projected by using modeling results in a relative sense to scale current observed visibility for the B20% and W20% visibility day s. This s caling is done using RRFs, the ratios of future

- ye ar modeling results to current

- ye ar results. Each of the six components of light extinction in the I MPROVE reconstructed mass extinction equation is scaled separately . Because the modeled species do not exa ctly match up with the IM PROVE measured PM species, assumptions must be made to map the modeled PM species to the I MPROVE measured species for the purpose of projecting visibility improvements. For example, in the model’s chemistry (which explicitly

simula tes ammonium), sulfate may or may not be fully

neutralized; the I MPROVE extinction equation, on the other hand, assumes that observed sulfate is fully

neutralized by

ammonium. For the CMAQ v4.5 model (September 2005 release) used in the WRAP RMC modeling, the mapping of modeled species to I MPROVE measured PM species is listed in Table 2

.

**Table 2. Mapping of CMAQ v4.5 modeled species concentrations** 

**to IMPROVE measured components. IMPROVE**  **Component CMAQ**  **V 4.3 Species**  Sulfate 1.375 x (ASO4J + ASO4I) Nitrate 1.29 x (ANO3J + ANO3I) OMC AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI LAC AECJ + AECI Soil A25J + A25I CM ACORS + ASEAS + ASOIL
Projecting Visibility

Changes Using Modeling Results RRFs calculated from modeling results can be used to pro ject future

- ye ar visibility

. For the urrent modeling efforts, RRFs are the ratio of the 2018 modeling results to the 2002 modeling results, and are specific to each Class I area and each PM species. RRFs are applied to the Baseline Condition observed PM sp ecies levels to project future

- ye ar PM levels, which are then used with the I MPROVE extinction equation listed above to assess visibility . The following six steps are used to project future

- ye ar visibility for the B20% and W20% visibility day s (the discuss ion below is for W20% day s but also applies to B20% day s): 1. For each Class I area and each monitored day , daily visibility is ranked using I MPROVE data and I MPROVE extinction equation for each y ear from the five

- ye ar baseline period (2000

- 2004) to identify the W20% visibility day s for each y ear . 2. Use an air quality

model to simulate a base

- ye ar period (ideally 2000

- 2004, but in reality just 2002) and a future y ear (e.g., 2018), then apply the resulting information to developClass

- I

- area - specific RRFs for eac h of the six components of light extinction in the

IM PROVE aerosol extinction equation. 3. Multiply

the RRFs by

the measured 24

- h PM data for each day from the W20% day s for each y ear from the five

- ye ar baseline period to obtain projected future

- ye ar (2018) 2 4

- h PM concentrations for the W20% day s. 4. Compute the future

- ye ar daily extinction using the I MPROVE aerosol extinction equa tion and the projected PM concentrations for each of the W20% day s in the five

- ye ar baseline from Step 3. 5. For each of the W20% day s within each y ear of the five

- ye ar baseline, convert the future

ye ar daily extinction to units of deciview and average the daily deciview values within each of the five y ears separately to obtain five y ears of average deciview visibility for the W20% day s. 6. Average the five y ears of average deciview visibility to obtain the future

- ye ar visibility Haze I ndex estimate that is compared with the 2018 progress goal. In calculating the RRFs, EPA draft guidance (U.S. EPA, 2001, 2006a) recommends selecting modeled PM species concentrations “near” the monitor by taking a spatial average of PM concentrations across a grid

- cell

- resolution

– dependent NX by

NY array of cells centered on the grid containing the monitor. For the WRAP 36

- km CMAQ modeling, the model estimates f or just the grid cell containing the monitor are used (i.e., NX=NY=1). For the preliminary

2018 visibility

projections, results are presented only for “Method 1,” which is the recommended approach in EPA’s draft modeling guidance documents (U.S. EPA, 200 1, 2006a). I n the Method 1 Average RRF Approach, an average RRF for the W20% day s from 2002 (Modeled Worst Day s) is obtained for the Plan02c and the Base18b CMAQ simulations by averaging the PM concentration components across the Modeled Worst Day s and the n calculating the (future y ear):(base y ear) ratio of the average PM concentrations. For example, if SO4 i,j is the measured sulfate concentrations at Class I area j for the i =1,..., N 20% worst visibility day s in 2002, then the RRF for sulfate on the W20% day s would be obtained as:



##    

 

    N

i ij N

i ij N

i ij N

i ij j SO SO SO N SO N SO RRF

1 1

1 1 ) 2002 ( 4 ) 2018 ( 4 ) 2002 ( 4 1 )

2018 ( 4 1 ) 4 (

For each Class I area and each of the W20% day s, the average RRF for each PM component would be applied to concentrations for the W20% day s from the 2000

- 2004 baseline period to estimate future

- ye ar PM concent rations for each of the W20% day s. Extinction and HI would then be calculated to obtain the projected future

- ye ar visibility conditions using the procedures given previously

. Glide Path to Natural ConditionsThe presumptive visibility target for 2018 is t he URP goal that is obtained by constructing a linear glide path from the current Baseline Conditions to Natural Conditions in 2064 (both expressed in deciviews). For instance, Figure 5 display s an example visibility glide path for the Grand Cany on Nationa l Park (GRCA) Class I area. EPA’s default Natural Conditions value for the W20% day s (U.S. EPA, 2003b), shown as the green line, is the 2064 visibility goal at GRCA of 6.95 dv. The blue diamonds at the left of the plot are the annual average current condit ions, based on I MPROVE observations for the W20% day s as obtained from the Visibility I nformation Exchange Web Sy stem (VI EWS) web site ( http://vista.cira.colostate.edu/views/ ). These annual average vi sibility

values for the 20% worst day s allow an assessment of trends and the y ear - to
ye ar variation in visibility . The Baseline Conditions are the average of the W20% visibility from 2000

- 2004, which is the starting point for the glide path in 2004 (12.04 dv for GRCA). A linear URP from the Baseline Conditions in 2004 to Natural Conditions in 2064 (sloping pink line with triangles) is assumed, and the value on the glide path at 2018 is the presumptive URP visibility target that the modeled 2018 projections are compared against to judge progress. I n this example, the visibility

progress goal in 2018 would be 10.85 dv. Meeting this would require a 1.19 dv reduction in visibility

by 2018 to meet that milestone y ear’s visibility progress target at the Grand Cany on National Park.

**Uniform Rate of Reasonable Progress Glide Path Grand Cany**  **on NP - 20% Worst Days**

**12.04 11.70**  **10.85 10.01**  **9.16 8.31**  **7.46 6.95**  **11.71**

0 5 10 15 20 25 30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064 YearHazinessIndex(Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction **Figure 5. Example of URP glide path using IMPROVE data from the Grand Canyon**  **National Park for the W20% days and comparison with Base18b visibility projections.**Preliminary Visibility

Projection Results For all of the WRAP Class I are as, the RMC perf ormed preliminary

2018 visibility

projections and compared them to the 2018 URP goal s using the Plan 02c and B ase18 b CMAQ modeling results and the Old and New I MPROVE equations. As an example, Figure 5 above compares the B ase1 8b vi sibility

p rojections with the URP goal based on the glide path for GRCA and the O ld IM PROVE equation. To achieve the 2018 URP goal , the mod eled 2018 visibility projection would have to show a 1.19 dv (=12.04

- 10.85) reduction. However, the mod eled 2018 visibility pro jection show s only

a 0.33 dv (=12.04

- 11.71) reduction by 2018, which indicates that the emission controls simulated in case Base18 b would not achieve the modeled URP goal ; the 2018 visibility projection achieve s only

28% of the goal (28% = 100 x 0.33/1.19) . Figure 6 display s the 2018 visibility

projections for all WRAP Class I areas , using both the Old and New IM PROVE equation s, expressed as a percentage of achieving the URP goal, with values of 100% or greater achieving the goal. Using the procedures outli ned above, none of the WRAP Class I areas are projected to achieve their URP goals. There are various reasons for this , such as the presence of W20% day s that are dominated by emissions from sources that are not controll able, such as wildfires, dus t, and/o r international transport. Additional analy sis of these res ults and alternative projection techniques are currently under study

.Method 1 predictions for Colorado Plateau and Desert Southwest sites

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%BRCA1CANY1GRCA2MEVE1SAPE1WEMI1ZION1BAND1BOAP1CHIR1GICL1GRSA1IKBA1SACR1SAGU1SIAN1WHIT1WHPE1**P e** **r c** **e n** **t o** **f t** **a r** **g e** **t r** **e d** **u c** **t i** **o n** **a c** **h i** **e v** **e d**  base18b old algorithm base18b new algorithm

Colorado Plateau Desert Southwest
Method 1 predictions for North, Great Basin and Rockies sites

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%BADL1CABI1GAMO1LOST1MELA1MONT1SULA1THRO1ULBE1WICA1CRMO1JARB1SAWT1BRID1NOAB1ROMO1WHRI1YELL2**P e** **r c** **e n** **t o** **f t** **a r** **g e** **t r** **e d** **u c** **t i** **o n** **a c** **h i** **e v** **e d**  base18b old algorithm base18b new algorithm

North Great Basin Rockies
Method 1 predictions for Pacific Northwest and California sites

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%HECA1KALM1MOHO1MORA1NOCA1OLYM1PASA1SNPA1STAR1THSI1WHPA1AGTI1BLIS1DOME1HOOV1JOSH1LAVO1PINN1REDW1SAGA1SAGO1YOSE1**P e** **r c** **e n** **t o** **f t** **a r** **g e** **t r** **e d** **u c** **t i** **o n** **a c** **h i** **e v** **e d**  base18b old algorithm base18b new algorithm

Pacific Northwest California **Figure 6. 2018 visibility projections at WRAP Class I areas expressed as a percent of achieving the 2018 URP goal using**  **the Old and New IMPROVE equation and the WRAP Base18c CMAQ 36**  **- km modeling results.** **PM Source Apportionment**

Im pairment of visibility

in Class I areas is caused by a combination of local air pollutants and regional pollutants that are transported long distan ces. To develop effective visibility improve

ment strategies , the WRAP member states and tribes need to know the relative contributions of local and transported pollutants , and which emissions sources are significant contributors to visibility impairment a t a given Class I area. A variety of modeling and data analy sis methods can be used to perform source apportionment of the PM observed at a given receptor site. Model sensitivity simulations have been used in which a “base case” model simulation is perfor med and then a particular source is “zeroed out” of the emissions. The importance of that source is assessed by evaluating the change in pollutants at the receptor site, calculated as pollutant concentration in the sensitivity case minus that in the base c a se. This approach is known as a “brute force” sensitivity because a separate model run is required for each sensitivity

. An alternative approach is to implement a mass

- tracking algorithm in the air quality model to explicitly track for a given emissions source the chemical transformations, transport , and removal of the PM that was formed from that source. Mass tracking methods have been implemented in both the CMAQ and CAMx air quality models. In itial work completed by the RMC during 2004 use d the CMAQ Ta gged Species Source Apportionment (TSSA) method. Unfortunately , t here were problems with mass conservation in the version of CMAQ used in that study , and these affected the TSSA results. A similar algorithm has been implemented in CAMx, the PM Source Appor tionment Technology (PSAT). Comparisons of TSSA and PSAT showed that the results were qualitatively

similar, that is , the relative ranking of the most significant source contributors were similar for the two methods. However, the total mass contributions d iffered. With separate funding from EPA, UCR has implement ed a version of TSSA in the new CMAQ release (v4.5) that corrects the mass conservation error, but given the uncertainty of the availability

of this update, the CA Mx/PS AT source apportion ment metho d was used for the WRAP modeling analy sis

. The main objective of apply ing CA Mx/PS AT is to e valuate the regional haze air quality for ty pical 2002 (Plan02c) and future

- ye ar 2018 (Base18b) conditions. These results are used

 to assess the contributions of di fferent geographic source regions (e.g., states) and source categories to current (2002) and future (2018) visibility impairment at Class I areas, to obtain improved understanding of (1) the causes of the impairment and (2) which states are included in the area of influence (AOI ) of a given Class I area; and

 to identify

the source regions and emissions categories that, if controlled, would produce the greatest visibility

improvements at a Class I area. CAMx/PSAT The PM Source Apportionment Technology perfo rms source apportionment based on user defined source group s . A source group is the com bination of a geographic source region and an emissions source category

. Examples of source regions include s tates, nonattainment area s, andcounties . E xamples of source categories include mobile sources, biogenic sources, and elevated

point sources ; PSAT can even focus on individual source s . The user defines a geographic source region map to specify

the source regions of interest

. He or she then inputs each source catego ry as separate , gridded low

- level emissions and/or elevated

- poin t
- source emissions. The model then determines each source group by overlay ing the source categories on the source region map. For further information, please refer to the white paper on the fe atures and capabilities of PSAT ( http://pah.cert.ucr.edu/aqm/308/reports/PSAT_White_Paper_111405_final_draft1.pdf ), with additional details available in the C AMx user’s guide (ENVI RON, 2005; http://www.camx.com )

. PM source apportionmen t modeling was performed for aerosol sulfate (SO 4 ) and aerosol nitrate (NO 3 ) and their related species (e.g., SO 2 , NO, NO 2 , HNO 3 , NH 3 , and NH 4 ) . The PSAT simulations include 9 tracers, 18 source regions , and 6 source groups. The computational cost for each of these species differs because additional tracers must be used to track chemical conversions of precursors to the secondary PM species SO 4 , NO 3 , NH 4 , and secondary organic aerosols ( SOA ). T able 3 summarizes the computer run time required for each species. Th e practical implication of this table for WRAP is that it is much more expensive to perform PSAT simulations for NO 3 and especially for S OA th an it is to perform simulations for other species.

**Table 3. Benchmarks for PSAT computational costs for each PM species.**  **Run time is for one day (01/02/2002) on the WRAP 36 -**  **km domain. Species**  **N o.**  **of S**  **pecies**

**Tracers RAM** 

**Memory Disk Storage** 

**per D**  **ay Run**  **Time with**

**1 CPU** SO 4 2 1.6 GB 1.1 GB 4.7 h /day NO 3 7 1.7 GB 2.6 GB 13.2 h /day SO 4 and NO 3 combined 9 1.9 GB 3.3 GB 16.8 h /day SOA 14 6.8 GB Not tested Not tested Primary PM species 6 1.5 GB 3.0 GB 10.8 h /day
Two annual 36

- km CAMx/PSAT model simulatio ns were performed: one with the Plan02c ty pical

- ye ar baseline case and the other with the Base18b future

- ye ar case. I t is expected that the states and tribes will use these results to assess the sources that contribute to visibility impairment at each Clas s I Area, and to guide the choice of emission control strategies. The RMC web site includes a full set of source apportionment spatial plots and receptor bar plots for both Plan02b and Base18b. These graphical display s of the PSAT results, as well as addi tional analy ses of these results are available on the TSS under http://vista.cira.colostate.edu/tss/Tools/ResultsSA.aspxCAMx/PSAT 2002 and 2018 Setup

PSAT source apportionment si mulations for 2002 and 2018 were performed using CAMx v 4.30

. Table 4 lists overall specifications for the 2002 PSAT simulations. The domain setup was identical to the standard WRAP CMAQ modeling domain. The CA Mx/PS AT run

- time options are shown in Table 5

. The CA Mx/PS AT computational cost for one simulation day with source tracking for sulfate (SO 4 ) and nitrate (NO 3 ) is approximately 14.5 CPU hours with an AMD Opteron CPU

. The source regions used in the PSAT simulations are shown in Figure 7 and Table 4 . Th e six emissions source groups are described in Table 6

. The development of these emissions data are described in more detail below. T he annual PSAT run was divided into four seasons for modeling . The initial condition s for the first season (Jan uary 1 to March 31, 2002) came from a CENRAP annual simulation. For the other three seasons, we allowed 15 model spin

- up day s prior to the beginning of each season. Based on the chosen set of source regions and groups, with nine tracers, and with a minimum requireme nt of 8 7,000 point sources and a horizontal domain of 148 by 112 grid cells with 19 vertical lay ers, the run

- time memory requirement is 1.9 GB. Total disk storage per day is approximately

3.3 GB. Although the RMC’s computation nodes are equipped with dual Opteron CPU s with 2 GB of RAM and 1 GB of swap space, the high run - time memory requirement s prevented ru nning PSAT simulations using the OpenMP shared memory multi processing capability implemented in CAMx

.

**Table 4. WRAP 2002 CAMx/PSAT specifications**  **. WRAP**  **PSAT Specs Description**  Model CAMx v4.30 OS/compiler Linux, pgf90 v.6.0

- 5 CPU type AMD Opteron with 2 GB of RAM Source region 18 source regions; see Figure 4.1 and Table 4.4 Emissions source groups Plan02b, 6 source groups; see Table 4.5 Initial cond itions From CENRAP (camx.v4.30.cenrap36.omp.2001365.inst.2) Boundary conditions 3

- h BC from GEOS

- Chem v2

**Table 5. WRAP CAMx/PSAT run -**  **time options. WRAP PSAT specs**  **Description** Advection solver PPM Chemistry parameters CAMx4.3.chemparam.4_CF Chemistry s olver CMC Plume

- in

- grid Not used**WRAP PSAT specs Description**  Probing tool PSAT Dry/wet deposition TRUE (turned on) Staggered winds TRUE (turned on)

**Table 6 .**  **WRAP CAMx/PS**  **AT source region s**  **cross -**  **reference table .**  **Source Region ID**  **Source Region**

**Description 1**  **Source**

**Region ID Sourc**  **e Region Description**  **1** 1 Arizona (AZ) 10 South Dakota (SD) 2 California (CA) 11 Utah (UT) 3 Colorado (CO) 12 Washington (WA) 4 Idaho (ID) 13 Wyoming (WY) 5 Montana (MT) 14 Pacific off

- shore & Sea of Cortez (OF) 6 Nevada (NV) 15 CENRAP states (CE) 7 New Mexico (NM) 16 Eastern U.S., Gulf of Mexico, & Atlantic Ocean (EA) 8 North Dakota (ND) 17 Mexico (MX) 9 Oregon (OR) 18 Canada (CN) 1 The abbreviations in parentheses are used to identify source regions in PSAT receptor bar plot s.

**-2736 -2412**  **-2088 -1764**  **-1 440**  **-1116 -792**  **-468 -144**  **180 504**  **828 1152**  **1476 1800**  **2124 2448**  **-2088 -1872**  **-1656 -1440**  **-1224 -1008**  **-792 -576**  **-360 -144**  **72 288**  **504 720**  **936 1152**  **1368 1584**  **1800**

**2 6** 

**9 12** 

**4 5**  **8**

**13**
**11 3** 

**1 7**  **15 15** 
**16**

**16 16**  **18 18** 

**14 17**  **14**

**10**

**Figure 7. WRAP CAM x/PSAT source region map. Table 6 defines the source region IDs.** **Table 7. WRAP CAMx/PSAT emissions source groups.**

**Emissions Source**  **Groups Low**  **- level Sources**  **Elevated Sources** 1 Low

- level point sources (including stationary off

shore) Elevated point sources (including stationary off

- shore) 2 Anthropogenic wildfires (WRAP only) Anthropogenic wild fires (WRAP only) 3 Total mobile (on

- road, off

- road, including planes, trains, ships in/near port, off

- shore shipping) 4 Natural emissions (natural fire, WRAP onl y, biogenics) Natural emissions (natural fire, WRAP only, biogenics) 5 Non

- WRAP wildfires (elevated fire sources in other RPOs) Non

- WRAP wild fires (elevated fire sources in other RPOs) 6 Everything else (area sources, all dust, fugitive ammonia, non

- ele vated fire sources in other RPOs) PSAT Results The source apportionment algorithms implemented in CAMx generate output files in the same format as the standard modeled species concentrations files . This ty pically consists of a two
- dimensional , gridded d ataset of hourly

- average surface concentrations for each source group tracer that gives the contribution of the tracer to all the surface grid cells in the model domain for each hour of the simulation. Three

- dimensional instantaneous concentrations are als o output for the l ast two hours of the simulation, which are used to restart the model. Although there are options to output hourly

3

- D average tracer concentrations, the model is usually configures to output only

the model ’s surface lay er concentrations b ecause of the vast disk storage space needed for the 3

- D file output for all the source group contributions

. The source apportionment model results are ty pically presented in two way s :

 Spatial plots showing the area of influence of a source group ’ s PM sp ecies contributions throughout the model domain, either at a given hourly

- average point in time or av eraged over some time interval (e.g., monthly average).

 Receptor b ar plots showing the rank order of source groupings that contribute to PM species at any given receptor site. These plots also can be at a particular point in time or averaged over selected time intervals

— for example , the average source contri butions for the 20% worst visibility

day s. If the 3

- D tracer output files are saved , it is also poss ible to prepare animations of PM species plumes from each of the source groups . H owever, these plots are less useful than the others for quantitative analy sis, are expensive to produce , and requir e saving 3

- D hourly output , which is disk

- space intensive. T he primary product s of the WRAP PSAT modeling were receptor bar plot s showing the emission source groups that contribute the most to the model grid cells containing each I MPROVE monitoring site and other receptor sites identified by WRAP.**Model Sensitivity Simulations** 

A variety of sensitivity

simulations were conducted by the RMC as part of their modeling efforts to support the WRAP in addressing the Regional Haze Rule requirements. These sensitivity simulations are described below. 200 2 Clean Case There are many

natural sources of ambient PM 2.5 , both direct emissions of primary PM 2.5 (such as windblown dust) and emissions of gaseous species that undergo photochemical transformation or condensation to form secondary PM 2.5 . Natural sources of PM 2.5 are of c oncern because they represent sources that cannot be controlled. Estimates of natural haze levels have been developed by EPA for visibility

planning purposes and are described in Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule (U.S. EPA, 2003 a ). These are the natural haze levels to be used in glide path calculations, such as those we performed as part of the visibility projections for 2018. However, the natural haze levels developed by EPA for glide path calculations were based on ambient data analy sis, not on visibility modeling. This question thus arises: Would modeled levels of natural haze be consistent with the values estimated by EPA for visibility planning? I f the natural haze levels calculated by the model were subs tantially higher than the levels used for planning purposes, this would make it more difficult for modeling studies to demonstrate progress in attaining visibility goals, because the model would predict haze levels that exceeded EPA’s natural haze levels e ven if all anthropogenic sources of PM 2.5 were removed from the modeling. The RMC explored this issue by conducting a CMAQ sensitivity “clean conditions” simulation There are many

uncertainties and unknowns regarding natural emissions. There have been only limited studies of natural emissions conditions. It is known that there are very large uncertainties in the categories of natural emissions included in the WRAP emissions inventories, and that some categories of natural emissions are not included at all. Also, it is difficult to know what truly natural emissions would have been like in the absence of human modifications of the environment. For example, wildfire emissions are a large source of natural emissions in our modeling, but how much larger might tha t source be in the absence of fire suppression efforts? For all of these reasons, it was decided to describe this sensitivity simulation as a “clean conditions” scenario rather than a “natural conditions” scenario. I n this simulation, all anthropogenic emi ssions were removed from the inventory and only those emissions that were defined as biogenic in the 2002 base case (Base02) were included . Thus, this model simulation does not represent true natural conditions. I t indicates instead the lowest haze levels that could be achieved in the model if all anthropogenic emissions were zeroed out. Emission Inventories T he emissions for the clean 2002 sensitivity case were derived from case Base02a. Because it was a sensitivity

analy sis to test the impacts of natural emissions sources on visibility , it is refer red to it as scenario Base02nt, where “nt” refers to natural. T he following emissions categories in Base02nt were included :

 Biogenics: Generated in case Base02a by BEI S3.12 using SMOKE. WR AP A mmonia: The Base02a ammonia emissions for the WRAP region were developed with a GI S by

ENVI RON. The five emissions category modeled included three anthropogenic sources (domestic animals, livestock, and fertilizer application) and two natural sources (soils and wildlife). O nl y the two natural sources in scenario Base02nt were used

.
 CENRAP and MRPO A mmonia: To create ammonia inventory files for only

natural sources, we used a list of SCCs representing natural sources to extract the emissions records of these sources from the mo nthly inventory

files that were used in Base02a. it was found that there were no natural ammonia sources in the MRPO monthly inventory files.

 Natural A rea S

ources: The Base02a area

- source inventory files included natural sources, such as wildfires and wild animals. T hese records were extracted from the stationary

area - source inventories. Note that the WRAP area

- source files did not include any natural sources.

 Natural F

ires: Of the five fire categories modeled in Base02a (wildfires, wildland fire use, non - F ederal rangeland prescribed fires, prescribed fires [which were split into natural and anthropogenic prescribed for this purpose of this sensitivity ], and agricultural fires), only the categories that represent natural fires (wildfires, wildland fire use, and natural prescribed fires) were included

.
 Wi ndblown D ust: We used the windblown dust inventory that ENVI RON and the RMC developed for use in case Base02a. Additional details on this dust inventory are available at http://www.cert.ucr.edu/aqm/308/wb_dust2002/wb_dust_ii_36k.shtml

. T he biogenic and windblown dust emissions from the Base02a SMOKE outputs that are stored at the RMC were used directly . For the fire (including b oth point and area fires), natural area, and ammonia emissions, these data were reprocessed specifically for scenario Base02nt using the same ancillary

data (temporal, chemical, and spatial allocation data) used in case Base02a. QA plots and documentation for scenario Base02nt are posted on the RMC web site at http://pah.cert.ucr.edu/aqm/308/qa_Base02nt36.shtml

. Modeling Results Figure 8 shows the model

- reconstructed light extinction in t he clean emissions model simulation. Because the natural fire emissions in the WRAP states were a major component of the clean emissions, the largest visibility impairment is in the regions with natural fire emissions. Contributions to light extinction fro m natural sources were small in regions without large fire emissions, as evidenced in the eastern U.S., where the extinction was only slightly larger (about 2 Mm - 1 ) than perfectly

clean Ray leigh conditions of 10 Mm - 1

. Although there are large uncertainties in the natural emissions, and it is known that there are missing ty

pes of natural emissions, the components of the natural inventory used in this sensitivity

simulation did contribute to relatively large visibility

impairment in regions where there were l arge wildfires. Extinction coefficients as large as 90 Mm - 1 were simulated in the southern Oregon and northern California regions; this was most likely a result of the large Biscuit fire in Oregon, plus contributions from smaller fires and other natural em issions. These visibilityimpairment levels exceed the natural visibility levels specified in the EPA regional haze natural visibility guidance document. I t will thus be more difficult for the modeling to demonstrate attainment of progress goals in areas o f the country subject to wildfires because of their large contribution to visibility

impairment that is not controllable. I n other regions of the country for which the inventories lacked large natural fire emissions, the modeled clean visibility was only s lightly greater than clean Ray leigh conditions. Note the model results may be overly optimistic in these regions because we lack a complete, accurate natural emissions inventory

.

**Figure 8**  **. Annual average model -**  **reconstructed “clean conditions” visibility as extinction coefficient.** 

These results are all very

tentative because of the large uncertainties in natural emissions. Considerable effort would be needed to more fully investigate natural conditions in future modeling studies. I t will alway s be difficul t to determine and quantify “clean conditions” based on observations because of the pervasive influence of anthropogenic emissions. Also as part of this sensitivity analy sis, the contributors to organic carbon aerosols (OC) for the clean conditions scenari o wer4e evaluated . The CMAQ model represents explicitly three classes of organic carbon aerosols:

 AO RGPA: Primary

anthropogenic OC resulting from direct organic mass emissions, such as primary

organic aerosol ( POA ).

 AORGA: Secondary

anthropogenic OC result ing from aromatic VOCs, such as xy lene, toluene, and cresols. AORGB: Secondary biogenic OC resulting from biogenic VOCs, such as terpenes. Because it was not cost effective to carry out CAMx/PSAT simulations with OC, the explicit OC results for the clean conditions case were analy zed , and then compared those results to the Base02b case in an attempt to infer the relative contributions of biogenic and anthropogenic VOCs to OC. These results are difficult to interpret for at least two reasons:

 Because of the simplified approach used by CMAQ and the Carbon Bond Mechanism version 4 (CB4) to represent these species, it is not possible to accurately classify all emissions into the CMAQ model as either biogenic or anthropogenic based simply on the species name. Th us, some biogenic OC might be included with AORGA, and some anthropogenic OC might be included in AORB.

 Some fire emissions are classified as anthropogenic, but these emissions might include species such as terpenes that are ty pically considered biogenic. Using the analy sis approach in which all terpenes are assumed biogenic then incorrectly causes some anthropogenic emissions to be labeled biogenic when we use the simplified approach of analy zing OC in terms of AORGPA, AORGA and AORGB. In spite of these difficulties, however, the results should classify the majority

of the emissions correctly as either biogenic or anthropogenic. For each of the above three components of OC, plots of the annual average mass in the Base02b case were prepared , and then the c ontrollable mass was estimated as the difference between the Base02b case the Base02nt clean emissions scenario. Figure 9 shows the annual average mass of OC contributed from AORGPA in case Base02b (top) and the portion of that mass attributed to controlla ble emissions (bottom). Comparing these two plots indicates that in the western U.S. there is considerable AORGPA mass that is not controllable. I t is likely that much of this mass is from fires, since uncontrollable AORGPA mass is present at the site of l arge fires in southern Oregon and north of Tucson, AZ. Figure 10 shows the annual average mass of secondary OC contributed from AORGA in the Base02b case (top) and the portion of that mass attributed to controllable emissions (bottom). These plots indicate that virtually all of the AORGA mass is controllable, since the bottom plot is almost identical to the top plot. Figure 11 shows the annual average mass of OC contributed from AORGPA in the Base02b case (top) and the portion of that mass attributed to con trollable emissions (bottom). These plots indicate that although most of the AORGB mass is not controllable, a significant amount of mass is controllable. I t is likely that the controllable AORGB mass results from VOC oxidation chemistry and the larger amo unt of biogenic mass that is oxidized and subsequently condenses to form OC in the Base02b case. These results indicate that controlling O 3 precursor emissions is effective at reducing a small but significant fraction of the biogenic OC.**Figure 9**  **. An nual average modeled primary anthropogenic OC (AORGPA) in Base02b** 

**(top) and the portion that is “controllable” primary anthropogenic OC (bottom). Figure**  **10 . Annual average modeled secondary anthropogenic OC (AORGA) in Base02b** 

**(top) and the portion th at is “controllable” secondary anthropogenic OC (bottom).** **Figure 11**  **. Annual average modeled primary biogenic OC (AORGB) in Base02b (top) and the portion that is “controllable” primary biogenic OC (bottom).** It might be difficult for the WRAP states an d tribes to use these results quantitatively in develop

ing emissions control strategies for visibility SI Ps and TI Ps. However, the results do provide some insight into the relative contributions of biogenic and anthropogenic OC as well as the amount of ea ch that is controllable in the model simulations. Finally , it is note d that there are uncertainties in the modeled emissions of anthropogenic VOCs, and larger uncertainties in the modeled emissions of biogenic VOCs. I t is not possible to evaluate the model performance individually for biogenic and anthropogenic OC because the OC measurements do not distinguish between those two forms. I nstead, only compar isons of total modeled OC to total measured OC can be made . Therefore, even when the model achieves good performance for total OC, it is possible that the model may be overpredicting one component of total OC and underpredicting the other. The inability to evaluate model performance for each component of OC increases the uncertainty of the results described here and illustrated in Figures 9 through 11 , so caution should be used when drawing conclusions about the sources of OC based on these results.

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