AIR QUALITY MODELING 

Overview

Visibility impairment occurs when fine particulate matter (PM2.5) in the atmosphere scatters and 

absorbs light, thereby creating haze. PM2.5 can be emitted into the atmosphere directly as 

primary particulates, or it can be produced in the atmosphere from photochemical reactions of 

gas-phase precursors and subsequent condensation to form secondary particulates. Examples of 

primary PM2.5 include crustal materials and elemental carbon; examples of secondary PM 

include ammonium nitrate, ammonium sulfates, and secondary organic aerosols (SOA). 

Secondary PM2.5 is generally smaller than primary PM2.5, and because the ability of PM2.5 to 

scatter light depends on particle size, with light scattering for fine particles being greater than for 

coarse particles, secondary PM2.5 plays an especially important role in visibility impairment. 

Moreover, the smaller secondary PM2.5 can remain suspended in the atmosphere for longer 

periods and is transported long distances, thereby contributing to regional-scale impacts of 

pollutant emissions on visibility. 

The sources of PM2.5 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 

to determine which pollutants should be controlled to most effectively improve visibility. 

Photochemical air quality models offer opportunity to better understand the sources of PM2.5 by 

simulating the emissions of pollutants and the formation, transport, and deposition of PM2.5. If an 

air quality model performs well for a historical episode, the model may then be useful for 

identifying the sources of PM2.5 and helping to select the most effective emissions reduction 

strategies for attaining visibility goals. Although several types of air quality modeling systems 

are available, the gridded, three-dimensional, Eulerian models provide the most complete spatial 

representation and the most comprehensive representation of processes affecting PM2.5, 

especially for situations in which multiple pollutant sources interact to form PM2.5. For less 

complex situations in which a few large point sources of emissions are the dominant source of 

PM2.5, trajectory models (such as the California Puff Model [CALPUFF]) may also be useful for 

simulating PM2.5. 

Air Quality Models

The WRAP RMC utilized two regulatory air quality modeling systems to conduct all regional 

haze modeling. A brief discussion of each of these models is provided below. 

Community Multi-Scale Air Quality Model

EPA initially developed the Community Multi-Scale Air Quality (CMAQ) modeling system in 

the late 1990s. The model source code and supporting data can be downloaded from the 

Community Modeling and Analysis System (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 system to encompass modeling of multiple pollutants 

and issues, including ozone, 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 Urban 
Airshed Model). CMAQ is an Eulerian model—that is, it is a grid-based model in which the 

frame of reference is a fixed, three-dimensional (3-D) grid with uniformly sized horizontal grid 

cells and variable vertical layer thicknesses. The number and size of grid cells and the number 

and thicknesses of layers 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 thermodynamics and 

phase transfer, aqueous chemistry, and wet 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 mechanisms 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 IV mechanism (CB-IV). 

Comprehensive Air Quality Model with Extensions 

The Comprehensive Air Quality Model with extensions (CAMx) model was initially developed 

by ENVIRON in the late 1990s as a nested-grid, gas-phase, Eulerian photochemical grid model. 

ENVIRON later revised CAMx to treat PM, visibility, and air toxics. While there are many 

similarities between the CMAQ and CAMx systems, there are also some significant differences 

in their treatment of advection, dispersion, aerosol formation, and dry and wet deposition. 

Model Versions 

Both EPA and ENVIRON periodically update and revise their models as new science or other 

improvements to the models are developed. For CMAQ, EPA typically provides a new release 

about once per year. The initial 2002 MPE for WRAP used CMAQ version 4.4, which was 

released in October 2004. In October 2005 EPA released CMAQ version 4.5, which includes the 

following updates and improvements to the modeling system: 

 A new vertical advection algorithm with improved mass conservation 

 Changes in deposition velocities for some PM species 

 A new sea-salt emissions model and inclusion of sea salt in the aerosol thermodynamics 

 An option to make vertical mixing parameters vary as a function of land use type 

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 dynamics module, and percent urban minimum vertical diffusivity) require the 

reprocessing of the MM5 data using the new version of MCIP (MCIP 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 MCIP version, MCIP v2.3, 

and the AE3 aerosol module that does not include active sea salt chemistry. 

ENVIRON releases updated versions of CAMx approximately every two years, 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, ISORROPIA aerosol thermodynamics, and PPM horizontal advection 

scheme. In 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 differences 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 analysis 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) sensitivity 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) parallelization (i.e., shared memory 

multiprocessors). CMAQ parallelization, on the other hand, is implemented using Message 

Passing Interface (MPI) multiprocessing and therefore can be run using any number of CPUs. 

Depending on the number of model simulations to be 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 air 

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 system. Each of these 

inventories has undergone a number of revisions throughout 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. In 

addition to various sensitivities 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 the actual conditions in calendar year 

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 databases 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 represent baseline emission patterns based on 

average, or “typical”, conditions. This inventory provides a basis for comparison with 
the future year 2018 projected emissions, as well as to gauge reasonable progress with 

respect to future year visibility. 

 The 2018 future-year base case emissions scenario, referred to as “2018 Base Case” or 

“Base18”. These emissions are used to represent conditions in future year 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 year ambient air quality and visibility metrics. 

Data Sources

The CMAQ model requires inputs of three-dimensional gridded wind, temperature, humidity, 

cloud/precipitation, and boundary layer parameters. The current version of CMAQ can only 

utilize output fields from the PSU/NCAR MM5 meteorological model. MM5 is a state-of-thescience atmosphere model that has proven useful for air quality applications 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. In-depth descriptions 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 simulations. 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 system. The development of these data has 

been discussed and documented elsewhere (Tonnesen, G. et al., 2006) 

Initial conditions (ICs) 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 ICs can affect model 

results for as many as 15 days, although the effect typically becomes very small after about 7 

days. A model spin-up period is included in each simulation to eliminate any effects from the 

ICs. For the WRAP modeling, the annual simulation is divided 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 ICs. 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 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 

year 2002 were applied. Additional 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 the 

CMAQ and CAMx models. This work was completed by Byun and coworkers (http://www-
as.harvard.edu/chemistry/trop/geos/meetings/2005/ppt/Expanding_Model_Capabilities/GEOSCMAQ_april_4_Byun.ppt

The CMAQ model options and configuration used for the WRAP 36-km model simulations are 

described in Tonnesen, G. et al., 2006. 

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 year 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 defined the modeling schedule. The purpose of each simulation, and expected results, 

including their implications, 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 systems for a historical episode—in this case, for calendar year 2002—to demonstrate 

the suitability of the modeling systems 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 performance 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., 2006. Also documented in Tonnesen, G. et al., 

2006 are the results of the model performance evaluation, a model inter-comparison and 

discussion of various sensitivity simulations. This information was used as the basis for 

recommending the selection of CMAQ and/or CAMx to complete the remaining modeling 

efforts in RMC’s support of WRAP. 

Model Performance 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 conditions. There are a number of 

challenges in completing an annual MPE for regional haze. The model must be compared to 

ambient 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 yet available from EPA. Therefore, the RMC 

experimented with many different approaches for showing model performance results. The plot 

types 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 predictions on the y-axis and ambient data on the x-axis 

 Spatial analysis 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 days or the worst-20% visibility days at each site; the higher the light 

extinction, the lower the visibility 

Examples of each of these MPE metrics and analysis products can be found in Tonnesen, G. et 

al., 2006. The results of the MPE are available from the WRAP RMC website 

(http://pah.cert.ucr.edu/aqm/308/eval.shtml) 

2002 Planning Scenario 

The 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 “typical”, conditions. This scenario provides a basis for comparison with 

the future year 2018 scenario based on projected emissions, as well as to gauge reasonable 

progress with respect to future year visibility. 

Plan02 Simulations Input Data 

Input data used for the 2002 Planning model simulations 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 

ancillary 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). In 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. Initial 

condition (IC) data for January 1, 2002, were developed using a 15-day spin-up period 

(December 16-31, 2001). 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://wwwas.harvard.edu/chemistry/trop/geos/) for calendar year 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, since errors in the emissions 

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 analysis of the Plan02 scenario, plots were prepared for QA purposes 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 

compare the CMAQ results of each revised Plan02 case to the previous iteration. These were 

prepared as Program for the Analysis and Visualization of Environmental data (PAVE) spatial 

plots showing the change in individual PM2.5 species concentrations as daily, monthly, and 

annual averages. The final set of analysis products, available on the RMC web site, include 

PAVE difference plots comparing the CMAQ-predicted annual average species concentrations 

from the Plan02c case with those from the Base02b case. Note that these plots are not useful for 

visibility planning 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 

emissions for the year 2002 to the “typical-year” emissions created for the final Plan02 scenario. 

The primary analysis “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. 

2018 Model Simulations 

The 2018 future-year 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 year 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 year ambient air quality and visibility metrics. 

Base18 Simulation Input Data 

Input data used for the 2018 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). In 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. Initial 

condition (IC) data for January 1, 2002, were developed using a 15-day spin-up period 

(December 16-31, 2001). 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://wwwas.harvard.edu/chemistry/trop/geos/) for calendar year 2002. 

Base18 Simulation Results

The purpose of modeling 2018 visibility is to compare the 2018 visibility predictions to the 2002 

typical-year visibility modeling results, as discussed below. Some improvements in visibility by 

2018 are expected because of reductions in emissions due to currently planned regulations and 

technology improvements. A brief summary is provided here of the comparison between the 

2018 and 2002 results using annual average PAVE spatial plots. The goal of this summary is to 

convey the scale and spatial extent of changes in key PM2.5 species from 2002 to 2018. For 

planning purposes, on the other hand, states and tribes should focus on the visibility projections 

and glide path calculations at individual Class I Areas. 

Figures 1 through 4 show the annual average concentrations for sulfate, nitrate, PM2.5 and 

model-reconstructed visibility (in deciviews), respectively. In each figure, the bottom two plots 

show the modeled concentration or deciviews for the Plan02b and Base18b cases, while the top 

plot shows the change in visibility calculated as Base18b minus Plan02b. The Plan02b results are 

presented here instead of Plan02c results because these plots had previously been prepared with 

version B. As the 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 figures, cool 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 SOx emissions reductions in the CENRAP region. There were smaller reductions in 

sulfate in the Los Angeles area, western Washington state, and southern Nevada. There were 

small increases of sulfate, mostly in Wyoming, due to growth in SOx 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 results for nitrate. In 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 NOx emissions. There were small reductions in the Phoenix area as 

well, also from reductions in mobile-source NOx emissions. 

Figure 3 shows the comparison of PM2.5 for 2002 and 2018. In most areas of the WRAP region, 

changes in PM2.5 were less than 1 g/m3

. Locations with increases in PM2.5 correspond to areas 

of increased sulfate (see Figure 3-1). Areas with the largest reductions in PM2.5 were the areas in 

California that had large reductions in modeled nitrate in 2018 (see Figure 3-2). Results for other 

species that contribute to PM2.5 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. Note that these results are 

calculated using the modeled relative humidity (RH), so they differ from the results that use site-
specific monthly average RH. Nonetheless, the results in Figure 4 are indicative of the direction 

and magnitude of visibility changes in from 2002 to 2018. Although the largest improvements 

are in California and the Pacific Northwest, there were improvements throughout the WRAP 

region. The change in deciviews is more dramatic than the change in PM2.5 mass (Figure 3) 

because the visibility in deciviews is a relative metric, so small mass changes in PM2.5 in good 

visibility areas can result in large relative improvements in visibility. 
Figure 1. Annual average aerosol sulfate (ASO4) concentration comparisons between 

Base18b and Plan02b. Top plot: difference between the two (Base18b – Plan02b); 

bottom left plot: Plan02b results; bottom right plot: Base18b results. 
Figure 2. Annual average aerosol nitrate (ANO3) concentration comparisons between 

Base18b and Plan02b. Top plot: difference between the two (Base18b – Plan02b); 

bottom left plot: Plan02b results; bottom right plot: Base18b results. 
Figure 3. Annual average PM2.5 concentration comparisons between Base18b 

and Plan02b. Top plot: difference between the two (Base18b – Plan02b); 

bottom left plot: Plan02b results; bottom right plot: Base18b results. 
Figure 4. Annual average deciview comparisons between Base18b and Plan02b. 

Top plot: difference between the two (Base18b – Plan02b); bottom left 

plot: Plan02b results; bottom right plot: Base18b results. 
Visibility Projections 

The Regional Haze Rule (RHR) goals include achieving natural visibility conditions at 156 

Federally mandated Class I areas by 2064. In more specific terms, that RHR goal is defined as 

(1) visibility improvement toward natural conditions for the 20% of days that have the worst 

visibility (termed “20% worst,” or W20%, visibility days) and (2) no worsening in visibility for 

the 20% of days that have the best visibility (“20% best,” or B20%, visibility days). One component 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 year 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-year (2000-2004) baseline period 

and at the other end natural visibility conditions in 2064; the visibility value that occurs on the 

glide path at year 2018 is the URP goal. 

Preliminary WRAP 2018 visibility projections have been made using the Plan02c and Base18b 

CMAQ 36-km modeling results, following EPA guidance that recommends applying the 

modeling results in a relative sense to project future-year 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-year modeling results to the current-year modeling results. The calculated 

RRFs are applied to the baseline observed visibility conditions to project future-year observed 

visibility. These projections can then be used to assess the effectiveness of the simulated 

emission control strategies that were included in the future-year 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 PM10 are divided into six major components; the first five 

are assumed to be PM2.5 and the sixth is PM2.5-10. 

 SO4 (sulfate) 

 NO3 (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 current 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 

[(NH4)2SO4] and all particulate nitrate is in the form of ammonium nitrate [NH4NO3]. 

To facilitate tracking the progress toward visibility goals, two important visibility parameters are 

required for each Class I area: 

 Baseline Conditions: “Baseline Conditions” represent visibility for the B20% and W20% 

days for the initial five-year baseline period of the regional haze program. Baseline 

Conditions are calculated using monitoring data collected during the 2000-2004 five-year 

period and are the starting point 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 conditions for the B20% 

and W20% days at a given Class I area. 

Baseline Conditions

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

measured at Interagency Monitoring of Protected Visual Environments (IMPROVE) monitors 

(Malm et al., 2000). Each Class I area in the WRAP domain has an associated IMPROVE PM 

monitor. The IMPROVE monitors do not measure visibility directly, but instead measure 

speciated fine particulate (PM2.5) and total PM2.5 and PM10 mass concentrations from which 

visibility is calculated using the IMPROVE aerosol extinction equation, discussed later. 

Visibility conditions are estimated starting with the IMPROVE 24-h average PM mass 

measurements related to six PM components of light extinction: 

 Sulfate [(NH4)2SO4] 

 Particulate nitrate [(NH4NO3] 

 Organic matter [OMC] 

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

 Soil 

 Coarse matter [CM] 

The IMPROVE monitors do not directly measure some of these species, so assumptions are 

made as to how the IMPROVE 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 (PM2.5) is speciated to obtain sulfate and 

nitrate measurements (that is, any coarse-mode sulfate and nitrate in the real atmosphere may be 

present in the IMPROVE CM measurement). Concentrations for the above six components of 

light extinction in the IMPROVE aerosol extinction equation are obtained from the IMPROVE 

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 NO3

-

OMC 1.4 x OC 

LAC EC 

Soil (2.2 x Al) + (2.49 x Si) + (1.63 x Ca) + (2.42 x Fe) + (1.94 x Ti) 

CM MT – MF 

where 

 S is elemental sulfur as determined from proton-induced x-ray emissions (PIXE) analysis 

of the IMPROVE Module A. To estimate the mass of the sulfate ion (SO4

=

), S is 

multiplied by 3 to account for the presence of oxygen. If S is missing then the sulfate 

(SO4) measured by ion chromatography analysis of Module B is used to replace (3 x S). 

For the IMPROVE aerosol extinction calculation, sulfate is assumed to be completely 

neutralized by ammonium (1.375 x SO4). 

 NO3

- is the particulate nitrate measured by ion chromatography analysis of Module B. 

For the IMPROVE aerosol extinction calculation, it is assumed to be completely 

neutralized by ammonium (1.29 x NO3). 

 The IMPROVE 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 PIXE) 

predominantly associated with soil (Al, Si, Ca, Fe, K, and Ti), adjusted to account for 

oxygen 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 PM10 and PM2.5 mass, respectively. 

Associated with each PM species is an extinction efficiency that converts concentrations (in 

g/m3

) to light extinction (in inverse megameters, Mm-1), as listed below. Sulfate and nitrate are 

hygroscopic, so relative humidity (RH) adjustment factors, f(RH), are used to increase the 

particles’ 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 hygroscopic properties, but the IMPROVE aerosol extinction equation assumes OMC 

is nonhygroscopic. 
βSulfate = 3 x f(RH) x [sulfate] 

βNitrate = 3 x f(RH) x [nitrate] 

βOM = 4 x [OMC] 

βEC = 10 x [EC] 

βSoil = 1 x [soil] 

βCM = 0.6 x [CM] 

The total light extinction (βext) is assumed to be the sum of the light extinctions due to the six 

PM species listed above plus Rayleigh (blue sky) background extinction (βRay), which is 

assumed to be 10 Mm-1. This is reflected in the IMPROVE extinction equation: 

βext = βRay + bSulfate + βNitrate + βEC +βOMC + βSoil + βCM

The total light extinction (βext) in Mm-1 is related to visual range (VR) in kilometers using the 

following relationship: 

VR = 3912 / βext

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(βext/10) 

The equations above, with measurements from the associated IMPROVE monitor, are used to 

estimate the daily average visibility at each Class I area for each IMPROVE monitored day. For 

each year from the 2000-2004 baseline period, these daily average visibility values are then 

ranked from highest to lowest. The “worst days” visibility for each of the five years in the 

baseline period is defined as the average visibility across the 20% worst-visibility days (highest 

deciview values); similarly, the “best days” visibility is defined as the average visibility across 

the 20% best-visibility days (lowest deciview values) for each year. The Baseline Conditions for 

the best and worst days are defined as the five-year average of the B20% visibility days and of 

the W20% visibility days, respectively, across the five-year baseline period. 

The set of equations given above for relating measured PM species to visibility (light extinction) 

are referred to as the “Old IMPROVE” equation. The IMPROVE Steering Committee has 

developed a “New IMPROVE” equation that they believe better represents the fit between 

measured PM species concentrations and visibility impairment. Although conceptually similar to 

the Old IMPROVE equation, the New IMPROVE equation includes updates to many of the 

parameters and the addition of extinctions due to NO2 absorption and sea salt. 2018 visibility 

projections and comparisons with the URP glide path goals were performed using both the New 

and Old IMPROVE equations. The reader is referred elsewhere for details on the New 

IMPROVE extinction equation (e.g., EPA, 2006a,b).

Mapping Model Results to IMPROVE Measurements

As noted above, future-year 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 days. This 
scaling is done using RRFs, the ratios of future-year modeling results to current-year results. 

Each of the six components of light extinction in the IMPROVE reconstructed mass extinction 

equation is scaled separately. Because the modeled species do not exactly match up with the 

IMPROVE measured PM species, assumptions must be made to map the modeled PM species to 

the IMPROVE measured species for the purpose of projecting visibility improvements. For 

example, in the model’s chemistry (which explicitly simulates ammonium), sulfate may or may 

not be fully neutralized; the IMPROVE 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 IMPROVE 

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 V4.3 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 project future-year 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 species levels to project future-year PM levels, which are then 

used with the IMPROVE extinction equation listed above to assess visibility. The following six 

steps are used to project future-year visibility for the B20% and W20% visibility days (the 

discussion below is for W20% days but also applies to B20% days): 

1. For each Class I area and each monitored day, daily visibility is ranked using IMPROVE 

data and IMPROVE extinction equation for each year from the five-year baseline period 

(2000-2004) to identify the W20% visibility days for each year. 

2. Use an air quality model to simulate a base-year period (ideally 2000-2004, but in reality 

just 2002) and a future year (e.g., 2018), then apply the resulting information to develop 

Class-I-area-specific RRFs for each of the six components of light extinction in the 

IMPROVE aerosol extinction equation. 
3. Multiply the RRFs by the measured 24-h PM data for each day from the W20% days for 

each year from the five-year baseline period to obtain projected future-year (2018) 24-h 

PM concentrations for the W20% days. 

4. Compute the future-year daily extinction using the IMPROVE aerosol extinction equation and the projected PM concentrations for each of the W20% days in the five-year 

baseline from Step 3. 

5. For each of the W20% days within each year of the five-year baseline, convert the futureyear daily extinction to units of deciview and average the daily deciview values within 

each of the five years separately to obtain five years of average deciview visibility for the 

W20% days. 

6. Average the five years of average deciview visibility to obtain the future-year visibility 

Haze Index 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 for 

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, 2001, 

2006a). In the Method 1 Average RRF Approach, an average RRF for the W20% days from 

2002 (Modeled Worst Days) is obtained for the Plan02c and the Base18b CMAQ simulations by 

averaging the PM concentration components across the Modeled Worst Days and then 

calculating the (future year):(base year) ratio of the average PM concentrations. For example, if 

SO4i,j is the measured sulfate concentrations at Class I area j for the i=1,...,N 20% worst 

visibility days in 2002, then the RRF for sulfate on the W20% days would be obtained as: 















   N

i

ij

N

i

ij

N

i

ij

N

i

ij

j

SO

SO

SO

N

SO

N SORRF

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% days, the average RRF for each PM component 

would be applied to concentrations for the W20% days from the 2000-2004 baseline period to 

estimate future-year PM concentrations for each of the W20% days. Extinction and HI would 

then be calculated to obtain the projected future-year visibility conditions using the procedures 

given previously. 

Glide Path to Natural Conditions

The presumptive visibility target for 2018 is the 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 displays an example visibility glide path for the 

Grand Canyon National Park (GRCA) Class I area. EPA’s default Natural Conditions value for 

the W20% days (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 conditions, 

based on IMPROVE observations for the W20% days as obtained from the Visibility 

Information Exchange Web System (VIEWS) web site (http://vista.cira.colostate.edu/views/). 

These annual average visibility values for the 20% worst days allow an assessment of trends and 

the year-to-year 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. In 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 year’s 

visibility progress target at the Grand Canyon National Park. 

Uniform Rate of Reasonable Progress Glide Path

Grand Canyon 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

Year

Haziness Index (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 areas, the RMC performed preliminary 2018 visibility projections 

and compared them to the 2018 URP goals using the Plan02c and Base18b CMAQ modeling 

results and the Old and New IMPROVE equations. As an example, Figure 5 above compares the 

Base18b visibility projections with the URP goal based on the glide path for GRCA and the Old 
IMPROVE equation. To achieve the 2018 URP goal, the modeled 2018 visibility projection 

would have to show a 1.19 dv (=12.04-10.85) reduction. However, the modeled 2018 visibility 

projection shows only a 0.33 dv (=12.04-11.71) reduction by 2018, which indicates that the 

emission controls simulated in case Base18b would not achieve the modeled URP goal; the 2018 

visibility projection achieves only 28% of the goal (28% = 100 x 0.33/1.19). Figure 6 displays 

the 2018 visibility projections for all WRAP Class I areas, using both the Old and New 

IMPROVE equations, expressed as a percentage of achieving the URP goal, with values of 

100% or greater achieving the goal. Using the procedures outlined 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% days that are dominated by emissions from sources that are not 

controllable, such as wildfires, dust, and/or international transport. Additional analysis of these 

results 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%

BRCA1

CANY1

GRCA2

MEVE1

SAPE1

WEMI1

ZION1

BAND1BOAP1

CHIR1

GICL1

GRSA1

IKBA1

SACR1

SAGU1

SIAN1

WHIT1

WHPE1

Percent of target reduction achieved

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%

BADL1

CABI1

GAMO1

LOST1

MELA1

MONT1

SULA1

THRO1

ULBE1

WICA1

CRMO1

JARB1

SAWT1

BRID1

NOAB1

ROMO1

WHRI1

YELL2

Percent of target reduction achieved

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%

HECA1

KALM1

MOHO1

MORA1

NOCA1

OLYM1

PASA1

SNPA1

STAR1

THSI1

WHPA1

AGTI1

BLIS1

DOME1

HOOV1

JOSH1LAVO1

PINN1

REDW1

SAGA1

SAGO1

YOSE1

Percent of target reduction achieved

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 

Impairment of visibility in Class I areas is caused by a combination of local air pollutants and 

regional pollutants that are transported long distances. To develop effective visibility improvement 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 at a given Class I area. 

A variety of modeling and data analysis 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 performed 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 case. 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. Initial work completed by the 

RMC during 2004 used the CMAQ Tagged Species Source Apportionment (TSSA) method. 

Unfortunately, there 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 Apportionment 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 

differed. With separate funding from EPA, UCR has implemented 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 CAMx/PSAT source apportionment method was used for the 

WRAP modeling analysis. 

The main objective of applying CAMx/PSAT is to evaluate the regional haze air quality for 

typical 2002 (Plan02c) and future-year 2018 (Base18b) conditions. These results are used 

 to assess the contributions of different 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 performs source apportionment based on userdefined source groups. A source group is the combination of a geographic source region and an 

emissions source category. Examples of source regions include states, nonattainment areas, and 

counties. Examples of source categories include mobile sources, biogenic sources, and elevated 
point sources; PSAT can even focus on individual sources. The user defines a geographic source 

region map to specify the source regions of interest. He or she then inputs each source category 

as separate, gridded low-level emissions and/or elevated-point-source emissions. The model then 

determines each source group by overlaying the source categories on the source region map. For 

further information, please refer to the white paper on the features 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 CAMx user’s guide (ENVIRON, 2005; 

http://www.camx.com). 

PM source apportionment modeling was performed for aerosol sulfate (SO4) and aerosol nitrate 

(NO3) and their related species (e.g., SO2, NO, NO2, HNO3, NH3, and NH4). 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 SO4, NO3, NH4, and secondary organic 

aerosols (SOA). Table 3 summarizes the computer run time required for each species. The 

practical implication of this table for WRAP is that it is much more expensive to perform PSAT 

simulations for NO3 and especially for SOA than 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 No. of Species 

Tracers 

RAM 

Memory 

Disk Storage 

per Day 

Run Time with 

1 CPU 

SO4 2 1.6 GB 1.1 GB 4.7 h/day 

NO3 7 1.7 GB 2.6 GB 13.2 h/day 

SO4 and NO3

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 simulations were performed: one with the Plan02c 

typical-year baseline case and the other with the Base18b future-year case. It is expected that the 

states and tribes will use these results to assess the sources that contribute to visibility 

impairment at each Class 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 displays of the PSAT results, as well as additional 

analyses of these results are available on the TSS under 

http://vista.cira.colostate.edu/tss/Tools/ResultsSA.aspx
CAMx/PSAT 2002 and 2018 Setup

PSAT source apportionment simulations for 2002 and 2018 were performed using CAMx v4.30. 

Table 4 lists overall specifications for the 2002 PSAT simulations. The domain setup was 

identical to the standard WRAP CMAQ modeling domain. The CAMx/PSAT run-time options 

are shown in Table 5. The CAMx/PSAT computational cost for one simulation day with source 

tracking for sulfate (SO4) and nitrate (NO3) 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. The six emissions source groups are described in Table 6. The development of these 

emissions data are described in more detail below. 

The annual PSAT run was divided into four seasons for modeling. The initial conditions for the 

first season (January 1 to March 31, 2002) came from a CENRAP annual simulation. For the 

other three seasons, we allowed 15 model spin-up days prior to the beginning of each season. 

Based on the chosen set of source regions and groups, with nine tracers, and with a minimum 

requirement of 87,000 point sources and a horizontal domain of 148 by 112 grid cells with 19 

vertical layers, 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 

CPUs with 2 GB of RAM and 1 GB of swap space, the high run-time memory requirements 

prevented running PSAT simulations using the OpenMP shared memory multiprocessing 

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 conditions 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 solver 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/PSAT source regions cross-reference table. 

Source 

Region ID 

Source Region 

Description1

Source 

Region ID 

Source Region 

Description1

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 plots. 

-2736 -2412 -2088 -1764 -1440 -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 CAMx/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 offshore) 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 only, 

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-elevated 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 typically consists of a 

two-dimensional, gridded dataset 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 also output for 

the last 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 layer concentrations because 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 typically presented in two ways : 

 Spatial plots showing the area of influence of a source group’s PM species contributions 

throughout the model domain, either at a given hourly-average point in time or averaged 

over some time interval (e.g., monthly average). 

 Receptor bar 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 contributions for 

the 20% worst visibility days. 

If the 3-D tracer output files are saved, it is also possible to prepare animations of PM species 

plumes from each of the source groups. However, these plots are less useful than the others for 

quantitative analysis, are expensive to produce, and require saving 3-D hourly output, which is 

disk-space intensive. The primary products of the WRAP PSAT modeling were receptor bar 

plots showing the emission source groups that contribute the most to the model grid cells 

containing each IMPROVE 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. 

2002 Clean Case

There are many natural sources of ambient PM2.5, both direct emissions of primary PM2.5 (such 

as windblown dust) and emissions of gaseous species that undergo photochemical transformation 

or condensation to form secondary PM2.5. Natural sources of PM2.5 are of concern 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, 2003a). 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 analysis, 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? If the natural haze levels calculated by the model were substantially 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 even if all anthropogenic sources of PM2.5

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 that 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. In this simulation, all 

anthropogenic emissions 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. It indicates instead the lowest haze levels that could 

be achieved in the model if all anthropogenic emissions were zeroed out. 

Emission Inventories 

The emissions for the clean 2002 sensitivity case were derived from case Base02a. Because it 

was a sensitivity analysis to test the impacts of natural emissions sources on visibility, it is 

referred to it as scenario Base02nt, where “nt” refers to natural. The following emissions 

categories in Base02nt were included: 

 Biogenics: Generated in case Base02a by BEIS3.12 using SMOKE. 
 WRAP Ammonia: The Base02a ammonia emissions for the WRAP region were 

developed with a GIS by ENVIRON. The five emissions category modeled included 

three anthropogenic sources (domestic animals, livestock, and fertilizer application) and 

two natural sources (soils and wildlife). Only the two natural sources in scenario 

Base02nt were used. 

 CENRAP and MRPO Ammonia: 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 monthly inventory files that were used in Base02a. it 

was found that there were no natural ammonia sources in the MRPO monthly inventory 

files. 

 Natural Area Sources: The Base02a area-source inventory files included natural sources, 

such as wildfires and wild animals. These records were extracted from the stationaryarea-source inventories. Note that the WRAP area-source files did not include any natural 

sources. 

 Natural Fires: Of the five fire categories modeled in Base02a (wildfires, wildland fire 

use, non-Federal 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. 

 Windblown Dust: We used the windblown dust inventory that ENVIRON 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. 

The biogenic and windblown dust emissions from the Base02a SMOKE outputs that are stored at 

the RMC were used directly. For the fire (including both 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 the 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 from 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 Rayleigh conditions of 10 Mm-1. 

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

missing types 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 large 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 emissions. These 
visibility impairment levels exceed the natural visibility levels specified in the EPA regional 

haze natural visibility guidance document. It will thus be more difficult for the modeling to 

demonstrate attainment of progress goals in areas of the country subject to wildfires because of 

their large contribution to visibility impairment that is not controllable. In other regions of the 

country for which the inventories lacked large natural fire emissions, the modeled clean visibility 

was only slightly greater than clean Rayleigh 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. It will always be difficult to determine and quantify “clean conditions” based 

on observations because of the pervasive influence of anthropogenic emissions. 

Also as part of this sensitivity analysis, the contributors to organic carbon aerosols (OC) for the 

clean conditions scenario wer4e evaluated. The CMAQ model represents explicitly three classes 

of organic carbon aerosols: 

 AORGPA: Primary anthropogenic OC resulting from direct organic mass emissions, such 

as primary organic aerosol (POA). 

 AORGA: Secondary anthropogenic OC resulting from aromatic VOCs, such as xylene, 

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 analyzed, 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. Thus, 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 typically considered biogenic. Using the analysis 

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 

analyzing 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 controllable 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 

controllable emissions (bottom). Comparing these two plots indicates that in the western U.S. 

there is considerable AORGPA mass that is not controllable. It is likely that much of this mass is 

from fires, since uncontrollable AORGPA mass is present at the site of large 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 controllable emissions (bottom). These plots 

indicate that although most of the AORGB mass is not controllable, a significant amount of mass 

is controllable. It is likely that the controllable AORGB mass results from VOC oxidation 

chemistry and the larger amount of biogenic mass that is oxidized and subsequently condenses to 

form OC in the Base02b case. These results indicate that controlling O3 precursor emissions is 

effective at reducing a small but significant fraction of the biogenic OC. 
Figure 9. Annual 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 that 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 and tribes to use these results quantitatively in developing emissions control strategies for visibility SIPs and TIPs. However, the results do provide 

some insight into the relative contributions of biogenic and anthropogenic OC as well as the 

amount of each that is controllable in the model simulations. 

Finally, it is noted that there are uncertainties in the modeled emissions of anthropogenic VOCs, 

and larger uncertainties in the modeled emissions of biogenic VOCs. It 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. Instead, only comparisons 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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