Appendix C: Chemical Transport Model Methods Page C-1

Appendix C: Chemical Transport Model Results 

for Sulfate Source Attribution Studies in the 

Northeast U.S. 

Model description and performance evaluation 

Shan He, Emily Savelli, Jung-Hun Woo, John Graham, Gary Kleiman

Northeast States for Coordinated Air Use Management (NESCAUM) 

Boston, MA 02114. 

ABSTRACT 

Regional Scale Air Quality Modeling is being conducted by NESCAUM with two 

primary modeling systems based on the CMAQ and REMSAD models. Modeling efforts 

are being conducted on a 12-km Eastern U.S. domain consistent with the RPO projection 

national domain. Modeling for the 2002 base year and two future years (2009, 2018) has 

been conducted. CMAQ provides one-atmosphere results for multiple pollutants while 

the REMSAD model is used primarily for attribution of sulfate species in the Eastern US 

via the species-tagging scheme included in Version 7.10 and newer versions of the 

model. Performance evaluations for both models are provided and will be refined for 

CMAQ once final data inputs have been developed. Current model output suggests 

reasonable performance for the species of interest for each platform. Only minor changes 

are anticipated for scenarios conducted with the final inventory version and revised 

model code. 
Appendix C: Chemical Transport Model Methods Page C-2

Appendix C: Chemical Transport Model Results for Sulfate 

Source Attribution Studies in the Northeast U.S. 

C.1. Introduction 

Air quality models have been extensively used as important tools within 

academia, federal and state governments, and other regulatory, policy, and environmental 

research communities to understand the underlying causes and formation mechanisms of 

air pollution on the local, regional and global scale. To assist states in developing 

effective solutions to air pollution issues, NESCAUM has established in-house air quality 

modeling capabilities that include emission processing, meteorological input analysis, 

and chemical transport modeling. Preliminary work has been conducted to develop a 

number of modeling platforms for a variety of applications (e.g. annual runs, episodic 

simulations, source tagging, etc.) and to evaluate model performance. These efforts form 

the foundation upon which future modeling studies will be built ranging from SIP 

modeling for Regional Haze, Ozone, and fine particles to studies of mercury deposition 

and potential air quality impacts of climate change. 

This appendix introduces air quality modeling platforms established by 

NESCAUM for Regional Haze and fine particle pollution, evaluates model performance 

with respect to spatial and temporal variations of PM species and their precursors, and 

summarizes the current status and future improvements of each platform’s development. 

C.2. Platforms 

Currently two regional-scale air quality models have been evaluated and used by 

NESCAUM to perform air quality simulations. These are the Community Multi-scale 

Air Quality modeling system (CMAQ; Byun and Ching, 1999) and the Regional 

Modeling System for Aerosols and Deposition (REMSAD; SAI, 2002). CMAQ was 

developed by USEPA, while REMSAD was developed by ICF Consulting/Systems 

Applications International (ICF/SAI) with USEPA support. CMAQ has undergone 

extensive community development and peer review (Amar et al. 2005) and has been 

successfully used in a number of regional air quality studies (Bell and Ellis, 

2003;Hogrefe et al., 2004; Jimenez and Baldasano, 2004; Mao and Talbot, 2003; Mebust 

et al. 2003). REMSAD has also has been peer reviewed (Seigneur et al., 1999) and used 

by EPA for regulatory applications 

(http://www.epa.gov/otaq/regs/hd2007/frm/r00028.pdf and 

http://www.epa.gov/clearskies/air_quality_tech.html) to study ambient concentrations 

and deposition of sulfate and other PM species. 

C.2.1. CMAQ 

The CMAQ modeling system is a three-dimensional Eulerian model that 

incorporates output fields from emissions and meteorological modeling systems and 

several other data sources through special interface processors into the CMAQ Chemical 

Transport Model (CCTM). The CCTM then performs chemical transport modeling for 

multiple pollutants on multiple scales. With this structure, CMAQ retains the flexibility 

to substitute other emissions processing systems and meteorological models. CMAQ is 
Appendix C: Chemical Transport Model Methods Page C-3

designed to provide an air quality modeling system with a “one atmosphere” capability 

containing state-of-science parameterizations of atmospheric processes affecting 

transport, transformation, and deposition of such pollutants as ozone, particulate matter, 

airborne toxics, and acidic and nutrient pollutant species (Byun and Ching, 1999). 

MANE-VU has adopted the Inter-RPO domain description for its modeling runs.1

 

This 36-km domain covers the continental United States, southern Canada and northern 

Mexico. The dimensions of this domain are 145 and 102 cells in the east-west and northsouth directions, respectively. A 12-km inner domain was selected to better characterize 

air quality in MANE-VU and surrounding RPO regions. This domain covers the 

Northeast region including northeastern, central and southeastern US as well as 

Southeastern Canada. It extends from 66oW~94oW in longitude and 29oN~50oN in 

latitude with 172X172 grid cells (Figure C-1). 

NY DEC has completed annual 2002 CMAQ modeling on the 36km domain to 

provide dynamic boundary conditions for all simulations performed on the 12km domain. 

Three-hourly boundary conditions for the outer domain were derived from an annual 

model run performed by researchers at Harvard University using the GEOS-CHEM 

global chemistry transport model (Park et al., 2004). Model resolution was species 

dependent at either 4° latitude by 5° longitude or 2° by 2.5°. 

To date, MANE-VU SIP modeling on both 36km and 12km domains used CMAQ 

V4.4 with IOAPI V2.2 and NETCDF V3.5 libraries. The CMAQ model is configured 

with the Carbon Bond IV mechanism (Gery et al., 1989) using the EBI solver for gas 

phase chemistry rather than the SAPRC-99 mechanism due to better computing 

efficiency with no significant model performance differences for Ozone and PM as 

compared to observations. 

Meteorological inputs for CMAQ are derived from the Fifth-Generation 

Pennsylvania State University/National Center for Atmospheric Research (NCAR) 

Mesoscale Model (MM5)2

 system meteorological fields. A modified Blackadar 

boundary layer scheme is used as well as physics options including explicit 

representations of cloud physics with simple ice microphysics (no mixed-phase 

processes) and the Kain-Fritsch cumulus parameterization. As shown in Figure C-2, out 

of the 29 MM5 vertical layers, CMAQ has 22 vertical layers from the ground surface up 

to ~200hPa, with 10 layers below 850hPa (including 1 layer below 10m) to resolve 

boundary layer processes, 6 layers in between 850hPa and 500hPa, and 3 layers in 

between 500hPa and 300hPa. The domain has a finer vertical resolution within the 

troposphere so that it can capture complex atmospheric circulations between the east 

coast of the US and the northern Atlantic Ocean in the boundary layer, the free and upper 

troposphere, and potentially cross-tropopause transport. 

 

1

 The modeling system for 2002 annual simulation is applied with a Lambert Conformal Conic projection 

with parallels at 33N and 45N. A spherical earth radius of 6370km is used for all elements of the system 

(MM5/SMOKE/CMAQ). 

2

http://www.mmm.ucar.edu/mm5/ 
Appendix C: Chemical Transport Model Methods Page C-4

Figure C-1. Modeling domains used in MANE-VU air quality modeling studies with 

CMAQ. Outer (blue) domain grid is 36 km and inner (red) domain is 12 km grid. 

The gridlines are shown at 180 km intervals (5 x 5 36km cells or 15 x 15 12km cells 

Five modeling centers are working collectively to maximize efficiency of 

computing resources in MANE-VU for SIP modeling. These centers include NY DEC, 

NJ DEP/Rutgers, VA DEQ, UMD, and NESCAUM. Annual CMAQ modeling on 12km 

domain is divided into 5 periods. UMD is responsible for the period from January 1 to 

February 28; NJ DEP and Rutgers are responsible for the period from March 1 to May 

14; NY DEC is responsible for the period from May 15 to September 30; VA DEQ is 

responsible for the period from October 1 to October 31; and NESCAUM is responsible 

for the period from November 1 to December 31. Each period uses a 15 day spin up run 

to minimize/eliminate the impact of the default initial concentration fields. Each group 

performs CMAQ simulation on its period for a series of scenarios including 2002 Base 

Case, 2009 Base Case, 2018 Base Case, 2009 Control Case, and 2018 Control Case. All 

scenarios adopt the same meteorological field (2002) and boundary conditions, varying 

only emission inputs. To ensure consistency, a benchmark test was conducted by each 

modeling group. 

C.2.1.1. Meteorology 

All meteorological fields have been simulated using the (MM5) system. 

Originally developed by Penn State and NCAR, MM5 is a model with limited-area 

primitive equations of momentum, thermodynamics, and moisture with the option of 

hydrostatic and non-hydrostatic physics. It is designed to simulate mesoscale 
Appendix C: Chemical Transport Model Methods Page C-5

atmospheric circulation. Domains are uniform rectangular grids representing threedimensional regions of the atmosphere. The horizontal coordinated system is equally 

spaced geographically and uses the Arakawa-B gridding scheme. The resolution can be 

as high as 1km. Sigma (σ) is a terrain-following vertical coordinate that is a function of 

pressure at the point (for hydrostatic) or reference state pressure (non-hydrostatic) (P), the 

surface pressure (Ps0), and the pressure at the top (Ptop) of the model. σ = (p-ptop) / (ps0-

ptop) 

Figure C-2. Vertical Structure of Air Quality Modeling Domains 

0

1

2

3

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5

6

7

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9

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Sigma Level

Vertical Layer 

MM5 29 Layers

CMAQ 22 Layers

10

1133

622

2330

3976

5257

10796

Altitude (m)

185

68

Professor Dalin Zhang’s group from University of Maryland (UMD) was tasked 

to provide the 2002 annual meteorological field for air quality modeling. The UMD 

MM5 model runs are made on two nested domains: the 36km resolution National US 

Continental domain (Domain 1) and the 12km resolution Eastern US domain (Domain 2). 

The inner (12km) domain uses a finer resolution terrain data. Initially, a set of test runs 

for the period of August 6 to 16, 2002 was conducted. UMD ran the non-hydrostatic 

MM5 v3.5.3 with 3 PBL schemes (a) modified Blackadar [BL], (b) the Pleim-Xiu 

scheme with the soil module [P-X], and (c) modified Blackadar with soil module [SSIB]. 

The simulated meteorological fields were compared to the measurements from 
Appendix C: Chemical Transport Model Methods Page C-6

Techniques Development Laboratory of National Weather Service (TDL NWS) and 

Clean Air Status and Trends Network (CASTNET). The TDL data are reflective of 

urban/suburban settings, while the CASTNET sites are more representative of rural areas. 

There are 48 CASTNET sites and about 800 TDL sites within Domain 2 (as shown in 

Figure C-3). Overall, the BL scheme shows a better correspondence to the measured data 

than the other two schemes, although it poorly captures the diurnal pattern of humidity. 

While the P-X scheme shows a better correspondence with the observed diurnal pattern 

for humidity, it fails to perform well for wind speed and temperature (Hao et al., 2004). 

The model utilizes a terrain-following sigma coordinate with 29 layers with the first level 

at 10 m and a radiative upper-boundary condition at 50hPa. Based on test run results, the 

boundary layer processes are determined using the Blackadar high-resolution planetary 

boundary layer parameterization. Physics options also included explicit representations 

of cloud physics with simple ice microphysics (no mixed-phase processes) and the KainFritsch cumulus parameterization. The model was initialized with the analyses of the 

National Center for Environmental Prediction (Eta Model). TDL data are used for MM5 

nudging. A modeled wind field map (Figure C-4) shows typical prevailing mesoscale 

flows from the midwest US to the east coast. 

Figure C-3. Observation Network sites within 12km resolution domain 
Appendix C: Chemical Transport Model Methods Page C-7

Figure C-4. MM5 modeled wind field map at 12:00 UTC on August 8, 2002 

The MM5 generated 2002 annual 12km resolution meteorological field has been 

evaluated by NESCAUM using ENVIRON's METSTAT program. Model results of 

surface wind speed, wind direction, temperature, and humidity are paired with 

measurements from CASTNET and TDL networks by hour and by location and then 

statistically compared. 

Figure C-5 presents domain-wide average hourly bias of wind speed (left panel) 

and wind direction (right panel) between MM5 results and two sets of measurement for 

every season in 2002 (a-Winter including Jan. Feb. and Dec.; b-Spring including Mar. 

Apr. and May; c-Summer including Jun. Jul. and Aug; d-Fall including Sep. Oct. and 

Nov.). It shows that MM5 capably predicts wind speed with reasonably small bias and 

equal consistency. Within the domain, MM5 tends to overestimate wind speed (hourly 

bias up to 1.7m/s) at CASTNET sites, and underestimate wind speed (hourly bias up to -

1.85m/s) at TDL sites. Seasonal mean bias of MM5 wind speed to CASTNET wind 

speed is ~0.3 to 0.4m/s, while seasonal mean bias of MM5 wind speed to TDL wind 

speed is about ~-0.5 to -0.6m/s. No significant seasonal variation on this wind speed bias 

is observed. MM5 prediction of wind direction shows a larger variation with CASTNET 

wind direction (hourly bias from ~-30 degree to ~30 degree) than with TDL wind 

direction (hourly bias from ~-5 degree to ~10 degree). However, seasonal mean bias of 

MM5 wind direction to CASTNET wind direction (~2 degree) is smaller than seasonal 

mean bias of MM5 wind direction to TDL wind direction (~3 degree) because the large 

variation of positive and negative bias offset each other. 
Appendix C: Chemical Transport Model Methods Page C-8

Figure C-5. 2002 Seasonal Average Hourly Bias of Wind Speed and Direction 

 Wind Speed Wind Direction 

Bias Wind Speed

-2.0

-1.5

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-0.5

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0.5

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Index of Agreement (IOA) is a statistical measure of difference between 

prediction and measurement, calculated as a ratio of Root Mean Square Error to the sum 

of the difference between prediction and mean observation and difference between 

observation and mean observation. IOA varies from 0 to 1, with a value of 1 indicating 

the prefect agreement between model prediction and observation, and a value larger than 

0.5 IOA indicating acceptable model performance. Domain wide average hourly IOA of Winter Spring Summer Fall
Appendix C: Chemical Transport Model Methods Page C-9

wind speed are presented in Figure C-6. MM5 predicted wind speed values are in good 

agreement (IOA from ~0.5 to ~0.9) to both CASTNET data and TDL data with similar 

IOA variation. Seasonal mean values of IOA are ~ 0.7. No particular season of the year 

stands out in terms of its agreement with measurement. 

Figure C-6. 2002 Seasonal Hourly Average Index of Agreement for Wind Speed 

 a) Winter b) Spring 

 

 c) Summer d) Fall 

 

Quarterly correlation coefficients in Figure C-7 show good MM5 performance on 

hourly wind speed for each observation site. MM5 predictions exhibit similar spatial 

patterns of correlation with CASTNET (left panel) and TDL (right panel) measurements 

– stronger correlation in north than in south. Over the year, the model has stronger 

correlation in the 1st quarter (Jan. Feb. Mar., top 1st row), 2nd quarter (Apr. May Jun., 2nd

row) and 4th quarter (Oct. Nov. and Dec., bottom row) than it does in the 3rd quarter (Jun. 

Jul. Aug., 3rd row), with an average of 0.1 correlation coefficient difference. Generally 

MM5 predictions and measurements have strongest correlation (0.8~0.9) within Midwest 

US, strong correlation (0.7~0.8) within Northeastern US and along coastline, and 

acceptable correlation (0.5~0.7) within Southern US and inland coast. MM5 predictions 

consistently show very similar spatial patterns and temporal variations for wind direction 

(as shown in Figure C-8) and wind speed. There is strong correlation (>0.7) between 

prediction and measurement for wind direction at most of sites. 
Appendix C: Chemical Transport Model Methods Page C-10

Figure C-7. Quarterly correlation coefficient (r) of hourly wind speed between 

modeling and measurement for each observation site in 2002 
Appendix C: Chemical Transport Model Methods Page C-11

Figure C-8. Quarterly correlation coefficient (r) of hourly wind direction between 

modeling and measurement for each observation site in 2002 
Appendix C: Chemical Transport Model Methods Page C-12

Figure C-9 presents domain wide average hourly bias of surface temperature 

between MM5 results and CASTNET and TDL for every season. MM5 tends to 

underestimate temperature at TDL sites throughout the year and at CASTNET sites for 

non-ozone season months. The seasonal mean temperature bias values are from ~-1K 

(Winter) to ~-0.3K (Summer) for TDL sites and ~-1K(Winter) to ~0.5K(Summer) for 

CASTNET sites. MM5 predictions show significantly larger variations of temperature 

bias at CASTNET sites (-4K~9K) than at TDL sites (-3K~1K). Domain wide average 

hourly IOA values of temperature are shown in Figure C-10. Model predicted 

temperatures have significantly better agreement with TDL data (average IOA as ~0.95) 

than with CASTNET data (average IOA as ~0.85), although both indicate accurate MM5 

performance on temperature. Figure C-11 shows the spatial distribution of quarterly 

correlation coefficients between MM5 prediction and measurement of surface 

temperature. It reveals very strong correlation (>0.95) over most of the domain for TDL 

data, with strong correlation (>0.8) for the majority of CASTNET sites. No spatial 

patterns or quarterly variations are apparent. MM5 performs consistently well throughout 

the year and the domain. 

The TDL network also provides humidity measurements. Comparison between 

MM5 prediction of hourly surface humidity and TDL measurement are presented in 

Figure C-12. MM5 captures the general trend of humidity change. It tends to 

underestimate humidity during the ozone season (seasonal mean bias as ~0.35g/kg), and 

overestimate it during the rest of year (seasonal mean bias range from ~0.17 to ~0.4), as 

shown in. Domain wide average hourly humidity bias shows a large diurnal variation, as 

much as 2g/kg. Domain wide average hourly IOA in Figure C-13 shows that MM5 

predicted humidity values are in good agreement with TDL data (average IOA as ~0.9) 

throughout year. Spatial distribution of quarterly correlation coefficient in Figure C-14 

shows a distinctive spatial pattern and temporal trend. MM5 results have stronger 

correlation to TDL data in the northern US than in the Southern US. Through the year, 

the strongest correlation between MM5 prediction and measurement occurs in the 4th 

Quarter (>0.95), followed by the 1st and 2nd Quarters, and finally, the 3rd Quarter, which 

shows the weakest correlation (0.5~0.9). 

Based on this statistical comparison between model prediction and data from two 

networks for wind speed, wind direction, temperature, and humidity, MM5 performs 

well. An acceptable small bias, high index of agreement and strong correlation with 

CASTNET and TDL data are shown. Since MM5 uses TDL data for nudging, the model 

predictions are in better agreement with TDL data than with CASTNET data. MM5 

performs better in Midwest and Northeast than Southeastern US. 
Appendix C: Chemical Transport Model Methods Page C-13

Figure C-9. 2002 Seasonal Hourly 

Average Bias of Temperature 

 

 

 

Figure C-10. 2002 Seasonal Hourly 

Average Index of Agreement for 

Temperature 

 

 Winter Spring Summer Fall

Winter Spring Summer Fall
Appendix C: Chemical Transport Model Methods Page C-14

Figure C-11. Quarterly correlation coefficient (r) of hourly temperature between 

modeling and measurement for each observation site in 2002 
Appendix C: Chemical Transport Model Methods Page C-15

Figure C-12. 2002 Seasonal Average 

Hourly Bias of Humidity 

Figure C-13. 2002 Seasonal Hourly 

Average Index of Agreement for 

Humidity 

Winter Spring Summer FallWinter Spring Summer Fall
Appendix C: Chemical Transport Model Methods Page C-16

Figure C-14. Quarterly correlation coefficient (r) of hourly humidity between 

modeling and measurement for each observation site in 2002 

C.2.1.2. CMAQ Emissions 

Emission scenarios are simulated using the Sparse Matrix Operator Kernel 

Emissions (SMOKE) Modeling System. SMOKE is primarily an emissions processing 

system designed to create gridded, speciated, hourly emissions for input into a variety of 

air quality models such as CMAQ, REMSAD, the Comprehensive Air quality Model 

with extensions (CAMX) and the Urban Airshed Model (UAM). SMOKE supports area, 

biogenic, mobile (both onroad and nonroad), and point source emissions processing for 

criteria, particulate, and toxic pollutants. For biogenic emissions modeling, SMOKE uses 

the Biogenic Emission Inventory System, version 2.3 (BEIS2) and version 3.09 and 3.12 

(BEIS3). SMOKE is also integrated with the on-road emissions model MOBILE6. The 

sparse matrix approach used throughout SMOKE permits rapid and flexible processing of 

emissions data. Flexible processing comes from splitting the processing steps of 

inventory growth, controls, chemical speciation, temporal allocation, and spatial 

allocation into independent steps whenever possible. The results from these steps are 

merged together in the final stage of processing using vector-matrix multiplication. It 

allows individual steps (such as adding a new control strategy, or processing for a 

different grid) to be performed and merged without having to redo all of the other 

processing steps (http://cf.unc.edu/cep/empd/products/smoke/version2.1/html/). 

The emission processing for CMAQ for the 36km national domain and 12km 

Eastern domain (Domain 2) has been performed by New York State Department of 

Environmental Conservation (NYS DEC) (for base year 2002 and future year 2009) and 

by NESCAUM (for future year 2018) using SMOKE v2.1 compiled on a Red Hat 9.0 
Appendix C: Chemical Transport Model Methods Page C-17

Linux operating system with the Portland group Fortran compiler version 5.1. They use 

the 2002 static emission inventory, CEM data, and surrogates data based on the 2002 

RPO data. Biogenic emissions are calculated using BEIS3 with BELD3 data. Mobile 

source emissions are processed using MOBILE6. The updated Canada inventory for 2000 

and 1999 Mexico inventory were used for processing. 

The emissions processing was performed on a month-by-month and RPO-by-RPO 

basis, i.e. SMOKE processing was performed for each of the RPOs (MANE-VU, 

VISTAS, CENRAP, MRPO, WRAP) individually as well as for Canada and Mexico. 

Note the processing of WRAP and Mexican emissions was necessary for use with the 36 

km grid modeling only. For each month/RPO combination, a separate SMOKE 

ASSIGNS file was created, and the length of the episode in each of these ASSIGNS files 

was set to the entire month. Specific data sources for individual source categories are 

listed below and the examples of processed emissions outputs are shown in Figure C-15. 

Emissions Inventory 

Area 

MANE-VU: 

MANEVU_AREA_SMOKE_INPUT_ANNUAL_SUMMERDAY_011705.txt 

Fugitive dust correction: This was applied as county-specific correction 

factors for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; 

the correction factors were obtained from 

http://www.epa.gov/ttn/chief/emch/invent/transportfractions.xls; this 

adjustment was performed outside of SMOKE with in-house Fortran 

programs 

VISTAS: 

ida_ar_2002_rev_29sep04.vistas.emis, arinv_2002_ncnox_01apr05.emis, 

ida_ar_fire_typ_29nov04.vistas.emis, ida_ar_dust_2002_wfac_27nov04.vistas.emis 

All files were obtained from Greg Stella (VISTAS) and were processed to 

extract VISTAS-only emissions as described above. Note: the header lines 

of these files indicate that the fugitive dust correction was already applied, 

so no further correction was performed. 

MRPO: 

ida_ar_2002_rev_29sep04.mrpo.emis, 

ida_ar_fire_typ_29nov04.mrpo.emis, 

ida_ar_dust_2002_wfac_27nov04.mrpo.emis 

All files were obtained from Greg Stella (VISTAS) and processed to 

extract MRPO-only emissions as described above. Note: the header lines 

of these files indicate that the fugitive dust correction was already applied, 

so no further correction was performed. 

CENRAP: 

CENRAP_AREA_SMOKE_INPUT_ANN_STATES_120704.txt, 

CENRAP_AREA_MISC_SMOKE_INPUT_ANN_STATE_120704.txt, 

CENRAP_AREA_BURNING_SMOKE_INPUT_ANN_TX_AR_NELI_1

20704.txt, 
Appendix C: Chemical Transport Model Methods Page C-18

CENRAP_AREA_MISC_SMOKE_INPUT_NH3_MONTH_{MMM} 

_120304.txt, where {MMM} is MAY, JUN, JUL, AUG, or SEP 

CENRAP_AREA_SMOKE_INPUT_NH3_MONTH_{MMM} 

_120304.txt where {MMM} is MAY, JUN, JUL, AUG, or SEP 

All files were downloaded from the CENRAP website 

http://www.cenrap.org/emission_document.asp. Fugitive dust correction: 

This was applied as county-specific correction factors for SCC’s listed at: 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the correction 

factors were obtained from 

http://www.epa.gov/ttn/chief/emch/invent/transportfractions.xls; this 

adjustment was performed outside of SMOKE with in-house Fortran 

programs 

CANADA: 

AS2000_SMOKEready.txt obtained from 

ftp://ftp.epa.gov/EmisInventory/canada_2000inventory . Fugitive dust 

correction: We applied “divide-by-four” correction for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; this 

adjustment was performed outside SMOKE with in-house Fortran 

programs. No county/province-specific correction factors were available 

for Canada 

Nonroad 

MANE-VU: 

CT_NRD2002.IDA, DC_NRD2002.IDA, DE_NRD2002.IDA, 

MA_NRD2002.IDA, MD_NRD2002.IDA, ME_NRD2002.IDA, 

NH_NRD2002.IDA, NJ_NRD2002.IDA, NY_NRD2002.IDA, 

PA_NRD2002.IDA, RI_NRD2002.IDA, and VT_NRD2002.IDA 

contained in the “MANE-VU Nrd SMOKE files.zip” file prepared by 

PECHAN 

VISTAS: 

ida_nr_2002_rev_01oct.vistas.emis was obtained from Greg Stella 

(VISTAS) and processed to extract VISTAS-only emissions as described 

above. 

MRPO: 

ida_nr_2002_rev_01oct.wrap.emis, obtained from Greg Stella (VISTAS) 

and processed to extract WRAP-only emissions as described above. 

CENRAP: 

CENRAP_NONROAD_SMOKE_INPUT_ANN_120704.txt downloaded 

from the CENRAP website http://www.cenrap.org/emission_document.asp

CANADA: 

File: NONROAD2000_SMOKEready.txt obtained from 

ftp://ftp.epa.gov/EmisInventory/canada_2000inventory

On-road 

MANE-VU: 

VMT/Speed: MANEVU_2002_mbinv.txt prepared by PECHAN 
Appendix C: Chemical Transport Model Methods Page C-19

VISTAS: 

VMT/Speed file: mbinv_2002_ida_vmt_22sep04.vistas.txt, obtained from 

Greg Stella (VISTAS) and was processed to extract VISTAS-only VMT 

as described above. 

MRPO: 

VMT/Speed file: mbinv_2002_ida_vmt_22sep04.mrpo.txt, obtained from 

Greg Stella (VISTAS) and were processed to extract MRPO-only VMT as 

described above in the VISTAS section. Note: Per email exchange 

between Gopal Sistla, Mark Janssen and Jeff Vukovich, it was determined 

that the VMT information used by VISTAS for their revised Phase II 

modeling reflects the latest MRPO information. Therefore, the MRPOportion of the mobile source files obtained from Greg Stella (VISTAS), 

were used in this work. 

CENRAP: 

VMT/Speed files: mbinv02_vmt_cenrap_ce.ida, 

mbinv02_vmt_cenrap_no.ida, mbinv02_vmt_cenrap_so.ida, and 

mbinv02_vmt_cenrap_we.ida, downloaded from the CENRAP website 

http://www.cenrap.org/emission_document.asp

CANADA: 

MOBILE2000_SMOKEready.txt obtained from 

ftp://ftp.epa.gov/EmisInventory/canada_2000inventory . Fugitive dust 

correction: applied “divide-by-four” correction for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; this 

adjustment was performed outside of SMOKE with in-house Fortran 

programs. No county/province-specific correction factors were available 

for Canada. 

Point 

MANE-VU: 

MANEVU_Point_SMOKE_INPUT_ANNUAL_SUMMERDAY_122004.

txt prepared by PECHAN was downloaded from ftp.marama.org

(username mane-vu, password exchange). Fugitive dust correction: This 

was applied as county-specific correction factors for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the correction 

factors were obtained from 

http://www.epa.gov/ttn/chief/emch/invent/transportfractions.xls; this 

adjustment was performed outside of SMOKE with in-house Fortran 

programs. Emission corrections were made for “THE HARTFORD 

STEAM CO” in Connecticut, Plant ID P0250. 

VISTAS: 

Annual: ptinv_2002typ_28nov04.vistas.ida and 

ptinv_fires_{MM}_typ.vistas.txt where {MM} is 01, 02, 03, etc. 

depending on the month. 
Appendix C: Chemical Transport Model Methods Page C-20

Hour-specific: pthour_rev2002typ_{MMM}_08nov04.vistas.ems and 

pthour_fires_{MM}_typ.vistas.ida where {MMM} is jan, feb, mar, etc. 

and {MM} is 01, 02, 03, etc. depending on the month

All files were obtained from Greg Stella (VISTAS) and were processed to 

extract VISTAS-only emissions as described above. 

Note: the header lines of these files indicate that the fugitive dust 

correction was already applied, so no further correction was performed. 

MRPO: 

Annual: ptinv_2002typ_28nov04.mrpo.ida and 

ptinv_fires_{MM}_typ.mrpo.txt where {MM} is 01, 02, 03, etc. 

depending on the month. Hour-specific: 

pthour_rev2002typ_{MMM}_08nov04.mrpo.ems and 

pthour_fires_{MM}_typ.mrpo.ida where {MMM} is jan, feb, mar, etc. 

and {MM} is 01, 02, 03, etc. depending on the month. All files were 

obtained from Greg Stella (VISTAS) and processed to extract MRPO-only 

emissions as described above. Note: the header lines of these files indicate 

that the fugitive dust correction was already applied, so no further 

correction was performed 

CENRAP: 

Annual: CENRAP_Point_SMOKE_INPUT_ANN_121004.txt. Hourspecific CEM: pthour.{QQ}.{ST}.txt where {QQ} is the quarter (q1, q2, 

q3, or q4) and {ST} is the state (AR, IA, KS, LA, MN, MO, NE, OK, TX) 

All files were downloaded from the CENRAP website 

http://www.cenrap.org/emission_document.asp . Fugitive dust correction: 

This was applied as county-specific correction factors for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the correction 

factors were obtained from 

http://www.epa.gov/ttn/chief/emch/invent/transportfractions.xls; this 

adjustment was performed outside of SMOKE with in-house Fortran 

programs 

CANADA : 

There has long been difficulty in obtaining an up-to-date Canadian criteria 

emissions inventory for point sources. This is due largely to 

confidentiality rights afforded to Canadian facilities. Thus far, the most 

recent inventory of Canadian point sources is rooted in the 1985 NAPAP 

data and is close to two decades old. Because there are a number of high 

emitting industrial facilities in southern Canada it is of particular 

importance to have a reasonably accurate inventory of these sources 

especially when modeling air quality over the Northeast and Midwest 

United States. Toward this end, an effort was made to obtain more recent 

Canadian point source data and incorporate it into an inventory database. 

Perhaps the most accurate and publicly accessible source of Canadian 

pollutant data is now available from the National Pollutant Release 

Inventory (NPRI) database. This database contains 268 substances. 

Facilities that manufacture, process or otherwise use one of these 
Appendix C: Chemical Transport Model Methods Page C-21

substances and that meet reporting thresholds are required to report these 

emissions to Environment Canada on an annual basis. The NPRI data are 

available at Environment Canada’s website and can be found at the link 

http://www.ec.gc.ca/pdb/npri/npri_home_e.cfm. The page hosts an on-line 

search engine where one can locate emissions by pollutant or location. In 

addition, the entire database is available for download as an MS Access or 

Excel file. The NPRI database contains numerous pages with a rather 

comprehensive list of information. Detailed information is available about 

each facility, including location, activity and annual emissions. In addition, 

facilities having stacks with a height of 50 meters or more are required to 

report stack parameters. 

Unfortunately, one of the limitations of the NPRI database for modeling 

purposes is that the data are only available at the facility level. Emissions 

models require process level information, so in order to use this data, a 

few generalizations had to be made. Each facility has a Standard 

Industrial Classification (SIC) code associated with it; however, emissions 

models require Source Classification Codes (SCC’s). SCC’s are of critical 

importance as the emissions models use these codes for assignment of 

temporal and speciation profiles. SIC codes describe the general activity 

of a facility while SCC codes describe specific processes taking place at 

each facility. While no direct relationship exists between these two codes, 

a general albeit subjective association can be made. 

For the purposes of creating a model-ready inventory file it was necessary 

to obtain the whole NPRI database. After merging all the necessary 

components from the NPRI database required in the SMOKE inventory 

file, the SIC code from each facility was examined and assigned an SCC 

code. In most cases, only a SCC3 level code was assigned with 

confidence. While this is admittedly a less than desirable process, it does 

allow for the use of the most recent emissions from the NPRI database to 

be used in modeling. Furthermore, having some level of SCC associated 

with these emissions will ensure that they will be assigned a temporal and 

speciation profile by the model, other than the default. Once the modelready inventory file was developed, it was processed through SMOKE. 
Appendix C: Chemical Transport Model Methods Page C-22

Emissions Processing Files 

Temporal Allocation 

MANE-VU: 

Area and nonroad sources: amptpro.m3.us+can.manevu.030205.txt and 

amptref.m3.manevu.012405.txt 

Mobile source: MANEVU_2002_mtpro.txt and MANEVU_2002_mtref.txt 

Point sources: Based on the same files as for the MANE-VU area and 

nonroad temporal files listed above, but added the VISTAS-generated 

CEM-based 2002 state-specific temporal profiles and cross-references for 

EGU sources for the MANE-VU states 

No CEM-based hour-specific EGU emissions were used.

CENRAP: 

The following temporal profiles and cross-reference files were used for all 

source categories: amptpro.m3.us_can.cenrap.010605.txt, 

amptref.m3.cenrap.010605.txt 

These files were downloaded from the CENRAP website

http://www.cenrap.org/emission_document.asp

For point sources, the CEM-based hour-specific EGU emissions described 

in Section 2.2.4 were utilized to override the annual-total based emissions 

whenever a match could be established by SMOKE 

VISTAS, WRAP and MRPO: 

The following month-specific temporal profiles and cross-reference files 

were used for all source categories: 

amptpro_typ_us_can_{MMM}_vistas_27nov04.txt where {MMM} is jan, feb, 

mar, etc., amptref_2002_us_can_vistas_17dec04.txt 

These files were obtained from Greg Stella (VISTAS)

For point sources (EGU and fires), the hour-specific emission files 

described in Sections 2.3.4 and 2.5.4 were utilized for the VISTAS and 

WRAP states to override the annual-total based emissions whenever a 

match could be established by SMOKE 

Canada and Mexico: 

For Canada and Mexico, the SMOKE2.1 default temporal profiles and 

cross-reference files (amptpro.m3.us+can.txt and amptref.m3.us+can.txt) 

were utilized. 

Chemical speciation 

The same speciation profiles (gspro.cmaq.cb4p25.txt) and cross-references 

(gsref.cmaq.cb4p25.txt) were utilized for all regions and all source categories. 

Different versions of these files were obtained (SMOKE2.1 default, EPA-CAIR 

modeling, VISTAS, CENRAP and MANE-VU) and compared. After comparing 

the creation dates and header lines of these files, it was determined that the EPACAIR and MANE-VU files had the most recent updates, and consequently the 

final speciation profile and cross-reference files used for all regions and source 

categories was based on the EPA-CAIR files with the addition of MANE-VU 

specific updates. 
Appendix C: Chemical Transport Model Methods Page C-23

 Spatial Allocation 

U.S. 

The spatial surrogates for the 12 km and 36 km domains were extracted 

from the national grid 12 km and 36 km U.S. gridding surrogates posted at 

EPA’s website at 

http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html

The gridding cross-references were also obtained from this website, but 

for the processing of MANE-VU area source emissions, MANE-VU 

specific cross-reference entries posted on the MARAMA ftp site were 

added. 

Canada 

The spatial surrogates for Canadian emissions for the 12 km and 36 km 

domains were extracted from the national grid 12 km and 36 km Canadian 

gridding surrogates posted at EPA’s website at 

http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html

The gridding cross-references were also obtained from this website. 

Mexico 

The spatial surrogates for Mexican emissions the 36 km domain were 

extracted from the national 36 km gridding surrogates used by EPA in the 

CAIR modeling. These files were obtained from EPA’s CAIR NODA ftp 

site http://www.airmodelingftp.com . The gridding cross-references were 

also obtained from this ftp site. 
Appendix C: Chemical Transport Model Methods Page C-24

Figure C-15. Examples of processed model-ready emissions 

(a): SO2 from Point; (b): NO2 from Area; (c): NO2 from On-road; (d): NO2 from Non-Road; 

(e): ISOP from Biogenic; (f): SO2 from all source categories) 

a b 

c d 

e f 
Appendix C: Chemical Transport Model Methods Page C-25

C.2.2. REMSAD 

The Regional Modeling System for Aerosols and Deposition (REMSAD) is also a 

three-dimensional Eulerian model designed to support a better understanding of the 

distributions, sources, and removal processes relevant to fine particles and other airborne 

pollutants. It calculates the concentrations of both inert and chemically reactive 

pollutants by simulating the physical and chemical processes in the atmosphere that affect 

pollutant concentrations. The basis for the model is the atmospheric diffusion equation 

representing a mass balance in which all of the relevant emissions, transport, diffusion, 

chemical reactions, and removal processes are expressed in mathematical terms. The 

REMSAD model performs a four-step solution procedure: emissions, horizontal 

advection/diffusion, vertical advection/diffusion and deposition, and chemical 

transformations during one half of each advective time step, and then reverses the order 

for the following half time step. The maximum advective time step for stability is a 

function of the grid size and the maximum wind velocity or horizontal diffusion 

coefficient. Vertical diffusion is solved on fractions of the advective time step to keep 

their individual numerical schemes stable. 

REMSAD uses a flexible horizontal and vertical coordinate system with nestedgrid capabilities and user-defined vertical layers. It accepts a geodetic 

(latitude/longitude) horizontal coordinate system or a Cartesian horizontal coordinate 

system measured in kilometers. REMSAD uses a simplified version of CB-IV chemistry 

mechanism which is based on a reduction in the number of different organic compound 

species and also includes radical-radical termination reactions. The organic portion of the 

chemistry is based on three primary organic compound species and one carbonyl species. 

The model parameterizes aerosol chemistry and dynamics for PM and calculates SOA 

yields from emitted hydrocarbons. REMSAD V7.12 and newer versions have 

capabilities that allow model tags of sulfur species (up to 11 tags), nitrogen (4 tags), 

mercury (up to 24 tags), and cadmium (up to 10 tags) to identify the impact of specific 

tagged species. 

Unlike CMAQ, REMSAD provides no choice of chemical and physical 

mechanisms. The modeling configuration for future work with REMSAD will be similar 

to the CMAQ modeling setup. The initial concentrations and boundary conditions will be 

generated using the same concentration profile used by CMAQ. The approach is to use 

similar model inputs to allow comparison of REMSAD with CMAQ to better understand 

differences between the two models. Due to the simplified chemistry mechanism, 

REMSAD may not simulate atmospheric processes as well as CMAQ. However, 

advantages such as the tagging feature for sulfur, more efficient modeling, and reasonable 

correspondence with measurements for many species, make REMSAD an important 

source apportionment tool for MANE-VU. 

In our present REMSAD modeling, the same 12km domain (i.e. domain2) 

presented in the previous section is used for a three full annual runs for the base year 

(2002). Multiple runs are necessary to permit tagging of sulfur emissions for all of the 

states in the domain, Canada and the boundary conditions. 
Appendix C: Chemical Transport Model Methods Page C-26

C.2.2.1. REMSAD Meteorology 

For the regional haze modeling study, an entire year of MM5 generated 

meteorological data for 2002 was provided by UMD and used to drive annual CMAQ and 

REMSAD modeling as described in the previous section. Meteorological inputs for these 

runs were developed by applying a simple translation script (MM52REMSAD) provided 

by the University of Maryland (Jeff Stehr, personal communication) to generate hourly 

meteorological field required by REMSAD. 

C.2.2.2. REMSAD Emissions 

Emissions processed for REMSAD take advantage of the SO2 tagging 

capabilities, whereas CMAQ treats all SO2 as indistinguishable. NESCAUM has 

developed emissions tagging techniques for application in air quality impact analyses 

using the REMSAD tagging scheme incorporated in REMSAD version 7.10 and higher. 

In general, these emissions tagging schemes can be used to assess source contributions in 

various ways including: (1) by size and susceptibility to transport (e.g., as between large 

elevated sources vs. small, low-level sources); (2) by sectors/types (e.g. by SCCs or by 

point, area, or mobile source categories); (3) by regions (e.g. by country/state/county); or 

(4) by combinations (e.g. largest electricity generating unit (EGU) in a specific state). 

The emissions inventory used for emissions tagging for REMSAD processing was 

primarily the same as the inventory described in the previous section (i.e. CMAQ 

platform). Some differences in emissions, however, do exist because of emissions 

updates (MANE-VU) and the necessity of simplified emissions processing to compensate 

for the added complexity introduced by tagging. Since REMSAD is a simpler model 

than CMAQ and is used mostly for longer term (> monthly) impact analysis, using a 

simplified emissions processing approach is reasonable. The major differences are: 

1) The point source emissions for MANE-VU were updated from ver.1 to ver. 2. 

2) CEM data were not used 

3) RPO-by-RPO emissions files were merged first, prior to processing in SMOKE 

4) Anthropogenic emissions were tagged 

MANE-VU had already conducted some preliminary runs to inform the early 

stages of regional haze planning. NESCAUM has taken the additional step of processing 

source emission files such that the model input is formatted to take advantage of 

REMSAD’s tagging capabilities. Thus, all combustion and industrial process emissions 

sources in over 30 Eastern states in the modeling domain have been tagged according to 

their state of origin, providing an estimate of the contribution those sources in each state 

make toward simulated sulfate concentrations at Eastern receptor sites. Moreover, 

boundary conditions were tagged to assess out-of-domain impact. The tagging scheme 

employed for this analysis is shown in Figure C-16. 
Appendix C: Chemical Transport Model Methods Page C-27

Figure C-16. REMSAD modeling tagging schemes. 

(black: group 1, red: group 2, and blue: group 3) 

Note: Sulfur species from anthropogenic emission sources are tagged by states for 

three sets of tags. Tag group 3 also includes boundary conditions. The color of 

the numbers represents tag groups (black: group 1, red: group 2, and blue: group 3) 

C.3. Model Evaluation and Results 

C.3.1. CMAQ 

C.3.1.1. CMAQ Performance evaluation of PM2.5 species 

CMAQ modeling has been conducted for the year 2002 (completed by 

cooperative modeling efforts from NYDEC, UMD, NJDEP, Rutgers, VADEP, and 

NESCAUM) under Base A emission scenario and for 5-month summer period of 2002 

(completed by NYDEC) under Base A1 (refined from Base A) emission scenario. 

CMAQ performance for PM2.5 species and visibility is examined based on these two 

CMAQ runs on a 12km resolution domain. Measurements from IMPROVE and STN 

networks are used to pair with model predictions by location and time for evaluation. 

Figure C-17 presents the domain wide paired comparison of PM2.5 species (Sulfate, 
Appendix C: Chemical Transport Model Methods Page C-28

Nitrate, OC, EC, Fine Soil, and PM2.5) daily average concentration between two sets of 

simulations (BaseA & BaseA1) and two sets of observations (STN & IMPROVE). It 

shows that predicted PM2.5 Sulfate (top row left panel) and measured Sulfate are in a 

good 1:1 linear relationship with r2 varying from 0.6 to 0.7. PM2.5 Nitrate (top row right 

panel) also has close to a 1:1 linear relationship between the model and observations, 

although the r2 values are much lower (from ~0.2 to ~0.5) than for Sulfate. Paired OC 

(mid row left panel) concentrations have a scattered distribution with over- and underestimating and a very weak linear relationship (r2

 of ~0.1). CMAQ tends to overestimate 

EC (mid row right panel) and fine soil (bottom row left panel) concentration. EC and soil 

are inert species not involved in chemical transformation. Poor emission inventory data 

may be the main cause for the weak linear relationships between prediction and 

measurement. In addition, there is no fire emission considered in CMAQ modeling. The 

wild fire in Quebec, Canada in early July of 2002 led to high concentrations of observed 

OC, EC, and Fine Soil that are not predicted by CMAQ. Since Sulfate is the dominant 

PM2.5 species, modeled PM2.5 (bottom row right panel) also shows a relatively strong 

near 1:1 linear relationship (slope between 0.7~0.8 with r2

 of 0.4~0.5). 

Similar comparisons of PM2.5 species between prediction and observation from 

other RPOs’ CMAQ modeling work (Ku et al., 2005) are shown in Figure C-18. Results 

from WRAP CMAQ run and CENRAP CMAQ run paired at 21 IMPROVE sites in July 

2002 are on the left panel; while results from VISTAS CMAQ run and CENRAP CAMx 

run at 17 IMPROVE sites in July 2002 are on the right panel. The rows from top to 

bottom are Sulfate, Nitrate, OC, EC, and Fine Soil. For PM2.5 Sulfate, other RPOs’ 

prediction show a rather strong 1:1 linear relation with measurement (r2

 of 0.6~0.7), 

similar to our CMAQ performance. Other PM2.5 species all show poor linear 

relationships between model prediction and observations. Values of r2 are ~0.1 for 

Nitrate; 0.2~0.4 for OC; 0.2~0.4 for EC; <0.02 for Fine Soil. Therefore, our CMAQ 

performance of PM2.5 is in the same range as other RPOs. 

Figure C-19 describes the spatial distribution of the correlation coefficient of 

sulfate between CMAQ prediction and observations (STN data on the top row and 

IMPROVE data on the bottom row) at network sites. CMAQ predictions show a similar 

spatial pattern of correlation with both networks. Generally, the north region of the 

domain has stronger correlations than do the south region. Correlation coefficients 

within MANE-VU region are highest (~0.9 in average) compared to other RPO regions. 

The fact that summer time correlation coefficients are higher than annual values indicates 

CMAQ performs better for summer than for other seasons. No significant improvement 

in correlation is observed for the BaseA1 case over the BaseA case. The spatial 

distribution of correlation coefficient for PM2.5 is presented in Figure C-20. The PM2.5

correlation coefficient spatial pattern follows PM2.5 sulfate correlation coefficient, 

although at the same observation site coefficient values are ~0.1 lower than the sulfate 

coefficient value. Like PM2.5 sulfate, CMAQ also performs the best for PM2.5 in the 

MANE-VU region with ~0.7 annual average and ~0.8 summer average of the correlation 

coefficient. 

In 2004 James Boylan from VISTAS suggested the goal and the criteria for PM2.5

evaluation (Boylan, 2004). This standard has been adopted by every RPO for SIP 

modeling. The proposed performance goals are: Mean Fractional Error (MFE) <= 
Appendix C: Chemical Transport Model Methods Page C-29

+50%, and Mean Fraction Bias (MFB) <= +-30%; while the criteria is proposed as: MFE 

<= +75%, and MFB <= +-60%. 

CMAQ prediction of PM2.5 species from 40 STN sites and 17 IMPROVE sites 

within MANE-VU region are paired with measurements and statistically analyzed to 

generate MFE and MFB values. Figure C-21 presents MFE of PM2.5 Sulfate, Nitrate, 

OC, EC, Fine Soil, and PM2.5, and curves of the goal and criteria. MFB values are shown 

in Figure C-22. Considering CMAQ performance in terms of both MFE and MFB, 

Sulfate, Nitrate, OC, EC, and PM2.5 all have the majority of data points within the goal 

curve, some are between the goal and acceptable criteria, and only a few outside the 

criteria curve. Only fine soil has the majority of points outside the criteria curve, but 

there are some sites still within the goal. For the MANE-VU region, CMAQ performs 

best for PM2.5 Sulfate, followed by PM2.5, EC, Nitrate, OC, and finally the fine soil. 

Regional haze modeling also requires a CMAQ performance evaluation for 

aerosol extinction coefficient (Bext) and the haze index. Modeled daily aerosol 

extinction at each IMPROVE site is calculated following the IMPROVE formula with 

modeled daily PM2.5 species concentration and relative humidity factor from IMPROVE. 

Figure C-23 shows the paired comparison of domain wide daily aerosol extinction 

coefficient for 2002 between prediction and measurement with 1:1 line and ±20% lines. 

The modeled Bext shows a near 1:1 linear relationship (slope of 0.74 and r2 of 0.53) with 

IMPROVE observed Bext. Mean bias is -6.31 Mm-1, which is less than 1% compared to 

mean Bext of either observation (76.54 Mm-1) or prediction (70.22 Mm-1). MFE of 35% 

and MFB of -13% both meet the standard goal. CMAQ prediction of the aerosol 

extinction coefficient agrees well with IMPROVE observation because CMAQ performs 

well on sulfate, which dominates aerosol extinction. Further, the modeled haze index 

(HI) is calculated based on modeled Bext. Figure C-24 presents the paired comparison of 

HI values at 4 Class I sites in the Eastern US between CMAQ prediction and IMPROVE 

measurement for 2002 with 1:1 line and +-20% lines. The majority of the data points are 

within the ±20% regime. Acadia shows the best model performance with a slope of 0.96 

and r2 of 0.64, mean bias of 0.05 compared to mean HI of ~15. Next is Brigantine, with a 

slope of 0.86 and r2 of 0.5, mean bias of 0.2 compared to an HI of ~20. Then is Lye 

Brook with a slope of 0.77 and r2 of 0.6, mean bias of 1.55 compared to an HI of ~14. 

Finally is Shenandoah with a slope of 0.6 and r2 of 0.4, mean bias of 1.62 compared to an 

HI of ~21. MFE and MFB of all 4 sites meet the performance goal. 

Overall, NESCAUM CMAQ modeling on the 12km resolution domain for 2002 

accurately portrays sulfate, PM2.5, aerosol extinction coefficient and the Haze Index. It 

provides reasonable performance for PM2.5 Nitrate, OC, and EC. The model performs 

better for summertime than for wintertime, and better in the MANE-VU region than in 

others regions. 

In late 2005, CMAQ V4.5 was released to the public. Although our current 

modeling results are acceptable, MANE-VU modelers have been considering a shift from 

CMAQ V4.4 to V4.5. CMAQ V4.5 is reported to significantly improve the PM science 

in the model. The more recent model version has added sea salt (fine equilibrium; noninteractive coarse mode) and updated the module of PM treatment (AERO4). It revised 

the aerosol dry deposition algorithm and corrected inconsistencies. It improved 

ISORROPIA and fixed discontinuities. It also added a new sub-grid cloud mixing 
Appendix C: Chemical Transport Model Methods Page C-30

algorithm/module. In addition, CMAQ V4.5 updated the planetary boundary layer (PBL) 

module to use urban fraction for setting minimum vertical diffusivity (KZ). Comparison 

studies (Appel et al., 2005) between CMAQ V4.5 and V4.4 show significantly improved 

performance on PM2.5 Sulfate and Nitrate using V4.5 over V4.4, while V4.5 maintains 

the same acceptable ozone performance as V4.4. While other RPOs have already used 

CMAQ V4.5 in their SIP modeling, MANE-VU recently decided to switch to CMAQ 

V4.5 based on results of comparison study conducted by NJ DEP. MANE-VU believes 

that V4.5 would improve PM performance for Regional Haze and PM2.5 SIP modeling for 

MANE-VU. 
Appendix C: Chemical Transport Model Methods Page C-31

Figure C-17. Domain wide paired comparison of daily average PM2.5 species 

between CMAQ predictions and measurements from IMPROVE and STN networks 
Appendix C: Chemical Transport Model Methods Page C-32

Figure C-18. Paired comparison of PM2.5 species from other RPOs’ modeling work 

(Ku et al.,2005 )

 

PM2.5 EC PM2.5 Sulfate PM2.5 Soil PM2.5 OC PM2.5 Nitrate
Appendix C: Chemical Transport Model Methods Page C-33

Figure C-19. Spatial distribution of correlation coefficient between PM2.5 Sulfate 

and measurement 

Figure C-20. Spatial distribution of correlation coefficient between PM2.5 and 

measurement 

 
Appendix C: Chemical Transport Model Methods Page C-34

Figure C-21. Mean Fractional Error of PM2.5 species within MANE-VU region 

PM2.5 SO4 2002 MANE-VU

0

20

40

60

80

100

120

140

160

180

200

0 1 2 3 4 5 6 7 8 9 10

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 NO3 2002 MANE-VU

0

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0 0.5 1 1.5 2 2.5 3 3.5 4

Mean Observation (g/m3)

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BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 EC 2002 MANE-VU

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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 OC 2002 MANE-VU

0

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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 2002 MANE-VU

0

20

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60

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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 Soil 2002 MANE-VU

0

20

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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 Sulfate PM2.5 Nitrate

PM2.5 Organic Carbon PM2.5 Elemental Carbon

PM2.5 Fine Soil PM2.5

PM2.5 SO4 2002 MANE-VU

0

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0 1 2 3 4 5 6 7 8 9 10

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 NO3 2002 MANE-VU

0

20

40

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0 0.5 1 1.5 2 2.5 3 3.5 4

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 EC 2002 MANE-VU

0

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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 OC 2002 MANE-VU

0

20

40

60

80

100

120

140

160

180

200

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 2002 MANE-VU

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 Soil 2002 MANE-VU

0

20

40

60

80

100

120

140

160

180

200

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Mean Observation (g/m3)

MFE (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 Sulfate PM2.5 Nitrate

PM2.5 Organic Carbon PM2.5 Elemental Carbon

PM2.5 Fine Soil PM2.5
Appendix C: Chemical Transport Model Methods Page C-35

Figure C-22. Mean Fraction Bias of PM2.5 species within MANE-VU region 

PM2.5 2002 MANE-VU

-200

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 Soil 2002 MANE-VU

-200

-180

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-100

-80

-60

-40

-20

0

20

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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 OC 2002 MANE-VU

-200

-180

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-80

-60

-40

-20

0

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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 EC 2002 MANE-VU

-200

-180

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-80

-60

-40

-20

0

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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 NO3 2002 MANE-VU

-200

-180

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-40

-20

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0 0.5 1 1.5 2 2.5 3 3.5 4

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 SO4 2002 MANE-VU

-200

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

20

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100

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0 1 2 3 4 5 6 7 8 9 10

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 Sulfate PM2.5 Nitrate

PM2.5 Organic Carbon PM2.5 Elemental Carbon

PM2.5 Fine Soil PM2.5

PM2.5 2002 MANE-VU

-200

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-20

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Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 Soil 2002 MANE-VU

-200

-180

-160

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-100

-80

-60

-40

-20

0

20

40

60

80

100

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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 OC 2002 MANE-VU

-200

-180

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-60

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-20

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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 EC 2002 MANE-VU

-200

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

20

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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 NO3 2002 MANE-VU

-200

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

160

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200

0 0.5 1 1.5 2 2.5 3 3.5 4

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 SO4 2002 MANE-VU

-200

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

160

180

200

0 1 2 3 4 5 6 7 8 9 10

Mean Observation (g/m3)

MFB (%)

BaseA STN

BaseA1 STN

BaseA IMPROVE

BaseA1 IMPROVE

Goal

Criteria

PM2.5 Sulfate PM2.5 Nitrate

PM2.5 Organic Carbon PM2.5 Elemental Carbon

PM2.5 Fine Soil PM2.5
Appendix C: Chemical Transport Model Methods Page C-36

Figure C-23. Paired comparison of extinction coefficient between CMAQ prediction 

and IMPROVE measurement 

Mobs Mpre MB ME FB(%) FE(%) NMB(%) NME(%) MNB(%) MNE(%)

76.54 70.22 -6.31 25.67 -12.64 35.34 -8.25 33.54 -3.25 34.22

Figure C-24. Paired Comparison of Haze Index between CMAQ prediction and 

IMPROVE measurement at Class I sites within Northeastern US 

Haze Index (deciview) at Class I Areas in Northeastern U.S.

y = 0.9556x + 0.69

R

2

 = 0.64

y = 0.8574x + 3.1194

R

2

 = 0.4922

y = 0.7683x + 4.7298

R

2

 = 0.5805

y = 0.595x + 9.7159

R

2

 = 0.3851

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

IMPROVE

CMAQ

Acadia

Brigantine

Lye Brook

Shenandoah

Class I area Mobs Mpre MAXobs MAXpre MB ME NMB(%) NME(%) FB(%) FE(%) MNB(%) MNE(%)

Acadia 14.50 14.54 30.77 32.08 0.05 3.39 0.32 23.35 -4.58 27.28 0.71 26.20

Brigantine 20.47 20.67 32.75 32.08 0.20 3.28 0.98 16.02 -1.37 17.15 1.22 16.45

Lye Brook 13.71 15.26 32.72 29.64 1.55 3.59 11.34 26.22 11.01 26.07 19.68 32.75

Shenandoah 20.00 21.61 33.46 34.53 1.62 4.05 8.08 20.25 7.28 21.58 12.41 24.36
Appendix C: Chemical Transport Model Methods Page C-37

C.3.1.2. CMAQ Control Scenario Results 

The results from preliminary annual simulations of the 2002 baseline, 2009 and 

2018 future case scenarios have been reviewed. These model runs provide insight into 

the current and expected ambient levels of fine particles and haze causing constituents. 

The general character should remain consistent with future model runs that may use 

updated model code (CMAQ V4.5) and revised emissions (MANE-VU v3.0 inventory). 

The six maps in 

Figure C-25 show annual average results for PM2.5 and sulfate for three modeling 

runs (2002/2009/2018). The other PM25 constituents are of lower concern for regional 

haze in MANE-VU, as sulfate dominates visibility degradation in the region. The total 

PM25 maps on the left provide the spatial distribution throughout the modeling domain, 

with levels in urban centers highest for all three model runs. The spatial distribution of 

annual sulfate levels is somewhat different from the PM2.5. Specifically, sulfate levels 

along the Ohio River valley are greater than surrounding areas for the baseline run, with 

the gradients becoming much less for future scenarios. 

Sulfate results are investigated further in Figure C-26. The set of six maps in this 

figure show relative reduction factors for sulfate. Model results are used in a relative 

sense to address potential uncertainties in the absolute results; uncertainty in relative 

changes is believed to be smaller than the absolute uncertainty. The top two figures 

display the ratio of sulfate results from future case runs to the base case for 2009 and 

2018 respectively. For regional haze purposes, site specific reduction factors were 

generated for the best and worst 20% days. The annual average results are spatially 

consistent with the seven Class 1 site factors derived for the worst 20% days

3

. These model results predict the greatest percentage sulfate reduction to occur in 

West Virginia and its immediate surrounds, with minor reductions calculated for areas 

west of the Mississippi River and moderate declines in the more northeastern section of 

MANE-VU. 

The bottom four panels of Figure C-26 provide quarterly estimates of relative 

reduction in sulfate for 2009. For PM2.5 mass, EPA guidance recommends the use of a 

quarterly reduction factor, unlike the 20% best and worst factors used in haze 

calculations. These results are instructive and improve the understanding of the seasonal 

impacts to be expected in the future due to emission changes. The broadest reductions 

occur in the third quarter, followed closely by the second quarter. The pattern for the first 

and fourth quarters differs substantially from the warmer months, with much of the 

domain predicted to exhibit increases in sulfate during the colder months of 2009 relative 

to the base year 2002. For the most-part, the changes modeled in the colder months are 

modest, falling within 10% of the base-year calculations. 

 

3

 To project future haze levels, relative reduction factors were determined for best and worst days for all six 

haze relevant constituents (sulfate, nitrate, organic carbon, elemental carbon, fine soil and coarse 

particulate). The seven sites investigated were Acadia, Brigantine, Lye Brook, Moosehorn, Great Gulf, 

Dolly Sods and Shenandoah. 
Appendix C: Chemical Transport Model Methods Page C-38

Figure C-25. CMAQ results for PM2.5 and Sulfate for 2002, 2009 and 2018 
Appendix C: Chemical Transport Model Methods Page C-39

Figure C-26. Sulfate Relative Reduction Factors for 2009 and 2018. Quarterly 

factors are shown for 2009 in the bottom four panels 

Relative reduction factors for all haze relevant PM constituents at seven Class 1 

sites are displayed in Table C-1. For each site and specie, the modeled change relative to 

the baseline year 2002 is shown. Therefore, negative values imply a modeled decrease 

while a positive value represents an increase. The values for 2009 and 2018 are additive, 
Appendix C: Chemical Transport Model Methods Page C-40

such that when added, the sum corresponds to the overall change from the baseline year 

to 2018. Using Lye Brook as an example, the table reveals a 28% decrease in 

concentrations from 2002 to 2009, followed by another 10% decline (relative to 2002) 

between 2009 and 2018, yielding a total reduction of 38% between 2002 and 2018. For 

fine soil, levels increase 17% in the first modeled period, then decline by 4% in the 

second period. The net change between 2002 and 2018 sums these two values (17 + (-4) 

= 13), implying an overall rise in fine soil concentrations of 13 percent. As a point of 

interest, the model results do not provide evidence of nitrate replacement at these sites 

between the period of 2002 and 2018, despite the substantial reductions in predicted 

ambient sulfate levels. 

The results for modeled sulfate in Figure C-27 show the application of the 

reduction factors from Table C-1 to the baseline measured ammonium sulfate on the 20% 

worst days. The yellow bar gives the five-year average sulfate levels on the worst days 

for each site. Using the reduction factors for 2009 yields sulfate concentrations shown at 

the red bar, while applying the 2018 factor predicts mass values given by the blue bar. 

These bars clearly indicate more substantial reductions in sulfate levels are expected to 

occur by 2009 with smaller reduction in the latter modeled timeframe. 

The final set of CMAQ results are graphed in Figure C-28(a) and (b). These plot 

the modeled progress combining all six species’ reduction factors. Based on the 

modeling, all sites except one are shown to meet their uniform progress goal by 2018. 

Brigantine Wilderness Area in New Jersey is projected to fall about a half deciview shy 

of the uniform rate under existing emission reduction plans. 

Table C-1. Relative Reduction factors by site and specie. Change is relative to 

baseline modeled year 2002 and overall change from 2002 to 2018 is additive 

 YEAR Sulfate Nitrate Organic Carbon 

Elemental

Carbon 

Fine 

Soil Coarse 

Acadia 2009 -31% 0% -7% -19% 5% 6%

2018 -7% -5% -6% -17% -1% 8%

2009 -29% -1% -8% -23% 13% 11% Brigantine 

2018 -10% -11% -9% -20% -2% 6%

2009 -24% -3% -5% -15% 16% 15% Great Gulf 

2018 -9% -2% -8% -16% -4% 7%

2009 -28% 4% 0% -16% 17% 10% Lye Brook 

2018 -10% -3% -8% -19% -4% 5%

2009 -27% -2% -3% -13% 9% 6% Moosehorn

2018 -6% -4% -5% -14% -1% 6%

2009 -33% -15% 4% -10% 29% 34% Dolly Sods 2018 -16% -11% -11% -22% 0% 11%

2009 -29% -24% 2% -13% 23% 15% Shenandoah

2018 -14% -17% -16% -29% -2% 8%
Appendix C: Chemical Transport Model Methods Page C-41

Figure C-27. Ammonium Sulfate mass predicted reduction for 20% worst days 

0

2

4

6

8

10

12

14

Dolly Sods

Shenandoah

Brigantine

Lye Brook

Great Gulf

Acadia

Moosehorn

Mass (μg/m

3

)

Baseline (2000-04)

2009

2018
Appendix C: Chemical Transport Model Methods Page C-42

Figure C-28 (a) and (b). CMAQ Integrated SIP Modeling Platform simulation 

results for 2002, 2009 and 2018 relative to Uniform Progress Goals calculated 

according to current USEPA Guidance for (a) Northeast Class I sites in MANE-VU 

and (b) Mid-Atlantic Class I sites in or near MANE-VU. 

(a) 

17

18

19

20

21

22

23

24

25

26

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Deciview

Acadia Uniform Goal

Camp Dodge Uniform Goal

Lye Brook Uniform Goal

Moosehorn Uniform Goal

Acadia Modeled

Camp Dodge Modeled

Lye Brook Modeled

Moosehorn Modeled

(b) 

22

23

24

25

26

27

28

29

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Deciview

Brigantine Uniform Goal

Dolly Sods Uniform Goal

Shenandoah Uniform Goal

Brigantine Modeled

Dolly Sods Modeled

Shenandoah Modeled
Appendix C: Chemical Transport Model Methods Page C-43

C.3.2. REMSAD 

C.3.2.1. Model performance 

REMSAD has been evaluated by EPA OAQPS for their CSA base case study 

using 1996 meteorology and 1996 NET Inventory.4

 Modeling results were compared 

with IMPROVE measurement as summarized in Table C-2. It shows that REMSAD 

performs better in the Eastern US than in the Western US on PM sulfate and PM2.5, 

although it underestimates ambient levels countrywide. Emissions may contribute to 

poor performance on soil, carbonaceous aerosols and PM nitrate. 

Table C-2. Normalized error of annual mean model prediction to annual mean 

observation on PM species between IMPROVE measurements and REMSAD 1996 

annual simulation (after Timin, B. et al., 2002) 

IMPROVE PM Species National East West 

PM2.5 -32% -15% -49% 

Sulfate -19% -10% -39% 

Nitrate 5% 82% -55% 

Elemental Carbon 1% 23% -20% 

Organic Aerosols -45% -42% -47% 

Soil/Other 38% 225% -18% 

NESCAUM’s previous REMSAD modeling exercise used 1996 meteorology 

along with the 2001 Proxy emission inventory, thus a direct comparison of modeling 

results to daily observations could not be completed. To evaluate REMSAD in that stage, 

NESCAUM first compared its own modeling results with EPA’s CSA 2001 case 

modeling results, which also used 1996 meteorology. As shown in Figure C-29, 

NESCAUM’s results exactly match with EPA’s REMSAD modeling on PM2.5 and PM 

sulfate distributions. In addition, NESCAUM compared the long term modeling average 

(annual mean) of PM species to IMPROVE annual means5 for three sites. The results are 

presented in Figure C-30. It shows good agreement for REMSAD modeling of PM 

sulfate, NH4, OC and EC. Emission inaccuracies may explain the model over-prediction 

of soil mass, while incomplete chemistry may cause observed differences for nitrate. 

NESCAUM’s present REMSAD modeling uses a 12km Eastern domain with 

2002 RPO emissions and meteorology. Figure C-31 and Figure C-32 show the gridded 

SO2 emissions with tags in our 12km modeling domain and some examples of annual 

average REMSAD sulfate concentrations by selected Northeast States, respectively. 

Figure C-32 illustrates the spatial distribution of the REMSAD simulated tagged 

emissions concentration fields. These fields are strongest in their own state and generally 

have the largest outside state impact toward the northeast. 

 

4

 Also see Clear Skies Act Air Quality Modeling Technical Support Document at: 

http://www.epa.gov/air/clearskies/aq_modeling_tsd_csa2003.pdf 

5

 Multi-year averages were computed from the measurements to better account for the lack of 

correspondence between emissions year (2001) and meteorological year (1996). 
Appendix C: Chemical Transport Model Methods Page C-44

Figure C-29. Comparison of annual average PM2.5 and PM sulfate between 

NESCAUM REMSAD modeling and EPA REMSAD modeling 

Figure C-30. Comparison of annual average PM species between NESCAUM 

REMSAD modeling and multi-year average IMPROVE measurements 

NESCAUM REMSAD vs. IMPROVE Mean

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

IMPROVE 

NESCAUM .

Sulfate

Nitrate

NH4

OC

EC

Soil

1:1
Appendix C: Chemical Transport Model Methods Page C-45

Figure C-31. Gridded SO2 emissions distribution and tag numbers 

Figure C-32. Sample sulfate concentration by state 
Appendix C: Chemical Transport Model Methods Page C-46

A spatial performance evaluation of REMSAD simulations for sulfate on the 

12km northeast US domain for the year 2002 was conducted through comparison with 

IMPROVE/STN measurements, as illustrated in Figure C-33. These comparisons are 

inexact because the discrete measurements represent irregular areas whereas model 

outputs represent a uniform gridded concentration field. This approach, however, does 

provide a first order examination of measurement and modeling results, which is 

appropriate for an annual averaged analysis. 

Figure C-33. Sulfate concentrations from IMPROVE/STN measurements and 

REMSAD model. 

Figure C-34. Intercomparison of measurement and model data for 5 different 

annual model simulations. 

Sulfate Ion LyeBrook 

y = 0.92x - 0.06 R2 = 0.81

y = 1.00x - 0.14 R2 = 0.84

y = 0.56x + 0.76 R2 = 0.62

y = 0.61x + 0.99 R2 = 0.30

y = 0.39x + 0.75 R2 = 0.27

0

5

10

15

20

0 5 10 15 20

Monitored Mass (ug/m3)

Modeled Mass (ug/m3)

CMAQ A

CMAQ A1

REMSAD

MD_CalPuff

VT_CalPuff

Sulfate Ion Shenandoah

y = 0.72x + 1.00 R2 = 0.57

y = 1.14x - 0.02 R2 = 0.68

y = 1.09x + 0.03 R2 = 0.68

y = 0.40x + 1.95 R2 = 0.21

y = 0.31x + 1.31 R2 = 0.21

0

5

10

15

20

25

0 5 10 15 20 25

Monitored Mass (ug/m3)

Modeled Mass (ug/m3)

CMAQ A

CMAQ A1

REMSAD

MD_CalPuff

VT_CalPuff

Sulfate Ion LyeBrook 

y = 0.92x - 0.06 R2 = 0.81

y = 1.00x - 0.14 R2 = 0.84

y = 0.56x + 0.76 R2 = 0.62

y = 0.61x + 0.99 R2 = 0.30

y = 0.39x + 0.75 R2 = 0.27

0

5

10

15

20

0 5 10 15 20

Monitored Mass (ug/m3)

Modeled Mass (ug/m3)

CMAQ A

CMAQ A1

REMSAD

MD_CalPuff

VT_CalPuff

Sulfate Ion Shenandoah

y = 0.72x + 1.00 R2 = 0.57

y = 1.14x - 0.02 R2 = 0.68

y = 1.09x + 0.03 R2 = 0.68

y = 0.40x + 1.95 R2 = 0.21

y = 0.31x + 1.31 R2 = 0.21

0

5

10

15

20

25

0 5 10 15 20 25

Monitored Mass (ug/m3)

Modeled Mass (ug/m3)

CMAQ A

CMAQ A1

REMSAD

MD_CalPuff

VT_CalPuff
Appendix C: Chemical Transport Model Methods Page C-47

In general, REMSAD’s simulation field is well-matched with measurement data. 

Figure C-34 shows the comparison of paired 24-hourly surface sulfate concentrations 

between five different air quality model results (including REMSAD) and IMPROVE 

measurements during the year 2002. For Lye Brook, which is a Class I area in Vermont, 

the two CMAQ model runs show the best performance in terms of slope, intercept and 

coefficient of determination (r2

). The REMSAD result shows the 2nd best performance 

with the two CALPUFF results matching least to measurements. This trend remains 

similar for Shenandoah. Along with EPA’s previous evaluation (Timin B. et al., 2002), 

REMSAD performs reasonably well for longer-term sulfate simulation. 

C.3.2.2. Contribution assessment 

In addition to the REMSAD tagged sulfur modeling, NESCAUM and its MANEVU partners performed other analysis techniques to assess states’ impact on PM levels 

over the Northeast US (e.g. CALPUFF modeling, Percent-time Upwind, and E/D 

analysis). Figure C-35 shows modeling results of region/country specific contributions to 

PM sulfate in the Acadia, Brigantine, Lye Brook, and Shenendoah Class I areas from five 

different contribution assessment techniques. In general, the five different techniques 

show similar contribution of sulfate to Class I areas, but MANE-VU’s contributions are 

estimated to be relatively higher from the REMSAD result and lower from the Percent 

upwind. 

For Acadia, the analysis reveals about 37% of sulfate is derived from in-region 

sources, while 30% comes from other RPOs and Canada. The “Other” tag from 

REMSAD, which explains non-tagged emissions and boundary conditions (about ~33% 

of the total contribution to Acadia), was normalized, then included in other relative 

contribution analysis techniques because only REMSAD predicted those impacts. For 

Brigantine, about 35% of sulfate comes from in-region sources, 40% from adjacent RPO 

regions (i.e. MRPO and VISTAS), 10% from CENRAP (in-domain) and Canada, and the 

“Other” tag explains the remaining ~16 percent. The contribution to Lye Brook shows 

similar composition to Brigantine’s case, with higher contribution of MRPO and lower 

contribution of VISTAS given the relative locations of sources and receptors. 

Shenandoah shows higher contribution from VISTAS and MRPO (about 60%) and lower 

contribution from MANE-VU and Others (about 20% each) due to its location (i.e. VA). 

Figure C-36 shows monthly contributions by four different Class I areas in the 

Northeast region. The contribution of “Other” and MANE-VU region are relatively big 

in Acadia (in Maine) because it is located at the Northeast boundary of the modeling 

domain. The concentrations are generally high in the summer months (i.e. June, July, 

and August) for all the regions with the relative contribution of MRPO and VISTAS 

higher than other seasons, likely due to stronger westerly/southwesterly winds in 

summer. The “Other” and MANE-VU regions’ contributions are still relatively big at 

Lye Brook, but MRPO and VISTAS’ contributions are more significant than in Acadia’s 

case because Vermont is closer to those regions. The monthly concentration shows a 

similar pattern to that from Acadia. For Brigantine, MANE-VU’s contribution remains 

biggest, followed by VISTAS, “Other”, and MRPO. The monthly contribution from the 

“Other” tag decreases after June in contrast to MRPO’s. The VISTAS and MRPO’s 

contributions are relatively large at Shenandoah due to their proximity. 
Appendix C: Chemical Transport Model Methods Page C-48

Figure C-35. Contribution of tagged sources by different apportionment methods 

0.73

0.65 1.03

0.58

1.20

0.89 1.37 1.07 0.68

0.56 0.83 0.59

0.81 0.56 0.83

0.96

0.24

0.40 0.60

0.49

0.63

0.61

0.94 0.58 0.46 0.57 0.77

0.52

1.05 0.94 1.22 0.88

0.18 0.26 0.39

0.45

0.77 0.87 1.04

0.84

0.29

0.36 0.51

0.42

1.36 1.11 1.49 1.25

0.02 0.05 0.09

0.05

0.04

0.07 0.10

0.05

0.04

0.07 0.12

0.05

0.06 0.08 0.09

0.07

0.18 0.19 0.29 0.35

0.24

0.08 0.11 0.20

0.27

0.12 0.17

0.28

0.19 0.08 0.11

0.26

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

Acadia Brigantine Lye Brook Shenendoah

MANEVU MIDWEST VISTAS CENRAP CANADA OUTSIDE DOMAIN

Given the reassuring results for sulfate, NESCAUM conducted REMSAD tagged 

sulfur modeling to assess states’ impact on PM levels over the Northeast US. Figure 

C-37 shows modeling results of state specific contributions to PM sulfate in the Acadia, 

ME Class I area. Similar plots are shown for Brigantine, Lye Brook and Shenandoah 

(Figure C-38, Figure C-39, and Figure C-40 respectively). For Acadia, “Other”, MA, 

Canada, PA, ME, OH, and NY contribute more than 75% of sulfate. The higher 

contribution of “Other”, MA, and Canada are explained by their relative location - 

Acadia National Park is located at the northeast boundary of our 12km modeling domain. 

In the case of Brigantine, PA, OH, and NY’s contributions are relatively higher compared 

to Acadia’s case because Brigantine is located nearer to those high emission states. In 

general, Lye Brook shows similar distribution to Brigantine, except that it shows a little 

higher contribution from Canada. Other than the “Other” tag, OH, PA, WV, VA, and IN 

show bigger contributions to Shenandoah, with Canada’s contribution relatively smaller 

compared to other Class I areas, as it is located further south. In Shenandoah’s case, the 

fraction of non-tagged emissions (e.g. biomass burning in VISTAS states - as opposed to 

boundary conditions) explains the higher contribution of “Other” tag even with smaller 

contribution of Canada. 
Appendix C: Chemical Transport Model Methods Page C-49

Figure C-36. Contribution of tagged sources for different Class-I areas in Northeast 

(monthly average sulfate concentration). 

Acadia NP

Jan

F eb

M ar

Apr

M ay

Jun

Jul

Aug

Sep

Oct

Nov

Dec

VISTAS

M-RPO

MANE-VU

0.0 OTHER

0.3

0.6

0.9

1.2

1.5

1.8

Lye Brook Wilderness

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

VISTAS

M-RPO

MANE-VU

0.0 OTHER

0.3

0.6

0.9

1.2

1.5

Shenandoah NP

Jan

Feb

M ar

Apr

M ay

JunJul

Aug

Sep

Oct

Nov

Dec

VISTAS

M-RPO

MANE-VU

0.0 OTHER

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7

Brigantine NWR

Jan

Feb

Mar

Apr

MayJunJul

Aug

Sep

Oct

Nov

Dec

VISTAS

M-RPO

MANE-VU

0.0 OTHER

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7
Appendix C: Chemical Transport Model Methods Page C-50

Figure C-37. Eastern states’ contribution to annual PM sulfate in Acadia, ME 

OTHER

33.7%

MA

10.1% CANADA

8.7%

PA

6.8%

ME

6.5%

OH

5.6%

NY

4.7% WV

2.4%

NH

2.2%

MD

2.2%

IN

2.1%

MI

2.0%

VA

1.5%

NC

1.5%

NJ

1.4%

CENRAP

0.9%

IL

1.4% KY

1.2%

DE

1.0%

GA

0.7%

CT

0.8%

WI

0.8% RI

0.3%

DC

0.0%

MS

0.0%

VT

0.1%

TN

0.6%

AL

0.3%

SC

0.4%

Figure C-38. Eastern states’ contribution to annual PM sulfate in Brigantine, NJ 

PA

12.8%

OH

9.1%

CANADA

NY 7.1%

5.6%

MD

5.0%

WV

4.7%

CT

0.5%

ME

0.2%

NH

0.6%

VT

0.1% MS

0.0%

DC

0.0%

RI

0.1%

WI

1.2%

CENRAP

1.1%

AL

1.1%

SC

1.7%

IL

KY 1.8%

2.3%

TN

1.6%

GA

2.3% MA

2.7%

MI

2.8% DE

3.2%

IN

3.3%

NJ

4.0%

NC

4.2%

VA

4.3%

OTHER

16.7%
Appendix C: Chemical Transport Model Methods Page C-51

Figure C-39. Eastern states’ contribution to annual PM sulfate in Lye Brook, VT 

CANADA

12.4%

PA

11.7%

OH

9.9%

NY

9.0%

IN

3.9%

OTHER

19.0%

WV

3.7%

SC

0.6%

CT

0.6%

AL

0.8%

RI

0.1%

MS

0.0% DC

0.0%

ME

0.3%

TN

1.2%

VT

1.0%

DE

0.9%

NJ

1.4%

WI

CENRAP 1.5%

1.7%

GA

1.3%

NH

1.7% NC

VA 1.9%

2.0% KY

2.2%

IL

2.4% MA

2.4%

MD

2.7% MI

3.7%

Figure C-40. Eastern states’ contribution to annual PM sulfate in Shenandoah, VA 

OTHER

12.4%

PA

12.0%

WV

9.6%

VA

6.2%

IN

5.2%

OH

14.8%

NC

4.8%

NH

0.1%

MS

0.1%

CT

0.1%

ME

0.0% RI

0.0% VT

0.0%

DC

0.0%

DE

0.6%

NJ

0.5%

MA

0.3% SC

1.3%

CENRAP

1.5%

AL

1.8%

WI

1.1%

NY

2.0%

IL

2.4%

TN

2.7%

MI

3.2%

GA

3.5% KY

4.4%

CANADA

4.7% MD

4.8%
Appendix C: Chemical Transport Model Methods Page C-52

Also, the 20% worst and best visibility days results were used to evaluate 

contributions to PM sulfate from emission sources in each RPO region, as shown in 

Figure C-41 and Figure C-42. Unlike previous presentations, three more Class I areas are 

added (e.g. Dolly Sods Wilderness, Great Gulf Wilderness, and Moosehorn Wilderness) 

for analysis and all sites are arranged from the southwest to northeast. As shown in the 

Figure C-41, each site tends to show the greatest contribution to poor visibility from 

nearby regions. This tendency reveals the atmospheric transport impact that adjacent 

regions’ strong and fresh emissions have at nearby receptor sites. The 20% best visibility 

days (Figure C-42) seem to occur when the contribution from boundaries are bigger 

unlike the behavior observed for worst case days. In comparing the annual average 

impact LYBR, BRIG, and SHEN show similar contribution patterns whereas Acadia 

shows higher contribution (45% vs. 37%) of MANE-VU on the 20% worst visibility 

days. 

Statistical parameters used in model performance evaluation. Pi and Oi are paired 

model prediction and observation, respectively. MB, ME, and RMSE have the same units 

as Pi and Oi, while other parameters have units of percent. 

Root Mean Square Error (RMSE) 

Fractional Error (FE) 

Normalized Mean Error (NME) 

Fractional Bias (FB) 

Normalized Mean Bias (NMB) 

Mean Normalized Bias (MNB) 

Mean Bias (MB) 

Mean Normalized Error (MNE) 

Mean Error (ME) 

∑

∑

=

=

−

N

i

i

N

i

i i

O

P O

1

1

( )

2

1

1

1 2













∑ −

=

N

i

Pi Oi

N

∑= +

−

N

i i i

i i

P O

P O

N 1

2

∑=

−

N

i

Pi Oi

N 1

1

∑=

−

N

i i

i i

O

P O

N 1

1

∑( )

=

−

N

i

Pi Oi

N 1

1

( ) ∑=

−

N

i i

i i

O

P O

N 1

1

∑=

















+

−

N

i i i

i i

P O

P O

N 1

2

∑

∑

=

=

−

N

i

i

N

i

i i

O

P O

1

1

( )
Appendix C: Chemical Transport Model Methods Page C-53

Figure C-41. Comparison of Sulfate Extinctions on 20% Worst Visibility Days 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

DOSO SHEN BRIG LYBR GRGU ACAD MOOS

MANEVU MIDWEST VISTAS CENRAP CANADA OUTSIDE DOMAIN

Figure C-42. Comparison of Sulfate Extinctions on 20% Best Visibility Days 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

DOSO SHEN BRIG LYBR GRGU ACAD MOOS

MANEVU MIDWEST VISTAS CENRAP CANADA OUTSIDE DOMAIN
Appendix C: Chemical Transport Model Methods Page C-54

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