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 conduc ted by NESCAUM with two primary modeling systems based on the CMAQ and REMS AD 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 a nd two future years (2009, 2018) has been conducted. CMAQ provides one-atmosphere result s 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. C urrent model output suggests reasonable performance for the species of interest for each platform. Only minor changes are anticipated for scenarios conducted with the fi nal 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 im portant tools within academia, federal and state governments, and other regulatory, policy, and environmental research communities to understand the underlying c auses and formation mechanisms of air pollution on the local, regional and global sca le. To assist states in developing effective solutions to air pollution issues, NESCAU M has established in-house air quality modeling capabilities that include emission process ing, meteorological input analysis, and chemical transport modeling. Preliminary work has been conducted to develop a number of modeling platforms for a variety of appli cations (e.g. annual runs, episodic simulations, source tagging, etc.) and to evaluate model performance. These efforts form the foundation upon which future modeling studies w ill be built ranging from SIP modeling for Regional Haze, Ozone, and fine particl es to studies of mercury deposition and potential air quality impacts of climate change

. This appendix introduces air quality modeling platf orms established by NESCAUM for Regional Haze and fine particle polluti on, evaluates model performance with respect to spatial and temporal variations of PM species and their precursors, and summarizes the current status and future improvemen ts of each platform’s development.



### C.2. Platforms Currently two regional-scale air quality models hav e 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 I CF Consulting/Systems Applications International (ICF/SAI) with USEPA sup port. CMAQ has undergone extensive community development and peer review (Am ar et al. 2005) and has been successfully used in a number of regional air quali ty studies (Bell and Ellis, 2003;Hogrefe et al., 2004; Jimenez and Baldasano, 2 004; Mao and Talbot, 2003; Mebust et al. 2003). REMSAD has also has been peer review ed (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 Eul erian model that incorporates output fields from emissions and meteo rological modeling systems and several other data sources through special interfac e processors into the CMAQ Chemical Transport Model (CCTM). The CCTM then performs che mical transport modeling for multiple pollutants on multiple scales. With this structure, CMAQ retains the flexibility to substitute other emissions processing systems an d meteorological models. CMAQ isAppendix 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 at mospheric processes affecting transport, transformation, and deposition of such p ollutants as ozone, particulate matter, airborne toxics, and acidic and nutrient pollutant species (Byun and Ching, 1999). MANE-VU has adopted the Inter-RPO domain descriptio n for its modeling runs. 1 This 36-km domain covers the continental United Sta tes, 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 doma in was selected to better characterize air quality in MANE-VU and surrounding RPO regions. This domain covers the Northeast region including northeastern, central an d southeastern US as well as Southeastern Canada. It extends from 66 o W~94 o W in longitude and 29 o N~50 o N in latitude with 172 X 172 grid cells (Figure C-1). NY DEC has completed annual 2002 CMAQ modeling on t he 36km domain to provide dynamic boundary conditions for all simulat ions performed on the 12km domain. Three-hourly boundary conditions for the outer doma in were derived from an annual model run performed by researchers at Harvard Unive rsity 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., 198 9) using the EBI solver for gas phase chemistry rather than the SAPRC-99 mechanism due to better computing efficiency with no significant model performance di fferences for Ozone and PM as compared to observations. Meteorological inputs for CMAQ are derived from the Fifth-Generation Pennsylvania State University/National Center for A tmospheric Research (NCAR) Mesoscale Model (MM5) 2 system meteorological fields. A modified Blackada r boundary layer scheme is used as well as physics op tions including explicit representations of cloud physics with simple ice mi crophysics (no mixed-phase processes) and the Kain-Fritsch cumulus parameteriz ation. 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 850hP a and 500hPa, and 3 layers in between 500hPa and 300hPa. The domain has a finer v ertical resolution within the troposphere so that it can capture complex atmosphe ric 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 trans port.

1 The modeling system for 2002 annual simulation is app lied with a Lambert Conformal Conic projection with parallels at 33N and 45N. A spherical earth radius o f 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 qu ality 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 m aximize efficiency of computing resources in MANE-VU for SIP modeling. Th ese centers include NY DEC, NJ DEP/Rutgers, VA DEQ, UMD, and NESCAUM. Annual CM AQ modeling on 12km domain is divided into 5 periods. UMD is responsibl e 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 1 5 to September 30; VA DEQ is responsible for the period from October 1 to Octobe r 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 ini tial 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 benc hmark 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, an d moisture with the option of hydrostatic and non-hydrostatic physics. It is des igned to simulate mesoscaleAppendix C: Chemical Transport Model Methods Page C-5

atmospheric circulation. Domains are uniform recta ngular grids representing threedimensional regions of the atmosphere. The horizon tal coordinated system is equally spaced geographically and uses the Arakawa-B griddi ng scheme. The resolution can be as high as 1km. Sigma ( σ

 ) is a terrain-following vertical coordinate that i s a function of pressure at the point (for hydrostatic) or referenc e state pressure (non-hydrostatic) (P), the surface pressure (P s0 ), and the pressure at the top (P top ) of the model. σ = (p-p top ) / (p s0 - p top )
Figure C-2. Vertical Structure of Air Quality Model ing Domains

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1



### Sigma LevelVertical Layer



### MM5 29 Layers

CMAQ 22 Layers

10 1133

622 2330 3976 5257 10796Altitude (m)

185

68

Professor Dalin Zhang’s group from University of Ma ryland (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 resoluti on Eastern US domain (Domain 2). The inner (12km) domain uses a finer resolution ter rain data. Initially, a set of test runs for the period of August 6 to 16, 2002 was conducte d. UMD ran the non-hydrostatic MM5 v3.5.3 with 3 PBL schemes (a) modified Blackada r [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 t o the measurements fromAppendix C: Chemical Transport Model Methods Page C-6

Techniques Development Laboratory of National Weath er Service (TDL NWS) and Clean Air Status and Trends Network (CASTNET). The TDL data are reflective of urban/suburban settings, while the CASTNET sites ar e 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 capt ures 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 spe ed and temperature (Hao et al., 2004). The model utilizes a terrain-following sigma coordi nate 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 B lackadar high-resolution planetary boundary layer parameterization. Physics options a lso included explicit representations of cloud physics with simple ice microphysics (no m ixed-phase processes) and the KainFritsch cumulus parameterization. The model was in itialized with the analyses of the National Center for Environmental Prediction (Eta M odel). TDL data are used for MM5 nudging. A modeled wind field map (Figure C-4) sho ws typical prevailing mesoscale flows from the midwest US to the east coast.
Figure C-3. Observation Network sites within 12km r esolution domainAppendix 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 meteo rological field has been evaluated by NESCAUM using ENVIRON's METSTAT progra m. Model results of surface wind speed, wind direction, temperature, an d 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 result s 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 spe ed 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 underestim ate wind speed (hourly bias up to - 1.85m/s) at TDL sites. Seasonal mean bias of MM5 w ind 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 se asonal variation on this wind speed bias is observed. MM5 prediction of wind direction show s 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 deg ree) is smaller than seasonal mean bias of MM5 wind direction to TDL wind directi on (~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 W ind Speed and Direction Wind Speed Wind Direction

Bias Wind Speed

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

1 2 3

4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 18 1 9 2 0 2 1 2 2 2 3 2 4 25 2 6 2 7 2 8 29 3 0 3 1 32 33 3 4 3 5 36 3 7 3 8 39 40 4 1 4 2 43 44 4 5 46 47 48 4 9 50 51 52 5 3 54 55 5 6 57 58 59 33 5 336 337 338 3 39 340 341 342 3 43 344 345 3 46 3 47 348 349 3 50 351 352 3 53 3 54 355 356 3 57 3 58 359 360 3 61 3 62 363 3 64 3 65 Julian Daym/s CASTNet TDL Bias Wind Direction

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60

1 2 3

4 5 6 7 8 9 10 1 1 12 1 3 14 1 5 16 1 7 18 1 9 2 0 21 2 2 2 3 2 4 25 2 6 27 2 8 29 30 3 1 3 2 33 34 3 5 36 3 7 38 3 9 4 0 41 42 4 3 4 4 45 4 6 47 4 8 4 9 50 51 5 2 53 54 5 5 56 5 7 58 59 33 5 3 36 337 3 38 33 9 340 3 41 342 3 43 34 4 3 45 346 3 47 348 3 49 3 50 351 3 52 353 35 4 355 3 56 357 3 58 35 9 360 3 61 362 3 63 3 64 3 65 Julian DayDegree CASTNet TDL

Index of Agreement (IOA) is a statistical measure o f difference between prediction and measurement, calculated as a ratio o f Root Mean Square Error to the sum of the difference between prediction and mean obser vation 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. D omain wide average hourly IOA ofWinterSpringSummerFallAppendix C: Chemical Transport Model Methods Page C-9

wind speed are presented in Figure C-6. MM5 predic ted wind speed values are in good agreement (IOA from ~0.5 to ~0.9) to both CASTNET dat a 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 measureme nt.
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 sh ow good MM5 performance on hourly wind speed for each observation site. MM5 p redictions exhibit similar spatial patterns of correlation with CASTNET (left panel) a nd TDL (right panel) measurements – stronger correlation in north than in south. Ove r the year, the model has stronger correlation in the 1 st

quarter (Jan. Feb. Mar., top 1 st row), 2 nd quarter (Apr. May Jun., 2 nd row) and 4 th quarter (Oct. Nov. and Dec., bottom row) than it d oes in the 3 rd quarter (Jun. Jul. Aug., 3 rd row), with an average of 0.1 correlation coefficie nt difference. Generally MM5 predictions and measurements have strongest cor relation (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 mo st 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 2002Appendix 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 2002Appendix 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 s eason. MM5 tends to underestimate temperature at TDL sites throughout t he year and at CASTNET sites for non-ozone season months. The seasonal mean tempera ture 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 (-3 K~1K). Domain wide average hourly IOA values of temperature are shown in Figur e C-10. Model predicted temperatures have significantly better agreement wi th TDL data (average IOA as ~0.95) than with CASTNET data (average IOA as ~0.85), altho ugh 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 majori ty 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 m easurement are presented in Figure C-12. MM5 captures the general trend of hum idity change. It tends to underestimate humidity during the ozone season (sea sonal mean bias as ~0.35g/kg), and overestimate it during the rest of year (seasonal m ean 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 F igure C-13 shows that MM5 predicted humidity values are in good agreement wit h 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 tr end. 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 an d measurement occurs in the 4th Quarter (>0.95), followed by the 1st and 2nd Quarte rs, 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, temperatur e, and humidity, MM5 performs well. An acceptable small bias, high index of agree ment 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 t han with CASTNET data. MM5 performs better in Midwest and Northeast than South eastern 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

TemperatureWinterSpringSummerFallWinterSpringSummerFallAppendix 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 2002Appendix 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

HumidityWinterSpringSummerFallWinterSpringSummerFallAppendix 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 M atrix Operator Kernel Emissions (SMOKE) Modeling System. SMOKE is primar ily an emissions processing system designed to create gridded, speciated, hourl y emissions for input into a variety of air quality models such as CMAQ, REMSAD, the Compre hensive Air quality Model with extensions (CAM X ) and the Urban Airshed Model (UAM). SMOKE support s area, biogenic, mobile (both onroad and nonroad), and poi nt source emissions processing for criteria, particulate, and toxic pollutants. For b iogenic 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 permit s rapid and flexible processing of emissions data. Flexible processing comes from spl itting the processing steps of inventory growth, controls, chemical speciation, te mporal allocation, and spatial allocation into independent steps whenever possible . The results from these steps are merged together in the final stage of processing us ing vector-matrix multiplication. It allows individual steps (such as adding a new contr ol 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 natio nal 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 9.0Appendix C: Chemical Transport Model Methods Page C-17

Linux operating system with the Portland group Fort ran compiler version 5.1. They use the 2002 static emission inventory, CEM data, and s urrogates data based on the 2002 RPO data. Biogenic emissions are calculated using BEIS3 with BELD3 data. Mobile source emissions are processed using MOBILE6. The u pdated Canada inventory for 2000 and 1999 Mexico inventory were used for processing. The emissions processing was performed on a month-b y-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 w as necessary for use with the 36 km grid modeling only. For each month/RPO combinat ion, a separate SMOKE ASSIGNS file was created, and the length of the epi sode 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 emission s 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 count y-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/transportf ractions.xls ; this adjustment was performed outside of SMOKE with in-h ouse Fortran programs VISTAS: ida_ar_2002_rev_29sep04.vistas.emis, arinv_2002_ncn ox_01apr05.emis, ida_ar_fire_typ_29nov04.vistas.emis, ida_ar_dust_20 02_wfac_27nov04.vistas.emis All files were obtained from Greg Stella (VISTAS) a nd were processed to extract VISTAS-only emissions as described above. N ote: the header lines of these files indicate that the fugitive dust corr ection 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) a nd processed to extract MRPO-only emissions as described above. Not e: the header lines of these files indicate that the fugitive dust corr ection 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 S EP 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 fact ors 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/transportf ractions.xls ; this adjustment was performed outside of SMOKE with in-h ouse Fortran programs CANADA: AS2000_SMOKEready.txt obtained from ftp://ftp.epa.gov/EmisInventory/canada_2000inventor y . 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-hous e Fortran programs. No county/province-specific correction fa ctors 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 emiss ions as described above. MRPO: ida_nr_2002_rev_01oct.wrap.emis, obtained from Greg Stella (VISTAS) and processed to extract WRAP-only emissions as des cribed above. CENRAP: CENRAP_NONROAD_SMOKE_INPUT_ANN_120704.txt downloade d from the CENRAP website http://www.cenrap.org/emission_document.asp CANADA: File: NONROAD2000_SMOKEready.txt obtained from ftp://ftp.epa.gov/EmisInventory/canada_2000inventor y 

### On-road MANE-VU: VMT/Speed: MANEVU_2002_mbinv.txt prepared by PECHANAppendix C: Chemical Transport Model Methods Page C-19

VISTAS: VMT/Speed file: mbinv_2002_ida_vmt_22sep04.vistas.t xt, obtained from Greg Stella (VISTAS) and was processed to extract V ISTAS-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 em ail exchange between Gopal Sistla, Mark Janssen and Jeff Vukovic h, it was determined that the VMT information used by VISTAS for their r evised Phase II modeling reflects the latest MRPO information. Ther efore, the MRPOportion of the mobile source files obtained from Gr eg 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.id a, and mbinv02_vmt_cenrap_we.ida, downloaded from the CENR AP website http://www.cenrap.org/emission_document.asp CANADA: MOBILE2000_SMOKEready.txt obtained from ftp://ftp.epa.gov/EmisInventory/canada_2000inventor y . 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-h ouse Fortran programs. No county/province-specific correction fa ctors 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 du st correction: This was applied as county-specific correction factors f or 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/transportf ractions.xls ; this adjustment was performed outside of SMOKE with in-h ouse Fortran programs. Emission corrections were made for “THE H ARTFORD 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, 0 2, 03, etc. depending on the month.Appendix C: Chemical Transport Model Methods Page C-20

Hour-specific: pthour_rev2002typ_{MMM}_08nov04.vist as.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) a nd 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 corre ction 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 line s of these files indicate that the fugitive dust correction was already appli ed, so no further correction was performed CENRAP: Annual: CENRAP_Point_SMOKE_INPUT_ANN_121004.txt. Ho urspecific CEM: pthour.{QQ}.{ST}.txt where {QQ} is th e quarter (q1, q2, q3, or q4) and {ST} is the state (AR, IA, KS, LA, M N, 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 fact ors 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/transportf ractions.xls ; this adjustment was performed outside of SMOKE with in-h ouse Fortran programs CANADA : There has long been difficulty in obtaining an up-t o-date Canadian criteria emissions inventory for point sources. This is due largely to confidentiality rights afforded to Canadian facilit ies. Thus far, the most recent inventory of Canadian point sources is roote d in the 1985 NAPAP data and is close to two decades old. Because ther e are a number of high emitting industrial facilities in southern Canada i t is of particular importance to have a reasonably accurate inventory of these sources especially when modeling air quality over the North east 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 s ource of Canadian pollutant data is now available from the National P ollutant Release Inventory (NPRI) database. This database contains 2 68 substances. Facilities that manufacture, process or otherwise u se one of theseAppendix C: Chemical Transport Model Methods Page C-21

substances and that meet reporting thresholds are r equired to report these emissions to Environment Canada on an annual basis. The NPRI data are available at Environment Canada’s website and can b e 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 pol lutant or location. In addition, the entire database is available for down load as an MS Access or Excel file. The NPRI database contains numerous pag es with a rather comprehensive list of information. Detailed inform ation is available about each facility, including location, activity and ann ual 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 d atabase for modeling purposes is that the data are only available at the facility level. Emissions models require process level information, so in ord er to use this data, a few generalizations had to be made. Each facility has a Standard Industrial Classification (SIC) code associated wit h 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 descri be the general activity of a facility while SCC codes describe specific pro cesses 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 inventor y file it was necessary to obtain the whole NPRI database. After merging a ll the necessary components from the NPRI database required in the S MOKE inventory file, the SIC code from each facility was examined and assigned an SCC code. In most cases, only a SCC3 level code was as signed with confidence. While this is admittedly a less than d esirable process, it does allow for the use of the most recent emissions from the NPRI database to be used in modeling. Furthermore, having some leve l 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 processe d 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_200 2_mtref.txt Point sources: Based on the same files as for the M ANE-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 annu al-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-specifi c 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 emis sions whenever a match could be established by SMOKE Canada and Mexico: For Canada and Mexico, the SMOKE2.1 default tempora l profiles and cross-reference files (amptpro.m3.us+can.txt and am ptref.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 regio ns and all source categories. Different versions of these files were obtained (SM OKE2.1 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 doma ins were extracted from the national grid 12 km and 36 km U.S. griddin g surrogates posted at EPA’s website at http://www.epa.gov/ttn/chief/emch/spatial/newsurrog ate.html The gridding cross-references were also obtained fr om this website, but for the processing of MANE-VU area source emissions , MANE-VU specific cross-reference entries posted on the MARA MA ftp site were added. Canada The spatial surrogates for Canadian emissions for t he 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/newsurrog ate.html The gridding cross-references were also obtained fr om this website. Mexico The spatial surrogates for Mexican emissions the 36 km domain were extracted from the national 36 km gridding surrogat es 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 emis sions (a): SO2 from Point; (b): NO2 from Area; (c): NO2 f rom On-road; (d): NO2 from Non-Road; (e): ISOP from Biogenic; (f): SO2 from all source c ategories)

a b

c d

e fAppendix C: Chemical Transport Model Methods Page C-25



### C.2.2. REMSAD The Regional Modeling System for Aerosols and Depos ition (REMSAD) is also a three-dimensional Eulerian model designed to suppor t a better understanding of the distributions, sources, and removal processes relev ant to fine particles and other airborne pollutants. It calculates the concentrations of bo th 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 rel evant emissions, transport, diffusion, chemical reactions, and removal processes are expre ssed in mathematical terms. The REMSAD model performs a four-step solution procedur e: emissions, horizontal advection/diffusion, vertical advection/diffusion a nd deposition, and chemical transformations during one half of each advective t ime step, and then reverses the order for the following half time step. The maximum adve ctive time step for stability is a function of the grid size and the maximum wind velo city or horizontal diffusion coefficient. Vertical diffusion is solved on fract ions of the advective time step to keep their individual numerical schemes stable.
REMSAD uses a flexible horizontal and vertical coor dinate system with nestedgrid capabilities and user-defined vertical layers. It accepts a geodetic (latitude/longitude) horizontal coordinate system o r a Cartesian horizontal coordinate system measured in kilometers. REMSAD uses a simpl ified version of CB-IV chemistry mechanism which is based on a reduction in the numb er of different organic compound species and also includes radical-radical terminati on reactions. The organic portion of the chemistry is based on three primary organic compoun d species and one carbonyl species. The model parameterizes aerosol chemistry and dynam ics for PM and calculates SOA yields from emitted hydrocarbons. REMSAD V7.12 and newer versions have capabilities that allow model tags of sulfur specie s (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 concentrat ions 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 sim plified chemistry mechanism, REMSAD may not simulate atmospheric processes as we ll 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 domai n (i.e. domain2) presented in the previous section is used for a thr ee full annual runs for the base year (2002). Multiple runs are necessary to permit tagg ing of sulfur emissions for all of the states in the domain, Canada and the boundary condi tions.Appendix C: Chemical Transport Model Methods Page C-26



### C.2.2.1. REMSAD Meteorology For the regional haze modeling study, an entire yea r of MM5 generated meteorological data for 2002 was provided by UMD an d used to drive annual CMAQ and REMSAD modeling as described in the previous sectio n. Meteorological inputs for these runs were developed by applying a simple translatio n 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 th e SO 2 tagging capabilities, whereas CMAQ treats all SO 2 as indistinguishable. NESCAUM has developed emissions tagging techniques for applicat ion in air quality impact analyses using the REMSAD tagging scheme incorporated in REM SAD 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 susceptibil ity 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 r egions (e.g. by country/state/county); or (4) by combinations (e.g. largest electricity gener ating 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 th e previous section (i.e. CMAQ platform). Some differences in emissions, however, do exist because of emissions updates (MANE-VU) and the necessity of simplified e missions processing to compensate for the added complexity introduced by tagging. Si nce REMSAD is a simpler model than CMAQ and is used mostly for longer term (> mon thly) impact analysis, using a simplified emissions processing approach is reasona ble. The major differences are:

1) The point source emissions for MANE-VU were upda ted from ver.1 to ver. 2. 2) CEM data were not used 3) RPO-by-RPO emissions files were merged first, pr ior 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 take n 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 combustio n and industrial process emissions sources in over 30 Eastern states in the modeling d omain 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 Eas tern receptor sites. Moreover, boundary conditions were tagged to assess out-of-do main 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 co nditions. 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 PM 2.5 

### 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-m onth summer period of 2002 (completed by NYDEC) under Base A1 (refined from Ba se A) emission scenario. CMAQ performance for PM 2.5 species and visibility is examined based on these two CMAQ runs on a 12km resolution domain. Measurement s 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 compari son of PM 2.5 species (Sulfate,Appendix C: Chemical Transport Model Methods Page C-28

Nitrate, OC, EC, Fine Soil, and PM 2.5 ) daily average concentration between two sets of simulations (BaseA & BaseA1) and two sets of observ ations (STN & IMPROVE). It shows that predicted PM 2.5 Sulfate (top row left panel) and measured Sulfate are in a good 1:1 linear relationship with r 2 varying from 0.6 to 0.7. PM 2.5 Nitrate (top row right panel) also has close to a 1:1 linear relationship between the model and observations, although the r 2 values are much lower (from ~0.2 to ~0.5) than for S ulfate. Paired OC (mid row left panel) concentrations have a scattere d distribution with over- and underestimating and a very weak linear relationship (r 2 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 transfor mation. Poor emission inventory data may be the main cause for the weak linear relations hips between prediction and measurement. In addition, there is no fire emissio n considered in CMAQ modeling. The wild fire in Quebec, Canada in early July of 2002 l ed to high concentrations of observed OC, EC, and Fine Soil that are not predicted by CMA Q. Since Sulfate is the dominant PM 2.5 species, modeled PM 2.5 (bottom row right panel) also shows a relatively s trong near 1:1 linear relationship (slope between 0.7~0.8 with r 2 of 0.4~0.5). Similar comparisons of PM 2.5 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 VIST AS CMAQ run and CENRAP CAMx run at 17 IMPROVE sites in July 2002 are on the rig ht panel. The rows from top to bottom are Sulfate, Nitrate, OC, EC, and Fine Soil. For PM 2.5 Sulfate, other RPOs’ prediction show a rather strong 1:1 linear relation with measurement (r 2 of 0.6~0.7), similar to our CMAQ performance. Other PM 2.5 species all show poor linear relationships between model prediction and observat ions. Values of r 2 are ~0.1 for Nitrate; 0.2~0.4 for OC; 0.2~0.4 for EC; <0.02 for F ine Soil. Therefore, our CMAQ performance of PM 2.5 is in the same range as other RPOs. Figure C-19 describes the spatial distribution of t he correlation coefficient of sulfate between CMAQ prediction and observations (S TN 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 seas ons. No significant improvement in correlation is observed for the BaseA1 case over the BaseA case. The spatial distribution of correlation coefficient for PM 2.5 is presented in Figure C-20. The PM 2.5 correlation coefficient spatial pattern follows PM 2.5 sulfate correlation coefficient, although at the same observation site coefficient v alues are ~0.1 lower than the sulfate coefficient value. Like PM 2.5 sulfate, CMAQ also performs the best for PM 2.5 in the MANE-VU region with ~0.7 annual average and ~0.8 summ er average of the correlation coefficient. In 2004 James Boylan from VISTAS suggested the goal and the criteria for PM 2.5 evaluation ( Boylan, 2004). This standard has been adopted by every RP O 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 PM 2.5 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 PM 2.5 Sulfate, Nitrate, OC, EC, Fine Soil, and PM 2.5 , and curves of the goal and criteria. MFB values are shown in Figure C-22. Considering CMAQ performance in te rms of both MFE and MFB, Sulfate, Nitrate, OC, EC, and PM 2.5 all have the majority of data points within the go al curve, some are between the goal and acceptable cri teria, 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 th e MANE-VU region, CMAQ performs best for PM 2.5 Sulfate, followed by PM 2.5 , EC, Nitrate, OC, and finally the fine soil. Regional haze modeling also requires a CMAQ perform ance evaluation for aerosol extinction coefficient (Bext) and the haze index. Modeled daily aerosol extinction at each IMPROVE site is calculated follo wing the IMPROVE formula with modeled daily PM 2.5 species concentration and relative humidity factor from IMPROVE. Figure C-23 shows the paired comparison of domain w ide daily aerosol extinction coefficient for 2002 between prediction and measure ment with 1:1 line and ±20% lines. The modeled Bext shows a near 1:1 linear relationsh ip (slope of 0.74 and r 2 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 obs ervation 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 betw een 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 mode l performance with a slope of 0.96 and r 2 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 compa red to an HI of ~20. Then is Lye Brook with a slope of 0.77 and r 2 of 0.6, mean bias of 1.55 compared to an HI of ~14. Finally is Shenandoah with a slope of 0.6 and r 2 of 0.4, mean bias of 1.62 compared to an HI of ~21. MFE and MFB of all 4 sites meet the perf ormance goal. Overall, NESCAUM CMAQ modeling on the 12km resoluti on domain for 2002 accurately portrays sulfate, PM 2.5 , aerosol extinction coefficient and the Haze Index . It provides reasonable performance for PM 2.5 Nitrate, OC, and EC. The model performs better for summertime than for wintertime, and bett er 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 h ave been considering a shift from CMAQ V4.4 to V4.5. CMAQ V4.5 is reported to signif icantly improve the PM science in the model. The more recent model version has add ed 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 mixingAppendix C: Chemical Transport Model Methods Page C-30

algorithm/module. In addition, CMAQ V4.5 updated th e planetary boundary layer (PBL) module to use urban fraction for setting minimum ve rtical diffusivity (K Z ). Comparison studies (Appel et al., 2005) between CMAQ V4.5 and V4.4 show significantly improved performance on PM 2.5 Sulfate and Nitrate using V4.5 over V4.4, while V4 .5 maintains the same acceptable ozone performance as V4.4. Whil e other RPOs have already used CMAQ V4.5 in their SIP modeling, MANE-VU recently d ecided 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 PM 2.5 SIP modeling for MANE-VU.Appendix C: Chemical Transport Model Methods Page C-31

Figure C-17. Domain wide paired comparison of dail y average PM 2.5 species between CMAQ predictions and measurements from IMPR OVE and STN networksAppendix C: Chemical Transport Model Methods Page C-32

Figure C-18. Paired comparison of PM 2.5 species from other RPOs’ modeling work ( Ku et al.,2005 )PM2.5 ECPM2.5 SulfatePM2.5 SoilPM2.5 OCPM2.5 NitrateAppendix C: Chemical Transport Model Methods Page C-33

Figure C-19. Spatial distribution of correlation c oefficient between PM 2.5 Sulfate and measurement

Figure C-20. Spatial distribution of correlation c oefficient between PM 2.5 and measurementAppendix C: Chemical Transport Model Methods Page C-34

Figure C-21. Mean Fractional Error of PM 2.5 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 20 40 60 80 100 120 140 160 180 200

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 20 40 60 80 100 120 140 160 180 200

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 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 20 40 60 80 100 120 140 160 180 200

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 20 40 60 80 100 120 140 160 180 200

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

Figure C-22. Mean Fraction Bias of PM 2.5 species within MANE-VU region

PM2.5 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 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 -160 -140 -120 -100 -80 -60 -40 -20 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)MFB (%) BaseA STN BaseA1 STN BaseA IMPROVE BaseA1 IMPROVE Goal Criteria PM2.5 OC 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 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 40 60 80 100 120 140 160 180 200

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 80 100 120 140 160 180 200

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 PM2.5 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 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 -160 -140 -120 -100 -80 -60 -40 -20 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)MFB (%) BaseA STN BaseA1 STN BaseA IMPROVE BaseA1 IMPROVE Goal Criteria PM2.5 OC 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 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 40 60 80 100 120 140 160 180 200

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 80 100 120 140 160 180 200

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

Figure C-23. Paired comparison of extinction coeffi cient 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 betwe en CMAQ prediction and IMPROVE measurement at Class I sites within Northea stern US

Haze Index (deciview) at Class I Areas in Northeast ern 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
IMPROVECMAQ

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 -1.3 7 17.15 1.22 16.45 Lye Brook 13.71 15.26 32.72 29.64 1.55 3.59 11.34 26.22 11.0 1 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 24.36Appendix 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. The se model runs provide insight into the current and expected ambient levels of fine par ticles 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 emission s (MANE-VU v3.0 inventory). The six maps in

Figure C-25 show annual average results for PM 2.5 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 de gradation in the region. The total PM25 maps on the left provide the spatial distribut ion 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 th e PM 2.5 . Specifically, sulfate levels along the Ohio River valley are greater than surrou nding areas for the baseline run, with the gradients becoming much less for future scenari os. 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 abs olute 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 ca se runs to the base case for 2009 and 2018 respectively. For regional haze purposes, sit e specific reduction factors were generated for the best and worst 20% days. The ann ual average results are spatially consistent with the seven Class 1 site factors deri ved for the worst 20% days

3 . These model results predict the greatest percent age sulfate reduction to occur in West Virginia and its immediate surrounds, with min or 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 quart erly estimates of relative reduction in sulfate for 2009. For PM 2.5 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 im prove the understanding of the seasonal impacts to be expected in the future due to emissio n 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 du ring the colder months of 2009 relative to the base year 2002. For the most-part, the chan ges modeled in the colder months are modest, falling within 10% of the base-year calcula tions.

3 To project future haze levels, relative reduction factors wer e determined for best and worst days for all six haze relevant constituents (sulfate, nitrate, organic carbon, e lemental carbon, fine soil and coarse particulate). The seven sites investigated were Acadia, Brigant ine, Lye Brook, Moosehorn, Great Gulf, Dolly Sods and Shenandoah.Appendix C: Chemical Transport Model Methods Page C-38

Figure C-25. CMAQ results for PM 2.5 and Sulfate for 2002, 2009 and 2018Appendix 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 panel s

Relative reduction factors for all haze relevant PM constituents at seven Class 1 sites are displayed in Table C-1. For each site an d specie, the modeled change relative to the baseline year 2002 is shown. Therefore, negati ve 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 ov erall 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 anoth er 10% decline (relative to 2002) between 2009 and 2018, yielding a total reduction o f 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 201 8 sums these two values (17 + (-4) = 13), implying an overall rise in fine soil concen trations 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 su bstantial 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 me asured ammonium sulfate on the 20% worst days. The yellow bar gives the five-year ave rage sulfate levels on the worst days for each site. Using the reduction factors for 200 9 yields sulfate concentrations shown at the red bar, while applying the 2018 factor predict s mass values given by the blue bar. These bars clearly indicate more substantial reduct ions 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’ red uction factors. Based on the modeling, all sites except one are shown to meet th eir uniform progress goal by 2018. Brigantine Wilderness Area in New Jersey is project ed to fall about a half deciview shy of the uniform rate under existing emission reducti on plans.
Table C-1. Relative Reduction factors by site and s pecie. 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

2009 -31% 0% -7% -19% 5% 6% Acadia 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 reduct ion for 20% worst days

0 2 4 6 8 10 12 14Dolly SodsShenandoahBrigantineLye BrookGreat GulfAcadiaMoosehornMass (μg/m3) 

## Baseline (2000-04)
2009
2018Appendix C: Chemical Transport Model Methods Page C-42

Figure C-28 (a) and (b). CMAQ Integrated SIP Modeli ng Platform simulation results for 2002, 2009 and 2018 relative to Uniform Progress Goals calculated according to current USEPA Guidance for (a) Northea st 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 2020Deciview 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 2020Deciview Brigantine Uniform Goal Dolly Sods Uniform Goal Shenandoah Uniform Goal Brigantine Modeled Dolly Sods Modeled Shenandoah ModeledAppendix 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 CS A 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 Weste rn US on PM sulfate and PM 2.5 , although it underestimates ambient levels countrywi de. Emissions may contribute to poor performance on soil, carbonaceous aerosols and PM nitrate.
Table C-2. Normalized error of annual mean model p rediction to annual mean observation on PM species between IMPROVE measureme nts and REMSAD 1996

annual simulation (after Timin, B. et al., 2002) IMPROVE PM Species National East West PM 2.5 -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 19 96 meteorology along with the 2001 Proxy emission inventory, thus a direct comparison of modeling results to daily observations could not be complete d. To evaluate REMSAD in that stage, NESCAUM first compared its own modeling results wit h 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 m odeling on PM 2.5 and PM sulfate distributions. In addition, NESCAUM compar ed the long term modeling average (annual mean) of PM species to IMPROVE annual means 5 for three sites. The results are presented in Figure C-30. It shows good agreement for REMSAD modeling of PM sulfate, NH 4 , 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 Easte rn domain with 2002 RPO emissions and meteorology. Figure C-31 an d Figure C-32 show the gridded SO 2 emissions with tags in our 12km modeling domain an d some examples of annual average REMSAD sulfate concentrations by selected N ortheast States, respectively. Figure C-32 illustrates the spatial distribution of the REMSAD simulated tagged emissions concentration fields. These fields are st rongest in their own state and generally have the largest outside state impact toward the no rtheast.

4 Also see Clear Skies Act Air Quality Modeling Technical Suppo rt Document at: http://www.epa.gov/air/clearskies/aq_modeling_tsd_csa2003.pd f 5 Multi-year averages were computed from the measurements to bet ter 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 specie s between NESCAUM REMSAD modeling and multi-year average IMPROVE meas urements

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

IMPROVENESCAUM . Sulfate

Nitrate

NH4

OC

EC

Soil

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

Figure C-31. Gridded SO 2 emissions distribution and tag numbers

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

A spatial performance evaluation of REMSAD simulati ons for sulfate on the 12km northeast US domain for the year 2002 was cond ucted 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 f ield. This approach, however, does provide a first order examination of measurement an d modeling results, which is appropriate for an annual averaged analysis.
Figure C-33. Sulfate concentrations from IMPROVE/ST N measurements and REMSAD model.

Figure C-34. Intercomparison of measurement and mod el data for 5 different annual model simulations.

Sulfate Ion LyeBrook

y = 0.92x - 0.06 R 2 = 0.81 y = 1.00x - 0.14 R 2 = 0.84 y = 0.56x + 0.76 R 2 = 0.62 y = 0.61x + 0.99 R 2 = 0.30 y = 0.39x + 0.75 R 2 = 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 R 2 = 0.57 y = 1.14x - 0.02 R 2 = 0.68 y = 1.09x + 0.03 R 2 = 0.68

y = 0.40x + 1.95 R 2 = 0.21 y = 0.31x + 1.31 R 2 = 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 R 2 = 0.81 y = 1.00x - 0.14 R 2 = 0.84 y = 0.56x + 0.76 R 2 = 0.62 y = 0.61x + 0.99 R 2 = 0.30 y = 0.39x + 0.75 R 2 = 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 R 2 = 0.57 y = 1.14x - 0.02 R 2 = 0.68 y = 1.09x + 0.03 R 2 = 0.68

y = 0.40x + 1.95 R 2 = 0.21 y = 0.31x + 1.31 R 2 = 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_CalPuffAppendix C: Chemical Transport Model Methods Page C-47

In general, REMSAD’s simulation field is well-match ed with measurement data. Figure C-34 shows the comparison of paired 24-hourl y surface sulfate concentrations between five different air quality model results (i ncluding 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 i n terms of slope, intercept and coefficient of determination (r 2 ). The REMSAD result shows the 2nd best performanc e with the two CALPUFF results matching least to meas urements. This trend remains similar for Shenandoah. Along with EPA’s previous evaluation (Timin B. et al., 2002), REMSAD performs reasonably well for longer-term sul fate simulation.



### C.3.2.2. Contribution assessment In addition to the REMSAD tagged sulfur modeling, N ESCAUM and its MANEVU partners performed other analysis techniques to assess states’ impact on PM levels over the Northeast US (e.g. CALPUFF modeling, Perce nt-time Upwind, and E/D analysis). Figure C-35 shows modeling results of r egion/country specific contributions to PM sulfate in the Acadia, Brigantine, Lye Brook, an d Shenendoah Class I areas from five different contribution assessment techniques. In g eneral, the five different techniques show similar contribution of sulfate to Class I are as, but MANE-VU’s contributions are estimated to be relatively higher from the REMSAD r esult and lower from the Percent upwind. For Acadia, the analysis reveals about 37% of sulfa te 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 bou ndary conditions (about ~33% of the total contribution to Acadia), was normalize d, then included in other relative contribution analysis techniques because only REMSA D predicted those impacts. For Brigantine, about 35% of sulfate comes from in-regi on 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 high er contribution of MRPO and lower contribution of VISTAS given the relative locations of sources and receptors. Shenandoah shows higher contribution from VISTAS an d MRPO (about 60%) and lower contribution from MANE-VU and Others (about 20% eac h) due to its location (i.e. VA). Figure C-36 shows monthly contributions by four dif ferent 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 N ortheast boundary of the modeling domain. The concentrations are generally high in t he summer months (i.e. June, July, and August) for all the regions with the relative c ontribution of MRPO and VISTAS higher than other seasons, likely due to stronger w esterly/southwesterly winds in summer. The “Other” and MANE-VU regions’ contribut ions are still relatively big at Lye Brook, but MRPO and VISTAS’ contributions are m ore significant than in Acadia’s case because Vermont is closer to those regions. T he monthly concentration shows a similar pattern to that from Acadia. For Brigantin e, MANE-VU’s contribution remains biggest, followed by VISTAS, “Other”, and MRPO. Th e monthly contribution from the “Other” tag decreases after June in contrast to MRP O’s. The VISTAS and MRPO’s contributions are relatively large at Shenandoah du e to their proximity.Appendix C: Chemical Transport Model Methods Page C-48

Figure C-35. Contribution of tagged sources by diff erent 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%REMSADCALPUFF(VT)CALPUFF(MD)E/DPercent UpwindREMSADCALPUFF(VT)CALPUFF(MD)E/DPercent UpwindREMSADCALPUFF(VT)CALPUFF(MD)E/DPercent UpwindREMSADCALPUFF(VT)CALPUFF(MD)E/DPercent Upwind

Acadia Brigantine Lye Brook Shenendoah

MANEVU MIDWEST VISTAS CENRAP CANADA OUTSIDE DOMAIN
Given the reassuring results for sulfate, NESCAUM c onducted REMSAD tagged sulfur modeling to assess states’ impact on PM leve ls over the Northeast US. Figure C-37 shows modeling results of state specific contr ibutions to PM sulfate in the Acadia, ME Class I area. Similar plots are shown for Briga ntine, Lye Brook and Shenandoah (Figure C-38, Figure C-39, and Figure C-40 respecti vely). 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 explain ed by their relative location - Acadia National Park is located at the northeast bo undary of our 12km modeling domain. In the case of Brigantine, PA, OH, and NY’s contrib utions are relatively higher compared to Acadia’s case because Brigantine is located near er to those high emission states. In general, Lye Brook shows similar distribution to Br igantine, except that it shows a little higher contribution from Canada. Other than the “O ther” tag, OH, PA, WV, VA, and IN show bigger contributions to Shenandoah, with Canad a’s contribution relatively smaller compared to other Class I areas, as it is located f urther south. In Shenandoah’s case, the fraction of non-tagged emissions (e.g. biomass burn ing in VISTAS states - as opposed to boundary conditions) explains the higher contributi on 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 dif ferent Class-I areas in Northeast (monthly average sulfate concentration).



### Acadia NPJanF ebM arAprM ayJunJulAugSepOctNovDec VISTAS M-RPO MANE-VU OTHER 0.0 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 OTHER 0.0 0.3 0.6 0.9 1.2 1.5



### Shenandoah NPJanFebM arAprM ayJunJulAugSepOctNovDec VISTAS M-RPO MANE-VU OTHER 0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7



### Brigantine NWRJanFebMarAprMayJunJulAugSepOctNovDec VISTAS M-RPO MANE-VU OTHER 0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7Appendix 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 7.1% NY 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 1.8% KY 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 1.5% CENRAP 1.7% GA

1.3%

NH 1.7% NC 1.9% VA 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 result s were used to evaluate contributions to PM sulfate from emission sources i n each RPO region, as shown in Figure C-41 and Figure C-42. Unlike previous prese ntations, three more Class I areas are added (e.g. Dolly Sods Wilderness, Great Gulf Wilde rness, and Moosehorn Wilderness) for analysis and all sites are arranged from the so uthwest to northeast. As shown in the Figure C-41, each site tends to show the greatest c ontribution to poor visibility from nearby regions. This tendency reveals the atmosphe ric 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 contribut ion from boundaries are bigger unlike the behavior observed for worst case days. In comparing the annual average impact LYBR, BRIG, and SHEN show similar contributi on patterns whereas Acadia shows higher contribution (45% vs. 37%) of MANE-VU on the 20% worst visibility days. Statistical parameters used in model performance ev aluation. P i and O i are paired model prediction and observation, respectively. MB, ME, and RMSE have the same units as P i and O i , 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 O P

1 1 

### ( ) 2

1

1 2 1 

    

− 

## ∑

= N

i i i O P N



## ∑

= + − N

i i i i i O P O P N 1 2 

## ∑

= − N

i i i O P N 1 1



## ∑

= − N

i i i i O O P
N 1 1 

### ( ) 

## ∑

= − N

i i i O P N 1 1



### ( ) 

## ∑

= − N

i i i i O O P N 1 1



## ∑

= 

      

+ − N

i i i i i O P O P N 1 2 ∑ ∑

= = −

N

i i N

i i

i

O O P

1 1 ) (Appendix C: Chemical Transport Model Methods Page C-53

Figure C-41. Comparison of Sulfate Extinctions on 2 0% 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 2 0% 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 DOMAINAppendix C: Chemical Transport Model Methods Page C-54



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