# Air Quality Modeling Technical Support Document: Final EGU NESHAPEPA-454/R-11-009 October 2011



# Air Quality Modeling Technical Support Document: Final EGU NESHAP

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, NC 277111 I Introduction This document describes the air quality modelin g performed by EPA in support of the final National Emissions Standard for Hazardous Air Pollutants (NESHAP) related to electrical generating utilities. A national scale air quality modeling analysis was performed to estimate the impact of the sector emissions changes on futu re year annual and 24-hour PM2.5 concentrations, 8-hr maximum ozone, as well as vi sibility impairment. Air quality benefits are estimated with the Community Multi-scale Air Quality (CMAQ) model. CMAQ simulates the numerous physical and chemical processes involved in the formation, transport, and destructi on of ozone, particulate matter and other air pollutants. In addition to the CMAQ model, the modeling platform includes the emissions, meteorology, and initial and bounda ry condition data which are inputs to this model. Emissions and air quality modeling decisions are made early in the analytical process. For this reason, it is important to note that the inventories used in the air quality modeling may be slightly different than the final utility sector inventorie s presented in the RIA. However, the air quality inventories and the final rule i nventories are generally consiste nt, so the air quality modeling adequately reflects the effects of the rule.
II. Photochemical Model Versio n, Inputs and Configuration Photochemical grid models use state of the sc ience numerical algorithms to estimate pollutant formation, transport, and deposition over a variety of spatial scales that range from urban to continental. Emissions of precursor species are in jected into the model where they react to form secondary species such as ozone and then transport around the modeling domain before ultimately being removed by deposition or chemical reaction. The 2005-based CMAQ modeling platform was used as the basis for the air quality modeling for this rule. This platform represents a structured system of connected modeling-related tools and data that provide a consistent and transparent basis for assessing the air quality response to projected changes in emissions. The base year of data used to construct this platform includes emissions and meteorology for 2005. The modeling sy stem treats the emissions, transport, and fate of criteria pollutants. This modeling plat form and analysis is described below. As part of the analysis for this rulemaking, the modeling system was used to calculate daily and annual PM2.5 concentrations, 8-hr maximum ozone, and visibility impairment. Model predictions are used to estimate future-year desi gn values of PM2.5 and ozone. Specifically, we compare a 2017 reference scenario to a 2017 cont rol scenario. This is done by calculating the simulated air quality ratios betw een any particular future year simulation and the 2005 base. These predicted ratios are then applied to ambien t base year design values. The design value projection methodology used here followed EPA gui dance for such analyses (USEPA, 2007).2 A. Model version The Community Multi-scale Air Quality (C MAQ) model v4.7.1 (www.cmaq-model.org) is a state of the science three-dimensional Eularian “one-atmosphere” photochemical transport model used to estimate air quality (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 2006). CMAQ simulates the formation and fate of photochemical oxidants, ozone, primary and secondary PM concentrations, and other pollutant s over regional and urban spatial scales for given input sets of meteorologi cal conditions and emissions. CMAQ is applied with the AERO5 aerosol module, which includes the ISORROPIA i norganic chemistry (Nenes et al., 1998) and a secondary organic aerosol module (Carlton et al., 2010). The CMAQ model is applied with sulfur and organic oxidation aqueous phase chem istry (Carlton et al., 2008) and the carbon-bond 2005 (CB05) gas-phase chemistry module (Gery et al., 1989). B. Model domain and grid resolution The modeling analyses were performed for a domai n covering the continental United States as shown in Figure II-1. This domain has a parent horizontal grid of 36 km with two finer-scale 12 km grids over portions of the eastern and wester n U.S. The model extends vertically from the surface to 100 millibars (approximately 15 km) usi ng a sigma-pressure coordinate system. Air quality conditions at the outer boundary of the 36 km domain were taken from a global model and vary in time and space. The 36 km grid was only used to establish the incoming air quality concentrations along the boundaries of the 12 km grids. Only the finer grid data were used in determining the impacts of the emissions change s. Table II-1 provides geographic information about the photochemical model domains.
Table II-1. Geographic elements of domain s used in photochemical modeling. Photochemical Modeling Configuration

National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid Map Projection Lambert Conformal Projection

Grid Resolution 36 km 12 km 12 km

Coordinate Center 97 deg W, 40 deg N

True Latitudes 33 deg N and 45 deg N

Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14

Vertical extent 14 Layers: Surface to 100 millib ar level (see Table II-3)3 Figure II-1. Map of the photochemical modeling domains. The black outer box denotes the 36 km national modeling domain; the red inner box is the 12 km west ern U.S. grid; and the blue inner box is the 12 km eastern U.S. grid.

C. Modeling Time-period The 36 km and both 12 km modeling domains were modeled for the entire year of 2005. Data from the entire year were utilized when looking at the estimation of PM2.5, total mercury deposition, and visibility impact s from the regulation. Data from April through October is used to estimate ozone impacts. D. Model Inputs: Emissions, Meteorology and Boundary Conditions The 2005-based modeling platform was used for the air quality modeling of future emissions scenarios. In addition to the photochemical model, the modeling platform also consists of the base- and future-year emissions estimates, meteorological fields, as well as initial and boundary condition data which are all i nputs to the air quality model. 1. Emissions Input Data The emissions data used in the base year and fu ture reference and future emissions adjustment case are based on the 2005 v4.3 platform. Emissi ons are processed to photochemical model inputs with the SMOKE emissions modeling sy stem (Houyoux et al., 2000). The 2017 reference case is intended to represent the emissions associ ated with growth and c ontrols in that year projected from the 2005 simulation year. The Un ited States EGU point source emissions estimates for the future year reference and c ontrol case are based on an Integrated Planning Model (IPM) run for criteria polluta nts. Both control and growth factors were applied to a subset of the 2005 non-EGU point and non-point emissions to create the 2017 reference case. The 2005 v4 platform projection factors were the star ting point for most of the 2017 SMOKE-based4 projections. The estimated total an thropogenic emissions and emissions for the utility sector used in this modeling assessment over the entire model domain are shown in Table II.2.
Table II.2 Model domain total estimated total inventory and EGU sector emissions for each modeling scenario.

Other North American emissions are based on a 2006 Canadian inventory and 1999 Mexican inventory. Both inventories are not grown or controll ed when used as part of future year baseline inventories. Global emissions of criteria and toxic pollutants are included in the modeling system through boundary condition inflow. 2. Meteorological Input Data The gridded meteorological input data for the en tire year of 2005 were derived from simulations of the Pennsylvania State University / Nationa l Center for Atmospheric Research Mesoscale Model. This model, commonly referred to as MM5, is a limited-area, nonhydrostatic, terrainfollowing system that solves for the full set of physical and thermodynamic equations which govern atmospheric motions. Meteorological model input fields were prepared separately for each of the three domains shown in Figure II-1 using MM5 version 3.7.4. The MM5 simulations were run on the same map projec tion as shown in Figure II-1. All three meteorological model r uns were configured similarly. The selections for key MM5 physics options are shown below:
• Pleim-Xiu PBL and land surface schemes • Kain-Fritsh 2 cumulus parameterization • Reisner 2 mixed phase moisture scheme • RRTM longwave radiation scheme • Dudhia shortwave radiation scheme Three dimensional analysis nudging for temper ature and moisture was applied above the boundary layer only. Analysis nudging for the wind field was applied above and below the boundary layer. The 36 km domain nudging weighting factors were 3.0 x 10 4 for wind fields and temperatures and 1.0 x 10 5 for moisture fields. The 12 km domain nudging weighting factors were 1.0 x 10 4 for wind fields and te mperatures and 1.0 x 10 5 for moisture fields. Scenario Sector CO NOX NH3 SO2 SULF Primary PM2.5 2005 baseline EGU (PTIPM) 603,788 3,729,157 21,995 10,380,870 224,859 496,874 All Other 102,946,238 22,523,479 4,805,389 6,675,740 88,714 4,567,808

2017 baseline EGU (PTIPM) 873,345 1,930,767 40,259 3,281,361 73,994 276,428 All Other 71,652,291 16,171,166 4,998,214 6,063,388 50,506 4,040,380

2017 control case EGU (PTIPM) 707,641 1,789,788 35,493 1,866,245 41,592 223,319 All Other 71,652,291 16,171,166 4,998,214 6,063,388 50,506 4,040,3805 Table II-3 . Vertical layer structure (heights are layer top). CMAQ Layers MM5 Layers Sigma P Approximate Height (m) Approximate Pressure (mb) 0 0 1.000 0 1000 1 1 0.995 38 995 2 2 0.990 77 991 3 3 0.985 115 987 4 0.980 154 982 4 5 0.970 232 973 6 0.960 310 964 5 7 0.950 389 955 8 0.940 469 946
6 9 0.930 550 937 10 0.920 631 928 11 0.910 712 919
7 12 0.900 794 910 13 0.880 961 892 14 0.860 1,130 874
8 15 0.840 1,303 856 16 0.820 1,478 838 17 0.800 1,657 820 9 18 0.770 1,930 793 19 0.740 2,212 766 10 20 0.700 2,600 730 21 0.650 3,108 685 11 22 0.600 3,644 640 23 0.550 4,212 595
12 24 0.500 4,816 550 25 0.450 5,461 505 26 0.400 6,153 460

13 27 0.350 6,903 415 28 0.300 7,720 370 29 0.250 8,621 325 30 0.200 9,625 280

14 31 0.150 10,764 235 32 0.100 12,085 190 33 0.050 13,670 145 34 0.000 15,674 100 All three sets of model runs we re conducted in 5.5 day segments with 12 hours of overlap for spin-up purposes. All three domains contained 34 vertical layers with an approximately 38 m deep surface layer and a 100 millib ar top. The MM5 and CMAQ vertic al structures are shown in Table II-3 and do not vary by horizontal grid re solution. The meteorological outputs from all three MM5 sets were processed to create m odel-ready inputs for CMAQ using the MCIP processor. Before initiating the air quality simulations, it is important to identify the biases and errors associated with the meteorological modeli ng inputs. The 2005 MM5 model performance evaluations used an approach which include d a combination of qualitative and quantitative analyses to assess the adequacy of the MM5 si mulated fields. The qualitative aspects involved comparisons of the model-estimated synoptic patterns against obser ved patterns from historical weather chart archives. Additionally, the evaluati ons compared spatial patterns of estimated to observed monthly average rainfall and check ed maximum planetary boundary layer (PBL) heights for reasonableness.6 Qualitatively, the model fields closely matched the observed synoptic patterns, which is not unexpected given the use of nudging. The ope rational evaluation in cluded statistical comparisons of model/observed pairs (e.g., mean normalized bias, mean normalized error, index of agreement, root mean square errors, etc.) for multiple meteorological parameters. For this portion of the evaluation, five meteorological parameters were investigated: temperature, humidity, shortwave downward radiation, wind speed, and wind di rection. The three individual MM5 evaluations are described el sewhere (Baker, 2009a, b, c). It wa s ultimately determined that the bias and error values associated with a ll three sets of 2005 mete orological data were generally within the rang e of past meteorological modeling resu lts that have been used for air quality applications. 3. Initial and Boundary Conditions The lateral boundary and initial species concentrations are provided by a three-dimensional global atmospheric chemistry model, the GEOS -CHEM model (standard version 7-04-11). The global GEOS-CHEM model simulates atmospheric chemical and physical processes driven by assimilated meteorological observations from the NASA’s Goddard Earth Observing System (GEOS). This model was run for 2005 with a grid resolution of 2.0 degree x 2.5 degree (latitude-longitude) and 30 verti cal layers up to 100 mb. The pred ictions were used to provide one-way dynamic boundary conditions at three-hour intervals and an initial concentration field for the 36 km CMAQ simulations. The 36 km phot ochemical model simulation is used to supply initial and hourly boundary concen trations to the 12 km domains. The 36 km domain simulation includes 10 days of spin-up before the start of each calendar quarter that are not used in the analysis. The 12 km domain simula tions include 3 days of spin-up before each calendar quarter. Initial and boundary conditions for the projected fu ture year 36 km simulations are the same as the 2005 base year.
III. Base Case Model Performance Evaluation
A. PM2.5 An operational model performance evaluation for the speciated components of PM2.5 (e.g., sulfate, nitrate, elemental carbon, organic ca rbon, etc.) was conducte d using 2005 state/local monitoring data in order to estimate the ability of the modeling system to replicate base year concentrations. The evaluation of PM2.5 componen t species includes comp arisons of predicted and observed concentrations of sulfate (SO4), nitrate (NO3), ammonium (NH4), elemental carbon (EC), and organic carbon (OC). PM2.5 ambi ent measurements for 2005 were obtained from the Chemical Speciation Network (CSN) and the Interagency Monitoring of PROtected Visual Environments (IMPROVE). The CSN sites are generally located within urban areas and the IMPROVE sites are typically in rural/re mote areas. The measurements at CSN and IMPROVE sites represent 24-hour average concentr ations. In calculating the model performance metrics, the modeled hourly specie s predictions were aggregated to the averaging times of the measurements.Fi g ure I I
Model p e concentr a and Rus s o b servat i when m e represen t in recent (bias) an d found b y model p e platform purposes I I-1 . Specia t

e rformance s a tions. Metr s ell, 2006; U i on pairs ar e e trics appro a t poor mode l regional P M d mean erro r y other grou p e rformance r

provides a s of this asse t ed PM2.5 m

s tatistics we r ics estimate d U SEPA, 200 7 e shown by c a ch 0. The fr a l performan c M 2.5 model a

r (error) sta t p s in recent a r esults give u s cientificall y ssment. m onitors inc l

r e calculate d d include b i a 7 ). The aggr e c hemical sp e a ctional bia s c e. Model p e a pplications t istics show n a pplications u s confiden c y credible a p l uded in the

d for observ e a s, error, fra e gated metr i e cie and qua r s and error m e rformance w

for other, n o n in Table II I

(Doraiswa m c e that our a p p proach for a model perf o

e d/predicte d ctional bias , i cs and num b r ter in Table m etrics are b w as compar e o n-EPA stu d I -1 are with i m y, 2010; T e

p plication o f a ssessing P M o rmance eva

d pairs of dai

, and fractio n b er (N) of p r III-1. Perfo ound by 20 0 e d to the pe r d ies. Overal l i n the range e sche et al., 2 f CMAQ us i M 2.5 concen t luation.
ly n al error (B o r edictionrmance is b e 0 %, which w r formance f o l , the mean b or close to t h 2 006). The i ng this mo d t rations for t

7 o ylan

e st w ould o und b ias h at d eling t he8 TABLE III-1. Model performance metrics for sp eciated PM2.5 averaged by quarter.
Specie 1 2 3 4 N Sulfate Ion 8,493 8,916 8,229 8,155 Nitrate Ion 8,143 8,518 7,942 8,016 Ammonium Ion 4,723 4,746 4,546 4,461 Organic Carbon 8,038 8,485 7,939 7,874 Elemental Carbon 8,543 8,857 8,145 8,218 Mean Observed (ug/m 3 ) Sulfate Ion 2.1 3.0 4.3 2.0 Nitrate Ion 2.1 0.8 0.5 1.4 Ammonium Ion 1.7 1.4 1.7 1.3 Organic Carbon 1.7 1.8 2.3 2.2 Elemental Carbon 0.5 0.4 0.5 0.6 Mean Predicted (ug/m 3 ) Sulfate Ion 1.9 2.7 3.4 1.9 Nitrate Ion 1.9 0.9 0.4 1.4 Ammonium Ion 1.2 1.2 1.1 1.1 Organic Carbon 1.7 1.2 1.6 1.5 Elemental Carbon 0.8 0.6 0.7 0.8 Bias (ug/m 3 ) Sulfate Ion

‐ 0.2

‐ 0.2

‐ 0.8

‐ 0.1 Nitrate Ion

‐ 0.1 0.2

‐ 0.1 0.1 Ammonium Ion 0.0 0.2

‐ 0.2 0.2 Organic Carbon 0.0

‐ 0.7

‐ 0.7

‐ 0.6 Elemental Carbon 0.3 0.2 0.2 0.2 Error (ug/m 3 ) Sulfate Ion 0.9 1.0 1.4 0.6 Nitrate Ion 1.3 0.7 0.5 1.1 Ammonium Ion 0.8 0.7 0.7 0.7 Organic Carbon 1.0 1.0 1.2 1.2 Elemental Carbon 0.5 0.3 0.4 0.4 Fractional Bias (%) Sulfate Ion 0.2

‐ 3.8

‐ 11.9 6.2 Nitrate Ion

‐ 33.0

‐ 47.2

‐ 85.5

‐ 25.5 Ammonium Ion 4.5 27.3 7.6 27.5 Organic Carbon 0.8

‐ 35.8

‐ 33.1

‐ 23.2 Elemental Carbon 29.8 16.9 22.3 13.4 Fractional Error (%) Sulfate Ion 43.3 35.3 39.6 39.1 Nitrate Ion 86.3 103.5 119.8 97.8 Ammonium Ion 50.9 56.4 56.6 59.4 Organic Carbon 55.2 62.2 59.9 60.1 Elemental Carbon 63.3 60.9 60.8 58.1 Quarte rB. Ozon e An oper a was con d concentr a in the Ai r

Fi g ure I I

The ozo n maximu m USEPA, observe d the predi was appl Aggrega t e

a tional mod e d ucted in or d a tions. Ozo n r Quality S y

I I-2 . Ozone

n e metrics c o m ozone bia s

2007). The d pairs that w

ction-obser v ied to evalu a t ed perform a e l performa n d er to estim a n e measure m y stem (AQS )

monitors in

o vered in th i s , error, frac t evaluation p w ere paired i n v ation pairs w a te the mod e a nce metric s n ce evaluati o a te the abilit y m ents were t a

) Aerometri c

cluded in m o

i s evaluatio n t ional bias, a p rincipally c o n time and s w here obse r e l on days o f s by ozone s e o n for hourl y y of the mo d a ken from t h c Informatio n

o del perfor m

n include on e a nd fraction a o nsists of st a s pace. This o r ved ozone e f elevated o z e ason mont h y and eight- h d eling syste m h e 2005 Stat e n Retrieval S

m ance evalu a

e -hour and e a l error (Bo y a tistical ass e o zone mode xcee d ed or e z one which a h are shown h our daily m m

to replicat e e /local mon i S ystem (AI R

a tion.
ight-hour a v y lan and Ru s e ssments of m l performan c e qualed 60 p a re more po l in Table III

aximum oz o e the base y e i toring site d R S).

v e r age daily s sell, 2006; m odel vers u c e was limit p pb. This cu t l icy relevan t

- 2.

9 o ne e ar d ata

u s

ed to t off t

.10 TABLE III-2 . Model performance metrics for daily maximum ozone by month.

This model performance is consistent with photochemical modeling published in literature (Appel et al., 2007).

V. References Appel, K.W., Bhave, P.V., Gilliland, A.B., Sarwar, G., Roselle, S.J., 2008. Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II - particulate matter. Atmospheric Environment 42, 6057-6066. Appel, K.W., Gilliland, A.B., Sarwar, G., Gilliam, R.C., 2007. Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance Part I - Ozone. Atmospheric Environment 41, 9603-9615. Baker, K., Dolwick, P., 2009a. Meteorological Modeling Performance Evaluation for the Annual 2005 Continental U.S. 36-km Domain Simulation. US Environmental Protection Agency OAQPS. Baker, K., Dolwick, P., 2009b. Meteorological Modeling Performance Evaluation for the Annual 2005 Eastern U.S. 12-km Domain Simulation. US Environmental Protection Agency OAQPS, RTP. Baker, K., Dolwick, P., 2009c. Meteorological Modeling Performance Evaluation for the Annual 2005 Western U.S. 12-km Domain Simulation, in: EPA, U. (Ed.). US Environmental Protection Agency OAQPS. Boylan, J.W., Russell, A.G., 2006. PM and light extinction m odel performance metrics, goal s, and criteria for threedimensional air quality models. Atmospheric Environment 40, 4946-4959. Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews 59, 51- 77. 456789

N Daily 1

‐ hr maximum 5,704 7,173 9,553 9,522 8,433 7,118 Daily 8

‐ hr maximum 5,705 7,180 9,557 9,529 8,437 7,120 Mean observed (ppb) Daily 1

‐ hr maximum 73 75 79 81 81 78 Daily 8

‐ hr maximum 68 68 71 72 71 70 Mean predicted (ppb) Daily 1

‐ hr maximum 64 67 72 77 75 70 Daily 8

‐ hr maximum 59 62 66 70 68 64 Bias (ppb) Daily 1

‐ hr maximum

‐ 9

‐ 8

‐ 7

‐ 4

‐ 5

‐ 8 Daily 8

‐ hr maximum

‐ 9

‐ 7

‐ 5

‐ 2

‐ 3

‐ 6 Error (ppb) Daily 1

‐ hr maximum 10 10 10 11 11 10 Daily 8

‐ hr maximum 9 8 8 9 9 9 Fractional bias (%) Daily 1

‐ hr maximum

‐ 13

‐ 12

‐ 10

‐ 6

‐ 7

‐ 11 Daily 8

‐ hr maximum

‐ 14

‐ 11

‐ 8

‐ 3

‐ 5

‐ 10 Fractional error (%) Daily 1

‐ hr maximum 14 14 14 14 15 14 Daily 8

‐ hr maximum 15 13 12 12 13 13 Month11 Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.D., Sarwar, G., Pinder, R.W., Pouliot, G.A., Houyoux, M., 2010. Model Representation of Secondary Organic Aerosol in CMAQv4.7. Environmental Science & Technology 44, 8553-8560. Carlton, A.G., Turpin, B.J., Altieri, K.E., Seitzinger, S.P., Mathur, R., Ro selle, S.J., Weber, R.J., 2008. CMAQ Model Performance Enhanced When In-Cloud Secondary Organic Aerosol is Included: Comparisons of Organic Carbon Predictions with Measurements. Environmental Science & Technology 42, 8798-8802. Doraiswamy, P., Hogrefe, C., Hao, W., Civerolo, K., Ku , J., Sistla, G., 2010. A Retrospective Comparison of Model-Based Forecasted PM2.5 Concen trations with Measurements. Jour nal of Air & Waste Management Association 60, 1293-1308. Gery, M.W., Whitten, G.Z., Killus, J. P., Dodge, M.C., 1989. A Photochemical Kinetics Mechanism for Urban and Regional Scale Computer Modeling. Journal of Geophysical Research-Atmospheres 94, 12925-12956. Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S., 2000. Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. Journal of Geophysical Research-Atmospheres 105, 9079-9090. Nenes, A., Pandis, S.N., Pilinis, C., 1998. ISORROPIA: A new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquatic Geochemistry 4, 123-152. Tesche, T.W., Morris, R., Tonnesen, G., McNally, D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx annual 2002 performance evaluation over the eastern US. Atmospheric Environment 40, 4906-4919. USEPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, RTP.United StatesEnvironmental ProtectionAgencyOffice of Air Quality Planning and StandardsAir Quality Assessment DivisionResearch Triangle Park, NCPublication No. EPA-454/R-11-009October 2011