# Air Quality Modeling Technical Support Document: EGU Mercury Analysis

### EPA-454/R-11-008 September 2011



# Air Quality Modeling Technical Support Document: EGU Mercury Analysis

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, NC 27711

### 1 I Introduction

This document describes the air quality modeling performed by EPA in support of air quality and mercury deposition assessments related to large stat ionary point sources that generate electricity. A national scale air quality modeli ng analysis was performed to estimate the impact of the sector emissions changes on future year annual a nd 24-hour PM2.5 concentrations, 8-hr maximum ozone, total mercury deposition, as well as visi bility impairment. Air quality benefits are estimated with the Community Multi-scale Ai r Quality (CMAQ) model. CMAQ simulates the numerous physical and chemical pr ocesses involved in the forma tion, transport, and destruction of ozone, particulate matter and air toxics. In addition to the CMAQ model, the modeling platform includes the emissions, meteorology, an d initial and boundary 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 and the benefits modeling may be slightly different than the final utility sector inventories presented in the RIA. However, the air quality inventories and the final rule inventories are generally consistent, 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 and certain toxics including hydrogen chloride (HCL) and speciated mercury: Hg(0) (gaseous elemental), Hg(II) (oxidi zed gaseous), and Hg(p) (particle-bound). This modeling platform 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 ozon e, annual total mercury deposition levels and visibility impairment. Model predictions are used to estimate future-year design values of PM2.5 and ozone. Specifically, we co mpare a 2016 reference scenario, a scenario wit hout the boiler sector controls, to a 2016 control scenario whic h includes the adjustments to the boiler sector. This is done by calculating the simulated air qual ity ratios between any particular future year simulation and the 2005 base. These predicted ra tios are then applied to ambient base year design values. The design value projection methodology used here followed EPA guidance for such analyses (USEPA, 2007). Additionally, the ra w model outputs are also used in a relative sense as inputs to the health and welfare impact functions of the benefits analysis. Only model

### 2 predictions for mercury deposition were anal yzed using absolute model changes, although percent changes between the control case and two future baselines are also estimated.

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 air toxics ove r regional and urban spatial scales for given input sets of meteorological conditions a nd 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). Mercury ox idation pathways are represented for both the gas and aqueous phases in addition to aqueous phase reduction reactions (Bullock and Brehme, 2002). Mercury estima tes from CMAQ have been compared to observations and other mercury modeling systems in several peer reviewed publications (Bullock et al., 2008, 2009; Lin et al., 2007).

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 v4.1 platform. The emissi ons cases use different emissions data for some pollutants than the official v4 platform to use data intended only for this rule. Unlike the 2005 v4 platform, the configuration for this m odeling application incl uded mercury emissions

### 4 from the National Air Toxics Assessment Invent ory and some industrial boiler sector mercury emissions more consistent with the engineering analysis for the Industrial/Commercial/Institutional Boilers and Process Heaters NESHAP. Emissions for the future years for the EGU sect or utilized information colle cted from the utility MACT information collection request. The information co llection request informed existing HCL, NOX, HG, and PM controls. In addition this data was used to supply HCL removal rates from selected control technology. Emissions are processed to photochemical model inputs with the SMOKE emissions modeling system (Houyoux et al., 2000).

The 2016 reference case is intende d to represent the emissions associated with growth and controls in that year projected from the 2005 simulation year. The Un ited States EGU point source emissions estimates for the future year reference and control case are based on an Integrated Planning Model (IPM) run for criteria pollutants, hydrochloric acid, and mercury in 2016. Both control and growth factors were appl ied to a subset of the 2005 non-EGU point and non-point to create the 2016 re ference case. The 2005 v4 platfo rm 2014 projection factors were the starting point for most of the 2016 SMOKE-b ased projections. The mercury projections for non–EGU point sources accounted for emission reduc tions expected in the future due to NESHAP for various non-EGU source categories that were finalized or expected to be finalized prior to the rule proposal in cluding the Boiler MACT, Gold Mine NESHAP and Electric Arc Furnace NESHAP. The estimated total anthropoge nic emissions and emissions for the utility sector used in this modeling a ssessment are shown in Table II.2.

Table II.2 Estimated total inventory and EGU sector emissions for each modeling scenario.

Scenario Sector VOC NOx CO SO2 PM10 PM2.5 2005 baseline EGU (PTIPM) 40,950 3,726,459 601,564 10,380,786 615,095 508,903 All 17,613,543 22,216,093 83,017,436 15,050,209 13,031,716 4,400,680

2016 baseline EGU (PTIPM) 40,845 1,769,764 691,310 3,577,698 523,504 384,320 All 14,390,421 15,019,836 59,148,384 7,245,595 12,772,091 4,022,846

2016 control case EGU (PTIPM) 38,217 1,618,199 656,245 1,220,379 358,165 291,044 All 14,387,792 14,868,270 59,113,319 4,888,276 12,606,752 3,929,570

Scenario Sector Hg(0) Hg(II) Hg(p) HCL CL2 NH3
2005 baseline EGU (PTIPM) 30 21 1.6 351,592 99 21,684 All 64 33 8.5 429,223 6,409 3,762,641

2016 baseline EGU (PTIPM) 21 7 0.7 74,089 36,655 All 42 16 5.9 140,638 6,050 3,897,033

2016 control case EGU (PTIPM) 5 2 0.4 8,802 36,982 All 26 11 5.6 75,351 6,050 3,897,360 Emissions (tons/year)

Emissions (tons/year)



### Other North American emissions of criteria and toxic pollutants (includi ng mercury) are based on a 2006 Canadian inventory and 1999 Mexican inventory. Both i nventories are not grown or

### 5 controlled when used as part of future year base line inventories. Global em issions of criteria and toxic pollutants (including mercury) are in cluded in the modeling system through boundary condition inflow.

All mercury emissions from facilities included in the PTIPM sector were removed, or “zeroedout” in both the 2005 baseline and 2016 baseline scenarios to provide information about the contribution of mercury from this sector.

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.

### 6 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.

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

### 7 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. Initial and boundary conditions for the projected future year (2016) 36 km simu lations are the same as the 2005 base year. The first 10 days of the 36 km modeling simulation ar e not used in the analysis, which is beyond the number of days necessary to remove the infl uence of initial conditi ons on mercury deposition estimates (Pongprueksa et al., 2008).

Mercury initial and boundary conditions were based on a GEOS-CHEM simulation using a 2000 based global anthropogenic emissions inventory that includes 1,278 Mg/yr of Hg(0), 720 Mg/yr of Hg(II), and 192 Mg/yr of particle bound mercur y (Selin et al., 2007). A comparison of global mercury emissions by continent for 2000 and 2006 is published in (Streets et al., 2009). Total mercury emissions from China (and Oceania ) total 1,306 Mg/yr in 2000 and 1,317 Mg/yr in 2006 (Streets et al., 2009). Given these consis tent emissions estimates from Asia, the 2005 boundary inflow to the 36 km CMAQ domain was not adjusted. Recent research has shown that ambient mercury concentrations have been de creasing in the norther n hemisphere since 2000 (Slemr et al., 2011). Since emissions from Ch ina have not appreciably changed between 2000 and 2006, ambient mercury concentrations have been decreasing, and the large uncertainties surrounding projected mercury global inventories the mercury boundary conditions are the same for both the 2005 and 2016 simulations.

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

### 8 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.

Figure III-1. Speciated PM2.5 monitors used in the model performance evaluation.

Model performance statistics were calculat ed for observed/predicted pairs of daily concentrations. Estimated metrics include bias, e rror, fractional bias, and fractional error (Boylan and Russell, 2006). The aggregated metrics and nu mber (N) of prediction-observation pairs are shown by chemical specie and quarter in Table II I-1. The “acceptability ” of model performance was judged by comparing our 2005 performance re sults to the range of performance found in recent regional PM2.5 model applications for ot her, non-EPA studies. Overall, the mean bias (bias) and mean error (error) statistics shown in Ta ble III-1 are within the range or close to that found by other groups in recent applications (D oraiswamy, 2010; Tesche et al., 2006). The model performance results give us confidence th at our application of CMAQ using this modeling platform provides a scientifica lly credible approach for assessing PM2.5 concentrations for the purposes of this assessment.

### 9 TABLE III-1. Model performance metrics for sp eciated PM2.5 averaged by quarter.

1234 N Sulfate Ion 6,927 7,248 6,819 6,372 Nitrate Ion 6,577 6,850 6,532 6,240 Ammonium Ion 4,752 4,777 4,576 4,303 Organic Carbon 7,049 7,219 6,726 6,386 Elemental Carbon 6,977 7,182 6,830 6,464
Mean Observed (ug/m 3 ) Sulfate Ion 2.5 3.6 5.1 2.3 Nitrate Ion 2.5 0.9 0.6 1.6 Ammonium Ion 1.7 1.4 1.7 1.3 Organic Carbon 0.6 0.5 0.5 0.7 Elemental Carbon 0.4 0.7 0.8 0.6
Mean Predicted (ug/m 3 ) Sulfate Ion 2.3 3.2 4.1 2.2 Nitrate Ion 2.3 1.2 0.5 1.8 Ammonium Ion 1.5 1.4 1.3 1.3 Organic Carbon 0.9 0.7 0.7 0.9 Elemental Carbon 5.5 4.1 4.8 5.8
Bias (ug/m 3 ) Sulfate Ion

‐ 0.2

‐ 0.3

‐ 0.8

‐ 0.1 Nitrate Ion

‐ 0.1 0.4 0.0 0.3 Ammonium Ion 0.0 0.3

‐ 0.1 0.2 Organic Carbon 0.4 0.2 0.2 0.3 Elemental Carbon 5.1 3.6 4.1 5.3
Error (ug/m 3 ) Sulfate Ion 1. 0 1.2 1. 6 0.7 Nitrate Ion 1.5 0.9 0.5 1.3 Ammonium Ion 0.8 0.7 0.7 0.7 Organic Carbon 0.5 0.4 0.4 0.5 Elemental Carbon 5.2 3.7 4.3 5.3 Fractional Bias (%) Sulfate Ion

‐ 6.6

‐ 6.0

‐ 13.4 1.0 Nitrate Ion

‐ 17.9

‐ 24.8

‐ 67.0

‐ 6.3 Ammonium Ion 6.2 28.5 10.4 31.1 Organic Carbon 33.3 17.7 15.6 18.5 Elemental Carbon 159.6 124.0 127.9 151.8 Fractional Error (%) Sulfate Ion 42.9 35.8 38.7 36.8 Nitrate Ion 76.7 95.8 111.7 91.1 Ammonium Ion 51.8 58.3 57.8 60.8 Organic Carbon 61.9 61.1 59.1 55.8 Elemental Carbon 161.5 134.0 136.9 154.0 Quarte r

### 10 B. Ozone

An operational model performance evaluation fo r hourly and eight-hour daily maximum ozone was conducted in order to estimate the ability of the modeling system to replicate the base year concentrations. Ozone measurements were taken from the 2005 State/local monitoring site data in the Air Quality System (AQS) Aerometric In formation Retrieval System (AIRS). The ozone metrics covered in this evaluation bias, error, fr actional bias, and fractional error for both daily maximum 1-hr ozone and daily maximum 8-hr ozone (Boylan and Russell, 2006).

Figure III-2 . Ozone monitors used in the model performance evaluation.

The evaluation principally consists of statistical assessments of model versus observed pairs that were paired in time and space. This ozone m odel performance was limited to the predictionobservation pairs where observed ozone exceeded or equaled 60 ppb. This cutoff was applied to evaluate the model on days of elevated ozone which are more policy relevant. Aggregated performance metrics by quarter are shown in Table III-2.

### 11 TABLE III-2 . Model performance metrics for daily maximum ozone by quarter.

56789 N Daily peak 1

‐ hr ozone 7,173 9,553 9,522 8,433 7,118 Daily peak 8

‐ hr ozone 7,180 9,557 9,529 8,437 7,120 Mean observed (ppb) Daily peak 1

‐ hr ozone 75 79 81 81 78 Daily peak 8

‐ hr ozone 68 71 72 71 70 Mean predicted (ppm) Daily peak 1

‐ hr ozone 68 74 78 77 72 Daily peak 8

‐ hr ozone 63 68 70 69 64 Bias (ppb) Daily peak 1

‐ hr ozone

‐ 7

‐ 5

‐ 3

‐ 4

‐ 6 Daily peak 8

‐ hr ozone

‐ 6

‐ 4

‐ 1

‐ 2

‐ 5 Error (ppb) Daily peak 1

‐ hr ozone 9 9 11 11 10 Daily peak 8

‐ hr ozone 8 8 9 9 9 Fractional bias (%) Daily peak 1

‐ hr ozone

‐ 9

‐ 7

‐ 5

‐ 6

‐ 9 Daily peak 8

‐ hr ozone

‐ 9

‐ 6

‐ 2

‐ 4

‐ 8 Fractional error (%) Daily peak 1

‐ hr ozone 13 12 14 14 14 Daily peak 8

‐ hr ozone 12 11 12 13 13 Month



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

C. Mercury Wet Deposition

Model estimated weekly mercury wet deposition is compared to observation data to assess model skill simulating this component of mercury de position. Mercury wet deposition measurements are weekly totals taken at sites that are part of the Merc ury Deposition Network ( http://nadp.sws.uiuc.edu/MDN/ ) which operates under the National Atmospheric Deposition Program. In addition to mercury wet deposition, the network sites also collect rainfall data which is also evaluated against estimates used by the photochemical model from prognostic meteorological model output. Previous versions of the CMAQ modeling system has been applied by other researchers at a continental and regi onal scale and evaluation has been published (Bullock et al., 2008, 2009; Lin et al., 2007; Pongprueksa et al ., 2008; Vijayaraghavan et al., 2007).

Model performance is characterized using a variety of statistical metrics common in photochemical model evaluation journal articles: bi as, error, fractional bias, and fractional error (Boylan and Russell, 2006). These metrics are estim ated for total mercury wet deposition and for rainfall. Performance is best when the metrics approach 0. The fractional bias and error metrics are bound by ±200%, which would be considered poor performance.

### 12 Figure III-1. Mercury deposition network monitors us ed for evaluation of the 12 and 36 km domains.

CMAQ estimates of total mercury wet deposit ion are paired in space and time with MDN observations. Model performance metrics are aver aged by season and shown in Table III-3 for the 12 km domain and Table III-4 for the 36 km model domain. Other published mercury modeling studies show a positive bias for annua l total mercury wet deposition (Bullock et al., 2009; Lin et al., 2007; Vijayaraghavan et al., 2007) . This CMAQ application also shows an over prediction bias except during the summer months in the eastern United States.

An annual mercury modeling applicati on done by ENVIRON and Atmospheric and Environmental Research for Lake Michigan Ai r Directors Consortium show seasonal average normalized bias between 70 and 158% and seas onal average normalized error between 72 and 503% (Yarwood et al, 2003). The model performan ce shown by EPA is consistent with other long term mercury modeling applic ations and often more robust in terms of estimated metrics that are more stringently paired in space and ti me before averaging to an annual or seasonal temporal scale (Seigneur et al., 2006; Vijayaraghavan et al., 2008).

### 13 TABLE III-3. Model performance metrics for mercury wet deposition and rainfall by quarter. 12 km domain.

Quarter 1 2 3 4 N 753 795 773 733
Observed (ng/m 2 ) 155 255 317 13 4
Predicted (ng/m 2 ) 29 0 31 4 183 209
Bias (ng/m 2 ) 135 59

‐ 134 75
Error (ng/m 2 ) 185 24 0 246 136 Fractional Bias (%) 37 11

‐ 46 28 Fractional Error (%) 80 83 97 81

Rainfall N 753 795 773 733 Observed (mm) 23 24 27 27 Predicted (mm) 23 28 31 23 Bias (mm)

‐ 0.04 3.43 4.67

‐ 3.52 Error (mm) 10 16 23 11 Fractional Bias (%) 3 5 1

‐ 3 Fractional Error (%) 14 19 22 15 12 km Domain

Total Mercury Wet Deposition



### TABLE III-4. Model performance metrics for mercury wet deposition and rainfall by quarter. 36 km domain.

Quarter 1 2 3 4 N 783 822 797 754
Observed (ng/m 2 ) 152 252 313 133
Predicted (ng/m 2 ) 262 276 187 183
Bias (ng/m 2 ) 110 24

‐ 125 50
Error (ng/m 2 ) 165 211 235 114 Fractional Bias (%) 29 6

‐ 40 19 Fractional Error (%) 82 84 91 79

Rainfall N 783 822 797 754 Observed (mm) 23 24 27 27 Predicted (mm) 21 24 29 21 Bias (mm)

‐ 1.73

‐ 0.09 1.87

‐ 6.16 Error (mm) 10 15 20 12 Fractional Bias (%) 1 2 0

‐ 5 Fractional Error (%) 14 19 21 15 36 km Domain

Total Mercury Wet Deposition

### 14 IV. Post Processing Mercury Deposition

CMAQ outputs hourly wet and dry deposition estima tes (kg/ha) in each grid cell of speciated mercury: Hg0, Hg2, and PM2.5 Hg. Hourly outputs are summed to an annual estimate. CMAQ model estimates of annual to tal mercury deposition from both 12 km model domains (12EUS1 and 12WUS1) were joined into a single 12 km m odel file covering the entire continental United States. Where both the eastern and western 12 km domains intersect in the integrated 12 km domain, the average of the 12 km eastern US and 12 km western US is assigned to the integrated 12 km domain. Total mercury deposition is define d as the sum of all wet and dry deposition of elemental mercury, divalent gas- phase mercury, and PM2.5 mercury.

### 15 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.

Bullock, O.R., Atkinson, D., Braverman, T., Civerolo, K., Dastoor, A., Davignon, D., Ku, J.Y., Lohman, K., Myers, T.C., Park, R.J., Seigneur, C., Selin, N.E., Sistla, G., Vijayaraghavan, K., 2008. The North American Mercury Model Intercomparison Study (NAMMIS): Study description and model-to-model comparisons. Journal of Geophysical Research-Atmospheres 113.

Bullock, O.R., Atkinson, D., Braverman, T., Civerolo, K., Dastoor, A., Davignon, D., Ku, J.Y., Lohman, K., Myers, T.C., Park, R.J., Seigneur, C., Selin, N.E., Sistla, G., Vi jayaraghavan, K., 2009. An analysis of simulated wet deposition of mercury from the North American Mercury Model Intercomparison Study. Journal of Geophysical Research-Atmospheres 114.

Bullock, O.R., Brehme, K.A., 2002. Atmospheric mercury simulation using the CMAQ model: formulation description and analysis of wet deposition results. Atmospheric Environment 36, 2135-2146.

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.

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.

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