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 stationary point sources that generate electricity. 

A national scale air quality modeling analysis was performed to estimate the impact of the sector 

emissions changes on future year annual and 24-hour PM2.5 concentrations, 8-hr maximum 

ozone, total mercury deposition, as well as visibility 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 destruction 

of ozone, particulate matter and air toxics. In addition to the CMAQ model, the modeling 

platform includes the emissions, meteorology, and 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 Version, Inputs and Configuration 

Photochemical grid models use state of the science 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 injected 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 system 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) (oxidized 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 ozone, 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 compare a 2016 reference scenario, a scenario without the boiler 

sector controls, to a 2016 control scenario which includes the adjustments to the boiler sector. 

This is done by calculating the simulated air quality ratios between any particular future year 

simulation and the 2005 base. These predicted ratios 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 raw model outputs are also used in a relative 

sense as inputs to the health and welfare impact functions of the benefits analysis. Only model 
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predictions for mercury deposition were analyzed 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 (CMAQ) 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 over regional and urban spatial scales for given 

input sets of meteorological conditions and emissions. CMAQ is applied with the AERO5 

aerosol module, which includes the ISORROPIA inorganic 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 chemistry (Carlton et al., 2008) and the carbon-bond 

2005 (CB05) gas-phase chemistry module (Gery et al., 1989). Mercury oxidation pathways are 

represented for both the gas and aqueous phases in addition to aqueous phase reduction reactions 

(Bullock and Brehme, 2002). Mercury estimates 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 domain 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 western U.S. The model extends vertically from the 

surface to 100 millibars (approximately 15 km) using 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 changes. Table II-1 provides geographic information 

about the photochemical model domains. 

Table II-1. Geographic elements of domains 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 millibar 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 western 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 impacts 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 inputs to the air quality model. 

1. Emissions Input Data 

The emissions data used in the base year and future reference and future emissions adjustment 

case are based on the 2005 v4.1 platform. The emissions 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 modeling application included mercury emissions 
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from the National Air Toxics Assessment Inventory 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 sector utilized information collected from the utility MACT 

information collection request. The information collection 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 intended to represent the emissions associated with growth and 

controls in that year projected from the 2005 simulation year. The United 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 applied to a subset of the 2005 non-EGU point and 

non-point to create the 2016 reference case. The 2005 v4 platform 2014 projection factors were 

the starting point for most of the 2016 SMOKE-based projections. The mercury projections for 

non–EGU point sources accounted for emission reductions 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 including the Boiler MACT, Gold Mine NESHAP and Electric Arc 

Furnace NESHAP. The estimated total anthropogenic emissions and emissions for the utility 

sector used in this modeling assessment 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 (including mercury) are based 

on a 2006 Canadian inventory and 1999 Mexican inventory. Both inventories are not grown or 
5 

controlled when used as part of future year baseline inventories. Global emissions of criteria and 

toxic pollutants (including mercury) are included 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 entire year of 2005 were derived from simulations 

of the Pennsylvania State University / National 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 projection as shown in Figure II-1. 

All three meteorological model runs 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 temperature 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 104

 for wind fields and 

temperatures and 1.0 x 105

 for moisture fields. The 12 km domain nudging weighting factors 

were 1.0 x 104 for wind fields and temperatures and 1.0 x 105 for moisture fields. 
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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 were 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 millibar top. The MM5 and CMAQ vertical structures are shown in 

Table II-3 and do not vary by horizontal grid resolution. The meteorological outputs from all 

three MM5 sets were processed to create model-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 modeling inputs. The 2005 MM5 model performance 

evaluations used an approach which included a combination of qualitative and quantitative 

analyses to assess the adequacy of the MM5 simulated fields. The qualitative aspects involved 

comparisons of the model-estimated synoptic patterns against observed patterns from historical 

weather chart archives. Additionally, the evaluations compared spatial patterns of estimated to 

observed monthly average rainfall and checked 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 operational evaluation included statistical 
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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 direction. The three individual 

MM5 evaluations are described elsewhere (Baker, 2009a, b, c). It was ultimately determined that 

the bias and error values associated with all three sets of 2005 meteorological data were 

generally within the range of past meteorological modeling results 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 vertical layers up to 100 mb. The predictions 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 photochemical model simulation is used to supply 

initial and hourly boundary concentrations to the 12 km domains. Initial and boundary conditions 

for the projected future year (2016) 36 km simulations are the same as the 2005 base year. The 

first 10 days of the 36 km modeling simulation are not used in the analysis, which is beyond the 

number of days necessary to remove the influence of initial conditions 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 mercury (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 consistent 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 decreasing in the northern hemisphere since 2000 

(Slemr et al., 2011). Since emissions from China 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 carbon, etc.) was conducted 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 component species includes comparisons of predicted 
8 

and observed concentrations of sulfate (SO4), nitrate (NO3), ammonium (NH4), elemental 

carbon (EC), and organic carbon (OC). PM2.5 ambient 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/remote areas. The measurements at CSN and 

IMPROVE sites represent 24-hour average concentrations. In calculating the model performance 

metrics, the modeled hourly species 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 calculated for observed/predicted pairs of daily 

concentrations. Estimated metrics include bias, error, fractional bias, and fractional error (Boylan 

and Russell, 2006). The aggregated metrics and number (N) of prediction-observation pairs are 

shown by chemical specie and quarter in Table III-1. The “acceptability” of model performance 

was judged by comparing our 2005 performance results to the range of performance found in 

recent regional PM2.5 model applications for other, non-EPA studies. Overall, the mean bias 

(bias) and mean error (error) statistics shown in Table III-1 are within the range or close to that 

found by other groups in recent applications (Doraiswamy, 2010; Tesche et al., 2006). The 

model performance results give us confidence that our application of CMAQ using this modeling 

platform provides a scientifically credible approach for assessing PM2.5 concentrations for the 

purposes of this assessment. 
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TABLE III-1. Model performance metrics for speciated 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/m3

) 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/m3

) 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/m3

) 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/m3

) 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

Quarter
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B. Ozone 

An operational model performance evaluation for 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 Information Retrieval System (AIRS). The ozone 

metrics covered in this evaluation bias, error, fractional 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 model 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. 
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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 deposition. Mercury wet deposition measurements 

are weekly totals taken at sites that are part of the Mercury 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 regional 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: bias, error, fractional bias, and fractional error 

(Boylan and Russell, 2006). These metrics are estimated 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 used for evaluation of the 12 and 36 km 

domains. 

CMAQ estimates of total mercury wet deposition are paired in space and time with MDN 

observations. Model performance metrics are averaged 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 annual 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 application done by ENVIRON and Atmospheric and 

Environmental Research for Lake Michigan Air Directors Consortium show seasonal average 

normalized bias between 70 and 158% and seasonal average normalized error between 72 and 

503% (Yarwood et al, 2003). The model performance shown by EPA is consistent with other 

long term mercury modeling applications and often more robust in terms of estimated metrics 

that are more stringently paired in space and time 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/m2

) 155 255 317 134

Predicted (ng/m2) 290 314 183 209

Bias (ng/m2

) 135 59 ‐134 75

Error (ng/m2

) 185 240 246 136

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/m2

) 152 252 313 133

Predicted (ng/m2

) 262 276 187 183

Bias (ng/m2

) 110 24 ‐125 50

Error (ng/m2

) 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 estimates (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 total mercury deposition from both 12 km model domains (12EUS1 

and 12WUS1) were joined into a single 12 km model 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 defined 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 model performance metrics, goals, 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., Vijayaraghavan, 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., Roselle, 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 Concentrations with Measurements. Journal 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. 
16 

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. 

Lin, C.J., Pongprueks, P., Rusell Bulock, O., Lindberg, S.E., Pehkonen, S.O., Jang, C., Braverman, T., Ho, T.C., 

2007. Scientific uncertainties in atmospheric mercury models II: Sensitivity analysis in the CONUS domain. 

Atmospheric Environment 41, 6544-6560. 

Nenes, A., Pandis, S.N., Pilinis, C., 1998. ISORROPIA: A new thermodynamic equilibrium model for multiphase 

multicomponent inorganic aerosols. Aquatic Geochemistry 4, 123-152. 

Pongprueksa, P., Lin, C.J., Lindberg, S.E., Jang, C., Braverman, T., Bullock, O.R., Ho, T.C., Chu, H.W., 2008. 

Scientific uncertainties in atmospheric mercury models III: Boundary and initial conditions, model grid resolution, 

and Hg(II) reduction mechanism. Atmospheric Environment 42, 1828-1845. 

Seigneur, C., Lohman, K., Vijayaraghavan, K., Jansen, J., Levin, L., 2006. Modeling atmospheric mercury 

deposition in the vicinity of power plants. Journal of the Air & Waste Management Association 56, 743-751. 

Selin, N.E., Jacob, D.J., Park, R.J., Yantosca, R.M., Strode, S., Jaegle, L., Jaffe, D., 2007. Chemical cycling and 

deposition of atmospheric mercury: Global constraints from observations. Journal of Geophysical ResearchAtmospheres 112. 

Slemr, F., Brunke, E.G., Ebinghaus, R., Kuss, J., 2011. Worldwide trend of atmospheric mercury since 1995. 

Atmospheric Chemistry and Physics 11, 4779-4787. 

Streets, D.G., Zhang, Q., Wu, Y., 2009. Projections of Global Mercury Emissions in 2050. Environmental Science 

& Technology 43, 2983-2988. 

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. 

Vijayaraghavan, K., Karamchandani, P., Seigneur, C., Balmori, R., Chen, S.-Y., 2008. Plume-in-grid modeling of 

atmospheric mercury. Journal of Geophysical Research-Atmospheres 113. 

Vijayaraghavan, K., Seigneur, C., Karamchandani, P., Chen, S.Y., 2007. Development and application of a 

multipollutant model for atmospheric mercury deposition. Journal of Applied Meteorology and Climatology 46, 

1341-1353. 

Yarwood, G, Lau, S., Jia, Y., Karamchandani, P., Vijayaraghavan, K. 2003. Final Report: Modeling Atmospheric 

Mercury Chemistry and Deposition with CAMx for a 2002 Annual Simulation. Prepared for Wisconsin Department 

of Natural Resources. http://www.gypsymoth.wi.gov/air/toxics/mercury/hg_X97579601_appB.pdf
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

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