# 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

### I Introduction

This document describes the air quality modeling performed b y 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 anal ys is 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
- scal e 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 em issions, meteorology , and initial and boundary condition data which are inputs to this model. Emissions and air quality modeling decisions are made early in the anal ytical 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 model ing 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 o f 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

depo sition 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 s ys tem 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 ye ar of data used to construct this platform includes emiss ions and meteorology for 2005. The modeling s ys tem 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 anal ys is for this rulemaking, the modeling s ys tem 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
- ye ar design value s of PM2.5 and ozone. Specificall y, 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 ye ar 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 anal ys es (USEPA, 2007) . Additionally, the raw model outputs are also u sed in a relative sense as inputs to the health and welfare impact functions of the benefits analysis. Onl y model

### predictions for mercury deposition were anal yz ed 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 mode l used to estimate air quality (Appel et al., 2008; Appel et al., 2007; By un 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 m eteorological conditions and emissions. CMAQ is applied with the AERO5 aerosol module, which includes the IS ORROPIA 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 pha se reduction reactions (Bullock and Brehme, 2002)
. Mercury estimates from CMAQ have be en compared to observations and other mercury modeling s ys tems in several peer reviewed publications (Bullock et al., 2008, 2009; Lin et al., 2007)
. **B. Model domain and grid resolution** The mo deling anal ys es 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 verticall y from the surface to 100 millibars (approximately 15 km) using a sigma
-pressure coordinate s ys tem. 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. Onl y the finer grid data were used in deter mining 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)

### Figure II **- 1.**  Map of the photochemical modeling domain s . 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 ye ar 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
- ye ar 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 onl y for this rule. Unlike the 2005 v4 platform, the configuration for this modeling application included mercury emissions

### from the National Air Toxi cs 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 ye ars for the EGU sector util ized 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 ye ar 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 T able II.2. **Ta ble II.2**  Estimated total inventory and EGU sector emissions for each modeling scenario.

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 Sce nari o Sector VO C NOx CO SO2 PM 10 PM 2. 5 2005 b ase lin e EGU ( PT IPM ) 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 b ase lin e EGU ( PT IPM ) 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 co ntrol ca se EGU ( PT IPM ) 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
Sce nari o Sector Hg( 0) Hg( II) Hg( p) HCL CL2 NH 3 2005 b ase lin e EGU ( PT IPM ) 30 21 1. 6 351, 592 99 21, 684

All 64 33 8. 5 429, 223 6, 409 3, 762, 641 2016 b ase lin e EGU ( PT IPM ) 21 7 0. 7 74, 089 36, 655

All 42 16 5. 9 140, 638 6, 050 3, 897, 033 2016 co ntrol ca se EGU ( PT IPM ) 5 2 0. 4 8, 802 36, 982

All 26 11 5. 6 75, 351 6, 050 3, 897, 360 Emi ssi ons (t ons/ye ar)

Emi ssi ons (t ons/ye ar)

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

toxic pollutants (including mercury) are included in the modeling s ys tem through boundary condition inflow. All mercury

emissions from facilities included in the PTIPM sector were removed, or “zeroed
out” 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, nonhydrostati c, terrain
following s ys tem that solves for the full set of physical and thermodynamic equations which govern atmospheric motions. Meteorological model input fields were prepared separatel y for each of the three domains shown in Figure II
- 1 using MM5 versi on 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 PBLand land surface schemes • Kain
- Fritsh 2 cumulus parameterization
• Reisner 2 mixed phase moisture scheme
• RRTM longwave radiation scheme
• Dudhia shortwave radiation scheme Three dimensional anal ysis 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 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 temperatures and 1.0 x 10 5 

### for moisture fields.

### Table II **- 3**  . Vertical layer structure (heights are layer top). **CMAQ Layers**  **MM5 Layers Sigm**  **a P Approximate Height**  **(m )**  **Approximate Pressure (m** **b)** 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 gri d 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 anal ys es to assess the adequacy of the MM5 simulated fields. The qualitative aspects involved c omparisons of the model
- estimated sy noptic 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 s ynoptic patterns, which is not unexpected given the use of nudging. The operational evaluation included statistical

### comparisons of model/observ ed 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, humi dity, 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 th e bias and error values associated with all three sets of 2005 meteorological data were generall y 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 phys ical processes driven by as similated 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 dy namic 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 dom ains. 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/yrof 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/y r 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 appreci abl y 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 s ys tem to replicate base year concentrations. The evaluation of PM2.5 component species includes comparisons of predicted

### and observed concentrations of sulfate (SO4),

carbon (EC), and organic carbon (OC). PM2.5 ambient measurements for 2005 were obtained from the Chemical Speciation Network (CSN) and the I nteragency

Monitoring of PROtected Visual Environments (IMPROVE). The CSN the IMPROVE sites are ty pically in rural/remote areas. The measurements at CSN and IMPROVE sites represent 24
- hour average concentrations. I n calculating the model performance metrics, the modeled hourly s pecies 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 and Russell, 2006) . The aggregated metrics and number (N) of prediction shown by chemical specie and quarter in Table III was judged b y comparing our 2005 performance results to the range of performance found in recent regional PM2.5 model applications for other, non (bias) and mean error (error) statistics shown in T found by other groups in recent applications 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. 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 I nteragency

Monitoring of PROtected Visual Environments (IMPROVE). The CSN sites are generally located within urban areas and the IMPROVE sites are ty pically in rural/remote areas. The measurements at CSN and hour average concentrations. I n calculating the model performance pecies predictions were aggregated to the averaging times of the

Speciated PM2.5 monitors used in the model performance evaluation. Model performance statistics were calculated for observed/predicted pairs of daily ntrations. Estimated metrics include bias, error, fractional bias, and fractional error

. 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 b y 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 T able III
- 1 are within the range or close to that found by other groups in recent applications (Doraiswamy, 2010; Tesche et al., 2006) 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

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 I nteragency

Monitoring of PROtected sites are generally

located within urban areas and the IMPROVE sites are ty pically in rural/remote areas. The measurements at CSN and hour average concentrations. I n calculating the model performance pecies predictions were aggregated to the averaging times of the

Speciated PM2.5 monitors used in the model performance evaluation. Model performance statistics were calculated for observed/predicted pairs of daily ntrations. Estimated metrics include bias, error, fractional bias, and fractional error (Boylan observation pairs are ” of model performance was judged b y comparing our 2005 performance results to the range of performance found in EPA studies. Overall, the mean bias 1 are within the range or close to that (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

### TABLE II **I -**  **1 .**  Model perform ance metrics for speciated PM2.5 averaged by quarter. 1 2 3 4 N Sul fa te I on 6, 927 7, 248 6, 819 6, 372 Nit rate I on 6, 577 6, 850 6, 532 6, 240 Ammoni um Ion 4, 752 4, 777 4, 576 4, 303 Organi c Carb on 7, 049 7, 219 6, 726 6, 386 El em en tal C arb on 6, 977 7, 182 6, 830 6, 464 Me an O bse rv ed ( ug/m 3 ) Sul fa te I on 2. 5 3. 6 5. 1 2. 3 Nit rate I on 2. 5 0. 9 0. 6 1. 6 Ammoni um Ion 1. 7 1. 4 1. 7 1. 3 Organi c Carb on 0. 6 0. 5 0. 5 0. 7 El em en tal C arb on 0. 4 0. 7 0. 8 0. 6 Me an P re di cte d (ug/m 3 ) Sul fa te I on 2. 3 3. 2 4. 1 2. 2 Nit rate I on 2. 3 1. 2 0. 5 1. 8 Ammoni um Ion 1. 5 1. 4 1. 3 1. 3 Organi c Carb on 0. 9 0. 7 0. 7 0. 9 El em en tal C arb on 5. 5 4. 1 4. 8 5. 8 Bi as ( ug/m 3 ) Sul fa te I on -0 .2 -0 .3 -0 .8 -0 .1 Nit rate I on -0 .1 0. 4 0. 0 0. 3 Ammoni um Ion 0. 0 0. 3 -0 .1 0. 2 Organi c Carb on 0. 4 0. 2 0. 2 0. 3 El em en tal C arb on 5. 1 3. 6 4. 1 5. 3 Erro r ( ug/m 3 ) Sul fa te I on 1. 0 1. 2 1. 6 0. 7 Nit rate I on 1. 5 0. 9 0. 5 1. 3 Ammoni um Ion 0. 8 0. 7 0. 7 0. 7 Organi c Carb on 0. 5 0. 4 0. 4 0. 5 El em en tal C arb on 5. 2 3. 7 4. 3 5. 3 Fr acti onal Bi as ( %) Sul fa te I on -6 .6 -6 .0 -1 3. 4 1. 0 Nit rate I on -1 7. 9 -2 4. 8 -67. 0 -6 .3 Ammoni um Ion 6. 2 28. 5 10. 4 31. 1 Organi c Carb on 33. 3 17. 7 15. 6 18. 5 El em en tal C arb on 159. 6 124. 0 127. 9 151. 8 Fr acti onal Erro r ( %) Sul fa te I on 42. 9 35. 8 38. 7 36. 8 Nit rate I on 76. 7 95. 8 111. 7 91. 1 Ammoni um Ion 51. 8 58. 3 57. 8 60. 8 Organi c Carb on 61. 9 61. 1 59. 1 55. 8 El em en tal C arb on 161. 5 134. 0 136. 9 154. 0 Quarte r

### B. Ozone

An operational model performance evaluation for hourly and eight was conducted in order to e stimate the ability of the concentrations
. Ozone measurements were taken from the 2005 State/local monitoring site data in the Air Quality

S ys tem (AQS) Aerometric Information Retrieval Sy stem (AIRS). The ozone metrics covered in this evaluation maximum 1
- hr ozone and daily maximum 8

**Figure III -**  **2** . Ozone monitors used in the model performance evaluation. The evaluation principally consists of statistical assessments of model were paired in time and space. This ozone model performance w observation pairs where observed ozone exceeded or evaluate the model on days of elevated ozone performance metrics b y quarter are shown in Table III An operational model performance evaluation for hourly and eight
- hour daily maximum ozone stimate the ability

of the modeling s ys tem to replicate the base ye a Ozone measurements were taken from the 2005 State/local monitoring site data in the Air Quality

S ys tem (AQS) Aerometric Information Retrieval Sy stem (AIRS). The ozone metrics covered in this evaluation bias, error, fractional bias, and f ractional error for both daily hr ozone and daily maximum 8
- hr ozone (Bo yl an and Russell, 2006)

. 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 w as limited to the prediction observation 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 b y quarter are shown in Table III
- 2. hour daily maximum ozone modeling s ys tem to replicate the base ye a r Ozone measurements were taken from the 2005 State/local monitoring site data in the Air Quality

S ys tem (AQS) Aerometric Information Retrieval Sy stem (AIRS). The ozone ractional error for both daily (Bo yl an and Russell, 2006)
.

versus observed pairs that as limited to the prediction
cutoff was applied to which are more policy

relevant. Aggregated

### TABLE II **I -**  **2** . Model performance metrics for daily maximum ozone by

quarter.

This model performance is consistent with photochemical modeling used to support other national regulations (USEPA, 2010)
. C. Mercury

Wet Deposition Model estimated weekl y mercury wet deposition is compared to observation data to assess model skill simulating this component of mercury deposition
. Mercury

wet deposition measurements are weekl y 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 s ystem 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 (Bo yl an 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 b y ±200%, which would be considered poor performance. 5 6 7 8 9 N Dai ly pea k 1- hr ozone 7, 173 9, 553 9, 522 8, 433 7, 118 Dai ly pea k 8- hr ozone 7, 180 9, 557 9, 529 8, 437 7, 120 Me an o bse rve d (ppb) Dai ly pea k 1- hr ozone 75 79 81 81 78 Dai ly pea k 8- hr ozone 68 71 72 71 70 Me an p re dicte d (p pm ) Dai ly pea k 1- hr ozone 68 74 78 77 72 Dai ly pea k 8- hr ozone 63 68 70 69 64 Bi as ( ppb) Dai ly pea k 1- hr ozone -7 -5 -3 -4 -6 Dai ly pea k 8- hr ozone -6 -4 -1 -2 -5 Error ( ppb) Dai ly pea k 1- hr ozone 9 9 11 11 10 Dai ly pea k 8- hr ozone 8 8 9 9 9 Fracti onal b ia s (%) Dai ly pea k 1- hr ozone -9 -7 -5 -6 -9 Dai ly pea k 8- hr ozone -9 -6 -2 -4 -8 Fracti onal e rror ( %) Dai ly pea k 1- hr ozone 13 12 14 14 14 Dai ly pea k 8- hr ozone 12 11 12 13 13 Mo nth

### Figure III **- 1.**  Mercury deposition network monitors used for evaluation of the 12 and 36 km domains. CMAQ estim ates 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 the 12 km domain and Table III
- 4 for the 36 km model domain. mode ling studies show a positive bias for annual total mercury wet deposition 2009; Lin et al., 2007; Vijayaraghavan et al., 2007) prediction bias except during the summer months in the eastern United States. An annual mercury

modeling application Environmental Research for Lake Michigan Air Directors Consortium show seasonal average normalized bias between 70 and 158% an 503% (Yarwood et al, 2003). The mo 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)

Mercury deposition network monitors used for evaluation of the 12 and 36 km ates 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 4 for the 36 km model domain. Other published ling 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 ring the summer months in the eastern United States. An annual mercury

modeling application done b y ENVIRON and Atmosph eric and for Lake Michigan Air Directors Consortium show seasonal average normalized bias between 70 and 158% an d seasonal average normalized error between 72 and 503% (Yarwood et al, 2003). The mo del performance shown b y EPA is consistent with other m mercury

modeling applications and often more robust in terms of estimated metrics paired in space and time before averaging to an annual or seasonal (Seigneur et al., 2006; Vijayaraghavan et al., 2008)
. Mercury deposition network monitors used for evaluation of the 12 and 36 km ates 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 published mercury (Bullock et al ., This CMAQ application also shows an over

eric and for Lake Michigan Air Directors Consortium show seasonal average

d seasonal average normalized error between 72 and consistent with other m mercury

modeling applications and often more robust in terms of estimated metrics paired in space and time before averaging to an annual or seasonal

### TABLE II **I -** **3 .**  Model performance metrics for mercury wet deposition and rainfall b y quarter. 12 km domain.

**TABLE II I**  **- 4**  **.** Model performance metrics for mercury wet deposition and rainfall b y quarter. 36 km domain. Quarte r 1 2 3 4 N 753 795 773 733 Obse rve d (ng/m 2 ) 155 255 317 134 Pr ed icte d (ng/m 2 ) 290 314 183 209 Bi as ( ng/m 2 ) 135 59 -1 34 75 Error ( ng/m 2 ) 185 240 246 136 Fr acti onal B ia s (%) 37 11 -4 6 28 Fr acti onal Erro r (% ) 80 83 97 81 Rai nf al l N 753 795 773 733 Obse rve d (m m) 23 24 27 27 Pr ed icte d (m m) 23 28 31 23 Bi as ( mm) -0 .04 3. 43 4. 67 -3 .5 2 Error ( mm) 10 16 23 11 Fr acti onal B ia s (%) 3 5 1 -3 Fr acti onal Erro r (% ) 14 19 22 15 12 k m Do mai n To tal M er cu ryWe t De posi ti on

Quarte r 1 2 3 4 N 783 822 797 754 Obse rve d (ng/m 2 ) 152 252 313 133 Pr ed icte d (ng/m 2 ) 262 276 187 183 Bi as ( ng/m 2 ) 110 24 -1 25 50 Error ( ng/m 2 ) 165 211 235 114 Fr acti onal B ia s (%) 29 6 -40 19 Fr acti onal Erro r (% ) 82 84 91 79 Rai nf al l N 783 822 797 754 Obse rve d (m m) 23 24 27 27 Pr ed icte d (m m) 21 24 29 21 Bi as ( mm) -1 .7 3 -0 .09 1. 87 -6 .1 6 Error ( mm) 10 15 20 12 Fr acti onal B ia s (%) 1 2 0 -5 Fr acti onal Erro r (% ) 14 19 21 15 36 k m Do mai n To tal M er cu ryWe t De posi ti on

### 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. Hourl y 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
.

### V. References

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### United States

Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality

Assessment Division Research Triangle Park, NC Publication No. EPA
- 454/R
- 11
- 008 September 2011