# Air Quality Modeling Technical Support Document: Changes to the Renewable Fuel Standard Program2 EPA 454/R-10-001 January 2010



# Air Quality Modeling Technical Support Document:

Changes to the Renewable Fuel Standard Program

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, NC 27711 January 20103 

### I. Introduction

This document describes the air quality m odeling performed by EPA in support of the final revisions to the National Renewable Fuel Standard rule (commonly known as RFS2). A national scale air quality modeling analysis was performed to estimate the effect of the rule on future year: annual and 24-hour PM 2.5 concentrations, daily maximum 8-hour ozone concentrations, annual nitrogen and sulfur depos ition levels, and select annual and seasonal air toxic concentrations (formaldehyde, acetaldehyde, ethanol, benzene, 1,3-butadiene, acrolein). To model the air quality benefits of this rule we used the Community Multiscale Air Quality (CMAQ) 1 model. CMAQ simulates the numerous physic al and chemical processes involved in the formation, transport, and destruction of ozone , particulate matter and ai r toxics. In addition to the CMAQ model, the modeling platform incl udes the emissions, mete orology, and initial and boundary condition data which are inputs to this model.

It is critical to note that a key limitation of the air quality modeling analysis is that it employed interim emission inventories, which were somewhat enhanced compared to what was described in the proposal, but due to the timing of the analysis di d not include some of the later enhancements and corrections of the final emi ssion inventories presented in the FRM (see Section 3.3 of the RIA). Most significantly, ou r modeling of the air quality impacts of the renewable fuel volumes required by RFS2 relie d upon interim inventorie s that assumed that ethanol will make up 34 of the 36 billion gallon renewable fuel mandate, that approximately 20 billion gallons of this ethanol will be in the form of E85, and that the use of E85 results in fewer emissions of direct PM 2.5 from vehicles. The emission impact s and air quality results would be different if, instead of E85, more non-ethanol biofuels are used or mid-level ethanol blends are approved. There are additional, important limitations and uncertainties associated with the interim inventories that must be kept in mind when considering the results. These limitations and uncertainties are described in more detail in Section 3.4.1.3 of the RIA.



### II. CMAQ Model Version, Inputs and Configuration

The 2005-based CMAQ modeling platform was us ed as the basis for the air quality modeling of the two future baselin es and the RFS2 future control scenario for this final rule. This platform represents a stru ctured system of connected mode ling-related tools and data that provide a consistent and transp arent basis for assessing the air quality response to projected changes in emissions. The base year of data us ed to construct this platform includes emissions and meteorology for 2005. The platform was devel oped by the U.S. EPA’s Office of Air Quality Planning and Standards in collaboration with th e Office of Research a nd Development and is intended to support a variety of regulatory and re search model applications and analyses. This modeling platform and analysis is fully described below.

1 Byun, D.W., and K. L. Schere, 2006: Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling Sy stem. Applied Mechanics Reviews, Volume 59, Number 2 (March 2006), pp. 51-77.4 A. Model version

CMAQ is a non-proprietary computer model th at simulates the formation and fate of photochemical oxidants, primary and secondary PM concentrations, acid deposition, and air toxics, over regional and urban sp atial scales for given input se ts of meteorological conditions and emissions. The CMAQ model version 4.7 was mo st recently peer-reviewed in February of 2009 for the U.S. EPA. 2 The CMAQ model is a well-know n and well-respected tool and has been used in numerous national and international applications. 3,4,5,6 This 2005 multi-pollutant modeling platform used the latest publicly-released CMAQ version 4.7 7 with a minor internal change made by the U.S. EPA CMAQ model developers intended to speed model runtimes when only a small subset of toxics species are of interest. 8 This version reflects updates in a number of areas to improve the under lying science, including:

1) an enhanced secondary organic aerosol (SOA) mechanism to include chemistry of isoprene, sesquiterpene, and aged in-cl oud biogenic SOA in addition to terpene,

2) an improved vertical convective mixing algorithm;

3) an improved heterogeneous reacti on involving nitrate formation, and

4) an updated gas-phase chemistry mechanis m, Carbon Bond 05 (CB05), with extensions to model explicit concentrations of air toxic species as we ll as chlorine and mercury.

This mechanism, CB05-toxics, also computes c oncentrations of species that are involved in aqueous chemistry and that are precursors to aeros ols. Chapter 3 of the RIA discusses in detail

2 Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M. (February 2009 Draft Version). Report on the Peer Review of the Atmospheric Mode ling and Analysis Division, NERL/ORD/E PA. U.S. EPA, Research Triangle Park, NC

3 Hogrefe, C., Biswas, J., Lynn, B., Civerolo, K., Ku, J.Y. , Rosenthal, J., et al. (2004). Simulating regional-scale ozone climatology over the eastern Un ited States: model evaluation results. Atmospheric Environment, 38(17), 2627-2638.

6 United States Environmental Protection Agency. (2008). Technical support document for the final locomotive/marine rule: Air quality modeling analyses. Research Triangle Park, N.C.: U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Air Qua lity Assessment Division.

5 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S. (2008). Long-range transport of acidifying substances in East Asia-Part I: Model evaluation and sensitivity studies. Atmospheric Environment, 42(24), 5939- 5955.

6 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S. (2008). Long-range transport of acidifying substances in East Asia-Part I: Model evaluation and sensitivity studies. Atmospheric Environment, 42(24), 5939- 5955.

7 CMAQ version 4.7 was released on D ecember, 2008. It is available from the Community Modeling and Analysis System (CMAS) as well as pr evious peer-review reports at : http://www.cmascenter.org. 8 CMAQ version 4.7 was released on D ecember, 2008. It is available from the Community Modeling and Analysis System (CMAS) as well as pr evious peer-review reports at : http://www.cmascenter.org.5 the chemical mechanism, SOA formation, and de tails about the improvements made to the SOA mechanism within this recent release of CMAQ.

B. Model domain and grid resolution

The CMAQ modeling analyses were performed for a domain covering the continental United States, as shown in Figure II-1. This dom ain has a parent horizontal grid of 36 km with two finer-scale 12 km grids over portions of th e eastern and western U.S. The model extends vertically from the surface to 100 millibars (a pproximately 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 did not change over the simulations. In turn, the 36 km grid was only used to establish the incoming ai r quality concentrations along th e boundaries of the 12 km grids. Only the finer grid data were used in determ ining the impacts of the RFS2 program changes. Table II-1 provides some basic geographic information regarding the CMAQ domains.

Table II-1. Geographic elements of domains used in RFS2 modeling. CMAQ 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)6 Figure II-1. Map of the CMAQ modeling doma in. The black outer box denotes the 36 km national modeling domain; the red inner box is the 12 km western U.S. fine grid; and the blue inner box is the 12 km eastern U.S. fine grid.

C. Valid Modeling Days

The 36 km and both 12 km CMAQ modeling do mains were modeled for the entire year of 2005. 9 For the 8-hour ozone results, we are only using modeling results from the period between May 1 and September 30, 2005. This 153-da y period generally conforms to the ozone season across most parts of the U.S. and contains the majority of days with observed high ozone concentrations in 2005. Data from the entire ye ar were utilized when looking at the toxics impacts from the regulation.

Normally, all 365 model days would also ha ve been used in the estimation of PM 2.5 and visibility impacts; however during the RFS2 mo deling, an error was discovered in the aqueous

9 We also modeled 10 days at the end of December 2004 as a modeled "ramp up" period. These days are used to minimize the effects of initial conditions and are not considered as part of the output analyses.7 phase chemistry routines of CMAQ v4.7. This error 10 caused simulated hourly sulfate concentrations to increase sporadically and in an unrealistic manner over a very limited number of grid-cell hours over the RFS2 si mulations. While this artifact has subsequently been removed from CMAQ v4.7, the RFS2 modeling schedule did not allow for the simulations to be redone. Instead, we simply invalidated any day that contained evidence of the aqueous phase problem and used the remaining data to determine the “true” model signal from the RFS2 scenarios. The following invalidation criteria were used. Any day in which there were five or more grid cellhours that had greater than 50 ug/m 3 difference in sulfate concentra tions between the future base and control cases was invalidate d. Additionally any day with a single grid cell-hour difference exceeding 250 ug/m 3 was invalidated. Based on these i nvalidation criteria, nine days were removed from the EUS12 analysis and two da ys were removed from the WUS12 analysis 11

. D. Model Inputs: Emissions, Meteorology and Boundary Conditions

The 2005-based CMAQ modeling platform was used for the air quality modeling of future baseline emissions and control scenarios. As noted in the introduc tion, in addition to the CMAQ model, the modeling platform also consis ts of the base- and future-year emissions estimates (both anthropogenic and biogenic), mete orological fields, as well as initial and boundary condition data which are all inputs to the air quality model.

1. Base Year and Future Baseline Emissions: The emissions modeling TSD, found in the docket for this rule (EPA-HQ-OAR-2005-0161), cont ains a detailed discu ssion of the emissions inputs used in our air quality modeling as well as Section 3.1 in the final RFS2 RIA. We have provided a brief summary of the base year and fu ture baseline emissions used for the air quality modeling. The emissions data used in the base year and each of the future base cases are based on the 2005 v4 platform. The RFS2 cases use some different emissions data than the official v4 platform for two reasons: (1) the RFS2 mode ling was done prior to the completion of the platform and (2) the RFS2 modeling used data intended only for the rule development and not for general application. The US EGU point source emissions estimates for all 2022 future year base cases are based on an Integrated Planni ng Model (IPM) run for criteria pollutants, hydrochloric acid, and mercury in 2020. The year 2020 was used since it was the year closest to the 2022 modeling year supported by the IPM model. Both control and growth factors were applied to a subset of the 2005 non-EGU point and nonpoint to create each of the 2022 future base cases. The 2002 v3.1 platform 2020 projection f actors were the starting point for most of the RFS2 year 2022 SMOKE-based projections. Et hanol plant replacements and additions were included in the 2005 base and 2022 future baselines as well as biodiesel additions and portable fuel containers.

It should be noted that the emission inventories used in the air quality and benefits modeling were enhanced compared to what was described in the proposal , but did not include

10 This model artifact is discussed in more detail in an August 5 th , 2009 document prepared by Shawn Roselle and Ann Marie Carlton. This document has been placed in the rule docket (EPA-HQ- OAR-2005-0161-DRAFT-2902).

11 The days to be removed for the EUS12 are: 1/25, 2/25, 3/04, 3/05, 3/13, 3/14. 12/08, 12/09, 12/12. The days to be removed for the WUS12 are: 1/04 and 1/27.8 some of the later enhancements a nd corrections of the final emissi on inventories presented in this FRM.

2. RFS2 Modeling Scenarios: As part of our analysis for this rulemaking, the CMAQ modeling system was used to calculate daily and annual PM 2.5 concentrations, 8-hour ozone concentrations, annual and seasonal air toxics concentrations, and tota l nitrogen and sulfur deposition levels for each of the following emissions scenarios:

2005 base year

2022 baseline projection (RFS1 Mandate) of 7.5 billion gallons of renewable fuels

2022 baseline projection (Annual En ergy Outlook (AEO) 2007) volume of roughly 14 billion gallons of renewable fuels

2022 control case projection (implementation of RFS2; also referred to as EISA (Energy Independence and Security Act of 2007)

Model predictions are used in a relative sense to estimate scenario-specific, future-year design values of PM 2.5 and ozone. This is done by calculating the simulated air quality ratios between any particular future year simulation and the 2005 base. These predicted change ratios are then applied to ambient base year desi gn values. The design value projection methodology used here followed EPA guidance 12 for such analyses. Additionally, the raw model outputs are also used in a relative sense as inputs to the he alth and welfare impact functions of the benefits analysis. Model predictions fo r air toxics as well as nitr ogen and sulfur deposition were analyzed for an absolute change and percent ch ange between the contro l case and two future baselines.

3. Sensitivity analyses look ing at impacts of chos en speciation profiles: During the course of the RFS2 modeling, tw o issues arose concer ning the approaches used to speciate certain classes of mobile source emissions into the chemical mechanism used by CMAQ. In order to determine what effect, if any, these pa rticular RFS2 speciation assumptions may have had on the modeling results, a limited set of se nsitivity modeling runs were performed and are summarized below.

The first analysis considered the impacts of an error in the emissions processing of nonroad gasoline emissions. Inadvertently, the sp eciation profiles for highway sources, which reflect a mix of pre-/post-Tier 2 vehicles and a mix of E0, E10, and E85 gasoline, had also been applied to nonroad gasoline engines which do not have similar advanced Tier-2 emissions controls, nor do they use E85 gaso line. The concern was that this error would resu lt in potential overestimates of ethanol and potential underestimat es of acetaldehyde in the control case. The corrected RFS2 emissions contained 9.1% less et hanol and 1.1% more acetaldehyde than what was modeled in the original scenario. Th e RFS2 control case was remodeled with the appropriate speciation profiles for four months in 2005 (January, April, July, and October). The

12 U.S. EPA, Guidance on the Use of Models and Other An alyses in Attainment Demonstrations for the 8-hour Ozone NAAQS; EPA-454/R-05-002; Research Triangle Park, NC; October 2005.9 sensitivity modeling showed that the original simulations (i.e ., the ones summarized herein) do overestimate the expected ethanol changes betw een the baselines (i.e., AEO and RFS1) and the control case (RFS2). However, the CMAQ modeling indicated that the impa cts of the speciation fixes were very small for ozone, PM 2.5 , and key toxics species. Ou tside of ethanol, the impacts of the fix were generally at leas t one order of magnitude smaller than the differences between the RFS1 base and the control scenario. As a result , it was determined that the original modeling was sufficient for isolating the impacts of RFS2.

The second analysis evolved out of initial comparisons of the RFS1 mandate reference case with the RFS2 control case, where the modeling showed decreases in acetaldehyde concentrations in the summer and winter in urba n areas. Decreases are less pronounced in winter when there is less secondary formation of acet aldehyde. The main reason for the decrease in urban areas is determined to be due to re ductions in emissions of certain acetaldehyde precursors. In particular, reducti ons in alkenes (olefins) were not ed, driven by differences in the E0 gasoline headspace speciation pr ofiles used for the control case and the reference cases, as discussed in Section 3.4.1.3 of the RFS2 RIA. Headspace profiles are used to speciate hydrocarbon emissions from gasoline storage, ga soline distribution, and gas cans. After the initial modeling was completed, EPA noticed that the headspace profiles used in the reference case scenarios exhibited a reduction in alkene levels going from E0 to E10 that was inconsistent with what one would expect as a result of increa sed ethanol use. In th ese cases, the E0 gasoline headspace profile has 13% of the VOC as alkenes and the E10 profile has an alkene content of 4%. To address this issue, EPA conducted a sens itivity analysis by adjusting the E0 headspace profile in the RFS1 mandate reference case for the Eastern U.S. modeling domain 13 (based on the assumption that the emissions have an alkene cont ent of 4%, consistent with the percent alkene content of the E10 headspace profile 14 ). A sensitivity analysis was conducted for the month of July and EPA compared results with th e control case for the following two cases:

1) RFS1 case with no change in alkene leve ls between headspace profiles for E0 and E10 (i.e., adjusted E0 profile)

2) RFS1 case with higher alkene le vels for E0 headspace profile

Because of these uncharacteristic differen ces, EPA reran the control case using the adjusted E0 gasoline headspace profile. Due to tim e constraints, we were not able to make this improvement for the reference cases. Thus, alkene levels associated with the E0 use are lower in the control case than the reference cases, l eading to a reduction in secondarily formed acetaldehyde.

The results of the sensitivity analysis showed that acetaldehyde levels were significantly higher for the comparison between Case 1 and the control case than for the comparison between Case 2 and the control case. The sensitivity analysis thus confirmed that the decrease in these

13 Details of the sensitivity run are discussed in the emissions modeling TSD, found in the docket for this rule (EPAHQ-OAR-2005-0161).

14 U.S. Environmental Protection Agency. 2010. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. Draft Report EPA-420-D-10-001, January 2010.10 acetaldehyde precursors between the reference cases and the control case E0 headspace profile is driving the decrease in ambient concentrations of acetaldehyde in urban areas. Thus, while the air quality modeling results pres ented in the RFS2 RIA and in Section III.C.1 below suggest impacts of increased renewable fuel use on ambi ent acetaldehyde are not substantial and there may be decreases in urban areas, there is consider able uncertainty associated with these results. In fact, if the reference cases were rerun w ith revised E0 headspace profiles, some of the observed decreases could become increases.

4. Meteorological Input Data: The gridded meteorological i nput data for the entire year of 2005 were derived from simulations of the Penns ylvania State University / National Center for Atmospheric Research Mesoscale Model. This model, commonly referred to as MM5, is a limited-area, nonhydrostatic, terrain-following system that solves for the full set of physical and thermodynamic equations which govern atmospheric motions. 15 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 CMAQ.

All three meteorological model runs 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.

All three sets of model runs were conducted in 5.5 day segmen ts 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 resolution.

Table II-3. Vertical layer structure for MM5 and CMAQ (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

15 Grell, G., J. Dudhia, and D. Stauffer, 1994: A Descri ption of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO.11 4 0.980 154 982 5 0.970 232 973 4 6 0.960 310 964 7 0.950 389 955 5 8 0.940 469 946 9 0.930 550 937 10 0.920 631 928 6 11 0.910 712 919 12 0.900 794 910 13 0.880 961 892 7 14 0.860 1,130 874 15 0.840 1,303 856 16 0.820 1,478 838 8 17 0.800 1,657 820 18 0.770 1,930 793 9 19 0.740 2,212 766 20 0.700 2,600 730 10 21 0.650 3,108 685 22 0.600 3,644 640 11 23 0.550 4,212 595 24 0.500 4,816 550 25 0.450 5,461 505 12 26 0.400 6,153 460 27 0.350 6,903 415 28 0.300 7,720 370 29 0.250 8,621 325 13
30 0.200 9,625 280 31 0.150 10,764 235 32 0.100 12,085 190 33 0.050 13,670 145 14
34 0.000 15,674 100

The meteorological outputs from all three MM5 sets were processed to create modelready inputs for CMAQ using the Meteorology-Ch emistry Interface Processor (MCIP), version 3.4, to derive the specific inputs to CMAQ. 16

Before initiating the air quality simulations, it is important to identify the biases and errors associated with the meteorological m odeling 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 qua litative aspects involved comparisons of the model-estimated synoptic patterns against obser ved patterns from historical weather chart archives. Additionally, the eval uations compared spatial patterns of monthly average rainfall and monthly maximum planetar y boundary layer (PBL) heights. Qualitatively, the model fields closely matched the observed sy noptic patterns, which is not unexpected given the use of nudging. The operational evaluati on included statistical comparisons of model/observed pairs (e.g., mean normalized bias, mean normalized error, index of agreement, root mean square errors, etc.) for multiple meteorological parameters. For this portion of the

16 Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA Models-3 Community Multiscale Air Quality (CMAQ modeling system, EPA/600/R-99/03 0, Office of Research and Development).12 evaluation, five meteorological parameters were investigated: temperature, humidity, shortwave downward radiation, wind speed, and wind direction. The three individual MM5 evaluations are described elsewhere. 17,18,19 It was ultimately determined that the bias and error values associated with all three sets of 2005 meteorological data were ge nerally within the range of past meteorological modeling results that have been used for air quality applications.

5. Initial and Boundary Conditions: The lateral boundary and initial species concentrations are provided by a three-dimens ional global atmospheric chemistry model, the GEOS-CHEM 20 model (standard version 7-04-11 21 ). The global GEOS-CHEM model simulates atmospheric chemical and physical processes driv en by assimilated meteorological observations from the NASA’s Goddard Earth Observing Syst em (GEOS). This model was run for 2005 with a grid resolution of 2.0 degree x 2.5 degree (latitude-longitude) a nd 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 fo r the 36-km CMAQ simulations. The future base conditions from the 36 km coarse grid modeling were used as th e initial/boundary state for all subsequent 12 km finer grid modeling.

E. CMAQ Base Case Model Performance Evaluation

1. PM 2.5 : An operational model performance evaluation for PM 2.5 and its related speciated components (e.g., sulf ate, nitrate, elemental car bon, organic carbon, etc.) was conducted using 2005 state/local monitoring data in order to estimate the ability of the CMAQ modeling system to replicate base year con centrations. In summa ry, model performance statistics were calculated for observed/predicted pairs of daily/monthly/seasonal/annual concentrations. Statistics were generated fo r the following geographic groupings: domain wide, Eastern vs. Western (divided along the 100th meridian), and each Regional Planning Organization (RPO) region 22 . The “acceptability” of model performance was judged by

17 Baker K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Eastern U.S. 12-km Domain Simulation, USEPA/OAQPS, February 2, 2009.

18 Baker K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Western U.S. 12-km Domain Simulation, USEPA/OAQPS, February 2, 2009.

19 Baker K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Continental U.S. 36-km Domain Simulation, USEPA/OAQPS, February 2, 2009.

20 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, October 15, 2004.

21 Henze, D.K., J.H. Seinfeld, N.L. Ng, J.H. Kroll, T-M. Fu, D.J. Jacob, C.L. Heald, 2008. Global modeling of secondary organic aerosol formation from aromatic hydrocarbons: high-vs.low-yield pathways. Atmos. Chem. Phys

., 8, 2405-2420.

22 Regional Planning Organization regions include: Mid- Atlantic/Northeast Visibility Union (MANEVU), Midwest Regional Planning Organization – Lake Michigan Air Directors Consortium (MWRPO-LADCO), Visibility Improvement State and Tribal Association of the Southeast (VISTAS), Central States Regional Air Partnership (CENRAP), and the Western Regional Air Partnership (WRAP).13 comparing our CMAQ 2005 performance results to the range of performance found in recent regional PM 2.5 model applications for other, non-EPA studies 23 . Overall, the fractional bias, fractional error, normalized mean bias, and normali zed mean error statistic s shown in Table II-4 are within the range or close to that found by other groups in recent applications. The model performance results give us confidence that our application of CM AQ using this modeling platform provides a scientifically credible approach for assessing PM 2.5 concentrations for the purposes of the RFS2 assessment. A detail ed summary of the 2005 CMAQ model performance evaluation is available in Appendix B 24

. Table II-4. 2005 CMAQ annual PM 2.5 species model performance statistics.

CMAQ 2005 Annual No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 11797 -0.8 37.6 -2.4 38.7 12-km WUS 3440 -10.0 45.0 -9.5 44.4 Midwest 2318 8.5 35.2 9.2 33.5 Northeast 2977 10.8 41.6 9.9 38.8 Southeast 2960 -13.7 34.0 -14.5 37.1 Central U.S. 2523 -6.3 39.8 -9.8 44.3 STN

West 2826 -10.9 46.1 -10.6 45.0 12-km EUS 9321 -6.4 41.6 -7.2 43.6 12-km WUS 10411 -19.9 44.6 -21.9 48.4 Midwest 571 1.3 36.8 1.3 37.2 Northeast 2339 12.1 47.7 8.1 44.3 Southeast 1694 -17.6 37.5 -16.9 43.1 Central U.S. 2376 -13.3 41.7 -11.9 46.2 PM 2.5 Total Mass

IMPROVE

West 9258 -22.9 44.8 -23.5 48.6 12-km EUS 13897 -12.3 33.2 -9.2 35.9 12-km WUS 3920 -17.0 42.3 -7.8 42.8 Midwest 2495 -5.2 34.0 0.7 34.8 Northeast 3441 -7.7 32.1 -4.0 33.9 Southeast 3499 -14.5 30.9 -12.2 33.4 Central U.S. 2944 -22.5 37.2 -19.5 41.6 STN

West 3157 -15.5 45.8 -6.7 44.0 12-km EUS 9034 -15.2 34.5 -6.2 38.9 12-km WUS 10002 -14.4 41.0 2.7 44.5 Midwest 531 -12.7 32.5 -5.0 34.6 Northeast 2253 -7.7 34.3 -0.2 37.3 Southeast 1685 -17.8 32.7 -12.2 36.2 Central U.S. 2350 -23.5 36.6 -16.1 40.7 IMPROVE

West 8896 -10.5 42.3 5.0 45.1 Sulfate

CASTNet 12-km EUS 3170 -19.3 24.8 -18.5 27.8

23 These other modeling studies represent a wide range of modeling analyses which cover various models, model configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules.

24 U.S. Environmental Protection Agency, Air Quality Modeling Technical Support Document: Changes to Renewable Fuel Standard Program, Appendix B: CMAQ Model Performance Evaluation for Ozone, Particulate Matter and Toxics. January, 2010 (EPA-454/R-10-001A).14 12-km WUS 1142 -24.6 33.6 -16.6 35.2 Midwest 615 -16.7 23.4 -14.8 24.8 Northeast 786 -14.2 22.6 -12.3 24.8 Southeast 1099 -21.5 24.7 -23.6 27.7 Central U.S. 300 -32.2 34.3 -33.6 38.4 West 1091 -23.7 33.8 -15.8 35.2 12-km EUS 12741 25.5 70.4 -8.1 78.1 12-km WUS 3655 -41.7 65.3 -70.8 97.5 Midwest 2495 29.8 64.7 17.1 63.9 Northeast 3442 37.0 73.9 3.2 73.6 Southeast 3499 13.1 78.2 -27.5 86.0 Central U.S. 1812 5.2 58.8 -6.2 71.6 STN

West 31339 -47.3 65.4 -79.1 99.9 12-km EUS 9027 34.2 86.9 -31.6 101.6 12-km WUS 9987 -29.7 72.5 -93.8 124.0 Midwest 531 20.7 69.8 -7.3 82.1 Northeast 2248 66.4 106.9 -1.0 95.4 Southeast 1685 42.5 106.6 -37.5 105.2 Central U.S. 2350 22.8 72.7 -20.5 95.0 Nitrate

IMPROVE

West 8881 -45.7 75.6 -102.3 128.4 12-km EUS 3170 27.3 40.3 21.9 38.1 12-km WUS 1142 -3.3 34.5 6.4 39.9 Midwest 615 26.1 37.4 26.9 34.1 Northeast 786 40.6 46.0 34.4 42.4 Southeast 1099 25.7 41.4 17.2 39.2 Central U.S. 300 10.4 33.9 7.3 33.3 Total Nitrate (NO3 + HNO3) CASTNet

West 1091 -4.7 35.7 6.4 40.5 12-km EUS 13897 6.7 42.3 12.6 45.4 12-km WUS 3893 -14.9 55.3 7.1 55.0 Midwest 2495 14.3 42.0 22.3 42.2 Northeast 3498 14.7 44.3 25.0 46.3 Southeast 3882 0.8 39.1 6.5 42.0 Central U.S. 3059 -4.3 43.6 -0.2 50.4 STN

West 3130 -20.5 59.0 5.8 57.2 12-km EUS 3170 -1.3 34.6 0.4 35.8 12-km WUS 1142 -14.5 38.8 -12.1 40.1 Midwest 615 8.1 34.6 12.8 33.3 Northeast 786 6.2 37.2 11.3 35.9 Southeast 1099 -13.6 32.6 -13.7 36.5 Central U.S. 300 -4.2 35.4 -1.1 39.6 Ammonium

CASTNet

West 1091 -20.8 39.1 -13.9 40.2 12-km EUS 14038 25.9 66.0 18.2 54.3 12-km WUS 3814 31.1 77.7 19.5 62.5 Midwest 2502 18.7 51.7 20.1 47.1 Northeast 3479 37.7 70.5 26.7 54.7 Southeast 3877 10.7 59.1 8.1 49.4 Central U.S. 3221 48.1 86.3 26.5 64.3 STN

West 3015 38.7 82.8 21.0 65.1 12-km EUS 8668 -25.9 49.1 -28.3 56.0 12-km WUS 9495 -10.2 57.2 -17.8 60.4 Elemental

Carbon

IMPROVE

Midwest 602 -16.8 41.8 -28.3 50.415 Northeast 2117 -4.8 48.8 -15.5 54.1 Southeast 1584 -46.1 51.4 -51.5 62.8 Central U.S. 2123 -31.3 49.5 -30.1 56.5 West 8518 -9.6 58.2 -18.2 61.5 12-km EUS 12619 -35.1 52.6 -32.5 63.9 12-km WUS 3582 -32.1 56.7 -28.2 61.3 Midwest 2380 -37.3 51.9 -31.0 62.5 Northeast 3323 -17.4 52.7 -13.7 60.3 Southeast 3802 -45.7 52.9 -48.7 66.7 Central U.S. 2259 -38.8 53.4 -37.3 66.9 STN

West 3060 -31.7 57.6 -27.8 61.4 12-km EUS 8662 -29.9 50.6 -34.3 59.1 12-km WUS 9495 -24.0 57.1 -29.4 62.8 Midwest 601 -33.1 43.6 -39.3 53.2 Northeast 2116 -7.7 52.4 -14.4 53.5 Southeast 1587 -37.8 46.7 -48.0 60.2 Central U.S. 2123 -42.5 54.2 -46.8 65.2 Organic Carbon

IMPROVE

West 8518 -22.3 57.3 -28.1 62.7

2. Ozone: An operational model performance eval uation for hourly and eight-hour daily maximum ozone was conducted in order to estima te the ability of the CMAQ modeling system to replicate the base year concentrations fo r the 12-km Eastern and Western United States domain shown in Figure II-1. Ozone measurements from 1194 sites (817 in the East and 377 in the West) were included in the evaluation and were taken from the 2005 State/local monitoring site data in the Air Quality System (AQS) Aero metric Information Retrieval System (AIRS). The ozone metrics covered in this evalua tion include one-hour daily maximum ozone concentrations and eight-hour daily maximum ozone concentrations. The evaluation principally consists of statistical assessments of model vers us observed pairs that were paired in time and space on an hourly and/or daily basis, de pending on the sampling frequency of each measurement site (measured data). This ozone model performance was limited to the ozone season (May through September) that was mode led for the RFS2 final rule. Appendix B contains a more detailed summary of ozone model performance over the 12km Eastern and Western U.S. grid. A summary of the evaluation is presented here.

As with the national, annual PM 2.5 CMAQ modeling, the “acceptability” of model performance was judged by comparing our CMAQ 2005 performance results to the range of performance found in recent regional ozone model applications (e.g., EPA’s Proposal to Designate an Emissions Contro l Area for Nitrogen Oxides 25 and the Clean Air Interstate Rule 26 ). Overall, the normalized mean bias and error (NMB and NME), as well as the fractional bias and error (FB and FE) statistics shown in Tables II-5 and II-6 indicate that CMAQ-predicted 2005 hourly and eight-hour daily maximum ozone residua ls (i.e., observation vs. model predictions) are within the range of ot her recent regional modeling ap plications. The CMAQ model

25 U.S. Environmental Protection Agency, Proposal to Designate an Emissions Control Area for Nitrogen Oxides, Sulfur Oxides, and Particulate Matter: Technical Support Document. EPA-420-R-007, 329pp., 2009. (http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007.pdf)

26 U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule: Air Quality Modeling; Office of Air Quality Planning and Standards; Research Triangle Park, NC; March 2005.16 performance results give us confidence that our applications of CMAQ using this modeling platform provide a scientifical ly credible approach for asse ssing ozone concentration changes resulting from the final R FS2 emissions reductions.

Table II-5. 2005 CMAQ one-hour daily maxi mum ozone model performance statistics calculated for a threshold of 40 ppb.

CMAQ 2005 One-Hour Maximum Ozone: Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 21394 -1.6 11.5 -0.8 11.6 12-km WUS 9631 -3.4 12.8 -2.8 12.7 Midwest 4418 0.8 10.0 1.0 10.2 Northeast 4102 5.4 11.8 5.9 11.7 Southeast 6424 -3.6 11.3 -3.0 11.5 Central U.S. 4328 -6.4 13.4 -5.5 13.4 May

West 8294 -3.5 12.9 -3.0 12.8 12-km EUS 19517 -3.5 12.8 -2.8 12.9 12-km WUS 9056 -3.7 13.0 -3.2 13.0 Midwest 4639 -4.6 12.3 -4.0 12.4 Northeast 4148 -1.0 14.1 -0.1 14.2 Southeast 4644 -2.7 12.5 -2.2 12.6 Central U.S. 4062 -6.2 13.2 -5.4 13.3 June

West 7737 -4.0 13.1 -3.6 13.1 12-km EUS 19692 1.2 14.2 1.8 14.1 12-km WUS 9443 0.4 16.0 1.0 15.8 Midwest 4923 0.4 12.7 0.9 12.6 Northeast 4445 4.2 15.2 4.8 14.9 Southeast 4733 4.2 15.1 4.6 14.8 Central U.S. 3521 -3.8 14.8 -3.1 14.9 July

West 8168 0.2 16.2 0.7 16.0 12-km EUS 19643 0.1 13.9 0.8 13.8 12-km WUS 9562 -0.8 15.5 -0.6 15.5 Midwest 4549 0.2 12.2 1.0 12.3 Northeast 4139 0.2 13.2 1.2 13.1 Southeast 5303 3.6 14.9 3.9 14.5 Central U.S. 3589 -4.1 16.2 -2.9 16.1 August

West 8357 -1.0 15.7 -1.0 15.7 12-km EUS 18085 -2.2 12.0 -1.3 12.0 12-km WUS 8725 -3.6 14.1 -3.2 14.3 Midwest 4002 -3.6 10.7 -3.0 10.8 Northeast 3667 -1.8 11.3 -0.7 11.3 Southeast 5259 -0.1 12.1 0.8 12.1 Central U.S. 3286 -6.1 14.5 -5.1 14.5 September

West 7530 -4.1 14.3 -3.8 14.4 12-km EUS 98331 -1.2 12.9 -0.5 12.8 12-km WUS 46417 -2.1 14.3 -1.7 14.2 Midwest 22531 -1.4 11.7 -0.8 11.7 Northeast 20501 1.4 13.3 2.3 13.1 Southeast 26363 0.1 13.1 0.7 13.0 Central U.S. 18786 -5.4 14.4 -4.4 14.4 Seasonal Aggregate (May – September)

West 40086 -2.3 14.5 -2.1 14.417 Table II-6. 2005 CMAQ eight-hour daily maxi mum ozone model performance statistics calculated for a threshold of 40 ppb.

CMAQ 2005 Eight-Hour Maximum Ozone: Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 19310 -1.0 10.9 -0.4 11.0 12-km WUS 8445 -1.6 12.0 -1.2 12.0 Midwest 3858 0.2 10.0 0.7 10.2 Northeast 3528 5.2 11.4 5.4 11.2 Southeast 6019 -2.1 10.5 -1.6 10.6 Central U.S. 3927 -5.8 12.8 -5.2 13.0 May

West 7234 -1.8 12.1 -1.5 12.0 12-km EUS 17404 -2.1 11.9 -1.5 12.0 12-km WUS 8102 -1.9 11.9 -1.6 11.9 Midwest 4324 -3.8 11.6 -3.4 11.8 Northeast 3590 0.3 13.1 1.0 13.2 Southeast 3924 -0.3 11.4 0.1 11.5 Central U.S. 3663 -5.5 12.1 -5.0 12.3 June

West 6889 -2.2 12.1 -2.0 12.1 12-km EUS 17045 3.3 13.4 3.6 13.3 12-km WUS 8556 3.7 15.0 3.9 14.7 Midwest 4429 1.8 11.8 2.3 11.8 Northeast 3856 6.6 14.6 6.8 14.3 Southeast 3806 7.4 15.0 7.3 14.5 Central U.S. 3057 -2.3 13.2 -2.1 13.5 July

West 7407 3.5 15.1 3.6 14.9 12-km EUS 16953 1.9 12.9 2.2 12.9 12-km WUS 8523 1.6 13.9 1.5 13.9 Midwest 4027 0.9 11.3 1.4 11.4 Northeast 3530 1.4 12.3 2.0 12.2 Southeast 4447 7.4 14.7 7.2 14.1 Central U.S. 3096 -3.4 14.4 -3.1 14.8 August

West 7469 1.4 14.1 1.2 14.0 12-km EUS 15190 -1.8 11.2 -1.3 11.3 12-km WUS 7465 -2.4 13.4 -2.6 13.9 Midwest 3265 -4.2 10.2 -4.0 10.4 Northeast 2856 -2.3 10.6 -1.8 10.7 Southeast 4647 1.5 11.2 2.1 11.2 Central U.S. 2798 -6.5 13.6 -6.1 14.0 September

West 6446 -2.9 13.7 -3.1 14.1 12-km EUS 85902 0.1 12.1 0.5 12.1 12-km WUS 41091 0.0 13.3 0.1 13.3 Midwest 19903 -0.9 11.1 -0.5 11.2 Northeast 17360 2.4 12.6 2.9 12.4 Southeast 22843 2.3 12.3 2.6 12.2 Central U.S. 16541 -4.8 13.2 -4.4 13.4 Seasonal Aggregate (May – September)

West 35445 -0.2 13.5 -0.3 13.518 3. Hazardous air pollutants

An operational model performance evaluation for daily, monthly, seasonal, and annual specific air toxics (formalde hyde, acetaldehyde, benzene, ac rolein, and 1,3-butadiene) was conducted in order to estimate th e ability of the CMAQ modeling sy stem to replicate the base year concentrations for the 12-km Eastern a nd Western United States domains. Toxic measurements from 471 sites in the East and 135 sites in the West were included in the evaluation and were taken from the 2005 State/lo cal monitoring site data in the National Air Toxics Trends Stations (NATTS). Similar to PM 2.5 and ozone, the evaluation principally consists of statistical assessments of model vers us observed pairs that were paired in time and space on daily basis. Appendix B contains a more detailed summary of air toxics model performance over the 12km Eastern and Western U.S. grid. A summary of the evaluation is presented here.

Model predictions of annual formaldehyde, ace taldehyde and benzene showed relatively small bias and error percentages when compared to observations. The model yielded larger bias and error results for 1,3 butadiene and acrolein ba sed on limited monitoring sites. As with the national, annual PM 2.5 and ozone CMAQ modeling, the “accep tability” of model performance was judged by comparing our CMAQ 2005 performa nce results to the limited performance found in recent regional multi-pollutant model applications. 27,28,29 Overall, the normalized mean bias and error (NMB and NME), as well as the fr actional bias and error (FB and FE) statistics shown in Table II-7 indicate that CMAQ-pred icted 2005 toxics (i.e., observation vs. model predictions) are within th e range of recent regional modeling applications.

27 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform: Air Toxics, Ozone, and Particulate Matter, 7 th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.

28 Strum, M., Wesson, K., Phillips, S., Co ok, R., Michaels, H., Brzezinski, D., Poll ack, A., Jimenez, M., Shepard, S. Impact of using lin-level emissions on multi-pollutant air quality model predictions at regional and local scales. 17 th Annual International Emission Inventory Conference, Portland, Oregon , June 2-5, 2008.

29 Wesson, K., N. Fann, and B. Timin, 2010: Draft Manusc ript: Air Quality and Benefits Model Responsiveness to Varying Horizontal Resolution in the Detroit Urban Ar ea, Atmospheric Pollution Research, Special Issue: Air Quality Modeling and Analysis.19 Table II-7. 2005 CMAQ annual toxics model performance statistics

CMAQ 2005 Annual No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 6365 -55.5 65.3 -39.2 65.6 12-km WUS 1928 -28.4 52.1 -30.1 60.7 Midwest 771 -77.1 85.4 -25.8 74.0 Northeast 1982 -30.5 51.3 -28.5 61.6 Southeast 1246 -66.2 72.2 -51.3 70.4 Central U.S. 1815 -43.5 51.0 -41.4 61.5 Formaldehyde

West 1746 -25.5 52.3 -26.0 59.8 12-km EUS 6094 -4.2 62.0 -8.2 60.3 12-km WUS 1892 -19.2 53.7 -19.5 59.6 Midwest 703 -12.6 58.0 -12.1 60.0 Northeast 1969 -9.5 62.8 -9.0 63.7 Southeast 1231 0.4 63.5 -6.2 62.2 Central U.S. 1640 1.8 57.0 -4.3 51.1 Acetaldehyde

West 1709 -20.4 54.1 -20.1 60.6 12-km EUS 11615 -32.6 66.8 -13.5 62.8 12-km WUS 3369 -38.4 60.8 -30.4 63.9 Midwest 1425 -8.3 72.7 25.2 62.4 Northeast 2589 21.6 53.3 18.1 46.8 Southeast 2426 -41.1 68.6 -17.2 59.8 Central U.S. 4737 -47.0 68.3 -32.7 69.4 Benzene

West 2333 -30.5 61.2 -19.2 63.4 12-km EUS 8102 -74.7 85.6 -49.4 91.6 12-km WUS 1976 -51.9 82.1 -34.5 91.7 Midwest 516 -78.7 86.2 -48.3 81.9 Northeast 1902 -41.6 55.5 -54.8 71.3 Southeast 1226 -85.4 86.4 -106.2 111.5 Central U.S. 4142 -66.5 85.9 -20.0 89.2 1,3-Butadiene

West 1082 -40.8 77.5 -41.9 85.3 12-km EUS 1660 -94.4 95.0 -131.3 142.2 12-km WUS 783 -95.7 95.7 -168.1 170.4 Midwest n/a n/a n/a n/a n/a Northeast 850 -90.4 91.5 -120.5 134.2 Southeast 278 -97.0 97.0 -156.4 157.0 Central U.S. n/a n/a n/a n/a n/a Acrolein

West 592 -95.9 95.9 -177.6 177.6



### III. CMAQ Model Results

A. Impacts of RFS2 Changes on Future PM 2.5 Levels

It is important to remember that there are uncertainties and limitations related to the air quality modeling (see Section 3.4.1.3 in RFS2 RIA) , including the projected amount of E85 in20 use. The modeled projected usage of E85 is hi gher than what was included in the final rule inventory, which could overestimate the decreases in PM 2.5 . These differences in the air quality modeling inventories and the final rule inventories are discussed in detail in Section 3.3 of the RFS2 RIA.

After the air quality modeling was complete an error was found in the PM inventory for locomotives, therefore only design value change s over all 577 modeled c ounties are reported. A large majority of the modeled counties will have relatively minor annual average PM 2.5 design value changes of between -0.05 μg/m 3 and +0.05 μg/m 3 . On a population-weighted basis, the average modeled future-year annual PM 2.5 design values are proj ected to decrease by 0.002 μg/m 3 when compared with the RFS1 mandate or AEO reference case. 30 Likewise, daily PM 2.5 design values show the majority of the modele d counties will experience changes of between - 0.25 μg/m 3 and +0.25 μg/m 3 . On a population-weighted basis, the average modeled future-year daily PM 2.5 design value is project ed to decrease by 0.06 μg/m 3 when compared with the RFS1 mandate scenario or 0.05 μg/m 3 when compared with the AEO scenario.

The changes in ambient PM 2.5 described above are likely due to both increased emissions at biofuel production plants and from biofuel tr ansport, and reductions in SOA formation and reduced emissions from gasoline refineries. In addition, decreases in am bient PM are predicted because our modeling inventory assumed large vol umes of E85 use and also that E85 usage reduces PM tailpipe emissions. As mentioned previously and in mo re detail in Section 3.4 of the RIA, these direct PM emission re ductions would not occur with fina l rule inventory assumptions.

B. Impacts of RFS2 Changes on Future 8-Hour Ozone Levels

This section summarizes the results of our m odeling of ozone air quality impacts in the future due to the required renewable fuel volum es. Our modeling indica tes that the renewable fuel standards will result in increases in ozone design value concentrations in many areas of the country as well as decreases in ozone design valu e concentrations in a small number of areas. Figures III-1 and III-2 display the projected c ounty-level, 8-hour ozone design value changes expected when the RFS2 control scenario is co mpared to the RFS1 mandate reference case and the AEO 2007 reference case respectively. 31 The air quality modeling of the expected impacts of the final rule shows that in 2022, most counties with modeled data, esp ecially those in the southeast U.S., will see increases in their ozone design values. The bulk of these design value increases are less than 0.5 ppb. On a population- weighted basis, the average modeled futureyear 8-hour ozone design values are projecte d to increase by 0.15 ppb in 2022 when compared with the RFS1 mandate reference case and in crease by 0.27 ppb when comparing with the AEO reference case. On a population-weighted basis t hose counties that are projected to be above the

30 Note that the change in annual average PM 2.5 for design values differs from the change in national populationweighted annual average PM 2.5 discussed in Sections I and VIII of the preamble and Chapter 5 of the RIA. The discussion of national population-weighted annual average PM 2.5 with respect to health impacts in Sections I and VIII of the preamble and Chapter 5 of this RIA is based on modeling data from all grid cells rather than just those counties with monitors. It finds that there is a small increase in annual average PM 2.5 . 31 The air quality modeling used a different speciation prof ile for E10 gasoline headspace emissions in the RFS2 control case than was used for the RFS1 and AEO reference cases. This inconsistency is described in Section 3.4.1.3 in the RFS2 RIA.21 2008 ozone standard in 2022 will see decreases of 0.18 when compared with the AEO reference case and 0.17 ppb when compared with the RFS1 mandate reference case.

When comparing the changes in projected ozone it is important to note the differences in the inventories used for the air quality modeling and the inventories presented in the RFS2 final rule. The most important difference and uncertain ty has to do with the fact that the modeled inventory assumes increases in NOx for vehicl es using E10 fuel. The air quality modeling indicates that the NOx increases required from the renewable fuel volumes contribute to the ozone increases in NOx-limited areas as well as the ozone decreases in VOC-limited areas.

Figure III-1. Model-projected change in a nnual 8-hour Ozone design values between the RFS2 Control Scenario and the RFS1 Manda te Scenario in 2022. Units are ppb.22 Figure III-2. Model-projected change in a nnual 8-hour Ozone design values between the RFS2 Control Scenario and AEO Scenario in 2022. Units are ppb.

C. Impacts of RFS2 Changes on Toxic Air Pollutant Levels

This section summarizes the results of our m odeling of ambient air toxics impacts in the future from the renewable fuel volumes require d by RFS2. Specifically, we compare the RFS1 mandate and AEO reference scenarios to the RFS2 control scenario for 2022 (see Section 3.3 of the RIA for more information on the scenarios). 32 Our modeling indicates that, while there are some localized impacts, the renewable fuel volumes required by RFS2 have relatively little impact on national average ambient concentrations of the modeled air toxics. An exception is increased ambient concentrations of ethanol. Si nce the overall impacts are relatively small, we concluded that assessing exposure to ambient c oncentrations and conduc ting a quantit ative risk assessment of air toxic impacts was not warrant ed. Although, we developed population metrics, including the population living in ar eas with increases or decreases in concentrations of various magnitudes. We also estimated aggregat ed populations above and below reference concentrations for noncancer effects.

32 We used a different speciation profile for E10 gasolin e headspace emissions in the RFS2 control case than was used for the RFS1 and AEO reference cases. This inconsistency is described in Section 3.4.1.3 of the RIA.23 1. Acetaldehyde

Overall, the air quality modeling does not show substantial nationwide impacts on ambient concentrations of acetaldehyde due to th e renewable fuel volumes required by this rule. Figure III-3 shows the annual percent changes in ambient concentrations of acetaldehyde are less than 1% for most of the country. Several urba n areas show decreases in ambient acetaldehyde concentrations ranging from 1 to 10%, and some ru ral areas associated with new ethanol plants show increases in ambient acetaldehyde concentrat ions ranging from 1 to 10% with RFS2. In Figure III-4, the annual absolute changes in ambien t concentrations of acetaldehyde are generally less than 0.1 μg/m3. As noted above, the results show that th e largest increases in ambient acetaldehyde concentrations with RFS2 volumes occur in areas associated with new ethanol plants. This result is due to an increase in emissions of primary ace taldehyde and precursor emissions from ethanol plants not included in the RFS1 mandate reference scenario.

Figure III-3. Acetaldehyde Annual Percent Ch ange in Concentration Between the RFS2 Mandate Reference Case and the RFS2 Control Case in 202224 Figure III-4. Acetaldehyde Annual Absolute Changes in Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3)

Figures III-5 and III-6 show the comparison of the RFS1 mandate reference case with the RFS2 control case for summer and winter shows decreases in ambient acetaldehyde concentrations in urban areas. Decreases are less pronounced in winter when there is less secondary formation of acetaldehyde (Figures III-6 ). As stated above, the main reason for the decrease in urban areas is reductions in certa in acetaldehyde precursors, primarily alkenes (olefins) that are related to the differences in the E0 gasoline headspace speciation profiles used for the control case and the reference cases. While the air quality modeling results presented here and in the RFS2 RIA suggest impacts of increased renewable fuel use on ambient acetaldehyde are not substantial and there may be d ecreases in urban areas, there is considerable uncertainty associated with these results. Thus, if the reference cases were rerun with revised E0 headspace profiles, some of the observed decreases could become increases. Additional research is underway to address these unc ertainties, e.g., measurement of representative fuels to create better headspace speciation prof iles (Section 3.4.1.3 in the RFS2 RIA) and improvements in other speciation profiles based on additional results from the EPAct emissions test program. 33

33 . EPAct Phase I II, and III Testing: Comprehensive Gasoline Light-Duty Exhaust Fuel Effects Test Program to Cover Multiple Fuel Properties. EPA Contract: EPC-07-028EP A. Southwest Research Institute, San Antonio, TX. Phase III of the EPAct emission test program is scheduled for completion in 2010.25 Figure III-5. Summer Changes in Acetaldehy de Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes (μg/m3)

Figure III-6. Winter Changes in Acetaldehyde Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes (μg/m3)

2. Formaldehyde

Our air quality modeling results do not show substantial impacts on ambient concentrations of formaldehyde from the renewa ble fuel volumes required by this rule. As 

### b a a b26 shown in Figure III-7, most of the U.S. e xperiences a 1% or less change in ambient formaldehyde concentrations. Decreases in ambi ent formaldehyde concentr ations range between 1 and 5% in a few urban areas. Increases ra nge between 1 and 2.5% in some rural areas associated with new ethanol plants; this result is due to increases in emissions of primary formaldehyde and formaldehyde precursors from th e new ethanol plants. Figure III-8 shows that absolute changes in ambient concentrations of form aldehyde are generally less than 0.1 μg/m3.

Figure III-7. Formaldehyde Annual Percent Ch ange in Concentration Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 202227 Figure III-8. Formaldehyde Annual Percent Ch anges in Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3)

3. Ethanol

Our modeling projects that the renewable fuel volumes required by this rule will lead to significant nationwide increases in ambient ethanol concentrations. Figure III-9 shows increases ranging between 10 to 50% that are seen across mo st of the country. The largest increases (more than 100%) occur in urban areas with high amou nts of onroad emissions and in rural areas associated with new ethanol plants. Absolute increases in ambient ethanol concentrations are above 1.0 ppb in some urban areas (Figure III-10). The location of these localized increases is limited by uncertainties in the placement of the new ethanol plants, as discussed in Section 3.4.1.3 of the RFS2 RIA. It should be noted here that these increases are overestimated because the speciated profile combination used for mo deling nonroad emissions was misapplied. While sensitivity analyses suggest that the impact of this error was negligible for other pollutants, it resulted in overestimates of ethanol impacts by more than 10% across much of the modeling domain. Details on the ethanol impacts are discussed in the em issions modeling TSD, found in the docket for this rule (EPA-HQ-OAR-2005-0161).28 Figure III-9. Ethanol Annual Percent Changes Change in Concentration Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022

Figure III-10. Ethanol Annual Absolute Change s in Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (ppb)29 4. Benzene

Our modeling projects that the renewable fuel volumes required by this rule will lead to small nationwide decreases in ambient benzene c oncentrations. Figure III-11, show decreases in ambient benzene concentrations that range between 1 and 10% acr oss most of the country and can be higher in a few urban areas. Figure III-12 indicates absolute changes in ambient concentrations of benzene show reductions up to 0.2 μg/m3.

Figure III-11. Benzene Annua l Percent Change in Concentration Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 202230 Figure III-12. Benzene Annual Ab solute Changes in Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3)

5. 1,3-Butadiene

The results of our air quality modeling show small increases and decreases in ambient concentrations of 1,3-butadiene in parts of the U.S. as a result of the renewable fuel volumes required this rule. Overall, as seen in Figure III-13, decreases occur in some southern areas of the country and increases occur in some northern areas and areas with high altitudes. Percent changes in 1,3-butadiene concentrations are ove r 50% in several areas; but the changes in absolute concentrations of am bient 1,3-butadiene are generally less than 0.005 μg/m3 (Figure III14). Annual increases in ambient concentratio ns of 1,3-butadiene are driven by wintertime rather than summertime changes (Figures III-15 a nd III-16). These increases appear in rural areas with cold winters and low ambient leve ls but high contributions of emissions from snowmobiles, and a major reason for this modele d increase may be deficiencies in available emissions test data used to estimate snowmobile 1,3-butadiene emission inventories. These data were based on tests using only three engines, which showed significantly higher 1,3-butadiene emissions with 10% ethanol. However, they ma y not have been representative of real-world response of snowmobile engines to ethanol.31 Figure III-13. 1,3-Butadiene Annual Percent Ch ange in Concentration Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022

Figure III-14. 1,3-Butadiene Annual Absolu te Changes in Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3)32 Figure III-15. Summer Changes in 1,3-Butadiene Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes (μg/m3)

Figure III-16. Winter Changes in 1,3-Butadi ene Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes (μg/m3)



### a b a b33 6. Acrolein

Our air quality modeling shows small regi onal increases and decreases in ambient concentrations of acrolein as a result of the renewable fuel vol umes required by this rule. As shown in Figure III-17, decreases in acrolein concentrations occur in some eastern and southern parts of the U.S. and increases occur in some northern areas and areas associated with new ethanol plants. Figure III-18 in dicates that changes in absolu te ambient concentrations of acrolein are between ± 0.001 μg/m3 with the exception of the increases associated with new ethanol plants. These increases can be up to and above 0.005 μg/m 3 with percent changes above 50% and are due to increases in emissions of acr olein from the new plants. As discussed in Section 3.4.1.3 of the RFS RIA, uncertainties in the placement of new ethanol plants limit the model’s projected location of a ssociated emission increases. Ambient acrolein increases in upper Michigan, Canada, the Northeast, and the Rocky Mountain region are driven by wintertime changes (Figures III-19 and III-20), and occur in the sa me areas of the country that have wintertime rather than summertime increas es in ambient 1,3-butadiene. 1,3-butadiene is a precursor to acrolein, and these increases are like ly associated with the same emission inventory issues in areas of high snowmobile usage s een for 1,3-butadiene, as described above.

Figure III-17. Acrolein Annual Percent Change s Change in Concentration Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 202234 Figure III-18. Acrolein Annual Absolute Chan ges in Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3)

Figure III-19. Summer Changes in Acrolein Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes (μg/m3)



### a b35 Figure III-20. Winter Changes in Acrolein Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes (μg/m3)



### a b36 

### Appendix A: 8-Hour Ozone Design Values for RFS-2 Scenarios (units are ppb)

State Name County Name Baseline DV 2022 RFS1 DV 2022 AEO DV 2022 RFS2 DV Alabama Baldwin 77.30 63.76 64.20 64.40 Alabama Clay 74.00 56.43 56.65 56.88 Alabama Colbert 72.00 50.47 50.77 51.00 Alabama Elmore 70.70 52.07 52.38 52.63 Alabama Etowah 71.70 54.66 54.92 55.14 Alabama Houston 71.00 57.36 57.55 57.82 Alabama Jefferson 83.70 62.27 62.55 62.73 Alabama Lawrence 72.00 55.16 55.40 56.02 Alabama Madison 77.30 59.03 59.39 59.81 Alabama Mobile 76.70 63.66 64.07 64.24 Alabama Montgomery 69.30 49.96 50.26 50.51 Alabama Morgan 77.30 62.50 62.80 64.07 Alabama Russell 71.30 55.24 55.47 56.26 Alabama Shelby 85.70 63.20 63.50 63.65 Alabama Sumter 64.00 53.65 53.78 54.02 Alabama Talladega 72.00 52.98 53.18 53.40 Alabama Tuscaloosa 73.30 53.52 53.74 53.93 Arizona Cochise 71.30 62.66 62.74 62.74 Arizona Coconino 73.00 64.74 64.77 64.80 Arizona Gila 80.30 62.93 63.10 63.10 Arizona La Paz 72.00 62.13 62.17 62.20 Arizona Maricopa 83.00 68.91 69.06 68.99 Arizona Pima 76.00 63.54 63.67 63.67 Arizona Pinal 79.30 62.93 63.10 63.08 Arizona Yavapai 72.00 62.68 62.74 62.77 Arizona Yuma 75.00 63.11 63.18 63.19 Arkansas Crittenden 87.30 66.66 66.84 67.01 Arkansas Newton 72.70 58.70 58.90 59.46 Arkansas Polk 75.00 62.86 63.01 63.44 Arkansas Pulaski 79.70 59.98 60.21 60.65 California Alameda 78.30 70.90 70.90 70.89 California Amador 83.00 71.11 71.11 71.21 California Butte 83.70 69.84 69.85 70.39 California Calaveras 91.30 80.22 80.23 80.36 California Colusa 67.00 57.77 57.77 57.96 California Contra Co sta 73.30 69.50 69.50 69.49 California El Dorado 96.00 79.46 79.47 79.51 California Fresno 98.30 86.26 86.29 86.37 California Glenn 65.50 56.24 56.24 56.44 California Imperial 85.00 74.04 74.05 74.04 California Inyo 82.30 71.67 71.72 71.76 California Kern 110.00 98.45 98.49 98.47 California Kings 85.70 73.42 73.46 73.55 California Lake 60.70 53.07 53.08 53.16 California Los Angeles 114.00 103.39 103.39 103.23 California Madera 79.30 68.48 68.51 68.6137 California Marin 49.70 45.74 45.74 45.69 California Mariposa 86.30 75.74 75.77 75.88 California Mendocino 56.70 48.97 48.99 49.03 California Merced 89.30 76.57 76.63 76.82 California Monterey 61.00 54.77 54.77 54.80 California Napa 59.30 52.18 52.20 52.23 California Nevada 96.30 80.00 80.01 80.06 California Orange 84.30 81.40 81.35 80.83 California Placer 94.00 78.00 78.01 78.05 California Riverside 112.30 108.72 108.66 108.06 California Sacrament o 97.30 81.05 81.05 81.09 California San Benito 75.00 65.39 65.40 65.46 California San Bernar dino 123.30 121.22 121.17 120.64 California San Dieg o 87.70 76.76 76.75 76.66 California San Franci sco 46.00 45.37 45.37 45.34 California San Joaqui n 75.30 67.86 67.91 68.00 California San Luis Ob ispo 70.70 62.46 62.48 62.56 California San Mat eo 53.70 51.43 51.42 51.39 California Santa Barb ara 76.00 68.26 68.27 68.27 California Santa Clar a 75.30 65.26 65.26 65.25 California Santa Cruz 61.30 55.65 55.64 55.60 California Shasta 79.30 67.23 67.25 67.45 California Siskiyou 63.50 54.67 54.72 54.93 California Solano 72.70 63.93 63.94 63.98 California Sonoma 47.70 41.27 41.28 41.32 California Stanislau s 84.70 74.98 75.05 75.21 California Sutter 82.00 70.06 70.06 70.20 California Tehama 82.70 70.26 70.28 70.58 California Tulare 103.70 89.07 89.34 89.41 California Tuolumne 80.00 70.43 70.44 70.56 California Ventura 89.70 78.66 78.68 78.65 California Yolo 78.70 67.63 67.64 67.67 Colorado Adams 69.00 62.90 62.99 62.91 Colorado Arapahoe 78.70 69.39 69.52 69.51 Colorado Boulder 77.00 67.41 67.52 67.51 Colorado Denver 73.00 66.55 66.64 66.56 Colorado Douglas 83.00 73.88 73.99 73.93 Colorado El Paso 73.30 64.60 64.71 64.74 Colorado Jefferson 81.70 74.36 74.44 74.33 Colorado La Plata 63.70 59.00 59.04 59.06 Colorado Larimer 76.00 66.07 66.19 66.20 Colorado Montezuma 72.00 66.07 66.12 66.15 Colorado Weld 76.70 67.37 67.49 67.52 Connecticut Fairfield 92.30 78.29 78.30 78.22 Connecticut Hartford 84.30 67.41 67.50 67.67 Connecticut Litchfield 87.70 70.05 70.15 70.35 Connecticut Middlesex 90.30 75.10 75.15 75.24 Connecticut New Haven 90.30 76.14 76.17 76.16 Connecticut New London 85.30 70.51 70.54 70.50 Connecticut Tolland 88.70 71.08 71.16 71.33 Delaware Kent 80.30 64.51 64.59 64.71 Delaware New Castle 82.30 68.12 68.18 68.27 Delaware Sussex 82.70 69.10 69.16 69.2538 D.C. Washington 84.70 68.78 68.87 69.00 Florida Alachua 72.00 54.16 54.45 54.67 Florida Baker 68.70 53.65 53.90 54.11 Florida Bay 78.70 61.49 61.98 62.23 Florida Brevard 71.30 57.01 57.34 57.38 Florida Broward 65.00 58.55 58.56 58.63 Florida Collier 68. 30 53.82 54.19 54.28 Florida Columbia 72.00 56.97 57.20 57.41 Florida Duval 77.70 62.74 63.05 63.06 Florida Escambia 82.70 67.06 67.72 67.86 Florida Highlands 72.30 60.69 60.89 61.15 Florida Hillsborough 80.70 64.92 65.19 65.16 Florida Holmes 70.30 56.17 56.39 56.67 Florida Lake 76.70 59.32 59.76 59.80 Florida Lee 70.30 55.56 56.30 56.37 Florida Leon 71.00 53.22 53.59 53.90 Florida Manatee 77.30 60.49 60.76 60.80 Florida Marion 73.00 55.13 55.46 55.60 Florida Miami-Dade 71.30 65.33 65.38 65.39 Florida Orange 79.30 62.40 62.87 62.89 Florida Osceola 72.00 53.73 54.18 54.24 Florida Palm Beach 65.00 57.86 57.97 58.28 Florida Pasco 76.30 59.87 60.19 60.23 Florida Pinellas 72.70 56.09 56.39 56.44 Florida Polk 74.70 57.85 58.14 58.13 Florida St Lucie 66.50 54.79 55.10 55.25 Florida Santa Rosa 80.00 65.21 66.25 66.45 Florida Sarasota 77.30 58.88 59.19 59.24 Florida Seminole 76.00 59.45 59.90 59.91 Florida Volusia 68.30 51.74 52.04 52.18 Florida Wakulla 71.30 57.05 57.40 57.69 Georgia Bibb 81.00 65.09 65.36 65.55 Georgia Chatham 68.30 57.09 57.38 57.71 Georgia Chattooga 75.00 55.41 55.70 55.92 Georgia Clarke 80.70 56.49 56.90 57.15 Georgia Cobb 82.70 59.53 59.97 60.18 Georgia Columbia 73.00 56.28 56.50 57.48 Georgia Coweta 82.00 64.12 64.36 64.57 Georgia Dawson 76.30 52.93 53.38 53.62 Georgia De Kalb 88.70 70.62 70.88 71.01 Georgia Douglas 87.30 63.96 64.29 64.51 Georgia Fayette 85.70 66.46 66.73 66.90 Georgia Fulton 91.70 73.01 73.28 73.42 Georgia Glynn 67.00 52.25 52.53 53.63 Georgia Gwinnett 88.70 65.80 66.20 66.38 Georgia Henry 89.70 66.91 67.26 67.45 Georgia Murray 78.00 60.28 60.54 60.88 Georgia Muscogee 75.70 56.81 57.09 58.02 Georgia Paulding 80.30 56.54 56.86 57.06 Georgia Richmond 80.30 60.76 61.00 62.18 Georgia Rockdale 90.00 65.63 65.99 66.18 Georgia Sumter 72.30 57.41 57.61 57.88 Idaho Ada 76.00 69.69 69.81 69.8039 Idaho Canyon 66.00 59.37 59.53 59.54 Idaho Elmore 63.00 57.30 57.42 57.44 Idaho Kootenai 67.00 57.90 58.14 58.20 Illinois Adams 70.00 58.60 58.74 59.40 Illinois Champaign 68.30 56.65 56.79 57.92 Illinois Clark 66. 00 54.50 54.65 55.15 Illinois Cook 77.70 69.45 69.59 69.84 Illinois Du Page 69.00 62.49 62.60 62.89 Illinois Effingham 70.00 57.72 57.88 58.43 Illinois Hamilton 73.00 58.96 59.13 59.74 Illinois Jersey 78.70 60.58 60.69 61.37 Illinois Kane 74.30 63.36 63.48 63.87 Illinois Lake 78.00 69.37 69.50 69.76 Illinois McHenry 73.30 60.54 60.68 61.07 Illinois McLean 73.00 59.13 59.32 60.09 Illinois Macon 71.30 58.95 59.09 59.82 Illinois Macoupin 73.00 55.79 55.89 56.46 Illinois Madison 83.00 67.06 67.18 67.76 Illinois Peoria 72.70 61.99 62.11 62.76 Illinois Randolph 72.00 60.39 60.54 61.28 Illinois Rock Island 65.30 55.10 55.22 55.90 Illinois St Clair 81.70 66.77 66.90 67.49 Illinois Sangamon 70.00 55.21 55.34 55.91 Illinois Will 71.70 60.94 61.07 61.47 Illinois Winnebago 69.00 57.39 57.55 58.31 Indiana Allen 79.30 63.96 64.25 65.09 Indiana Boone 79.70 63.90 64.06 64.52 Indiana Carroll 74.00 60.27 60.47 61.28 Indiana Clark 80.30 63.04 63.19 63.33 Indiana Delaware 76.30 60.84 61.15 61.86 Indiana Elkhart 79.00 64.27 64.54 65.34 Indiana Floyd 77.70 63.61 63.75 63.84 Indiana Greene 78.30 62.41 62.58 63.52 Indiana Hamilton 82.70 65.75 65.95 66.52 Indiana Hancock 78.00 62.44 62.63 63.17 Indiana Hendricks 75.30 61.36 61.51 61.85 Indiana Huntington 75.00 61.28 61.55 62.17 Indiana Jackson 74.70 60.27 60.42 61.17 Indiana Johnson 76.70 63.38 63.53 63.98 Indiana Lake 81.00 72.55 72.76 73.17 Indiana La Porte 78.50 67.43 67.61 68.08 Indiana Madison 76.70 60.45 60.66 61.36 Indiana Marion 78.70 64.00 64.16 64.66 Indiana Morgan 77.00 63.02 63.21 63.81 Indiana Perry 81.00 63.99 64.18 64.53 Indiana Porter 78.30 69.47 69.70 70.09 Indiana Posey 71.70 57.31 57.46 58.66 Indiana St Joseph 79.30 64.84 65.09 65.83 Indiana Shelby 77.30 64.89 65.06 65.69 Indiana Vanderburgh 77.30 62.01 62.16 63.11 Indiana Vigo 74.00 61.42 61.67 62.23 Indiana Warrick 77.70 63.17 63.32 63.80 Iowa Bremer 66.30 56.49 56.71 57.5840 Iowa Clinton 71.30 60.46 60.61 61.37 Iowa Harrison 74.70 63.57 63.71 64.42 Iowa Linn 68.30 58.14 58.60 59.40 Iowa Montgomery 65.70 55.56 55.71 56.26 Iowa Palo Alto 61.00 52.63 52.80 53.94 Iowa Polk 63.00 51.30 51.45 52.21 Iowa Scott 72.00 60.00 60.15 60.88 Iowa Story 61.00 49.97 50.12 50.99 Iowa Van Buren 69.00 58.37 58.53 59.31 Iowa Warren 64.50 52.00 52.19 53.03 Kansas Douglas 73.00 59.93 60.12 60.58 Kansas Johnson 75.30 62.36 62.53 62.96 Kansas Leavenworth 75.00 63.88 64.02 64.28 Kansas Linn 73.30 59.50 59.72 60.28 Kansas Sedgwick 71.30 59.33 59.51 60.09 Kansas Sumner 71.70 59.82 60.01 60.51 Kansas Trego 70.70 63.18 63.33 63.58 Kansas Wyandotte 75.30 64.35 64.48 64.68 Kentucky Bell 71.70 53.68 53.94 54.25 Kentucky Boone 75. 70 60.67 60.79 60.97 Kentucky Boyd 77.30 63.49 63.61 63.74 Kentucky Bullitt 74.00 59.93 60.07 60.21 Kentucky Campbell 75.00 62.36 62.52 62.78 Kentucky Carter 71.00 56.67 56.80 56.96 Kentucky Christi an 78.00 60.18 60.35 60.57 Kentucky Daviess 75.70 61.46 61.63 61.98 Kentucky Edmonson 73.70 59.28 59.44 59.79 Kentucky Fayette 70.30 57.37 57.53 57.72 Kentucky Greenup 76.70 63.26 63.38 63.53 Kentucky Hancock 74.00 58.82 58.98 59.29 Kentucky Hardin 74.70 60.02 60.16 60.39 Kentucky Henderso n 75.30 61.35 61.49 61.94 Kentucky Jefferson 78.30 64.51 64.65 64.77 Kentucky Jessami ne 73.30 59.35 59.50 59.75 Kentucky Kenton 78.70 63.53 63.72 64.01 Kentucky Livingsto n 73.70 61.16 61.38 61.66 Kentucky McCrack en 73.30 61.71 61.89 62.20 Kentucky McLean 73.00 58.86 59.01 59.46 Kentucky Oldham 83.00 63.73 63.89 64.08 Kentucky Perry 72.30 58.50 58.66 58.97 Kentucky Pike 66.70 53.76 53.91 54.13 Kentucky Pulaski 70.30 58.12 58.26 58.48 Kentucky Simpson 75.70 58.21 58.36 58.76 Kentucky Trigg 70.00 55.17 56.03 56.36 Kentucky Warren 72.00 58.85 58.98 59.24 Louisiana Ascension 82.00 72.39 72.47 72.71 Louisiana Beauregard 75.00 67.63 67.70 67.92 Louisiana Bossier 78.00 62.91 63.20 63.40 Louisiana Caddo 79.00 63.73 63.99 64.31 Louisiana Calcasieu 82.00 72.48 72.63 72.96 Louisiana East Baton Rouge 92.00 81.32 81.44 81.71 Louisiana Grant 73.00 61.93 62.09 62.47 Louisiana Iberville 85.00 75.62 75.70 75.8741 Louisiana Jefferson 83.00 72.83 73.15 73.25 Louisiana Lafayette 82.00 70.27 70.41 70.81 Louisiana Lafourche 79.30 70.35 70.47 70.58 Louisiana Livingston 78.30 69.46 69.54 69.76 Louisiana Orleans 70.00 61.82 62.06 62.13 Louisiana Ouachita 75.30 62.20 62.44 62.68 Louisiana Pointe Coupee 83.70 74.74 74.80 75.10 Louisiana St Bernard 78.00 67.71 67.96 68.09 Louisiana St Charles 77.30 67.58 67.93 68.01 Louisiana St James 76.30 67.78 67.88 68.00 Louisiana St John The Baptis 79.00 69.83 70.00 70.13 Louisiana St Mary 76.00 66.29 66.42 66.59 Louisiana West Baton Rouge 84.30 74.24 74.34 74.60 Maine Cumberland 72.00 58.25 58.28 58.36 Maine Hancock 82.00 67.38 67.53 67.68 Maine Kennebec 69.70 56.68 56.75 56.85 Maine Knox 75.30 61.11 61.18 61.29 Maine Oxford 61.00 51.06 51.18 51.48 Maine Penobscot 67.00 56.74 56.83 56.96 Maine Sagadahoc 68.50 55.72 55.77 55.85 Maine York 74.00 60.68 60.74 60.80 Maryland Anne Arundel 89.70 69.53 69.64 69.80 Maryland Baltimore 85.30 73.83 74.01 74.01 Maryland Calvert 81.00 63.80 64.07 64.20 Maryland Carroll 83.30 64.12 64.22 64.37 Maryland Cecil 90.70 70.33 70.46 70.61 Maryland Charles 86.00 65.14 65.28 65.49 Maryland Frederick 80.30 62.78 62.85 62.99 Maryland Garrett 75.50 60.08 60.25 60.57 Maryland Harford 92.70 78.87 79.07 79.10 Maryland Kent 82.00 63.70 63.79 63.91 Maryland Montgomery 83.00 66.14 66.23 66.33 Maryland Prince Georges 91.00 71.16 71.27 71.44 Maryland Washington 78.30 62.37 62.48 62.67 Massachusetts Barnstable 84.70 69.99 70.07 69.91 Massachusetts Berkshire 79.70 64.49 64.64 64.92 Massachusetts Bristol 82.70 69.53 69.58 69.63 Massachusetts Dukes 83.00 71.26 71.30 71.34 Massachusetts Essex 83.30 71.29 71.39 71.40 Massachusetts Hampden 87.30 70.03 70.12 70.29 Massachusetts Hampshire 85.00 67.67 67.77 67.96 Massachusetts Middlesex 79.00 63.92 64.01 64.13 Massachusetts Norfolk 84.70 68.40 68.56 68.42 Massachusetts Suffolk 80.30 65.30 65.40 65.40 Massachusetts Worcester 80.00 62.82 62.92 63.15 Michigan Allegan 90.00 75.26 75.49 76.18 Michigan Benzie 81.70 68.76 68.99 69.68 Michigan Berrien 82.30 69.63 69.83 70.33 Michigan Cass 80.70 66.37 66.63 67.42 Michigan Clinton 75.70 60.43 60.67 61.16 Michigan Genesee 79.30 64.31 64.56 65.07 Michigan Huron 75.70 63.63 63.84 64.30 Michigan Ingham 76.00 61.87 62.12 62.7242 Michigan Kalamazoo 75.30 61.55 61.83 62.79 Michigan Kent 81.00 65.22 65.50 66.13 Michigan Leelanau 75.70 63.85 64.33 65.03 Michigan Lenawee 78.70 64.93 65.19 65.64 Michigan Macomb 86.00 70.95 71.30 71.69 Michigan Mason 79.70 65.67 65.86 66.57 Michigan Missaukee 73.70 60.43 60.67 61.32 Michigan Muskegon 85.00 71.44 71.61 72.28 Michigan Oakland 78.00 66.14 66.41 66.84 Michigan Ottawa 81.70 67.25 67.45 68.07 Michigan St Clair 82.30 67.67 67.93 68.51 Michigan Schoolcraft 79.30 65.40 65.53 66.22 Michigan Washtenaw 78.30 66.16 66.38 66.68 Michigan Wayne 82.00 69.27 69.51 69.86 Minnesota Anoka 67.70 63.38 63.50 64.14 Minnesota St Louis 65.00 55.52 55.67 56.34 Mississippi Adams 74.70 64.38 64.50 64.82 Mississippi Bolivar 74.30 60.09 60.28 60.72 Mississippi De Soto 82.70 65.16 65.32 65.66 Mississippi Hancock 79.00 66.94 67.23 67.38 Mississippi Harrison 83.00 68.47 68.70 68.87 Mississippi Hinds 71.30 51.08 51.42 51.61 Mississippi Jackson 80.30 67.43 67.67 67.81 Mississippi Lauderdale 74.30 58.34 58.56 58.91 Mississippi Lee 73.70 54.53 54.83 55.11 Missouri Cass 74.70 61.11 61.28 61.72 Missouri Cedar 75.70 62.38 62.56 63.26 Missouri Clay 84.30 70.48 70.64 70.89 Missouri Clinton 83.00 68.47 68.64 68.90 Missouri Greene 73.00 59.73 59.85 60.34 Missouri Jefferson 82.30 69.75 69.91 70.74 Missouri Lincoln 87.00 71.24 71.40 72.26 Missouri Monroe 71.70 58.85 59.00 59.62 Missouri Perry 77.50 63.18 63.34 63.85 Missouri Platte 77.00 65.23 65.37 65.57 Missouri St Charles 87.00 68.85 68.98 69.82 Missouri Ste Genevieve 79.70 66.50 66.63 67.22 Missouri St Louis 88.00 73.02 73.18 74.19 Missouri St Louis Ci ty 84.00 69.19 69.32 69.98 Montana Yellowstone 59.00 54.67 54.70 54.76 Nebraska Douglas 68.70 59.73 59.84 60.48 Nebraska Lancaster 56.00 46.74 46.85 47.41 Nevada Clark 83.70 74.23 74.33 74.20 Nevada Washoe 70.70 60.56 60.62 60.67 Nevada White Pine 72.30 64.93 64.97 65.01 Nevada Carson City 65.00 55.81 55.82 55.87 New Hampshire Belknap 71.30 56.01 55.64 55.73 New Hampshire Cheshire 70.70 56.59 56.65 56.78 New Hampshire Coos 77.00 63.97 64.14 64.43 New Hampshire Grafton 67.00 55.17 55.27 55.44 New Hampshire Hillsbor ough 78.70 63.46 63.53 63.64 New Hampshire Merrimack 71.70 56.02 56.07 56.16 New Hampshire Rockingham 75.00 61.50 61.56 61.6243 New Hampshire Sulliv an 70.00 57.34 57.43 57.60 New Jersey Atlantic 79.30 65.49 65.53 65.60 New Jersey Bergen 86.00 74.46 74.48 74.46 New Jersey Camden 89.30 72.65 72.73 72.88 New Jersey Cumberland 83.30 66.32 66.39 66.54 New Jersey Gloucest er 87.00 71.61 71.68 71.78 New Jersey Hudson 85.70 73.50 73.49 73.47 New Jersey Hunterdo n 89.00 70.25 70.37 70.59 New Jersey Mercer 88.00 72.37 72.46 72.61 New Jersey Middlesex 88.30 71.71 71.78 71.91 New Jersey Monmouth 87.30 74.07 74.07 74.07 New Jersey Morris 83.30 67.01 67.10 67.26 New Jersey Ocean 93.00 75.75 75.80 75.92 New Jersey Passai c 81.00 67.07 67.13 67.24 New Mexico Bernalillo 73.70 61.89 62.02 62.04 New Mexico Dona Ana 75.30 68.52 68.58 68.60 New Mexico Eddy 69.00 64.38 64.44 64.52 New Mexico Grant 66.00 59.96 60.01 60.03 New Mexico Lea 69.50 65.30 65.34 65.40 New Mexico Sandoval 73.30 61.55 61.68 61.70 New Mexico San Juan 71.30 67.01 67.05 67.06 New York Albany 73.70 59.59 59.72 59.97 New York Bronx 74.70 67.68 67.65 67.53 New York Chautauqua 86.70 73.83 74.02 74.51 New York Chemung 68.70 57.63 57.75 58.04 New York Dutchess 75.70 61.25 61.34 61.48 New York Erie 85.00 70.86 71.07 71.58 New York Essex 77.00 64.66 64.81 65.23 New York Hamilton 71.70 60.64 60.77 61.12 New York Herkimer 68.30 58.08 58.21 58.54 New York Jefferson 78.00 64.47 64.72 64.90 New York Madison 72.00 59.75 59.87 60.21 New York Monroe 75.00 62.57 62.81 63.03 New York Niagara 82.70 71.53 71.75 71.96 New York Oneida 68.30 57.13 57.30 57.68 New York Onondaga 73.70 62.92 63.10 63.43 New York Orange 82.00 66.60 66.67 66.78 New York Oswego 78.00 66.62 66.83 67.11 New York Putnam 84.30 69.71 69.76 69.84 New York Queens 80.00 69.57 69.56 69.54 New York Rensselaer 77.30 62.29 62.46 62.73 New York Richmond 88.30 75.53 75.54 75.54 New York Saratoga 79.70 64.39 64.56 64.85 New York Schenectady 70.00 56.91 57.05 57.34 New York Suffolk 90.30 81.86 81.83 81.77 New York Ulster 77.30 62.86 62.98 63.25 New York Wayne 68.00 57.22 57.42 57.64 New York Westchester 87.70 76.61 76.60 76.49 North Carolina Alexander 77.00 57.64 57.72 58.15 North Carolina Avery 70.00 56.78 56.89 57.23 North Carolina Buncombe 74.00 59.31 59.43 59.81 North Carolina Caldwell 74.30 54.93 55.00 55.52 North Carolina Caswell 76.30 58.13 58.22 58.7444 North Carolina Chatham 73.30 56.71 56.79 57.21 North Carolina Cumberland 81.70 61.89 61.96 63.08 North Carolina Davie 81.30 61.84 61.93 62.45 North Carolina Durham 77.00 57.26 57.33 57.88 North Carolina Edgecom be 77.00 58.76 58.84 59.31 North Carolina Forsyth 80.00 62.34 62.43 63.18 North Carolina Franklin 78.70 60.04 60.12 60.63 North Carolina Graham 78.30 61.41 61.64 61.97 North Carolina Granville 82.00 62.59 62.69 63.34 North Carolina Guilford 82.00 61.78 61.88 62.55 North Carolina Haywood 78.30 63.42 63.59 63.90 North Carolina Jackson 76.00 60.49 60.65 61.01 North Carolina Johnston 77.30 57.23 57.29 57.82 North Carolina Lenoir 75.30 60.34 60.41 60.80 North Carolina Lincoln 81.00 60.65 60.73 61.21 North Carolina Martin 75.00 62.23 62.32 62.66 North Carolina Mecklenburg 89.30 68.17 68.22 68.54 North Carolina New Hanov er 72.30 61.79 61.85 62.20 North Carolina Person 77.30 59.81 59.90 60.55 North Carolina Pitt 76.30 57.93 58.02 58.46 North Carolina Rockingham 77.00 57.75 57.83 58.42 North Carolina Rowan 86.70 65.70 65.78 66.31 North Carolina Swain 66.30 52.49 52.64 52.96 North Carolina Union 79.30 58.52 58.58 58.98 North Carolina Wake 80.30 60.77 60.84 61.34 North Carolina Yancey 76.00 61.17 61.33 61.67 North Dakota Billings 61.50 56.57 56.73 56.83 North Dakota Burke 57.50 52.72 52.78 52.96 North Dakota Cass 60.00 51.18 51.52 52.66 North Dakota McKenzie 61.30 56.46 56.59 56.71 North Dakota Oliver 57.70 52.93 52.97 53.04 Ohio Allen 78.70 65.15 65.43 66.13 Ohio Ashtabula 89.00 75.07 75.37 75.86 Ohio Butler 83.30 67.28 67.44 67.76 Ohio Clark 81.00 64.18 64.37 64.80 Ohio Clermont 81.00 65.88 66.04 66.39 Ohio Clinton 82.30 63.54 63.71 63.93 Ohio Cuyahoga 79.70 67.43 67.31 67.51 Ohio Delaware 78.30 63.38 63.60 64.21 Ohio Franklin 86.30 69.39 69.66 70.35 Ohio Geauga 79.30 63.42 63.72 64.17 Ohio Greene 80.30 63.77 63.94 64.26 Ohio Hamilton 84.70 67.74 67.91 68.23 Ohio Jefferson 78.00 62.64 62.84 63.09 Ohio Knox 77.70 61.44 61.69 62.32 Ohio Lake 86.30 71.72 71.46 71.55 Ohio Lawrence 70.70 58.31 58.42 58.56 Ohio Licking 78.00 61.92 62.15 62.73 Ohio Lorain 76.70 64.21 64.00 64.13 Ohio Lucas 81.30 67.70 67.98 68.17 Ohio Madison 79.70 62.16 62.40 62.94 Ohio Mahoning 78.70 61.66 61.93 62.54 Ohio Medina 80.30 65.36 65.66 65.8945 Ohio Miami 76.70 60.06 60.25 60.64 Ohio Montgomery 74.00 58.37 58.52 58.79 Ohio Portage 83.70 66.80 67.14 67.70 Ohio Preble 73.00 57.60 57.77 58.14 Ohio Stark 81.00 64.54 64.81 65.35 Ohio Summit 83.70 67.28 67.64 68.15 Ohio Trumbull 84.30 66.18 66.47 66.97 Ohio Warren 87.70 69.70 69.89 70.27 Ohio Washington 82.70 68.43 68.63 68.89 Ohio Wood 80.00 65.36 65.66 66.26 Oklahoma Adair 75.70 63.78 63.97 64.62 Oklahoma Canadian 76.00 61.58 61.92 62.27 Oklahoma Cherokee 75.70 66.40 66.54 67.53 Oklahoma Cleveland 74.70 62.14 62.38 62.84 Oklahoma Comanche 77.50 64.22 64.39 65.02 Oklahoma Creek 76.70 62.92 63.18 64.18 Oklahoma Dewey 72.70 61.02 61.16 61.45 Oklahoma Kay 78.00 64.01 64.21 64.64 Oklahoma Mc Clain 72.00 59.90 60.12 60.44 Oklahoma Mayes 78.50 69.61 69.75 70.39 Oklahoma Oklahoma 80.00 65.50 65.85 66.18 Oklahoma Ottawa 78.00 65.99 66.20 66.89 Oklahoma Pittsburg 72.00 61.32 61.51 61.91 Oklahoma Tulsa 79.30 67.89 68.12 68.78 Oregon Clackamas 66.30 62.18 62.22 62.26 Oregon Columbia 58.70 54.33 54.45 54.47 Oregon Jackson 68.00 56.05 56.26 56.39 Oregon Lane 69.30 59.58 59.79 60.21 Oregon Marion 65.70 57.84 58.00 58.20 Oregon Multnomah 56.30 57.83 57.67 57.54 Pennsylvania Adams 76.30 60.02 60.12 60.29 Pennsylvania Allegheny 83.70 67.83 67.98 68.28 Pennsylvania Armstrong 83.00 65.65 65.83 66.13 Pennsylvania Beaver 83.00 68.78 68.95 69.26 Pennsylvania Berks 76.00 60.39 60.56 60.80 Pennsylvania Blair 74.30 58.94 59.11 59.40 Pennsylvania Bucks 88.00 73.97 74.03 74.13 Pennsylvania Cambria 74.70 60.00 60.15 60.46 Pennsylvania Centre 78.30 62.20 62.42 62.86 Pennsylvania Chester 86.00 66.77 66.91 67.07 Pennsylvania Clearfield 78.30 62.17 62.36 62.71 Pennsylvania Dauphin 79.30 64.44 64.57 64.79 Pennsylvania Delaware 83.30 68.12 68.18 68.24 Pennsylvania Erie 81.30 68.93 69.15 69.65 Pennsylvania Franklin 72.30 56.99 57.07 57.23 Pennsylvania Greene 80.00 63.42 63.58 63.93 Pennsylvania Indiana 80.00 63.12 63.28 63.58 Pennsylvania Lackawanna 75.30 59.63 59.87 60.26 Pennsylvania Lancaster 83.30 66.27 66.45 66.67 Pennsylvania Lawrence 72.30 57.98 58.17 58.56 Pennsylvania Lehigh 83.30 66.03 66.20 66.47 Pennsylvania Luzerne 76.30 60.42 60.64 60.96 Pennsylvania Lycoming 77.30 62.99 63.16 63.5246 Pennsylvania Mercer 82.00 64.94 65.20 65.75 Pennsylvania Montgomery 85.70 71.24 71.32 71.45 Pennsylvania Northamp ton 84.30 66.73 66.89 67.16 Pennsylvania Perry 77.00 60.90 61.12 61.35 Pennsylvania Philadelphia 90.30 76.12 76.20 76.31 Pennsylvania Tioga 77.70 64.53 64.68 65.00 Pennsylvania Washington 78.30 63.45 63.62 63.93 Pennsylvania Westmoreland 79.00 63.69 63.81 64.08 Pennsylvania York 82.00 65.36 65.46 65.63 Rhode Island Kent 84.30 69.67 69.72 69.80 Rhode Island Providence 82.30 67.79 67.85 67.98 Rhode Island Washington 86.00 71.10 71.15 71.25 South Carolina Abbeville 79.00 60.78 60.90 61.39 South Carolina Aiken 76.00 56.79 57.03 57.94 South Carolina Anderso n 76.50 57.05 57.14 57.65 South Carolina Barnwell 73.00 54.00 54.21 54.90 South Carolina Berkel ey 67.30 52.82 52.90 53.37 South Carolina Charleston 74.00 62.55 62.65 63.11 South Carolina Cherokee 74.00 57.67 57.76 58.27 South Carolina Chester 75.70 56.52 56.58 57.06 South Carolina Chesterfield 75.00 59.34 59.43 59.77 South Carolina Colleton 72.30 56.81 56.99 57.46 South Carolina Darlington 76.30 59.45 59.54 59.95 South Carolina Edgefield 70.00 52.89 53.12 54.04 South Carolina Oconee 73.00 55.09 55.19 55.71 South Carolina Pickens 78.70 59.16 59.23 59.81 South Carolina Richland 82.30 60.63 60.70 61.25 South Carolina Spartan burg 82.30 60.61 60.72 61.90 South Carolina Union 76.00 60.12 60.19 60.62 South Carolina Williamsbu rg 69.30 55.09 55.20 55.56 South Carolina York 76.70 57.92 57.98 58.48 South Dakota Custer 70.00 64.60 64.67 64.73 South Dakota Jackso n 67.00 61.35 61.43 61.53 South Dakota Minnehaha 66.00 56.41 56.64 57.41 Tennessee Anderson 77.30 55.86 56.17 56.44 Tennessee Blount 85.30 62.23 62.52 62.76 Tennessee Davidson 77.70 58.48 58.66 58.83 Tennessee Hamilton 81.00 61.14 61.38 61.55 Tennessee Jefferson 82.30 60.37 60.65 60.88 Tennessee Knox 85.00 61.89 62.17 62.41 Tennessee Loudon 83.00 60.51 61.02 61.29 Tennessee Meigs 80.00 59.26 59.56 59.96 Tennessee Rutherford 76.30 57.32 57.51 57.77 Tennessee Sevier 80.70 59.07 59.34 59.63 Tennessee Shelby 80.70 61.50 61.70 61.91 Tennessee Sullivan 80.30 67.64 67.75 67.90 Tennessee Sumner 83.00 62.79 62.97 63.12 Tennessee Williamson 75.30 56.51 56.66 56.84 Tennessee Wilson 78.70 60.64 60.82 61.02 Texas Bexar 85.00 73.28 73.42 73.59 Texas Brazoria 94.70 82.10 82.18 82.28 Texas Brewster 64.00 56.38 56.47 56.61 Texas Collin 90.30 72.47 72.63 72.8547 Texas Dallas 88.30 74.61 74.73 74.94 Texas Denton 94.00 72.51 72.69 72.94 Texas Ellis 81.70 64.91 65.04 65.25 Texas El Paso 77.70 70.26 70.36 70.39 Texas Galveston 80.30 71.73 71.82 71.98 Texas Gregg 84.30 73.47 73.59 73.80 Texas Harris 100.70 89.17 89.24 89.29 Texas Harrison 79.00 65.63 65.84 66.13 Texas Hidalgo 65.70 57.30 57.43 57.59 Texas Hood 83.00 62.70 62.87 63.15 Texas Hunt 78.00 65.38 65.54 65.81 Texas Jefferson 84.70 74.98 75.10 75.36 Texas Johnson 87.00 67.73 67.89 68.13 Texas Kaufman 74.70 63.61 63.76 63.99 Texas Montgomery 85.00 72.76 72.80 72.80 Texas Nueces 72.30 64.40 64.65 64.84 Texas Orange 78.00 67.62 67.76 68.01 Texas Parker 88.70 66.46 66.64 66.91 Texas Rockwall 79.70 68.23 68.36 68.57 Texas Smith 81.00 69.03 69.18 69.45 Texas Tarrant 95.30 73.84 73.99 74.24 Texas Travis 81.30 66.57 66.78 67.01 Texas Victoria 72.30 63.52 63.62 63.80 Texas Webb 61.30 54.61 54.69 54.84 Utah Box Elder 76.00 68.29 68.41 68.51 Utah Cache 68.70 60.96 61.09 61.19 Utah Davis 81.30 71.92 72.27 72.37 Utah Salt Lake 81.00 71.69 71.82 71.91 Utah San Juan 70.30 64.99 65.03 65.08 Utah Tooele 78.00 67.86 68.25 68.41 Utah Utah 76.70 71.42 71.47 71.52 Utah Washington 78.50 68.96 69.04 69.10 Utah Weber 80.30 70.51 70.78 70.90 Vermont Bennington 72.00 57.74 57.90 58.18 Vermont Chittenden 69.70 58.20 58.37 58.63 Virginia Arlington 86.70 71.91 71.99 72.12 Virginia Caroline 80.00 61.41 61.50 61.75 Virginia Charles City 80.30 65.69 65.75 66.03 Virginia Chesterfield 76.70 62.43 62.48 62.71 Virginia Fairfax 90.00 71.76 71.85 72.04 Virginia Fauquier 72.70 57.67 57.76 57.96 Virginia Frederick 72.30 57.62 57.72 57.93 Virginia Hanover 81.30 64.59 64.66 64.93 Virginia Henrico 82.00 66.09 66.14 66.37 Virginia Loudoun 80.70 62.34 62.41 62.59 Virginia Madison 77.70 63.21 63.29 63.50 Virginia Page 74.00 60.13 60.23 60.48 Virginia Prince William 78.70 61.91 61.98 62.13 Virginia Roanoke 74.70 60.78 60.88 61.22 Virginia Rockbridge 69.70 58.20 58.30 58.59 Virginia Stafford 81.70 63.01 63.07 63.32 Virginia Wythe 72.70 59.33 59.45 59.79 Virginia Alexandria City 81.70 65.14 65.22 65.3948 Virginia Hampton City 76.70 67.57 67.60 67.76 Virginia Suffolk City 76.70 70.76 70.77 70.70 Washington Clark 59.50 59.23 59.22 59.23 Washington King 72.30 66.42 66.48 66.37 Washington Klickitat 64.50 59.53 59.67 59.73 Washington Pierce 68.70 62.11 62.13 62.06 Washington Skagit 46.00 45.83 45.79 45.78 Washington Spokane 68.30 58.43 58.72 58.75 Washington Thurston 65.00 56.82 57.17 57.24 Washington Whatcom 57.00 55.54 55.52 55.50 West Virginia Berkeley 75.00 60.18 60.29 60.48 West Virginia Cabell 78.70 64.75 64.86 65.00 West Virginia Greenbrier 69.70 59.86 59.98 60.36 West Virginia Hancock 75.70 61.88 62.05 62.35 West Virginia Kanawha 77.30 60.83 61.01 61.25 West Virginia Monongalia 75.30 58.16 58.31 58.70 West Virginia Ohio 78.30 62.93 63.12 63.42 West Virginia Wood 79.00 64.31 64.50 64.75 Wisconsin Ashland 61.50 53.14 53.27 53.94 Wisconsin Brown 73.70 62.25 62.40 62.97 Wisconsin Columbia 72.70 59.48 59.69 60.62 Wisconsin Dane 72.00 59.65 59.86 60.83 Wisconsin Dodge 74.70 61.74 62.02 62.90 Wisconsin Door 88.70 72.97 73.11 73.75 Wisconsin Florence 66.30 57.03 57.19 57.74 Wisconsin Fond Du Lac 73.70 61.94 62.17 62.88 Wisconsin Forest 69.50 59.77 59.94 60.53 Wisconsin Jefferson 74.30 61.37 61.46 62.05 Wisconsin Kenosha 84.70 76.27 76.39 76.62 Wisconsin Kewaunee 82.70 68.76 68.87 69.40 Wisconsin Manitowoc 85.00 71.47 71.59 72.11 Wisconsin Marathon 70.00 60.32 60.55 61.30 Wisconsin Milwaukee 82.70 71.63 71.75 72.16 Wisconsin Oneida 69.00 59.60 59.76 60.42 Wisconsin Outagamie 74.00 61.98 62.20 62.89 Wisconsin Ozaukee 83.30 71.67 71.78 72.19 Wisconsin Racine 80.30 71.56 71.68 71.91 Wisconsin Rock 74.00 61.24 61.41 62.34 Wisconsin St Croix 69.00 58.71 58.85 59.84 Wisconsin Sauk 69.70 57.88 58.14 58.97 Wisconsin Sheboygan 88.00 74.71 74.84 75.33 Wisconsin Vernon 69.70 59.00 59.21 59.99 Wisconsin Vilas 68.70 59.51 59.67 60.31 Wisconsin Walworth 75.70 62.74 62.91 63.50 Wisconsin Washington 72.30 61.14 61.32 61.93 Wisconsin Waukesha 75.00 63.24 63.38 63.88 Wyoming Campbell 67.30 64.63 64.68 64.70 Wyoming Sublette 70.00 65.10 65.15 65.21 Wyoming Teton 62.70 57.02 57.12 57.1849United StatesEnvironmental ProtectionAgencyOffice of Air Quality Planning and StandardsAir Quality Assessment DivisionResearch Triangle Park, NCPublication No. EPA 454/R-10-001January 2010EPA 454/R-10-001A January 2010



# Appendix B: 2002 CMAQ Model Performance Evaluation for Ozone, Particulate Matter and Air Toxics

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, NC 27711 January 2010A. Introduction

An operational model performance evaluation for ozone, PM 2.5 and its related speciated components, and specific air t oxics (i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene, acrolein, and naphthalene) was conducted using 2005 St ate/local monitoring sites data in order to estimate the ability of the CMAQ modeling system to replicate the base year concentrations for the 12-km Eastern and West ern United States domain 1 . This evaluation principally comprises statistical assessments of model versus observed pairs that were paired in space and time on a daily or weekly basis, depending on the sampling frequency of each network (measured data). For certain time periods with missing ozone, PM 25 and air toxic observations we excluded the CMAQ predictions from those time periods in our calculations. It should be noted when pairing model and observed data that each CMAQ concentr ation represents a grid-cell volume-averaged value, while the ambient network measurements are made at specific lo cations. In conjunction with the model performance statistics, we also pr ovide spatial plots for individual monitors of the calculated bias and error statistics (defined be low). Statistics were generated for the 12-km Eastern US domain (EUS), 12- km Western US domain (WUS), and five large subregions 2 : Midwest, Northeast, Southeast, Central, and West U.S. The Atmospheric Model Evaluation Tool (AMET) was used to conduct the evaluation described in this document. 3

The ozone evaluation primarily focuses on obs erved and predicted one-hour daily maximum ozone concentrations and eight-hour daily maxi mum ozone concentrations at a threshold of 40ppb. This ozone model performance was limited to the ozone season modeled for the Final National Renewable Fuel Standard Rule (commo nly known as RFS2): May, June, July, August, and September. Ozone ambient measurements for 2005 were obtained from the Air Quality System (AQS) Aerometric Information Retrieval System (AIRS). A total of 1194 ozone measurement sites were included for evaluation. The ozone data were measured and reported on an hourly basis.

The PM 2.5 evaluation focuses on PM 2.5 total mass and its compone nts including sulfate (SO 4 ), nitrate (NO 3 ), total nitrate (TNO 3 =NO 3 +HNO 3 ), ammonium (NH 4 ), elemental carbon (EC), and organic carbon (OC). The PM 2.5 performance statistics were calculated for each month and season individually and for the entire year, as a whole. Seasons were defined as: winter (December-January-February), spring (March-April -May), summer (June-July-August), and fall (September-October-November). PM 2.5 ambient measurements for 2002 were obtained from the

1 See Air Quality Modeling Technical Support Document, 20 10 (EPA 454/R-10-001): Changes to the Renewable Fuel Standard Program (Figure II-1) for the map of the CMAQ modeling domain.

2 The subregions are defined by States where: Midwes t is IL, IN, MI, OH, and WI; Northeast is CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, and VT; Southeast is AL, FL, GA, KY, MS, NC, SC, TN, VA, and WV; Central is AR, IA, KS, LA, MN, MO, NE, OK, and TX; West is AK, CA, OR, WA, AZ, NM, CO, UT, WY, SD, ND, MT, ID, and NV.

3 Gilliam, R. C., W. Appel, and S. Phillips. The Atmosp heric Model Evaluation Tool (AMET): Meteorology Module. Presented at 4th Annual CMAS Models-3 Users Conference, Chapel Hill, NC, September 26 - 28, 2005. (http://www.cmascenter.org/)following networks for model evaluation: S peciation T rends N etwork (STN- total of 260 sites), I nteragency M onitoring of PRO tected V isual E nvironments (IMPROVE- total of 204), C lean A ir S tatus and T rends Net work (CASTNet- total of 93), and N ational A cid D eposition P rogram /N ational T rends ( NADP/NTN- toal of 297). The pollutant species included in the evaluation for each network are listed in Table A-1. For PM 2.5 species that are measured by more than one network, we calculated separate sets of statistics for each network.

Table A-1. PM 2.5 monitoring networks and polluta nts species included in the CMAQ performance evaluation.

Particulate Species Wet Deposition

Species Ambient Monitoring

Networks PM2.5 Mass SO 4 NO 3 TNO 3 a EC NH 4 OC SO 4 NO 3
IMPROVE X X X X X X CASTNet X X X STN X X X X X X N ADP X X

a TNO 3 = (NO 3 + HNO 3 )

The air toxics evaluation focuse s on specific species relevant to the RFS2 final rule, i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene, acrolein, and naphthalene. Similar to the PM2.5 evaluation, the air toxics performance stat istics were calculated for each month and season individually and for the entire year, as a whole to estimate the ability of the CMAQ modeling system to replicate th e base year concentrations for the 12-km Eastern and Western United States domains. As mentioned above, se asons were defined as: winter (DecemberJanuary-February), spring (March -April-May), summer (June-July- August), and fall (SeptemberOctober-November). Toxic measurements for 2 005 were obtained from the National Air Toxics Trends Stations (NATTS). Toxic measurements from 471 sites in the East and 135 sites in the West were included in the evaluation for the 12km Eastern and Western U.S. grids, respectively.

There are various statistical metrics availabl e and used by the scienc e community for model performance evaluation. For a robust evaluation, the principal evaluation statistics used to evaluate CMAQ performance were two bias metric s, normalized mean bias and fractional bias; and two error metrics, normalized me an error and fractional error.

Normalized mean bias (NMB) is used as a norma lization to facilitate a range of concentration magnitudes. This statistic averages the differe nce (model - observed) over the sum of observed values. NMB is a useful model performance indicator because it avoids over inflating the observed range of values, especially at low concentrations. Norma lized mean bias is defined as:NMB = 

### ( )

( ) P O

O n

n − 

## ∑ ∑

1

1 *100

Normalized mean error (NME) is also similar to NMB, where the performanc e statistic is used as a normalization of the mean error. NME calcula tes the absolute value of the difference (model - observed) over the sum of observed values. Normalized mean error is defined as:

NME = 

### ( ) P O

O n

n − 

## ∑

∑ 1

1 *100

Fractional bias is defined as:

FB = 

### ( ) ( ) 1

2 1

1 n P O

P O n

n −

+ ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ ⎛

⎝ ⎜ ⎜ ⎜ ⎜ ⎞

⎠ ⎟ ⎟ ⎟ ⎟ 

# ∑
∑ *100, where P = predicted concentrations and O = observed

concentrations. FB is a useful model perfor mance indicator because it has the advantage of equally weighting positive and negative bias estim ates. The single largest disadvantage in this estimate of model performance is that the estimated concentration (i.e., prediction, P) is found in both the numerator and denominator. Fractional erro r (FE) is similar to fractional bias except the absolute value of the difference is used so that the error is always positive. Fractional error is defined as:

FE = 

### ( ) 1

2 1

1 n P O

P O n

n −

+ ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ ⎛

⎝ ⎜ ⎜ ⎜ ⎜ ⎞

⎠ ⎟ ⎟ ⎟ ⎟ 

# ∑
∑ *100

The “acceptability” of model performance was judged by comparing our CMAQ 2005 performance results to the range of perf ormance found in recent regional ozone, PM 2.5 , and air toxic 4 , 5 , 6 model applications (e.g., Clean Air Interstate Rule 7 , Final PM NAAQS Rule 8 , and EPA’s

4 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform: Air Toxics, Ozone, and Particulate Matter, 7 th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.

5 Strum, M., Wesson, K., Phillips, S., Co ok, R., Michaels, H., Brzezinski, D., Poll ack, A., Jimenez, M., Shepard, S. Impact of using lin-level emissions on multi-pollutant air quality model predictions at regional and local scales. 17 th Annual International Emission Inventory Conference, Portland, Oregon , June 2-5, 2008.

6 Wesson, K., N. Fann, and B. Timin, 2010: Draft Manusc ript: Air Quality and Benefits Model Responsiveness to Varying Horizontal Resolution in the Detroit Urban Ar ea, Atmospheric Pollution Research, Special Issue: Air Quality Modeling and Analysis.Proposal to Designate an Emissions Control Area for Nitrogen Oxides 9 ). These other modeling studies represent a wide range of modeling analyses which cover various models, model configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules. Overall, the NMB, NME, FB, and FE statistics shown in Sections B through P below for CMAQ predicted 2005 ozone, PM 2.5 , and air toxics concentrations are within the range or close to that found in recent OAQPS applications. The CMAQ m odel performance results give us confidence that our applications of CMAQ using this 2005 modeling platform provide a scientifically credible approach for assessing ozone and PM 2.5 concentrations for the purposes of the RFS2 Final Rule. We discuss in the following secti ons the bias and error results for the one-hour maximum ozone concentrations and eight-hour da ily maximum ozone concentrations evaluated at a threshold of 40 ppb, the annual and seasonal PM 2.5 and its related speciated components as well as specific air toxic concentrations.

7 See: U.S. Environmental Protection Ag ency; Technical Support Document for the Final Clean Air Interstate Rule: Air Quality Modeling; Office of Air Quality Planning and Standards; RTP, NC; March 2005 (CAIR Docket OAR2005-0053-2149).

8 U.S. Environmental Protection Agency, 2006. Technical Support Document for the Final PM NAAQS Rule: Office of Air Quality Planning and Stan dards, Research Triangle Park, NC.

9 U.S. Environmental Protection Agency, Proposal to Designate an Emissions Control Area for Nitrogen Oxides, Sulfur Oxides, and Particulate Matter: Technical Support Document. EPA-420-R-007, 329pp., 2009. (http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007.pdf)B. One-Hour Daily M aximum Ozone Performance
Ozone Performance: Threshold of 40 ppb Table B-1 provides one-hour daily maximum ozone model performance statistics calculated for a threshold of 40 ppb of observed and modeled co ncentrations, restricted to the ozone season modeled for the 12-km Eastern and Western U.S. domain and the five subregions (Midwest, Northeast, Southeast, Central and Western U.S.). Spatial plots of the NMB and NME statistics (units of percent) for individual monitors are also provided as a complement to the tabular statistical data (Figures B-1 – B-24). Ov erall, one-hour daily maximum ozone model performance is slightly under-predicted or ne ar negligible in both the 12-km EUS and WUS when applying a threshold of 40 ppb for the modeled ozone season (May-September). For the 12-km Eastern domain, the bias and error statistics are comparable for the aggregate of the ozone season and for each individual ozone month mode led, with a NMB range of -1% to -5% and a FB range of -0.5% to -4%, and a NME and FE range of 11% to 14%. Likewise, for the 12-km Western domain, the bias and error statistics are similar between the ozone seasonal aggregate and the individual months, with a NMB and FB approximately -2%, and a NME and FE approximately 14%. Hourly ozone model pe rformance when compared across the five subregions shows slightly better performance in the Southeast. In general, the Northeast, Midwest, Central and West U.S. exhibit similar bi as and error statistics for the episodes modeled. The month of August shows a sli ghtly better bias and error mode l performance results, although the results are spatially and temporally comparable across the months modeled.
Table B-1. 2005 CMAQ one-hour daily maxi mum ozone model performance statistics calculated for a threshold of 40 ppb.

CMAQ 2005 One-Hour Maximum Ozone: Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 21394 -1.6 11.5 -0.8 11.6 12-km WUS 9631 -3.4 12.8 -2.8 12.7 Midwest 4418 0.8 10.0 1.0 10.2 Northeast 4102 5.4 11.8 5.9 11.7 Southeast 6424 -3.6 11.3 -3.0 11.5 Central U.S. 4328 -6.4 13.4 -5.5 13.4 May

West 8294 -3.5 12.9 -3.0 12.8 12-km EUS 19517 -3.5 12.8 -2.8 12.9 12-km WUS 9056 -3.7 13.0 -3.2 13.0 Midwest 4639 -4.6 12.3 -4.0 12.4 Northeast 4148 -1.0 14.1 -0.1 14.2 Southeast 4644 -2.7 12.5 -2.2 12.6 Central U.S. 4062 -6.2 13.2 -5.4 13.3 June

West 7737 -4.0 13.1 -3.6 13.1 12-km EUS 19692 1.2 14.2 1.8 14.1 12-km WUS 9443 0.4 16.0 1.0 15.8 Midwest 4923 0.4 12.7 0.9 12.6 Northeast 4445 4.2 15.2 4.8 14.9 July

Southeast 4733 4.2 15.1 4.6 14.8Central U.S. 3521 -3.8 14.8 -3.1 14.9 West 8168 0.2 16.2 0.7 16.0 12-km EUS 19643 0.1 13.9 0.8 13.8 12-km WUS 9562 -0.8 15.5 -0.6 15.5 Midwest 4549 0.2 12.2 1.0 12.3 Northeast 4139 0.2 13.2 1.2 13.1 Southeast 5303 3.6 14.9 3.9 14.5 Central U.S. 3589 -4.1 16.2 -2.9 16.1 August

West 8357 -1.0 15.7 -1.0 15.7 12-km EUS 18085 -2.2 12.0 -1.3 12.0 12-km WUS 8725 -3.6 14.1 -3.2 14.3 Midwest 4002 -3.6 10.7 -3.0 10.8 Northeast 3667 -1.8 11.3 -0.7 11.3 Southeast 5259 -0.1 12.1 0.8 12.1 Central U.S. 3286 -6.1 14.5 -5.1 14.5 September

West 7530 -4.1 14.3 -3.8 14.4 12-km EUS 98331 -1.2 12.9 -0.5 12.8 12-km WUS 46417 -2.1 14.3 -1.7 14.2 Midwest 22531 -1.4 11.7 -0.8 11.7 Northeast 20501 1.4 13.3 2.3 13.1 Southeast 26363 0.1 13.1 0.7 13.0 Central U.S. 18786 -5.4 14.4 -4.4 14.4 Seasonal Aggregate (May – September)

West 40086 -2.3 14.5 -2.1 14.4Figure B-1. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., May 2005.

Figure B-2. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., May 2005.Figure B-3. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., June 2005.

Figure B-4. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., June 2005.Figure B-5. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., July 2005.

Figure B-6. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., July 2005.Figure B-7. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., August 2005.

Figure B-8. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., August 2005.Figure B-9. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Ea stern U.S., September 2005.

Figure B-10. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Ea stern U.S., September 2005.Figure B-11. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for East ern U.S., seasonal aggregate 2005.

Figure B-12. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for East ern U.S., seasonal aggregate 2005.Figure B-13. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., May 2005.

Figure B-14. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., May 2005.Figure B-15. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., June 2005.

Figure B-16. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., June 2005.Figure B-17. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., July 2005.

Figure B-18. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., July 2005.Figure B-19. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Western U.S., August 2005.

Figure B-20. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for Western U.S., August 2005.Figure B-21. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for We stern U.S., September 2005.

Figure B-22. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for We stern U.S., September 2005.Figure B-23. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for West ern U.S., seasonal aggregate 2005.

Figure B-24. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb threshold) by monitor for West ern U.S., seasonal aggregate 2005.C. Eight-hour Daily Maximum Ozone Performance
Ozone Performance: Threshold of 40 ppb Table C-1 presents eight-hour daily maximum ozon e model performance bias and error statistics for the entire range of observed and modeled concentrations at a th reshold of 40 ppb for the ozone season modeled for the 12-km Eastern and Western U.S. doma in and the corresponding subregions defined above. Spatial plots of the NMB and NME st atistics (units of percent) for individual monitors based on the aggregat e and the individual ozone months modeled respectively are shown in Figures C-1 through C-24. In general, CMAQ slightly under-predicts eight-hour daily maximum ozone with a threshold of 40 ppb in the months of May, June and August. Likewise, model predictions in the EU S and WUS are slightly over-predicted in the months of July and August. For the 12-km Ea stern domain, the bias st atistics are within the range of approximately -4% to 7%, while the er ror statistics range from 11% to 14% for the aggregate of the ozone season and for most of the months modeled. For the 12-km Western domain, the bias statistics are within the rang e of approximately 3% to -3%, while the error statistics range from 11% to 13% for the aggr egate of the ozone seas on and for the individual months modeled. The five subregions show re latively similar eight-hour daily maximum ozone performance.

Table C-1. 2005 CMAQ eight-hour daily maxi mum ozone model performance statistics calculated for a threshold of 40 pbb.

CMAQ 2005 Eight-Hour Maximum Ozone: Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 19310 -1.0 10.9 -0.4 11.0 12-km WUS 8445 -1.6 12.0 -1.2 12.0 Midwest 3858 0.2 10.0 0.7 10.2 Northeast 3528 5.2 11.4 5.4 11.2 Southeast 6019 -2.1 10.5 -1.6 10.6 Central U.S. 3927 -5.8 12.8 -5.2 13.0 May

West 7234 -1.8 12.1 -1.5 12.0 12-km EUS 17404 -2.1 11.9 -1.5 12.0 12-km WUS 8102 -1.9 11.9 -1.6 11.9 Midwest 4324 -3.8 11.6 -3.4 11.8 Northeast 3590 0.3 13.1 1.0 13.2 Southeast 3924 -0.3 11.4 0.1 11.5 Central U.S. 3663 -5.5 12.1 -5.0 12.3 June

West 6889 -2.2 12.1 -2.0 12.1 12-km EUS 17045 3.3 13.4 3.6 13.3 12-km WUS 8556 3.7 15.0 3.9 14.7 Midwest 4429 1.8 11.8 2.3 11.8 Northeast 3856 6.6 14.6 6.8 14.3 July

Southeast 3806 7.4 15.0 7.3 14.5Central U.S. 3057 -2.3 13.2 -2.1 13.5 West 7407 3.5 15.1 3.6 14.9 12-km EUS 16953 1.9 12.9 2.2 12.9 12-km WUS 8523 1.6 13.9 1.5 13.9 Midwest 4027 0.9 11.3 1.4 11.4 Northeast 3530 1.4 12.3 2.0 12.2 Southeast 4447 7.4 14.7 7.2 14.1 Central U.S. 3096 -3.4 14.4 -3.1 14.8 August

West 7469 1.4 14.1 1.2 14.0 12-km EUS 15190 -1.8 11.2 -1.3 11.3 12-km WUS 7465 -2.4 13.4 -2.6 13.9 Midwest 3265 -4.2 10.2 -4.0 10.4 Northeast 2856 -2.3 10.6 -1.8 10.7 Southeast 4647 1.5 11.2 2.1 11.2 Central U.S. 2798 -6.5 13.6 -6.1 14.0 September

West 6446 -2.9 13.7 -3.1 14.1 12-km EUS 85902 0.1 12.1 0.5 12.1 12-km WUS 41091 0.0 13.3 0.1 13.3 Midwest 19903 -0.9 11.1 -0.5 11.2 Northeast 17360 2.4 12.6 2.9 12.4 Southeast 22843 2.3 12.3 2.6 12.2 Central U.S. 16541 -4.8 13.2 -4.4 13.4 Seasonal Aggregate (May – September)

West 35445 -0.2 13.5 -0.3 13.5Figure C-1. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., May 2005.

Figure C-2. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., May 2005.Figure C-3. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., June 2005.

Figure C-4. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., June 2005.Figure C-5. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., July 2005.

Figure C-6. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., July 2005.Figure C-7. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., August 2005.

Figure C-8. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Eastern U.S., August 2005.Figure C-9. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Ea stern U.S., September 2005.

Figure C-10. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Ea stern U.S., September 2005.Figure C-11. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for East ern U.S., seasonal aggregate 2005.

Figure C-12. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for East ern U.S., seasonal aggregate 2005.Figure C-13. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., May 2005.

Figure C-14. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., May 2005.Figure C-15. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., June 2005.

Figure C-16. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., June 2005.Figure C-17. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., July 2005.

Figure C-18. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor fo r Western U.S., July 2005.Figure C-19. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Western U.S., August 2005.

Figure C-20. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for Western U.S., August 2005.Figure C-21. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for We stern U.S., September 2005.

Figure C-22. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for We stern U.S., September 2005.Figure C-23. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for West ern U.S., seasonal aggregate 2005.

Figure C-24. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb threshold) by monitor for West ern U.S., seasonal aggregate 2005.D. Annual PM 2.5 Species Evaluation Table D-1 provides annual model performance statistics for PM 2.5 and its component species for the 12-km Eastern domain, 12-km Western domain, a nd five subregions de fined above (Midwest, Northeast, Southeast, Central, and West U.S.). Spatial plots of the NMB and NME statistics (units of percent) for individual monitors are also provided as a complement to the tabular statistical data (Figures D-1 – D- 28). In the East, annual total PM 2.5 mass is under-predicted when compared at STN and IMPROVE sites in th e Southeast and Central U.S. In the West, annual total PM 2.5 mass is under-predicted when evaluate d at STN sites and IMPROVE sites, with better performance at STN network (bia s ~ -10). Although not shown here, the mean observed concentrations of PM 2.5 are approximately twice as high at the STN sites (EUS = ~13μg m -3 ; WUS = ~11μg m -3 ) as the IMPROVE sites (EUS = ~7μg m -3 ; WUS = ~4μg m -3 ), thus illustrating the statistical differences betw een the urban STN and rural IMPROVE networks. Sulfate is consistently under-predicted at STN, IMPROVE, and CASTNet sites, with NMB values ranging from -32% to -5%. Overall, sulfate performance is best in the East at urban STN sites. Nitrate is over-predicted in the 12-km Ea stern domain (NMB in the range of 5% to 66%), while nitrate is under-predicted in the 12-km Western domain (NMB in the range of -29% to - 47%). Likewise, model performance of total ni trate at CASTNet sites shows an over-prediction in the East (NMB = 27%) and an under-predict ion in the West (NMB = -3%). Ammonium model performance varies across the STN and CA STNet in the East and West, with a mix of over and under-predictions in the Eastern domai n and also an under-prediction in the West. Elemental carbon is over-predicted at STN sites in the East and West with a bias of ~30% and error of ~60%. Although, EC is under-predicted at IMPROVE sites in the East and West with a NMB of ~ -20% and error of ~45%. Organic carbon is moderately under-predicted for all domains in the STN and IMPROVE networks (bia s ~ -35% and error ~ 60%. Differences in model predictions between IMPROVE and STN networ ks could be attributed to both the rural versus urban characteristics as well as differences in the measurement methodology between the two networks (e.g. blank correction factors, and filter technology used).
Table D-1. 2005 CMAQ annual PM 2.5 species model performance statistics.

CMAQ 2005 Annual PM 2.5 species No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 11797 -0.8 37.6 -2.4 38.7 12-km WUS 3440 -10.0 45.0 -9.5 44.4

Midwest 2318 8.5 35.2 9.2 33.5 Northeast 2977 10.8 41.6 9.9 38.8 Southeast 2960 -13.7 34.0 -14.5 37.1 Central U.S. 2523 -6.3 39.8 -9.8 44.3 STN

West 2826 -10.9 46.1 -10.6 45.0 12-km EUS 9321 -6.4 41.6 -7.2 43.6 12-km WUS 10411 -19.9 44.6 -21.9 48.4

Midwest 571 1.3 36.8 1.3 37.2 PM 2.5 Total Mass

IMPROVE

Northeast 2339 12.1 47.7 8.1 44.3Southeast 1694 -17.6 37.5 -16.9 43.1 Central U.S. 2376 -13.3 41.7 -11.9 46.2 West 9258 -22.9 44.8 -23.5 48.6 12-km EUS 13897 -12.3 33.2 -9.2 35.9 12-km WUS 3920 -17.0 42.3 -7.8 42.8

Midwest 2495 -5.2 34.0 0.7 34.8 Northeast 3441 -7.7 32.1 -4.0 33.9 Southeast 3499 -14.5 30.9 -12.2 33.4 Central U.S. 2944 -22.5 37.2 -19.5 41.6 STN

West 3157 -15.5 45.8 -6.7 44.0 12-km EUS 9034 -15.2 34.5 -6.2 38.9 12-km WUS 10002 -14.4 41.0 2.7 44.5

Midwest 531 -12.7 32.5 -5.0 34.6 Northeast 2253 -7.7 34.3 -0.2 37.3 Southeast 1685 -17.8 32.7 -12.2 36.2 Central U.S. 2350 -23.5 36.6 -16.1 40.7 IMPROVE

West 8896 -10.5 42.3 5.0 45.1 12-km EUS 3170 -19.3 24.8 -18.5 27.8 12-km WUS 1142 -24.6 33.6 -16.6 35.2

Midwest 615 -16.7 23.4 -14.8 24.8 Northeast 786 -14.2 22.6 -12.3 24.8 Southeast 1099 -21.5 24.7 -23.6 27.7 Central U.S. 300 -32.2 34.3 -33.6 38.4 Sulfate

CASTNet

West 1091 -23.7 33.8 -15.8 35.2 12-km EUS 12741 25.5 70.4 -8.1 78.1 12-km WUS 3655 -41.7 65.3 -70.8 97.5

Midwest 2495 29.8 64.7 17.1 63.9 Northeast 3442 37.0 73.9 3.2 73.6 Southeast 3499 13.1 78.2 -27.5 86.0 Central U.S. 1812 5.2 58.8 -6.2 71.6 STN

West 31339 -47.3 65.4 -79.1 99.9 12-km EUS 9027 34.2 86.9 -31.6 101.6 12-km WUS 9987 -29.7 72.5 -93.8 124.0

Midwest 531 20.7 69.8 -7.3 82.1 Northeast 2248 66.4 106.9 -1.0 95.4 Southeast 1685 42.5 106.6 -37.5 105.2 Central U.S. 2350 22.8 72.7 -20.5 95.0 Nitrate

IMPROVE

West 8881 -45.7 75.6 -102.3 128.4 12-km EUS 3170 27.3 40.3 21.9 38.1 12-km WUS 1142 -3.3 34.5 6.4 39.9

Midwest 615 26.1 37.4 26.9 34.1 Northeast 786 40.6 46.0 34.4 42.4 Southeast 1099 25.7 41.4 17.2 39.2 Total Nitrate (NO3 + HNO3) CASTNet

Central U.S. 300 10.4 33.9 7.3 33.3West 1091 -4.7 35.7 6.4 40.5 12-km EUS 13897 6.7 42.3 12.6 45.4 12-km WUS 3893 -14.9 55.3 7.1 55.0

Midwest 2495 14.3 42.0 22.3 42.2 Northeast 3498 14.7 44.3 25.0 46.3 Southeast 3882 0.8 39.1 6.5 42.0 Central U.S. 3059 -4.3 43.6 -0.2 50.4 STN

West 3130 -20.5 59.0 5.8 57.2 12-km EUS 3170 -1.3 34.6 0.4 35.8 12-km WUS 1142 -14.5 38.8 -12.1 40.1

Midwest 615 8.1 34.6 12.8 33.3 Northeast 786 6.2 37.2 11.3 35.9 Southeast 1099 -13.6 32.6 -13.7 36.5 Central U.S. 300 -4.2 35.4 -1.1 39.6 Ammonium

CASTNet

West 1091 -20.8 39.1 -13.9 40.2 12-km EUS 14038 25.9 66.0 18.2 54.3 12-km WUS 3814 31.1 77.7 19.5 62.5

Midwest 2502 18.7 51.7 20.1 47.1 Northeast 3479 37.7 70.5 26.7 54.7 Southeast 3877 10.7 59.1 8.1 49.4 Central U.S. 3221 48.1 86.3 26.5 64.3 STN

West 3015 38.7 82.8 21.0 65.1 12-km EUS 8668 -25.9 49.1 -28.3 56.0 12-km WUS 9495 -10.2 57.2 -17.8 60.4

Midwest 602 -16.8 41.8 -28.3 50.4 Northeast 2117 -4.8 48.8 -15.5 54.1 Southeast 1584 -46.1 51.4 -51.5 62.8 Central U.S. 2123 -31.3 49.5 -30.1 56.5 Elemental

Carbon

IMPROVE

West 8518 -9.6 58.2 -18.2 61.5 12-km EUS 12619 -35.1 52.6 -32.5 63.9 12-km WUS 3582 -32.1 56.7 -28.2 61.3

Midwest 2380 -37.3 51.9 -31.0 62.5 Northeast 3323 -17.4 52.7 -13.7 60.3 Southeast 3802 -45.7 52.9 -48.7 66.7 Central U.S. 2259 -38.8 53.4 -37.3 66.9 STN

West 3060 -31.7 57.6 -27.8 61.4 12-km EUS 8662 -29.9 50.6 -34.3 59.1 12-km WUS 9495 -24.0 57.1 -29.4 62.8

Midwest 601 -33.1 43.6 -39.3 53.2 Northeast 2116 -7.7 52.4 -14.4 53.5 Southeast 1587 -37.8 46.7 -48.0 60.2 Central U.S. 2123 -42.5 54.2 -46.8 65.2 Organic Carbon

IMPROVE

West 8518 -22.3 57.3 -28.1 62.7Figure D-1. Normalized Mean Bias (%) of annual PM 2.5 by monitor for Eastern U.S., 2005.

Figure D-2. Normalized Mean Error (%) of annual PM 2.5 by monitor for Eastern U.S., 2005.Figure D-3. Normalized Mean Bias (%) of annual sulfate by monitor for Eastern U.S., 2005.

Figure D-4. Normalized Mean Error (%) of a nnual sulfate by monitor for Eastern U.S., 2005.Figure D-5. Normalized Mean Bias (%) of an nual nitrate by monitor for Eastern U.S., 2005.

Figure D-6. Normalized Mean Error (%) of annual nitrate by monitor for Eastern U.S., 2005.Figure D-7. Normalized Mean Bias (%) of annual total nitrate by monitor for Eastern U.S., 2005.

Figure D-8. Normalized Mean Error (%) of a nnual total nitrate by monitor for Eastern U.S., 2005.Figure D-9. Normalized Mean Bias (%) of an nual ammonium by monitor for Eastern U.S., 2005.

Figure D-10. Normalized Mean Error (%) of annual ammonium by monitor for Eastern U.S., 2005.Figure D-11. Normalized Mean Bias (%) of annual elemental carbon by monitor for Eastern U.S., 2005.

Figure D-12. Normalized Mean Error (%) of annual elemental carbon by monitor for Eastern U.S., 2005.Figure D-13. Normalized Mean Bias (%) of a nnual organic carbon by monitor for Eastern U.S., 2005.

Figure D-14. Normalized Mean Error (%) of annual organic carbon by monitor for Eastern U.S., 2005.Figure D-15. Normalized Mean Bias (%) of annual PM 2.5 by monitor for Western U.S., 2005.

Figure D-16. Normalized Mean Error (%) of annual PM 2.5 by monitor for Western U.S., 2005.Figure D-17. Normalized Mean Bias (%) of annual sulfate by monitor for Western U.S., 2005.

Figure D-18. Normalized Mean Error (%) of annual sulfate by monitor for Western U.S., 2005.Figure D-19. Normalized Mean Bias (%) of annual nitrate by monitor for Western U.S., 2005.

Figure D-20. Normalized Mean Error (%) of annual nitrate by monitor for Western U.S., 2005.Figure D-21. Normalized Mean Bias (%) of a nnual total nitrate by monitor for Western U.S., 2005.

Figure D-22. Normalized Mean Error (%) of a nnual total nitrate by monitor for Western U.S., 2005.Figure D-23. Normalized Mean Bias (%) of annual ammonium by monitor for Western U.S., 2005.

Figure D-24. Normalized Mean Error (%) of annual ammonium by monitor for Western U.S., 2005.Figure D-25. Normalized Mean Bias (%) of annual elemental carbon by monitor for Western U.S., 2005.

Figure D-26. Normalized Mean Error (%) of annual elemental carbon by monitor for Western U.S., 2005.Figure D-27. Normalized Mean Bias (%) of a nnual organic carbon by monitor for Western U.S., 2005.

Figure D-28. Normalized Mean Error (%) of annual organic carbon by monitor for Western U.S., 2005.E. Seasonal PM 2.5 Total Mass Performance Seasonal model performance statistics for PM 2.5 total mass are shown in Table E-1. Spatial plots of the NMB and NME statistics (units of percent) for individual m onitors are also provided as a complement to the tabular statistical data (Figures E-1 – E-16). Total PM 2.5 mass is generally over-predicted in the cooler seas ons (winter and fall) in the 12 -km Eastern domain for both STN and IMPROVE networks. In the fall season, PM 2.5 is over-predicted for Eastern STN sites with NMB values ranging from 0% to 15% whereas PM 2.5 is under-predicted at Eastern IMPROVE sites. In the winter season, PM 2.5 is over-predicted for Eastern STN and IMPROVE networks with NMB values ranging from 3% to 39% and FB values ranging from 60% to 72%. However, in the 12-km Western domain, PM 2.5 is under-predicted in the winter (NMB in the range of -2% to -11%) and the fall (NMB in th e range of -7% to -26%). Note that for comparison of West versus East STN sites, the total number of Western sites is usually less than a third of the Eastern sites. In the spring, CM AQ generally over-predicts PM 2.5 in the East and We st at urban STN and rural IMPROVE sites. In the summer season, PM 2.5 is under-predicted in the East and West for STN and IMPROVE (NMB = ~ 30% and NME = ~40%).

Table E-1. CMAQ 2005 seasonal mode l performance statistics for PM 2.5 total mass.

CMAQ 2005 PM2.5 total mass No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 2861 8.8 37.8 5.5 36.9
12-km WUS 895 -6.2 54.7 -3.2 53.0
Northeast 716 22.8 40.9 18.1 35.7
Midwest 542 7.3 32.3 8.5 31.1
Southeast 762 -2.6 35.7 -5.0 36.6
Central 635 10.0 43.0 4.5 43.0 STN

West 739 -10.9 55.0 -8.7 54.6
12-km EUS 2213 16.3 44.7 13.4 43.3
12-km WUS 2493 -1.9 49.3 -2.2 49.0
Northeast 573 39.4 54.3 28.7 44.0
Midwest 143 7.1 34.3 4.7 34.3
Southeast 406 3.6 38.6 -0.9 41.0
Central 546 5.6 43.1 5.5 44.6 Winter

IMPROVE

West 2217 -8.8 49.3 -4.3 49.4
12-km EUS 3159 10.5 41.2 6.5 39.1
12-km WUS 964 0.5 43.2 -1.1 41.2
Northeast 795 31.1 51.4 24.6 43.3
Midwest 612 31.2 46.2 25.1 39.5
Southeast 798 -7.3 33.1 -7.8 34.2
Central 752 -6.6 37.1 -9.4 40.8 STN

West 773 3.2 45.5 -0.2 41.9 Spring

IMPROVE 12-km EUS 2385 3.4 42.5 0.2 41.512-km WUS 2692 -24.3 43.0 -26.6 47.0
Northeast 630 33.9 55.7 20.2 45.5
Midwest 153 20.6 44.4 16.7 41.5
Southeast 429 -8.6 34.5 -7.0 36.3
Central 601 -14.6 39.4 -12.5 43.5
West 2389 -27.1 44.4 -28.2 47.5
12-km EUS 2950 -29.9 37.8 -36.2 46.5
12-km WUS 935 -20.9 35.9 -21.0 40.3
Northeast 754 -27.7 36.1 -30.5 41.6
Midwest 558 -21.1 30.8 -21.6 33.8
Southeast 722 -39.1 42.7 -49.9 54.8
Central 701 -29.5 40.4 -38.8 53.1 STN

West 758 -18.2 36.2 -19.0 40.3
12-km EUS 2277 -38.1 43.7 -45.2 53.4
12-km WUS 2433 -27.7 43.5 -31.1 48.5
Northeast 580 -32.6 41.2 -41.3 50.5
Midwest 156 -31.1 63.6 -33.8 41.9
Southeast 421 -46.6 48.4 -63.1 67.7
Central 568 -40.7 45.9 -49.1 57.7 Summer

IMPROVE

West 2155 -26.0 43.7 -29.7 48.0
12-km EUS 2802 -0.9 36.8 1.3 37.0
12-km WUS 962 -6.9 46.2 -4.9 45.3
Northeast 755 15.1 44.8 10.9 39.2
Midwest 606 0.1 32.2 4.4 31.6
Southeast 785 -17.0 32.5 -16.0 36.0
Central 435 6.9 42.4 11.1 42.9 STN

West 812 -9.1 47.2 -7.8 45.8
12-km EUS 2256 -7.4 41.8 -8.2 43.3
12-km WUS 2600 -21.6 46.9 -27.2 51.1
Northeast 556 14.55 48.2 7.1 43.1
Midwest 119 -5.3 37.1 -2.7 39.2
Southeast 438 -23.6 36.5 -22.8 42.4
Central 549 -8.01 44.3 -5.4 47.4 Fall

IMPROVE

West 2304 -26.5 46.0 -30.3 51.1Figure E-1. Normalized Mean Bias (%) of PM 2.5 by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure E-2. Normalized Mean Error (%) of PM 2.5 by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure E-3. Normalized Mean Bias (%) of PM 2.5 by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure E-4. Normalized Mean Error (%) of PM 2.5 by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure E-5. Normalized Mean Bias (%) of PM 2.5 by monitor for 12-km Eastern U.S. domain, Summer 2005.

Figure E-6. Normalized Mean Error (%) of PM 2.5 by monitor for 12-km Eastern U.S. domain., Summer 2005.Figure E-7. Normalized Mean Bias (%) of PM 2.5 by monitor for 12-km Eastern U.S. domain, Fall 2005.

Figure E-8. Normalized Mean Error (%) of PM 2.5 by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure E-9. Normalized Mean Bias (%) of PM 2.5 by monitor for 12-km Western U.S. domain, Winter 2005.

Figure E-10. Normalized Mean Error (%) of PM 2.5 by monitor for 12-km Western U.S. domain, Winter 2005.Figure E-11. Normalized Mean Bias (%) of PM 2.5 by monitor for 12-km Western U.S. domain, Spring 2005.

Figure E-12. Normalized Mean Error (%) of PM 2.5 by monitor for 12-km Western U.S. domain, Spring 2005.Figure E-13. Normalized Mean Bias (%) of PM 2.5 by monitor for 12-km Western U.S. domain, Summer 2005.

Figure E-14. Normalized Mean Error (%) of PM 2.5 by monitor for 12-km Western U.S. domain, Summer 2005.Figure E-15. Normalized Mean Bias (%) of PM 2.5 by monitor for 12-km Western U.S. domain, Fall 2005.

Figure E-16. Normalized Mean Error (%) of PM 2.5 by monitor for 12-km Western U.S. domain, Fall 2005.F. Seasonal Sulfate Performance As seen in Table F-1, CMAQ generally under-predi cts sulfate in the 12-km Eastern and Western domains throughout the entire year. Spatial pl ots of the NMB and NME statistics (units of percent) for individual monitors are also provided in Figures F-1 – F-16. Sulfate predictions during the winter season show NM B values ranging from -7% to - 35% and in the East and with NMB values range from -1% to -10% in the West . In the fall season, sulfate predictions show NMB values ranging from -14% to -29%, acros s STN, IMPROVE, and CASTNet networks in the East and West. In the spring, sulfate predicti ons for the most part are under-predicted in the East and West, with NMB values ranging from -2% to -31%. Sulfate predictions during the summer season are moderately under-predicted in the East and West across the available monitoring data (NMB values rage from -17% to -38%.
Table F-1. CMAQ 2005 seasonal model p erformance statistics for sulfate.

CMAQ 2005 Sulfate No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 3385 -15.1 38.0 -16.7 40.7 12-km WUS 1033 -7.3 55.4 -2.6 53.7 Northeast 828 -17.3 35.9 -21.5 37.7 Midwest 598 -7.4 39.4 -12.7 42.2 Southeast 963 -13.1 36.7 -12.4 37.9 Central 766 -22.0 39.3 -21.2 44.1 STN

West 830 -10.3 58.1 -3.5 54.9 12-km EUS 2018 -16.2 37.1 -6.5 42.8 12-km WUS 2370 -1.3 50.6 22.1 53.1 Northeast 502 -16.3 31.8 -18.9 34.7 Midwest 129 -7.1 35.6 -12.0 36.8 Southeast 386 -13.7 35.7 -9.5 37.3 Central 511 -27.1 42.6 -21.6 47.2 IMPROVE

West 2120 8.5 51.9 26.6 53.4 12-km EUS 760 -20.4 25.4 -20.2 30.2 12-km WUS 267 -5.4 33.2 9.4 35.5 Northeast 193 -19.7 24.4 -24.3 28.7 Midwest 142 -16.9 23.4 -20.2 29.1 Southeast 264 -21.2 3-23.9 -21.5 25.3 Central 72 -35.7 36.6 -40.2 41.7 Winter

CASTNet

West 255 -1.6 34.1 11.3 35.5 12-km EUS 3626 -7.3 32.9 -6.2 33.6 12-km WUS 1085 -12.3 35.0 -3.4 35.8 Northeast 894 4.2 34.8 2.9 34.0 Midwest 637 11.3 39.2 10.8 36.1 Southeast 988 -13.7 28.0 -13.7 30.4 Central 875 -23.0 33.9 -19.2 35.9 STN

West 867 -6.2 37.5 0.3 36.7 Spring

IMPROVE 12-km EUS 2378 -10.3 34.0 -4.6 35.412-km WUS 2642 -9.2 34.8 2.2 36.8 Northeast 630 4.3 37.2 4.2 38.3 Midwest 147 -2.3 36.2 2.2 36.0 Southeast 436 -14.7 29.7 -11.9 31.2 Central 605 -25.8 35.4 -18.9 36.5 West 2352 -5.1 35.3 4.6 37.1 12-km EUS 832 -10.9 23.6 -9.6 24.6 12-km WUS 287 -18.1 26.3 -14.8 26.3 Northeast 206 0.4 24.4 1.1 26.2 Midwest 155 -4.9 22.2 -2.9 21.6 Southeast 292 -15.8 21.9 -17.1 23.8 Central 78 -31.2 34.3 -27.9 34.1 CASTNet

West 274 -16.9 25.9 -14.2 26.1 12-km EUS 3512 -28.1 36.2 -27.3 42.7 12-km WUS 1075 -35.3 43.4 -28.8 45.1 Northeast 874 -22.9 32.1 -16.5 35.3 Midwest 621 -22.8 33.1 -12.9 34.0 Southeast 941 -31.3 37.5 -34.2 44.8 Central 847 -37.9 44.4 -43.8 56.4 STN

West 853 -35.4 45.8 -27.3 45.6 12-km EUS 2269 -32.8 40.3 -28.4 46.7 12-km WUS 2340 -32.2 43.8 -23.9 47.2 Northeast 590 -26.3 37.2 -15.4 42.6 Midwest 158 -31.3 37.0 -20.5 40.5 Southeast 427 -37.8 42.3 -42.3 52.4 Central 572 -37.7 43.9 -34.8 50.9 IMPROVE

West 2066 -30.1 44.2 -22.3 47.2 12-km EUS 792 -22.4 25.7 -25.3 31.7 12-km WUS 295 -38.1 41.2 -40.2 45.8 Northeast 192 -17.9 21.9 -14.5 24.1 Midwest 161 -18.6 23.0 -16.8 25.1 Southeast 270 -24.6 27.0 -31.9 35.0 Central 75 -36.2 38.9 -42.5 48.3 Summer

CASTNet

West 282 -38.2 41.6 -40.1 45.9 12-km EUS 3349 -21.0 32.3 -12.6 33.6 12-km WUS 1095 -17.2 44.2 -7.9 44.5 Northeast 902 -17.0 35.8 -0.2 38.3 Midwest 639 -21.4 32.0 -13.3 32.0 Southeast 990 -22.2 30.9 -17.7 31.9 Central 571 -21.0 35.0 -12.3 36.7 STN

West 900 -14.6 48.8 -6.9 46.4 12-km EUS 2147 -23.7 35.1 -11.7 39.1 12-km WUS 2427 -19.3 41.4 -4.0 44.0 Fall

IMPROVE

Northeast 531 -17.0 35.8 -0.2 38.3Midwest 97 -29.3 33.8 -27.1 36.5 Southeast 436 -26.6 34.2 -20.8 38.3 Central 548 -26.0 35.0 -17.7 40.2 West 2135 -14.9 43.3 -1.5 44.6 12-km EUS 786 -21.8 24.1 -19.4 24.9 12-km WUS 293 -22.4 30.6 -18.2 32.9 Northeast 195 -17.2 20.8 -12.5 20.3 Midwest 157 -23.0 24.9 -19.6 23.8 Southeast 273 -22.9 24.5 -24.3 26.8 Central 75 -25.2 26.7 -24.5 29.9 CASTNet

West 280 -21.5 31.1 -17.6 33.0Figure F-1. Normalized Mean Bias (%) of su lfate by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure F-2. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure F-3. Normalized Mean Bias (%) of su lfate by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure F-4. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure F-5. Normalized Mean Bias (%) of su lfate by monitor for 12-km Eastern U.S. domain, Summer 2005.

Figure F-6. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure F-7. Normalized Mean Bias (%) of su lfate by monitor for 12-km Eastern U.S. domain, Fall 2005.

Figure F-8. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure F-9. Normalized Mean Bias (%) of su lfate by monitor for 12-km Western U.S. domain, Winter 2005.

Figure F-10. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S. domain, Winter 2005.Figure F-11. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S. domain, Spring 2005.

Figure F-12. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S. domain, Spring 2005.Figure F-13. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S. domain, Summer 2005.

Figure F-14. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S. domain, Summer 2005.Figure F-15. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S. domain, Fall 2005.

Figure F-16. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S. domain, Fall 2005.G. Seasonal Nitrate Performance Table G-1 provides the seasonal model performance statistics for nitrate and total nitrate for the 12-km Eastern and Western domains. Spatial plot s of the NMB and NME statistics (units of percent) for individual monitors are also provided as a complement to the tabular statistical data (Figures G-1 – G-32). Overall, nitrate and total nitrat e performance is over-predicted in the EUS and under-predicted in the WUS for all of the se asonal assessments except in the winter and fall season, where total nitrate is under-predicted in the EUS a nd WUS and in the spring where nitrate is over-predicted in the EUS. Likewi se, in the East, nitrate and total nitrate are moderately over-predicted during the spring an d summer seasons (NMB values ranging from 0.4% to 70%). In the East and West, nitrate and total nitrate are moderately under-predicted during the winter season when nitrate is most abunda nt (NMB values ranging from -2% to -46%).

Table G-1. CMAQ 2005 seasonal model p erformance statistics for nitrate.

CMAQ 2005 Nitrate No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 3099 -10.9 45.7 -16.1 57.8 12-km WUS 973 -42.8 61.8 -53.1 82.1 Northeast 829 9.1 44.7 9.1 47.5 Midwest 598 -18.7 38.0 -15.9 44.2 Southeast 963 -14.7 57.2 -34.6 72.8 Central 479 -16.3 46.8 -10.8 56.7 STN

West 831 -46.9 64.1 -60.5 86.1 12-km EUS 2018 -2.0 59.2 -25.0 81.6 12-km WUS 2368 -33.6 62.1 -78.4 108.5 Northeast 502 40.0 71.7 29.3 72.2 Midwest 129 -29.5 43.2 -35.7 64.8 Southeast 386 3.4 75.5 -36.2 82.1 Central 511 -12.6 50.9 -20.1 71.9 Nitrate (Winter)

IMPROVE

West 2118 -46.3 74.2 -84.5 113.9 12-km EUS 760 10.5 27.9 16.9 31.9 12-km WUS 267 13.1 40.4 26.4 49.7 Northeast 193 24.5 30.9 30.0 34.2 Midwest 142 -6.0 19.9 0.3 20.4 Southeast 264 17.7 31.4 14.5 32.1 Central 72 11.4 30.3 13.3 31.1 Total Nitrate (Winter) CASTNet

West 255 14.4 47.4 27.2 51.4 12-km EUS 3254 38.8 72.6 16.8 70.4 12-km WUS 987 -33.5 55.5 -56.9 81.4 Northeast 894 48.8 81.3 39.0 70.5 Midwest 637 47.2 69.3 36.4 60.4 Southeast 988 37.2 86.8 -3.0 81.2 Nitrate (Spring) STN

Central 503 11.6 52.3 7.9 60.3West 859 -38.4 56.8 -63.8 84.3 12-km EUS 2378 41.5 84.1 -6.8 91.3 12-km WUS 2636 -26.8 67.0 -81.2 112.3 Northeast 630 68.9 107.3 29.9 91.3 Midwest 147 42.3 74.5 11.1 79.3 Southeast 436 54.7 97.9 -9.6 89.7 Central 605 21.1 62.6 -14.0 86.3 IMPROVE

West 2346 -39.6 76.5 -88.7 116.9 12-km EUS 832 12.0 35.2 15.7 32.9 12-km WUS 287 -12.2 31.1 -3.6 32.3 Northeast 206 36.9 40.0 31.6 36.9 Midwest 155 27.9 33.5 24.3 30.0 Southeast 292 16.5 34.5 9.7 33.7 Central 78 -1.4 31.0 -1.1 31.3 Total Nitrate (Spring) CASTNet

West 274 -11.8 31.9 -3.0 32.6 12-km EUS 3150 0.4 79.9 -51.1 92.7 12-km WUS 992 -68.2 74.6 -120.1 127.1 Northeast 874 -8.6 78.9 -49.3 91.2 Midwest 621 30.3 79.8 -9.3 74.3 Southeast 941 -28.4 75.3 -73.0 100.4 Central 485 11.1 88.1 -41.5 89.7 STN

West 846 -70.6 74.1 -126.0 130.0 12-km EUS 2269 -3.6 94.2 -78.4 115.2 12-km WUS 2339 -65.9 82.3 -135.3 144.1 Northeast 590 7.1 102.3 -55.8 104.7 Midwest 158 16.2 86.7 -34.0 91.2 Southeast 427 -4.9 96.3 -68.8 112.3 Central 572 0.1 92.0 -64.2 106.9 Nitrate (Summer)

IMPROVE

West 2065 -72.2 82.2 -142.3 148.6 12-km EUS 792 27.0 41.9 14.4 37.6 12-km WUS 295 -13.3 30.9 -13.0 34.1 Northeast 192 40.3 49.6 26.3 43.3 Midwest 161 40.6 45.1 33.1 37.3 Southeast 270 16.8 39.6 8.2 39.0 Central 75 -6.3 26.5 -12.0 29.4 Total Nitrate (Summer) CASTNet

West 282 -14.5 31.5 -13.6 34.6 12-km EUS 3238 72.2 104.6 4.1 83.0 12-km WUS 1048 -42.0 72.4 -52.3 91.6 Northeast 902 82.7 109.2 2.3 81.3 Midwest 639 63.1 88.4 28.5 69.7 Southeast 990 76.6 126.2 -12.3 94.1 Central 460 78.8 107.8 16.1 81.5 STN

West 896 -48.9 69.1 -63.2 92.5 N itrate (Fall)

IMPROVE 12-km EUS 2140 102.3 144.2 -12.7 100.212-km WUS 2421 -5.4 98.3 -65.0 112.1 Northeast 526 112.6 147.0 -4.3 95.0 Midwest 97 85.9 123.8 16.8 92.8 Southeast 436 98.4 161.4 -25.5 107.4 Central 548 119.5 150.5 6.0 98.9 West 2129 -38.1 83.3 -75.4 114.1 12-km EUS 786 60.9 66.6 40.8 50.1 12-km WUS 293 6.0 38.2 17.6 44.1 Northeast 195 73.9 76.7 49.7 55.6 Midwest 157 65.6 66.2 47.0 0.5 Southeast 273 57.6 67.6 36.9 52.3 Central 75 40.7 51.1 29.7 41.3 Total Nitrate (Fall) CASTNet

West 280 2.2 37.0 16.9 44.4Figure G-1. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure G-2. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure G-3. Normalized Mean Bias (%) of tota l nitrate by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure G-4. Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure G-5. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure G-6. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure G-7. Normalized Mean Bias (%) of tota l nitrate by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure G-8 Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure G-9. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S. domain, Summer 2005.

Figure G-10. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure G-11. Normalized Mean Bias (%) of tota l nitrate by monitor fo r 12-km Eastern U.S. domain, Summer 2005.

Figure G-12. Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure G-13. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S. domain, Fall 2005.

Figure G-14. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure G-15. Normalized Mean Bias (%) of tota l nitrate by monitor fo r 12-km Eastern U.S. domain, Fall 2005.

Figure G-16. Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure G-17. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S. domain, Winter 2005.

Figure G-18. Normalized Mean Error (%) of nitrate by monitor fo r 12-km Western U.S. domain, Winter 2005.Figure G-19. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western U.S. domain, Winter 2005.

Figure G-20. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western U.S. domain, Winter 2005.Figure G-21. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S. domain, Spring 2005.

Figure G-22. Normalized Mean Error (%) of nitrate by monitor fo r 12-km Western U.S. domain, Spring 2005.Figure G-23. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western U.S. domain, Spring 2005.

Figure G-24. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western U.S. domain, Spring 2005.Figure G-25. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S. domain, Summer 2005.

Figure G-26. Normalized Mean Error (%) of nitrate by monitor fo r 12-km Western U.S. domain, Summer 2005.Figure G-27. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western U.S. domain, Summer 2005.

Figure G-28. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western U.S. domain, Summer 2005.Figure G-29. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S. domain, Fall 2005.

Figure G-30. Normalized Mean Error (%) of nitrate by monitor fo r 12-km Western U.S. domain, Fall 2005.Figure G-31. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western U.S. domain, Fall 2005.

Figure G-32. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western U.S. domain, Fall 2005.H. Seasonal Ammonium Performance Table H-1 lists the performance statistics for ammonium PM at the STN and CASTNet sites. Spatial plots of the NMB and NME statistics (units of percent) for individual monitors are also provided in Figures H-1 – H-16. In the winter, ammonium perf ormance at STN and CASTNet networks shows an under-predic tion in the EUS and WUS (NMB values range from -1% to - 31%), except in the Northeast (NMB values ra nge from 5% to 20%). Likewise, ammonium performance for the summer season shows an under-p rediction in the East and West. However, in the spring, model pred ictions in the East are over-predict ed, whereas ammonia predictions are under-predicted in the West. Ammonium predictions in the summer are moderately underpredicted for the East and West in both the rural and urban site s (NMB values ranging from -6% to - 37%). Table H-1. CMAQ 2002 seasonal model p erformance statistics for ammonium.

CMAQ 2002 Ammonium No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 3385 -5.3 37.5 -1.8 41.6 12-km WUS 1032 -24.0 58.9 -8.0 63.4 Northeast 828 4.7 34.0 8.7 34.0 Midwest 598 -9.2 32.5 -3.7 33.6 Southeast 963 -6.7 41.5 -4.7 44.3 Central 766 -8.9 41.9 -5.5 50.2 STN

West 829 -31.0 61.1 -14.5 66.0 12-km EUS 760 -1.4 29.8 2.7 31.9 12-km WUS 267 -6.8 37.0 1.2 40.7 Northeast 193 20.5 37.3 22.9 34.8 Midwest 142 -12.7 24.7 -7.8 25.1 Southeast 264 -7.4 27.7 -7.3 29.3 Central 72 -5.8 35.4 -4.9 42.5 Winter

CASTNet

West 255 -12.1 42.1 0.7 41.4 12-km EUS 3626 18.8 46.4 18.5 44.4 12-km WUS 1077 -2.0 47.4 17.5 48.7 Northeast 894 34.7 54.9 38.0 51.7 Midwest 637 39.0 55.0 35.7 48.6 Southeast 988 5.9 37.1 7.1 37.8 Central 875 -4.3 39.3 0.5 42.6 STN

West 859 -2.5 52.2 20.4 51.4 12-km EUS 832 24.1 39.8 18.8 34.3 12-km WUS 287 -5.3 33.1 -3.3 32.0 Northeast 206 42.6 48.3 33.9 38.6 Midwest 155 42.2 49.6 35.7 40.3 Southeast 292 5.3 29.1 4.7 29.4 Central 78 3.7 34.1 4.9 32.9 Spring

CASTNet

West 274 -10.4 32.7 -4.7 32.1 Summer STN 12-km EUS 3512 -18.2 38.5 -9.0 48.012-km WUS 1071 -31.6 49.6 -8.9 51.7 Northeast 874 -17.5 36.9 -1.4 45.2 Midwest 621 -8.5 35.8 9.1 40.7 Southeast 941 -21.4 37.3 -14.3 44.6 Central 847 -27.7 45.7 -27.8 60.3 West 849 -34.7 54.6 -7.2 54.2 12-km EUS 792 -22.6 31.3 -28.0 38.5 12-km WUS 295 -32.9 41.6 -37.3 47.8 Northeast 192 -23.4 29.7 -25.9 34.5 Midwest 161 -6.8 25.2 -3.6 27.0 Southeast 270 -32.8 35.6 -44.8 48.1 Central 75 -20.7 31.4 -24.5 38.1 CASTNet

West 282 -36.9 43.5 -39.1 49.0 12-km EUS 3349 4.4 43.2 18.6 46.8 12-km WUS 1081 -18.3 62.5 8.6 59.8 Northeast 902 15.5 49.9 29.5 51.3 Midwest 639 1.9 37.6 17.8 41.1 Southeast 990 -2.8 40.1 9.7 43.2 Central U.S. 571 6.9 48.5 18.6 51.0 STN

West 886 -24.9 64.7 5.5 61.2 12-km EUS 786 3.0 38.7 7.2 38.5 12-km WUS 293 -3.8 43.4 -7.3 39.9 Northeast 195 7.4 38.8 12.3 35.5 Midwest 157 18.7 44.3 25.5 40.2 Southeast 273 -11.3 35.6 -9.0 39.7 Central 75 10.9 42.0 19.5 45.3 Fall

CASTNet

West 280 -14.5 37.9 -10.8 38.3Figure H-1. Normalized Mean Bias (%) of a mmonium by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure H-2. Normalized Mean Error (%) of ammonium by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure H-3. Normalized Mean Bias (%) of a mmonium by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure H-4. Normalized Mean Error (%) of a mmonium by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure H-5. Normalized Mean Bias (%) of a mmonium by monitor for 12-km Eastern U.S. domain, Summer 2005.

Figure H-6. Normalized Mean Error (%) of a mmonium by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure H-7. Normalized Mean Bias (%) of a mmonium by monitor for 12-km Eastern U.S. domain, Fall 2005.

Figure H-8. Normalized Mean Error (%) of a mmonium by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure H-9. Normalized Mean Bias (%) of a mmonium by monitor fo r 12-km Western U.S. domain, Winter 2005.

Figure H-10. Normalized Mean Error (%) of ammonium by monitor for 12-km Western U.S. domain, Winter 2005.Figure H-11. Normalized Mean Bias (%) of a mmonium by monitor for 12-km Western U.S. domain, Spring 2005.

Figure H-12. Normalized Mean Error (%) of ammonium by monitor for 12-km Western U.S. domain, Spring 2005.Figure H-13. Normalized Mean Bias (%) of a mmonium by monitor for 12-km Western U.S. domain, Summer 2005.

Figure H-14. Normalized Mean Error (%) of ammonium by monitor for 12-km Western U.S. domain, Summer 2005.Figure H-15. Normalized Mean Bias (%) of a mmonium by monitor for 12-km Western U.S. domain, Fall 2005.

Figure H-16. Normalized Mean Error (%) of ammonium by monitor for 12-km Western U.S. domain, Fall 2005.I. Seasonal Elemental Carbon Performance Table I-1 presents the seasonal pe rformance statistics of elementa l carbon for the urban and rural 2005 monitoring data. Spatial plots of the NMB and NME statistics (units of percent) for individual monitors are also provi ded as a complement to the tabular statistical data (Figures I1 – I16). In the winter, elemen tal carbon performance is mixe d across the STN and IMPROVE networks in the EUS and WUS, with a moderate over-prediction at STN sites and a moderate under-prediction at the IMPROVE sites (except during the summer season in the WUS). These biases and errors are not unexpected since th ere are known uncertainties among the scientific community in carbonaceous emissions/measurements, transport, and deposition processes.

Table I-1. CMAQ 2005 seasonal model perfor mance statistics for elemental carbon.

CMAQ 2005 Elemental Carbon No. of Ob s. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 3441 36.3 73.2 24.7 55.5 12-km WUS 657 8.1 71.3 5.4 65.2 Northeast 831 53.3 74.7 36.3 53.8 Midwest 602 53.5 78.1 41.2 56.9 Southeast 964 17.5 60.1 7.9 46.0 Central 811 43.7 90.5 31.0 65.6 STN

West 520 10.4 71.0 0.4 66.1 12-km EUS 2072 -10.1 49.2 -16.4 53.3 12-km WUS 2279 -19.4 57.1 -31.7 68.2 Northeast 522 13.3 51.3 6.1 47.2 Midwest 166 11.3 46.1 -1.9 42.4 Southeast 386 -33.4 45.0 -34.2 52.8 Central 474 -17.3 51.4 -13.6 52.5 Winter

IMPROVE

West 2066 -23.6 56.7 -35.7 70.0 12-km EUS 3672 31.7 68.1 22.1 54.1 12-km WUS 1064 49.5 86.4 24.2 63.8 Northeast 881 52.1 80.4 36.6 58.5 Midwest 637 14.7 49.7 19.5 45.6 Southeast 985 18.9 61.2 15.5 49.1 Central 937 44.9 81.9 22.5 60.9 STN

West 822 65.2 97.3 28.5 67.2 12-km EUS 2296 -23.9 48.5 -17.7 52.3 12-km WUS 2563 -7.6 54.7 -11.4 54.0 Northeast 565 1.9 48.2 -3.1 52.5 Midwest 160 -15.3 42.0 -29.4 50.0 Southeast 408 -43.9 49.7 -42.2 52.2 Central 578 44.9 81.9 22.5 60.9 Spring

IMPROVE

West 2289 -3.6 55.1 -11.4 54.2 12-km EUS 3529 27.4 66.9 17.2 58.0 Summer STN 12-km WUS 1030 62.1 91.3 34.6 62.5Northeast 866 30.4 66.1 24.1 55.7 Midwest 621 3.8 40.1 8.7 44.2 Southeast 940 18.6 66.9 15.5 55.9 Central 571 62.5 98.4 21.2 72.0 West 806 78.9 10.7 41.0 65.8 12-km EUS 2182 -42.2 51.1 -46.8 64.1 12-km WUS 2301 1.4 60.8 0.8 56.6 Northeast 512 -38.1 47.8 -54.2 66.6 Midwest 160 -35.5 40.6 -52.9 58.6 Southeast 384 -58.7 59.8 -81.2 87.0 Central 561 -42.7 51.7 -51.9 65.5 IMPROVE

West 2055 5.1 63.0 4.7 57.1 12-km EUS 3396 10.8 56.9 8.3 49.4 12-km WUS 1063 21.4 70.3 9.0 59.6 Northeast 901 18.8 63.1 10.6 50.9 Midwest 642 8.8 43.6 11.9 42.3 Southeast 988 -5.6 52.2 -6.2 46.9 Central 602 42.4 73.7 34.1 56.4 STN

West 867 26.4 74.3 7.6 61.8 12-km EUS 2118 -25.0 47.4 -32.2 54.5 12-km WUS 2352 -16.0 55.6 -29.2 63.7 Northeast 518 3.6 47.7 -12.5 50.4 Midwest 116 36.6 -25.4 -30.5 51.1 Southeast 406 -47.8 60.9 -49.4 60.3 Central 510 -28.4 44.1 -29.3 50.6 Fall

IMPROVE

West 2108 -16.8 56.8 -30.7 65.5Figure I-1. Normalized Mean Bias (%) of elem ental carbon by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure I-2. Normalized Mean Error (%) of elemental carbon by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure I-3. Normalized Mean Bias (%) of elem ental carbon by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure I-4. Normalized Mean Error (%) of elemental carbon by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure I-5. Normalized Mean Bias (%) of elem ental carbon by monitor for 12-km Eastern U.S. domain, Summer 2005.

Figure I-6. Normalized Mean Error (%) of elemental carbon by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure I-7. Normalized Mean Bias (%) of elem ental carbon by monitor for 12-km Eastern U.S. domain, Fall 2005.

Figure I-8. Normalized Mean Error (%) of elemental carbon by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure I-9. Normalized Mean Bias (%) of elem ental carbon by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure I-10. Normalized Mean Error (%) of elemental carbon by monitor for 12-km Western U.S. domain, Winter 2005.Figure I-11. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Western U.S. domain, Spring 2005.

Figure I-12. Normalized Mean Error (%) of elemental carbon by monitor for 12-km Western U.S. domain, Spring 2005.Figure I-13. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Western U.S. domain, Summer 2005.

Figure I-14. Normalized Mean Error (%) of elemental carbon by monitor for 12-km Western U.S. domain, Summer 2005.Figure I-15. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Western U.S. domain, Fall 2005.

Figure I-16. Normalized Mean Error (%) of elemental carbon by monitor for 12-km Western U.S. domain, Fall 2005.J. Seasonal Organic Carbon Performance Seasonal organic carbon performan ce statistics are provided in Table J-1. Spatial plots of the NMB and NME statistics (units of percent) for individual mon itors are also provided as a complement to the tabular statis tical data (Figures J-1 – J-16). The model predictions generally show moderate under-predictions for all Easter n sites located in the urban STN sites (NMB values range from -12% to -67%) and rural IM PROVE sites (NMB values range from -3% to - 50%). Organic carbon performance in the EU S and WUS shows the largest under estimations during the summer season. For IMPROVE, orga nic carbon performance shows a negative bias in the West (NMB= -3%) and a positive bias in the East (NMB=24%). For STN, organic carbon is under-predicted in the East (NMB= -12%) and West (NMB= -26%). Th ese biases and errors reflect sampling artifacts among each monitoring network. In a ddition, uncertainties exist for primary organic mass emissions and secondary orga nic aerosol formation. Research efforts are ongoing to improve fire emission estimates a nd understand the formati on of semi-volatile compounds, and the partitioning of SOA be tween the gas and particulate phases.
Table J-1. CMAQ 2002 seasonal model perf ormance statistics for organic carbon.

CMAQ 2002 Organic Carbon No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 2063 -12.4 51.2 -2.5 55.2 12-km WUS 606 -25.6 62.0 -22.4 62.6 Northeast 284 -18.1 58.7 -0.7 62.7 Midwest 552 24.4 61.6 34.0 60.4 Southeast 520 -32.4 44.0 -27.8 51.5 Central 568 -22.9 45.4 -13.2 48.5 STN

West 507 -24.2 63.0 -22.0 64.3 12-km EUS 2032 24.5 65.4 14.1 55.4 12-km WUS 2432 -2.9 59.9 -4.3 58.9 Northeast 149 4.6 43.1 16.6 45.0 Midwest 512 89.2 104.2 63.5 71.9 Southeast 439 -12.9 42.9 -22.1 49.9 Central 366 -5.1 52.9 -4.5 51.4 Winter

IMPROVE

West 2307 -4.8 59.3 -5.1 59.4 12-km EUS 2241 -19.1 53.6 -15.7 60.8 12-km WUS 656 -19.8 63.4 -13.7 64.5 Northeast 337 -19.9 57.1 -6.6 63.8 Midwest 583 8.5 55.3 9.8 55.8 Southeast 590 -25.9 47.5 -22.7 54.0 Central 579 -38.7 53.2 -45.7 68.3 STN

West 560 -15.9 65.9 -7.1 65.5 12-km EUS 1917 -27.2 53.0 -32.0 58.5 12-km WUS 2369 -23.7 54.5 -26.4 56.3 Northeast 147 -29.7 43.9 -28.6 52.1 Spring

IMPROVE

Midwest 508 15.6 46.4 8.5 45.0Southeast 442 -42.8 58.3 -60.6 73.0 Central 293 -53.9 59.6 -71.6 81.0 West 2307 -21.5 53.6 -24.4 55.1 12-km EUS 2690 -67.5 69.4 -95.0 100.3 12-km WUS 832 -55.4 60.0 -76.2 83.6 Northeast 408 -69.2 69.8 -98.4 101.2 Midwest 754 -68.1 71.5 -88.4 97.7 Southeast 683 -68.7 69.5 -100.9 104.5 Central 659 -63.3 64.9 -91.1 95.7 STN

West 684 -54.0 59.3 -73.3 81.8 12-km EUS 2133 -66.7 69.0 -95.5 99.0 12-km WUS 2595 -50.4 63.7 -61.9 76.0 Northeast 153 -70.4 70.7 -103.1 104.0 Midwest 523 -66.2 68.5 -73.8 80.0 Southeast 460 -73.8 74.8 -122.3 123.2 Central 411 -74.2 74.5 -120.0 120.7 Summer

IMPROVE

West 2435 -49.4 63.2 -58.1 73.0 12-km EUS 2732 -37.6 50.2 -37.8 60.6 12-km WUS 809 -40.4 57.9 -38.7 63.4 Northeast 418 -44.1 51.3 -42.0 60.1 Midwest 752 -14.6 45.4 -13.2 51.9 Southeast 681 -47.6 52.3 -53.1 65.9 Central 698 -43.7 50.7 -49.2 63.5 STN

West 657 -41.0 59.9 -39.6 66.8 12-km EUS 2205 -25.5 47.2 -32.4 57.6 12-km WUS 2686 -29.4 56.5 -30.0 59.9 Northeast 149 -41.0 44.6 -44.0 53.0 Midwest 514 4.1 43.9 0.5 45.9 Southeast 459 -43.6 50.8 -71.3 77.4 Central 461 -37.8 51.8 -47.2 64.6 Fall

IMPROVE

West 2459 -29.9 56.4 -29.5 59.5Figure J-1. Normalized Mean Bias (%) of or ganic carbon by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure J-2. Normalized Mean Error (%) of or ganic carbon by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure J-3. Normalized Mean Bias (%) of or ganic carbon by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure J-4. Normalized Mean Error (%) of or ganic carbon by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure J-5. Normalized Mean Bias (%) of or ganic carbon by monitor for 12-km Eastern U.S. domain, Summer 2005.

Figure J-6. Normalized Mean Error (%) of or ganic carbon by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure J-7. Normalized Mean Bias (%) of or ganic carbon by monitor for 12-km Eastern U.S. domain, Fall 2005.

Figure J-8. Normalized Mean Error (%) of or ganic carbon by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure J-9. Normalized Mean Bias (%) of or ganic carbon by monitor for 12-km Western U.S. domain, Winter 2005.

Figure J-10. Normalized Mean Error (%) of organic carbon by monitor for 12-km Western U.S. domain, Winter 2005.Figure J-11. Normalized Mean Bias (%) of or ganic carbon by monitor for 12-km Western U.S. domain, Spring 2005.

Figure J-12. Normalized Mean Error (%) of organic carbon by monitor for 12-km Western U.S. domain, Spring 2005.Figure J-13. Normalized Mean Bias (%) of or ganic carbon by monitor for 12-km Western U.S. domain, Summer 2005.

Figure J-14. Normalized Mean Error (%) of organic carbon by monitor for 12-km Western U.S. domain, Summer 2005.Figure J-15. Normalized Mean Bias (%) of or ganic carbon by monitor for 12-km Western U.S. domain, Fall 2005.

Figure J-16. Normalized Mean Error (%) of organic carbon by monitor for 12-km Western U.S. domain, Fall 2005.K. Annual Hazardous Air Pollutants Performance

An annual and seasonal operational model perfor mance evaluation for specific hazardous air pollutants (formaldehyde, acetaldehyde, benzene, acrolein, and 1,3-butadiene) was conducted in order to estimate the ability of the CMAQ modeling system to replicate the base year concentrations for the 12-km Eastern and West ern United States domains. The annual model performance results are presented in Table K- 1 below. Spatial plots of the NMB and NME statistics (units of percent) fo r individual monitors are also pr ovided as a complement to the tabular statistical data (Figures K-1 – K-24). The seasonal results follow in Sections L-P. Toxic measurements from 471 sites in the East and 135 sites in the West were included in the evaluation and were taken from the 2005 State/lo cal monitoring site data in the National Air Toxics Trends Stations (NATTS). Similar to PM 2.5 and ozone, the evaluation principally consists of statistical assessments of model vers us observed pairs that were paired in time and space on daily basis.

Model predictions of annual formaldehyde, acetald ehyde and benzene show ed relatively small bias and error percentages when compared to ob servations. The model yi elded larger bias and error results for 1,3 butadiene and acrolein based on limited monitoring sites. Model performance for HAPs is not as good as model performance for ozone and PM 2.5 . Technical issues in the HAPs data consist of (1) un certainties in monito ring methods; (2) limited measurements in time/space to characterize ambi ent concentrations (“local in nature”); (3) commensurability issues between measurements and model predictions; (4) emissions and science uncertainty issues may also affect mode l performance; and (5) limited data for estimating intercontinental transport that effects the estimation of boundary c onditions (i.e., boundary estimates for some species are much higher than predicted values inside the domain).

As with the national, annual PM 2.5 and ozone CMAQ modeling, the “acceptability” of model performance was judged by comparing our CM AQ 2005 performance results to the limited performance found in recent regional multi-pollutant model applications. 10 , 11 , 12 Overall, the normalized mean bias and error (NMB and NME), as well as the fractional bias and error (FB and FE) statistics shown in Table J-1 indi cate that CMAQ-predic ted 2005 toxics (i.e., observation vs. model predictions) are within the ra nge of recent regional m odeling applications.

10 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform: Air Toxics, Ozone, and Particulate Matter, 7 th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.

11 Strum, M., Wesson, K., Phillips, S., Co ok, R., Michaels, H., Brzezinski, D., Poll ack, A., Jimenez, M., Shepard, S. Impact of using lin-level emissions on multi-pollutant air quality model predictions at regional and local scales. 17 th Annual International Emission Inventory Conference, Portland, Oregon , June 2-5, 2008.

12 Wesson, K., N. Fann, and B. Timin, 2010: Draft Manusc ript: Air Quality and Benefits Model Responsiveness to Varying Horizontal Resolution in the Detroit Urban Ar ea, Atmospheric Pollution Research, Special Issue: Air Quality Modeling and Analysis.Table K-1. CMAQ 2005 annual model perf ormance statistics for air toxics.

CMAQ 2005 Annual No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 6365 -55.5 65.3 -39.2 65.6 12-km WUS 1928 -28.4 52.1 -30.1 60.7 Midwest 771 -77.1 85.4 -25.8 74.0 Northeast 1982 -30.5 51.3 -28.5 61.6 Southeast 1246 -66.2 72.2 -51.3 70.4 Central U.S. 1815 -43.5 51.0 -41.4 61.5 Formaldehyde

West 1746 -25.5 52.3 -26.0 59.8 12-km EUS 6094 -4.2 62.0 -8.2 60.3 12-km WUS 1892 -19.2 53.7 -19.5 59.6 Midwest 703 -12.6 58.0 -12.1 60.0 Northeast 1969 -9.5 62.8 -9.0 63.7 Southeast 1231 0.4 63.5 -6.2 62.2 Central U.S. 1640 1.8 57.0 -4.3 51.1 Acetaldehyde

West 1709 -20.4 54.1 -20.1 60.6 12-km EUS 11615 -32.6 66.8 -13.5 62.8 12-km WUS 3369 -38.4 60.8 -30.4 63.9 Midwest 1425 -8.3 72.7 25.2 62.4 Northeast 2589 21.6 53.3 18.1 46.8 Southeast 2426 -41.1 68.6 -17.2 59.8 Central U.S. 4737 -47.0 68.3 -32.7 69.4 Benzene

West 2333 -30.5 61.2 -19.2 63.4 12-km EUS 8102 -74.7 85.6 -49.4 91.6 12-km WUS 1976 -51.9 82.1 -34.5 91.7 Midwest 516 -78.7 86.2 -48.3 81.9 Northeast 1902 -41.6 55.5 -54.8 71.3 Southeast 1226 -85.4 86.4 -106.2 111.5 Central U.S. 4142 -66.5 85.9 -20.0 89.2 1,3-Butadiene

West 1082 -40.8 77.5 -41.9 85.3 12-km EUS 1660 -94.4 95.0 -131.3 142.2 12-km WUS 783 -95.7 95.7 -168.1 170.4 Midwest n/a n/a n/a n/a n/a Northeast 850 -90.4 91.5 -120.5 134.2 Southeast 278 -97.0 97.0 -156.4 157.0 Central U.S. n/a n/a n/a n/a n/a Acrolein

West 592 -95.9 95.9 -177.6 177.6Figure K-1. Normalized Mean Bias (%) of annual formaldehyde by monitor for Eastern U.S., 2005.

Figure K-2. Normalized Mean Error (%) of annual formaldehyde by monitor for Eastern U.S., 2005.Figure K-3. Normalized Mean Bias (%) of annual acetaldehyde by monitor for Eastern U.S., 2005.

Figure K-4. Normalized Mean Error (%) of annual acetaldehyde by monitor for Eastern U.S., 2005.Figure K-5. Normalized Mean Bias (%) of annual benzene by monitor for Eastern U.S., 2005.

Figure K-6. Normalized Mean Error (%) of annual benzene by monitor for Eastern U.S., 2005.Figure K-7. Normalized Mean Bias (%) of annual 1,3-butadiene by monitor for Eastern U.S., 2005.

Figure K-8. Normalized Mean Error (%) of annual 1,3-butadiene by monitor for Eastern U.S., 2005.Figure K-9. Normalized Mean Bias (%) of an nual acrolein by monitor for Eastern U.S., 2005.

Figure K-10. Normalized Mean Error (%) of a nnual acrolein by monitor for Eastern U.S., 2005.Figure K-15. Normalized Mean Bias (%) of annual formaldehyde by monitor for Western U.S., 2005.

Figure K-16. Normalized Mean Error (%) of annual formaldehyde by monitor for Western U.S., 2005.Figure K-17. Normalized Mean Bias (%) of annual acetaldehyde by monitor for Western U.S., 2005.

Figure K-18. Normalized Mean Error (%) of annual acetaldehyde by monitor for Western U.S., 2005.Figure K-19. Normalized Mean Bias (%) of annual benzene by monitor for Western U.S., 2005.

Figure K-20. Normalized Mean Error (%) of annual benzene by monitor for Western U.S., 2005.Figure K-21. Normalized Mean Bias (%) of annual 1,3-butadiene by monitor for Western U.S., 2005.

Figure K-22. Normalized Mean Error (%) of annual 1,3-butadiene by monitor for Western U.S., 2005.Figure K-23. Normalized Mean Bias (%) of a nnual acrolein by monitor for Western U.S., 2005.

Figure K-24. Normalized Mean Error (%) of a nnual acrolein by monitor for Western U.S., 2005.L. Seasonal Formaldehyde Performance Seasonal formaldehyde performance statistics are provided in Table L-1. Spatial plots of the NMB and NME statistics (units of percent) for individual mon itors are also provided as a complement to the tabular statistical data (Figur es L-1 – L-16). The model predictions generally show moderate under-predictions (bias and error results) for a ll seasons in the Eastern and Western sites (NMB values range from -19% to -84%; NME values range from 44% to 90%). Formaldehyde performance in the EUS and WUS shows the largest under estimations during the Midwest and Southeast areas. These biases a nd errors reflect sampling artifacts mentioned previously among the NATTS monitoring network.
Table L-1. CMAQ 2005 seasonal model perf ormance statistics for formaldehyde.

CMAQ 2005 Formaldehyde No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 1532 -54.2 63.0 -46.9 70.1 12-km WUS 389 -25.1 70.9 -40.1 79.9 Northeast 468 -35.9 48.9 -35.5 57.0 Midwest 171 -64.2 74.1 -39.7 75.9 Southeast 306 -66.2 68.9 -68.5 77.9 Central 456 -49.0 62.0 -31.5 65.9 Winter

West 347 -19.7 72.5 -35.1 79.8 12-km EUS 1480 -55.4 66.3 -37.5 69.3 12-km WUS 440 -34.4 60.3 -26.4 65.9 Northeast 472 -36.7 57.9 -26.9 66.3 Midwest 181 -59.9 71.9 -26.7 67.9 Southeast 249 -70.6 79.1 -41.0 77.8 Central 435 -48.8 57.1 -42.2 69.0 Spring

West 396 -29.4 60.2 -19.6 63.5 12-km EUS 1693 -55.8 65.6 -32.0 60.8 12-km WUS 585 -26.7 44.3 -23.6 47.8 Northeast 608 -27.5 50.6 -20.3 60.7 Midwest 244 -84.0 90.6 -16.9 76.4 Southeast 283 -50.6 58.3 -39.1 56.9 Central 425 -43.2 47.2 -51.3 57.9 Summer

West 538 -25.1 44.3 -20.8 47.1 12-km EUS 1660 -55.8 65.3 -41.0 63.1 12-km WUS 514 -28.9 48.2 -33.2 56.3 Northeast 434 -26.3 49.0 -34.3 62.8 Midwest 175 -67.4 79.4 -23.5 75.0 Southeast 408 -72.8 77.6 -53.2 69.6 Central 499 -36.0 44.4 -41.3 54.1 Fall

West 465 -27.0 48.8 -30.7 56.2Figure L-1. Normalized Mean Bias (%) of fo rmaldehyde by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure L-2. Normalized Mean Error (%) of fo rmaldehyde by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure L-3. Normalized Mean Bias (%) of fo rmaldehyde by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure L-4. Normalized Mean Error (%) of fo rmaldehyde by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure L-5. Normalized Mean Bias (%) of fo rmaldehyde by monitor for 12-km Eastern U.S. domain, Summer 2005.

Figure L-6. Normalized Mean Error (%) of fo rmaldehyde by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure L-7. Normalized Mean Bias (%) of fo rmaldehyde by monitor for 12-km Eastern U.S. domain, Fall 2005.

Figure L-8. Normalized Mean Error (%) of fo rmaldehyde by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure L-9. Normalized Mean Bias (%) of fo rmaldehyde by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure L-10. Normalized Mean Error (%) of formaldehyde by monitor for 12-km Western U.S. domain, Winter 2005.Figure L-11. Normalized Mean Bias (%) of formaldehyde by monitor for 12-km Western U.S. domain, Spring 2005.

Figure L-12. Normalized Mean Error (%) of formaldehyde by monitor for 12-km Western U.S. domain, Spring 2005.Figure L-13. Normalized Mean Bias (%) of formaldehyde by monitor for 12-km Western U.S. domain, Summer 2005.

Figure L-14. Normalized Mean Error (%) of formaldehyde by monitor for 12-km Western U.S. domain, Summer 2005.Figure L-15. Normalized Mean Bias (%) of formaldehyde by monitor for 12-km Western U.S. domain, Fall 2005.

Figure L-16. Normalized Mean Error (%) of formaldehyde by monitor for 12-km Western U.S. domain, Fall 2005.M. Seasonal Acetaldehyde Performance Seasonal acetaldehyde performance statistics ar e provided in Table M-1. Spatial plots of the NMB and NME statistics (units of percent) for individual mon itors are also provided as a complement to the tabular statistical data (Figur es M-1 – M-16). Overall, the model predictions show moderate under-predictions for all Eastern and Western sites (NMB values range from -3% to -47%). Although, in the summer, acetaldehyd e performance in the EUS and WUS shows a positive bias with over-predictions ranging from 15% in the Midwest to 84% in the Southeast. Similar to formaldehyde results the biases and er rors reflect technical issues with observational data (uncertainties in monitoring methods and limited measurements in time and space).
Table M-1. CMAQ 2005 seasonal model perf ormance statistics for acetaldehyde.

CMAQ 2005 Acetaldehyde No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 1454 -44.8 52.0 -46.5 60.5 12-km WUS 388 -30.2 69.9 -44.4 79.7 Northeast 470 -47.8 54.4 -48.7 62.2 Midwest 141 -46.1 51.8 -52.7 66.6 Southeast 304 -41.6 52.6 -44.0 63.9 Central 408 -34.8 40.8 -34.4 44.3 Winter

West 346 -27.9 72.1 -42.1 81.7 12-km EUS 1417 -29.9 50.8 -25.6 56.5 12-km WUS 429 -29.1 54.8 -26.3 60.8 Northeast 473 -33.1 57.4 -23.0 66.3 Midwest 164 -44.9 50.7 -45.5 55.9 Southeast 245 -12.9 52.2 -4.1 57.0 Central 392 -30.5 43.4 -29.4 44.6 Spring

West 383 -29.7 58.3 -25.7 63.8 12-km EUS 1622 47.8 83.6 44.9 66.9 12-km WUS 572 -5.6 47.0 5.9 45.4 Northeast 576 37.6 76.1 40.9 65.6 Midwest 238 15.0 63.5 32.0 56.9 Southeast 278 84.8 103.7 51.9 70.0 Central 397 54.0 87.9 49.2 69.4 Summer

West 525 -8.9 46.1 2.6 45.0 12-km EUS 1601 -8.7 55.3 -11.9 56.9 12-km WUS 503 -24.4 52.2 -23.3 59.3 Northeast 450 -16.0 57.6 -16.9 59.8 Midwest 160 -3.5 60.4 -7.6 63.1 Southeast 404 -16.0 52.9 -19.0 58.8 Central 443 2.2 47.8 -2.3 46.6 Fall

West 455 -26.0 51.9 -25.0 59.9Figure M-1. Normalized Mean Bias (%) of acetaldehyde by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure M-2. Normalized Mean Error (%) of acetaldehyde by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure M-3. Normalized Mean Bias (%) of acetaldehyde by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure M-4. Normalized Mean Error (%) of acetaldehyde by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure M-5. Normalized Mean Bias (%) of acetaldehyde by monitor for 12-km Eastern U.S. domain, Summer 2005.

Figure M-6. Normalized Mean Error (%) of acetaldehyde by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure M-7. Normalized Mean Bias (%) of acetaldehyde by monitor for 12-km Eastern U.S. domain, Fall 2005.

Figure M-8. Normalized Mean Error (%) of acetaldehyde by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure M-9. Normalized Mean Bias (%) of acetaldehyde by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure M-10. Normalized Mean Error (%) of acetaldehyde by monitor for 12-km Western U.S. domain, Winter 2005.Figure M-11. Normalized Mean Bias (%) of acetaldehyde by monitor for 12-km Western U.S. domain, Spring 2005.

Figure M-12. Normalized Mean Error (%) of acetaldehyde by monitor for 12-km Western U.S. domain, Spring 2005.Figure M-13. Normalized Mean Bias (%) of acetaldehyde by monitor for 12-km Western U.S. domain, Summer 2005.

Figure M-14. Normalized Mean Error (%) of acetaldehyde by monitor for 12-km Western U.S. domain, Summer 2005.Figure M-15. Normalized Mean Bias (%) of acetaldehyde by monitor for 12-km Western U.S. domain, Fall 2005.

Figure M-16. Normalized Mean Error (%) of acetaldehyde by monitor for 12-km Western U.S. domain, Fall 2005.N. Seasonal Benzene Performance Seasonal benzene performance sta tistics are provided in Table N-1. Spatial plots of the NMB and NME statistics (units of percent) for individu al monitors are also pr ovided as a complement to the tabular statistical data (Figures N-1 – N-16). The model predictions typically show moderate bias (under-predictions ) and error results for all Eastern and Western NATTS sites (NMB values range from -3% to -52%; NME va lues range from 50% to 75%). However, benzene performance in the Northeast shows ove r-predictions during a ll the seasons (NMB values range from 11% in the Summer and Fall to 30% in the Spring and Winter). Similar to the other HAPs modeled, these biases and errors reflect sampling artifacts among the NATTS monitoring network.
Table N-1. CMAQ 2005 seasonal model p erformance statistics for benzene.

CMAQ 2005 Benzene No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 2846 -28.6 69.7 -6.0 60.0 12-km WUS 781 -39.2 59.1 -39.4 64.9 Northeast 592 31.1 56.7 25.2 46.2 Midwest 322 27.2 72.0 35.0 55.0 Southeast 591 -30.2 67.3 -6.4 55.8 Central 1217 -50.3 74.1 -23.4 66.4 Winter

West 523 -31.4 58.9 -33.8 67.4 12-km EUS 2888 -35.8 65.3 -19.9 63.2 12-km WUS 769 -29.8 56.8 -30.6 62.1 Northeast 632 28.8 52.1 19.5 45.2 Midwest 331 -20.6 67.7 13.1 54.4 Southeast 618 -51.9 70.9 -22.2 61.0 Central 1169 -42.0 62.3 -37.4 69.6 Spring

West 516 -17.0 57.0 -16.1 60.2 12-km EUS 2955 -36.8 67.2 -15.8 67.0 12-km WUS 943 -28.9 58.7 -19.2 61.8 Northeast 751 11.4 52.9 17.2 51.0 Midwest 396 -3.2 76.8 31.4 75.3 Southeast 563 -48.4 69.2 -26.3 62.3 Central 1167 -51.7 68.0 -41.7 73.8 Summer

West 675 -16.9 57.6 -3.0 58.2 12-km EUS 2926 -30.9 64.0 -12.3 60.9 12-km WUS 876 -47.9 65.8 -34.2 67.0 Northeast 614 11.7 49.8 10.9 44.1 Midwest 376 -29.2 74.9 20.8 62.2 Southeast 654 -34.8 66.8 -14.3 60.2 Central 1184 -41.8 64.9 -28.6 67.9 Fall

West 619 -44.0 67.5 -27.1 68.3Figure N-1. Normalized Mean Bias (%) of benzene by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure N-2. Normalized Mean Error (%) of be nzene by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure N-3. Normalized Mean Bias (%) of benzene by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure N-4. Normalized Mean Error (%) of be nzene by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure N-5. Normalized Mean Bias (%) of benzene by monitor for 12-km Eastern U.S. domain, Summer 2005.

Figure N-6. Normalized Mean Error (%) of be nzene by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure N-7. Normalized Mean Bias (%) of benzene by monitor for 12-km Eastern U.S. domain, Fall 2005.

Figure N-8. Normalized Mean Error (%) of be nzene by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure N-9. Normalized Mean Bias (%) of benzene by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure N-10. Normalized Mean Error (%) of be nzene by monitor for 12-km Western U.S. domain, Winter 2005.Figure N-11. Normalized Mean Bias (%) of be nzene by monitor for 12-km Western U.S. domain, Spring 2005.

Figure N-12. Normalized Mean Error (%) of be nzene by monitor for 12-km Western U.S. domain, Spring 2005.Figure N-13. Normalized Mean Bias (%) of be nzene by monitor for 12-km Western U.S. domain, Summer 2005.

Figure N-14. Normalized Mean Error (%) of be nzene by monitor for 12-km Western U.S. domain, Summer 2005.Figure N-15. Normalized Mean Bias (%) of be nzene by monitor for 12-km Western U.S. domain, Fall 2005.

Figure N-16. Normalized Mean Error (%) of be nzene by monitor for 12-km Western U.S. domain, Fall 2005.O. Seasonal 1,3-Butadiene Performance Table O-1 presents the seasonal 1.3-butadiene perf ormance statistics. Spatial plots of the NMB and NME statistics (units of percent) for individu al monitors are also pr ovided as a complement to the tabular statistical data (Figures O-1 – O-16). In general, the model predictions show moderate to large under-predic tions for all Eastern and Wester n sites during all the seasons (NMB values range from -29% to -90%). Pe rformance of 1,3-butadiene shows the largest under estimations in the areas of the Southeast and Cent ral U.S. Likewise, the error results are large ranging from approximately 50% to 100%. These bi ases and errors reveal the underlying issues in the HAPs data (i.e., uncertainties in monitori ng methods; limited measurements in time/space, proportionality issues between measurements and model predictions, emissions and science uncertainty issues, as well as boundary condition estimates).

Table O-1. CMAQ 2005 seasonal model perf ormance statistics for 1,3-butadiene.

CMAQ 2005 1,3-Butadiene No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 2028 -69.8 84.9 -26.0 88.3 12-km WUS 501 -55.3 87.6 -30.7 100.7 Northeast 453 -32.0 53.1 -32.5 60.0 Midwest 133 -34.9 70.5 -2.6 64.5 Southeast 313 -78.4 80.4 -92.9 100.1 Central 1045 -65.2 89.0 6.7 92.4 Winter

West 280 -39.9 83.2 -42.7 95.2 12-km EUS 1963 -80.3 88.8 -52.1 93.6 12-km WUS 491 -36.5 79.4 -26.5 85.6 Northeast 446 -42.7 52.6 -56.0 69.9 Midwest 105 -66.6 75.3 -65.3 93.2 Southeast 294 -83.8 84.4 -108.1 111.0 Central 1011 -67.8 88.1 -17.8 88.4 Spring

West 281 -29.4 75.3 -33.8 81.0 12-km EUS 2003 -79.1 86.7 -63.1 93.8 12-km WUS 482 -47.9 72.6 -40.2 86.0 Northeast 489 -47.8 55.7 -66.4 77.0 Midwest 122 -94.5 95.3 -73.6 89.9 Southeast 279 -90.6 91.0 -122.1 125.3 Central 1059 -71.8 85.0 -39.5 90.0 Summer

West 246 -42.1 65.5 -46.7 78.0 12-km EUS 2108 -69.3 81.1 -56.2 90.6 12-km WUS 502 -57.0 80.3 -40.6 94.3 Northeast 514 -49.1 60.7 -62.4 77.1 Midwest 156 -62.0 70.3 -56.1 82.9 Southeast 340 -87.7 88.8 -103.7 111.1 Central 1027 -60.3 80.5 -29.2 86.0 Fall

West 275 -47.9 76.7 -45.0 86.3Figure O-1. Normalized Mean Bias (%) of 1,3-butadiene by moni tor for 12-km Eastern U.S. domain, Winter 2005.

Figure O-2. Normalized Mean Error (%) of 1,3-butadiene by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure O-3. Normalized Mean Bias (%) of 1,3-butadiene by moni tor for 12-km Eastern U.S. domain, Spring 2005.

Figure O-4. Normalized Mean Error (%) of 1,3-butadiene by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure O-5. Normalized Mean Bias (%) of 1,3-butadiene by moni tor for 12-km Eastern U.S. domain, Summer 2005.

Figure O-6. Normalized Mean Error (%) of 1,3-butadiene by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure O-7. Normalized Mean Bias (%) of 1,3-butadiene by moni tor for 12-km Eastern U.S. domain, Fall 2005.

Figure O-8. Normalized Mean Error (%) of 1,3-butadiene by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure O-9. Normalized Mean Bias (%) of 1,3-butadiene by moni tor for 12-km Eastern U.S. domain, Winter 2005.

Figure O-10. Normalized Mean Error (%) of 1,3-butadiene by monitor for 12-km Western U.S. domain, Winter 2005.Figure O-11. Normalized Mean Bias (%) of 1,3-butadiene by monitor for 12-km Western U.S. domain, Spring 2005.

Figure O-12. Normalized Mean Error (%) of 1,3-butadiene by monitor for 12-km Western U.S. domain, Spring 2005.Figure O-13. Normalized Mean Bias (%) of 1,3-butadiene by monitor for 12-km Western U.S. domain, Summer 2005.

Figure O-14. Normalized Mean Error (%) of 1,3-butadiene by monitor for 12-km Western U.S. domain, Summer 2005.Figure O-15. Normalized Mean Bias (%) of 1,3-butadiene by monitor for 12-km Western U.S. domain, Fall 2005.

Figure O-16. Normalized Mean Error (%) of 1,3-butadiene by monitor for 12-km Western U.S. domain, Fall 2005.P. Seasonal Acrolein Performance Seasonal acrolein performance statistics are provid ed in Table P-1. Spatial plots of the NMB and NME statistics (units of percent) for individual monitors are al so provided as a complement to the tabular statistical data (Fi gures P-1 – P-16). The model predictions generally show large under-predictions for all Eastern and Western sites (NMB values range from -85% to -97%). Acrolein performance in the EUS and WUS show s the similar under estimations during each season. These biases and errors reflect sampling artifacts among each monitoring network mentioned above.
Table P-1. CMAQ 2005 seasonal model p erformance statistics for acrolein.

CMAQ 2005 Acrolein No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 423 -91.9 92.7 -123.1 133.8 12-km WUS 195 -93.7 93.7 -160.5 162.1 Northeast 216 -85.6 87.2 -108.4 119.7 Midwest n/a n/a n/a n/a n/a Southeast 80 -96.6 96.6 -165.9 165.9 Central n/a n/a n/a n/a n/a Winter

West 153 -94.0 94.0 -169.1 169.1 12-km EUS 298 -86.3 87.3 -118.2 128.0 12-km WUS 190 -94.7 94.7 -167.1 167.9 Northeast 180 -87.7 88.7 -121.3 129.2 Midwest n/a n/a n/a n/a n/a Southeast 25 -76.7 76.9 -107.4 108.1 Central n/a n/a n/a n/a n/a Spring

West 143 -95.1 95.1 -177.3 177.3 12-km EUS 447 -95.8 96.5 -132.9 151.5 12-km WUS 187 -96.4 96.4 -171.7 177.3 Northeast 251 -93.0 94.8 -119.1 147.9 Midwest n/a n/a n/a n/a n/a Southeast 52 -93.9 93.9 -142.1 142.2 Central n/a n/a n/a n/a n/a Summer

West n/a n/a n/a n/a n/a 12-km EUS 492 -96.3 96.4 -144.8 149.5 12-km WUS 211 -96.8 96.8 -172.8 174.3 Northeast 203 -92.9 93.1 -134.5 137.3 Midwest 29 -98.3 98.5 -145.4 153.5 Southeast 121 -98.1 98.2 -166.5 167.5 Central n/a n/a n/a n/a n/a Fall

West 160 -97.0 97.0 -180.9 180.9Figure P-1. Normalized Mean Bias (%) of a crolein by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure P-2. Normalized Mean Error (%) of a crolein by monitor for 12-km Eastern U.S. domain, Winter 2005.Figure P-3. Normalized Mean Bias (%) of a crolein by monitor for 12-km Eastern U.S. domain, Spring 2005.

Figure P-4. Normalized Mean Error (%) of a crolein by monitor for 12-km Eastern U.S. domain, Spring 2005.Figure P-5. Normalized Mean Bias (%) of a crolein by monitor for 12-km Eastern U.S. domain, Summer 2005.

Figure P-6. Normalized Mean Error (%) of a crolein by monitor for 12-km Eastern U.S. domain, Summer 2005.Figure P-7. Normalized Mean Bias (%) of a crolein by monitor for 12-km Eastern U.S. domain, Fall 2005.

Figure P-8. Normalized Mean Error (%) of a crolein by monitor for 12-km Eastern U.S. domain, Fall 2005.Figure P-9. Normalized Mean Bias (%) of a crolein by monitor for 12-km Eastern U.S. domain, Winter 2005.

Figure P-10. Normalized Mean Error (%) of a crolein by monitor for 12-km Western U.S. domain, Winter 2005.Figure P-11. Normalized Mean Bias (%) of a crolein by monitor for 12-km Western U.S. domain, Spring 2005.

Figure P-12. Normalized Mean Error (%) of a crolein by monitor for 12-km Western U.S. domain, Spring 2005.Figure P-13. Normalized Mean Bias (%) of a crolein by monitor for 12-km Western U.S. domain, Summer 2005.

Figure P-14. Normalized Mean Error (%) of a crolein by monitor for 12-km Western U.S. domain, Summer 2005.Figure P-15. Normalized Mean Bias (%) of a crolein by monitor for 12-km Western U.S. domain, Fall 2005.

Figure P-16. Normalized Mean Error (%) of a crolein by monitor for 12-km Western U.S. domain, Fall 2005.Q. Annual Nitrate and Sulfate Deposition Performance Annual nitrate and sulfate depositi on performance statistics are pr ovided in Table Q-1. Spatial plots of the NMB and NME statisti cs (units of percent) for indi vidual monitors are also provided as a complement to the tabular statistical data (Figures Q-1 – Q-8). The model predictions for annual nitrate deposition genera lly show small under-predictions for the Eastern and Western NADP sites (NMB values range from -3% to -18 %). Sulfate deposition performance in the EUS and WUS shows the similar over pr edictions (NMB values range from 3% to 14%), except for predicted under-prediction in the Central US (NMB = -9.9%). Th e errors for both annual nitrate and sulfate are relatively moderate with values ranging from 54% to 87% which reflect scatter in the model predictions o observation comparison.
Table Q-1. CMAQ 2005 seasonal model p erformance statistics for acrolein.

CMAQ 2005 Total Deposition No. of Obs. NMB (%) NME (%) FB (%) FE (%)

12-km EUS 7381 -8.6 61.3 -5.1 54.2 12-km WUS 2732 -16.3 68.4 -16.1 83.3 Northeast 1658 1.0 57.4 -3.1 65.7 Midwest 1391 -3.3 59.7 -2.0 67.5 Southeast 1980 -3.7 63.8 -6.5 70.7 Central 1229 -18.5 61.4 -17.6 78.6 Nitrate

West 2400 -13.4 71.8 -15.4 84.1 12-km EUS 7381 6.2 66.5 4.7 75.3 12-km WUS 2732 3.7 75.9 3.2 86.5 Northeast 1658 11.3 63.7 16.0 67.2 Midwest 1391 13.9 61.7 21.4 69.9 Southeast 1980 7.4 71.0 6.0 73.8 Central 1229 -9.9 64.4 -3.5 80.3 Sulfate

West 2400 11.6 80.99 5.4 87.1Figure Q-1. Normalized Mean Bias (%) of annual nitrate deposition by monitor for Eastern U.S., 2005.

Figure Q-2. Normalized Mean Error (%) of annual nitrate by monitor for Eastern U.S., 2005.Figure Q-3. Normalized Mean Bias (%) of annual nitrate deposition by monitor for Western U.S., 2005.

Figure Q-4. Normalized Mean Error (%) of annual nitrate deposition by monitor for Western U.S., 2005.Figure Q-5. Normalized Mean Bias (%) of annual sulfate deposition by monitor for Eastern U.S., 2005.

Figure Q-6. Normalized Mean Error (%) of annual sulfate deposition by monitor for Eastern U.S., 2005.Figure Q-7. Normalized Mean Bias (%) of annual sulfate deposition by monitor for Western U.S., 2005.

Figure Q-8. Normalized Mean Error (%) of annual sulfate deposition by monitor for Western U.S., 2005.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-10-001A January 2010