Air Quality Modeling Technical Support Document: Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule £% United States Environmental Protect Agency ------- Air Quality Modeling Technical Support Document: Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule Air Quality Assessment Division Office of Air Quality Planning and Standards U.S. Environmental Protection Agency and Assessment and Standards Division Office of Transportation and Air Quality U.S. Environmental Protection Agency 4>EPA Environmental Protection EPA.420-R-16-007 Agency August 2016 ------- Table of Contents I. Introduction 1 II. Air Quality Modeling Platform 2 A. Air Quality Model 2 B. Model Domains and Grid Resolution 3 C. Modeling Simulation Periods 4 D. Modeling Scenarios 5 E. Meteorological Input Data 7 F. Initial and Boundary Conditions 9 G. CMAQ Base Case Model Performance Evaluation 10 III. CMAQ Model Results 10 A. Impacts of HDGHG Phase 2 Standards on Future 8-Hour Ozone Levels 10 B. Impacts of HDGHG Phase 2 Standards on Future Annual PM2.5 Levels 11 C. Impacts of HDGHG Phase 2 Standards on Future 24-hour PM2.5 Levels 13 D. Impacts of HDGHG Phase 2 Standards on Future Nitrogen Dioxide Levels 14 E. Impacts of HDGHG Phase 2 Standards on Future Ambient Air Toxic Concentrations 15 F. Impacts of HDGHG Phase 2 Standards on Future Annual Nitrogen and Sulfur Deposition Levels 25 G. Impacts of HDGHG Phase 2 Standards on Future Visibility Levels 26 Appendices 1 ------- List of Appendices Appendix A. Model Performance Evaluation for the 2011-Based Air Quality Modeling Platform Appendix B. 8-Hour Ozone Design Values for Air Quality Modeling Scenarios Appendix C. Annual PM2.5 Design Values for Air Quality Modeling Scenarios Appendix D. 24-Hour PM2.5 Design Values for Air Quality Modeling Scenarios 11 ------- I. Introduction This document describes the air quality modeling performed by EPA in support of the Heavy-Duty Greenhouse Gas (HDGHG) Phase 2 motor vehicle emission and fuel standards. A national scale air quality modeling analysis was performed to estimate the impact of the Phase 2 standards on future year annual and 24-hour PM2.5 concentrations, daily maximum 8-hour ozone concentrations, annual nitrogen dioxide concentrations, annual nitrogen and sulfur deposition levels, specific annual and seasonal air toxic concentrations (formaldehyde, acetaldehyde, benzene, 1,3-butadiene, acrolein and naphthalene) as well as visibility impairment. To model the air quality benefits of this rule we used the Community Multiscale Air Quality (CMAQ) model.1 CMAQ simulates the numerous physical and chemical processes involved in the formation, transport, and destruction of ozone, particulate matter and air toxics. In addition to the CMAQ model, the modeling platform includes the emissions, meteorology, and initial and boundary condition data which are inputs to this model. Emissions and air quality modeling decisions are made early in the analytical process to allow for sufficient time required to conduct emissions and air quality modeling. For this reason, it is important to note that the inventories used in the air quality modeling and the benefits modeling are slightly different than the final emissions inventories. The standards in the air quality modeling inventory are based on the Phase 2 proposal. As mentioned in Chapter 5.5.2.3 and 6.2.2.3 of the RIA, the air quality inventories and the final rule inventories are generally consistent, however there are some important differences. For example, the air quality modeling inventory predicted increases in downstream PM2.5 emissions that are not expected to occur. The air quality modeling inventory also predicts larger reductions in NOx emissions than the final inventory. The implications of these differences are noted in the following discussion of the air quality modeling results. Air quality modeling was performed for three emissions cases: a 2011 base year, a 2040 reference case projection without the HDGHG Phase 2 rule standards and a 2040 control case projection with HDGHG Phase 2 standards in place. The year 2011 was selected for the HDGHG Phase 2 base year because this is the most recent year for which EPA had a complete national emissions inventory at the time of emissions and air quality modeling. The remaining sections of the Air Quality Modeling TSD are as follows. Section II describes the air quality modeling platform and the evaluation of model predictions of PM2.5 and ozone using corresponding ambient measurements. In Section III we present the results of modeling performed for 2040 to assess the impacts on air quality of the Phase 2 vehicle standards. Information on the development of emissions inventories for the HDGHG Phase 2 Rule and the steps and data used in creating emissions inputs for air quality modeling can be found in the Emissions Inventory for Air Quality Modeling TSD (EITSD; EPA-HQ-OAR-2014- 0827; EPA-420-R-16-008). The docket for this rulemaking also contains state/sector/pollutant emissions summaries for each of the emissions scenarios modeled. 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 System. Applied Mechanics Reviews, Volume 59, Number 2 (March 2006), pp. 51-77. 1 ------- II. Air Quality Modeling Platform The 2011-based CMAQ modeling platform was used as the basis for the air quality modeling of the HDGHG Phase 2 final rule. This platform represents a structured system of connected modeling-related tools and data that provide a consistent and transparent basis for assessing the air quality response to projected changes in emissions. The base year of data used to construct this platform includes emissions and meteorology for 2011. The platform was developed by the U.S. EPA's Office of Air Quality Planning and Standards in collaboration with the Office of Research and Development and is intended to support a variety of regulatory and research model applications and analyses. This modeling platform and analysis is fully described below. A. Air Quality Model CMAQ is a non-proprietary computer model that simulates the formation and fate of photochemical oxidants, primary and secondary PM concentrations, acid deposition, and air toxics, over regional and urban spatial scales for given input sets of meteorological conditions and emissions. The CMAQ model version 5.1, which was an upcoming new community version in late 2015, was most recently peer-reviewed in September of 2015 for the U.S. EPA.2 The CMAQ model is a well-known and well-respected tool and has been used in numerous national and international applications.3'4'5 CMAQ includes numerous science modules that simulate the emission, production, decay, deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in the atmosphere. This 2011 multi-pollutant modeling platform used the most recent multi-pollutant CMAQ code available at the time of air quality modeling (CMAQ version 5.0.2; multipollutant version6). CMAQ v5.0.2 reflects updates to version 5.0 to improve the underlying science algorithms which are detailed at http://www.cmascenter.org.7-8'9 2 Moran, M., Astitha, M., Barsanti, K.C., Brown, N.J., Kaduwela, A., McKeen, S.A., Pickering, K.E. (28 September 2015). Final Report: Fifth Peer Review of the CMAQ Model, NERL/ORD/EPA. U.S. EPA, Research Triangle Park, NC. https://www.cmascenter.org/PDF/CMAO 5th peer review report.pdf. It is available from the Community Modeling and Analysis System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org. 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 United States: model evaluation results. Atmospheric Environment, 38(17), 2627-2638. 4 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 Quality 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 CMAQ version 5.0.2 was released on April 2014. It is available from the Community Modeling and Analysis System (CMAS) website: http://www.cmascenter.org. 7 Community Modeling and Analysis System (CMAS) website: http://www.cmascenter.org.. RELEASENOTES for CMAQv5.0 - February 2012. 8 Community Modeling and Analysis System (CMAS) website: http://www.cmascenter.org.. RELEASE NOTES for CMAQv5.0.1 - July 2012. 9 Community Modeling and Analysis System (CMAS) website: http://www.cmascenter.org.. CMAQ version 5.0.2 (April 2014 release) Technical Documentation. - May 2014. 2 ------- B. Model Domains and Grid Resolution The CMAQ modeling analyses were performed for a 12 kilometer (km) domain covering the continental United States, as shown in Figure II-1. The model extends vertically from the surface to 50 millibars (approximately 17,600 meters) using a sigma-pressure coordinate system with 25 vertical layers. Table II-l provides some basic geographic information regarding the CMAQ domains. In addition to the CMAQ model, the HDGHG Phase 2 modeling platform includes (1) emissions for the 2011 base year, 2040 reference and control case projections, (2) meteorology for the year 2011, and (3) estimates of intercontinental transport (i.e., boundary concentrations) for the year 2011 from a global photochemical model. Using these input data, CMAQ was run to generate hourly predictions of ozone, PM2.5 component species, nitrogen and sulfate deposition, nitrogen dioxide, and a subset of air toxics (formaldehyde, acetaldehyde, acrolein, benzene, 1,3- butadiene, and naphthalene) concentrations for each grid cell in the modeling domains. The development of 2011 meteorological inputs and initial and boundary concentrations are described below. The emissions inventories used in the HDGHG Phase 2 air quality modeling are described in the EITSD found in the docket for this rule (EPA-420-R-16-008). Table II-1. Geographic elements of domains used in HDGHG Phase 2 modeling, CMAQ Modeling Configuration Grid Resolution 12 km National Grid Map Projection Lambert Conformal Projection Coordinate Center 97 deg W, 40 deg N True Latitudes 33 deg N and 45 deg N Dimensions 396 x246 x25 Vertical extent 25 Layers: Surface to 50 millibar level (see Table II-3) 3 ------- 12US2 domain 1 \ x,y origin: -2412000r|i, (>1621 col: 396 row:246 1 , Figure 11-1. Map of the CMAQ 12 km modeling domain (noted by the purple box). C. Modeling Simulation Periods The 12 km CMAQ modeling domain was modeled for the entire year of the 2011 base year and 2040 reference and control scenarios. These annual simulations were performed in two half-year segments (i.e., January through June, July through December) for each emissions scenario. With this approach to segmenting an annual simulation we were able to reduce the overall throughput time for an annual simulation. The 12 km domain simulations included a "ramp-up" period, comprised of 10 days before the beginning of each half-year segment, to mitigate the effects of initial concentrations. The ramp-up period is not considered as part of the output analyses. For the 8-hour ozone results, we are only using modeling results from the period between May I and September 30, 2011. This 153-day 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 2011. Data from the entire year were utilized when looking at the estimation of PM2.5, total nitrogen and sulfate deposition, nitrogen dioxide, toxics and visibility impacts from this rulemaking. 4 ------- D. Modeling Scenarios As part of our analysis for this rulemaking, the CMAQ modeling system was used to calculate daily and annual PM2.5 concentrations, 8-hour ozone concentrations, annual NO2 concentrations, annual and seasonal air toxics concentrations, annual total nitrogen and sulfur deposition levels and visibility impairment for each of the following emissions scenarios: 2011 base year 2040 reference case projection without the HDGHG Phase 2 standards 2040 control case projection with the HDGHG Phase 2 standards Model predictions are used in a relative sense to estimate scenario-specific, future-year design values of PM2.5 and ozone. For example, we compare a 2040 reference scenario (a scenario without the vehicle standards) to a 2040 control scenario which includes the vehicle standards. This is done by calculating the simulated air quality ratios between the 2040 future year simulation and the 2011 base. These predicted change ratios are then applied to ambient base year design values. The ambient air quality observations are average conditions, on a site- by-site basis, for a period centered around the model base year (i.e., 2009-2013). The raw model outputs are also used in a relative sense as inputs to the health and welfare impact functions of the benefits analysis. The difference between the 2040 reference case and 2040 control case was used to quantify the air quality benefits of the rule. Additionally, the differences in projected annual average PM2.5 and seasonal average ozone were used to calculate monetized benefits by the BenMAP model (see Section 8.6 of the RIA). The design value projection methodology used here followed EPA guidance10 for such analyses. For each monitoring site, all valid design values (up to 3) from the 2009-2013 period were averaged together. Since 2011 is included in all three design value periods, this has the effect of creating a 5-year weighted average, where the middle year is weighted 3 times, the 2nd and 4th years are weighted twice, and the 1st and 5th years are weighted once. We refer to this as the 5-year weighted average value. The 5-year weighted average values were then projected to the future years that were analyzed. Concentrations of PM2.5 in 2040 were estimated by applying the modeled 201 l-to-2040 relative change in PM2.5 species to the 5 year weighted average (2009-2013) design values. Monitoring sites were included in the analysis if they had at least one complete design value in the 2009-2013 period. EPA followed the procedures recommended in the modeling guidance for projecting PM2.5 by projecting individual PM2.5 component species and then summing these to calculate the concentration of total PM2.5. The PM2.5 species are defined as sulfates, nitrates, ammonium, organic carbon mass, elemental carbon, crustal mass, water, and blank mass (a fixed value of 0.5 |ig/m3). EPA's Modeled Attainment Test Software (MATS) was used to calculate 10 U.S. EPA, 2014: Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze (Draft version of the updated Ozone, PM2 5, and Regional Haze modeling guidance document). Office of Air Quality Planning and Standards, Research Triangle Park, NC. https ://www3. epa.gov/ttn/scram/guidance/guide/Draft_03 -PM-RH_Modeling_Guidance -2014 .pdf 5 ------- the future year design values. The software (including documentation) is available at: http://www.epa.gov/scram001/modelingapps mats.htm. To calculate 24-hour PM2.5 design values, the measured 98th percentile concentrations from the 2009-2013 period at each monitor are projected to the future. The procedures for calculating the future year 24-hour PM2.5 design values have been updated. The updates are intended to make the projection methodology more consistent with the procedures for calculating ambient design values. A basic assumption of the old projection methodology is that the distribution of high measured days in the base period will be the same in the future. In other words, EPA assumed that the 98th-percentile day could only be displaced "from below" in the instance that a different day's future concentration exceeded the original 98th-percentile day's future concentration. This sometimes resulted in overstatement of future-year design values for 24-hour PM2.5 at receptors whose seasonal distribution of highest-concentration 24-hour PM2.5 days changed between the 2009-2013 period and the future year modeling. In the revised methodology, we do not assume that the seasonal distribution of high days in the base period years and future years will remain the same. We project a larger set of ambient days from the base period to the future and then re-rank the entire set of days to find the new future 98th percentile value (for each year). More specifically, we project the highest 8 days per quarter (32 days per year) to the future and then re-rank the 32 days to derive the future year 98th percentile concentrations. More details on the methodology can be found in a guidance memo titled "Update to the 24 Hour PM2.5 NAAQS Modeled Attainment Test" which can be found here: http://www.epa.gov/ttn/scram/guidance/guide/Update to the 24- hour PM25 Modeled Attainment Test.pdf. The future year 8-hour average ozone design values were calculated in a similar manner as the PM2.5 design values. The May-to-September daily maximum 8-hour average concentrations from the 2011 base case and the 2040 cases were used to project ambient design values to 2040. The calculations used the base period 2009-2013 ambient ozone design value data for projecting future year design values. Relative response factors (RRF) for each monitoring site were calculated as the percent change in ozone on days with modeled ozone greater than 70 ppb11. We also conducted an analysis to compare the absolute and percent differences between the 2040 control case and the 2040 reference case for annual and seasonal nitrogen dioxide, formaldehyde, acetaldehyde, benzene, 1,3-butadiene, acrolein, and naphthalene as well as annual nitrate and sulfate deposition. These data were not compared in a relative sense due to the limited observational data available. 11 If there are less than 5 days > 70 ppb for a site, then the threshold is lowered in 1 ppb increments to as low as 60 ppb. If there are not 5 days > 60 ppb, then the site is excluded. If a county has no sites that meet the 70 ppb threshold, then the county design value is calculated from the sites that meet the 60 ppb threshold. 6 ------- E. Meteorological Input Data The gridded meteorological input data for the entire year of 2011 were derived from simulations of the Weather Research and Forecasting Model (WRF) version 3.4, Advanced Research WRF (ARW) core12 for the entire year of 2011 over a model domain slightly larger than that shown in Figure II-1. Meteorological model input fields were prepared for the 12 km domain shown in Figure II-1. The WRF simulation was run on the same map projection as CMAQ. The selections for key WRF physics options are shown below13: • Pleim-Xiu PBL and land surface schemes • Asymmetric Convective Model version 2 planetary boundary layer scheme • Kain-Fritsh cumulus parameterization utilizing the moisture-advection trigger • Morrison double moment microphysics • RRTMG longwave and shortwave radiation schemes The WRF model was initialized using the 12km North American Model (12NAM) analysis product provided by National Climatic Data Center (NCDC). Where 12NAM data was unavailable, the 40km Eta Data Assimilation System (EDAS) analysis (ds609.2) from the National Center for Atmospheric Research (NCAR) was used. Three dimensional analysis nudging for temperature, wind, and moisture was applied above the boundary layer only. The meteorological simulations were conducted in 5.5 day blocks with soil moisture and temperature carried from one block to the next via the ipxwrf program.14 Landuse and land cover data are based on the U.S. Geological Survey (USGS) data. The 36km and 12km meteorological modeling domains contained 35 vertical layers with an approximately 19 m deep surface layer and a 50 millibar top. The WRF and CMAQ vertical structures are shown in Table II-3 and do not vary by horizontal grid resolution. Table II-3. Vertical layer structure for WRF and CMAQ (heights are layer top). CMAQ Layers WRF Layers Sigma P Approximate Height (m) 25 35 0.0000 17,556 34 0.0500 14,780 24 33 0.1000 12,822 32 0.1500 11,282 12 Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W., Powers, J.G., 2008. A Description of the Advanced Research WRF Version 3. 13 Gilliam, R.C., Pleim, J.E., 2010. Performance Assessment of New Land Surface and Planetary Boundary Layer Physics in the WRF-ARW. Journal of Applied Meteorology and Climatology 49, 760-774. 14 Gilliam, R.C., Pleim, J.E., 2010. Performance Assessment of New Land Surface and Planetary Boundary Layer Physics in the WRF-ARW. Journal of Applied Meteorology and Climatology 49, 760-774. 7 ------- 23 31 0.2000 10,002 30 0.2500 8,901 22 29 0.3000 7,932 28 0.3500 7,064 21 27 0.4000 6,275 26 0.4500 5,553 20 25 0.5000 4,885 24 0.5500 4,264 19 23 0.6000 3,683 18 22 0.6500 3,136 17 21 0.7000 2,619 16 20 0.7400 2,226 15 19 0.7700 1,941 14 18 0.8000 1,665 13 17 0.8200 1,485 12 16 0.8400 1,308 11 15 0.8600 1,134 10 14 0.8800 964 9 13 0.9000 797 12 0.9100 714 8 11 0.9200 632 10 0.9300 551 7 9 0.9400 470 8 0.9500 390 6 7 0.9600 311 5 6 0.9700 232 4 5 0.9800 154 4 0.9850 115 3 3 0.9900 77 2 2 0.9950 38 1 1 0.9975 19 Surface 1.0000 0 The 2011 meteorological outputs from the 12km WRF simulation were processed to create model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor (MCIP), version 4.1.3.15,16 15 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-99/030, Office of Research and Development). 16 Otte, T.L., Pleim, J.E., 2010. The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1. Geoscientific Model Development 3, 243-256. 8 ------- Before initiating the air quality simulations, it is important to identify the biases and errors associated with the meteorological modeling inputs. The 2011 WRF model performance evaluations used an approach which included a combination of qualitative and quantitative analyses to assess the adequacy of the WRF simulated fields. The qualitative aspects involved comparisons of the model-estimated synoptic patterns against observed patterns from historical weather chart archives. Additionally, the evaluations compared spatial patterns of monthly average rainfall and monthly maximum planetary boundary layer (PBL) heights. The operational evaluation included statistical comparisons of model/observed pairs (e.g., mean bias, mean (gross) error, fractional bias, and fractional error17) for multiple meteorological parameters. For this portion of the evaluation, five meteorological parameters were investigated: temperature, humidity, shortwave downward radiation, wind speed, and wind direction. The 36 km and 12 km WRF evaluations are described elsewhere.18 The results of these analyses indicate that the bias and error values associated with all three sets of 2011 meteorological data were generally within the range of past meteorological modeling results that have been used for air quality applications. F. Initial and Boundary Conditions The lateral boundary concentrations are provided by a three-dimensional global atmospheric chemistry model, the GEOS-CHEM19 model (standard version 8-03-02 with version 8-02-03 chemistry). The global GEOS-CHEM model simulates atmospheric chemical and physical processes driven by assimilated meteorological observations from the NASA's Goddard Earth Observing System (GEOS-5; additional information available at: http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS- 5). This model was run for 2011 with a grid resolution of 2.0 degree x 2.5 degree (latitude- longitude) and 46 vertical layers up to 0.01 hPa. The predictions were processed using the GEOS-2-CMAQ tool20'21 and used to provide one-way dynamic boundary conditions at one-hour intervals and an initial concentration field for the CMAQ simulations. A GEOS-Chem model evaluation was conducted for the purpose of validating the 2011 GEOS-Chem simulation outputs for their use as inputs to the CMAQ modeling system. This evaluation included using satellite retrievals paired with GEOS-Chem grid cell concentrations.22 17Boylan, J.W., Russell, A.G., 2006. PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models. Atmospheric Environment 40, 4946-4959. 18 Misenis, Chris, Meteorological Model Performance Evaluation for Annual 2011 WRF v3.4 Simulation, USEPA/OAQPS, November, 2014. 19 Yantosca, B. and Carouge, C., 2010, GEOS-Chem v8-03-01 User's Guide, Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, http://acmg.seas.harvard.edu/geos/doc/archive/man.v8-03- 02/index.html 20 Akhtar, F., Henderson, B., Appel, W., Napelenok, S., Hutzell, B., Pye, H., Foley, K., 2012. Multiyear Boundary Conditions for CMAQ 5.0 from GEOS-Chem with Secondary Organic Aerosol Extensions, 11th annual Community Modeling and Analysis System conference, Chapel Hill, NC, October 2012. 21 Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.L., Hutzell, W.T., 2013. A database and tool for boundary conditions for regional air quality modeling: description and evaluation, Geoscientific Model Development Discussions, 6, 4665-4704. 22 Lam, Y.F., Fu, J.S., Jacob, D.J., Jang, C., Dolwick, P., 2010 2006-2008 GEOS-Chem for CMAQ Initial and Boundary Conditions. 9th Annual CMAS Conference, October 11-13, 2010, Chapel Hill, NC. 9 ------- More information is available about the GEOS-Chem model and other applications using this tool at: http://www-as.harvard.edu/chemistry/trop/geos. G. CMAQ Base Case Model Performance Evaluation The CMAQ predictions for ozone, fine particulate matter, sulfate, nitrate, ammonium, organic carbon, elemental carbon, nitrogen and sulfur deposition, and specific air toxics (formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and acrolein) from the 2011 base year evaluation case were compared to measured concentrations in order to evaluate the performance of the modeling platform for replicating observed concentrations. This evaluation was comprised of statistical and graphical comparisons of paired modeled and observed data. Details on the model performance evaluation including a description of the methodology, the model performance statistics, and results are provided in Appendix A. III. CMAQ Model Results As described above, we performed a series of air quality modeling simulations for the continental U.S in order to assess the impacts of the HDGHG Phase 2 standards. We looked at impacts on future ambient levels of daily and annual PM2.5 concentrations, 8-hour maximum ozone concentrations and annual NO2 concentrations, as well as changes in annual and seasonal (summer and winter) ambient concentrations of the following air toxics: formaldehyde, acetaldehyde, acrolein, benzene, 1,3-butadiene, and naphthalene . The air quality modeling results also include impacts on deposition of nitrogen and sulfur and on visibility levels due to this rule. In this section, we present the air quality modeling results for the 2040 HDGHG Phase 2 control case relative to the 2040 reference case. A. Impacts of HDGHG Phase 2 Standards on Future 8-Hour Ozone Levels This section summarizes the results of our modeling of ozone air quality impacts in the future with the HDGHG Phase 2 fuel and vehicle standards. Specifically, for the year 2040 we compare a reference scenario (a scenario without the proposed HDGHG Phase 2 standards) to a control scenario which includes the Phase 2 standards. Our modeling indicates that there will be reductions in 8-hour maximum ozone across most of the country as a result of the HDGHG Phase 2 standards. The decreases in 8-hour ozone design values (DV), max reduction of 1.7 ppb, are likely due to the projected reductions in both upstream and downstream NOx and VOC emissions. As described in the RIA Section 5.5.2.3, assumptions about the usage of diesel- powered APUs differs between the air quality inventories and the final rule inventories. The air quality inventories assumed more widespread usage of diesel-powered APUs than was assumed for the final rule. The APU assumptions mean that the NOx reductions assumed in the air quality inventories are larger than we expect to occur and reductions in 8-hour ozone are over- estimated in the air quality modeling. The magnitude of the reductions in 8-hour ozone DV from the final rule inventories is difficult to estimate due to the complex, non-linear chemistry governing ozone formation. However, EPA does expect reductions in ambient ozone concentrations due to these final standards. 10 ------- Figure III-1 presents the changes in 8-hour ozone design value concentrations between the projected air quality modeling inventories for the 2040 reference case and the 2040 control case. Appendix B details the state and county 8-hour maximum ozone design values for the 2011 ambient baseline and the 2040 future reference and control cases. Legend Number of Counties | >= -2,0 to < -1,50 ppb 2 >- -1.50 to <-1.0 14 | >= -1.0 to < -0.75 62 | >= -0.75 to < -0.5 271 >= -0.5 to < -0,25 267 _J >= -0.25 to < -0.1 55 ~ >= -0.1 to <= 0.1 35 Difference in Ozone DV 2040ei_ldghgp2_ctl minus 2040ei_hdghgp2_ref Figure III-l. Projected Change in 2040 8-hour Ozone Design Values Between the Reference Case and Control Case Using Air Quality Modeling Inventories B. Impacts of HDGHG Phase 2 Standards on Future Annual PM2.5 Levels This section summarizes the results of our modeling of annual average PM2.5 air quality impacts in the future due to the HDGHG Phase 2 fuel and vehicle standards. Specifically, for the year of 2040 we compare a reference scenario (a scenario without the standards) to a control scenario that includes the standards. Our modeling indicates that by 2040 annual PM2.5 design values in the majority of the modeled counties would decrease due to the standards. The 23 An 8-hour ozone design value is the concentration that determines whether a monitoring site meets the 8-hour ozone NAAQS. The full details involved in calculating an 8-hour ozone design value are given in Appendix I of 40 CFR part 50. 11 ------- decreases in annual PM2.5 DV, less than 0.05 |ig/m3, are likely due to the projected reductions in upstream primary PM2.5 emissions, and reductions in both upstream and downstream NOx, SOx and VOCs. As described in the RIA Section 5.5.3.2 and 6A2.1, the air quality modeling used inventories that do not reflect the new requirements for controlling PM2.5 emissions from APUs installed in new tractors and therefore show increases in downstream PM2.5 emissions that we now do not expect to occur. Although in most areas this direct PM2.5 increase is outweighed by reductions in secondary PM2.5, the air quality modeling does predict ambient PM2.5 increases in a few places. We do not expect to actually see increases in PM2.5 DV from the HDGHG Phase 2 program. In addition, assumptions about the usage of diesel-powered APUs also differs between the air quality inventories and the final rule inventories. The air quality inventories assumed more widespread usage of diesel-powered APUs than was assumed for the final rule. The APU assumptions mean that the NOx reductions assumed in the air quality inventories are larger than we expect to occur and reductions in ambient PM2.5 due to secondary nitrate formation are over- estimated in the air quality modeling. The magnitude of the reductions in PM2.5 DV from the HDGHG Phase 2 final rule inventories is difficult to estimate due to the differences in the air quality inventories, namely overestimation of nitrate reductions and underestimation of direct PM2.5 reductions. However, EPA does expect reductions in ambient concentrations of PM2.5 due to these final standards. Figure III-2 presents the projected impacts of the air quality modeling inventories on annual PM2.5 design values in 2040.24 Appendix C details the state and county annual PM2.5 design values for the ambient 2011 baseline and the 2040 future reference and control cases. 24 An annual PM2 5 design value is the concentration that determines whether a monitoring site meets the annual NAAQS for PM2 5. The full details involved in calculating an annual PM2 5 design value are given in appendix N of 40 CFR part 50. 12 ------- I >= -0.01 to <= 0.0 I > 0.0 to <= 0.01 i > 0.01 to <= 0.05 I >0.05 to <=0.10 329 Difference in Annual PM2.5 DV 2040ei_hdghgp2_ctl minus 2040ei_hdghgp2_ref Figure ELI-2. Projected Change in 2040 Annual PM2.5 Design Values Between the Reference Case and Control Case Using Air Quality Modeling Inventories C. Impacts of HDGHG Phase 2 Standards on Future 24-hour P1VI2.5 Levels This section summarizes the results of our modeling of 24-hour PM2.5 air quality impacts in the future due to the HDGHG Phase 2 final rule. Specifically, for the year 2040 we compare a reference scenario (a scenario without the proposed standards) to a 2040 control scenario that includes the standards. Our modeling indicates that 24-hour PM2.5 design values in the majority of the modeled counties would decrease due to the standards. The daily PM2.5 decreases, less than 0.6 jig/m3, are likely due to the projected reductions in upstream primary PM2.5 emissions, and reductions in both upstream and downstream NOx, SOx and VOCs. As described in Section 5.5.2.3 of the RIA, the air quality modeling used inventories that do not reflect the new requirements for controlling PM2.5 emissions from APUs installed in new tractors and therefore show increases in downstream PM2.5 emissions. Although in most areas this direct PM2.5 increase is outweighed by reductions in secondary PM2.5, the air quality modeling does predict ambient PM2.5 increases in a few places. We do not expect to actually see increases in PM2.5 DV from the Phase 2 program. In addition, assumptions about the usage of diesel-powered APUs also differs between the air quality inventories and the final rule inventories. The air quality 13 ------- inventories assumed more widespread usage of diesel-powered APUs than was assumed for the final rule. The APU assumptions mean that the NOx reductions assumed in the air quality inventories are larger than we expect to occur and reductions in ambient PM2.5 due to secondary nitrate formation are over-estimated in the air quality modeling. The magnitude of the reductions in PM2.5 DV from the final rule inventories is difficult to estimate due to the differences in the air quality inventories, namely overestimation of nitrate reductions and underestimation of direct PM2.5 reductions. However, EPA does expect reductions in ambient concentrations of PM2.5 due to these final standards. Figure III-3 shows the projected impacts of the air quality inventories on 24-hour PM2.5 DVs. Legend Number of Counties Difference in Daily PM2.5 DV 2040ei_ldghgp2_ctl minus 2040ei_hdghgp2_ref Figure III-3. Projected Change in 2040 24-hour PM2.5 Design Values Between the Reference Case and the Control Case Using Air Quality Modeling Inventories D. Impacts of HDGHG Phase 2 Standards on Future Nitrogen Dioxide Levels This section summarizes the results of our modeling of annual average nitrogen dioxide (NO2) air quality impacts in the future due to the final HDGHG Phase 2 standards. Specifically, we compare a 2040 reference scenario (a scenario without the HDGHG Phase 2 standards) to a 14 ------- 2040 control scenario that includes the HDGHG Phase 2 standards. Figure III-4 presents the changes in annual NO2 concentrations in 2040 based on percent changes and absolute changes. Air quality modeling results indicate that annual average NO2 concentrations will be reduced across the country. However, the magnitude of the reductions that will actually result from the final standards is difficult to estimate because the air quality modeling inventories included larger NOx emission reductions than we now expect to occur. As described in Section 5.5.2.3, the air quality inventories and the final rule inventories make different assumptions about the usage of diesel-powered APUs. The air quality inventories assumed more widespread usage of diesel-powered APUs than was assumed for the final rule, and as a result the reductions in ambient NO2 concentrations are overestimated in the air quality modeling. l*- v / 'S'A 5*4=J: Figure EEI-4. Projected Change in 2040 Annual NO2 Concentrations Between the Reference Case and Control Case Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in ppb (right) E. Impacts of HDGHG Phase 2 Standards on Future Ambient Air Toxic Concentrations This section summarize the results of our modeling of air toxics impacts in the future from the HDGHG Phase 2 fuel and vehicle emission standards. Our modeling indicates that the standards have relatively little impact on national average ambient concentrations of the modeled air toxics. Annual absolute changes in ambient concentrations are generally less than 0.2 |ig/m5 for benzene, formaldehyde, and acetaldehyde and less than 0.005 ug/m ' for acrolein and 1,3- butadiene. Naphthalene changes are in the range of 0.005 lig'nr' along major roadways and in urban areas. Air toxics concentration maps are presented below in Figures 111-5 through 111-22 along with Table III-l showing the percent of the population experiencing changes in ambient toxic concentrations. 15 ------- Figure III-5. Annual Changes in Acetaldehyde Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in jig/m3 (right) Figure III-6. Winter Changes in Acetaldehyde Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in fig/m' (right) 16 ------- Figure III-7. Summer Changes in Acetaldehyde Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in jig/m3 (right) Figure III-8. Changes in Formaldehyde Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in (ig/m3 (right) 17 ------- Figure III-9. Winter Changes in Formaldehyde Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in jig/m3 (right) Figure 111-10. Summer Changes in Formaldehyde Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in fig/m3 (right) 18 ------- Figure III-ll. Annual Changes in Acrolein Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in jig/m3 (right) Figure 111-12. Winter Changes in Acrolein Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in fig/m' (right) 19 ------- Figure 111-13. Summer Changes in Acrolein Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in jig/m3 (right) _tt owl l Figure 111-14. Annual Changes in Benzene Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in jig/m3 (right) 20 ------- Figure 111-15. Winter Changes in Benzene Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in jig/m3 (right) Figure 111-16. Summer Changes in Benzene Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in fig/m' (right) 21 ------- Figure 111-17. Changes In 1,3-Butadiene Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in jig/m3 (right) \ Tfr .'* / I Lcgond \ ¦i A H '-'ab — -•* V Si \ ¦¦ ~JI»- l*« ¦B "B* ^ t-.J ne~- m nr, uromn >=»» »OtOI XHOOOl BB ' fteJH-iiaai B MndalTMktvntvlvr f,J S*i«lwrr - WWlinn Figure 111-18, Winter Changes in 1,3-Butadiene Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in fig/m' (right) 22 ------- Figure 111-19. Summer Changes in 1,3-Butadiene Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in jig/m1 (right) Figure 111-20. Changes in Naphthalene Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in (ig/m3 (right) 23 ------- Figure 111-21. Winter Changes in Naphthalene Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in jig/m3 (right) Figure 111-22. Summer Changes in Naphthalene Ambient Concentrations between the Reference Case and the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in fig/m3 (right) 24 ------- Table III-l. Percent of Total Population Experiencing Changes in Annual Ambient Concentrations of Toxic Pollutants in 2040 as a Result of the HDGHG Phase 2 Standards Percent Change Acet aldehyde Acrolein Benzene 1,3-Butadiene Formaldehyde Naphthalene <-50 0% 0% > -50 to < -25 1% 4% > -25 to <-10 8% 1% 20% > -10 to < -5 0% 15% 0% 2% 24% > -5 to < -2.5 0% 25% 1% 5% 21% > -2.5 to <-1 3% 28% 5% 1% 18% 15% > -1 to < 1 97% 23% 94% 99% 74% 15% > 1 to < 2.5 0% >2.5 to <5 > 5 to < 10 > 10 to < 25 > 25 to < 50 >50 F. Impacts of HDGHG Phase 2 Standards on Future Annual Nitrogen and Sulfur Deposition Levels Our air quality modeling projects decreases in both nitrogen and sulfur deposition due to this rule (Figures 111-23 and 111-24). However, the magnitude of the reductions that will actually result from the final standards is difficult to estimate because the air quality modeling inventories included larger NOx emission reductions than we now expect to occur. As described in the RIA Section 5.5.2.3, the air quality inventories and the final rule inventories make different assumptions about the usage of diesel-powered APUs. The air quality inventories assumed more widespread usage of diesel-powered APLIs than was assumed for the final rule, and as a result the reductions in ambient NOx deposition are overestimated in the air quality modeling. Figure III-23. Changes in Nitrogen Deposition between the Reference Case and the Control Case in 2040 using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in kg/ha (right) 25 ------- Legend Figure 111-24. Changes in Sulfur Deposition between the Reference Case and the Control Case in 2040 using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in kg/ha (right) G. Impacts of HDGHG Phase 2 Standards on Future Visibility Levels Air quality modeling conducted for the HDGHG Phase 2 final rule was used to project visibility conditions in 135 Mandatory Class I Federal areas across the U S in 2040. The impacts of this action were examined in terms of the projected improvements in visibility on the 20 percent worst visibility days at Class I areas. We quantified visibility impacts at the Class I areas which have complete IMPROVE: ambient data for 2011 or are represented by IMPROVE monitors with complete data. Sites were used in this analysis if they had at least 3 years of complete data for the 2009-2013 period23. Visibility for the 2040 reference and control cases were calculated using the regional haze methodology outlined in section 6 of the photochemical modeling guidance, which applies modeling results in a relative sense, using base year ambient data. The PM2.5 and regional haze modeling guidance recommends the calculation of future year changes in visibility in a similar manner to the calculation of changes in PM2.5 design values https://www3.epa.gOv/scram001/guidance/guide/Draft_03-PM-RH_Modeling_Guidance- 2014.pdf). The regional haze methodology for calculating future year visibility impairment is included in MATS (http://www.epa.gov/scram001/modelingapps mats.htm) In calculating visibility impairment, the extinction coefficient values26 are made up of individual component species (sulfate, nitrate, organics, etc). The predicted change in visibility (on the 20 percent worst days) is calculated as the modeled percent change in the mass for each of the PM2.5 species (on the 20% worst observed days) multiplied by the observed concentrations. The future mass is converted to extinction and then daily species extinction 25 Since the base case modeling used meteorology' for 2011, one of the complete years must be 2011. 26 Extinction coefficient is in units of inverse megameters (Mm1). It is a measure of how much light is absorbed or scattered as it passes through a medium. Light extinction is commonly used as a measure of visibility impairment in the regional haze program. 26 ------- coefficients are summed to get a daily total extinction value (including Rayleigh scattering). The daily extinction coefficients are converted to deciviews and averaged across all 20 percent worst days. In this way, we calculate an average change in deciviews from the base case to a future case at each IMPROVE site. For example, subtracting the 2040 reference case from the corresponding 2040 reference case deciview values gives an estimate of the visibility benefits in Class I areas that are expected to occur from the rule. The following options were chosen in MATS for calculating the future year visibility values for the rule: New IMPROVE algorithm Use model grid cells at (IMPROVE) monitor Temporal adjustment at monitor- 3x3 for 12km grid, (lxl for 36km grid) Start monitor year- 2009 End monitor year- 2013 Base model year 2011 Minimum years required for a valid monitor- 3 The "base model year" was chosen as 2011 because it is the base case meteorological year for the HDGHG Phase 2 final rule modeling. The start and end years were chosen as 2009 and 2013 because that is the 5 year period which is centered on the base model year of 2011. These choices are consistent with using a 5 year base period for regional haze calculations. The results show that in 2040 all the modeled areas would continue to have annual average deciview levels above background and the rule would improve visibility in the majority of these areas.27 Table III-2 contains the full visibility results from 2040 for the 135 analyzed areas. Table III-2. Visibility Levels (in Deciviews) for Mandatory Class I Federal Areas on the 20 Percent Worst Days Using Air Quality Inventories (with and without HDGHG Phase 2 Rule) Class 1 Area (20% worst days) State 2011 Baseline Visibility 2040 Reference 2040 HDGHGP2 Control Natural Background Sipsey Wilderness Alabama 22.93 18.16 18.07 10.99 Mazatzal Wilderness Arizona 12.03 11.40 11.38 6.68 Pine Mountain Wilderness Arizona 12.03 11.40 11.38 6.68 Superstition Wilderness Arizona 12.72 11.82 11.80 6.54 Chiricahua NM Arizona 12.08 11.54 11.53 7.20 Chiricahua Wilderness Arizona 12.08 11.54 11.53 7.20 27 The level of visibility impairment in an area is based on the light-extinction coefficient and a unitless visibility index, called a "deciview", which is used in the valuation of visibility. The deciview metric provides a scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, the average person can generally perceive a change of one deciview. The higher the deciview value, the worse the visibility. Thus, an improvement in visibility is a decrease in deciview value. 27 ------- Class 1 Area (20% worst days) State 2011 Baseline Visibility 2040 Reference 2040 HDGHGP2 Control Natural Background Galiuro Wilderness Arizona 12.08 11.54 11.53 7.20 Grand Canyon NP Arizona 10.92 10.53 10.52 7.04 Petrified Forest NP Arizona 11.92 11.64 11.63 6.49 Sycamore Canyon Wilderness Arizona 14.62 14.00 14.01 6.65 Caney Creek Wilderness Arkansas 22.23 19.01 18.96 11.58 Upper Buffalo Wilderness Arkansas 22.12 19.00 18.95 11.57 Joshua Tree NM California 15.07 13.49 13.47 7.19 Kings Canyon NP California 20.82 17.93 17.91 7.70 San Rafael Wilderness California 16.46 14.51 14.49 7.57 San Gorgonio Wilderness California 16.85 14.11 14.09 7.30 San Jacinto Wilderness California 16.85 14.11 14.09 7.30 Sequoia NP California 20.82 17.93 17.91 7.70 Agua Tibia Wilderness California 18.44 15.66 15.65 7.64 Ansel Adams Wilderness (Minarets) California 14.27 13.01 13.00 7.12 Desolation Wilderness California 11.82 11.02 11.01 6.05 Dome Land Wilderness California 17.23 15.93 15.92 7.46 Emigrant Wilderness California 14.75 14.16 14.15 7.64 Hoover Wilderness California 10.78 10.31 10.30 7.71 John Muir Wilderness California 14.27 13.01 13.00 7.12 Kaiser Wilderness California 14.27 13.01 13.00 7.12 Marble Mountain Wilderness California 14.10 13.34 13.33 7.90 Mokelumne Wilderness California 11.82 11.02 11.01 6.05 Pinnacles NM California 16.15 14.42 14.41 7.99 Ventana Wilderness California 16.15 14.42 14.41 7.99 Yolla Bolly Middle Eel Wilderness California 14.10 13.34 13.33 7.90 Yosemite NP California 14.75 14.16 14.15 7.64 Caribou Wilderness California 13.49 12.83 12.83 7.31 Lava Beds NM California 13.38 12.93 12.93 7.85 Lassen Volcanic NP California 13.49 12.83 12.83 7.31 Point Reyes NS California 20.98 19.93 19.93 15.77 Redwood NP California 17.38 16.82 16.82 13.91 South Warner Wilderness California 13.38 12.93 12.93 7.85 Thousand Lakes Wilderness California 13.49 12.83 12.83 7.31 Rocky Mountain NP Colorado 11.84 10.93 10.91 7.15 Black Canyon of the Gunnison NM Colorado 9.88 9.71 9.70 6.21 La Garita Wilderness Colorado 9.88 9.71 9.70 6.21 Weminuche Wilderness Colorado 9.88 9.71 9.70 6.21 Eagles Nest Wilderness Colorado 8.48 8.04 8.03 6.06 Flat Tops Wilderness Colorado 8.48 8.04 8.03 6.06 Great Sand Dunes NM Colorado 11.57 11.50 11.49 6.66 28 ------- Class 1 Area (20% worst days) State 2011 Baseline Visibility 2040 Reference 2040 HDGHGP2 Control Natural Background Maroon Bells-Snowmass Wilderness Colorado 8.48 8.04 8.03 6.06 Mount Zirkel Wilderness Colorado 9.11 8.70 8.69 6.08 Rawah Wilderness Colorado 9.11 8.70 8.69 6.08 West Elk Wilderness Colorado 8.48 8.04 8.03 6.06 Mesa Verde NP Colorado 11.22 11.37 11.37 6.81 Chassahowitzka Florida 21.34 18.21 18.17 11.03 St. Marks Florida 22.23 18.74 18.70 11.67 Everglades NP Florida 18.15 17.65 17.62 12.15 Cohutta Wilderness Georgia 22.71 17.47 17.43 10.78 Okefenokee Georgia 22.68 18.82 18.78 11.44 Wolf Island Georgia 22.68 18.82 18.78 11.44 Craters of the Moon NM Idaho 14.05 12.93 12.80 7.53 Sawtooth Wilderness Idaho 15.64 15.44 15.44 6.42 Selway-Bitterroot Wilderness Idaho 14.89 14.77 14.77 7.43 Mammoth Cave NP Kentucky 25.09 19.83 19.75 11.08 Acadia NP Maine 17.93 15.81 15.80 12.43 Moosehorn Maine 16.83 15.27 15.26 12.01 Roosevelt Campobello International Park Maine 16.83 15.27 15.26 12.01 Seney Michigan 20.56 17.15 17.08 12.65 Isle Royale NP Michigan 18.92 16.06 16.01 12.37 Boundary Waters Canoe Area Minnesota 18.82 16.66 16.60 11.61 Hercules-Glades Wilderness Missouri 22.89 19.57 19.51 11.30 Mingo Missouri 24.31 20.91 20.86 11.62 Medicine Lake Montana 17.98 17.07 17.06 7.89 Bob Marshall Wilderness Montana 14.43 14.33 14.32 7.73 Cabinet Mountains Wilderness Montana 12.73 12.24 12.23 7.52 Glacier NP Montana 16.03 15.82 15.81 9.18 Mission Mountains Wilderness Montana 14.43 14.33 14.32 7.73 Red Rock Lakes Montana 11.98 11.73 11.72 6.44 Scapegoat Wilderness Montana 14.43 14.33 14.32 7.73 UL Bend Montana 14.11 13.77 13.76 8.16 Anaconda-Pintler Wilderness Montana 14.89 14.77 14.77 7.43 Jarbidge Wilderness Nevada 11.97 11.90 11.90 7.87 Great Gulf Wilderness New Hampshire 16.66 13.61 13.60 11.99 Presidential Range-Dry River Wilderness New Hampshire 16.66 13.61 13.60 11.99 Brigantine New Jersey 23.75 19.64 19.61 12.24 Bosque del Apache New Mexico 14.02 14.37 14.34 6.73 Salt Creek New Mexico 17.42 18.32 18.30 6.81 Bandelier NM New Mexico 11.92 12.22 12.21 6.26 Carlsbad Caverns NP New Mexico 15.32 15.09 15.08 6.65 29 ------- Class 1 Area (20% worst days) State 2011 Baseline Visibility 2040 Reference 2040 HDGHGP2 Control Natural Background Pecos Wilderness New Mexico 9.93 9.84 9.83 6.08 San Pedro Parks Wilderness New Mexico 10.02 10.02 10.01 5.72 Wheeler Peak Wilderness New Mexico 9.93 9.84 9.83 6.08 White Mountain Wilderness New Mexico 14.19 14.56 14.56 6.80 Linville Gorge Wilderness North Carolina 21.60 15.94 15.91 11.22 Swanquarter North Carolina 21.77 16.75 16.73 11.55 Theodore Roosevelt NP North Dakota 16.96 15.96 15.95 7.80 Wichita Mountains Oklahoma 21.24 18.83 18.76 7.53 Hells Canyon Wilderness Oregon 16.58 15.10 14.94 8.32 Eagle Cap Wilderness Oregon 14.87 14.20 14.17 8.92 Strawberry Mountain Wilderness Oregon 14.87 14.20 14.17 8.92 Kalmiopsis Wilderness Oregon 15.01 14.52 14.51 9.44 Mount Hood Wilderness Oregon 13.35 12.72 12.71 8.43 Mount Jefferson Wilderness Oregon 15.77 15.52 15.51 8.79 Mount Washington Wilderness Oregon 15.77 15.52 15.51 8.79 Three Sisters Wilderness Oregon 15.77 15.52 15.51 8.79 Crater Lake NP Oregon 11.64 11.33 11.33 7.62 Diamond Peak Wilderness Oregon 11.64 11.33 11.33 7.62 Gearhart Mountain Wilderness Oregon 11.64 11.33 11.33 7.62 Mountain Lakes Wilderness Oregon 11.64 11.33 11.33 7.62 Cape Romain South Carolina 23.17 19.02 18.99 12.12 Wind Cave NP South Dakota 14.04 12.85 12.82 7.71 Badlands NP South Dakota 15.67 14.32 14.30 8.06 Great Smoky Mountains NP Tennessee 22.50 16.99 16.95 11.24 Joyce-Kilmer-Slickrock Wilderness Tennessee 22.50 16.99 16.95 11.24 Guadalupe Mountains NP Texas 15.32 15.09 15.08 6.65 Big Bend NP Texas 16.30 16.54 16.54 7.16 Arches NP Utah 10.83 10.53 10.50 6.43 Canyonlands NP Utah 10.83 10.53 10.50 6.43 Capitol Reef NP Utah 10.18 9.69 9.66 6.03 Bryce Canyon NP Utah 10.61 10.21 10.19 6.80 Lye Brook Wilderness Vermont 19.26 14.94 14.92 11.73 James River Face Wilderness Virginia 22.55 17.28 17.24 11.13 Shenandoah NP Virginia 21.82 15.20 15.16 11.35 Alpine Lake Wilderness Washington 16.14 14.86 14.80 8.43 Mount Rainier NP Washington 15.50 14.43 14.41 8.54 Olympic NP Washington 14.10 13.50 13.48 8.44 Pasayten Wilderness Washington 12.44 11.83 11.81 8.25 Glacier Peak Wilderness Washington 13.51 12.82 12.81 8.39 Goat Rocks Wilderness Washington 12.37 11.77 11.76 8.35 30 ------- Class 1 Area (20% worst days) State 2011 Baseline Visibility 2040 Reference 2040 HDGHGP2 Control Natural Background North Cascades NP Washington 13.51 12.82 12.81 8.01 Mount Adams Wilderness Washington 12.37 11.77 11.76 8.35 Dolly Sods Wilderness West Virginia 22.40 16.06 16.03 10.39 Otter Creek Wilderness West Virginia 22.40 16.06 16.03 10.39 Bridger Wilderness Wyoming 10.25 9.91 9.90 6.45 Fitzpatrick Wilderness Wyoming 10.25 9.91 9.90 6.45 Grand Teton NP Wyoming 11.98 11.73 11.72 6.44 Teton Wilderness Wyoming 11.98 11.73 11.72 6.44 Yellowstone NP Wyoming 11.98 11.73 11.72 6.44 31 ------- Air Quality Modeling Technical Support Document: Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule Appendix A Model Performance Evaluation for the 2011-Based Air Quality Modeling Platform A-l ------- A.l. Introduction An operational model performance evaluation for ozone, PM2.5 and its related speciated components, specific air toxics (i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and acrolein), as well as nitrate and sulfate deposition was conducted using 2011 State/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 Continental United States domain (Figure A-l) \ Included in this evaluation are statistical measures 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, PM2.5, air toxic observations and nitrate and sulfate deposition 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 concentration represents a grid-cell volume-averaged value, while the ambient network measurements are made at specific locations. Model performance statistics were calculated for several spatial scales and temporal periods (statistics are defined in Section A. 1.2). Statistics were calculated for individual monitoring sites and for each of the nine National Oceanic and Atmospheric Administration (NOAA) climate regions of the 12-km U.S. modeling domain (Figure A-2)2. The regions include the Northeast, Ohio Valley, Upper Midwest, Southeast, South, Southwest, Northern Rockies, Northwest and West3'4 as were originally identified in Karl and Koss (1984)5. The statistics for each site and climate region were calculated by season ("winter" is defined as average of December, January, and February; "spring" is defined as average of March, April, and May; "summer" is defined as average of June, July, and August; and "fall" is defined as average of September, October, and December). For 8-hour daily maximum ozone, we also calculated performance statistics by region for the May through September ozone season6. In addition to the performance statistics, we prepared several graphical presentations of model performance. These graphical presentations include regional maps which show the mean bias, mean error, normalized mean bias and normalized mean error calculated for each season at individual monitoring sites. 1 See section 6 A. 1. of the RIA document (Figure 6 A-l for the description and map of the CMAQ modeling domain. 2 NOAA, National Centers for Environmental Information scientists have identified nine climatically consistent regions within the contiguous U.S., http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php. 3 The nine climate regions are defined by States where: Northeast includes CT, DE, ME, MA, MD, NH, NJ, NY, PA, RI, and VT; Ohio Valley includes IL, IN, KY, MO, OH, TN, and WV; Upper Midwest includes IA, MI, MN, and WI; Southeast includes AL, FL, GA, NC, SC, and VA; South includes AR, KS, LA, MS, OK, and TX; Southwest includes AZ, CO, NM, and UT; Northern Rockies includes MT, NE, ND, SD, WY; Northwest includes ID, OR, and WA; and West includes CA and NV. 4 Note most monitoring sites in the West region are located in California (see Figure A-2), therefore statistics for the West will be mostly representative of California ozone air quality. 5 Karl, T. R. and Koss, W. J., 1984: "Regional and National Monthly, Seasonal, and Annual Temperature Weighted by Area, 1895-1983." Historical Climatology Series 4-3, National Climatic Data Center, Asheville, NC, 38 pp. 6 In calculating the ozone season statistics we limited the data to those observed and predicted pairs with observations that exceeded 60 ppb in order to focus on concentrations at the upper portion of the distribution of values. A-2 ------- 12US2 domain x.y origin: -2412C col; 396 row:246 Figure A-l. Map of the CMAQ 12 km Modeling Domain Used for HDGHG Phase 2 rule (noted by the purple box). U.S. Climate Regions Figure A-2. NOAA Nine Climate Regions (source: litti)://www.nc(lc.noaa.gov/monitoriiig- referenccs/mai)s/us-climate-rcgions.i)hi)#references) A-3 ------- A.l.l Monitoring Networks The model evaluation for ozone was based upon comparisons of model predicted 8-hour daily maximum concentrations to the corresponding ambient measurements for 2011 at monitoring sites in the EPA Air Quality System (AQS) and the Clean Air Status and Trends Network (CASTNet). The observed ozone data were measured and reported on an hourly basis. The PM2.5 evaluation focuses on concentrations of PM2.5 total mass and its components including sulfate (SO4), nitrate (NO3), total nitrate (TNO3), ammonium (NH4), elemental carbon (EC), and organic carbon (OC) as well as wet deposition for nitrate and sulfate. The PM2.5 performance statistics were calculated for each season (e.g., "winter" is defined as December, January, and February). PM2.5 ambient measurements for 2011 were obtained from the following networks: Chemical Speciation Network (CSN), Interagency Monitoring of PROtected Visual Environments (IMPROVE), Clean Air Status and Trends Network (CASTNet), and National Acid Deposition Program/National Trends (NADP/NTN). NADP/NTN collects and reports wet deposition measurements as weekly average data. The pollutant species included in the evaluation for each monitoring network are listed in Table A-l. For PM2.5 species that are measured by more than one network, we calculated separate sets of statistics for each network. The CSN and IMPROVE networks provide 24-hour average concentrations on a 1 in every 3 day, or 1 in every 6 day sampling cycle. The PM2.5 species data at CASTNet sites are weekly integrated samples. In this analysis we use the term "urban sites" to refer to CSN sites; "suburban/rural sites" to refer to CASTNet sites; and "rural sites" to refer to IMPROVE sites. Table A-l. PM2.5 monitoring networks and pollutants species included in the CMAQ performance evaluation. Ambient Monitoring Networks Particulate Species Wet Deposition Species PM2.5 Mass S04 N03 TN03a EC OC nh4 S04 NO3 IMPROVE X X X X X CASTNet X X X CSN X X X X X X NADP X X a TNO3 = (NO3 + HNO3) The air toxics evaluation focuses on specific species relevant to the HDGHG Phase 2 standards and rulemaking, i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and acrolein. Similar to the PM2.5 evaluation, the air toxics performance statistics were calculated for each season to estimate the ability of the CMAQ modeling system to replicate the base year concentrations for the 12 km continental U.S. domain. Toxic measurements for 2011 were obtained from the air toxics archive, http://www.epa.g0v/ttn/amtic/t0xdat.html#data. While most of the data in the archive are from the AQS database including the National Air Toxics Trends Stations (NATTS) (downloaded in July 2014), additional data (e.g., special studies) are included in the archive but not reported in the AQS. A-4 ------- A.1.2 Model Performance Statistics The Atmospheric Model Evaluation Tool (AMET) was used to conduct the evaluation described in this document.7 There are various statistical metrics available and used by the science community for model performance evaluation. For this evaluation of the 2011 CMAQ modeling platform, we have selected the mean bias, mean error, normalized mean bias, and normalized mean error to characterize model performance, statistics which are consistent with the recommendations in Simon et al. (2012)8 and the draft photochemical modeling guidance9. Mean bias (MB) is used as average of the difference (predicted - observed) divided by the total number of replicates (n). Mean bias is given in units of ppb and is defined as: MB = ~Hi(P ~ 0) , where P = predicted and O = observed concentrations. Mean error (ME) calculates the absolute value of the difference (predicted - observed) divided by the total number of replicates (n). Mean error is given in units of ppb and is defined as: ME = i£I|P-0| Normalized mean bias (NMB) is used as a normalization to facilitate a range of concentration magnitudes. This statistic averages the difference (predicted - 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. Normalized mean bias is given in percentage units and is defined as: i(p-o) NMB = *100 n I(O) 1 Normalized mean error (NME) is also similar to NMB, where the performance statistic is used as a normalization of the mean error. NME calculates the absolute value of the difference (predicted - observed) over the sum of observed values. Normalized mean error is given in percentage units and is defined as: 7 Appel, K.W., Gilliam, R.C., Davis, N., Zubrow, A., and Howard, S.C.: Overview of the Atmospheric Model Evaluation Tool (AMET) vl.l for evaluating meteorological and air quality models, Environ. Modell. Softw.,26, 4, 434-443, 2011. (http://www.cmascenter.org/) 8 Simon, H., Baker, K., Phillips, S., 2012: Compilation and interpretation of photochemical model performance statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139. 9 U.S. Environmental Protection Agency (US EPA), Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2 5, and Regional Haze. December 2014, U.S. EPA, Research Triangle Park, NC, 27711. A-5 ------- YJ\p-o\ NME = *100 1(0) The "acceptability" of model performance was judged by comparing our CMAQ 2011 performance results in light of the range of performance found in recent regional ozone model applications.10'11'12'13'14'151617'18'19'20 These other modeling studies represent a wide range of modeling analyses that cover various models, model configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules. Overall, the ozone model performance results for the 2011 CMAQ simulations are within the range found in other recent peer-reviewed and regulatory applications. The model performance results, as described in this document, demonstrate that that our applications of CMAQ using this 2011 modeling platform provide a scientifically credible approach for assessing ozone and PM2.5 concentrations for the purposes of the HDGHG Phase 2 final rule. 10 National Research Council (NRC), 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations, Washington, DC: National Academies Press. 11 Appel, K.W., Roselle, S.J., Gilliam, R.C., and Pleim, J.E, 2010: Sensitivity of the Community Multiscale Air Quality (CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers. Geoscientific Model Development, 3, 169-188. 12 Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O., Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., and Bash, J.O., 2010: Incremental testing of the Community multiscale air quality (CMAQ) modeling system version 4.7. Geoscientific Model Development, 3, 205-226. 13 Hogrefe, G., Civeroio, K.L., Hao, W., Ku, J-Y., Zalewsky, E.E., and Sistla, G., Rethinking the Assessment of Photochemical Modeling Systems in Air Quality Planning Applications. Air & Waste Management Assoc., 58:1086-1099, 2008. 14 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, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008. (http://www.cmascenter.org/conference/2008/agenda.cfm). 15 Simon, H., Baker, K.R., and Phillips, S., 2012. Compilation and interpretation of photochemical model performance statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139. http://dx.doi.Org/10.1016/i.atmosenv.2012.07.012 16 Strum, M., Wesson, K., Phillips, S., Pollack, A., Shepard, S., Jimenez, M., M., Beidler, A., Wilson, M., Ensley, D., Cook, R., Michaels H., and Brzezinski, D. Link Based vs NEI Onroad Emissions Impact on Air Quality Model Predictions. 17th Annual International Emission Inventory Conference, Portland, Oregon, June 2-5, 2008. dittp://www.epa.gov/ttn/chief/conference/eil7/sessionl 1/strum pres.pdf) 17 Tesche, T.W., Morris, R., Tonnesen, G., McNally, D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx annual 2002 performance evaluation over the eastern United States. Atmospheric Environment 40, 4906-4919. 18 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; RTP, NC; March 2005 (CAIR Docket OAR-2005- 0053-2149). 19 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/otaa/regs/nonroad/marine/ci/420r09007.pdf) 20 U.S. Environmental Protection Agency, 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. EPA-420-R-10-006. February 2010. Sections 3.4.2.1.2 and 3.4.3.3. Docket EPA-HQ-0AR-2009-0472- 11332. (http://www.epa.gov/oms/renewableluels/420rl0006.pdf) A-6 ------- A.2. Evaluation for 8-hour Daily Maximum Ozone The 8-hour ozone model performance bias and error statistics for each climate region, for each season defined above and for each monitor network (AQS and CASTNet) are provided in Table A-2. Spatial plots of the mean bias and error as well as the normalized mean bias and error for individual monitors are shown in Figures A-la through A-lh. The statistics shown in these two figures were calculated over the ozone season using data pairs on days with observed 8-hour ozone of > 60 ppb. As indicated by the statistics in Table A-2, bias and error for 8-hour daily maximum ozone are relatively low in each climate region. In general the winter shows under prediction except at AQS sites in the Southeast and West and also at rural CASTNet sites in the Northeast. Likewise, the model tends to under predict in the spring with the exception of slight over predictions at AQS sites in the Ohio Valley, South and Southeast in addition to CASTNet sites in the Southeast and Northwest. Model predictions for the summer season typically show slight over predictions apart from rural CASTNet sites in the Upper Midwest, Southwest, Northern Rockies, and West and at AQS sites in the Northwest and Southwest. Figures A-la and A-le show MB for 8-hour ozone > 60 ppb during the ozone season in the range of ±10 ppb at the majority of ozone AQS and CASTNet measurement sites. At both AQS and CASTNet sites, NMB is within the range of ±20 percent (Figures A-lc and A-lg). Model error for 8-hour maximum ozone > 60 ppb, as seen from Figure A-lb and A-lf, is 10 ppb or less at most of the sites across the modeling domain. Table A-2. Daily Maximum 8-hour Ozone Performance Statistics by Climate Region, by Season, and by Monitoring Network for the 2011 CMAQ Model Simulation. Climate Monitor Season No. of MB ME NMB NME Region Network Obs (PPb) (PPb) (%) (%) Winter 8,109 -2.5 5.1 -8.3 16.7 AQS Spring 15,432 -0.2 5.3 -0.4 12.4 Summer 17,223 1.4 7.1 3.0 14.8 Northeast Fall 14,105 3.2 5.1 9.9 19.9 Winter 1,188 -3.2 4.7 -9.3 13.8 CASTNet Spring 1,160 -1.2 5.0 -2.8 11.3 Summer 1,217 0.7 6.0 1.6 13.1 Fall 1,295 2.8 5.5 8.3 16.6 Winter 3,293 -1.4 5.0 -4.9 17.7 AQS Spring 15,995 0.1 5.8 0.2 13.0 Summer 19,865 1.4 7.3 2.7 13.9 Ohio Valley Fall 13,574 1.5 6.2 4.1 16.6 Winter 1,485 -1.1 4.8 -3.2 14.5 CASTNet Spring 1,461 -0.7 5.3 -1.5 11.4 Summer 1,393 0.7 6.1 1.4 11.7 Fall 1,501 1.2 5.4 3.1 13.8 Winter 1,048 -3.3 5.3 -10.7 17.0 AQS Spring 5,416 -0.3 4.9 -0.7 11.1 Upper Summer 8,149 1.4 6.4 3.2 14.5 Midwest Fall 4,727 2.8 5.7 7.9 16.3 CASTNet Winter 442 -4.4 5.6 -12.6 15.9 Spring 432 -3.0 5.4 -6.6 11.9 A-7 ------- Climate Monitor Season No. of MB ME NMB NME Region Network Obs (PPb) (PPb) (%) (%) Summer 403 -1.7 5.5 -3.8 12.8 Fall 393 1.7 4.5 5.0 13.1 Winter 6,117 0.5 5.0 1.3 13.6 AQS Spring 15,428 3.5 6.5 7.5 13.9 Summer 17,342 6.6 9.8 13.9 20.5 Southeast Fall 898 2.8 5.4 6.8 13.4 Winter 851 -1.4 4.5 -3.8 11.8 CASTNet Spring 910 1.2 5.4 2.4 11.2 Summer 892 5.1 8.0 10.6 16.6 Fall 14,169 4.1 6.6 10.6 16.9 Winter 11,863 -0.2 5.4 -0.8 16.7 AQS Spring 13,954 2.7 6.5 6.0 14.4 Summer 14,054 7.4 11.7 15.3 24.2 South Fall 13,407 1.6 6.4 3.5 14.1 Winter 566 -1.1 4.8 -3.0 13.1 CASTNet Spring 549 -0.1 5.9 -0.3 12.3 Summer 547 1.1 6.9 2.0 13.1 Fall 551 -0.3 5.0 -0.5 10.9 Winter 9,010 -1.0 6.30 -2.7 15.8 AQS Spring 10,867 -2.7 5.6 -5.0 10.4 Summer 11,989 -1.6 7.3 -2.8 12.7 Southwest Fall 10,711 2.9 5.8 6.6 13.1 Winter 640 -3.2 4.9 -7.1 10.7 CASTNet Spring 687 -4.8 6.2 -8.5 10.9 Summer 702 -3.0 6.8 -5.2 11.7 Fall 688 0.1 3.8 0.2 7.8 Winter 3,293 -6.2 7.5 -16.0 19.3 AQS Spring 3,673 -2.0 6.3 -4.2 13.1 Summer 4,148 2.4 6.0 5.1 12.7 Northern Fall 4,062 3.1 4.7 8.2 12.4 Rockies Winter 423 -5.8 6.8 -13.9 16.2 CASTNet Spring 403 -4.6 6.8 -8.9 13.1 Summer 421 -1.3 4.9 -2.6 9.5 Fall 386 1.9 4.3 4.4 10.3 Winter 654 -0.3 6.7 -0.9 23 AQS Spring 1,522 -1.3 5.7 -3.2 13.5 Summer 2,784 -0.1 5.5 -0.3 15.1 Northwest Fall 1,266 3.0 6.7 8.5 19.1 Winter 87 10.6 11.1 44.4 46.7 CASTNet Spring 92 2.4 4.0 6.0 10.2 Summer 84 5.4 6.3 18.3 21.1 Fall 78 13.1 13.6 51.4 53.1 Winter 15,225 3.6 6.4 11.3 19.9 AQS Spring 16,907 -1.7 6.0 -3.7 12.6 Summer 18,073 1.1 8.2 2.1 16.0 West Fall 17,064 2.8 7.5 6.6 17.4 Winter 551 -0.8 5.1 -1.9 11.7 CASTNet Spring 535 -5.2 6.9 -9.7 12.9 Summer 539 -6.7 8.9 -10.8 14.3 Fall 532 -1.7 6.6 -3.5 13.1 A-8 ------- 03_8hrmax MB (ppb) for run2011ei_cb05v2_hdghgp2_12US2 lor 20110501 to 20110930 units = ppb coverage limit = 75% fl>25 W20 — 15 — 10 CIRCLE=AQS_Daily; Figure A-la. Mean Bias (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period May-September 2011 at AQS monitoring sites in the modeling domain. 03_8hrmax ME (ppb) lor run2011ei_cb05v2_hdghgp2_12US2 for 20110501 to 20110930 units = ppb coverage limit = 75% CIRCLE=AQS_Daily; Figure A-lb. Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period May-September 2011 at AQS monitoring sites in the modeling domain. A-9 ------- 03_8hrmax NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for 20110501 to 20110930 units = % coverage limit = 75% CIRCLE=AQS_Daily; Figure A-lc. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over the period May-September AQS 2011 at monitoring sites in the modeling domain. 03_8hrmax NME (%) for run 2011ei_cb05v2_hdghgp2_12US2 for 20110501 to 20110930 units = % coverage limit = 75% CIRCLE=AQS_Daily; Figure A-ld. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over the period May-September AQS 2011 at monitoring sites in the modeling domain. A-10 ------- 03_8hrmax MB (ppb) for run2011ei_cb05v2_hdghgp2_12US2 for 20110501 to 20110930 units = ppb . coverage limit = 75% TRIANGLE=CASTNETJDaily; Figure A-le. Mean Bias (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period May-September 2011 at CASTNet monitoring sites in the modeling domain. 03_8hrmax ME (ppb) for run2011ei_cb05v2_hdghgp2_12US2for 20110501 to 20110930 units = ppb coverage limit = 75% TRIANGLE=CASTNET_Daily; Figure A-lf. Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period May-September 2011 at CASTNet monitoring sites in the modeling domain. A-11 ------- 03_8hrmax NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for 20110501 to 20110930 ^ units = % aA** coverage limit = 75% TRI ANGLE=CASTN ET_Daily; Figure A-lg. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over the period May-September CAST Net 2011 at monitoring sites in the modeling domain. 03_8hrmax NME (%) for run 2011ei_cb05v2_hdghgp2_12US2for 20110501 to 20110930 ^ "^j units = % .1^ coverage limit = 75% TRIANGLE=CASTNET_Daily; Figure A-111. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over the period May-September CAST Net 2011 at monitoring sites in the modeling domain. A-12 ------- A.3. Seasonal Evaluation of PM2.5 Component Species The evaluation of 2011 model predictions for PM2.5 covers the performance for the individual PM2.5 component species (i.e., sulfate, nitrate, organic carbon, elemental carbon, and ammonium). Performance results are provided for each PM2.5 species. As indicated above, for each species we present tabular summaries of bias and error statistics by climate region for each season. These statistics are based on the set of observed-predicted pairs of data for the particular quarter at monitoring sites within the nine NOAA climate regions. Separate statistics are provided for each monitoring network, as applicable for the particular species measured. For sulfate and nitrate we also provide a more refined temporal and spatial analysis of model performance that includes spatial maps which show the mean bias and error and the normalized mean bias and error by site, aggregated by season. A.3.1. Seasonal Evaluation for Sulfate The model performance bias and error statistics for sulfate for each climate region and each season by monitor network are provided in Table A-3. Spatial plots of the normalized mean bias and error by season for individual monitors are shown in Figures A-3 through A-6. As seen in Table A-3, CMAQ generally under predicts sulfate in the NOAA climate regions throughout the entire year except for the following: (1) at Southeast IMPROVE sites during the spring season, (2) at Northeast, Northern Rockies and Upper Midwest IMPROVE sites, as well as Northeast, Northern Rockies, and South CSN sites during the fall season, (3) at Southwest and West IMPROVE and CASTNet ozone sites in addition to CSN in the West, and (4) at Northwest IMPROVE, CASTNet and CSN during all seasons except for the summer at CASTNet sites. Table A-3. Sulfate Performance Statistics by Climate Region, by Season, and by Monitoring Network for the 2011 CMAQ Model Simulation. Climate Monitor Season No. of MB ME NMB NME Region Network Obs (ug/m3) (ug/m3) (%) (%) Winter 425 -0.2 0.3 -12.4 26.2 IMPROVE Spring 475 0.2 0.5 18.2 40.6 Summer 422 -0.2 0.7 -9.7 41.7 Fall 418 0.1 0.4 5.9 34.7 Winter 679 -0.4 0.7 -19.0 35.2 Northeast CSN Spring 717 -0.2 0.5 -8.1 29.7 Summer 721 -0.4 0.9 -13.6 28.8 Fall 685 0.1 0.6 5.3 33.1 Winter 170 -0.6 0.6 -32.6 33.2 CASTNet Spring 193 -0.3 0.4 -13.9 23.8 Summer 187 -0.7 0.7 -23.1 26.2 Fall 196 -0.2 0.3 -9.7 18.9 Winter 207 -0.5 0.7 -27.6 37.0 IMPROVE Spring 235 -0.4 0.7 -19.1 31.5 Summer 211 -0.9 1.2 -24.5 33.3 Ohio Valley Fall 226 -0.1 0.6 -7.3 34.1 Winter 588 -0.7 0.9 -31.5 39.0 CSN Spring 624 -0.5 0.8 -18.8 31.0 Summer 645 -0.7 1.2 17.0 30.3 Fall 611 -0.2 0.6 -12.1 31.6 A-13 ------- Climate Monitor Season No. of MB ME NMB NME Region Network Obs (ug/m3) (ug/m3) (%) (%) Winter 201 -0.9 1.0 -39.7 40.3 CASTNet Spring 214 -0.7 0.7 -26.5 27.7 Summer 207 -1.2 1.3 -28.6 30.2 Fall 214 -0.4 0.5 -20.0 23.5 Winter 210 -0.3 0.4 -23.5 37.2 IMPROVE Spring 205 0.0 0.4 -1.3 27.2 Summer 221 -0.2 0.5 -16.9 33.8 Fall 223 0.0 0.4 1.9 36.7 Winter 334 -0.4 0.6 -24.6 38.4 Upper CSN Spring 337 0.0 0.6 -0.1 30.5 Midwest Summer 335 -0.4 0.8 -17.6 33.1 Fall 340 0.0 0.5 -1.2 31.3 Winter 56 -0.5 0.5 -33.4 33.8 CASTNet Spring 62 -0.2 0.3 -12.5 18.3 Summer 65 -0.5 0.5 -24.7 25.7 Fall 62 -0.2 0.3 -12.4 19.0 Winter 329 -0.2 0.6 -11.0 34.7 IMPROVE Spring 346 -0.4 0.7 -16.3 31.0 Summer 331 -0.7 0.9 -22.4 31.0 Fall 319 -0.1 0.5 -4.9 30.2 Winter 435 -0.2 0.6 -8.9 35.6 Southeast CSN Spring 454 -0.4 0.8 -16.4 32.6 Summer 471 -0.6 1.0 -17.5 29.0 Fall 442 0.0 0.5 -0.3 29.7 Winter 138 -0.6 0.6 -30.0 30.3 CASTNet Spring 146 -0.8 0.9 -31.0 32.1 Summer 147 -1.2 1.2 -34.2 34.6 Fall 150 -0.4 0.5 -23.4 26.6 Winter 247 -0.2 0.5 -18.7 39.7 IMPROVE Spring 269 -0.5 0.7 -28.0 37.1 Summer 279 -0.7 0.8 -33.4 36.8 Fall 252 -0.1 0.3 -8.7 27.3 Winter 222 -0.2 0.7 -10.1 38.1 South CSN Spring 248 -0.6 0.8 -23.9 33.0 Summer 253 -0.7 0.8 -26.4 33.7 Fall 238 0.0 0.5 1.9 30.1 Winter 70 -0.6 0.6 -35.7 36.5 CASTNet Spring 85 -1.0 1.0 -39.9 40.0 Summer 88 -1.0 1.0 -40.6 42.0 Fall 76 -0.4 0.5 -22.3 27.2 Winter 904 0.1 0.2 39.5 60.6 IMPROVE Spring 920 -0.1 0.3 -14.8 42.7 Summer 922 -0.4 0.4 -43.1 44.9 Fall 916 -0.1 0.2 -21.3 34.9 Winter 185 0.0 0.3 -4.0 48.0 Southwest CSN Spring 190 0.0 0.3 -3.5 37.5 Summer 192 -0.4 0.4 -39.8 43.9 Fall 186 -0.1 0.2 -14.8 32.6 Winter 94 0.1 0.1 23.0 33.4 CASTNet Spring 102 -0.1 0.2 -18.3 31.9 Summer 102 -0.4 0.5 -39.0 45.9 A-14 ------- Climate Monitor Season No. of MB ME NMB NME Region Network Obs (ug/m3) (ug/m3) (%) (%) Fall 101 -0.2 0.2 -27.5 32.0 Winter 522 0.0 0.2 -5.4 49.1 IMPROVE Spring 590 0.0 0.3 -5.5 38.5 Summer 580 -0.1 0.2 -14.8 31.8 Fall 551 0.1 0.2 22.5 40.1 Winter 66 -0.2 0.3 -24.0 31.9 Northern CSN Spring 70 -0.3 0.4 -17.2 29.7 Rockies Summer 72 -0.2 0.4 -12.0 29.3 Fall 69 0.0 0.2 4.8 28.1 Winter 77 -0.1 0.2 -17.2 32.9 CASTNet Spring 76 -0.2 0.2 -20.8 26.6 Summer 88 -0.3 0.3 33.1 33.9 Fall 89 -0.1 0.1 -9.1 20.6 Winter 422 0.2 0.2 65.0 90. IMPROVE Spring 500 0.2 0.2 45.2 58.3 Summer 438 0.0 0.3 6.9 39.3 Fall 450 0.2 0.3 41.5 62.4 Winter 166 0.3 0.5 44.4 70.3 Northwest CSN Spring 167 0.3 0.4 54.6 63.0 Summer 172 0.2 0.4 15.9 39.9 Fall 164 0.4 0.5 48.4 65.6 Winter 12 0.1 0.1 48.6 54.2 CASTNet Spring 13 0.1 0.2 26.0 32.4 Summer 13 -0.1 0.2 -14.1 21.6 Fall 13 0.1 0.1 17.1 30.0 Winter 471 0.1 0.2 51.1 82.7 IMPROVE Spring 513 0.0 0.3 -1.4 47.3 Summer 526 -0.4 0.5 -46.5 53.7 Fall 525 -0.1 0.3 -20.6 44.3 Winter 226 0.0 0.4 4.6 55.8 West CSN Spring 242 -0.1 0.4 -9.9 39.2 Summer 246 -0.9 0.9 -49.5 52.3 Fall 229 -0.5 0.7 -36.1 46.8 Winter 69 0.0 0.2 5.1 40.8 CASTNet Spring 73 -0.2 0.3 -24.9 37.9 Summer 77 -0.6 0.6 -57.4 57.8 Fall 77 -0.3 0.4 -40.7 46.8 A-15 ------- S04 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for December to February 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-3a. Mean Bias (ug/m3) of sulfate during winter 2011 at monitoring sites in the modeling domain. S04 ME (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for December to February 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-3b. Mean Error (ug/m3) of sulfate during winter 2011 at monitoring sites in the modeling domain. A-16 ------- S04 NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for December to February 2011 units = % coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-3c. Normalized Mean Bias (%) of sulfate during winter 2011 at monitoring sites in the modeling domain. S04 NME (%) for run 2011ei_cb05v2_hdghgp2 12US2 for December to February 2011 units = % coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-3d. Normalized Mean Error (%) of sulfate during winter 2011 at monitoring sites in the modeling domain. A-17 ------- S04 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for March to May 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-4a. Mean Bias (ug/mJ) of sulfate during spring 2011 at monitoring sites in the modeling domain. S04 ME (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for March to May 2011 units = ug/m3 coverage limit = 75% CIRCLEdMPROVE; TRIANGLE=CSN; SQUARE=CASTNET: Figure A-4b. Mean Error (ug/m3) of sulfate during spring 2011 at monitoring sites in the modeling domain. A-18 ------- S04 NMB (%) for run 2011eLcb05v2_hdghgp2_12US2 for March to May 2011 units = % coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-4c. Normalized Mean Bias (%) of sulfate during spring 2011 at monitoring sites in the modeling domain. S04 NME (%) for run 2011ei_cb05v2_hdghgp2_12US2 for March to May 2011 units = % coverage limit = 75% CIRCLEdMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-4d. Normalized Mean Error (%) of sulfate during spring 2011 at monitoring sites in the modeling domain. A-19 ------- S04 MB (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for June to August 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-5a. Mean Bias (ug/m3) of sulfate during summer 2011 at monitoring sites in the modeling domain. S04 ME (ug/m3) for run2011ei_cb05v2Jidghgp2 12US21or June to August 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET: Figure A-5b. Mean Error (ug/m3) of sulfate during summer 2011 at monitoring sites in the modeling domain. A-20 ------- S04 NMB (%) for run 2011ei_cb05v2 hdghgp2 12US2 for June to August 2011 units = % coverage limit = 75% ' •• j im CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-5c. Normalized Mean Bias (%) of sulfate during summer 2011 at monitoring sites in the modeling domain. S04 NME {%) for run 2011ei_cb05v2_hdghgp2_12US2 for June to August 2011 units = % coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-5d. Normalized Mean Error (%) of sulfate during summer 2011 at monitoring sites in the modeling domain. A-21 ------- S04 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for September to November 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-6a. Mean Bias (ug/m3) of sulfate during fall 2011 at monitoring sites in the modeling domain. S04 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for September to November 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-6b. Mean Error (ug/mJ) of sulfate during fall 2011 at monitoring sites in the modeling domain. A-22 ------- S04 NMB (%) for run 2011ei_cb05v2_hdghgp2 12US2 for September to November 2011 units = % coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-6c. Normalized Mean Bias (%) of sulfate during fall 2011 at monitoring sites in the modeling domain. units = % coverage limit = 75% SQ4 NME (%) for run 2011ei Cb05v2 hdghgp2 12US2 for September to November 2011 CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET; Figure A-6d. Normalized Mean Error (%) of sulfate during fall 2011 at monitoring sites in the modeling domain. > 100 90 80 70 60 50 40 30 20 10 A-23 ------- A.3.1. Seasonal Evaluation for Nitrate The model performance bias and error statistics for nitrate for each climate region and each season are provided in Table A-4. This table includes statistics for particulate nitrate as measured at CSN and IMPROVE sites and total nitrate (NO3+HNO3) as measured at CASTNet sites. Spatial plots of the mean bias and error as well as normalized mean bias and error by season for individual monitors are shown in Figures A-7 through A-10. Overall, nitrate and total nitrate performance are over predicted in the Northeast, Ohio Valley, Upper Midwest, Southeast, South, Northern Rockies and Northwest U.S.; with the exception at the IMPROVE and CSN sites in the Southwest where nitrate is under predicted in the winter. Likewise, the model tends to over predict nitrate during the fall season except in the South and Southwest at CSN and CASTNet and in the Southwest at all three monitoring networks. During the spring, nitrate and total nitrate is also over predicted except in the South, Southeast and Southwest. Nitrate and total nitrate performance during the summer season typically shows an under prediction in most areas of the U.S. with the exception of the Northwest at urban CSN sites and the Ohio Valley region at rural CASTNet sites. Model performance shows an under prediction in the West for all of the seasonal assessments of nitrate. Table A-4. Nitrate and Total Nitrate Performance Statistics by Climate Region, by Season, and by Monitoring Network for the 2011 CMAQ Model Simulation. Climate Monitor Season No. of MB ME NMB NME Region Network Obs (ug/m3) (ug/m3) (%) (%) Winter 425 0.9 1.0 154.0 163.0 IMPROVE Spring 474 0.1 0.3 42.6 111.0 Summer 422 -0.1 0.2 -55.5 97.1 Fall 418 0.1 0.3 26.2 106.0 Winter 679 1.1 1.4 52.9 66.7 Northeast CSN Spring 717 0.1 0.6 6.5 58.8 Summer 721 -0.3 0.4 -54.1 72.5 Fall 685 0.1 0.5 16.0 63.8 Winter 170 0.8 0.9 38.7 40.9 CASTNet Spring 193 0.2 0.4 11.0 30.3 Summer 187 0.0 0.3 -1.7 27.5 Fall 196 0.4 0.6 35.4 46.1 Winter 207 0.6 1.2 29.5 57.7 IMPROVE Spring 235 0.6 0.9 70.5 110.0 Summer 211 -0.1 0.2 -62.5 81.3 Fall 226 0.2 0.4 38.9 83.0 Winter 588 1.0 1.4 35.0 49.0 Ohio Valley CSN Spring 624 0.7 1.1 43.0 68.5 Summer 645 -0.2 0.4 -33.0 69.4 Fall 611 0.3 0.1 38.0 67.0 Winter 201 0.6 0.9 16.1 25.4 CASTNet Spring 214 0.3 0.7 14.7 33.1 Summer 207 0.0 0.5 0.7 28.4 Fall 214 0.5 0.6 33.6 38.1 Winter 210 0.6 1.0 30.2 49.9 Upper IMPROVE Spring 205 0.4 0.7 35.7 59.3 Midwest Summer 221 -0.1 0.1 -33.2 69.0 Fall 222 0.5 0.6 74.9 88.7 A-24 ------- Climate Monitor Season No. of MB ME NMB NME Region Network Obs (ug/m3) (ug/m3) (%) (%) Winter 334 0.8 1.3 22.7 36.8 CSN Spring 337 0.7 1.1 30.5 50.8 Summer 335 -0.2 0.4 -38.1 72.1 Fall 340 0.6 0.8 47.5 62.9 Winter 56 0.5 0.7 18.6 23.4 CASTNet Spring 62 0.4 0.6 24.1 38.6 Summer 65 -0.1 0.4 -10.7 29.6 Fall 62 0.7 0.7 43.1 47.3 Winter 329 0.4 0.7 70.9 119.0 IMPROVE Spring 346 -0.1 0.4 -17.5 97.5 Summer 331 -0.2 0.2 -60.7 87.2 Fall 319 0.1 0.3 24.7 111.0 Winter 435 0.9 1.0 97.3 115.0 Southeast CSN Spring 454 0.0 0.4 -8.1 83.4 Summer 471 -0.1 0.2 -47.9 70.8 Fall 442 0.3 0.5 88.1 131.0 Winter 138 0.4 0.9 22.1 50.8 CASTNet Spring 146 -0.4 0.6 -25.8 39.2 Summer 147 -0.3 0.4 -21.0 34.5 Fall 150 0.1 0.5 11.2 41.7 Winter 247 0.1 0.7 3.9 51.8 IMPROVE Spring 269 0.0 0.5 -1.4 61.1 Summer 279 -0.2 0.3 -91.6 93.6 Fall 252 0.0 0.3 0.1 78.3 Winter 222 0.3 1.0 15.9 52.7 South CSN Spring 248 -0.1 0.6 -13.2 70.6 Summer 253 -0.3 0.3 -79.7 87.0 Fall 238 0.0 0.4 -1.0 65.2 Winter 70 0.3 0.7 11.7 27.7 CASTNet Spring 85 -0.5 0.8 -27.0 40.0 Summer 88 -0.7 0.7 -39.4 40.3 Fall 76 -0.2 0.4 -12.3 31.0 Winter 903 -0.1 0.3 -37.5 74.2 IMPROVE Spring 920 -0.1 0.2 -59.5 75.0 Summer 922 -0.1 0.2 -87.1 92.0 Fall 916 -0. 1 0.1 -49.6 93.0 Winter 185 -1.8 2.1 -51.9 62.6 Southwest CSN Spring 190 -0.2 0.4 -26.2 58.2 Summer 192 -0.1 0.3 -44.2 96.8 Fall 186 -0.2 0.6 -21.5 73.0 Winter 94 0.0 0.3 1.7 47.2 CASTNet Spring 102 -0.2 0.2 -26.6 36.2 Summer 102 -0.4 0.4 -40.2 45.4 Fall 101 -0.1 0.2 -15.7 38.6 Winter 520 0.5 0.6 130.0 166.0 IMPROVE Spring 588 0.3 0.4 67.1 101.0 Northern Rockies Summer 578 -0.1 0.1 -59.9 90.0 Fall 551 0.2 0.3 152 193 Winter 66.0 0.1 1.1 4.4 46.7 CSN Spring 70.0 0.2 0.8 12.6 48.4 Summer 72.0 -0.2 0.2 -64.2 83.2 A-25 ------- Climate Monitor Season No. of MB ME NMB NME Region Network Obs (ug/m3) (ug/m3) (%) (%) Fall 69.0 0.5 0.7 66.0 94.3 Winter 77 0.3 0.3 36.8 45.1 CASTNet Spring 76 0.1 0.3 13.4 33.6 Summer 88 -0.2 0.2 -19.5 23.8 Fall 89 0.1 0.2 23.9 35.9 Winter 416 0.1 0.4 16.5 109.0 IMPROVE Spring 498 0.1 0.1 37.3 103.0 Summer 436 0.0 0.1 -21.3 103.0 Fall 447 0.1 0.2 40.8 119.0 Winter 166 0.5 1.5 28.7 84.9 Northwest CSN Spring 167 0.2 0.4 49.6 84.6 Summer 172 0.1 0.3 40.5 113.0 Fall 164 0.4 0.6 61.1 99.6 Winter - - - - - CASTNet Spring - - - - - Summer - - - - - Fall - - - - - Winter 460 -0.4 0.6 -44.3 74.1 IMPROVE Spring 513 -0.2 0.3 -46.6 72.8 Summer 526 -0.3 0.3 -78.9 90.6 Fall 522 -0.1 0.4 -33.0 85.4 Winter 226 -2.5 2.9 -52.9 61.2 West CSN Spring 242 -0.7 1.0 -38.8 55.5 Summer 246 -1.5 1.5 -71.4 72.8 Fall 229 -2.0 2.3 -55.8 64.3 Winter 69 -0.2 0.6 -21.4 55.1 CASTNet Spring 73 -0.3 0.4 -31.8 41.7 Summer 77 -0.6 0.6 -34.3 38.9 Fall 77 -0.4 0.6 -29.1 40.1 A-26 ------- N03 MB (ug/m3) lor run2011ei_cb05v2 hdghgp2 12US2 for December to February 2011 units = ug/m3 coverage limit = 75% CIRCLEdMPROVE; TRIANGLE=CSN; Figure A-7a. Mean Bias (ug/m3) for nitrate during winter 2011 at monitoring sites in the modeling domain. N03 ME (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for December to February 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-7b. Mean Error (ug/m3) for nitrate during winter 2011 at monitoring sites in the modeling domain. A-27 ------- TN03 MB (ug/m3) for run2011ei_cb05v2 hdghgp2_12US2 for December to February 2011 units = ug/m3 coverage limit = 75% SQUARE=CASTNET; Figure A-7c. Mean Bias (ug/mJ) for total nitrate during winter 2011 at monitoring sites in the modeling domain. TN03 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for December to February 2011 units = ug/m3 coverage limit = 75% SQUARE=CASTN ET; Figure A-7d. Mean Error (ug/m3) for total nitrate during winter 2011 at monitoring sites in the modeling domain. A-28 ------- N03 NMB (%) lor run 2011ei_cb05v2_hdghgp2 12US2 for December to February 2011 units = % coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-7e. Normalized Mean Bias (%) for nitrate during winter 2011 at monitoring sites in the modeling domain. N03 NME (%) for run 2011 ei_cb05v2 hdghgp212US2 for December to February 2011 units = % coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-7f. Normalized Mean Error (%) for nitrate during winter 2011 at monitoring sites in the modeling domain. A-29 ------- TN03 NMB (%) for run 2011 ei_cb05v2 hdghgp2 12US2 for December to February 2011 units = % coverage limit = 75% > 100 <-100 SQUARE=CASTNET; Figure A-7g. Normalized Mean Bias (%) for total nitrate during winter 2011 at monitoring sites in the modeling domain. units = % coverage limit = 75% TN03 NME (%) for run 2011ei_cb05v2 hdghgp2 12US2 for December to February 2011 > 100 90 80 70 60 50 40 30 20 10 0 Figure A-7h. Normalized Mean Error (%) for total nitrate during winter 2011 at monitoring sites in the modeling domain. SQUARE=CASTNET; A-30 ------- N03 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for March to May 2011 units = ug/m3 coverage limit = 75% CIRCLE=IIVIPROVE; TRIANGLE=CSN; Figure A-8a. Mean Bias (ug/m3) for nitrate during spring 2011 at monitoring sites in the modeling domain. N03 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for March to May 2011 units = ug/m3 coverage limit = 75% f* CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-8b. Mean Error (ug/m3) for nitrate during spring 2011 at monitoring sites in the modeling domain. A-31 ------- TN03 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for March to May 2011 units = ug/m3 coverage limit = 75% SQUARE=CASTNET; Figure A-8c. Mean Bias (ug/nr') for total nitrate during spring 2011 at monitoring sites in the modeling domain. TN03 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 lor March to May 2011 units = ug/m3 coverage limit = 75% SQUARE=CASTN ET; Figure A-8d. Mean Error (ug/mJ) for total nitrate during spring 2011 at monitoring sites in the modeling domain. A-32 ------- N03 NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for March to May 2011 units = % coverage limit = 75% > 100 80 60 40 20 0 -20 -40 -60 -80 <-100 CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-8e. Normalized Mean Bias (%) for nitrate during spring 2011 at monitoring sites in the modeling domain. units = % coverage limit = 75% NQ3 NME (%) for run 2011eLcb05v2_hdghgp2_12US2 for March to May 2011 >100 90 80 70 60 50 40 30 20 10 0 Figure A-8f. Normalized Mean Error (%) for nitrate during spring 2011 at monitoring sites in the modeling domain. CIRCLE=IMPROVE; TRIANGLE=CSN; A-33 ------- TN03 NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for March to May 2011 units = % coverage limit = 75% SQUARE=CASTNET; Figure A-8g. Normalized Mean Bias (%) for total nitrate during spring 2011 at monitoring sites in the modeling domain. TN03 NME (%) for run 2011ei_cb05v2_hdghgp2_12US2 lor March to May 2011 units = % coverage limit = 75% SQUARE=CASTNET; Figure A-8h. Normalized Mean Error (%) for total nitrate during spring 2011 at monitoring sites in the modeling domain. A-34 ------- N03 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for June to August 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-9a. Mean Bias (ug/m3) for nitrate during summer 2011 at monitoring sites in the modeling domain. N03 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for June to August 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-9b. Mean Error (ug/m3) for nitrate during summer 2011 at monitoring sites in the modeling domain. A-35 ------- TN03 MB (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for June to August 2011 units = ug/m3 coverage limit = 75% SQUARE=CASTNET; Figure A-9c. Mean Bias (ug/m3) for total nitrate during summer 2011 at monitoring sites in the modeling domain. TN03 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for June to August 2011 units = ug/m3 coverage limit = 75% SQUARE=CASTNET; Figure A-9d. Mean Error (ug/m3) for total nitrate during summer 2011 at monitoring sites in the modeling domain. A-36 ------- N03 NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for June to August 2011 units = % coverage limit = 75% > 100 ¦¦ 80 60 40 n ¦ 20 0 _ -20 -40 -60 -80 <-100 CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-9e. Normalized Mean Bias (%) for nitrate during summer 2011 at monitoring sites in the modeling domain. NQ3 NME (%) for run 2011ei_cb05v2_hdghgp2_12US2 lor June to August 2011 units = % coverage limit = 75% > 100 90 80 70 60 50 40 30 20 10 0 Figure A-9f. Normalized Mean Error (%) for nitrate during summer 2011 at monitoring sites in the modeling domain. CIRCLE=IMPROVE; TRIANGLE=CSN; A-37 ------- TN03 NMB (%) for run 2011ei_cb05v2 hdghgp2 12US2 for June to August 2011 units = % coverage limit = 75% SQUARE=CASTNET; Figure A-9g. Normalized Mean Bias (%) for total nitrate during summer 2011 at monitoring sites in the modeling domain. units = % coverage limit = 75% TN03 NME (%) for run 2011ei_cb05v2 hdghgp2 12US2 for June to August 2011 ! -<«H V I \ . SQUARE=CASTNET; Figure A-9h. Normalized Mean Error (%) for total nitrate during summer 2011 at monitoring sites in the modeling domain. A-38 ------- N03 MB (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for September to November 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN: Figure A-lOa. Mean Bias (ug/m3) for nitrate during fall 2011 at monitoring sites in the modeling domain. N03 ME (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for September to November 2011 units = ug/m3 coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-lOb. Mean Error (ug/m3) for nitrate during fall 2011 at monitoring sites in the modeling domain. A-39 ------- TN03 MB (ug/m3) for run2011ei_cb05v2 hdghgp2_12US2 for September to November 2011 units = ug/m3 coverage limit = 75% SQUARE=CASTNET; Figure A-lOc. Mean Bias (ug/m3) for total nitrate during fall 2011 at monitoring sites in the modeling domain. TN03 ME (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for September to November 2011 units = ug/m3 coverage limit = 75% SQUARE=CASTNET; Figure A-lOd. Mean Error (ug/m3) for total nitrate during fall 2011 at monitoring sites in the modeling domain. A-40 ------- N03 NMB (%) for run 2011ei_cb05v2 hdghgp2_12US2 for September to November 2011 units = % coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-lOe. Normalized Mean Bias (%) for nitrate during fall 2011 at monitoring sites in the modeling domain. N03 NME (%) for run 2011ei_cb05v2 hdghgp2 12US2 for September to November 2011 units = % coverage limit = 75% CIRCLE=IMPROVE; TRIANGLE=CSN; Figure A-lOf. Normalized Mean Error (%) for nitrate during fall 2011 at monitoring sites in the modeling domain. A-41 ------- TN03 NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for September to November 2011 units = % coverage limit = 75% SQUAR E=CASTN ET; Figure A-lOg. Normalized Mean Bias (%) for total nitrate during fall 2011 at monitoring sites in the modeling domain. TN03 NME (%) for run 2011 ei_cb05v2 hdghgp212US2 for September to November 2011 units ¦ % coverage limit - 75% SQUARE=CASTNET; Figure A-lOh. Normalized Mean Error (%) for total nitrate during fall 2011 at monitoring sites in the modeling domain. A-42 ------- H. Seasonal Ammonium Performance The model performance bias and error statistics for ammonium for each climate region and season are provided in Table A-5. These statistics indicate model bias for ammonium is generally over predicted in the spring, fall and winter seasons except for the following exclusions: (1) the spring shows under predictions in the South, Southeast, and Southwest at both CSN and CASTNet sites; (2) the fall has under predictions at rural CASTNet sites in the Ohio Valley, Southeast, South, Southwest, and Northern Rockies as well as at the urban CSN sites in the Southwest; and (3) the winter performance shows under predictions in the Ohio Valley, Southeast, South, Southwest and Northern Rockies at CASTNet monitors in addition to CSN sites at the Northern Rockies. Generally, the West (California and Nevada) show under predictions of ammonia. Table A-5. Ammonium Performance Statistics by Climate Region, by Season, and by Monitoring Network for the 2011 CMAQ Model Simulation. Climate Monitor Season No. of MB ME NMB NME Region Network Obs (ug/m3) (ug/m3) (%) (%) Winter 679 0.2 0.5 20.9 39.3 CSN Spring 717 0.0 0.3 2.3 37.2 Summer 721 -0.1 0.3 -11.8 36.6 Northeast Fall 685 0.2 0.3 36.2 56.3 Winter 170 0.1 0.2 6.7 25.3 CASTNet Spring 193 0.0 0.2 -0.1 24.3 Summer 187 -0.3 0.3 -33.0 34.2 Fall 196 0.0 0.2 36.2 56.3 Winter 588 0.1 0.5 5.0 34.9 CSN Spring 624 0.1 0.4 4.4 33.7 Summer 645 -0.1 0.4 -11.5 34.7 Ohio Valley Fall 611 0.1 0.3 18.6 47.3 Winter 201 -0.2 0.3 -13.5 21.6 CASTNet Spring 214 0.0 0.3 -2.9 28.6 Summer 207 -0.5 0.5 -34.1 35.2 Fall 214 -0.1 0.3 -6.6 32.9 Winter 334 0.1 0.6 00 00 38.5 CSN Spring 337 0.2 0.4 17.8 35.2 Summer 335 -0.1 0.3 -11.0 43.2 Upper Fall 340 0.3 0.4 38.8 53.1 Midwest Winter 56 0.0 0.2 2.6 16.2 CASTNet Spring 62 0.1 0.2 12.3 29.0 Summer 65 -0.2 0.2 -30.6 32.1 Fall 62 0.0 0.2 6.9 28.0 Winter 435 0.2 0.3 23.0 47.3 CSN Spring 454 -0.1 0.3 -14.6 39.5 Summer 471 0.0 0.3 -4.0 35.9 Southeast Fall 442 0.2 0.3 56.5 71.6 Winter 138 -0.1 0.2 -8.0 23.4 CASTNet Spring 146 -0.2 0.2 -22.5 30.3 Summer 147 -0.3 0.4 -32.8 35.2 Fall 150 -0.1 0.2 -15.3 30.1 South CSN Winter 222 0.0 0.4 2.4 44.8 A-43 ------- Climate Region Monitor Network Season No. of Obs MB (ug/m3) ME (ug/m3) NMB (%) NME (%) Spring 248 -0.2 0.4 -23.2 40.9 Summer 253 -0.1 0.3 -24.3 45.0 Fall 238 0.0 0.3 8.4 50.9 CASTNet Winter 70 -0.1 0.3 -13.0 32.1 Spring 85 -0.2 0.3 -24.0 38.5 Summer 88 -0.3 0.3 -38.6 41.2 Fall 76 -0.1 0.2 -22.4 32.6 Southwest CSN Winter 185 -0.6 0.8 -53.9 65.2 Spring 190 -0.1 0.2 -44.5 68.9 Summer 471 0.0 0.3 -4.0 35.9 Fall 186 -0.2 0.2 -45.6 60.5 CASTNet Winter 94 0.0 0.1 13.0 44.9 Spring 102 -0.1 0.1 -52.8 61.7 Summer 147 -0.3 .4 -32.8 35.2 Fall 101 -0.1 0.1 -40.7 45.0 Northern Rockies CSN Winter 70 0.0 0.3 -1.5 33.1 Spring 66 0.1 0.4 5.9 40.5 Summer 72 0.0 0.1 1.8 39.8 Fall 69 0.2 0.3 57.9 86.4 CASTNet Winter 76 0.0 0.1 -1.7 28.7 Spring 77 0.1 0.1 23.4 37.6 Summer 88 -0.1 0.1 -49.4 51.0 Fall 89 0.0 0.1 -4.4 42.1 Northwest CSN Winter 166 0.3 0.6 48.6 108.0 Spring 167 0.2 0.2 111.0 135.0 Summer 172 0.0 0.1 11.7 58.8 Fall 164 0.2 0.3 109.0 152.0 CASTNet Winter 12 0.0 0.1 71.5 79.3 Spring 13 0.0 0.1 33.4 47.1 Summer 13 0.0 0.1 -18.3 25.0 Fall 13 0.0 0.1 27.3 50.6 West CSN Winter 226 -0.8 1.0 -49.9 62.9 Spring 242 -0.2 0.4 -32.0 60.0 Summer 246 -0.6 0.6 -66.8 68.6 Fall 229 -0.8 0.9 -56.0 66.0 CASTNet Winter 69 -0.1 0.2 -19.0 64.9 Spring 73 -0.1 0.2 -44.2 63.9 Summer 77 -0.3 0.3 -74.9 74.9 Fall 77 -0.2 0.2 -52.9 64.1 A-44 ------- I. Seasonal Elemental Carbon Performance The model performance bias and error statistics for elemental carbon for each of the nine climate regions and each season are provided in Table A-6. The statistics show clear over prediction at urban and rural sites in all climate regions with the exception of a slight under prediction during the winter in the Northern Rockies urban sites. In the Northwest, issues in the ambient data when compared to model predictions were found and thus removed from the performance analysis. Table A-6. Elemental Carbon Performance Statistics by Climate Region, by Season, and by Monitoring Network for the 2011 CMAQ Model Simulation. Climate Monitor Season No. of MB ME NMB NME Region Network Obs (ug/m3) (ug/m3) (%) (%) Winter 441 0.2 0.3 94.0 108.0 IMPROVE Spring 480 0.1 0.1 52.4 79.3 Summer 446 0.0 0.1 6.0 39.8 Northeast Fall 449 0.1 0.1 34.9 58.1 Winter 645 0.7 0.8 94.3 108.0 CSN Spring 687 0.3 0.4 58.7 81.8 Summer 699 0.1 0.4 17.5 48.9 Fall 625 0.3 0.5 48.1 68.8 Winter 222 0.2 0.2 62.4 71.8 IMPROVE Spring 238 0.0 0.1 15.8 44.0 Summer 225 0.0 0.1 6.0 31.9 Ohio Valley Fall 236 0.1 0.1 28.3 47.8 Winter 575 0.6 0.7 109.0 117.0 CSN Spring 604 0.3 0.4 50.2 67.8 Summer 662 0.3 0.4 33.9 52.8 Fall 611 0.4 0.5 55.4 70.3 Winter 222 0.1 0.2 88.5 102.0 IMPROVE Spring 232 0.1 0.1 65.8 22.2 Summer 231 0.0 0.1 0.6 41.6 Upper Fall 228 0.2 0.2 80.3 99.2 Midwest Winter 326 0.6 0.6 153.0 155.0 CSN Spring 330 0.4 0.4 96.3 103.0 Summer 333 0.2 0.3 37.7 53.4 Fall 340 0.4 0.4 78.1 82.3 Winter 345 0.2 0.3 51.0 66.8 IMPROVE Spring 374 0.0 0.2 5.1 50.4 Summer 359 0.0 0.2 9.5 53.0 Southeast Fall 350 0.1 0.2 26.0 49.6 Winter 417 0.4 0.5 52.8 74.5 CSN Spring 430 0.3 0.4 44.0 68.2 Summer 460 0.3 0.5 53.4 80.6 Fall 423 0.4 0.5 61.2 81.5 Winter 267 0.2 0.2 54.5 76.2 IMPROVE Spring 299 0.0 0.2 6.5 56.0 South Summer 279 0.0 0.1 1.2 47.1 Fall 251 0.1 0.1 26.2 52.0 CSN Winter 222 0.5 0.6 68.9 95.6 Spring 250 0.3 0.4 53.5 81.6 A-45 ------- Climate Region Monitor Network Season No. of Obs MB (ug/m3) ME (ug/m3) NMB (%) NME (%) Summer 257 0.4 0.5 94.1 111.0 Fall 240 0.4 0.5 67.7 85.5 Southwest IMPROVE Winter 946 0.1 0.1 36.5 68.3 Spring 965 0.1 0.1 68.0 101.0 Summer 987 0.1 0.1 37.7 96.7 Fall 948 0.1 0.1 40.2 78.9 CSN Winter 181 0.6 0.8 57.2 72.4 Spring 187 0.5 0.5 153.0 155.0 Summer 195 0.4 0.5 88.4 103.0 Fall 189 0.6 0.6 76.8 86.6 Northern Rockies IMPROVE Winter 541 0.0 0.1 52.6 91.0 Spring 594 0.0 0.0 18.3 59.2 Summer 583 0.0 0.1 32.1 69.6 Fall 568 0.1 0.1 52.5 82.8 CSN Winter 63 -0.1 0.9 -13.1 117.0 Spring 60 0.1 0.3 35.6 91.3 Summer 70 0.1 0.3 34.3 76.5 Fall 69 0.2 0.5 41.4 96.2 Northwest IMPROVE Winter - - - - - Spring - - - - - Summer - - - - - Fall - - - - - CSN Winter - - - - - Spring - - - - - Summer - - - - - Fall - - - - - West IMPROVE Winter 552 0.0 0.1 11.6 50.9 Spring 555 0.0 0.1 25.2 67.0 Summer 566 0.0 0.1 32.6 69.8 Fall 588 0.1 0.1 26.0 72.0 CSN Winter 226 0.1 0.6 9.5 41.2 Spring 237 0.5 0.5 100.0 105.0 Summer 244 0.4 0.4 77.0 80.3 Fall 226 0.4 0.5 39.0 52.7 A-46 ------- J. Seasonal Organic Carbon Performance The model performance bias and error statistics for organic carbon for each climate region and season are provided in Table A-7. The statistics in this table indicate a tendency for the modeling platform to under predict observed organic carbon concentrations during the spring and summer although over predict organic carbon during the fall and winter at urban and rural locations with the exceptions of the following: (1) the spring shows over predictions in the Northeast and Upper Midwest at MPROVE and CSN sites as well as in the Ohio Valley and Southeast at CSN sites; (2) the summer has over predictions at urban CSN sites in the South, Southeast and Southwest; (3) the fall under predicts at rural IMPROVE sites in the Ohio Valley, South and Southeast; and (4) the winter under predicts in the Northern Rockies at urban sites. In the West, organic carbon performance shows over predictions at urban sites during all seasons. However, in the West performance shows under predictions at rural sites during the entire year. These biases and errors reflect sampling artifacts among each monitoring network. In addition, uncertainties exist for primary organic mass emissions and secondary organic aerosol formation. Research efforts are ongoing to improve fire emission estimates and understand the formation of semi-volatile compounds, and the partitioning of SOA between the gas and particulate phases. Similar to the elemental carbon performance, issues in the ambient data when compared to model predictions were found in the Northwest as well as in the Southwest at urban sites and thus removed from the performance analysis. Table A-7. Organic Carbon Performance Statistics by Climate Region, by Season, and by Monitoring Network for the 2011 CMAQ Model Simulation. Climate Monitor Season No. of MB ME NMB NME Region Network Obs (ug/m3) (ug/m3) (%) (%) Winter 440 1.3 1.4 133.0 140.0 IMPROVE Spring 478 0.2 0.5 32.8 78.0 Summer 445 -0.7 0.8 -44.4 50.2 Northeast Fall 448 0.1 0.5 12.1 51.5 Winter 639 3.4 3.4 211.0 213.0 CSN Spring 682 0.9 1.2 84.0 106.0 Summer 698 -0.2 0.7 -10.9 36.1 Fall 622 1.1 1.2 74.3 84.3 Winter 222 0.6 0.9 45.7 65.9 IMPROVE Spring 238 -0.3 0.6 -18.7 45.9 Summer 225 -0.6 0.6 -29.4 36.3 Ohio Valley Fall 236 0.0 0.5 -0.9 42.6 Winter 570 1.9 2.0 121.0 126.0 CSN Spring 600 0.4 0.8 26.9 52.0 Summer 662 -0.1 0.7 -4.4 29.0 Fall 610 0.6 0.8 39.3 54.2 Winter 221 0.6 0.7 95.5 106.0 IMPROVE Spring 232 0.2 0.5 19.9 64.3 Summer 231 -0.7 0.7 -45.4 48.2 Upper Fall 228 0.2 1.2 12.1 73.3 Midwest Winter 324 2.4 2.4 186.0 190.0 CSN Spring 330 1.1 1.2 98.3 110.0 Summer 332 -0.1 0.7 -3.3 37.3 Fall 333 1.0 1.1 72.1 80.1 A-47 ------- Climate Region Monitor Network Season No. of Obs MB (ug/m3) ME (ug/m3) NMB (%) NME (%) Southeast IMPROVE Winter 345 0.5 1.0 33.5 63.9 Spring 374 -0.5 1.1 -26.4 56.8 Summer 361 -0.7 1.4 -31.9 61.6 Fall 349 -0.1 0.9 -6.1 61.1 CSN Winter 415 1.3 1.7 62.3 80.5 Spring 429 0.4 1.0 19.9 52.6 Summer 458 0.4 1.6 16.4 60.0 Fall 421 0.8 1.2 47.0 68.4 South IMPROVE Winter 266 0.3 0.5 33.5 67.9 Spring 299 -0.5 0.9 -29.7 57.4 Summer 281 -0.4 0.6 -28.5 41.6 Fall 251 0.0 0.4 -1.7 45.7 CSN Winter 220 1.2 1.7 61.7 89.3 Spring 250 -0.1 1.2 -7.0 57.6 Summer 257 0.5 1.0 27.6 55.8 Fall 239 0.7 1.1 42.5 64.1 Southwest IMPROVE Winter 930 0.1 0.4 18.3 67.8 Spring 962 0.0 0.2 -6.6 55.5 Summer 991 -0.3 0.6 -33.3 59.3 Fall 948 0.0 0.4 8.0 61.8 CSN Winter - - - - - Spring - - - - - Summer - - - - - Fall - - - - - Northern Rockies IMPROVE Winter 527 0.1 0.2 20.6 73.1 Spring 584 -0.1 0.2 -25.3 51.2 Summer 583 -0.3 0.7 -31.4 64.5 Fall 568 0.1 0.6 7.5 62.3 CSN Winter 63 -1.2 3.2 -38.8 107.0 Spring 58 0.0 0.7 4.4 67.6 Summer 70 -0.4 0.6 -30.2 39.3 Fall 68 0.1 1.1 4.1 61.2 Northwest IMPROVE Winter - - - - - Spring - - - - - Summer - - - - - Fall - - - - - CSN Winter - - - - - Spring - - - - - Summer - - - - - Fall - - - - - West IMPROVE Winter 538 0.0 0.3 -2.1 48.1 Spring 551 -0.1 0.3 -21.0 55.5 Summer 242 -0.3 0.6 -26.5 56.3 Fall 585 -0.2 0.8 -14.3 62.6 CSN Winter 224 1.3 2.1 34.1 56.5 Spring 237 1.2 1.3 87.9 96.5 Summer 242 0.1 0.6 7.1 38.8 Fall 225 0.7 1.3 27.2 45.7 A-48 ------- K. Seasonal Hazardous Air Pollutants Performance A seasonal operational model performance evaluation for specific hazardous air pollutants (i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene and acrolein) was conducted in order to estimate the ability of the CMAQ modeling system to replicate the base year concentrations for the 12 km Continental United States domain. The seasonal model performance results for the 12 km modeling domain are presented below in Table A-8. Toxic measurements included in the evaluation were taken from the 2011 air toxics archive, http ://www. epa. gov/ttn/amtic/toxdat.html#data. While most of the data in the archive are from the AQS database including the National Air Toxics Trends Stations (NATTS) (downloaded in July 2014), additional data (e.g., special studies) are included in the archive but not reported in the AQS. Similar to PM2.5 and ozone, the evaluation principally consists of statistical assessments of model versus observed pairs that were paired in time and space on daily basis. Model predictions of annual formaldehyde, acetaldehyde, benzene and 1,3 butadiene showed relatively small to moderate bias and error percentages when compared to observations. The model yielded larger bias and error results for acrolein based on limited monitoring sites. Model performance for HAPs is not as good as model performance for ozone and PM2.5. Technical issues in the HAPs data consist of (1) uncertainties in monitoring methods; (2) limited measurements in time/space to characterize ambient concentrations ("local in nature"); (3) ambient data below method detection limit (MDL); (4) commensurability issues between measurements and model predictions; (5) emissions and science uncertainty issues may also affect model performance; and (6) limited data for estimating intercontinental transport that effects the estimation of boundary conditions (i.e., boundary estimates for some species are much higher than predicted values inside the domain). As with the national, annual PM2.5 and ozone CMAQ modeling, the "acceptability" of model performance was judged by comparing our CMAQ 2011 performance results to the limited performance found in recent regional multi-pollutant model applications.21'22'23 Overall, the mean bias and error (MB and ME), as well as the normalized mean bias and error (NMB and NME) statistics shown below in Table A-8 indicate that CMAQ-predicted 2011 toxics (i.e., observation vs. model predictions) are within the range of recent regional modeling applications. 21 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, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008. 22 Strum, M., Wesson, K., Phillips, S., Cook, R., Michaels, H., Brzezinski, D., Pollack, A., Jimenez, M., Shepard, S. Impact of using in-line emissions on multi-pollutant air quality model predictions at regional and local scales. 17th Annual International Emission Inventory Conference, Portland, Oregon, June 2-5, 2008. 23 Wesson, K., N. Fann, and B. Timin, 2010: Draft Manuscript: Air Quality and Benefits Model Responsiveness to Varying Horizontal Resolution in the Detroit Urban Area, Atmospheric Pollution Research, Special Issue: Air Quality Modeling and Analysis. A-49 ------- Table A-8. Hazardous Air Toxics Performance Statistics by Season for the 2011 CMAQ odel Simulation. Air Toxic Species Season No. of Obs. MB (ug/m3) ME (ug/m3) NMB (%) NME (%) Formaldehyde Winter 1,070 -0.9 1.1 -50.8 59.9 Spring 1,067 -1.3 1.3 -59.1 62.4 Summer 1,044 -1.2 1.6 -32.8 41.8 Fall 1,055 -0.8 1.1 -41.3 42.9 Acetaldehyde Winter 1,056 -0.4 0.7 -30.0 52.9 Spring 1,069 -0.2 0.7 -12.0 55.0 Summer 1,063 1.6 1.9 88.5 106.0 Fall 1,095 0.0 0.8 2.6 55.9 Benzene Winter 2,498 0.2 0.7 25.5 76.4 Spring 2,386 -0.1 0.5 -11.0 64.7 Summer 2,504 -0.1 0.6 -14.0 79.0 Fall 2,448 0.0 0.6 -5.6 67.9 1,3-Butadiene Winter 2,373 0.0 0.1 -8.2 114.0 Spring 2,254 0.0 0.1 -24.7 115.0 Summer 2,330 0.0 0.1 -15.3 116.0 Fall 2,327 0.0 0.1 -38.3 100.0 Acrolein Winter 172 -0.4 0.4 -93.2 93.5 Spring 140 -0.3 0.3 -94.9 94.9 Summer 211 -0.5 0.5 -97.5 97.6 Fall 198 -0.4 0.4 -93.7 94.5 A-50 ------- L. Seasonal Nitrate and Sulfate Deposition Performance Seasonal nitrate and sulfate wet deposition performance statistics for the 12 km Continental U.S. domain are provided in Table A-9. The model predictions for seasonal nitrate deposition generally show under predictions for the continental U.S. NADP sites (NMB values range from -8% to -41%). Sulfate deposition performance shows the similar under predictions (NMB values range from -15% to 25%). The errors for both annual nitrate and sulfate are relatively moderate with values ranging from 51% to 69% which reflect scatter in the model predictions versus observation comparison. Table A-9. Nitrate and Sulfate Wet Deposition Performance Statistics by Season for the 2011 CMAQ Model Simulation. Wet Deposition Species Season No. of Obs. MB (kg/ha) ME (kg/ha) NMB (%) NME (%) Nitrate Winter 1,772 0.0 0.1 -15.4 58.4 Spring 2,006 -0.1 0.1 -26.7 50.9 Summer 1,892 -0.1 0.2 -40.8 64.1 Fall 1,934 0.0 0.1 -7.6 55.5 Sulfate Winter 1,772 0.0 0.1 -24.7 53.0 Spring 2,006 -0.1 0.1 -22.8 53.5 Summer 1,892 0.0 0.2 -19.1 68.7 Fall 1,934 0.0 0.1 -15.0 56.1 A-51 ------- Air Quality Modeling Technical Support Document: Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule Appendix B 8-Hour Ozone Design Values for Air Quality Modeling Scenarios B-l ------- Table B-l. 8-Hour Ozone Design Values for HDGHG Phase 2 Scenarios (units are ppb) State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Alabama Baldwin 70.0 48.94 48.42 Alabama Colbert 65.0 42.70 42.29 Alabama De Kalb 66.0 49.11 48.40 Alabama Elmore 66.3 46.73 46.05 Alabama Etowah 61.7 43.59 42.74 Alabama Houston 63.7 47.11 46.60 Alabama Jefferson 76.7 55.69 54.88 Alabama Madison 70.7 50.92 50.31 Alabama Mobile 73.0 50.79 50.33 Alabama Montgomery 67.3 47.97 47.23 Alabama Morgan 68.7 51.73 51.09 Alabama Russell 66.0 48.06 47.58 Alabama Shelby 73.3 50.70 49.93 Alabama Sumter 61.0 47.57 47.01 Alabama Tuscaloosa 58.7 43.54 42.93 Arizona Cochise 72.0 66.64 66.38 Arizona Coconino 71.0 62.68 62.48 Arizona Gila 73.7 59.06 58.11 Arizona La Paz 71.3 61.74 61.34 Arizona Maricopa 79.7 63.29 62.15 Arizona Navajo 68.7 61.14 60.83 Arizona Pima 71.3 55.84 54.87 Arizona Pinal 75.0 60.30 59.53 Arizona Yavapai 68.0 61.76 61.46 Arizona Yuma 75.3 60.91 60.55 Arkansas Crittenden 77.3 57.18 56.39 Arkansas Newton 68.0 53.10 52.55 Arkansas Polk 72.3 57.34 56.68 Arkansas Pulaski 75.7 52.21 50.93 Arkansas Washington 71.0 56.87 56.37 California Alameda 73.3 61.69 61.59 California Amador 72.0 54.63 54.55 California Butte 76.3 56.58 56.49 California Calaveras 75.0 56.50 56.41 California Colusa 61.0 49.03 48.89 California Contra Costa 71.7 59.24 59.13 California El Dorado 82.7 59.99 59.89 B-2 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV California Fresno 97.0 75.88 75.80 California Glenn 64.3 51.51 51.40 California Imperial 81.0 72.19 72.06 California Inyo 71.7 64.98 64.94 California Kern 91.7 73.89 73.76 California Kings 87.0 68.38 68.30 California Lake 58.3 46.68 46.64 California Los Angeles 97.3 79.22 79.14 California Madera 85.0 68.04 67.95 California Marin 52.3 47.87 47.81 California Mariposa 77.3 64.73 64.67 California Merced 82.7 65.69 65.60 California Monterey 58.0 46.87 46.81 California Napa 62.3 48.11 48.03 California Nevada 77.7 56.30 56.20 California Orange 72.0 62.51 62.45 California Placer 84.0 60.93 60.84 California Riverside 100.7 79.14 79.04 California Sacramento 93.3 66.68 66.57 California San Benito 70.0 56.83 56.75 California San Bernardino 105.0 87.81 87.70 California San Diego 81.0 60.49 60.45 California San Joaquin 79.0 64.19 64.07 California San Luis Obispo 78.0 62.78 62.70 California Santa Barbara 68.3 58.74 58.69 California Santa Clara 71.3 58.57 58.51 California Santa Cruz 53.0 44.46 44.40 California Shasta 68.0 54.14 54.08 California Solano 68.0 53.93 53.82 California Sonoma 48.0 36.06 36.02 California Stanislaus 87.0 69.89 69.78 California Sutter 65.0 49.49 49.38 California Tehama 75.3 58.90 58.80 California Tulare 94.7 74.01 73.94 California Tuolumne 73.3 55.65 55.56 California Ventura 81.0 63.90 63.81 California Yolo 69.0 55.97 55.87 Colorado Adams 76.0 64.84 64.32 Colorado Arapahoe 76.7 64.64 64.21 Colorado Boulder 74.7 65.21 64.85 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Colorado Denver 71.0 60.57 60.09 Colorado Douglas 80.7 66.77 66.33 Colorado El Paso 72.7 61.40 61.10 Colorado Garfield 65.0 63.21 63.06 Colorado Jefferson 80.3 69.89 69.46 Colorado La Plata 73.0 64.05 63.81 Colorado Larimer 78.0 71.33 70.94 Colorado Mesa 67.0 60.82 60.65 Colorado Montezuma 68.3 59.24 59.02 Colorado Rio Blanco 77.0 71.10 70.90 Colorado Weld 74.7 68.41 68.07 Connecticut Fairfield 84.3 77.78 77.32 Connecticut Hartford 73.7 55.79 55.28 Connecticut Litchfield 70.3 54.48 54.13 Connecticut Middlesex 79.3 59.52 59.06 Connecticut New Haven 85.7 67.33 66.86 Connecticut New London 80.3 62.78 62.45 Connecticut Tolland 75.3 56.94 56.45 Delaware Kent 74.3 55.93 55.36 Delaware New Castle 78.0 56.34 55.70 Delaware Sussex 77.7 60.20 59.69 D.C. Washington 80.7 56.67 56.19 Florida Alachua 63.7 49.36 48.72 Florida Baker 61.7 49.17 48.69 Florida Bay 68.0 49.31 48.80 Florida Brevard 64.0 50.68 50.25 Florida Broward 59.3 48.20 48.05 Florida Collier 59.5 45.95 45.64 Florida Columbia 62.7 50.26 49.75 Florida Duval 64.3 49.67 49.20 Florida Escambia 72.0 50.77 50.17 Florida Highlands 63.3 52.16 51.86 Florida Hillsborough 71.7 55.26 55.00 Florida Holmes 62.3 45.97 45.46 Florida Indian River 65.0 51.03 50.64 Florida Lake 65.7 51.28 51.00 Florida Lee 63.7 48.17 47.80 Florida Leon 64.3 46.19 45.72 Florida Manatee 67.0 51.20 50.89 Florida Marion 65.0 49.23 48.85 B-4 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Florida Miami-Dade 64.0 52.53 52.32 Florida Okaloosa 66.0 47.21 46.68 Florida Orange 71.7 54.13 53.82 Florida Osceola 66.0 48.71 48.31 Florida Palm Beach 62.7 51.92 51.73 Florida Pasco 66.7 51.56 51.31 Florida Pinellas 66.7 56.12 55.91 Florida Polk 68.3 51.87 51.61 Florida Santa Rosa 71.7 50.44 49.81 Florida Sarasota 71.3 53.31 52.91 Florida Seminole 67.3 50.42 50.07 Florida Volusia 63.3 48.91 48.41 Florida Wakulla 63.7 48.30 47.78 Georgia Bibb 72.3 48.55 47.79 Georgia Chatham 63.3 48.07 47.53 Georgia Chattooga 66.3 47.19 46.43 Georgia Clarke 70.7 45.68 45.05 Georgia Cobb 76.0 50.41 49.75 Georgia Columbia 68.7 48.53 47.87 Georgia Coweta 65.0 43.36 42.84 Georgia Dawson 66.3 45.22 44.56 Georgia De Kalb 77.3 50.19 49.47 Georgia Douglas 73.3 48.53 47.85 Georgia Fulton 81.0 54.12 53.44 Georgia Glynn 60.0 45.73 45.04 Georgia Gwinnett 76.7 49.02 48.42 Georgia Henry 80.0 53.99 53.26 Georgia Murray 70.3 49.42 48.33 Georgia Muscogee 66.0 47.98 47.46 Georgia Paulding 70.7 45.48 44.62 Georgia Richmond 70.0 49.94 49.26 Georgia Rockdale 77.0 49.96 49.22 Georgia Sumter 64.7 49.50 49.00 Idaho Ada 67.5 57.33 56.32 Idaho Butte 62.3 58.82 58.65 Illinois Adams 67.0 53.67 53.14 Illinois Champaign 71.0 54.89 54.04 Illinois Clark 66.0 52.80 52.19 Illinois Cook 77.7 59.44 58.96 Illinois Du Page 66.3 52.60 52.18 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Illinois Effingham 68.3 53.12 52.16 Illinois Hamilton 74.3 59.35 58.71 Illinois Jersey 76.0 56.37 55.44 Illinois Jo Daviess 68.0 54.59 54.18 Illinois Kane 69.7 57.70 57.14 Illinois Lake 79.3 51.44 51.14 Illinois McHenry 69.7 57.84 57.26 Illinois McLean 70.3 53.67 52.89 Illinois Macon 71.3 54.33 53.57 Illinois Macoupin 71.3 52.56 51.59 Illinois Madison 78.3 57.18 56.10 Illinois Peoria 70.7 53.98 53.39 Illinois Randolph 67.7 53.90 53.01 Illinois Rock Island 58.3 46.76 46.40 Illinois St Clair 74.7 56.45 55.14 Illinois Sangamon 72.0 54.39 53.50 Illinois Will 64.0 52.23 51.62 Illinois Winnebago 67.3 54.64 54.02 Indiana Allen 69.3 54.26 53.73 Indiana Boone 72.3 56.74 56.19 Indiana Carroll 69.0 55.04 54.42 Indiana Clark 78.0 60.20 59.31 Indiana Delaware 68.7 52.08 51.47 Indiana Elkhart 67.7 52.05 51.37 Indiana Floyd 76.0 57.91 57.32 Indiana Greene 77.0 65.65 65.24 Indiana Hamilton 71.0 54.71 54.20 Indiana Hancock 66.7 50.84 50.33 Indiana Hendricks 67.0 52.23 51.77 Indiana Huntington 65.0 51.86 51.34 Indiana Jackson 66.0 54.92 54.41 Indiana Johnson 69.0 54.93 54.29 Indiana Knox 73.0 61.01 60.63 Indiana Lake 69.7 54.06 53.55 Indiana La Porte 79.3 64.26 64.00 Indiana Madison 68.3 51.45 50.84 Indiana Marion 72.7 55.62 55.08 Indiana Morgan 69.0 53.98 53.40 Indiana Perry 72.7 58.26 58.13 Indiana Porter 70.3 55.09 54.75 B-6 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Indiana Posey 70.3 57.59 57.22 Indiana St Joseph 69.3 53.69 52.95 Indiana Shelby 74.0 56.94 56.28 Indiana Vanderburgh 74.0 61.24 60.87 Indiana Vigo 65.7 52.03 51.52 Indiana Warrick 71.0 59.41 59.04 Iowa Bremer 64.0 50.54 50.02 Iowa Clinton 66.7 53.05 52.63 Iowa Harrison 67.7 54.06 53.60 Iowa Linn 64.3 50.92 50.37 Iowa Montgomery 65.3 53.56 53.08 Iowa Palo Alto 66.7 54.62 54.14 Iowa Polk 59.7 46.00 45.48 Iowa Scott 66.0 52.93 52.52 Iowa Story 61.3 47.85 47.32 Iowa Van Buren 65.7 51.23 50.58 Iowa Warren 63.7 49.72 49.16 Kansas Johnson 72.7 56.24 55.72 Kansas Leavenworth 72.0 54.95 54.33 Kansas Linn 70.0 55.80 55.25 Kansas Sedgwick 75.7 58.67 58.22 Kansas Shawnee 71.7 54.64 54.08 Kansas Sumner 76.0 61.85 61.31 Kansas Trego 72.3 63.91 63.60 Kansas Wyandotte 65.7 50.15 49.70 Kentucky Bell 63.3 48.37 47.58 Kentucky Boone 68.0 56.50 55.93 Kentucky Boyd 70.0 56.02 55.53 Kentucky Bullitt 72.3 57.48 56.53 Kentucky Campbell 76.7 60.10 59.45 Kentucky Carter 67.0 54.06 53.55 Kentucky Christian 70.7 49.97 49.32 Kentucky Daviess 76.3 60.56 60.14 Kentucky Edmonson 72.0 53.82 52.61 Kentucky Fayette 71.3 54.79 54.14 Kentucky Greenup 69.7 56.82 56.31 Kentucky Hancock 73.7 59.24 58.84 Kentucky Hardin 70.3 53.80 52.97 Kentucky Henderson 76.3 62.77 62.39 Kentucky Jefferson 82.0 67.22 66.68 B-7 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Kentucky Jessamine 70.0 51.87 51.23 Kentucky Livingston 72.3 54.78 54.22 Kentucky McCracken 73.7 55.29 54.79 Kentucky Oldham 82.0 63.12 62.32 Kentucky Perry 65.3 54.27 53.86 Kentucky Pike 65.7 53.81 53.40 Kentucky Pulaski 66.7 47.03 46.40 Kentucky Simpson 69.3 49.79 48.95 Kentucky Trigg 69.0 52.39 51.64 Kentucky Warren 64.0 46.97 46.02 Kentucky Washington 69.0 53.44 52.82 Louisiana Ascension 74.7 59.74 59.26 Louisiana Bossier 77.3 62.52 62.11 Louisiana Caddo 74.7 59.76 59.34 Louisiana Calcasieu 73.3 59.86 59.39 Louisiana East Baton Rouge 78.7 63.68 63.21 Louisiana Iberville 76.0 61.76 61.30 Louisiana Jefferson 73.7 58.99 58.53 Louisiana Lafayette 71.0 56.01 55.53 Louisiana Lafourche 72.3 57.83 57.30 Louisiana Livingston 74.0 58.43 57.94 Louisiana Orleans 69.3 56.68 56.26 Louisiana Ouachita 63.3 52.36 52.07 Louisiana Pointe Coupee 75.3 58.17 57.73 Louisiana St Bernard 69.0 55.24 54.85 Louisiana St Charles 70.0 55.72 55.28 Louisiana St James 68.0 54.75 54.28 Louisiana St John the Baptist 74.0 57.79 57.24 Louisiana St Tammany 73.3 60.17 59.75 Louisiana West Baton Rouge 70.3 55.57 55.08 Maine Androscoggin 61.0 47.87 47.36 Maine Cumberland 69.3 53.82 53.38 Maine Hancock 71.7 57.50 57.12 Maine Kennebec 62.7 47.18 46.63 Maine Knox 67.7 52.92 52.31 Maine Oxford 54.3 44.20 43.79 Maine Sagadahoc 61.0 47.78 47.28 Maine Washington 58.3 46.94 46.64 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Maine York 73.7 56.74 56.20 Maryland Anne Arundel 83.0 57.66 57.07 Maryland Baltimore 80.7 63.14 62.63 Maryland Calvert 79.7 62.26 61.90 Maryland Carroll 76.3 57.04 56.40 Maryland Cecil 83.0 60.36 59.61 Maryland Charles 79.0 55.03 54.58 Maryland Dorchester 75.0 58.20 57.62 Maryland Frederick 76.3 57.88 57.17 Maryland Garrett 72.0 54.68 54.28 Maryland Harford 90.0 68.70 68.06 Maryland Kent 78.7 55.75 55.05 Maryland Montgomery 75.7 55.07 54.45 Maryland Prince Georges 82.3 56.71 56.17 Maryland Washington 72.7 55.55 54.87 Maryland Baltimore City 73.7 57.84 57.38 Massachusetts Barnstable 73.0 58.28 57.83 Massachusetts Berkshire 69.0 54.77 54.37 Massachusetts Bristol 74.0 58.34 57.86 Massachusetts Dukes 77.0 62.53 62.03 Massachusetts Essex 71.0 55.74 55.28 Massachusetts Hampden 73.7 55.62 55.12 Massachusetts Hampshire 71.3 53.44 52.97 Massachusetts Middlesex 67.3 51.31 50.83 Massachusetts Norfolk 72.3 54.65 54.54 Massachusetts Suffolk 68.3 49.82 49.61 Massachusetts Worcester 69.0 51.55 51.09 Michigan Allegan 82.7 65.91 65.20 Michigan Benzie 73.0 58.45 57.66 Michigan Berrien 79.7 61.82 61.07 Michigan Cass 76.7 58.75 57.93 Michigan Chippewa 63.5 56.16 55.84 Michigan Clinton 69.3 53.41 52.70 Michigan Genesee 73.0 57.23 56.58 Michigan Huron 71.3 57.28 56.62 Michigan Ingham 70.3 53.37 52.66 Michigan Kalamazoo 73.7 57.25 56.46 Michigan Kent 73.0 55.67 54.85 Michigan Lenawee 75.5 58.06 57.38 Michigan Macomb 77.3 63.92 63.36 B-9 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Michigan Manistee 72.3 58.23 57.53 Michigan Mason 73.3 58.53 57.87 Michigan Missaukee 68.3 54.51 53.88 Michigan Muskegon 79.7 63.98 63.42 Michigan Oakland 76.3 61.56 60.94 Michigan Ottawa 76.0 58.42 57.53 Michigan St Clair 75.3 61.23 60.57 Michigan Schoolcraft 71.7 56.74 55.90 Michigan Washtenaw 73.3 57.32 56.71 Michigan Wayne 78.7 64.84 64.25 Minnesota Anoka 67.0 52.98 52.52 Minnesota Crow Wing 62.0 49.64 49.20 Minnesota Goodhue 62.5 50.51 50.10 Minnesota Lyon 64.5 53.62 53.23 Minnesota Mille Lacs 59.7 45.61 45.35 Minnesota Olmsted 63.5 50.36 49.93 Minnesota St Louis 61.3 42.83 42.71 Minnesota Scott 63.5 51.03 50.67 Minnesota Stearns 61.5 51.18 50.73 Minnesota Wright 63.5 52.22 51.88 Mississippi Bolivar 71.7 58.68 58.28 Mississippi De Soto 72.3 53.58 52.75 Mississippi Hancock 66.3 50.28 49.82 Mississippi Harrison 72.3 51.65 51.07 Mississippi Hinds 67.0 44.87 44.17 Mississippi Jackson 71.7 54.60 53.99 Mississippi Lauderdale 62.7 47.51 46.78 Mississippi Lee 65.0 48.56 48.08 Mississippi Yalobusha 63.0 50.49 50.08 Missouri Andrew 73.3 56.14 55.53 Missouri Boone 69.0 53.72 53.01 Missouri Callaway 67.7 52.88 52.27 Missouri Cass 70.0 53.85 53.36 Missouri Cedar 71.7 57.05 56.50 Missouri Clay 77.7 59.25 58.65 Missouri Clinton 78.0 58.64 57.93 Missouri Greene 71.7 54.92 54.44 Missouri Jasper 76.7 60.36 59.77 Missouri Jefferson 76.3 57.29 55.57 Missouri Lincoln 77.0 58.68 57.65 B-10 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Missouri Monroe 68.7 54.83 54.19 Missouri Perry 74.3 57.73 56.91 Missouri St Charles 82.3 60.36 59.24 Missouri Ste Genevieve 72.3 57.18 56.30 Missouri St Louis 79.0 59.55 58.23 Missouri Taney 69.0 54.17 53.59 Missouri St Louis City 75.7 56.93 55.57 Montana Powder River 55.0 50.47 50.25 Montana Rosebud 55.5 52.08 51.93 Nebraska Douglas 67.0 54.55 54.08 Nebraska Knox 68.0 59.37 59.03 Nebraska Lancaster 53.3 45.22 44.92 Nevada Churchill 56.7 50.48 50.36 Nevada Clark 76.0 64.62 64.28 Nevada Lyon 68.5 58.89 58.71 Nevada Washoe 67.3 56.90 56.65 Nevada White Pine 72.0 63.30 63.10 Nevada Carson City 66.0 57.09 56.99 New Hampshire Belknap 62.3 49.20 48.88 New Hampshire Cheshire 62.3 47.79 47.39 New Hampshire Coos 69.3 56.84 56.38 New Hampshire Grafton 59.7 47.04 46.58 New Hampshire Hillsborough 69.0 52.95 52.52 New Hampshire Merrimack 64.7 49.83 49.34 New Hampshire Rockingham 68.0 52.76 52.22 New Jersey Atlantic 74.3 56.05 55.56 New Jersey Bergen 77.0 58.86 58.37 New Jersey Camden 82.7 62.81 62.25 New Jersey Cumberland 72.0 53.98 53.45 New Jersey Essex 78.0 60.44 59.93 New Jersey Gloucester 84.3 62.41 61.75 New Jersey Hudson 77.0 62.20 61.73 New Jersey Hunterdon 78.0 57.77 57.27 New Jersey Mercer 78.3 59.37 58.87 New Jersey Middlesex 81.3 61.60 61.08 New Jersey Monmouth 80.0 63.08 62.62 New Jersey Morris 76.3 55.94 55.47 New Jersey Ocean 82.0 60.92 60.40 New Jersey Passaic 73.3 57.14 56.66 New Jersey Warren 66.0 47.90 47.48 B-ll ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV New Mexico Bernalillo 72.0 59.63 59.25 New Mexico Dona Ana 71.0 59.08 58.70 New Mexico Eddy 70.3 66.47 66.21 New Mexico Grant 65.0 58.53 58.16 New Mexico Lea 62.7 60.51 60.34 New Mexico Luna 63.0 54.72 54.31 New Mexico Sandoval 63.0 56.56 56.37 New Mexico San Juan 71.0 60.79 60.61 New Mexico Santa Fe 64.3 58.04 57.72 New Mexico Valencia 68.5 56.21 55.70 New York Albany 68.0 53.85 53.46 New York Bronx 74.0 73.23 73.06 New York Chautauqua 74.0 58.29 57.80 New York Chemung 66.5 53.89 53.46 New York Dutchess 72.0 54.42 54.05 New York Erie 71.3 58.34 57.79 New York Essex 70.3 58.12 57.70 New York Hamilton 66.0 51.96 51.46 New York Herkimer 62.0 49.79 49.32 New York Jefferson 71.7 57.89 57.58 New York Madison 67.0 53.93 53.45 New York New York 73.3 70.61 70.39 New York Niagara 72.3 62.56 62.24 New York Oneida 61.5 49.39 48.98 New York Onondaga 69.3 55.75 55.34 New York Orange 67.0 52.41 52.01 New York Oswego 68.0 54.68 54.37 New York Putnam 70.0 52.36 51.93 New York Queens 78.0 76.00 75.80 New York Rensselaer 67.0 52.68 52.31 New York Richmond 81.3 73.85 73.47 New York Rockland 75.0 57.67 57.18 New York Saratoga 67.0 52.18 51.77 New York Steuben 65.3 53.43 53.01 New York Suffolk 83.3 72.82 72.49 New York Ulster 69.0 55.74 55.37 New York Wayne 65.0 53.78 53.45 New York Westchester 75.3 74.17 73.94 North Carolina Alexander 66.7 47.39 46.95 North Carolina Avery 63.3 48.76 48.19 B-12 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV North Carolina Buncombe 66.7 48.11 47.55 North Carolina Caldwell 66.0 47.68 47.24 North Carolina Caswell 70.7 49.49 48.60 North Carolina Chatham 64.0 45.06 44.37 North Carolina Cumberland 70.7 50.75 50.11 North Carolina Davie 71.0 47.93 47.17 North Carolina Durham 70.0 47.10 46.27 North Carolina Edgecombe 70.0 49.85 49.25 North Carolina Forsyth 75.3 52.22 51.53 North Carolina Franklin 69.3 48.12 47.36 North Carolina Graham 70.3 52.47 51.83 North Carolina Granville 70.7 49.49 48.62 North Carolina Guilford 74.0 51.87 51.22 North Carolina Haywood 67.7 52.83 52.27 North Carolina Jackson 67.0 51.93 51.39 North Carolina Johnston 71.7 49.65 49.08 North Carolina Lenoir 67.7 51.44 50.84 North Carolina Lincoln 72.7 49.35 48.76 North Carolina Martin 66.3 48.67 48.16 North Carolina Mecklenburg 80.0 54.96 54.15 North Carolina Montgomery 66.0 46.25 45.50 North Carolina New Hanover 63.0 45.18 44.79 North Carolina Person 71.0 48.37 47.59 North Carolina Pitt 69.7 51.59 50.94 North Carolina Rockingham 71.0 50.07 49.30 North Carolina Rowan 75.3 50.83 50.08 North Carolina Swain 60.7 46.52 46.02 North Carolina Union 71.0 47.47 46.81 North Carolina Wake 73.0 51.61 51.06 North Carolina Yancey 69.7 53.01 52.50 Ohio Allen 73.0 56.88 56.34 Ohio Ashtabula 77.3 58.00 57.44 Ohio Athens 69.0 54.72 54.22 Ohio Butler 79.7 62.26 61.56 Ohio Clark 75.0 56.06 55.40 Ohio Clermont 78.7 58.58 57.86 Ohio Clinton 78.7 57.77 56.98 Ohio Cuyahoga 77.7 57.10 56.83 Ohio Delaware 73.0 54.45 53.80 Ohio Fayette 72.0 52.14 51.55 B-13 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Ohio Franklin 80.3 60.19 59.51 Ohio Geauga 74.7 57.09 56.53 Ohio Greene 73.0 53.98 53.28 Ohio Hamilton 82.0 64.25 63.48 Ohio Jefferson 70.3 58.34 57.96 Ohio Knox 73.7 54.68 54.02 Ohio Lake 80.0 56.20 55.83 Ohio Lawrence 70.0 57.07 56.55 Ohio Licking 74.3 54.27 53.48 Ohio Lorain 71.7 52.72 52.39 Ohio Lucas 74.3 55.56 55.13 Ohio Madison 74.3 54.46 53.76 Ohio Mahoning 70.7 54.12 53.63 Ohio Medina 69.0 52.97 52.40 Ohio Miami 73.3 56.85 56.33 Ohio Montgomery 76.7 57.30 56.64 Ohio Portage 68.3 51.55 51.05 Ohio Preble 72.3 55.49 54.89 Ohio Stark 76.7 57.34 56.72 Ohio Summit 72.0 55.29 54.69 Ohio Trumbull 76.3 57.53 57.01 Ohio Warren 77.7 58.18 57.44 Ohio Washington 71.3 55.56 55.07 Ohio Wood 71.3 55.79 55.25 Oklahoma Adair 73.7 58.39 57.81 Oklahoma Caddo 74.7 59.54 59.02 Oklahoma Canadian 75.7 56.27 55.68 Oklahoma Cherokee 73.7 58.51 58.02 Oklahoma Cleveland 75.0 59.25 58.71 Oklahoma Comanche 74.7 62.71 62.19 Oklahoma Creek 77.0 58.51 57.99 Oklahoma Dewey 72.3 64.28 63.92 Oklahoma Kay 73.0 59.16 58.61 Oklahoma Mc Clain 74.0 59.15 58.60 Oklahoma Mc Curtain 68.0 56.84 56.27 Oklahoma Mayes 76.3 61.60 61.06 Oklahoma Oklahoma 78.3 62.06 61.52 Oklahoma Ottawa 74.0 57.69 57.13 Oklahoma Pittsburg 73.3 62.38 61.79 Oklahoma Sequoyah 72.0 58.49 57.90 B-14 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Oklahoma Tulsa 79.0 60.94 60.42 Oregon Clackamas 64.0 51.19 50.51 Oregon Columbia 51.3 42.33 41.83 Oregon Deschutes 58.5 52.28 52.14 Oregon Jackson 61.7 53.21 52.87 Oregon Lane 60.0 47.17 46.60 Oregon Marion 59.3 47.20 46.68 Oregon Multnomah 56.7 49.10 48.69 Oregon Umatilla 61.3 50.07 49.52 Pennsylvania Allegheny 80.7 67.30 66.98 Pennsylvania Armstrong 74.3 62.54 62.26 Pennsylvania Beaver 74.7 64.12 63.78 Pennsylvania Berks 76.3 57.11 56.61 Pennsylvania Blair 72.7 61.24 60.99 Pennsylvania Bucks 80.3 60.12 59.58 Pennsylvania Cambria 70.3 58.15 57.89 Pennsylvania Centre 72.0 59.88 59.60 Pennsylvania Chester 76.3 54.53 53.86 Pennsylvania Clearfield 72.3 59.26 58.90 Pennsylvania Dauphin 74.7 56.94 56.42 Pennsylvania Delaware 75.7 55.55 54.96 Pennsylvania Erie 74.0 57.49 56.98 Pennsylvania Franklin 67.0 52.35 51.89 Pennsylvania Greene 69.0 54.24 53.78 Pennsylvania Indiana 75.7 63.39 63.10 Pennsylvania Lackawanna 71.0 56.37 55.86 Pennsylvania Lancaster 78.0 57.50 57.01 Pennsylvania Lawrence 71.0 57.22 56.84 Pennsylvania Lebanon 76.0 57.19 56.65 Pennsylvania Lehigh 76.0 57.34 56.84 Pennsylvania Luzerne 65.0 49.80 49.36 Pennsylvania Lycoming 67.0 54.25 53.78 Pennsylvania Mercer 76.3 58.40 57.89 Pennsylvania Monroe 66.7 50.23 49.77 Pennsylvania Montgomery 76.3 57.76 57.21 Pennsylvania Northampton 76.0 57.19 56.73 Pennsylvania Perry 68.3 56.16 55.87 Pennsylvania Philadelphia 83.3 63.33 62.77 Pennsylvania Somerset 65.0 49.75 49.44 Pennsylvania Tioga 69.7 56.88 56.48 B-15 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Pennsylvania Washington 70.7 59.40 59.02 Pennsylvania Westmoreland 71.7 58.88 58.59 Pennsylvania York 74.3 53.02 52.52 Rhode Island Kent 73.7 57.09 56.61 Rhode Island Providence 74.0 57.94 57.66 Rhode Island Washington 76.3 60.16 59.75 South Carolina Abbeville 62.0 43.88 43.22 South Carolina Aiken 64.3 45.53 44.89 South Carolina Anderson 70.0 49.12 48.50 South Carolina Berkeley 62.3 46.89 46.45 South Carolina Charleston 64.7 48.73 48.17 South Carolina Chesterfield 64.3 46.32 45.82 South Carolina Colleton 61.0 46.36 45.90 South Carolina Darlington 68.0 49.75 49.27 South Carolina Edgefield 61.3 43.48 42.91 South Carolina Greenville 68.0 47.73 47.22 South Carolina Pickens 69.7 49.75 49.10 South Carolina Richland 71.7 51.14 50.34 South Carolina Spartanburg 73.7 51.25 50.60 South Carolina York 64.0 43.46 42.82 South Dakota Brookings 63.3 53.08 52.66 South Dakota Custer 61.7 56.23 56.00 South Dakota Jackson 57.0 51.10 50.79 South Dakota Meade 58.5 51.25 50.96 South Dakota Minnehaha 66.0 54.45 53.97 South Dakota Union 62.5 52.34 51.96 Tennessee Anderson 70.7 51.11 50.06 Tennessee Blount 76.7 56.35 55.53 Tennessee Claiborne 62.0 46.70 45.83 Tennessee Davidson 70.3 51.22 50.27 Tennessee Hamilton 73.3 52.69 51.68 Tennessee Jefferson 74.7 53.65 52.53 Tennessee Knox 71.7 50.85 49.96 Tennessee Loudon 72.3 53.18 51.65 Tennessee Meigs 71.3 51.94 51.02 Tennessee Rutherford 68.5 48.73 47.79 Tennessee Sevier 74.3 55.42 54.52 Tennessee Shelby 78.0 57.73 56.95 Tennessee Sullivan 71.7 57.80 57.38 Tennessee Sumner 76.7 54.48 53.51 B-16 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Tennessee Williamson 70.3 50.51 49.63 Tennessee Wilson 71.7 50.33 49.46 Texas Bell 74.5 59.64 59.11 Texas Bexar 78.7 61.17 60.68 Texas Brazoria 88.0 68.78 68.15 Texas Brewster 70.0 67.21 67.10 Texas Cameron 62.7 55.55 55.29 Texas Collin 82.7 62.63 61.92 Texas Dallas 82.0 63.53 62.77 Texas Denton 84.3 65.28 64.46 Texas Ellis 75.7 61.15 60.37 Texas El Paso 71.0 57.85 57.43 Texas Galveston 77.3 64.97 64.41 Texas Gregg 77.7 66.39 65.96 Texas Harris 83.0 69.77 69.19 Texas Harrison 72.7 60.39 59.91 Texas Hidalgo 61.0 53.42 53.17 Texas Hood 76.7 61.73 61.07 Texas Hunt 71.7 56.31 55.81 Texas Jefferson 78.0 63.33 62.71 Texas Johnson 79.0 64.01 63.31 Texas Kaufman 70.7 58.45 57.91 Texas Mc Lennan 72.7 60.22 59.63 Texas Montgomery 77.3 59.06 58.57 Texas Navarro 71.0 61.12 60.64 Texas Nueces 71.0 62.97 62.66 Texas Orange 72.7 59.46 58.87 Texas Parker 78.7 64.28 63.62 Texas Rockwall 77.0 60.08 59.48 Texas Smith 75.0 62.19 61.69 Texas Tarrant 87.3 68.61 67.91 Texas Travis 73.7 59.17 58.76 Texas Victoria 68.7 59.23 58.82 Utah Box Elder 67.7 58.53 57.96 Utah Cache 64.3 55.98 55.61 Utah Carbon 69.0 62.53 62.36 Utah Davis 69.3 60.81 60.36 Utah Duchesne 68.0 61.45 61.27 Utah Salt Lake 76.0 65.58 65.10 Utah San Juan 68.7 61.83 61.67 B-17 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Utah Tooele 72.0 62.45 61.76 Utah Utah 70.0 61.80 61.35 Utah Washington 71.7 63.79 63.61 Utah Weber 72.7 62.96 62.59 Vermont Bennington 63.7 50.24 49.85 Virginia Albemarle 66.7 50.83 50.17 Virginia Arlington 81.7 58.02 57.43 Virginia Caroline 71.7 51.54 50.97 Virginia Charles City 75.7 56.19 55.53 Virginia Chesterfield 72.0 53.39 52.90 Virginia Fairfax 82.3 56.61 56.07 Virginia Fauquier 62.7 47.85 47.35 Virginia Frederick 66.7 51.57 51.05 Virginia Giles 63.0 46.56 46.09 Virginia Hanover 73.7 54.82 54.16 Virginia Henrico 75.0 55.26 54.63 Virginia Loudoun 73.0 56.88 56.30 Virginia Madison 70.7 57.08 56.65 Virginia Page 66.3 53.68 53.28 Virginia Prince Edward 62.0 47.64 47.06 Virginia Prince William 70.0 55.13 54.59 Virginia Roanoke 67.3 51.97 51.27 Virginia Rockbridge 62.3 51.41 50.99 Virginia Rockingham 66.0 52.40 51.96 Virginia Stafford 73.0 48.99 48.42 Virginia Wythe 64.3 51.01 50.39 Virginia Alexandria City 80.0 56.13 55.68 Virginia Hampton City 74.0 57.97 57.49 Virginia Suffolk City 71.3 58.23 57.78 Washington Clark 56.0 47.54 47.14 Washington Spokane 59.0 48.55 48.13 Washington Whatcom 45.0 41.77 41.74 West Virginia Berkeley 68.0 52.55 51.97 West Virginia Cabell 69.3 55.39 54.85 West Virginia Gilmer 60.0 50.28 49.92 West Virginia Greenbrier 64.7 51.72 51.17 West Virginia Hancock 73.0 60.87 60.48 West Virginia Kanawha 72.3 61.84 61.35 West Virginia Monongalia 69.7 57.92 57.55 West Virginia Ohio 72.3 55.98 55.47 B-18 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV West Virginia Wood 68.3 52.89 52.44 Wisconsin Brown 68.3 53.20 52.70 Wisconsin Columbia 67.0 53.51 52.88 Wisconsin Dane 66.3 53.03 52.44 Wisconsin Dodge 71.5 57.72 57.01 Wisconsin Door 75.7 59.18 58.36 Wisconsin Eau Claire 62.0 48.34 47.74 Wisconsin Fond Du Lac 70.0 56.32 55.65 Wisconsin Jefferson 68.5 54.83 54.28 Wisconsin Kenosha 81.0 53.84 53.54 Wisconsin Kewaunee 75.0 59.25 58.44 Wisconsin La Crosse 63.3 50.41 49.91 Wisconsin Manitowoc 78.7 62.03 61.33 Wisconsin Marathon 63.3 50.06 49.44 Wisconsin Milwaukee 80.0 61.95 61.48 Wisconsin Outagamie 69.3 55.64 55.07 Wisconsin Ozaukee 76.3 64.23 63.87 Wisconsin Racine 77.7 55.40 55.15 Wisconsin Rock 69.5 56.14 55.54 Wisconsin Sauk 65.0 52.44 51.75 Wisconsin Sheboygan 84.3 68.51 67.97 Wisconsin Walworth 69.3 56.53 55.93 Wisconsin Waukesha 66.7 54.29 53.77 Wyoming Campbell 63.7 58.62 58.40 Wyoming Carbon 63.0 58.18 58.02 Wyoming Fremont 68.0 62.77 62.58 Wyoming Laramie 68.0 61.36 61.06 Wyoming Sublette 77.3 72.16 71.99 Wyoming Sweetwater 66.0 59.28 59.00 Wyoming Teton 65.3 61.61 61.44 Wyoming Uinta 64.3 56.49 56.18 B-19 ------- Air Quality Modeling Technical Support Document: Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule Appendix C Annual PM2.5 Design Values for Air Quality Modeling Scenarios c-i ------- Table C-l. Annual PM2.5 Design Values for HDGHG Phase 2 Scenarios (units are ug/m3) State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Alabama Baldwin 9.49 7.94 7.92 Alabama Clay 9.74 7.74 7.73 Alabama Colbert 9.70 8.28 8.27 Alabama DeKalb 10.41 8.38 8.37 Alabama Etowah 10.70 8.74 8.73 Alabama Houston 9.62 8.19 8.18 Alabama Jefferson 12.59 11.03 11.03 Alabama Madison 10.48 9.18 9.17 Alabama Mobile 9.45 8.06 8.05 Alabama Montgomery 10.88 9.49 9.49 Alabama Morgan 10.02 8.74 8.73 Alabama Russell 11.87 9.95 9.94 Alabama Shelby 9.75 8.22 8.21 Alabama Talladega 11.05 9.05 9.04 Alabama Tuscaloosa 10.21 8.81 8.80 Alabama Walker 10.84 9.03 9.02 Arizona Cochise 6.77 7.21 7.21 Arizona Coconino 5.47 5.34 5.35 Arizona Maricopa 11.48 10.18 10.22 Arizona Pima 5.52 4.89 4.89 Arizona Pinal 9.36 8.61 8.64 Arizona Santa Cruz 10.07 10.02 10.02 Arizona Yavapai 4.14 4.10 4.10 Arizona Yuma 7.70 7.29 7.28 Arkansas Arkansas 10.51 8.71 8.70 Arkansas Ashley 10.48 9.10 9.09 Arkansas Crittenden 10.94 8.56 8.56 Arkansas Faulkner 10.76 9.02 9.00 Arkansas Garland 10.75 9.04 9.02 Arkansas Jackson 10.00 8.11 8.10 Arkansas Phillips 10.67 8.75 8.74 Arkansas Polk 10.67 9.22 9.21 Arkansas Pope 11.34 9.70 9.68 Arkansas Pulaski 12.01 9.89 9.89 Arkansas Union 11.07 9.59 9.57 Arkansas Washington 10.67 9.11 9.10 Arkansas White 11.26 9.52 9.51 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV California Alameda 9.37 8.20 8.19 California Butte 10.09 9.19 9.19 California Calaveras 7.76 6.56 6.55 California Colusa 6.56 5.95 5.95 California Contra Costa 7.43 6.53 6.51 California Fresno 16.44 13.86 13.84 California Humboldt 6.21 6.01 6.01 California Imperial 13.64 14.88 14.86 California Inyo 7.38 7.06 7.05 California Kern 17.02 13.75 13.73 California Kings 16.33 13.87 13.85 California Lake 3.51 3.25 3.25 California Los Angeles 12.92 10.57 10.55 California Madera 18.75 16.25 16.22 California Marin 9.53 8.59 8.58 California Mendocino 8.55 7.91 7.91 California Merced 14.54 12.81 12.80 California Monterey 6.15 5.43 5.42 California Nevada 6.39 5.84 5.84 California Orange 10.77 8.75 8.73 California Placer 7.54 6.57 6.56 California Plumas 9.59 8.92 8.91 California Riverside 15.31 12.43 12.41 California Sacramento 9.94 8.82 8.81 California San Benito 5.51 4.74 4.74 California San Bernardino 13.03 10.64 10.62 California San Diego 10.79 9.48 9.48 California San Francisco 9.51 8.36 8.34 California San Joaquin 12.09 10.47 10.45 California San Luis Obispo 11.33 9.91 9.90 California San Mateo 8.80 7.73 7.72 California Santa Barbara 9.59 8.61 8.61 California Santa Clara 9.79 8.56 8.55 California Santa Cruz 6.25 5.53 5.53 California Shasta 5.42 5.10 5.09 California Siskiyou 5.54 5.29 5.28 California Solano 9.15 8.09 8.07 California Sonoma 8.15 7.54 7.54 California Stanislaus 15.27 13.12 13.10 California Sutter 7.30 6.45 6.44 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV California Tulare 15.54 13.06 13.04 California Ventura 8.98 8.02 8.02 California Yolo 6.87 5.99 5.98 Colorado Adams 8.06 6.88 6.87 Colorado Arapahoe 6.29 5.33 5.32 Colorado Boulder 6.92 6.22 6.21 Colorado Denver 7.63 6.51 6.50 Colorado Douglas 5.68 4.85 4.84 Colorado El Paso 5.87 5.08 5.08 Colorado Larimer 6.32 5.66 5.66 Colorado Mesa 8.60 7.79 7.79 Colorado Montezuma 6.05 5.77 5.77 Colorado Rio Blanco 9.55 9.50 9.50 Colorado Weld 7.49 6.89 6.88 Connecticut Fairfield 9.35 7.26 7.26 Connecticut Hartford 8.78 7.23 7.23 Connecticut Litchfield 5.63 4.48 4.48 Connecticut New Haven 9.45 7.37 7.37 Connecticut New London 8.19 6.51 6.51 Delaware Kent 8.93 6.63 6.62 Delaware New Castle 10.35 7.83 7.81 Delaware Sussex 8.97 6.68 6.67 District Of Co District of Columbia 10.29 7.91 7.90 Florida Palm Beach 7.37 6.79 6.79 Georgia Bibb 12.78 10.61 10.60 Georgia Chatham 10.70 8.57 8.57 Georgia Clarke 10.35 7.87 7.86 Georgia Clayton 11.97 9.02 9.02 Georgia Cobb 11.10 8.26 8.25 Georgia DeKalb 11.31 8.34 8.34 Georgia Dougherty 12.05 10.29 10.28 Georgia Floyd 11.72 9.22 9.20 Georgia Fulton 13.08 9.87 9.87 Georgia Hall 10.22 7.80 7.79 Georgia Houston 10.45 8.57 8.57 Georgia Muscogee 12.58 10.55 10.53 Georgia Richmond 12.05 9.85 9.84 Georgia Walker 10.16 7.73 7.73 Georgia Wilkinson 12.27 10.19 10.18 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Idaho Bannock 6.45 6.02 6.01 Idaho Lemhi 11.59 11.09 11.09 Idaho Shoshone 12.34 11.69 11.69 Indiana Allen 10.51 7.78 7.75 Indiana Clark 12.91 9.88 9.87 Indiana Delaware 10.74 8.02 8.00 Indiana Dubois 12.23 8.99 8.97 Indiana Elkhart 11.10 8.39 8.35 Indiana Floyd 11.60 8.71 8.69 Indiana Gibson 11.43 8.51 8.49 Indiana Greene 9.89 7.02 7.00 Indiana Henry 10.43 7.66 7.63 Indiana Howard 11.61 8.76 8.72 Indiana Knox 11.70 8.68 8.66 Indiana Lake 12.04 9.24 9.21 Indiana LaPorte 9.96 7.41 7.38 Indiana Madison 10.08 7.42 7.39 Indiana Marion 12.57 9.34 9.32 Indiana Monroe 10.14 7.32 7.29 Indiana Porter 10.73 8.09 8.06 Indiana St. Joseph 10.54 7.94 7.90 Indiana Spencer 11.82 8.70 8.68 Indiana Tippecanoe 10.51 7.80 7.77 Indiana Vanderburgh 12.06 9.21 9.19 Indiana Vigo 11.80 8.69 8.67 Indiana Whitley 9.61 7.11 7.08 Iowa Black Hawk 10.63 8.39 8.37 Iowa Clinton 11.34 8.81 8.78 Iowa Delaware 9.49 7.40 7.37 Iowa Johnson 10.29 8.09 8.07 Iowa Lee 11.14 8.90 8.87 Iowa Linn 10.19 8.03 8.01 Iowa Montgomery 9.10 7.38 7.34 Iowa Muscatine 12.10 9.53 9.50 Iowa Palo Alto 8.82 7.11 7.08 Iowa Polk 9.52 7.29 7.24 Iowa Pottawattamie 10.70 8.55 8.52 Iowa Scott 11.42 8.81 8.77 Iowa Van Buren 9.40 7.50 7.46 Iowa Woodbury 9.65 7.88 7.85 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Kansas Johnson 8.32 6.64 6.62 Kansas Linn 9.08 7.47 7.44 Kansas Sedgwick 9.24 7.79 7.77 Kansas Shawnee 9.10 7.57 7.55 Kansas Sumner 8.56 7.22 7.19 Kansas Wyandotte 10.09 8.19 8.17 Kentucky Bell 10.83 8.50 8.50 Kentucky Boyd 10.44 7.82 7.81 Kentucky Bullitt 12.18 9.67 9.66 Kentucky Campbell 10.53 7.14 7.11 Kentucky Carter 8.71 6.40 6.39 Kentucky Christian 10.51 7.99 7.98 Kentucky Daviess 11.73 8.78 8.76 Kentucky Fayette 10.59 7.59 7.58 Kentucky Hardin 11.08 8.07 8.06 Kentucky Henderson 11.22 8.44 8.43 Kentucky Jefferson 12.38 9.39 9.38 Kentucky McCracken 10.84 8.19 8.17 Kentucky Madison 9.37 6.58 6.56 Kentucky Pike 9.42 7.08 7.07 Kentucky Warren 11.03 8.45 8.45 Louisiana Caddo 11.50 10.27 10.26 Louisiana Calcasieu 8.80 7.61 7.58 Louisiana East Baton Rouge 9.95 8.71 8.70 Louisiana Iberville 9.78 8.74 8.74 Louisiana Jefferson 9.03 7.40 7.39 Louisiana Lafayette 8.89 7.87 7.87 Louisiana Ouachita 9.14 7.91 7.91 Louisiana Rapides 8.56 7.34 7.33 Louisiana St. Bernard 10.23 8.42 8.41 Louisiana Tangipahoa 8.80 7.26 7.25 Louisiana Terrebonne 8.26 7.18 7.19 Louisiana West Baton Rouge 10.50 9.26 9.25 Maine Androscoggin 7.50 6.24 6.24 Maine Aroostook 6.53 6.00 6.00 Maine Cumberland 8.37 7.01 7.01 Maine Hancock 4.59 4.06 4.06 Maine Kennebec 7.16 5.99 5.99 Maine Oxford 8.20 7.11 7.11 Maine Penobscot 7.21 6.19 6.19 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Maryland Anne Arundel 10.53 8.20 8.20 Maryland Baltimore 10.79 8.27 8.27 Maryland Cecil 10.27 7.72 7.70 Maryland Garrett 8.93 6.90 6.90 Maryland Harford 10.11 7.59 7.58 Maryland Kent 10.16 7.75 7.74 Maryland Montgomery 10.14 8.04 8.03 Maryland Prince George's 10.53 8.44 8.44 Maryland Washington 10.89 8.45 8.45 Maryland Baltimore (City) 10.97 8.51 8.51 Massachusetts Berkshire 8.68 7.12 7.11 Massachusetts Bristol 7.58 6.00 6.00 Massachusetts Essex 7.91 6.67 6.68 Massachusetts Hampden 9.22 7.75 7.75 Massachusetts Middlesex 7.49 6.28 6.29 Massachusetts Plymouth 7.85 6.35 6.35 Massachusetts Suffolk 9.87 8.10 8.10 Massachusetts Worcester 8.71 7.29 7.29 Michigan Allegan 8.42 6.40 6.38 Michigan Bay 7.81 6.19 6.17 Michigan Berrien 8.66 6.52 6.48 Michigan Chippewa 6.23 5.62 5.62 Michigan Genesee 8.35 6.48 6.45 Michigan Ingham 8.65 6.76 6.74 Michigan Kalamazoo 9.16 6.95 6.92 Michigan Kent 9.53 7.44 7.41 Michigan Lenawee 9.13 6.99 6.97 Michigan Macomb 8.73 6.84 6.82 Michigan Manistee 6.58 5.28 5.26 Michigan Missaukee 5.96 4.86 4.85 Michigan Monroe 9.72 7.27 7.24 Michigan Muskegon 8.48 6.59 6.56 Michigan Oakland 9.23 7.12 7.10 Michigan Ottawa 8.99 6.89 6.87 Michigan St. Clair 9.13 7.52 7.51 Michigan Washtenaw 9.35 7.25 7.23 Michigan Wayne 11.47 9.33 9.31 Minnesota Anoka 8.44 7.28 7.25 Minnesota Dakota 8.89 7.65 7.62 Minnesota Hennepin 8.94 7.76 7.74 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Minnesota Olmsted 8.96 7.20 7.17 Minnesota Ramsey 9.86 8.57 8.54 Minnesota Saint Louis 6.59 5.80 5.79 Minnesota Scott 8.62 7.37 7.34 Minnesota Stearns 8.34 7.09 7.07 Minnesota Washington 9.21 7.84 7.81 Mississippi DeSoto 9.76 7.78 7.78 Mississippi Forrest 11.36 9.40 9.39 Mississippi Grenada 9.41 7.56 7.56 Mississippi Hancock 9.44 7.84 7.83 Mississippi Harrison 9.66 7.93 7.91 Mississippi Hinds 10.83 8.97 8.97 Mississippi Jackson 9.38 7.64 7.63 Mississippi Jones 11.67 9.68 9.66 Mississippi Lauderdale 10.86 8.92 8.90 Mississippi Lee 10.77 8.86 8.86 Missouri Cass 10.65 8.81 8.79 Missouri Cedar 10.48 8.87 8.85 Missouri Clay 9.38 7.47 7.44 Missouri Greene 10.15 8.46 8.45 Missouri Jackson 10.25 8.28 8.26 Missouri Jefferson 10.05 7.63 7.62 Missouri Saint Louis 10.89 8.10 8.07 Missouri St. Louis City 11.61 8.68 8.66 Montana Lewis and Clark 8.45 8.10 8.10 Montana Lincoln 11.43 10.97 10.97 Montana Missoula 10.83 10.40 10.41 Montana Powder River 5.83 5.72 5.72 Montana Ravalli 10.00 9.76 9.76 Montana Richland 6.81 6.53 6.53 Montana Silver Bow 10.07 9.24 9.24 Nebraska Douglas 10.34 8.32 8.29 Nebraska Hall 7.24 5.98 5.96 Nebraska Lancaster 8.57 6.97 6.94 Nebraska Sarpy 11.26 9.06 9.03 Nebraska Washington 9.09 7.34 7.30 Nevada Clark 8.16 7.13 7.14 Nevada Washoe 6.90 6.14 6.14 New Hampshire Belknap 5.91 4.99 4.99 New Hampshire Cheshire 9.27 7.75 7.75 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV New Hampshire Grafton 6.75 5.52 5.53 New Hampshire Hillsborough 7.78 6.59 6.60 New Hampshire Merrimack 8.48 7.22 7.22 New Hampshire Rockingham 7.49 6.35 6.35 New Jersey Atlantic 8.91 6.83 6.82 New Jersey Bergen 9.17 6.84 6.84 New Jersey Camden 9.51 7.23 7.21 New Jersey Essex 9.45 7.25 7.24 New Jersey Gloucester 9.30 6.77 6.74 New Jersey Hudson 11.10 8.53 8.53 New Jersey Mercer 9.54 7.48 7.48 New Jersey Middlesex 8.01 6.16 6.15 New Jersey Morris 8.39 6.43 6.43 New Jersey Ocean 8.48 6.48 6.47 New Jersey Passaic 9.32 7.08 7.08 New Jersey Union 11.24 8.45 8.44 New Jersey Warren 9.24 7.18 7.17 New Mexico Bernalillo 6.36 6.00 6.00 New Mexico Dona Ana 5.78 5.98 5.98 New Mexico Lea 8.02 8.30 8.30 New Mexico San Juan 4.60 4.97 4.97 New Mexico Santa Fe 4.55 4.63 4.63 New York Albany 8.05 6.37 6.37 New York Bronx 11.91 9.08 9.08 New York Chautauqua 7.43 5.69 5.68 New York Erie 9.43 7.42 7.41 New York Essex 4.33 3.61 3.61 New York Kings 9.98 7.57 7.56 New York Nassau 8.88 6.73 6.72 New York New York 11.75 9.08 9.08 New York Onondaga 7.52 5.87 5.86 New York Orange 8.04 6.20 6.19 New York Queens 9.08 6.91 6.91 New York Richmond 9.47 7.01 7.01 New York Steuben 6.85 5.28 5.28 New York Suffolk 8.31 6.14 6.14 New York Westchester 9.09 6.83 6.83 North Carolina Alamance 9.53 7.09 7.11 North Carolina Buncombe 9.07 6.77 6.77 North Carolina Caswell 8.66 6.30 6.30 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV North Carolina Catawba 10.14 7.74 7.73 North Carolina Chatham 8.08 5.93 5.93 North Carolina Cumberland 9.78 7.58 7.57 North Carolina Davidson 10.77 8.23 8.23 North Carolina Duplin 8.57 6.52 6.51 North Carolina Durham 9.12 6.76 6.75 North Carolina Edgecombe 8.73 6.51 6.50 North Carolina Forsyth 9.53 6.99 6.99 North Carolina Gaston 10.00 7.54 7.53 North Carolina Guilford 9.29 6.82 6.82 North Carolina Haywood 9.65 7.82 7.82 North Carolina Jackson 8.96 7.02 7.02 North Carolina Johnston 8.76 6.55 6.55 North Carolina Lenoir 8.88 6.74 6.73 North Carolina McDowell 9.48 7.42 7.42 North Carolina Martin 8.30 6.12 6.12 North Carolina Mecklenburg 10.65 8.21 8.21 North Carolina Mitchell 8.94 7.07 7.07 North Carolina Montgomery 8.88 6.74 6.73 North Carolina New Hanover 7.77 5.76 5.75 North Carolina Pitt 8.27 6.13 6.12 North Carolina Robeson 9.56 7.83 7.83 North Carolina Rowan 9.97 7.66 7.66 North Carolina Swain 9.36 7.37 7.36 North Carolina Wake 9.97 7.64 7.63 North Carolina Watauga 7.99 6.07 6.07 North Carolina Wayne 9.51 7.40 7.40 North Dakota Billings 4.38 4.11 4.10 North Dakota Burke 6.76 6.43 6.43 North Dakota Burleigh 6.60 5.98 5.97 North Dakota Cass 7.70 6.80 6.79 North Dakota McKenzie 6.46 6.18 6.17 North Dakota Mercer 6.14 5.76 5.75 Ohio Athens 8.80 6.21 6.20 Ohio Butler 12.39 9.02 8.99 Ohio Clark 11.83 8.49 8.46 Ohio Clermont 11.34 7.87 7.84 Ohio Cuyahoga 12.82 9.92 9.91 Ohio Franklin 11.63 8.31 8.28 Ohio Greene 11.18 7.90 7.87 C-10 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Ohio Hamilton 13.17 9.52 9.50 Ohio Jefferson 12.07 8.72 8.70 Ohio Lake 9.54 7.01 7.00 Ohio Lawrence 10.97 8.34 8.33 Ohio Lorain 9.64 7.31 7.29 Ohio Lucas 10.89 8.28 8.25 Ohio Mahoning 11.14 8.45 8.43 Ohio Montgomery 12.06 8.63 8.59 Ohio Portage 10.26 7.45 7.43 Ohio Preble 10.66 7.76 7.74 Ohio Scioto 10.37 7.62 7.61 Ohio Stark 12.85 9.89 9.87 Ohio Summit 11.85 8.70 8.68 Ohio Trumbull 10.57 7.92 7.90 Ohio Warren 11.54 8.23 8.19 Oklahoma Oklahoma 9.61 8.48 8.47 Oklahoma Pittsburg 10.25 9.15 9.13 Oklahoma Sequoyah 10.68 9.26 9.25 Oklahoma Tulsa 10.46 9.10 9.08 Oregon Crook 9.02 8.93 8.92 Oregon Harney 9.05 8.72 8.71 Oregon Jackson 9.43 9.19 9.19 Oregon Josephine 7.76 7.59 7.59 Oregon Klamath 10.67 10.20 10.20 Oregon Lake 9.66 9.37 9.37 Oregon Lane 9.32 8.94 8.93 Oregon Multnomah 7.61 7.24 7.23 Oregon Umatilla 7.41 7.02 7.02 Oregon Washington 7.82 7.49 7.49 Pennsylvania Adams 11.49 8.93 8.92 Pennsylvania Allegheny 14.40 10.57 10.56 Pennsylvania Armstrong 11.60 9.05 9.04 Pennsylvania Beaver 12.00 9.36 9.35 Pennsylvania Berks 10.88 8.31 8.29 Pennsylvania Blair 11.89 8.66 8.65 Pennsylvania Bucks 10.88 8.61 8.61 Pennsylvania Cambria 12.34 9.35 9.34 Pennsylvania Centre 9.36 6.94 6.93 Pennsylvania Chester 12.33 9.56 9.54 Pennsylvania Cumberland 11.00 8.32 8.31 C-ll ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Pennsylvania Dauphin 11.97 9.18 9.16 Pennsylvania Delaware 12.81 10.00 9.98 Pennsylvania Erie 11.60 9.49 9.48 Pennsylvania Lackawanna 9.16 7.13 7.13 Pennsylvania Lancaster 12.01 9.05 9.03 Pennsylvania Lebanon 12.56 9.59 9.57 Pennsylvania Mercer 10.44 7.90 7.88 Pennsylvania Monroe 7.90 5.99 5.98 Pennsylvania Montgomery 9.90 7.68 7.67 Pennsylvania Northampton 12.90 10.44 10.44 Pennsylvania Philadelphia 11.15 8.40 8.37 Pennsylvania Washington 11.81 8.70 8.68 Pennsylvania Westmoreland 12.63 9.91 9.90 Pennsylvania York 11.48 8.78 8.75 Rhode Island Kent 6.15 4.75 4.75 Rhode Island Providence 9.38 7.59 7.60 South Carolina Charleston 8.89 7.02 7.02 South Carolina Chesterfield 9.15 7.10 7.09 South Carolina Edgefield 9.75 7.81 7.81 South Carolina Florence 10.26 8.25 8.24 South Carolina Greenville 10.74 8.44 8.44 South Carolina Lexington 10.89 8.75 8.75 South Carolina Richland 10.41 8.36 8.36 South Carolina Spartanburg 10.53 8.28 8.27 South Dakota Brookings 8.34 6.99 6.97 South Dakota Brown 7.67 6.73 6.71 South Dakota Codington 9.11 7.87 7.86 South Dakota Custer 4.20 4.00 3.99 South Dakota Jackson 3.96 3.66 3.66 South Dakota Minnehaha 8.83 7.19 7.17 South Dakota Pennington 5.89 5.58 5.58 South Dakota Union 9.22 7.62 7.59 Tennessee Hamilton 10.79 8.26 8.25 Texas Bexar 9.03 8.74 8.73 Texas Bowie 10.94 9.56 9.55 Texas Dallas 10.07 8.89 8.89 Texas El Paso 10.39 10.80 10.79 Texas Harris 12.05 11.24 11.23 Texas Harrison 10.65 9.32 9.31 Texas Hidalgo 10.37 10.74 10.74 C-12 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV Texas Nueces 10.28 9.79 9.78 Texas Tarrant 10.59 9.70 9.69 Texas Travis 10.01 9.51 9.50 Utah Box Elder 8.03 6.86 6.82 Utah Cache 9.40 8.03 8.00 Utah Davis 8.65 7.24 7.19 Utah Salt Lake 9.60 7.88 7.83 Utah Tooele 6.48 5.55 5.50 Utah Utah 8.71 7.19 7.14 Utah Washington 4.63 4.51 4.51 Utah Weber 9.38 7.80 7.75 Vermont Bennington 6.83 5.62 5.61 Vermont Chittenden 7.12 6.03 6.02 Vermont Rutland 9.49 7.85 7.85 Virginia Albemarle 8.40 6.40 6.40 Virginia Charles 8.61 6.37 6.36 Virginia Chesterfield 9.54 7.19 7.19 Virginia Fairfax 9.23 7.03 7.02 Virginia Frederick 10.04 7.81 7.80 Virginia Henrico 9.22 6.97 6.96 Virginia Loudoun 9.27 7.25 7.24 Virginia Page 8.79 6.84 6.84 Virginia Rockingham 9.66 7.67 7.67 Virginia Alexandria City 10.74 8.34 8.34 Virginia Bristol City 9.58 7.54 7.54 Virginia Hampton City 7.85 5.78 5.78 Virginia Lynchburg City 8.40 6.37 6.36 Virginia Norfolk City 9.20 6.98 6.98 Virginia Roanoke City 9.85 7.53 7.53 Virginia Salem City 9.59 7.29 7.29 Virginia Virginia Beach City 9.11 6.85 6.85 Washington Clark 7.34 6.87 6.87 Washington King 10.13 8.88 8.88 Washington Pierce 7.88 7.00 7.00 Washington Snohomish 7.62 6.88 6.88 Washington Spokane 7.69 7.19 7.19 Washington Yakima 8.91 7.77 7.74 West Virginia Berkeley 11.38 8.90 8.90 West Virginia Brooke 12.41 8.98 8.97 West Virginia Cabell 11.36 8.54 8.53 C-13 ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV West Virginia Hancock 11.17 8.16 8.15 West Virginia Kanawha 11.76 8.80 8.80 West Virginia Marion 11.34 8.71 8.70 West Virginia Marshall 12.46 9.65 9.64 West Virginia Monongalia 10.20 7.56 7.56 West Virginia Ohio 11.35 8.23 8.21 West Virginia Raleigh 9.06 6.60 6.60 West Virginia Wood 11.51 8.69 8.68 Wisconsin Ashland 5.32 4.58 4.56 Wisconsin Brown 9.57 7.86 7.84 Wisconsin Dane 10.07 7.97 7.94 Wisconsin Dodge 8.99 7.08 7.05 Wisconsin Forest 5.57 4.55 4.54 Wisconsin Grant 10.04 7.83 7.80 Wisconsin Kenosha 9.33 7.25 7.22 Wisconsin La Crosse 8.98 7.32 7.29 Wisconsin Milwaukee 10.82 8.52 8.50 Wisconsin Outagamie 9.22 7.49 7.46 Wisconsin Ozaukee 9.02 7.08 7.05 Wisconsin Sauk 8.36 6.43 6.40 Wisconsin Taylor 7.62 6.34 6.31 Wisconsin Vilas 5.76 4.82 4.80 Wisconsin Waukesha 11.26 8.84 8.81 Wyoming Albany 4.97 4.64 4.64 Wyoming Fremont 8.19 8.03 8.02 Wyoming Laramie 4.54 4.21 4.21 Wyoming Natrona 4.79 4.64 4.64 Wyoming Park 4.55 4.53 4.52 Wyoming Sheridan 8.04 7.80 7.80 Wyoming Sublette 3.82 3.68 3.68 Wyoming Sweetwater 5.77 5.17 5.16 Wyoming Teton 4.94 4.71 4.71 C-14 ------- Air Quality Modeling Technical Support Document: Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule Appendix D 24-Hour PM2.5 Design Values for Air Quality Modeling Scenarios D-l ------- Table D-l. 24-hour PM2.5 Design Values for HDGHG Phase 2 Scenarios (units are ug/m3) State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV Alabama Baldwin 19.0 15.4 15.5 Alabama Clay 20.7 16.8 16.8 Alabama Colbert 19.5 16.7 16.7 Alabama DeKalb 20.7 17.0 17.0 Alabama Etowah 21.6 18.0 17.9 Alabama Houston 19.3 17.0 16.9 Alabama Jefferson 25.6 23.1 23.1 Alabama Madison 21.0 19.4 19.4 Alabama Mobile 20.0 16.3 16.3 Alabama Montgomery 23.1 20.2 20.2 Alabama Morgan 20.1 17.3 17.2 Alabama Russell 26.2 22.9 22.9 Alabama Shelby 20.1 16.4 16.4 Alabama Talladega 21.4 18.3 18.3 Alabama Tuscaloosa 22.9 19.4 19.4 Alabama Walker 22.0 17.8 17.7 Arizona Cochise 12.8 14.1 14.1 Arizona Coconino 12.6 12.5 12.5 Arizona Maricopa 27.2 23.0 23.2 Arizona Pima 12.2 10.6 10.6 Arizona Pinal 28.9 26.6 26.6 Arizona Santa Cruz 28.1 27.9 27.9 Arizona Yavapai 9.7 9.7 9.7 Arizona Yuma 15.5 14.9 14.9 Arkansas Arkansas 21.5 17.4 17.3 Arkansas Ashley 22.5 18.6 18.6 Arkansas Crittenden 22.7 16.9 17.0 Arkansas Faulkner 20.1 16.4 16.4 Arkansas Garland 21.4 17.7 17.6 Arkansas Jackson 21.4 17.0 16.9 Arkansas Phillips 20.6 16.7 16.7 Arkansas Polk 22.0 18.8 18.8 Arkansas Pope 22.8 19.3 19.3 Arkansas Pulaski 25.2 20.0 20.1 Arkansas Union 22.5 18.7 18.6 Arkansas Washington 22.3 19.0 18.9 Arkansas White 21.7 18.5 18.4 D-2 ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV California Alameda 27.5 22.0 21.9 California Butte 34.6 30.5 30.4 California Calaveras 19.0 14.1 14.1 California Colusa 22.3 19.5 19.5 California Contra Costa 27.0 21.6 21.6 California Fresno 58.9 49.8 49.7 California Humboldt 22.7 22.1 22.1 California Imperial 30.8 30.8 30.7 California Inyo 35.3 33.7 33.7 California Kern 61.6 41.2 41.1 California Kings 60.1 44.3 44.2 California Lake 8.7 7.9 7.9 California Los Angeles 31.1 26.3 26.2 California Madera 52.3 41.0 41.0 California Marin 24.3 21.3 21.2 California Mendocino 19.2 16.7 16.7 California Merced 41.7 35.7 35.7 California Monterey 13.9 11.9 11.9 California Nevada 17.5 15.3 15.3 California Orange 26.6 21.0 21.0 California Placer 19.9 16.4 16.4 California Plumas 32.1 30.1 30.1 California Riverside 36.7 26.0 25.9 California Sacramento 34.0 29.5 29.5 California San Benito 14.3 11.2 11.2 California San Bernardino 29.5 26.8 26.7 California San Diego 23.2 19.5 19.5 California San Francisco 25.3 21.6 21.5 California San Joaquin 39.8 34.3 34.2 California San Luis Obispo 30.2 27.8 27.8 California San Mateo 24.5 20.1 20.0 California Santa Barbara 18.9 17.1 17.1 California Santa Clara 32.1 26.6 26.5 California Santa Cruz 13.0 11.5 11.5 California Shasta 15.3 14.3 14.3 California Siskiyou 18.4 17.8 17.8 California Solano 28.5 24.8 24.8 California Sonoma 22.4 19.6 19.6 California Stanislaus 50.9 40.5 40.4 California Sutter 27.2 23.1 23.0 D-3 ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV California Tulare 49.8 36.7 36.6 California Ventura 20.2 17.0 16.9 California Yolo 21.3 18.4 18.4 Colorado Adams 21.1 17.6 17.5 Colorado Arapahoe 14.5 12.4 12.3 Colorado Boulder 20.2 17.6 17.5 Colorado Denver 18.8 15.7 15.7 Colorado Douglas 15.5 13.1 13.0 Colorado El Paso 13.7 11.9 11.9 Colorado Larimer 17.7 15.2 15.2 Colorado Mesa 33.5 28.0 27.9 Colorado Montezuma 13.6 13.3 13.3 Colorado Rio Blanco 20.6 21.3 21.3 Colorado Weld 22.8 19.6 19.6 Connecticut Fairfield 24.8 19.9 19.9 Connecticut Hartford 22.9 18.2 18.2 Connecticut Litchfield 16.4 11.4 11.4 Connecticut New Haven 25.5 19.3 19.3 Connecticut New London 21.4 16.8 16.8 Delaware Kent 22.9 17.5 17.4 Delaware New Castle 25.7 20.5 20.3 Delaware Sussex 23.6 16.2 16.2 District Of Co District of Columbia 25.9 20.0 19.9 Florida Alachua 20.1 19.3 19.3 Florida Brevard 14.8 12.8 12.8 Florida Broward 14.5 13.8 13.8 Florida Citrus 17.0 14.3 14.3 Florida Duval 20.9 18.3 18.3 Florida Escambia 19.5 15.8 15.9 Florida Hillsborough 16.1 14.1 14.1 Florida Lee 14.0 12.3 12.3 Florida Leon 23.8 21.3 21.3 Florida Miami-Dade 15.2 13.9 13.9 Florida Orange 15.6 13.9 13.9 Florida Palm Beach 15.1 13.8 13.8 Florida Pinellas 16.7 15.2 15.2 Florida Polk 15.2 13.4 13.4 Florida Sarasota 15.5 13.3 13.3 Florida Seminole 17.4 14.9 14.9 Florida Volusia 16.2 13.8 13.8 D-4 ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV Georgia Bibb 26.7 22.3 22.2 Georgia Chatham 29.6 23.4 23.4 Georgia Clarke 21.7 16.7 16.6 Georgia Cobb 21.4 16.3 16.3 Georgia DeKalb 22.0 16.5 16.5 Georgia Dougherty 26.4 24.3 24.3 Georgia Hall 21.1 15.9 15.9 Georgia Houston 22.4 19.3 19.2 Georgia Walker 22.0 17.1 17.1 Georgia Wilkinson 22.8 20.0 20.0 Idaho Ada 33.1 31.5 31.0 Idaho Bannock 23.5 21.1 21.0 Idaho Benewah 28.4 27.8 27.8 Idaho Canyon 24.2 22.1 21.8 Idaho Franklin 42.2 35.5 35.4 Idaho Lemhi 37.0 35.3 35.3 Idaho Shoshone 38.1 36.3 36.3 Indiana Allen 24.9 18.0 17.8 Indiana Clark 26.8 20.7 20.7 Indiana Delaware 25.4 18.5 18.4 Indiana Dubois 25.3 17.8 17.7 Indiana Elkhart 29.2 21.9 21.7 Indiana Floyd 24.5 17.9 17.8 Indiana Gibson 25.3 17.3 17.3 Indiana Henry 24.2 18.0 17.8 Indiana Howard 26.0 18.9 18.8 Indiana Knox 25.8 18.1 18.0 Indiana Lake 30.0 23.9 23.6 Indiana La Porte 24.1 18.1 18.0 Indiana Madison 22.6 17.2 16.8 Indiana Marion 28.1 20.8 20.7 Indiana Monroe 21.9 15.1 15.0 Indiana Porter 26.6 20.8 20.7 Indiana St. Joseph 26.9 20.3 20.1 Indiana Spencer 25.7 17.5 17.3 Indiana Tippecanoe 23.8 17.7 17.5 Indiana Vanderburgh 25.5 19.9 19.8 Indiana Vigo 26.0 17.8 17.7 Indiana Whitley 22.2 16.0 15.9 Iowa Black Hawk 29.2 22.1 21.9 D-5 ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV Iowa Clinton 27.6 20.6 20.3 Iowa Delaware 21.9 15.7 15.5 Iowa Johnson 26.1 19.7 19.5 Iowa Lee 25.2 18.9 18.7 Iowa Linn 30.4 22.2 21.9 Iowa Montgomery 22.1 16.4 16.2 Iowa Muscatine 31.5 24.6 24.4 Iowa Palo Alto 22.0 15.9 15.8 Iowa Polk 24.3 17.8 17.5 Iowa Pottawattamie 25.2 19.0 18.9 Iowa Scott 28.7 20.8 20.6 Iowa Van Buren 23.8 18.2 18.0 Iowa Woodbury 25.9 19.4 19.1 Kansas Johnson 18.2 14.2 14.1 Kansas Linn 20.4 16.4 16.3 Kansas Sedgwick 22.1 17.6 17.5 Kansas Shawnee 20.0 18.3 18.2 Kansas Sumner 20.8 17.5 17.5 Kansas Wyandotte 22.4 18.1 18.0 Kentucky Bell 23.7 20.1 20.0 Kentucky Boyd 23.0 16.7 16.7 Kentucky Campbell 23.7 15.4 15.3 Kentucky Carter 18.3 14.1 14.1 Kentucky Christian 21.5 14.8 14.7 Kentucky Daviess 25.5 18.8 18.8 Kentucky Fayette 21.9 15.5 15.5 Kentucky Hardin 22.7 16.3 16.2 Kentucky Henderson 23.9 17.2 17.2 Kentucky Jefferson 26.0 20.5 20.5 Kentucky McCracken 23.4 15.9 15.8 Kentucky Madison 19.6 13.8 13.8 Kentucky Pike 21.5 16.3 16.3 Kentucky Warren 21.9 15.3 15.4 Louisiana Caddo 22.0 19.2 19.1 Louisiana Calcasieu 19.8 17.0 16.9 Louisiana East Baton Rouge 21.2 18.5 18.7 Louisiana Iberville 20.6 19.9 20.1 Louisiana Jefferson 18.7 16.1 16.3 Louisiana Lafayette 19.9 17.8 17.8 Louisiana Ouachita 19.9 16.9 16.9 D-6 ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV Louisiana Rapides 19.8 16.8 16.8 Louisiana St. Bernard 20.2 16.2 16.2 Louisiana Tangipahoa 18.3 15.2 15.3 Louisiana Terrebonne 17.5 14.4 14.6 Louisiana West Baton Rouge 21.9 18.9 18.9 Maine Androscoggin 20.9 16.0 16.0 Maine Aroostook 18.6 16.3 16.3 Maine Cumberland 20.4 16.1 16.1 Maine Hancock 14.8 12.7 12.7 Maine Kennebec 19.7 14.6 14.6 Maine Oxford 26.4 21.6 21.6 Maine Penobscot 19.5 15.4 15.4 Maryland Anne Arundel 24.5 18.9 18.9 Maryland Baltimore 27.0 21.6 21.6 Maryland Cecil 26.7 19.9 19.8 Maryland Garrett 19.5 14.2 14.2 Maryland Harford 23.6 17.9 17.8 Maryland Kent 24.1 18.9 18.8 Maryland Montgomery 24.5 19.8 19.7 Maryland Prince George's 24.4 19.9 19.8 Maryland Washington 27.4 21.8 21.8 Maryland Baltimore (City) 27.1 21.9 21.9 Massachusetts Berkshire 24.1 18.6 18.5 Massachusetts Bristol 19.5 13.9 13.9 Massachusetts Essex 18.8 15.4 15.4 Massachusetts Hampden 24.6 20.0 20.0 Massachusetts Middlesex 19.3 15.0 15.1 Massachusetts Plymouth 19.4 15.0 15.0 Massachusetts Suffolk 22.5 17.8 17.8 Massachusetts Worcester 21.6 17.1 17.1 Michigan Allegan 23.9 17.5 17.3 Michigan Bay 23.1 17.6 17.4 Michigan Berrien 21.2 16.2 16.1 Michigan Chippewa 16.2 13.6 13.5 Michigan Genesee 21.8 16.1 16.0 Michigan Ingham 22.4 17.1 17.1 Michigan Kalamazoo 22.6 17.0 16.8 Michigan Kent 24.2 18.5 18.3 Michigan Lenawee 24.0 18.6 18.5 Michigan Macomb 23.5 17.9 17.7 D-7 ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV Michigan Manistee 18.6 13.4 13.3 Michigan Missaukee 17.1 13.4 13.4 Michigan Monroe 24.4 18.0 17.9 Michigan Muskegon 23.7 17.5 17.3 Michigan Oakland 24.8 18.7 18.6 Michigan Ottawa 23.8 18.1 17.9 Michigan St. Clair 23.8 19.3 19.2 Michigan Washtenaw 23.3 17.8 17.6 Michigan Wayne 28.4 22.7 22.5 Minnesota Anoka 22.6 17.7 17.6 Minnesota Dakota 25.9 20.6 20.4 Minnesota Hennepin 25.7 21.1 21.0 Minnesota Olmsted 25.7 19.1 19.0 Minnesota Ramsey 28.5 23.0 22.9 Minnesota Saint Louis 21.2 17.7 17.6 Minnesota Scott 24.8 19.4 19.2 Minnesota Stearns 24.4 18.4 18.2 Mississippi DeSoto 18.9 14.6 14.7 Mississippi Forrest 21.7 18.3 18.4 Mississippi Grenada 19.5 15.3 15.3 Mississippi Hancock 19.2 16.4 16.3 Mississippi Harrison 18.3 14.5 14.5 Mississippi Hinds 21.2 17.5 17.5 Mississippi Jackson 20.4 16.2 16.2 Mississippi Jones 22.6 19.5 19.5 Mississippi Lauderdale 21.0 17.4 17.3 Mississippi Lee 21.1 16.3 16.3 Missouri Cass 23.4 19.6 19.5 Missouri Cedar 22.4 18.7 18.7 Missouri Clay 21.6 17.6 17.4 Missouri Greene 22.0 18.6 18.6 Missouri Jackson 23.0 19.1 19.1 Missouri Jefferson 22.9 17.6 17.5 Missouri Saint Louis 25.4 20.2 20.0 Missouri St. Louis City 25.3 20.0 19.7 Montana Lewis and Clark 33.3 31.9 31.9 Montana Missoula 31.5 31.2 31.1 Montana Ravalli 51.3 50.3 50.3 Montana Richland 15.7 15.2 15.2 Montana Silver Bow 39.7 36.1 36.0 D-8 ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV Nebraska Douglas 22.7 17.3 17.3 Nebraska Hall 18.9 14.4 14.2 Nebraska Lancaster 21.3 16.3 16.2 Nebraska Sarpy 25.7 19.8 19.5 Nebraska Washington 22.7 17.1 16.9 Nevada Clark 21.3 18.4 18.4 Nevada Washoe 22.8 19.0 18.9 New Hampshire Belknap 16.3 13.0 13.0 New Hampshire Cheshire 28.4 23.0 23.0 New Hampshire Grafton 19.0 14.4 14.4 New Hampshire Hillsborough 20.5 16.5 16.5 New Hampshire Merrimack 21.8 17.4 17.4 New Hampshire Rockingham 22.8 18.2 18.3 New Jersey Atlantic 23.2 17.0 17.0 New Jersey Bergen 23.5 17.3 17.3 New Jersey Camden 22.6 16.5 16.4 New Jersey Essex 22.8 18.0 18.0 New Jersey Gloucester 22.2 15.9 15.8 New Jersey Hudson 26.8 20.1 20.1 New Jersey Mercer 25.0 19.9 19.9 New Jersey Middlesex 19.3 14.7 14.7 New Jersey Morris 21.1 15.4 15.4 New Jersey Ocean 22.7 16.3 16.3 New Jersey Passaic 24.3 18.9 18.8 New Jersey Union 29.4 22.3 22.2 New Jersey Warren 25.3 19.1 19.0 New Mexico Bernalillo 19.1 16.2 16.2 New Mexico Dona Ana 12.7 14.3 14.3 New Mexico Lea 19.4 20.3 20.3 New Mexico San Juan 13.4 15.3 15.3 New Mexico Santa Fe 9.9 10.1 10.1 New York Albany 21.8 16.5 16.4 New York Bronx 28.0 21.9 21.9 New York Chautauqua 21.1 14.3 14.3 New York Erie 24.5 18.5 18.5 New York Essex 14.2 9.9 9.9 New York Kings 24.1 18.1 18.1 New York Nassau 23.0 17.2 17.1 New York New York 25.7 20.3 20.3 New York Onondaga 20.7 14.4 14.3 D-9 ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV New York Orange 21.7 16.5 16.4 New York Queens 24.2 18.5 18.4 New York Richmond 23.0 17.8 17.8 New York Steuben 19.3 13.5 13.5 New York Suffolk 21.9 16.6 16.6 New York Westchester 25.4 18.4 18.4 North Carolina Alamance 19.8 14.6 14.7 North Carolina Buncombe 17.8 13.1 13.1 North Carolina Caswell 17.8 12.3 12.2 North Carolina Catawba 20.6 16.0 16.0 North Carolina Chatham 18.1 12.7 12.7 North Carolina Cumberland 21.0 16.8 16.8 North Carolina Davidson 20.8 15.6 15.6 North Carolina Duplin 19.3 14.5 14.5 North Carolina Durham 18.7 13.4 13.3 North Carolina Edgecombe 19.6 14.4 14.4 North Carolina Forsyth 19.9 14.7 14.6 North Carolina Gaston 21.6 16.2 16.1 North Carolina Guilford 20.4 15.4 15.4 North Carolina Haywood 21.1 18.6 18.6 North Carolina Jackson 17.4 13.8 13.8 North Carolina Johnston 18.9 13.8 13.8 North Carolina Lenoir 21.4 15.4 15.4 North Carolina McDowell 18.4 15.1 15.1 North Carolina Martin 22.9 16.5 16.5 North Carolina Mecklenburg 22.6 17.6 17.5 North Carolina Mitchell 18.0 13.8 13.8 North Carolina Montgomery 19.6 14.6 14.6 North Carolina New Hanover 22.0 15.8 15.8 North Carolina Pitt 20.6 15.0 15.0 North Carolina Robeson 20.5 17.5 17.5 North Carolina Rowan 19.3 14.8 14.8 North Carolina Swain 19.4 15.4 15.4 North Carolina Wake 21.9 16.9 16.9 North Carolina Watauga 16.9 12.5 12.5 North Carolina Wayne 20.3 15.6 15.6 North Dakota Billings 10.9 9.7 9.7 North Dakota Burke 14.7 13.9 13.9 North Dakota Burleigh 15.7 14.1 14.0 North Dakota Cass 20.2 17.1 17.0 D ------- State County 2011 Baseline DV 2040 Reference DV 2040 HDGHGP2 Control DV North Dakota McKenzie 15.2 14.5 14.5 North Dakota Mercer 14.9 13.9 13.9 Oh o Athens 17.1 12.1 12.1 Oh o Butler 27.0 20.2 20.0 Oh o Clark 26.4 18.7 18.6 Oh o Clermont 26.6 18.1 18.0 Oh o Cuyahoga 29.4 22.7 22.7 Oh o Franklin 24.8 18.0 17.9 Oh o Greene 21.8 15.5 15.4 Oh o Hamilton 28.9 21.0 20.7 Oh o Jefferson 27.2 19.6 19.5 Oh o Lake 22.3 15.7 15.6 Oh o Lawrence 22.3 17.7 17.7 Oh o Lorain 22.7 16.2 16.2 Oh o Lucas 25.6 19.8 19.7 Oh o Mahoning 24.8 18.8 18.7 Oh o Montgomery 26.6 19.7 19.6 Oh o Portage 24.1 17.0 16.9 Oh o Preble 24.0 17.6 17.5 Oh o Scioto 21.1 14.8 14.8 Oh o Stark 27.9 21.9 21.9 Oh o Summit 26.5 18.5 18.5 Oh o Trumbull 23.9 17.4 17.3 Oh o Warren 26.3 18.8 18.7 Oklahoma Oklahoma 19.6 17.1 17.1 Oklahoma Pittsburg 20.2 18.1 18.0 Oklahoma Sequoyah 22.2 18.8 18.7 Oklahoma Tulsa 22.3 19.3 19.2 Oregon Crook 33.5 33.2 33.2 Oregon Harney 32.6 31.3 31.3 Oregon Jackson 32.2 31.4 31.4 Oregon Josephine 26.2 25.9 25.9 Oregon Klamath 35.8 33.7 33.7 Oregon Lake 41.8 40.4 40.4 Oregon Lane 24.6 23.9 23.9 Oregon Multnomah 27.2 26.2 26.2 Oregon Umatilla 23.4 22.1 22.0 Oregon Washington 28.9 27.9 27.9 Pennsylvania Adams 28.8 21.2 21.1 Pennsylvania Allegheny 41.4 33.7 33.6 D-ll ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV Pennsylvania Armstrong 25.5 18.9 18.8 Pennsylvania Beaver 27.4 21.6 21.6 Pennsylvania Berks 27.4 22.2 22.1 Pennsylvania Blair 29.8 22.5 22.5 Pennsylvania Bucks 28.8 24.0 23.9 Pennsylvania Cambria 30.2 23.0 23.0 Pennsylvania Centre 25.0 18.2 18.2 Pennsylvania Chester 29.6 23.1 23.0 Pennsylvania Cumberland 30.9 24.1 24.1 Pennsylvania Dauphin 31.5 25.3 25.2 Pennsylvania Delaware 29.7 24.1 23.9 Pennsylvania Erie 26.6 20.6 20.6 Pennsylvania Lackawanna 23.6 17.6 17.5 Pennsylvania Lancaster 30.9 24.6 24.5 Pennsylvania Mercer 24.8 18.7 18.5 Pennsylvania Monroe 20.4 14.7 14.6 Pennsylvania Montgomery 25.8 19.9 19.8 Pennsylvania Northampton 32.1 25.3 25.2 Pennsylvania Philadelphia 30.4 22.5 22.5 Pennsylvania Washington 26.4 19.0 18.9 Pennsylvania York 28.6 22.6 22.5 Rhode Island Kent 16.0 11.4 11.4 Rhode Island Providence 23.3 17.6 17.6 South Carolina Charleston 21.0 16.8 16.7 South Carolina Chesterfield 19.5 17.3 17.3 South Carolina Edgefield 20.3 16.4 16.3 South Carolina Florence 21.9 17.4 17.4 South Carolina Greenville 22.4 18.7 18.7 South Carolina Lexington 22.8 19.4 19.4 South Carolina Richland 22.8 18.9 18.9 South Carolina Spartanburg 21.3 17.1 17.0 South Dakota Brookings 21.8 16.2 16.0 South Dakota Brown 20.9 15.2 15.1 South Dakota Codington 21.1 15.8 15.7 South Dakota Custer 12.6 11.7 11.7 South Dakota Jackson 11.9 11.0 11.0 South Dakota Minnehaha 22.8 16.5 16.3 South Dakota Pennington 14.9 14.2 14.2 South Dakota Union 23.1 17.6 17.5 Tennessee Hamilton 22.5 18.0 18.0 D ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV Texas Bexar 22.6 22.2 22.2 Texas Bowie 21.8 18.5 18.5 Texas Dallas 20.9 18.8 18.8 Texas El Paso 29.2 32.0 32.0 Texas Harris 23.7 21.4 21.4 Texas Harrison 21.1 17.5 17.5 Texas Nueces 27.8 26.4 26.3 Texas Tarrant 22.3 21.0 21.0 Texas Travis 22.3 21.1 21.1 Utah Box Elder 38.1 31.1 30.7 Utah Cache 41.7 32.7 32.5 Utah Davis 36.5 29.0 28.6 Utah Salt Lake 41.2 32.4 32.0 Utah Tooele 26.4 20.4 19.9 Utah Utah 42.5 32.7 32.1 Utah Washington 10.8 10.6 10.6 Utah Weber 39.4 31.7 31.3 Vermont Bennington 18.2 13.2 13.2 Vermont Chittenden 20.4 16.1 16.1 Vermont Rutland 28.1 22.4 22.4 Virginia Albemarle 18.6 12.7 12.7 Virginia Charles 20.3 13.2 13.2 Virginia Chesterfield 21.0 14.8 14.8 Virginia Fairfax 23.0 17.7 17.6 Virginia Frederick 23.4 18.2 18.2 Virginia Henrico 21.4 15.6 15.5 Virginia Loudoun 20.2 16.0 15.9 Virginia Page 20.8 15.5 15.4 Virginia Rockingham 21.8 17.4 17.4 Virginia Bristol City 19.5 15.7 15.6 Virginia Hampton City 20.6 14.7 14.7 Virginia Lynchburg City 18.2 13.5 13.4 Virginia Norfolk City 21.6 16.0 15.9 Virginia Roanoke City 21.5 16.6 16.6 Virginia Salem City 19.8 14.6 14.6 Virginia Virginia Beach City 23.1 16.6 16.5 Washington Clark 27.9 25.8 25.7 Washington King 23.8 21.7 21.7 Washington Pierce 31.8 28.5 28.5 Washington Snohomish 28.4 26.7 26.7 D ------- State County 2011 2040 2040 HDGHGP2 Baseline DV Reference DV Control DV Washington Spokane 26.0 24.3 24.3 Washington Yakima 32.7 27.3 27.1 West Virginia Berkeley 29.1 22.4 22.4 West Virginia Brooke 26.2 18.1 18.1 West Virginia Cabell 23.3 17.3 17.3 West Virginia Hancock 27.0 19.0 19.0 West Virginia Kanawha 24.1 17.8 17.7 West Virginia Marion 24.2 18.5 18.5 West Virginia Marshall 27.6 22.4 22.4 West Virginia Monongalia 23.6 16.4 16.4 West Virginia Ohio 25.2 16.8 16.7 West Virginia Raleigh 19.4 13.6 13.6 West Virginia Wood 24.2 17.7 17.7 Wisconsin Ashland 17.2 13.6 13.5 Wisconsin Brown 28.5 21.7 21.4 Wisconsin Dane 27.4 20.8 20.7 Wisconsin Dodge 25.0 18.4 18.2 Wisconsin Forest 19.5 14.2 14.0 Wisconsin Grant 25.1 18.9 18.7 Wisconsin Kenosha 25.5 19.3 19.0 Wisconsin La Crosse 24.6 18.1 17.8 Wisconsin Milwaukee 29.6 22.5 22.3 Wisconsin Outagamie 27.2 19.9 19.7 Wisconsin Ozaukee 23.6 17.6 17.4 Wisconsin Sauk 24.3 18.1 18.0 Wisconsin Taylor 23.8 17.6 17.4 Wisconsin Vilas 17.5 12.3 12.2 Wisconsin Waukesha 27.3 21.0 20.8 Wyoming Albany 13.0 12.2 12.1 Wyoming Fremont 29.6 30.0 30.0 Wyoming Laramie 11.3 10.9 10.9 Wyoming Natrona 14.1 14.1 14.1 Wyoming Park 12.6 12.8 12.8 Wyoming Sheridan 22.0 21.9 21.9 Wyoming Sweetwater 15.6 14.0 14.0 Wyoming Teton 14.1 13.6 13.6 D ------- |