Technical Support Document for the Final 

Locomotive/Marine Rule: Air Quality 

Modeling Analyses 
2

EPA 454/R-08-002 

January 2008 

Technical Support Document for the Final 

Locomotive/Marine Rule: Air Quality Modeling 

Analyses 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 

January 2008 
3

I. Introduction

 This document describes the air quality modeling performed by EPA in support of 

the final Control of Emissions of Air Pollution from Locomotive Engines and Marine 

Compression-Ignition Engines Less than 20 Liters per Cylinder (Locomotive/Marine) 

rule. A national scale air quality modeling analysis was performed to estimate the effect 

of the rule on future annual fine particulate matter (PM2.5) concentrations, future 8-hour 

ozone concentrations, and future visibility levels. To estimate the air quality changes 

expected to result from this rule we used the Community Multiscale Air Quality (CMAQ) 

model1

. The CMAQ model simulates the multiple physical and chemical processes 

involved in the formation, transport, and destruction of fine particulate matter and ozone. 

 The overall CMAQ modeling platform has been revised from what was used at 

proposal. A modeling platform is a structured system of connected modeling-related 

tools and data that provide a consistent and transparent basis for assessing the air quality 

response to changes in emissions and/or meteorology. A platform typically consists of a 

specific air quality model, base year and future year baseline emissions estimates, and a 

set of meteorological model inputs. The final Locomotive/Marine rule modeling analyses 

were based on a 2002 modeling platform which reflects: a) an updated version of the 

CMAQ model, b) higher resolution PM2.5 modeling, c) a longer period of ozone 

modeling, and d) updated emissions and meteorological data. These updates from the 

previous 2001 platform will be described in more detail in subsequent sections of this 

technical support document (TSD). 

II. CMAQ Model Version, Inputs and Configuration 

A. Model version 

CMAQ is a non-proprietary computer model that simulates the formation and fate 

of photochemical oxidants, including PM2.5 and ozone, for given input sets of 

meteorological conditions and emissions. This analysis employed a version of CMAQ 

based on the latest publicly-released version of CMAQ available at the time of the final 

Locomotive/Marine rule modeling (i.e., version 4.6)2. CMAQ version 4.6 reflects recent 

updates intended to improve the underlying science from version 4.5, which was used in 

the proposal. These model enhancements include: 

1) an updated Carbon Bond chemical mechanism (CB-05) and associated Euler 

Backward Iterative (EBI) solver was added; 

 

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. 

2

 CMAQ version 4.6 was released on September 30, 2006. It is available from the Community Modeling 

and Analysis System (CMAS) at: http://www.cmascenter.org . 
4

2) an updated version of the ISORROPIA aerosol thermodynamics module was 

added; 

3) the heterogeneous N2O5 reaction probability is now temperature- and humiditydependent; 

4) the gas-phase reactions involving N2O5 and H2O are now included; and 

5) an updated version of the vertical diffusion module was added (ACM2). 

 Additionally, there were a few minor changes made to the release version of 

CMAQ v4.6 by the EPA model developers subsequent to its release. The relatively 

minor changes and new features of this internal version that was ultimately used in this 

analysis (version 4.6.1i) are described elsewhere.3

 

B. Model domain and grid resolution 

The CMAQ modeling analyses were performed for a domain covering the 

continental United States, as shown in Figure II-1. This domain has a parent horizontal 

grid of 36 km with two finer-scale 12 km grids over portions of the eastern and western 

U.S. The model extends vertically from the surface to 100 millibars (approximately 15 

km) using a sigma-pressure coordinate system. Air quality conditions at the outer 

boundary of the 36 km domain were taken from a global model and did not change over 

the simulations. In turn, the 36 km grid was only used to establish the incoming air 

quality concentrations along the boundaries of the 12 km grids. All of the modeling 

results assessing the emissions reductions from the Locomotive/Marine rule were taken 

from the 12 km grids. Table II-1 provides some basic geographic information regarding 

the CMAQ domains. 

Table II-1. Geographic elements of domains used in Locomotive/Marine modeling. 

CMAQ Modeling Configuration 

National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid 

Map Projection Lambert Conformal Projection 

Grid Resolution 36 km 12 km 12 km 

Coordinate Center 97 deg W, 40 deg N 

True Latitudes 33 deg N and 45 deg N 

Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14 

Vertical extent 14 Layers: Surface to 100 millibar level (see Table II-3) 

 

3

 See the 4/09/07 e-mail from Shawn Roselle, Office of Research and Development to Carey Jang, Office 

of Air Quality Planning and Standards which is included in the docket for this rulemaking. 
5

Figure II-1. Map of the CMAQ modeling domain. The black outer box denotes the 

36 km national modeling domain; the red inner box is the 12 km western U.S. fine 

grid; and the blue inner box is the 12 km eastern U.S. fine grid. 

C. Modeling Period / Ozone Episodes 

 The 36 km and both 12 km CMAQ modeling domains were modeled for the 

entire year of 2002.4 All 365 model days were used in the calculations of the impacts of 

the locomotive/marine controls on annual average levels of PM2.5. For the 8-hour ozone 

results, we are only using modeling results from the period between May 1 and 

September 30, 2002. 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 2002. 

D. Model Inputs: Emissions, Meteorology and Boundary Conditions 

1. Base Year and Future Baseline Emissions: As noted in the introduction 

section, a 2001-based platform was used for the proposed rule modeling and a 2002-

based platform was used for the final rule modeling. The 2002-based platform builds 

upon the general concepts, tools and emissions modeling data from the 2001-based 

 

4

 We also modeled 10 days at the end of December 2001 as a modeled "ramp up" period. These days are 

used to minimize the effects of initial conditions and are not considered as part of the output analyses. 
6

platform, while updating and enhancing many of the emission inputs and tools. A 

summary of the emissions inventory development is described below. More detailed 

documentation on the methods and data summaries of the 2002-based platform emissions 

for base and future years is also available separately.5

We used version 3 of the 2002-based platform which takes emission inventories 

from the 2002 National Emissions Inventory (NEI) version 3.0. These inventories, with 

the exception of California, include monthly onroad and nonroad emissions generated 

from the National Mobile Inventory Model (NMIM) using versions of MOBILE6.0 and 

NONROAD2005 consistent with recent national rule analyses.6,7,8 The locomotive and 

marine inventories are based on national level estimates developed for the proposed rule 

making.9 That is, the base year emissions for locomotive and marine sectors did not 

change between the proposal and final modeling. The 2002-based platform and its 

associated chemical mechanism (CB05) employs updated speciation profiles using data 

included in the SPECIATE4.0 database.10 In addition, the 2002-based platform 

incorporates several temporal profile updates for both mobile and stationary sources. 

The 2002-based platform includes emissions for a 2002 base year model 

evaluation case, a 2002 base case and several projection years. The projection years 

include 2020 and 2030, which were used as the future years for the locomotive/marine 

rule analyses. The model evaluation case uses prescribed burning and wildfire emissions 

specific to 2002, which were developed and modeled as day-specific, location-specific 

emissions using an updated version of Sparse Matrix Operator Kernel Emissions 

(SMOKE) system, version 2.3, which computes plume rise and vertically allocates the 

fire emissions. It also includes continuous emissions monitoring (CEM) data for 2002 for 

electric generating units (EGUs) with CEMs. The 2002 and projection year baselines 

 

5

 Technical Support Document: Preparation of Emissions Inventories for the 2002-based Platform, 

Version 3.0, Criteria Air Pollutants, January 2008. This file is available in the docket for this rulemaking. 

6

 The California Air Resources Board submitted annual emissions for California. These were allocated to 

monthly resolution prior to emissions modeling using data from the National Mobile Inventory Model 

(NMIM). 

7

 MOBILE6 version was used in the Mobile Source Air Toxics Rule: Regulatory Impact Analysis for Final 

Rule: Control of Hazardous Air Pollutants from Mobile Sources, U.S. Environmental Protection Agency, 

Office of Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, 

EPA420-R-07-002, February 2007. 

8

 NONROAD2005 version was used in the proposed rule for small spark ignition (SI) and marine SI rule: 

Draft Regulatory Impact Analysis: Control of Emissions from Marine SI and Small SI Engines, Vessels, 

and Equipment , U.S. Environmental Protection Agency, Office of Transportation and Air Quality, Office 

of Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI, EPA420-D-07-

004, April 2007. 

9

U.S. Environmental Protection Agency, Draft Regulatory Impact Analysis: Control of Emissions of Air 

Pollution from Locomotive Engines and Marine Compression-Ignition Engines Less than 30 Liters per 

Cylinder, EPA420-D-07-001, January 2007. 

10 See http://www.epa.gov/ttn/chief/software/speciate/index.html for more details. 
7

include an average fire sector and temporally averaged emissions (i.e., no CEM data) for 

EGUs. Projections from 2002 were developed to account for the expected impact of 

national regulations, consent decrees or settlements, known plant closures, and, for some 

sectors, activity growth. For 2030, stationary sources used 2020 projections (i.e., no 

activity growth between 2020 and 2030). For the locomotive and marine sectors, the 

baseline emissions have not changed from the proposal modeling. Percent reductions 

were applied to the 2020 and 2030 baseline emissions to reflect the impacts of the final 

Locomotive/Marine rulemaking as shown in Tables II-2a and II-2b. The first five source 

sectors are locomotive sectors and the last three are marine sectors. 

Table II-2a. Percentage reductions applied to locomotive/marine sectors in 2020 to 

reflect the impacts of the final rule. 

Pollutant VOC NOX PM10 PM2.5 SO2

Class I Railroads 49% 19% 41% 41% 0% 

Class II/III Railroads 0% 0% 0% 0% 0% 

Commuter Railroads 51% 19% 43% 43% 0% 

Passenger Railroads 51% 19% 43% 43% 0% 

Switch Railroads 19% 8% 21% 21% 0% 

Commercial Marine Vessels 28% 26% 29% 29% 5% 

Pleasure Craft: Inboard 7% 5% 3% 3% 0% 

Pleasure Craft: Outboard 13% 13% 8% 8% 0% 

Table II-2b. Percentage reductions applied to locomotive/marine sectors in 2030 to 

reflect the impacts of the final rule. 

Pollutant VOC NOX PM10 PM2.5 SO2

Class I Railroads 69% 52% 63% 63% 0% 

Class II/III Railroads 0% 0% 0% 0% 0% 

Commuter Railroads 72% 53% 66% 66% 0% 

Passenger Railroads 72% 53% 66% 66% 0% 

Switch Railroads 35% 23% 38% 38% 0% 

Commercial Marine Vessels 60% 54% 58% 58% 16% 

Pleasure Craft: Inboard 14% 11% 8% 8% 0% 

Pleasure Craft: Outboard 23% 29% 17% 17% 0% 

2. Meteorological Input Data: The gridded meteorological input data for the 

entire year of 2002 were derived from simulations of the Pennsylvania State University / 

National Center for Atmospheric Research Mesoscale Model. This model, commonly 

referred to as MM5, is a limited-area, nonhydrostatic, terrain-following system that 

solves for the full set of physical and thermodynamic equations which govern 

atmospheric motions.11 Meteorological model input fields were prepared separately for 

each of the domains shown in Figure II-1. The MM5 simulations were run on the same 

 

11 Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Penn State/NCAR 

Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, 

Boulder CO. 
8

map projection as CMAQ. The 36 km national domain was modeled using MM5 v.3.6.0 

using land-surface modifications that were added in v3.6.3. The 12 km eastern U.S grid 

was modeled with MM5 v3.7.2. These two sets of meteorological inputs were developed 

by EPA. For the 12 km western U.S. domain, we utilized existing MM5 meteorological 

model data prepared by the Western Regional Air Partnership (WRAP).12

 

The three meteorological model runs used similar sets of physics options. All 

three simulations used the Pleim-Xiu planetary boundary layer and vertical diffusion 

scheme, the RRTM longwave radiation scheme, and the Reisner 1 microphysics scheme. 

The EPA simulations used the Kain-Fritsch 2 subgrid convection scheme while the 

WRAP simulation used the Betts-Miller scheme for subgrid convection. In the EPA 

simulations, analysis nudging was utilized above the boundary layer for temperature and 

water vapor, and in all locations for the wind components using relatively weak nudging 

coefficients. The WRAP runs employed similar four-dimensional data assimilation, but 

also included observational nudging of surface winds. All three sets of model runs were 

conducted in 5.5 day segments with 12 hours of overlap for spin-up purposes. All three 

domains contained 34 vertical layers with an approximately 38 m deep surface layer and 

a 100 millibar top. The MM5 and CMAQ vertical structures are shown in Table II-3 and 

do not vary by horizontal grid resolution. 

Table II-3. Vertical layer structure for MM5 and CMAQ (heights are layer top). 

CMAQ Layers MM5 Layers Sigma P Approximate 

Height (m) 

Approximate 

Pressure (mb) 

0 0 1.000 0 1000 

1 1 0.995 38 995 

2 2 0.990 77 991 

3 0.985 115 987 3 4 0.980 154 982 

5 0.970 232 973 4 6 0.960 310 964 

7 0.950 389 955 5 8 0.940 469 946 

9 0.930 550 937 

6 10 0.920 631 928 

11 0.910 712 919 

12 0.900 794 910 

7 13 0.880 961 892 

14 0.860 1,130 874 

15 0.840 1,303 856 

8 16 0.820 1,478 838 

17 0.800 1,657 820 

18 0.770 1,930 793 9 19 0.740 2,212 766 

 

12 Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen. 2004. 2002 Annual MM5 

Simulation to Support WRAP CMAQ Visibility Modeling for the Section 308 SIP/TIP – MM5 Sensitivity 

Simulations to Identify a More Optimal MM5 Configuration for Simulating Meteorology in the Western 

United States. Western Regional Air Partnership, Regional Modeling Center. December 10. 

(http://pah.cert.ucr.edu/aqm/308/reports/mm5/MM5SensitivityRevRep_Dec_10_2004.pdf) 
9

20 0.700 2,600 730 10 21 0.650 3,108 685 

22 0.600 3,644 640 11 23 0.550 4,212 595 

24 0.500 4,816 550 

12 25 0.450 5,461 505 

26 0.400 6,153 460 

27 0.350 6,903 415 

28 0.300 7,720 370 

29 0.250 8,621 325 13 

30 0.200 9,625 280 

31 0.150 10,764 235 

32 0.100 12,085 190 

33 0.050 13,670 145 14 

34 0.000 15,674 100 

The meteorological outputs from all three MM5 sets were processed to create 

model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor 

(MCIP), version 3.1, to derive the specific inputs to CMAQ.13

Before initiating the air quality simulations, it is important to identify the biases 

and errors associated with the meteorological modeling inputs. The 2002 MM5 model 

performance evaluations used an approach which included a combination of qualitative 

and quantitative analyses to assess the adequacy of the MM5 simulated fields. The 

qualitative aspects involved comparisons of the model-estimated synoptic patterns 

against observed patterns from historical weather chart archives. Qualitatively, the model 

fields closely matched the observed synoptic patterns, which is expected given the use of 

nudging. The operational evaluation included statistical comparisons of model/observed 

pairs (e.g., mean normalized bias, mean normalized error, index of agreement, root mean 

square errors, etc.) for multiple meteorological parameters. For this portion of the 

evaluation, four meteorological parameters were investigated: temperature, humidity, 

wind speed, and wind direction. The operational piece of the analyses focuses on surface 

parameters. The Atmospheric Model Evaluation Tool (AMET) was used to conduct the 

analyses as described in this report.14 The three individual MM5 evaluations are 

described elsewhere.15,16,17 It was ultimately determined that the bias and error values 

 

13 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). 

14 Gilliam, R. C., W. Appel, and S. Phillips. The Atmospheric Model Evaluation Tool (AMET): 

Meteorology Module. Presented at 4th Annual CMAS Models-3 Users Conference, Chapel Hill, NC, 

September 26 - 28, 2005. 

15 Brewer J., P. Dolwick, and R. Gilliam. Regional and Local Scale Evaluation of MM5 Meteorological 

Fields for Various Air Quality Modeling Applications, Presented at the 87th Annual American 

Meteorological Society Annual Meeting, San Antonio, TX, January 15-18, 2007. 

16 Dolwick, P, R. Gilliam, L. Reynolds, and A. Huffman. Regional and Local-scale Evaluation of 2002 

MM5 Meteorological Fields for Various Air Quality Modeling Applications, Presented at 6th Annual 

CMAS Models-3 Users Conference, Chapel Hill, NC, October 1 - 3, 2007. 
10

associated with all three sets of 2002 meteorological data were generally within the range 

of past meteorological modeling results that have been used for air quality applications.18

3. Initial and Boundary Conditions: The lateral boundary and initial species 

concentrations are provided by a three-dimensional global atmospheric chemistry model, 

the GEOS-CHEM model.19 The global GEOS-CHEM model simulates atmospheric

chemical and physical processes driven by assimilated meteorological observations from 

the NASA’s Goddard Earth Observing System (GEOS). This model was run for 2002 

with a grid resolution of 2.0 degree x 2.5 degree (latitude-longitude) and 20 vertical 

layers. The predictions were used to provide one-way dynamic boundary conditions at 

three-hour intervals and an initial concentration field for the CMAQ simulations. More 

information is available about the GEOS-CHEM model and other applications using this 

tool at: http://www-as.harvard.edu/chemistry/trop/geos. 

E. CMAQ Base Case Model Performance Evaluation 

 An operational model performance evaluation for ozone and PM2.5 and its related 

speciated components was conducted using 2002 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 eastern and western domains. In summary, model 

performance statistics were calculated for observed-predicted pairs of daily, monthly, 

seasonal, and annual concentrations. Statistics were generated for the following 

geographic groupings: the entire 12-km Eastern US domain (EUS), the entire 12-km 

Western US domain (WUS), and five large subregions: Midwest, Northeast, Southeast, 

Central, and West U.S.20 The “acceptability” of model performance was judged by 

comparing our CMAQ 2002 performance results to the range of performance found in the 

2001 CMAQ results used in the proposal, as well as recent regional ozone and PM2.5

model applications (e.g., Clean Air Interstate Rule, Final PM NAAQS Rule).21 These 

 

17 Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang, and G. Tonnesen. Annual 2002 MM5 

Meteorological Modeling to Support Regional Haze Modeling of the Western United States, 

Prepared for The Western Regional Air Partnership (WRAP), 1515 Cleveland Place, Suite 200 

Denver, CO 80202, March 2005. 

18 Environ, Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Episodes, 

August 2001. 

19 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling 

Group, Harvard University, Cambridge, MA, October 15, 2004. 

20 The subregions are defined by States where: Midwest is IL, IN, MI, OH, and WI; Northeast is CT, DE, 

MA, MD, ME, NH, NJ, NY, PA, RI, and VT; Southeast is AL, FL, GA, KY, MS, NC, SC, TN, VA, and 

WV; Central is AR, IA, KS, LA, MN, MO, NE, OK, and TX; West is CA, OR, WA, AZ, NM, CO, UT, 

WY, SD, ND, MT, ID, and NV. 

21 See: 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); and U.S. Environmental Protection Agency, 2006. Technical 

Support Document for the Final PM NAAQS Rule: Office of Air Quality Planning and Standards, Research 

Triangle Park, NC 
11

other modeling studies represent a wide range of modeling analyses which cover various 

models, model configurations, domains, years and/or episodes, chemical mechanisms, 

and aerosol modules. 

 There are various statistical metrics available and used by the scientific 

community for model performance evaluation. The principal evaluation statistics used to 

evaluate CMAQ performance were two bias metrics, normalized mean bias and fractional 

bias; and two error metrics, normalized mean error and fractional error. Normalized 

mean bias (NMB) is used as a normalization to facilitate a range of concentration 

magnitudes. This statistic averages the difference (model - observed) over the sum of 

observed values. NMB is a useful model performance indicator because it avoids over 

inflating the observed range of values, especially at low concentrations. Normalized 

mean bias is defined as:

NMB = 

( )

( )

P O

O

n

n

∑ −

∑

1

1

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

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 (model - observed) over the sum of observed values. Normalized mean error 

is defined as: 

NME = 

( )

P O

O

n

n

∑ −

∑

1

1

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

Fractional bias is defined as: 

FB = 

( )

( )

1

2

1

1

n

P O

P O

n

n

−

 +



































∑

∑

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

FB is a useful model performance indicator because it has the advantage of equally 

weighting positive and negative bias estimates. The single largest disadvantage in this 

estimate of model performance is that the estimated concentration (i.e., prediction, P) is 

found in both the numerator and denominator. 

 
12

Fractional error (FE) is similar to fractional bias except the absolute value of the 

difference is used so that the error is always positive. Fractional error is defined as: 

FE = ( )

1

2

1

1

n

P O

P O

n

n

−

 +



































∑

∑

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

 Overall, the bias and error statistics shown in Table II-4 below indicate that the 

base case model ozone and PM2.5 concentrations are within the range or close to that 

found in recent OAQPS applications. The CMAQ model performance results give us 

confidence that our applications of CMAQ using this 2002 modeling platform provide a 

scientifically credible approach for assessing ozone and PM2.5 concentrations for the 

purposes of the final Locomotive/Marine rule. A detailed summary of the CMAQ model 

performance evaluation is available in the docket for this rulemaking.22 A summary of 

the PM2.5 and ozone evaluation is presented here. 

1. PM2.5: The PM2.5 evaluation focuses on PM2.5 total mass and its components 

including sulfate (SO4), nitrate (NO3), total nitrate (TNO3=NO3+HNO3), ammonium 

(NH4), elemental carbon (EC), and organic carbon (OC). The PM2.5 performance 

statistics were calculated for each month and season individually and for the entire year, 

as a whole. Seasons were defined as: winter (December-January-February), spring 

(March-April-May), summer (June-July-August), and fall (September-OctoberNovember). PM2.5 ambient measurements for 2002 were obtained from the following 

networks for model evaluation: Speciation Trends Network (STN- total of 199 sites), 

Interagency Monitoring of PROtected Visual Environments (IMPROVE- total of 150), 

and Clean Air Status and Trends Network (CASTNet- total of 83). For PM2.5 species 

that are measured by more than one network, we calculated separate sets of statistics for 

each network. For brevity, Table II-4 provides annual model performance statistics for 

PM2.5 and its component species for the 12-km Eastern domain, 12-km Western domain, 

and five subregions defined above (Midwest, Northeast, Southeast, Central, and West 

U.S.). 

Table II-4. Summary of 2002 CMAQ annual PM2.5 species model performance 

statistics. 

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

PM2.5 12-km EUS 10307 10.8 42.8 5.4 42.6 

Total Mass 

STN 

12-km WUS 3000 -5.8 46.9 -3.1 45.0 

 

22 Technical Support Document: 2002 CMAQ Model Performance Evaluation for Ozone and Particulate 

Matter, January 2008. This file is available in the docket for this rulemaking. 
13

Northeast 1516 14.9 35.6 13.2 34.4 

Midwest 2780 20.5 48.2 16.6 42.6 

Southeast 2554 -3.9 36.0 -10.0 39.7 

Central 2738 14.5 49.1 6.0 49.4 

West 2487 -7.4 46.8 -4.5 44.8 

12-km EUS 8436 -2.3 49.0 -5.7 51.4 

12-km WUS 10123 -26.4 53.5 -26.3 57.5 

Northeast 592 8.6 41.5 2.4 41.0 

Midwest 2060 21.0 59.4 17.4 51.6 

Southeast 1803 -13.1 41.2 -19.8 49.9 

Central 1624 -13.1 49.4 -17.6 57.0 

IMPROVE 

West 9543 -27.8 53.1 -27.1 57.2 

12-km EUS 10157 -3.9 33.6 -9.7 38.4 

12-km WUS 2926 -20.6 41.9 -12.2 43.5 

Northeast 1487 3.6 34.9 -2.9 36.2 

Midwest 2730 -4.3 29.1 -8.8 33.6 

Southeast 2541 -7.6 33.4 -16.3 38.8 

Central 2686 -3.2 39.2 -7.2 44.3 

STN 

West 2446 -26.1 44.9 -15.8 44.8 

12-km EUS 8532 -10.8 33.0 -7.2 40.6 

12-km WUS 10232 -7.5 42.4 7.6 45.7 

Northeast 597 -4.9 29.9 -10.0 35.7 

Midwest 2070 -12.3 30.1 -9.9 36.1 

Southeast 1805 -9.5 32.9 -16.8 40.5 

Central 1671 -16.1 35.0 -16.0 42.4 

IMPROVE 

West 9645 -5.5 43.5 8.6 45.9 

12-km EUS 3173 -11.3 20.5 -16.3 26.1 

12-km WUS 1158 -21.3 34.6 -11.2 35.9 

Northeast 663 -8.3 19.3 -16.3 24.3 

Midwest 839 -12.3 17.9 -15.6 21.6 

Southeast 1085 -11.2 21.5 -17.8 27.2 

Central 229 -20.7 27.3 -27.4 33.6 

Sulfate 

CASTNet 

West 1118 -20.4 35.3 -10.7 36.1 

12-km EUS 8770 18.3 65.9 -29.1 84.5 

12-km WUS 2726 -45.0 63.1 -70.6 95.0 

Northeast 1488 17.4 59.1 -5.0 67.3 

Midwest 2731 32.7 70.4 -10.9 78.1 

Southeast 2540 8.6 84.6 -64.7 107.5 

Central 1298 12.7 52.5 -13.4 69.1 

STN 

West 2446 -47.5 62.8 -73.8 95.4 

12-km EUS 8514 48.4 106.8 -52.8 116.4 

Nitrate 

IMPROVE 

12-km WUS 10110 -34.8 80.67 -101.0 130.0 
14

Northeast 597 43.0 86.0 -37.0 102.8 

Midwest 2069 122.2 153.8 3.5 107.5 

Southeast 1803 33.5 112.2 -78.5 130.8 

Central 1672 18.1 81.0 -59.6 114.1 

West 9522 -39.6 81.1 -104.0 131.1 

12-km EUS 3171 24.4 37.3 16.8 35.1 

12-km WUS 1157 -19.5 44.2 -12.0 46.0 

Northeast 662 20.5 29.4 16.3 25.3 

Midwest 839 39.1 46.5 29.0 39.7 

Southeast 1085 22.9 39.5 15.8 37.2 

Central 229 6.2 35.6 0.6 36.2 

Total Nitrate 

(NO3+HNO3) CASTNet 

West 1117 -20.4 45.8 -12.1 46.6 

12-km EUS 10157 11.9 40.6 14.4 45.2 

12-km WUS 2926 -23.6 55.7 7.2 58.1 

Northeast 1488 16.0 39.6 21.8 42.8 

Midwest 2731 12.3 38.4 19.2 42.4 

Southeast 2540 7.3 38.4 6.0 41.8 

Central 2685 15.0 46.6 14.3 52.1 

STN 

West 2446 -30.6 56.7 2.9 59.7 

12-km EUS 3166 5.3 30.8 2.7 31.6 

12-km WUS 1156 -16.8 42.5 -13.0 41.1 

Northeast 661 15.3 27.6 13.6 25.2 

Midwest 837 9.8 34.7 11.9 33.9 

Southeast 1085 -7.7 30.1 -9.7 33.6 

Central 229 7.4 33.1 3.0 35.6 

Ammonium 

CASTNet 

West 1116 -21.1 43.5 -14.4 41.4 

12-km EUS 10031 45.0 78.9 22.1 56.9 

12-km WUS 2975 43.1 82.6 18.2 61.3 

Northeast 1498 37.1 58.9 24.5 48.3 

Midwest 2744 53.1 76.7 26.3 54.7 

Southeast 2506 16.9 66.0 7.2 51.7 

Central 2570 91.7 118.0 41.0 68.1 

STN 

West 2475 49.0 86.2 17.1 62.7 

12-km EUS 8282 -15.0 49.2 -23.4 52.8 

12-km WUS 10069 -14.1 67.2 -29.5 62.1 

Northeast 599 -22.6 37.5 -27.4 46.5 

Midwest 2056 11.6 57.5 0.5 50.8 

Southeast 1795 -32.4 44.6 -42.0 55.6 

Central 1532 -24.3 47.6 -29.8 55.9 

Elemental 

Carbon 

IMPROVE 

West 9493 -15.5 67.8 -31.3 62.7 

Organic 12-km EUS 9726 -39.9 58.0 -41.1 70.5 

Carbon 

STN 

12-km WUS 2903 -37.6 60.3 -40.4 69.3 
15

Northeast 1447 -45.2 60.9 -41.6 73.1 

Midwest 2641 -26.5 61.7 -19.7 67.6 

Southeast 2474 -47.4 55.3 -53.7 70.7 

Central 2504 -43.6 54.0 -51.3 69.7 

West 2408 -36.3 61.4 -37.9 70.2 

12-km EUS 8287 -32.4 60.5 -37.1 67.9 

12-km WUS 10082 -34.8 60.0 -31.2 63.0 

Northeast 598 -42.4 54.8 -40.2 63.8 

Midwest 2057 -6.4 68.2 -0.7 60.8 

Southeast 1800 -46.1 58.4 -69.7 81.3 

Central 1531 -47.9 61.6 -61.2 79.6 

IMPROVE 

West 9508 -34.5 59.6 -29.7 61.9 

2. Ozone: The ozone evaluation focuses on the observed and predicted hourly 

ozone concentrations and eight-hour daily maximum ozone concentrations using a 

(observation) threshold of 40 ppb. This ozone model performance was limited to the 

period used in the calculation of projected design values within the analysis, that is: May, 

June, July, August, and September. Ozone ambient measurements for 2002 were 

obtained from the Air Quality System (AQS) Aerometric Information Retrieval System 

(AIRS). A total of 1178 ozone measurement sites were included for evaluation. These 

ozone data were measured and reported on an hourly basis. 

Table II-5 and II-6 provide hourly and eight-hour daily maximum ozone model 

performance statistics, respectively, for the 12-km Eastern and Western U.S. domain and 

the five subregions. Generally, hourly and eight-hour ozone model performance are 

under-predicted in both the 12-km EUS and WUS when applying a threshold of 40 ppb 

for the modeled ozone season (May-September). For the 12-km EUS and WUS domain, 

the bias and error statistics are comparable for the aggregate of the ozone season and for 

each individual ozone month modeled. 

Table II-5. Summary of CMAQ 2002 hourly ozone model performance statistics 

CMAQ 2002 Hourly Ozone: 

Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 241185 -0.7 15.9 -2.0 17.1 

12-km WUS 124931 -3.7 15.9 -5.0 17.3 

Northeast 51055 1.2 17.1 -0.3 18.2 

Midwest 55859 3.3 16.2 2.4 16.9 

Southeast 69073 -2.5 14.1 -3.1 14.8 

Central 41728 -6.4 17.3 -9.2 20.3 

May 

West 111385 -3.9 16.1 -5.2 17.6 

12-km EUS 256263 -7.5 16.8 -9.0 18.6 

12-km WUS 125662 -8.4 17.7 -9.3 19.1 

June 

Northeast 61354 -8.5 17.3 -9.9 19.1 
16

Midwest 54515 -7.2 17.9 -8.3 19.6 

Southeast 67867 -7.2 15.3 -7.6 16.3 

Central 46026 -10.0 17.5 -13.5 21.2 

West 109157 -8.8 18.2 -9.9 19.7 

12-km EUS 257076 -5.3 17.7 -6.6 19.2 

12-km WUS 116785 -12.0 21.5 -14.9 24.3 

Northeast 66774 -3.9 17.0 -4.8 18.0 

Midwest 59360 -10.5 19.4 -12.3 21.7 

Southeast 68619 -3.6 16.5 -3.9 17.2 

Central 36021 -3.6 18.7 -6.3 21.1 

July 

West 104321 -13.6 21.8 -16.8 24.9 

12-km EUS 235090 -8.7 17.8 -10.2 19.7 

12-km WUS 125575 -7.9 20.1 -10.2 22.1 

Northeast 53837 -6.4 16.7 -7.4 18.0 

Midwest 54179 -10.8 19.1 -12.4 21.4 

Southeast 62506 -9.4 17.3 -9.9 18.5 

Central 41456 -9.3 18.7 -12.8 22.4 

August 

West 110225 -8.5 20.6 -11.1 22.8 

12-km EUS 179156 -9.9 17.2 -11.8 19.5 

12-km WUS 99710 -10.7 19.0 -12.7 21.1 

Northeast 44678 -8.7 16.3 -10.6 18.4 

Midwest 34285 -11.4 18.5 -12.9 20.4 

Southeast 41627 -8.2 16.5 -9.0 17.8 

Central 41549 -12.8 18.8 -16.6 22.8 

September 

West 83921 -11.7 20.0 -13.8 22.1 

12-km EUS 1168770 -6.4 17.1 -7.7 18.8 

12-km WUS 592663 -8.4 18.8 -10.3 20.7 

Northeast 277698 -5.4 16.9 -6.5 18.4 

Midwest 258198 -7.3 18.3 -8.4 20.0 

Southeast 309692 -6.0 15.9 -6.4 16.8 

Central U 206780 -8.6 18.2 -11.9 21.6 

Seasonal Aggregate 

West 519009 -9.2 19.3 -11.2 21.3 

Table II-6. Summary of CMAQ 2002 eight-hour daily maximum ozone model 

performance statistics. 

CMAQ 2002 Eight-Hour Maximum Ozone: 

Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 19172 3.9 12.7 4.3 12.6 

12-km WUS 9223 0.2 12.6 0.6 12.8 

Northeast 4255 6.7 14.3 6.8 14.2 

Midwest 4198 7.8 13.7 8.2 13.5 

May 

Southeast 5470 0.6 10.9 1.1 11.0 
17

Central 3379 0.3 12.3 0.7 12.4 

West 8155 -0.1 12.8 0.3 12.9 

12-km EUS 19462 -3.9 12.3 -3.3 12.4 

12-km WUS 9029 -4.9 14.1 -4.2 14.2 

Northeast 4608 -5.3 12.5 -4.7 12.7 

Midwest 4104 -3.2 12.7 -2.2 12.8 

Southeast 5110 -4.8 11.8 -4.1 11.9 

Central 3603 -4.5 12.2 -4.4 12.7 

June 

West 7818 -5.3 14.5 -4.7 14.7 

12-km EUS 20565 -1.6 13.5 -1.0 13.6 

12-km WUS 8809 -7.4 17.1 -8.1 18.0 

Northeast 5380 -0.7 13.0 -0.2 12.9 

Midwest 4368 -6.5 14.2 -5.8 14.4 

Southeast 5633 -0.9 13.0 -0.1 13.0 

Central 3114 1.3 14.4 1.2 14.7 

July 

West 7784 -9.0 17.2 -9.9 18.2 

12-km EUS 19260 -5.1 13.2 -4.4 13.4 

12-km WUS 9551 -2.8 15.8 -3.1 16.1 

Northeast 4667 -2.9 12.4 -2.2 12.4 

Midwest 4012 -8.1 13.9 -7.5 14.2 

Southeast 5067 -6.4 13.4 -5.4 13.4 

Central 3543 -4.0 13.5 -3.9 14.1 

August 

West 8311 -3.2 16.1 -3.6 16.5 

12-km EUS 15865 -6.2 12.6 -5.9 12.9 

12-km WUS 8185 -6.7 15.0 -6.9 15.5 

Northeast 4074 -6.0 11.8 -6.0 12.3 

Midwest 3120 -7.2 13.3 -6.5 13.3 

Southeast 3671 -4.5 12.6 -3.8 12.7 

Central 3492 -8.5 13.8 -8.7 14.5 

September 

West 6911 -7.3 15.9 -7.6 16.4 

12-km EUS 94324 -2.6 12.9 -1.9 13.0 

12-km WUS 44797 -4.3 14.9 -4.2 15.3 

Northeast 22984 -1.9 12.8 -1.2 12.9 

Midwest 19802 -3.6 13.6 -2.5 13.7 

Southeast 24951 -3.1 12.4 -2.3 12.4 

Central 17131 -3.3 13.2 -3.1 13.7 

Seasonal Aggregate 

West 38979 -4.9 15.3 -5.0 15.7 

F. CMAQ Locomotive/Marine Modeling Scenarios 

The CMAQ modeling system was used to calculate annual PM2.5 concentrations, 

daily 8-hour ozone concentrations, and visibility estimates for each of the following 

seven emissions scenarios: 
18

1) 2002 base case 

2) 2020 future baseline 

3) 2020 future control case – locomotive and marine controls 

4) 2020 future control case – marine controls only 

5) 2030 future baseline 

6) 2030 future control case – locomotive and marine controls 

7) 2030 future control case – marine controls only 

Model predictions are used in a relative sense to estimate scenario-specific, 

future-year design values of PM2.5 and ozone. This is done by calculating the simulated 

air quality ratios between any particular future year simulation and the 2002 base. These 

predicted change ratios are then applied to ambient base year design values. The design 

value projection methodology used in this analysis followed EPA guidance for such 

analyses23 We used the 5-year weighted average 2000-2004 design values as the starting 

point for the projections. Additionally, the raw model outputs are also used in a relative 

sense for creating inputs to the health and welfare impact functions of the benefits 

analysis. 

III. CMAQ Model Results 

A. Impacts of Locomotive/Marine Rule on Future PM2.5 Annual Averages 

 

This section summarizes the results of our modeling of PM2.5 air quality impacts 

in the future due to the reductions in locomotive and commercial marine diesel emissions. 

Appendix A contains annual average PM2.5 design values by county for each modeling 

scenario. The modeling results indicate that the emissions reductions from this rule will 

contribute to lower ambient PM2.5 levels in future years. Tables III-1 and III-2 show the 

projected improvements in average annual PM2.5 design values, for various years as a 

result of the Locomotive/Marine control scenarios discussed in Section II.F.

 

23 U.S. EPA, Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-

hour Ozone NAAQS; EPA-454/R-05-002; Research Triangle Park, NC; October 2005. 
19

Table III-1. Model-projected change in annual average PM2.5 design values 

resulting from the Locomotive/Marine modeling scenarios for several categories of 

counties. Units are μg/m3

. 

Average change 

2020 

marine 

controls 

only 

2020 

locomotive 

and 

marine 

controls 

2030 

marine 

controls 

only 

2030 

locomotive 

and 

marine 

controls 

Average change in all counties -0.02 -0.04 -0.04 -0.08 

Average change in counties whose base year 

design value is above the NAAQS -0.03 -0.06 -0.05 -0.11 

Average change in counties whose base year 

design value is within 10% of the NAAQS -0.03 -0.06 -0.06 -0.12 

Average change in counties whose projection year 

design value is above the NAAQS -0.06 -0.10 -0.14 -0.22 

Average change in counties whose projection year 

design value is within 10% of the NAAQS -0.04 -0.09 -0.09 -0.18 

Table III-2. Model-projected, population-weighted, change in annual average PM2.5 

design values resulting from the Locomotive/Marine modeling scenarios for several 

categories of counties. Units are μg/m3. 

Average change 

2020 

marine 

controls 

only 

2020 

locomotive 

and 

marine 

controls 

2030 

marine 

controls 

only 

2030 

locomotive 

and 

marine 

controls 

Average change in all counties -0.03 -0.06 -0.08 -0.12 

Average change in counties whose base year 

design value is above the NAAQS -0.05 -0.08 -0.11 -0.16 

Average change in counties whose base year 

design value is within 10% of the NAAQS -0.04 -0.08 -0.10 -0.16 

Average change in counties whose projection year 

design value is above the NAAQS -0.07 -0.11 -0.16 -0.21 

Average change in counties whose projection year 

design value is within 10% of the NAAQS -0.05 -0.09 -0.12 -0.18 

The modeling projects that 11 counties will have design values greater than 15.0 

μg/m3

 in 2020 and 2030. Over 24 million people are projected to live in a PM2.5

nonattainment county in the future. The controls from the final Locomotive/Marine rule 

modeling are enough to bring one of those counties (Merced Co., CA) into attainment by 

2030. As can be seen from Table III-1, the final Locomotive/Marine rule controls will 

lower PM2.5 concentrations on average by 0.04 μg/m3 in 2020 and 0.08 μg/m3 in 2030. 

Greater improvements in PM2.5 air quality are projected in areas where present-day and 

future-projected PM2.5 levels are above or near the NAAQS. For instance, when 

considering only those counties whose future year design values are projected to exceed 

the NAAQS, the average improvement resulting from this rule is 0.10 μg/m3 in 2020 and 

0.22 μg/m3 in 2030. Additionally, as shown in Table III-2, the improvements resulting 

from the rule are larger when population-weighted. The greatest impacts from the final 
20

Locomotive/Marine rule emissions reductions tend to occur in areas with high 

populations. The largest reduction in annual average PM2.5 occurs in Houston TX (Harris 

Co.) where the rule is projected to result in a 0.38 μg/m3

 improvement in 2020 and 0.81 

μg/m3 in 2030. The largest reduction in an area that is projected to exceed the PM2.5

NAAQS in 2020 and 2030 is in the Los Angeles region (Riverside Co.) where the annual 

average PM2.5 design value is projected to drop from 22.67 to 22.48 μg/m3 in 2020, and 

22.54 to 22.13 μg/m3 in 2030 as a result of the final Locomotive/Marine rule. The 

modeling indicated that both the locomotive and marine components of the rule improved 

PM2.5 air quality relatively equally. 

Figures III-1 through III-4 display the projected county-level, annual PM2.5 design 

value changes expected from various locomotive/marine control scenarios and years 

associated with this rule. The largest impacts tend to be in areas near water, where 

commercial marine source contributions can be large. 
21

Figure III-1. Model-projected change in annual PM2.5 design values from the 

Locomotive/Marine control scenario in 2020. Units are μg/m3

. 

Number of Counties

0

4

43

142

367

0 Differences due to scenario 2020cc Locomarine

Legend

-1.00 to -0.50

-0.49 to -0.25

-0.24 to -0.10

-0.09 to -0.05

-0.04 to 0.00

> 0.00

Figure III-2. Model-projected change in annual PM2.5 design values from the 

Marine-only control scenario in 2020. Units are μg/m3. 

Number of Counties

0

3

19

37

496

1 Differences due to scenario 2020cc Marine-only

Legend

-1.00 to -0.50

-0.49 to -0.25

-0.24 to -0.10

-0.09 to -0.05

-0.04 to 0.00

> 0.00
22

Figure III-3. Model-projected change in annual PM2.5 design values from the 

Locomotive/Marine control scenario in 2030. Units are μg/m3

. 

Number of Counties

4

20

143

193

196

0 Differences due to scenario 2030cc Locomarine

Legend

-1.00 to -0.50

-0.49 to -0.25

-0.24 to -0.10

-0.09 to -0.05

-0.04 to 0.00

> 0.00

Figure III-4. Model-projected change in annual PM2.5 design values from the 

Marine-only control scenario in 2030. Units are μg/m3. 

Legend

-1.00 to -0.50

-0.49 to -0.25

-0.24 to -0.10

-0.09 to -0.05

-0.04 to 0.00

> 0.00

Number of Counties

4

11

46

46

449

0 Differences due to scenario 2030cc Marine-only
23

B. Impacts of the Locomotive/Marine Rule on Future 8-Hour Ozone Levels 

This section summarizes the results of our modeling of ozone air quality impacts 

in the future due to the reductions in locomotive and commercial marine diesel emissions. 

Appendix B contains 8-hour ozone design values by county for each modeling scenario. 

The modeling results indicate that the emissions reductions from this rule will contribute 

to lower ambient 8-hour ozone design values in future years. Tables III-3 and III-4 show 

the projected improvements in average 8-hour ozone design values, for various years as a 

result of the four Locomotive/Marine control scenarios. 

Table III-3. Model-projected change in average 8-hour ozone design values 

resulting from the Locomotive/Marine modeling scenarios for several categories of 

counties. Units are ppb. 

Average change 

2020 

marine 

controls 

only 

2020 

locomotive 

and 

marine 

controls 

2030 

marine 

controls 

only 

2030 

locomotive 

and 

marine 

controls 

Average change in all counties -0.23 -0.45 -0.51 -1.15 

Average change in counties whose base year 

design value is above the NAAQS -0.22 -0.45 -0.51 -1.18 

Average change in counties whose base year 

design value is within 10% of the NAAQS -0.23 -0.46 -0.53 -1.18 

Average change in counties whose projection year 

design value is above the NAAQS 0.00 -0.19 0.00 -0.50 

Average change in counties whose projection year 

design value is within 10% of the NAAQS -0.20 -0.41 -0.65 -1.24 

Table III-4. Model-projected, population-weighted, change in average 8-hour ozone 

design values resulting from the Locomotive/Marine modeling scenarios for several 

categories of counties. Units are ppb. 

Average change 

2020 

marine 

controls 

only 

2020 

locomotive 

and 

marine 

controls 

2030 

marine 

controls 

only 

2030 

locomotive 

and 

marine 

controls 

Average change in all counties -0.12 -0.30 -0.32 -0.85 

Average change in counties whose base year 

design value is above the NAAQS -0.09 -0.27 -0.26 -0.78 

Average change in counties whose base year 

design value is within 10% of the NAAQS -0.12 -0.31 -0.32 -0.86 

Average change in counties whose projection year 

design value is above the NAAQS -0.01 -0.13 -0.24 -0.62 

Average change in counties whose projection year 

design value is within 10% of the NAAQS +0.05 -0.08 -0.42 -0.79 
24

The modeling projects that 9 counties will have design values greater than 0.08 

ppm in 2020 and 6 counties will exceed that level in 2030. Based on this modeling, over 

22 million people are projected to live in a ozone nonattainment county in 2020. The 

controls from the final Locomotive/Marine rule modeling are enough to bring one of 

those counties (Kenosha Co., WI) into attainment by 2020. Further, two of the nine 

counties will be at least 10 percent closer to a design value of less than 85 ppb, and on 

average all nine counties will be about 18 percent closer to a design value of less than 85 

ppb (including Kenosha Co., WI). 

As can be seen from Table III-3, the final Locomotive/Marine rule controls will 

lower ozone design values on average by 0.30 ppb in 2020 and 0.85 ppb in 2030. Unlike 

PM2.5, there are instances in which the final Locomotive/Marine rule controls are 

projected to increase ozone levels. As a result of where these "disbenefit" areas are 

located, the improvements resulting from the rule are smaller when population-weighted. 

The largest increase in a county-level 8-hour ozone design values occurs in the Los 

Angeles area (Orange Co.) where the rule is projected to result in an increase of 2.6 ppb 

in 2020 and 5.5 ppb in 2030. This increase is highly localized. The county with the 

second largest increase from the rule is Riverside Co. CA which is projected to have an 

0.5 ppb increase from the rule in 2030. The largest county-level decrease resulting from 

the rule is north of the Los Angeles area (Santa Barbara Co.) where ozone levels are 

projected to drop by 1.8 and 4.6 ppb, respectively, in 2020 and 2030. Again, the 

modeling indicated that both the locomotive and marine components of the rule improved 

air quality relatively equally, but it was the marine reductions that tended to lead to the 

"disbenefit" regions. 

Figures III-5 through III-8 display the projected county-level, 8-hour ozone 

design value changes expected from the final Locomotive/Marine rule control scenarios. 

The largest impacts tend to be in areas near water, where commercial marine source 

contributions can be large. 
25

Figure III-5. Model-projected change in annual 8-hour ozone design values from 

the Locomotive/Marine control scenario in 2020. Units are ppb. 

Number of Counties

0

14

241

315

5

3

Effect of Locomarine in 2020 1

Legend

<= -2.0

> -2.0 to <= -1.0

> -1.0 to <= -0.5

> -0.5 to <= -0.1

= 0.0

> 0.0 to <= 1.0

> 1.0

Figure III-6. Model-projected change in annual 8-hour ozone design values from 

the marine-only control scenario in 2020. Units are ppb. 

Number of Counties

0

9

63

450

48

8

Effect of Marine-only in 2020 1

Legend

<= -2.0

> -2.0 to <= -1.0

> -1.0 to <= -0.5

> -0.5 to <= -0.1

= 0.0

> 0.0 to <= 1.0

> 1.0
26

Figure III-7. Model-projected change in annual 8-hour ozone design values from 

the Locomotive/Marine control scenario in 2030. Units are ppb. 

Number of Counties

27

338

190

18

1

4

Effect of LocoMarine in 2030 1

Legend

<= -2.0

> -2.0 to <= -1.0

> -1.0 to <= -0.5

> -0.5 to <= -0.1

= 0.0

> 0.0 to <= 1.0

> 1.0

Figure III-8. Model-projected change in annual 8-hour ozone design values from 

the marine-only control scenario in 2030. Units are ppb. 

Number of Counties

9

71

170

306

13

9

Effect of Marine-only in 2030 1

Legend

<= -2.0

> -2.0 to <= -1.0

> -1.0 to <= -0.5

> -0.5 to <= -0.1

= 0.0

> 0.0 to <= 1.0

> 1.0
27

C. Impacts of the Locomotive/Marine Rule on Visibility 

The modeling conducted for the Locomotive/Marine rule was also used to project 

the impacts of the reductions on visibility conditions over 133 mandatory class I federal 

areas across the U.S. in 2020 and 2030. The results indicate that reductions in regional 

haze would occur in all 133 of these federal areas. The model projects that average 

visibility on the 20% worst days would improve by 0.02 deciviews.24 The average 

deciview improvement on the 20% worst days is 0.06 in 2030. The greatest visibility 

improvement due to this rule would occur at the San Gorgonio Wilderness where a 0.24 

deciview improvement is projected by 2030 as a result of the locomotive/marine controls 

in this rule. Appendix C contains the visibility results from the locomotive/marine 

scenario over the 133 Class 1 areas. 

 

24 The level of visibility impairment in an area is based on the light-extinction coefficient and a unit less 

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. 
28 

Appendix A: Annual Average PM2.5 Design Values for Locomotive/Marine Scenarios (u

State Name County Name Baseline DV 2020 Base 2020 Marine only 2020 Locomotive 

/ Marine 

2030 Base 

Alabama Baldwin Co 11.38 9.14 9.13 9.12 9.19 Alabama Clay Co 13.50 10.45 10.44 10.41 10.45 Alabama Colbert Co 13.02 10.23 10.22 10.20 10.24 Alabama DeKalb Co 14.86 11.21 11.20 11.18 11.20 Alabama Escambia Co 12.82 10.48 10.47 10.46 10.50 Alabama Etowah Co 15.69 12.02 12.01 11.98 11.99 Alabama Jefferson Co 18.36 15.00 14.99 14.95 14.95 Alabama Madison Co 14.20 10.74 10.74 10.72 10.75 Alabama Mobile Co 12.80 10.72 10.58 10.56 10.95 Alabama Montgomery Co 14.50 11.76 11.75 11.72 11.75 Alabama Morgan Co 13.36 10.35 10.34 10.32 10.36 Alabama Russell Co 16.04 13.15 13.14 13.13 13.14 Alabama Shelby Co 14.50 11.36 11.36 11.32 11.38 Alabama Sumter Co 12.26 9.78 9.77 9.75 9.81 Alabama Talladega Co 14.96 11.48 11.48 11.44 11.47 Arizona Cochise Co 6.97 6.60 6.60 6.60 6.61 Arizona Gila Co 9.52 8.89 8.89 8.88 8.87 Arizona Maricopa Co 11.45 10.30 10.30 10.29 10.31 Arizona Pima Co 6.75 6.13 6.13 6.12 6.12 Arizona Pinal Co 8.14 7.45 7.45 7.42 7.59 Arizona Santa Cruz Co 11.74 11.24 11.24 11.23 11.27 Arkansas Arkansas Co 12.13 10.08 10.06 10.03 10.09 Arkansas Ashley Co 12.14 10.28 10.26 10.24 10.33 Arkansas Crittenden Co 12.91 10.66 10.60 10.55 10.93 Arkansas Faulkner Co 12.40 10.33 10.32 10.28 10.34 Arkansas Mississippi Co 11.84 9.66 9.62 9.60 9.73 Arkansas Phillips Co 12.16 10.20 10.12 10.10 10.38 Arkansas Polk Co 11.08 9.02 9.02 9.00 9.04 Arkansas Pope Co 12.23 10.13 10.12 10.10 10.12 Arkansas Pulaski Co 14.12 11.63 11.61 11.55 11.60 Arkansas Sebastian Co 12.33 10.33 10.33 10.31 10.33 Arkansas Union Co 12.64 11.01 10.99 10.98 11.04 Arkansas White Co 11.63 9.57 9.56 9.52 9.57 

California Alameda Co 11.76 10.77 10.75 10.73 10.68 
29 

California Butte Co 13.69 11.84 11.83 11.80 11.76 California Calaveras Co 8.85 7.83 7.82 7.80 7.84 California Colusa Co 9.44 8.78 8.78 8.77 8.75 California Contra Costa Co 11.32 10.53 10.50 10.49 10.57 California Fresno Co 20.02 17.54 17.52 17.47 17.14 California Humboldt Co 8.48 7.34 7.33 7.33 7.36 California Imperial Co 14.44 13.92 13.91 13.87 13.97 California Inyo Co 6.17 5.94 5.95 5.94 5.96 California Kern Co 21.77 19.28 19.26 19.21 18.87 California Kings Co 18.77 16.82 16.80 16.76 16.55 California Lake Co 5.03 4.73 4.72 4.72 4.73 California Los Angeles Co 23.16 20.69 20.64 20.61 20.86 California Mendocino Co 8.00 7.22 7.21 7.21 7.20 California Merced Co 16.47 15.15 15.14 15.11 15.11 California Monterey Co 8.23 7.74 7.73 7.72 7.75 California Nevada Co 7.97 7.25 7.25 7.23 7.31 California Orange Co 18.27 15.73 15.60 15.58 15.93 California Placer Co 11.65 10.21 10.20 10.17 10.07 California Riverside Co 27.15 22.67 22.53 22.48 22.54 California Sacramento Co 12.56 11.47 11.46 11.43 11.36 California San Bernardino Co 24.63 21.79 21.69 21.66 21.97 California San Diego Co 15.65 14.64 14.61 14.60 14.91 California San Francisco Co 11.58 11.08 10.94 10.93 11.58 California San Joaquin Co 14.84 13.58 13.54 13.51 13.57 California San Luis Obispo Co 9.20 8.79 8.79 8.78 8.85 California San Mateo Co 10.58 9.77 9.76 9.74 9.70 California Santa Barbara Co 9.32 9.06 9.05 9.05 9.12 California Santa Clara Co 11.33 10.10 10.09 10.08 9.95 California Santa Cruz Co 8.17 7.38 7.38 7.37 7.30 California Shasta Co 9.01 7.74 7.74 7.73 7.76 California Solano Co 12.03 11.64 11.58 11.57 11.88 California Sonoma Co 9.91 9.22 9.21 9.21 9.21 California Stanislaus Co 16.49 14.57 14.54 14.50 14.30 California Sutter Co 11.39 10.14 10.13 10.10 10.07 California Tulare Co 21.33 18.71 18.69 18.64 18.24 California Ventura Co 14.34 12.54 12.45 12.44 12.59 California Yolo Co 10.04 9.11 9.10 9.08 8.99 Colorado Adams Co 10.29 8.91 8.91 8.87 8.80 
30 

Colorado Arapahoe Co 8.70 7.76 7.76 7.74 7.80 Colorado Archuleta Co 5.99 5.55 5.55 5.55 5.54 Colorado Boulder Co 9.27 8.20 8.19 8.17 8.19 Colorado Delta Co 8.26 7.44 7.44 7.42 7.40 Colorado Denver Co 10.58 9.22 9.22 9.19 9.12 Colorado El Paso Co 7.86 7.16 7.16 7.14 7.14 Colorado Elbert Co 4.33 3.92 3.92 3.91 3.98 Colorado Gunnison Co 6.69 6.26 6.26 6.26 6.25 Colorado Larimer Co 7.88 7.21 7.21 7.19 7.16 Colorado Pueblo Co 7.79 7.16 7.16 7.13 7.16 Colorado Routt Co 7.40 6.98 6.98 6.98 6.96 Colorado San Miguel Co 5.33 4.99 4.99 4.99 4.98 Colorado Weld Co 9.33 8.14 8.14 8.10 8.16 Connecticut Fairfield Co 12.85 11.39 11.38 11.37 11.54 Connecticut Hartford Co 11.50 9.84 9.84 9.84 9.86 Connecticut New Haven Co 13.71 11.48 11.47 11.46 11.52 Connecticut New London Co 11.57 9.60 9.60 9.60 9.66 Delaware Kent Co 12.81 9.54 9.53 9.52 9.61 Delaware New Castle Co 15.96 12.35 12.33 12.30 12.52 Delaware Sussex Co 13.65 10.52 10.51 10.50 10.54 District of Columbia District of Columbia 15.75 11.72 11.71 11.69 11.73 Florida Alachua Co 10.12 7.71 7.71 7.71 7.70 Florida Bay Co 11.04 9.01 8.98 8.98 9.03 Florida Brevard Co 7.88 5.65 5.64 5.64 5.65 Florida Broward Co 8.30 6.29 6.23 6.23 6.40 Florida Citrus Co 9.13 6.73 6.73 6.72 6.73 Florida Duval Co 10.64 8.49 8.43 8.43 8.62 Florida Escambia Co 11.56 9.63 9.62 9.62 9.63 Florida Hillsborough Co 11.25 8.40 8.31 8.30 8.57 Florida Lee Co 8.42 6.10 6.10 6.10 6.08 Florida Leon Co 12.67 10.19 10.18 10.16 10.13 Florida Manatee Co 9.31 6.56 6.55 6.55 6.57 Florida Marion Co 10.04 7.56 7.56 7.56 7.54 Florida Miami-Dade Co 9.66 7.51 7.39 7.39 7.60 Florida Orange Co 10.19 7.37 7.37 7.37 7.39 Florida Palm Beach Co 7.54 5.75 5.75 5.75 5.80 Florida Pinellas Co 10.46 7.84 7.83 7.82 7.84 Florida Polk Co 10.36 7.49 7.49 7.49 7.52 
31 

Florida Sarasota Co 9.28 6.70 6.70 6.70 6.69 Florida Seminole Co 9.31 6.63 6.62 6.62 6.61 Florida St. Lucie Co 8.61 6.26 6.26 6.26 6.26 Florida Volusia Co 9.28 6.73 6.73 6.73 6.72 Georgia Bibb Co 15.69 12.37 12.36 12.34 12.33 Georgia Chatham Co 13.87 11.94 11.81 11.79 12.10 Georgia Clarke Co 15.75 11.51 11.51 11.49 11.46 Georgia Clayton Co 16.48 12.38 12.38 12.36 12.35 Georgia Cobb Co 16.29 12.28 12.28 12.26 12.26 Georgia DeKalb Co 16.22 12.43 12.43 12.42 12.48 Georgia Dougherty Co 14.24 11.65 11.65 11.64 11.62 Georgia Floyd Co 15.71 11.77 11.77 11.74 11.76 Georgia Fulton Co 18.29 14.22 14.21 14.18 14.27 Georgia Glynn Co 11.89 9.67 9.63 9.62 9.71 Georgia Gwinnett Co 16.20 12.29 12.28 12.27 12.21 Georgia Hall Co 15.14 11.71 11.71 11.69 11.70 Georgia Houston Co 13.04 10.15 10.15 10.13 10.14 Georgia Lowndes Co 12.14 9.85 9.85 9.83 9.82 Georgia Muscogee Co 14.86 12.15 12.15 12.14 12.15 Georgia Paulding Co 14.30 10.48 10.48 10.45 10.50 Georgia Richmond Co 15.05 12.13 12.13 12.12 12.11 Georgia Walker Co 15.44 11.79 11.77 11.74 11.79 Georgia Washington Co 14.21 11.09 11.08 11.06 11.08 Georgia Wilkinson Co 15.15 11.77 11.77 11.75 11.77 Idaho Ada Co 9.17 8.56 8.55 8.54 8.53 Idaho Bannock Co 8.56 8.31 8.31 8.29 8.30 Idaho Canyon Co 9.26 8.31 8.31 8.28 8.22 Idaho Power Co 9.82 9.53 9.53 9.51 9.52 Idaho Shoshone Co 12.72 12.02 12.02 12.01 11.99 Illinois Adams Co 12.88 10.63 10.61 10.54 10.58 Illinois Champaign Co 12.56 10.26 10.25 10.21 10.21 Illinois Cook Co 17.06 13.85 13.84 13.74 13.79 Illinois DuPage Co 14.36 11.52 11.51 11.42 11.46 Illinois Kane Co 13.93 11.27 11.26 11.18 11.19 Illinois Lake Co 12.54 10.56 10.55 10.51 10.59 Illinois Macon Co 13.87 11.29 11.28 11.23 11.23 Illinois Madison Co 17.27 14.38 14.31 14.27 14.53 Illinois McHenry Co 12.73 10.32 10.31 10.27 10.28 
32 

Illinois McLean Co 13.41 10.80 10.79 10.74 10.73 Illinois Peoria Co 13.83 11.35 11.33 11.29 11.27 Illinois Randolph Co 12.41 9.78 9.76 9.71 9.78 Illinois Rock Island Co 12.04 9.80 9.77 9.74 9.72 Illinois Sangamon Co 13.14 10.65 10.63 10.60 10.55 Illinois St. Clair Co 16.19 13.36 13.29 13.25 13.49 Illinois Will Co 14.50 11.80 11.79 11.70 11.82 Illinois Winnebago Co 12.87 10.56 10.55 10.51 10.44 Indiana Allen Co 14.29 11.41 11.40 11.35 11.35 Indiana Clark Co 16.33 12.96 12.92 12.91 13.13 Indiana Delaware Co 14.34 11.08 11.06 11.03 11.02 Indiana Dubois Co 15.84 12.46 12.44 12.41 12.45 Indiana Elkhart Co 14.99 12.27 12.26 12.21 12.21 Indiana Floyd Co 14.90 11.38 11.31 11.29 11.52 Indiana Henry Co 13.26 10.08 10.07 10.04 10.04 Indiana Howard Co 14.55 11.49 11.48 11.44 11.43 Indiana Knox Co 13.75 10.60 10.58 10.54 10.58 Indiana La Porte Co 13.25 10.88 10.86 10.80 10.86 Indiana Lake Co 15.01 12.88 12.86 12.75 12.87 Indiana Madison Co 14.54 11.21 11.20 11.16 11.15 Indiana Marion Co 16.54 13.02 13.00 12.97 12.94 Indiana Porter Co 13.75 11.48 11.46 11.39 11.51 Indiana Spencer Co 14.02 10.67 10.64 10.62 10.69 Indiana St. Joseph Co 14.08 11.43 11.42 11.37 11.38 Indiana Vanderburgh Co 15.29 12.06 11.99 11.96 12.09 Indiana Vigo Co 14.51 11.51 11.49 11.43 11.43 Iowa Black Hawk Co 11.16 9.25 9.25 9.21 9.14 Iowa Clinton Co 12.06 9.75 9.73 9.67 9.69 Iowa Emmet Co 8.82 7.31 7.30 7.27 7.21 Iowa Johnson Co 11.43 9.44 9.43 9.39 9.37 Iowa Linn Co 11.00 9.15 9.14 9.08 9.07 Iowa Muscatine Co 12.80 10.56 10.54 10.49 10.50 Iowa Polk Co 10.52 8.60 8.60 8.56 8.47 Iowa Pottawattamie Co 10.43 8.72 8.71 8.66 8.64 Iowa Scott Co 12.33 10.00 9.97 9.93 9.92 Iowa Van Buren Co 10.33 8.39 8.38 8.33 8.32 Iowa Woodbury Co 10.07 8.46 8.46 8.41 8.38 Kansas Johnson Co 11.54 9.59 9.59 9.53 9.52 
33 

Kansas Linn Co 10.66 8.92 8.92 8.86 8.90 Kansas Sedgwick Co 11.06 9.58 9.58 9.55 9.53 Kansas Shawnee Co 10.86 9.17 9.17 9.11 9.07 Kansas Sumner Co 10.19 8.79 8.79 8.76 8.79 Kansas Wyandotte Co 13.55 11.42 11.41 11.33 11.51 Kentucky Bell Co 14.64 10.97 10.97 10.94 10.97 Kentucky Boyd Co 14.88 11.41 11.34 11.31 11.65 Kentucky Bullitt Co 14.88 11.43 11.41 11.39 11.52 Kentucky Campbell Co 14.00 10.24 10.19 10.15 10.31 Kentucky Carter Co 12.18 8.84 8.83 8.80 8.89 Kentucky Christian Co 13.47 10.47 10.46 10.44 10.49 Kentucky Daviess Co 14.70 11.52 11.49 11.48 11.55 Kentucky Fayette Co 15.60 11.94 11.93 11.89 11.72 Kentucky Franklin Co 13.67 10.03 10.01 9.99 10.04 Kentucky Hardin Co 13.97 10.50 10.48 10.46 10.51 Kentucky Jefferson Co 16.58 13.65 13.62 13.61 13.92 Kentucky Kenton Co 14.88 11.03 10.99 10.95 11.16 Kentucky Laurel Co 12.20 8.89 8.88 8.85 8.88 Kentucky Madison Co 13.53 9.86 9.85 9.81 9.93 Kentucky McCracken Co 13.39 10.61 10.59 10.57 10.63 Kentucky Perry Co 13.14 9.62 9.62 9.59 9.60 Kentucky Pike Co 13.67 10.02 10.01 9.98 10.02 Kentucky Warren Co 13.81 10.55 10.54 10.53 10.55 Louisiana Caddo Parish 12.63 10.77 10.76 10.73 10.83 Louisiana Calcasieu Parish 11.35 9.83 9.79 9.78 9.95 Louisiana Concordia Parish 11.10 9.48 9.41 9.40 9.65 Louisiana East Baton Rouge Parish 13.11 11.99 11.72 11.71 12.85 Louisiana Iberville Parish 12.55 11.41 11.20 11.19 12.06 Louisiana Jefferson Parish 12.17 10.32 10.08 10.07 10.87 Louisiana Lafayette Parish 11.02 9.38 9.37 9.36 9.49 Louisiana Orleans Parish 12.23 10.13 10.02 10.01 10.44 Louisiana Ouachita Parish 11.46 9.80 9.79 9.76 9.84 Louisiana St. Bernard Parish 10.69 8.74 8.66 8.65 8.94 Louisiana Tangipahoa Parish 11.17 9.21 9.19 9.18 9.35 Louisiana Terrebonne Parish 10.40 8.69 8.66 8.66 8.85 Louisiana West Baton Rouge Parish 12.81 11.72 11.46 11.45 12.58 Maine Androscoggin Co 10.73 9.40 9.40 9.40 9.38 Maine Aroostook Co 11.29 10.60 10.59 10.59 10.59 
34 

Maine Cumberland Co 11.59 10.27 10.21 10.20 10.43 Maine Hancock Co 6.25 5.37 5.37 5.37 5.39 Maine Kennebec Co 10.49 9.12 9.12 9.12 9.10 Maine Oxford Co 10.13 8.99 8.99 8.99 8.98 Maine Penobscot Co 10.06 8.71 8.71 8.71 8.73 Maryland Anne Arundel Co 15.36 11.87 11.86 11.84 11.96 Maryland Baltimore city 16.63 13.17 13.13 13.09 13.38 Maryland Baltimore Co 15.01 11.15 11.14 11.11 11.19 Maryland Harford Co 13.01 9.67 9.67 9.64 9.71 Maryland Montgomery Co 12.82 9.39 9.38 9.37 9.39 Maryland Washington Co 14.25 10.38 10.37 10.34 10.36 Massachusetts Berkshire Co 11.72 10.46 10.45 10.44 10.46 Massachusetts Hampden Co 13.30 11.59 11.59 11.58 11.60 Massachusetts Plymouth Co 10.86 9.02 9.02 9.01 9.12 Massachusetts Suffolk Co 13.68 11.79 11.77 11.76 11.83 Michigan Allegan Co 12.29 10.14 10.13 10.10 10.15 Michigan Bay Co 10.94 9.34 9.33 9.32 9.34 Michigan Berrien Co 12.35 10.07 10.06 10.02 10.05 Michigan Chippewa Co 8.09 7.45 7.44 7.44 7.49 Michigan Genesee Co 12.37 10.29 10.29 10.26 10.23 Michigan Ingham Co 13.13 10.77 10.76 10.73 10.75 Michigan Kalamazoo Co 14.38 11.78 11.77 11.72 11.72 Michigan Kent Co 13.55 10.95 10.94 10.90 10.84 Michigan Macomb Co 13.13 10.68 10.67 10.65 10.67 Michigan Monroe Co 14.99 11.74 11.70 11.65 11.78 Michigan Muskegon Co 11.99 10.06 10.06 10.03 10.03 Michigan Oakland Co 14.64 11.73 11.72 11.69 11.70 Michigan Ottawa Co 13.14 10.73 10.72 10.69 10.67 Michigan Saginaw Co 10.70 9.06 9.05 9.04 9.07 Michigan St. Clair Co 13.77 11.65 11.64 11.62 11.68 Michigan Washtenaw Co 14.39 11.31 11.30 11.27 11.30 Michigan Wayne Co 19.32 16.60 16.59 16.55 16.59 Minnesota Dakota Co 9.43 8.05 8.05 8.02 7.98 Minnesota Hennepin Co 10.29 8.85 8.84 8.80 8.77 Minnesota Mille Lacs Co 7.12 6.18 6.18 6.15 6.13 Minnesota Olmsted Co 10.72 9.14 9.13 9.10 9.03 Minnesota Ramsey Co 11.91 10.19 10.15 10.11 10.20 Minnesota Scott Co 9.98 8.48 8.47 8.44 8.40 
35 

Minnesota St. Louis Co 8.14 7.30 7.27 7.26 7.36 Minnesota Stearns Co 9.21 7.93 7.92 7.88 7.86 Mississippi Adams Co 11.20 9.34 9.29 9.28 9.49 Mississippi Bolivar Co 12.56 10.74 10.72 10.71 10.79 Mississippi DeSoto Co 12.60 10.23 10.22 10.18 10.34 Mississippi Forrest Co 13.37 11.04 11.03 11.00 11.07 Mississippi Hancock Co 10.34 8.33 8.32 8.30 8.42 Mississippi Harrison Co 11.57 9.59 9.53 9.52 9.75 Mississippi Hinds Co 13.38 11.19 11.18 11.14 11.22 Mississippi Jackson Co 11.84 9.75 9.66 9.65 9.97 Mississippi Jones Co 14.48 11.81 11.80 11.77 11.87 Mississippi Lauderdale Co 13.16 10.57 10.57 10.54 10.60 Mississippi Lee Co 12.48 9.98 9.97 9.95 9.99 Mississippi Lowndes Co 13.36 10.75 10.74 10.73 10.75 Mississippi Pearl River Co 11.70 9.59 9.57 9.55 9.66 Mississippi Rankin Co 13.10 10.94 10.93 10.89 10.97 Mississippi Scott Co 11.82 9.56 9.55 9.52 9.61 Mississippi Warren Co 12.26 10.61 10.45 10.44 10.97 Missouri Cass Co 11.22 9.33 9.33 9.26 9.31 Missouri Cedar Co 11.45 9.38 9.37 9.33 9.35 Missouri Clay Co 11.73 9.74 9.74 9.67 9.72 Missouri Greene Co 12.11 10.08 10.08 10.04 10.04 Missouri Jefferson Co 14.43 11.90 11.88 11.85 11.92 Missouri Monroe Co 11.03 8.94 8.93 8.88 8.89 Missouri St. Charles Co 14.08 11.43 11.40 11.36 11.46 Missouri St. Louis city 15.16 12.45 12.38 12.34 12.57 Missouri St. Louis Co 14.02 11.46 11.44 11.41 11.46 Missouri Ste. Genevieve Co 13.66 10.94 10.90 10.86 10.98 Montana Cascade Co 5.70 5.26 5.26 5.25 5.25 Montana Flathead Co 10.01 9.36 9.36 9.32 9.34 Montana Gallatin Co 8.40 7.96 7.96 7.95 7.94 Montana Lake Co 9.41 8.69 8.69 8.68 8.66 Montana Lincoln Co 15.85 14.72 14.71 14.55 14.66 Montana Missoula Co 10.20 9.39 9.39 9.39 9.38 Montana Rosebud Co 6.78 6.55 6.55 6.54 6.54 Montana Sanders Co 6.48 6.17 6.17 6.16 6.15 Montana Silver Bow Co 8.30 7.61 7.61 7.61 7.59 Montana Yellowstone Co 7.43 6.86 6.86 6.85 6.86 
36 

Nebraska Cass Co 10.26 8.61 8.61 8.55 8.58 Nebraska Douglas Co 10.55 8.82 8.82 8.78 8.72 Nebraska Lancaster Co 9.58 8.06 8.06 8.01 8.01 Nebraska Lincoln Co 7.13 6.30 6.30 6.17 6.26 Nebraska Sarpy Co 10.07 8.44 8.43 8.38 8.43 Nebraska Scotts Bluff Co 5.96 5.41 5.41 5.33 5.38 Nebraska Washington Co 9.71 8.15 8.15 8.10 8.09 Nevada Clark Co 9.51 8.74 8.74 8.73 8.68 Nevada Washoe Co 8.86 8.32 8.32 8.30 8.26 New Hampshire Belknap Co 7.15 5.96 5.96 5.95 5.96 New Hampshire Cheshire Co 11.68 10.07 10.07 10.07 10.08 New Hampshire Coos Co 9.68 8.78 8.78 8.78 8.77 New Hampshire Hillsborough Co 10.33 8.77 8.77 8.76 8.77 New Hampshire Rockingham Co 9.49 8.05 8.03 8.02 8.13 New Hampshire Sullivan Co 9.86 8.44 8.44 8.44 8.45 New Jersey Atlantic Co 11.41 8.79 8.78 8.77 8.86 New Jersey Bergen Co 13.65 10.94 10.92 10.91 11.01 New Jersey Camden Co 14.31 11.12 11.04 11.03 11.33 New Jersey Essex Co 13.68 10.75 10.72 10.70 10.84 New Jersey Gloucester Co 13.68 10.45 10.39 10.38 10.72 New Jersey Hudson Co 14.93 12.48 12.33 12.31 12.88 New Jersey Mercer Co 13.91 10.74 10.72 10.71 10.87 New Jersey Middlesex Co 12.46 9.52 9.51 9.50 9.58 New Jersey Morris Co 12.38 9.52 9.52 9.51 9.59 New Jersey Ocean Co 11.16 8.57 8.57 8.56 8.62 New Jersey Passaic Co 13.09 10.26 10.25 10.23 10.30 New Jersey Union Co 15.66 12.40 12.32 12.29 12.68 New Jersey Warren Co 13.36 10.03 10.03 10.01 10.06 New Mexico Bernalillo Co 6.55 6.03 6.03 6.02 6.02 New Mexico Chaves Co 6.70 6.17 6.17 6.16 6.17 New Mexico Dona Ana Co 11.29 10.29 10.29 10.23 10.14 New Mexico Grant Co 6.13 5.84 5.84 5.84 5.84 New Mexico Lea Co 6.75 6.13 6.13 6.12 6.12 New Mexico San Juan Co 6.49 6.14 6.14 6.14 6.14 New Mexico Sandoval Co 4.98 4.59 4.59 4.58 4.58 New Mexico Santa Fe Co 4.93 4.59 4.59 4.58 4.60 New York Bronx Co 15.78 13.01 12.98 12.97 13.05 New York Chautauqua Co 10.71 8.17 8.16 8.14 8.16 
37 

New York Erie Co 13.90 11.07 11.05 11.02 10.98 New York Essex Co 6.39 5.48 5.47 5.47 5.49 New York Kings Co 14.65 12.06 12.02 12.01 12.20 New York Nassau Co 12.21 9.86 9.85 9.84 10.00 New York New York Co 17.16 14.35 14.34 14.33 14.37 New York Niagara Co 12.03 9.85 9.84 9.83 9.93 New York Onondaga Co 10.56 10.13 10.13 10.10 10.11 New York Orange Co 11.50 9.95 9.94 9.93 10.02 New York Queens Co 13.23 10.76 10.75 10.75 10.96 New York Richmond Co 12.13 10.80 10.73 10.72 10.97 New York St. Lawrence Co 8.49 7.78 7.77 7.77 7.77 New York Steuben Co 9.83 7.42 7.42 7.42 7.43 New York Suffolk Co 12.11 10.05 10.04 10.03 10.65 New York Westchester Co 12.33 10.06 10.05 10.04 10.32 North Carolina Alamance Co 13.93 10.15 10.14 10.13 10.12 North Carolina Buncombe Co 13.19 9.95 9.95 9.93 9.90 North Carolina Cabarrus Co 14.68 10.75 10.74 10.72 10.76 North Carolina Caswell Co 13.43 9.61 9.61 9.60 9.62 North Carolina Catawba Co 15.60 11.52 11.52 11.50 11.47 North Carolina Chatham Co 12.34 9.00 9.00 8.99 9.04 North Carolina Cumberland Co 14.17 10.81 10.80 10.79 10.78 North Carolina Davidson Co 15.94 11.96 11.96 11.93 12.01 North Carolina Duplin Co 12.08 9.35 9.34 9.34 9.36 North Carolina Durham Co 14.09 10.44 10.44 10.43 10.38 North Carolina Forsyth Co 14.79 10.96 10.96 10.95 10.92 North Carolina Gaston Co 14.23 10.55 10.55 10.53 10.55 North Carolina Guilford Co 14.27 10.25 10.25 10.24 10.22 North Carolina Haywood Co 13.48 10.58 10.58 10.57 10.57 North Carolina Jackson Co 12.27 9.32 9.32 9.32 9.33 North Carolina Lenoir Co 11.57 8.84 8.83 8.83 8.86 North Carolina McDowell Co 14.41 10.72 10.72 10.69 10.71 North Carolina Mecklenburg Co 15.19 12.48 12.48 12.48 12.89 North Carolina Mitchell Co 13.56 10.22 10.22 10.19 10.23 North Carolina Montgomery Co 12.40 9.08 9.08 9.07 9.09 North Carolina Onslow Co 11.27 8.66 8.66 8.65 8.66 North Carolina Orange Co 13.24 9.82 9.82 9.81 9.81 North Carolina Pitt Co 12.40 9.85 9.85 9.84 9.88 North Carolina Robeson Co 12.69 10.06 10.06 10.05 10.05 
38 

North Carolina Swain Co 12.67 9.52 9.52 9.51 9.52 North Carolina Wake Co 13.96 10.85 10.85 10.84 10.97 North Carolina Watauga Co 11.55 8.30 8.30 8.29 8.28 North Carolina Wayne Co 13.76 10.83 10.83 10.82 10.83 North Dakota Billings Co 4.57 4.27 4.27 4.23 4.26 North Dakota Burke Co 5.74 4.36 4.36 4.36 4.34 North Dakota Burleigh Co 6.63 5.89 5.89 5.86 5.85 North Dakota Cass Co 7.78 6.65 6.65 6.61 6.56 North Dakota McKenzie Co 5.26 4.82 4.82 4.81 4.81 North Dakota Mercer Co 6.20 4.98 4.98 4.97 4.96 Ohio Athens Co 12.31 8.70 8.68 8.67 8.74 Ohio Butler Co 16.12 12.68 12.66 12.62 12.68 Ohio Clark Co 14.69 11.20 11.18 11.16 11.21 Ohio Cuyahoga Co 18.36 14.52 14.44 14.39 14.60 Ohio Franklin Co 16.52 12.50 12.49 12.44 12.41 Ohio Hamilton Co 17.76 13.35 13.32 13.27 13.37 Ohio Jefferson Co 17.48 13.38 13.33 13.31 13.42 Ohio Lake Co 13.25 10.49 10.45 10.42 10.54 Ohio Lawrence Co 15.70 12.55 12.48 12.42 12.67 Ohio Lorain Co 13.60 10.47 10.40 10.36 10.59 Ohio Lucas Co 14.70 11.51 11.43 11.36 11.51 Ohio Mahoning Co 15.11 11.18 11.16 11.12 11.17 Ohio Montgomery Co 15.73 12.09 12.08 12.05 12.01 Ohio Portage Co 14.19 10.72 10.71 10.67 10.74 Ohio Preble Co 13.34 10.07 10.06 10.03 10.11 Ohio Scioto Co 17.11 13.07 13.04 12.97 13.13 Ohio Stark Co 17.26 13.16 13.14 13.10 13.11 Ohio Summit Co 16.42 12.61 12.60 12.57 12.61 Ohio Trumbull Co 14.96 11.40 11.39 11.36 11.40 Oklahoma Caddo Co 8.79 7.30 7.30 7.28 7.31 Oklahoma Canadian Co 8.96 7.45 7.45 7.43 7.48 Oklahoma Carter Co 10.10 8.27 8.27 8.25 8.29 Oklahoma Cherokee Co 11.56 9.66 9.65 9.63 9.66 Oklahoma Garfield Co 9.85 8.58 8.58 8.55 8.56 Oklahoma Kay Co 10.64 9.43 9.43 9.40 9.44 Oklahoma Lincoln Co 9.96 8.34 8.34 8.32 8.37 Oklahoma Mayes Co 11.87 10.13 10.12 10.08 10.12 Oklahoma Muskogee Co 12.05 10.26 10.25 10.21 10.25 
39 

Oklahoma Oklahoma Co 10.44 8.49 8.49 8.47 8.42 Oklahoma Ottawa Co 11.73 9.66 9.66 9.63 9.65 Oklahoma Pittsburg Co 11.37 9.56 9.55 9.51 9.57 Oklahoma Seminole Co 9.64 8.04 8.04 8.02 8.06 Oklahoma Tulsa Co 11.73 9.87 9.86 9.84 9.85 Oregon Columbia Co 6.29 5.87 5.84 5.83 5.99 Oregon Jackson Co 11.39 10.68 10.68 10.67 10.67 Oregon Klamath Co 10.69 10.09 10.09 10.07 10.07 Oregon Lane Co 13.28 12.51 12.51 12.44 12.48 Oregon Linn Co 8.23 7.90 7.90 7.89 7.90 Oregon Multnomah Co 8.67 8.31 8.24 8.23 8.51 Oregon Wasco Co 7.53 7.03 7.01 6.98 7.01 Oregon Washington Co 7.77 7.39 7.37 7.37 7.44 Pennsylvania Adams Co 13.31 9.37 9.36 9.34 9.36 Pennsylvania Allegheny Co 20.99 16.21 16.08 16.03 16.34 Pennsylvania Beaver Co 15.79 11.97 11.95 11.90 12.00 Pennsylvania Berks Co 16.41 12.25 12.24 12.21 12.26 Pennsylvania Bucks Co 14.10 11.04 11.01 11.00 11.35 Pennsylvania Cambria Co 15.62 11.47 11.46 11.42 11.47 Pennsylvania Centre Co 12.95 9.38 9.37 9.35 9.37 Pennsylvania Chester Co 14.80 10.74 10.72 10.70 10.79 Pennsylvania Cumberland Co 14.93 10.88 10.87 10.84 10.83 Pennsylvania Dauphin Co 15.57 11.17 11.16 11.12 11.12 Pennsylvania Delaware Co 15.31 11.91 11.80 11.78 12.26 Pennsylvania Erie Co 13.14 10.18 10.16 10.13 10.18 Pennsylvania Lackawanna Co 12.32 9.17 9.16 9.15 9.14 Pennsylvania Lancaster Co 16.95 12.01 12.00 11.96 11.99 Pennsylvania Lehigh Co 14.21 10.68 10.67 10.65 10.66 Pennsylvania Luzerne Co 12.70 9.45 9.45 9.44 9.43 Pennsylvania Mercer Co 14.00 10.48 10.47 10.44 10.48 Pennsylvania Montgomery Co 13.74 10.36 10.35 10.33 10.55 Pennsylvania Northampton Co 14.33 10.86 10.85 10.83 10.84 Pennsylvania Perry Co 12.83 9.49 9.48 9.42 9.51 Pennsylvania Philadelphia Co 15.97 12.73 12.63 12.61 13.00 Pennsylvania Washington Co 15.37 11.14 11.11 11.07 11.31 Pennsylvania Westmoreland Co 15.38 10.77 10.76 10.72 10.77 Pennsylvania York Co 16.92 12.53 12.52 12.49 12.51 Rhode Island Kent Co 8.62 6.98 6.98 6.97 7.03 
40 

Rhode Island Providence Co 12.60 10.57 10.56 10.56 10.61 South Carolina Beaufort Co 10.91 8.52 8.51 8.51 8.54 South Carolina Charleston Co 11.80 9.23 9.22 9.21 9.24 South Carolina Chesterfield Co 12.37 9.34 9.34 9.33 9.34 South Carolina Edgefield Co 12.71 9.57 9.57 9.55 9.57 South Carolina Florence Co 12.66 10.20 10.20 10.19 10.18 South Carolina Georgetown Co 12.75 10.28 10.25 10.24 10.32 South Carolina Greenville Co 15.73 11.94 11.94 11.92 11.89 South Carolina Greenwood Co 13.36 9.99 9.99 9.96 9.98 South Carolina Horry Co 11.22 8.79 8.78 8.78 8.78 South Carolina Lexington Co 13.87 10.62 10.62 10.60 10.57 South Carolina Oconee Co 10.76 7.72 7.71 7.71 7.70 South Carolina Richland Co 13.86 10.65 10.65 10.63 10.60 South Carolina Spartanburg Co 13.82 10.30 10.30 10.28 10.29 South Dakota Brookings Co 9.44 8.05 8.05 8.03 7.97 South Dakota Brookings Co 9.44 8.04 8.04 8.02 7.95 South Dakota Brown Co 8.22 7.17 7.17 7.15 7.10 South Dakota Brown Co 8.22 7.14 7.14 7.12 7.06 South Dakota Jackson Co 5.44 5.06 5.06 5.04 5.04 South Dakota Jackson Co 5.44 5.06 5.06 5.05 5.05 South Dakota Meade Co 6.28 5.87 5.87 5.86 5.84 South Dakota Meade Co 6.28 5.87 5.87 5.87 5.85 South Dakota Minnehaha Co 9.84 8.12 8.12 8.09 8.02 South Dakota Minnehaha Co 9.84 8.12 8.12 8.10 8.01 South Dakota Pennington Co 7.55 7.05 7.05 7.04 7.02 South Dakota Pennington Co 7.55 7.05 7.05 7.05 7.03 Tennessee Blount Co 14.36 10.86 10.86 10.84 10.92 Tennessee Davidson Co 14.44 11.25 11.23 11.21 11.30 Tennessee Dyer Co 12.05 9.77 9.75 9.73 9.80 Tennessee Hamilton Co 16.34 12.58 12.56 12.53 12.58 Tennessee Knox Co 16.67 12.58 12.57 12.53 12.51 Tennessee Lawrence Co 11.94 9.12 9.11 9.10 9.14 Tennessee Maury Co 12.90 10.78 10.78 10.77 10.80 Tennessee McMinn Co 14.85 10.89 10.89 10.86 10.88 Tennessee Montgomery Co 13.21 10.40 10.39 10.38 10.43 Tennessee Putnam Co 13.43 10.00 9.99 9.98 9.98 Tennessee Roane Co 14.31 10.44 10.43 10.38 10.47 Tennessee Shelby Co 14.12 11.68 11.60 11.56 11.84 
41 

Tennessee Sullivan Co 14.59 12.12 12.12 12.10 12.06 Tennessee Sumner Co 13.58 10.45 10.44 10.42 10.47 Texas Bowie Co 13.67 11.60 11.59 11.55 11.59 Texas Brewster Co 4.98 4.52 4.52 4.51 4.52 Texas Cameron Co 9.88 8.79 8.78 8.78 8.80 Texas Dallas Co 13.67 11.35 11.35 11.34 11.43 Texas Ector Co 7.67 6.76 6.76 6.76 6.77 Texas Galveston Co 9.79 7.96 7.86 7.86 8.19 Texas Gregg Co 12.37 10.36 10.35 10.33 10.40 Texas Harris Co 14.22 12.88 12.51 12.50 13.61 Texas Harrison Co 11.46 9.52 9.51 9.49 9.57 Texas Hidalgo Co 10.81 9.67 9.67 9.66 9.65 Texas Jefferson Co 11.20 9.91 9.73 9.73 10.35 Texas Montgomery Co 11.23 9.70 9.69 9.68 9.84 Texas Nueces Co 10.02 8.54 8.50 8.49 8.73 Texas Orange Co 11.43 9.66 9.64 9.63 9.79 Texas Potter Co 6.17 5.27 5.27 5.25 5.26 Texas Tarrant Co 12.73 10.63 10.63 10.60 10.65 Utah Box Elder Co 9.14 8.65 8.64 8.64 8.64 Utah Cache Co 12.82 11.89 11.89 11.88 11.85 Utah Salt Lake Co 12.89 11.81 11.81 11.78 11.63 Utah Utah Co 10.89 9.80 9.80 9.78 9.87 Utah Weber Co 12.81 11.78 11.78 11.76 11.84 Vermont Chittenden Co 9.38 7.96 7.96 7.95 7.91 Virginia Arlington Co 14.61 10.72 10.72 10.70 10.66 Virginia Bristol city 14.51 10.99 10.98 10.96 10.92 Virginia Charles City Co 12.80 9.33 9.32 9.30 9.35 Virginia Chesterfield Co 13.73 9.73 9.72 9.70 9.73 Virginia Fairfax Co 14.15 10.34 10.34 10.32 10.37 Virginia Hampton city 12.51 9.82 9.73 9.72 9.96 Virginia Henrico Co 13.80 10.04 10.03 9.99 10.05 Virginia Loudoun Co 13.63 9.71 9.70 9.68 9.75 Virginia Norfolk city 12.97 10.56 10.38 10.37 10.84 Virginia Page Co 12.96 9.24 9.24 9.22 9.25 Virginia Roanoke city 14.36 10.51 10.51 10.41 10.45 Virginia Salem city 14.78 10.93 10.93 10.86 10.90 Virginia Virginia Beach city 12.58 9.98 9.96 9.95 10.03 Washington Clark Co 9.74 8.77 8.76 8.75 8.86 
42 

Washington King Co 11.37 10.84 10.72 10.71 11.06 Washington Pierce Co 10.92 10.66 10.65 10.64 10.72 Washington Snohomish Co 11.20 11.14 11.13 11.12 11.21 Washington Spokane Co 10.19 9.09 9.09 9.06 9.07 Washington Yakima Co 10.38 9.08 9.08 9.07 9.06 West Virginia Berkeley Co 16.23 12.35 12.35 12.31 12.33 West Virginia Brooke Co 16.69 12.70 12.65 12.63 12.74 West Virginia Cabell Co 16.54 13.07 13.04 13.00 13.33 West Virginia Hancock Co 17.30 13.65 13.61 13.59 13.68 West Virginia Harrison Co 13.99 10.28 10.28 10.27 10.29 West Virginia Kanawha Co 17.08 13.32 13.30 13.27 13.37 West Virginia Marion Co 15.32 11.25 11.24 11.23 11.25 West Virginia Marshall Co 15.61 11.47 11.42 11.40 11.55 West Virginia Mercer Co 12.67 9.20 9.20 9.18 9.26 West Virginia Monongalia Co 14.81 10.44 10.43 10.42 10.44 West Virginia Ohio Co 15.08 11.03 10.98 10.97 11.06 West Virginia Raleigh Co 13.05 9.55 9.55 9.53 9.55 West Virginia Summers Co 10.10 7.19 7.19 7.17 7.20 West Virginia Wood Co 16.07 11.82 11.77 11.75 11.89 Wisconsin Brown Co 11.27 9.91 9.90 9.88 9.87 Wisconsin Dane Co 12.36 10.50 10.49 10.46 10.45 Wisconsin Dodge Co 11.12 9.24 9.23 9.19 9.20 Wisconsin Grant Co 11.36 9.45 9.44 9.38 9.38 Wisconsin Kenosha Co 11.50 9.54 9.53 9.49 9.57 Wisconsin Manitowoc Co 9.81 8.25 8.25 8.22 8.24 Wisconsin Milwaukee Co 13.10 11.08 11.07 11.03 11.10 Wisconsin Outagamie Co 10.70 9.17 9.17 9.14 9.16 Wisconsin Vilas Co 6.40 5.63 5.62 5.61 5.61 Wisconsin Waukesha Co 13.11 11.01 11.00 10.97 10.99 Wyoming Campbell Co 6.30 6.09 6.09 6.07 6.09 Wyoming Converse Co 3.66 3.51 3.51 3.49 3.50 Wyoming Fremont Co 9.12 8.38 8.38 8.38 8.37 Wyoming Laramie Co 4.95 4.57 4.57 4.53 4.57 Wyoming Sheridan Co 10.49 9.87 9.87 9.79 9.82 
43 

Appendix B: 8-Hour Ozone Design Values for Locomotive/Marine Scenarios (units 

State Name County Name Baseline DV 2020 Base 2020 Marine only 2020 Locomotive 

/ Marine 

2030 Base 

Alabama Baldwin 78.0 66.1 65.8 65.7 67.0 Alabama Clay 79.3 57.7 57.6 57.3 56.3 Alabama Elmore 76.7 55.9 55.8 55.4 53.9 Alabama Etowah 75.0 55.2 55.1 54.8 53.8 Alabama Jefferson 83.7 60.8 60.7 60.3 58.9 Alabama Lawrence 76.3 56.2 56.0 55.7 54.9 Alabama Madison 79.7 58.8 58.6 58.3 56.9 Alabama Mobile 79.0 66.6 66.3 66.2 67.6 Alabama Montgomery 75.0 55.7 55.6 55.3 54.0 Alabama Morgan 82.0 61.8 61.6 61.3 60.6 Alabama Shelby 88.0 62.6 62.6 62.1 60.6 Alabama Sumter 71.7 52.3 52.2 52.0 51.2 Alabama Tuscaloosa 75.5 53.3 53.2 52.9 51.8 Arizona Maricopa 85.7 71.8 71.7 71.6 69.3 Arizona Pima 74.0 64.5 64.4 64.3 63.0 Arizona Pinal 82.0 66.5 66.4 66.2 64.6 Arizona Yavapai 78.7 67.8 67.5 67.3 66.0 Arkansas Crittenden 91.0 70.9 70.1 69.7 70.3 Arkansas Pulaski 81.7 63.1 62.8 62.5 61.3 

California Alameda 82.7 72.6 72.3 72.1 70.0 California Amador 85.7 71.6 71.3 71.0 67.9 California Butte 88.7 73.1 72.9 72.5 68.7 California Calaveras 91.0 76.8 76.4 76.1 73.4 California Colusa 73.3 61.5 61.3 61.1 58.4 California Contra Costa 79.3 72.9 72.7 72.6 71.1 California El Dorado 105.0 86.2 86.0 85.6 80.7 California Fresno 110.0 96.1 95.9 95.6 92.4 California Glenn 72.3 61.2 61.1 60.9 58.3 California Imperial 86.0 74.8 74.5 74.3 72.8 California Inyo 80.7 71.4 71.3 71.1 69.5 California Kern 114.3 100.9 100.8 100.6 97.6 California Kings 95.7 81.5 81.3 81.1 77.8 California Lake 64.3 56.2 56.2 56.1 54.5 California Los Angeles 121.3 109.0 108.8 108.7 105.5 
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California Madera 91.0 79.5 79.3 79.0 76.5 California Marin 48.0 42.1 42.1 42.1 40.8 California Mariposa 89.7 76.3 75.9 75.7 73.2 California Mendocino 56.7 47.9 47.8 47.7 45.6 California Merced 101.7 84.9 84.6 84.4 80.6 California Monterey 66.0 57.2 57.1 57.0 54.9 California Napa 64.7 53.3 53.1 53.0 50.7 California Nevada 97.7 80.4 80.2 79.7 75.8 California Orange 85.3 76.7 79.2 79.3 76.3 California Placer 98.3 80.7 80.5 80.2 75.6 California Riverside 115.0 103.1 103.5 103.5 101.9 California Sacramento 99.0 82.1 81.9 81.6 76.8 California San Benito 81.0 69.6 69.4 69.3 66.6 California San Bernardino 128.7 124.7 125.3 125.1 123.0 California San Diego 92.3 80.5 80.2 80.1 78.0 California San Joaquin 81.0 71.0 70.7 70.5 68.6 California San Luis Obispo 73.3 63.1 63.0 62.8 60.5 California San Mateo 56.7 52.5 52.5 52.4 51.4 California Santa Barbara 82.7 72.7 71.1 70.9 70.5 California Santa Clara 84.0 70.1 70.0 69.9 66.4 California Santa Cruz 65.0 57.1 57.0 56.9 55.0 California Shasta 72.3 60.8 60.7 60.4 57.7 California Solano 70.3 59.9 59.7 59.6 57.8 California Sonoma 62.0 50.7 50.6 50.5 47.6 California Stanislaus 95.0 81.9 81.6 81.5 78.4 California Sutter 87.3 72.2 72.0 71.8 68.3 California Tehama 84.0 69.7 69.6 69.1 66.0 California Tulare 105.7 88.8 88.6 88.4 84.6 California Tuolumne 91.0 77.3 76.9 76.7 74.0 California Ventura 94.7 81.5 80.9 80.8 78.7 California Yolo 81.7 68.3 68.1 67.9 64.6 Colorado Adams 65.3 57.7 57.7 57.6 57.1 Colorado Arapahoe 78.7 70.4 70.4 70.3 69.5 Colorado Boulder 75.3 63.9 63.9 63.7 62.9 Colorado Denver 74.0 65.4 65.4 65.3 64.7 Colorado Douglas 83.0 73.8 73.8 73.7 72.7 Colorado El Paso 72.3 63.2 63.2 63.0 62.6 Colorado Jefferson 84.7 74.2 74.2 74.0 73.2 
45 

Colorado La Plata 59.0 52.2 52.2 52.1 51.9 Colorado Larimer 80.3 67.8 67.8 67.6 66.8 Colorado Montezuma 68.0 63.0 62.9 62.9 62.6 Colorado Weld 76.7 64.9 64.9 64.7 64.9 Connecticut Fairfield 98.3 81.7 81.6 81.5 81.3 Connecticut Hartford 88.0 67.9 67.6 67.5 66.8 Connecticut Litchfield 86.0 66.7 66.3 66.2 66.2 Connecticut Middlesex 95.7 75.6 75.3 75.2 74.8 Connecticut New Haven 98.3 79.1 78.8 78.6 78.4 Connecticut New London 90.0 70.9 70.6 70.5 70.2 Connecticut Tolland 92.3 70.6 70.3 70.1 69.2 D.C. Washington 92.7 70.5 70.4 70.1 68.9 Delaware Kent 88.3 71.8 71.3 71.2 71.4 Delaware New Castle 92.7 72.4 72.1 71.9 71.7 Delaware Sussex 90.0 71.9 71.7 71.5 71.1 Florida Bay 79.3 62.9 62.7 62.6 61.8 Florida Brevard 72.7 52.8 52.5 52.5 51.7 Florida Duval 74.0 54.3 53.9 53.6 53.4 Florida Escambia 81.0 66.5 66.4 66.2 66.2 Florida Hillsborough 78.7 67.1 67.0 66.9 66.2 Florida Lake 76.0 56.3 56.2 56.1 55.4 Florida Manatee 76.3 62.3 62.1 62.1 61.6 Florida Miami-Dade 67.0 54.4 54.3 54.3 53.6 Florida Orange 76.3 57.6 57.5 57.4 56.6 Florida Pasco 76.7 58.8 58.6 58.5 57.7 Florida Pinellas 74.7 62.1 61.9 61.9 61.4 Florida Polk 76.3 59.8 59.5 59.4 59.0 Florida Santa Rosa 81.3 65.3 65.2 65.0 65.0 Florida Sarasota 79.7 62.2 61.8 61.7 61.6 Florida Seminole 77.5 58.3 58.2 58.1 57.2 Georgia Bibb 88.0 65.8 65.7 65.4 64.3 Georgia Chatham 68.3 54.4 54.1 53.9 53.6 Georgia Cherokee 78.0 54.7 54.6 54.4 52.0 Georgia Clarke 78.0 55.0 55.0 54.8 52.6 Georgia Cobb 91.0 64.9 64.9 64.7 61.8 Georgia Coweta 88.7 66.3 66.3 66.1 64.4 Georgia Dawson 80.0 57.3 57.3 57.1 55.0 Georgia De Kalb 91.0 68.3 68.3 68.0 65.9 
46 

Georgia Douglas 91.0 65.1 65.0 64.8 62.5 Georgia Fayette 85.3 63.4 63.3 63.1 60.8 Georgia Fulton 94.3 71.8 71.8 71.5 69.2 Georgia Glynn 72.3 55.8 55.5 55.3 54.9 Georgia Gwinnett 87.7 62.8 62.8 62.6 59.6 Georgia Henry 91.7 65.9 65.8 65.6 63.2 Georgia Murray 85.0 60.7 60.6 60.3 58.9 Georgia Muscogee 75.0 54.9 54.8 54.6 53.4 Georgia Paulding 88.0 61.4 61.3 61.1 59.2 Georgia Richmond 84.3 65.3 65.2 65.0 63.3 Georgia Rockdale 91.0 65.6 65.5 65.3 62.9 Georgia Sumter 75.0 55.2 55.0 54.7 54.0 Idaho Ada 76.0 69.9 69.9 69.8 68.7 Idaho Canyon 68.0 59.6 59.6 59.5 58.0 Illinois Adams 75.3 61.4 61.0 60.7 60.8 Illinois Champaign 75.0 59.8 59.6 59.3 58.9 Illinois Clark 73.0 54.4 54.2 54.0 53.8 Illinois Cook 85.3 75.0 75.1 75.1 74.3 Illinois Du Page 71.7 63.1 63.0 62.8 62.7 Illinois Effingham 74.7 58.3 58.1 57.9 57.4 Illinois Hamilton 79.3 60.6 60.0 59.8 60.2 Illinois Jersey 87.7 69.5 68.9 68.7 68.5 Illinois Kane 77.0 65.3 65.1 64.7 64.7 Illinois Lake 84.7 73.7 73.6 73.4 73.2 Illinois Macon 75.0 59.3 59.1 58.9 58.6 Illinois Macoupin 78.0 58.6 58.3 58.1 57.7 Illinois Madison 85.7 68.0 67.5 67.3 67.0 Illinois McHenry 82.0 69.7 69.5 69.1 69.0 Illinois McLean 76.0 59.4 59.2 58.9 58.4 Illinois Peoria 78.0 65.2 65.0 64.7 64.3 Illinois Randolph 77.7 60.9 60.5 60.2 60.2 Illinois Rock Island 68.7 56.3 56.1 55.8 55.1 Illinois Sangamon 75.3 54.8 54.6 54.4 53.6 Illinois St Clair 83.0 67.7 67.3 67.0 66.7 Illinois Will 78.3 63.9 63.8 63.4 63.1 Illinois Winnebago 75.0 60.2 60.0 59.6 59.0 Indiana Allen 87.0 68.7 68.5 68.1 67.4 Indiana Boone 88.0 69.4 69.2 68.9 68.2 
47 

Indiana Carroll 83.0 64.2 63.9 63.6 63.1 Indiana Clark 90.0 70.3 69.8 69.5 69.7 Indiana Delaware 85.5 66.2 66.0 65.6 64.9 Indiana Elkhart 87.0 69.2 69.0 68.6 68.1 Indiana Floyd 84.3 68.1 67.6 67.4 67.5 Indiana Gibson 73.0 52.5 52.2 52.1 51.7 Indiana Greene 87.0 63.9 63.6 63.3 63.0 Indiana Hamilton 93.7 72.0 71.8 71.6 70.4 Indiana Hancock 91.3 69.4 69.2 69.0 67.8 Indiana Hendricks 84.7 66.8 66.6 66.4 65.3 Indiana Huntington 83.3 65.7 65.5 65.2 64.5 Indiana Jackson 83.3 64.0 63.6 63.4 63.3 Indiana Johnson 85.3 66.3 66.1 65.9 65.2 Indiana La Porte 90.3 76.1 76.0 75.8 75.4 Indiana Lake 88.3 78.5 78.6 78.5 77.7 Indiana Madison 91.7 69.5 69.3 69.1 67.9 Indiana Marion 90.0 70.7 70.5 70.3 69.2 Indiana Morgan 85.0 67.4 67.2 67.0 66.2 Indiana Porter 86.3 76.0 76.1 76.0 75.3 Indiana Posey 84.0 63.5 62.9 62.6 62.7 Indiana Shelby 91.3 71.0 70.8 70.6 69.3 Indiana St Joseph 90.3 72.5 72.3 71.8 71.5 Indiana Vanderburgh 82.7 62.0 61.4 61.2 61.1 Indiana Vigo 85.0 68.0 67.8 67.5 67.1 Indiana Warrick 84.0 65.5 65.1 64.9 64.9 Iowa Clinton 76.3 64.2 63.9 63.6 63.4 Iowa Harrison 75.7 63.5 63.4 63.1 62.8 Iowa Montgomery 67.0 57.0 57.0 56.7 56.5 Iowa Polk 57.3 47.5 47.5 47.2 45.7 Iowa Scott 77.7 63.0 62.7 62.4 61.8 Iowa Story 60.7 49.6 49.5 49.3 47.8 Kansas Linn 74.3 61.3 61.2 60.9 60.6 Kansas Sedgwick 79.0 65.0 64.9 64.7 63.2 Kansas Sumner 75.7 63.6 63.5 63.3 62.5 Kansas Wyandotte 79.0 64.2 64.1 63.7 63.0 Kentucky Bell 82.3 57.4 57.3 57.0 55.5 Kentucky Boone 83.7 64.8 64.3 64.1 64.4 Kentucky Boyd 88.3 73.1 72.5 72.2 73.3 
48 

Kentucky Bullitt 81.0 63.3 62.8 62.6 62.5 Kentucky Campbell 90.7 72.3 71.8 71.6 71.6 Kentucky Carter 77.0 59.7 59.2 59.0 60.1 Kentucky Christian 84.0 59.1 58.7 58.5 58.7 Kentucky Daviess 75.3 60.1 59.6 59.5 59.7 Kentucky Edmonson 80.3 60.5 60.1 59.9 59.6 Kentucky Fayette 75.0 58.6 58.4 58.1 57.1 Kentucky Graves 79.0 61.1 60.7 60.5 60.3 Kentucky Greenup 81.3 67.2 66.6 66.3 67.6 Kentucky Hancock 81.7 64.9 64.4 64.2 64.6 Kentucky Hardin 78.3 59.3 58.8 58.6 58.6 Kentucky Henderson 79.3 61.8 61.4 61.2 61.2 Kentucky Jefferson 82.7 66.4 66.0 65.8 65.7 Kentucky Jessamine 76.3 58.6 58.4 58.1 57.4 Kentucky Kenton 85.0 67.7 67.1 66.9 67.1 Kentucky Livingston 82.7 63.2 62.7 62.5 62.8 Kentucky McCracken 79.0 65.3 64.9 64.7 64.7 Kentucky McLean 82.0 60.5 60.2 60.0 59.9 Kentucky Oldham 85.3 65.0 64.4 64.2 64.5 Kentucky Perry 75.7 56.7 56.5 56.2 55.9 Kentucky Pike 73.3 56.1 55.8 55.6 55.3 Kentucky Pulaski 77.3 59.9 59.8 59.4 59.0 Kentucky Scott 68.7 51.9 51.6 51.3 51.5 Kentucky Simpson 79.7 57.9 57.6 57.4 56.6 Kentucky Trigg 73.0 54.6 53.9 53.8 54.4 Kentucky Warren 82.0 61.4 61.1 60.8 60.5 Louisiana Ascension 79.3 70.8 70.0 69.9 70.9 Louisiana Bossier 79.7 62.2 62.0 61.6 60.9 Louisiana Caddo 77.3 60.0 59.7 59.4 58.9 Louisiana Calcasieu 78.7 68.1 67.5 67.3 68.1 Louisiana East Baton Rouge 87.0 78.1 77.7 77.6 77.9 Louisiana Iberville 84.3 75.0 74.1 74.0 75.1 Louisiana Jefferson 83.0 72.3 71.7 71.5 72.4 Louisiana Lafayette 79.3 68.1 67.1 66.9 68.6 Louisiana Lafourche 78.0 67.4 66.6 66.5 67.5 Louisiana Livingston 79.7 70.8 69.8 69.7 70.9 Louisiana Orleans 69.7 60.2 59.6 59.5 60.3 Louisiana Ouachita 77.7 62.6 62.2 62.0 61.8 
49 

Louisiana Pointe Coupee 73.3 65.1 64.6 64.5 65.0 Louisiana St Bernard 78.0 65.9 65.3 65.1 65.8 Louisiana St Charles 78.7 69.3 68.5 68.4 69.5 Louisiana St James 74.0 65.4 65.2 65.1 65.2 Louisiana St John The Baptist 78.7 69.9 69.6 69.5 69.4 Louisiana St Mary 74.7 63.9 62.6 62.5 64.5 Louisiana West Baton Rouge 84.0 75.6 74.9 74.8 75.6 Maine Cumberland 84.3 65.2 65.0 64.9 64.2 Maine Hancock 91.7 73.1 72.8 72.6 72.2 Maine Kennebec 78.0 61.6 61.4 61.3 60.6 Maine Knox 83.7 65.0 64.8 64.6 64.1 Maine Sagadahoc 79.0 61.2 61.1 60.9 60.4 Maine York 88.3 68.5 68.3 68.1 67.5 Maryland Anne Arundel 98.3 74.1 74.0 73.7 72.8 Maryland Baltimore 91.3 72.4 72.3 72.1 71.6 Maryland Carroll 88.7 66.8 66.6 66.3 65.5 Maryland Cecil 97.7 73.2 73.0 72.7 72.1 Maryland Charles 93.0 66.7 66.5 66.3 65.2 Maryland Frederick 87.3 67.2 67.0 66.8 66.1 Maryland Harford 100.3 78.9 78.7 78.5 78.0 Maryland Kent 95.3 71.7 71.5 71.2 70.6 Maryland Montgomery 86.7 65.8 65.7 65.5 64.6 Maryland Prince Georges 94.0 71.0 70.9 70.7 69.4 Maryland Washington 85.3 65.4 65.2 64.9 64.1 Massachusetts Barnstable 92.0 73.8 73.4 73.2 73.4 Massachusetts Berkshire 87.0 70.3 70.1 70.0 69.1 Massachusetts Bristol 91.0 71.9 71.7 71.5 71.5 Massachusetts Essex 90.0 72.8 72.7 72.5 72.0 Massachusetts Hampden 92.0 70.3 70.1 69.9 69.0 Massachusetts Hampshire 86.7 68.1 67.9 67.8 67.0 Massachusetts Middlesex 85.7 66.5 66.3 66.1 65.2 Massachusetts Norfolk 91.0 76.2 76.2 76.1 75.4 Massachusetts Suffolk 88.7 71.6 71.5 71.4 70.9 Massachusetts Worcester 85.5 66.9 66.6 66.5 65.9 Michigan Allegan 94.0 77.2 77.0 76.5 76.4 Michigan Benzie 85.7 69.7 69.5 69.2 68.9 Michigan Berrien 88.0 74.0 73.9 73.6 73.4 Michigan Cass 90.7 71.7 71.5 71.0 70.5 
50 

Michigan Clinton 82.7 66.7 66.5 66.2 65.5 Michigan Genesee 86.3 68.5 68.3 68.0 67.1 Michigan Huron 83.0 70.8 70.7 70.5 70.3 Michigan Ingham 82.3 65.9 65.8 65.5 64.8 Michigan Kalamazoo 82.7 65.1 64.9 64.5 63.9 Michigan Kent 84.7 67.4 67.2 66.9 66.2 Michigan Lenawee 85.0 69.4 69.2 69.0 68.6 Michigan Macomb 92.3 76.6 76.5 76.3 75.6 Michigan Mason 86.0 68.9 68.7 68.3 67.8 Michigan Missaukee 78.3 64.0 63.8 63.5 63.1 Michigan Muskegon 90.0 73.4 73.2 72.7 72.5 Michigan Oakland 87.7 74.2 74.1 73.9 73.3 Michigan Ottawa 86.0 69.3 69.1 68.7 68.3 Michigan Schoolcraft 77.0 65.0 64.9 64.6 64.5 Michigan St Clair 88.0 72.5 72.3 72.1 71.5 Michigan Washtenaw 87.3 71.4 71.3 71.1 70.5 Michigan Wayne 86.0 72.9 72.8 72.6 71.9 Mississippi Adams 77.7 63.1 61.9 61.7 63.2 Mississippi De Soto 83.3 64.0 63.6 63.2 62.8 Mississippi Hancock 81.0 66.1 65.4 65.1 65.7 Mississippi Harrison 80.3 65.1 64.4 64.2 65.0 Mississippi Hinds 72.7 52.1 51.8 51.4 50.1 Mississippi Jackson 80.0 68.6 68.3 68.1 68.5 Mississippi Lauderdale 73.3 52.3 52.1 51.8 50.9 Mississippi Lee 78.3 57.4 57.1 56.8 56.2 Mississippi Madison 74.3 55.2 54.9 54.5 53.7 Mississippi Warren 73.7 54.9 53.6 53.4 54.9 Missouri Cass 77.7 61.9 61.8 61.5 60.9 Missouri Cedar 79.7 64.8 64.6 64.2 63.7 Missouri Clay 83.7 66.7 66.5 66.0 65.0 Missouri Greene 74.5 59.8 59.6 59.3 58.6 Missouri Jefferson 84.7 68.9 68.4 68.2 67.6 Missouri Monroe 76.7 61.9 61.6 61.3 61.2 Missouri Platte 80.3 65.0 64.9 64.5 64.0 Missouri St Charles 90.0 73.0 72.6 72.4 71.6 Missouri St Louis 88.3 72.6 72.1 71.8 71.3 Missouri St Louis City 87.7 72.9 72.4 72.2 71.8 Missouri Ste Genevieve 82.7 66.8 66.4 66.1 66.0 
51 

Nebraska Douglas 67.3 57.3 57.2 57.0 56.6 Nebraska Lancaster 55.0 46.6 46.5 46.3 45.8 Nevada Clark 84.7 74.9 74.8 74.6 74.0 Nevada Douglas 69.0 61.1 61.0 60.9 59.4 Nevada Washoe 73.3 65.0 65.0 64.9 63.6 New Hampshire Belknap 76.5 61.5 61.4 61.2 60.4 New Hampshire Cheshire 74.3 58.4 58.2 58.1 57.6 New Hampshire Hillsborough 85.3 66.7 66.5 66.3 65.6 New Hampshire Merrimack 74.7 59.1 58.9 58.8 58.2 New Hampshire Rockingham 83.5 65.9 65.7 65.5 65.2 New Hampshire Strafford 78.5 61.1 60.9 60.8 60.2 New Jersey Atlantic 88.0 69.9 69.3 69.2 69.5 New Jersey Bergen 91.3 76.2 76.0 75.9 75.5 New Jersey Camden 99.7 79.6 79.1 78.9 78.9 New Jersey Cumberland 94.0 73.7 73.2 73.0 73.3 New Jersey Essex 67.0 54.1 54.1 54.0 53.5 New Jersey Gloucester 98.0 77.9 77.6 77.4 77.4 New Jersey Hudson 84.0 67.7 67.8 67.7 66.9 New Jersey Hunterdon 94.7 73.6 73.1 73.0 72.5 New Jersey Mercer 97.7 78.2 77.7 77.5 77.5 New Jersey Middlesex 96.0 75.4 75.0 74.9 74.4 New Jersey Monmouth 95.3 75.6 75.3 75.1 75.0 New Jersey Morris 95.3 73.2 72.9 72.7 72.2 New Jersey Ocean 105.7 82.1 81.5 81.3 81.1 New Jersey Passaic 86.7 69.2 69.0 68.9 68.3 New Mexico Bernalillo 76.3 66.4 66.3 66.1 64.5 New Mexico Dona Ana 78.3 70.2 70.2 70.0 66.8 New Mexico San Juan 74.3 70.4 70.4 70.3 70.0 New Mexico Sandoval 74.0 64.9 64.8 64.6 63.1 New Mexico Valencia 67.5 58.0 58.0 57.7 56.6 New York Albany 83.0 66.2 66.0 65.8 64.9 New York Bronx 82.7 71.1 71.1 70.9 70.7 New York Chautauqua 93.0 75.4 75.0 74.7 75.0 New York Chemung 80.3 63.1 63.0 62.7 61.9 New York Dutchess 92.0 71.0 70.7 70.5 70.4 New York Erie 95.7 77.7 77.3 77.0 77.1 New York Essex 89.0 70.6 70.4 70.2 69.6 New York Herkimer 74.0 60.3 60.1 59.9 59.4 
52 

New York Jefferson 91.3 74.9 74.7 74.5 74.3 New York Madison 79.7 63.7 63.6 63.3 62.5 New York Monroe 84.0 68.8 68.7 68.5 67.5 New York Niagara 91.7 77.2 77.0 76.8 76.8 New York Oneida 79.7 64.5 64.3 64.1 63.6 New York Onondaga 84.0 68.8 68.7 68.5 67.9 New York Orange 84.7 65.6 65.3 65.2 64.5 New York Oswego 68.0 55.1 55.0 54.8 54.4 New York Putnam 91.3 73.4 73.1 72.9 72.9 New York Queens 84.5 71.6 71.5 71.4 71.1 New York Rensselaer 86.0 68.4 68.2 68.0 67.0 New York Richmond 93.0 75.3 75.4 75.2 74.1 New York Saratoga 87.0 68.8 68.6 68.4 67.4 New York Schenectady 77.7 62.8 62.6 62.4 61.2 New York Suffolk 97.0 82.0 82.1 82.0 81.4 New York Ulster 81.3 65.2 65.0 64.8 64.2 New York Wayne 84.0 67.5 67.3 67.1 66.3 New York Westchester 91.3 76.3 76.1 76.0 75.7 North Carolina Alexander 87.0 63.2 63.1 62.9 61.5 North Carolina Avery 77.7 60.1 60.0 59.8 58.8 North Carolina Buncombe 80.0 61.6 61.5 61.3 59.9 North Carolina Caldwell 83.3 61.8 61.7 61.4 60.2 North Carolina Caswell 87.7 62.0 61.9 61.7 60.1 North Carolina Chatham 81.3 59.9 59.8 59.6 58.5 North Carolina Cumberland 86.0 63.1 63.0 62.7 61.1 North Carolina Davie 91.3 65.5 65.4 65.2 63.7 North Carolina Duplin 80.0 60.6 60.5 60.3 59.2 North Carolina Durham 88.7 62.8 62.7 62.5 60.6 North Carolina Edgecombe 87.3 64.6 64.5 64.3 62.7 North Carolina Forsyth 91.3 64.8 64.7 64.5 62.7 North Carolina Franklin 89.7 64.7 64.6 64.3 62.2 North Carolina Granville 92.3 65.8 65.7 65.5 64.2 North Carolina Guilford 88.7 61.3 61.2 61.0 58.7 North Carolina Haywood 84.7 65.8 65.7 65.5 64.5 North Carolina Jackson 86.0 64.9 64.8 64.5 63.6 North Carolina Johnston 84.3 61.4 61.3 61.1 59.1 North Carolina Lenoir 80.0 61.2 61.1 60.9 59.8 North Carolina Lincoln 90.7 65.7 65.6 65.4 63.8 
53 

North Carolina Martin 81.0 61.3 61.2 61.0 60.0 North Carolina Mecklenburg 97.3 73.0 72.9 72.7 70.8 North Carolina New Hanover 77.3 58.0 57.7 57.5 56.9 North Carolina Northampton 84.0 63.2 63.1 62.9 62.0 North Carolina Person 89.3 64.0 63.9 63.8 63.1 North Carolina Pitt 82.0 60.5 60.4 60.2 58.6 North Carolina Randolph 83.5 58.9 58.8 58.6 56.7 North Carolina Rockingham 88.3 63.0 62.9 62.7 61.6 North Carolina Rowan 97.3 70.1 70.0 69.8 67.8 North Carolina Swain 73.0 54.6 54.5 54.2 53.4 North Carolina Union 87.0 63.5 63.4 63.2 61.1 North Carolina Wake 92.5 65.8 65.7 65.5 62.9 North Carolina Yancey 83.0 64.1 64.0 63.8 62.5 Ohio Allen 88.0 69.7 69.4 69.2 68.7 Ohio Ashtabula 95.7 78.9 78.6 78.3 78.6 Ohio Butler 89.7 70.1 69.6 69.3 69.2 Ohio Clark 88.3 68.1 67.9 67.6 67.4 Ohio Clermont 89.3 70.8 70.3 70.1 70.0 Ohio Clinton 94.3 71.4 71.0 70.6 70.4 Ohio Cuyahoga 88.0 70.4 70.3 70.1 70.1 Ohio Delaware 89.0 68.4 68.2 67.9 67.0 Ohio Franklin 93.0 70.8 70.5 70.2 69.0 Ohio Geauga 99.0 79.5 79.2 78.9 78.6 Ohio Greene 87.7 68.0 67.6 67.3 67.2 Ohio Hamilton 90.3 71.3 70.8 70.5 70.3 Ohio Jefferson 84.3 65.0 64.7 64.5 64.5 Ohio Knox 87.0 66.5 66.3 66.0 65.1 Ohio Lake 92.7 75.3 75.2 75.0 75.0 Ohio Lawrence 81.7 67.5 66.9 66.6 68.0 Ohio Licking 88.0 67.2 67.0 66.7 65.7 Ohio Lorain 87.0 69.8 69.8 69.6 69.3 Ohio Lucas 90.0 72.8 72.8 72.5 71.9 Ohio Madison 88.7 67.2 66.9 66.7 66.3 Ohio Mahoning 87.0 67.1 66.8 66.5 66.0 Ohio Medina 87.0 69.7 69.5 69.2 69.3 Ohio Miami 87.0 66.5 66.2 66.0 65.2 Ohio Montgomery 86.5 67.5 67.3 67.0 66.4 Ohio Portage 91.0 71.1 70.7 70.4 70.0 
54 

Ohio Preble 80.0 62.0 61.7 61.5 61.1 Ohio Stark 88.3 67.8 67.6 67.3 66.5 Ohio Summit 93.3 73.6 73.4 73.1 72.7 Ohio Trumbull 92.0 70.8 70.5 70.2 69.7 Ohio Warren 90.7 70.3 70.0 69.7 69.3 Ohio Washington 85.7 63.2 62.7 62.5 63.1 Ohio Wood 87.7 70.4 70.1 69.7 69.5 Oklahoma Canadian 76.0 58.1 58.0 57.8 56.0 Oklahoma Cleveland 75.3 61.3 61.1 60.9 60.3 Oklahoma Comanche 77.3 62.4 62.2 62.0 61.7 Oklahoma Dewey 71.0 59.3 59.1 59.0 58.9 Oklahoma Kay 75.0 61.6 61.5 61.2 61.0 Oklahoma Mc Clain 77.0 63.0 62.8 62.6 62.2 Oklahoma Oklahoma 80.3 62.4 62.3 62.1 59.8 Oklahoma Ottawa 78.0 63.5 63.4 63.1 62.7 Oklahoma Pittsburg 73.0 61.6 61.4 61.1 61.0 Oklahoma Tulsa 83.0 67.7 67.6 67.4 65.6 Pennsylvania Adams 80.0 61.0 60.7 60.5 59.9 Pennsylvania Allegheny 91.3 73.9 73.6 73.3 73.3 Pennsylvania Armstrong 90.7 70.3 69.6 69.4 69.8 Pennsylvania Beaver 91.3 72.8 72.5 72.3 72.1 Pennsylvania Berks 88.7 67.4 67.2 67.0 66.2 Pennsylvania Blair 83.3 62.3 62.0 61.8 61.5 Pennsylvania Bucks 99.0 80.7 80.1 80.0 80.1 Pennsylvania Cambria 85.0 65.1 64.8 64.5 64.4 Pennsylvania Centre 84.7 63.7 63.5 63.2 62.7 Pennsylvania Chester 95.0 72.9 72.7 72.5 71.9 Pennsylvania Clearfield 87.3 66.7 66.3 66.1 66.0 Pennsylvania Dauphin 86.7 66.6 66.4 66.2 65.4 Pennsylvania Delaware 91.7 72.7 72.4 72.2 72.2 Pennsylvania Erie 89.0 73.2 72.7 72.5 72.9 Pennsylvania Franklin 90.7 68.8 68.5 68.2 67.3 Pennsylvania Greene 87.7 65.5 65.0 64.8 65.2 Pennsylvania Lackawanna 83.3 63.3 63.2 63.0 62.0 Pennsylvania Lancaster 91.0 69.2 69.1 68.8 68.0 Pennsylvania Lawrence 78.3 59.2 59.0 58.7 58.3 Pennsylvania Lehigh 90.7 68.6 68.4 68.2 67.3 Pennsylvania Luzerne 83.7 63.5 63.4 63.2 62.3 
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Pennsylvania Lycoming 82.0 62.7 62.5 62.3 61.7 Pennsylvania Mercer 91.3 70.1 69.7 69.4 69.0 Pennsylvania Montgomery 92.3 73.4 73.1 72.9 72.7 Pennsylvania Northampton 90.0 68.2 68.0 67.8 66.8 Pennsylvania Perry 83.3 63.1 63.0 62.7 62.0 Pennsylvania Philadelphia 96.7 79.7 79.2 79.0 79.2 Pennsylvania Tioga 85.0 66.4 66.2 66.0 65.4 Pennsylvania Washington 86.3 68.2 68.0 67.8 67.7 Pennsylvania Westmoreland 88.0 70.9 70.3 70.1 70.3 Pennsylvania York 89.0 68.5 68.4 68.1 67.4 Rhode Island Kent 93.0 72.6 72.2 72.0 72.1 Rhode Island Providence 92.0 72.0 71.5 71.4 71.4 Rhode Island Washington 92.7 73.7 73.2 73.1 73.3 South Carolina Abbeville 82.3 61.5 61.4 61.2 59.6 South Carolina Aiken 82.7 62.7 62.6 62.3 60.9 South Carolina Anderson 85.3 64.8 64.8 64.6 62.3 South Carolina Barnwell 79.3 60.0 59.9 59.6 58.4 South Carolina Berkeley 71.5 54.5 54.1 53.9 53.6 South Carolina Charleston 72.5 56.3 55.9 55.7 55.5 South Carolina Cherokee 83.7 62.2 62.2 62.0 60.6 South Carolina Chester 82.7 60.6 60.5 60.3 58.9 South Carolina Chesterfield 80.0 60.1 60.0 59.7 58.6 South Carolina Colleton 77.7 59.4 59.3 59.1 58.2 South Carolina Darlington 82.7 62.5 62.4 62.2 61.0 South Carolina Edgefield 79.7 60.3 60.2 60.0 58.7 South Carolina Oconee 83.7 61.8 61.7 61.5 59.8 South Carolina Pickens 83.0 61.0 61.0 60.8 58.9 South Carolina Richland 89.3 67.8 67.7 67.5 65.3 South Carolina Spartanburg 87.0 63.7 63.7 63.4 61.4 South Carolina Union 79.7 59.9 59.8 59.6 58.1 South Carolina Williamsburg 71.7 53.4 53.3 53.1 52.3 South Carolina York 83.0 60.8 60.8 60.6 59.1 Tennessee Anderson 87.0 60.2 60.0 59.7 57.6 Tennessee Blount 92.3 66.6 66.4 66.1 64.4 Tennessee Davidson 77.7 58.2 57.9 57.6 56.7 Tennessee Hamilton 88.3 63.7 63.5 63.1 61.3 Tennessee Haywood 83.5 61.5 61.2 61.0 60.4 Tennessee Jefferson 91.0 63.4 63.3 63.0 60.2 
56 

Tennessee Knox 92.0 63.4 63.3 62.9 59.9 Tennessee Lawrence 77.0 57.8 57.6 57.3 56.9 Tennessee Meigs 89.0 62.8 62.7 62.3 60.8 Tennessee Putnam 84.0 63.1 63.0 62.8 61.9 Tennessee Rutherford 80.7 59.5 59.3 59.1 58.1 Tennessee Sevier 92.3 68.0 67.8 67.5 66.3 Tennessee Shelby 87.7 68.3 67.5 67.2 67.6 Tennessee Sullivan 86.7 67.3 67.3 67.0 66.0 Tennessee Sumner 85.7 63.5 63.3 63.0 61.9 Tennessee Williamson 84.3 62.1 61.8 61.6 60.5 Tennessee Wilson 82.0 61.6 61.4 61.2 60.2 Texas Bexar 88.7 70.6 70.4 70.1 69.4 Texas Brazoria 94.0 80.2 79.1 78.9 80.6 Texas Collin 90.0 71.4 71.2 71.0 70.3 Texas Dallas 90.0 70.5 70.4 70.2 69.7 Texas Denton 97.3 76.2 76.1 75.9 75.4 Texas El Paso 79.3 70.5 70.5 70.3 67.3 Texas Ellis 85.0 64.4 64.3 64.1 63.3 Texas Galveston 89.7 76.6 76.6 76.4 76.6 Texas Gregg 84.3 69.0 68.8 68.6 68.2 Texas Harris 102.0 92.4 92.4 92.3 92.1 Texas Harrison 78.5 62.8 62.5 62.3 62.1 Texas Hood 83.0 59.6 59.5 59.2 58.1 Texas Jefferson 91.0 78.6 77.4 77.2 79.0 Texas Johnson 89.7 67.3 67.2 67.0 66.0 Texas Kaufman 72.0 56.0 55.9 55.6 55.3 Texas Montgomery 88.3 77.3 76.7 76.6 77.3 Texas Nueces 80.3 68.9 68.0 67.8 69.2 Texas Orange 81.0 69.6 68.6 68.4 69.8 Texas Parker 87.0 64.8 64.7 64.5 63.6 Texas Rockwall 82.0 63.2 63.1 62.9 62.2 Texas Smith 82.0 65.1 64.9 64.7 64.2 Texas Tarrant 98.7 77.0 76.9 76.7 76.2 Texas Travis 84.7 65.7 65.4 65.2 64.5 Texas Victoria 77.7 65.1 64.4 64.2 65.0 Texas Webb 64.7 55.3 54.8 54.7 55.2 Utah Box Elder 77.5 67.0 67.0 66.7 66.2 Utah Cache 69.3 58.7 58.7 58.4 57.5 
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Utah Davis 81.0 72.8 72.8 72.6 71.7 Utah Salt Lake 79.7 72.8 72.7 72.4 72.2 Utah Utah 77.7 69.3 69.3 69.0 68.3 Utah Weber 79.3 68.1 68.1 67.8 66.8 Vermont Bennington 79.3 62.5 62.3 62.1 61.1 Virginia Alexandria City 90.0 68.1 68.0 67.8 66.7 Virginia Arlington 96.7 74.2 74.1 73.8 72.6 Virginia Caroline 82.3 60.7 60.6 60.3 59.5 Virginia Charles City 89.3 70.2 70.1 69.9 69.1 Virginia Chesterfield 84.7 67.6 67.5 67.4 66.7 Virginia Fairfax 96.7 73.2 73.1 72.8 71.6 Virginia Fauquier 79.3 59.7 59.5 59.3 58.8 Virginia Frederick 82.7 63.3 63.1 62.8 62.2 Virginia Hampton City 88.3 72.9 72.7 72.5 72.4 Virginia Hanover 92.0 71.2 71.1 70.9 70.0 Virginia Henrico 88.3 68.9 68.8 68.6 67.7 Virginia Loudoun 90.0 68.5 68.3 68.1 67.4 Virginia Madison 84.7 64.2 64.1 63.9 63.2 Virginia Page 79.7 59.3 59.1 58.9 58.3 Virginia Prince William 85.0 64.9 64.7 64.5 63.8 Virginia Roanoke 83.7 63.2 63.1 62.8 61.6 Virginia Rockbridge 76.7 58.0 57.9 57.7 57.0 Virginia Stafford 86.0 64.8 64.7 64.2 63.5 Virginia Suffolk City 87.0 72.0 71.8 71.6 71.7 West Virginia Berkeley 83.0 63.7 63.5 63.2 62.5 West Virginia Cabell 85.7 70.9 70.5 70.3 71.7 West Virginia Hancock 84.7 65.5 65.3 65.1 65.0 West Virginia Kanawha 84.0 63.6 63.2 63.0 63.1 West Virginia Monongalia 78.7 56.7 56.5 56.4 56.2 West Virginia Ohio 83.3 64.6 64.2 64.1 64.3 West Virginia Wood 85.7 63.9 63.3 63.1 63.8 Wisconsin Brown 80.3 67.9 67.8 67.5 67.4 Wisconsin Columbia 76.3 62.1 61.9 61.7 61.1 Wisconsin Dane 76.0 62.6 62.5 62.2 61.6 Wisconsin Dodge 79.3 65.7 65.5 65.3 65.1 Wisconsin Door 91.0 75.0 74.8 74.4 74.4 Wisconsin Fond Du Lac 77.3 63.7 63.6 63.4 63.0 Wisconsin Jefferson 80.0 65.0 64.9 64.6 64.1 
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Wisconsin Kenosha 98.3 85.0 84.9 84.7 84.4 Wisconsin Kewaunee 89.3 74.3 74.1 73.8 73.9 Wisconsin Manitowoc 87.0 72.2 72.0 71.6 71.6 Wisconsin Milwaukee 91.0 77.2 77.1 76.8 76.6 Wisconsin Ozaukee 93.0 78.0 77.9 77.5 77.3 Wisconsin Racine 91.7 78.0 77.9 77.7 77.4 Wisconsin Rock 81.7 66.1 65.9 65.5 64.7 Wisconsin Sauk 72.0 59.4 59.3 59.0 58.6 Wisconsin Sheboygan 97.0 81.1 80.9 80.5 80.4 Wisconsin St Croix 71.3 61.2 61.1 61.0 60.7 Wisconsin Walworth 81.3 66.3 66.2 65.8 65.4 Wisconsin Washington 80.3 67.4 67.2 67.0 66.9 Wisconsin Waukesha 79.0 66.0 65.9 65.6 65.5 
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Appendix C: Visibility Levels on 20% Worst Days for Locomotive/Marine Scenario (unit

Class 1 Area State Baseline Visibility 2020 Base 2020 Locomotive / Marine 2030 Base 2

Loco

Ma

Sipsey Wilderness AL 29.03 23.78 23.73 23.77 Caney Creek Wilderness AR 26.36 22.11 22.05 22.06 Upper Buffalo Wilderness AR 26.27 22.41 22.35 22.34 Chiricahua NM AZ 13.43 13.09 13.09 13.10 Chiricahua Wilderness AZ 13.43 13.09 13.09 13.10 Galiuro Wilderness AZ 13.43 13.08 13.07 13.11 Grand Canyon NP AZ 11.66 11.13 11.09 11.15 Mazatzal Wilderness AZ 13.35 12.74 12.72 12.77 Petrified Forest NP AZ 13.21 12.90 12.83 12.87 Pine Mountain Wilderness AZ 13.35 12.59 12.58 12.59 Saguaro NM AZ 14.83 14.50 14.47 14.49 Sierra Ancha Wilderness AZ 13.67 13.22 13.20 13.18 Sycamore Canyon Wilderness AZ 15.25 14.96 14.94 14.98 

Agua Tibia Wilderness CA 23.50 21.23 21.14 21.16 

Caribou Wilderness CA 14.15 13.62 13.60 13.55 Cucamonga Wilderness CA 19.94 17.42 17.36 17.14 Desolation Wilderness CA 12.63 12.15 12.13 12.15 Dome Land Wilderness CA 19.43 18.37 18.34 18.20 Emigrant Wilderness CA 17.63 17.22 17.21 17.24 Hoover Wilderness CA 12.87 12.73 12.72 12.75 Joshua Tree NM CA 19.62 17.95 17.93 17.85 Lassen Volcanic NP CA 14.15 13.56 13.54 13.48 Lava Beds NM CA 15.05 14.45 14.42 14.40 Mokelumne Wilderness CA 12.63 12.32 12.30 12.34 Pinnacles NM CA 18.46 17.37 17.36 17.16 Point Reyes NS CA 22.81 22.01 21.99 21.87 Redwood NP CA 18.45 17.89 17.86 17.86 San Gabriel Wilderness CA 19.94 17.30 17.25 17.01 San Gorgonio Wilderness CA 22.17 20.28 20.22 19.94 San Jacinto Wilderness CA 22.17 19.92 19.87 19.61 South Warner Wilderness CA 15.05 14.61 14.59 14.57 
60 

Thousand Lakes Wilderness CA 14.15 13.54 13.52 13.46 Ventana Wilderness CA 18.46 17.67 17.64 17.67 Yosemite NP CA 17.63 17.16 17.14 17.15 Black Canyon of the Gunnison NM CO 10.33 9.80 9.79 9.79 Eagles Nest Wilderness CO 9.61 9.05 9.03 8.99 Flat Tops Wilderness CO 9.61 9.31 9.31 9.32 Great Sand Dunes NM CO 12.78 12.36 12.36 12.37 La Garita Wilderness CO 10.33 9.90 9.89 9.89 Maroon Bells-Snowmass Wilderness CO 9.61 9.24 9.24 9.24 Mesa Verde NP CO 13.03 12.40 12.39 12.40 Mount Zirkel Wilderness CO 10.52 10.06 10.05 10.07 Rawah Wilderness CO 10.52 10.04 10.04 10.06 Rocky Mountain NP CO 13.83 13.10 13.08 13.06 Weminuche Wilderness CO 10.33 9.86 9.86 9.86 West Elk Wilderness CO 9.61 9.24 9.24 9.24 Chassahowitzka FL 26.09 21.96 21.94 21.96 Everglades NP FL 22.30 19.75 19.77 19.97 St. Marks FL 26.03 21.84 21.82 21.88 Cohutta Wilderness GA 30.30 23.36 23.33 23.34 Okefenokee GA 27.13 23.46 23.42 23.50 Wolf Island GA 27.13 23.40 23.37 23.40 Craters of the Moon NM ID 14.00 13.00 12.97 12.90 Sawtooth Wilderness ID 13.78 13.64 13.63 13.64 Mammoth Cave NP KY 31.37 25.53 25.48 25.54 

Acadia NP ME 22.89 19.79 19.77 19.86 

Moosehorn ME 21.72 18.65 18.63 18.68 Roosevelt Campobello International Park ME 21.72 18.47 18.45 18.51 Isle Royale NP MI 20.74 19.15 19.10 19.16 Seney MI 24.16 21.77 21.72 21.78 Voyageurs NP MN 19.27 17.62 17.58 17.53 Hercules-Glades Wilderness MO 26.75 23.00 22.93 22.96 

Anaconda-Pintler Wilderness MT 13.41 13.15 13.14 13.13 

Bob Marshall Wilderness MT 14.48 14.14 14.13 14.11 Cabinet Mountains Wilderness MT 14.09 13.57 13.54 13.52 Gates of the Mountains Wilderness MT 11.29 10.92 10.91 10.89 Medicine Lake MT 17.72 16.25 16.22 16.18 Mission Mountains Wilderness MT 14.48 14.06 14.04 14.02 Scapegoat Wilderness MT 14.48 14.17 14.16 14.14 
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Selway-Bitterroot Wilderness MT 13.41 13.06 13.04 13.02 UL Bend MT 15.14 14.66 14.64 14.62 Linville Gorge Wilderness NC 28.77 22.48 22.45 22.47 Swanquarter NC 25.49 21.17 21.15 21.20 Lostwood ND 19.57 17.73 17.70 17.67 Theodore Roosevelt NP ND 17.74 16.65 16.54 16.61 Great Gulf Wilderness NH 22.82 19.48 19.45 19.50 Presidential Range-Dry River Wilderness NH 22.82 19.48 19.45 19.50 Brigantine NJ 29.01 24.88 24.85 24.99 Bandelier NM NM 12.22 11.43 11.41 11.38 Bosque del Apache NM 13.80 12.96 12.90 12.94 Gila Wilderness NM 13.11 12.55 12.54 12.55 Pecos Wilderness NM 10.41 10.01 10.00 10.02 Salt Creek NM 18.03 16.61 16.59 16.58 San Pedro Parks Wilderness NM 10.17 9.53 9.52 9.53 Wheeler Peak Wilderness NM 10.41 9.96 9.95 9.97 White Mountain Wilderness NM 13.70 13.07 13.05 13.07 Jarbidge Wilderness NV 12.07 11.86 11.86 11.86 Wichita Mountains OK 23.81 20.67 20.62 20.68 Crater Lake NP OR 13.74 13.29 13.27 13.26 Diamond Peak Wilderness OR 13.74 13.25 13.20 13.22 Eagle Cap Wilderness OR 18.57 17.86 17.83 17.79 Gearhart Mountain Wilderness OR 13.74 13.39 13.37 13.36 Hells Canyon Wilderness OR 18.55 17.26 17.20 17.18 Kalmiopsis Wilderness OR 15.51 15.00 14.98 14.97 Mount Hood Wilderness OR 14.86 14.19 14.13 14.28 Mount Jefferson Wilderness OR 15.33 14.80 14.77 14.82 Mount Washington Wilderness OR 15.33 14.77 14.75 14.78 Mountain Lakes Wilderness OR 13.74 13.26 13.24 13.23 Strawberry Mountain Wilderness OR 18.57 17.77 17.73 17.69 Three Sisters Wilderness OR 15.33 14.84 14.82 14.84 Cape Romain SC 26.48 22.77 22.74 22.77 Badlands NP SD 17.14 15.87 15.84 15.80 Wind Cave NP SD 15.84 14.94 14.91 14.94 Great Smoky Mountains NP TN 30.28 23.96 23.93 23.93 Joyce-Kilmer-Slickrock Wilderness TN 30.28 23.46 23.43 23.43 Big Bend NP TX 17.30 16.15 16.13 16.18 Carlsbad Caverns NP TX 17.19 15.93 15.92 15.92 
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Guadalupe Mountains NP TX 17.19 15.89 15.88 15.89 

Arches NP UT 11.24 11.14 11.11 11.05 

Bryce Canyon NP UT 11.65 11.36 11.34 11.34 Canyonlands NP UT 11.24 10.84 10.81 10.83 Zion NP UT 13.24 12.96 12.92 12.89 James River Face Wilderness VA 29.12 23.43 23.34 23.43 Shenandoah NP VA 29.31 22.83 22.80 22.83 Lye Brook Wilderness VT 24.45 21.10 21.08 21.17 

Alpine Lake Wilderness WA 17.84 16.77 16.71 16.72 

Glacier Peak Wilderness WA 13.96 13.62 13.60 13.69 Goat Rocks Wilderness WA 12.76 12.06 12.05 12.07 Mount Adams Wilderness WA 12.76 12.03 12.01 12.02 Mount Rainier NP WA 18.24 17.27 17.24 17.27 North Cascades NP WA 13.96 13.58 13.57 13.68 Olympic NP WA 16.74 15.85 15.82 15.95 Pasayten Wilderness WA 15.23 14.85 14.84 14.83 Dolly Sods Wilderness WV 29.04 22.38 22.35 22.38 Otter Creek Wilderness WV 29.04 22.31 22.29 22.32 Bridger Wilderness WY 11.12 10.81 10.81 10.80 Fitzpatrick Wilderness WY 11.12 10.86 10.85 10.86 Grand Teton NP WY 11.76 11.36 11.35 11.33 North Absaroka Wilderness WY 11.45 11.17 11.16 11.15 Red Rock Lakes WY 11.76 11.44 11.43 11.42 Teton Wilderness WY 11.76 11.41 11.40 11.39 Washakie Wilderness WY 11.45 11.18 11.17 11.16 Yellowstone NP WY 11.76 11.39 11.38 11.36 
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United States Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

Publication No. E