### 1 MEMORANDUM

SUBJECT: Non

- Hg Case Study Chronic Inhalation Risk Assessment for the Utility MACT Appropriate and Necessary Analysis

FROM: Madeleine Strum, Emissions Inventory and Analysis Group ( C339

- 02 ) James Thurman, Air Quality Modeling Group ( C439

- 01 )
Mark Morris

, Air Toxics Assessment Group (C539

- 02)

TO: D ocket EPA

- HQ

- OAR

- 2009

- 0234

DATE: March 1 6 , 2011

To estimate the potential for human health impacts from current emissions of HAPs other than mercury from coal

- and oil

- fired electric utility steam generating units (EGUs) , several
facilities were selected as case studies for a chronic inhalation risk assessment , and this memo documents the methods and results of the assessment for these case studies

. The assessment was performed in support of the appropriate and necessary analysis for coal

- and oil

- fired EGU s

.

1. Case S tudy S election

An initial set of eight case study facilities was selected based on several factors. First, we considered facilities with the highest estimated cancer and non

- cancer risks using the 2005 National Emissions Inventory (NEI) data and the Human Exposure Model (HEM

- 3 ) 1 

### . The 2005 NEI data were used because the initial set of case study facilities was selected before we received
the bulk of the emissions data from the “Information Collection Effort for New and Existing
Coal

- and Oil

- fired Electric Utility Steam Generating Units” (EPA ICR No. 2362.01; OMB
Control Number 2060

- 0631)

. The criteria considered in the selection of the initial set of cas e stud y facilities a re given below :

o Focus ed on the highest risk facilities based upon previous studies. W e identified the top
facilities in terms of total inhalation cancer and non

- cancer risk based on the 2005 NEI and
the use of HEM

- 3, following the same approach used for risk assessments performed for section 112(f) of the Clean Air Act

.

o Screen ed the top facilities to exclude those that (a) were not power generating facilities that contribute to the grid, (b) for which ICR facility data were not available, and (c) install ed emissions control equipment, suggested that the 2005 NEI may not reflect current conditions
at those facilities (and, therefore, may have significantly lower risks)

.

o Provide d coverage, to the extent possible, for the different cancer and non

- cancer ris k drivers identified in risk modeling for the top EGUs in each category.

o Provide d coverage for different regions of the country, including those believed to have higher US EGU mercury impacts (e.g., eastern portion of the country, primarily NE and SE).

o Vi sual ly inspect ed aerial images o f each EGU location to verify that there are potential
residential loca tions located nearby and

, consequently, possible near

- field inhalation impacts.

1 http://www.epa.gov/ttn/fera/hem_download.html

### 2 Based on application of the se criteria , the initial set of case study facilities included : Xcel Bayfront

, SC&E Canadys

, Dominion Chesapeake Energy Center

, Exelon Cromby Generating
Station

, Spruance Genco

, PSI Energy

– Wabash River

, Heco Waiau

, and Dominion

– Yorktown

.

After the receipt of more data through the ICR, addit ional case study facilities were selected, based on the magnitude of emissions, heat input values (throughput), and level of emission
control. For the initial set of case study facilities, emissions were estimated for all HAPs
included in the available IC R data. Because dispersion modeling and risk estimates were performed for the initial set of case study facilities before the selection of the additional facilities,
the results of the initial set were used to focus the assessment of the additional facili ties. The results for the initial set indicated that nickel, hexavalent chromium, and arsenic were the cancer risk drivers, and that non

- cancer risks were not significant. Although the non

- cancer risks were
low ( the maximum chronic hazard index was 0.4 )

, they were driven by emissions of nickel, arsenic and hydrogen chloride. For the se reasons , the selection of additional case study facilities
focused on nickel, hexavalent chromium, and arsenic . The following criteria were used to select
the additional c ase study facilities:

o Considered the potential emissions of EGU s based on generating capacity ( > 300 MW)

.

o Focus ed on coal

- fired EGU s with the highest emission rates of chromium and arsenic from
test data. W e computed an approximate emission rate using 2008 heat input data from the
Clean Air Markets data and maps 2



### o Focused on untested EGU s with the highest throug hput and minimal emission controls

. For throughput, we used 2008 heat input data

. , a unit

- specific emission factor estimate d from the ICR
data , and average capacity utilization based on 2007

- 2009 data provided in the ICR data

.

An additional eight case study facilities were selected using these criteria: Cambria Cogen

, Conesville

, TVA Gallatin

, City Utilities of Springfield

- James River

, Amerenue

- Labadie

, PSHNH

– Merrimack

, Monticello Steam Electric Plant

, and OG&E

– Muskogee

. Considering both sets of case study facilities, t here were a total of sixteen facilities , fifteen that use coal as fuel, and one that uses oil. The locations of the case stu d y facilities are shown in Figure 1.

It is important to note that the case study facilities likely do not represent the highest

- emitting (and highest

- risk) sources. Although facility selection criteria included high estimated cancer
and non

- cancer risks using the 2005 NEI data , high throughput

, and minimal emission control

, another necessary criterion was the availability of ICR data for the EGUs at those facilities (or for similar EGUs at other facilities). Because the ICR data were collected for the p urpose of developing the MACT standards, the ICR was targeted towards better performing sources, with a smaller set of random recipients. Therefore, facilities for which ICR data were available may not represent the highest

- emitting sources.

2 http://camddataandmaps.epa.gov/gdm/

### 3 Figure 1. Location of C ase S tudy S ites

.

2

. Methods

This section describes the methods used to estimate emissions for the case study facilities, and the dispersion modeling and risk estimation methods used.

2.1 Emissions Estimation

For the initial set of case study facilities, emissions were estimated using ICR data for metals, HCl, and s everal organic compounds, such as ethylene dibromide, polycyclic aromatic hydrocarbons, and naphthalene . However, because the ICR emission rate est imates of organic
HAP emissions were based on minimum detectable levels , they were not used in the assessment
of any of the case study facilities. As discussed above, only emissions of nickel, hexavalent chromium, and arsenic were estimated for the additi onal set of case study facilities.

For all case study facilities, e missions were developed using the data from P art II I of the
ICR. Emission factors , in lbs of pollutant per million BTU, were developed directly from the test data, where available. Where test data were not available for a specific unit

, emission factors were derived from similarly configured units at the facility (site

- average across tested units) or from similarly configured units from other facilities tested (average across tested units with

### 4 similar configuration). Only EGU s were considered in the assessment ; o ther emitting processes
located at the facilities were not considered.

Speciated chromium emissions data were not available from the ICR. Speciated chromium
emissions data are a vailable from existing test data on four coal

- fired boilers and seven oil

- fired boilers. These test data indicate that hexavalent chromium is on average twelve percent and eighteen percent of the emissions from coal

- and oil

- fired boilers, respectively, a nd we applied
these values to the total chromium emissions estimates to estimate hexavalent chromium
emissions.

“Actual” annual e missions were computed with the following equation :

In the above equation, the annual emission rate was computed in tons per year using an
emission factor in lb/MMBtu and the maximum heat input in MMBtu /hr . The capacity factor
was taken as the boiler

- specific 3

- year average capacity factor provided by ICR respondents of

Part I of the ICR. If a respondent did not provide a 3

- year average capacity factor for a boiler, a
default value of 100 percent was used. For the set of additional case stud y facilities , potential emissions were computed using a 100 percent average capacity factor.

Hourly emissions for e ach hour in years 2005

- 2009 were generated from the actual annual emissions by using hourly heat input as a temporalization factor. That is, the hourly heat input divided by the annual heat input was multiplied by the annual emissions to get hourly emissi ons for each pollutant. The same annual emissions value was used for all 5 years. Hourly heat input
values were obtained from Clean Air Markets continuous emission monitoring (CEM) data.
Where data were missing for several months for a specific year, the data for the same period of a different year were used. In situations where hourly heat inputs w ere unavailable for the entire

year, a S tate average temporalization for the year 200 5 was used for all years. The S tate average data were readily available from national scale modeling efforts . For the Heco Waiau case study, neither unit nor S tate average data were available

, so we used facilities with CEM data
from Broward and Miami

- Dade C ounties as a surrogate for Hawaii CEM heat input for each of the five years. Hourly emissions derived from the potential emissions were created assuming the
same emissions for each hour.

Tabl e 1 provides facility and unit location

, and stack parameter data for all units in the case
studies . Table 2 provides estimates of actual and potential emissions

.

### 5 Table 1. Unit Locations and Stack Parameters.

Facility L ocatio n Unit Type Unit ID L at L on g S tack Height

(m) Stack

T emperature (K) Stack

V elocity

(m/s) Stack

D iameter (m)
Xcel Bayfront Ashland, WI 1 coal 5 46.5872

- 90.9018 59.44 371.48 13.05 1.86

Cambria Cogen Ebensburg, PA 2 coal B1 40.4748

- 78.703 70.10 466.48 27.83 2.29

B2 40.4748

- 78.703 70.10 466.48 27.83 2.29

SC&E Canadys Canadys, SC 3 coal CAN001 33.0646

- 80.6235 60.96 415.37 11.00 4.88

CAN002 33.0653

- 80.6232 60.96 412.04 12.62 4.88

CAN003 33.065

- 80.6218 60.96 413.71 19.93 4.88

Dominion Chesapeake
Energy Center Chesapeake, VA 4 coal Unit 1 36.7705

- 76.3012 53.34 414.26 17.68 3.96

Unit 2 36.7706

- 76.3011 53.34 412.59 18.25 3.96

Unit 3 36.7709

- 76.3009 60.96 413.15 21.29 3.66

Unit 4 36.7712

- 76.3008 60.96 382.04 21.45 4.27

Conesville Conesville, OH 4 coal 3 40.1648

- 81.9044 137.16 416.48 12.19 5.33

4 40.1862

- 81.8787 243.84 416.48 25.36 7.92

5 40.1856

- 81.8798 243.84 324.82 23.92 7.92

6 40.1856

- 81.8798 243.84 324.82 23.92 7.92

Exelon Cromby Generating

Station Phoenixville, PA coal (unit 1) oil (unit 2) Unit 1 40.1524

- 75.5303 91.44 388.71 17.92 4.27

Unit 2 40.152

- 75.5304 91.44 388.71 17.11 4.27

TVA Gallatin Gallatin, TN 4 coal 1 36.3156

- 86.4005 152.86 408.15 21.76 7.62

2 36.3156

- 86.4005 152.86 408.15 21.76 7.62

3 36.3151

- 86.4009 152.86 408.15 24.57 7.62

4 36.3151

- 86.4009 152.86 408.15 24.57 7.62

City Utilities of Springfield

- James River Springfield, MO 3 coal 3 37.1084

- 93.2602 60.96 422.04 4.57 3.66

4 37.1084

- 93.2598 60.96 422.04 5.70 3.66

5 37.1084

- 93.2605 106.68 422.04 14.72 2.53

Amerenue

- Labadie Labadie, MO 4 coal 1 38.5626

- 90.8381 213.36 444.26 28.04 6.25

2 38.5621

- 90.8377 213.36 444.26 28.04 6.25

3 38.5614

- 90.8371 213.36 444.26 28.04 8.84

4 38.5614

- 90.8371 213.36 444.26 28.04 8.84

PSHNH

- Merrimack Bow, NH 2 coal 1 43.142

- 71.4685 69.19 433.15 45.42 2.59

### 6 Facility L ocatio n Unit Type Unit ID L at L on g S tack Height

(m) Stack

T emperature (K) Stack

V elocity

(m/s) Stack

D iameter (m)

2 43.1418

- 71.4682 97.23 449.82 31.09 4.42

Monticello Steam Electric

Plant Mount Pleasant,

TX 3 coal 1 33.0907

- 95.0375 121.92 453.15 24.69 6.55

2 33.0914

- 95.038 121.92 453.15 24.69 6.55

3 33.0923

- 95.0378 140.21 354.26 26.52 2.44

OG&E

- Muskogee Fort Gibson, OK 3 coal 4 35.7618

- 95.2886 106.68 402.04 17.13 7.32

5 35.7619

- 95.288 106.68 402.04 17.13 7.32

6 35.7621

- 95.2872 152.40 402.04 24.84 6.55

Spruance Genco Richmond, VA 8 coal GEN1 37.4552

- 77.4312 76.20 355.37 17.03 2.62128

GEN2 37.4555

- 77.4309 76.20 355.37 17.03 2.62128

GEN3 37.4557

- 77.4307 76.20 355.37 17.03 2.62128

GEN4 37.4559

- 77.4304 76.20 355.37 17.03 2.62128

PSI Energy

– Wabash River West Terre
Haute, IN 1 coal

- gas (unit 1) 2 coal (units 4&6) PG7221FA 39.5303

- 87.4256 68.58 452.59 19.17 5.49

4 39.5274

- 87.4232 137.16 410.93 34.26 7.62

6 39.5274

- 87.4232 137.16 410.93 34.26 7.62

Heco Waiau Waiau, HI 6 oil W3 21.3891

- 157.9615 42.09 469.26 12.25 3.05

W4 21.389

- 157.9613 42.09 469.26 12.25 3.05

W5 21.3888

- 157.9612 41.91 414.26 12.25 2.74

W6 21.3887

- 157.961 41.91 414.26 12.25 2.74

W7 21.3885

- 157.9606 41.91 392.04 16.12 3.2

W8 21.3884

- 157.9603 41.91 392.04 16.12 3.2

Dominion

- Yorktown Yorktown, VA 2 coal (units 1&2) 1 oil (unit 3) Units 1&2 37.2154

- 76.4622 98.76 402.04 32.77 5.18

Unit 3 37.2152

- 76.4612 149.05 415.93 33.52 6.86

### 7 Table 2. Unit Emissions Estimates.

Facility L ocation Unit Type Unit ID A s ( TPY )

actual A s ( TPY ) potential Cr +6
( TPY )*

actual Cr +6
( TPY )* potential Nickel ( TPY )

actua l Nickel ( TPY ) potential HC l ( TPY )

actual Basis of Emissions

Factor (lbs/MMBtu) **

Xcel Bayfront Ashland, WI 1 coal 5 n/a n/a n/a 40 similar config

Cambria

Cogen Ebensburg, PA 2 coal B1 used potential 1.0E

- 03 used potential 3.6E

- 02 used potential 2.2E

- 01 not
modeled test

B2 1.1E

- 03 7.8E

- 03 7.2E

- 02 test

SC&E Canadys Canadys, SC 3 coal CAN001 5.1E

- 02 not
modeled 1.9E

- 02 not
modeled 1.3E

- 01 not
modeled 114 similar config

CAN002 4.9E

- 02 1.8E

- 02 1.3E

- 01 111 similar config

CAN003 2.2E

- 02 2.4E

- 03 9.6E

- 02 4.8 test (except HCl )

Dominion Chesapeake
Energy

Center Chesapeake,

VA 4 coal Unit 1 5.1E

- 02

not
modeled 1.9E

- 02

not
modeled 1.3E

- 01

not
modeled 113 similar config

Unit 2 5.6E

- 02 2.1E

- 02 1.4E

- 01 125 similar config

Unit 3 1.0E

- 01 3.8E

- 02 2.7E

- 01 232 similar config

Unit 4 1.4E

- 01 5.0E

- 02 3.5E

- 01 304 similar config

Conesville Conesville, OH 4 coal 3 9.1E

- 02 1.2E

- 01 6.6E

- 01 8.5E

- 01 3.5E+00 4.5E+00

not
modeled test

4 1.0E

- 01 1.6E

- 01 8.1E

- 03 1.3E

- 02 1.9E

- 01 3.0E

- 01 similar config

5 6.8E

- 02 8.2E

- 02 5.3E

- 03 6.5E

- 03 1.2E

- 01 1.5E

- 01 similar config

6 6.5E

- 02 8.2E

- 02 5.1E

- 03 6.5E

- 03 1.2E

- 01 1.5E

- 01 similar config

Exelon Cromby Generating

Station Phoenixville,

PA coal (unit 1) oil (unit 2) Unit 1 6.6E

- 02

not
modeled 3.0E

- 03

not
modeled 1.8E

- 02

not
modeled 5.7 similar config

Unit 2 2.4E

- 03 6.0E

- 04 3.4E

- 01 0.97 test for arsenic only

TVA Gallatin Gallatin, TN 4 coal 1 3.3E

- 03 4.8E

- 03 2.6E

- 01 3.8E

- 01 1.2E+00 1.8E+00

not
modeled site average

2 3.3E

- 03 4.8E

- 03 2.7E

- 01 3.8E

- 01 1.2E+00 1.8E+00 test

3 3.8E

- 03 5.3E

- 03 3.0E

- 01 4.2E

- 01 1.4E+00 2.0E+00 site average

4 3.8E

- 03 5.3E

- 03 3.1E

- 01 4.2E

- 01 1.4E+00 2.0E+00 site average City Utilities

of Springfield

- James River Springfield,

MO 3 coal 3 4.1E

- 03 5.1E

- 03 1.1E

- 01 1.3E

- 01 6.6E

- 01 8.1E

- 01

not
modeled site average

4 4.6E

- 03 5.4E

- 03 4.1E

- 02 4.9E

- 02 4.5E

- 01 5.4E

- 01 test

5 9.1E

- 03 1.1E

- 02 3.5E

- 01 4.4E

- 01 1.9E+00 2.4E+00 test

Amerenue

Labadie Labadie, MO 4 coal 1 3.9E

- 01 5.4E

- 01 1.3E

- 01 1.8E

- 01 9.5E

- 01 1.3E+00

not
modeled similar config

2 4.3E

- 01 5.4E

- 01 1.5E

- 01 1.8E

- 01 1.1E+00 1.3E+00 similar config

3 4.4E

- 01 5.4E

- 01 1.5E

- 01 1.8E

- 01 1.1E+00 1.3E+00 similar config

4 4.6E

- 01 5.4E

- 01 1.5E

- 01 1.8E

- 01 1.1E+00 1.3E+00 similar config

PSHNH

- Bow, NH 2 coal 1 8.2E

- 02 9.4E

- 02 2.7E

- 02 3.2E

- 02 2.0E

- 01 2.3E

- 01 not similar config

### 8 Facility L ocation Unit Type Unit ID A s ( TPY )

actual A s ( TPY ) potential Cr +6
( TPY )*

actual Cr +6
( TPY )* potential Nickel ( TPY )

actua l Nickel ( TPY ) potential HC l ( TPY )

actual Basis of Emissions

Factor (lbs/MMBtu) **

Merrimack 2 1.5E

- 01 2.1E

- 01 2.0E

- 02 2.9E

- 02 8.5E

- 02 1.2E

- 01 modeled test

Monticello

Steam
Electric

Plant Mount Pleasant, TX 3 coal 1 6.1E

- 02 6.7E

- 02 1.16E

- 01 1.26E

- 01 4.1E

- 01 4.5E

- 01

not
modeled site average

2 6.2E

- 02 6.7E

- 02 1.17E

- 01 1.26E

- 01 4.1E

- 01 4.5E

- 01 test

3 1.1E

- 01 1.2E

- 01 1.98E

- 02 2.09E

- 02 1.7E

- 01 1.8E

- 01 similar config

OG&E

- Muskogee Fort Gibson,

OK 3 coal 4 3.1E

- 01 4.7E

- 01 1.0E

- 01 1.6E

- 01 7.5E

- 01 1.2E+00 not
modeled similar config

5 2.8E

- 01 4.7E

- 01 9.5E

- 02 1.6E

- 01 6.9E

- 01 1.2E+00 similar config

6 3.2E

- 01 4.6E

- 01 1.1E

- 01 1.5E

- 01 7.9E

- 01 1.1E+00 similar config

Spruance

Genco Richmond, VA 8 coal GEN1 8.4E

- 04 not
modeled 3.1E

- 04 not
modeled 2.4E

- 03 4.5 test

GEN2 9.0E

- 04 3.2E

- 04 8.3E

- 03 0.0 test

GEN3 3.5E

- 03 5.6E

- 04 6.2E

- 03 0.0 test

GEN4 2.4E

- 03 3.6E

- 04 4.1E

- 03 0.1 test

PSI Energy

–

Wabash

River West Terre
Haute, IN 1 coal

- gas (unit 1) 2 coal (units 4&6) PG7221FA 1.3E

- 03 not
modeled 7.5E

- 04

not
modeled 5.0E

- 03

not
modeled 1.1 test

4 1.1E

- 01 4.5E

- 03 4.1E

- 02 43.6 test

6 3.3E

- 01 1.4E

- 02 1.2E

- 01 131.0 test

Heco Waiau Waiau, HI 6 oil W3 1.4E

- 03

not
modeled 7.7E

- 05

not
modeled 2.1E

- 01

not
modeled 0.2 site average

W4 1.6E

- 03 8.7E

- 05 2.4E

- 01 0.2 site average

W5 2.0E

- 03 1.1E

- 04 3.0E

- 01 0.2 site average

W6 2.0E

- 03 1.1E

- 04 3.1E

- 01 0.2 site average

W7 8.3E

- 03 1.2E

- 03 1.3E+00 0.3 tested

W8 9.3E

- 03 1.3E

- 03 1.5E+00 0.4 tested

Dominion

- Yorktown Yorktown, VA 2 coal (units 1&2) 1 oil (unit 3) Units 1&2 1.9E

- 01

not
modeled 6.8E

- 02 not
modeled 4.8E

- 01

not
modeled 415.5 similar config

Unit 3 2.4E

- 02 3.6E

- 01 1.8E+01 1.6 tested

* hexavalent chromium was assumed to be 12 percent of total chromium for coal fired units (same value used in the EPRI report) and 18 percent for oil fired units. Coal gas sp eciation was assigned the coal speciation (12 percent ).

** Emission factors were based on unit

- specific test data ("test"), data from the average of tested similar units at the same facility ("site average"), or units th at had similar boiler / control con figurations ("similar config")

### 9 2. 2 Dispersion Modeling

Dispersion m odeling for the case stud y facilities was done using AERMOD 3



### , EPA’s preferred model for near

- field dispersion. The AERMOD modeling system has two input data processors that are regulatory components of the system: AERMET, a meteorological data preprocessor that incorporates air dispersion based on planetary boundary layer turbulence structure and scaling concepts, and AERMAP, a terrain data preprocessor that incorporates complex terrain using USGS d igital e levation d ata. Other nonregulatory components of the
AERMOD system include AERSURFACE, a surface characteristics preprocessor , and the Building Profile Input Program for PR IME (BPIPPRM)

, a pro cessor that calculates building parameters (e.g., height and projected building width) for use in building downwash algorithms
within AERMOD.

For each facility listed in Table 1, each boiler or combination of boilers ( e.g., Yorktown
Un its 1 and 2) was modeled as an individual emission point. Sources were modeled with a unit emission rate of 1 g/s. Annual and hourly emissions were used in post

- processing to calculate
concentrations for all modeled HAPs. The EGU location coordinates an d stack parameters used in the modeling are given in Table 1 . When stack parameters such as stack height and diameter differed among the boilers, plane view and aerial photos were used to assign the correct source
identifier to the stacks. Source locatio ns ( in UTM coordinates ) were entered into AERMAP to calculate source elevations. Census block centroids within 20 km of each facility were used as model receptors . Receptor coordinates (in UTM coordinates) were entered into AERMAP to calculate receptor e levations.

Because data w ere not readily available for building parameters, building downwash was ignored except for Waiau, Cambria, and Yorktown. Photos of these facilities revealed that downwash may play a role in dispersion. Building parameters were generated using aerial and plane view photos. Later analysis revealed that downwash may not play a role in Cambria and Yorktown. Table 3 summarizes the use of downwash for each facility.

The urban/rural classification for each facility was based on methodologies listed in Section 7.2.3 of the Guideline on Air Quality Models 4 

### . These methodologies are a landuse method and a population density based method. With both methods, a circle with 3 km radius is centered on
the source. If 50 percent of the area within the circle is commercial, industrial, or high intensity residential landuse, the source is considered urban. If the population density within the circle is 750 people per sq. km or more, the source is considered urban. Also, if a source is ne ar an urban area but not considered urban based on the above methods, it was still modeled as urban. Based on these methods, three facilities, Dominion Chesapeake, Spruance Genco, and Heco Waiau were considered urban. Urban populations were based on near by city populations . All others
were modeled as rural sources.

Five years of meteorological data were used as input for the modeling. The most recent five complete years ( 2005 through 2009 ) were chosen. Integrated Surface Hourly (ISH)

3 http://www.epa.gov/ttn/scram/dispersion_prefrec.htm#aermod

4 U.S. EPA, 2005. Guideline on Air Quality Models . 40 CFR Part 51 Appendix W. http://www.epa.gov/ttn/scram/guidance/guide/appw_05.pdf

.

### 10 meteorological da ta and upper air data were processed through a beta version of AERMET which includes the input of hourly averaged winds from a beta preprocessor ( AERMINUTE ) that
processes two

- minute wind speeds for Automated Surface Observing Stations ( ASOS ) . Surface
cha racteristics were processed using AERSURFACE. For each station, the most representative National Weather Service surface and upper air stations were chosen. This was often the closest station or station with the least amount of missing data when multiple stations were considered. A list of selected stations for each facility is shown in Table 3 , along with urban/rural
classification and whether downwash effects were modeled

.

Table 3. Model Scenario Information

.

Facility Downwash Urban/rural (population) Surface station Upper air station Xcel Bayfront

No Rural Ashland Kennedy Memorial Airport, WI Minneapolis, MN

Cambria Cogen

Yes Rural Johnstown Cambria County Airport, PA Pittsburgh, PA

SC&E Canadys

No Rural Charleston Intl. Airport, SC Charleston Intl. Airport, SC Dominion Chesapeake Energy Center No Urban (200,000) Norfolk Intl. Airport,

VA Washington Dulles,

VA

Conesville

No Rural Zanesville Municipal Airport, OH Wilmington, OH

Exelon Cromby Generating Station No Rural Philadelphia Intl. Airport, PA Washington Dulles,

VA

TVA Gallatin

No Rural Nashville Intl. Airport,

TN Nashville Intl. Airport, TN City Utilities of Springfield

- James

River No Rural Springfield Regional Airport, MO Springfield Regional Airport, MO

Amerenue

- Labadie

No Rural St. Louis Lambert Intl. Airport, MO Lincoln, IL

PSHNH

- Merrimack

No Rural Concord Municipal Airport, NH Albany, NY

Monticello Steam

Electric Plant No Rural Tyler Pounds Field, TX Shreveport, LA

OG&E

- Muskogee

No Rural Muskogee Davis Field,

OK Norman, OK

Spruance Genco

No Urban (200,000) Richmond Intl. Airport,

VA Washington Dulles,

VA PSI Energy

– Wabash

River No Rural Terre Haute Hulman Regional Airport ,IN Wilmington, OH

Heco Waiau

Yes Urban (300,000) Honolulu Intl Airport,

HI Lihue, HI

Dominion

- Yorktown

Yes Rural Newport News Intl. Airport, VA Washington Dulles,

VA

### 11 AERSURFACE was used to process 1992 land cover data to calculate the following surface characteristics at the surface meteorological stations: albedo , Bowen ratio and surface roughness. All variables were allowed to vary seasonally. Bowen ratio was also allowed to vary seasonally and by year, as it is affected by the moisture conditions at the station (average, dry, or wet) based on precipitation amo unts. Precipitation was calculated for each season for each year
from 2005 to 2009. For stations that do not use default month

- season assignments (e.g., March may not be considered spring at some locations), AERSURFACE output was varied monthly but preci pitation used to define whether the location experienced dry, wet, or average moisture conditions was based on seasonal totals. A season was considered average if the seasonal precipitation was within 25 percent of the seasonal normal. A season was dry i f the seasonal precipitation was less than 75 percent of the seasonal normal and a season was wet if the season
exceeded 125 percent of the seasonal normal. Surface roughness also varied spatially at a location, based on aerial photos and analysis of the 1992 land cover data. Surface roughness may vary spatially around a station because of the proximity of vegetation and structures around the meteorological tower.

A potential concern related to the use of NWS meteorological data for dispersion modeling i s the often high incidence of calms and variable wind conditions reported for the
ASOS stations in use at most NWS stations since the mid

- 1990’s. In the METAR 5 

### coding used to report surface observations beginning July 1996, a calm wind is defined as a win d speed less than 3 knots and is assigned a value of 0 knots. The METAR code also introduced the variable wind observation that may include wind speeds up to 6 knots, but the wind direction is reported as missing if the wind direction varies more than 60 degrees during the 2

- minute averaging period for the observation. The AERMOD model currently cannot simulate dispersion under calm or missing wind conditions. To reduce the number of calms and missing winds in the surface data, archived 1

- minute winds fo r the ASOS stations can be used to calculate hourly average wind speed and directions, which are used to supplement the standard archive of hourly observed winds processed in AERMET.

Recently, the National Climatic Data Center (NCDC) began archiving the 2

- minute average wind speeds for each minute of the hour for most ASOS stations for public access.
These 2

- minute values have not been subjected to the METAR coding for calm and variable winds, but have also been subjected to only limited quality control measures. The 1

- minute ASOS wind data consists of running 2

- minute average winds, reported every minute, for
commissioned ASOS stations. The 1

- minute ASOS wind data can be obtained without cost through the National Climatic Data Center (NCDC) website ( ftp://ftp.ncdc.noaa.gov/pub/data/asos

- onemin/ )

. AERMINUTE reads the one minute data files and calculates hourly average winds for each hour from available minutes. In AERMET processing, the hourly average winds supersede the winds from the standard observations also processed by AERMET.

For actual emissions, AERMOD hourly concentrations were output at each receptor for each boiler at a facility for the period 2005

- 2009. For example, if a fa cility had three boilers, each receptor would have an hourly concentration contribution from each boiler. A unit

5 METAR literally trans lates to Météorologique Aviation Régulière. The Federal Aviation Administration defines the report as the aviation routine weather report.

### 12 emission rate of 1 g/s for each boiler was modeled in AERMOD

. The resulting modeled concentration at each receptor was scaled by the hourly e missions of each HAP to calculate an hourly concentration for each HAP. A 5

- year concentration for each HAP was calculated by averaging the hourly values (that were not calm or missing hours) for the 2005

- 2009 period

. The methodology for estimating concen trations due to potential emissions was similar, except the potential emissions were uniformly distributed throughout a year (i.e., no hourly variability).

2. 3 Chronic Inhalation Risk Assessment

In the risk assessment for chronic inhalation exposures, we used the 5

- year average ambient concentrations of HAP estimated from the refined dispersion modeling. The estimated ambient concentration at each nearby census block centroid was used as a surro gate for the chronic inhalation exposure concentration for all the people who reside in that census block. We calculated the maximum individual risk, or MIR, for each facility as the cancer risk associated with a continuous lifetime (24 hours per day, 7 da ys per week, and 52 weeks per year for a 70

- year period) exposure to the maximum concentration at the centroid of an inhabited census block. Individual cancer risks were calculated by multiplying the estimated lifetime exposure to
the ambient concentratio n of each carcinogenic HAP (in micrograms per cubic meter) by its cancer unit risk estimate (URE), which is an upper bound estimate of an individual's probability of contracting cancer over a lifetime of exposure to a concentration of 1 microgram of the

po llutant per cubic meter of air. We used URE values for arsenic and hexavalent chromium from EPA’s Integrated Risk Information System (IRIS), which is a human health assessment program that evaluates quantitative and qualitative risk information on effects that may result from exposure to environmental contaminants. Unit risk estimates in IRIS have undergone both internal and external peer review.

The IRIS URE for nickel subsulfide was derived from evidence of the carcinogenic effects of
insoluble nickel c ompounds in crystalline form. Soluble nickel species such as nickel sulfate, and insoluble species in amorphous form, do not appear to produce genotoxic effects by the same toxic mode of action as insoluble crystalline nickel, although other agencies, incl uding the International Agency on Research in Cancer 6 

### and the Danish EPA 7 

### for example, have determined that nickel sulfate, specifically, and nickel compounds, in general, are carcinogenic. Although there is some discussion of how risk varies with differe nt combinations of nickel compounds, the collection of epidemiology studies provides strong evidence of carcinogenicity specifically with respect to soluble nickel (nickel sulfate). In 2009, the National Toxicology Program (NTP) 8

6 World Health Organization International Agency for Research on Cancer, 1997. IARC Monographs on the
Evaluation of Carcinogenic Risks to Humans, Volume 49, Chromium, Nickel and Welding. http://monographs.iarc.fr/ENG/Monographs/vol49/volume49.pdf 

### noted that "The combined results of epidemiological studies, mechanistic studies, and carcinogenesis studies in rodents support the concept that nickel compounds generate nickel ions in target cells at sites critical for carcinogenesis, thus allowing consideration
and evaluation o f these compounds as a single group." Nickel speciation information for large combustion sources (including oil combustion, coal combustion, and others) suggests that at least

7 Danish Environmental Protection Agency, 2008. European Union Risk Assessment Report, Nickel and Nickel Compounds.

8 Repor t on Carcinogens, Eleventh Edition; U.S. Department of Health and Human Services, Public Health Service, National Toxicology Program.

### 13 35% of total nickel emissions may be soluble compounds, while the remaining ins oluble nickel
emissions are not well

- characterized. More recent data specifically from oil combustion suggest
that the nickel emissions can be 85

- 100% nickel sulfate. EPA does not have an inhalation unit
risk estimate for nickel sulfate and is not aware of any other organization that has developed one. However, the EPA does have a unit risk for one of the insoluble nickel compounds, nickel
subsulfide. This unit risk is 4.8 X 10

- 4 per ug/m3. A draft assessment from Texas Commission on Environmental Qual ity (May 2009) derives a unit risk for total nickel which is between 3.9 X
10

- 4 and 9.0 X 10

- 4 per ug/m3 (these are upper bound estimates, where the range corresponds to different adjustment procedures in the modeling). This estimate is roughly similar to the IRIS unit risk for nickel subsulfide. For the purposes of this assessment, we have assumed that 65% of emitted nickel is insoluble, that all insoluble nickel is crystalline, and that the URE for nickel subsulfide can be applied to this fraction. On t his basis, the URE for nickel subsulfide (representing pure insoluble crystalline nickel) was multiplied by 0.65 and applied to all nickel compounds. 9 

### We note that this approach, while attempting to account for potential differences in toxicity between t he soluble and insoluble forms of nickel, is not completely consistent with the NTP suggestion that all forms of nickel can be considered as a single group, and, therefore, this approach may result in an underestimation of the health risks from nickel emis sions from
EGUs.

To assess the risk of non

- cancer health effects from chronic exposures, following the approach recommended in EPA’s Mixtures Guidelines 10

, 11



### , we summed the hazard quotients (HQ) for all HAPs that affect a common target organ system to obtain the HI for that target organ system (target

- organ

- specific HI, or TOSHI). The HQ for chronic exposures is the estimated chronic exposure (again, based on t he estimated 5

- year average ambient concentration at each nearby census block centroid) divided by the chronic non

- cancer reference level, which is usually the EPA Reference Concentration (RfC), defined as ``an estimate (with uncertainty spanning perhaps a n order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.'' In cases where an IRIS RfC is not available, EPA utilizes the following prioritized sources for chronic dose

- response values: (1) The Agency for Toxic Substances and Disease Registry (ATSDR) Minimum Risk Level (MRL), which is defined as ``an estimate of daily human exposure to a substance that is likely to be without an appreciable risk of adverse effects (other than cancer) over a specified duration of exposure''; and (2) the California Environmental Protection Agency chronic Reference Exposure Level (REL), which is
defined as ``the concentration level at or below which no adverse health effects are anticipated for a specified exposure duration.'' In this assessment, we used the IRIS RfC values for hexavalent chromium and hydrogen chloride, the ATSDR MRL for nickel compounds , and the
California Environm ental Protection Agency REL for arsenic.

9 Recent data from industry have attempted to further characterize the insoluble nickel. While these data show that
the in soluble nickel is primarily in a spinel form, they do not attempt to characterize the toxicity of that form, noting only that it is not in the insoluble crystalline form. As such, it does not provide us with a better means for characterizing the risks tha n the approach described above.

10 US EPA, 1986, Guidelines for the Health Risk Assessment of Chemical Mixtures, EPA

- 630

- R

- 98

- 002. http://www.epa.gov/NCEA/raf/pdfs/chem_mix/chemmix_1986.pdf.

11 US EPA, 2000. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures. EPA

630/R

- 00

- 002. http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf.

### 14 The health reference values used in the assessment are given in Table 4.

Table 4. Health Reference Values Used in the Assessment.

Pollutant CAS Number URE (1/μg/m3) Source RfC (mg/m 3 

### ) Source

Arsenic 7440382 4.3E

- 03 IRIS 0.000015 CalEPA Chromium (VI) 18540299 0.012 IRIS 0.0001 IRIS

Nickel 7440020 0.000312 IRIS 0.00009 CalEPA

HCl 7647010 0.02 IRIS



### 3. Results

The results of th e assessment are given in Table 5. Based on actual emissions, t he highest estimated lifetime cancer risk from any of the sixteen case study facilities was 10 in a million, driven by nickel emissions from the one case study facility with oil

- fired EGU s. For the
facilities with coal

- fired EGU s, there were three with ma ximum cancer risks greater than 1 in a million (the highest was 8 in a million), all driven by hexavalent chromium, and there were four
with cancer risks at 1 in a million. All of the facilities had non

- cancer TOSHI values less than one, with a maximum T OSHI value of 0.4 (also driven by nickel emissions from the one case study facility with oil

- fired EGU s).

The cancer risk estimates from this assessment indicate that the EGU source category would not be eligible for delisting under section 112(c)(9)(B)(i ) of the CAA, which specifies that a category may be delisted only when the Administrator determines “... that no source in the category (or group of sources in the case of area sources) emits such hazardous air pollutants in quantities which may cause a lif etime risk of cancer greater than one in one million to the individual in the population who is most exposed to emissions of such pollutants from the
source....” We note that, since these case studies do not cover all facilities in the category, and
since o ur assessment does not include the potential for impacts from different EGU facilities to overlap one another (i.e., these case studies only look at facilities in isolation), the maximum risk estimates from the case studies may underestimate true maximum r isks.

### 15 Table 5

. Chronic Inhalation Risk Assessment Results.

Facility Actual emissions Potential emissions

Max Risk Risk Driver Max HI HI Driver Max risk Risk Driver Max HI HI Driver Xcel Bayfront 4.0x10 - 9 

### Formaldehyde 0.005 HCl N/A N/A N/A N/A Cambria Cogen 5.0x10 - 7 

### Chromium VI 0.003 Nickel 5.0x10 - 7 

### Chromium VI 0.003 Nickel SC&E Canadys 6.0x10 - 7 

### Arsenic 0.009 HCl N/A N/A N/A N/A Dominion Chesapeake Energy Center 3.0x10 - 6 

### Chromium VI 0.05 HCl N/A N/A N/A N/A

Conesville 3.0x10 - 6 

### Chromium VI 0.01 Nickel 3.0x10 - 6 

### Chromium VI 0.01 Nickel Exelon Cromby Generating Station 3.0x10 - 7 

### Arsenic 0.008 Nickel N/A N/A N/A N/A

TVA Gallatin 1.0x10 - 6 

### Chromium VI 0.006 Nickel 1.0x10 - 6 

### Chromium VI 0.005 Nickel City Utilities of Springfield

- James River 8.0x10 - 6 

### Chromium VI 0.04 Nickel 9.0x10 - 6 

### Chromium VI 0.05 Nickel

Amerenue

- Labadie 8.0x10 - 7 

### Arsenic 0.006 Arsenic 9.0x10 - 7 

### Arsenic 0.007 Arsenic

PSHNH

- Merrimack 1.0x10 - 6 

### Arsenic 0.01 Arsenic 2.0x10 - 6 

### Arsenic 0.01 Arsenic

Monticello Steam Electric

Plant 6.0x10 - 7 

### Chromium VI 0.003 Arsenic 7.0x10 - 7 

### Chromium VI 0.003 Arsenic

OG&E

- Muskogee 1.0x10 - 6 

### Arsenic 0.01 Arsenic 2.0x10 - 6 

### Arsenic 0.01 Arsenic Spruance Genco 8.0x10 - 8 

### Arsenic 0.007 HCl N/A N/A N/A N/A
PSI Energy

– Wabash

River 1.0x10 - 7 

### Arsenic 0.001 Arsenic N/A N/A N/A N/A

Heco Waiau 1.0x10 - 5 

### Nickel 0.4 Nickel N/A N/A N/A N/A

Dominion

- Yorktown 1.0x10 - 6 

### Chromium VI 0.02 Nickel N/A N/A N/A N/A