# Technical Support Document: Huma n-health Multipathway Residualrisk Assessment for the Ferroalloy-production Source Category

DRAFT

April 24, 2015

Prepared For:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

Prepared By:

ICF International

2635 Meridian Pkwy.

Suite 200

Durham, NC 27713

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# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft i April 2015 

# CONTENTS

Tables ........................................................................................................................ ................... ii

Figures ....................................................................................................................... .................. iii

1. Introduction .................................................................................................................. ......... 1

2. Screening-level Assessments ............................................................................................... 2

2.1 Tier-1 Emission-screening Assessment ...................................................................... 2

2.2 Tier-2 Emission-screening Assessment ...................................................................... 3

2.2.1 Adjustment Factors Applied to Tier-1 Screening Values .............................. 4

2.2.2 Calculating Refined Tier-2 Fisher Screening Values .................................... 5

2.2.3 Finalizing Tier-2 Farmer Screening Values ................................................... 6

2.2.4 Tier-2 Screening Results for this Source Category ....................................... 6

3. Scope of Site-specific Risk Assessment ............................................................................. 11

4. Assessment Methods ......................................................................................................... 13

4.1 Fate-and-transport Modeling ..................................................................................... 14

4.1.1 Overview of the Area Surrounding the Facility ............................................ 14

4.1.2 Meteorological Data for Modeling ............................................................... 16

4.1.3 Characteristics of the Modeled Emission-source

Compartment .............................................................................................. 18

4.1.4 Modeling Domain and Parcel Design .......................................................... 21

4.1.5 Abiotic Environment .................................................................................... 28

4.1.6 Biotic Environment ...................................................................................... 29

4.2 Methods for Exposure and Risk Modeling ................................................................. 29

4.2.1 Methods for Calculating Exposure Concentrations ..................................... 30

4.2.2 Ingestion-exposure Assessment ................................................................. 31

4.2.3 Risk Characterization .................................................................................. 35

5. Results ....................................................................................................................... ......... 35

5.1 Media Concentrations ............................................................................................... 35

5.2 Risk-assessment Results .......................................................................................... 35

5.2.1 Risk-assessment Summary ........................................................................ 35

5.2.2 Hazard Quotients Associated With Mercury Exposure ............................... 36

5.2.3 Hazard Quotients Associated With Cadmium Exposure ............................. 36

5.2.4 Cancer Risks Associated With PAH Exposure ........................................... 43

5.2.5 Cancer Risks Associated With Dioxin Exposure ......................................... 46

5.2.6 Comparison to Screening-level-assessment Results ................................. 49

6. Discussion of Uncertainties and Limitations ....................................................................... 51

6.1 Uncertainties Related to Fate-and-transport Modeling (TRIM.FaTE) ........................ 52

6.2 Uncertainties Related to Exposure Modeling and Risk

Characterization (MIRC) ........................................................................................... 55

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft ii April 2015 

# 7. References .................................................................................................................... ..... 57

Appendix A: User-Specified Values for TRIM.FaTE Properties ................................................ A-1

Appendix B: Modeled Media Concentrations ............................................................................ B-1



# TABLES

Table 2-1. Tier-1 Screening Results for the Ferroalloy-production Source

Category ................................................................................................................. 3

Table 2-2. Tier-2 Screening Results for the Ferroalloy-production Source

Category ................................................................................................................. 8

Table 2-3. Tiers 1 and 2 Screening Results for NEI11660, by PB-HAP Chemical

and PB-HAP Group Total ....................................................................................... 9

Table 2-4. Meteorological and Lake Location Information Used in Tier 1 (all

facilities) and in Tier 2 (specific to NEI11660) ...................................................... 10

Table 4-1. Wind and Precipitation Statistics for the Meteorological Data Used in

this Site-specific Assessment ............................................................................... 17

Table 4-2. Mixing-height Statistics for the Meteorological Data Used in this Sitespecific Assessment ............................................................................................. 18

Table 4-3. Modeled Emissions Amounts for Each Grouped Emission Source ........................... 19

Table 4-4. Lake Sizes ......................................................................................................... ........ 27

Table 4-5. Farm-food-chain and Soil Ingestion Rates Used for Farmer Scenario ...................... 32

Table 4-6. Fish-ingestion Rates Used for Fisher Scenarios Evaluated....................................... 34

Table 5-1. Hazard Quotients from Exposure to Methyl Mercury for Fisher

Scenarios ............................................................................................................. 37

Table 5-2. Hazard Quotients from Exposure to Divalent Mercury for Subsistencefarmer Scenarios .................................................................................................. 38

Table 5-3. Hazard Quotients by Ingested Medium, per Age Group, from

Exposure to Divalent Mercury at Farm SE ........................................................... 39

Table 5-4. Hazard Quotients from Exposure to Cadmium for Fisher Scenarios ......................... 40

Table 5-5. Hazard Quotients from Exposur e to Cadmium for Subsistence-farmer

Scenarios ............................................................................................................. 41

Table 5-6. Hazard Quotients by Ingested Medium, per Age Group, from

Exposure to Cadmium at Farm SE ....................................................................... 42

Table 5-7. Incremental Lifetime Cancer Risks from Exposure to PAHs for Fisher

Scenarios ............................................................................................................. 43

Table 5-8. Incremental Lifetime Cancer Risk from Exposure to PAHs for

Subsistence-farmer Scenarios ............................................................................. 43

Table 5-9. Incremental Lifetime Cancer Risk by Ingested Medium from Exposure

to PAHs at Farm NNW ......................................................................................... 45

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft iii April 2015 

# Table 5-10. Incremental Lifetime Cancer Risks from Exposure to Dioxins for

Fisher Scenarios .................................................................................................. 46

Table 5-11. Incremental Lifetime Cancer Risk from Exposure to Dioxins for

Subsistence-farmer Scenarios ............................................................................. 46

Table 5-12. Incremental Lifetime Canc er Risk by Ingested Medium from

Exposure to Dioxins at Farm NNW ...................................................................... 48

Table 5-13. Comparison of Site-specific Hazard Quotients and Cancer Risks to

the Results of the Screening-level Assessments ................................................. 50

Table 6-1. Sources of Uncertainty in the Current Site-specific Assessment ............................... 52



# FIGURES

Figure 3-1. Conceptual Exposure Model for the Subsistence-farmer Scenario .......................... 13

Figure 3-2. Conceptual Exposure Model for the Fisher Scenario ............................................... 13

Figure 4-1. Overview of Methods for Evaluating Ingestion Exposure and Risk .......................... 14

Figure 4-2. Location of the Assessed Facility (NEI11660) .......................................................... 15

Figure 4-3. Wind Rose for the Meteorological Data Used in this Site-specific

Assessment .......................................................................................................... 17

Figure 4-4. Modeled Surface Parcels for NEI11660 and Their Modeled

Vegetation or Soil Types ...................................................................................... 22

Figure 4-5. Modeled Surface Parcels for NEI11660 and Land-Cover Base Map ....................... 23

Figure 4-6. Modeled Surface Parcels for NEI11660 and Terrain Base Map ............................... 24

Figure 4-7. Modeled Surface Parcels for NEI11660 and County Base Map ............................... 25

Figure 4-8. Modeled Surface Parcels for NEI11660, Zoomed on Farms and

Lakes near the Facility ......................................................................................... 26

Figure 4-9. Modeled Air Parcels for NEI11660 ........................................................................... 28

Figure 5-1. Contribution to Hazard Quotients by Ingested Medium, per Age

Group, from Exposure to Divalent Mercury at Farm SE ....................................... 38

Figure 5-2. Contribution to Hazard Quotients by Ingested Medium, per Age

Group, from Exposure to Cadmium at Farm SE .................................................. 41

Figure 5-3. Contribution to Incremental Li fetime Cancer Risk by Ingested Medium

from Exposure to PAHs at Farm NNW ................................................................. 44

Figure 5-4. Contribution to Incremental Li fetime Cancer Risk by Ingested Medium

from Exposure to Dioxins at Farm NNW .............................................................. 47

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# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 1 April 2015 

# 1. Introduction

This report contains the details and results of an assessment of human-health risks, via the

ingestion pathway, from exposure to persistent and bioaccumulative hazardous air pollutants

(PB-HAPs) emitted to the air from a ferroalloy- production facility. Cadmium, dioxins, mercury,

and polycyclic aromatic hydrocarbons ( PAHs) were the evaluated PB-HAP groups, 1 

# selected

from a larger list of PB-HAPs based in part on ranking their toxicity-weighted air emissions

across all source categories. 2 

# The risks evaluated were chronic cancer risks (for PAHs and

dioxins) and chronic non-cancer hazards (for cadmium and mercury). This assessment was

conducted to show an example of the differences in risk estimates between the multipathway

“tiered” screening results and those of a site-specific multipathway assessment.

As described in Section 4 of this document and Appendix 4 to the Risk Report, EPA uses the

TRIM.FaTE 3 

# model to evaluate the environmental transport, transfer, and fate of PB-HAP

emissions. The MIRC 4 

# program then calculates farm-food-chain (FFC) chemical uptake, human

exposure, and human-health risk. For the assessment described in this report, these models

were used to conduct one site-specific case study for a ferroalloy-production facility with PBHAP emissions that might deposit into the environment, concentrate in food products and soil,

and elevate risks to human health when people ingest those contaminated food products and

soil. The selected facility is the Eramet facility near Marietta in Washington County, Ohio

(NEI11660 in the National Emissions Inventory [NEI]; approximately 39.37°N, 81.52°W).

Two ferroalloy-production facilities make up the source category: NEI11660 and

NEIWV053FELMAN. In the iterative (“tiered”) screening-level ingestion assessment discussed

in greater detail in Section 2 of this document, both facilities exceeded the Tier-1 and Tier-2

emission-screening thresholds for at least one of the PB-HAP groups evaluated. NEI11660

exceeded the Tier-2 thresholds of dioxins, mercury (for fish consumption only), and PAHs, the

latter two chemicals by larger margins than the ot her facility in the source category, making it

the facility with the largest Tier-2 exceedances in the source category. NEI11660 was selected

based on its Tier-2 screening results and based on the feasibility, with respect to the modeling

framework, of parameterizing the region su rrounding the facility. The ingestion-exposure

scenarios assessed for the selected facility do not necessarily represent the highest potential

ingestion exposures and risks for all humans living in the vicinity of ferroalloy-production

facilities, but the exposure and risk estimates should be among the highest possible for this

source category.

The approach, data, assumptions, and results of the site-specific assessment are presented in

this report. In addition to the description and discussion of the site-specific assessment

presented in this document, the Protocol for Developing a TRIM.FaTE Model Scenario to

Support a Site-specific Risk Assessment in the RTR Program (which is Appendix 9 to the Risk

Report) contains additional considerations, suggested guidelines, and justifications for

1 

# The phrase “PB-HAP group” is used to distinguish the in dividual PB-HAP chemicals and congeners from the overall

family (grouping) of those chemicals. For example, EPA’ s ingestion-risk methods in its Risk and Technology Review

(RTR) program evaluate emissions of 17 individual cong eners of chlorinated dibenzo-p-dioxins and chlorinated

dibenzofurans in the “dioxin” PB-HAP group.

2 

# Explained further in Appendix 4 to the Risk Report.

3 

# TRIM.FaTE stands for the Fate, Transport, and Ecologica l Exposure module of the Total Risk Integrated

Methodology modeling system. Additional TRIM.FaTE information can be found at: http://www2.epa. gov/fera/totalrisk-integrated-method ology-trim-trimfate. Explained further in Appendix 4 to the Risk Report (Section 2.2.5).

4 

# MIRC stands for the Multimedia Ingestion Risk Calculator.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 2 April 2015 

# developing a site-specific assessment (not spec ific to this source category). EPA generally

followed this protocol in developing this site-specific assessment. The Technical Support

Document for the TRIM-Based Multipathway Tiered Screening Methodology for RTR 5 

# , contains

a more comprehensive description of the iterative approach (focusing especially on Tiers 1 and

2) that EPA uses to evaluate the potential for in gestion risks resulting from PB-HAP emissions.



# 2. Screening-level Assessments

EPA developed an iterative screening approach for evaluating the potential for human-health

risks above levels of concern resulting from non-inhalation exposures (i.e., via the ingestion

pathway) to selected PB-HAPs in air emissions. 2 

# In the screening approach, PB-HAP emissions

are “screened out” if they are unlikely to pos e human-health ingestion risks above levels of

concern, therefore enabling EPA to focus additional resources on emission sources of greater

concern. The levels of concern are an increm ental lifetime human cancer risk above 1-in-one

million (for PAHs and dioxins) and a chronic non-cancer hazard quotient (HQ) above 1 (for

cadmium and mercury). 6 

# The Tiers 1 and 2 screening assessments and results are discussed in

Section 2.1 and Section 2.2.



# 2.1 Tier-1 Emission-screening Assessment

The objective of the Tier-1 emission-screening assessment is to rank PB-HAP emission sources

within a source category (or across source categories), enabling EPA to screen out emission

sources unlikely to pose human-health ingestion risks above levels of concern. 7 

# The confidence

in screening out emission sources is achi eved by avoiding the underestimation of media

concentrations and human exposure resulting from the emissions.

The Tier-1 emission-screening thresholds are intended to apply to any U.S. facility; that is, they

are designed to be applicable to every facility regardless of the facility’s characteristics and

surrounding environment and the population exposed in its vicinity. 8 

# Each evaluated PB-HAP

group has a single Tier-1 screening threshold, as shown in Table 2-1. For Tier 1, the emissions

of all dioxin congeners are normalized to 2,3,7,8-tetrachlorodibenzo-p-dioxin-equivalent

emissions, and the emissions of all PAH congeners are normalized to benzo(a)pyreneequivalent emissions. 9 

# The equivalency factors used to relate individual compounds to the

surrogates are based on oral toxicity factors and factors related to the congeners’ environmental

fate-and-transport characteristics as analyzed in the TRIM.FaTE model. 10 

# For mercury,

emissions of divalent mercury are evaluated, but the Tier-1 emission-screening threshold

reflects ingestion of methyl mercury from cont aminated fish, only because ingestion of fish is the

dominant exposure medium for mercury and because methyl mercury (formed from the

methylation of divalent mercury) represents 95 percent or more of the total mercury

concentration in fish.

5 

# Appendix 4 to the Risk Report.

6 

# EPA considers “cancer risks above 1-in-one million” to refer to risks of at least 1.5-in-one million, and “non-cancer

HQs above 1” to refer to HQs of at least 1.5. That is, risks and HQs are rounded to the nearest whole number.

7 

# Explained further in Appendix 4 to the Risk Report (Sections 1.1 and 2).

8 

# Explained further in Appendix 4 to the Risk Report (Section 2).

9 

# 2,3,7,8-TCDD and BaP are sometimes used in this report as shorthand terms for 2,3,7,8-tetrachlorodibenzo-pdioxin and benzo(a)pyrene, respectively.

10 

# Explained further in Appendix 4 to the Risk Report (Section 2.2.7).

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 3 April 2015 

# As shown in Table 2-1, the emissions from both ferroalloy-production facilities exceeded the

Tier-1 emission-screening thresholds for mercury, dioxins, and PAHs, and from one facility

(NEI11660) for cadmium. Tier-1 screening values (SVs) were calculated as the ratio of a

facility’s emissions to the Tier-1 emission-screening threshold. Where a facility’s PB-HAPspecific emission rate exceeds the Tier-1 screening-threshold emission rate, the SV is 2 or

greater when rounded. The list below contains the highest SVs estimated in the source

category, with NEI11660 being the highest emitter for mercury, cadmium, and PAHs.

 

# Mercury: SV=100 (meaning the highest-emitting facility exceeded the Tier-1 emissionscreening threshold by a factor of 100) at NEI11660

 

# Cadmium: SV=10 at NEI11660

 

# PAHs: SV=500 at NEI11660

 

# Dioxins: SV=100 at NEIWV053FELMAN (40 at NEI11660)

 

# PAHs + Dioxins (total cancer): SV=500 at NEI11660

When the Tier-1 screening values were summed across the four PB-HAP groups (not shown),

NEI11660 ranked first in the source category.

Table 2-1. Tier-1 Screening Results for the Ferroalloy-production Source Category

Facility PB-HAP

Group Tie r -1 Emission-screening Assessment

(a) Emissionscreening

Threshold

(TPY) a 

# (b) Emissions

(TPY) a,b 

# (c) Screening

Value

[(b)/(a)] c



# NEI11660 Mercury d 

# (non-cancer) 3.16E-04 4.19E-02 100

Cadmium (non-cancer) 1.18E-02 1.17E-01 10

PAHs as BaP (cancer) 2.58E-03 1.25E-00 500

Dioxins as 2,3,7,8-TCDD (cancer) 2.81E-09 1.17E-07 40

PAHs + Dioxins (cancer) N/A N/A 500

NEIWV053FELMAN Mercury d 

# (non-cancer) 3.16E-04 3.57E-03 10

Cadmium (non-cancer) 1.18E-02 2.27E-03 0.2

PAHs as BaP (cancer) 2.58E-03 5.57E-02 20

Dioxins as 2,3,7,8-TCDD (cancer) 2.81E-09 3.53E-07 100

PAHs + Dioxins (cancer) N/A N/A 100

a 

# TPY = tons per year

b 

# PAH and dioxin emissions in this column were normalized to BaP and 2,3,7,8-TCDD, respectively, for oral toxicity and Tier-1

modeled environmental fate and transport.

c 

# Red highlights indicate SVs that are 2 or larger (wit h 1.5 rounding to 2; SVs rounde d to one significant figure).

d 

# The emission-screening threshold for mercury applies to emissi ons of divalent mercury, al though it was derived based on

hazards associated with ingestion of fish c ontaminated with methyl mercury (i.e., emissi ons of divalent mercury, which transfor ms

to methyl mercury within the environment and accumulates in fish tissue).



# 2.2 Tier-2 Emission-screening Assessment

The generic site characteristics used in the Tier-1 screening assessment can differ from those

actually present at many facilities. Further, the ingestion scenario used in Tier 1 (i.e., a

combined diet of 90th-percentile consumption of farm food products and soil and 99th-percentile

consumption of fish) is very health protective and likely is more food than most people regularly

consume. In a Tier-2 screening assessment, some site-specific characteristics are taken into

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 4 April 2015 

# account when developing Tier-2 emission-screening thresholds, and the ingestion scenario

assesses 90th-percentile farm-food consumption separate from 99th-percentile fish

consumption (i.e., an exposed person consumes farm foods or fish, but not both). 11 

# In Tier 2, the

potential risk from consumption of contaminat ed fish is assessed as a “fisher SV” and the

potential risk from consumption of FFC media is assessed as a “farmer SV.”

For a facility undergoing Tier-2 screening, the 50-km area around the facility is partitioned into

eight “wedges” or “octants” centered on the facility and compartmentalizing areas north of the

facility, northeast, east, and so on. As described in Section 2.2.1, the lake locations and some

meteorological conditions related to a facility can vary by octant in Tier 2. Though the distance

from the facility to the modeled farm remains fixed for each octant, the location of the modeled

lake is adjusted based on actual lake locations. This affects chemical concentrations in some

FFC media due to modeled erosion and runoff patterns that are directed mostly toward the lake.

Therefore, for every relevant PB-HAP emitted by a facility, the fisher SV of each lake is

calculated using the distance from facility to lake and the meteorology conditions of the octant

the lake is in; and, since lake location can affect the farmer SV, a corresponding farmer SV is

calculated for each lake scenario.

2.2.1 Adjustment Factors Applied to Tier-1 Screening Values

In developing the adjustment factors used in Tier 2, hundreds of TRIM.FaTE and MIRC model

runs were conducted corresponding to ranges of meteorological characteristics expected to be

found at most U.S. locations, and corresponding to lake locations ranging from 2 km to 40 km

away from the facility. As in Tier 1, the model runs provided emission rates corresponding to risk

and hazard levels of concern. Because each modeled scenario has different environmental fateand-transport characteristics, each scenario also has different equivalency factors to relate PAH

and dioxin congeners to BaP and 2,3,7,8-TCDD, res pectively. Unlike in Tier 1, screening values

are calculated separately in Tier 2 for the fisher and farmer scenarios. The Tier-2 adjustment

factors are the ratios of the Tier-2 equivalency factors and thresholds to those of Tier 1. The

Tier-2 adjustment factors are applied to Tier-1 SVs in order to calculate Tier-2 fisher SVs and

Tier-2 farmer SVs. An additional adjustment factor based on wind direction is discussed below.

In order to match an assessed facility in Tier 2 with its appropriate meteorology- and lakerelated Tier-2 adjustment factors, the facility is matched with the same meteorology station to

which it is matched in the RTR assessment of inhalation risks (i.e., typically the closest station),

and qualifying lakes around the facility are also i dentified. Some of a facility’s meteorology

patterns vary by octant, and qualifying lakes (d iscussed below) can be scattered around multiple

octants and at multiple distances from the facility. Therefore, for each lake in each octant

around a facility, its combination of meteorology data and lake distance are matched to its

appropriate scenario in the Tier-2 model runs and, thus, to the appropriate Tier-2 adjustment

factors.

The meteorology data used in Tier 2 are based on the same dataset used in RTR assessments

of inhalation risk, consisting of more than 800 meteorology stations nationwide. Hourly data

from the meteorology stations were processed to derive typical station-specific values of wind

speed, wind direction, and mixing height, and 1981-2010 30-year station-normal annual totals of

precipitation were also used. For each station, the combination of these values was matched

with the most representative meteorological scenario used in the Tier-2 model runs. The

matching method is health-protective (i.e., wind speeds and mixing heights match to their

11 

# Explained further in Appendix 4 to the Risk Report (Sections 1 and 3).

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 5 April 2015 

# closest, smaller value in the Tier-2 model runs, and a precipitation value to its closest, larger

value). The frequency of winds blowing toward each of the octants around each station was also

calculated. The frequency of winds blowing into an octant is compared to the frequency used in

Tier 1, creating a Tier-2 wind-frequency adjustment factor.

All qualifying lakes within 50 km of the facility are analyzed. A qualifying lake (1) is at least 25

acres in surface area but no larger than 100,000 acres; 12 

# (2) is classified by the U.S. Geological

Survey as a lake or reservoir but not used for disposal, treatment, or evaporator purposes; (3)

does not have a name suggesting it is not intended for public use (e.g., “Gavin Fly Ash

Impoundment”); and, (4) has not been removed in previous source-category assessments

based on these and other inspection methods. Each lake is assigned to its respective octant

around the assessed facility and to the closest lake-distance value used in the Tier-2 model

runs, where the matching method is health-protective (i.e., a lake’s distance matches to its

closest, smaller value in the Tier-2 model runs). In cases where there are no qualifying lakes

around the facility, there is no exposure or risk to the fisher.

The Tier-2 fisher and farmer SVs calculated at this point in the assessment are “initial SVs”; this

is because subsequent steps in Tier 2 refine the fisher SVs (see Section 2.2.2) and select one

of the potentially many farmer SVs (see Section 2.2.3).

2.2.2 Calculating Refined Tier-2 Fisher Screening Values

The subsistence fisher in Tier 1 consumes fish from one lake at the subsistence ingestion rates

(i.e., 99th percentile), regardless of the lake’s ability to support a fish population that allows for

that level of consumption across many years. In Tier 2, the lake’s capacity to accommodate the

assumed fish ingestion rates is considered. For any particular lake, the fisher in Tier 2 catches

and consumes fish from that lake at an amount proportional to the area of the lake, which

serves as an approximation of the lake’s biological productivity. 12 

# In order to consume fish at

the ingestion rate used in Tier 2, the fisher might need to fish multiple lakes (and be exposed to

several different chemical-concentration levels in fish).

The Tier-1 SVs and the initial Tier-2 fisher SVs use a fixed set of fish-ingestion rates covering a

range of consumer ages. The highest fish-ingestion rate among the age groups is 373 g-fish/d

for adults, which for simplicity is used to estimate a lake’s capacity to support subsistence

fishing. Smaller lakes are unlikely to support this level of fish consumption over time (i.e., the

lake would be “fished out”), although the fisher could catch and consume that amount of fish

collectively from multiple small lakes. In Tier 2, it is assumed that a fisher can catch and

consume no more than 1 g-fish/d/acre-lake without impacting the lake’s long-term sustainability.

In order to reach the assumed ingestion rate, it is assumed that the fisher catches and

consumes fish from one or more lakes until he is able to fish a total of 373 acres of lake and

consume a total of 373 g-fish/d (i.e., the adult subsistence fish-ingestion rate) or until there are

no more qualifying lakes to fish to reach 373 total acres.

The initial Tier-2 fisher SVs calculated for all qualifying lakes around a facility, along with the

acreage values of those lakes, are used to identify which lake(s) the fisher catches and

consumes fish from and the amount of fish caught from each lake. Using this information, the

Tier-2 fisher SV is refined based on a scenario assuming consumption of fish from multiple

lakes. The steps below are conducted for each PB-HAP group emitted by a facility.

12 

# Explained further in Appendix 4 to the Risk Report (Section 3.5 and Attachment E).

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 6 April 2015 

# 1. Identify the lake with the highest chemical concentration in fish for a given PB-HAP

group, according to the initial Tier-2 fisher SVs.

2. If the lake with the highest concentration is at least 373 acres in area, then the fisher

consumes fish from only that lake, at the subsistence-level ingestion rates (the adult rate

being 373 g-fish/d). That is, the refined Tier-2 fisher SV equals the initial Tier-2 fisher

SV.

3. If the lake with the highest concentration is smaller than 373 acres, then the fisher

catches only the amount of fish consistent with the assumption of 1 g-fish/d/acre-lake.

That is, multiply the initial Tier-2 fisher SV for that lake by [its acreage]÷[373 acres]; put

another way, multiply the Tier-2 fisher SV by [the daily mass of fish consumed from the

lake]÷[373 g-fish/d]. Apply the following logic to subsequent lakes.

a. The fisher catches and consumes fish from one or more additional lakes, in order

of highest-to-lowest concentration. If multiple lakes have the same concentration,

then he fishes them in order of largest-to-smallest acreage. He stops fishing

when he fishes lakes totaling 373 acres or when there are no more qualifying

lakes to fish to reach 373 total acres. Scale each additional lake’s initial Tier-2

fisher SV by acreage (i.e., by fish-consumption rate) in the same way as the first

fished lake (see above).

b. If the cumulative area of all qualifying lakes is smaller than 373 acres (i.e., if all

lakes together cannot support a 373 g-fish/d ingestion rate for adults, assuming 1

g-fish/d/acre-lake), then the fisher does not consume the full 373 g-fish/d.

c. If the cumulative area of all qualifying lakes is larger than 373 acres, the fisher

consumes only the amount of fish from the final lake necessary to reach 373 gfish/day. That is, scale the initial Tier-2 fisher SV for that lake by [the remaining

acreage necessary to reach 373 acres, after fishing the prior lake(s)]÷[373

acres].

d. The refined Tier-2 fisher SV (reflecting the refined fisher methods) is the sum of

the adjusted Tier-2 fisher SVs of all fished lakes as calculated above.

2.2.3 Finalizing Tier-2 Farmer Screening Values

For a given PB-HAP group emitted by a facility, the highest Tier-2 farmer SV is identified from

all the relevant meteorology and lake-distance scenarios. This helps to avoid underestimating

exposure and risk.

2.2.4 Tier-2 Screening Results for this Source Category

Summary-level Tier-2 screening results for the ferroalloy-production facilities are shown in Table

2-2. At NEI11660, emissions of cadmium screened out for both the fisher and farmer scenarios;

mercury emissions exceeded the threshold for fishers by 10 and screened out of the farmer

scenario; PAH and dioxin emissions remained above thresholds but my much smaller margins

than in Tier 1. At NEIWV053FELMAN, emissions of mercury and PAHs screened out; emissions

of cadmium remained below thresholds, while emissions of dioxins remained above but by

much smaller margins than in Tier 1. The largest Tier-2 screening values are listed below.

 

# Mercury: SV fish 

# =8 at NEI11660, SV farm 

# =0.009 at NEI11660

 

# Cadmium: SV fish 

# =0.7 at NEI11660, SV farm 

# =0.07 at NEI11660

 

# PAHs: SV fish 

# =10 at NEI11660, SV farm 

# =30 at NEI11660

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 7 April 2015  

# Dioxins: SV fish 

# =7 at NEIWV053FELMAN, SV farm 

# =7 at NEIWV053FELMAN)

 

# PAHs + Dioxins (total cancer): SV fish 

# =20 at NEI11660, SV farm 

# =40 at NEI11660

Error! Reference source not found. Table 2-3 contains both Tier-1 and Tier-2 screening

results for NEI11660 for each chemical (i.e., for emissions of cadmium compounds, divalent

mercury, 17 congeners of dioxins, and 18 congeners of PAHs) and chemical group.

Key observations on the Tier-2 screening results for NEI11660 are listed below.

 

# For mercury, the fisher screening value (8) was much smaller than the Tier-1 screening

value (100) and was the largest in the source category (same rank as in Tier 1).

 

# Cadmium emissions screened out, although the fisher screening value (0.7, down from a

Tier-1 screening value of 10) was the largest in the source category (same rank as in

Tier 1).

 

# For PAHs, the fisher and farmer screening values (10 and 30, respectively) were much

smaller than the Tier-1 screening value (500) and were the largest in the source

category (same rank as in Tier 1), driven primarily by benzo(b)fluoranthene,

benzo(k)fluoranthene and benzo(a)pyrene.

 

# For dioxins, the fisher and farmer screening values (2 each) were much smaller than the

Tier-1 screening value (40), and were the second largest in the source category (same

rank as in in Tier 1), driven primarily by 1,2,3,7,8-PCDD, 2,3,4,7,8-PCDF, and 2,3,7,8-

TCDD.

 

# When the Tier-2 screening values are summed across the four PB-HAP groups, the

facility ranked first in the source category (same ranking as in Tier 1).

In the Tier-2 assessment for NEI11660, the meteorological statistics (shown in Table 2-4 Error!

Reference source not found. ) were derived from the Mid-Ohio Valley Regional Airport surface

station (Weather Bureau Army Navy [WBAN] ID 03804; 8 km east-southeast of NEI11660) and

from the Pittsburgh International Airport upper-air station (WBAN ID 94823). This pair of surface

and upper-air stations is the same as that used in the RTR assessment of inhalation risk for this

facility. The Tier-2 refined-fisher analysis identified three lakes as having the largest

concentrations of cadmium, mercury, PAHs, and dioxins and together potentially containing

enough fish to sustain the subsistence-level ingestion rates. These lakes were Veto Lake (12

km west-southwest of the facility, matched with the 10-km lake scenario in Tier 2), Wolf Run

Lake (48 km from the facility, matched with the 40-km lake scenario in Tier 2), and Mountwood

Park Lake (24 km from the facility, matched with the 20-km lake scenario in Tier 2). All these

Tier-2 lakes were farther from the facility than the generic lake in Tier 1 (which was

approximately 1.4 km from the facility), which reduces modeled chemical deposition to the lake

in Tier 2 if all other parameters are held constant. As discussed later in this report, the modeled

octant containing Wolf Run Lake (the north octant) corresponded to the largest FFC chemical

concentrations.

The modeled meteorological conditions for these higher-concentration octants for NEI11660 are

also shown in Table 2-4, along with the values used for all facilities in Tier 1. Annual

precipitation and average mixing height are not modeled by compass direction, and the

modeled Tier-2 mixing height remained unchanged from the Tier-1 assessment, while the

modeled Tier-2 precipitation was smaller than in Tier 1. Tier-2 modeled wind speeds were also

unchanged from Tier 1. Winds blew into each Tier-2 octant much less frequently than into the

Tier-1 domain, decreasing modeled c hemical deposition to the domain.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryDraft 8 April 2015Table 2-2. Tier-2 Screening Results for the Ferroalloy-production Source CategoryFacilityPB-HAPGroupTier-2 Emission-screening AssessmentFisher Scenario Farmer Scenario(a)EmissionscreeningThreshold(TPY)a(b)Emissions(TPY)a,b(c)ScreeningValue[(b)/(a)]c(a)EmissionscreeningThreshold(TPY)a(b)Emissions(TPY)a,b(c)ScreeningValue[(b)/(a)]cNEI11660Mercuryd(non-cancer)5.24E-03 4.19E-028 4.66E+00 4.19E-02 0.009Cadmium (non-cancer) 1.67E-01 1.17E-01 0.7 1.67E+00 1.17E-01 0.07PAHs as BaP (cancer) 4.08E-02 4.08E-0110 1.02E-02 3.07E-0130Dioxins as 2,3,7,8-TCDD(cancer) 6.50E-08 1.30E-072 8.13E-08 1.41E-072PAHs + Dioxins (cancer) N/A N/A20 N/A N/A40NEIWV053FELMANMercuryd(non-cancer)3.97E-03 3.57E-03 0.9 4.46E+00 3.57E-03 0.0008Cadmium (non-cancer) 1.14E-01 2.27E-03 0.02 2.27E+00 2.27E-03 0.001PAHs as BaP (cancer) 2.43E-02 1.70E-02 0.7 1.28E-02 1.28E-02 1Dioxins as 2,3,7,8-TCDD(cancer) 5.86E-08 4.10E-077 7.11E-08 4.95E-077PAHs + Dioxins (cancer) N/A N/A8 N/A N/A8aTPY = tons per yearbEmissions of PAHs and dioxins in this column were normalized toBaP and 2,3,7,8-TCDD, respectively for oral toxicity and Tier-2 modeled environmental fate and transport.cRed highlights indicate screening values that are 2 or larger(with 1.5 rounding to 2; screening values rounded to one significant figure).dThe emission-screening threshold for mercury applies to emissions of divalent mercury, although it was derived based on hazardsassociated with ingestion of fishcontaminated with methyl mercury (i.e., emissions of divalent mercury, which transforms to methyl mercury within the environment and accumulates in fish tissue).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryDraft 9 April 2015Table 2-3. Tiers 1 and 2 Screening Results for NEI11660, by PB-HAP Chemical and PB-HAP Group TotalChemical Information Tier-1 Emission-screening Assessment Tier-2 Emission-screening AssessmentPB-HAPGroup PB-HAP Chem.Raw Emissions(TPY)aScreeningValue (byChem.)Screening Value (byPB-HAP Group)bFisher Scenario Farmer ScenarioScreeningValue (byChem.)ScreeningValue (by PBHAP Group)bScreeningValue (byChem.)ScreeningValue (by PB-HAP Group)bMercurycDivalent mercury 4.19E-02 130100 7.78 0.00890.009Cadmium Cadmium compounds 1.17E-01 1010 0.680.70.0690.07PAHs2-Methylnaphthalene 9.19E-01 0.395000.0083100.002530Acenaphthene 2.34E-01 0.15 0.0034 0.0045Acenaphthylene 9.18E-01 0.92 0.021 0.0023Anthracene 5.08E-01 0 0 0Benz(a)anthracene 4.46E-01 2.5 0.019 0.74Benzo(a)pyrene 6.52E-02 25 0.078 9.4Benzo(b)fluoranthene 3.75E-01 270 7.5 10.0Benzo(e)pyrened1.86E-01 22 0.26 4.0Benzo(g,h,i)perylene 2.42E-02 2.8 0.036 0.54Benzo(k)fluoranthene 1.18E-01 130 5.1 5.8Chrysene 9.87E-01 1.6 0.025 0.35Dibenzo(a,h)anthracene 4.89E-03 8.6 0.17 1.2Fluoranthene 4.78E+00 4.8 0.097 0.51Fluorene 4.07E-01 0.33 0.0080 0.00088Indeno(1,2,3-c,d)pyrene 2.68E-02 7.7 0.12 1.5Perylened1.05E-23 0.33 0.0077 0.033Phenanthrene 4.71E+00 0 0 0Pyrene 3.40E+00 0 0 0DioxinsHeptaCDD, 1,2,3,4,6,7,8- 3.07E-08 0.11400.004820.00902HeptaCDF, 1,2,3,4,6,7,8- 3.62E-08 0.021 0.00015 0.0090HeptaCDF, 1,2,3,4,7,8,9- 1.71E-08 0.011 0.00028 0.0029HexaCDD, 1,2,3,4,7,8- 1.63E-08 0.90 0.042 0.043HexaCDD, 1,2,3,6,7,8- 1.58E-08 0.57 0.026 0.032HexaCDD, 1,2,3,7,8,9 - 1.67E-08 0.25 0.011 0.014HexaCDF, 1,2,3,4,7,8- 1.84E-08 0.21 0.0072 0.030HexaCDF, 1,2,3,6,7,8- 1.41E-08 0.23 0.0076 0.037HexaCDF, 1,2,3,7,8,9- 1.70E-08 0.29 0.0099 0.044HexaCDF, 2,3,4,6,7,8- 1.99E-08 0.14 0.0046 0.021OctaCDD, 1,2,3,4,6,7,8,9- 2.63E-07 0.031 0.00026 0.012OctaCDF, 1,2,3,4,6,7,8,9- 4.44E-08 0.00079 0.0000016 0.00040PentaCDD, 1,2,3,7,8- 2.29E-08 31 1.4 1.1PentaCDF, 1,2,3,7,8- 1.82E-08 0.082 0.0024 0.016PentaCDF, 2,3,4,7,8- 2.66E-08 1.0 0.033 0.18TetraCDD, 2,3,7,8- 1.88E-08 6.7 0.35 0.15TetraCDF, 2,3,7,8- 2.62E-08 0.12 0.0043 0.013Note: All values in the “Screening Value (by PB-HAP Group)” columns are rounded to one significant figure, and all values in the “Screening Value (by Chem.)” and “Raw Emissions(TPY)” columns are rounded to two and three significant figures, respectively.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryDraft 10 April 2015Chemical Information Tier-1 Emission-screening Assessment Tier-2 Emission-screening AssessmentPB-HAPGroup PB-HAP Chem.Raw Emissions(TPY)aScreeningValue (byChem.)Screening Value (byPB-HAP Group)bFisher Scenario Farmer ScenarioScreeningValue (byChem.)ScreeningValue (by PBHAP Group)bScreeningValue (byChem.)ScreeningValue (by PB-HAP Group)baTPY = tons per yearbRed highlights indicate PB-HAP-group screening values that are 2 orlarger (with 1.5 rounding to 2). Gray font indicates screening values smaller than 2 (with 1.5 rounding to 2).Green highlights indicate emissions thatscreened out in Tier 2 but not in Tier 1. Mercury and cadmium are evaluated for non-cancer effects, while PAHs and dioxins are evaluatedfor cancer effects.cThe emission-screening threshold for mercuryapplies to emissions of divalent mercury, although it was derived based on hazards associated with ingestion of fish contaminatedwith methyl mercury (i.e., emissions of divalent mercury thattransforms to methyl mercury within the environment and accumulates in fish tissue).dThese PAH congeners are not currently fully parameterized in TRIM.FaTE to evaluate their specificfate, transport, and transformation properties. Their exposure-equivalencyfactors (to relate them to the benzo(a)pyrene surrogate used in screening) were based on how their Kowvalues related to the Kowvalues of the fully-parameterized congeners. SeeAppendix 4 to the Risk Report (specifically, Section 2.2.7) for more information.Table 2-4. Meteorological and Lake Location Information Used in Tier 1 (all facilities) and in Tier 2 (specificto NEI11660)Parameter (As Modeled) Tier 1 Tier 2Octant Analyzed– W N SEWind Speed (m/s) 2.8 2.8 2.8 2.8Frequency of Winds Blowing into Modeled Domain (fraction) 0.430.07 0.19 0.10Annual Precipitation (mm), site-wide(same for all octants) 1,500 1,187Mixing Height(m), site-wide (same forall octants) 710 710Distance to Lake (km) 1.4 10 (Veto Lake) 40 (Wolf Run Lake) 20 (Mountwood Park Lake)

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 11 April 2015 

# 3. Scope of Site-specific Risk Assessment

This site-specific risk assessment evaluated the emissions of four PB-HAP groups (mercury,

cadmium, dioxins, and PAHs) emitted from t he selected ferroalloy-production facility. This

section describes the conceptual exposure model and overall scope of the site-specific

assessment.

For an assessment of inhalation risk, the risk can be approximated (taking into account a range

of assumptions) using modeled long-term-average air concentrations associated with a source

and information on where people reside. For an evaluation of non-inhalation exposures,

however, estimating the risk to the “most-exposed” individual can be more difficult because

chemical concentrations in environment al media to which people are exposed and individual

exposure patterns associated with ingestion can vary greatly, depending on location, timing, and

other factors. For example, people can be exposed to chemicals that accumulate in the FFC by

consuming a variety of fruits and vegetables, each of which might or might not be grown in the

vicinity of the source. The amount of each type of produce consumed can vary widely among

the individuals in a population, as can the fraction of each type of produce that is actually

impacted by emissions from a source.

To simplify the assessment of exposure and risk related to PB-HAPs for the selected facility for

this risk assessment, a scenario approach was employed. This approach involved evaluating a

combination of exposure media by which an individual is most likely to be exposed to elevated

concentrations of PB-HAPs (i.e., a set of “exposure scenarios”). The scenario approach

provides a systematic method for evaluating the relative importance of exposure media (e.g.,

consumption of farm-food products versus consumption of fish) that are of potential concern for

different chemicals and locations. Typically, only scenarios that are plausible for the situation of

interest are evaluated, and the assessment us ually focuses on those scenarios that are

assumed a priori to lead to the highest individual exposure and risks. Risk metrics such as

incremental lifetime cancer risk and chronic non-c ancer HQ are calculated as appropriate for

each scenario. If warranted, information regarding the likelihood of a specific exposure scenario

actually occurring could be used to develop estimates of uncertainty for each scenario and the

variations thereof.

For this site-specific residual-risk assessment, exposure estimates and corresponding risks

were calculated for two basic exposure scenarios:

 

# A subsistence-farmer scenario , involving an individual living for a 70-year lifetime on a

farm homestead in the vicinity of the source and consuming produce grown on, and

meat and animal products raised on, the farm. The individual also incidentally ingests

surface soil at the location of the farm homestead.

 

# A fisher scenario , involving an individual who regular ly consumes fish caught in a

freshwater lake(s) in the vicinity of the source of interest over the course of a 70-year

lifetime.

Variations of these two scenarios were evaluated using different assumptions regarding food

source (i.e., location of the farm homestead, or location of the water body from which fish are

obtained), the age of the individual exposed (for non-cancer hazards), the assumed ingestion

rate of each food type, and other factors. In particular, a range of fish-ingestion rates was

evaluated to determine the possible health risk s associated with that important medium.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 12 April 2015 

# In addition, exposure estimates and risks for infants consuming contaminated breast milk

were evaluated in the case of dioxins, with the assumption that the nursing mother was exposed

to chemicals via one of the two basic scenarios listed above.

The conceptual exposure model for the subsistence-farmer scenario is presented in Figure 3-1.

The arrows represent the movement of the chemical of concern through the environment and

FFC. In this exposure scenario, the hypothetical human receptor consumes produce, meat, and

animal products, and he or she incidentally ingests soil. The conceptual exposure model for the

fisher scenario is presented in Figure 3-2. The hy pothetical human receptor consumes fish from

a contaminated water body. Note that the groundwater parts of these conceptual models were

not used in this assessment of the ferroalloy- production facility. Chemical transport into a

modeled lake via horizontal groundwater flow and recharge is negligibly small compared to

other chemical inputs into the lake. The vadose soil zone also was not used in this assessment

because theoretical considerations (and sensitivit y runs with TRIM.FaTE) suggest that chemical

transported into lower soil layers will not substantially move back into the surface soil. Removing

the compartments for the vadose soil zone and for groundwater improves model runtime and

has no appreciable impact on human chemic al exposure in the TRIM.FaTE modeled

environment.

These scenarios are expected to cover the highest-possible long-term exposures and risks for

the chemicals evaluated. In addition to ingestion, non-inhalation exposure to PB-HAPs also can

occur by the dermal pathway. Risk from dermal exposure, however, was expected to be a small

fraction of the risk from inhalat ion exposure or ingestion exposure. 13 

# Therefore, the risk from

dermal exposure was not calculated for this assessment.

These three exposure scenarios (subsistence farmer , fisher, and infant consuming breast milk)

were evaluated for each of the four PB-HAP groups as appropriate (note, for example, that for

methyl mercury, only the fisher scenario is rele vant). As described in Sections 1 and 2 of this

report, the facility evaluated was the Eramet facility near Marietta, Ohio (NEI ID NEI11660).

NEI11660 was selected for this site-specific assessment based on its Tier-2 screening results

and based on the feasibility of parameterizing th e environment surrounding the facility for the

TRIM.FaTE and MIRC models. EPA anticipates that the results for this facility and these

scenarios are among the highest that might be encountered for the ferroalloy-production source

category.

13 

# Explained further in Appendix 4 to the Risk Report (Attachments A and C).

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 13 April 2015 

# Figure 3-1. Conceptual Exposure Model for the Subsistence-farmer

Scenario

Note: The compartments for soil vadose zone and ground water were not modeled in this assessment,

which had no noticeable impact on m odeled human chemical exposure.



# Figure 3-2. Conceptual Exposure Model for the Fisher Scenario

Note: The compartments for soil vadose zone and ground water were not modeled in this assessment,

which had no noticeable impact on m odeled human chemical exposure.



# 4. Assessment Methods

An overview of the processes analyzed by TRIM.FaTE and MIRC for assessing site-specific

multipathway risks from the assessed ferroalloy-production facility is shown in Figure 4-1. The

approach can be divided into the following four steps, which correspond to the green boxes in

Figure 4-1.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 14 April 2015 

# Figure 4-1. Overview of Methods for Evaluating Ingestion Exposure and Risk

1. TRIM.FaTE: Fate-and-transport modeling of PB- HAPs that are emitted to air by the

source and partition into soil, water, and other environmental media (including fish).

2. MIRC: Modeling of transfer and uptake of PB-HAPs into FFC media (produce, livestock,

and dairy products) from soil and air.

3. MIRC: Estimating ingestion exposures (i.e., average daily ingestion rates) resulting from

contact by a hypothetical human receptor with the various selected media.

4. MIRC: Calculating estimates of incremental lifetime cancer risks or chronic non-cancer

HQs, as appropriate, for each PB-HAP and comparing these metrics to health-effect

levels of concern used in the RTR.

Site-specific aspects of the fate-and-transport modeling for this facility are discussed in Section

4.1. The methods used for the exposure and risk modeling are presented in Section 4.2. 14



# 4.1 Fate-and-transport Modeling

This section describes the TRIM.FaTE modeling conducted for this assessment. Most of the

material presented here describes the assumptions and data sources used to develop

TRIM.FaTE inputs and settings related to meteorological inputs used by the model, the spatial

aspects of the modeled region, the characteri stics of abiotic environmental compartments and

plants included in the scenario, and the aquatic ecosystems set up in each water body of

interest.

4.1.1 Overview of the Area Surrounding the Facility

Figure 4-2 shows the location of NEI11660, along with satellite imagery, water bodies, terrain,

and land cover in the larger region around the facility. The facility is located in Washington

County, OH—approximately 8 km southwest of Marietta, OH and 5 km northeast of Vienna, WV.

The area around the facility is generally forested with relatively small areas of pasture and

14 

# Explained further in Appendix 4 to the Risk Report. TRIM.F aTE: Sections 2.2.1, 2.2. 5. MIRC: Section 2.2.6;

Attachments A and E. Note: Appendix 4 focuses on develop ment of Tiers 1 and 2 of the multipathway screening

assessment, although general descriptions of TRIM.FaT E and MIRC and their components and purposes still apply

to this site-spe cific assessment.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-prod uction Source Category

Draft 15 April 2015 

# farmland. Some industrial and other development is located along the Ohio River, generally

running northeast-to-southwest directly past the facility. Most crop-farming activities are related

to soybeans and corn (not shown in Figure 4-2) and occur on the Ohio side of the river

(northwest of the facility). The elevation in most nearby areas is 200–300 m above sea level,

with areas farther north in Ohio, northeast in Pennsylvania, and east/southeast in West Virginia

exceeding 300 m. The number of people in Washingt on County, OH is relatively small

(approximately 62,000 people in 2010; U.S. Census Bureau 2013; not shown in Figure 4-2).

Wood County, WV, directly across the river from the facility, also has a relatively small

population (approximately 87,000 people in 2010). The modeling domain for the facility occupies

parts of seven counties for which the 2010 total population exceeded 236,000 (all of the

counties, not just the parts inside the modeling domain).

Figure 4-2. Location of the Assessed Facility (NEI11660)

Note: Top Left Panel: Basemap: ESRI World Imagery (ESRI 2013) ; Foreground: ESRI Water Body Types (Specifically, the

geospatial file in the ESRI Data & Maps 2009 Data Update for Ar cGIS version 9.3.1; derived by USGS, EPA, and ESRI from the

USGS National Hydrography Dataset (USGS 2012). Top Right Panel: Shuttle Radar Topography Mission elevation data (see:

http://www.esri.com/news/arcnews/s pring07articles/global-elevation.html ). Bottom Panel: 2006 National Land Cover Dataset (MLRC

2006).

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 16 April 2015 

# 4.1.2 Meteorological Data for Modeling

In the RTR assessments of inhalation risk and Tier-2 ingestion screening (Section 2.2),

NEI11660 was matched to the surface station (WBAN 03804) at the Mid-Ohio Valley Regional

Airport because it was the closest Automated Surface Observing Station (8 km east-southeast

of NEI11660) with relatively few missing data in the data period used in those assessments

(2011). That surface station was matched to the upper-air observing station closest to it—

Pittsburgh International Airport (WBAN ID 94823; approximately 160 km northeast of the

surface station, approximately 175 km northeast of NEI11660).

EPA followed the suggested meteorological-data-dev elopment steps described in Section 3 of

the site-specific-assessment protocol provided in Appendix 9 to the Risk Report. For this sitespecific assessment, the same surface and upper-air stations were selected as in Tier 2. A

recent 4-year period (2008 through 2011) was used after ensuring that the period-averaged

annual precipitation total was not much larger or much smaller than the 30-year average.

Multiple years of meteorological data often are used in chemical-dispersion and -advection

models to sample a wider range of meteorological conditions and to reduce the impact of

individual extreme weather events on estimates of long-term exposure. For this assessment,

TRIM.FaTE was run for 50 years on hourly data to allow the emitted chemical to bioaccumulate

and persist in the environment. Obtaining, analyzing, and processing 50 years of meteorological

data are impractical (based on cost and availability of such a long period of record).

Consequently, 4 years of meteorological data were used because repeating it over and over to

create a 50-year meteorological dataset is simpler while still representing long-term

meteorological patterns on the whole.

TRIM.FaTE requires five meteorological parameters: temperature, wind speed, wind direction,

mixing height, and precipitation amount. EPA obtained hourly mixing-height values for the sitespecific assessment by running AERMOD’s meteorology processor 15 

# on the surface data,

upper-air data, and the 1992 National Land Cover Dataset. 16 

# Approximately 7 percent of the

meteorological data was missing, so EPA used automated methods to replace all missing data

based on reasonable values from surrounding observation hours. The methods of replacing

missing data were based in part on EPA’s guidance for substituting missing meteorological data

for regulatory air-quality models (Atkinson and Lee 1992; see also Section 3.5 of the sitespecific protocol, Appendix 9 to the Risk Report) . EPA also substituted all calm winds (which

occurred 6 percent of the time) and winds speeds less than 0.75 m/s (which occurred 7 percent

of the time) with a wind speed value of 0.75 m/s, and EPA replaced all mixing heights less than

20 m (which occurred less than 1 percent of the time) with a value of 20 m. As evident from

Figure 4-3 and Table 4-1, winds most often blew toward the east (19 percent frequency) during

the assessment period, and winds blowing toward the north or northeast also were common (17

and 16 percent frequency, respectively). Winds with a strong westward or southward wind

component were least frequent (8 percent toward the southwest; 9 percent each toward the

south and northwest; 10 percent toward the west). The average wind speed was 2.7 m/s,

although that average varied by ±0.9 m/s depending on wind direction.

15 

# AERMET version 12345—the most current when this a ssessment began in late 2013—with modifications to reduce

the impact of a known error with friction-velocity calcul ations; includes processing of 1-minute winds with EPA’s

AERMINUTE processor.

16 

# When this assessment began in late 2013, EPA’s AERSURFACE tool (used in conjunction with AERMET)

accepted only 1992 land-cover data, which did not change significantly for the modeled domain by 2006, which was

the latest data available from the National Land Cover Database.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 17 April 2015 Figure 4-3. Wind Rose for the Meteorological Data Used in this

Site-specific Assessment

Note: Winds are shown “blowing from.” That is, the cone in the northeastern octant

indicates that winds blow toward the southwest approximately 8 percent of the time.



# Table 4-1. Wind and Precipitation Statistics for the Meteorological Data Used in this

Site-specific Assessment

Directional

Octant All Times When Precipitating

(9% of the time) When Not Precipitating

(91% of the time)

Wind

Frequency

Toward

Octant Average

Wind Speed

(m/s) A verage

Annual

Precipitation

Total (mm) a 

# Wind

Frequency

Toward

Octant Average

Wind Speed

(m/s) Wind

Frequency

Toward

Octant Average

Wind Speed

(m/s)

N 17% 2.8 204 18% 3.4 17% 2.7

NE 16% 3.2 169 16% 3.8 16% 3.2

E 19% 3.6 157 17% 4.0 19% 3.6

SE 12% 2.4 108 10% 3.0 12% 2.4

S 9% 2.1 100 9% 2.7 9% 2.0

SW 8% 2.2 75 8% 2.9 8% 2.1

W 10% 1.8 124 11% 2.5 9% 1.8

NW 9% 1.8 101 11% 2.7 9% 1.7

Average

Regardless

of Octant – 2.7 1,038 – 3.2 – 2.6

Calm Calm winds (<0.75 m/s) occurred 6% of the time in the raw meteorological data. For TRIM.FaTE, calm

values and values less than 0.75 m/s (occurred 7% of the time) were replaced with 0.75 m/s.

Missing Missing wind data occurred 1% of the time. For TRIM.FaTE, missing values were replaced with

averages of surrounding values.

a 

# The average annual precipitation totals in t he individual octant rows r epresent only the precipitation that fell when winds wer e

blowing into that octant. The “Average Regardless of Octant” val ue is the total precipitation without regard to wind direction. NORTH

SOUTH WEST EAST 4% 8% 12% 16% 20%

WIND SPEED

(m/s)

>= 10.0

7.5 - 10.0

5.0 - 7.5

4.0 - 5.0

3.0 - 4.0

2.0 - 3.0

1.0 - 2.0

0.8 - 1.0

Calms: 0.00%

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 18 April 2015 

# Precipitation occurred 9 percent of the time and was usually associated with stronger winds

than times without precipitation. Approximately 46 percent of the annual precipitation occurred

when winds had a northward component. The average annual precipitation total was 1,038 mm

(Table 4-1), which was approximately equal to the 1981–2010 30-year average (not shown).

Hourly mixing heights typically were less than 1,000 m, and

more than 64 percent of hours modeled had mixing heights

smaller than 500 m (Table 4-2). The average of the maximum

daily mixing heights was approximately 1,254 m, and the

average of the minimum daily mixing heights was

approximately 67 m (not shown).

4.1.3 Characteristics of the Modeled Emission-source

Compartment

For TRIM.FaTE, the modeled emissions location, amounts,

and release heights were derived from the same NEI

emissions data used for the RTR inhalation assessment for

the ferroalloy-production source category. The emissionsource compartment in TRIM.FaTE was approximately

centered on the facility and generally encompass ed the locations of all of its 16 PB-HAPemitting sources.

The height of the source compartment represents the chemical-release height, and the release

height’s only impact in TRIM.FaTE is to determine whether the emissions are released within

the mixing layer or above it. Chemicals released above the mixing layer are not available for

ground-level exposure (i.e., they are emitted to an upper-air sink). When determining whether

emissions are within the mixing layer, one must consider not only the physical release height of

the emission source but also the buoyancy and vertical momentum of the emissions.

TRIM.FaTE does not explicitly model the buoy ancy and vertical-momentum characteristics

related to the emissions’ exit-gas temperatures and velocities, which have the real-world effect

of ejecting the chemical vertically beyond exiti ng the source (i.e., plume rise). The effect of

plume rise, however, can be represented in TRIM. FaTE by varying the release height with time

to account for plume-rise effects appropriately. The use of time-varying release heights can

increase model runtime, so this adjustment wa s implemented only when plume-rise estimations

indicated that more than 5 percent of the modeled chemical emissions would be lost to the

upper-air sink.

Among the 16 PB-HAP-emitting sources at NEI11660 were five unique release-height values

ranging from 13 m to 38 m. Other physical stack parameters (exit diameter, exit-gas

temperature, and exit-gas velocity) also had widely-varying values, leading to seven unique

combinations of release height, exit diameter, exit-gas temperature, and exit-gas velocity. EPA

examined these seven unique combinations at NEI11660 and reduced the data to three

modeled sources generally representing three unique combinations of physical characteristics.

These groups, and their emission amounts, are shown in Table 4-3. Plume-rise values were

estimated for each emission source for each hour of modeling, using the hourly meteorology

developed for this site-specific assessment and using methods summarized by Seinfeld and

Pandis (1998). 17

17 

# See also Appendix 9 to the Risk Report (Section 3.7). 

# Table 4-2. Mixing-height

Statistics for the Meteorological

Data Used in this

Site-specific Assessment

Hourly Mixing Heights

(m) Frequency

= 2 0 a 

# 0.1%

21

− 418 58%

419

− 816 21%

817

− 1,214 11%

1,215

− 1,612 7%

1,613

− 2,010 3%

2,011

− 2,408 1%

2,409

− 4,000 0.37%

a 

# Mixing heights below 20 m are set equal

to 20 m.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryDraft 19 April 2015Table 4-3. Modeled Emissions Amounts for Each Grouped Emission SourceModeled PB-HAP GroupModeled ChemicalEmissions(tonsperyear)StackAStack BStack CTotalRelease Ht.: 28.8 mRelease Ht.: Varies byHour for Plume-rise PurposesMercuryDivalent Mercury1.48E-03 1.05E-05 4.04E-02 4.19E-02Elemental Mercurya5.93E-03 4.20E-05 1.62E-01 1.68E-01Cadmium Cadmium 8.67E-02 4.82E-05 3.06E-02 1.17E-01PAHs2-Methylnaphthalene 4.01E-01 8.11E-03 5.10E-01 9.19E-01Acenaphthene 1.31E-01 3.18E-03 1.00E-01 2.34E-01Acenaphthylene 4.05E-01 6.55E-03 5.06E-01 9.17E-01Benz(a)anthracene 2.18E-01 2.01E-04 2.29E-01 4.46E-01Benzo(a)pyreneb3.79E-02 7.69E-05 4.07E-02 7.86E-02Benzo(b)fluoranthene 1.79E-01 1.66E-04 1.97E-01 3.75E-01Benzo(g,h,i)perylene 1.15E-02 9.20E-05 1.26E-02 2.42E-02Benzo(k)fluoranthene 5.63E-02 8.32E-05 6.16E-02 1.18E-01Chrysene 4.77E-01 3.42E-04 5.10E-01 9.87E-01Dibenzo(a,h)anthracene 2.20E-03 1.31E-05 2.68E-03 4.89E-03Fluoranthene 2.06E+00 2.42E-03 2.73E+00 4.78E+00Fluorene 1.90E-01 3.66E-03 2.14E-01 4.07E-01Indeno(1,2,3-c,d)pyrene 1.22E-02 3.94E-05 1.45E-02 2.68E-02DioxinsHeptaCDD, 1,2,3,4,6,7,8- 2.36E-08 1.05E-09 6.12E-09 3.07E-08HeptaCDF, 1,2,3,4,6,7,8- 3.19E-08 1.53E-09 2.70E-09 3.62E-08HeptaCDF, 1,2,3,4,7,8,9- 1.33E-08 5.74E-10 3.22E-09 1.71E-08HexaCDD, 1,2,3,4,7,8- 1.30E-08 5.74E-10 2.73E-09 1.63E-08HexaCDD, 1,2,3,6,7,8- 1.26E-08 5.56E-10 2.69E-09 1.59E-08HexaCDD, 1,2,3,7,8,9 - 1.29E-08 5.65E-10 3.20E-09 1.67E-08HexaCDF, 1,2,3,4,7,8- 1.51E-08 6.75E-10 2.66E-09 1.84E-08HexaCDF, 1,2,3,6,7,8- 1.16E-08 5.21E-10 2.05E-09 1.42E-08HexaCDF, 1,2,3,7,8,9- 1.39E-08 6.18E-10 2.65E-09 1.71E-08HexaCDF, 2,3,4,6,7,8- 1.69E-08 7.84E-10 2.23E-09 1.99E-08OctaCDD, 1,2,3,4,6,7,8,9- 1.90E-07 8.72E-09 6.41E-08 2.63E-07OctaCDF, 1,2,3,4,6,7,8,9- 3.54E-08 1.54E-09 7.50E-09 4.44E-08PentaCDD, 1,2,3,7,8- 1.88E-08 8.45E-10 3.24E-09 2.29E-08PentaCDF, 1,2,3,7,8- 1.50E-08 6.79E-10 2.53E-09 1.82E-08PentaCDF, 2,3,4,7,8- 2.28E-08 1.07E-09 2.74E-09 2.66E-08TetraCDD, 2,3,7,8- 1.46E-08 6.18E-10 3.53E-09 1.88E-08TetraCDF, 2,3,7,8- 2.08E-08 9.02E-10 4.42E-09 2.62E-08aEmissions of elemental mercury are included in site-specific modeling because it can transform in the environment to other forms of mercury.bModeled emissions of BaP shown here include emissions of benzo(e)pyrene and perylene, which are not currently fully parameterized in TRIM.FaTE to evaluate their specific fate, transport,and transformation properties. See text in this section on adjusting emissions of these two chemicals to the BaP surrogate based on relative toxicity. Emissions of benzo(e)pyrene and perylenerespectively make up 16.20% and <1% of the modeled emissions of BaP.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 20 April 2015 

# Two of the source groups (Sources B and C) had physical release heights, exit-gas

temperatures, and exit-gas velocities that were large enough to produce large plume-rise values

during some meteorological conditions. Stacks B and C were estimated to emit chemical into

the upper-air sink 17 percent and 31 percent of the time, respectively, so they were modeled

with hourly-varying release heights in TRIM .FaTE. The median plume-rise value above the

physical release points was 34 m for Stack B and 101 m for Stack C. Chemical emissions from

Stack A rarely had enough buoyancy or vertical momentum to release into the upper-air sink, so

they were modeled with a static release height of approximately 29 m.

As is evident in Table 4-3, most of the mercury emissions (96 percent) came from Stack C, with

nearly all the remainder coming from Stack A, so approximately 30 percent of total mercury

emissions was released into the upper-air sink and was unavailable for human exposure. Most

of the cadmium emissions (74 percent) came from modeled Stack A, with nearly all the

remainder coming from Stack C, so approximately 8 percent of total cadmium emissions was

released into the upper-air sink and was unavailable for human exposure. The PAH (BaPequivalent) emissions were split evenly between Stacks A and C, with less than 1 percent

emitted from Stack B, so approximately 17 percent of total BaP-equivalent PAH emissions was

released into the upper-air sink and was unavailable for human exposure. Most of the dioxin

(2,3,7,8-TCDD-equivalent) emissions (81 percent) came from Stack A, with 4 and 15 percent

respectively from Stacks B and C, so approximately 5 percent of total 2,3,7,8-TCDD-equivalent

emissions was released into the upper-air sink and was unavailable for human exposure. These

BaP- and 2,3,7,8-TCDD-equivalent emissions are not shown in Table 4-3.

Two PAH compounds emitted from NEI11660 (benzo(e)pyrene—BeP for simplicity—and

perylene) are not currently parameterized in TRIM.FaTE. Surrogate compounds were selected

to approximate their environmental fate, transpo rt, and transformation characteristics in the

modeling. To select an appropriate surrogate chemical, a brief literature search was conducted

to obtain information on physicochemical and environmental-fate properties of these congeners.

These properties included number of benzene rings, molecular weight, K

ow 

# , K

oc 

# , Henry’s Law

Constant, solubility in water, vapor pressure, biotransformation half-life in fish, and

environmental partitioning and persistence. Data sources included the National Institutes of

Health’s TOXNET (U.S. NIH 2013b) and ChemID (U.S. NIH 2013a) and British Columbia’s

Ministry of Environment (B.C. MOELP 1993), among others. These properties were compared

to those of congeners already parameterized in TRIM.FaTE. The number of benzene rings and

molecular weight were the priority properties; a reasonable surrogate for a chemical would

usually have the same number of benzene rings and a similar molecular weight. For the other

properties, similarity between chemicals was qu antified by grouping chemicals with similar

values for each property and noting roughly the number of times each TRIM-parameterized

chemical was grouped with the chemical requiri ng a surrogate. Additional considerations

included the number and types of additional groups (e.g., methyl, chlorine) attached to one or

more benzene rings and the overall configuration of the molecule (e.g., planar, bent). The

weight of evidence, considered along with professional judgment, suggested that BaP was an

appropriate surrogate for both congeners. The compound-to-surrogate ratio of oral-pathway

toxicities (i.e., the oral toxic-equivalency factor) was 0.0685 for both congeners. That is,

emissions of BeP and perylene were multiplied by 0.0685 and added to the emissions of BaP.

The emissions of BaP shown in Table 4-3 reflect this process. BeP and perylene emissions

make up approximately 16 and less than 1 percent, respectively, of the modeled emissions of

BaP.

The properties of the modeled chemicals are shown in various tables in Appendix A to this

report. These properties include diffusivity, Henry’s Law Constant, molecular weight, K

ow 

# ,

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 21 April 2015 

# deposition velocity, transformation rates, and half-lives, and various assimilation, absorption,

and elimination rates in aquatic species.

4.1.4 Modeling Domain and Parcel Design

The spatial configurations of the TRIM.FaTE surface and air parcels are described below.

These parcels subdivide the surface and air modeling domains in two dimensions. The

modeling compartments associated with a parce l have well-mixed chemical concentrations and

are where chemical transformations and transport take place. The parcels were designed stepby-step, generally following the design recommen dations provided in Section 4 of the sitespecific protocol. 18 

# These steps first considered the locations of the modeled features of interest

(i.e., farms and lakes), and then considered other parcels adjacent to (including upwind and

downwind from) those features. Finally, the s paces were methodically filled in between the

source, those features of interest, and the desired outer domain of the modeling, considering

important land-cover, elevation, soil-type, and watershed qualities where appropriate. Air and

surface parcels were generally collocated, except for the features of interest and areas directly

crosswind of those features, and except w here important land characteristics changed

significantly beneath a single air parcel. For t he parcel design overall, runtime also was

considered because each additional air or surface parcel introduces additional runtime. Model

runtime also increases with increasing number of points (i.e., vertices) used to draw a parcel, so

parcels for features of interest were drawn with as few points as was reasonable to represent

the modeled area. The rationale for this system of parcel designed is described in Section 4 of

the site-specific protocol. 18



# 4.1.4.1 Surface Parcels

The surface-parcel design for NEI11660 is shown in Figure 4-4. The name of each modeled

water body is shown, and each of the other parcels is labeled based roughly on its location

relative to the source parcel (e.g., “NW1,” “SE3,” and so on). The parcel shading generally

corresponds to the type of vegetation used in the modeling (or the tilled or untilled nature of the

soil, in the case of modeled farms and other parcels with heavy farm use). The selection of the

water bodies and farms and the assignment of v egetation and soil types are described below.

Figure 4-5, Figure 4-6, and Figure 4-7, respective ly, show the surface parcels with land-cover,

elevation, and county-boundary base maps. Figure 4-8 is similar to Figure 4-4 but is zoomed in

on farms and lakes near the facility to show additional resolution.

The characteristics of all potentially-fishable lakes 19 

# within approximately 50 km of the facility

were analyzed. Such lakes included those used in the Tier-2 assessment plus a small number

of other lakes. Most of the potential lakes, upon examination using satellite data and Internet

searches, appeared unlikely to be fishable or were close to a much larger lake that was more

likely to be open to fishing and to support the larger fish populations that are more likely to be

supportive of a subsistence fisher.

18 

# Appendix 9 to the Risk Report.

19 

# Based on available data, for RTR mu ltipathway emission-screening assessment s, EPA defines potentially-fishable

lakes as those that are 25–100,000 acre s. Even a 25-acre lake is unlikely to be large enough to sustain harvesting

the number of piscivorous fish required for the current screeni ng ingestion rate (i.e., 373 grams wet weight of fillet per

day). This is discussed in Section 3.4. 1 in Appendix 4 to the Risk Report. Howe ver, EPA includes these smaller lakes

in RTR multipathway assessments to be health protective.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 22 April 2015 

# Figure 4-4. Modeled Surface Parcels for NEI11660 and Their Modeled

Vegetation or Soil Types

Notes: Parcel shadings correspond to modeled vegetation or soil types. As explained in the text (Footnote 20),

Goodfellows Park Lake ultimately wa s not included in the risk results.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 23 April 2015 

# Figure 4-5. Modeled Surface Parcels for NEI11660 and Land-Cover Base

Map

Notes: Source of base map: MLRC (2006). As explained in the text (Footnote 20), Goodfellows Park Lake

ultimately was not included in the risk results.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 24 April 2015 

# Figure 4-6. Modeled Surface Parcels for NEI11660 and Terrain Base Map

Notes: Source of base map: Shuttle R adar Topography Mission elevation data (see:

http://www.esri.com/news/arcnews/s pring07articles/global-elevation.html ). As explained in the text (Footnote

20), Goodfellows Park Lake ultimately was not included in the risk results.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 25 April 2015 

# Figure 4-7. Modeled Surface Parcels for NEI11660 and County Base Map

Note: As explained in the text (Footnote 20), Goodfe llows Park Lake ultimately was not included in the risk

results.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 26 April 2015 

# Figure 4-8. Modeled Surface Parcels for NEI11660, Zoomed on Farms and

Lakes near the Facility

Note: As explained in the text (Footnote 20), Goodfe llows Park Lake ultimately was not included in the risk

results.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 27 April 2015 

# Three lakes were selected to be included in t he site-specific TRIM.FaTE modeling and then

used to evaluate exposures via the fish-c onsumption pathway—Veto Lake (12 km westsouthwest of the facility), Mountwood Park Lake (23 km southeast of the facility), and Wolf Run

Lake (48 km north of the facility). 20 

# These same lakes also were used in the Tier-2 emissionscreening assessment. The modeled surface areas for each lake were nearly identical to their

actual surface areas (Table 4-4). For the other properties needed to model surface water and

sediment, some site-specific values were available

from literature and Internet sources, and default

state, regional, or national values or estimation

methods were used as a last resort. Three

significant rivers (Ohio, Muskingum, and Little

Kanawha) crossed through the modeled domain.

These rivers were not explicitly modeled, although

the water runoff they collect from surrounding land (carrying chemical out of the domain) was

accounted for.

EPA used recent land-cover data (MLRC 2006) and farming data (USDA 2011, USDA 2007) to

identify areas that were within approximately 5 km of the emission source and that appeared to

support farming activities. Most of the crops were soybeans and corn. Local animal husbandry

included cattle, hogs, sheep, and poultry. EPA se lected three farming areas close to the

facility—one to the north-northwest (labeled “Farm NNW” in Figure 4-4; see Figure 4-8 for

greater resolution), one to the west-southwest (“Farm WSW”), and one to the southeast (“Farm

SE”). A modeled farm parcel does not necessarily represent a single, entire farm owned by one

entity; instead, the farms were delineated to represent where farming likely would occur in

general. EPA split in half each modeled farmi ng area—one half was modeled with tilled soil

(representing crop farming), the other half with untilled soil (representing where animals graze).

For each non-lake parcel other than those m odeled for subsistence-farming activities, EPA

analyzed land-cover data (MLRC 2006) to identify the major land-cover type and then related

those land-cover types to the types used in TRIM.FaTE (i.e., coniferous forest, deciduous forest,

grasses/herbs, tilled soil, untilled soil, and bare earth). The other properties of soil

compartments unrelated to vegetation were set to site-specific values when readily available

(i.e., from literature, Internet sources, and the U.S. Department of Agriculture Web Soil Survey

[USDA 2012]), and default state, regional, or national values or estimation methods otherwise.

See Appendix A to this report for these values and other modeled properties of soil-related

compartments.

4.1.4.2 Air Parcels

The air parcel design for NEI11660 is shown in Figure 4-9. Each lake and farm surface parcel,

and any irregularly-shaped surface parcels that were included adjacent to those parcels, had a

single air parcel overlaying it. For all other parcels, air parcels were collocated with surface

parcels.

20 

# Goodfellows Park Lake is present in some of the tables, figures, and setup discussion in this report and

accompanying appendices, but it is not included in the mode ling results. This is because it was initially part of the

assessment, but it was later learned that it is likely still owned by a private company, is not for public access, and was

dry in some recent years. Very little data on the lake were available for modeling. 

# Table 4-4. Lake Sizes

Lake Name Modeled Lake Size

(same as actual size)

km 2 

# (acres)

Veto Lake 0.51 ( 126 )

Mountwood Park Lake 0.16 ( 39.5 )

Wolf Run Lake 0.92 ( 227 )

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 28 April 2015 

# Figure 4-9. Modeled Air Parcels for NEI11660

Notes: For reference, the base map (the lightly-c olored parcels) shows the surface parcels shaded as

in Figure 4-4 and without labels. As explained in the text (Footnote 20), Goodfellows Park Lake

ultimately was not included in the risk results.



# 4.1.5 Abiotic Environment

TRIM.FaTE requires various environmental properti es for each abiotic compartment included in

a scenario. Examples of abiotic environmental properties include the depth of surface soil, soil

porosity and water content, erosion and runoff rates for surface-soil compartments, and

suspended-sediment concentrations in surface water. The values used in this assessment are

shown in Appendix A to this report. Following the site-specific protocol, 18 

# site-specific inputs

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 29 April 2015 

# were used for this assessment where data supporting such values were readily available, and

these site-specific values are indicated in the tables in Appendix A using shading and footnotes.

State, regional, or national default values or estimation methods were used for all other inputs,

especially for those not expected to strongly in fluence chemical concentrations. Following the

site-specific protocol, examples of properties that were at least partially site-specific for at least

some parcels included: soil pH; concentration of suspended sediment in surface water; and

surface-water pH, depth, algal density, temperature, and flush rate. USLE (universal soil-loss

equation) erosion factors were site-specific because they were readily available. Fraction of

precipitation that runs off (i.e., “runoff fractions”) was also site-specific, following the “with

sophisticated GIS software” steps in Section 5.3 of the site-specific protocol. 18



# The approach to parameterizing flush rates for la kes in TRIM.FaTE was to rely on measured

site-specific values, where available, from literature or other reliable sources. In the absence of

such values (i.e., for Veto Lake), EPA computes lake flush-rate estimates from precipitation

input to the lake or assumptions about the lake’s watershed area, the fraction of precipitation

that runs off toward the lake from other non-water surface parcels, lake evaporation rate, lake

volume, and inflow from other water bodies to the lake. Properties of the surface waters of

individual lakes, and of surrounding land parcels, are shown in Appendix A to this report.

4.1.6 Biotic Environment

The TRIM.FaTE biotic environment includes terrestrial plants and aquatic ecosystems.

Examples of biotic environmental properties incl ude leaf-litterfall rate and leaf-lipid content,

water content of plant parts, chemical-transformation rates and -transfer factors, and biomass

densities in surface water and sediment. The values used in this assessment are shown in

Appendix A to this report. Because site-specific information is generally lacking, default state,

regional, or national values or estimation methods were used for most biotic properties.

Examples of properties that were at least partia lly site-specific for at least some parcels included

litterfall period (October 1–October 30) and the period when leaves are on trees (April 10–

October 1), both estimated based on local frost and freeze data. Site-specific values are

indicated in the tables in Appendix A using shading and footnotes.



# 4.2 Methods for Exposure and Risk Modeling

This section describes the methods applied to conduct the exposure assessment and risk

characterization for the scenarios described above. Specifically, this section describes the

methods for modeling chemical concentrations in FFC and other media relevant to the selected

exposure scenarios (Section 4.2.1); estimating hu man exposures associated with ingestion of

FFC media, incidental ingestion of soil, consumpt ion of fish, and infant consumption of breast

milk, where applicable (Section 4.2.2); and estimating metrics of human-health risk associated

with these exposure media (Section 4.2.3). All calculations were conducted using the MIRC

exposure model.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 30 April 2015 

# 4.2.1 Methods for Calculating Exposure Concentrations

Farm-food-chain Media

For foodstuffs that are part of the FFC, MIRC was used to calculate concentrations of PBHAPs. 21 

# The FFC media in the evaluated exposure scenarios included

 

# exposed and protected fruit,

 

# exposed, protected, and root vegetables,

 

# beef and dairy products,

 

# pork, and

 

# poultry and eggs.

The algorithms used in MIRC were obtained from EPA’s Human Health Risk Assessment

Protocol for Hazardous Waste Combustion Facilities (HHRAP; U.S. EPA 2005). These

algorithms model the transfer of concentrations of PB-HAPs in FFC media using biotransfer

factors. Environmental-media concentrations (i .e., the chemical source terms in these

algorithms) were obtained from TRIM.FaTE. TRIM .FaTE outputs included as inputs to MIRC to

calculate exposure concentrations in farm foodstuffs included

 

# PB-HAP concentrations in air,

 

# air-to-surface deposition rates for PB-HAPs in both particle and vapor phases, and

 

# PB-HAP concentrations in surface soil and root-zone soil.

In general, plant- and animal-specific parameter va lues, including chemical-specific transfer

factors for FFC media, were obtained from the Hazardous Waste Companion Database

included in HHRAP (U.S. EPA 2005). Except wher e noted specifically in this report, the

parameter values used in MIRC for this risk assessment matched those used to develop Tier-1

and Tier-2 screening thresholds. 2 

# Surface-soil concentrations were obtained from the estimated

untilled surface-soil compartments represent ative of hypothetical farm locations.

Fish

For the fish-consumption exposure scenarios, PB-HAP concentrations in uncooked fish tissue

were estimated by TRIM.FaTE. All individuals consuming fish were assumed to eat fish

obtained exclusively from one of the modeled wate r bodies. Fishers consumed fish from two of

the five fish compartments: 50 percent bioma ss from water-column carnivores and 50 percent

from benthic carnivores (as modeled in TRIM. FaTE). The average size of fish in those two

compartments is 2 kg, or approximately 1 pound per fish. Although fishers can and probably do

also consume smaller “pan” fish from many lakes, the health-protective assumption is made that

all fish consumed are top predatory fish in the lake.

Because the fish-consumption data are reported “as consumed” and the estimated fish-tissue

concentrations are for uncooked fish, the concentrations were adjusted to reflect possible

changes in concentration due to cooking. When cooked, fish tends to lose both water and fat,

with the amount and proportion of each dependent on the cooking method. These losses affect

chemical concentration in the fish as served. For chemicals that bind primarily to muscle

21 

# Explained further in Attachment 4 to the Risk Repor t (Attachment A, Section A.4.1 and Addendum 2).

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 31 April 2015 

# proteins, such as mercury and cadmium, the loss of water and fat will reduce the overall weight

of the fish serving without reducing the mass of chemical retained in the muscle. Cooking,

therefore, effectively increases the concentrations of mercury and cadmium in the fish tissue. A

cooking conversion factor of 1.5, based on data presented in Morgan et al. (1997, as cited in

U.S. EPA 2011a), was used for mercury and cadmium to account for this potential.

Dioxins, however, are lipophilic and have been dem onstrated to be lost during cooking. Based

on a literature review, a conversion factor of 0.7 was applied to dioxin concentrations to account

for the losses during the cooking process (Schecter et al., 1998; Reinert et al., 1972; Zabik and

Zabik 1995).

Although assuming losses of lipophilic PAHs dur ing the cooking process might be reasonable,

information is insufficient to distinguish what the net loss (or gain) during cooking might be

because cooking can create PAHs from proteins in the tissue. The literature acknowledges

these competing processes but does not provide information sufficient to disentangle the gain

and loss mechanisms. As such, a neutral approach was taken, which is to assume an

adjustment factor of 1.0 (i.e., no adjustment) for PAHs. 22



# Breast Milk

For dioxins, exposure scenarios were evaluated in this assessment to cover exposures to

nursing infants during the first year of life. Only dioxins were evaluated, given the potential for

these lipophilic chemicals to partition to breast milk and the availability of methods to assess

dioxins for this exposure medium. 2



# 4.2.2 Ingestion-exposure Assessment

The following subsections describe the ingestion-exposure scenarios and corresponding

exposure factors used in this risk assessment.

Ingestion-exposure Media and Routes of Uptake

MIRC was used to estimate ingestion rates as average daily doses normalized to body weight

for a range of exposure media. Exposure medi a included were consumption of fish, produce,

and farm animals and related products, as well as incidental ingestion of soil and also including

intermediate exposure media such as ingestion of forage for farm animals. 2



# Summary of Exposure Scenarios

The exposure scenarios described previously were evaluated by defining ingestion-activity

patterns (i.e., estimating how much of each medium was consumed and the fraction of the

consumed medium that was grown in or obtained from contaminated areas) and the

characteristics of the hypothetical human exposed (e.g., age and body weight). MIRC computes

exposure doses and risks for each ingestion medium separately, with variants of three exposure

scenarios evaluated in this assessment and listed below.

1. Consumption of farm-grown fruits, vegetables, and animal products, and incidental

ingestion of soil.

2. Consumption of self-caught fish from local water bodies.

22 

# Explained further in Appendix 4 to the Risk Report (Attachm ent A; Sections 2 and 3; At tachment B, Section 6.4.4).

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 32 April 2015 

# 3. Ingestion of contaminated breast milk by an infant.

For the first of these scenarios, exposed individuals were assumed to consume produce and

livestock products grown at one of the farm loca tions. Fruits and vegetables were assumed to

be contaminated from air and soil at those fa rms. Contaminant uptake by plants included

deposition to leaves/plants, vapor transfer, and root uptake. Livestock were assumed to ingest

contaminants in locally-grown feed plants (i.e., forage, silage, and grain) and in soil incidentally

ingested during grazing. The selection of the evaluated farm locations is described in the

preceding section on fate-and-transport modeling. In the fisher scenario, the exposed

individuals were assumed to eat freshwater game fish caught in one of the modeled lakes and

included in the TRIM.FaTE evaluation.

In the breast-milk scenario, an infant was assumed to be exposed to contaminants ingested in

the fat-content (i.e., lipid phase) of the mother’s breast milk. In estimating contaminant

concentrations in breast milk for the subsistence-farmer scenario, the mother was assumed to

be exposed through consumption of farm-grown fruits, vegetables, and animal products and

through incidental ingestion of soil. For the fisher scenario, maternal exposure was assumed to

include only consumption of self-caught fish from modeled water bodies.

Exposure-scenario Characterization

With the exception of ingestion rates, exposures were estimated for each scenario evaluated

using the same exposure-factor assumptions applied in developing the Tiers 1 and 2

multipathway screening thresholds. 2 

# This approach is likely to result in a relatively high estimate

of exposure, given the assumption that all foods specific to the scenario were obtained from the

local farm or lake, consumption of contaminated foods occurred continuously (i.e., exposure

frequency was daily without interruption), and the exposure duration was long. With respect to

ingestion rates, higher-end assumptions equal to those used to derive screening thresholds

were used, as were other alternative exposure assumptions.

For the subsistence-farmer scenario, the two variants listed below were evaluated using two

sets of ingestion rates.

 

# A reasonable-maximum-exposure (RME) estimate was evaluated that uses farmfoodstuff ingestion rates corresponding to the 90th percentile of consumers who produce

their own food items. Soil-ingestion rates corresponded to the 90th percentile of the

general population. These rates are equal to the inputs used to develop the Tiers 1 and

2 screening thresholds.

 

# A central-tendency-exposure (CTE) estimate was evaluated that uses ingestion rates

corresponding to the mean of the distribution of consumers who produce their own food.

Soil-ingestion rates were set to the mean of the general population.

Farm-food-chain and soil ingestion rates used for the farmer scenarios are summarized in Table

4-5.

Table 4-5. Farm-food-chain and Soil Ingestion Rates Used for Farmer Scenario

Product Child (age in y r s) Adult

(20–70 yrs) <1 1

– 2 3

– 5 6

– 11 12

– 19

Mean ingestion rates (g/kg-day)

Beef a 

# N/A 4.14 4.00 3.77 1.72 1.93

Dairy b 

# N/A 91.6 50.9 27.4 13.6 2.96

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 33 April 2015 Product Child (age in y r s) Adult

(20–70 yrs) <1 1

– 2 3

– 5 6

– 11 12

– 19

Eggs a 

# N/A 2.46 1.42 0.86 0.578 0.606

Exposed Fruit a 

# N/A 6.14 2.60 2.52 1.33 1.19

Exposed Vegetable a 

# N/A 3.48 1.74 1.39 1.07 1.38

Pork a 

# N/A 2.23 2.15 1.50 1.28 1.10

Poultry a 

# N/A 3.57 3.35 2.14 1.50 1.37

Protected Fruit a 

# N/A 16.6 12.4 8. 50 2.96 5.19

Protected Vegetable a 

# N/A 2.46 1.30 1.10 0.78 0.862

Root Vegetable a 

# N/A 2.52 1.28 1.32 0.94 1.03

Soil (mg/day) c 

# N/A 50 50 50 50 20

90th-percentile ingestion rates (g/kg-day) d



# Beef a 

# N/A 9.49 8.83 11.4 3.53 4.41

Dairy b 

# N/A 185 92.5 57.4 30.9 6.16

Eggs a 

# N/A 4.90 3.06 1.90 1.30 1.31

Exposed Fruit a 

# N/A 12.7 5.41 6. 98 3.41 2.37

Exposed Vegetable a 

# N/A 10.7 3.47 3. 22 2.35 3.09

Pork a 

# N/A 4.90 4.83 3.72 3.69 2.23

Poultry a 

# N/A 7.17 6.52 4.51 3.13 2.69

Protected Fruit a 

# N/A 44.8 32 23.3 7.44 15.1

Protected Vegetable a 

# N/A 3.88 2.51 2.14 1.85 1.81

Root Vegetable a 

# N/A 7.25 4.26 3.83 2.26 2.49

Soil (mg/day) N/A 200 d 

# 200 d 

# 201 e 

# 201 e 

# 201 e

a 

# Primary source for values was the 1987–1988 NFCS survey; co mpiled results are presented in Chapter 13 of 2011 Exposure

Factors Handbook (U.S. EPA 2011b). When data were unavailable fo r a particular age group, intake rate for all age groups was

used multiplied by the age-specific ratio of intake based on national-population intake rates from CSFII.

b 

# Primary source for values was 1987–1988 NFCS survey, compil ed results presented in Chapter 13 of 2011 Exposure Factors

Handbook (U.S. EPA 2011b). When data were unavailable for a parti cular age group, intake rate for all age groups was used

multiplied by the age-specific ratio of intake based on nati onal-population intake rates from an NHANES 2003–2006 analysis in

Chapter 11 of the Exposure Factors Handbook.

c 

# The recommended general-population central-tendency rates from the 2011 Exposure Factors Handbook; Table ES-1 Chapter 5

(U.S. EPA 2011b).

d 

# The recommended general-population “upper-percentile” rate for children aged 3 to <6 from the 2011 Exposure Factors Handbook

(U.S. EPA 2011b).

e 

# 90th-percentile adult ingestion rate ca lculated in Stanek et al. 1997; used to represent older children and adults.



# For the fisher scenario, multiple variants involving fish-consumption rates for adults and children

were evaluated given the importance of this parameter to multipathway exposures and the

potentially wide range of fish-ingestion rates.

 

# A subsistence fisher was evaluated that corresponds to the same fish-ingestion rates

used to develop the Tiers 1 and 2 screening thresholds. 23 

# The adult rate is the 99thpercentile value for adult females from Burger (2002) and is considered representative of

male and female subsistence fishers. Fish-ingestion rates for children are based on the

99th-percentile, consumer-only fish-ingestion rates from U.S. EPA (2002). Rates were

adjusted to be representative of the age groups modeled in MIRC. 24



#  

# A high-end recreational fisher was assessed by using the 95th-percentile ingestion

rates from the same two data sets that were used for the subsistence fisher (i.e., Burger

[2002] and U.S. EPA [2002]).

 

# A central-tendency recreational fisher was evaluated by using mean values from

Burger (2002) and U.S. EPA (2002).

 

# Fish-ingestion rates for the general population were represented by three statistics:

mean, 90th percentile, and 99th percentile. These values are from the per-capita

23 

# See Exhibit_Att A 16 in Attachment A of Appendix 4 to the Risk Report.

24 

# Explained further in Appendix 4 to the Risk Report (Attachment A; Attachment B, Section 6.3.4.1).

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 34 April 2015 

# ingestion rates from U.S. EPA (2002) for adults and children. Per-capita ingestion rates

are based on the entire population rather than the subset of the population that ingests

the particular food category (i.e., consumer-only). Thus, the general population includes

fishers and non-fishers, including individuals who rarely or never consume fish.

Fish-ingestion rates used in these fisher scenarios are summarized in Table 4-6.

Table 4-6. Fish-ingestion Rates Used for Fisher Scenarios Evaluated

Scenario Fish Ingestion

Rates (g/day) Source

Subsistence Fishe r a



# Adult 373 Female 99th %-ile ingestion rate of wild-caught fish from Burger (2002)

Child 1–2 108 Based on the 99th %-ile, cons umer-only ingestion rates from U.S. EPA (2002);

rates were adjusted to represent the age groups used in MIRC Child 3–5 159

Child 6–11 268

Child 12–19 331

High-end Recreational Fishe r

Adult 172 Female 95th %-ile ingestion rate of wild-caught fish from Burger (2002)

Child 1–2 65 Based on the 95th %-ile, cons umer-only ingestion rates from U.S. EPA (2002);

rates were adjusted to represent the age groups used in MIRC Child 3–5 96

Child 6–11 181

Child 12–19 167

Central-tendency Recreational Fishe r

Adult 39 Female mean ingestion rate of wild-caught fish from Burger (2002)

Child 1–2 18 Based on the mean, consumer-onl y ingestion rates from U.S. EPA (2002); rates

were adjusted to represent the age groups used in MIRC Child 3–5 27

Child 6–11 44

Child 12–19 58

General-population 99th Percentile

Adult 105 Based on 99th %-ile percentile pe r-capita consumption rates from U.S. EPA

(2002); rates were adjusted to represent the age groups used in MIRC Child 1–2 26.23

Child 3–5 38.72

Child 6–11 62.29

Child 12–19 81.39

General-population 90th Percentile

Adult 17 Based on 90th %-ile per-capita c onsumption rates from U.S. EPA (2002); rates

were adjusted to represent the age groups used in MIRC Child 1–2 3.24

Child 3–5 4.79

Child 6–11 6.9

Child 12–19 8.95

General-population Central Tendency

Adult 6.9 Based on mean per-capita consum ption rates from U.S. EPA ( 2002); rates

were adjusted to represent the age groups used in MIRC Child 1–2 1.37

Child 3–5 2.03

Child 6–11 2.71

Child 12–19 3.90

a 

# This ingestion scenario is the same as that evaluated in the tiered screening assessment.



# Evaluation of Combined Exposures

Beyond the Tier-1 assessment, exposures and the associated health risks were evaluated

separately as described above for the subsistence-farmer and -fisher scenarios, based on the

assumption that a single individual at this location would be unlikely to obtain primary dietary

needs (and especially protein) simultaneously from both fishing and farming. In other words,

fish- and farm-related exposures were not combined after the Tier-1 stage of the assessment.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 35 April 2015 

# 4.2.3 Risk Characterization

MIRC was used to calculate estimated incremental lifetime cancer risks and chronic non-cancer

HQs using the calculated average daily doses and ingestion dose-response values. Chemical

dose-response data included cancer slope factors for ingestion and non-cancer oral reference

doses (RfDs) for chronic exposures. The cancer slope factors and RfDs for the PB-HAPs used

to evaluate risks for this assessment are the same as those used to develop screening

thresholds. 2



# 5. Results

5.1 Media Concentrations

The modeled concentrations in environmental media are presented in Appendix B to this report

for the chemicals assessed. These are annual-average concentrations for the 50th year of the

modeling period.

For mercury (Table B-1), the relative speciation results indicate that divalent mercury was the

dominant species in surface water, sediment, and surface soil. Methyl mercury was the

dominant species in the higher trophic levels of the aquatic biota, progressively bioaccumulating

in the food chain until it represents more than 95 percent of total mercury in game fish. These

speciation trends are consistent with literature (Raymond and Rossmann 2009). The media

concentrations for cadmium (Table B-2), PAH congeners (Table B-3), and dioxin congeners

(Table B-4) also are provided in Appendix B.



# 5.2 Risk-assessment Results

The human-health risk-assessment results for NEI11660 are presented in this section. An

overview of the risk-assessment results for the exposure scenarios evaluated is provided in

Section 5.2.1, while Sections 5.2.2, 5.2.3, 5.2.4, and 5.2.5 contain the results for each of the

four groups of PB-HAPs. In Section 5.2.6, these results are compared to the screening-level

results.

5.2.1 Risk-assessment Summary

The annual-average concentrations estimated by TRIM.FaTE and MIRC for the 50th year of the

modeling period were used to calculate individual , incremental lifetime cancer risks and chronic

non-cancer HQs attributable to source emission s. Exposures to the emitted chemicals were

based on two scenarios: a subsistence farmer who grows and consumes the majority of his food

on a farm near the facility (including animal products and incidental soil ingestion) and a fisher

who ingests self-caught fish from nearby lake s. Because dioxin exposure can occur via

consumption of breast milk by nursing infants, this medium was included in the incremental

lifetime cancer risk where indicated.

Exposures to four groups of PB-HAPs were in cluded in this assessment: mercury HQs (divalent

for farm exposure, and methyl for fish exposur e), cadmium HQs, dioxin incremental lifetime

cancer risks (with 17 individual congeners), and PAH incremental lifetime cancer risks (with 13

individual congeners; 3 of the 18 emitted PAH congeners were not carcinogenic and not

modeled, and the emissions of 2 other congeners were merged with another congener because

they are not parameterized in TRIM.FaTE). The cancer risks for dioxin congeners were summed

into a total dioxin risk, and likewise for PAH congeners.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 36 April 2015 

# Non-cancer HQs from mercury or cadmium did no t exceed 1 (interpreted as below 1.5) for any

exposure location or scenario exposure. For methyl mercury, the largest HQ was 1 (for the

subsistence fisher). For cadmium, the largest HQ was 0.09 (for the subsistence fisher).

Cancer results from exposure to PAHs included in cremental lifetime cancer risks above 1-in-one

million for some ingestion scenarios from exposure at Veto Lake and Wolf Run Lake (risks up to

7-in-one million) and from exposure at a ll modeled farms (risks up to 10-in-one million).

Cancer risks from exposure to dioxins did not exceed 1-in-one million for fisher and farmer

exposures.

For each PB-HAP group, these site-specific-assessment hazard and risk estimates were

smaller than the screening-level assessment results presented in Section 2 using the

subsistence farmer at 90th-percentile ingestion rates and the subsistence-fisher ingestion rates

(which were the screening-assessment exposur e scenarios). This demonstrates that the

screening-level assessments avoided underestimating risk for this facility.

More detailed results for each PB-HAP chemical or group are presented in the following

subsections, along with a more comprehensive comparison with the screening-levelassessment results.

5.2.2 Hazard Quotients Associated With Mercury Exposure

Non-cancer HQs from exposure to methyl merc ury (from fish exposure only) and to divalent

mercury (from farm exposure only) are presented in Table 5-1 and Table 5-2, respectively. The

HQs from exposure to methyl mercury via fish ingestion did not exceed 1 (i.e., were below 1.5).

HQs for fisher scenarios are not shown here for divalent mercury because they were several

orders of magnitude less than the methyl-mercury values. The HQs from exposure to divalent

mercury via the farm were several orders of magnitude below than 1; HQs for the farm

scenarios are not shown for methyl mercury because they were over an order of magnitude

below the divalent-mercury values.

Figure 5-1 shows the levels at which each ingested farm medium (beef, eggs, pork, etc.)

contributed to the total farmer HQ for each age group (farm-medium-specific HQs are provided

in Table 5-3). No single medium contributed a ma jority of the farmer HQ value; overall,

protected fruit, root vegetables, soil, protected vegetables, and exposed fruit each tended to

contribute at least 10 percent of the value and together accounted for most of the value.

5.2.3 Hazard Quotients Associated With Cadmium Exposure

Non-cancer HQs from exposure to cadmium via fish exposure only and via farm exposure only

are presented in Table 5-4 and Table 5-5, respectively. All HQs were several orders of

magnitude below 1.

Figure 5-2 shows the levels at which each ingested farm medium (beef, eggs, pork, etc.)

contributed to the total farmer HQ for each age group (farm-medium-specific HQs are provided

in Table 5-6). Exposure to protected fruit tended to contribute close to the majority of the total

farmer HQ value, followed distantly by exposed fruit and protected vegetables.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 37 April 2015 

# Table 5-1. Hazard Quotients from Exposure to Methyl Mercury for Fisher Scenarios

Fisher

Population Ingestion

Level Period of

Exposure

(years of age) Hazard Quotient

Wolf Run

Lake V eto

Lake Mountwood

Park Lake

Subsistence

Fisher 99th %ile 1–2 1.E+00 2.E-01 2.E-02

3–5 1.E+00 2.E-01 2.E-02

6–11 1.E+00 2.E-01 2.E-02

12–19 8.E-01 1.E-01 1.E-02

20–70 (adult) 8.E-01 1.E-01 1.E-02

Recreational

Fisher Central Tendency

(Mean) 1–2 2.E-01 4.E-02 3.E-03

3–5 2.E-01 4.E-02 3.E-03

6–11 2.E-01 3.E-02 3.E-03

12–19 1.E-01 2.E-02 2.E-03

20–70 ( adult) 8.E-02 1.E-02 1.E-03

High-end

(95th %ile) 1–2 8.E-01 1.E-01 1.E-02

3–5 8.E-01 1.E-01 1.E-02

6–11 8.E-01 1.E-01 1.E-02

12–19 4.E-01 7.E-02 6.E-03

20–70 (adult) 3.E-01 5.E-02 5.E-03

General

Population Central Tendency

(Mean ) 1–2 2.E-02 3.E-03 2.E-04

3–5 2.E-02 3.E-03 3.E-04

6–11 1.E-02 2.E-03 2.E-04

12–19 1.E-02 2.E-03 1.E-04

20–70 (adult) 1.E-02 2.E-03 2.E-04

90th %ile 1–2 4.E- 02 7.E-03 6.E-04

3–5 4.E-02 7.E-03 6.E-04

6–11 3.E-02 5.E-03 4.E-04

12–19 2.E-02 4.E-03 3.E-04

20–70 (adult) 3.E-02 5.E-03 5.E-04

99th %ile 1–2 3.E- 01 5.E-02 5.E-03

3–5 3.E-01 5.E-02 5.E-03

6–11 3.E-01 4.E-02 4.E-03

12–19 2.E-01 3.E-02 3.E-03

20–70 (adult) 2.E-01 3.E-02 3.E-03

Notes: Bolded, underlined cell indicates the largest value in th is table. Hazard quotients rounded to one significant figure.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 38 April 2015

# Table 5-2. Hazard Quotients from Exposure to Divalent Mercury for Subsistencefarmer Scenarios

Ingestion

Level Period of Exposure

(years of age) Hazard Quotient

Farm SE Farm NNW Farm WSW

Mean 1–2 4.E-03 2.E-03 6.E-04

3–5 3.E-03 1.E-03 4.E-04

6–11 2.E-03 9.E-04 3.E-04

12–19 1.E-03 5.E-04 2.E-04

20–70 (adult) 1.E-03 6.E-04 2.E-04

90th %ile 1–2 1.E-02 5.E-03 2.E-03

3–5 7.E-03 3.E-03 1.E-03

6–11 5.E-03 3.E-03 8.E-04

12–19 3.E-03 1.E-03 4.E-04

20–70 (adult) 3.E-03 2.E-03 5.E-04

Notes: Bolded, underlined cell indicates the largest value in th is table. Hazard quotients rounded to one significant figure.



# Figure 5-1. Contribution to Hazard Quotients by Ingested Medium, per Age

Group, from Exposure to Divalent Mercury at Farm SE

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%PercentContributiontoHazardQuotient



# Age Group (years of age) Soil

Total Dairy

Eggs

Beef

Pork

Poultry

Exposed Vegetable

Protected Vegetable

Root Vegetable

Exposed Fruit

Protected Fruit



# Mean Ingestion 90th %ile IngestionHuman-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryDraft 39 April 2015Table 5-3. Hazard Quotients by Ingested Medium, per Age Group, from Exposure to Divalent Mercury at Farm SEIngestionLevelPeriod ofExposure(yearsof age)Hazard QuotientBreastMilk SoilTotalDairy Eggs Beef Pork PoultryExposedVegetableProtectedVegetableRootVegetableExposedFruitProtectedFruitMean 1-2 0 5.4E-04 7.4E-06 1.8E-04 1.7E-05 1.8E-06 1.3E-04 2.4E-044.5E-04 9.2E-04 4.9E-04 1.2E-033-5 0 3.7E-04 4.1E-06 1.1E-04 1.6E-05 1.8E-06 1.2E-04 1.2E-04 2.4E-04 4.7E-04 2.1E-04 9.0E-046-11 0 1.9E-04 2.2E-06 6.5E-051.5E-05 1.2E-06 7.7E-05 9.8E-052.0E-04 4.8E-04 2.0E-04 6.2E-0412-19 0 1.1E-04 1.1E-06 4.3E-05 6.9E-06 1.1E-06 5.4E-05 7.5E-05 1.4E-04 3.4E-04 1.1E-04 2.2E-0420-70(adult)0 3.4E-05 2.4E-07 4.6E-05 7.7E-06 9.0E-07 4.9E-05 9.7E-05 1.6E-04 3.7E-04 9.5E-05 3.8E-0490th %ile 1-2 0 2.2E-03 1.5E-053.7E-04 3.8E-05 4.0E-06 2.6E-047.5E-04 7.1E-04 2.6E-03 1.0E-033.3E-033-5 0 1.5E-03 7.5E-06 2.3E-04 3.5E-05 4.0E-06 2.3E-04 2.4E-04 4.6E-04 1.6E-03 4.4E-04 2.3E-036-11 0 7.6E-04 4.7E-06 1.4E-044.6E-05 3.1E-06 1.6E-04 2.3E-043.9E-04 1.4E-03 5.6E-04 1.7E-0312-19 0 4.3E-04 2.5E-06 9.8E-05 1.4E-05 3.0E-06 1.1E-04 1.7E-04 3.4E-04 8.3E-04 2.7E-04 5.4E-0420-70(adult)0 3.4E-04 5.0E-07 9.8E-05 1.8E-05 1.8E-06 9.7E-05 2.2E-04 3.3E-04 9.1E-04 1.9E-04 1.1E-03Notes: The numbers in this table were used to make the cumulative stacked bar chart shown in Figure 5-1. The bolded, underlinedcell indicates the largest risk value in this table. Hazard quotientsrounded to two significant figures.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 40 April 2015 

# Table 5-4. Hazard Quotients from Exposure to Cadmium for Fisher Scenarios

Fisher

Population Ingestion

Level Period of

Exposure Hazard Quotient

Wolf Run

Lake V eto

Lake Mountwood

Park Lake

Subsistence

Fisher 99th %ile 1–2 5.E-02 9.E-02 3.E-02

3–5 5.E-02 9.E-02 3.E-02

6–11 4.E-02 7.E-02 3.E-02

12–19 3.E-02 5.E-02 2.E-02

20–70 (adult) 3.E-02 5.E-02 2.E-02

Recreational

Fisher Central

Tendency

(Mean) 1–2 9.E-03 1.E-02 5.E-03

3–5 9.E-03 1.E-02 5.E-03

6–11 7.E-03 1.E-02 4.E-03

12–19 5.E-03 9.E-03 3.E-03

20–70 (adult) 3.E-03 5.E-03 2.E-03

High-end

(95th %ile) 1–2 3.E-02 5.E-02 2.E-02

3–5 3.E-02 5.E-02 2.E-02

6–11 3.E-02 5.E-02 2.E-02

12–19 2.E-02 3.E-02 9.E-03

20–70 (adult) 1.E-02 2.E-02 7.E-03

General

Population Central

Tendency

(Mean ) 1–2 6.E-04 1.E-03 4.E-04

3–5 7.E-04 1.E-03 4.E-04

6–11 4.E-04 8.E-04 3.E-04

12–19 4.E-04 6.E-04 2.E-04

20–70 (adult) 5.E-04 9.E-04 3.E-04

90th %ile 1–2 2.E- 03 3.E-03 9.E-04

3–5 2.E-03 3.E-03 9.E-04

6–11 1.E-03 2.E-03 6.E-04

12–19 8.E-04 1.E-03 5.E-04

20–70 (adult) 1.E-03 2.E-03 7.E-04

99th %ile 1–2 1.E- 02 2.E-02 7.E-03

3–5 1.E-02 2.E-02 7.E-03

6–11 1.E-02 2.E-02 6.E-03

12–19 8.E-03 1.E-02 4.E-03

20–70 (adult) 8.E-03 1.E-02 4.E-03

Notes: Bolded, underlined cell indicates the largest value in th is table. Hazard quotients rounded to one significant figure.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 41 April 2015 

# Table 5-5. Hazard Quotients from Exposure to Cadmium for Subsistence-farmer

Scenarios

Ingestion Level Period of Exposure

(years of age) Hazard Quotient

Farm SE Fa r m NNW Farm WSW

Mean 1–2 2.E-02 5.E-03 1.E-02

3–5 1.E-02 3.E-03 8.E-03

6–11 9.E-03 2.E-03 6.E-03

12–19 4.E-03 1.E-03 3.E-03

20–70 (adult) 6.E-03 1.E-03 4.E-03

90th %ile 1–2 4.E-02 1.E-02 3.E-02

3–5 3.E-02 6.E-03 2.E-02

6–11 2.E-02 6.E-03 2.E-02

12–19 1.E-02 3.E-03 8.E-03

20–70 (adult) 1.E-02 3.E-03 1.E-02

Notes: Bolded, underlined cell indicates the largest value in th is table. Hazard quotients rounded to one significant figure.



# Figure 5-2. Contribution to Hazard Quotients by Ingested Medium, per Age Group,

from Exposure to Cadmium at Farm SE

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1

‐ 2 3

‐ 5 6

‐ 11 12

‐ 19 20

‐ 70

(adult) 1

‐ 2 3

‐ 5 6

‐ 11 12

‐ 19 20

‐ 70

(adult)PercentContributiontoHazardQuotient



# Age Group (years of age) Soil

Total Dairy

Eggs

Beef

Pork

Poultry

Exposed Vegetable

Protected Vegetable

Root Vegetable

Exposed Fruit

Protected Fruit



# Mean Ingestion 90th %ile IngestionHuman-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryDraft 42 April 2015Table 5-6. Hazard Quotients by Ingested Medium, per Age Group, from Exposure to Cadmium at Farm SEIngestionLevelPeriod ofExposure(years ofage)Hazard QuotientBreastMilk SoilTotalDairy Eggs Beef Pork PoultryExposedVegetableProtectedVegetableRootVegetableExposedFruitProtectedFruitMean1-2 02.8E-04 3.2E-04 1.3E-05 1.0E-04 1.3E-05 4.0E-04 1.8E-03 2.9E-03 1.2E-03 3.8E-03 7.7E-033-5 01.9E-04 1.8E-04 7.7E-06 9.7E-05 1.3E-05 3.7E-04 9.1E-04 1.5E-03 6.1E-04 1.6E-03 5.7E-036-11 09.7E-05 9.6E-05 4.7E-06 9.2E-05 9.0E-06 2.4E-04 7.3E-04 1.3E-03 6.3E-04 1.6E-03 3.9E-0312-19 05.4E-05 4.8E-05 3.1E-06 4.2E-05 7.7E-06 1.7E-04 5.6E-04 9.1E-04 4.5E-04 8.2E-04 1.4E-0320-70(adult)01.7E-05 1.0E-05 3.3E-06 4.7E-05 6.6E-06 1.5E-04 7.2E-04 1.0E-03 4.9E-04 7.3E-04 2.4E-0390th %ile1-2 01.1E-03 6.5E-04 2.7E-05 2.3E-04 2.9E-058.0E-04 5.6E-03 4.5E-03 3.5E-03 7.8E-032.1E-023-5 07.5E-04 3.2E-04 1.7E-05 2.2E-04 2.9E-05 7.2E-04 1.8E-03 2.9E-03 2.0E-03 3.3E-03 1.5E-026-11 03.9E-04 2.0E-04 1.0E-05 2.8E-04 2.2E-05 5.0E-04 1.7E-03 2.5E-03 1.8E-03 4.3E-03 1.1E-0212-19 02.2E-04 1.1E-04 7.1E-06 8.6E-05 2.2E-05 3.5E-04 1.2E-03 2.2E-03 1.1E-03 2.1E-03 3.4E-0320-70(adult)01.8E-04 2.2E-05 7.1E-06 1.1E-04 1.3E-05 3.0E-04 1.6E-03 2.1E-03 1.2E-03 1.5E-03 7.0E-03Notes: The values in this table were used to make the cumulative stacked bar chart shown in Figure 5-2. The bolded, underlinedcell indicates the largest risk value in this table. Hazardquotients rounded to two significant figures.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 43 April 2015 

# 5.2.4 Cancer Risks Associated With PAH Exposure

Incremental lifetime cancer risks from exposur e to PAHs via fish exposure only and via farm

exposure only are presented in Table 5-7 and Table 5-8, respectively. The risks from exposure

to PAHs via fish ingestion were above 1-in-one million for Veto Lake at several ingestion

scenarios (i.e., 7-in-one million, 4-in-one million, and 2-in-one million) and for Wolf Run Lake at

the subsistence scenario (i.e., 3-in-one million), and these risks were driven primarily by

emissions of benzo(b)fluoranthene and secondarily by emissions of benzo(k)fluoranthene. The

risks from exposure to PAHs via the farm we re above 1-in-one million for all modeled farm

locations and ingestion scenarios (e.g., as large as 10-in-one million at Farm SE using the 90thpercentile ingestion rate) and were driven primarily by emissions of BaP and

benzo(b)fluoranthene and secondarily by emissions of benzo(k)fluoranthene. HQs for PAH

exposure are not shown because non-cancer RfDs were available only for a small subset of

congeners, and the HQs for these congeners summed to values many orders of magnitude less

than 1 (thus, the cancer assessment was more protective).

Figure 5-3 shows the levels at which each ingested farm medium (beef, eggs, pork, etc.)

contributed to the total incremental lifetime farm er risk (farm-medium-specific risks are provided

in Table 5-9). No one medium contributed a majority of the farm-risk value, although exposures

to dairy and exposed fruits together accounted for most of the farmer risk, followed by exposed

vegetables and beef.

Table 5-7. Incremental Lifetime Cancer Risks from Exposure to PAHs for Fisher

Scenarios

Fisher Population Ingestion Level Period of

Exposure Incremental Lifetime Cancer Risk

Wolf Run

Lake V eto

Lake Mountwood

Park Lake

Subsistence Fisher 99th %ile Lifetime 3.E-06 7.E-06 1.E-06

Recreational Fisher Central Tendency (Me an) Lifetime 4.E-07 1.E-06 2.E-07

95th %ile Lifetime 1.E-06 4.E-06 6.E-07

General Population Mean

Lifetime 4.E-08 1.E-07 2.E-08

90th %ile 9.E-08 2.E-07 4.E-08

99th %ile 6.E-07 2.E-06 3.E-07

Notes: Lifetime is ages 1 through 70. Risks rounded to one significant figure. Shading corresponds to values that exceed 1-in-one

million, rounded (which is defined as at l east 1.5-in-one million). Bolded, underlined ce ll indicates the largest value in this table.



# Table 5-8. Incremental Lifetime Cancer Risk from Exposure to PAHs for

Subsistence-farmer Scenarios

Ingestion Level Period of

Exposure Incremental Lifetime Cancer Risk

Farm SE Farm NNW Farm WSW

Mean Lifetime 6.E-06 6.E-06 2.E-06

90th %ile Lifetime 1.E-05 1.E-05 4.E-06

Notes: Lifetime is ages 1 through 70. Risks rounded to one signific ant figure. Shading corresponds to values that exceed 1-inone million, rounded (which is defined as at least 1.5-in-one m illion). Bolded, underlined cell indicates the largest value in this

table.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 44 April 2015 

# Figure 5-3. Contribution to Incremental Lifetime Cancer Risk by Ingested Medium

from Exposure to PAHs at Farm NNW

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%PercentContributiontoIncrementalLIfetimeCancerRisk 

# Soil

Total Dairy

Eggs

Beef

Pork

Poultry

Exposed Vegetable

Protected Vegetable

Root Vegetable

Exposed Fruit

Protected Fruit



# Mean Ingestion 90th %ile IngestionHuman-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryDraft 45 April 2015Table 5-9. Incremental Lifetime Cancer Risk by Ingested Medium from Exposure to PAHs at Farm NNWIngestionLevelPeriod ofExposureIncremental Lifetime Cancer RiskBreastMilk SoilTotalDairy Eggs Beef Pork PoultryExposedVegetableProtectedVegetableRootVegetableExposedFruitProtectedFruitMeanLifetime0 1.1E-07 2.4E-06 3.2E-08 4.8E-07 2.1E-08 5.6E-08 8.7E-07 2.9E-08 1.4E-07 1.6E-06 7.7E-0890th %ileLifetime0 5.3E-074.8E-066.8E-08 1.2E-06 4.9E-08 1.1E-07 2.1E-06 5.7E-08 3.8E-07 3.6E-06 2.1E-07Notes: The values in this table were used to make the cumulative stacked bar chart shown in Figure 5-3. The bolded, underlinedcell indicates the largest risk value in this table. Risksrounded to two significant figures.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 46 April 2015 

# 5.2.5 Cancer Risks Associated With Dioxin Exposure

Incremental lifetime cancer risks from exposure to dioxins via fish exposure only and via farm

exposure only are presented in Table 5-10 and Table 5-11, respectively. The risks from

exposure to dioxins did not exceed 1-in-one million for any farm or fish scenario. The fisher risks

were driven primarily by emi ssions of 1,2,3,7,8-PCDD and secondarily by 2,3,7,8-TCDD. The

farmer risks were driven primarily by emissions of 1,2,3,7,8-PCDD and secondarily by 2,3,4,7,8-

PCDF and 2,3,7,8-TCDD. HQs for dioxin expos ure are not shown because non-cancer RfDs

were available only for one modeled congener, and the HQs for that congener were well below

1 (thus, the cancer assessment was more protective).

Figure 5-4 shows the levels at which each ingested farm medium (beef, eggs, pork, etc.)

contributed to the total lifetime farmer risk (f arm-medium-specific risks are provided in Table

5-12). The dairy medium contributed a near-majorit y of the farmer risk, followed by infant

exposure via breast milk and beef exposure.

Table 5-10. Incremental Lifetime Cancer Risks from Exposure to Dioxins for Fisher

Scenarios

Fisher Population Ingestion

Level Period of

Exposure Incremental Lifetime Cancer Risk

Wolf Run

Lake V eto

Lake Mountwood

Park Lake

Subsistence Fisher 99th %ile Lifetime 3.E-07 5.E-07 1.E-07

Recreational Fisher Central

Tendency

(Mean) Lifetime 4.E-08 6.E-08 2.E-08

95th %ile Lifetime 2.E-07 3.E-07 7.E-08

General Population Mean

Lifetime 5.E-09 8.E-09 2.E-09

90th %ile 1.E-08 2.E-08 5.E-09

99th %ile 8.E-08 1.E-07 3.E-08

Notes: Lifetime is ages 1 through 70, including infant exposure to chemical from an adult mother’s breast milk, where the adult

mother’s exposure period did not include childhood. Risks rounded to one significant figure. Bol ded, underlined cell indicates the

largest value in this table.



# Table 5-11. Incremental Lifetime Cancer Risk from Exposure to Dioxins for

Subsistence-farmer Scenarios

Ingestion Level Period of Exposure Incremental Lifetime Cancer Risk

Farm SE Farm NNW Farm WSW

Mean Lifetime 7.E-07 7.E-07 2.E-07

90th %ile Lifetime 1.E-06 1.E-06 5.E-07

Notes: Lifetime is ages 1 through 70, including infant exposure to chemical from an adult mother’s breast milk, where the

adult mother’s exposure period did not include childhood. Risks rounded to one significant figure. Bolded, underlined cell

indicates the largest value in this table.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 47 April 2015 

# Figure 5-4. Contribution to Incremental Lifetime Cancer Risk by Ingested Medium

from Exposure to Dioxins at Farm NNW

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%PercentContributiontoIncrementalLifetimeCancerRisk 

# Breast Milk

Soil

Total Dairy

Eggs

Beef

Pork

Poultry

Exposed Vegetable

Protected Vegetable

Root Vegetable

Exposed Fruit

Protected Fruit



# Mean Ingestion 90th %ile IngestionHuman-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryDraft 48 April 2015Table 5-12. Incremental Lifetime Cancer Risk by Ingested Medium from Exposure to Dioxins at Farm NNWIngestionLevelPeriod ofExposureIncremental Lifetime Cancer RiskBreastMilk SoilTotalDairy Eggs Beef Pork PoultryExposedVegetableProtectedVegetableRootVegetableExposedFruitProtectedFruitMean Lifetime 2.0E-07 7.5E-10 3.4E-07 1.4E-10 1.3E-07 8.2E-09 2.7E-10 4.8E-09 1.1E-10 2.0E-09 7.2E-09 2.8E-1090th %ile Lifetime 4.4E-07 4.3E-097.0E-073.1E-10 3.0E-07 1.8E-08 5.3E-10 1.1E-08 2.3E-10 5.1E-09 1.6E-08 7.9E-10Notes: The values in this table were used to make the cumulative stacked bar chart shown in Figure 5-4. The bolded, underlinedcell indicates the largest risk value in this table. Risks rounded totwo significant figures.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 49 April 2015 

# 5.2.6 Comparison to Screening-level-assessment Results

Table 5-13 presents the screening-level-assessment results (from Section 2) as ratios of the

emissions for this facility to the screening thresholds. Although these values are not intended to

be interpreted as “risk estimates,” they can be used to evaluate the impact of site-specific fateand-transport modeling by comparing the screening values to the site-specific risk results. Note

that Tier-2 screening values and site-specific HQs and incremental lifetime cancer risks shown

here are shown separately for the farm and fish scenarios, whereas the Tier-1 screening value

combines the farm and fish scenarios.

As discussed in Section 2.2, the Tier-2 screening resulted in lower screening values than the

Tier-1 screening, and, as expected (and illustrated by the results shown in Table 5-13), the sitespecific risks and hazards were lower than the Tier-2 screening values.

 

# The largest site-specific fish HQ for mercury was 6 times smaller than the Tier-2 fish

screening value, while the largest site-specific farm HQ for mercury was 25 percent

larger than the Tier-2 farm screening value.

 

# For cadmium, the largest site-specific fish HQ was 8 times smaller than the Tier-2 fish

screening value, while the largest site-specific farm HQ was 2 times smaller than the

Tier-2 farm screening value.

 

# The largest fish and farm site-specific incr emental lifetime cancer risks from PAH

exposure were 2–3 times smaller than the corresponding Tier-2 screening values.

 

# For dioxins, the largest site-specific fish incremental lifetime cancer risk was 4 times

smaller than the Tier-2 fish screening value, while the largest site-specific farm risk was

slightly smaller than the Tier-2 farm screening value.

 

# The sum of the site-specific incremental lifetime cancer risks due to exposures to PAHs

and dioxins was 7-in-one million for the largest fish risks and 20-in-one million for the

largest farm risks, which were much smaller than the respective Tier-2 screening values

(interpreted for comparative purposes as 20-in-one million and 40-in-one million,

respectively).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryDraft 50 April 2015Table 5-13. Comparison of Site-specific Hazard Quotients and Cancer Risks to the Results of the Screening-levelAssessmentsPB-HAP GroupScreening ValueSite-specificHazard Quotientor RiskaFactor Changein HQ or RiskTier 1Tier 2 –Via FishOnlyTier 2 –ViaFarmOnlyVia FishOnlybViaFarmOnlycTier 1 toTier 2FishTier 1 toTier 2FarmTier 2 toSitespecific(Fish)Tier 2 toSitespecific(Farm)Tier 1 toSitespecificFishTier 1 toSitespecificFarmMercuryd(non-cancer)1008 0.009 1 0.01 17 14,862 6 0.8 96 11,806Cadmium(non-cancer)10 0.7 0.07 0.09 0.04 15 144 8 2 116 221PAHs(cancer)5001030710 36 14 2 3 72 37Dioxins(cancer)4022 0.5 1 22 24 4 1 83 28Total IncrementalLifetime CancerRisk (PAH +Dioxin)5002040720 34 15 2 2 73 36Note: Shading corresponds to values that are 2 or larger, whichis defined as at least 1.5. Rounding artifacts are present. Tier-1 assessments do not separately assess risks orhazards from fish and farm (they are aggregated); Tier-2 and site-specific assessmentsONLY assess risks or hazards from fish separately from farm (they are NOT aggregated).Also, site-specific assessments assess risksor hazards from individual lakes, whileTier-2 assessments assess risks or hazardsfrom potentially multiple lakes in combination.aSite-specific values are hazard quotientsfor mercury and cadmium, and lifetime incremental cancer risk as“in-one million” (i.e., divided by 1 × 10-6) for PAHs and dioxins. To becongruent with the setup of the screening assessments, these site-specific values use the Child 1–2 age group for HQs (the ingestion amount per body weight is largest for theChild 1–2 category), a lifetime defined as age 1 through age 70 (including infant breast milk for dioxin exposure) for cancer risk, the subsistence-fisherscenario for fish exposure,and the 90th-percentile ingestion ratefor subsistence-farmer exposure.bWolf Run Lake for Mercury, Veto Lake for others.cFarm_NNW for Dioxins, Farm_SE for others.dExposure to methyl mercury was evaluated for fish exposure, whileexposure to divalent mercurywas evaluated for subsistence-farmer exposure.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 51 April 2015 

# 6. Discussion of Uncertainties and Limitations

The exposure- and risk-modeling process attempts to simulate naturally-occurring physical,

chemical, and biological processes using ma thematical algorithms. For computational

tractability in a risk assessment, the modeling process generally involves simple representations

of many complex real-world processes. The simplification introduces uncertainty. Furthermore,

algorithms that describe the environmental movement of pollutants depend on numerous

environmental parameters for which the values might be naturally variable and for which

available data are limited.

The media-concentration and risk results presented in Section 5 therefore must be interpreted in

light of the uncertainties associated with the model assumptions, structure, and input values.

How mercury, cadmium, PAHs, and dioxins behave in the environment is highly complex, and

many natural processes are represented in a simplified manner by TRIM.FaTE, including

 

# gaseous and particulate deposition from air;

 

# biogeochemical cycling in the aquatic environment, and especially mercury

transformations through methylation and demethylation at the sediment-surface

interface;

 

# mixing processes in air, water, and sediment;

 

# suspended- and benthic-sediment dynamics in lakes; and

 

# biotic processes such as growth, reproduction, and predation.

In addition, the toxicology of the modeled chemicals is complex and uncertain, as are the

estimates of human exposure to these chemicals given the available empirical data. Note that

surrogate congeners were used for emissions of two PAH congeners that currently are not

parameterized in TRIM.FaTE.

Examples of parameters for which values are variable and uncertain include aquatic food web

structure (e.g., diet of each fish species), biokinetic parameters that influence bioaccumulation

(e.g., assimilation efficiencies and elimination rates), topographic characteristics (e.g., lake

depth, runoff rates, and erosion rates), meteorological parameters (e.g., evaporation and

precipitation rates), chemical transformation rates (e.g., methylation and demethylation rates, in

the case of mercury), human exposure parameter s (especially fish-consumption rates), and the

reference dose/cancer slope factors used to assess potential adverse health effects.

EPA has conducted several analyses of the sensitiv ity of risk estimates to parameter values

over the past decade. For those parameters to wh ich the model is particularly sensitive, EPA

has continued to collect additional data to better quantify the variability and distribution of

values. Nonetheless, because of the large number of parameters included, considering the

model in total, a probabilistic risk assessment wa s not attempted. Instead, this assessment

relied on central-tendency values and combinations of values that would lead to estimates of

reasonable-maximum exposures to bound risk estimates.

This assessment evaluated exposure to chemic als that could be attributed to the modeled

facility; background levels of the modeled chemicals were not included. Thus, the assessment

focuses on incremental risks attributable to the facility. The number or fraction of people

potentially affected in the modeling zones were not estimated, nor were specific endpoints like

IQ decrements or cardiovascular effects. The assessment focused on possible central-tendency

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 52 April 2015 

# and high-end exposures for selected exposure scenarios for individuals that might live near a

facility now or in the future.

Major sources of uncertainty affecting model re sults are shown in Table 6-1. Table 6-1 provides

the assessment input of concern, a qualitative judgment of the sensitivity of risk results to the

assessment input (in general, not limited just to this facility), and a general comment. The table

also provides a qualitative judgment of the im pact on modeled risk based on the sensitivity to

the assessment input and based on the range of values manifested by the assessment input in

the environment. In other words, the second column (Sensitivity of Risk Results) describes the

sensitivity of model results to changes in the assessment input, and the fourth column

(Estimated Impact of Uncertainty on Results) ta kes into account both the sensitivity (column

two) and the variability of the assessment input. Inputs that exhibit high risk sensitivity could

have a limited impact in terms of overall uncer tainty, and vice versa, depending on the range of

values manifested by the input. Each source of uncertainty also is discussed in greater detail

below.



# 6.1 Uncertainties Related to Fate-a nd-transport Modeling (TRIM.FaTE)

The algorithms representing the transport and eventual fate of the modeled chemicals in air,

surface water, sediment, and biotic media are simplified representations of complex natural

processes. Estimated chemical-deposition rates and concentrations will vary across different

environmental models and might be most accurate in specific conditions that meet restrictive

assumptions. The TRIM.FaTE model represents all fate-and-transport processes in terms of

first-order differential equations; however, some processes like chemical diffusion are known to

follow second-order dynamics. Other algorithms—like those dealing with methylation and

demethylation in the case of mercury, biotic chemical cycling, and sediment dynamics—do not

consider all the factors known to affect these processes, or the processes might not be well

understood. This section expands on some of the major uncertainties included in Table 6-1 that

are specific to the fate-and-transport modeling conducted for the case studies.

Table 6-1. Sources of Uncertainty in the Current Site-specific Assessment

Assessment Input Sensitivity of

Risk Results Comment Estimated Impact

of Uncertainty on

Results

Aquatic-food-web

parameters High Limited data on chemical- and species-specific

parameters, such as assimilation efficiency and

elimination rates. Medium

Depth of lakes High Based on limited data. Averaged over lake area and

time. Perfect mixing assumed in estimating

concentrations. Medium

Toxicity reference

value (RfD) and

cancer slope factors High Used EPA-recommended values where possible

(estimated from TEFs for most dioxin congeners), but

estimate includes inherent variability and uncertainty.

Surrogate congeners were used for the emissions of

two PAH congeners that are not currently fully

parameterized in TRIM.FaTE. Low to Medium

Fate-and-transport

modeling process High TRIM.FaTE model might not capture all natural

processes or describe them precisely for particular

sites. Low to Medium

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 53 April 2015

Assessment Input Sensitivity of

Risk Results Comment Estimated Impact

of Uncertainty on

Results

Ingestion-exposure

parameters High There is a great deal of variability and uncertainty in

ingestion rates, which is compounded when assessing

exposures from multiple food groups. In general, used

EPA-recommended health-protective values. Medium

Methylation and

demethylation rates in

sediment, wetlands,

and surface water

(mercury only) High Model uses fixed-rate constants for methylation and

demethylation in abiotic media. Model does not

capture complex dependence of rate constants on

environmental conditions. Medium

Retention time/flush

rate Medium-High From literature (for Mount wood Park Lake and Wolf

Run Lake) or calculated from lake dimensions and flow

formulas. Independent data would help validate inflows

and runoff fractions. Low to Medium

Modeling resolution

and layout Medium Resolution of compartmen ts in modeling zone is

relatively coarse. Larger area-averaging could dilute

exposure point concentrations. Low

Erosion and runoff

flow directions Medium Estimated based on topography, watersheds, and

streamflows. Could not validate owing to lack of field

measurements. Low to Medium

Erosion rates Medium Based on site-specific universal soil-loss equation

factors. May not be reflective of small areas with

higher or lower erosion rates. Low to Medium

Precipitation rate Medium Average annual precipitation total for the 4-year period

used in modeling can differ from the 30-year average.

Affects deposition quantity and type. Low to Medium

Evaporation rate Medium Not a direct input to TRIM.FaTE—used in offline

estimations of chemical mo vement (e.g., runoff rates)

and flush rate. Limited site-specific data. Impacts

media concentrations. Low to Medium

Wind speed and

direction Medium Data for 4-year period might not represent average

conditions. Excessive diffusion could underestimate

risk. Low to Medium

Sediment dynamics Medium Model uses simplistic algorithms to describe

deposition and resuspension of sediment. Low to Medium

Other soil, surfacewater, air, and

vegetation

physicochemical

parameters Medium Limited site-specific data available. Default

assumptions often used. Low to Medium

Cooking correction

factor Medium Limited studies available. Single value does not

account for different styles of cooking. Medium

Breast milk pathway

parameters Medium All dioxins were assumed to exhibit the same

partitioning behavior as 2,3,7,8-TCDD. Medium



# Aquatic Food Web Parameters. Estimates of pollutant concentrations in fish that people

consume are sensitive to aquatic-food-web paramet ers. Chemical-, site-, and species-specific

data on food-web structure (e.g., species, diets, biomass per species) and chemical- and

species-specific biokinetic and biodynamic parameters that influence bioaccumulation (e.g., gill

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 54 April 2015 

# absorption from water, assimilation efficiency from food, elimination rates) are limited. A

simplified food web was developed to include both benthic and water column organisms that are

consistent with data reported for many lake food webs (Great Lakes excluded), and central

tendency values were identified for chemical biokinetic/dynamic parameters.

Lake-modeling Assumptions. The concentration of pollutants in modeled lakes (and thereafter

up the aquatic food chain) depends on the depth of lake assumed in the model. The

assessment used depth data specific to each lake. Each lake is modeled as a single watercolumn compartment with a surface-sediment layer and a sediment sink. That approach

assumes perfect and instantaneous mixing of chemical in the water body at each modeling time

step. The approach does not therefore capture possible spatial variation in chemical

concentration within the lake (e.g., would not identify local pockets of high concentrations).

Similarly, the screening-level simulation does not simulate possible short-term high

concentrations that might follow a snow melt or strong storm event.

Surface-water Retention Time/Flush Rate. Retention time, which is inversely proportional to

flush rate, determines how quickly pollutants are passed out of a lake. A flush rate that is too

high (or retention time that is too low) could result in an underestimate of pollutant

concentrations in surface water and in the aquatic food chain. For this assessment, site-specific

retention times were available for Mountwood Park Lake and for Wolf Run Lake from literature

sources. For Veto Lake, retention time was calculated from estimated values for inflow to the

lake and lake volume.

Methylation and Demethylation Rates (Mercury). Methylation and demethylation in sediment

and surface water are key processes governing biogeochemical cycling of mercury in the

aquatic environment. How prevalent these processes are greatly influences the sensitivity of an

aquatic ecosystem to mercury inputs and specifically influences the amount of methyl mercury

available for accumulation. Some water bodies that are efficient at methylating inorganic

mercury can show significant methyl-mercury conc entrations in biota despite a relatively small

mercury input into the system. The representation of these processes in the TRIM.FaTE model

does not explicitly account for known dependencies of transformation rates on redox potential,

pH, sulfite concentration, dissolved-organi c-carbon content, and hydrodynamics at the

sediment-surface water interface. Both methylat ion and demethylation can occur either biotically

or abiotically. Certain conditions, like specific ranges of chloride, sulfide, and dissolved-organicmatter concentrations, can increase the bioavailab ility of divalent mercury for methylation.

Redox conditions can influence the rate of abi otic demethylation. These process mechanics,

potentially antagonistic interactions with heavy metals like selenium, and the potential for

photodegradation of methyl mercury are not captured in the TRIM.FaTE model. Instead, usersupplied first-order rate constants were used to model methylation and demethylation in surface

water and sediment. Site-specific data were limited and the regional default rate constants used

might not represent conditions at the modeled lakes.

Sediment Dynamics. The suspension and burial of sedime nt can have a significant impact on

surface-water concentration and mercury speciation by influencing the methylation and

demethylation process. Suspended-solids concentrat ions also affect the amount of chemical

transported from the water body during flushing. Resuspension of buried sediment could

remobilize previously-deposited contaminants in to the water body. In the TRIM.FaTE model,

these processes are simplistically represented by default sediment-deposition rates and

suspended-solids concentrations, which do not account for hydrodynamic sediment cycling

processes. These parameters were not based on site-specific data.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 55 April 2015 

# Erosion and Runoff Flow Directions. For pollutants for which the risks are transmitted chiefly

by the fish-consumption pathway, the amount of pollu tant entering lakes is a significant variable.

Because erosion and runoff can account for a significant portion of the pollutant transported into

the lake in some locations, erosion and runoff flows from the watershed are a potentiallysensitive parameter in the model. These flow directions were estimated based on information

about the surrounding topography.

Erosion Rates. Similar to runoff rates, erosion rates can affect the quantity of pollutants

transported into a water body. Erosion rates were estimated using the USLE, which is a

generalized estimate that depends on local topography, land use, and climate. Site-specific

USLE factors were used in this assessment. Local erosion rates could differ from the USLE

estimate.

Precipitation Rate. The precipitation rate in the model affects the rate at which pollutants are

transported between surface-soil compartments and water bodies and also the rate at which

pollutants are flushed from water bodies. This assessment used precipitation data for a 4-year

period. Although the annual-average precipitation total for that 4-year period was approximately

equal to the 30-year annual average, it contained periods (e.g., months, seasons, and individual

years) where precipitation was considerably smaller or larger than the 30-year average.

Evaporation Rate. Evaporation rate is not directly input to TRIM.FaTE; it is used in offline

estimations of chemical movement (e.g., chemical-runoff rates from land) and flush rates.

Consequently, evaporation rate affects lake-concentration and aquatic-biota-concentration

estimates. Limited site-specific data were available and regional estimates had to be used.

Wind Speed and Direction. Wind speed and direction affect advection and diffusion of the

pollutant in the model. Because these data were der ived from a single, 4-year period, they might

not be representative of average or future conditions.

Other Soil, Surface-water, Air, and Vegetation Physicochemical Parameters. Default or

national-average values or estimation methods were used for some soil, surface-water, and air

and vegetation physical and chemical parameters due to a lack of easily-accessible site-specific

data.

Algorithm Uncertainty. The TRIM.FaTE model represents all fate-and-transport processes in

terms of first-order differential equations. Some processes like diffusion, however, are known to

follow second-order dynamics. TRIM.FaTE also does not explicitly deal with lateral or vertical

dispersion in the air compartments. As noted earlier, some algorithms like methylation and

sediment transport do not consider all the factors known to affect the process. Biotic processes

including chemical absorption, chemical elimi nation, growth, reproduction, predation, and death

have been represented relatively simplistically in the model. Although the model’s algorithms

have been validated and are based on professional judgment, some level of uncertainty results

from such simplifications.



# 6.2 Uncertainties Related to Exposur e Modeling and Risk Characterization

(MIRC)

Ingestion Exposure Parameters. All the surveys and data analyses in EPA’s 2011 Exposure

Factors Handbook (Chapter 10) (U.S. EPA 2011b) were analyzed. For possible subsistence

populations and high-end recreational-fish consumers, numerous studies have reported catch

and ingestion rates, predominantly for adults, but some for children as well. All of these studies

can be characterized as having relatively small sample sizes (tens to a few hundred) and as

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 56 April 2015 

# applying to a localized population with varying lakes and rivers available for fishing. Many of the

studies also focus on specific racial or ethnic groups with culturally-higher fish-harvesting

practices. As a consequence, extrapolating fi sh-ingestion rates from one population or location

to another or using any single study to represent a cultural group is difficult. Therefore, one of

the studies reporting high-end consumption rates by avid sports fishers (Burger 2002) was used

to represent subsistence fishers. One advantage of the Burger (2002) data is that all ingestion

rates were reported for self- or wild-caught fish (many fish-ingestion surveys do not distinguish

wild-caught from store-bought fish). A difficulty with all of the studies, however, is that given the

small sample sizes, the certainty associated with upper-percentile values is relatively low.

To represent fish consumption for children and for the general population, two large (i.e.,

hundreds to thousands surveyed) data sets are available. EPA’s 2002 analysis of the U.S.

Department of Agriculture’s Continuing Survey of Food Intakes by Individuals (CSFII) for 1994–

1996 and 1998 (USDA 2000) was used. Another more -recent large data set is available from

the 2003–2006 National Health and Nutrition Exami nation Survey (NHANES) conducted by the

Centers for Disease Control and Prevention. EPA’s analysis of those data by consumers and

non-consumers and by age categories is pres ented in EPA’s 2011 Exposure Factors Handbook.

Total sample size for each age category from the NHANES data set is somewhat smaller at this

time than from the CSFII data set (U.S. EPA 201 1b). The overall fish-ingestion rates from the

CSFII data set are somewhat higher than for the NHANES data set. Several factors might

contribute to that trend (e.g., lower fish-ingestion rates for more-recent data as people heed fishconsumption warnings; shellfish included as a separate category in the NHANES data set but

included with fish in the CSFII data set). EPA’s 2002 analysis of the CSFII data set was used for

this risk assessment to be somewhat conservati ve, given the difficulty in evaluating temporal

trends in fish consumption at this time, and because the data for adults as summarized by EPA

(2002) were more similar to the data reported for adults in the Burger (2002) study.

For site-specific risk assessment, using data on actual fish-harvesting and -consumption

practices by people living in the vicinity of the sp ecific facility would be preferable. Such data,

however, were not available for the facility evaluated in this report. High-end values from

nationwide surveys should be protective of possible future populations around any given facility.

The likelihood of any families or residents in the vicinity of a given facility being subsistence

fishers now or in the future is low.

Cooking Correction Factor. As described in Section 4.2.1, a cooking conversion factor of 1.5

was used for mercury and cadmium to account for the fact that cooking fish reduces the overall

mass of the fish without reducing the amount of the chemical bound to proteins, which

effectively increases the chemical concentration in the cooked fish. The cooking conversion

factor assumption was developed based on information presented by Morgan et al. (1997, as

cited in U.S. EPA 2011a) and might overestimate the loss of fat and water from fish when

steamed or poached and underestimate the loss of water by some dry-cooking methods (e.g.,

broiling, grilling).

A conversion factor of 0.7 was applied to dioxin concentrations to account for loss of the

chemical along with loss of fats during cooking methods that include grilling, broiling, and

steaming, and poaching. The value was assumed based on data from several sources

(Schecter et al., 1998; Reinert et al., 1972; Zabik and Zabik 1995) and might not apply to all

species of fish or cooking methods.

Although PAHs in fish taken up from their environment also might be lost along with lipids during

cooking, PAHs also can be created by cooking, particularly at higher heats often associated with

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 57 April 2015 

# broiling or grilling. Although one could argue for reducing the PAH concentration in fish due to

loss of lipids during cooking, the TRIM.FaTE-estimated PAH concentrations in fish were not

adjusted because of the uncertainty associated with both the loss and gain processes.

Breast-milk-pathway Parameters. Exposure to dioxins via the breast-milk pathway was

estimated using algorithms and assumptions to simulate the partitioning behavior of the

chemical in breast milk and the rate and duration of breast-milk consumption. All dioxins were

assumed to exhibit the same partitioning behavior as 2,3,7,8-TCDD, owing to a lack of data.

Although the methods and assumptions are supported by scientific literature, they have not

been evaluated against empirical biomonitoring data or other models.

Toxicity Reference Value. Incremental HQs and incremental lifetime cancer risks were

calculated using EPA’s recommended oral refe rence doses and cancer slope factors where

available (for most dioxins, cancer slope fact ors were estimated from toxicity-equivalency

factors). Reference doses and cancer slope factors are typically estimated after building in

uncertainty factors for pharmacokinetic variability and uncertainty, pharmacodynamic variability

and uncertainty, inter- or intra-species variability, and potentially other factors. An awareness of

the values and ranges of these uncertainty factors (documented in EPA’s IRIS data base) could

help inform risk-management decisions.



# 7. References

Atkinson, D., and R.F. Lee. 1992. Procedures for Substituting Values for Missing NWS

Meteorological Data for Use in Regulatory Air Quality Models. Available at:

http://www.epa.gov/ttn/scram/surface/missdata.txt

B.C. MOELP (Province of British Columbia Ministry of Environment, Lands and Parks). 1993.

Ambient Water Quality Criteria for Polycyclic Aromatic Hydrocarbons (PAHs). Available at:

http://www.env.gov.bc.ca/wat/wq/BC guidelines/pahs/index.html#TopOfPage

.

Burger, J. 2002. Daily consumption of w ild fish and game: Exposures of high end

recreationalists. Environmental Health Research 12(4):343–354.

ESRI (Environmental Systems Research Institute). 2013. World Imagery. Accessed January 15,

2013. Available at:

http://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9

.

MLRC. 2006. National Land Cover Database. Available at:

http://www.mlrc.gov/nlcd06_data.php

.

Morgan, J.N., M.R. Berry, and R.L. Graves. (19 97). Effects of commonly used cooking practices

on total mercury concentration in fish and their impact on exposure assessments. Journal of

Exposure Analysis and Environmental Epidemiology 7(1):119–133.

Raymond, B., and R. Rossmann. 2009. Total and methyl mercury accumulation in 1994

− 1995

Lake Michigan lake trout and forage fish. Journal of Great Lakes Research 35:438

− 446.

Reinert, R.E., D. Stewart, and H.L. Seagran. 1972. Effects of dressing and cooking on DDT

concentrations in certain fish from Lake Michigan. Journal of the Fisheries Research Board

of Canada 29:525–529.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 58 April 2015 

# Schecter A., M. Dellarco, O. Päpke, and J. Olson. 1998. A comparison of dioxins, dibenzofurans

and coplanar PCBS in uncooked and broiled ground beef, catfish, and bacon. Chemosphere

37(9–12):1723–1730.

Seinfeld, J.H., and S.N. Pandis. 1998. Atmospheric Chemistry and Physics: From Air Pollution

to Climate Change, Wiley-Interscience, New York, pp. 931–933.

Stanek, E.J., E.J. Calabrese, R. Barnes, P. Pekow. 1997. Soil ingestion in adults – results of a

second pilot study. Toxicol. Environ. Safety 36:249-257.

U.S. Census Bureau. 2013. Available at: http://www.census.gov/.

U.S. EPA (U.S. Environmental Protection Agency). 2002. Estimated Per Capita Fish

Consumption in the United States. Office of Water, Office of Science and Technology,

Washington, D.C. EPA-821- C- 02-003. August. Available at:

http://www.epa.gov/waterscience/fish/files/consumption_report.pdf

.

U.S. EPA. 2005. Human Health Risk Assessmen t Protocol for Hazardous Waste Combustion

Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R05-006. September. Available at: http://www.epa.gov/osw/hazard/tsd/td/combust/risk.htm

.

U.S. EPA. 2011a. Revised Technical Support Document: National-Scale Assessment of

Mercury Risk to Populations with High Consumption of Self-caught Freshwater Fish, In

Support of the Appropriate and Necessary Finding for Coal- and Oil-Fired Electric

Generating Units. Office of Air Quality Planning and Standards, Health and Environmental

Impacts Division. Research Triangle Park, North Carolina. Available at:

http://www.epa.gov/mats/pdfs/20111216MercuryRiskAssessment.pdf.

U.S. EPA. 2011b. Exposure Factors Handbook: 2011 Edition. Office of Research and

Development, Washington, D.C. EPA/600/R-090/052F. September. Available at:

http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252.

USDA (U.S. Department of Agriculture). 2000. 1994–96, 1998 Continuing Survey of Food

Intakes by Individuals (CSFII). CD-ROM. Agricultural Research Service, Beltsville Human

Nutrition Research Center, Beltsville, MD. Available from the National Technical Information

Service, Springfield, VA, Accession Number PB-2000500027.

USDA. 2007. Census of Agriculture. Available at: http://www.agcensus.usda.gov/

.

USDA. 2011. Cropland Data Layer. Available at: http://nassgeodata.gmu.edu/CropScape/.

USDA. 2012. Web Soil Survey. Available at:

http://websoilsurvey.nrcs.usda.gov/app/HomePage.htm ).

U.S. NIH (U.S. National Institutes of Health). 2013a. ChemIDplus Advanced. Available at:

http://chem.sis.nlm.nih.gov/chemidplus/.

U.S. NIH. 2013b. Toxicology Data Network (TOXNET). Available at: http://toxnet.nlm.nih.gov/

.

USGS (U.S. Geological Survey). 2012. National Hydrography Dataset. Available online at

http://nhd.usgs.gov/

.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

Draft 59 April 2015 

# Zabik, M.E., and M.J. Zabik. 1995. Tetradiocholorodibenzo-p-dioxin residue reduction by

cooking/processing of fish fillets harvested from the Great Lakes. Bulletin of Environmental

Contamination and Toxicology 55:264-269.

# [This page intentionally left blank.]

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category



# Appendix A

User-specified Values for TRIM.FaTE Properties

The properties of the TRIM.FaTE scenario dev eloped to model the environmental fate-andtransport of mercury, cadmium, dioxins, and polyc yclic aromatic hydrocarbons (PAHs) for this

risk assessment are shown in the following tables . Shaded cells in these tables indicate where

site-specific values were developed for this site -specific assessment, with additional information

provided for some values in table notes. In some instances where an entire table shows sitespecific values (such as the values for estimated erosion rates for modeled surface-soil

compartments), table notes are used and cells are left unshaded. All values presented in

unshaded and otherwise unmarked tables were unchanged from the Tiers 1 and 2 screening

assessments.

# [This page intentionally left blank.]

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

A-1 Table A-1. TRIM.FaTE Simulation Parameters a



# Parameter Name Units Value Reference

Start of simulation date/time 1/1/1990,

midnight Consistent with met data.

End of simulation date/time 1/1/2040,

midnight Consistent with met data set; selected to

provide a 50-year modeling period.

Simulation time step hr 1 Selected value.

Output time step b 

# hr 4 Selected value.

a 

# All values were unchanged from the Tiers 1 and 2 Scr eening assessments (no site-specific values used).

b 

# Output time step is set in TRIM.FaTE using the scenario prope rties "simulationStepsPerOutputS tep" and "simulationTimeStep."

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

A-2 Table A-2. Meteorological Inputs a



# Parameter Name Units Value Reference

Meteorological Inputs

Air temperature degrees K Varies by hour Hourly surface meteorological data

from NOAA; see Section 4 of main

report for details on station and data

processing.

Horizontal wind speed m/sec Varies by hour Hourly surface meteorological data

from NOAA; see Section 4 of main

report for details on station and data

processing.

Vertical wind speed m/sec 0.0 Professional judgment; vertical wind

speed not used by any of the

algorithms in the version of the

TRIM.FaTE library used for the

ferroalloy assessment.

Wind direction degrees clockwise

from N (blowing

from) Varies by hour Hourly surface meteorological data

from NOAA; see Section 4 of main

report for details on station and data

processing.

Rainfall rate m 3 

# [rain]/m 2 

# [surface

area]-day Varies by hour Hourly surface meteorological data

from NOAA; see Section 4 of main

report for details on station and data

processing.

Mixing height (used to set air

VE property named “top”) m Varies by hour Twice-daily upper-air meteorological

data from NOAA; see Section 4 of

main report for details on station

and data processing.

isDay_SteadyState_forAir unitless

- Value not used in current dynamic

runs (would need to be reevaluated

if steady-state runs are needed). isDay_SteadyState_forOther unitless

-

a 

# Shaded values indicate where refined values were developed for this site-specific asse ssment. All other values (unshaded) were

unchanged from the Tiers 1 and 2 screening assessments.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

A-3 Table A-3. Air Parameters a



# Parameter Name Units Value Reference

Atmospheric dust load kg[dust]/m 3 

# [air] 6.15E-08 Bidleman 1988.

Density of air g/cm 3 

# 0.0012 U.S. EPA 1997.

Dust density kg[dust]/m 3 

# [dust] 1,400 Bidleman 1988.

Fraction organic matter on

particulates unitless 0.2 Harner and Bidleman 1998.

a 

# All values were unchanged from the Tiers 1 and 2 screeni ng assessments (no site-specific values used).

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

A-4 Table A-4. Soil and Ground Water Parameters 

# *

Parameter

Name Units Value

Soil-Tilled

(incl.

farms) SoilUntilled

(incl.

farms) Grasses/

Herbs Deciduous

Forest References

Surface Soil Compartment Type

Air content volume[air]/vo

lume

[compartment

] 0.25 varies 2 

# varies 2 

# varies 2 

# See additional Soil

table.

Average

vertical

velocity of

water

(percolation) m/day 0.000624 0.000624 0.000624 0.000624 = Average Annual

Precipitation / 365 ×

Fraction of Precipitation

that Percolates, where

365 = days per year;

percolation fraction is

professional judgment

= 0.20

Boundary

layer

thickness

above

surface soil m 0.006 0.006 0.006 0.006 Thibodeaux 1996;

McKone et al. 2001

(Table 3).

Density of soil

solids (dry

weight) kg[soil]/m 3 

# [soi

l] 2600 2600 2600 2600 Default in McKone et

al. 2001 (Table 3)

Thickness 1 

# m 0.2 a 

# 0.01 b 

# 0.01 b 

# 0.01 b a 

# U.S. EPA 2005b

b 

# McKone et al. 2001 (p.

30)

Erosion

fraction unitless varies 2 

# varies 2 

# varies 2 

# varies 2 

# See Erosion and

Runoff Fraction table.

Fraction of

area

available for

erosion m 2 

# [area

available]/m 2 

# [t

otal] 1 1 1 1 Professional judgment;

area is rural.

Fraction of

area

available for

runoff m 2 

# [area

available]/m 2 

# [t

otal] 1 1 1 1 Professional judgment;

area is rural.

Fraction of

area

available for

vertical

diffusion m 2 

# [area

available]/m 2 

# [t

otal] 1 1 1 1 Professional judgment;

area is rural.

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

A-5 Table A-4. Soil and Ground Water Parameters (Cont.) 

# *

Parameter

Name Units Value

SoilTilled

(incl.

farms) SoilUntilled

(incl.

farms) Grasses/

Herbs Deciduous

Forest References

Surface Soil Compartment Type

Fraction

sand unitless 0.181 varies 2 

# 0.181 varies 2 

# USDA Web Soil

Survey

Organic

carbon

fraction unitless varies 2 

# varies 2 

# varies 2 

# varies 2 

# See additional Soil

table

pH unitless varies 2 

# varies 2 

# varies 2 

# varies 2 

# See additional Soil

table

Runoff

fraction unitless varies 2 

# varies 2 

# varies 2 

# varies 2 

# See Erosion and

Runoff Fraction table.

Total

erosion rate kg

[soil]/m 2 

# /day varies 2 

# varies 2 

# varies 2 

# varies 2 

# See Total Erosion

Rates table.

Total runoff

rate m 3 

# [water]/m 2



# /day 0.00125 0.00125 0.00125 0.00125 Calculated using

scenario-specific

precipitation rate and

assumptions

associated with water

balance.(= Average

Annual Precipitation /

365 × Fraction of

Precipitation that

Runs Off, where 365

= days per year;

runoff fraction is

professional

judgment = 0.4)

Water

content volume[wate

r]/volume[compar

tment] 0.22 varies 2 

# varies 2 

# varies 2 

# McKone et al. 2001

(Table A-3).

Root Zone Soil Compartment Type

Air content volume[air]/v

olume[compar

tment] 0.25 varies 2 

# varies 2 

# varies 2 

# See additional Soil

table

# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category

A-6 Table A-4. Soil and Ground Water Parameters (Cont.) 

# *

Parameter

Name Units Value

SoilTilled

(incl.

farms) SoilUntilled

(incl.

farms) Grasses/

Herbs Deciduous

Forest References

Root Zone Soil Compartment Type

Average

vertical

velocity of

water

(percolation) m/day 0.000624 0.000624 0.000624 0.000624 = Average Annual

Precipitation / 365 ×

Fraction of

Precipitation that

Percolates, where

365 = days per year;

percolation fraction is

professional

judgment = 0.20

Density of

soil solids

(dry weight) kg[soil]/m 3 

# [s

oil] 2601 2601 2600 2601 Default in McKone et

al. 2001 (Table 3)

Fraction

sand unitless 0.181 varies 2 

# 0.181 varies 2 

# See additional Soil

table

Thickness 1 

# m 0.6 a 

# 0.79 a 

# 0.79 a 

# 0.79 a a 

# McKone et al. 2001

(Table 16 directly or

adjusted).

Organic

carbon

fraction unitless varies 2 

# varies 2 

# varies 2 

# varies 2 

# See additional Soil

table

pH unitless varies 2 

# varies 2 

# varies 2 

# varies 2 

# See additional Soil

table

Water

content volume[wate

r]/volume[compar

tment] 0.22 varies 2 

# varies 2 

# varies 2 

# See additional Soil

table

* 

# Shaded values indicate where refined va lues were developed for this site-spec ific assessment. All other values

(unshaded) were unchanged from the Tiers 1 and 2 screening assessments.

1 

# Set using the volume element properties file.

2 

# See separate tables for val ues of these parameters.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-7Table A-5. Additional Soil Parameters*Surface ParcelSurface Soil Compartment TypeRoot Zone Compartment TypeAirContentcFractionOrganicCarbonaWaterContentbpHcFractionOrganicCarbonaFractionSandcWaterContentbpHcFarm_NNW_Tilled0.250.0072670.225.30.0072670.1810.225.3Farm_NNW_Untilled0.250.0072670.225.30.0072670.1810.225.3Farm_SE_Tilled0.250.0101740.225.50.0101740.1810.225.5Farm_SE_Untilled0.250.0101740.225.50.0101740.1810.225.5Farm_WSW_Tilled0.250.0072670.225.30.0072670.1810.225.3Farm_WSW_Untilled0.250.0072670.225.30.0072670.1810.225.3E10.30.0101740.25.50.0101740.1810.25.5E20.30.0101740.25.50.0101740.1810.25.5N10.250.0101740.225.30.0101740.1810.225.3N20.250.0145350.225.50.0145350.3670.225.5N3e0.250.0101740.225.10.0101740.3040.225.1N3w0.250.0101740.225.10.0101740.3040.225.1N40.250.0101740.225.10.0101740.3040.225.1NE10.250.0101740.225.30.0101740.1810.225.3NNW10.250.0072670.225.30.0072670.1810.225.3NNW2e0.250.0072670.225.30.0072670.1810.225.3NNW2w0.250.0072670.225.30.0072670.1810.225.3NNW30.250.0101740.225.30.0101740.1810.225.3NNW40.250.0072670.225.30.0072670.1810.225.3NW10.250.0101740.225.30.0101740.1810.225.3NW20.250.0101740.225.30.0101740.1810.225.3NW30.250.0101740.225.30.0101740.1810.225.3S10.30.0101740.25.50.0101740.1810.25.5S20.30.0101740.25.50.0101740.1810.25.5S30.30.0101740.25.50.0101740.1810.25.5SE10.30.0101740.25.50.0101740.1810.25.5SE20.30.0101740.25.50.0101740.1810.25.5Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-8Table A-5. Additional Soil Parameters (Cont.)*Surface ParcelSurface Soil Compartment TypeRoot Zone Compartment TypeAirContentcFractionOrganicCarbonaWaterContentbpHcFractionOrganicCarbonaFractionSandcWaterContentbpHcSE30.30.0101740.25.50.0101740.1810.25.5SE4n0.30.0101740.25.50.0101740.1810.25.5SE4s0.30.0101740.25.50.0101740.1810.25.5SE50.30.0101740.25.50.0101740.1810.25.5Source0.280.0080.196.90.0080.250.216.9SW10.250.0101740.225.30.0101740.1810.225.3SW20.250.0101740.225.30.0101740.1810.225.3SW30.250.0101740.225.30.0101740.1810.225.3W10.250.0072670.225.30.0072670.1810.225.3W20.250.010170.225.30.010170.1810.225.3W3n0.250.010170.225.30.010170.1810.225.3W3s0.250.0101740.225.30.0101740.1810.225.3W40.250.010170.225.30.010170.1810.225.3W50.250.010.225.30.010.1810.225.3WNW10.250.0101740.225.30.0101740.1810.225.3WNW2n0.250.0101740.225.30.0101740.1810.225.3WNW2s0.250.0101740.225.30.0101740.1810.225.3WNW30.250.010170.225.30.010170.1810.225.3WNW40.250.010170.225.30.010170.1810.225.3WNW50.250.0101740.225.30.0101740.1810.225.3WSW10.250.007270.225.30.007270.1810.225.3WSW20.250.0070.225.30.0070.1810.225.3WSW3n0.250.0072670.225.30.0072670.1810.225.3WSW3s0.250.0072670.225.30.0072670.1810.225.3WSW40.250.010170.225.30.010170.1810.225.3WSW50.250.0101740.225.30.0101740.1810.225.3WSW60.250.01020.225.30.01020.1810.225.3*All values shown were refined for this site-specific assessment (versus the valuesused in Tiers 1 and 2 screening assessments).a=fraction organic matter / 1.72, as per McKone et al. 2001.bMcKone et al. 2001 (Table A-3).cUSDA Web Soil Survey.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-9Table A-6. Runoff FractionsaReceiving parcelSending parcel Sink E1 E2 Farm_NNW_Tilled Farm_NNW_Untilled Farm_SE_Tilled Farm_SE_Untilled Farm_WSW_Tilled Farm_WSW_Untilled Goodfellows_Park_Lake Mountwood_Park_Lake N1 N2 N3e N3w N4 NE1 NNW1 NNW2e NNW2w NNW3 NNW4 NW1 NW2 NW3 S1 S2 S3 SE1 SE2E1 .200.24000.3500 0 000000.200 0 0 0 0 0 0 0000.010E2 .48 .010000000 0 000000.410 0 0 0 0 0 0 0000 0.01Farm_NNW_Tilled .94000.020000 0 00000000 0.040 0 0 0 0000 0 0Farm_NNW_Untilled .1300.4000000 0 00000000.460 0 0 0 0 0000 0 0Farm_SE_Tilled000000.0600 0 00000000 0 0 0 0 0 0 0000.88 .06Farm_SE_Untilled0.02000.96000 0 00000000 0 0 0 0 0 0 0000.020Farm_WSW_Tilled00000000.330 00000000 0 0 0 0 0 0 0000 0 0Farm_WSW_Untilled .1100000 0.020 0 00000000 0 0 0 0 0 0 0000 0 0Goodfellows_Park_Lake0000000001.0000000000 0 0 0 0 0 0 0000 0 0Mountwood_Park_Lake000000000 01.000000000 0 0 0 0 0 0 0000 0 0N1000000000 0 00.04000.92 .01 .010 0 0 0 0 0000 0 0N2 .0500000000 0 000.03 .010.78000.02 .110 0 0000 0 0N3e .0100000000 0 00.800 0.10 .080 0 0 0 0 0 0 0000 0 0N3w000000000 0 00.10 .040.6900000.070 0 0000 0 0N4 .4500000000 0 000.32 .23000 0 0 0 0 0 0 0000 0 0NE1 .47 .01 .01000000 0 00.5100000 0 0 0 0 0 0 0000 0 0NNW1000000000 0 0.24000000.04000.680 0000 0 0NNW2e000000000 0 0.7600000.040 0.190 0 0 0000 0 0NNW2w .4700000000 0 0000000.11 .020.020.380 0000 0 0NNW3000000000 0 00.0700000.190 0.260.25 .23000 0 0NNW4 .2400000000 0 00.590.0300000.01000.13000 0 0NW1000000000 0 0000000.1600000.290000 0 0NW2000000000 0 0000000000.120.020.42000 0 0NW3 .1700000000 0 00000000000.180.510000 0 0S1 .160000.06000 0 000000000 00 00 000.170.150S2 .6300000000 0 00000000 0 0 0 0 0 0 000.180.12S3 .5900000000 0 000000000 00 00 000.19000SE1 .23 .740000000 0 000000000000000.0200 0 0SE200.3300.01000 0 000000000 00 00 000.59000SE300.35000000 0 000000000 00 00 000.430 0.01SE4n .0400000000 0.010000000 0 0 0 0 0 0 0000 0 0SE4s .0500000000 0 000000000 00 00 000.05000SE5 .9100000000 0 00000000 0 0 0 0 0 0 0000 0 0Source 100000000 0 00000000 0 0 0 0 0 0 0000 0 0Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-10Table A-6. Runoff Fractions (Cont.)aReceiving parcelSending parcel Sink E1 E2 Farm_NNW_Tilled Farm_NNW_Untilled Farm_SE_Tilled Farm_SE_Untilled Farm_WSW_Tilled Farm_WSW_Untilled Goodfellows_Park_Lake Mountwood_Park_Lake N1 N2 N3e N3w N4 NE1 NNW1 NNW2e NNW2w NNW3 NNW4 NW1 NW2 NW3 S1 S2 S3 SE1 SE2SW1 .4300000000 0 000000000000000.02 .10000SW2 .1800000000 0 00000000 0 0 0 0 0 0 0000 0 0SW3 .9300000000 0 00000000 0 0 0 0 0 0 000.020 0Veto_Lake000000000 0 00000000 0 0 0 0 0 0 0000 0 0W1000000000 0 00000000 0 0 0 0 0 0 0000 0 0W2000000000 0 00000000 0 0 0 0 0 0 0000 0 0W3n .6700000000 0 00000000 0 0 0 0 0 0 0000 0 0W3s .6600000000 0 00000000 0 0 0 0 0 0 0000 0 0W4000000000 0 00000000 0 0 0 0 0 0 0000 0 0W5 .3000000000 0 00000000 0 0 0 0 0 0 0000 0 0WNW1000000000 0 000000000000.03 .040000 0 0WNW2n .5300000000 0 0000000000000.310000 0 0WNW2s .1500000000.4300000000 0 0 0 0 0 0 0000 0 0WNW3000000000 0 00000000000000.27000 0 0WNW4000000000 0 00000000000000.28000 0 0WNW5 .2300000000 0 00000000000000.13000 0 0Wolf_Run_Lake000000000 0 00000000 0 0 0 0 0 0 0000 0 0WSW1000000000 0 00000000 0 0 0 0 0 0 0000 0 0WSW2000000000 0 00000000 0 0 0 0 0 0 0000 0 0WSW3n .7900000 0.01 .110 00000000 0 0 0 0 0 0 0000 0 0WSW3s .0100000 0.410 0 00000000 0 0 0 0 0 0 0000 0 0WSW4000000000 0 00000000 0 0 0 0 0 0 0000 0 0WSW5000000000 0 00000000 0 0 0 0 0 0 0000 0 0WSW6 .1900000000 0 00000000 0 0 0 0 0 0 0000 0 0Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-11Table A-6. Runoff Fractions (Cont.)aReceiving parcelSending parcel SE3 SE4n SE4s SE5 Source SW1 SW2 SW3 Veto_Lake W1 W2 W3n W3s W4 W5 WNW1 WNW2n WNW2s WNW3 WNW4 WNW5 Wolf_Run_Lake WSW1 WSW2 WSW3n WSW3s WSW4 WSW5 WSW6E10000.01000000000000000000000000E2 .100000000000000000000000000000Farm_NNW_Tilled00000000000000000000000000000Farm_NNW_Untilled00000000000000000000000000000Farm_SE_Tilled00000000000000000000000000000Farm_SE_Untilled00000000000000000000000000000Farm_WSW_Tilled0000000000000000000000000.67000Farm_WSW_Untilled0000000000000000000000000.86000Goodfellows_Park_Lake00000000000000000000000000000Mountwood_Park_Lake00000000000000000000000000000N10000.02000000000000000000000000N200000000000000000000000000000N3e00000000000000000000000000000N3w000000000000000000000.100000000N400000000000000000000000000000NE100000000000000000000000000000NNW10000.03000000000000000000000000NNW2e00000000000000000000000000000NNW2w00000000000000000000000000000NNW300000000000000000000000000000NNW400000000000000000000000000000NW10000.030000000000.520000000000000NW2000000000000000.43 .01000000000000NW3000000000000000000.02 .02 .1000000000S100000.4500000000000000000000000S2 .050000.0200000000000000000000000S30000000.22000000000000000000000SE100000000000000000000000000000SE2 .070000000000000000000000000000SE30.15 .0600000000000000000000000000SE4n .4500.490000000000000000000000000SE4s .1100.790000000000000000000000000SE50.07 .0300000000000000000000000000Source00000000000000000000000000000Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-12Table A-6. Runoff Fractions (Cont.)aReceiving parcelSending parcel SE3 SE4n SE4s SE5 Source SW1 SW2 SW3 Veto_Lake W1 W2 W3n W3s W4 W5 WNW1 WNW2n WNW2s WNW3 WNW4 WNW5 Wolf_Run_Lake WSW1 WSW2 WSW3n WSW3s WSW4 WSW5 WSW6SW10000.010.22 .0800000000000000.09 .010.03000SW200000.050.10000000000000000000.42 .03 .23SW3000000.02000000000000000000000.02Veto_Lake000000001.0000000000000000000000W10000000000.770000.03000000.06 .09 .040000W2000000000.350.04 .0200000.100000000.4900W3n00000000.22000.01 .0100000.08000000000W3s00000000.07000000000000000000.260W400000000000.06 .020.1900000.010000000.72W50000000000000.31000000.180000000.21WNW1000000000.73000000.17 .0300000000000WNW2n000000000000000.060.07 .040000000000WNW2s000000000.2500000.04 .120.010000000000WNW30000000000.5900000.07 .020.05000000000WNW400000000000.28000000.120.3200000000WNW50000000000000.24 .170000.22000000000Wolf_Run_Lake0000000000000000000001.000000000WSW10000.02 .42000.200000000000000.3600000WSW200000.31000.17000000000000.050.35 .11000WSW3n00000000000000000000000.0400.0600WSW3s00000.1000000000000000000.0400.4500WSW4000000.39000.260000000000000000.340WSW5000000.5400000.250000000000000.090.12WSW6000000.02 .4200000.15 .06000000000000.160aAll values shown were refined for this site-specific assessment (versus the valuesused in the Tiers 1 and 2 screening assessments).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-13Table A-7. USLE Erosion Parameters*Soil ParcelAreaRainfall/ErosivityIndexaSoilErodibilityIndexaLengthSlopeFactoraCoverMgmtFactoraSupportingPracticesFactoraUnit Soil LossbSedimentDeliveryRatiocCalculated(Adjusted)Erosion Ratedm2R(100 ftton/ac)K(ton/ac/(100ft-ton/acre))LS(USCS)C(USCS)PA(ton/ac/yr)A(kg/m2/d)SDRcalculated(adjusted)erosion rate(kg/m2/d)E11,312,9591250.43---0.00310.115e0.00007060.326572.31E-05E276,917,8821250.43---0.00110.25e0.00015350.124001.90E-05Farm_NNW_Tilled24,2001250.325.40.11123.760.01459260.594638.68E-03Farm_NNW_Untilled24,2001250.325.40.11123.760.01459260.594638.68E-03Farm_SE_Tilled152,6801250.43---0.11125e0.01535410.472337.25E-03Farm_SE_Untilled152,6821250.43---0.11125e0.01535410.472337.25E-03Farm_WSW_Tilled49,6001250.325.40.11123.760.01459260.543617.93E-03Farm_WSW_Untilled49,6001250.325.40.11123.760.01459260.543617.93E-03N11,622,4591250.373.390.00110.1567880.00009630.318043.06E-05N2371,817,1291250.375.40.00110.249750.00015340.050927.81E-06N3e36,817,6141250.175.40.00110.114750.00007050.135969.58E-06N3w7,353,6311250.175.40.00110.114750.00007050.194011.37E-05N492,484,5261250.175.40.00110.114750.00007050.121188.54E-06NE1216,092,4371250.373.390.00110.1567880.00009630.108981.05E-05NNW11,195,8201250.325.40.00110.2160.00013270.330404.38E-05NNW2e340,0311250.325.40.00110.2160.00013270.386655.13E-05NNW2w38,1961250.325.40.00110.2160.00013270.561657.45E-05NNW39,611,6001250.0372.20.1111.119250.00068740.187621.29E-04Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-14Table A-7. USLE Erosion Parameters (Cont.)*Soil ParcelAreaRainfall/ErosivityIndexaSoilErodibilityIndexaLengthSlopeFactoraCoverMgmtFactoraSupportingPracticesFactoraUnit Soil LossbSedimentDeliveryRatiocCalculated(Adjusted)Erosion Ratedm2R(100 ftton/ac)K(ton/ac/(100ft-ton/acre))LS(USCS)C(USCS)PA(ton/ac/yr)A(kg/m2/d)SDRcalculated(adjusted)erosion rate(kg/m2/d)NNW4165,843,0921250.0325.40.00110.02160.00001330.112651.49E-06NW13,049,7891250.0372.20.00110.0101750.00000620.216571.35E-06NW27,387,4491250.0372.20.00110.0101750.00000620.193891.21E-06NW3126,334,1231250.0372.20.00110.0101750.00000620.116547.28E-07S11,768,9321250.43---0.00110.25e0.00015350.314624.83E-05S2142,826,0531250.43---0.00110.25e0.00015350.114771.76E-05S328,312,9551250.43---0.00310.115e0.00007060.140509.92E-06SE1393,2441250.43---0.00110.25e0.00015350.379685.83E-05SE27,835,8571250.43---0.00110.25e0.00015350.192472.96E-05SE384,872,1121250.43---0.00110.25e0.00015350.122491.88E-05SE4n4,347,5961250.43---0.00110.25e0.00015350.207183.18E-05SE4s2,097,3601250.43---0.00110.25e0.00015350.308004.73E-05SE545,631,5521250.43---0.00110.25e0.00015350.132362.03E-05Source714,0661250.391.50.2135.10.021560.352400.00E+00SW110,272,5131250.373.320.00110.153550.00009430.186071.75E-05SW232,293,7811250.373.320.00310.460650.00028290.138213.91E-05SW355,275,3221250.373.320.00110.153550.00009430.129231.22E-05W15,516,9371250.325.40.00110.2160.00013270.201102.67E-05Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-15Table A-7. USLE Erosion Parameters (Cont.)*Soil ParcelAreaRainfall/ErosivityIndexaSoilErodibilityIndexaLengthSlopeFactoraCoverMgmtFactoraSupportingPracticesFactoraUnit Soil LossbSedimentDeliveryRatiocCalculated(Adjusted)Erosion Ratedm2R(100 ftton/ac)K(ton/ac/(100ft-ton/acre))LS(USCS)C(USCS)PA(ton/ac/yr)A(kg/m2/d)SDRcalculated(adjusted)erosion rate(kg/m2/d)W214,501,4791250.372.20.00110.101750.00006250.178221.11E-05W3n5,333,2021250.372.20.00110.101750.00006250.201951.26E-05W3s2,168,4421250.372.20.00110.101750.00006250.306721.92E-05W427,245,6811250.372.20.00110.101750.00006250.141188.82E-06W534,264,7231250.372.20.00110.101750.00006250.137198.57E-06WNW14,208,5811250.372.20.00310.305250.00018750.208023.90E-05WNW2n235,2541250.372.20.00110.101750.00006250.447482.80E-05WNW2s991,9851250.372.20.00110.101750.00006250.338212.11E-05WNW314,464,8741250.372.20.00310.305250.00018750.178273.34E-05WNW48,391,7751250.372.20.00110.101750.00006250.190831.19E-05WNW552,557,6941250.372.20.00310.305250.00018750.130052.44E-05WSW11,162,0681250.325.40.00110.2160.00013270.331594.40E-05WSW23,051,9071250.325.40.00310.6480.00039800.216558.62E-05WSW3n459,8801250.325.40.00110.2160.00013270.372334.94E-05WSW3s707,6481250.325.40.00110.2160.00013270.352804.68E-05WSW414,537,1251250.372.20.00310.305250.00018750.178163.34E-05WSW57,870,7581250.372.20.00110.101750.00006250.192361.20E-05WSW648,298,3211250.372.20.00110.101750.00006250.131438.21E-06*All values shown were refined for thissite-specific assessment (versus the values used in the Tiers 1 and 2 screening assessments)aUSDA Web Soil Survey, unless otherwise noted.b=R*K*LS*C*P, with proper unit and timeconversions, unless otherwise noted.cCalculated using SD = a * (AL)-b;where a is the empirical intercept coefficient (based on the size of the watershed), ALis the total watershed area receiving deposition (m2), and b is theempirical slope coefficient (always 0.125).dCalculated as A*SDR*E, where E = enrichment ratio for inorganics = 1.eDerived using Ohio EPA (2009) weighted average KSLP for Beech Forkin the absence of LS data in USDA Web Soil Survey DataHuman-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-16Table A-8. Terrestrial Plant PlacementSurface Soil Volume ElementGrasses/HerbsDeciduousForestNoneE1 xE2 xFarm_NNW_Tilled xFarm_NNW_Untilled xFarm_SE_Tilled xFarm_SE_Untilled xFarm_WSW_Tilled xFarm_WSW_Untilled xN1 xN2 xN3e xN3w xN4 xNE1 xNNW1 xNNW2e xNNW2w xNNW3 xNNW4 xNW1 xNW2 xNW3 xS1 xS2 xS3 xSE1 xSE2 xSE3 xSE4n xHuman-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-17Table A-8. Terrestrial Plant Placement (Cont.)Surface Soil Volume ElementGrasses/HerbsDeciduousForestNoneSE4s xSE5 xSource xSW1 xSW2 xSW3 xW1 xW2 xW3n xW3s xW4 xW5 xWNW1 xWNW2n xWNW2s xWNW3 xWNW4 xWNW5 xWSW1 xWSW2 xWSW3n xWSW3s xWSW4 xWSW5 xWSW6 xHuman-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-18Table A-9. Terrestrial Plant Parameters*Parameter NameUnitsGrass/Herbs1DeciduousForestReferenceLeafCompartment TypeAllow exchange 1=yes, 0=noseasonal2seasonal2-Average leaf area index m2[leaf]/ m2[area]5a3.4baMid-range of 4–6 for oldfields, Scurlock et al. 2001bCDIAC 2010 (HarvardForest, dom. red oak and redmaple).Calculate wet dep interceptionfraction (Boolean)1=yes, 0=no00 Professional judgment.Correction exponent, octanal tolipidunitless0.760.76 Trapp 1995 (from roots).Degree stomatal opening unitless11Set to 1 for daytime based onprofessional judgment(stomatal diffusion is turnedoff at night using a differentproperty, IsDay).Density of wet leaf kg/m3820820 Paterson et al. 1991.Leaf wetting factor m3.00E-043.00E-04Muller and Prohl 1993 (1E-04to 6E-04 for different cropsand elements).Length of leaf m0.05 0.1 Professional judgment.Lipid content kg/kg wet weight0.002240.00224Riederer 1995 (Europeanbeech).Litterfall rate 1/dayseasonal3seasonal3-Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-19Table A-9. Terrestrial Plant Parameters (Cont.)*Parameter NameUnitsGrass/Herbs1DeciduousForest1ReferenceLeafCompartment TypeStomatal area normalizedeffective diffusion path length1/m200200 Wilmer and Fricker 1996.Vegetation attenuation factor m2/kg2.92.9Baes et al. 1984(Grass/hay).Water content unitless0.80.8 Paterson et al. 1991.Wet dep interception fraction unitlessCalculated withinTRIM.FaTECalculatedwithinTRIM.FaTECalculated based on themeteorology data usedwithin TRIM.FaTEWet mass of leaf per soil areakg[freshleaf]/m2[area]0.6a0.6baCalculated from leaf areaindex and Leith 1975.bSimonich and Hites 1994(Calculated from leaf areaindex, leaf thickness,density of wet foliage).Particle on Leaf Compartment TypeAllow exchange 1=yes, 0=noseasonal2seasonal2-Volume particle per area leafm3[leafparticles]/m2[leaf]1.00E-091.00E-09Coe and Lindberg 1987(based on particle densityand size distribution foratmospheric particlesmeasured on an adhesivesurface).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-20Table A-9. Terrestrial Plant Parameters (Cont.)*Parameter NameUnitsGrass/Herbs1DeciduousForest1ReferenceRoot Compartment Type – Nonwoody OnlyAllow exchange1=yes, 0=noseasonal2seasonal2-Correction exponent, octanol tolipidunitless0.76-Trapp 1995.Lipid content of root kg/kg wet weight0.011- Calculated.Water content of rootkg/kg wet weight0.8- Professional judgment.Wet density of root kg/m3820- Paterson et al. 1991(soybean).Wet mass per soil area kg/m21.4- Jackson et al. 1996(temperate grassland).Stem Compartment Type – Nonwoody OnlyAllow exchange1=yes, 0=noseasonal2seasonal2-Correction exponent, octanol tolipidunitless0.76-Trapp 1995.Density of phloem fluid kg/m31,000- Professional judgment.Density of xylem fluid kg/cm3900- Professional judgment.Flow rate of transpired water perleaf aream3[water]/m2[leaf]0.0048-Crank et al. 1981.Fraction of transpiration flow ratethat is phloem rateunitless0.05-Paterson et al. 1991.Lipid content of stem kg/kg wet weight0.00224- Riederer 1995 (Europeanbeech).Water content of stem unitless0.8- Paterson et al. 1991.Wet density of stem kg/m3830- Professional judgment.Wet mass per soil area kg/m20.24a-aCalculated from leaf and rootbiomass density based onprofessional judgment.*Shaded values indicate where refined valueswere developed for this site-specific assessment. All other values (unshaded) wereunchanged from the Tiers 1and 2 screening assessments.1See separate table for assignment of plant types to surface soil compartments.2Begins April 10 (set to 1), ends after October1 (set to 0). Based on local frost/freeze data.3Begins October 1, ends after October 30; rate = 0.15/day during this time (value assumes 99 percent of leaves fall in the 30 days beginning with the last dayof allow-exchange).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-21Table A-10. Surface Water Parameters*Parameter NameUnitsValueReferenceGoodfellowsPark LakeMountwood ParkLakeVeto LakeWolf Run LakeAlgal carbon content(fraction)unitless 0.465 0.465 0.465 0.465 APHA 1995.Algal density in watercolumng[algae]/L[water]0.0025 0.0025 0.0025 0.0025Millard et al. 1996 ascited in ICF 2005.Algal growth rate 1/day 0.7 0.7 0.7 0.7Hudson et al. 1994 ascited in Mason et al.1995b.Algal radiusμm2.52.52.52.5 Mason et al. 1995b.Algal water content(fraction)unitless0.90.90.90.9 APHA 1995.Average algal celldensity (per vol cell,not water)g[algae]/m3[algae]1,000,0001,000,0001,000,0001,000,000Mason et al. 1995b;Mason et al. 1996.Boundary layerthickness abovesedimentm 0.02 0.02 0.020.02 Cal EPA 1993.Chloride concentration mg/L13121311USGS Water AlertData.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-22Table A-10. Surface Water Parameters (Cont.)*Parameter NameUnitsValueReferenceGoodfellows ParkLakeMountwood ParkLakeVeto LakeWolf Run LakeChlorophyllconcentrationmg/L0.01970.00759440.01970.0017USGS 1984; Fulmer1990.Depth m2.23a4.57b2.23a10.5caUSGS 1984 (Vetoused as surrogate forGoodfellows)bWest Virginia DNRcFulmer 1990Dimensionless viscoussublayer thicknessunitless 4 4 4 4 Ambrose et al. 1995.Drag coefficient forwater bodyunitless 0.0011 0.0011 0.0011 0.0011 Ambrose et al. 1995.Flush rate 1/year4.879.35897619.0220.595238 Calculated.Fraction sand unitless 0.25 0.25 0.25 0.25 Professional judgment.Organic carbonfraction in suspendedsedimentsunitless 0.02 0.02 0.02 0.02 Professional judgment.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-23Table A-10. Surface Water Parameters (Cont.)*Parameter NameUnitsValueReferenceGoodfellows ParkLakeMountwood ParkLakeVeto LakeWolf Run LakepH unitless7.33a7.3b7.33a7.85aaUSGS Water AlertDatabDefault; professionaljudgmentSuspended sedimentdeposition velocitym/day 2 2 2 2 U.S. EPA 1997.Total suspendedsedimentconcentrationkg[sediment]/m3[watercolumn]0.042a0.05b0.042a0.001caUSGS 1984 andUSGS Water Alert DatabU.S. EPA 2005bcUSGS Water AlertDataWater temperature degrees K291.85a298b291.85a288.05caUSGS 1984bU.S. EPA 2005bcUSGS WaterAlert Data*Shaded values indicate where refined valueswere developed for this site-specific assessment. All other values (unshaded) wereunchanged from the Tiers 1 and 2 screeningassessments.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-24Table A-11. Sediment Parameters*Parameter NameUnitsValueReferenceGoodfellowsPark LakeMountwoodPark LakeVeto LakeWolf Run LakeDepth1m 0.05a1.09728b0.05a0.05aaMcKone et al. 2001(Table 3).bEPA 1998bFraction Sand unitless 0.25 0.25 0.25 0.25Professionaljudgment.Organic carbonfractionunitless 0.02 0.02 0.02 0.02Professionaljudgment.Porosity of thesediment zonevolume[total porespace]/volume[sedimentcompartment]0.6 0.6 0.6 0.6 U.S. EPA 1998a.Solid material densityin sedimentkg[sediment]/m3[sediment]2600 2600 2600 2600McKone et al. 2001(Table 3)pH unitless7.337.27.337.85Same as surfacewater.Sedimentresuspension velocitym/day8.28388E-059.6426E-058.2424E-051.9265E-06Calculated from waterbalance model.*Shaded values indicate where refined valueswere developed for this site-specific assessment. All other values (unshaded) wereunchanged from the Tiers 1 and 2 screeningassessments.1Set using the volume element properties named "top" and "bottom."Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-25Table A-12. Aquatic Animals Food Chain, Density, and Mass*Aquatic Biota(ConsumingOrganism)Fraction DietBiomass(kg/m2)BodyWeight (kg)Reference

Algae Zooplankton Benthic Invertebrate Water Column Herbivore Benthic Omnivore Water Column Omnivore Benthic Carnivore Water Column CarnivoreBenthicinvertebrate0%0%0%0%0%0%0%0%0.0202.55E-04 Professional judgment.Water columnherbivore0%100%0%0%0%0%0%0%0.0020.025 Professional judgment.Benthicomnivore0%0%100%0%0%0%0%0%0.0020.25 Professional judgment.Water columnomnivore0%0%0%100%0%0%0%0%0. 00050.25 Professional judgment.Benthiccarnivore0%0%50%0%50%0%0%0%0.0012.0 Professional judgment.Water columncarnivore0%0%0%0%0%100%0%0%0.00022.0 Professional judgment.Zooplankton100%0%0%0%0%0%0%0%0.00645.70E-08 Professional judgment.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-26Table A-13. Cadmium Chemical-Specific Parameters*Parameter NameaUnitsValueReferenceCAS numberbunitless7440-43-9 -Diffusion coefficient in pureairm2[air]/day0.71 U.S. EPA 1999 (Table A-2-35).Diffusion coefficient in purewaterm2[water]/day8.16E-05 U.S. EPA 1999 (Table A-2-35).Henry's Law constant Pa-m3/mol1.00E-37U.S. EPA 1999 (Table A-2-35; assumedto be zero).Melting point degrees K594 ATSDR 1999.Molecular weight g/mol112.41 ATSDR 1999.Octanol-air partitioncoefficient (Koa)m3[air]/m3[octanol]- -Octanol-carbon partitioncoefficient (Koc)m3[carbon]/m3[octanol]- -Octanol-water partitioncoefficient (Kow)L[water]/kg[octanol]- -*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).aAll parameters in this table are TRIM.FaTE chemical properties.bCAS numbers apply to elemental Cd; however,the cations of cadmium are being modeled.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-27Table A-14. Mercury Chemical-Specific Parameters*Parameter NameaUnitsValueReferenceHg(0)bHg(2)bMHgbCAS number unitless7439-97-614302-87-522967-92-6-Diffusion coefficient in pureairm2[air]/day0.4780.4780.456 U.S. EPA 1997.Diffusion coefficient in purewaterm2[water]/day5.54E-055.54E-055.28E-05 U.S. EPA 1997.Henry's Law constant Pa-m3/mol7197.19E-050.0477 U.S. EPA 1997.Melting point degrees K2345.50E+02443 CARB 1994.Molecular weight g/mol201201216 U.S. EPA 1997.Octanol-water partitioncoefficient (Kow)L[water]/kg[octanol]4.153.331.7 Mason et al. 1996.Vapor washout ratio m3[air]/m3[rain]1,2001.6E+060U.S. EPA 1997,based on Petersen etal. 1995.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).aAll parameters in this table are TRIM.FaTE chemical properties.bOn this and all following tables, Hg(0) = elemental mercury,Hg(2) = divalent mercury, and MHg = methyl mercury.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-28Table A-15. PAH Chemical-Specific Parameters*ParameterNameaUnitsValue2MethylAcenaphtheneAcenaphthyleneBaABaPBbFBghiPCAS number unitless91-57-6 83-32-9 208-96-8 56-55-3 50-32-8 205-99-2 191-24-2Diffusioncoefficient inpure airm2/day0.451 0.009 0.388 0.441 0.372 0.009 0.190Diffusioncoefficient inpure waterm2/day6.70E-05 8.64E-05 6.03E-05 7.78E-05 7.78E-05 8.64E-05 4.54E-05Henry's LawconstantPa-m3/mol50.56 18.50 12.70 1.22 0.07 0.05 0.03Melting point degrees K307.75 366.15 365.65 433 452 441 550.15Molecularweightg/mol142.20 154.21 152.20 228.29 252.32 252.32 276.34Octanol-waterpartitioncoefficient(Kow)L[water]/L[octanol]7.24E+03 8.32E+03 1.00E+04 6.17E+05 9.33E+05 6.03E+05 4.27E+06Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-29Table A-15. PAH Chemical-Specific Parameters (Cont.)*ParameterNameaUnitsValueBkFChrDahAFluorantheneFluoreneIcdPCAS number unitless207-08-9 218-01-9 53-70-3 206-44-0 86-73-7 193-39-5Diffusioncoefficient inpure airm2/day0.009 0.009 0.009 0.009 0.009 0.009Diffusioncoefficient inpure waterm2/day8.64E-05 8.64E-05 8.64E-058.64E-05 8.64E-05 8.64E-05Henry's LawconstantPa-m3/mol0.04 0.53 0.01 1.96 9.81 0.03Melting point degrees K490 531 539 383.15 383.15 437Molecularweightg/mol252.32 228.29 278.33 202.26 166.20 276.34Octanol-waterpartitioncoefficient(Kow)L[water]/L[octanol]8.71E+05 5.37E+05 3.16E+061.45E+05 1.51E+04 5.25E+06Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-30Table A-15. PAH Chemical-Specific Parameters (Cont.)*ParameterNameaUnitsReferenceCAS number unitless -Diffusioncoefficient inpure airm2/dayU.S. EPA 2005b. Exceptions include Siemens 2007(2-Methylnaphthalene,Acenaphthylene, andBenzo(g,h,i)perylene).Diffusioncoefficient inpure waterm2/dayU.S. EPA 2005b. Exceptions include Siemens 2007(2-Methylnaphthalene,Acenaphthylene, andBenzo(g,h,i)perylene).Henry's LawconstantPa-m3/molU.S. EPA 2005b. Exceptionsinclude U.S. EPA 2003 (2-Methylnaphthalene), HSDB2001a (Acenaphthylene), andHSDB 2001b (Benzo(g,h,i)perylene).Melting point degrees KBudavari 1996. Exceptions include U.S. EPA 2003 (2-Methylnaphthalene), HSDB 2001a(Acenaphthylene), HSDB2001b (Benzo(g,h,i)perylene),and U.S. EPA 2005b (Acenaphthene,Fluoranthene, and Fluorene).Molecularweightg/molBudavari 1996. Exceptions include U.S. EPA 2003 (2-Methylnaphthalene), HSDB 2001a(Acenaphthylene), HSDB2001b (Benzo(g,h,i)perylene),and U.S. EPA 2005b (Acenaphthene,Fluoranthene, and Fluorene).Octanol-waterpartitioncoefficient(Kow)L[water]/L[octanol]Hansch et al. 1995. Exceptions include Passivirtaet al. 1999 (Acenaphthylene, Benzo(k)fluoranthene, andIndeno(1,2,3-cd)pyrene), and Sangster 1993 (Benzo(b)fluoranthene).*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).aAll parameters in this table are TRIM.FaTE chemical properties.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-31Table A-16. Dioxin Chemical-Specific Parameters*ParameterName1UnitsValue1,2,3,4,6,7,8-HpCDD1,2,3,4,7,8-HxCDF1,2,3,7,8-PeCDF2,3,7,8-TCDD1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDFCAS number unitless35822-46-970648-26-957117-41-61746-01-63268-87-9 39001-02-0 67562-39-4Diffusioncoefficient inpure airm2/day0.7820.1830.1920.8990.0883 0.123 0.129Diffusioncoefficient inpure waterm2/day6.91E-056.91E-056.91E-054.84E-053.08E-06 3.15E-05 3.33E-05Henry's LawconstantPa-m3/mol1.221.450.5073.330.68 0.19 1.43Melting point degrees K538a499a499b578a603 259 236.5Molecularweightg/mol425.2a374.87a340.42b322a460.0 443.76 409.31Octanol-waterpartitioncoefficient (Kow)L[water]/L[octanol]1.00E+081.00E+076.17E+066.31E+061.58E+08 1.00E+08 2.51E+07Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-32Table A-16. Dioxin Chemical-Specific Parameters (Cont.)*ParameterName1UnitsValue1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF2,3,4,6,7,8-HxCDF1,2,3,7,8-PeCDDCAS number unitless57653-85-757117-44-919408-74-372918-21-960851-34-5 40321-76-4Diffusioncoefficient inpure airm2/day0.09580.1350.09580.1350.135 0.101Diffusioncoefficient inpure waterm2/day3.43E-053.53E-053.43E-053.53E-053.53E-05 3.65E-05Henry's LawconstantPa-m3/mol1.080.741.080.740.74 3.33Melting point degrees K558.0506.0517.0509.0512.5 513.0Molecularweightg/mol390.84374.9390.8374.9374.9 356.4Octanol-waterpartitioncoefficient (Kow)L[water]/L[octanol]1.62E+088.24E+071.62E+083.80E+078.31E+07 4.37E+06Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-33Table A-16. Dioxin Chemical-Specific Parameters (Cont.)*ParameterName1UnitsValueReference2,3,4,7,8-PeCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD2,3,7,8-TCDFCAS number unitless 57117-31-4 55673-89-7 39227-28-6 51207-31-9 -Diffusioncoefficient inpure airm2/day 0.142 0.129 0.09580.149 U.S. EPA 2005b.Diffusioncoefficient inpure waterm2/day 3.76E-05 3.33E-05 3.43E-05 4.04E-05 U.S. EPA 2005b.Henry's LawconstantPa-m3/mol 0.5 1.43 1.081.46 U.S. EPA 2005.Melting point degrees K 469.3222546500.0aMackay et al. 2000.bATSDR 1998.Molecularweightg/mol 340.4409.31391.0306.0aMackay et al. 2000.bATSDR 1998.Octanol-waterpartitioncoefficient (Kow)L[water]/L[octanol]3.16E+06 7.94E+06 6.31E+07 1.26E+06Mackay et al. 1992 as cited in U.S.EPA 2000.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).1All parameters in this table are TRIM.FaTE chemical properties.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-34Table A-17. Cadmium Chemical-SpecificParameters for Abiotic Compartments*Parameter NameUnitsValueReferenceAir Compartment TypeParticle dry deposition velocitym/day260Calculated from Muhlbaierand Tisue 1981.Washout ratio m3[air]/m3[rain]200,000 MacKay et al. 1986.Surface Soil Compartment TypeUse input characteristic depth(Boolean)0 = no, Else = yes0 Professional judgment.Root Zone Soil Compartment TypeUse input characteristic depth(Boolean)0 = no, Else = yes0 Professional judgment.Surface Water Compartment TypeRatio of concentration in water toconcentration in algae toconcentration dissolved in waterL[water]/g[algal wet wt]1.87 McGeer et al. 2003.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-35Table A-18. Mercury Chemical-SpecificParameters for Abiotic CompartmentsParameter NameUnitsValueReferenceHg(0)Hg(2)MHgAir Compartment TypeParticle dry depositionvelocitym/day500500500 McKone et al. 2001 (CalTOX value).Demethylation rate 1/dayN/AN/A0 Professional judgment.Methylation rate 1/day000 Professional judgment.Oxidation rate 1/day0.0038500U.S. EPA 1997 (low end of half-life range (6months to 2 years)).Reduction rate 1/day000 Professional judgment.Washout ratio m3[air]/m3[rain]200,000200,000200,000 Professional judgment.Surface Soil Compartment TypeUse input characteristicdepth (Boolean)0 = no, Else =yes000 Professional judgment.Soil-water partitioncoefficientL[water]/kg[soilwet wt]1,00058,0007,000 U.S. EPA 1997.Vapor dry deposition velocity m/day50a2500b0aLindberg et al. 1992 .bEstimate by U.S. EPA using the Industrial SourceComplex (ISC) Model [See Vol. III, App. A of theMercury Study Report (U.S. EPA 1997)].Demethylation rate 1/day N/A N/A 0.06 Rangereported in Porvari, P. and M. Verta. 1995.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-36Table A-18. Mercury Chemical-Specific Parameters for Abiotic Compartments (Cont.)Parameter NameUnitsValueReferenceHg(0)Hg(2)MHgSurface Soil Compartment TypeMethylation rate 1/day 0 0.001 0 Professional judgment.Oxidation rate 1/day 0 0 0 U.S. EPA 1997.Reduction rate 1/day 0 1.25E-5 0 Professional judgment.Root Zone Soil Compartment TypeUse input characteristicdepth (Boolean)0 = no, Else =yes000 Professional judgment.Soil-water partitioncoefficientL[water]/kg[soilwet wt]1,00058,0007,000 U.S. EPA 1997.Demethylation rate 1/day N/A N/A 0.06Range reported in Porvari and Verta 1995 is 3E-2to 6E-2 /day; value is average maximum potentialdemethylation rate constant under anaerobicconditions.Methylation rate 1/day 0 0.001 0Range reported in Porvari and Verta 1995 is 2E-4to 1E-3 /day; value is average maximum potentialmethylation rate constant under anaerobicconditions.Oxidation rate 1/day 0 0 0 U.S. EPA 1997.Reduction rate 1/day 0 3.25E-06 0 U.S. EPA 2005aHuman-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-37Table A-18. Mercury Chemical-Specific Parameters for Abiotic Compartments (Cont.)Parameter NameUnitsValueReferenceHg(0)Hg(2)MHgSurface Water Compartment TypeAlgal surface area-specificuptake rate constantnmol/[μm2-daynmol]02.04E-103.60E-10Assumes radius = 2.5mm, Mason et al. 1995b,Mason et al. 1996; Hg(0) assumed same as Hg(2).Dow ("overall Kow")L[water]/kg[octanol]0-1-2Mason et al. 1996.Solids-water partitioncoefficientL[water]/kg[solids wet wt]1,000100,000100,000 U.S. EPA 1997.Vapor dry deposition velocity m/dayN/A2500U.S. EPA 1997 (Vol. III, App. A).Demethylation rate 1/dayN/AN/A0.013Gilmour and Henry 1991 (average of range of 1E-3to 2.5E-2/day).Methylation rate 1/day00.0010U.S. EPA 1997; Gilmourand Henry 1991 (range isfrom 1E-4 to 3E-4/day).Oxidation rate 1/day000 Professional judgment.Reduction rate 1/day00.00750U.S. EPA 1997; reported values range from lessthan 5E-3/day for depths greater than 17m, up to3.5/day (Xiao et al. 1995; Vandal et al. 1995;Mason et al. 1995a; Amyot et al. 1997).Sediment Compartment TypeSolids-water partitioncoefficientL[water]/kg[solids wet wt]3,00050,0003,000 U.S. EPA 1997.Demethylation rate 1/dayN/AN/A0.0501Gilmour and Henry 1991 (average of range of 2E-4to 1E-1/day).Methylation rate 1/day01.0E-40U.S. EPA 1997; Gilmourand Henry 1991 (range isfrom 1E-5 to 1E-3/day).Oxidation rate 1/day000 Professional judgment.Reduction rate 1/day01.00E-060U.S. EPA 1997; Vandal et al. 1995; (inferred valuebased on presence of Hg(0) in sedimentporewater).1TRIM.FaTE Formula Property, which varies from 0.025 to 1.625 depending on pH and chloride concentration.2TRIM.FaTE Formula Property, which varies from 0.075to 1.7 depending on pH and chloride concentration.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-38Table A-19. PAH Chemical-Specific Parameters for Abiotic Compartments*Parameter NameUnitsValues2MethylAcenaphtheneAcenaphthyleneBaABaPBbFBghiPBkFAir Compartment TypeParticle drydeposition velocitym/day500500500500500500500500Half-life day0.1540.30.2080.1250.0460.5960.2150.458Washout ratiom3[air]/m3[rain]200000200000200000200000200000200000200000200000Surface Soil Compartment TypeUser inputcharacteristicdepth (Boolean)0 = No,Else = Yes00000000Half-life day185666.56805306104152140Root Zone Soil Compartment TypeUser inputcharacteristicdepth (Boolean)0 = No,Else = Yes00000000Half-life day185666.56805306104152140Surface Water Compartment TypeRatio ofconcentration inalgae toconcentrationdissolved in water(g[chem]/kg[algae]) /(g[chem]/L[water])2.633.73255103171539473Half-life day78251840.3750.13890167062.4Sediment Compartment TypeHalf-lifeday22902290229022902290229022902290Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-39Table A-19. PAH Chemical-Specific Parameters for Abiotic Compartments (Cont.)*Parameter NameUnitsValueReferenceChrDahAFluorantheneFluoreneIcdPAir Compartment TypeParticle drydeposition velocitym/day500500500500500 McKone et al. 2001.Half-life day0.3340.1780.460.460.262Howard et al. 1991 / upper bound measured orestimated value. Exceptions include ATSDR2005 (2-Methylnaphthalene), U.S. EPA 2005b(Benzo(g,h,i)perylene, and Fluoranthene) /average of range, HSDB2001c (Acenaphthene),HSDB 2001a (Acenaphthylene), and Spero et al.2000 (Fluorene).Washout ratiom3[air]/m3[rain]200000200000200000200000200000 Mackay et al. 1986.Surface Soil Compartment TypeUser inputcharacteristicdepth (Boolean)0 = No,Else = Yes00000 Professional judgment.Half-life day100094027533730MacKay et al. 2000 / average of range.Exceptions include ATSDR 2005 (2-Methylnaphthalene), U.S. EPA 2005b(Benzo(g,h,i)perylene, and Fluoranthene) /average of range, HSDB2001c (Acenaphthene),HSDB 2001a (Acenaphthylene), and HSDB2001d (Fluorene).Root Zone Soil Compartment TypeUser inputcharacteristicdepth (Boolean)0 = No,Else = Yes00000 Professional judgment.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-40Table A-19. PAH Chemical-Specific Parameters for Abiotic Compartments (Cont.)*Parameter NameUnitsValueReferenceChrDahAFluorantheneFluoreneIcdPRoot Zone Soil Compartment TypeHalf-lifeday100094027533730Howard et al. 1991 / upper bound measured orestimated value. Exceptions include ATSDR2005 (2-Methylnaphthalene), U.S. EPA 2005b(Benzo(g,h,i)perylene, and Fluoranthene) /average of range, HSDB2001c (Acenaphthene),HSDB 2001a (Acenaphthylene), and HSDB2001d (Fluorene).Surface Water Compartment TypeRatio ofconcentration inalgae toconcentrationdissolved in water(g[chem]/kg[algae]) /(g[chem]/L[water])280138867.45.81653 Kowfrom Del Vento and Dachs 2002.Half-life day1.62697.81608.5750Howard et al. 1991 / upper bound measured orestimated value. Exceptions include HSDB 2005(2-Methylnaphthalene), HSDB 2001c(Acenaphthene), HSDB 2001a(Acenaphthylene), and HSDB 2001b(Benzo(g,h,i)perylene), Montgomery 2000(Fluoranthene), and Boyle 1985 (Fluorene).Sediment Compartment TypeHalf-lifeday22902290229022902290Mackay et al. 1992 (PAH values are the meanhalf-life of the log class that Mackay et al.assigned for sediment, except for BbF and IcdP,which were not on Table 2.3).*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-41Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments*Parameter NameUnitsValue1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDDAir Compartment TypeDeposition velocity m/day500500500500500500Half-life day1623216413712242Washout ratio m3[air]/m3[rain]91000220006400032000320009000Surface Soil Compartment TypeInput characteristic depth m0.080.080.080.080.080.08Use input characteristic depth(Boolean)0 = No, Else = Yes000000Half-life day365036503650365036503650Root Zone Soil Compartment TypeInput characteristic depth m0.080.080.080.080.080.08Use input characteristic depth 0 = No, Else = Yes000000Half-life day365036503650365036503650Half-lifeday100810081008100810081008Surface Water Compartment TypeRatio of concentration inalgae to concentrationdissolved in water(g[chem]/g[algae])/(g[chem]/L[water])5.314.544.542.831.93.88Half-life day0.670.58470.580.586.3Sediment Compartment TypeHalf-lifeday109510951095109510951095Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-42Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments (Cont.)*Parameter NameUnitsValue1,2,3,4,7,8-HxCDF1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF1,2,3,7,8-PeCDDAir Compartment TypeDeposition Velocity m/day500500500500500500Half-life day782855285118Washout ratio m3[air]/m3[rain]1000090001000090001000018000Surface Soil Compartment TypeInput characteristic depth m0.080.080.080.080.080.08Use input characteristic depth(Boolean)0 = No, Else = Yes000000Half-life day365036503650365036503650Root Zone Soil Compartment TypeInput characteristic depth m0.080.080.080.080.080.08Use input characteristic depth 0 = No, Else = Yes000000Half-life day365036503650365036503650Half-lifeday100810081008100810081008Surface Water Compartment TypeRatio of concentration inalgae to concentrationdissolved in water(g[chem]/g[algae])/(g[chem]/L[water])2.065.364.255.363.261.55Half-life day0.586.30.586.30.582.7Sediment Compartment TypeHalf-lifeday109510951095109510951095Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-43Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments (Cont.)*Parameter NameUnitsValue1,2,3,7,8-PeCDF2,3,4,6,7,8-HxCDF2,3,4,7,8-PeCDF2,3,7,8-TCDD2,3,7,8-TCDFAir Compartment TypeDeposition velocity m/day500500500500500Half-life day3159331219Washout ratio m3[air]/m3[rain]1300010000140001800019000Surface Soil Compartment TypeInput characteristic depth m0.080.080.080.080.08Use input characteristic depth(Boolean)0 = No, Else = Yes00000Half-life day36503650365036503650Root Zone Soil Compartment TypeInput characteristic depth m0.080.080.080.080.08Use input characteristic depth 0 = No, Else = Yes00000Half-life day36503650365036503650Surface Water Compartment TypeRatio of concentration inalgae to concentrationdissolved in water(g[chem]/g[algae])/(g[chem]/L[water])1.754.261.391.760.71Half-life day0.190.580.192.70.18Sediment Compartment TypeHalf-lifeday10951095109510951095Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-44Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments (Cont.)*Parameter NameReferencesAir Compartment TypeDeposition Velocity McKone et al. 2001.Half-life Atkinson 1996 as cited in U.S. EPA 2000;vapor-phase reaction with hydroxyl radical.Washout ratio Vulykh et al. 2001.Surface Soil Compartment TypeInput characteristic depth Not used (model set to calculate value).Use input characteristic depth(boolean)Professional judgment.Half-life Mackay et al. 2000; the degradation rate wascited by multiple authors,value is for 2,3,7,8-TCDD.Root Zone Soil Compartment TypeInput characteristic depth Not used (model set to calculate value).Use input characteristic depth Professional judgment.Half-life Mackay et al. 2000; the degradation rate wascited by multiple authors,value is for 2,3,7,8-TCDD.Half-lifeAverage value of the range presented in Mackayet al. 2000; based on estimated unacclimated aerobicbiodegradation half-life, value is for 2,3,7,8-TCDD.Surface Water Compartment TypeRatio of concentration inalgae to concentrationdissolved in waterEstimated from Kowvalue using model from Del Vento and Dachs 2002.Half-life Kim and O’Keefe 1998 as cited in U.S. EPA 2000.Sediment Compartment TypeHalf-life Estimation based on Adriaens and Grbic-Galic 1992,1993and Adriaens et al. 1995 as cited in U.S. EPA 2000.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used)Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-45Table A-21. Cadmium Chemical-SpecificParameters for Plant Compartments*Parameter NameUnitsValueReferenceLeaf Compartment TypeTransfer factor to leaf particle 1/day0.002 Professional judgment.Particle on Leaf Compartment TypeTransfer factor to leaf 1/day0.200 Professional judgment.Root Compartment Type - Grasses/HerbsaRoot to root soil partition - alpha of steady stateunitless0.95Henning et al. 2001.Root to root soil partition - partitioning coefficientm3[bulk root soil]/m3[root]0.23Nriagu 1980 (based on average valuecalculated from various agricultural plantspecies).Root to root soil partition - time to reach alpha day28Henning et al. 2001.Stem Compartment Type - Grasses/HerbsaTranspiration stream concentration factor (TSCF) m3[soil pore water]/m3[xylem fluid]0.45Tsiros et al. 1999.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-46Table A-22. Mercury Chemical-SpecificParameters for Plant Compartments*Parameter NameUnitsValueReferenceHg(0)Hg(2)MHgLeaf Compartment TypeTransfer factor to leaf particle 1/day0.0020.0020Professional judgment (assumed 1% oftransfer factor from leaf particle to leaf).Demethylation rate1/dayN/AN/A0.03Calculated from Bache et al. 1973.Methylation rate1/day000 Assumed from Gay 1975, Bache et al. 1973.Oxidation rate1/day1.0E+0600Professional judgment; assumed close toinstantaneous.Reduction rate1/day000 Professional judgment.Particle on Leaf Compartment TypeTransfer factor to leaf 1/day0.20.20.2 Professional judgment.Demethylation rate1/dayN/AN/A0 Professional judgment.Methylation rate1/day000 Professional judgment.Oxidation rate1/day000 Professional judgment.Reduction rate1/day000 Professional judgment.Root Compartment Type - Grasses/HerbsaAlpha for root-root zone bulk soilunitless0.950.950.95Selected value.Root/root-zone-soil-water partitioncoefficientm3[bulk root soil]/m3[root]00.18a1.2baGeometric mean Leonard et al. 1998, John1972, Hogg et al. 1978bMHg- assumed, based on Hogg et al. 1978.t-alpha for root-root zone bulk soil day212121 Professional judgment.Demethylation rate1/dayN/AN/A0 Professional judgment.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-47Table A-22. Mercury Chemical-Specific Parameters for Plant Compartments (Cont.)*Parameter NameUnitsValueReferenceHg(0)Hg(2)MHgRoot Compartment Type - Grasses/HerbsaMethylation rate1/day000 Professional judgment.Oxidation rate1/day000 Professional judgment.Reduction rate1/day000 Professional judgment.Stem Compartment Type - Grasses/HerbsaTranspiration stream concentrationfactor (TSCF)m3[soil pore water]/m3[xylem fluid]00.50.2Bishop et al. 1998 (Norway spruce, Scotspine).Demethylation rate1/dayN/AN/A0.03Calculated from Bache et al. 1973.Methylation rate1/day000 Professional judgment.Oxidation rate1/day000 Professional judgment.Reduction rate1/day000 Professional judgment.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-48Table A-23. PAH Chemical-Specific Parameters for Plant Compartments*ParameterNameUnitsValue2MethylAcenaphtheneAcenaphthyleneBaABaPBbFBghiPBkFLeaf Compartment TypeTransfer factorto leaf particle1/day1.00E-041.00E-041.00E-041.00E-041.00E-041.00E-041.00E-041.00E-04Half-life day3.503.503.503.503.503.503.503.50Particle on Leaf Compartment TypeTransfer factorto leaf1/day1.00E-041.00E-041.00E-041.00E-041.00E-041.00E-041.00E-041.00E-04Half-life day2.312.312.311.842.313.562.3117.80Root Compartment Type - Grasses/HerbsaHalf-life day34.6034.6034.6034.6034.6034.6034.6034.60Root soil waterinteraction -alphaunitless0.950.950.950.950.950.950.950.95Stem Compartment Type - Grasses/HerbsaHalf-life day3.503.503.503.503.503.503.503.50Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-49Table A-23. PAH Chemical-Specific Parameters for Plant Compartments (Cont.)*ParameterNameUnitsValueReferenceChrDahAFluorantheneFluoreneIcdPLeaf Compartment TypeTransfer factorto leaf particle1/day1.00E-041.00E-041.00E-041.00E-041.00E-04 Professional judgment.Half-life day3.503.503.503.503.50Edwards 1988 (as cited in Efroymson 1997)/calculated from metabolic rate constant.Particle on Leaf Compartment TypeTransfer factorto leaf1/day1.00E-041.00E-041.00E-041.00E-041.00E-04 Professional judgment.Half-life day4.1217.802.312.3117.80Calculated as 2 times the measuredphotolysis half-life from Mackay et al. 1992.Exceptions include values that have been setequal to Benzo(a)pyrene (2-Methylnaphthalene; Acenaphthene;Acenaphthylene; Benzo(ghi)perylene;Fluoranthene; and Fluorene)Root Compartment Type - Grasses/HerbsaHalf-life day34.6034.6034.6034.6034.60Edwards 1988 (as cited in Efroymson 1997)/calculated from metabolic rate constant.Root soil waterinteraction -alphaunitless0.950.950.950.950.95 Professional judgment.Stem Compartment Type - Grasses/HerbsaHalf-life day3.503.503.503.503.50Edwards 1988 (as cited in Efroymson 1997)/calculated from metabolic rate constant.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-50Table A-24. Dioxin Chemical-Specific Parameters for Plant Compartments*Parameter NameUnitsValueReferenceAll DioxinsLeaf Compartment TypeTransfer factor to leaf particle 1/day0.003 Calculated as 1 percent of transfer factor to leaf; highly uncertain.Half-life day70Arjmand and Sandermann 1985, as cited in Komoba et al. 1995 (soybeanroot cell culture metabolism test data for DDE).Particle on Leaf Compartment TypeTransfer factor to leaf 1/day0.3Professional judgment based on U.S. EPA 2000 (an estimate for mercury)and Trapp 1995 (highly uncertain).Half-life day4.4McCrady and Maggard 1993 (photodegradation sorbed to grass foliage insunlight; assumed 10 sunlight per day).Root Compartment Type - Grasses/HerbsaHalf-lifeday70Arjmand and Sandermann 1985, as cited in Komoba, et al. 1995 (soybeanroot cell culture metabolism test data for DDE).Root soil water interaction - alphaunitless0.95 Professional judgment.Stem Compartment Type - Grasses/HerbsaHalf-lifeday70Arjmand and Sandermann 1985, as cited in Komoba, et al. 1995 (soybeanroot cell culture metabolism test data for DDE).aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-51Table A-25. Cadmium Chemical-Specific Parameters for Aquatic Species*Parameter NameUnitsValueReferenceZooplankton Compartment TypeAbsorption rate constantL[water]/kg[fish wet wt]-day1500 Goulet 2007.Assimilation efficiency from algaeunitless0.5 Goulet 2007.Elimination rate constant1/day0.03 Goulet 2007.Benthic Invertebrate Compartment TypeSediment partitioning - alpha of equilibrium unitless0.95 Professional judgment.Sediment partitioning - partition coefficientkg[bulk sed/kg[invertebratewet wt]0.27 Professional judgment.Sediment partitioning - time to reach alphaof equilibriumday21 Hare et al. 2001.Benthic Omnivore Compartment TypeAssimilation efficiency from food unitless0.1Professional judgment based on Yan and Wang2002.Absorption rate constantunitless1.23Calculated based on body weight from regressionin Hendriks & Heikens 2001.Elimination rate constantunitless1.73E-02 Professional judgment.Benthic Carnivore Compartment TypeAssimilation efficiency from food unitless0.1Professional judgment based on Yan and Wang2002.Absorption rate constantunitless0.66Calculated based on body weight from regressionin Hendriks & Heikens 2001.Elimination rate constantunitless1.68E-03 Professional judgment.Water-column Herbivore Compartment TypeAssimilation efficiency from food unitless0.1Professional judgment based on Yan and Wang2002.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-52Table A-25. Cadmium Chemical-SpecificParameters for Aquatic Species (Cont.)AParameter NameUnitsValueReferenceWater-column Herbivore Compartment TypeAssimilation efficiency from plants unitless0.1Professional judgment based on Yan and Wang2002.Absorption rate constantunitless2.46Calculated based on body weight from regressionin Hendriks & Heikens 2001.Elimination rate constantunitless1.73E-02 Professional judgmentr.Water-column Omnivore Compartment TypeAssimilation efficiency from food unitless0.1Professional judgment based on Yan and Wang2002.Assimilation efficiency from plants unitless0.1Professional judgment based on Yan and Wang2002.Absorption rate constantunitless1.23Calculated based on body weight from regressionin Hendriks & Heikens 2001.Elimination rate constantunitless1.73E-02 Professional judgment.Water-column Carnivore Compartment TypeAssimilation efficiency from food unitless0.1Professional judgment based on Yan and Wang2002.Absorption rate constantunitless0.66Calculated based on body weight from regressionin Hendriks & Heikens 2001.Elimination rate constantunitless1.73E-02 Professional judgment.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-53Table A-26. Mercury Chemical-Specific Parameters for Aquatic Species*Parameter NameUnitsValueReferenceHg(0)Hg(2)MHgZooplankton Compartment TypeAssimilationEfficiencyFromAlgae unitless0.20.0150.5 Environment Canada 2002.Half-life day1.0E+091.0E+091.0E+09 Professional judgment.HowMuchFasterHgEliminationIsThanForMHg unitless331 Professional judgment.Methylation rate 1/day000 Professional judgment.Oxidation rate 1/day01.0E+060 Professional judgment.Reduction rate 1/day000 Professional judgment.Benthic Invertebrate Compartment TypeAlpha of equilibrium for sediment partitioning unitless0.950.950.95 Selected value.Benthic invertebrate-bulksediment partitioncoefficientkg[bulk sediment]/kg[invertebrate wet wt]0.0824a0.0824b5.04baAssumed based on Hg(2) value.bSaouter et al. 1991.t-alpha for equilibrium for sedimentpartitioningday141414Saouter et al. 1991 (experimentduration).All Fish Compartments Types1Elimination adjustment factorunitless331Trudel and Rasmussen 1997.Assimilation efficiency from food unitless0.060.060.5Williams et al. 2010. The 0.5value was used for MHg (insteadof 0.8) to calibrate the model tomatch the ratio of Hgconcentrations at different trophiclevels within the same food webfrom published literature.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-54Table A-26. Mercury Chemical-SpecificParameters for Aquatic Species (Cont.)*Parameter NameUnitsValueReferenceHg(0)Hg(2)MHgAll Fish Compartments Types1Demethylation rate1/dayN/AN/A0 Professional judgment.Methylation rate1/day000 Professional judgment.Oxidation rate1/day1.0E+0600 Professional judgment.Reduction rate1/day000 Professional judgment.Water-column Herbivore Compartment TypeAssimilation efficiency from plankton unitless0.060.060.5Williams et al. 2010. The 0.5value was used for MHg (insteadof 0.8) to calibrate the model tomatch the ratio of Hgconcentrations at different trophiclevels within the same food webfrom published literature.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).1Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore,and Water-column Carnivore.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-55Table A-27. PAH Chemical-Specific Parameters for Aquatic Species*Parameter NameUnitsValue2MethylAcenaphtheneAcenaphthyleneBaABaPBbFBghiPBkFZooplankton Compartment TypeAbsorption rateconstantL[water]/kg[fish wet wt]-day7904223142302.242650.842652.842650.742655.842653Assimilationefficiency fromalgaeunitless0.50.50.50.460.250.250.250.25Elimination rateconstant1/day169.68148.07123.442.0731.38642.120.331.48Half-lifeday0.007790.002390.002391.28416.5171717Benthic Invertebrate Compartment TypeClearanceconstantunitless100.6100.6100.6100.6100.6100.6100.6100.6Vd(ratio ofconcentration inbenthicinvertebrates toconcentration inwater)mL/g72357235723572357235723572357235Half-lifeday0.7220.7220.7221.28416.5171717All Fish Compartment TypesaGamma fish unitless0.20.20.20.20.20.20.20.2Assimilationefficiency fromfoodunitless0.50.50.320.150.150.150.150.15Half-lifeday0.20.20.20.4081.925222Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-56Table A-27. PAH Chemical-Specific Parameters for Aquatic Species (Cont.)*Parameter NameUnitsValueReferenceChrDahAFluorantheneFluoreneIcdPZooplankton Compartment TypeAbsorption rateconstantL[water]/kg[fish wet wt]-day4265042655.51420001500042655.9Kowfrom Arnot et al. 2004. Exception isBerrojalbiz et al. 2009 (2-Methylnaphthalene,Fluoranthene, and Fluorene).Assimilationefficiency fromalgaeunitless0.460.250.490.50.25Kowfrom Arnot et al. 2004. Exception ismaximum value from Wang and Wang 2006(Benzo(a)pyrene, Benzo(b)fluoranthene,Benzo(g,h,i)perylene, Benzo(k)fluoranthene,Dibenz(a,h)anthracene, and Indeno(1,2,3-cd)pyrene).Elimination rateconstant1/day2.37460.43318.67881.870.269 Kowfrom Arnot et al. 2004.Half-lifeday0.495170.002390.00024817McElroy 1990. Exceptions include Berrojalbiz etal. 2009 (2-Methylnaphthalene, Fluoranthene,and Fluorene) and Moermond et al. 2007(Benz(a)anthracene and Benzo(a)pyrene).Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-57Table A-27. PAH Chemical-Specific Parameters for Aquatic Species (Cont.)*Parameter NameUnitsValueReferenceChrDahAFluorantheneFluoreneIcdPBenthic Invertebrate Compartment TypeClearanceconstantunitless100.6100.6100.6100.6100.6 Stehly et al. 1990.Vd(ratio ofconcentration inbenthicinvertebrates toconcentration inwater)mL/g72357235723572357235 Stehly et al. 1990.Half-lifeday0.495170.7220.72217 Moermond et al. 2007.All Fish Compartment TypesaGamma fish unitless0.20.20.20.20.2 Thomann 1989.Assimilationefficiency fromfoodunitless0.150.150.140.140.15Lemair et al. 1992. Exceptions include Barber2008 & Wang and Wang 2006 (2-Methylnaphthalene and Acenaphthene) andNiimi and Palazzo 1986 (Acenaphthylene,Fluoranthene, and Fluorene).Half-lifeday0.53320.1650.22 Moermond et al. 2007.*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).aBenthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore,and Water-column Carnivore.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-58Table A-28. Dioxin Chemical-Specific Parameters for Aquatic Species*Parameter NameUnitsValue1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7, 8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD1,2,3,4,7,8-HxCDF1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDFBenthic Invertebrate CompartmentClearance constant unitless000000000Sediment partitioning partitioncoefficientkg/kg0.00130.00170.00550.00120.0420.0330.00810.0130.02Sediment partitioning alpha ofequilibriumunitless0.950.950.950.950.950.950.950.950.95Sediment partitioning time toreach alpha of equilibriumdays1204212042421204212042Vd(ratio of concentrationin benthic invertebrates toconcentration in water)mL/g000000000Half-life day5776.25776.25776.25776.25776.25776.25776.25776.25776.2Zooplankton Compartment TypeAssimilation efficiency from food unitless0.410.410.410.410.410.410.410.410.41Absorption rate constant L/kg(ww)/day380.0380.0380.0380.0380.0380.0380.0380.0380.0Elimination rate constant /day5.5E-45.5E-45.5E-45.5E-45.5E-45.5E-45.5E-45.5E-45.5E-4Half life day1E091E091E091E091E091E091E091E091E09All Fish CompartmentsaAssimilation efficiency from food unitless0.030.030.50.50.50.50.50.50.5Chemical uptake rate via gillL[water]/kg[fish wetwt]/day142N/Ab14N/AbN/Ab127N/Ab127N/AbGamma_fish unitlessN/Ab0.2N/Ab0.20.2N/Ab0.2N/Ab0.2Half-life day693.15346.57346.57346.57346.57495.11495.11495.11495.11Water Column Herbivore CompartmentAssimilation efficiency from plants unitless0.030.030.50.50.50.50.50.50.5Water Column Omnivore CompartmentAssimilation efficiency from plants unitless0.030.030.50.50.50.50.50.50.5Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-59Table A-28. Dioxin Chemical-Specific Parameters for Aquatic Species (Cont.)*Parameter NameUnitsValue1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF1,2,3,7,8-PeCDD1,2,3,7,8-PeCDF2,3,4,6,7,8-HxCDF2,3,4,7,8-PeCDF2,3,7,8-TCDD2,3,7,8-TCDFBenthic Invertebrate CompartmentClearance constant unitless00000000Sediment partitioning partitioncoefficientkg/kg0.0150.0670.0980.0240.0720.170.2050.056Sediment partitioning alpha ofequilibriumunitless0.950.950.950.950.950.950.950.95Sediment partitioning time to reachalpha of equilibriumdays1204212042424212042Vd(ratio of concentration in benthicinvertebrates to concentration inwater)mL/g00000000Half-life day5776.25776.25776.25776.25776.25776.25776.25776.2All Fish CompartmentsaAssimilation efficiency from food unitless0.50.50.50.50.50.50.50.5Chemical uptake rate via gillL[water]/kg[fish wetwt]/day127N/Ab700N/AbN/AbN/Ab380N/AbGamma_fish unitlessN/Ab0.2N/Ab0.20.20.2N/Ab0.2Half-life day495.11495.11420.09420.09495.11420.095251.15251.1Water Column Herbivore CompartmentAssimilation efficiency from plants unitless0.50.50.50.50.50.50.50.5Water Column Omnivore CompartmentAssimilation efficiency from plants unitless0.50.50.50.50.50.50.50.5Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryA-60Table A-28. Dioxin Chemical-Specific Parameters for Aquatic Species (Cont.)*Parameter NameUnitsReferenceBenthic Invertebrate CompartmentClearance constant unitless Professional judgment.Sediment partitioning partitioncoefficientkg/kgTCDD data for sandworm in Rubenstein et al. 1990; dry weight sediment. PeCDF,multiplied TCDD partition coefficientfor sandworm by congener-specificbioaccumulation equivalency factor in GLWQI from U.S. EPA 1999.Sediment partitioning alpha ofequilibriumunitless Professional judgment.Sediment partitioning time to reachalpha of equilibriumdaysTCDD: professional judgment; PeCDF: Rubinstein et al. 1990; data for TCDF insandworm.Vd(ratio of concentration in benthicinvertebrates to concentration inwater)mL/g Professional judgment.Half-life day Change source to f-passZooplankton Compartment TypeAssimilation efficiency from food unitless Morrison et al. 1999.Absorption rate constant L/kg(ww)-day Based on fish value in Muir et al. 1986Elimination rate constant /day Professionaljudgment based on water column herbivore value.Half Life day Professional judgment.All Fish CompartmentsaAssimilation efficiency from food unitlessTCDD: calculated from data in Kleeman etal. 1986b trout data as cited in U.S.EPA 1993; PeCDF: used assimilationefficiency for TCDD in trout.Chemical uptake rate via gillL[water]/kg[fish wetwt]-dayMuir et al. 1986.Gamma_fish unitless Thomann 1989Half-life day Change source to f-passWater Column Herbivore CompartmentAssimilation efficiency from plants unitlessTCDD: calculated from data in Kleeman etal. 1986b trout data as cited in U.S.EPA 1993; PeCDF: used assimilationefficiency for TCDD in trout.Water Column Omnivore CompartmentAssimilation efficiency from plants unitlessTCDD: calculated from data in Kleeman etal. 1986b trout data as cited in U.S.EPA 1993; PeCDF: used assimilationefficiency for TCDD in trout.*All values were unchanged from the Tiers 1 and 2 screening assessments (no site-specific values used).aFerroalloys scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.bN/A = not applicable. 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# Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category



# Appendix B

Modeled Media Concentrations

This appendix provides the concentrations of PB- HAPs estimated in TRIM.FaTE compartments.

The values are the annual averages for the 50th year of the simulation; these outputs were used

in the risk-characterization estimates.

# [This page intentionally left blank.]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-1Table B-1. Concentration Estimates and Speciations for MercuryLocation and MediumUnitsTotal HgDivalent HgElemental HgMethyl HgaValueValue% Total HgValue% Total HgValue% TotalHgFarm_NNWTilled Surface Soilμg g-1[dryweight]1.05E-02 9.94E-03 94.36% 4.28E-04 4.07% 1.66E-04 1.57%Untilled SurfaceSoilμg g-1[dryweight]2.02E-02 1.99E-02 98.34% 7.22E-06 0.04% 3.28E-04 1.62%Farm_SETilled Surface Soilμg g-1[dryweight]2.23E-02 2.11E-02 94.51% 8.73E-04 3.91% 3.51E-04 1.57%Untilled SurfaceSoilμg g-1[dryweight]4.17E-02 4.10E-02 98.33% 1.29E-05 0.03% 6.82E-04 1.63%Farm_WSWTilled Surface Soilμg g-1[dryweight]3.00E-03 2.83E-03 94.36% 1.22E-04 4.07% 4.72E-05 1.57%Untilled SurfaceSoilμg g-1[dryweight]7.81E-03 7.68E-03 98.33% 3.53E-06 0.05% 1.27E-04 1.63%Wolf RunLakeSurface water mg L-15.43E-083.00E-0855.16%2.36E-0843.46%7.48E-101.38%Macrophytemg kg-1[wetweight]4.67E-084.42E-0894.57%2.50E-160.00%2.54E-095.43%Zooplanktonmg kg-1[wetweight]3.88E-041.81E-0446.63%0.00E+000.00%2.07E-0453.37%Water ColumnHerbivoremg kg-1[wetweight]8.36E-047.32E-058.76%0.00E+000.00%7.63E-0491.24%Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-2Table B-1. Concentration Estimates and Speciations for Mercury (Cont.)Location and MediumUnitsTotal HgDivalent HgElemental HgMethyl HgaValueValue% Total HgValue% Total HgValue% TotalHgWolf RunLakeWater ColumnOmnivoremg kg-1[wetweight]3.81E-03 3.14E-05 0.82% 0.00E+00 0.00% 3.78E-03 99.18%Water ColumnCarnivoremg kg-1[wetweight]2.13E-02 1.73E-05 0.08% 0.00E+00 0.00% 2.12E-02 99.92%Sedimentμg g-1[dryweight]1.27E-03 1.23E-03 97.23% 3.26E-05 2.57% 2.54E-06 0.20%BenthicInvertebratemg kg-1[wetweight]7.40E-05 6.43E-05 86.94% 1.70E-06 2.30% 7.96E-06 10.76%Benthic Omnivoremg kg-1[wetweight]6.08E-05 3.06E-05 50.29% 1.42E-11 0.00% 3.02E-05 49.71%Benthic Carnivoremg kg-1[wetweight]2.88E-04 2.70E-05 9.36% 1.30E-11 0.00% 2.61E-04 90.64%Veto LakeSurface water mg L-11.33E-07 1.20E-07 90.62% 1.20E-08 9.05% 4.34E-10 0.33%Macrophytemg kg-1[wetweight]3.08E-08 3.05E-08 98.85% 2.74E-16 0.00% 3.54E-10 1.15%Zooplanktonmg kg-1[wetweight]1.82E-04 1.53E-04 84.33% 0.00E+00 0.00% 2.85E-05 15.67%Water ColumnHerbivoremg kg-1[wetweight]1.68E-04 6.21E-05 36.93% 0.00E+00 0.00% 1.06E-04 63.07%Water ColumnOmnivoremg kg-1[wetweight]5.61E-04 2.66E-05 4.75% 0.00E+00 0.00% 5.34E-04 95.25%Water ColumnCarnivoremg kg-1[wetweight]2.95E-03 1.47E-05 0.50% 0.00E+00 0.00% 2.93E-03 99.50%Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-3Table B-1. Concentration Estimates and Speciations for Mercury (Cont.)Location and MediumUnitsTotal HgDivalent HgElemental HgMethyl HgaValueValue% Total HgValue% Total HgValue% TotalHgVeto LakeSedimentμg g-1[dryweight]2.24E-032.23E-0399.22%1.28E-050.57%4.57E-060.20%BenthicInvertebratemg kg-1[wetweight]1.31E-041.16E-0488.59%6.69E-070.51%1.43E-0510.90%Benthic Omnivoremg kg-1[wetweight]1.07E-045.31E-0549.52%9.59E-120.00%5.41E-0550.48%Benthic Carnivoremg kg-1[wetweight]5.16E-044.68E-059.06%4.00E-120.00%4.70E-0490.94%MountwoodPark LakeSurface water mg L-11.53E-081.26E-0882.66%2.59E-0917.00%5.28E-110.35%Macrophytemg kg-1[wetweight]4.07E-094.03E-0999.08%1.04E-160.00%3.76E-110.92%Zooplanktonmg kg-1[wetweight]1.64E-051.35E-0582.14%0.00E+000.00%2.93E-0617.86%Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-4Table B-1. Concentration Estimates and Speciations for Mercury (Cont.)Location and MediumUnitsTotal HgDivalent HgElemental HgMethyl HgaValueValue% Total HgValue% Total HgValue% TotalHgMountwoodPark LakeWater ColumnHerbivoremg kg-1[wetweight]1.62E-055.37E-0633.10%0.00E+000.00%1.09E-0566.90%Water ColumnOmnivoremg kg-1[wetweight]5.66E-052.28E-064.03%0.00E+000.00%5.43E-0595.97%Water ColumnCarnivoremg kg-1[wetweight]2.83E-041.21E-060.43%0.00E+000.00%2.81E-0499.57%Sedimentμg g-1[dryweight]1.30E-041.28E-0498.20%2.08E-061.60%2.56E-070.20%Benthic Invertebratemg kg-1[wetweight]7.55E-066.65E-0688.09%1.08E-071.43%7.91E-0710.48%Benthic Omnivoremg kg-1[wetweight]5.93E-062.97E-0650.13%3.03E-120.00%2.96E-0649.87%Benthic Carnivoremg kg-1[wetweight]2.71E-052.57E-069.51%1.10E-120.00%2.45E-0590.49%aMethyl mercury concentrations represent the mass of mercury as methyl mercury.Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-5Table B-2. Concentration Estimates for CadmiumLocation and MediumUnitsValuesFarm_NNWTilled Surface Soil μg g-1[dry weight]1.07E-02Untilled Surface Soil μg g-1[dry weight]8.83E-03Farm_SETilled Surface Soil μg g-1[dry weight]5.18E-02Untilled Surface Soil μg g-1[dry weight]6.98E-02Farm_WSWTilled Surface Soil μg g-1[dry weight]3.83E-02Untilled Surface Soil μg g-1[dry weight]2.85E-02Wolf Run LakeSurface water mg L-11.79E-06Macrophyte mg kg-1[wet weight]1.78E-04Zooplankton mg kg-1[wet weight]1.14E-01Water Column Herbivore mg kg-1[wet weight]5.67E-02Water Column Omnivore mg kg-1[wet weight]1.97E-02Water Column Carnivore mg kg-1[wet weight]5.62E-03Sediment μg g-1[dry weight]4.43E-03Benthic Invertebrate mg kg-1[wet weight]7.55E-04Benthic Omnivore mg kg-1[wet weight]3.89E-04Benthic Carnivore mg kg-1[wet weight]2.32E-03Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-6Table B-2. Concentration Estimates for Cadmium (Cont.)Location and MediumUnitsValuesVeto LakeSurface watermg L-11.93E-06Macrophytemg kg-1[wet weight]1.84E-04Zooplanktonmg kg-1[wet weight]1.24E-01Water Column Herbivoremg kg-1[wet weight]7.51E-02Water Column Omnivoremg kg-1[wet weight]3.19E-02Water Column Carnivoremg kg-1[wet weight]1.14E-02Sedimentμg g-1[dry weight]2.26E-03Benthic Invertebratemg kg-1[wet weight]3.84E-04Benthic Omnivoremg kg-1[wet weight]2.90E-04Benthic Carnivoremg kg-1[wet weight]1.96E-03Mountwood ParkLakeSurface watermg L-12.32E-07Macrophytemg kg-1[wet weight]2.20E-05Zooplanktonmg kg-1[wet weight]1.64E-02Water Column Herbivoremg kg-1[wet weight]1.41E-02Water Column Omnivoremg kg-1[wet weight]8.28E-03Water Column Carnivoremg kg-1[wet weight]4.27E-03Sedimentμg g-1[dry weight]1.68E-04Benthic Invertebratemg kg-1[wet weight]2.85E-05Benthic Omnivoremg kg-1[wet weight]3.17E-05Benthic Carnivoremg kg-1[wet weight]2.43E-04Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-7Table B-3. Concentration Estimates for PAH CongenersLocation and MediumUnitsValues

A cenaphthene A cenaphthylene Benzo(A)anthracene Benzo(A)pyrene Benzo(B)flouranthene Benzo(g,h,i)perylene Benzo(K)fluorantheneFarm_NNWTilled Surface Soilμg g-17.77E-087.07E-071.27E-041.75E-041.46E-031.14E-042.11E-03[dry weight]Untilled SurfaceSoilμg g-11.45E-063.14E-051.59E-032.25E-031.75E-021.59E-031.31E-02[dry weight]Farm_SETilled Surface Soilμg g-11.07E-071.15E-061.62E-042.28E-041.85E-031.50E-043.08E-03[dry weight]Untilled SurfaceSoilμg g-12.38E-064.18E-052.04E-032.86E-032.16E-022.02E-031.85E-02[dry weight]Farm_WSWTilled Surface Soilμg g-16.27E-086.64E-075.01E-054.45E-055.59E-043.23E-058.35E-04[dry weight]Untilled SurfaceSoilμg g-16.83E-071.21E-056.29E-046.20E-047.04E-034.93E-045.59E-03[dry weight]Wolf Run LakeSurface water mg L-11.08E-097.54E-095.48E-105.09E-115.49E-082.50E-091.57E-08Macrophytemg kg-12.19E-081.85E-073.66E-073.33E-083.66E-051.11E-061.04E-05[wet weight]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-8Table B-3. Concentration Estimates for PAH Congeners (Cont.)Location and MediumUnitsValues

A cenaphthene A cenaphthylene Benzo(A)anthracene Benzo(A)pyrene Benzo(B)flouranthene Benzo(g,h,i)perylene Benzo(K)fluorantheneWolf Run LakeZooplanktonmg kg-11.06E-077.86E-071.64E-051.99E-061.34E-034.01E-045.68E-04[wet weight]Water ColumnHerbivoremg kg-19.71E-077.51E-062.31E-068.06E-071.07E-032.47E-052.65E-04[wet weight]Water ColumnOmnivoremg kg-16.82E-074.81E-064.33E-071.66E-072.33E-043.09E-065.55E-05[wet weight]Water ColumnCarnivoremg kg-15.59E-073.93E-063.43E-071.21E-071.71E-042.01E-064.04E-05[wet weight]Sedimentμg g-12.43E-083.06E-077.87E-061.12E-069.52E-048.05E-053.36E-04[dry weight]BenthicInvertebratemg kg-14.58E-073.31E-063.04E-063.70E-076.36E-047.86E-061.56E-04[wet weight]Benthic Omnivoremg kg-16.78E-074.79E-064.38E-071.55E-072.22E-042.62E-065.26E-05[wet weight]Benthic Carnivoremg kg-15.58E-073.93E-063.48E-071.23E-071.75E-042.06E-064.15E-05[wet weight]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-9Table B-3. Concentration Estimates for PAH Congeners (Cont.)Location and MediumUnitsValues

A cenaphthene A cenaphthylene Benzo(A)anthracene Benzo(A)pyrene Benzo(B)flouranthene Benzo(g,h,i)perylene Benzo(K)fluorantheneVeto LakeSurface water mg L-13.08E-091.46E-085.98E-097.46E-101.68E-071.01E-085.62E-08Macrophytemg kg-16.26E-083.56E-073.50E-063.66E-079.29E-052.95E-062.97E-05[wet weight]Zooplanktonmg kg-13.02E-071.52E-061.81E-042.51E-053.85E-031.28E-031.88E-03[wet weight]Water ColumnHerbivoremg kg-12.77E-061.44E-052.22E-059.19E-062.75E-038.75E-057.84E-04[wet weight]Water ColumnOmnivoremg kg-11.95E-069.26E-064.10E-061.89E-066.11E-049.41E-061.65E-04[wet weight]Water ColumnCarnivoremg kg-11.60E-067.57E-063.22E-061.34E-064.37E-045.38E-061.16E-04[wet weight]Sedimentμg g-12.23E-072.04E-069.15E-051.50E-052.72E-032.50E-041.07E-03[dry weight]Benthic Invertebratemg kg-14.21E-062.21E-053.53E-054.94E-061.81E-032.44E-054.97E-04[wet weight]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-10Table B-3. Concentration Estimates for PAH Congeners (Cont.)Location and MediumUnitsValues

A cenaphthene A cenaphthylene Benzo(A)anthracene Benzo(A)pyrene Benzo(B)flouranthene Benzo(g,h,i)perylene Benzo(K)fluorantheneVeto LakeBenthic Omnivoremg kg-11.97E-069.32E-064.20E-061.75E-065.80E-047.17E-061.55E-04[wet weight]Benthic Carnivoremg kg-11.61E-067.61E-063.30E-061.38E-064.53E-045.55E-061.21E-04[wet weight]MountwoodPark LakeSurface water mg L-11.10E-096.05E-091.19E-091.41E-102.82E-081.44E-098.73E-09Macrophytemg kg-12.24E-081.48E-076.76E-076.57E-081.50E-053.94E-074.45E-06[wet weight]Zooplanktonmg kg-11.09E-076.40E-074.66E-055.93E-067.89E-042.37E-043.68E-04[wet weight]Water ColumnHerbivoremg kg-19.88E-075.98E-064.63E-061.83E-064.68E-042.04E-051.29E-04[wet weight]Water ColumnOmnivoremg kg-16.99E-073.85E-068.10E-073.71E-071.07E-041.89E-062.70E-05[wet weight]Water ColumnCarnivoremg kg-15.73E-073.15E-066.25E-072.45E-077.22E-057.58E-071.78E-05[wet weight]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-11Table B-3. Concentration Estimates for PAH Congeners (Cont.)Location and MediumUnitsValues

A cenaphthene A cenaphthylene Benzo(A)anthracene Benzo(A)pyrene Benzo(B)flouranthene Benzo(g,h,i)perylene Benzo(K)fluorantheneMountwoodPark LakeSedimentμg g-14.08E-083.61E-072.87E-063.92E-076.82E-055.39E-062.48E-05[dry weight]Benthic Invertebratemg kg-17.68E-073.91E-061.11E-061.29E-074.55E-055.25E-071.15E-05[wet weight]Benthic Omnivoremg kg-16.97E-073.83E-067.73E-072.88E-078.55E-058.65E-072.11E-05[wet weight]Benthic Carnivoremg kg-15.73E-073.15E-066.26E-072.38E-077.06E-057.12E-071.74E-05[wet weight]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-12Table B-3. Concentration Estimates for PAH Congeners (Cont.)Location and MediumUnitsValues

Chrysene Dibenz[a,h]anthracene Fluoranthene Fluorene Indeno(1,2,3-cd)pyrene Methylnaphthalene, 2-Farm_NNWTilled Surface Soilμg g-19.12E-046.12E-051.26E-041.50E-072.50E-049.41E-08[dry weight]Untilled Surface Soilμg g-18.69E-036.13E-041.81E-033.04E-062.82E-036.72E-06[dry weight]Farm_SETilled Surface Soilμg g-11.15E-038.33E-051.53E-041.79E-073.30E-041.12E-07[dry weight]Untilled Surface Soilμg g-11.08E-027.91E-042.45E-033.94E-063.53E-037.24E-06[dry weight]Farm_WSWTilled Surface Soilμg g-13.99E-041.81E-057.66E-058.52E-087.60E-056.26E-08[dry weight]Untilled Surface Soilμg g-13.95E-032.08E-048.68E-041.35E-069.83E-042.35E-06[dry weight]Wolf RunLakeSurface water mg L-14.61E-095.45E-105.00E-081.83E-093.41E-092.77E-09Macrophytemg kg-13.02E-062.54E-071.68E-056.85E-081.47E-064.90E-08[wet weight]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-13Table B-3. Concentration Estimates for PAH Congeners (Cont.)Location and MediumUnitsValues

Chrysene Dibenz[a,h]anthracene Fluoranthene Fluorene Indeno(1,2,3-cd)pyrene Methylnaphthalene, 2-Wolf RunLakeZooplanktonmg kg-19.14E-056.86E-052.35E-051.06E-086.55E-041.76E-08[wet weight]Water ColumnHerbivoremg kg-12.61E-055.12E-061.23E-042.28E-063.63E-052.31E-06[wet weight]Water ColumnOmnivoremg kg-15.08E-067.57E-072.42E-051.19E-063.95E-061.74E-06[wet weight]Water ColumnCarnivoremg kg-13.99E-065.16E-071.95E-059.67E-072.47E-061.43E-06[wet weight]Sedimentμg g-16.04E-051.67E-059.58E-059.95E-081.10E-046.78E-08[dry weight]Benthic Invertebratemg kg-19.78E-063.00E-061.82E-041.20E-068.79E-061.01E-06[wet weight]Benthic Omnivoremg kg-14.96E-067.00E-072.44E-051.18E-063.18E-061.73E-06[wet weight]Benthic Carnivoremg kg-14.01E-065.39E-071.96E-059.67E-072.51E-061.43E-06[wet weight]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-14Table B-3. Concentration Estimates for PAH Congeners (Cont.)Location and MediumUnitsValues

Chrysene Dibenz[a,h]anthracene Fluoranthene Fluorene Indeno(1,2,3-cd)pyrene Methylnaphthalene, 2-Veto LakeSurface water mg L-13.63E-082.13E-099.59E-087.91E-091.29E-085.77E-09Macrophytemg kg-12.00E-057.58E-073.14E-052.95E-073.44E-061.02E-07[wet weight]Zooplanktonmg kg-16.98E-042.47E-044.50E-054.68E-081.86E-034.13E-08[wet weight]Water Column Herbivoremg kg-11.74E-041.95E-052.29E-049.78E-061.18E-044.79E-06[wet weight]Water Column Omnivoremg kg-13.36E-052.51E-064.52E-055.09E-061.10E-053.60E-06[wet weight]Water Column Carnivoremg kg-12.62E-051.55E-063.62E-054.15E-065.86E-062.96E-06[wet weight]Sedimentμg g-15.23E-045.39E-053.73E-041.40E-063.20E-044.73E-07[dry weight]Benthic Invertebratemg kg-18.46E-059.67E-067.08E-041.69E-052.54E-057.02E-06[wet weight]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-15Table B-3. Concentration Estimates for PAH Congeners (Cont.)Location and MediumUnitsValues

Chrysene Dibenz[a,h]anthracene Fluoranthene Fluorene Indeno(1,2,3-cd)pyrene Methylnaphthalene, 2-Veto LakeBenthic Omnivoremg kg-13.28E-052.17E-064.65E-055.12E-067.74E-063.63E-06[wet weight]Benthic Carnivoremg kg-12.64E-051.64E-063.69E-054.16E-066.01E-062.98E-06[wet weight]MountwoodPark LakeSurface water mg L-16.88E-093.33E-103.52E-082.47E-091.91E-092.28E-09Macrophytemg kg-13.67E-061.13E-071.14E-059.19E-084.73E-074.02E-08[wet weight]Zooplanktonmg kg-11.69E-045.08E-051.76E-051.55E-083.54E-042.05E-08[wet weight]Water Column Herbivoremg kg-13.34E-054.75E-068.34E-053.05E-062.91E-051.89E-06[wet weight]Water Column Omnivoremg kg-16.30E-065.15E-071.65E-051.59E-062.43E-061.43E-06[wet weight]Water Column Carnivoremg kg-14.82E-062.41E-071.32E-051.29E-068.56E-071.17E-06[wet weight]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-16Table B-3. Concentration Estimates for PAH Congeners (Cont.)Location and MediumUnitsValues

Chrysene Dibenz[a,h]anthracene Fluoranthene Fluorene Indeno(1,2,3-cd)pyrene Methylnaphthalene, 2-MountwoodPark LakeSedimentμg g-11.56E-051.20E-062.74E-051.85E-077.41E-068.89E-08[dry weight]Benthic Invertebratemg kg-12.52E-062.15E-075.20E-052.23E-065.89E-071.32E-06[wet weight]Benthic Omnivoremg kg-15.88E-062.82E-071.64E-051.59E-069.63E-071.42E-06[wet weight]Benthic Carnivoremg kg-14.80E-062.31E-071.33E-051.30E-067.93E-071.17E-06[wet weight]Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-17Table B-4. Concentration Estimates for Dioxin CongenersLocation and MediumUnitsValues

HeptaCDD, 1,2,3,4,6,7,8 ‐

HexaCDF, 1,2,3,4,7,8 ‐ PentaCDF, 1,2,3,7,8 ‐ TetraCDD, 2,3,7,8 ‐ OctaCDF, 1,2,3,4,6,7,8, 9 ‐ TetraCDF, 2,3,7,8 ‐Farm_NNWTilled Surface Soilμg g-1[dryweight]1.49E-09 2.90E-10 4.23E-109.94E-11 2.66E-09 7.58E-11Untilled Surface Soilμg g-1[dryweight]6.91E-09 1.34E-09 1.94E-094.96E-10 1.22E-08 3.56E-10Farm_SETilled Surface Soilμg g-1[dryweight]2.30E-09 4.38E-10 6.52E-101.65E-10 4.05E-09 1.45E-10Untilled Surface Soilμg g-1[dryweight]9.57E-09 1.87E-09 2.80E-097.80E-10 1.68E-08 7.39E-10Farm_WSWTilled Surface Soilμg g-1[dryweight]4.50E-10 1.11E-10 1.58E-104.55E-11 7.84E-10 5.60E-11Untilled Surface Soilμg g-1[dryweight]2.68E-09 6.25E-10 8.73E-102.49E-10 4.69E-09 2.22E-10Wolf RunLakeSurface watermg L-12.24E-15 9.93E-17 4.57E-171.84E-16 3.80E-16 2.90E-17Macrophytemg kg-1[wetweight]4.36E-11 2.46E-12 9.40E-133.84E-12 7.34E-12 1.98E-13Zooplanktonmg kg-1[wetweight]3.95E-10 5.03E-12 1.58E-126.43E-12 7.99E-11 2.42E-13Water ColumnHerbivoremg kg-1[wetweight]5.65E-10 1.03E-11 4.18E-121.99E-11 3.33E-11 1.17E-12Water ColumnOmnivoremg kg-1[wetweight]6.20E-10 1.41E-11 6.95E-123.49E-11 9.89E-12 2.47E-12Water ColumnCarnivoremg kg-1[wetweight]3.90E-10 2.22E-11 1.49E-117.68E-11 2.45E-12 6.81E-12Sedimentμg g-1[dryweight]6.17E-11 5.50E-13 1.66E-136.87E-13 1.02E-11 2.48E-14Benthic Invertebratemg kg-1[wetweight]2.01E-13 2.72E-15 2.41E-157.85E-14 1.09E-14 8.32E-16Benthic Omnivoremg kg-1[wetweight]4.76E-12 1.21E-12 7.96E-136.58E-12 9.21E-14 6.62E-13Benthic Carnivoremg kg-1[wetweight]6.74E-12 2.53E-12 1.97E-121.59E-11 1.06E-13 1.82E-12Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-18Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)Location and MediumUnitsValues

HeptaCDD, 1,2,3,4,6,7,8 ‐

HexaCDF, 1,2,3,4,7,8 ‐ PentaCDF, 1,2,3,7,8 ‐ TetraCDD, 2,3,7,8 ‐ OctaCDF, 1,2,3,4,6,7,8, 9 ‐ TetraCDF, 2,3,7,8 ‐Veto LakeSurface watermg L-11.48E-14 1.14E-15 6.13E-161.37E-15 5.29E-15 3.34E-16Macrophytemg kg-1[wetweight]1.26E-11 5.11E-12 3.22E-127.29E-12 4.27E-12 1.43E-12Zooplanktonmg kg-1[wetweight]1.08E-10 1.07E-11 5.56E-121.23E-11 4.62E-11 1.72E-12Water ColumnHerbivoremg kg-1[wetweight]1.83E-10 2.42E-11 1.57E-114.04E-11 2.31E-11 8.38E-12Water ColumnOmnivoremg kg-1[wetweight]2.44E-10 3.59E-11 2.73E-117.59E-11 8.70E-12 1.83E-11Water ColumnCarnivoremg kg-1[wetweight]2.02E-10 7.17E-11 7.22E-112.07E-10 2.85E-12 6.09E-11Sedimentμg g-1[dryweight]2.37E-10 1.51E-11 7.23E-121.70E-11 7.98E-11 2.11E-12Benthic Invertebratemg kg-1[wetweight]7.63E-13 7.40E-14 1.03E-131.89E-12 8.50E-14 6.98E-14Benthic Omnivoremg kg-1[wetweight]2.40E-12 2.47E-12 2.70E-121.52E-11 8.46E-14 4.39E-12Benthic Carnivoremg kg-1[wetweight]2.79E-12 5.72E-12 7.33E-123.92E-11 8.08E-14 1.36E-11MountwoodPark LakeSurface watermg L-11.76E-15 1.60E-16 8.81E-171.83E-16 7.58E-16 5.34E-17Macrophytemg kg-1[wetweight]1.46E-12 7.51E-13 4.75E-139.87E-13 6.40E-13 2.21E-13Zooplanktonmg kg-1[wetweight]1.05E-11 1.30E-12 6.93E-131.45E-12 6.21E-12 2.56E-13Water ColumnHerbivoremg kg-1[wetweight]2.51E-11 4.25E-12 2.68E-126.09E-12 5.09E-12 1.45E-12Water ColumnOmnivoremg kg-1[wetweight]4.59E-11 8.53E-12 6.15E-121.43E-11 2.76E-12 3.83E-12Water ColumnCarnivoremg kg-1[wetweight]5.43E-11 2.61E-11 2.57E-115.91E-11 1.27E-12 1.99E-11Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-19Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)Location and MediumUnitsValues

HeptaCDD, 1,2,3,4,6,7,8 ‐

HexaCDF, 1,2,3,4,7,8 ‐ PentaCDF, 1,2,3,7,8 ‐ TetraCDD, 2,3,7,8 ‐ OctaCDF, 1,2,3,4,6,7,8, 9 ‐ TetraCDF, 2,3,7,8 ‐MountwoodPark LakeSedimentμg g-1[dryweight]4.53E-12 4.11E-13 2.03E-134.24E-13 2.05E-12 6.72E-14Benthic Invertebratemg kg-1[wetweight]1.41E-14 1.98E-15 2.85E-154.45E-14 2.18E-15 2.16E-15Benthic Omnivoremg kg-1[wetweight]1.62E-13 3.06E-13 3.25E-131.43E-12 8.57E-15 6.18E-13Benthic Carnivoremg kg-1[wetweight]2.83E-13 1.02E-12 1.34E-125.64E-12 1.08E-14 2.90E-12Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-20Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)Location and MediumUnitsValues

PentaCDD, 1,2,3,7,8 ‐ PentaCDF, 2,3,4,7,8 ‐ HexaCDD, 1,2,3,4,7,8 ‐ HexaCDD, 1,2,3,6,7,8 ‐ HexaCDF, 1,2,3,6,7,8 ‐ HexaCDF, 1,2,3,7,8,9 ‐Farm_NNWTilled Surface Soilμg g-1[dryweight]1.02E-09 4.13E-10 6.86E-107.98E-10 7.36E-10 6.52E-10Untilled Surface Soilμg g-1[dryweight]4.72E-09 1.90E-09 3.18E-093.70E-09 3.39E-09 3.00E-09Farm_SETilled Surface Soilμg g-1[dryweight]1.62E-09 6.58E-10 1.07E-091.25E-09 1.12E-09 9.84E-10Untilled Surface Soilμg g-1[dryweight]6.86E-09 2.94E-09 4.49E-095.20E-09 4.64E-09 4.09E-09Farm_WSWTilled Surface Soilμg g-1[dryweight]3.17E-10 1.80E-10 2.11E-102.37E-10 2.23E-10 2.10E-10Untilled Surface Soilμg g-1[dryweight]1.84E-09 9.17E-10 1.26E-091.42E-09 1.33E-09 1.24E-09Wolf RunLakeSurface watermg L-17.47E-16 6.52E-17 6.88E-166.09E-16 1.14E-16 1.25E-16Macrophytemg kg-1[wetweight]2.05E-11 9.17E-13 1.62E-119.01E-12 2.42E-12 3.36E-12Zooplanktonmg kg-1[wetweight]5.65E-11 1.27E-12 9.60E-119.70E-11 1.79E-11 1.51E-11Water ColumnHerbivoremg kg-1[wetweight]1.49E-10 4.17E-12 2.16E-101.85E-10 3.39E-11 2.91E-11Water ColumnOmnivoremg kg-1[wetweight]2.46E-10 7.56E-12 2.75E-102.37E-10 4.33E-11 3.76E-11Water ColumnCarnivoremg kg-1[wetweight]5.30E-10 1.72E-11 4.11E-103.57E-10 6.45E-11 5.67E-11Sedimentμg g-1[dryweight]6.95E-12 1.30E-13 1.49E-111.99E-11 2.80E-12 1.96E-12Benthic Invertebratemg kg-1[wetweight]3.79E-13 1.34E-14 2.82E-131.49E-13 3.42E-14 8.03E-14Benthic Omnivoremg kg-1[wetweight]2.68E-11 1.58E-12 3.10E-124.35E-12 8.19E-13 1.28E-12Benthic Carnivoremg kg-1[wetweight]6.59E-11 3.88E-12 6.14E-128.92E-12 1.67E-12 2.57E-12Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-21Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)Location and MediumUnitsValues

PentaCDD, 1,2,3,7,8 ‐ PentaCDF, 2,3,4,7,8 ‐ HexaCDD, 1,2,3,4,7,8 ‐ HexaCDD, 1,2,3,6,7,8 ‐ HexaCDF, 1,2,3,6,7,8 ‐ HexaCDF, 1,2,3,7,8,9 ‐Veto LakeSurface watermg L-16.76E-15 8.34E-16 5.58E-155.81E-15 1.57E-15 1.63E-15Macrophytemg kg-1[wetweight]2.18E-11 4.68E-12 7.08E-123.11E-12 1.51E-12 3.06E-12Zooplanktonmg kg-1[wetweight]5.97E-11 6.53E-12 4.02E-113.08E-11 1.09E-11 1.40E-11Water ColumnHerbivoremg kg-1[wetweight]1.72E-10 2.22E-11 1.03E-106.70E-11 2.28E-11 2.95E-11Water ColumnOmnivoremg kg-1[wetweight]3.07E-10 4.18E-11 1.50E-109.88E-11 3.27E-11 4.23E-11Water ColumnCarnivoremg kg-1[wetweight]8.29E-10 1.15E-10 2.92E-101.95E-10 6.40E-11 8.26E-11Sedimentμg g-1[dryweight]9.76E-11 8.14E-12 8.68E-119.19E-11 2.36E-11 2.40E-11Benthic Invertebratemg kg-1[wetweight]5.16E-12 8.26E-13 1.61E-126.70E-13 2.86E-13 9.75E-13Benthic Omnivoremg kg-1[wetweight]3.68E-11 8.92E-12 3.96E-122.52E-12 9.59E-13 2.73E-12Benthic Carnivoremg kg-1[wetweight]9.47E-11 2.33E-11 6.96E-124.98E-12 1.83E-12 4.97E-12MountwoodPark LakeSurface watermg L-18.62E-16 1.22E-16 6.62E-166.95E-16 2.18E-16 2.24E-16Macrophytemg kg-1[wetweight]2.84E-12 6.79E-13 8.64E-133.80E-13 2.24E-13 4.51E-13Zooplanktonmg kg-1[wetweight]6.35E-12 8.48E-13 3.85E-122.93E-12 1.25E-12 1.61E-12Water ColumnHerbivoremg kg-1[wetweight]2.40E-11 3.75E-12 1.40E-118.83E-12 3.79E-12 4.98E-12Water ColumnOmnivoremg kg-1[wetweight]5.43E-11 8.97E-12 2.67E-111.68E-11 7.26E-12 9.70E-12Water ColumnCarnivoremg kg-1[wetweight]2.22E-10 3.82E-11 7.80E-114.87E-11 2.14E-11 2.89E-11Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-22Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)Location and MediumUnitsValues

PentaCDD, 1,2,3,7,8 ‐ PentaCDF, 2,3,4,7,8 ‐ HexaCDD, 1,2,3,4,7,8 ‐ HexaCDD, 1,2,3,6,7,8 ‐ HexaCDF, 1,2,3,6,7,8 ‐ HexaCDF, 1,2,3,7,8,9 ‐MountwoodPark LakeSedimentμg g-1[dryweight]2.19E-12 2.34E-13 1.82E-121.92E-12 6.08E-13 6.24E-13Benthic Invertebratemg kg-1[wetweight]1.09E-13 2.32E-14 3.23E-141.34E-14 7.25E-15 2.50E-14Benthic Omnivoremg kg-1[wetweight]3.03E-12 1.01E-12 1.84E-131.65E-13 7.15E-14 1.78E-13Benthic Carnivoremg kg-1[wetweight]1.20E-11 4.08E-12 5.13E-135.05E-13 2.15E-13 5.18E-13Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-23Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)Location and MediumUnitsValues

HexaCDD, 1,2,3,7,8,9 ‐ HexaCDF, 2,3,4,6,7,8 ‐ HeptaCDF, 1,2,3,4,7,8,9 ‐

OctaCDD, 1,2,3,4,6,7,8,9 ‐ HeptaCDF, 1,2,3,4,6,7,8 ‐Farm_NNWTilled Surface Soilμg g-1[dry weight]8.22E-10 9.92E-10 2.33E-10 1.46E-08 1.14E-09Untilled Surface Soilμg g-1[dry weight]3.81E-09 4.57E-09 1.07E-09 6.72E-08 5.24E-09Farm_SETilled Surface Soilμg g-1[dry weight]1.29E-09 1.50E-09 3.51E-10 2.25E-08 1.68E-09Untilled Surface Soilμg g-1[dry weight]5.36E-09 6.21E-09 1.51E-09 9.34E-08 7.04E-09Farm_WSWTilled Surface Soilμg g-1[dry weight]2.45E-10 3.04E-10 9.19E-11 4.24E-09 3.85E-10Untilled Surface Soilμg g-1[dry weight]1.46E-09 1.81E-09 5.11E-10 2.54E-08 2.26E-09Wolf RunLakeSurface watermg L-16.35E-16 1.58E-16 9.00E-17 2.33E-15 2.52E-16Macrophytemg kg-1[wetweight]9.38E-12 3.33E-12 2.06E-12 3.46E-11 7.05E-12Zooplanktonmg kg-1[wetweight]1.01E-10 2.48E-11 3.80E-12 5.12E-10 2.55E-11Water ColumnHerbivoremg kg-1[wetweight]1.93E-10 4.70E-11 5.40E-12 3.28E-10 1.84E-11Water ColumnOmnivoremg kg-1[wetweight]2.47E-10 6.00E-11 5.32E-12 1.48E-10 9.49E-12Water ColumnCarnivoremg kg-1[wetweight]3.72E-10 8.94E-11 5.41E-12 5.76E-11 4.50E-12Sedimentμg g-1[dry weight]2.08E-11 3.88E-12 4.08E-13 7.48E-11 2.95E-12Benthic Invertebratemg kg-1[wetweight]1.79E-13 1.71E-13 1.06E-14 5.97E-14 2.22E-15Benthic Omnivoremg kg-1[wetweight]4.56E-12 1.31E-12 3.20E-13 7.54E-13 4.05E-13Benthic Carnivoremg kg-1[wetweight]9.34E-12 2.53E-12 5.38E-13 9.46E-13 5.33E-13Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-24Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)Location and MediumUnitsValues

HexaCDD, 1,2,3,7,8,9 ‐ HexaCDF, 2,3,4,6,7,8 ‐ HeptaCDF, 1,2,3,4,7,8,9 ‐

OctaCDD, 1,2,3,4,6,7,8,9 ‐ HeptaCDF, 1,2,3,4,6,7,8 ‐Veto LakeSurface watermg L-16.02E-15 2.17E-15 1.01E-15 3.25E-14 3.19E-15Macrophytemg kg-1[wet weight]3.22E-12 2.08E-12 4.95E-12 1.72E-11 8.30E-12Zooplanktonmg kg-1[wet weight]3.19E-11 1.51E-11 9.38E-12 2.46E-10 3.09E-11Water ColumnHerbivoremg kg-1[wet weight]6.94E-11 3.15E-11 1.50E-11 1.90E-10 2.58E-11Water ColumnOmnivoremg kg-1[wet weight]1.02E-10 4.52E-11 1.68E-11 1.08E-10 1.60E-11Water ColumnCarnivoremg kg-1[wet weight]2.01E-10 8.86E-11 2.18E-11 5.64E-11 9.64E-12Sedimentμg g-1[dry weight]9.51E-11 3.27E-11 1.28E-11 4.99E-10 4.66E-11Benthic Invertebratemg kg-1[wet weight]8.00E-13 1.42E-12 3.31E-13 3.96E-13 3.49E-14Benthic Omnivoremg kg-1[wet weight]2.80E-12 3.15E-12 1.13E-12 5.94E-13 4.87E-13Benthic Carnivoremg kg-1[wet weight]5.45E-12 5.31E-12 1.80E-12 6.39E-13 6.66E-13MountwoodPark LakeSurface watermg L-17.26E-16 2.96E-16 1.47E-16 4.83E-15 4.36E-16Macrophytemg kg-1[wet weight]3.96E-13 3.04E-13 7.40E-13 2.60E-12 1.19E-12Zooplanktonmg kg-1[wet weight]3.06E-12 1.69E-12 1.19E-12 3.33E-11 3.66E-12Water ColumnHerbivoremg kg-1[wet weight]9.21E-12 5.16E-12 2.94E-12 3.92E-11 5.15E-12Water ColumnOmnivoremg kg-1[wet weight]1.75E-11 9.91E-12 4.62E-12 3.05E-11 4.66E-12Water ColumnCarnivoremg kg-1[wet weight]5.08E-11 2.92E-11 8.91E-12 2.19E-11 3.98E-12Human-health Multipathway Residual-risk Assessmentfor the Ferroalloy-production Source CategoryB-25Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)Location and MediumUnitsValues

HexaCDD, 1,2,3,7,8,9 ‐ HexaCDF, 2,3,4,6,7,8 ‐ HeptaCDF, 1,2,3,4,7,8,9 ‐

OctaCDD, 1,2,3,4,6,7,8,9 ‐ HeptaCDF, 1,2,3,4,6,7,8 ‐MountwoodPark LakeSedimentμg g-1[dry weight]2.00E-12 8.33E-13 3.52E-13 1.23E-11 1.17E-12Benthic Invertebratemg kg-1[wet weight]1.61E-14 3.58E-14 9.01E-15 9.62E-15 8.76E-16Benthic Omnivoremg kg-1[wet weight]1.77E-13 1.55E-13 1.14E-13 5.83E-14 6.46E-14Benthic Carnivoremg kg-1[wet weight]5.36E-13 4.09E-13 2.67E-13 8.51E-14 1.07E-13