Technical Support Document: Human-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 Exposure 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 Cancer 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 Lifetime Cancer Risk by Ingested Medium 

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

Figure 5-4. Contribution to Incremental Lifetime 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.FaTE3

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

emissions. The MIRC4 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 other 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 surrounding 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 individual 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 congeners 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 Ecological 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-methodology-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 specific 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 RTR5

, contains 

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

2) that EPA uses to evaluate the potential for ingestion 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 pose 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 incremental 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 achieved 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 contaminated 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 

Tier-1 Emission-screening Assessment

(a) Emissionscreening 

Threshold 

(TPY)a (b) Emissions 

(TPY)a,b

(c) Screening 

Value 

[(b)/(a)]c

NEI11660 

Mercuryd (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 

Mercuryd (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 (with 1.5 rounding to 2; SVs rounded to one significant figure). 

d

 The emission-screening threshold for mercury applies to emissions of divalent mercury, although it was derived based on 

hazards associated with ingestion of fish contaminated with methyl mercury (i.e., emissions of divalent mercury, which transforms 

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 contaminated 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, respectively. 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 identified. Some of a facility’s meteorology 

patterns vary by octant, and qualifying lakes (discussed 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: SVfish=8 at NEI11660, SVfarm=0.009 at NEI11660 

 Cadmium: SVfish=0.7 at NEI11660, SVfarm=0.07 at NEI11660 

 PAHs: SVfish=10 at NEI11660, SVfarm=30 at NEI11660 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category 

Draft 7 April 2015 

 Dioxins: SVfish=7 at NEIWV053FELMAN, SVfarm=7 at NEIWV053FELMAN) 

 PAHs + Dioxins (total cancer): SVfish=20 at NEI11660, SVfarm=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 2-4Error! 

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 chemical deposition to the domain.
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

Draft 8 

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

Facility 

PB-HAP 

Group 

Tier-2 Emission-screening Assessment

Fisher Scenario Farmer 

(a)Emissionscreening 

Threshold 

(TPY)a

(b) 

Emissions 

(TPY)a,b

(c)Screening 

Value 

[(b)/(a)]c

(a)Emissionscreening 

Threshold 

(TPY)a

(

Emis

(TP

NEI11660 

Mercuryd (non-cancer) 5.24E-03 4.19E-02 8 4.66E+00 4.19Cadmium (non-cancer) 1.67E-01 1.17E-01 0.7 1.67E+00 1.17PAHs as BaP (cancer) 4.08E-02 4.08E-01 10 1.02E-02 3.07Dioxins as 2,3,7,8-TCDD (cancer) 6.50E-08 1.30E-07 2 8.13E-08 1.41PAHs + Dioxins (cancer) N/A N/A 20 N/A N

NEIWV053FELMAN 

Mercuryd (non-cancer) 3.97E-03 3.57E-03 0.9 4.46E+00 3.57Cadmium (non-cancer) 1.14E-01 2.27E-03 0.02 2.27E+00 2.27PAHs as BaP (cancer) 2.43E-02 1.70E-02 0.7 1.28E-02 1.28Dioxins as 2,3,7,8-TCDD (cancer) 5.86E-08 4.10E-07 7 7.11E-08 4.95PAHs + Dioxins (cancer) N/A N/A 8 N/A N

a TPY = tons per yearb Emissions of PAHs and dioxins in this column were normalized to BaP and 2,3,7,8-TCDD, respectively for oral toxicity and Tier-2 modeled environmc Red highlights indicate screening values that are 2 or larger (with 1.5 rounding to 2; screening values rounded to one significant figure).d The emission-screening threshold for mercury applies to emissions of divalent mercury, although it was derived based on hazards associated with 

contaminated with methyl mercury (i.e., emissions of divalent mercury, which transforms to methyl mercury within the environment and accumulates 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

Draft 9 

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

Chemical Information Tier-1 Emission-screening Assessment Tier-2 Emission-screening 

PB-HAP 

Group PB-HAP Chem. 

Raw Emissions 

(TPY)a

Screening 

Value (by 

Chem.) 

Screening Value (by 

PB-HAP Group)b

Fisher Scenario 

Screening 

Value (by 

Chem.) 

Screening 

Value (by PBHAP Group)b Sc

V

Mercuryc Divalent mercury 4.19E-02 130 100 7.7 8 Cadmium Cadmium compounds 1.17E-01 10 10 0.68 0.7 PAHs 

2-Methylnaphthalene 9.19E-01 0.39 

500 

0.0083 

10 

Acenaphthene 2.34E-01 0.15 0.0034 Acenaphthylene 9.18E-01 0.92 0.021 Anthracene 5.08E-01 0 0 Benz(a)anthracene 4.46E-01 2.5 0.019 Benzo(a)pyrene 6.52E-02 25 0.078 Benzo(b)fluoranthene 3.75E-01 270 7.5 Benzo(e)pyrened 1.86E-01 22 0.26 Benzo(g,h,i)perylene 2.42E-02 2.8 0.036 Benzo(k)fluoranthene 1.18E-01 130 5.1 Chrysene 9.87E-01 1.6 0.025 Dibenzo(a,h)anthracene 4.89E-03 8.6 0.17 Fluoranthene 4.78E+00 4.8 0.097 Fluorene 4.07E-01 0.33 0.0080 0Indeno(1,2,3-c,d)pyrene 2.68E-02 7.7 0.12 Perylened 1.05E-23 0.33 0.0077 Phenanthrene 4.71E+00 0 0 Pyrene 3.40E+00 0 0 

Dioxins 

HeptaCDD, 1,2,3,4,6,7,8- 3.07E-08 0.11 

40 

0.0048 

2 

HeptaCDF, 1,2,3,4,6,7,8- 3.62E-08 0.021 0.00015 HeptaCDF, 1,2,3,4,7,8,9- 1.71E-08 0.011 0.00028 HexaCDD, 1,2,3,4,7,8- 1.63E-08 0.90 0.042 HexaCDD, 1,2,3,6,7,8- 1.58E-08 0.57 0.026 HexaCDD, 1,2,3,7,8,9 - 1.67E-08 0.25 0.011 HexaCDF, 1,2,3,4,7,8- 1.84E-08 0.21 0.0072 HexaCDF, 1,2,3,6,7,8- 1.41E-08 0.23 0.0076 HexaCDF, 1,2,3,7,8,9- 1.70E-08 0.29 0.0099 HexaCDF, 2,3,4,6,7,8- 1.99E-08 0.14 0.0046 OctaCDD, 1,2,3,4,6,7,8,9- 2.63E-07 0.031 0.00026 OctaCDF, 1,2,3,4,6,7,8,9- 4.44E-08 0.00079 0.0000016 0PentaCDD, 1,2,3,7,8- 2.29E-08 31 1.4 PentaCDF, 1,2,3,7,8- 1.82E-08 0.082 0.0024 PentaCDF, 2,3,4,7,8- 2.66E-08 1.0 0.033 TetraCDD, 2,3,7,8- 1.88E-08 6.7 0.35 TetraCDF, 2,3,7,8- 2.62E-08 0.12 0.0043 

Note: 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 C

(TPY)” columns are rounded to two and three significant figures, respectively.
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

Draft 10 

Chemical Information Tier-1 Emission-screening Assessment Tier-2 Emission-screening 

PB-HAP 

Group PB-HAP Chem. 

Raw Emissions 

(TPY)a

Screening 

Value (by 

Chem.) 

Screening Value (by 

PB-HAP Group)b

Fisher Scenario 

Screening 

Value (by 

Chem.) 

Screening 

Value (by PBHAP Group)b Sc

V

a TPY = tons per yearb Red highlights indicate PB-HAP-group screening values that are 2 or larger (with 1.5 rounding to 2). Gray font indicates screening values smaller than 

Green highlights indicate emissions that screened out in Tier 2 but not in Tier 1. Mercury and cadmium are evaluated for non-cancer effects, while PAHs for cancer effects.

c The emission-screening threshold for mercury applies to emissions of divalent mercury, although it was derived based on hazards associated with inge

with methyl mercury (i.e., emissions of divalent mercury that transforms to methyl mercury within the environment and accumulates in fish tissue).

d These PAH congeners are not currently fully parameterized in TRIM.FaTE to evaluate their specific fate, transport, and transformation properties. Thei

factors (to relate them to the benzo(a)pyrene surrogate used in screening) were based on how their Kow values related to the Kow values of the fully-para

Appendix 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 Tie

to NEI11660) 

Parameter (As Modeled) Tier 1 Tier 2

Octant Analyzed – W N SWind Speed (m/s) 2.8 2.8 2.8 2Frequency of Winds Blowing into Modeled Domain (fraction) 0.43 0.07 0.19 0.Annual Precipitation (mm), site-wide (same for all octants) 1,500 1,187 Mixing Height (m), site-wide (same for all octants) 710 710 Distance to Lake (km) 1.4 10 (Veto Lake) 40 (Wolf Run Lake) 20 (Mountwo
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 the 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 environmental 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 usually 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-cancer 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 regularly 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 risks 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 hypothetical 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 sensitivity 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 chemical 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 inhalation 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 relevant). 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 the 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 groundwater were not modeled in this assessment, 

which had no noticeable impact on modeled human chemical exposure. 

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

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

which had no noticeable impact on modeled 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 characteristics 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.FaTE: Sections 2.2.1, 2.2.5. MIRC: Section 2.2.6; 

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

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

to this site-specific assessment. 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production 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 Washington 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 ArcGIS 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/spring07articles/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-development 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 processor15 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 assessment began in late 2013—with modifications to reduce 

the impact of a known error with friction-velocity calculations; 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) 

Average 

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 the individual octant rows represent only the precipitation that fell when winds were 

blowing into that octant. The “Average Regardless of Octant” value 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 encompassed 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 buoyancy 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 exiting 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 was 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

= 20a 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.
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Table 4-3. Modeled Emissions Amounts for Each Grouped Emission Source 

Modeled PB-HAP Group Modeled Chemical

Emissions (tons per year)

Stack A Stack B Stac

Release Ht.: 28.8 m Release Ht.: Varies by Hour for Plume-rise P

Mercury Divalent Mercury 1.48E-03 1.05E-05 4.04E

Elemental Mercurya 5.93E-03 4.20E-05 1.62E

Cadmium Cadmium 8.67E-02 4.82E-05 3.06EPAHs 

2-Methylnaphthalene 4.01E-01 8.11E-03 5.10E

Acenaphthene 1.31E-01 3.18E-03 1.00EAcenaphthylene 4.05E-01 6.55E-03 5.06EBenz(a)anthracene 2.18E-01 2.01E-04 2.29EBenzo(a)pyreneb 3.79E-02 7.69E-05 4.07EBenzo(b)fluoranthene 1.79E-01 1.66E-04 1.97EBenzo(g,h,i)perylene 1.15E-02 9.20E-05 1.26EBenzo(k)fluoranthene 5.63E-02 8.32E-05 6.16EChrysene 4.77E-01 3.42E-04 5.10EDibenzo(a,h)anthracene 2.20E-03 1.31E-05 2.68EFluoranthene 2.06E+00 2.42E-03 2.73EFluorene 1.90E-01 3.66E-03 2.14EIndeno(1,2,3-c,d)pyrene 1.22E-02 3.94E-05 1.45E

Dioxins 

HeptaCDD, 1,2,3,4,6,7,8- 2.36E-08 1.05E-09 6.12E

HeptaCDF, 1,2,3,4,6,7,8- 3.19E-08 1.53E-09 2.70EHeptaCDF, 1,2,3,4,7,8,9- 1.33E-08 5.74E-10 3.22EHexaCDD, 1,2,3,4,7,8- 1.30E-08 5.74E-10 2.73EHexaCDD, 1,2,3,6,7,8- 1.26E-08 5.56E-10 2.69EHexaCDD, 1,2,3,7,8,9 - 1.29E-08 5.65E-10 3.20EHexaCDF, 1,2,3,4,7,8- 1.51E-08 6.75E-10 2.66EHexaCDF, 1,2,3,6,7,8- 1.16E-08 5.21E-10 2.05EHexaCDF, 1,2,3,7,8,9- 1.39E-08 6.18E-10 2.65EHexaCDF, 2,3,4,6,7,8- 1.69E-08 7.84E-10 2.23EOctaCDD, 1,2,3,4,6,7,8,9- 1.90E-07 8.72E-09 6.41EOctaCDF, 1,2,3,4,6,7,8,9- 3.54E-08 1.54E-09 7.50EPentaCDD, 1,2,3,7,8- 1.88E-08 8.45E-10 3.24EPentaCDF, 1,2,3,7,8- 1.50E-08 6.79E-10 2.53EPentaCDF, 2,3,4,7,8- 2.28E-08 1.07E-09 2.74ETetraCDD, 2,3,7,8- 1.46E-08 6.18E-10 3.53ETetraCDF, 2,3,7,8- 2.08E-08 9.02E-10 4.42E

a Emissions of elemental mercury are included in site-specific modeling because it can transform in the environment to other forms of mercury. b Modeled emissions of BaP shown here include emissions of benzo(e)pyrene and perylene, which are not currently fully parameterized in TRIM.FaTE to evaluate 

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 respectively 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, transport, 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, Kow, Koc, 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 quantified 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 requiring 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, Kow, 
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 parcel have well-mixed chemical concentrations and 

are where chemical transformations and transport take place. The parcels were designed stepby-step, generally following the design recommendations 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 spaces 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 where important land characteristics changed 

significantly beneath a single air parcel. For the 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 vegetation and soil types are described below. 

Figure 4-5, Figure 4-6, and Figure 4-7, respectively, 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 lakes19 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 multipathway emission-screening assessments, EPA defines potentially-fishable 

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

the number of piscivorous fish required for the current screening 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. However, EPA includes these smaller lakes 

in RTR multipathway assessments to be health protective. 
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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 was not included in the risk results. 
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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. 
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Draft 24 April 2015 

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

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

http://www.esri.com/news/arcnews/spring07articles/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), Goodfellows 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), Goodfellows 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 the site-specific TRIM.FaTE modeling and then 

used to evaluate exposures via the fish-consumption 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 selected 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 farming 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 modeled 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 modeling 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) 

km2 (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-colored 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 properties 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 influence 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 lakes 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 include 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 partially 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 human exposures associated with ingestion of 

FFC media, incidental ingestion of soil, consumption 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. 
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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 values, including chemical-specific transfer 

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

included in HHRAP (U.S. EPA 2005). Except where 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 representative 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 water bodies. Fishers consumed fish from two of 

the five fish compartments: 50 percent biomass 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 Report (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 demonstrated 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 during 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 media 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 (Attachment A; Sections 2 and 3; Attachment 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 locations. Fruits and vegetables were assumed to 

be contaminated from air and soil at those farms. 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 yrs) Adult 

<1 1–2 3–5 6–11 12–19 (20–70 yrs)

Mean ingestion rates (g/kg-day)

Beefa N/A 4.14 4.00 3.77 1.72 1.93 

Dairyb 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 yrs) Adult 

<1 1–2 3–5 6–11 12–19 (20–70 yrs)

Eggsa N/A 2.46 1.42 0.86 0.578 0.606 

Exposed Fruita N/A 6.14 2.60 2.52 1.33 1.19 

Exposed Vegetablea N/A 3.48 1.74 1.39 1.07 1.38 

Porka N/A 2.23 2.15 1.50 1.28 1.10 

Poultrya N/A 3.57 3.35 2.14 1.50 1.37 

Protected Fruita N/A 16.6 12.4 8.50 2.96 5.19 

Protected Vegetablea N/A 2.46 1.30 1.10 0.78 0.862 

Root Vegetablea 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

Beefa N/A 9.49 8.83 11.4 3.53 4.41 

Dairyb N/A 185 92.5 57.4 30.9 6.16 

Eggsa N/A 4.90 3.06 1.90 1.30 1.31 

Exposed Fruita N/A 12.7 5.41 6.98 3.41 2.37 

Exposed Vegetablea N/A 10.7 3.47 3.22 2.35 3.09 

Porka N/A 4.90 4.83 3.72 3.69 2.23 

Poultrya N/A 7.17 6.52 4.51 3.13 2.69 

Protected Fruita N/A 44.8 32 23.3 7.44 15.1 

Protected Vegetablea N/A 3.88 2.51 2.14 1.85 1.81 

Root Vegetablea N/A 7.25 4.26 3.83 2.26 2.49 

Soil (mg/day) N/A 200d 200d 201e 201e 201e a

 Primary source for values was the 1987–1988 NFCS survey; compiled results are presented in Chapter 13 of 2011 Exposure 

Factors Handbook (U.S. EPA 2011b). When data were unavailable for 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, compiled results presented in Chapter 13 of 2011 Exposure Factors 

Handbook (U.S. EPA 2011b). When data were unavailable for 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 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 calculated 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 Fisher

a

Adult 373 Female 99th %-ile ingestion rate of wild-caught fish from Burger (2002) 

Child 1–2 108 Based on the 99th %-ile, consumer-only ingestion rates from U.S. EPA (2002); 

Child 3–5 159 rates were adjusted to represent the age groups used in MIRC 

Child 6–11 268 

Child 12–19 331 

High-end Recreational Fisher

Adult 172 Female 95th %-ile ingestion rate of wild-caught fish from Burger (2002) 

Child 1–2 65 Based on the 95th %-ile, consumer-only ingestion rates from U.S. EPA (2002); 

Child 3–5 96 rates were adjusted to represent the age groups used in MIRC 

Child 6–11 181 

Child 12–19 167 

Central-tendency Recreational Fisher

Adult 39 Female mean ingestion rate of wild-caught fish from Burger (2002) 

Child 1–2 18 Based on the mean, consumer-only ingestion rates from U.S. EPA (2002); rates 

Child 3–5 27 were adjusted to represent the age groups used in MIRC 

Child 6–11 44 

Child 12–19 58 

General-population 99th Percentile 

Adult 105 Based on 99th %-ile percentile per-capita consumption rates from U.S. EPA 

Child 1–2 26.23 (2002); rates were adjusted to represent the age groups used in MIRC 

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 consumption rates from U.S. EPA (2002); rates 

Child 1–2 3.24 were adjusted to represent the age groups used in MIRC 

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 consumption rates from U.S. EPA ( 2002); rates 

Child 1–2 1.37 were adjusted to represent the age groups used in MIRC 

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 emissions. 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 lakes. 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 included in this assessment: mercury HQs (divalent 

for farm exposure, and methyl for fish exposure), 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 not 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 incremental 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 all 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 exposure 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 mercury (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 majority 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 

Veto 

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 this 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 this 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%

Percent Contribution to Hazard Quotient

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 Ingestion
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source C

Draft 39 

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

Ingestion 

Level 

Period of 

Exposure 

(years 

of age) 

Hazard Quotient 

Breast 

Milk Soil 

Total 

Dairy Eggs Beef Pork Poultry 

Exposed 

Vegetable 

Protected 

Vegetable 

Ro

Vege

Mean 1-2 0 5.4E-04 7.4E-06 1.8E-04 1.7E-05 1.8E-06 1.3E-04 2.4E-04 4.5E-04 9.2E

3-5 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 4.7E6-11 0 1.9E-04 2.2E-06 6.5E-05 1.5E-05 1.2E-06 7.7E-05 9.8E-05 2.0E-04 4.8E12-19 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 3.4E20-70 (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

90th %ile 1-2 0 2.2E-03 1.5E-05 3.7E-04 3.8E-05 4.0E-06 2.6E-04 7.5E-04 7.1E-04 2.6E

3-5 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 1.6E6-11 0 7.6E-04 4.7E-06 1.4E-04 4.6E-05 3.1E-06 1.6E-04 2.3E-04 3.9E-04 1.4E12-19 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 8.3E20-70 (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

Notes: The numbers in this table were used to make the cumulative stacked bar chart shown in Figure 5-1. The bolded, underlined cell indicates the larges

rounded 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 

Veto 

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 this 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 Farm 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 this 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)

Percent Contribution to Hazard Quotient

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

Draft 42 

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

Ingestion 

Level 

Period of 

Exposure 

(years of 

age) 

Hazard Quotient 

Breast 

Milk Soil 

Total 

Dairy Eggs Beef Pork Poultry 

Exposed 

Vegetable 

Protected 

Vegetable 

Ro

Vege

Mean 1-2 0 2.8E-04 3.2E-04 1.3E-05 1.0E-04 1.3E-05 4.0E-04 1.8E-03 2.9E-03 1.2E

3-5 0 1.9E-04 1.8E-04 7.7E-06 9.7E-05 1.3E-05 3.7E-04 9.1E-04 1.5E-03 6.1E6-11 0 9.7E-05 9.6E-05 4.7E-06 9.2E-05 9.0E-06 2.4E-04 7.3E-04 1.3E-03 6.3E12-19 0 5.4E-05 4.8E-05 3.1E-06 4.2E-05 7.7E-06 1.7E-04 5.6E-04 9.1E-04 4.5E20-70 (adult) 0 1.7E-05 1.0E-05 3.3E-06 4.7E-05 6.6E-06 1.5E-04 7.2E-04 1.0E-03 4.9E

90th %ile 1-2 0 1.1E-03 6.5E-04 2.7E-05 2.3E-04 2.9E-05 8.0E-04 5.6E-03 4.5E-03 3.5E

3-5 0 7.5E-04 3.2E-04 1.7E-05 2.2E-04 2.9E-05 7.2E-04 1.8E-03 2.9E-03 2.0E6-11 0 3.9E-04 2.0E-04 1.0E-05 2.8E-04 2.2E-05 5.0E-04 1.7E-03 2.5E-03 1.8E12-19 0 2.2E-04 1.1E-04 7.1E-06 8.6E-05 2.2E-05 3.5E-04 1.2E-03 2.2E-03 1.1E20-70 (adult) 0 1.8E-04 2.2E-05 7.1E-06 1.1E-04 1.3E-05 3.0E-04 1.6E-03 2.1E-03 1.2E

Notes: The values in this table were used to make the cumulative stacked bar chart shown in Figure 5-2. The bolded, underlined cell indicates the largest quotients 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 exposure 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 were 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 farmer 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 

Veto 

Lake 

Mountwood 

Park Lake 

Subsistence Fisher 99th %ile Lifetime 3.E-06 7.E-06 1.E-06 

Recreational Fisher Central Tendency (Mean) 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 least 1.5-in-one million). Bolded, underlined cell 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 significant figure. Shading corresponds to values that exceed 1-inone million, rounded (which is defined as at least 1.5-in-one million). Bolded, underlined cell indicates the largest value in this 

table. 
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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%

Percent Contribution to Incremental LIfetime Cancer

Risk

Soil

Total Dairy

Eggs

Beef

Pork

Poultry

Exposed Vegetable

Protected Vegetable

Root Vegetable

Exposed Fruit

Protected Fruit

Mean Ingestion 90th %ile Ingestion
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Draft 45 

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

Ingestion 

Level 

Period of 

Exposure 

Incremental Lifetime Cancer Risk

Breast 

Milk Soil 

Total 

Dairy Eggs Beef Pork Poultry 

Exposed 

Vegetable 

Protected 

Vegetable 

Roo

Vegeta

Mean Lifetime 0 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-90th %ile Lifetime 0 5.3E-07 4.8E-06 6.8E-08 1.2E-06 4.9E-08 1.1E-07 2.1E-06 5.7E-08 3.8E-

Notes: The values in this table were used to make the cumulative stacked bar chart shown in Figure 5-3. The bolded, underlined cell indicates the largest r

rounded to two significant figures.
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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 emissions 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 exposure 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 (farm-medium-specific risks are provided in Table 

5-12). The dairy medium contributed a near-majority 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 

Veto 

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. Bolded, 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. 
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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%

Percent Contribution to Incremental Lifetime Cancer

Risk

Breast Milk

Soil

Total Dairy

Eggs

Beef

Pork

Poultry

Exposed Vegetable

Protected Vegetable

Root Vegetable

Exposed Fruit

Protected Fruit

Mean Ingestion 90th %ile Ingestion
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Table 5-12. Incremental Lifetime Cancer Risk by Ingested Medium from Exposure to Dio

Ingestion 

Level 

Period of 

Exposure 

Incremental Lifetime Cancer Risk

Breast 

Milk Soil 

Total 

Dairy Eggs Beef Pork Poultry 

Exposed 

Vegetable 

Protected 

Vegetable 

R

Veg

Mean 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 2.090th %ile Lifetime 4.4E-07 4.3E-09 7.0E-07 3.1E-10 3.0E-07 1.8E-08 5.3E-10 1.1E-08 2.3E-10 5.1

Notes: The values in this table were used to make the cumulative stacked bar chart shown in Figure 5-4. The bolded, underlined cell indicates the largest r

two significant figures.
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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 incremental 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 Assessment for the Ferroalloy-production Source Categor

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

Assessments 

PB-HAP Group

Screening Value

Site-specific

Hazard Quotient 

or Riska

Factor Change 

in HQ or Risk

Tier 1

Tier 2 – 

Via Fish 

Only

Tier 2 – 

Via 

Farm 

Only

Via Fish 

Onlyb

Via 

Farm 

Onlyc

Tier 1 to 

Tier 2 

Fish

Tier 1 to 

Tier 2 

Farm

Tier 2 to 

Sitespecific 

(Fish)

Tier 2 to 

Sitespecific 

(Farm)

Mercuryd

(non-cancer) 100 8 0.009 1 0.01 17 14,862 6 0.8 

Cadmium (non-cancer) 10 0.7 0.07 0.09 0.04 15 144 8 2 PAHs (cancer) 500 10 30 7 10 36 14 2 3 Dioxins (cancer) 40 2 2 0.5 1 22 24 4 1 Total Incremental Lifetime Cancer Risk (PAH + Dioxin) 

500 20 40 7 20 34 15 2 2 

Note: Shading corresponds to values that are 2 or larger, which is defined as at least 1.5. Rounding artifacts are present. Tier-1 assessments do not sep

hazards from fish and farm (they are aggregated); Tier-2 and site-specific assessments ONLY assess risks or hazards from fish separately from farm (th

Also, site-specific assessments assess risks or hazards from individual lakes, while Tier-2 assessments assess risks or hazards from potentially multiple 

a Site-specific values are hazard quotients for mercury and cadmium, and lifetime incremental cancer risk as “in-one million” (i.e., divided by 1 × 10-6) for 

congruent 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 

Child 1–2 category), a lifetime defined as age 1 through age 70 (including infant breast milk for dioxin exposure) for cancer risk, the subsistence-fisher sand the 90th-percentile ingestion rate for subsistence-farmer exposure. 

b Wolf Run Lake for Mercury, Veto Lake for others. c Farm_NNW for Dioxins, Farm_SE for others. d Exposure to methyl mercury was evaluated for fish exposure, while exposure to divalent mercury was evaluated for subsistence-farmer exposure. 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Category 

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6. Discussion of Uncertainties and Limitations 

The exposure- and risk-modeling process attempts to simulate naturally-occurring physical, 

chemical, and biological processes using mathematical 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 parameters (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 sensitivity of risk estimates to parameter values 

over the past decade. For those parameters to which 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 was 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 chemicals 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 
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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 results 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 impact 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) takes 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 uncertainty, 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-and-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 
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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 Mountwood 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 compartments 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 movement (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 parameters. 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 
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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 concentrations 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-organic-carbon content, and hydrodynamics at the 

sediment-surface water interface. Both methylation and demethylation can occur either biotically 

or abiotically. Certain conditions, like specific ranges of chloride, sulfide, and dissolved-organicmatter concentrations, can increase the bioavailability of divalent mercury for methylation. 

Redox conditions can influence the rate of abiotic 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 sediment can have a significant impact on 

surface-water concentration and mercury speciation by influencing the methylation and 

demethylation process. Suspended-solids concentrations also affect the amount of chemical 

transported from the water body during flushing. Resuspension of buried sediment could 

remobilize previously-deposited contaminants into 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. 
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Erosion and Runoff Flow Directions. For pollutants for which the risks are transmitted chiefly 

by the fish-consumption pathway, the amount of pollutant 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 derived 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 elimination, 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 Exposure 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 
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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 fish-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 Examination 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 presented 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 2011b). 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 conservative, 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 specific 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 reference doses and cancer slope factors where 

available (for most dioxins, cancer slope factors 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/BCguidelines/pahs/index.html#TopOfPage. 

Burger, J. 2002. Daily consumption of wild 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. (1997). 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 Assessment 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.
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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 developed to model the environmental fate-andtransport of mercury, cadmium, dioxins, and polycyclic 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 Parametersa 

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 stepb hr 4 Selected value. a

All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used). b

Output time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep." 

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

A-2 

Table A-2. Meteorological Inputsa 

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 m3

[rain]/m2[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 assessment. 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 Parametersa 

Parameter Name Units Value Reference 

Atmospheric dust load kg[dust]/m3[air] 6.15E-08 Bidleman 1988. 

Density of air g/cm3 0.0012 U.S. EPA 1997. 

Dust density kg[dust]/m3[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 screening 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 varies2 varies2 varies2 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]/m3

[soi

l] 2600 2600 2600 2600 Default in McKone et al. 2001 (Table 3) 

Thickness1 m 0.2a 0.01b 0.01b 0.01b

a

U.S. EPA 2005b 

b

McKone et al. 2001 (p. 

30)

Erosion 

fraction unitless varies2 varies2 varies2 varies2 See Erosion and 

Runoff Fraction table.

Fraction of 

area 

available for 

erosion 

m2

[area 

available]/m2

[t

otal] 

1 1 1 1 Professional judgment; 

area is rural.

Fraction of 

area 

available for 

runoff 

m2

[area 

available]/m2[t

otal] 

1 1 1 1 Professional judgment; 

area is rural.

Fraction of 

area 

available for 

vertical 

diffusion 

m2

[area 

available]/m2

[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 varies2 0.181 varies2 USDA Web Soil Survey 

Organic 

carbon 

fraction 

unitless varies2 varies2 varies2 varies2 See additional Soil 

table 

pH unitless varies2 varies2 varies2 varies2 See additional Soil 

table 

Runoff 

fraction unitless varies2 varies2 varies2 varies2 See Erosion and 

Runoff Fraction table. 

Total 

erosion rate 

kg 

[soil]/m2

/day varies2 varies2 varies2 varies2 See Total Erosion 

Rates table. 

Total runoff 

rate 

m3[water]/m2

/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 varies2 varies2 varies2 McKone et al. 2001 

(Table A-3). 

Root Zone Soil Compartment Type

Air content 

volume[air]/v

olume[compar

tment] 

0.25 varies2 varies2 varies2 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]/m3

[s

oil] 2601 2601 2600 2601 Default in McKone et 

al. 2001 (Table 3) 

Fraction 

sand unitless 0.181 varies2 0.181 varies2 See additional Soil 

table 

Thickness1 m 0.6a 0.79a 0.79a 0.79a

a

McKone et al. 2001 

(Table 16 directly or 

adjusted). 

Organic 

carbon 

fraction 

unitless varies2 varies2 varies2 varies2 See additional Soil 

table 

pH unitless varies2 varies2 varies2 varies2 See additional Soil 

table 

Water 

content 

volume[wate

r]/volume[compar

tment] 

0.22 varies2 varies2 varies2 See additional Soil 

table 

*

Shaded values indicate where refined values were developed for this site-specific 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 values of these parameters. 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

A-7 

Table A-5. Additional Soil Parameters*

Surface Parcel 

Surface Soil Compartment Type Root Zone Compartment T

Air 

Contentc 

Fraction 

Organic 

Carbona

Water 

Contentb pHc

Fraction 

Organic 

Carbona

Fraction 

Sandc

Water 

Conten

Farm_NNW_Tilled 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 Farm_NNW_Untilled 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 Farm_SE_Tilled 0.25 0.010174 0.22 5.5 0.010174 0.181 0.22 Farm_SE_Untilled 0.25 0.010174 0.22 5.5 0.010174 0.181 0.22 Farm_WSW_Tilled 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 Farm_WSW_Untilled 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 E1 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 E2 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 N1 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 N2 0.25 0.014535 0.22 5.5 0.014535 0.367 0.22 N3e 0.25 0.010174 0.22 5.1 0.010174 0.304 0.22 N3w 0.25 0.010174 0.22 5.1 0.010174 0.304 0.22 N4 0.25 0.010174 0.22 5.1 0.010174 0.304 0.22 NE1 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 NNW1 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 NNW2e 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 NNW2w 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 NNW3 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 NNW4 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 NW1 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 NW2 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 NW3 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 S1 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 S2 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 S3 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 SE1 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 SE2 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

A-8 

Table A-5. Additional Soil Parameters (Cont.)*

Surface Parcel 

Surface Soil Compartment Type Root Zone Compartment Typ

Air 

Contentc 

Fraction 

Organic 

Carbona

Water 

Contentb pHc

Fraction 

Organic 

Carbona

Fraction 

Sandc

Water 

Conten

SE3 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 SE4n 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 SE4s 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 SE5 0.3 0.010174 0.2 5.5 0.010174 0.181 0.2 Source 0.28 0.008 0.19 6.9 0.008 0.25 0.21 SW1 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 SW2 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 SW3 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 W1 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 W2 0.25 0.01017 0.22 5.3 0.01017 0.181 0.22 W3n 0.25 0.01017 0.22 5.3 0.01017 0.181 0.22 W3s 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 W4 0.25 0.01017 0.22 5.3 0.01017 0.181 0.22 W5 0.25 0.01 0.22 5.3 0.01 0.181 0.22 WNW1 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 WNW2n 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 WNW2s 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 WNW3 0.25 0.01017 0.22 5.3 0.01017 0.181 0.22 WNW4 0.25 0.01017 0.22 5.3 0.01017 0.181 0.22 WNW5 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 WSW1 0.25 0.00727 0.22 5.3 0.00727 0.181 0.22 WSW2 0.25 0.007 0.22 5.3 0.007 0.181 0.22 WSW3n 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 WSW3s 0.25 0.007267 0.22 5.3 0.007267 0.181 0.22 WSW4 0.25 0.01017 0.22 5.3 0.01017 0.181 0.22 WSW5 0.25 0.010174 0.22 5.3 0.010174 0.181 0.22 WSW6 0.25 0.0102 0.22 5.3 0.0102 0.181 0.22 

*

All values shown were refined for this site-specific assessment (versus the values used 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 Assessment for the Ferroalloy-production Source Categor

A-9 

Table A-6. Runoff Fractionsa

Receiving parcel 

Sending 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 

E1 .20 0 .24 0 0 0 .35 00 0 000000 .20 0 0 0 0 0 0 E2 .48 .01 0 0 0 0000 0 000000 .41 0 0 0 0 0 0 

Farm_NNW_Tilled .94 0 0 0 .02 0000 0 000000 0 0 0 .04 0 0 0 

Farm_NNW_Untilled .13 0 0 .40 0 0000 0 000000 0 0 .46 0 0 0 0 

Farm_SE_Tilled 0 0 0 0 0 0 .06 00 0 000000 0 0 0 0 0 0 0 

Farm_SE_Untilled 0 .02 0 0 0 .96 000 0 000000 0 0 0 0 0 0 0 

Farm_WSW_Tilled 0 0 0 0 0 000 .33 0 000000 0 0 0 0 0 0 0 

Farm_WSW_Untilled .11 0 0 0 0 0 0 .02 0 0 000000 0 0 0 0 0 0 0 

Goodfellows_Park_L

ake 0 0 0 0 0 0000 1.00 000000 0 0 0 0 0 0 0 

Mountwood_Park_L

ake 0 0 0 0 0 0000 0 1.00 00000 0 0 0 0 0 0 0 

N1 0 0 0 0 0 0000 0 00 .04 000 .92 .01 .01 0 0 0 0 N2 .05 0 0 0 0 0000 0 000 .03 .01 0 .78 000 .02 .11 0 

N3e .01 0 0 0 0 0000 0 00 .80 0 0 .10 .08 0 0 0 0 0 0 

N3w 0 0 0 0 0 0000 0 00 .10 .04 0 .69 0 0000 .07 0 

N4 .45 0 0 0 0 0000 0 000 .32 .23 0 0 0 0 0 0 0 0 

NE1 .47 .01 .01 0 0 0000 0 00 .51 000 0 0 0 0 0 0 0 

NNW1 0 0 0 0 0 0000 0 0 .24 0000 0 0 .04 000 .68 

NNW2e 0 0 0 0 0 0000 0 0 .76 0000 0 .04 0 0 .19 0 0 

NNW2w .47 0 0 0 0 0000 0 000000 0 .11 .02 0 .02 0 .38 

NNW3 0 0 0 0 0 0000 0 00 .07 000 0 0 .19 0 0 .26 0 NNW4 .24 0 0 0 0 0000 0 00 .59 0 .03 0 0 000 .01 00

NW1 0 0 0 0 0 0000 0 000000 0 .16 00000 NW2 0 0 0 0 0 0000 0 000000 0 000 .12 0 .02 NW3 .17 0 0 0 0 0000 0 000000 0 0000 .18 0 

S1 .16 0 0 0 0 .06 000 0 000000 0 00 00 00 S2 .63 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 S3 .59 0 0 0 0 0000 0 000000 0 00 00 00 

SE1 .23 .74 0 0 0 0000 0 000000 0 000000SE2 0 0 .33 0 0 .01 000 0 000000 0 00 00 00 SE3 0 0 .35 0 0 0000 0 000000 0 00 00 00 

SE4n .04 0 0 0 0 0000 0 .01 00000 0 0 0 0 0 0 0 

SE4s .05 0 0 0 0 0000 0 000000 0 00 00 00 

SE5 .91 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 

Source 1 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

A-10 

Table A-6. Runoff Fractions (Cont.)a

Receiving parcel 

Sending 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 

SW1 .43 0 0 0 0 0000 0 000000 0 000000SW2 .18 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 SW3 .93 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 

Veto_Lake 0 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 

W1 0 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 W2 0 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 

W3n .67 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 

W3s .66 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 

W4 0 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 W5 .30 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 

WNW1 0 0 0 0 0 0000 0 000000 0 00000 .03 

WNW2n .53 0 0 0 0 0000 0 000000 0 000000 

WNW2s .15 0 0 0 0 0000 .43 000000 0 0 0 0 0 0 0 

WNW3 0 0 0 0 0 0000 0 000000 0 000000WNW4 0 0 0 0 0 0000 0 000000 0 000000WNW5 .23 0 0 0 0 0000 0 000000 0 000000

Wolf_Run_Lake 0 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 

WSW1 0 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 WSW2 0 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 

WSW3n .79 0 0 0 0 0 0 .01 .11 0 000000 0 0 0 0 0 0 0 

WSW3s .01 0 0 0 0 0 0 .41 0 0 000000 0 0 0 0 0 0 0 

WSW4 0 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 WSW5 0 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 WSW6 .19 0 0 0 0 0000 0 000000 0 0 0 0 0 0 0 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-6. Runoff Fractions (Cont.)a

Receiving parcel 

Sending 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 

E1 0 0 0 0 .01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 E2 .10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Farm_NNW_Tilled 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Farm_NNW_Untilled 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Farm_SE_Tilled 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Farm_SE_Untilled 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Farm_WSW_Tilled 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Farm_WSW_Untilled 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Goodfellows_Park_Lake 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Mountwood_Park_Lake 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

N1 0 0 0 0 .02 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 N2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

N3e 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

N3w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .10 0 

N4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

NE1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

NNW1 0 0 0 0 .03 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

NNW2e 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

NNW2w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

NNW3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NNW4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

NW1 0 0 0 0 .03 0 0 0 0 0 0 0 0 0 0 .52 0 0 0 0 0 0 0 NW2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .43 .01 0 0 0 0 0 0 NW3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .02 .02 .10 0 0 

S1 0 0 0 0 0 .45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S2 .05 0 0 0 0 .02 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S3 0 0 0 0 0 0 0 .22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

SE1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SE2 .07 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SE3 0 .15 .06 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

SE4n .45 0 0 .49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

SE4s .11 0 0 .79 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

SE5 0 .07 .03 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Source 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-6. Runoff Fractions (Cont.)a

Receiving parcel 

Sending 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 

SW1 0 0 0 0 .01 0 .22 .08 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .09 SW2 0 0 0 0 0 .05 0 .10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SW3 0 0 0 0 0 0 .02 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Veto_Lake 0 0 0 0 0 0 0 0 1.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

W1 0 0 0 0 0 0 0 0 0 0 .77 0 0 0 0 .03 0 0 0 0 0 0 .06 W2 0 0 0 0 0 0 0 0 0 .35 0 .04 .02 0 0 0 0 0 .10 0 0 0 0 

W3n 0 0 0 0 0 0 0 0 .22 0 0 0 .01 .01 0 0 0 0 0 .08 0 0 0 

W3s 0 0 0 0 0 0 0 0 .07 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

W4 0 0 0 0 0 0 0 0 0 0 0 .06 .02 0 .19 0 0 0 0 0 .01 0 0 W5 0 0 0 0 0 0 0 0 0 0 0 0 0 .31 0 0 0 0 0 0 .18 0 0 

WNW1 0 0 0 0 0 0 0 0 0 .73 0 0 0 0 0 0 .17 .03 0 0 0 0 0 

WNW2n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .06 0 .07 .04 0 0 0 0 

WNW2s 0 0 0 0 0 0 0 0 0 .25 0 0 0 0 0 .04 .12 0 .01 0 0 0 0 

WNW3 0 0 0 0 0 0 0 0 0 0 .59 0 0 0 0 0 .07 .02 0 .05 0 0 0 WNW4 0 0 0 0 0 0 0 0 0 0 0 .28 0 0 0 0 0 0 .12 0 .32 0 0 WNW5 0 0 0 0 0 0 0 0 0 0 0 0 0 .24 .17 0 0 0 0 .22 0 0 0 

Wolf_Run_Lake 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.00 0 

WSW1 0 0 0 0 .02 .42 0 0 0 .20 0 0 0 0 0 0 0 0 0 0 0 0 0 WSW2 0 0 0 0 0 .31 0 0 0 .17 0 0 0 0 0 0 0 0 0 0 0 0 .05 

WSW3n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

WSW3s 0 0 0 0 0 .10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

WSW4 0 0 0 0 0 0 .39 0 0 0 .26 0 0 0 0 0 0 0 0 0 0 0 0 WSW5 0 0 0 0 0 0 .54 0 0 0 0 0 .25 0 0 0 0 0 0 0 0 0 0 WSW6 0 0 0 0 0 0 .02 .42 0 0 0 0 0 .15 .06 0 0 0 0 0 0 0 0 

aAll values shown were refined for this site-specific assessment (versus the values used in the Tiers 1 and 2 screening assessments).
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-7. USLE Erosion Parameters*

Soil Parcel 

Area 

Rainfall/

Erosivity 

Indexa

Soil 

Erodibility 

Indexa

LengthSlope 

Factora

Cover 

Mgmt 

Factora

Supporting 

Practices 

Factora

Unit Soil Lossb

m2

R 

(100 ftton/ac) 

K 

(ton/ac/(100 

ft-ton/acre)) 

LS 

(USCS) 

C 

(USCS) P 

A 

(ton/ac/yr

) 

A 

(kg/m2/d) 

E1 1,312,959 125 0.43 --- 0.003 1 0.115e 0.0000706 E2 76,917,882 125 0.43 --- 0.001 1 0.25e 0.0001535 Farm_NNW_Tilled 24,200 125 0.32 5.4 0.11 1 23.76 0.0145926 Farm_NNW_Untilled 24,200 125 0.32 5.4 0.11 1 23.76 0.0145926 Farm_SE_Tilled 152,680 125 0.43 --- 0.11 1 25e 0.0153541 Farm_SE_Untilled 152,682 125 0.43 --- 0.11 1 25e 0.0153541 Farm_WSW_Tilled 49,600 125 0.32 5.4 0.11 1 23.76 0.0145926 Farm_WSW_Untilled 49,600 125 0.32 5.4 0.11 1 23.76 0.0145926 N1 1,622,459 125 0.37 3.39 0.001 1 0.156788 0.0000963 N2 371,817,129 125 0.37 5.4 0.001 1 0.24975 0.0001534 N3e 36,817,614 125 0.17 5.4 0.001 1 0.11475 0.0000705 N3w 7,353,631 125 0.17 5.4 0.001 1 0.11475 0.0000705 N4 92,484,526 125 0.17 5.4 0.001 1 0.11475 0.0000705 NE1 216,092,437 125 0.37 3.39 0.001 1 0.156788 0.0000963 NNW1 1,195,820 125 0.32 5.4 0.001 1 0.216 0.0001327 NNW2e 340,031 125 0.32 5.4 0.001 1 0.216 0.0001327 NNW2w 38,196 125 0.32 5.4 0.001 1 0.216 0.0001327 NNW3 9,611,600 125 0.037 2.2 0.11 1 1.11925 0.0006874 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-7. USLE Erosion Parameters (Cont.)*

Soil Parcel 

Area 

Rainfall/

Erosivity 

Indexa

Soil 

Erodibility 

Indexa

LengthSlope 

Factora

Cover 

Mgmt 

Factora

Supporting 

Practices 

Factora

Unit Soil Lossb

m2

R 

(100 ftton/ac) 

K 

(ton/ac/(100 

ft-ton/acre)) 

LS 

(USCS) 

C 

(USCS) P 

A 

(ton/ac/yr

) 

A 

(kg/m2/d) 

NNW4 165,843,092 125 0.032 5.4 0.001 1 0.0216 0.0000133 NW1 3,049,789 125 0.037 2.2 0.001 1 0.010175 0.0000062 NW2 7,387,449 125 0.037 2.2 0.001 1 0.010175 0.0000062 NW3 126,334,123 125 0.037 2.2 0.001 1 0.010175 0.0000062 S1 1,768,932 125 0.43 --- 0.001 1 0.25e 0.0001535 S2 142,826,053 125 0.43 --- 0.001 1 0.25e 0.0001535 S3 28,312,955 125 0.43 --- 0.003 1 0.115e 0.0000706 SE1 393,244 125 0.43 --- 0.001 1 0.25e 0.0001535 SE2 7,835,857 125 0.43 --- 0.001 1 0.25e 0.0001535 SE3 84,872,112 125 0.43 --- 0.001 1 0.25e 0.0001535 SE4n 4,347,596 125 0.43 --- 0.001 1 0.25e 0.0001535 SE4s 2,097,360 125 0.43 --- 0.001 1 0.25e 0.0001535 SE5 45,631,552 125 0.43 --- 0.001 1 0.25e 0.0001535 Source 714,066 125 0.39 1.5 0.2 1 35.1 0.02156 SW1 10,272,513 125 0.37 3.32 0.001 1 0.15355 0.0000943 SW2 32,293,781 125 0.37 3.32 0.003 1 0.46065 0.0002829 SW3 55,275,322 125 0.37 3.32 0.001 1 0.15355 0.0000943 W1 5,516,937 125 0.32 5.4 0.001 1 0.216 0.0001327 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-7. USLE Erosion Parameters (Cont.)*

Soil Parcel 

Area 

Rainfall/

Erosivity 

Indexa

Soil 

Erodibility 

Indexa

LengthSlope 

Factora

Cover 

Mgmt 

Factora

Supporting 

Practices 

Factora

Unit Soil Lossb

m2

R 

(100 ftton/ac) 

K 

(ton/ac/(100 

ft-ton/acre)) 

LS 

(USCS) 

C 

(USCS) P 

A 

(ton/ac/yr

) 

A 

(kg/m2/d) 

W2 14,501,479 125 0.37 2.2 0.001 1 0.10175 0.0000625 W3n 5,333,202 125 0.37 2.2 0.001 1 0.10175 0.0000625 W3s 2,168,442 125 0.37 2.2 0.001 1 0.10175 0.0000625 W4 27,245,681 125 0.37 2.2 0.001 1 0.10175 0.0000625 W5 34,264,723 125 0.37 2.2 0.001 1 0.10175 0.0000625 WNW1 4,208,581 125 0.37 2.2 0.003 1 0.30525 0.0001875 WNW2n 235,254 125 0.37 2.2 0.001 1 0.10175 0.0000625 WNW2s 991,985 125 0.37 2.2 0.001 1 0.10175 0.0000625 WNW3 14,464,874 125 0.37 2.2 0.003 1 0.30525 0.0001875 WNW4 8,391,775 125 0.37 2.2 0.001 1 0.10175 0.0000625 WNW5 52,557,694 125 0.37 2.2 0.003 1 0.30525 0.0001875 WSW1 1,162,068 125 0.32 5.4 0.001 1 0.216 0.0001327 WSW2 3,051,907 125 0.32 5.4 0.003 1 0.648 0.0003980 WSW3n 459,880 125 0.32 5.4 0.001 1 0.216 0.0001327 WSW3s 707,648 125 0.32 5.4 0.001 1 0.216 0.0001327 WSW4 14,537,125 125 0.37 2.2 0.003 1 0.30525 0.0001875 WSW5 7,870,758 125 0.37 2.2 0.001 1 0.10175 0.0000625 WSW6 48,298,321 125 0.37 2.2 0.001 1 0.10175 0.0000625 

*All values shown were refined for this site-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 time conversions, unless otherwise noted. cCalculated using SD = a * (AL)-b; where a is the empirical intercept coefficient (based on the size of the watershed), AL is the total watershed area receiv

empirical 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 Fork in the absence of LS data in USDA Web Soil Survey Data 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

A-16 

Table A-8. Terrestrial Plant Placement

Surface Soil Volume Element Grasses/ Herbs Deciduous Forest None 

E1 x E2 x Farm_NNW_Tilled x Farm_NNW_Untilled x Farm_SE_Tilled x Farm_SE_Untilled x Farm_WSW_Tilled x Farm_WSW_Untilled x N1 x N2 x N3e x N3w x N4 x NE1 x NNW1 x NNW2e x NNW2w x NNW3 x NNW4 x NW1 x NW2 x NW3 x S1 x S2 x S3 x SE1 x SE2 x SE3 x SE4n x 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

A-17 

Table A-8. Terrestrial Plant Placement (Cont.)

Surface Soil Volume Element Grasses/ Herbs Deciduous Forest None 

SE4s x SE5 x Source x SW1 x SW2 x SW3 x W1 x W2 x W3n x W3s x W4 x W5 x WNW1 x WNW2n x WNW2s x WNW3 x WNW4 x WNW5 x WSW1 x WSW2 x WSW3n x WSW3s x WSW4 x WSW5 x WSW6 x 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

A-18 

Table A-9. Terrestrial Plant Parameters*

Parameter Name Units Grass/Herbs1 Deciduous Forest Refer

Leaf Compartment TypeAllow exchange 1=yes, 0=no seasonal2 seasonal2 - Average leaf area index m2[leaf]/ m2[area] 5a 3.4b

aMid-range of 4

fields, Scurlock 

bCDIAC 2010 (

Forest, dom. remaple). 

Calculate wet dep interception fraction (Boolean) 1=yes, 0=no 0 0 Professional juCorrection exponent, octanal to lipid unitless 0.76 0.76 Trapp 1995 (froDegree stomatal opening unitless 1 1 

Set to 1 for dayprofessional jud(stomatal diffusoff at night usinproperty, IsDay

Density of wet leaf kg/m3 820 820 Paterson et al. Leaf wetting factor m 3.00E-04 3.00E-04 

Muller and Proto 6E-04 for difand elements). 

Length of leaf m 0.05 0.1 Professional juLipid content kg/kg wet weight 0.00224 0.00224 Riederer 1995 

beech). 

Litterfall rate 1/day seasonal3 seasonal3 - 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

A-19 

Table A-9. Terrestrial Plant Parameters (Cont.)*

Parameter Name Units Grass/Herbs1 Deciduous Forest 1 Refer

Leaf Compartment TypeStomatal area normalized effective diffusion path length 1/m 200 200 Wilmer and FrVegetation attenuation factor m2/kg 2.9 2.9 Baes et al. 198

(Grass/hay). 

Water content unitless 0.8 0.8 Paterson et alWet dep interception fraction unitless Calculated within TRIM.FaTE Calculated within TRIM.FaTE Calculated bas

meteorology dwithin TRIM.Fa

Wet mass of leaf per soil area kg[fresh leaf]/m2[area] 0.6a 0.6b

aCalculated fro

index and Leit

bSimonich and 

(Calculated froindex, leaf thicdensity of wet 

Particle on Leaf Compartment TypeAllow exchange 1=yes, 0=no seasonal2 seasonal2 - Volume particle per area leaf m3[leaf particles]/m2[leaf] 1.00E-09 1.00E-09 

Coe and Lindb(based on partand size distribatmospheric pmeasured on asurface). 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-9. Terrestrial Plant Parameters (Cont.)*

Parameter Name Units Grass/Herbs1 Deciduous Forest1 Refer

Root Compartment Type – Nonwoody OnlyAllow exchange 1=yes, 0=no seasonal2 seasonal2 - Correction exponent, octanol to lipid unitless 0.76 - Trapp 1995. Lipid content of root kg/kg wet weight 0.011 - Calculated. Water content of root kg/kg wet weight 0.8 - Professional judWet density of root kg/m3 820 - Paterson et al. 

(soybean). 

Wet mass per soil area kg/m2 1.4 - Jackson et al. 1

(temperate gras

Stem Compartment Type – Nonwoody OnlyAllow exchange 1=yes, 0=no seasonal2 seasonal2 - Correction exponent, octanol to lipid unitless 0.76 - Trapp 1995. Density of phloem fluid kg/m3 1,000 - Professional judDensity of xylem fluid kg/cm3 900 - Professional judFlow rate of transpired water per leaf area 

m3[water]/ 

m2[leaf] 0.0048 

- 

Crank et al. 198

Fraction of transpiration flow rate that is phloem rate unitless 0.05 - Paterson et al. Lipid content of stem kg/kg wet weight 0.00224 - Riederer 1995 (

beech). 

Water content of stem unitless 0.8 - Paterson et al. Wet density of stem kg/m3 830 - Professional judWet mass per soil area kg/m2 0.24a

- 

aCalculated from 

biomass density professional jud

*

Shaded values indicate where refined values were developed for this site-specific assessment. All other values (unshaded) were unchanged 

and 2 screening assessments. 1See separate table for assignment of plant types to surface soil compartments. 2Begins April 10 (set to 1), ends after October 1 (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 

of allow-exchange). 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-10. Surface Water Parameters* 

Parameter Name Units 

Value 

Goodfellows 

Park Lake 

Mountwood Park 

Lake Veto Lake Wolf Run Lake 

Algal carbon content (fraction) unitless 0.465 0.465 0.465 0.465 Algal density in water column 

g[algae]/L 

[water] 0.0025 0.0025 0.0025 0.0025 

Algal growth rate 1/day 0.7 0.7 0.7 0.7 Algal radius μm 2.5 2.5 2.5 2.5 Algal water content (fraction) unitless 0.9 0.9 0.9 0.9 Average algal cell density (per vol cell, not water) 

g[algae]/m3

[algae] 1,000,000 1,000,000 1,000,000 1,000,000 

Boundary layer thickness above sediment 

m 0.02 0.02 0.02 0.02 

Chloride concentration mg/L 13 12 13 11 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-10. Surface Water Parameters (Cont.)* 

Parameter Name Units 

Value 

Goodfellows Park 

Lake 

Mountwood Park 

Lake Veto Lake Wolf Run Lake 

Chlorophyll concentration mg/L 0.0197 0.0075944 0.0197 0.0017 U

1

Depth m 2.23a 4.57b 2.23a 10.5c 

a

uG

bc

Dimensionless viscous sublayer thickness unitless 4 4 4 4 ADrag coefficient for water body unitless 0.0011 0.0011 0.0011 0.0011 AFlush rate 1/year 4.87 9.358976 19.022 0.595238 CFraction sand unitless 0.25 0.25 0.25 0.25 POrganic carbon fraction in suspended sediments 

unitless 0.02 0.02 0.02 0.02 P
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

A-23 

Table A-10. Surface Water Parameters (Cont.)* 

Parameter Name Units 

Value 

Goodfellows Park 

Lake 

Mountwood Park 

Lake Veto Lake Wolf Run Lake 

pH unitless 7.33a 7.3b 7.33a 7.85a

a

D

b

ju

Suspended sediment deposition velocity m/day 2 2 2 2 UTotal suspended sediment concentration 

kg[sedime

nt]/ 

m3[water 

column] 

0.042a 0.05b 0.042a 0.001c

a

U

b

c

D

Water temperature degrees K 291.85a 298b 291.85a 288.05c

ab

c

*

Shaded values indicate where refined values were developed for this site-specific assessment. All other values (unshaded) were unchanged from 

assessments. 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

A-24 

Table A-11. Sediment Parameters*

 Parameter Name Units 

Value 

Goodfellows 

Park Lake 

Mountwood 

Park Lake Veto Lake Wolf Run Lake 

Depth1 m 0.05a 1.09728b 0.05a 0.05a

a

(

b

Fraction Sand unitless 0.25 0.25 0.25 0.25 P

j

Organic carbon fraction unitless 0.02 0.02 0.02 0.02 P

j

Porosity of the sediment zone 

volume[total pore 

space]/volume[se

diment 

compartment] 

0.6 0.6 0.6 0.6 U

Solid material density in sediment 

kg[sediment]/m3[s

ediment] 2600 2600 2600 2600 M

(

pH unitless 7.33 7.2 7.33 7.85 S

w

Sediment resuspension velocity m/day 8.28388E-05 9.6426E-05 8.2424E-05 1.9265E-06 C

b

*

Shaded values indicate where refined values were developed for this site-specific assessment. All other values (unshaded) were unchanged from the T

assessments.

1Set using the volume element properties named "top" and "bottom." 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

A-25 

Table A-12. Aquatic Animals Food Chain, Density, and Mass* 

Aquatic Biota 

(Consuming 

Organism) 

Fraction Diet 

Biomass

(kg/m2) Body Weight (kg) 

Algae 

Zooplankton 

Benthic 

Invertebrate 

Water 

Column 

Herbivore 

Benthic 

Omnivore 

Water 

Column 

Omnivore 

Benthic 

Carnivore 

Water 

Column 

Carnivore 

Benthic invertebrate 0% 0% 0% 0% 0% 0% 0% 0% 0.020 2.55E-04 ProWater column herbivore 0% 100% 0% 0% 0% 0% 0% 0% 0.002 0.025 ProBenthic omnivore 0% 0% 100% 0% 0% 0% 0% 0% 0.002 0.25 ProWater column omnivore 0% 0% 0% 100% 0% 0% 0% 0% 0. 0005 0.25 ProBenthic carnivore 0% 0% 50% 0% 50% 0% 0% 0% 0.001 2.0 ProWater column carnivore 0% 0% 0% 0% 0% 100% 0% 0% 0.0002 2.0 ProZooplankton 100% 0% 0% 0% 0% 0% 0% 0% 0.0064 5.70E-08 Pro*

All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used). 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-13. Cadmium Chemical-Specific Parameters*

Parameter Namea Units Value Reference 

CAS numberb unitless 7440-43-9 - Diffusion coefficient in pure air m2[air]/day 0.71 U.S. EPA 1999 (Table A-2Diffusion coefficient in pure water m2[water]/day 8.16E-05 U.S. EPA 1999 (Table A-2Henry's Law constant Pa-m3/mol 1.00E-37 U.S. EPA 1999 (Table A-2

to be zero). 

Melting point degrees K 594 ATSDR 1999. Molecular weight g/mol 112.41 ATSDR 1999. Octanol-air partition coefficient (Koa) m3[air]/m3[octanol] - - Octanol-carbon partition coefficient (Koc) m3[carbon]/m3[octanol] - - Octanol-water partition coefficient (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 Assessment for the Ferroalloy-production Source Categor

A-27 

Table A-14. Mercury Chemical-Specific Parameters*

Parameter Namea Units Value R

Hg(0)b Hg(2)b MHgb

CAS number unitless 7439-97-6 14302-87-5 22967-92-6 Diffusion coefficient in pure air m2[air]/day 0.478 0.478 0.456 U.S. EPDiffusion coefficient in pure water m2[water]/day 5.54E-05 5.54E-05 5.28E-05 U.S. EPHenry's Law constant Pa-m3/mol 719 7.19E-05 0.0477 U.S. EPMelting point degrees K 234 5.50E+02 443 CARB 1Molecular weight g/mol 201 201 216 U.S. EPOctanol-water partition coefficient (Kow) L[water]/kg[octanol] 4.15 3.33 1.7 Mason Vapor washout ratio m3[air]/m3[rain] 1,200 1.6E+06 0 

U.S. EPbased oal. 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 Assessment for the Ferroalloy-production Source Categor

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Table A-15. PAH Chemical-Specific Parameters*

Parameter 

Namea Units Value 2Methyl Acenaphthene Acenaphthylene BaA BaP BbF 

CAS number unitless 91-57-6 83-32-9 208-96-8 56-55-3 50-32-8 205-99Diffusion coefficient in pure air 

m2/day 0.451 0.009 0.388 0.441 0.372 0.00

Diffusion coefficient in pure water 

m2/day 6.70E-05 8.64E-05 6.03E-05 7.78E-05 7.78E-05 8.64E

Henry's Law constant Pa-m3/mol 50.56 18.50 12.70 1.22 0.07 0.05 Melting point degrees K 307.75 366.15 365.65 433 452 441 Molecular weight g/mol 142.20 154.21 152.20 228.29 252.32 252.3Octanol-water partition coefficient (Kow) 

L[water]/L[

octanol] 7.24E+03 8.32E+03 1.00E+04 6.17E+05 9.33E+05 6.03E+
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-15. PAH Chemical-Specific Parameters (Cont.)* 

Parameter 

Namea Units Value BkF Chr DahA Fluoranthene Fluoren

CAS number unitless 207-08-9 218-01-9 53-70-3 206-44-0 86-73-Diffusion coefficient in pure air 

m2/day 0.009 0.009 0.009 0.009 0.009 

Diffusion coefficient in pure water 

m2/day 8.64E-05 8.64E-05 8.64E-05 8.64E-05 8.64E-0

Henry's Law constant Pa-m3/mol 0.04 0.53 0.01 1.96 9.81 Melting point degrees K 490 531 539 383.15 383.15 Molecular weight g/mol 252.32 228.29 278.33 202.26 166.20 Octanol-water partition coefficient (Kow) 

L[water]/L 

[octanol] 8.71E+05 5.37E+05 3.16E+06 1.45E+05 1.51E+
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Table A-15. PAH Chemical-Specific Parameters (Cont.)* 

Parameter 

Namea Units Reference 

CAS number unitless - Diffusion coefficient in pure air 

m2/day U.S. EPA 2005b. Exceptions include Siemens 2007 (2-Methylnaphthalene, Acenaphthylene

Benzo(g,h,i)perylene). 

Diffusion coefficient in pure water 

m2/day U.S. EPA 2005b. Exceptions include Siemens 2007 (2-Methylnaphthalene, Acenaphthylene

Benzo(g,h,i)perylene). 

Henry's Law constant Pa-m3/mol U.S. EPA 2005b. Exceptions include U.S. EPA 2003 (2-Methylnaphthalene), HSDB 2001a (

HSDB 2001b (Benzo(g,h,i)perylene). 

Melting point degrees K Budavari 1996. Exceptions include U.S. EPA 2003 (2-Methylnaphthalene), HSDB 2001a (Ac

2001b (Benzo(g,h,i)perylene), and U.S. EPA 2005b (Acenaphthene, Fluoranthene, and Fluo

Molecular weight g/mol Budavari 1996. Exceptions include U.S. EPA 2003 (2-Methylnaphthalene), HSDB 2001a (Ac

2001b (Benzo(g,h,i)perylene), and U.S. EPA 2005b (Acenaphthene, Fluoranthene, and Fluo

Octanol-water partition coefficient (Kow) 

L[water]/L[

octanol] 

Hansch et al. 1995. Exceptions include Passivirta et al. 1999 (Acenaphthylene, Benzo(k)fluo

Indeno(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. 
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Table A-16. Dioxin Chemical-Specific Parameters*

Parameter 

Name1 Units 

Value 

1,2,3,4,6,7,8

-HpCDD 

1,2,3,4,7,8-

HxCDF 

1,2,3,7,8-

PeCDF 

2,3,7,8-

TCDD 

1,2,3,4,6,7,

8,9-OCDD 

1,2,3,4,6

OCD

CAS number unitless 35822-46-9 70648-26-9 57117-41-6 1746-01-6 3268-87-9 39001-Diffusion coefficient in pure air 

m2/day 0.782 0.183 0.192 0.899 0.0883 0.12

Diffusion coefficient in pure water 

m2/day 6.91E-05 6.91E-05 6.91E-05 4.84E-05 3.08E-06 3.15E

Henry's Law constant Pa-m3/mol 1.22 1.45 0.507 3.33 0.68 0.19 Melting point degrees K 538a 499a 499b 578a 603 259 Molecular weight g/mol 425.2a 374.87a 340.42b 322a 460.0 443.7Octanol-water partition coefficient (Kow) 

L[water]/ 

L[octanol] 1.00E+08 1.00E+07 6.17E+06 6.31E+06 1.58E+08 1.00E+
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Table A-16. Dioxin Chemical-Specific Parameters (Cont.)*

Parameter 

Name1 Units 

Value 

1,2,3,6,7,8-

HxCDD 

1,2,3,6,7,8-

HxCDF 

1,2,3,7,8,9-

HxCDD 

1,2,3,7,8,9-

HxCDF 

2,3,4,6,7

HxCD

CAS number unitless 57653-85-7 57117-44-9 19408-74-3 72918-21-9 60851-3Diffusion coefficient in pure air 

m2/day 0.0958 0.135 0.0958 0.135 0.135 

Diffusion coefficient in pure water 

m2/day 3.43E-05 3.53E-05 3.43E-05 3.53E-05 3.53E-0

Henry's Law constant Pa-m3/mol 1.08 0.74 1.08 0.74 0.74 Melting point degrees K 558.0 506.0 517.0 509.0 512.5 Molecular weight g/mol 390.84 374.9 390.8 374.9 374.9 Octanol-water partition coefficient (Kow) 

L[water]/L[octanol

] 1.62E+08 8.24E+07 1.62E+08 3.80E+07 8.31E+0
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Table A-16. Dioxin Chemical-Specific Parameters (Cont.)*

Parameter 

Name1 Units 

Value 

Ref

2,3,4,7,8-PeCDF 1,2,3,4,7,8,9 -HpCDF 1,2,3,4,7,8- HxCDD 2,3,7,8- TCDF 

CAS number unitless 57117-31-4 55673-89-7 39227-28-6 51207-31-9 - Diffusion coefficient in pure air 

m2/day 0.142 0.129 0.0958 0.149 U.S. EPA 2005b. 

Diffusion coefficient in pure water 

m2/day 3.76E-05 3.33E-05 3.43E-05 4.04E-05 U.S. EPA 2005b. 

Henry's Law constant Pa-m3/mol 0.5 1.43 1.08 1.46 U.S. EPA 2005. Melting point degrees K 469.3 222 546 500.0

aMackay et al. 200

bATSDR 1998. 

Molecular weight g/mol 340.4 409.31 391.0 306.0

aMackay et al. 200

bATSDR 1998. 

Octanol-water partition coefficient (Kow) 

L[water]/L[octanol

] 3.16E+06 7.94E+06 6.31E+07 1.26E+06 Mackay et al. 199

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.
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Table A-17. Cadmium Chemical-Specific Parameters for Abiotic Compartments*

Parameter Name Units Value Reference 

Air Compartment Type Particle dry deposition velocity m/day 260 Calculated from Muhlbai

and Tisue 1981. 

Washout ratio m3[air]/m3[rain] 200,000 MacKay et al. 1986. Surface Soil Compartment Type Use input characteristic depth (Boolean) 0 = no, Else = yes 0 Professional judgment. Root Zone Soil Compartment Type Use input characteristic depth (Boolean) 0 = no, Else = yes 0 Professional judgment. Surface Water Compartment Type Ratio of concentration in water to concentration in algae to concentration dissolved in water 

L[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). 
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Table A-18. Mercury Chemical-Specific Parameters for Abiotic Compartments

Parameter Name Units Value Referen

Hg(0) Hg(2) MHg 

Air Compartment Type Particle dry deposition velocity m/day 500 500 500 McKone et al. 2001 (CalTOX Demethylation rate 1/day N/A N/A 0 Professional judgment. Methylation rate 1/day 0 0 0 Professional judgment. Oxidation rate 1/day 0.00385 0 0 U.S. EPA 1997 (low end of h

months to 2 years)). 

Reduction rate 1/day 0 0 0 Professional judgment. Washout ratio m3[air]/m3[rain] 200,000 200,000 200,000 Professional judgment. Surface Soil Compartment Type Use input characteristic depth (Boolean) 

0 = no, Else = 

yes 

0 0 0 Professional judgment. 

Soil-water partition coefficient 

L[water]/kg[soil 

wet wt] 1,000 58,000 7,000 U.S. EPA 1997. 

Vapor dry deposition velocity m/day 50a 2500b 0 

aLindberg et al. 1992 . bEstimate by U.S. EPA using 

Complex (ISC) Model [See VMercury Study Report (U.S. 

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari, P
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-18. Mercury Chemical-Specific Parameters for Abiotic Compartments (Cont.)

Parameter Name Units Value Referen

Hg(0) Hg(2) MHg 

Surface Soil Compartment Type Methylation 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 Type Use input characteristic depth (Boolean) 

0 = no, Else = 

yes 

0 0 0 Professional judgment. 

Soil-water partition coefficient 

L[water]/kg[soil 

wet wt] 1,000 58,000 7,000 U.S. EPA 1997. 

Demethylation rate 1/day N/A N/A 0.06 

Range reported in Porvari anto 6E-2 /day; value is averagdemethylation rate constant conditions. 

Methylation rate 1/day 0 0.001 0 

Range reported in Porvari anto 1E-3 /day; value is averagmethylation rate constant unconditions. 

Oxidation rate 1/day 0 0 0 U.S. EPA 1997. Reduction rate 1/day 0 3.25E-06 0 U.S. EPA 2005a 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-18. Mercury Chemical-Specific Parameters for Abiotic Compartments (Cont.)

Parameter Name Units Value Referen

Hg(0) Hg(2) MHg 

Surface Water Compartment Type Algal surface area-specific uptake rate constant 

nmol/[μm2-daynmol] 0 2.04E-10 3.60E-10 Assumes radius = 2.5mm, M

Mason et al. 1996; Hg(0) ass

Dow ("overall Kow") L[water]/kg[octa nol] 0 -1 -2 Mason et al. 1996. Solids-water partition coefficient 

L[water]/kg[solid

s wet wt] 1,000 100,000 100,000 U.S. EPA 1997. 

Vapor dry deposition velocity m/day N/A 2500 U.S. EPA 1997 (Vol. III, AppDemethylation rate 1/day N/A N/A 0.013 Gilmour and Henry 1991 (av

to 2.5E-2/day). 

Methylation rate 1/day 0 0.001 0 U.S. EPA 1997; Gilmour and 

from 1E-4 to 3E-4/day). 

Oxidation rate 1/day 0 0 0 Professional judgment. Reduction rate 1/day 0 0.0075 0 

U.S. EPA 1997; reported valthan 5E-3/day for depths gre3.5/day (Xiao et al. 1995; VaMason et al. 1995a; Amyot e

Sediment Compartment TypeSolids-water partition coefficient 

L[water]/kg[solid

s wet wt] 3,000 50,000 3,000 U.S. EPA 1997. 

Demethylation rate 1/day N/A N/A 0.0501 Gilmour and Henry 1991 (av

to 1E-1/day). 

Methylation rate 1/day 0 1.0E-4 0 U.S. EPA 1997; Gilmour and 

from 1E-5 to 1E-3/day). 

Oxidation rate 1/day 0 0 0 Professional judgment. Reduction rate 1/day 0 1.00E-06 0 

U.S. EPA 1997; Vandal et albased on presence of Hg(0) porewater). 

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 0.075 to 1.7 depending on pH and chloride concentration. 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-19. PAH Chemical-Specific Parameters for Abiotic Compartments*

Parameter Name Units 

Values 

2Methyl Acenaphthene Acenaphthylene BaA BaP BbF 

Air Compartment Type Particle dry deposition velocity m/day 500 500 500 500 500 500 Half-life day 0.154 0.3 0.208 0.125 0.046 0.596 Washout ratio m3[air]/m3 [rain] 200000 200000 200000 200000 200000 20000Surface Soil Compartment Type User input characteristic depth (Boolean) 

0 = No, 

Else = Yes 0 0 0 0 0 0 

Half-life day 18 56 66.5 680 530 610 Root Zone Soil Compartment Type User input characteristic depth (Boolean) 

0 = No, 

Else = Yes 0 0 0 0 0 0 

Half-life day 18 56 66.5 680 530 610 Surface Water Compartment Type Ratio of concentration in algae to concentration dissolved in water 

(g[chem]/kg

[algae]) / 

(g[chem]/ 

L[water]) 

2.6 3 3.7 325 510 317 

Half-life day 78 25 184 0.375 0.138 90 Sediment Compartment Type Half-life day 2290 2290 2290 2290 2290 2290 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-19. PAH Chemical-Specific Parameters for Abiotic Compartments (Cont.)* 

Parameter Name Units 

Value 

Referen

Chr DahA Fluoranthene Fluorene IcdP 

Air Compartment Type Particle dry deposition velocity m/day 500 500 500 500 500 McKone et al. 2001. Half-life day 0.334 0.178 0.46 0.46 0.262 

Howard et al. 1991 / upper estimated value. Exception2005 (2-Methylnaphthalene(Benzo(g,h,i)perylene, and average of range, HSDB 20HSDB 2001a (Acenaphthyl2000 (Fluorene). 

Washout ratio m3[air]/m3 [rain] 20000 0 200000 200000 200000 200000 Mackay et al. 1986. Surface Soil Compartment Type User input characteristic depth (Boolean) 

0 = No, 

Else = Yes 0 0 0 0 0 Professional judgment. 

Half-life day 1000 940 275 33 730 

MacKay et al. 2000 / averaExceptions include ATSDR Methylnaphthalene), U.S. E(Benzo(g,h,i)perylene, and average of range, HSDB 20HSDB 2001a (Acenaphthyl2001d (Fluorene). 

Root Zone Soil Compartment Type User input characteristic depth (Boolean) 

0 = No, 

Else = Yes 0 0 0 0 0 Professional judgment. 
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Table A-19. PAH Chemical-Specific Parameters for Abiotic Compartments (Cont.)*

Parameter Name Units 

Value 

Referen

Chr DahA Fluoranthene Fluorene IcdP 

Root Zone Soil Compartment Type Half-life day 1000 940 275 33 730

Howard et al. 1991 / upper estimated value. Exception2005 (2-Methylnaphthalene(Benzo(g,h,i)perylene, and average of range, HSDB 20HSDB 2001a (Acenaphthyl2001d (Fluorene).

Surface Water Compartment Type Ratio of concentration in algae to concentration dissolved in water 

(g[chem]/k

g[algae]) / 

(g[chem]/ 

L[water]) 

280 1388 67.4 5.8 1653 Kow from Del Vento and Da

Half-life day 1.626 97.8 160 8.5 750 

Howard et al. 1991 / upper estimated value. Exception(2-Methylnaphthalene), HS(Acenaphthene), HSDB 20(Acenaphthylene), and HSD(Benzo(g,h,i)perylene), Mo(Fluoranthene), and Boyle 

Sediment Compartment Type Half-life day 2290 2290 2290 2290 2290 

Mackay et al. 1992 (PAH vhalf-life of the log class thaassigned for sediment, excwhich were not on Table 2.

*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used). 
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Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments*

Parameter Name Units 

Value 

1,2,3,4,6,7,

8,9-OCDD 

1,2,3,4,6,

7,8,9-

OCDF 

1,2,3,4,6,7,8

-HpCDD 

1,2,3,4,6,7,8

-HpCDF 

1,2,3

9-Hp

Air Compartment Type Deposition velocity m/day 500 500 500 500 5Half-life day 162 321 64 137 1Washout ratio m3[air]/m3[rain] 91000 22000 64000 32000 32Surface Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0Use input characteristic depth (Boolean) 0 = No, Else = Yes 0 0 0 0 Half-life day 3650 3650 3650 3650 36Root Zone Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 Half-life day 3650 3650 3650 3650 36Half-life day 1008 1008 1008 1008 10Surface Water Compartment Type Ratio of concentration in algae to concentration dissolved in water 

(g[chem]/g 

[algae])/(g[chem]/L[wate

r]) 

5.31 4.54 4.54 2.83 1

Half-life day 0.67 0.58 47 0.58 0Sediment Compartment Type Half-life day 1095 1095 1095 1095 10
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments (Cont.)*

Parameter Name Units 

Value 

1,2,3,4,7,8-

HxCDF 

1,2,3,6,7,8

-HxCDD 

1,2,3,6,7,8-

HxCDF 

1,2,3,7,8,9-

HxCDD 

1,2,

Hx

Air Compartment Type Deposition Velocity m/day 500 500 500 500 Half-life day 78 28 55 28 Washout ratio m3[air]/m3[rain] 10000 9000 10000 9000 1Surface Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0Use input characteristic depth (Boolean) 0 = No, Else = Yes 0 0 0 0 Half-life day 3650 3650 3650 3650 3Root Zone Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 Half-life day 3650 3650 3650 3650 3Half-life day 1008 1008 1008 1008 1Surface Water Compartment Type Ratio of concentration in algae to concentration dissolved in water 

(g[chem]/g[algae])/(g[ch

em]/L[water]) 2.06 5.36 4.25 5.36 3

Half-life day 0.58 6.3 0.58 6.3 0Sediment Compartment Type Half-life day 1095 1095 1095 1095 1
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Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments (Cont.)*

Parameter Name Units 

Value 

1,2,3,7,8-

PeCDF 

2,3,4,6,7,8-

HxCDF 

2,3,4,7,8-

PeCDF 

2,3,7,8-

TCDD 

Air Compartment Type Deposition velocity m/day 500 500 500 500 Half-life day 31 59 33 12 Washout ratio m3[air]/m3[rain] 13000 10000 14000 18000 Surface Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 Use input characteristic depth (Boolean) 0 = No, Else = Yes 0 0 0 0 Half-life day 3650 3650 3650 3650 Root Zone Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 Half-life day 3650 3650 3650 3650 Surface Water Compartment Type Ratio of concentration in algae to concentration dissolved in water 

(g[chem]/g[algae])/(g[ch

em]/ 

L[water]) 

1.75 4.26 1.39 1.76 

Half-life day 0.19 0.58 0.19 2.7 Sediment Compartment Type Half-life day 1095 1095 1095 1095 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments (Cont.)*

Parameter Name References 

Air Compartment Type Deposition 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 Type Input 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 was cited by multiple authors, value is for 2,3,7,8Root Zone Soil Compartment Type Input characteristic depth Not used (model set to calculate value). Use input characteristic depth Professional judgment. Half-life Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8Half-life Average value of the range presented in Mackay et al. 2000; based on estimated unacclim

biodegradation half-life, value is for 2,3,7,8-TCDD. 

Surface Water Compartment Type Ratio of concentration in algae to concentration dissolved in water 

Estimated from Kow value using model from Del Vento and Dachs 2002. 

Half-life Kim and O’Keefe 1998 as cited in U.S. EPA 2000. Sediment Compartment Type Half-life Estimation based on Adriaens and Grbic-Galic 1992,1993 and Adriaens et al. 1995 as cite*All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used)
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-21. Cadmium Chemical-Specific Parameters for Plant Compartments*

Parameter Name Units Value Re

Leaf Compartment Type Transfer factor to leaf particle 1/day 0.002 Professional judgmParticle on Leaf Compartment Type Transfer factor to leaf 1/day 0.200 Professional judgmRoot Compartment Type - Grasses/HerbsaRoot to root soil partition - alpha of steady state unitless 0.95 Henning et al. 200Root to root soil partition - partitioning coefficient m3[bulk root soil]/m3[root] 0.23 Nriagu 1980 (base

calculated from vaspecies). 

Root to root soil partition - time to reach alpha day 28 Henning et al. 200Stem Compartment Type - Grasses/HerbsaTranspiration stream concentration factor (TSCF) m3[soil pore water]/m3[xylem fluid] 0.45 Tsiros 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 Assessment for the Ferroalloy-production Source Categor

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Table A-22. Mercury Chemical-Specific Parameters for Plant Compartments*

Parameter Name Units 

Value 

Refe

Hg(0) Hg(2) MHg 

Leaf Compartment Type Transfer factor to leaf particle 1/day 0.002 0.002 0 Professional judgmen

transfer factor from le

Demethylation rate 1/day N/A N/A 0.03 Calculated from BachMethylation rate 1/day 0 0 0 Assumed from Gay 19Oxidation rate 1/day 1.0E+06 0 0 Professional judgmen

instantaneous. 

Reduction rate 1/day 0 0 0 Professional judgmenParticle on Leaf Compartment Type Transfer factor to leaf 1/day 0.2 0.2 0.2 Professional judgmenDemethylation rate 1/day N/A N/A 0 Professional judgmenMethylation rate 1/day 0 0 0 Professional judgmenOxidation rate 1/day 0 0 0 Professional judgmenReduction rate 1/day 0 0 0 Professional judgmenRoot Compartment Type - Grasses/HerbsaAlpha for root-root zone bulk soil unitless 0.95 0.95 0.95 Selected value. Root/root-zone-soil-water partition coefficient 

m3[bulk root soil]/ 

m3[root] 0 0.18a 1.2b

aGeometric mean Leo1972, Hogg et al. 197bMHg- assumed, base

t-alpha for root-root zone bulk soil day 21 21 21 Professional judgmenDemethylation rate 1/day N/A N/A 0 Professional judgmen
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

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Table A-22. Mercury Chemical-Specific Parameters for Plant Compartments (Cont.)*

Parameter Name Units 

Value 

Refe

Hg(0) Hg(2) MHg 

Root Compartment Type - Grasses/HerbsaMethylation rate 1/day 0 0 0 Professional judgmentOxidation rate 1/day 0 0 0 Professional judgmentReduction rate 1/day 0 0 0 Professional judgmentStem Compartment Type - Grasses/HerbsaTranspiration stream concentration factor (TSCF) 

m3[soil pore water]/ 

m3[xylem fluid] 0 0.5 0.2 Bishop et al. 1998 (No

pine). 

Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache Methylation rate 1/day 0 0 0 Professional judgmentOxidation rate 1/day 0 0 0 Professional judgmentReduction rate 1/day 0 0 0 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 Assessment for the Ferroalloy-production Source Categor

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Table A-23. PAH Chemical-Specific Parameters for Plant Compartments*

Parameter 

Name Units 

Value 

2Methyl Acenaphthene Acenaphthylene BaA BaP BbF 

Leaf Compartment Type Transfer factor to leaf particle 1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1Half-life day 3.50 3.50 3.50 3.50 3.50 3.50 Particle on Leaf Compartment Type Transfer factor to leaf 1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1Half-life day 2.31 2.31 2.31 1.84 2.31 3.56 Root Compartment Type - Grasses/HerbsaHalf-life day 34.60 34.60 34.60 34.60 34.60 34.60 Root soil water interaction - alpha 

unitless 0.95 0.95 0.95 0.95 0.95 0.95 

Stem Compartment Type - Grasses/HerbsaHalf-life day 3.50 3.50 3.50 3.50 3.50 3.50 
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Table A-23. PAH Chemical-Specific Parameters for Plant Compartments (Cont.)*

Parameter 

Name Units 

Value 

Refere

Chr DahA Fluoranthene Fluorene IcdP 

Leaf Compartment Type Transfer factor to leaf particle 1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 Professional judgment. Half-life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 (as cited 

calculated from metabo

Particle on Leaf Compartment Type Transfer factor to leaf 1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 Professional judgment. Half-life day 4.12 17.80 2.31 2.31 17.80 

Calculated as 2 times thphotolysis half-life from Exceptions include valuequal to Benzo(a)pyrenMethylnaphthalene; AceAcenaphthylene; BenzoFluoranthene; and Fluo

Root Compartment Type - Grasses/HerbsaHalf-life day 34.60 34.60 34.60 34.60 34.60 Edwards 1988 (as cited 

calculated from metabo

Root soil water interaction - alpha 

unitless 0.95 0.95 0.95 0.95 0.95 Professional judgment. 

Stem Compartment Type - Grasses/HerbsaHalf-life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 (as cited 

calculated from metabo

*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. 
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Table A-24. Dioxin Chemical-Specific Parameters for Plant Compartments*

Parameter Name Units 

Value 

Reference 

All Dioxins 

Leaf Compartment Type Transfer factor to leaf particle 1/day 0.003 Calculated as 1 percent of transfer factor to leaf; higHalf-life day 70 Arjmand and Sandermann 1985, as cited in Komoba 

root cell culture metabolism test data for DDE). 

Particle on Leaf Compartment Type Transfer factor to leaf 1/day 0.3 Professional judgment based on U.S. EPA 2000 (an 

and Trapp 1995 (highly uncertain). 

Half-life day 4.4 McCrady and Maggard 1993 (photodegradation sor

sunlight; assumed 10 sunlight per day). 

Root Compartment Type - Grasses/HerbsaHalf-life day 70 Arjmand and Sandermann 1985, as cited in Komoba

root cell culture metabolism test data for DDE). 

Root soil water interaction - alpha unitless 0.95 Professional judgment. Stem Compartment Type - Grasses/HerbsaHalf-life day 70 Arjmand and Sandermann 1985, as cited in Komoba

root cell culture metabolism test data for DDE). 

a Roots 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). 
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Table A-25. Cadmium Chemical-Specific Parameters for Aquatic Species*

Parameter Name Units Value Refere

Zooplankton Compartment Type Absorption rate constant L[water]/ kg[fish wet wt]-day 1500 Goulet 2007. Assimilation efficiency from algae unitless 0.5 Goulet 2007. Elimination rate constant 1/day 0.03 Goulet 2007. Benthic Invertebrate Compartment Type Sediment partitioning - alpha of equilibrium unitless 0.95 Professional judgment. Sediment partitioning - partition coefficient kg[bulk sed/kg[invertebrate wet wt] 0.27 Professional judgment. Sediment partitioning - time to reach alpha of equilibrium day 21 Hare et al. 2001. Benthic Omnivore Compartment Type Assimilation efficiency from food unitless 0.1 Professional judgment bas

2002. 

Absorption rate constant unitless 1.23 Calculated based on body 

in Hendriks & Heikens 200

Elimination rate constant unitless 1.73E-02 Professional judgment. Benthic Carnivore Compartment Type Assimilation efficiency from food unitless 0.1 Professional judgment bas

2002. 

Absorption rate constant unitless 0.66 Calculated based on body 

in Hendriks & Heikens 200

Elimination rate constant unitless 1.68E-03 Professional judgment. Water-column Herbivore Compartment Type Assimilation efficiency from food unitless 0.1 Professional judgment bas

2002. 
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Table A-25. Cadmium Chemical-Specific Parameters for Aquatic Species (Cont.)A 

Parameter Name Units Value Refere

Water-column Herbivore Compartment Type Assimilation efficiency from plants unitless 0.1 Professional judgment bas

2002. 

Absorption rate constant unitless 2.46 Calculated based on body 

in Hendriks & Heikens 200

Elimination rate constant unitless 1.73E-02 Professional judgmentr. Water-column Omnivore Compartment Type Assimilation efficiency from food unitless 0.1 Professional judgment bas

2002. 

Assimilation efficiency from plants unitless 0.1 Professional judgment bas

2002. 

Absorption rate constant unitless 1.23 Calculated based on body 

in Hendriks & Heikens 200

Elimination rate constant unitless 1.73E-02 Professional judgment. Water-column Carnivore Compartment Type Assimilation efficiency from food unitless 0.1 Professional judgment bas

2002. 

Absorption rate constant unitless 0.66 Calculated based on body 

in Hendriks & Heikens 200

Elimination rate constant unitless 1.73E-02 Professional judgment. *All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used). 
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Table A-26. Mercury Chemical-Specific Parameters for Aquatic Species*

Parameter Name Units 

Value 

Hg(0) Hg(2) MHg 

Zooplankton Compartment Type AssimilationEfficiencyFromAlgae unitless 0.2 0.015 0.5 EnvironmeHalf-life day 1.0E+09 1.0E+09 1.0E+09 ProfessionaHowMuchFasterHgEliminationIsThanForMHg unitless 3 3 1 ProfessionaMethylation rate 1/day 0 0 0 ProfessionaOxidation rate 1/day 0 1.0E+06 0 ProfessionaReduction rate 1/day 0 0 0 ProfessionaBenthic Invertebrate Compartment Type Alpha of equilibrium for sediment partitioning unitless 0.95 0.95 0.95 Selected vaBenthic invertebrate-bulk sediment partition coefficient 

kg[bulk sediment]/ 

kg[invertebrate wet wt] 0.0824a 0.0824b 5.04b

aAssumed bSaouter et 

t-alpha for equilibrium for sediment partitioning day 14 14 14 Saouter et 

duration). 

All Fish Compartments Types1Elimination adjustment factor unitless 3 3 1 Trudel and Assimilation efficiency from food unitless 0.06 0.06 0.5 

Williams et value was uof 0.8) to camatch the rconcentratilevels withifrom publis
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Table A-26. Mercury Chemical-Specific Parameters for Aquatic Species (Cont.)*

Parameter Name Units 

Value 

Hg(0) Hg(2) MHg 

All Fish Compartments Types1Demethylation rate 1/day N/A N/A 0 ProfessionaMethylation rate 1/day 0 0 0 ProfessionaOxidation rate 1/day 1.0E+06 0 0 ProfessionaReduction rate 1/day 0 0 0 ProfessionaWater-column Herbivore Compartment Type Assimilation efficiency from plankton unitless 0.06 0.06 0.5 

Williams et value was uof 0.8) to camatch the rconcentratilevels withifrom publis

*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. 
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Table A-27. PAH Chemical-Specific Parameters for Aquatic Species*

Parameter Name Units 

Value 

2Methyl Acenaphthene Acenaphthylene BaA BaP BbF 

Zooplankton Compartment Type Absorption rate constant 

L[water]/ 

kg[fish wet wt]-

day 

790 42231 42302.2 42650.8 42652.8 42650.7 

Assimilation efficiency from algae 

unitless 0.5 0.5 0.5 0.46 0.25 0.25 

Elimination rate constant 1/day 169.68 148.07 123.44 2.073 1.3864 2.12 Half-life day 0.00779 0.00239 0.00239 1.284 16.5 17 Benthic Invertebrate Compartment Type Clearance constant unitless 100.6 100.6 100.6 100.6 100.6 100.6 Vd (ratio of 

concentration in benthic invertebrates to concentration in water) 

mL/g 7235 7235 7235 7235 7235 7235 

Half-life day 0.722 0.722 0.722 1.284 16.5 17 All Fish Compartment TypesaGamma fish unitless 0.2 0.2 0.2 0.2 0.2 0.2 Assimilation efficiency from food 

unitless 0.5 0.5 0.32 0.15 0.15 0.15 

Half-life day 0.2 0.2 0.2 0.408 1.925 2 
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Table A-27. PAH Chemical-Specific Parameters for Aquatic Species (Cont.)*

Parameter Name Units 

Value 

Referen

Chr DahA Fluoranthene Fluorene IcdP 

Zooplankton Compartment Type Absorption rate constant 

L[water]/ 

kg[fish wet wt]-

day 

42650 42655.5 142000 15000 42655.9 

Kow from Arnot et al. 2004. 

Berrojalbiz et al. 2009 (2-MFluoranthene, and Fluoren

Assimilation efficiency from algae 

unitless 0.46 0.25 0.49 0.5 0.25 

Kow from Arnot et al. 2004. 

maximum value from Wang (Benzo(a)pyrene, Benzo(bBenzo(g,h,i)perylene, BenzDibenz(a,h)anthracene, ancd)pyrene). 

Elimination rate constant 1/day 2.3746 0.4331 8.678 81.87 0.269 Kow from Arnot et al. 2004. Half-life day 0.495 17 0.00239 0.000248 17 

McElroy 1990. Exceptions al. 2009 (2-Methylnaphthaland Fluorene) and Moermo(Benz(a)anthracene and B
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Table A-27. PAH Chemical-Specific Parameters for Aquatic Species (Cont.)*

Parameter Name Units 

Value 

Referen

Chr DahA Fluoranthene Fluorene IcdP 

Benthic Invertebrate Compartment Type Clearance constant unitless 100.6 100.6 100.6 100.6 100.6 Stehly et al. 1990. Vd (ratio of 

concentration in benthic invertebrates to concentration in water) 

mL/g 7235 7235 7235 7235 7235 Stehly et al. 1990. 

Half-life day 0.495 17 0.722 0.722 17 Moermond et al. 2007. All Fish Compartment TypesaGamma fish unitless 0.2 0.2 0.2 0.2 0.2 Thomann 1989. Assimilation efficiency from food 

unitless 0.15 0.15 0.14 0.14 0.15 

Lemair et al. 1992. Excepti2008 & Wang and Wang 2Methylnaphthalene and AcNiimi and Palazzo 1986 (AFluoranthene, and Fluoren

Half-life day 0.533 2 0.165 0.2 2 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. 
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Table A-28. Dioxin Chemical-Specific Parameters for Aquatic Species*

Parameter Name Units 

Value 

1,2,3,

4,6,7,8,

9-OCDD

1,2,3,

4,6,7,8,

9-OCDF

1,2,3,4, 

6,7, 8-

HpCDD 

1,2,3,4,

6,7,8-

HpCDF

1,2,3,4,

7,8,9-

HpCDF

1,2,3,4,

7,8-

HxCDD

1,2,3

7,8

HxC

Benthic Invertebrate Compartment Clearance constant unitless 0 0 0 0 0 0 0 Sediment partitioning partition coefficient kg/kg 0.0013 0.0017 0.0055 0.0012 0.042 0.033 0.00Sediment partitioning alpha of equilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.9Sediment partitioning time to reach alpha of equilibrium days 120 42 120 42 42 120 42 Vd (ratio of concentration 

in benthic invertebrates to concentration in water) 

mL/g 0 0 0 0 0 0 0 

Half-life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776Zooplankton Compartment TypeAssimilation efficiency from food unitless 0.41 0.41 0.41 0.41 0.41 0.41 0.4Absorption rate constant L/kg(ww)/day 380.0 380.0 380.0 380.0 380.0 380.0 380Elimination rate constant /day 5.5E-4 5.5E-4 5.5E-4 5.5E-4 5.5E-4 5.5E-4 5.5EHalf life day 1E09 1E09 1E09 1E09 1E09 1E09 1E0All Fish CompartmentsaAssimilation efficiency from food unitless 0.03 0.03 0.5 0.5 0.5 0.5 0.5 Chemical uptake rate via gill L[water]/kg[fish wet wt]/day 142 N/Ab 14 N/Ab N/Ab 127 N/AGamma_fish unitless N/Ab 0.2 N/Ab 0.2 0.2 N/Ab 0.2 Half-life day 693.15 346.57 346.57 346.57 346.57 495.11 495.Water Column Herbivore Compartment Assimilation efficiency from plants unitless 0.03 0.03 0.5 0.5 0.5 0.5 0.5 Water Column Omnivore Compartment Assimilation efficiency from plants unitless 0.03 0.03 0.5 0.5 0.5 0.5 0.5 
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Table A-28. Dioxin Chemical-Specific Parameters for Aquatic Species (Cont.)*

Parameter Name Units 

Value

1,2,3,7,8,9-

HxCDD 

1,2,3,7,8,9-

HxCDF 

1,2,3,7,8-

PeCDD 

1,2,3,7,8-

PeCDF 

2,3,4,6,7,

8-HxCDF

2,3,4,7

PeCD

Benthic Invertebrate Compartment Clearance constant unitless 0 0 0 0 0 0 Sediment partitioning partition coefficient kg/kg 0.015 0.067 0.098 0.024 0.072 0.17 Sediment partitioning alpha of equilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 Sediment partitioning time to reach alpha of equilibrium days 120 42 120 42 42 42 Vd (ratio of concentration in benthic 

invertebrates to concentration in water) 

mL/g 0 0 0 0 0 0 

Half-life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.All Fish CompartmentsaAssimilation efficiency from food unitless 0.5 0.5 0.5 0.5 0.5 0.5 Chemical uptake rate via gill L[water]/kg[fish wet wt]/day 127 N/Ab 700 N/Ab N/Ab N/Ab Gamma_fish unitless N/Ab 0.2 N/Ab 0.2 0.2 0.2 Half-life day 495.11 495.11 420.09 420.09 495.11 420.0Water Column Herbivore Compartment Assimilation efficiency from plants unitless 0.5 0.5 0.5 0.5 0.5 0.5 Water Column Omnivore Compartment Assimilation efficiency from plants unitless 0.5 0.5 0.5 0.5 0.5 0.5 
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Table A-28. Dioxin Chemical-Specific Parameters for Aquatic Species (Cont.)*

Parameter Name Units Reference 

Benthic Invertebrate Compartment Clearance constant unitless Professional judgment. Sediment partitioning partition coefficient kg/kg 

TCDD data for sandworm in Rubenstein et al. 1990; dry weigmultiplied TCDD partition coefficient for sandworm by congenbioaccumulation equivalency factor in GLWQI from U.S. EPA 

Sediment partitioning alpha of equilibrium unitless Professional judgment. Sediment partitioning time to reach alpha of equilibrium days TCDD: professional judgment; PeCDF: Rubinstein et al. 1990

sandworm. 

Vd (ratio of concentration in benthic 

invertebrates to concentration in water) 

mL/g Professional judgment. 

Half-life day Change source to f-pass Zooplankton 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. 1986 Elimination rate constant /day Professional judgment based on water column herbivore valuHalf Life day Professional judgment. All Fish CompartmentsaAssimilation efficiency from food unitless TCDD: calculated from data in Kleeman et al. 1986b trout dat

EPA 1993; PeCDF: used assimilation efficiency for TCDD in t

Chemical uptake rate via gill L[water]/kg[fish wet wt]-day Muir et al. 1986. Gamma_fish unitless Thomann 1989 Half-life day Change source to f-pass Water Column Herbivore Compartment Assimilation efficiency from plants unitless TCDD: calculated from data in Kleeman et al. 1986b trout dat

EPA 1993; PeCDF: used assimilation efficiency for TCDD in t

Water Column Omnivore Compartment Assimilation efficiency from plants unitless TCDD: calculated from data in Kleeman et al. 1986b trout dat

EPA 1993; PeCDF: used assimilation efficiency for TCDD in t

*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. b N/A = not applicable. This parameter is used in calculating the uptake when measured data are unavailable. 
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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. 
 

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 B-1 

Table B-1. Concentration Estimates and Speciations for Mercury 

Location and Medium Units 

Total Hg Divalent Hg Elemental Hg 

Value Value % Total Hg Value % Total Hg 

Farm_NNW 

Tilled Surface Soil μg g-1 [dry weight] 1.05E-02 9.94E-03 94.36% 4.28E-04 4.07% 

Untilled Surface Soil 

μg g-1 [dry 

weight] 2.02E-02 1.99E-02 98.34% 7.22E-06 0.04% 

Farm_SE 

Tilled Surface Soil μg g-1 [dry weight] 2.23E-02 2.11E-02 94.51% 8.73E-04 3.91% 

Untilled Surface Soil 

μg g-1 [dry 

weight] 4.17E-02 4.10E-02 98.33% 1.29E-05 0.03% 

Farm_WSW 

Tilled Surface Soil μg g-1 [dry weight] 3.00E-03 2.83E-03 94.36% 1.22E-04 4.07% 

Untilled Surface Soil 

μg g-1 [dry 

weight] 7.81E-03 7.68E-03 98.33% 3.53E-06 0.05% 

Wolf Run Lake 

Surface water mg L-1 5.43E-08 3.00E-08 55.16% 2.36E-08 43.46% 

Macrophyte mg kg-1 [wet weight] 4.67E-08 4.42E-08 94.57% 2.50E-16 0.00% Zooplankton mg kg-1 [wet weight] 3.88E-04 1.81E-04 46.63% 0.00E+00 0.00% Water Column Herbivore 

mg kg-1 [wet 

weight] 8.36E-04 7.32E-05 8.76% 0.00E+00 0.00% 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-2 

Table B-1. Concentration Estimates and Speciations for Mercury (Cont.) 

Location and Medium Units 

Total Hg Divalent Hg Elemental Hg 

Value Value % Total Hg Value % Total Hg 

Wolf Run Lake 

Water Column Omnivore 

mg kg-1 [wet 

weight] 3.81E-03 3.14E-05 0.82% 0.00E+00 0.00% 

Water Column Carnivore 

mg kg-1 [wet 

weight] 2.13E-02 1.73E-05 0.08% 0.00E+00 0.00% 

Sediment μg g-1 [dry weight] 1.27E-03 1.23E-03 97.23% 3.26E-05 2.57% Benthic Invertebrate 

mg kg-1 [wet 

weight] 7.40E-05 6.43E-05 86.94% 1.70E-06 2.30% 

Benthic Omnivore mg kg-1 [wet weight] 6.08E-05 3.06E-05 50.29% 1.42E-11 0.00% Benthic Carnivore mg kg-1 [wet weight] 2.88E-04 2.70E-05 9.36% 1.30E-11 0.00% 

Veto Lake 

Surface water mg L-1 1.33E-07 1.20E-07 90.62% 1.20E-08 9.05% 

Macrophyte mg kg-1 [wet weight] 3.08E-08 3.05E-08 98.85% 2.74E-16 0.00% Zooplankton mg kg-1 [wet weight] 1.82E-04 1.53E-04 84.33% 0.00E+00 0.00% Water Column Herbivore 

mg kg-1 [wet 

weight] 1.68E-04 6.21E-05 36.93% 0.00E+00 0.00% 

Water Column Omnivore 

mg kg-1 [wet 

weight] 5.61E-04 2.66E-05 4.75% 0.00E+00 0.00% 

Water Column Carnivore 

mg kg-1 [wet 

weight] 2.95E-03 1.47E-05 0.50% 0.00E+00 0.00% 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-3 

Table B-1. Concentration Estimates and Speciations for Mercury (Cont.)

Location and Medium Units 

Total Hg Divalent Hg Elemental Hg 

Value Value % Total Hg Value % Total Hg 

Veto Lake 

Sediment μg g-1 [dry weight] 2.24E-03 2.23E-03 99.22% 1.28E-05 0.57% 

Benthic Invertebrate 

mg kg-1 [wet 

weight] 1.31E-04 1.16E-04 88.59% 6.69E-07 0.51% 

Benthic Omnivore mg kg-1 [wet weight] 1.07E-04 5.31E-05 49.52% 9.59E-12 0.00% Benthic Carnivore mg kg-1 [wet weight] 5.16E-04 4.68E-05 9.06% 4.00E-12 0.00% 

Mountwood Park Lake 

Surface water mg L-1 1.53E-08 1.26E-08 82.66% 2.59E-09 17.00% 

Macrophyte mg kg-1 [wet weight] 4.07E-09 4.03E-09 99.08% 1.04E-16 0.00% Zooplankton mg kg-1 [wet weight] 1.64E-05 1.35E-05 82.14% 0.00E+00 0.00% 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-4 

Table B-1. Concentration Estimates and Speciations for Mercury (Cont.)

Location and Medium Units 

Total Hg Divalent Hg Elemental Hg 

Value Value % Total Hg Value % Total Hg 

Mountwood Park Lake 

Water Column Herbivore 

mg kg-1 [wet 

weight] 1.62E-05 5.37E-06 33.10% 0.00E+00 0.00% 

Water Column Omnivore 

mg kg-1 [wet 

weight] 5.66E-05 2.28E-06 4.03% 0.00E+00 0.00% 

Water Column Carnivore 

mg kg-1 [wet 

weight] 2.83E-04 1.21E-06 0.43% 0.00E+00 0.00% 

Sediment μg g-1 [dry weight] 1.30E-04 1.28E-04 98.20% 2.08E-06 1.60% Benthic Invertebrate mg kg-1 [wet weight] 7.55E-06 6.65E-06 88.09% 1.08E-07 1.43% Benthic Omnivore mg kg-1 [wet weight] 5.93E-06 2.97E-06 50.13% 3.03E-12 0.00% Benthic Carnivore mg kg-1 [wet weight] 2.71E-05 2.57E-06 9.51% 1.10E-12 0.00% 

aMethyl mercury concentrations represent the mass of mercury as methyl mercury. 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-5 

Table B-2. Concentration Estimates for Cadmium 

Location and Medium Units Values 

Farm_NNW Tilled Surface Soil μg g-1 [dry weight] 1.07E-02 Untilled Surface Soil μg g-1 [dry weight] 8.83E-03Farm_SE Tilled Surface Soil μg g-1 [dry weight] 5.18E-02 Untilled Surface Soil μg g-1 [dry weight] 6.98E-02Farm_WSW Tilled Surface Soil μg g-1 [dry weight] 3.83E-02 Untilled Surface Soil μg g-1 [dry weight] 2.85E-02Wolf Run Lake 

Surface water mg L-1 1.79E-06

Macrophyte 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-03
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-6 

Table B-2. Concentration Estimates for Cadmium (Cont.) 

Location and Medium Units Values 

Veto Lake 

Surface water mg L-1 1.93E-06

Macrophyte mg kg-1 [wet weight] 1.84E-04Zooplankton mg kg-1 [wet weight] 1.24E-01Water Column Herbivore mg kg-1 [wet weight] 7.51E-02Water Column Omnivore mg kg-1 [wet weight] 3.19E-02Water Column Carnivore mg kg-1 [wet weight] 1.14E-02Sediment μg g

-1 [dry weight] 2.26E-03

Benthic Invertebrate mg kg-1 [wet weight] 3.84E-04Benthic Omnivore mg kg-1 [wet weight] 2.90E-04Benthic Carnivore mg kg-1 [wet weight] 1.96E-03

Mountwood Park Lake 

Surface water mg L-1 2.32E-07

Macrophyte mg kg-1 [wet weight] 2.20E-05Zooplankton mg kg-1 [wet weight] 1.64E-02Water Column Herbivore mg kg-1 [wet weight] 1.41E-02Water Column Omnivore mg kg-1 [wet weight] 8.28E-03Water Column Carnivore mg kg-1 [wet weight] 4.27E-03Sediment μg g

-1 [dry weight] 1.68E-04

Benthic Invertebrate mg kg-1 [wet weight] 2.85E-05Benthic Omnivore mg kg-1 [wet weight] 3.17E-05Benthic Carnivore mg kg-1 [wet weight] 2.43E-04
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-7 

Table B-3. Concentration Estimates for PAH Congeners 

Location and Medium Units 

Values 

Acenaphthene Acenaphthylene 

Benzo(A)anthracene 

Benzo(A)pyrene 

Benzo(B)flouranthene 

Farm_NNW 

Tilled Surface Soil μg g-1 7.77E-08 7.07E-07 1.27E-04 1.75E-04 1.46E-03 

[dry weight] 

Untilled Surface Soil 

μg g-1

1.45E-06 3.14E-05 1.59E-03 2.25E-03 1.75E-02 

[dry weight] 

Farm_SE 

Tilled Surface Soil μg g-1 1.07E-07 1.15E-06 1.62E-04 2.28E-04 1.85E-03 

[dry weight] 

Untilled Surface Soil 

μg g-1

2.38E-06 4.18E-05 2.04E-03 2.86E-03 2.16E-02 

[dry weight] 

Farm_WSW 

Tilled Surface Soil μg g-1 6.27E-08 6.64E-07 5.01E-05 4.45E-05 5.59E-04 

[dry weight] 

Untilled Surface Soil 

μg g-1

6.83E-07 1.21E-05 6.29E-04 6.20E-04 7.04E-03 

[dry weight] 

Wolf Run Lake 

Surface water mg L-1 1.08E-09 7.54E-09 5.48E-10 5.09E-11 5.49E-08 

Macrophyte 

mg kg-1

2.19E-08 1.85E-07 3.66E-07 3.33E-08 3.66E-05 

[wet weight]
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-8 

Table B-3. Concentration Estimates for PAH Congeners (Cont.) 

Location and Medium Units 

Values 

Acenaphthene Acenaphthylene 

Benzo(A)anthracene 

Benzo(A)pyrene 

Benzo(B)flouranthene 

Wolf Run Lake 

Zooplankton mg kg-1 1.06E-07 7.86E-07 1.64E-05 1.99E-06 1.34E-03 

[wet weight] 

Water Column Herbivore 

mg kg-1 9.71E-07 7.51E-06 2.31E-06 8.06E-07 1.07E-03 

[wet weight] 

Water Column Omnivore 

mg kg-1 6.82E-07 4.81E-06 4.33E-07 1.66E-07 2.33E-04 

[wet weight] 

Water Column Carnivore 

mg kg-1 5.59E-07 3.93E-06 3.43E-07 1.21E-07 1.71E-04 

[wet weight] 

Sediment μg g-1 2.43E-08 3.06E-07 7.87E-06 1.12E-06 9.52E-04 

[dry weight] 

Benthic Invertebrate 

mg kg-1 4.58E-07 3.31E-06 3.04E-06 3.70E-07 6.36E-04 

[wet weight] 

Benthic Omnivore mg kg-1 6.78E-07 4.79E-06 4.38E-07 1.55E-07 2.22E-04 

[wet weight] 

Benthic Carnivore mg kg-1 5.58E-07 3.93E-06 3.48E-07 1.23E-07 1.75E-04 

[wet weight] 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-9 

Table B-3. Concentration Estimates for PAH Congeners (Cont.) 

Location and Medium Units 

Values 

Acenaphthene Acenaphthylene 

Benzo(A)anthracene 

Benzo(A)pyrene 

Benzo(B)flouranthene 

Veto Lake 

Surface water mg L-1 3.08E-09 1.46E-08 5.98E-09 7.46E-10 1.68E-07 

Macrophyte mg kg-1 6.26E-08 3.56E-07 3.50E-06 3.66E-07 9.29E-05 

[wet weight] 

Zooplankton mg kg-1 3.02E-07 1.52E-06 1.81E-04 2.51E-05 3.85E-03 

[wet weight] 

Water Column Herbivore 

mg kg-1 2.77E-06 1.44E-05 2.22E-05 9.19E-06 2.75E-03 

[wet weight] 

Water Column Omnivore 

mg kg-1 1.95E-06 9.26E-06 4.10E-06 1.89E-06 6.11E-04 

[wet weight] 

Water Column Carnivore 

mg kg-1 1.60E-06 7.57E-06 3.22E-06 1.34E-06 4.37E-04 

[wet weight] 

Sediment 

μg g-1

2.23E-07 2.04E-06 9.15E-05 1.50E-05 2.72E-03 

[dry weight] 

Benthic Invertebrate mg kg-1 4.21E-06 2.21E-05 3.53E-05 4.94E-06 1.81E-03 

[wet weight] 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-10 

Table B-3. Concentration Estimates for PAH Congeners (Cont.) 

Location and Medium Units 

Values 

Acenaphthene Acenaphthylene 

Benzo(A)anthracene 

Benzo(A)pyrene 

Benzo(B)flouranthene 

Veto Lake 

Benthic Omnivore mg kg-1 1.97E-06 9.32E-06 4.20E-06 1.75E-06 5.80E-04 

[wet weight] 

Benthic Carnivore mg kg-1 1.61E-06 7.61E-06 3.30E-06 1.38E-06 4.53E-04 

[wet weight] 

Mountwood Park Lake 

Surface water mg L-1 1.10E-09 6.05E-09 1.19E-09 1.41E-10 2.82E-08 

Macrophyte mg kg-1 2.24E-08 1.48E-07 6.76E-07 6.57E-08 1.50E-05 

[wet weight] 

Zooplankton mg kg-1 1.09E-07 6.40E-07 4.66E-05 5.93E-06 7.89E-04 

[wet weight] 

Water Column Herbivore 

mg kg-1 9.88E-07 5.98E-06 4.63E-06 1.83E-06 4.68E-04 

[wet weight] 

Water Column Omnivore 

mg kg-1

6.99E-07 3.85E-06 8.10E-07 3.71E-07 1.07E-04 

[wet weight] 

Water Column Carnivore 

mg kg-1 5.73E-07 3.15E-06 6.25E-07 2.45E-07 7.22E-05 

[wet weight] 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-11 

Table B-3. Concentration Estimates for PAH Congeners (Cont.) 

Location and Medium Units 

Values 

Acenaphthene Acenaphthylene 

Benzo(A)anthracene 

Benzo(A)pyrene 

Benzo(B)flouranthene 

Mountwood Park Lake 

Sediment μg g-1 4.08E-08 3.61E-07 2.87E-06 3.92E-07 6.82E-05 

[dry weight] 

Benthic Invertebrate mg kg-1 7.68E-07 3.91E-06 1.11E-06 1.29E-07 4.55E-05 

[wet weight] 

Benthic Omnivore mg kg-1 6.97E-07 3.83E-06 7.73E-07 2.88E-07 8.55E-05 

[wet weight] 

Benthic Carnivore 

mg kg-1

5.73E-07 3.15E-06 6.26E-07 2.38E-07 7.06E-05 

[wet weight] 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-12 

Table B-3. Concentration Estimates for PAH Congeners (Cont.) 

Location and Medium Units 

Values 

Chrysene 

Dibenz[a,h]anthracene 

Fluoranthene 

Fluorene 

Indeno(1,2,3-cd)pyrene 

Farm_NNW 

Tilled Surface Soil μg g-1 9.12E-04 6.12E-05 1.26E-04 1.50E-07 2.50E-04 

[dry weight] 

Untilled Surface Soil μg g-1 8.69E-03 6.13E-04 1.81E-03 3.04E-06 2.82E-03 

[dry weight] 

Farm_SE 

Tilled Surface Soil μg g-1 1.15E-03 8.33E-05 1.53E-04 1.79E-07 3.30E-04 

[dry weight] 

Untilled Surface Soil μg g-1 1.08E-02 7.91E-04 2.45E-03 3.94E-06 3.53E-03 

[dry weight] 

Farm_WSW 

Tilled Surface Soil μg g-1 3.99E-04 1.81E-05 7.66E-05 8.52E-08 7.60E-05 

[dry weight] 

Untilled Surface Soil μg g-1 3.95E-03 2.08E-04 8.68E-04 1.35E-06 9.83E-04 

[dry weight] 

Wolf Run Lake 

Surface water mg L-1 4.61E-09 5.45E-10 5.00E-08 1.83E-09 3.41E-09 

Macrophyte mg kg-1 3.02E-06 2.54E-07 1.68E-05 6.85E-08 1.47E-06 

[wet weight] 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-13 

Table B-3. Concentration Estimates for PAH Congeners (Cont.) 

Location and Medium Units 

Values 

Chrysene 

Dibenz[a,h]anthracene 

Fluoranthene 

Fluorene 

Indeno(1,2,3-cd)pyrene 

Wolf Run Lake 

Zooplankton mg kg-1 9.14E-05 6.86E-05 2.35E-05 1.06E-08 6.55E-04 

[wet weight] 

Water Column Herbivore 

mg kg-1 2.61E-05 5.12E-06 1.23E-04 2.28E-06 3.63E-05 

[wet weight] 

Water Column Omnivore 

mg kg-1 5.08E-06 7.57E-07 2.42E-05 1.19E-06 3.95E-06 

[wet weight] 

Water Column Carnivore 

mg kg-1 3.99E-06 5.16E-07 1.95E-05 9.67E-07 2.47E-06 

[wet weight] 

Sediment μg g-1 6.04E-05 1.67E-05 9.58E-05 9.95E-08 1.10E-04 

[dry weight] 

Benthic Invertebrate mg kg-1 9.78E-06 3.00E-06 1.82E-04 1.20E-06 8.79E-06 

[wet weight] 

Benthic Omnivore mg kg-1 4.96E-06 7.00E-07 2.44E-05 1.18E-06 3.18E-06 

[wet weight] 

Benthic Carnivore mg kg-1 4.01E-06 5.39E-07 1.96E-05 9.67E-07 2.51E-06 

[wet weight] 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-14 

Table B-3. Concentration Estimates for PAH Congeners (Cont.) 

Location and Medium Units 

Values 

Chrysene 

Dibenz[a,h]anthracene 

Fluoranthene 

Fluorene 

Indeno(1,2,3-cd)pyrene 

Veto Lake 

Surface water mg L-1 3.63E-08 2.13E-09 9.59E-08 7.91E-09 1.29E-08 

Macrophyte mg kg-1 2.00E-05 7.58E-07 3.14E-05 2.95E-07 3.44E-06 

[wet weight] 

Zooplankton mg kg-1 6.98E-04 2.47E-04 4.50E-05 4.68E-08 1.86E-03 

[wet weight] 

Water Column Herbivore mg kg-1 1.74E-04 1.95E-05 2.29E-04 9.78E-06 1.18E-04 

[wet weight] 

Water Column Omnivore mg kg-1 3.36E-05 2.51E-06 4.52E-05 5.09E-06 1.10E-05 

[wet weight] 

Water Column Carnivore mg kg-1 2.62E-05 1.55E-06 3.62E-05 4.15E-06 5.86E-06 

[wet weight] 

Sediment μg g-1 5.23E-04 5.39E-05 3.73E-04 1.40E-06 3.20E-04 

[dry weight] 

Benthic Invertebrate mg kg-1 8.46E-05 9.67E-06 7.08E-04 1.69E-05 2.54E-05 

[wet weight] 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-15 

Table B-3. Concentration Estimates for PAH Congeners (Cont.) 

Location and Medium Units 

Values 

Chrysene 

Dibenz[a,h]anthracene 

Fluoranthene 

Fluorene 

Indeno(1,2,3-cd)pyrene 

Veto Lake 

Benthic Omnivore mg kg-1 3.28E-05 2.17E-06 4.65E-05 5.12E-06 7.74E-06 

[wet weight] 

Benthic Carnivore mg kg-1 2.64E-05 1.64E-06 3.69E-05 4.16E-06 6.01E-06 

[wet weight] 

Mountwood Park Lake 

Surface water mg L-1 6.88E-09 3.33E-10 3.52E-08 2.47E-09 1.91E-09 

Macrophyte mg kg-1 3.67E-06 1.13E-07 1.14E-05 9.19E-08 4.73E-07 

[wet weight] 

Zooplankton mg kg-1 1.69E-04 5.08E-05 1.76E-05 1.55E-08 3.54E-04 

[wet weight] 

Water Column Herbivore mg kg-1 3.34E-05 4.75E-06 8.34E-05 3.05E-06 2.91E-05 

[wet weight] 

Water Column Omnivore mg kg-1 6.30E-06 5.15E-07 1.65E-05 1.59E-06 2.43E-06 

[wet weight] 

Water Column Carnivore mg kg-1 4.82E-06 2.41E-07 1.32E-05 1.29E-06 8.56E-07 

[wet weight] 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-16 

Table B-3. Concentration Estimates for PAH Congeners (Cont.) 

Location and Medium Units 

Values 

Chrysene 

Dibenz[a,h]anthracene 

Fluoranthene 

Fluorene 

Indeno(1,2,3-cd)pyrene 

Mountwood Park Lake 

Sediment μg g-1 1.56E-05 1.20E-06 2.74E-05 1.85E-07 7.41E-06 

[dry weight] 

Benthic Invertebrate mg kg-1 2.52E-06 2.15E-07 5.20E-05 2.23E-06 5.89E-07 

[wet weight] 

Benthic Omnivore mg kg-1 5.88E-06 2.82E-07 1.64E-05 1.59E-06 9.63E-07 

[wet weight] 

Benthic Carnivore mg kg-1 4.80E-06 2.31E-07 1.33E-05 1.30E-06 7.93E-07 

[wet weight] 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-17 

Table B-4. Concentration Estimates for Dioxin Congeners 

Location and Medium Units 

Values 

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‐ 

Farm_NNW 

Tilled Surface Soil μg g-1 [dry weight] 1.49E-09 2.90E-10 4.23E-10 9.94E-11 2.66E-09 

Untilled Surface Soil μg g-1 [dry weight] 6.91E-09 1.34E-09 1.94E-09 4.96E-10 1.22E-08 

Farm_SE 

Tilled Surface Soil μg g-1 [dry weight] 2.30E-09 4.38E-10 6.52E-10 1.65E-10 4.05E-09 

Untilled Surface Soil μg g-1 [dry weight] 9.57E-09 1.87E-09 2.80E-09 7.80E-10 1.68E-08 

Farm_WSW 

Tilled Surface Soil μg g-1 [dry weight] 4.50E-10 1.11E-10 1.58E-10 4.55E-11 7.84E-10 

Untilled Surface Soil μg g-1 [dry weight] 2.68E-09 6.25E-10 8.73E-10 2.49E-10 4.69E-09 

Wolf Run Lake 

Surface water mg L-1 2.24E-15 9.93E-17 4.57E-17 1.84E-16 3.80E-16 

Macrophyte mg kg-1 [wet weight] 4.36E-11 2.46E-12 9.40E-13 3.84E-12 7.34E-12 Zooplankton mg kg-1 [wet weight] 3.95E-10 5.03E-12 1.58E-12 6.43E-12 7.99E-11 Water Column Herbivore 

mg kg-1 [wet 

weight] 5.65E-10 1.03E-11 4.18E-12 1.99E-11 3.33E-11 

Water Column Omnivore 

mg kg-1 [wet 

weight] 6.20E-10 1.41E-11 6.95E-12 3.49E-11 9.89E-12 

Water Column Carnivore 

mg kg-1 [wet 

weight] 3.90E-10 2.22E-11 1.49E-11 7.68E-11 2.45E-12 

Sediment μg g-1 [dry weight] 6.17E-11 5.50E-13 1.66E-13 6.87E-13 1.02E-11 Benthic Invertebrate mg kg-1 [wet weight] 2.01E-13 2.72E-15 2.41E-15 7.85E-14 1.09E-14 Benthic Omnivore mg kg-1 [wet weight] 4.76E-12 1.21E-12 7.96E-13 6.58E-12 9.21E-14 Benthic Carnivore mg kg-1 [wet weight] 6.74E-12 2.53E-12 1.97E-12 1.59E-11 1.06E-13 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-18 

Table B-4. Concentration Estimates for Dioxin Congeners (Cont.) 

Location and Medium Units 

Values 

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‐ 

Veto Lake 

Surface water mg L-1 1.48E-14 1.14E-15 6.13E-16 1.37E-15 5.29E-15 

Macrophyte mg kg-1 [wet weight] 1.26E-11 5.11E-12 3.22E-12 7.29E-12 4.27E-12 Zooplankton mg kg-1 [wet weight] 1.08E-10 1.07E-11 5.56E-12 1.23E-11 4.62E-11 Water Column Herbivore 

mg kg-1 [wet 

weight] 1.83E-10 2.42E-11 1.57E-11 4.04E-11 2.31E-11 

Water Column Omnivore 

mg kg-1 [wet 

weight] 2.44E-10 3.59E-11 2.73E-11 7.59E-11 8.70E-12 

Water Column Carnivore 

mg kg-1 [wet 

weight] 2.02E-10 7.17E-11 7.22E-11 2.07E-10 2.85E-12 

Sediment μg g-1 [dry weight] 2.37E-10 1.51E-11 7.23E-12 1.70E-11 7.98E-11 Benthic Invertebrate mg kg-1 [wet weight] 7.63E-13 7.40E-14 1.03E-13 1.89E-12 8.50E-14 Benthic Omnivore mg kg-1 [wet weight] 2.40E-12 2.47E-12 2.70E-12 1.52E-11 8.46E-14 Benthic Carnivore mg kg-1 [wet weight] 2.79E-12 5.72E-12 7.33E-12 3.92E-11 8.08E-14 

Mountwood Park Lake 

Surface water mg L-1 1.76E-15 1.60E-16 8.81E-17 1.83E-16 7.58E-16 

Macrophyte mg kg-1 [wet weight] 1.46E-12 7.51E-13 4.75E-13 9.87E-13 6.40E-13 Zooplankton mg kg-1 [wet weight] 1.05E-11 1.30E-12 6.93E-13 1.45E-12 6.21E-12 Water Column Herbivore 

mg kg-1 [wet 

weight] 2.51E-11 4.25E-12 2.68E-12 6.09E-12 5.09E-12 

Water Column Omnivore 

mg kg-1 [wet 

weight] 4.59E-11 8.53E-12 6.15E-12 1.43E-11 2.76E-12 

Water Column Carnivore 

mg kg-1 [wet 

weight] 5.43E-11 2.61E-11 2.57E-11 5.91E-11 1.27E-12 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-19 

Table B-4. Concentration Estimates for Dioxin Congeners (Cont.) 

Location and Medium Units 

Values 

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‐ 

Mountwood Park Lake 

Sediment μg g

-1 [dry 

weight] 4.53E-12 4.11E-13 2.03E-13 4.24E-13 2.05E-12 

Benthic Invertebrate mg kg-1 [wet weight] 1.41E-14 1.98E-15 2.85E-15 4.45E-14 2.18E-15 Benthic Omnivore mg kg-1 [wet weight] 1.62E-13 3.06E-13 3.25E-13 1.43E-12 8.57E-15 Benthic Carnivore mg kg-1 [wet weight] 2.83E-13 1.02E-12 1.34E-12 5.64E-12 1.08E-14 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-20 

Table B-4. Concentration Estimates for Dioxin Congeners (Cont.) 

Location and Medium Units 

Values 

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‐ 

Farm_NNW 

Tilled Surface Soil μg g-1 [dry weight] 1.02E-09 4.13E-10 6.86E-10 7.98E-10 7.36E-10 

Untilled Surface Soil μg g-1 [dry weight] 4.72E-09 1.90E-09 3.18E-09 3.70E-09 3.39E-09 

Farm_SE 

Tilled Surface Soil μg g-1 [dry weight] 1.62E-09 6.58E-10 1.07E-09 1.25E-09 1.12E-09 

Untilled Surface Soil μg g-1 [dry weight] 6.86E-09 2.94E-09 4.49E-09 5.20E-09 4.64E-09 

Farm_WSW 

Tilled Surface Soil μg g-1 [dry weight] 3.17E-10 1.80E-10 2.11E-10 2.37E-10 2.23E-10 

Untilled Surface Soil μg g-1 [dry weight] 1.84E-09 9.17E-10 1.26E-09 1.42E-09 1.33E-09 

Wolf Run Lake 

Surface water mg L-1 7.47E-16 6.52E-17 6.88E-16 6.09E-16 1.14E-16 

Macrophyte mg kg-1 [wet weight] 2.05E-11 9.17E-13 1.62E-11 9.01E-12 2.42E-12 Zooplankton mg kg-1 [wet weight] 5.65E-11 1.27E-12 9.60E-11 9.70E-11 1.79E-11 Water Column Herbivore 

mg kg-1 [wet 

weight] 1.49E-10 4.17E-12 2.16E-10 1.85E-10 3.39E-11 

Water Column Omnivore 

mg kg-1 [wet 

weight] 2.46E-10 7.56E-12 2.75E-10 2.37E-10 4.33E-11 

Water Column Carnivore 

mg kg-1 [wet 

weight] 5.30E-10 1.72E-11 4.11E-10 3.57E-10 6.45E-11 

Sediment μg g-1 [dry weight] 6.95E-12 1.30E-13 1.49E-11 1.99E-11 2.80E-12 Benthic Invertebrate mg kg-1 [wet weight] 3.79E-13 1.34E-14 2.82E-13 1.49E-13 3.42E-14 Benthic Omnivore mg kg-1 [wet weight] 2.68E-11 1.58E-12 3.10E-12 4.35E-12 8.19E-13 Benthic Carnivore mg kg-1 [wet weight] 6.59E-11 3.88E-12 6.14E-12 8.92E-12 1.67E-12 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-21 

Table B-4. Concentration Estimates for Dioxin Congeners (Cont.) 

Location and Medium Units 

Values 

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‐ 

Veto Lake 

Surface water mg L-1 6.76E-15 8.34E-16 5.58E-15 5.81E-15 1.57E-15 

Macrophyte mg kg-1 [wet weight] 2.18E-11 4.68E-12 7.08E-12 3.11E-12 1.51E-12 Zooplankton mg kg-1 [wet weight] 5.97E-11 6.53E-12 4.02E-11 3.08E-11 1.09E-11 Water Column Herbivore 

mg kg-1 [wet 

weight] 1.72E-10 2.22E-11 1.03E-10 6.70E-11 2.28E-11 

Water Column Omnivore 

mg kg-1 [wet 

weight] 3.07E-10 4.18E-11 1.50E-10 9.88E-11 3.27E-11 

Water Column Carnivore 

mg kg-1 [wet 

weight] 8.29E-10 1.15E-10 2.92E-10 1.95E-10 6.40E-11 

Sediment μg g-1 [dry weight] 9.76E-11 8.14E-12 8.68E-11 9.19E-11 2.36E-11 Benthic Invertebrate mg kg-1 [wet weight] 5.16E-12 8.26E-13 1.61E-12 6.70E-13 2.86E-13 Benthic Omnivore mg kg-1 [wet weight] 3.68E-11 8.92E-12 3.96E-12 2.52E-12 9.59E-13 Benthic Carnivore mg kg-1 [wet weight] 9.47E-11 2.33E-11 6.96E-12 4.98E-12 1.83E-12 

Mountwood Park Lake 

Surface water mg L-1 8.62E-16 1.22E-16 6.62E-16 6.95E-16 2.18E-16 

Macrophyte mg kg-1 [wet weight] 2.84E-12 6.79E-13 8.64E-13 3.80E-13 2.24E-13 Zooplankton mg kg-1 [wet weight] 6.35E-12 8.48E-13 3.85E-12 2.93E-12 1.25E-12 Water Column Herbivore 

mg kg-1 [wet 

weight] 2.40E-11 3.75E-12 1.40E-11 8.83E-12 3.79E-12 

Water Column Omnivore 

mg kg-1 [wet 

weight] 5.43E-11 8.97E-12 2.67E-11 1.68E-11 7.26E-12 

Water Column Carnivore 

mg kg-1 [wet 

weight] 2.22E-10 3.82E-11 7.80E-11 4.87E-11 2.14E-11 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-22 

Table B-4. Concentration Estimates for Dioxin Congeners (Cont.) 

Location and Medium Units 

Values 

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‐ 

Mountwood Park Lake 

Sediment μg g

-1 [dry 

weight] 2.19E-12 2.34E-13 1.82E-12 1.92E-12 6.08E-13 

Benthic Invertebrate mg kg-1 [wet weight] 1.09E-13 2.32E-14 3.23E-14 1.34E-14 7.25E-15 Benthic Omnivore mg kg-1 [wet weight] 3.03E-12 1.01E-12 1.84E-13 1.65E-13 7.15E-14 Benthic Carnivore mg kg-1 [wet weight] 1.20E-11 4.08E-12 5.13E-13 5.05E-13 2.15E-13 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-23 

Table B-4. Concentration Estimates for Dioxin Congeners (Cont.) 

Location and Medium Units 

Values 

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_NNW Tilled Surface Soil μg g

-1 [dry weight] 8.22E-10 9.92E-10 2.33E-10 1.46E-08 1.14E-09 

Untilled Surface Soil μg g

-1 [dry weight] 3.81E-09 4.57E-09 1.07E-09 6.72E-08 5.24E-09 

Farm_SE Tilled Surface Soil μg g

-1 [dry weight] 1.29E-09 1.50E-09 3.51E-10 2.25E-08 1.68E-09 

Untilled Surface Soil μg g

-1 [dry weight] 5.36E-09 6.21E-09 1.51E-09 9.34E-08 7.04E-09 

Farm_WSW Tilled Surface Soil μg g

-1 [dry weight] 2.45E-10 3.04E-10 9.19E-11 4.24E-09 3.85E-10 

Untilled Surface Soil μg g

-1 [dry weight] 1.46E-09 1.81E-09 5.11E-10 2.54E-08 2.26E-09 

Wolf Run Lake 

Surface water mg L-1 6.35E-16 1.58E-16 9.00E-17 2.33E-15 2.52E-16 

Macrophyte mg kg-1 [wet weight] 9.38E-12 3.33E-12 2.06E-12 3.46E-11 7.05E-12 Zooplankton mg kg-1 [wet weight] 1.01E-10 2.48E-11 3.80E-12 5.12E-10 2.55E-11 Water Column Herbivore 

mg kg-1 [wet 

weight] 1.93E-10 4.70E-11 5.40E-12 3.28E-10 1.84E-11 

Water Column Omnivore 

mg kg-1 [wet 

weight] 2.47E-10 6.00E-11 5.32E-12 1.48E-10 9.49E-12 

Water Column Carnivore 

mg kg-1 [wet 

weight] 3.72E-10 8.94E-11 5.41E-12 5.76E-11 4.50E-12 

Sediment μg g

-1 [dry weight] 2.08E-11 3.88E-12 4.08E-13 7.48E-11 2.95E-12 

Benthic Invertebrate mg kg-1 [wet weight] 1.79E-13 1.71E-13 1.06E-14 5.97E-14 2.22E-15 Benthic Omnivore mg kg-1 [wet weight] 4.56E-12 1.31E-12 3.20E-13 7.54E-13 4.05E-13 Benthic Carnivore mg kg-1 [wet weight] 9.34E-12 2.53E-12 5.38E-13 9.46E-13 5.33E-13 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-24 

Table B-4. Concentration Estimates for Dioxin Congeners (Cont.) 

Location and Medium Units 

Values 

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 Lake 

Surface water mg L-1 6.02E-15 2.17E-15 1.01E-15 3.25E-14 3.19E-15 

Macrophyte mg kg-1 [wet weight] 3.22E-12 2.08E-12 4.95E-12 1.72E-11 8.30E-12 Zooplankton mg kg-1 [wet weight] 3.19E-11 1.51E-11 9.38E-12 2.46E-10 3.09E-11 Water Column Herbivore 

mg kg-1 [wet weight] 6.94E-11 3.15E-11 1.50E-11 1.90E-10 2.58E-11 

Water Column Omnivore 

mg kg-1 [wet weight] 1.02E-10 4.52E-11 1.68E-11 1.08E-10 1.60E-11 

Water Column Carnivore 

mg kg-1 [wet weight] 2.01E-10 8.86E-11 2.18E-11 5.64E-11 9.64E-12 

Sediment μg g

-1 [dry weight] 9.51E-11 3.27E-11 1.28E-11 4.99E-10 4.66E-11 

Benthic Invertebrate mg kg-1 [wet weight] 8.00E-13 1.42E-12 3.31E-13 3.96E-13 3.49E-14 Benthic Omnivore mg kg-1 [wet weight] 2.80E-12 3.15E-12 1.13E-12 5.94E-13 4.87E-13 Benthic Carnivore mg kg-1 [wet weight] 5.45E-12 5.31E-12 1.80E-12 6.39E-13 6.66E-13 

Mountwood Park Lake 

Surface water mg L-1 7.26E-16 2.96E-16 1.47E-16 4.83E-15 4.36E-16 

Macrophyte mg kg-1 [wet weight] 3.96E-13 3.04E-13 7.40E-13 2.60E-12 1.19E-12 Zooplankton mg kg-1 [wet weight] 3.06E-12 1.69E-12 1.19E-12 3.33E-11 3.66E-12 Water Column Herbivore 

mg kg-1 [wet weight] 9.21E-12 5.16E-12 2.94E-12 3.92E-11 5.15E-12 

Water Column Omnivore 

mg kg-1 [wet weight] 1.75E-11 9.91E-12 4.62E-12 3.05E-11 4.66E-12 

Water Column Carnivore 

mg kg-1 [wet weight] 5.08E-11 2.92E-11 8.91E-12 2.19E-11 3.98E-12 
Human-health Multipathway Residual-risk Assessment for the Ferroalloy-production Source Categor

 B-25 

Table B-4. Concentration Estimates for Dioxin Congeners (Cont.) 

Location and Medium Units 

Values 

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‐ 

Mountwood Park Lake 

Sediment μg g

-1 [dry weight] 2.00E-12 8.33E-13 3.52E-13 1.23E-11 1.17E-12 

Benthic Invertebrate mg kg-1 [wet weight] 1.61E-14 3.58E-14 9.01E-15 9.62E-15 8.76E-16 Benthic Omnivore mg kg-1 [wet weight] 1.77E-13 1.55E-13 1.14E-13 5.83E-14 6.46E-14 Benthic Carnivore mg kg-1 [wet weight] 5.36E-13 4.09E-13 2.67E-13 8.51E-14 1.07E-13