Section 1.0 Introduction

1-1

1.0 Introduction

In 1997, the U.S. Environmental Protection Agency’s (EPA) Office of Solid Waste

(OSW) and Office of Research and Development (ORD) began working together on the

development of the Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA)

modeling system. The 3MRA modeling system is intended to be one of EPA’s next generation

of multimedia exposure and risk models to support regulatory decisions. EPA designed the

modeling system specifically to meet the needs of OSW programs, but the 3MRA modeling

system has the flexibility to be used for many EPA applications. In particular, it was designed to

provide risk managers with information, at a national level, on exposure and risk to human and

ecological receptors due to the release of hazardous constituents from the management of solid

and hazardous wastes.

This document is the second volume of a four-volume set that describes the 3MRA

modeling system. 

# Volume I provides an overview of the system, including the reasons for its

development, its conceptual design, the modeling approach, and the underlying

science of each of the 3MRA modeling system component modules;

# Volume II (this volume) describes a representative national data set collected to

develop and test the 3MRA modeling system; 

# Volume III describes the verification and validation activities and peer reviews

undertaken to support the model development process;

# Volume IV provides a discussion of uncertainty and sensitivity.

# Volume V describes the 3MRA modeling system technology design and provides

a users’ guide.

The data collection effort for the 3MRA modeling system representative national data set

was designed to cover the range of environmental conditions across the United States and

implemented to demonstrate how to collect the data needed to run the 3MRA modeling system.

To document this data collection effort, this volume describes 

# What data were collected for use in the 3MRA modeling system;

# Where data were obtained (data sources);

# How data were compiled and processed (methodology);
Section 1.0 Introduction

1-2

# Quality assurance and quality control (QA/QC) measures to ensure that data

quality objectives (DQOs) were met; and

# Issues and uncertainties associated with data availability (data gaps) and

accuracy.

The rest of this section includes a description of the 3MRA modeling system

development process; an overview of the 3MRA modeling system; a summary of the approach to

data collection; a discussion of sites, settings, and areas of interest; a summary of data quality

objectives (DQOs); a description of the data collection methodology; and the organization of the

rest of this volume.

1.1 3MRA Modeling System Development

Program office scientists and risk managers in OSW collaborated with EPA scientists in

ORD to develop the 3MRA modeling system. A core team put together EPA’s science plan for

developing the system (U.S. EPA, 1999a). The science plan identified OSW needs and the

scientific approach to be taken. These needs were then described in terms of goals and

objectives. One overriding theme was to use existing regulatory models where appropriate, thus

relying on the science behind them and the level of acceptance associated with their use. The

following regulatory models have been incorporated into Version 1 of the 3MRA modeling

system: Industrial Source Complex Short-Term Model, Version 3 (ISCST3), for air dispersion

and deposition (U.S. EPA, 1995); EPA’s Composite Model for Leachate Migration with

Transformations Products (EPACMTP) (U.S. EPA, 1996a,b,c, 1997) for subsurface transport;

and EPA’s Exposure Analysis Modeling System II (EXAMS II) (Burns et al., 1982; Burns, 1997)

for surface water transport. Each of these legacy models was modified somewhat to provide

needed functionality within the 3MRA modeling system. All other source, fate and transport,

food web, exposure, and risk modules were developed specifically for the 3MRA modeling

system based on sound science and established principles.

The 3MRA modeling system represents the integration of more than 25 independent

software components developed by 5 different software development groups located across the

country. Core teams were formed to oversee the development of each set of modules. These

core teams consisted of OSW and ORD staff working together to design each module; see that

adequate test plans were developed; and oversee the testing, verification, and documentation of

the modules. 

1.2 Overview of the 3MRA Modeling System

The 3MRA modeling system was developed as a tool to provide risk assessment support

for the types of risk management decisions that are made within EPA’s OSW. OSW applies risk

assessment modeling tools in a variety of situations; one application is the use of tools to conduct

site-based national-level risk assessments to support rule making for the identification of

hazardous wastes. Consequently, EPA needed the 3MRA modeling system to model

environmental settings that are representative of the range of environmental settings found in the

United States, and within this broad range of settings, to simulate the release, fate, and transport

of many chemicals in waste undergoing a range of physical and biochemical processes. More
Section 1.0 Introduction

1-3

than 400 chemicals are regulated under the RCRA programs. EPA needs to consider the impacts

of these released chemicals on humans and the environment within the broad range of

environmental settings. This requires a modeling tool that encompasses releases to all media,

transport within those media, uptake in terrestrial and aquatic food webs, and exposure of

specific receptors to contaminated media and food items.

The 3MRA modeling system was designed to estimate national distributions of human

and ecological risks resulting from long-term (chronic) chemical release from land-based and

wastewater waste management units (WMUs) using a site-based approach. The national

distribution is constructed by performing “site-based” assessments at a statistically significant

number of randomly sampled industrial waste site locations across the United States. The

3MRA framework describes a tiered approach for populating data files for each site

characterization and evaluation. The approach is referred to as “site-based” because the

assignment of data values for the site being simulated occurs according to a tiered protocol. 

Data values are filled first with data at a site level. When site data are not available, a

statistically sampled value from a geographically relevant regional distribution of values is used. 

When a representative regional distribution for the variable is not available, a value from a

national distribution is assigned.

The 3MRA modeling system simulates contaminant releases from a WMU to the various

media (air, water, soil) based on the chemical/physical properties of the constituent, the

characteristics of the WMU that is modeled, and the environmental setting (e.g., meteorological

region) in which the facility is located. Once released from the WMU, the contaminant is

transported through environmental media and into biological compartments such as produce,

beef, and fish. Human and ecological receptors included in the simulation may be exposed

concurrently to contaminated media and food through multiple pathways and routes of exposure. 

For each receptor that is included in the simulation, the 3MRA modeling system performs

risk/hazard calculations based on aggregate exposures modeled through space and time.

Figure 1-1 provides an overview of the 3MRA modeling system design. As suggested in

this figure, the system performs three major functions: (1) the site definition, (2) the multimedia,

multipathway simulation, and (3) the exit level processing. The site definition in the figure

includes both the selection of site characteristics from three levels of data, as well as the

estimation and selection of chemical properties. The multimedia, multipathway simulation

includes all of the science modules linked together to predict behavior of chemicals from source

release through exposure and risk. The linkages among the science modules are depicted in

Figure 1-2. The exit level processing occurs after the simulation is completed and consists of

two-stage processing of the risk outputs followed by the risk visualization of exit level

distributions. At the top of Figure 1-1, the looping structure used to conduct national scale

analyses is summarized, including the site location loop, the WMU loop, the number of

iterations, and the number of chemicals (which are simulated individually).

The 3MRA modeling system was designed specifically to include Monte Carlo

simulation methods to address both uncertainty and variability in the risk outputs. Statistical

distributions were developed for many modeling parameters and provide a statistical measure of

variability and uncertainty, i.e., the range and distribution of potential exposures and risks 
1-4

Site Definition Processor

Site Simulation Files

Global Results Files

Risk Summary Output File

Site Definition Multimedia Multipathway 

Simulation

Cw Exit Level Processing

Waste Management Unit Loop (5 Source Types)

Sampled Input Data Iteration Loop (nr)

Chemical Loop (43 Metals & Organics)

Cw Loop

Waste Management Facility Loop (201 National Sites)

System User Interface (SUI)

Chemical 

Properties

Processor

Multimedia

Multipathway 

Simulation

Processor

Exit 

Level 

Processor

I

Exit

Leve

Process

II

Regional 

Database

National 

Database

Site-Based

Database

Chemical 

Properties

Database

MET

Database

Site Input Data

DatabData FProcesUser I

Cw≡ Waste stream concentration

Risk

Visualiz

Proces

List of Sites

List of Chemicals

Header Info froWarnings/Erro

Metal

Isotherms

Key

Site

Definition

Processor

Site Simulation Files

Global Results Files

Risk Summary Output File

Site Definition Multimedia Multipathway 

Simulation

Cw Exit Level Processing

Waste Management Unit Loop (5 Source Types)

Sampled Input Data Iteration Loop (nr)

Chemical Loop (43 Metals & Organics)

Cw Loop

Waste Management Facility Loop (201 National Sites)

System User Interface (SUI)

Chemical 

Properties

Processor

Multimedia

Multipathway 

Simulation

Processor

Exit 

Level 

Processor

I

Exit

Leve

Process

II

Regional 

Database

National 

Database

Site-Based

Database

Chemical 

Properties

Database

MET

Database

Site Input Data

DatabData FProcesUser I

Cw≡ Waste stream concentration

Risk

Visualiz

Proces

List of Sites

List of Chemicals

Header Info froWarnings/Erro

Metal

Isotherms

Key

Figure 1-1. 3MRA modeling system design.
1-5

Sources Transport Surface Water Vadose Zone Air Watershed Farm FoodChain Aquatic Food Web Terrestrial Food Web Foodchain Ecol

Expo

Ecol

R

Hu

Expo

Hu

R

Expos

Aquifer Land Application Unit Waste Pile Landfill Surface Impoundment Aerated Tank The dashed line indicates that soil concentrations for the local (land-based source) and regional watersheds may be a

estimate total soil concentrations for areas (e.g., habitats) that include both regional and local watershed components. Figure 1-2. Linkages among the source, fate, transport, exposure, and risk modules of the 3MRA mo
Section 1.0 Introduction

1-6

occurring at a site. When applied to the sites in a national assessment, the result is a national

distribution of risks with a statistical measure of variability and uncertainty. The sites currently

in the database were randomly selected from solid waste management sites across the United

States to represent the national variability in waste management scenarios and locations. The

methodology for selecting the sites allows for measures of protection to be calculated at the site

level and aggregated over all the sites to develop the national distribution of risks. 

1.3 Approach to Data Collection

As described in Volume I, the 3MRA modeling system contains 17 media-specific

pollutant release, fate, transport, exposure, and risk modules. The modules have varying data

requirements covering a wide range of general data categories: WMU characteristics; waste

properties, meteorological data; surface water and watershed layout and characteristics; soil

(vadose zone) properties; aquifer (saturated zone) properties; food chain or food web

characteristics; human and ecological exposure factors; and the types and locations of human

receptors and ecological receptors and habitats surrounding a WMU. In addition, for each

chemical to be assessed by the 3MRA modeling system, the modules require the following

chemical-specific data: chemical properties, bio-uptake and bioaccumulation factors, and human

and ecological health benchmarks. Table 1-1 lists the 46 chemicals included in the

representative national data set.

Table 1-1. Chemicals Included in the Representative National Data Set

Constituent CAS Chem Type

TCDD, 2,3,7,8- 1746-01-6 D

Mercury 7439-97-6 Hg

Mercury (elemental) 7439-97-6e Hg

Methyl mercury 7439-97-6m Hg

Antimony 7440-36-0 M

Arsenic 7440-38-2 M

Barium 7440-39-3 M

Beryllium 7440-41-7 M

Cadmium 7440-43-9 M

Chromium (total) 7440-47-3 M

Chromium III (insoluble salts) 16065-83-1 M

Chromium VI 18540-29-9 M

Lead 7439-92-1 M

Nickel 7440-02-0 M

Selenium 7783-79-1 M

Silver 7440-22-4 M

Thallium 7446-18-6 M

(continued)
Table 1-1. (continued)

Section 1.0 Introduction

Constituent CAS Chem Type

1-7

Vanadium 7440-62-2 M

Zinc 7440-66-6 M

Acetonitrile 75-05-8 O

Acrylonitrile 107-13-1 O

Aniline 62-53-3 O

Benzene 71-43-2 O

Carbon disulfide 75-15-0 O

Chlorobenzene 108-90-7 O

Chloroform 67-66-3 O

Dichlorophenoxyacetic acid, 2,4- (2,4-D) 94-75-7 O

Ethylene dibromide 106-93-4 O

Hexachloro-1,3-butadiene 87-68-3 O

Methoxychlor 72-43-5 O

Methyl ethyl ketone 78-93-3 O

Methyl methacrylate 80-62-6 O

Methylene chloride 75-09-2 O

Nitrobenzene 98-95-3 O

Pentachlorophenol 87-86-5 O

Phenol 108-95-2 O

Pyridine 110-86-1 O

Tetrachloroethylene 127-18-4 O

Thiram 137-26-8 O

Toluene 108-88-3 O

Trichloroethane, 1,1,1- 71-55-6 O

Trichloroethylene 79-01-6 O

Vinyl chloride 75-01-4 O

Benzo(a)pyrene 50-32-8 S

Bis(2-ethylhexyl) phthalate 117-81-7 S

Dibenz(a,h)anthracene 53-70-3 S

The representative national data set described in this volume was compiled to enable

development and comprehensive testing of the 3MRA modeling system. Although the 3MRA

modeling system is intended for national-scale assessments, its analyses are based on a regional,

site-based approach, in which risks are evaluated at a number of representative individual

nonhazardous industrial waste management sites across the country. To collect data for model

testing and development, EPA employed three data collection approaches consistent with this

modeling approach. In order of preference, they are
Section 1.0 Introduction

1-8

# Site-based–Input data are collected and passed to the model for each site.

# Regional–The nation is divided into regions of similar characteristics; data are

collected to characterize the variability within each region; and each site is

assigned to a region.

# National–Distributions or fixed values are collected for inputs that characterize

the nation as a whole.

For each variable, EPA selected one of these data collection approaches by considering the

preference for site-based data, data availability, and the level of effort for data collection. EPA

collected regional data (e.g., meteorological and aquifer data) and national data (e.g., exposure

factors, waste properties, chemical-specific data) only when site-based data were not readily

available. Table 1-2 shows the data collection approach by data type and gives the section

number for the section of the document that describes the data collection approach in detail.

Although this analysis is based primarily on site-based data that are collected by site location,

the 3MRA modeling system is not intended for a site-specific analysis. The site-based data are

intended to provide a representative data set for a national assessment. Thus, although a large

effort went into identifying, to the greatest extent possible, the locations of each site and

collecting accurate data to characterize each site, the data are not meant to be used for sitespecific risk assessments. Instead, the site-based data and analysis are intended to develop

nationwide chemical-specific distributions of risks.

EPA limited this data collection effort to information that could be obtained from readily

available data sources. The intent of the data set is to reasonably represent the nationwide

variability of environmental conditions that influence the fate and transport of chemicals from

industrial waste management units (WMUs). This was accomplished by collecting data from

extant data sources, such as published literature; existing geographic information system (GIS)

coverages; other EPA data collection efforts, such as the Exposure Factors Handbook (EFH);

and meteorological data sets. As such, the data collection effort was limited to secondary data

sources. Each source represented a proven and well-documented data set, with adequate

documentation to ensure consistent and known quality. Data sources that would require

extensive effort to document in terms of sources and data quality, such as permit records from

state or local governments, were not included.

1.4 Sites, Settings, and Area of Interest

The data collection effort centered around the collection of site-based data for

representative industrial nonhazardous waste management sites across the United States using

data from EPA’s 1985 Screening Survey of Industrial Subtitle D Establishments (Westat, 1987).

This survey was designed to represent a total population of nearly 150,000 establishments

generating nonhazardous industrial wastes. Although it is more than 10 years old, the survey

represents the largest consistent set of data available on Industrial D facility location and WMU
Section 1.0 Introduction

1-9

Table 1-2. Data Collection Approach, by Data Type 

Data Type (Report Section)

Data Collection Approach

Site-Based Regional National

WMU (Section 3) ! !

Waste properties (Section 16) !

Air Model (Section 16) !

Meteorological (Section 4) !

Watershed and waterbody layout (Section 5) !

Surface water (Section 6) ! !

Soil/vadose zone (Section 7) ! !

Aquifer (Section 16) ! !

Farm food chain/terrestrial food web (Section 10) !

Aquatic food web (Section 11) ! !

Human exposure factors (Section 8) !

Ecological exposure factors (Section 12) ! !

Chemical propertiesa (Section 17) !

Bio-uptake/bioaccumulation factorsa (Sections 8, 10, 11) !

Human health benchmarksa (Section 15) !

Human receptor type and location (Section 9) ! !

Ecological benchmarksa (Section 14) !

Ecological receptor and habitat type and location

(Section 13)

! !

Risk and control variables (Section 16) ! a

Chemical-specific variables.

dimensions. EPA selected a total of 201 sites from the 2,850 facilities (out of a total of 15,844

surveyed facilities) that had one or more of four types of WMUs onsite (landfill, waste pile, land

application unit [LAU], and surface impoundment).

EPA selected the sample of 201 facilities to represent the types, sizes, and geographic

locations of WMUs at which exempt waste could be currently disposed. In the representative

national data set, the locations of these facilities determine the sites where the 3MRA modeling

system is implemented and the screening survey data determine the type and size of WMUs that

are present at a particular site. The area of interest (AOI) is the spatial area within which data

are collected and risks are estimated. At each site, the AOI for testing and developing the 3MRA

modeling system is the WMU area plus the area encompassed by a 2 km radius extending from

the corner of the square WMU.
Section 1.0 Introduction

 1The Industrial D screening survey data do not include data for aerated tanks. EPA placed a tank at each surface

impoundment site from the 201 sites because the presence of the impoundment indicates that the facility manages

liquid wastes and therefore could have a treatment tank (see Section 3). 

1-10

 Because of difficulties in modeling multiple units at a single site, the data collection

effort assigned multiple AOIs and multiple settings for Industrial D sites with multiple WMU

types. The 3MRA modeling system defines a setting as a unique WMU type/site combination.

Thus, a site with a landfill and a waste pile would constitute two settings. Settings are the basic

spatial modeling unit for the 3MRA modeling system, with each model realization being run at a

single WMU/site setting. There are 419 unique WMU/site settings across the 201 sites selected 

for the example data set. Table 1-3 shows the breakout of WMU types across theses 419 settings

and compares these statistics with the overall survey data (for the 2,850 facilities in the screening

survey with onsite WMUs). The 419 settings include all the 282 unique Industrial D WMU/site

combinations plus 137 aerated tank settings.1

Table 1-3. Settings/WMU Type Distribution for the 201-Facility Sample.

WMU Type

Settings - 201 Sample

Facilitiesa

Settings - 2,850 Industrial D

Facilitiesb

Number Number

Landfill 56 827

Land application unit (LAU) 28 354

Waste pile 61 853

Surface impoundment 137 1,930

Aerated tank 137c –c

TOTAL 419 3,964

a Random sample, proportional by industry sector, from the 2,850 Industrial D facilities reporting onsite waste

management. A setting is a unique WMU type/site combination. b Facilities in 1985 Screening Survey of Industrial Subtitle D Establishments (Westat, 1987) reporting onsite

hazardous waste management (out of 15,844 establishments surveyed). c The representative national data set places an aerated tank at every surface impoundment facility (there are no

tank data in the Industrial D Screening Survey data). 

To estimate human and ecological risk for each of the 419 settings, the 3MRA modeling

system uses the Industrial D data on WMU dimensions along with site-based data on

waterbodies, watersheds, soils, and human and ecological receptor types and locations collected

within the 2 km AOI. To supplement these site-based data, EPA collected regional data (e.g.,

meteorological, water quality, and aquifer data) and national data (e.g., exposure factors, waste

properties, chemical-specific data) to satisfy the data requirements of all 17 modules (i.e., five

source, five fate and transport, three food chain/food web, two exposure, and two risk modules). 
Section 1.0 Introduction

1-11

1.5 Data Quality Objectives

Data collection for the 3MRA modeling system involved extracting data from secondary

sources, which are compilations of data that are well documented in terms of how the primary

data were obtained (e.g., through direct measurement of meteorological conditions or by

observation, characterization, and mapping of soil types). For such secondary data collection

efforts, DQOs can be described as clear specifications of the type, amount, and quality of data in 

terms of their end use, which, in this case, is an example data set for 3MRA modeling system

testing. 

Establishing DQOs for the 3MRA modeling system data collection effort involved

systematically planning the data collection methodology around these specifications to ensure

that a high-quality data set would be available for model testing. To assist in this planning

process, a pilot data collection study was conducted to investigate and establish the feasibility of

methods to be employed in the full-scale data collection effort. The end result was the 3MRA

modeling system data collection plan, which was followed and updated as needed during the data

collection effort. The updated plan became the basis for this volume. 

1.5.1 Data Type

For the 3MRA modeling system, the types of data that needed to be collected were

determined by both the input data requirements of the component modules, which are described

in Volume I, and the data collection methodologies that are employed to collect data across the

201 sites and the 419 site/WMU settings to be modeled. Planning the data collection effort

required developing methods for each data source and collection methodology. However, most

of the data types defined in this way were required by multiple 3MRA modeling system

component modules. For example, soil data are used by the Land-based Source Modules

(landfill, waste pile, and LAU), the Surface Impoundment Module, the Watershed Module, the

Vadose Zone Module, and the Aquifer Module, but are collected by a common methodology

(involving geographic information systems [GIS] and database processing) from a common data

source (the State Soil Geographic Data Base [STATSGO] soil database). 

To ensure that the data requirements of the 3MRA modeling system’s component

modules were fully addressed by the data collection effort, detailed crosswalks were developed

between the model inputs and the data types defined for the data collection effort. Table 1-4

illustrates the general crosswalk between these data types and the 3MRA modeling system

component modules by showing which data types each model requires and, for each data type,

whether data were collected for that model on a site-based, regional, or national basis. The data

collection effort was organized around the data types shown in Table 1-4, and this volume

reflects that organization. Each chapter addresses a specific data type and begins with the full list

of module inputs addressed by the collection methodology for that data type. 

1.5.2 Data Amount

The amount of data required for the 3MRA modeling system is substantial; as described

in Section 1.5, the databases and data files that make up the 201-site example data set contain 
1-12

Table 1-4. Data Types Used by the 3MRA Modeling System Module Data Type and Document Section WMU Sec. 3 Waste property Sec. 16 Meteor- ological Sec. 4 Watershed and waterbody layout Sec. 5 Surface water Sec. 6 Soil / vadose zone Sec.7 Aquifer (saturated zone) Sec. 16 Food web/ food chain Sec. 10, 11 Chemical propertya Sec. 17 Bio-uptake and bioaccumu- lation factorsa Sec. 8, 10, 11 Expo

fac

Sec. 

Source Modules Aerated Tank N N R N Landfill S, N N R S N LAU S, N N R S S N Surface Imp. S, N N R S N Waste Pile S, N N R S S N Fate and Transport Modules Air S R Watershed R S S N Surface Water R S R,N N Vadose Zone S N Aquifer S R,N N Aquatic Food Web S R N N N Farm Food Chain S N N N Terres. Food Web S N N N Exposure and Risk Modules Eco. Exposure S R S N N R,Human Exposure S S N N N Eco. Risk N Human Risk 

a Chemical-specific. S = Site-based Data; R = Regional Data; N = National Data; Blanks = Not Applicable to Module.
Section 1.0 Introduction

1-13

more than 700 variables and several million records and are more than 7 gigabytes in size. To

help ensure that data are complete, the 3MRA modeling system contains automated programs

that check the incoming data sets to ensure that all variables requested by the component

modules are present for every site, that all units are specified correctly, and that variables are

within reasonable ranges (see U.S. EPA, 1999b). Similar automated checks were also

programmed into the data processing system, and the outgoing data were thoroughly checked for

completeness and conformance to system specifications. 

1.5.3 Data Quality

The DQOs for the 3MRA modeling system data collection effort included (1) a technical

review of the quality of the incoming data (from the original data sources) to ensure that it was

scientifically correct, obtained from the best available source, and adequate to characterize

national conditions; and (2) comprehensive checks of both manual data entry and automated data

processing to ensure that the data were accurately transferred from the original data sources into

the 3MRA modeling system databases. For example, all manual data entries were checked

against the original source, and automated programs were checked for correct functioning by

hand calculations or independent programs. QA/QC procedures were developed for each data

type in the data collection plan and are included in each of the sections of this volume as well. 

Table 1-5 describes some of the specific data quality objectives and sample activities that

were taken to achieve them. Additional details may be found in the individual sections. 

Section 1.4.3 describes the overall QA/QC methodology and record keeping for the data

collection effort.

Table 1-5. 3MRA Data Quality Objectives: Specific Examples by Data Type

Data Type Data Quality Objectives Activities to Meet Objectives

Spatial Layout

(Section 2)

Accurate facility locations with respect to

waterbodies, land use

Facility matching to EPA locational data

Visual review and adjustment of facility

location

Accurate transfer of spatial layout to 3MRA

database

Consistent grid system for georeferencing

spatial data points

Automated and manual review of spatial

assignments

Waste Management

Unit (Section 3)

Complete and reasonable Industrial D area,

capacity, waste volume data for all sites 

Checks for outliers, unreasonable values

Conditional random replacement of missing

and culled values

Correlated, reasonable dimensional and

operational variables

Engineering relationships with WMU

dimensions, other correlated variables

Senior technical review

(continued)
Table 1-5. (continued)

Section 1.0 Introduction

Data Type Data Quality Objectives Activities to Meet Objectives

1-14

Meteorological

(Section 4)

Comprehensive coverage of 3MRA climatic

conditions

Met. station assignments via GIS processing

of data from every meteorological station

with adequate record length

Data sets with at least 10 years of complete

hourly records

Replacement routines for limited data gaps

Watersheds and

waterbodies

(Section 5)

Accurate representation of stream networks

in digital elevation model (DEMs) coverage

Procedure to burn RF3 stream networks into

DEMs

Correct connectivity between stream reaches

and watersheds; consistent delineations

given inconsistent data sources

Visual review and adjustment of delineated

watersheds and waterbody networks

Surface water

quality and flow

(Section 6)

Data quality concerns with STORET (e.g.,

unrealistic measurements)

Use median values of a 20-measurement

minimum data set (to minimize outlier bias)

Represent local water quality conditions

with adequate data to support median

statistic

Aggregate statistics over smallest drainage

basin meeting 20-measurement minimum

Soil

(Section 7)

Complete, consistent, well-documented

source for soil properties

Extract soil data from STATSGO nationwide

soil database

Represent site-to-site variability while

maintaining correlations between soil

hydrologic properties

Specify hydrologic properties by soil texture

through cross-correlation matrix based on

national distribution

Human exposure

factors (Section 8)

Distributions that accurately reflect

nationwide variability in exposure factors

Use base data from EPA Exposure Factors

Handbook as reputable, peer-reviewed source

Develop distributions using established

statistical techniques; use goodness of fit

routines to select best fit and quantify

uncertainty

Human receptor

locations (Section 9)

Accurate placement and population of

human receptors in area of interest

Use GIS coverages of U.S. Census, land use,

and agricultural census data

Correct assignment and calculation of

receptor types to receptor points

Technical review of methodology; parallel

checks of implementation

Farm food chain/

terrestrial food web

variables

(Section 10)

Best available values from extant literature Use reviewed EPA sources first, then other

compilations and selected literature reviews

to fill data gaps

Aquatic food web

variables

(Section 11)

Best available values from extant literature Develop and apply criteria to identify key,

highly reviewed data sources (compilations,

primary literature, and government databases)

(continued)
Table 1-5. (continued)

Section 1.0 Introduction

Data Type Data Quality Objectives Activities to Meet Objectives

1-15

Ecological exposure

factors (Section 12)

Defensible methods for calculating exposure

factors using data from accepted, well

reviewed sources

Follow methods in EPA Handbook, using

data from Handbook and other well reviewed

compilations of wildlife data

Ecological receptors

and habitats

(Section 13)

Representative habitats that encompass

nationwide variability, are spatially variable

at each site, and are of appropriate detail

Develop and apply classification criteria that

reflect species occurrence and existing

nationwide ecological classifications

Accurately delineated habitats that are

consistent with a site’s spatial layout

Application of GIS tool for delineation and

review of habitats at each site, based on land

use and the waterbody network

Ecological

benchmarks

(Section 14)

Defensible, well-documented benchmarks

for each chemical and receptor type

Assemble database of quantitative effects

from accepted synopses, databases, and

literature review (for data gaps)

Develop and apply taxa-specific methods to

derive benchmarks

Human health

benchmarks

(Section 15)

Defensible, well-documented benchmarks

that reflect latest EPA guidance and science

Apply hierarchy of EPA, State, and ATSDR

data sources to up-to-date database of extant

values,

Aquifer data

(Section 16)

Correlated aquifer inputs that reflect

nationwide variability in aquifer properties

Assign each site to a standard hydrogeologic

environment

Collect correlated data sets from EPA

Hydrogeologic Database

Chemical Properties

(Section 17)

Defensible chemical properties that are

consistent across chemicals

Calculate thermodynamic properties using

SPARC code

Expert criteria-structured literature review for

degradation rates

Media partition coefficients that reflect

nationwide geochemical conditions and

waste chemical concentrations

Calculation of MINTEQA2 sorption

isotherms for soil and groundwater

Comprehensive literature reviews, modeling,

and extrapolation for other media

The example data collection effort was limited to information that could be obtained from

readily available electronic and published data sources. The strategy for collecting data to

support the 3MRA modeling system included both automated and manual collection

methodologies, as follows: 

# Automated methods involved electronic processing of data using a combination of

GIS technology and conventional electronic databases.

# Manual techniques involved desktop analysis, such as literature reviews and

analyses, and manual data entry.
Section 1.0 Introduction

1-16

1.6 Data Collection Methodology

EPA considered data availability, accuracy, sample coverage, and available resources in

developing and choosing collection methodologies for particular data types. There was a general

preference for automated methods, although, in many cases, hybrid methods were required that

involved some manual interaction with data collection or processing programs.

Figure 1-3 provides an overview of the data collection process, showing data sources,

primary data processing operations, and the final files supplied to the 3MRA modeling system.

# Data sources included both conventional electronic data sources and GIS

coverages (spatial data), as well as published literature and professional judgment.

# Data processing operations used both GIS software and more conventional

database programs to process the data to the 3MRA modeling system

requirements. 

# Final data files were exported to the 3MRA modeling system in American

Standard Code for Information Interchange (ASCII) format (meteorological data)

and Microsoft Access databases.

Two separate lines of data processing are depicted in Figure 1-3. The Industrial Source

Complex Short-Term Model, Version 3, (ISCST3) air model requires ASCII files processed

through the PCRAMMET hourly data processing program. Other programs develop the

PCRAMMET input files from meteorological and land use data (from the GIS), as well as

derive, from the hourly data files, the longer-term average (climatic) meteorological ASCII data

files required by other 3MRA modeling system modules (daily, monthly, annual, and long-term

annual average).

The other processing system compiled the 3MRA modeling system input database and

grid data used by the 3MRA modeling system Site Layout Processor to place air points in the

input database. This system employed GIS programs (ArcInfo and ArcView), along with

conventional database processing using structured query language (SQL) and Visual Basic, to

automatically generate the input databases. Data collected from the literature or estimated using

professional judgment were entered into the system manually. Other data were processed from

electronic sources using a combination of GIS programs (for spatial data analysis) and database

programs. 

In general, GIS programs put data within the spatial context required by the modeling

assumptions (i.e., a 2 km AOI; 500 m, 1,000 m, and 2,000 m receptor rings; a single average

WMU for each setting) and interrelated different spatial data coverages using overlays to create

the site layout variables required by the model. Database programs processed these and all other

data to meet system requirements in terms of database format (table structure) and the variable

specifications in the 3MRA modeling system input dictionary (ssf.dic) files.
1-17

Data Sources Data Processing 3MRA Model Inpu

Meteorological Data SAMSON (hourly, surface data) Cooperative summaries (daily precip., surface data) Radiosonde data of North America (upper air data) Literature/Professional Judgment (various data) Conventional Databases Envirofacts (locations) Section 2 STATSGO/CONUS/USSOILS (soils) Section 7 Industrial D survey (WMU) Section 3 HGDB (aquifer) Section 16 STORET (flow data, water quality data) Section 6 GIS Data Coverages Industrial D site locations Section 2 Met. station locations Section 4 USGS Hydrologic Regions Section 6 Baileys Eco Regions Section 13 GIRAS (land use) Section 5,7,9 STATSGO (soils) Section 7 DEMs (topography) Section 5 RF3 (waterbodies) Section 5 NWI (wetlands) Section 5 U.S. Census (population) Section 9 Modeling Assumptions · AOI · Receptor ring distance · Single, average WMU 3MRA System Requirements (U.S. EPA, 1999) · Database format · SSF.DIC files Hourly data processing programs Climatic data processing programs Database programs (MS Access, SQL, Visual Basic) GIS programs (ArcInfo, ArcView) Meteorological Data F

(99 met. stations x 5 ASC

 · Hourly (11 variables) · Daily (10 variables) · Monthly (7 variables) · Annual (8 variables) · Long-Term (9 variables

 3MRA Input Databas

 (201 sites; 419 settin

Model input database tables: · National (439 variables

 · Regional (7 variables) · Site-based (241 variabl

settings) · Correlations · References Grid database tables: · Drinking water wells · Farms · Ecological habitats · Human receptors · Waterbodies · Watershed subbasins long-term only manual entry 

687 variables (0.8 GB) 45 variables (1.1 GB) 

land use, met. stations

Figure 1-3. Overview of 3MRA data collection and processing.
Section 1.0 Introduction 

1-18

1.6.1 GIS Data Collection and Processing

EPA selected a GIS as the platform for collecting much of the site-specific data for the

3MRA modeling system because it can be automated and can perform spatial overlay of

georegistered data. Most of the GIS processing used ArcInfo for UNIX workstations; some

processing occurred in the PC environment with ArcInfo and ArcView. This document uses the

term GIS “program” to refer to arc macro language (AML) scripts, a batch-process scripting 

language used with the ArcInfo GIS software. The term “coverage” refers to a GIS map layer

(e.g., geographically referenced digital points, lines, or polygons with attached data).

The GIS programs provided the following three primary data coverages for this risk

analysis:

# Waterbody and watershed layout (Section 5.0);

# Human receptor locations, including farms and residences (Section 9.0); and

# Ecological habitats and home ranges (Section 13.0).

The data collection effort also used GIS processing to assign sites to meteorological and

hydrologic regions (Sections 3.0 and 6.0), to identify soil types by watershed and WMU (Section

7.0), and to accurately locate facilities (Section 2.0). In general, these GIS methodologies

combined automated and manual techniques. Details on the various GIS methodologies used to

derive these data may be found in the previously referenced sections. Section 2.0 describes the

overall spatial framework used to conduct site-based GIS data collection, data processing, and

3MRA modeling system assessments.

1.6.2 Conventional Database Processing and Export to 3MRA Modeling System

A conventional Microsoft Access database, using Visual Basic and SQL programs for

data processing, was developed to import the GIS and other input data shown in Figure 1-3 and

to process them to create the Microsoft Access input databases required by the 3MRA modeling

system. As shown in Figure 1-3, the Access input data were exported in two primary file

formats: 

# The model input database (687 variables) includes national, regional, and sitebased data tables containing the input variables needed by the 3MRA modeling

system. Appendix 1A at the end of this section shows the structure of these data

tables and Appendix 1B contains a full listing of these variables, by data table

(national, regional, or site-based) and data group. Sections 3.0, 5.0 through 13.0,

and 16.0 of this report describe collection and processing of these data by data

type.

# The grid database includes six data tables containing x,y coordinates for

watersheds, waterbodies, farms, human receptor points, drinking water wells, and

ecological habitats. These are used by the 3MRA modeling system Site Layout

Processor (SLP) to place air receptor points for the Air Module into the model
Section 1.0 Introduction 

1-19

input database and to determine the coordinates for drinking water wells and

surface water discharge points for the Aquifer Module. Section 2.0 describes the

preparation and formatting of the grid database files.

As mentioned previously, meteorological data files (45 variables) were processed and

delivered separately from the Access database as a series of five sets of ASCII data files

containing hourly, daily, monthly, and annual time series data, as well as long-term annual

average climatic data for each meteorological station. Section 4.0 describes preparation of these

files in detail. Together, these data files and databases account for more than 700 variables

requested by the 3MRA modeling system and are almost 2 gigabytes in size uncompressed.

1.6.3 Quality Assurance/Quality Control and Record Keeping

Each of the subsequent sections of this report describes the QA/QC procedures that are

unique to the data type discussed in each section. In addition to these, there are certain

approaches that are common to all activities in this effort. These common approaches may or

may not fit a particular data type, depending on the specific data collection methodology. 

Prior to data collection, EPA developed a basic QA/QC protocol for each data type and

briefed all involved staff to ensure that they were aware of these requirements. Each section of

this report describes these simple protocols. Any necessary deviations from these protocols

during data collection were discussed with and approved by the team leader and the QA officer. 

QC staff checked 100 percent of the data manually entered into the input database from

hardcopy sources after a senior staff member reviewed the data source and highlighted the data

to be entered. Other general QA/QC and record-keeping procedures included the following:

# Recording the name of the staff member performing QC and the date as part of

QC records.

# Maintaining files documenting QC activities. These files were used to track data

sources, data entry, and changes to data, for instance, and included copies of the

hardcopy data sources. 

# Keeping metadata electronically for all electronic data sources.

# For automated import of data from electronic sources, using hand checks and

hand calculations to validate the data extraction system before use. After initial

system validation, QC staff manually checked a sufficient fraction of the data

(usually 5 to 10 percent) to ensure that the data processing system was

functioning properly. Automated checks were also built into the system to detect

data inconsistencies. Data types checked in this fashion included automated

portions of meteorological data, WMU data, soil data, watershed and waterbody

data, soil data, and human and ecological receptor data collection.

# Similar to validation of the automatic data collection, validating the 3MRA input

data processor system by manually checking a portion (usually 5 percent or more)

of the processed data for each variable to ensure that the system was functioning
Section 1.0 Introduction 

1-20

properly. The system also included automated checks to spot inconsistencies.

Finally, the 3MRA modeling system Site Definition Processor (SDP) checked

each database update for missing data and for consistency with the 3MRA

modeling system input data specifications (i.e., the ssf.dic dictionary files).

In several cases, QC checks found that source data were of unacceptable quality (e.g., see

Section 4.0, Meteorological Data). Approaches for spotting such errors and inconsistencies are

described under each data type, as well as how the issues were resolved.

1.7 Organization of This Document

Table 1-4 shows which modules use which types of data and provides a guide to the

organization of the rest of this volume. Section 2 describes the spatial framework of the data

collection effort, including site location and site layout, and provides an overview of the

collection and processing of site-based data. The rest of this report is then organized according to

the major types of model input data shown in Table 1-4. Each report section is generally

organized to provide the following information for the representative national data set data

collection effort: 

# Parameters addressed (by module)

# Data sources

# Data collection methodology, including

– Data selection and retrieval

– Data conversions and derivations

– QA/QC

# Data collected (by parameter)

# Significant issues and uncertainties associated with data collection

# References for data sources and collection methodologies.

Although each section generally follows this scheme and includes this information at a

minimum, some section-to-section variability in structure and content exists because of

differences in the data collection scope and methodologies.

1.8 References

Burns, L.A. 1997. Exposure Analysis Modeling System (EXAMS II): User’s Guide for

Version 2.97.5. EPA-600/R-97/047. U.S. Environmental Protection Agency, Athens,

GA. 

Burns, L.A., D.M. Cline, and R.R. Lassiter. 1982. Exposure Analysis Modeling System

(EXAMS): User Manual and System Documentation. EPA-600/3-83-023. U.S.

Environmental Protection Agency, Environmental Research Laboratory, Athens, GA. 

U.S. EPA (Environmental Protection Agency). 1995. User’s Guide for the Industrial Source

Complex (ISC3) Dispersion Models. Volume II: Description of Model Algorithms. EPA-
Section 1.0 Introduction 

1-21

454/B-95-003b. Office of Air Quality Planning and Standards, Research Triangle Park,

NC.

U.S. EPA (Environmental Protection Agency). 1996a. EPACMTP Sensitivity Analysis. Office

of Solid Waste, Washington, DC.

U.S. EPA (Environmental Protection Agency). 1996b. EPA’s Composite Model for Leachate

Migration with Transformation Products (EPACMTP): Background Document. Office

of Solid Waste, Washington, DC.

U.S. EPA (Environmental Protection Agency). 1996c. EPA’s Composite Model for Leachate

Migration with Transformation Products, EPACMTP, Background Document for Finite

Source Methodology for Chemicals with Transformation Products. R09-96-588. Office

of Solid Waste, Washington, DC.

U.S. EPA (Environmental Protection Agency). 1997. EPA’s Composite Model for Leachate

Migration with Transformation Products (EPACMTP): User’s Guide. Office of Solid

Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 1999a. Risk Characterization Report for the

HWIR 99 Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA).

Office of Solid Waste, Washington, DC.

U.S. EPA (Environmental Protection Agency). 1999b. FRAMES-HWIR Technology Software

System for 1999: System Overview. Athens, GA: Office of Research and Development.

July.

Westat. 1987. Screening Survey of Industrial Subtitle D Establishments. Draft Final Report.

Prepared for U.S. EPA. Rockville, MD: Westat, Inc. December.
Section 1.0 Introduction 

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Appendix 1A Structure of the 3MRA Modeling System Input Database

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Appendix 1A

Structure of the 3MRA Modeling System

Input Database
Appendix 1A Structure of the 3MRA Modeling System Input Database

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Appendix 1A Structure of the 3MRA Modeling System Input Database

1-25

Appendix 1A. Structure of the 3MRA Modeling System

Input Database

Figure 1A-1 shows the data structure for the 3MRA modeling system input database. The

data are stored in a Microsoft Access database composed of seven separate data tables (i.e., the

database is not relational). The table also lists the field (or column) names for each of these

tables.

Three primary data tables with identical structures hold most of the 3MRA modeling

system inputs:

# National_Variable_Distribution_Data, which contains data collected on a

national scale that characterize values across the nation. In this table, the

“Setting_ID” field is always “national” (i.e., for each variable, a single value or

distribution characterizes the variable’s value).

# Regional_Variable_Distribution_Data, which contains data collected for

individual regions to characterize the nationwide distribution of values. In this

table, the “Setting_ID” field contains the region (e.g., meteorological station, U.S.

Geological Survey [USGS] Hydrologic Region, hydrogeologic environment) to

which the data correspond. For a particular site, the 3MRA modeling system

reads the site’s regional assignments from the site-based data table and uses these

data to select the correct regional data from the regional data table.

# Site_Variable_Distribution_Data, which contains site-based data collected by

site. In this table, the “Setting_ID” field contains the setting ID, one of 419

unique site and WMU combinations.

Other 3MRA modeling system database data tables include Cross_Correlation_Data,

which provides correlation coefficients between correlated variables;

User_Defined_Distribution_Data, which provides empirical distributions for specific variables

with a nonnull entry in the “User_Defined_Distribution_Index” field in the primary data tables

above; Facility, which provides, for all 201 sites in the representative national data set, the

number of each type of WMU (landfill, surface impoundment, LAU, waste pile, and aerated

tank); and Reference_Data, which contains reference information (data sources) for each 3MRA

modeling system variable.

Additional detail on the design and structure of the 3MRA modeling system database and

its functioning within the 3MRA modeling system may be found in FRAMES-HWIR Technology

Software System for 1999: System Overview (U.S. EPA, 1999b) and its supporting documents.
Appendix 1A Structure of the 3MRA Modeling System Input Database

1-26

V1_Setting_ID TEXT(20) (IE)

V1_DataGroup TEXT(20)

V1_Variable TEXT(50)

V1_Units TEXT(20)

V1_Index_1 INT

V1_Index_2 INT

V1_Index_3 INT

V1_Index_4 INT

V1_Index_5 INT

V1_Index_6 INT

V2_Setting_ID TEXT(20) (IE)

V2_DataGroup TEXT(20)

V2_Variable TEXT(20)

V2_Units TEXT(20)

V2_Index_1 INT

V2_Index_2 INT

V2_Index_3 INT

V2_Index_4 INT

V2_Index_5 INT

V2_Index_6 INT

Cross_Correlation DOUBLE

Cross_Correlation_Data

Setting_ID TEXT(255)

LF LONG

SI LONG

LAU LONG

WP LONG

AT LONG

facility

RowID LONG (AK)

Setting_ID TEXT(20) (IE)

Data_Group_Name TEXT(20) (IE)

Variable_Name TEXT(20) (IE)

Units TEXT(20) (IE)

Index_1 INT (IE)

Index_2 INT (IE)

Index_3 INT (IE)

Index_4 INT

Index_5 INT

Index_6 INT

Data_Type TEXT(10)

Reference_Index TEXT(20)

Distribution_Type TEXT(12)

Central_Tendency DOUBLE

Variance DOUBLE (IE)

Maximum DOUBLE

Minimum DOUBLE

User_Defined_Dist_Index TEXT(20)

String_Value TEXT(80)

Logical_Value BOOL

Weight SINGLE

AutoFilled BOOL

National_Variable_Distribution_Data

Reference_Description MEMO

Reference_Index TEXT(20)

Reference_Data

RowID LONG (AK)

Setting_ID TEXT(20) (IE)

Data_Group_Name TEXT(20) (IE)

Variable_Name TEXT(20) (IE)

Units TEXT(20) (IE)

Index_1 INT (IE)

Index_2 INT (IE)

Index_3 INT (IE)

Index_4 INT (IE)

Index_5 INT (IE)

Index_6 INT (IE)

Data_Type TEXT(10)

Reference_Index TEXT(20)

Distribution_Type TEXT(12) (IE)

Central_Tendency DOUBLE

Variance DOUBLE (IE)

Maximum DOUBLE

Minimum DOUBLE

User_Defined_Dist_Index TEXT(20)

String_Value TEXT(80)

Logical_Value BOOL

Weight SINGLE

AutoFilled BOOL

Regional_Variable_Distribution_Data

RowID LONG

Setting_ID TEXT(20) (IE)

Data_Group_Name TEXT(20) (IE)

Variable_Name TEXT(20) (IE)

Units TEXT(20)

Index_1 INT (IE)

Index_2 INT (IE)

Index_3 INT (IE)

Index_4 INT (IE)

Index_5 INT (IE)

Index_6 INT (IE)

Data_Type TEXT(10)

Reference_Index TEXT(20)

Distribution_Type TEXT(12) (IE)

Central_Tendency DOUBLE

Variance DOUBLE

Maximum DOUBLE

Minimum DOUBLE

User_Defined_Dist_Index TEXT(20)

String_Value TEXT(80)

Logical_Value BOOL

Weight SINGLE

AutoFilled BOOL

Site_Variable_Distribution_Data

User_Defined_Dist_Index TEXT(20)

Value DOUBLE

CDF DOUBLE

User_Defined_Distribution_Data

Table Structure for 3MRA Input Database

Figure 1A-1. Table structure for 3MRA modeling system input database.
Appendix 1B Model Inputs by Database Table

1-27

Appendix 1B

Model Inputs by Database Table

Table 1B-1. Model Inputs in National Variable Distribution Table . . . . . . . . . . . . . . . . . . 1-23

Table 1B-2. Model Inputs in Regional Variable Distribution Table . . . . . . . . . . . . . . . . . . 1-36

Table 1B-3. Model Inputs in Site Variable Distribution Table . . . . . . . . . . . . . . . . . . . . . . 1-36

Note: Data groups in tables (column 1) correspond to specific 3MRA component modules

requiring inputs. Variables in site layout data group are generally shared by multiple

modules.
Appendix 1B Model Inputs by Database Table

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Appendix 1B Model Inputs by Database Table

1-29

Table 1B-1. Model Inputs in National Variable Distribution Table

Data Group Variable Name Description Units

Air AirSplineAngle angles used in polar mesh degrees

Air AirSplineDistance radial distances of polar mesh m

Air ArrayLen number of particle size categories unitless

Air DryDpStr dry depletion by WMU

Air IceScav scavenging coefficient for frozen precipitation h/s-mm

Air LiqScav scavenging coefficient for liquid precipitation h/s-mm

Air MASSFRAX fraction of each particle size category by WMU fraction

Air MASSFRAXOption flag for internal calculation of PMF (true) or (false) read from

ar.ssf

Air NumAirSplineAngle number of angles used to construct the polar mesh used to

construct the spline

unitless

Air NumAirSplineDist number of distances used to construct the polar mesh used to

construct the spline

unitless

Air PARTDIAM diameter of particles in the distribution :m

Air PARTSICE particle scavenging coefficient for frozen precipitation h/s-mm

Air PARTSLIQ particle scavenging coefficient for liquid precipitation h/s-mm

Air SCIMBYHR number of hours to skip in processing unitless

Air ScimStr first hour to start processing

Air SHight source height m

Air SplineOption 0=no spline; 1 = spline unitless

Air WetDpStr wet depletion, does not vary with WMU

Aquatic foodweb a_fish model slope of BCF regression equation across all tissues in

fish

unitless

Aquatic foodweb a_mus model slope of BCF regression equation for muscle tissue in

fish

unitless

Aquatic foodweb b_fish model intercept of BCF regression equation across all tissues

in fish

unitless

Aquatic foodweb b_mus model intercept of BCF regression equation for muscle tissue

in fish

unitless

Aquatic foodweb BiotaType biota categories

Aquatic foodweb BiotaTypeIndex index of biota unitless

Aquatic foodweb BWFish fish body weight kg

Aquatic foodweb c_fish error term in BCF regression equation across all tissues in fish unitless

Aquatic foodweb c_mus error term in BCF regression equation for muscle tissue in

fish

unitless

Aquatic foodweb FiletFrac fraction of fish that is filet unitless

Aquatic foodweb FishWaterFrac water fraction across all tissues of fish unitless

Aquatic foodweb LipFrac lipid fraction unitless

Aquatic foodweb LipFracMus lipid fraction in fish muscle (filet) unitless

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-30

Aquatic foodweb MaxPreyPref maximum dietary preference for items in the AqFW unitless

Aquatic foodweb MinPreyPref minimum dietary preference for items in the AqFW unitless

Aquatic foodweb MusWaterFrac water fraction in muscle (filet) of fish unitless

Aquatic foodweb NumBiotaTypes number of biota types in a given AqFW unitless

Aquatic foodweb rho_lip density (organic carbon) kg/L

Aquatic foodweb rho_OC density (lipids) kg/L

Aquatic foodweb T3EdibleFish edible T3 fish for human consumption unitless

Aquatic foodweb T3NumEdibleFish number of edible T3 fish in AqFW unitless

Aquatic foodweb T3NumFish number of T3 fish in AqFW fish

Aquifer AL Longitudinal dispersivity m

Aquifer ALATRatio Horizontal Transverse dispersivity m

Aquifer ALAVRatio Vertical Transverse dispersivity m

Aquifer ANIST Anisotropy ratio unitless

Aquifer AquAnaBioRandUnif Uniformly distributed random number used to choose the

anaerobic biodegradation regime: 0=methanogenic; 1= sulfate

reducing

unitless

Aquifer AquDoFracture Logical flag to turn fractures on or off unitless

Aquifer AquDoHetero Logical flag to turn heterogeneity on or off unitless

Aquifer AquRandFractUnif Uniformly distributed random number-used when

AquDoFracture==TRUE

unitless

Aquifer AquRandHeteroNorm Normally distributed random numbers with 0 mean and std of

1-used when AquDoHetero==TRUE

unitless

Aquifer AquRandHeteroUnif Uniformly distributed random number-used when

AquDoHetero==TRUE

unitless

Aquifer BDENS Bulk Density of soil g/cm3

Aquifer FOC Fraction Organic Carbon fraction

Aquifer POR Effective Porosity volume fraction

AT bio_yield biomass yield g/g

AT CBOD BOD (influent) g/cm3

AT d_imp impeller diameter cm

AT d_setpt fraction of tank occupied by sediments (max.) fraction

AT d_wmu depth (liquid) m

AT dmeanTSS particle diameter (mean, waste suspended solids) cm

AT EconLife economic life of a tank/SI year

AT F_aer fraction surface area-turbulent fraction

AT focW fraction organic carbon (waste solids) mass fraction

AT J oxygen transfer factor lb O2/h-hp

AT k_dec digestion (sediments) 1/s

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-31

AT kba1 biologically active solids/total solids (ratio) unitless

AT MWt_H2O molecular weight (liquid [water]) g/mol

AT n_imp impellers/aerators (number) unitless

AT NumEcon number of economic lifetimes

AT O2eff oxygen transfer correction factor unitless

AT Powr impellers/aerators (total power) hp

AT Q_wmu volumetric flow rate (tank) m3

/s

AT rho_l density (liquid [water]) g/cm3

AT rho_part solids density g/cm3

AT TSS_in total suspended solids (influent) g/cm3

AT w_imp impeller speed rad/s

Ecoexposure BodyWt_rec body weight for each receptor kg

Ecoexposure CR_food consumption rate of food items (e.g., plants, animals) for each

receptor

kg/d

Ecoexposure CR_water consumption rate of water for each receptor L/d

Ecoexposure CRfrac_sed consumption rate of sediment for each receptor mass fraction

Ecoexposure CRfrac_soil consumption rate of surficial soil for each receptor mass fraction

Ecoexposure HabitatIndex index of habitat types unitless

Ecoexposure HabitatType description of habitat type (e.g., grassland, pond, forested

wetland)

Ecoexposure MaxPreyPref_HabRange maximum dietary preference for items found in habitat range unitless

Ecoexposure MinPreyPref_HabRange minimum dietary preference for items found in habitat range unitless

Ecoexposure NumHabitat number of habitat types represented unitless

Ecoexposure NumPrey number of potential prey items unitless

Ecoexposure PreyIndex numerical index of potential prey items unitless

Ecoexposure PreyType text description of each potential prey item

Ecorisk DoExposed option on whether to include all receptors (false) or exposed

receptors only (true) in CDF calculations

unitless

Ecorisk EcoRegPercentile policy criterion for selecting critical year for maximum HQ unitless

Ecorisk HabitatIndex index of habitat types unitless

Ecorisk NumHabitat number of habitat types represented unitless

Farm foodchain Fforage_beef fraction of forage grown in contaminated soil ( beef cattle) fraction

Farm foodchain Fforage_dairy fraction of forage grown in contaminated soil ( dairy cattle) fraction

Farm foodchain Fgrain_beef fraction of grain grown in contaminated soil ( beef cattle) fraction

Farm foodchain Fgrain_dairy fraction of grain grown in contaminated soil ( dairy cattle) fraction

Farm foodchain Fsilage_beef fraction of silage grown in contaminated soil ( beef cattle) fraction

Farm foodchain Fsilage_dairy fraction of silage grown in contaminated soil ( dairy cattle) fraction

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-32

Farm foodchain Fw_exfruit fraction of wet deposition that adheres to plant unitless

Farm foodchain Fw_exveg fraction of wet deposition that adheres to plant unitless

Farm foodchain Fw_forage fraction of wet deposition that adheres to plant unitless

Farm foodchain Fw_silage fraction of wet deposition that adheres to plant unitless

Farm foodchain MAFexfruit moisture adjustment factor to convert DW into WW for

exposed above-ground fruits

percent

Farm foodchain MAFexveg moisture adjustment factor to convert DW into WW for

above-ground vegetables

percent

Farm foodchain MAFleaf moisture adjustment factor for wet leaf unitless

Farm foodchain MAFprofruit moisture adjustment factor to convert DW into WW for

protected above-ground fruits

percent

Farm foodchain MAFproveg moisture adjustment factor to convert DW into WW for

protected above-ground vegetables

percent

Farm foodchain MAFroot moisture adjustment factor to convert DW into WW for root

vegetables

percent

Farm foodchain Qp_forage_beef consumption rate: forage (beef cattle) kg DW/d

Farm foodchain Qp_forage_dairy consumption rate: forage (dairy cattle) kg DW/d

Farm foodchain Qp_grain_beef consumption rate: grain (beef cattle) kg DW/d

Farm foodchain Qp_grain_dairy consumption rate: grain (dairy cattle) kg DW/d

Farm foodchain Qp_silage_beef consumption rate: silage (beef cattle) kg DW/d

Farm foodchain Qp_silage_dairy consumption rate: silage (dairy cattle) kg DW/d

Farm foodchain Qs_beef consumption rate: soil ( beef cattle) kg/d

Farm foodchain Qs_dairy consumption rate: soil ( dairy cattle) kg/d

Farm foodchain Qw_beef consumption rate: water ( beef cattle) L/d

Farm foodchain Qw_dairy consumption rate: water ( dairy cattle) L/d

Farm foodchain rho_leaf leaf density g/L FW

Farm foodchain Rp_exfruit interception fraction unitless

Farm foodchain Rp_exveg interception fraction unitless

Farm foodchain Rp_forage interception fraction unitless

Farm foodchain Rp_silage interception fraction unitless

Farm foodchain tp_exfruit length of plant exposure to deposition yr

Farm foodchain tp_exveg length of plant exposure to deposition yr

Farm foodchain tp_forage length of plant exposure to deposition yr

Farm foodchain tp_silage length of plant exposure to deposition yr

Farm foodchain VapDdv vapor phase dry deposition velocity cm/s

Farm foodchain VGag_exfruit empirical correction factor unitless

Farm foodchain VGag_exveg empirical correction factor unitless

Farm foodchain VGag_forage empirical correction factor unitless

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-33

Farm foodchain VGag_silage empirical correction factor unitless

Farm foodchain VGbg_root empirical correction factor unitless

Farm foodchain Yp_exfruit crop yield kg DW/m2

Farm foodchain Yp_exveg crop yield kg DW/m2

Farm foodchain Yp_forage crop yield kg DW/m2

Farm foodchain Yp_silage crop yield kg DW/m2

Human exposure BF event frequency (shower) event/d

Human exposure Bri_cr1 inhalation (breathing) rate (child 1 resident) m3

/d

Human exposure Bri_cr2 inhalation (breathing) rate (child 2 resident) m3

/d

Human exposure Bri_cr3 inhalation (breathing) rate (child 3 resident) m3

/d

Human exposure Bri_cr4 inhalation (breathing) rate (child 4 resident) m3

/d

Human exposure Bri_r inhalation (breathing) rate (adult resident) m3

/d

Human exposure BWa body weight (adult) kg

Human exposure BWc1 body weight (child 1) kg

Human exposure BWc2 body weight (child 2) kg

Human exposure BWc3 body weight (child 3) kg

Human exposure BWc4 body weight (child 4) kg

Human exposure CRb_af consumption rate: beef (adult farmer) g WW/kg/d

Human exposure CRb_cf_2 consumption rate: beef (child 2 farmer) g WW/kg/d

Human exposure CRb_cf_3 consumption rate: beef (child 3 farmer) g WW/kg/d

Human exposure CRb_cf_4 consumption rate: beef (child 4 farmer) g WW/kg/d

Human exposure CRbm_cr_1 consumption rate: breast milk (child 1 resident) mL/d

Human exposure CRfr_cf_2 consumption rate: exposed fruit (child 2 farmer) g WW/kg/d

Human exposure CRfr_cf_3 consumption rate: exposed fruit (child 3 farmer) g WW/kg/d

Human exposure CRfr_cf_4 consumption rate: exposed fruit (child 4 farmer) g WW/kg/d

Human exposure CRfr_cg_2 consumption rate: exposed fruit (child 2 gardener) g WW/kg/d

Human exposure CRfr_cg_3 consumption rate: exposed fruit (child 3 gardener) g WW/kg/d

Human exposure CRfr_cg_4 consumption rate: exposed fruit (child 4 gardener) g WW/kg/d

Human exposure CRfr_f consumption rate: exposed fruit (farmer) g WW/kg/d

Human exposure CRfr_g consumption rate: exposed fruit (gardener) g WW/kg/d

Human exposure CRfs_a consumption rate: fish (adult) g/d

Human exposure CRfs_c_2 consumption rate: fish (child 2) g/d

Human exposure CRfs_c_3 consumption rate: fish (child 3) g/d

Human exposure CRfs_c_4 consumption rate: fish (child 4) g/d

Human exposure CRl_cf_2 consumption rate: exposed vegetables (child 2 farmer) g WW/kg/d

Human exposure CRl_cf_3 consumption rate: exposed vegetables (child 3 farmer) g WW/kg/d

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-34

Human exposure CRl_cf_4 consumption rate: exposed vegetables (child 4 farmer) g WW/kg/d

Human exposure CRl_cg2 consumption rate: exposed vegetables (child 2 gardener) g WW/kg/d

Human exposure CRl_cg3 consumption rate: exposed vegetables (child 3 gardener) g WW/kg/d

Human exposure CRl_cg4 consumption rate: exposed vegetables (child 4 gardener) g WW/kg/d

Human exposure CRl_f consumption rate: exposed vegetables ( adult farmer) g WW/kg/d

Human exposure CRl_g consumption rate: exposed vegetables (gardener) g WW/kg/d

Human exposure CRm_af consumption rate: milk (adult farmer) g WW/kg/d

Human exposure CRm_cf_2 consumption rate: milk (child 2 farmer) g WW/kg/d

Human exposure CRm_cf_3 consumption rate: milk (child 3 farmer) g WW/kg/d

Human exposure CRm_cf_4 consumption rate: milk (child 4 farmer) g WW/kg/d

Human exposure CRpfr_cf_2 consumption rate: protected fruit (child 2 farmer) g WW/kg/d

Human exposure CRpfr_cf_3 consumption rate: protected fruit (child 3 farmer) g WW/kg/d

Human exposure CRpfr_cf_4 consumption rate: protected fruit (child 4 farmer) g WW/kg/d

Human exposure CRpfr_cg_2 consumption rate: protected fruit (child 2 gardener) g WW/kg/d

Human exposure CRpfr_cg_3 consumption rate: protected fruit (child 3 gardener) g WW/kg/d

Human exposure CRpfr_cg_4 consumption rate: protected fruit (child 4 gardener) g WW/kg/d

Human exposure CRpfr_f consumption rate: protected fruit (adult farmer) g WW/kg/d

Human exposure CRpfr_g consumption rate: protected fruit (adult gardener) g WW/kg/d

Human exposure CRpl_cf_2 consumption rate: protected vegetables (child 2 farmer) g WW/kg/d

Human exposure CRpl_cf_3 consumption rate: protected vegetables (child 3 farmer) g WW/kg/d

Human exposure CRpl_cf_4 consumption rate: protected vegetables (child 4 farmer) g WW/kg/d

Human exposure CRpl_cg_2 consumption rate: protected vegetables (child 2 gardener) g WW/kg/d

Human exposure CRpl_cg_3 consumption rate: protected vegetables (child 3 gardener) g WW/kg/d

Human exposure CRpl_cg_4 consumption rate: protected vegetables (child 4 gardener) g WW/kg/d

Human exposure CRpl_f consumption rate: protected vegetables (adult farmer) g WW/kg/d

Human exposure CRpl_g consumption rate: protected vegetables (adult gardener) g WW/kg/d

Human exposure CRr_cf_2 consumption rate: root vegetables (child 2 farmer) g WW/kg/d

Human exposure CRr_cf_3 consumption rate: root vegetables (child 3 farmer) g WW/kg/d

Human exposure CRr_cf_4 consumption rate: root vegetables (child 4 farmer) g WW/kg/d

Human exposure CRr_cg_2 consumption rate: root vegetables (child 2 gardener) g WW/kg/d

Human exposure CRr_cg_3 consumption rate: root vegetables (child 3 gardener) g WW/kg/d

Human exposure CRr_cg_4 consumption rate: root vegetables (child 4 gardener) g WW/kg/d

Human exposure CRr_f consumption rate: root vegetables (farmer) g WW/kg/d

Human exposure CRr_g consumption rate: root vegetables (gardener) g WW/kg/d

Human exposure CRs_cr2 ingestion rate:soil (child 2 resident) kg/d

Human exposure CRs_cr3 ingestion rate:soil (child 3 resident) kg/d

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-35

Human exposure CRs_cr4 ingestion rate:soil (child 4 resident) kg/d

Human exposure CRs_r ingestion rate:soil (adult resident) kg/d

Human exposure CRw_cr1 ingestion rate: drinking water (child 1 resident) mL/d

Human exposure CRw_cr2 ingestion rate: drinking water (child 2 resident) mL/d

Human exposure CRw_cr3 ingestion rate: drinking water (child 3 resident) mL/d

Human exposure CRw_cr4 ingestion rate: drinking water (child 4 resident) mL/d

Human exposure CRw_r ingestion rate: drinking water (adult resident) mL/d

Human exposure DD water droplet diameter cm

Human exposure EFr exposure frequency (adult resident) d/y

Human exposure Fb_f fraction contaminated: beef (farmer) fraction

Human exposure fbp fraction of whole blood that is plasma fraction

Human exposure Ff_s fraction contaminated fish fraction

Human exposure ffm fraction of mother's weight that is fat fraction

Human exposure Ffr_f fraction homegrown: exposed fruit (farmer) fraction

Human exposure Ffr_g fraction homegrown: exposed fruit (gardener) fraction

Human exposure Fl_f fraction homegrown: exposed vegetables (farmer) fraction

Human exposure Fl_g fraction contaminated: homegrown exposed vegetables

(gardener)

fraction

Human exposure Fm_f fraction contaminated: milk (farmer) fraction

Human exposure fmbm fraction of fat in maternal breast milk fraction

Human exposure Fpfr_f fraction homegrown: protected fruit (farmer) fraction

Human exposure Fpfr_g fraction homegrown: protected fruit (gardener) fraction

Human exposure Fpl_f fraction homegrown: protected vegetables (farmer) fraction

Human exposure Fpl_g fraction homegrown: protected vegetables (gardener) fraction

Human exposure fpm fraction of mother's weight that is plasma fraction

Human exposure Fr_f fraction homegrown: root vegetables (farmer) fraction

Human exposure Fr_g fraction homegrown: root vegetables (gardener) fraction

Human exposure Fs fraction contaminated: soil fraction

Human exposure FT3fish fraction of fish consumed that is T3 fish fraction

Human exposure FT4fish fraction of fish consumed that is T4 fish fraction

Human exposure Fw fraction contaminated: drinking water fraction

Human exposure Hn nozzle height cm

Human exposure Rshower shower rate L/min

Human exposure t_sb time in shower and bathroom min

Human exposure t_shower shower time min

human exposure Vbath bathroom volume m3

Human exposure Vn terminal velocity of droplet cm/s

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-36

Human exposure VRbh bathroom to house ventilation rate L/min

Human exposure VRsb shower to bathroom ventilation rate L/min

Human exposure Vshower shower volume m3

Human risk DoExposed option on whether to include all receptors (false) or exposed

receptors only (true) in CDF calculations

unitless

Human risk ExDur_Car_Block exposure duration (carcinogens, residents) unitless

Human risk ExDur_Car_Farm exposure duration (carcinogens, farmers) unitless

Human risk ExDur_NCar_Block exposure duration (noncarcinogens, residents) unitless

Human risk ExDur_NCar_Farm exposure duration (noncarcinogens, farmers) unitless

Human risk LifeTime average receptor lifetime unitless

Human risk RegPercentile policy criterion defining regulatory percentile (not used in

example dataset)

unitless

LAU asdm mode of the aggregate size distribution (till zone surface) mm

LAU bcm boundary condition multiplier (lower boundary) unitless

LAU BDw dry bulk density (waste solids) g/cm3

LAU ConVs settling velocity (suspended solids) m/d

LAU CutOffYr operating life year

LAU Cwmu USLE cover factor (WMU) unitless

LAU deltDiv time step divider (for debugging) unitless

LAU effdust dust suppression control efficiency unitless

LAU focW fraction organic carbon (waste solids) mass fraction

LAU fwmu fraction hazardous waste in WMU mass fraction

LAU Infild input infiltration rate (for debugging) m/d

LAU Lc roughness ratio (till zone surface) unitless

LAU Pwmu USLE erosion control factor (WMU) unitless

LAU RunID run identification label (optional)

LAU solid percent solids (waste) mass percent

LAU Sw silt content (waste solids) mass percent

LAU thetawZ1d input volumetric water content in till zone (for debugging) volume fraction

LAU thetawZ2d input volumetric water content in LAU subsoil zone (for

debugging)

volume fraction

LAU veg fraction vegetative cover (inactive LAU) fraction

LAU vs vehicle speed (mean) km/h

LAU zava averaging depth upper (depth averaged soil concentration) m

LAU zavb averaging depth lower (depth averaged soil concentration) m

LAU zruf roughness height (inactive LAU) cm

LAU zZ1sa depth (modeled soil column, subareas other than WMU) m

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-37

LAU zZ2WMU subsoil layer thickness m

LF asdm mode of the aggregate size distribution (LF waste zone

surface)

mm

LF bcm boundary condition multiplier (lower boundary) unitless

LF BDw dry bulk density (waste) g/cm3

LF deltDiv time step divider (for debugging) unitless

LF DRZ_W depth (root zone in LF waste zone) cm

LF effdust dust suppression control efficiency unitless

LF focW fraction organic carbon (waste) mass fraction

LF fwmu fraction hazardous waste in WMU mass fraction

LF Infild input infiltration rate (for debugging) m/d

LF KsatW saturated hydraulic conductivity (waste) cm/h

LF Lc roughness ratio (LF waste zone surface) unitless

LF mcW volumetric water content (waste on trucks) volume percent

LF porW porosity (total, waste) volume fraction

LF RunID run identification label (optional)

LF SMbW soil moisture coefficient b (waste) unitless

LF SMFC_W soil moisture field capacity (LF waste zone) volume %

LF SMWP_W soil moisture wilting point (LF waste zone) volume %

LF Sw silt content (waste) mass %

LF thetawCd input volumetric water content in LF cover soil (for

debugging)

volume fraction

LF thetawSd input volumetric water content in LF subsoil zone (for

debugging)

volume fraction

LF thetawWd input volumetric water content in LF waste zone (for

debugging)

volume fraction

LF veg fraction vegetative cover (inactive LF cell) fraction

LF vs vehicle speed (mean) km/h

LF zava averaging depth upper (depth averaged soil concentration) m

LF zavb averaging depth lower (depth averaged soil concentration) m

LF zC optional soil cover thickness m

LF zruf roughness height (inactive LF cell) cm

LF zS thickness of liner (or subsoil zone) m

Saturated zone AL longitudinal dispersivity m

Saturated zone ALATratio longitudinal to transverse dispersivity ratio m

Saturated zone ALAVratio longitudinal to vertical dispersivity ratio m

Saturated zone ANIST anisotropy ratio unitless

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-38

Saturated zone DIAM particle diameter (d) mm

Saturated zone FOC fraction organic carbon (aquifer) fraction

SI bio_yield biomass yield g/g

SI CBOD BOD (influent) g/cm3

SI d_imp impeller diameter cm

SI dmeanTSS particle diameter (mean, waste suspended solids) cm

SI EconLife economic life of a tank/SI year

SI focW fraction organic carbon (waste solids) mass fraction

SI hydc_sed saturated hydraulic conductivity (sediment layer) m/s

SI J oxygen transfer factor lb O2/h-hp

SI k_dec digestion (sediments) 1/s

SI kba1 biologically active solids/total solids (ratio) unitless

SI MWt_H2O molecular weight (liquid [water]) g/mol

SI NumEcon number of economic lifetimes

SI O2eff oxygen transfer correction factor unitless

SI rho_l density (liquid [water]) g/cm3

SI rho_part solids density g/cm3

SI TSS_in total suspended solids (influent) g/cm3

SI w_imp impeller speed rad/s

Site Layout AquFEOX fraction iron-hydroxide adsorbent fraction

Site Layout AquLOM leachate organic matter mg/L

Site Layout AquPh average aquifer pH pH units

Site Layout ATIndex uniform distribution needed to select AT index for national

tank data

Site Layout BinRange_Min_C minimum values of bins for human risk -- cancer unitless

Site Layout BinRange_Min_NC minimum values of bins for human risk -- HQ unitless

Site layout BinRange_Label_C Labels for human risk bins -- cancer unitless

Site layout BinRange_Label_NC Labels for human risk bins -- noncancer unitless

Site layout EcoBinRange_Label Labels for ecological risk bins unitless

Site Layout EcoBinRange_Min minimum values of bins for eco risk HQ unitless

Site Layout FarmRcpType type of human receptor (beef farmer, dairy farmer, beef

farmer fisher, dairy farmer fisher)

Site Layout HabGroup group in which habitat type is attributed: 1 = terrestrial,

2 = aquatic, 3 = wetland

not applicable

Site Layout HabRangeRecType type of receptor (e.g., erbivert, omnivert, small

mammal, small bird)

not applicable

Site Layout HumRcpTemp typical shower temperature degrees Celsius

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-39

Site Layout HumRcpType type of human receptor (resident, home gardener, resident fisher, home

gardener fisher)

Site Layout NumBinC number of bins for human -- carcinogen unitless

Site Layout NumBinNC number of bins for human -- noncarcinogen unitless

Site Layout NumEcoBin number of HQ range bins to assign receptor-specific

HQs

unitless

Site Layout NumEcoRing number of eco rings (3rd ring is for entire site) unitless

Site Layout NumFarmRcpType number of farmer receptor types

Site Layout NumHabGroup number of general groups into which habitat types are

placed

unitless

Site Layout NumHumRcpType number of human receptor types

Site Layout NumReceptor complete receptor list across all habitat types unitless

Site Layout NumRecGroup total receptor groups considered (terrestrial plants;

aquatic plants; mammals; birds; amphibians; reptiles;

soil biota; sediment biota; aquatic biota)

unitless

Site Layout NumRing number of rings at site

Site Layout NumTrophicLevel number of possible trophic levels unitless

Site Layout NyrMax maximum model simulation time years

Site Layout ReceptorIndex indices assigned to each receptor unitless

Site Layout ReceptorName receptor name not applicable

Site Layout ReceptorType description of receptor (e.g., red-tailed hawk; aquatic

biota; frog; plants)

not applicable

Site Layout RecGroup general receptor groups (e.g., mammals, birds,

amphibians, reptiles, soil biota, terrestrial plants,

aquatic biota, sediment biota, aquatic plants)

not applicable

Site Layout RecTrophicLevel trophic level into which each receptor falls not applicable

Site Layout RingDistance distance of ring from source edge m

Site Layout SrcArea area of source m2

Site Layout SrcPh average waste/source pH pH units

Site Layout TermFrac peak output fraction for simulation termination fraction

Site Layout WBNfocAbS fraction organic carbon (abiotic solids) fraction

Site Layout WBNfocBioS biotic solids organic content fraction

Site Layout WBNfocSed fraction organic carbon in sediments of stream, lake,

and wetland reaches

fraction

Surface water ahyd_d stream depth hydraulic coefficient a m

Surface water ahyd_W stream width hydraulic coefficient a m

Surface water bhyd_d stream depth hydraulic coefficient b

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-40

Surface water bhyd_W stream width hydraulic coefficient b

Surface water DepthBenthos surficial sediment layer depth cm

Surface water DepthSedRes underlying sediment layer depth cm

Surface water E_sw sediment-water column diffusion coefficient cm2

/sec

Surface water E_thermocline thermocline diffusion coeff. cm2

/sec

Surface water k_PlankCMin plankton carbon mineralization rate constant yr-1

Surface water k_SedG2 sediment mineralization rate constant, G2 fraction yr-1

Surface water k_SedG3 sediment mineralization rate constant, G3 fraction yr-1

Surface water porBenthos surficial sediment layer porosity Lw/L

Surface water rhoDBenthos surficial sediment layer dry bulk density g/mL

Surface water porSedRes underlying sediment layer porosity Lw/L

Surface water rhoDSedRes underlying sediment layer dry bulk density g/mL

Surface water S_upstream upstream suspended solids concentration mg/L

Surface water TrophicIndex trophic index

Surface water v_bury underlying sediment layer burial rate mm/yr

Terrestrial

foodweb

Bv_ecf_plant empirical correction factor for Bv unitless

Terrestrial

foodweb

Fw_exfruit fraction of wet deposition that adheres to plant unitless

Terrestrial

foodweb

Fw_exveg fraction of wet deposition that adheres to plant unitless

Terrestrial

foodweb

Fw_forage fraction of wet deposition that adheres to plant unitless

Terrestrial

foodweb

Fw_silage fraction of wet deposition that adheres to plant unitless

Terrestrial

foodweb

MAFexfruit moisture adjustment factor to convert DW into WW for

exposed above-ground fruits

percent

Terrestrial

foodweb

MAFexveg moisture adjustment factor to convert DW into WW for

above-ground vegetables

percent

Terrestrial

foodweb

MAFforage moisture adjustment factor to convert DW into WW for

forage

percent

Terrestrial

foodweb

MAFgrain moisture adjustment factor to convert DW into WW for

grain (analogy to profruit)

percent

Terrestrial

foodweb

MAFleaf moisture adjustment factor for wet leaf unitless

Terrestrial

foodweb

MAFroot moisture adjustment factor to convert DW into WW for

root vegetables

percent

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-41

Terrestrial

foodweb

MAFsilage moisture adjustment factor to convert DW into WW for

silage

percent

Terrestrial

foodweb

rho_leaf leaf density g/L FW

Terrestrial

foodweb

Rp_exfruit interception fraction unitless

Terrestrial

foodweb

Rp_exveg interception fraction unitless

Terrestrial

foodweb

Rp_forage interception fraction unitless

Terrestrial

foodweb

Rp_silage interception fraction unitless

Terrestrial

foodweb

tp_exfruit length of plant exposure to deposition yr

Terrestrial

foodweb

tp_exveg length of plant exposure to deposition yr

Terrestrial

foodweb

tp_forage length of plant exposure to deposition yr

Terrestrial

foodweb

tp_silage length of plant exposure to deposition yr

Terrestrial

foodweb

VapDdv vapor phase dry deposition velocity cm/s

Terrestrial

foodweb

VGag_exfruit empirical correction factor unitless

Terrestrial

foodweb

VGag_exveg empirical correction factor unitless

Terrestrial

foodweb

VGag_forage empirical correction factor unitless

Terrestrial

foodweb

VGag_silage empirical correction factor unitless

Terrestrial

foodweb

VGbg_root empirical correction factor unitless

Terrestrial

foodweb

Yp_exfruit crop yield kg DW/m2

Terrestrial

foodweb

Yp_exveg crop yield kg DW/m2

Terrestrial

foodweb

Yp_forage crop yield kg DW/m2

Terrestrial

foodweb

Yp_silage crop yield kg DW/m2

Watershed bcm boundary condition multiplier (lower boundary) unitless

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-42

Watershed ConVs settling velocity (suspended solids) m/d

Watershed deltDiv time step divider (for debugging) unitless

Watershed Infild input infiltration rate (for debugging) m/d

Watershed RunID run identification label (optional)

Watershed thetawZ1d input volumetric water content in till zone (for

debugging)

volume

fraction

Watershed zava averaging depth upper (depth averaged soil

concentration)

m

Watershed zavb averaging depth lower (depth averaged soil

concentration)

m

Watershed zZ1sa depth (modeled soil column) m

WP bcm boundary condition multiplier (lower boundary) unitless

WP BDw dry bulk density (waste) g/cm3

WP ConVs settling velocity (suspended solids) m/d

WP CutOffYr operating life year

WP Cwmu USLE cover factor (WMU) unitless

WP deltDiv time step divider (for debugging) unitless

WP DRZ_W depth (WP root zone) cm

WP effdust dust suppression control efficiency unitless

WP focW fraction organic carbon (waste) mass fraction

WP fwmu fraction hazardous waste in WMU mass fraction

WP Infild input infiltration rate (for debugging) m/d

WP KsatW saturated hydraulic conductivity (waste) cm/h

WP mcW volumetric water content (waste on trucks) volume %

WP porW porosity (total, waste) volume

fraction

WP Pwmu USLE erosion control factor (WMU) unitless

WP RunID run identification label (optional)

WP SMbW soil moisture coefficient b (waste) unitless

WP SMFC_W soil moisture field capacity (WP) volume %

WP SMWP_W soil moisture wilting point (WP) volume %

WP Sw silt content (waste) mass %

WP thetawZ1d input volumetric water content in WP (for debugging) volume

fraction

WP thetawZ2d input volumetric water content in WP subsoil zone (for

debugging)

volume

fraction

(continued)
Table 1B-1. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-43

WP vs vehicle speed (mean) km/h

WP zava averaging depth upper (depth averaged soil

concentration)

m

WP zavb averaging depth lower (depth averaged soil

concentration)

m

WP zZ1sa depth (modeled soil column, subareas other than WMU) m

WP zZ2WMU subsoil layer thickness m

Table 1B-2. Model Inputs in Regional Variable Distribution Table

Data Group Variable Name Description Units

Site layout AquGrad regional groundwater gradient

Site layout AquSatk hydraulic conductivity in direction of gradient m/yr

Site layout AquThick saturated zone thickness m

Site layout GWClassIndex count of rows being passed for aquifer GWClass data unitless

Site layout VadThick vadose zone thickness m

Watershed a_BF regression coefficient "a" for baseflow model m/d

Watershed b_BF regression coefficient "b" for baseflow model unitless

Table 1B-3. Model Inputs in Site Variable Distribution Table

Data Group Variable Name Description Units

Air AirData station number for upper air data

Air AnemHght anemometer height m

Air RuralStr rural or urban

Air SHight source height m

Air StartYr first year of meteorological data in the met. file

Air SurfData station number of surface meteorological data

Data Group Variable Name Description Units

Aquifer AquFractureID Indicator for degree of fracturing of saturated porous

media

unitless

LAU C USLE cover factor unitless

LAU CN SCS curve number unitless

LAU CNwmu SCS curve number (WMU) unitless

LAU DRZ depth (root zone) cm

LAU fcult number of cultivations per application unitless

LAU fd frequency of surface disturbance per month (active

LAU)

1/mo

(continued)
Table 1B-3. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-44

LAU focS fraction organic carbon (surface soil) mass fraction

LAU HydroGroup hydrologic soil group

LAU K USLE erodibility factor kg/m2

LAU Ksat saturated hydraulic conductivity (surface soil) cm/h

LAU Kwmu USLE erodibility factor (WMU) kg/m2

LAU mt distance vehicle travels on LAU surface m

LAU Nappl waste applications per year 1/year

LAU nv vehicles/day (mean annual) 1/d

LAU nw wheels per vehicle (mean) unitless

LAU P USLE erosion control factor unitless

LAU Rappl wet waste application rate Mg/m2

-year

LAU SMb soil moisture coefficient b (surface soil) unitless

LAU SMFC field capacity volume %

LAU SMWP wilting point volume %

LAU Ss silt content (soil at lau) mass %

LAU Theta slope (local watershed) degrees

LAU vw vehicle weight (mean) Mg

LAU WCS saturated water content (surface soil) volume

fraction

LAU X flow length (local watershed, all subareas) m

LAU zZ1WMU depth (tilling, LAU) m

LF fd frequency of surface disturbance per month (active LF

cell)

1/mo

LF focC fraction organic carbon (cover soil) mass fraction

LF focS_lf fraction organic carbon (subsoil) mass fraction

LF KsatC saturated hydraulic conductivity (cover soil) cm/h

LF load waste loading rate (dry) Mg/y

LF mt distance vehicle travels on active LF cell surface m

LF Nly number of waste layers in a cell unitless

LF Nop spreading/compacting operations per day 1/d

LF nv vehicles/day (mean annual) 1/d

LF nw wheels per vehicle (mean) unitless

LF SMbC soil moisture coefficient b (cover soil) unitless

LF SMbS soil moisture coefficient b (subsoil) unitless

LF vw vehicle weight (mean) Mg

LF WCS_C saturated water content (cover soil) volume

fraction

LF zW waste zone thickness m

(continued)
Table 1B-3. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-45

Saturated zone AquFractureID indicator for degree of fracturing of saturated porous

media

Saturated zone AquRandHeteroNorm normally distributed random numbers with 0 mean

and std of 1 - used when AquDoHetero ==TRUE

SI d_setpt fraction of SI occupied by sediments (max.) fraction

SI d_wmu depth of wmu m

SI F_aer fraction surface area-turbulent fraction

SI n_imp impellers/aerators (number) unitless

SI Powr impellers/aerators (total power) hp

SI Q_wmu volumetric influent flow rate m3

/s

Site Layout AirTemp temperature (air, annual average, long-term) ° Celsius

Site Layout AquDir groundwater flow direction in degrees from North degrees

Site Layout AquId environmental setting ID for aquifer

Site Layout AquLWSIndex local watershed index for aquifer

Site Layout AquLWSSubAreaIndex LWS subarea index for aquifer

Site Layout AquTemp average aquifer temperature ° Celsius

Site Layout AquVadIndex vadose zone index per aquifer

Site Layout AquWellFracZ fraction of aquifer covered by well screen fraction

Site Layout AquWSSubIndex index of watershed for each aquifer

Site Layout EcoRingHabIndex index of habitat contained within ecoring (1 = 0 -

1km; 2 = 1 - 2 km)

unitless

Site Layout EcoRingNumHab number of habitats contained within each eco ring unitless

Site Layout FarmAquIndex well index associated with each farm

Site Layout FarmAquWellFrac fraction farm uses aquifer well as animal DW source fraction

Site Layout FarmAquWellIndex well index associated with each farm

Site Layout FarmArea area of farm m2

Site Layout FarmBlockGroup census block group associated with farm unitless

Site Layout FarmLWSIndex index of local watersheds associated with each farm not

applicable

Site Layout FarmLWSSubAreaFrac fraction of local watershed subarea on farm fraction

Site Layout FarmLWSSubAreaIndex indices of each local watershed on farm not

applicable

Site Layout FarmNumAquWell number of wells in each aquifer on farm (= 1)

Site Layout FarmNumLWS number of local watersheds on farm

Site Layout FarmNumLWSSubArea number of local watershed subareas on farm not

applicable

Site Layout FarmNumWBNRch number of WBN reach that impact farm or crop area

Site Layout FarmNumWSSub number of watersheds per farm unitless

Site Layout FarmPh pH (subsoil) pH units

(continued)

Site Layout FarmPopulation population of a farm unitless
Table 1B-3. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-46

Site Layout FarmTemp average farm food chain temperature ° Celsius

Site Layout FarmWBNIndex selected WBN associated with each farm

Site Layout FarmWBNRchFrac fraction of farm or crop area impacted by WBN reach fraction

Site Layout FarmWBNRchIndex selected WBNRch associated with each farm

Site Layout FarmWSSubFrac fraction of each watershed on farm unitless

Site Layout FarmWSSubIndex watershed indices associated with each farm not

applicable

Site Layout focS fraction organic carbon (surface soil) mass fraction

Site Layout HabArea area of habitat m2

Site Layout HabIndex index of habitat type unitless

Site Layout HabNumRange number of ranges per habitat unitless

Site Layout HabNumWBNRch number of WBN reaches that impact habitat range unitless

Site Layout HabRangeAreaFrac fraction of range that falls within habitat fraction

Site Layout HabRangeFishWBNInde

x

index of WBN containing fishable reaches that impact

habitat range

unitless

Site Layout HabRangeLWSIndex indices of local watersheds in a habitat range unitless

Site Layout HabRangeLWSSubAFra

c

fraction of LWS subarea within a habitat range fraction

Site Layout HabRangeLWSSubAInd

ex

index of LWS subarea in a habitat range unitless

Site Layout HabRangeNumLWSSub

A

number of local watershed subareas in a habitat range unitless

Site Layout HabRangeNumSISrc number of surface impoundments intersecting habitat

range

unitless

Site Layout HabRangeNumWBNRch number of WBN reaches found within habitat range unitless

Site Layout HabRangeNumWSSub number of watersheds that impact habitat range unitless

Site Layout HabRangeRecIndex receptor index associated with each habitat range (a

single receptor)

unitless

Site Layout HabRangeWBNIndex index of WBN that impacts habitat range unitless

Site Layout HabRangeWBNRchInde

x

Index of WBN reaches that impact habitat range unitless

Site Layout HabRangeWSSubFrac fraction of habitat range impacted by watershed fraction

Site Layout HabRangeWSSubIndex index of watershed that impacts habitat range unitless

Site Layout HabType type of representative habitat (e.g., grassland, pond,

wetland)

Not

applicable

Site Layout HabWBNIndex index of WBN that impacts habitat range unitless

Site Layout HabWBNRchFrac fraction of habitat range impacted by aquatic fraction

Site Layout HabWBNRchIndex index of WBN reaches that impact habitat unitless

(continued)
Table 1B-3. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-47

Site Layout HRangeFishWBNRchIn

de

index of WBN fishable reaches that impact habitat

range

unitless

Site Layout HRangeNumFishWBNR

ch

number of fishable reaches that cross habitat range unitless

Site Layout HumRcpAquIndex well index associated with each farm unitless

Site Layout HumRcpAquWellFrac fraction of population drinking from wells

Site Layout HumRcpAquWellIndex well index associated with a human receptor point unitless

Site Layout HumRcpLWSIndex local watershed indices for each human receptor point not

applicable

Site Layout HumRcpLWSSubAreaIn

de

LWS subarea indices for each human receptor point not

applicable

Site Layout HumRcpPopulation population represented by a human receptor point unitless

Site Layout HumRcpWSSubIndex watershed indices for each human receptor point not

applicable

Site Layout HydroGroup hydrologic soil group

Site Layout HydrologicRegion hydrologic regions

Site Layout MaxSrcArea maximum tank area (= SI SrcArea for AT, null for

other sources)

m2

Site Layout MetSta National Weather Service station number (surface)

Site Layout NumAqu number of aquifers

Site Layout NumAquWell number of wells in an aquifer

Site Layout NumFarm number of farms at site

Site Layout NumHab number of ecological habitats unitless

Site Layout NumHabType number of habitat types represented at the site unitless

Site Layout NumHumRcp number of human receptor points unitless

Site Layout NumVad number of vadose zones

Site Layout NumWBN number of waterbody networks

Site Layout NumWSSub number of watershed subbasins

Site Layout RingFarmFrac fraction of a farm in a ring fraction

Site Layout RingFarmIndex index of a farm in a ring unitless

Site Layout RingHumRcpIndex index of a human receptor point in a ring unitless

Site Layout RingNumFarm number of farms in a ring unitless

Site Layout RingNumHumRcp number of human receptor points in a ring unitless

Site Layout SettingID setting ID (SrcType+SiteID)

Site Layout SiteGeoRefX Easting in UTM coordinates (facility centroid) m

Site Layout SiteGeoRefY Northing in UTM coordinates (facility centroid) m

Site Layout SiteLatitude latitude (source) degrees

Site Layout SiteLongitude longitude (source) degrees

Site Layout SiteUTMZone UTM zone of SiteGeoRefX and SiteGeoRefY

Site Layout SoilType soil texture (subsoil)

(continued)
Table 1B-3. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-48

Site Layout SrcArea area of source m2

Site Layout SrcDepth depth of source (0 for AT, WP) m

Site Layout SrcId environmental setting ID for source

Site Layout SrcLocX WMU Easting in site coordinate system (0) m

Site Layout SrcLocY WMU Northing in site coordinate system (0) m

Site Layout SrcLWSNumSubArea number of local watershed subareas

Site Layout SrcLWSSubAreaArea area of LWS subarea m2

Site Layout SrcLWSSubAreaIndex local watershed subarea containing WMU unitless

Site Layout SrcNumLWS number of local watersheds

Site Layout SrcTemp average waste/source temperature ° Celsius

Site Layout SrcType WMU type (AT, SI, LAU, WP, or LF)

Site Layout VadALPHA soil moisture parameter alpha (subsoil) 1/cm

Site Layout VadBETA soil moisture parameter beta (subsoil) unitless

Site Layout VadId environmental setting ID for aquifer

Site Layout VadLWSIndex LWS index for vadose zone

Site Layout VadPh pH (subsoil) pH units

Site Layout VadSATK saturated hydraulic conductivity (subsoil) cm/h

Site Layout VadSoilType soil type/texture (subsoil)

Site Layout VadTemp soil column temperature (annual average) ° Celsius

Site Layout VadWCR residual water content (subsoil) L/L

Site Layout VadWCS saturated water content (subsoil) L/L

Site Layout WBNDOC dissolved organic carbon (stream, lake, wetland) mg/L

Site Layout WBNFishableRchIndex index of reaches that are fishable unitless

Site Layout WBNId environmental setting id of waterbody network

Site Layout WBNNumFishableRch number of fishable reaches unitless

Site Layout WBNNumRch number of reaches in waterbody network

Site Layout WBNpH pH of stream, lake, and wetland reaches in waterbody

network

pH units

Site Layout WBNRchAquFrac fraction of waterbody network reach impacted by the

aquifer

fraction

Site Layout WBNRchAquIndex index of aquifer that impacts waterbody network reach

Site Layout WBNRchArea reach surface area (nonstream reaches) m2

Site Layout WBNRchBodyType type of waterbody for each reach (lake, stream or

wetland)

Site Layout WBNRchHypoAreaFrac fraction of total surface area for hypolimnion fraction

Site Layout WBNRchLength waterbody reach length m

Site Layout WBNRchNumAqu number of aquifers that impact waterbody reach

Site Layout WBNRchNumRch number of waterbody reaches that impact waterbody

reach

(continued)
Table 1B-3. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-49

Site Layout WBNRchNumWSSub number of watershed subasins that impact waterbody

network reach

Site Layout WBNRchOrder stream order unitless

Site Layout WBNRchRchFrac fraction of waterbody network reach impacted by

another waterbody network reach

fraction

Site Layout WBNRchRchIndex index of waterbody network reach that impacts

waterbody network reach

Site Layout WBNRchSrcLWSFrac fraction of waterbody network reach area impacted by

the source local watershed

fraction

Site Layout WBNRchSrcLWSIndex index of local watershed from source

Site Layout WBNRchType type of waterbody network reach; selected from (headwater, exiting

or other)

Site Layout WBNRchWSSubFrac fraction of watershed subbasin impacting waterbody

network reach

fraction

Site Layout WBNRchWSSubIndex index of watershed subbasin that impacts waterbody

network reach

Site Layout WBNTemp median temperature of stream, lake, and wetland

reaches in waterbody network

° Celsius

Site Layout WBNTempMax maximum temperature of stream, lake, and wetland

reaches in waterbody network

° Celsius

Site Layout WBNTOC TOC of stream, lake, and wetland reaches in

waterbody network

mg/L

Site Layout WBNTSS TSS of stream, lake, and wetland reaches in

waterbody network

mg/L

Site Layout WBNWaterHardness water hardness mg CaCO3

eq/L

Site Layout WSPh pH (subsoil) pH units

Site Layout WSSubArea area (watershed subarea j) m2

Site Layout WSSubNumSubArea number of watershed subbasin subareas (= 1)

Site Layout WSTemp average watershed temperature ° Celsius

Surface water d_epil epilimnion depth m

Surface water d_hypol hypolimnion depth m

Surface water d_pond depth of pond m

Surface water d_wtlnd depth of wetland m

Surface water Q_upstream upstream flow rate m3

/day

Vadose zone POM percentage organic matter g/g

Vadose zone RHOB dry bulk density (subsoil) g/cm3

Watershed C USLE cover factor unitless

Watershed CN SCS curve number unitless

Watershed DRZ depth (root zone) cm

Watershed HydroGroup hydrologic soil group

Watershed K USLE erodibility factor kg/m2

(continued)
Table 1B-3. (continued)

Appendix 1B Model Inputs by Database Table

Data Group Variable Name Description Units

1-50

Watershed Ksat saturated hydraulic conductivity (surface soil) cm/h

Watershed P USLE erosion control factor unitless

Watershed SMb soil moisture coefficient b (surface soil) unitless

Watershed SMFC field capacity volume %

Watershed SMWP wilting point volume %

Watershed Theta slope (watershed) degrees

Watershed WCS saturated water content (surface soil) volume

fraction

Watershed X flow length (watershed) m

WP C USLE cover factor unitless

WP CN SCS curve number unitless

WP CNwmu SCS curve number (WMU) unitless

WP DRZ depth (root zone) cm

WP focS fraction organic carbon (surface soil) mass fraction

WP HydroGroup hydrologic soil group

WP K USLE erodibility factor kg/m2

WP Ksat saturated hydraulic conductivity (surface soil) cm/h

WP Kwmu USLE erodibility factor (WMU) kg/m2

WP load waste loading rate (dry) Mg/yr

WP mt distance vehicle travels on WP surface m

WP Nop spreading/compacting operations per day 1/d

WP nv vehicles/day (mean annual) 1/d

WP nw wheels per vehicle (mean) unitless

WP P USLE erosion control factor unitless

WP SMb soil moisture coefficient b (surface soil) unitless

WP SMFC field capacity volume %

WP SMWP wilting point volume %

WP Theta slope (local watershed) degrees

WP vw vehicle weight (mean) Mg

WP WCS saturated water content (surface soil) volume

fraction

WP X flow length (local watershed, all subareas) m

WP zZ1WMU height (WP) m