### 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, Multipathwa y, 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 co re 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 (E XAMS 

### 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 so ftware 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 wo rking 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 (c hronic) 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 f unctions: (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 pa rameters and provide a statistical measure of variability and uncertainty, i.e., the range and distribution of potential exposures and risks

### 1-4 Section 1.0 IntroductionSiteDefinitionProcessor Site Simulation Files Global Results Files Risk Summary Output FileSite DefinitionMultimedia MultipathwaySimulationCwExit Level Processing Protective Summary Output FileWaste Management Unit Loop (5 Source Types)Sampled Input Data Iteration Loop (nr)Chemical Loop (43 Metals & Organics)CwLoopWaste Management Facility Loop (201 National Sites)System User Interface (SUI)ChemicalPropertiesProcessorMultimediaMultipathwaySimulationProcessorExitLevelProcessorIExitLevelProcessorIIRegionalDatabaseNationalDatabaseSite-BasedDatabaseChemicalPropertiesDatabaseMETDatabaseSite Input DataDatabaseData FileProcessorUser InterfaceCw ≡Waste stream concentrationRiskVisualizationProcessorList of SitesList of ChemicalsHeader Info from SUIWarnings/Errors to SUIMetalIsothermsKeySiteDefinitionProcessor Site Simulation Files Global Results Files Risk Summary Output FileSite DefinitionMultimedia MultipathwaySimulationCwExit Level Processing Protective Summary Output FileWaste Management Unit Loop (5 Source Types)Sampled Input Data Iteration Loop (nr)Chemical Loop (43 Metals & Organics)CwLoopWaste Management Facility Loop (201 National Sites)System User Interface (SUI)ChemicalPropertiesProcessorMultimediaMultipathwaySimulationProcessorExitLevelProcessorIExitLevelProcessorIIRegionalDatabaseNationalDatabaseSite-BasedDatabaseChemicalPropertiesDatabaseMETDatabaseSite Input DataDatabaseData FileProcessorUser InterfaceCw ≡Waste stream concentrationRiskVisualizationProcessorList of SitesList of ChemicalsHeader Info from SUIWarnings/Errors to SUIMetalIsothermsKeyFigure 1-1. 3MRA modeling system design.

### 1-5 Section 1.0 IntroductionTransportSourcesSurfaceWaterVadoseZoneAirWatershedFarm FoodChainAquatic FoodWebTerrestrial FoodWebFoodchainEcologicalExposureEcologicalRiskHumanExposureHumanRiskExposure/RiskAquiferLandApplicationUnitWaste PileLandfillSurfaceImpoundmentAeratedTankThe dashed line indicates that soil concentrations for thelocal (land-based source) and regional watersheds may be added together toestimate 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 modeling system.

### 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 f actors; 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, bi o-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 andcollecting 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 effort s, 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 properties a 

### (Section 17) 

### ! Bio-uptake/bioaccumulation factors a 

### (Sections 8, 10, 11) 

### ! Human health benchmarks a 

### (Section 15) 

### ! Human receptor type and location (Section 9) 

### ! ! Ecological benchmarks a 

### (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

1 The 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 surv ey 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 Facilities a 

### Settings - 2,850 Industrial D Facilities b


### Number Number Landfill 56 827 Land application unit (LAU) 28 354 Waste pile 61 853 Surface impoundment 137 1,930 Aerated tank 137 c



### – c 

### TOTAL 419 3,964

a Random sample, proportional by industry sector, from th e 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) a nd 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 chai n/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 meas urement of meteorological conditions or by observation, characterization, and mapping of soil types). For such secondary data collection efforts, DQOs can be described as clear specifica tions 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 s ource 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 collec tion 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 Section 1.0 IntroductionTable 1-4. Data Types Used by the 3MRA Modeling SystemModuleData Type and Document SectionWMUSec. 3WastepropertySec. 16Meteor-ologicalSec. 4WatershedandwaterbodylayoutSec. 5SurfacewaterSec. 6Soil /vadosezoneSec.7Aquifer(saturatedzone)Sec. 16Food web/food chainSec. 10, 11ChemicalpropertyaSec. 17Bio-uptakeandbioaccumu-lation factorsaSec. 8, 10, 11ExposurefactorSec. 8, 12Receptor/habitattype andlocationSec. 9, 13Healthbench-markaSec. 14. 15Source ModulesAerated Tank N N R NLandfill S, N N R S NLAU S, N N R S S NSurface Imp. S, N N R S NWaste Pile S, N N R S S NFate and Transport ModulesAir S RSWatershed R S S NSurface Water R S R,N NVadose Zone S NAquifer S R,N N SAquatic Food Web S R N N N S,RFarm Food Chain S N N N S,RTerres. Food Web S N N N S,RExposure and Risk ModulesEco. Exposure S R S N N R,N S,RHuman Exposure S S N N N S,R,NEco. RiskN S,R NHuman RiskS,R,N NaChemical-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 Objectiv es 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 Objectiv es 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 Us e 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 Objectiv es 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 a nd 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 structur ed 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 syst em input dictionary (ssf.dic) files.

### 1-17 Section 1.0 IntroductionData Sources Data Processing 3MRA Model InputsMeteorological DataSAMSON (hourly, surface data)Cooperative summaries (daily precip.,surface data)Radiosonde data of North America (upperair data)Literature/Professional Judgment(various data)Conventional DatabasesEnvirofacts (locations)Section 2STATSGO/CONUS/USSOILS(soils)Section 7Industrial D survey (WMU)Section 3HGDB (aquifer)Section 16STORET (flow data, waterquality data)Section 6GIS Data CoveragesIndustrial D site locationsSection 2Met. station locationsSection 4USGS Hydrologic RegionsSection 6Baileys Eco RegionsSection 13GIRAS (land use)Section 5,7,9STATSGO (soils)Section 7DEMs (topography)Section 5RF3 (waterbodies)Section 5NWI (wetlands)Section 5U.S. Census (population)Section 9Modeling Assumptions· AOI· Receptor ring distance· Single, average WMU3MRA System Requirements(U.S. EPA, 1999)· Database format· SSF.DIC filesHourly data processing programsClimatic data processing programsDatabase programs(MS Access, SQL, Visual Basic)GIS programs(ArcInfo, ArcView)Meteorological Data Files(99 met. stations x 5 ASCII files):· Hourly (11 variables)· Daily (10 variables)· Monthly (7 variables)· Annual (8 variables)· Long-Term (9 variables)3MRA Input Databases(201 sites; 419 settings)Model input database tables:·National (439 variables)·Regional (7 variables)·Site-based (241 variables x 419settings)·Correlations·ReferencesGrid database tables:· Drinking water wells· Farms· Ecological habitats· Human receptors· Waterbodies· Watershed subbasinslong-term onlymanual entryTo 3MRA Model:687 variables(0.8 GB)45 variables(1.1 GB)732variables(1.9 GB)land use,met. stationsFigure 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 proce ssing 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 pr ocessing 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, depe nding on the specific data collection methodology.
Prior to data collection, EPA developed a ba sic QA/QC protocol for each data type and briefed all involved staff to ensure that they we re 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. Sec tion 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 (E XAMS 

### II): User’s Guide for Version 2.97.5. EPA-600/R-97/047. U.S. Environm ental Protection Agency, Athens, GA.
Burns, L.A., D.M. Cline, and R.R. Lassiter. 1982. Exposure Analysis Modeling System (E

XAMS 

### ): User Manual and System Documentation . EPA-600/3-83-023. U.S. Environmental Protection Agency, Environm ental Research Laboratory, Athens, GA.
U.S. EPA (Environmental Protection Agency). 1995. User’s Guide for the Industrial Source Complex (ISC3) Dispersion Models. Volu me 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



### 1-22 This page intentionally left blank

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



### 1-23 

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

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



### 1-24 This page intentionally left blank

### 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] Hydrologi c 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



### 1-28 This page intentionally left blank

### Appendix 1B Model Inputs by Database Table



### 1-29 Table 1B-1. Model Inputs in Nati onal 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 construc t the polar mesh used to construct the spline unitless

Air NumAirSplineDist number of distances used to constr uct 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 coeffi cient 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 regre ssion 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 frac tion across all tissues of fish unitless Aquatic foodweb LipFrac lipid fraction unitless Aquatic foodweb LipFracMus lipid frac tion 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 dietar y preference for items in the AqFW unitless Aquatic foodweb MinPreyPref minimum dietar y preference for items in the AqFW unitless Aquatic foodweb MusWaterFrac water fracti on 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 distri buted 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/cm 3 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 O 2 /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) m 3 /s AT rho_l density (liquid [water]) g/cm 3 AT rho_part solids density g/cm 3 AT TSS_in total suspended solids (influent) g/cm 3 AT w_imp impeller speed rad/s Ecoexposure BodyWt_rec body weight for each receptor kg
Ecoexposure CR_food consumption rate of f ood 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 habita t 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 ha bitat 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 incl ude 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 gr own in contaminated soil ( beef cattle) fraction Farm foodchain Fforage_dairy fraction of forage gr own 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 grow n in contaminated soil ( dairy cattle) fraction Farm foodchain Fsilage_beef fraction of silage grow n in contaminated soil ( beef cattle) fraction Farm foodchain Fsilage_dairy fraction of silage gr own 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 adjustme nt 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 adjustme nt factor to convert DW into WW for protected above-ground fruits percent

Farm foodchain MAFproveg moisture adjustme nt 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 consump tion rate: forage (beef cattle) kg DW/d Farm foodchain Qp_forage_dairy consumpti on 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 consumpti on rate: grain (dairy cattle) kg DW/d Farm foodchain Qp_silage_beef consumpti on rate: silage (beef cattle) kg DW/d Farm foodchain Qp_silage_dairy consumpti on 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/m 2 Farm foodchain Yp_exveg crop yield kg DW/m 2 Farm foodchain Yp_forage crop yield kg DW/m 2 Farm foodchain Yp_silage crop yield kg DW/m 2 Human exposure BF event frequency (shower) event/d Human exposure Bri_cr1 inhalation (br eathing) rate (child 1 resident) m 3 /d Human exposure Bri_cr2 inhalation (br eathing) rate (child 2 resident) m 3 /d Human exposure Bri_cr3 inhalation (br eathing) rate (child 3 resident) m 3 /d Human exposure Bri_cr4 inhalation (br eathing) rate (child 4 resident) m 3 /d Human exposure Bri_r inhalation (br eathing) rate (adult resident) m 3 /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 ra te: 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: e xposed fruit (child 2 gardener) g WW/kg/d Human exposure CRfr_cg_3 consumption rate: e xposed fruit (child 3 gardener) g WW/kg/d Human exposure CRfr_cg_4 consumption rate: e xposed 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: expos ed vegetables (child 2 farmer) g WW/kg/d Human exposure CRl_cf_3 consumption rate: expos ed 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: expos ed vegetables (child 4 farmer) g WW/kg/d Human exposure CRl_cg2 consumpti on rate: exposed vegetables (child 2 gardener) g WW/kg/d Human exposure CRl_cg3 consumpti on rate: exposed vegetables (child 3 gardener) g WW/kg/d Human exposure CRl_cg4 consumpti on rate: exposed vegetables (child 4 gardener) g WW/kg/d Human exposure CRl_f consumption rate: expos ed vegetables ( adult farmer) g WW/kg/d Human exposure CRl_g consumption rate: e xposed vegetables (gardener) g WW/kg/d Human exposure CRm_af consumption ra te: 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: pr otected fruit (child 2 farmer) g WW/kg/d Human exposure CRpfr_cf_3 consumption rate: pr otected fruit (child 3 farmer) g WW/kg/d Human exposure CRpfr_cf_4 consumption rate: pr otected fruit (child 4 farmer) g WW/kg/d Human exposure CRpfr_cg_2 consumption rate: pr otected fruit (child 2 gardener) g WW/kg/d Human exposure CRpfr_cg_3 consumption rate: pr otected fruit (child 3 gardener) g WW/kg/d Human exposure CRpfr_cg_4 consumption rate: pr otected fruit (child 4 gardener) g WW/kg/d Human exposure CRpfr_f consumption rate: pr otected fruit (adult farmer) g WW/kg/d Human exposure CRpfr_g consumption rate: pr otected fruit (adult gardener) g WW/kg/d Human exposure CRpl_cf_2 consumption rate: protect ed vegetables (child 2 farmer) g WW/kg/d Human exposure CRpl_cf_3 consumption rate: protect ed vegetables (child 3 farmer) g WW/kg/d Human exposure CRpl_cf_4 consumption rate: protect ed vegetables (child 4 farmer) g WW/kg/d Human exposure CRpl_cg_2 consumption rate: protect ed vegetables (child 2 gardener) g WW/kg/d Human exposure CRpl_cg_3 consumption rate: protect ed vegetables (child 3 gardener) g WW/kg/d Human exposure CRpl_cg_4 consumption rate: protect ed vegetables (child 4 gardener) g WW/kg/d Human exposure CRpl_f consumption rate: protect ed vegetables (adult farmer) g WW/kg/d Human exposure CRpl_g consumption rate: protect ed 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 ra te:soil (child 2 resident) kg/d Human exposure CRs_cr3 ingestion ra te: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 ra te:soil (child 4 resident) kg/d Human exposure CRs_r ingestion ra te:soil (adult resident) kg/d Human exposure CRw_cr1 ingestion rate: dr inking water (child 1 resident) mL/d Human exposure CRw_cr2 ingestion rate: dr inking water (child 2 resident) mL/d Human exposure CRw_cr3 ingestion rate: dr inking water (child 3 resident) mL/d Human exposure CRw_cr4 ingestion rate: dr inking 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 fre quency (adult resident) d/y Human exposure Fb_f fraction contam inated: 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 homegro wn: 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 contamin ated: homegrown exposed vegetables (gardener) fraction

Human exposure Fm_f fraction contam inated: milk (farmer) fraction Human exposure fmbm fraction of fat in maternal breast milk fraction Human exposure Fpfr_f fraction homegro wn: protected fruit (farmer) fraction Human exposure Fpfr_g fraction homegrown: protected fruit (gardener) fraction Human exposure Fpl_f fraction homegrown: pr otected vegetables (farmer) fraction Human exposure Fpl_g fraction homegrown: pr otected 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: r oot 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 contam inated: 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 m 3 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 m 3 Human risk DoExposed option on whether to in clude all receptors (false) or exposed receptors only (true) in CDF calculations unitless

Human risk ExDur_Car_Block exposure dur ation (carcinogens, residents) unitless Human risk ExDur_Car_Farm exposure dur ation (carcinogens, farmers) unitless Human risk ExDur_NCar_Block exposure dur ation (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/cm 3 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 conten t 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/cm 3 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 (w aste 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 c ontent 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/cm 3 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 O 2 /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/cm 3 SI rho_part solids density g/cm 3 SI TSS_in total suspended solids (influent) g/cm 3 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 n eeded 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 recep tor (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 (re sident, 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 m 2 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 cm 2 /sec Surface water E_thermocline thermocline diffusion coeff. cm 2 /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/m 2

Terrestrial foodweb Yp_exveg crop yield kg DW/m 2

Terrestrial foodweb Yp_forage crop yield kg DW/m 2

Terrestrial foodweb Yp_silage crop yield kg DW/m 2

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/cm 3 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 Regi onal 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 be ing passed for aquife r GWClass data unitless Site layout VadThick vadose zone thickness m Watershed a_BF regression coefficien t "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 degr ee 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/m 2 LAU Ksat saturated hydraulic conductivity (surface soil) cm/h LAU Kwmu USLE erodibility factor (WMU) kg/m 2 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/m 2 -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 m 3 /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 a quifer 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 m 2 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 m 2 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 ra nge 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 WB N reaches found within habitat range unitless Site Layout HabRangeNumWSSub number of wate rsheds 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 ha bitat range impacted by watershed fraction Site Layout HabRangeWSSubIndex index of wate rshed 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 ha bitat 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) m 2

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 m 2 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 m 2 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 su rface area (nonstream reaches) m 2 Site Layout WBNRchBodyType type of wate rbody 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 wate rshed 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 wate rshed 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 CaCO 3 eq/L Site Layout WSPh pH (subsoil) pH units Site Layout WSSubArea area (watershed subarea j) m 2 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 m 3 /day Vadose zone POM percentage organic matter g/g Vadose zone RHOB dry bulk density (subsoil) g/cm 3 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/m 2 (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/m 2 WP Ksat saturated hydraulic conductivity (surface soil) cm/h WP Kwmu USLE erodibility factor (WMU) kg/m 2 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