CRWR Online Report 03-02 Dilution attenuation factors in susceptibility assessments: A GIS based method by Gil Strassberg, M.S.E. Graduate Research Assistant and David R. Maidment, Ph.D. Lynn E. Katz, ph.D. Principle Investigators May 2003 CENTER FOR RESEARCH IN WATER RESOURCES Bureau of Engineering Research ? The University of Texas at Austin J.J. Pickle Research Campus ? Austin, TX 78712-4497 This document is available online via World Wide Web at http://www.crwr.utexas.edu/online.shtml Copyright by Gil Strassberg 2003 Dilution attenuation factors in susceptibility assessments: A GIS based method BY Gil Strassberg Report Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of Master of Science in Engineering The University of Texas at Austin May 2003 DEDICATION To my family ACKNOWLEDGEMENTS I would like to thank those that made this study and report possible. First to my advisors Dr. Maidment and Dr. Katz, I am grateful for your support and guidance throughout the project. I would also like to thank the USGS and TCEQ SWAP teams for their professional assistance and financial support. Special thanks to Randy Ulery, Brian Reece and Richard Kiesling, from USGS, and John Meyer and Greg Rogers, from TCEQ, for your personal assistance. Lastly, I would like to thank the CRWR research group for their friendship, assistance and team work. You all made this an exciting and educating experience. I ABSTRACT Dilution attenuation factors in susceptibility assessments: A GIS based method Gil Strassberg, M.S.E. The University of Texas at Austin, 2003 Supervisor: David R. Maidment The 1996 Amendments to the Safe Drinking Water Act require each state to prepare a source water assessment for all public water supplies, emphasizing the importance of protecting water sources. States are required to determine the drinking water source and the origin of contaminants for each public water supply. These assessments determine the susceptibility of individual water sources to contamination from various sources of contamination. The Texas Source Water Assessment Program is a joint effort of the U.S. Geological Survey (USGS) and the Texas Commission on Environmental Quality (TCEQ). Its objective is to construct a methodology for evaluating the relative susceptibility of Texas' Public Water Supplies (PWS) to contamination. The program is a combination of different source and transport components, that when linked together, yield the final susceptibility assessment. The work presented focuses on the development of a dilution attenuation factor component that is integrated into the ground water susceptibility II assessment using GIS. This component is based on a Tier 2 screening model presented in the Texas Risk Reduction Program (TRRP). The Tier 2 model is a steady state model that calculates concentration ratios between contaminated soils and groundwater. The model describes the major natural processes taking place in the environment during contaminants migration in groundwater systems. These processes include dilution, sorption, dispersion and degradation Model inputs include soil, aquifer and chemical properties. The output gives a dilution attenuation factor, which is the ratio between the concentration of pollutants in the soil, at the source of contamination, and in the groundwater at the water supply well. The numerical relationship, between sources of contamination and contaminant concentrations at water supply wells can be used to better estimate the susceptibility of water supplies to contamination. III Table of Contents 1. INTRODUCTION 1 2. TEXAS SOURCE WATER ASSESSMENT PROGRAM 4 2.1 IDENTIFICATION COMPONENT .............................................................. 5 2.2 DELINEATION OF THE CONTRIBUTING AREA COMPONENT ......... 6 2.3 POINT SOURCE COMPONENT ................................................................. 8 2.4 DILUTION ATTENUATION COMPONENT ........................................... 10 3. METHODOLOGY 11 3.1 SOIL SCREENING MODELS .................................................................... 11 3.2 TEXAS RISK REDUCTION PROGRAM ? TIERED APPROACH ......... 12 3.3 TIER 2 MODEL........................................................................................... 14 3.4 MATHEMATICAL FORMULATION ....................................................... 16 3.4.1 Phase 1 ? Soil To Groundwater Pathway............................................ 16 3.4.1.1 Partition equation .............................................................. 18 3.4.1.2 Lateral Dilution Factor ...................................................... 19 3.4.1.3 Assumptions in the soil to groundwater pathway ............. 22 3.4.2 Phase 2 - Advective Transport And Degradation In The Aquifer....... 23 3.4.2.1 Assumptions in the advective transport pathway.............. 26 3.4.3 Computing Dilution Attenuation Factors............................................ 27 3.4.4 Concentration at the source of contamination..................................... 28 3.4.5 Computing the concentration reaching the well.................................. 29 3.4.6 penetrating sources of contamination.................................................. 29 3.5 DATASETS AND APPLICATION OF THE TIER 2 MODEL.................. 30 3.5.1 Soil, aquifer and point sources datasets .............................................. 31 3.5.1.1 Soil datasets....................................................................... 31 IV 3.5.1.2 Aquifer datasets................................................................. 33 3.5.1.3 Precipitation information................................................... 34 3.5.1.4 Potential sources of contamination ................................... 34 3.5.2 Chemical database............................................................................... 36 3.5.2.1 CAS number assignment................................................... 37 3.5.2.2 Contaminant type .............................................................. 38 3.5.2.3 Partition coefficients ......................................................... 38 3.5.2.4 Degradation rates............................................................... 51 3.5.3 Model application using GIS............................................................... 53 3.5.4 Application example for one well ....................................................... 55 4. RESULTS 61 4.1 COMPARISON OF THE DILUTION ATTENUATION RESULTS WITH WATER QUALITY DATA ............................................................. 61 4.2 EXTRACTING SPATIAL TRENDS FROM THE RESULTS................... 66 4.2.1 analysing sources of contamination using spatial functions ............... 66 4.2.2 Correlation between density of contaminant sources, well distribution and water quality detections ............................................ 69 4.2.3 Spatial distribution of the dilution attenuation results ........................ 79 4.2.4 Influence of aquifer properties ............................................................ 86 V 5. CONCLUSIONS 94 APPENDIX 1 ? LIST OF CONTAMINANTS WITH CHEMICAL PHYSICAL PROPERTIES 98 APPENDIX 2 - ORIGINAL AND FINAL CAS NUMBERS AND CONTAMINANTS 105 APPENDIX 3 - PARTITION COEFFICIENTS FOR ORGANIC COMPOUNDS 112 APPENDIX 4 ? PARTITION COEFFITIONS AND DEGRADATION RATES FOR METALS AND INORGANIC COMPOUNDS 119 APPENDIX 5 - DEGRADATION RATES FOR ORGANIC COMPOUNDS 122 REFERENCES 127 List of Figures Figure 1 - Groundwater point source susceptibility process ...................................2 Figure 2 - Creating a drawdown surface from the drawdown of two wells............7 (cell size of grids 60 meters) ...................................................................................7 Figure 3 - Contributing area (for well 1) and the delineated time of travel (years) 8 Figure 4 - Identification of point sources of contamination within the capture zone of a well...........................................................................................................9 Figure 5 - Tiered approach and information needed .............................................13 Figure 6 ? Relationship between the source of contamination and the supply well .......................................................................................................................15 Figure 7 - Dilution of contaminant in the mixing zone.........................................17 Figure 8 - Transport from the end of the mixing zone to the well includes advection dispersion and degradation ...........................................................24 Figure 9 - Using spatial and non spatial information to describe potential sources of contamination............................................................................................35 Figure 10 - LogC-pH diagram for total Al +3 .........................................................48 Figure 11 - Model application using GIS..............................................................54 VI Figure 12 - Assessed well and Benzene potential sources of contamination........56 Figure 13 - Wells contributing to a monitoring point on the water supply network .......................................................................................................................62 Figure 14 - Potential sources of contamination for BTEX compounds ................67 Figure 15 - Density (number of sources per square km) of potential sources of contamination for BTEX compounds ...........................................................68 Figure 16 - Density of potential sources of contamination for TCE group compounds (sources per square km) .............................................................69 Figure 17 - BTEX measurements above the detection limit (0.1 microgram/l)....70 Figure 18 - BTEX detections compared with well density ...................................71 Figure 19 - TCE group detections compared with well density............................72 Figure 20 - BTEX detections aggregated by water supply compared with density of wells ..........................................................................................................73 Figure 21 - TCE group detections aggregated by water supply compared with well density ...........................................................................................................74 Figure 22 - Density of BTEX detections in the ?Finished Water? database.........75 Figure 23 - Density of BTEX detections aggregated by water supply..................76 Figure 24 - High density of BTEX detections in the ?Finished Water? database.77 Figure 25 - Density of BTEX detections when aggregated by PWS ....................78 Figure 26 - BTEX detections for both methods of aggregation............................79 Figure 27 - High BTEX concentrations computed in the dilution attenuation component .....................................................................................................80 Figure 28 - TCE High concentrations computed in the dilution attenuation component .....................................................................................................81 Figure 29 - BTEX detections in the ?Finished Water? database overlying a density map of high concentrations from the dilution attenuation component..........82 Figure 30 - TCE group detections in the ?Finished Water? overlying a density map of high concentrations from the dilution attenuation component..........83 VII Figure 31 - Densities of monitored (left) and predicted (right) high concentrations for BTEX compounds ...................................................................................84 Figure 32 - Densities of monitored (left) and predicted (right) high concentrations for TCE group compounds............................................................................85 Figure 33 - Trinity and Coastal lowlands aquifer systems....................................86 Figure 34 - Correlation chart of the coastal lowlands aquifer system (USGS, 1996)..............................................................................................................87 Figure 35 - Correlation chart of the Edwards - Trinity aquifer system (USGS, 1996)..............................................................................................................88 Figure 36 - Density of BTEX detections in the Dallas and Houston ....................89 Figure 37 - Density of detections of TCE group contaminants in the Dallas area from the ?Finished Water? database .............................................................90 Figure 38 - Density of detections of TCE group contaminants in the Houston area from the ?Finished Water? database .............................................................91 Figure 39 - Density of detections of TCE group contaminants from the aggregated ?Finished Water? database ............................................................................92 List of tables Table 1 ? Background pore water chemistry assumed for the MINEQL simulations (EPA, 1996) ...............................................................................46 Table 2 - Results of Aluminum modeling with MINEQL ....................................48 Table 3 - Results of Cadmium modeling with MINEQL......................................50 Table 4 ? Chemical properties of benzene ............................................................55 Table 5 - Source and soil properties......................................................................57 Table 6 - Aquifer and flow properties...................................................................57 Table 7 - Dilution Factor (DF) calculation results ................................................58 Table 8 - Results of the Attenuation Factor (AF) calculation...............................59 VIII Table 9 - Results of the DAF and concentration calculations...............................59 Table 10 - List of contaminants used in the comparison, their threshold and water quality standard .............................................................................................64 Table 11 - comparison of water supplies with detections and the ones that have identified sources of contamination ..............................................................65 1 1. INTRODUCTION The 1996 Amendments to the Safe Drinking Water Act require each state to prepare a source water assessment for all public water supplies, emphasizing the importance of protecting water sources. States are required to determine the drinking water source and the origin of contaminants for each public water supply. These assessments determine the susceptibility of individual water sources to contamination. The 1996 amendments resulted in the development of source water assessment programs in each state, supervised by the U.S. EPA. The Texas Source Water Assessment Program is a joint effort of the US Geological Survey (USGS) and the Texas Commission on Environmental Quality (TCEQ). Its objective is to construct a methodology for evaluating the relative susceptibility of Texas' Public Water Supplies (PWS) to contamination. These assessments may benefit the public by focusing source water protection efforts on highly susceptible water supplies, potentially reduce water supply monitoring costs and support the implementation of best management practices in water supplies. The methodology applied in the Texas Source Water Assessment Program is a combination of different source and transport components, that when linked together, yield the final susceptibility assessment. The program evaluates surface and groundwater water supplies. This research is focused on the groundwater section of the program, thus only the groundwater section is described. The components of the groundwater assessment include identification of water supplies, delineation of capture zones, 2 identification of non-point sources of contamination, point sources of contamination and attenuation. The susceptibility assessment begins with the identification of the water supply and the corresponding aquifer. Next, the delineation component defines the contributing area around the well, and point sources of contamination are identified within the contributing zone. Sources are then associated with specific constituents. Finally contaminants dilution and attenuation processes are simulated to determine the concentration of each contaminant at the water supply location. Figure 1 - Groundwater point source susceptibility process The work presented focuses on the development of a dilution attenuation factor component that can be integrated in the ground water susceptibility assessment. This component is based on a Tier 2 screening model presented in the Texas Risk Reduction Program (TRRP). The Tier 2 model is a steady state model that calculates concentration ratios between contaminated soils and groundwater. The model inputs include soil, aquifer and chemical properties. The output gives a dilution attenuation factor, which is the ratio between the concentration of pollutants in the soil, at the source of contamination, and in the groundwater at the supply well. Soil screening models are used extensively in risk reduction assessments to help decision making for contaminated soil sites. The screening models consider contaminant migration through the unsaturated zone to the water table 3 and the mixing of contaminants when they reach the aquifer. This process results in dilution of the contaminant?s concentration. Groundwater transport in the saturated zone, to the receptor well, further reduces the concentration through sorption, dispersion and degradation. The dilution attenuation factor combines these processes into one numerical value that relates contaminant concentrations at the source of contamination and the receptor well. In the Texas source water assessment program over 200 chemicals of concern are assessed for over 850,000 potential sources of contamination and 13,000 water supply wells. Therefore, construction of a computerized method based on Geographic Information Systems (GIS) is ideal for calculating dilution attenuation factors for each source of contamination. The Tier 2 screening model provides a logical method to combine the soil, aquifer and chemical properties into a GIS based method. This method uses GIS capabilities to retrieve source and aquifer properties and compute dilution attenuation factors, which are then used to determine the susceptibility of water supplies to point sources of contamination. The relationship between the sources identified in each contributing zone and their potential effects on water quality are utilized in determining the susceptibility of wells to contamination. This relationship allows one to not only identify the sources of contamination but to relate them to water quality through a physical model. The results of this model help determine the susceptibility of each well to contamination from potential point sources. 4 2. TEXAS SOURCE WATER ASSESSMENT PROGRAM The Texas source water assessment program was mandated to develop a scientifically defensible methodology for assessing the susceptibility of Texas public water supplies to contamination. Susceptibility of a public water supply is defined as the potential for the public water supply to withdraw water containing a listed contaminant(s), at a concentration that would pose concern (USGS, 2000). The program is divided into three main subjects: Software and database structure, ground water assessments and surface water assessments. This study focuses on the groundwater susceptibility section of the program, thus only a detailed description of the groundwater section is provided. Detailed information regarding the surface water component and the software and database structure design are presented in the documentation of the program (USGS, 2000 and USGS, 2002). The groundwater section of the assessment includes several components combined to assess the susceptibility of public water supply wells. Such components are the identification, delineation of contributing area, point and non point sources, contaminant occurrence, attenuation and susceptibility determination. The complete assessment utilizes the results of all components to yield the final susceptibility assessment. The attenuation component, which is the main subject of this study, is built upon other components in the assessment. Thus a brief description of the identification, delineation and point source components is provided. 5 2.1 IDENTIFICATION COMPONENT The first step in a water susceptibility assessment is to identify the source of the water, in the case of groundwater the source refers to the aquifer that the assessed well is deriving its water from. The hydrological and geological characteristics of the aquifer have a major effect on the water quality. Obviously different aquifers will yield varying susceptibilities due to differences in aquifer properties. Historically, nine major and twenty minor aquifers have been mapped in Texas (Ashworth and Hopkins, 1995). In the source water assessment program these aquifers were subdivided into about 40 aquifer codes for which sufficient aquifer detail is available (USGS, 2002). The aquifers were then assigned an aquifer type, which is used to determine the capture zone. The following aquifer types were used in the program. 1. Unconfined isotropic aquifers 2. Confined isotropic aquifers 3. Alluvial aquifers along major rivers 4. Anisotropic karst aquifers 5. Other aquifers Aquifer type 5 is used where the well is screened in an aquifer that is not included in the 40 aquifer codes, or when the aquifer type cannot be defined. 6 The source of the water, aquifer code, is determined for each well in the susceptibility assessment based on screening information. Once the aquifer and aquifer type are identified the contributing zone of the well can be delineated with the delineation component of the assessment. 2.2 DELINEATION OF THE CONTRIBUTING AREA COMPONENT The contributing area of the well and time of travel are delineated for each public water supply well. A grid based delineation process uses aquifer properties and well draws down to determine the contributing area of the well. Within this area flow paths and velocities can be computed and combined to compute the time of travel to the well. Different applications are used for varying aquifer types. All methods use a regional potentiometric surface representation of the aquifer and a draw down surface, computed using the Theis equation for unconfined and confined aquifers. The drawdown surface is deducted from the regional potentiometric surface to yield a new surface that describes the actual aquifer potentiometric surface including the influence of well discharges. Once this surface is computed flow paths and velocities can be derived using the gradients in the computed surface and aquifer properties. The following images demonstrate this process, a full description of the delineation processes are detailed in the groundwater section of the program's strategy (Texas Natural Resource Conservation Commission, 1999). 7 The following example illustrates the computation of the draw down surface and the delineation of the contributing area. In this example two wells create a draw down surface, which is then subtracted from the regional water table to create the actual potentiometric surface. Then the flow direction, and time of travel can be determined and a contributing area can be outlined. Figure 2 shows the summation of the drawdown surfaces (draw down values are given in feet and the grids? cell size is 60 meters). Figure 2 - Creating a drawdown surface from the drawdown of two wells (cell size of grids 60 meters) Once the drawdown surface is computed it is deducted from the regional potentiometric surface to create a new surface. This surface describes the actual Drawdown surface from Well 2 Well location Sum of drawdown surfaces Drawdown surface from Well 1 8 potentiometric surface/water table that includes the influence of discharge from multiple wells. From this surface gradients between cells can be computed and flow direction and velocities can be calculated using the gradients, hydraulic conductivity and the aquifer porosity. With the flow direction, flow paths can be determined and a contributing area can be delineated for each well. Using the flow path length and the flow velocity, the time of travel can be computed for each grid cell in the contributing area. Figure 3 shows the delineated area over the drawdown surface and the time of travel, in years, for water to reach the well. Figure 3 - Contributing area (for well 1) and the delineated time of travel (years) 2.3 POINT SOURCE COMPONENT In this component, potential point sources of contamination are identified within each delineated contributing area. Point and non point sources may introduce similar contaminants into the environment, but the point sources can be geographically located, assigned coordinates and categorized. In the assessment 9 each source type is related to a list of contaminants and source properties, such as the extent of the source, are estimated. When a point source of contamination is located in the contributing area of a well, one can use the list of related contaminants and the source properties to assess the impact on water quality at the well. Figure 4 illustrates this process, where coverage of potential sources is intersected with a contributing area. Then the potential sources of contamination and their properties are identified and the results are used as inputs in the dilution attenuation component. Figure 4 - Identification of point sources of contamination within the capture zone of a well The processes described in the delineation and point sources component are feasible due to extensive data and application development undertaken in the source water assessment program by the USGS and TCEQ. These processes provide the inputs for the dilution attenuation component described next. 10 2.4 DILUTION ATTENUATION COMPONENT Contaminants that are introduced into the ground water environment can undergo chemical, physical and biochemical processes that result in the reduction of contaminant concentrations. A soil-screening model is used to estimate the concentration reduction between the contaminant points of introduction, the potential sources of contamination, and the assessed well. The soil-screening model used in this component is a Tier 2 model, which is described in the Texas Risk Reduction Program (TRRP, Tier 2 PCL Equations). This is a steady state model that calculates dilution of contaminants leaching from soil layers into the water table and the transport and degradation of the constituents in the aquifer. The model incorporates a two-phased approach for describing the movement of contaminants from the soil, at the source of contamination, to the water supply well. This component yields a numerical relationship, a dilution attenuation factor, between the source of contamination and the assessed well. This relationship can be used to estimate the final concentration of contaminants at the water supply well and help in determining the wells susceptibility to contamination. The incorporation of the Tier 2 model into the susceptibility assessment is the main objective of this study. The model and its application are described in detail in the methodology section of this report. 11 3. METHODOLOGY The methodology section outlines the concepts of soil screening models, the formulation of the Tier 2 soil model and its incorporation in the source water susceptibility program. The model is used to compute the reduction in concentration between potential sources of contamination and water supply wells. 3.1 SOIL SCREENING MODELS Screening models can be described as generic models. These models are based on a simplified interpretation of the natural system, in this case the groundwater system, which enables the development of an analytical solution for the transport problem (Charbeneau and Weaver, 1992). Site-specific models can usually provide a higher level of detail and accuracy than analytical solutions, but they also require the application of numerical methods and detailed site information. The screening models have the advantage of simplicity and require less site-specific information and computation resources. Many states and regulatory agencies have adopted soil screening models for determining action based decisions at contaminated sites. Soil screening models allow the use of simple models to determine whether the site obtains any risk. Generally, at sites where contaminant concentrations fall below the soil screening levels, no further action is needed. In cases where the screening model determines the site might obtain a risk, more detailed studies or cleanup actions may be needed. 12 The U.S Environmental Protection Agency (EPA) developed soil screening guidance to help standardize and accelerate evaluations and cleanups of contaminated soils at sites on the National Priorities List (U.S EPA, 1996). This guidance outlines a method for calculating soil-screening levels to evaluate contaminated sites. The model used as the basis for this method is the EPA Composite Model For Leachate Migration with Transformation Products (EPACMTP). This model assesses groundwater quality impacts due to migration of wastes from surface waste sites. The model simulates the transport and attenuation of contaminants from their point of introduction to the water table and within the saturated zone. Applying this model with conservative assumptions and default values together with toxicity information allows the calculation of generic soil screening levels. The Texas Risk Reduction Program uses a similar approach. It uses a tiered approach where generic soil screening levels are determined in the first tier and detailed risk assessments are allowed in the second and third tiers, where more detailed information is available. This approach is described in detail in the following section, especially the Tier 2 model that is incorporated in the source water susceptibility assessment. 3.2 TEXAS RISK REDUCTION PROGRAM ? TIERED APPROACH The Texas Risk Reduction Program applies a tiered approach for evaluating contaminated sites. The most conservative method, in the Tier 1 part, applies pre-calculated general protective concentrations based on general soil and aquifer defaults and toxicity information. The assessment progresses into more 13 detailed risk assessments in Tiers 2 and 3. Tier 2 and 3 demand site-specific information regarding the soils and aquifer characteristics, while the Tier 1 model is only dependent on the area of the contaminant source. The Tier 2 model can also fall within the generic model category. Although it utilizes site-specific information, the mathematical formulation is generalized to provide an analytical solution for the groundwater transport problem. This solution provides a more detailed screening model than the Tier 1 model, but the mathematical formulation can still be solved without the use of numerical methods and extensive information is not required. The Tier 2 model also allows the use of general default values where site-specific information is not available. The following figure shows the relationship between the applied model and the information needed. Figure 5 - Tiered approach and information needed 14 3.3 TIER 2 MODEL The Tier 2 model described in the Texas Risk Reduction Program (TRRP, Tier 2 PCL Equations) is used in the susceptibility assessment to compute dilution attenuation factors. Using the dilution attenuation factors one can determine the potential of a water source to be contaminated. The model simulates the downward infiltration of contaminants from sources of contamination into the aquifer and then the transport within the saturated zone to the well. The processes simulated by the model include dilution within the aquifer, sorption to soil, dispersion and degradation. A standard linear soil-water equilibrium equation is used to estimate conservative contaminant concentrations in the soil at the source of contamination. The model is a combination of two separate modules that solve independent parts of the transport problem. The first phase of the model describes the migration from soil to the ground water. This section simulates the mixing of contaminants into the aquifer due to vertical infiltration from the contaminated soil layers above the water table, resulting in a dilution factor. The second phase describes the transport within the saturated zone to the receptor well. This part simulates sorption, dispersion and degradation occurring over this pathway. The result of this section is an attenuation factor that represents the reduction of the concentration due to these processes. Both pathways are combined to yield a dilution attenuation factor that represents the overall concentration reduction for the complete pathway (from the 15 contaminated soil to the assessed well). The dilution attenuation factor is combined with a source term to yield a conservative concentration reaching the well from a contaminated site. This relationship is shown in equation 1 DAFCC sourcewell ?= (1) where C source is the concentration in contaminated soil at the source of contamination (mg/kg-soil), DAF is the dilution attenuation factor between the source and the well, and C well is the contaminant concentration reaching the well from the source (mg/l). The computed concentrations can be used to estimate the wells susceptibility to contamination. The following section presents the mathematical formulation of the model. Figure 6 ? Relationship between the source of contamination and the supply well 16 3.4 MATHEMATICAL FORMULATION The soil screening methodology was designed for use during early stages of site evaluation, when information about the soil and aquifer characteristics is limited. These constraints led to the development of a methodology that is based on conservative, simplifying assumptions simulating the release and transport of contaminants in groundwater systems. The model is described here as two separate pathways and a source term and the simplifying assumptions for each are also presented. The development of the model is described in detail in the EPA soil screening guidance (U.S EPA, 1996). Obviously, any transport model is dependent on soil and aquifer properties as well as the physical-chemical characteristics of the contaminant modeled. In this section a generic model is shown, this model can simulate the transport of any contaminant when the appropriate information is provided. The assembly of a chemical database that holds the appropriate information for the assessed contaminants is described in section 3.5.2. 3.4.1 PHASE 1 ? SOIL TO GROUNDWATER PATHWAY The soil to ground water pathway simulates the dilution of the contaminant infiltrating into the aquifer. This results in a Dilution Factor (DF), which describes the ratio of contaminant concentrations between the groundwater and the soil. The model is based on simple water and mass balances. A basic assumption is the creation of a mixing zone where contaminant concentrations are well mixed and diluted. The horizontal dimensions of the mixing zone depend on the extent of the contaminated site, and its depth is a function of the infiltration 17 rate and aquifer properties. Figure 6 illustrates the steady-state dilution model where a constant flux of contaminant is introduced into a mixing zone within the aquifer, resulting in a surface of constant concentration at the end of the mixing zone. The concentration from the end of the mixing zone is advectively transported and attenuated during this process. Figure 7 - Dilution of contaminant in the mixing zone Equation 2 is the governing equation used to calculate the ratio between groundwater concentration, C GW (g/cm 3 ), and the soil concentration, C soil (g/g- soil). This formula uses the partition equation to estimate the contaminant release from the soil into the water phase and the lateral dilution formula to compute the 18 concentration after dilution. A description of both formulations is provided in sections 3.4.1.1 and 3.4.1.2. Equation 2 yields the dilution factor (DF), which is the first term required for the dilution attenuation factor calculation 1 2 ' L L LDF asH bd K ws b Soil C GW C DF ? ? ? ? ? ? ? ? ? ? ? = ++ = ??? ? (2) where b ? is the soil bulk density (kg/liter), ws? the volumetric water content of vadose zone soils (cm 3 -water/cm 3 -soil), d K the soil water partition coefficient (cm 3 -water/g-soil), H? the Henry's low constant and as ? the volumetric air content of vadose zone soils. L 1 and L 2 are the thickness of affected soil and the depth from the affected soils top to the groundwater table, respectively. 3.4.1.1 Partition equation The soil-water partition equation is used to estimate contaminant release into soil leachate. The model is based on linear partitioning relations and local equilibrium between the phases of the soil. The local equilibrium assumption allows the expression of the bulk concentration in the soil in terms of a single phase. These relationships can be written with the water phase serving as the reference phase, and the bulk concentration (the mass of constituent per bulk volume) can be written as a function of the soil properties and partitioning properties (Charbeneau, 2000). This assumption allows the calculation of the 19 concentration in the water phase based on the bulk soil concentration. Eq. 3 shows this relationship where m is the bulk concentration (mg/l-soil). w CasH bd K ws m )'( ??? ++= (3) The bulk concentration can also be expressed as bt Cm ??= (4) where C t is the total mass of contaminant in the soil (mg/kg-soil) and C w the concentration in the water phase of the soil (mg/l). Combining equations 3 and 4 yields the formulation used in equation 5 to represent the contaminant release from soil leachate. asH bd K ws b t C w C ??? ? '++ = (5) This equation gives a ratio between the concentration of contaminant in soil and the concentration in the water phase. Using this relationship, in equation 2, the groundwater concentration in the mixing zone can be computed with the soil concentration and the lateral dilution factor. 3.4.1.2 Lateral Dilution Factor The lateral dilution factor is computed using a simple water balance equation. The calculation assumes the existence of a mixing zone where contaminants infiltrating into the aquifer mix and dilute to create a surface of 20 constant concentration at the end of the mixing zone (as shown in figure 7). The following equation gives the lateral dilution factor sf gwgw WI U LDF ? +=1 (6) where gw U is the groundwater Darcy velocity (cm/year), f I the net infiltration rate through soil (cm/year), gw ? the ground water mixing zone thickness (meters) and W s the lateral width of affected vadose zone in direction of groundwater flow (meters). In the susceptibility assessment the lateral width of contaminated soil (W s ) is estimated as the width of its source. TCEQ has estimated areas of contaminant sources based on the type of the source. Within the water assessment program every point source type is associated with an area, the width is then calculated assuming the source of contamination has a rectangular shape. The mixing zone depth ( gw ? ) is estimated with the method used in the EPA Composite Model for Landfills (EPAMCL). The following equation shows the formulation for computing the depth of contaminant penetration, which defines the mixing zone depth ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ?+= gwgw sf gwsvgw bU WI bW exp1)2( 5.0 ?? (7) where v ? is the vertical groundwater dispersivity (meters), b gw the aquifer thickness (meters) and f I the net infiltration rate through soil (cm/year). 21 The first term in this formula, 5.0 )2( sv W? , estimates the vertical dispersivity along the travel path underneath the contaminated site. The vertical dispersivity is estimated using an empirical relationship between the vertical and longitudinal dispersivities presented by Gelhar and Axness (1983) Lv ?? ?= 056.0 (8) where v ? and L ? are the vertical and transverse dispersivities. L ? is assumed to be 10% of the flow distance (U.S EPA, 1996). By substituting L ? with 0.1 s W the transverse dispersivity can be described as, sLv W0056.0056.0 =?= ?? (9) The second term in the equation, ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ? gwgw sf gw bU WI b exp1 , estimates the depth due to the downward velocity of infiltrating water. Theses two terms are added together to estimate the depth of the mixing zone. This depth can also be described as the depth of leachate penetration into the aquifer (Charbeneau, 2000). Infiltration rates are calculated depending on the soil type. In the Texas risk reduction program three types of soils (sand, silt and clay) are used to calculate infiltration rates as shown in the following equations, which relate the infiltration rate and soil type (TRRP, Tier 2 equations) Sand 2 )(0018.0 PI f = (10) Silt 2 )(009.0 PI f = (11) Clay 2 )(00018.0 PI f = (12) 22 where f I is the net infiltration rate through soil (cm/year) and P the mean annual precipitation (cm / year). Once the lateral dilution factor is computed it can be combined with the partition equation to yield the dilution factor shown in equation 2. The following section describes the general assumptions used in the first phase of the model formulation, the soil to groundwater pathway. 3.4.1.3 Assumptions in the soil to groundwater pathway Conservative simplifying equations are applied in this pathway to create the analytical solution described above. The following assumptions are used in this phase of the model: ? The model is assumed to be at steady-state, where all variables are constant over time. Emissions from the source of contamination are continuous and result in a constant contaminant concentration in the soil. ? Local equilibrium is assumed between the phases of the soil (water, air and soil). ? The constituent is modeled as being released at the surface. This means that L 2 (the depth from the affected soils top to the groundwater table) is equal to the depth from the surface to the water table. ? The soil contamination extends from the surface to the water table, meaning that the ratio L 2 /L 1 is equal to one. Suggesting a conservative approach where the depth of the contaminated soil (L 1 ) equals the depth from the surface to the water table (L 2 ). Although this 23 assumption is conservative, especially in deep aquifers, a separate component (the intrinsic component) of the Texas susceptibility assessment determines the possibility of contaminants reaching the aquifer from the surface. Thus only contaminants that ?passed? the intrinsic component test will be assessed in the dilution attenuation component. ? There is no chemical or biological degradation in the unsaturated zone. ? The source of contamination is assumed to have a rectangular shape. ? NAPLs are not present at the site. The method used is applicable for compounds dissolved in the water phase and does not model transport and migration of NAPLs as a separate phase. ? The aquifer is unconfined. Only penetrating sources were assessed for confined aquifers (see section 3.4.6) 3.4.2 PHASE 2 - ADVECTIVE TRANSPORT AND DEGRADATION IN THE AQUIFER The second phase of the model describes the transport of contaminants in the saturated zone of the aquifer from the end of the mixing zone to the receptor well. The model uses a rectangular surface, with a constant concentration, at the end of the mixing zone as the source term within the aquifer. This concept is demonstrated in figure 8, where the results of the first phase are used as the source term for the second phase of the model. 24 Figure 8 - Transport from the end of the mixing zone to the well includes advection dispersion and degradation This section of the model simulates advection, dispersion and decay of contaminants in the saturated zone. These processes are modeled to determine the reduction of contaminant concentrations between the end of the mixing zone and the assessed well. The ratio between the concentration at the end of the mixing zone (C MZ ) and the concentration at the well (C well ) is the Attenuation Factor (AF), this relationship is demonstrated in the following equation ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? +?== )'(24 411 2 exp GWGWzGWy COC xg x GW MZ well LorL D erf L W erf v D L C C AF ?? ? ? (13) * ' GW L is used in the vertical dispersivity term when ' GWGW LL ? where GW L is the down gradient flow distance from the source of contamination to the recipient well (meters), g D the first order decay constant (day -1 ), COC v the contaminant retarded velocity (meter/day) and W and D are the source width and depth respectively (meters). x ? , y ? and z ? are the longitudinal transverse and 25 vertical groundwater dispersivityies (meters). ' GW L can be determined from equation 13a. z gw GW Db L ? 2 )( ' ? = (13a) This is a simplification of the analytical solution for the transport equation of a decaying contaminant (Domenico, 1986). The formulation simulates the advection transport with dispersion and first order degradation. The solution assumes a steady-state case and the equations are simplified to model the centerline of the contaminant plume. Spreading in the vertical direction is allowed only downward, because the source of contamination is assumed to be at the water table. The retarded velocity of the contaminant can be estimated using the groundwater seepage velocity ( w v ) and a retardation factor (R i ) as shown in equation 14. i w COC R v v = (14) The retardation factor is a measure of the velocity difference between solute migration and the water flow. The solute migrates slower than the water because it sorbs onto the soil matrix and has time periods of immobility. Thus, the velocity of a contaminant is lower than the water velocity, depending on the interaction of the contaminant and the soil. Equation 15 is used to compute the retardation factor T bd i K R ? ? +=1 (15) 26 where K d is the soil-water partition coefficient, b ? the soil bulk density and T ? the total soil porosity. Dispersivities in all directions can be calculated using simple equations derived from estimation models for land disposals regulations, based on field observations (Charbeneau, 2000). The equations estimate the dispersivities as a function of the distance of the transport to the receptor well. The Tier 2 model uses the following relationships between the longitudinal transverse and vertical dispersivities. Equations 16 - 18 show these relationships GWx L1.0=? (16) GWxy L033.0330.0 == ?? (17) GWxz L005.005.0 == ?? (18) where x ? , y ? and z ? are the longitudinal transverse and vertical groundwater dispersivities (meters) and L GW is the down gradient distance from the source of contamination to the well. The following section describes the general assumptions used in the second phase of the model formulation, the advective transport and degradation within the aquifer. 3.4.2.1 Assumptions in the advective transport pathway Conservative simplifying equations are applied to create the analytical solution for the pathway described above. The following assumptions are used in this phase of the model: 27 ? The model is at steady-state; all variables are constant over time. ? The depth of the source (D) is set equal to the depth of the mixing zone in the first phase ( gw ? ). ? The width of the source (W) is equal to the lateral width of affected vadose zone in direction of groundwater flow (W s ) from the soil to groundwater pathway. ? The groundwater velocity for the transport between the mixing zone and the well is computed using the time of travel and the accumulated flow distance. This yields an average velocity over the transport path. ? Soil properties (porosity and bulk density) are constant over the flow path and set equal to soil properties at the source of contamination. 3.4.3 COMPUTING DILUTION ATTENUATION FACTORS The Dilution Attenuation Factor (DAF) brings together both phases of the model, to give the ratio between concentrations of the contaminant at the recipient well and in the soil at the source of contamination. The DAF is calculated by multiplying the Dilution Factor (DF) and the Attenuation Factor (AF), as shown in the following equation. AFDFDAF ?= (19) 28 The dilution attenuation factor can be combined with a source concentration to estimate a conservative contaminant concentration at the receptor well, as shown in Eq. 1. The source term is presented in the following section. 3.4.4 CONCENTRATION AT THE SOURCE OF CONTAMINATION The completion of the dilution attenuation factor calculation yields a ratio between the initial contaminant concentration, in the soil at the source of contamination, and the groundwater concentration reaching the recipient well. The next step is to combine the dilution attenuation factor with an initial concentration to estimate the contaminant concentration reaching the receiving well. The initial concentration in the soil, at the source of contamination, is a function of the physical-chemical properties of the constituent and the soil properties. A conservative method is used to estimate the saturation concentration (C sat ). The saturation concentration is the maximum theoretical concentration for a specific contaminant in the soil without creating a non-aqueous phase. To compute the saturation limit the contaminant concentration in the water phase of the soil is set equal to the solubility and local equilibrium between the different phases of the soil (water, air and soil) is applied. Eq. 20 yields the saturation limit as shown )'( HK S C asbdws b sat ??? ? ++= (20) where C sat is the theoretical soil saturation limit (mg/kg-soil), S is the solubility of the contaminant (mg/l), b ? the soil bulk density (kg/liter), ws? the volumetric 29 water content (cm 3 -water/cm 3 -soil), K d the soil water partition coefficient (cm 3 - water/g-soil), H? the Henry's law constant and as ? is the volumetric air content. 3.4.5 COMPUTING THE CONCENTRATION REACHING THE WELL The saturation concentration is used as the source term in the model. This term is combined with the dilution attenuation factor to estimate the concentration at the assessed well. The concentration at the source of contamination is set equal to the saturation limit (C sat ) and multiplying by the DAF reduces the concentration to simulate the reduction occurring in the transport processes. Eq. 21 is used to estimate the concentration reaching the assessed well. DAFCDAFCC satsourcewell ?=?= (21) Both the source term and the dilution attenuation factor are results of conservative methods. These methods are used to compute the maximum effect a potential source of contamination may have on the water supply well. Concentrations can be calculated for each potential source of contamination that is identified in the contributing area of the assessed well. These concentrations are then used to assess the susceptibility of the well to contamination from point sources. 3.4.6 PENETRATING SOURCES OF CONTAMINATION Penetrating sources of contamination, which introduce contaminants directly into the water table, are modeled using only the advective phase of the Tier 2 model. Sources such as oil and gas wells, petroleum storage tanks and landfills may penetrate the aquifer and release contaminants directly into the 30 water table. In these cases the soil to groundwater phase is not applicable and the only reduction in concentration comes from the advective transport phase. The depth of penetration in these cases is set equal to the aquifer thickness and instead of the initial concentration in the soil the solubility of the contaminant is used as the initial concentration at the end of the mixing zone. The dilution factor in this case can be considered equal to 1 (no dilution) and the following equation is used to calculate the concentration reaching the well. AFilitySoDAFCC sourcewell ?=?= lub (22) 3.5 DATASETS AND APPLICATION OF THE TIER 2 MODEL The application of the Tier 2 model utilizes spatial and non-spatial information assembled in the source water assessment program. These sources are combined together using GIS software to extract values from spatial datasets, representing soil and aquifer data, and relating this information with chemical properties and data regarding the potential sources of contamination. Datasets of chemical properties were assembled for the dilution attenuation component of the assessment. Due to the number of constituents assessed in the dilution attenuation component, the range of the contaminants physical properties and the sensitivity of the assessment to the chemical and physical behavior of the constituents in the environment, it is important to utilize detailed information for each contaminant rather than general groupings or common default values to represent the differentiation between contaminants. 31 3.5.1 SOIL, AQUIFER AND POINT SOURCES DATASETS Detailed datasets were developed in the source water assessment program to support the susceptibility assessments. These include a variety of spatial datasets for assessing groundwater as well as surface water for the entire state of Texas. In the groundwater point source attenuation component the following spatial datasets were used to calculate the dilution attenuation factor using the Tier 2 model: 3.5.1.1 Soil datasets Soil properties at the source of contamination are used in the calculation of the dilution factor as well as in the source term, to calculate the saturation concentration in the soil. The soil properties were derived from the State Soil Geographic Database (STATSGO). The STATSGO data comes in vector format and was converted into 60-meter resolution grids. Descriptions of the datasets and the physical meaning of the variables are provided. ? Soil type ? the soil type categorizes the soils into groups to characterize the soils texture and its physical makeup. Many types and subtypes of soils exist in reality; these are grouped into classes using classification schemes. Standard classifications, such as the USDA standard classification soil texture triangle (Charbeneau, 2000), use the fractions of sand, silt and clay particles in the soil to classify groups. The Texas risk reduction program categorizes the soils into three types, sand, silt and clay. These classes are used in the Tier 2 model to estimate the infiltration rate. 32 ? Bulk Density ? Bulk density is the ratio of the mass of soil to its total volume (solids and pores together). The units are kg/liter-soil. In the Tier 2 model the bulk density is used to compute the concentrations of contaminants in soil and for computing the retardation factor. The Tier 2 model suggests a default bulk density of 1.67 where no site- specific data is available. ? Porosity - Porosity is a measure of the volume of air and water filled pores in the soil. Porosity is a dimensionless (volume/volume) property of the soil; its values can range from 0 in rocks to 0.65 in clays (Charbeneau, 2000). Porosity can also be estimated using equation 23 s b T ? ? ? ?=1 (23) where b ? is the bulk density and s ? the particle density (g/cm 3 ). In the Tier 2 model the particle density has a default value of 2.65. ? Volumetric water content ? Volumetric water content can be described as the fraction of soil pores occupied by water. The volumetric water content is a dimensionless property that can range between 0 and the value of the porosity, when all the pores are filled with water. The Tier 2 model allows the use of a default value, 0.16, when no site-specific data is available. ? Volumetric air - The fraction of soil pores occupied by air. Air content is a dimensionless property of the soil. Air content values can range from 0, when the soil is saturated, to the porosity value when 33 the soil is completely dry. The Tier 2 model allows the use of a default value, 0.21, when no site-specific data is available. ? Fraction of organic carbon in soil ? The fraction of organic carbon in the soil is used mainly for estimating sorption of organic pollutants to the soil particles. The organic carbon fraction is a mass ratio between the carbon mass and the total soil mass (g-carbon/g-soil). The Tier 2 model allows the use of a default value, 0.002 (0.2%), where no site-specific data is available. 3.5.1.2 Aquifer datasets Aquifer characteristics are used in the Tier 2 model to compute the dilution attenuation factor. Extensive datasets describing the aquifers in Texas were developed in the source water assessment program. These datasets provide the basis for the groundwater component of the susceptibility assessment. The following datasets were used to execute the Tier 2 model and compute the dilution attenuation factors. ? Saturated thickness grid ? The saturated thickness is a measure (meters) of the saturated soil within the aquifer. For a confined aquifer the saturated thickness usually equals the aquifer thickness, while for an unconfined aquifer the saturated thickness is less then the aquifer thickness (because of the vadose zone). For the unconfined aquifers the saturated thickness is the difference between the water table/potentiometric surface and the aquifer base. The saturated 34 thickness is used as the aquifer thickness (b gw ) in the dilution attenuation calculation. ? Darcy velocity grid ? The flow velocity (length/time) is calculated between cells using the Darcy equation that relates the head gradient between the cells to the flow rate. The Darcy velocity is calculated in the delineation process using grids of hydraulic head, porosity and hydraulic conductivity (TNRCC, 1999). The velocity is used in the dilution factor calculation to compute the lateral dilution of contaminants in the aquifer. ? Time of travel grid ? The time of travel grid is an output of the delineation process. Each cell in the grid has a value that represents the time it takes (years) for water to flow from the cell to the assessed well. The time of travel is used to compute the average flow velocity over the flow path and is incorporated in the attenuation factor calculation. 3.5.1.3 Precipitation information An average annual precipitation grid is used in the assessment to compute the infiltration rate of water into the aquifer. The infiltration rate is then used in the dilution factor calculation. 3.5.1.4 Potential sources of contamination Spatial and non-spatial datasets are used to describe the potential sources of contamination. The spatial information identifies the location of the source, to relate it with the contributing zone of assessed wells. The non-spatial information 35 describes the related contaminants and the source extent. Spatial intersections are used to identify potential sources of contamination within the delineated contributing area of an assessed well. Then specific contaminants can be introduced at these locations, using the relationship between the source type and a contaminant list. As described in section 3.4.1.2 an estimated area for each source type is used to determine the site dimensions. Figure 9 - Using spatial and non spatial information to describe potential sources of contamination 36 3.5.2 CHEMICAL DATABASE Physical-chemical properties have a significant impact on the mobilization and degradation of contaminants in the environment. When one models the transport of contaminants and processes such as retardation and degradation it is important to address the variation in chemical properties between the modeled contaminants. In the Tier 2 model a number of physical-chemical properties are required as inputs for the model, these properties were categorized into two classes. The first describes physical properties, such as partitioning coefficients and chemical type and the second class gives its degradation rate. To support the Tier 2 model a chemical database was established for the dilution attenuation component. The database is developed for all contaminants assessed in the water assessment program. The development process addressed the physical properties and the degradation rates separately using different sources of information and decision rules for each dataset. Each dataset was built from a set of sources that were compiled together, then a set of logical rules were applied to estimate the conservative properties for each contaminant in the database. Conservative properties are the ones that will result in less dilution and attenuation, yielding a higher dilution attenuation factor. Meaning a smaller reduction in concentration occurs during the transport process and the concentration reaching the well will be higher. In soil screening models this will be considered more conservative, due to lower concentrations allowed in the soil at the contaminant source. 37 The chemical properties required in the dilution attenuation calculation are listed below: ? Type ? the type of the constituent (Organic, Inorganic or Metal). ? H - Henry?s law constant (unitless or m 3 -liq/m 3 -air). ? K d ? Soil water partition coefficient (cm 3 -water/g-soil). ? K oc ? Soil organic carbon partition coefficient (cm 3 -water/g- carbon). ? Solubility (mg/l). ? First order degradation rate (1/day). The list of contaminants and the physical/chemical properties used is described in Appendix 1. A description of the process taken to develop a database with these properties is shown in the following sections. 3.5.2.1 CAS number assignment The first step in the database compilation was to assign Chemical Abstract Service (CAS) numbers for each constituent. CAS numbers are used as key identifiers in most datasets, literature and computer models. The database compilation started with an initial contaminant list, based on the TRRP PCL table, provided by TNRCC and was completed using SciFinder scholar 2001 software (CAS website). The SciFinder was used where contaminant CAS numbers were missing or where the CAS number was found to be incorrect or to represent an inappropriate form or species of the constituent. 38 A decision on the contaminant species to be modeled was required in this process. Some contaminants, especially metals, can be present in groundwater in different oxidation states and as either anions or cations. For example, metals are typically modeled in solution in their ionic form and each ion has a different CAS number with different chemical properties. The most common ion or the most mobile form of the metal was usually selected for use in the model. A list of the original contaminants, corresponding CAS numbers, and CAS modifications is provided in appendix 2. 3.5.2.2 Contaminant type The Tier 2 model categorizes contaminants into three basic groups. These are organics, inorganics and metals. The techniques for selecting or estimating contaminant properties varied among the groups. For example, partitioning of the organic compounds to soil was estimated using local equilibrium/linear partitioning to the organic fraction of the soil. In contrast partitioning of inorganic compounds and metals is often a pH dependent process. As a result, it is necessary to estimate or select pH dependent partition coefficients (U.S EPA, 1996. Soil Screening Guidance and U.S EPA, 1996. Chemical properties for SSL development). 3.5.2.3 Partition coefficients The Tier 2 model uses linear partitioning equations and a local equilibrium assumption to relate the concentrations of contaminants in the phases 39 of the soil. Water is used as the reference phase and the partition coefficients are used to estimate concentrations in the soil and air phases based on the water phase. Partition coefficients are used in the model to calculate the saturation concentration, the soil concentration in the dilution factor and the retardation factor used to estimate the contaminants velocity during the transport in the saturated zone. The partitioning coefficients that are needed in the Tier 2 model are Henry?s law constant, soil water partition coefficient, soil organic carbon and the solubility. These properties were assembled using a variety of sources and a characteristic value was chosen to represent the partitioning of the contaminant. Generally, the most conservative value was chosen to represent the maximum partitioning into the water phase resulting in higher concentrations of the contaminant in the groundwater. The following section gives a description of the partitioning coefficients and the logical rules applied to select conservative values together with the sources of information used in this process. Varying sources of information were used for organic contaminants vs. metals and inorganic contaminants. The interaction of metals and inorganic contaminants within the environment is highly dependent on environmental conditions such as pH and the water composition. This makes the estimation of partition coefficients for inorganic constituents and metals more challenging. In cases where no information was available the contaminant was referenced to a similar contaminant for which values were found. In cases where 40 contaminants are modeled as a group of constituents (i.e. Organotins and PCBs) the constituent with the most conservative partition coefficients was selected to represent the group. A full list of the partitioning coefficients is presented in Appendices 3 and 4. Different sources of information and methods were used for the organic and inorganic compounds. Appendix 3 shows the organic compounds partitioning coefficients and Appendix 4 gives the coefficients for the inorganic compounds. Henry?s law constant Henry?s law gives a linear relationship between the water and vapor states of a constituent under equilibrium conditions (Charbeneau, 2000). This relationship is expressed using the Henry?s law constant, which is shown in equation 24 as the ratio of the vapor pressure to the mole fraction of the substance in the solution. X P K vp H =' (24) Where P vp is the vapor pressure (atm) and X is the mole fraction of the substance in solution ( 3 m mole ). The resulting constant has the dimensions of mol matm 3 ? . Solubility can be used as an estimate of the mole fraction because it represents the maximum concentration that can be dissolved into the water. The following equation shows this relationship, where the solubility (S) is given in units of mole per cubic meter. S P K vp H =' (25) 41 This expression can be normalized using the gas constant (R) and the temperature (T) to yield a non-dimensional Henry?s law constant, as shown in equation 26. RT K K H H ' = (26) Where R equals Kmol matm ? ? ? ? 3 5 102.8 and T is the temperature in degrees Kelvin. The non-dimensional Henry?s law constant is used in the estimation of mass in the system, using the linear partitioning theory and local equilibrium. The smallest Henry?s law constant was selected to represent smaller partitioning into the air phase resulting in higher concentrations in the water phase of the soil. This leads to higher concentrations of contaminants infiltrating into the aquifer, thus a smaller constant is considered conservative. Solubility The solubility of the constituents is used in the source component to estimate the saturation concentration of the soil at the source of contamination. The largest solubility was selected to represent larger partitioning into the water phase resulting in higher concentrations in the water phase of the soil. This leads to higher concentrations of contaminants infiltrating into the aquifer. 42 Soil water partition coefficients The methodology used to relate soil and water concentrations is based on linear partitioning. The basic relationship between the soil and water concentrations is shown in the following equation )( water soil d C C K = (27) where K d is the linear soil-water partition coefficient (L/kg), C soil the concentration sorbed to the soil (mg/kg) and C water the concentration in solution. The soil water partition coefficient is used in the model to calculate the saturation concentration, the soil partitioning in the dilution factor and the retardation factor in the attenuation factor calculation. K d values vary significantly by the physical conditions in the soil and different methods are used to derive values for organic compounds and metals, due to varying dominant processes. For organic compounds the K d is a function of the hydrophobic character of the compound and the fraction of organic matter present in the soil (Charbeneau, 2000). Equation 28 shows this relationship ococd fKK ?= (28) where K oc is the soil organic carbon partition coefficient (cm 3 -water/grm-cabon) and f oc is the fraction of organic carbon in soil (g-carbon/g-soil). Unlike organic compounds, for which the K d values can be estimated based on one parameter, the organic matter fraction in the soil. K d values for metals are affected by a variety of parameters. The most important are pH, oxidation-reduction conditions, iron oxide content, soil organic matter content, cation exchange capacity and major ion chemistry (U.S EPA, 1996). 43 In order to estimate the K d one must assume a certain pH. A pH of 6.8 was chosen as the typical pH (U.S EPA, 1996 Attachment C) and used to extract specific values from pH dependent information. The smallest K d value is selected to represent less sorption to the soil yielding greater mobility of the contaminant and higher concentrations in the ground water. Sorption to soil has a dominant effect on concentrations of degrading constituents. High K d values will result in a large retardation factor suggesting an immobile contaminant, which will have longer time to degrade before reaching the well. Sources of information for partitioning coefficients of organic compounds The following sources of information were used to compile the database for organic contaminants. Partitioning information from the varying sources was compared and the logical rules were applied to select the most conservative value to be used in the Tier 2 model. ? TNRRC Texas Risk Reduction PCL table (TRRP, 30 TAC Chapter 350) ? EPI Suite (EPIWIN V3.10) software was used to retrieve partitioning coefficients of organic compounds. Henry?s law constant and sorption coefficients (K d and K oc ) were selected from the Level III Fugacity model inputs. Solubility was extracted from the WSKOW section of the model output. 44 ? Physical Properties Database (PHYSPROP) online Database Demo was used to retrieve Henry?s law constants and solubility values. Sources of partitioning coefficients for metals and inorganic compounds The following sources of information were used to develop the database for inorganic contaminants and metals. The default pH (6.8) was used when pH dependent information was available. ? TNRRC Texas Risk Reduction PCL table (TRRP, 30 TAC Chapter 350). ? EPA Soil Screening Guidance, User's Guide Attachment C: Chemical Properties for SSL Development (U.S EPA, 1996). ? EPA Soil Screening Guidance, Technical Background Document Part 5: Chemical-Specific Parameters (U.S EPA, 1996). ? EPA Soil Screening Guidance for Radionuclides, Radiological Properties for SSL Development Attachment C (EPA, 2000) ? Environmental Organic Chemistry ( Schwarzenbach et al 1993). Using MINEQL to calculate metals solubility The solubility of metals varies considerably with changes in the chemical composition of the water, the pH and solid precipitation. Usually metals solubilities are reported using LogC?pH diagrams, which describe the concentration of the metal in the aqueous phase as a function of pH for a given water composition. Solid precipitation is also a dominant process that may determine the aqueous concentration of the metal in solution. In order to model 45 metals solubility in an aqueous systems one has to take into consideration a variety of factors affecting the metals solubility. This makes the task of determining one characteristic value of solubility very difficult. To highlight theses difficulties two examples of solubility analysis were processed using MINEQL software. A default pH of 6.8 was used and a background pore water composition was assumed. The examples presented show the variation in the solubility based on changes in the systems characteristics and the chemical interactions considered. The following water composition (Table 1) was used in the examples. This water composition was used by the EPA in a similar modeling process to determine K d values for metals (EPA, 1996). The water composition was converted from mg/l to mole per liter, which is required as an input parameter to the MINEQL model. 46 Parameter Concentration (mg/l) (mole/l) Aluminum 0.2 7.412E-06 Bromine 0.3 3.755E-06 Calcium 48 1.198E-03 Carbonate 187 3.116E-03 Chlorine 15 4.231E-04 Iron(+3) 0.2 3.581E-06 Magnesium 14 5.760E-04 Manganese(+2) 0.04 7.281E-07 Nitrate 1 1.613E-05 Phosphate 0.09 9.477E-07 Potassium 2.9 7.417E-05 Sodium 22 9.570E-04 Sulfate 25 2.602E-04 Table 1 ? Background pore water chemistry assumed for the MINEQL simulations (EPA, 1996) Cadmium and aluminum were selected as examples. Each metal was modeled separately with a concentration of 1 mole per liter as a conservative high concentration of total cadmium or aluminum. pH was set constant at 6.8 and both opened and closed systems were considered for computing total CO 3 . An open system suggests that total CO 3 is not a limiting factor in the chemical reactions 47 because the system is open to the atmosphere. Solubility and complexation data were taken from the MINEQL database (Environmental Research Software, MINEQL+ software). MINEQL simulation of Aluminum MINEQL is an equilibrium code. As a result, precipitation of a particular solid phase during a simulation does not consider rates of precipitation. To account for the possibility that the most thermodynamically favorable precipitate may not be the most likely phase to form multiple simulations were performed. The first simulation did not exclude any solid phases from consideration. The model was formulated using a total aluminum concentration of 1M in a closed system. The simulation resulted in precipitation of Diaspore (AlO(OH)). In a second simulation, Diaspore was removed from the active database. In the second simulation precipitation of Gibbsite (Al(OH) 6 -3 ) resulted in small concentrations of aqueous aluminum. In the next simulation, gibbsite was assumed to be present in the system and simulations of open and closed systems were preformed to determine the maximum Al concentration in solution at pH 6.8. The total Al +3 concentrations are considered as the solubility. The following table shows the total dissolved concentrations from the simulations. 48 Simulation Precipitating solids Fixed solids Type of system Total dissolved concentration (M) 1 Diaspore - closed 1.27E-9 2 Gibbsite - closed 3.34E-8 3 Gibsite closed 3.34E-8 4 Gibsite open 3.31E-8 Table 2 - Results of Aluminum modeling with MINEQL The aluminum precipitants that dominate the system are created by hydroxides and are not dependent on the presence of carbonate. Thus, the open and closed systems simulations were very similar. Systems with hydroxide precipitants are dominated by variations in pH. The following LogC-pH diagram shows the change of total dissolved aluminum as a function of pH. Log C-pH diagram for Total Al+3 1.00E-08 1.00E-06 1.00E-04 1.00E-02 1.00E+00 1.00E+02 1.00E+04 1.00E+06 1.00E+08 1.00E+10 0123456789101121314 pH Log {A l+3} Figure 10 - LogC-pH diagram for total Al +3 49 The above diagram shows the sensitivity of the Al +3 dissolved concentration to pH variation. The changes in the solubility with variations in pH can be on orders of magnitude, making it difficult to predict a specific solubility. MINEQL simulation of Cadmium The second simulation set modeled the interaction of 1M total cadmium added into the system. The first simulation of a closed system did not result in any precipitation of solids and over 99% of the cadmium remained in solution. This suggests that the solubility (equal to the total dissolved concentration) is dominated by the initial input of Cd +2 into the system. Two open system simulations were preformed and the partial pressure of CO 2 was varied (to run the simulations Cd 3 (PO 4 ) 2 and Cd 4 (OH) 6 SO 4 were removed from consideration). These simulations resulted in much lower dissolved concentrations of cadmium due to Otavite (CdCO 3 ) precipitation. These simulations show the sensitivity of the cadmium system to carbonate. Another simulation with fixed CaCO 3 was preformed to simulate the interactions in calcareous formations, which may be a source of carbonate to groundwater systems. The following table shows the dissolved concentrations from the simulations. 50 Simulation Precipitating solids Type of system Total dissolved concentration (M) 1 None closed 0.996 2 Otavite Opened (LogPCO2 =-3.5) 1.6E-4 3 Otavite Opened (LogPCO2 =-1.5) 1.68E-6 4 Otavite Closed, Fixed CaCO 3 3.1E-4 Table 3 - Results of Cadmium modeling with MINEQL The above results show the variation in cadmium solubility with respect to carbonate availability. The more carbonate available in the system, more solid precipitates and the lower the solubility. The interaction of cadmium within the groundwater systems will depend on the carbonate available from the atmosphere or the geological formations. The examples shown for aluminum and cadmium highlight the difficulty of modeling metals interactions within groundwater systems. Solubility may vary by orders of magnitude depending on the water composition, the pH of the system and the precipitation of solids. Using a single value of solubility to model metals transport through groundwater systems will not be accurate. A better approach 51 might use a number of solubility values depending on the aquifer water composition, pH and rock formation. 3.5.2.4 Degradation rates Degradation rates have a major impact on the results of the dilution attenuation component. Degradation half-lives represent a range of individual processes that decrease the contaminant concentration over time. These processes can be related to microbiological processes, such as aerobic and anaerobic biodegradation, or to physical-chemical processes such as hydrolysis, photolysis oxidation-reduction processes and radiological decay. All these processes have to be addressed to result in one overall half-life. Degradation rates reported in literature are highly variable, depending on the source of information. A conservative approach was taken, meaning the lowest degradation rate (longest half life) found was selected, resulting in higher concentrations of the contaminant for longer travel distances in the aquifer. The following sources of information were used to develop the degradation rate database. The full list of degradation rates is presented in appendix 5 for organic compounds and in appendix 4 for inorganic compounds. 52 Sources of degradation rates for organic compounds ? Handbook of Environmental Degradation Rates (Howard et al, 1991). This book presents overall half lives for many of the assessed contaminants. The book gives a range of values rather than showing a single half live value. The smallest half life was chosen as the conservative value. ? EPI Suite (EPIWIN V3.10) The Level III Fugacity section of the EPI model was used to determine half-lives of organic compounds. The EPI Suite model gives the half-lives in surface water. Howard et al (1991) suggest that biodegradation and hydrolysis, to a lesser extent, are the principal means of degradation. They also recognize that biodegradation in groundwater proceeds at a slower rate compared to surface water. To account for this difference degradation rates in groundwater were taken as half the degradation rate in surface water, unless other information was available. The half-lives from the available sources were given in hours, these selected half-lives were converted into degradation rates by applying the following equation that relates the degradation rate to the half-life of contaminants 2/1 24 )5.0ln( T D g ? ?= (29) where g D is the first order degradation rate (day -1 ) and T 1/2 the half-life of the constituent (hours). 53 Sources of degradation rates for metals and inorganic compounds Degradation rates for metals and most of the inorganic compounds were set to zero, in most cases these compounds do not degrade but change forms due to chemical reactions. For a number of the metals and the radioactive elements, half-lives were obtained from: the following sources: ? EPA Soil Screening Guidance for Radionuclides, Radiological Properties for SSL Development Attachment C (EPA, 2000) ? EPA Soil Screening Guidance, User's Guide Attachment C: Chemical Properties for SSL Development (U.S EPA, 1996). ? Oak Ridge Reservation Annual Site Environmental Report 2000, Appendix G (ASER, 2000). This report was used only for the degradation rate of Strontium 89. 3.5.3 MODEL APPLICATION USING GIS An application to calculate dilution attenuation factors from point sources of contamination to water supply wells was created using ArcGIS and Microsoft Access databases. The ability of ArcGIS to combine spatial information and tabular datasets and the simple linkage between it to relational database programs provides an adequate environment to execute this type of assessment. The mathematical formulation is solved in one Microsoft Access database that assembles all the information necessary from the spatial and non-spatial datasets. The assembled databases and flow of information is shown in figure 11. 54 Figure 11 - Model application using GIS The following spatial and non-spatial querying procedures are needed to calculate the dilution attenuation factor from every source in the capture zone of the water supply well: ? Spatial datasets are queried based on the sources spatial location to extract soil and aquifer properties as well as precipitation rates for each source. ? Each source is associated with an area and a contaminant list in the point source component. ? Contaminants are related to chemical properties by querying the chemical database. Once all information is assembled into the calculation table, the Tier 2 model application calculates a dilution attenuation factor from each potential source to the assessed water supply well for all contaminants related to the sources in the capture zone. 55 3.5.4 APPLICATION EXAMPLE FOR ONE WELL An example of the model?s application for one well is presented to demonstrate the computation of the dilution attenuation factor and the aggregation of the results to determine overall susceptibility. In the example, one well is assessed for one contaminant. Benzene was selected as the contaminant of concern in the example. Table 4 presents the chemical properties of Benzene used in the example. Chemical property Units Value Chemical Type - Organic Henry?s Law constant (H) Unitless 0.227 Soil organic carbon ? water partition coefficient, Log(Koc) cm 3 -water/g- carbon 1.74 Degradation Rate (Dg) 1/day 1.671E-06 Solubility (S) mg/l 2,000 Table 4 ? Chemical properties of benzene The first step in assessing the susceptibility of the well is to identify the potential sources of contamination within its contributing area. The following image shows the assessed well and 5 potential sources of benzene identified in its capture zone (this is the outcome of the identification and delineation components). 56 Figure 12 - Assessed well and Benzene potential sources of contamination Once potential sources of contamination are identified, the types of the sources their area and soil and aquifer properties are extracted from the spatial datasets and the area of the contamination sources are estimated. Tables 5 and 6 present the source, soil and aquifer properties of the identified sources of contamination. 57 Source ID Penetrating code Area Bulk density foc soil type Volumetric air content Volumetric water content Precipitation 0 = not penetrating -1 = penetrating sq. meters Kg/liter g-carbon/g- soil cm/year 891459 0 1000 1.1716 0.27 Silt 0.21 0.6456 65.7 918210 0 1000 1.1716 0.27 Silt 0.21 0.6456 65.7 918980 0 1000 1.1716 0.27 Silt 0.21 0.6456 65.7 938894 0 1000 1.1716 0.27 Silt 0.21 0.6456 65.7 946212 0 1000 1.1716 0.27 Silt 0.21 0.6456 65.7 Table 5 - Source and soil properties ID Aquifer Thickness Flow Distance Time of travel Darcy Velocity at the source of contamination meters meters days cm/year 891459 5.5 205 234 21554 918210 6.1 205 119 42769 918980 4.9 170 423 7645 938894 5.8 170 116 30773 946212 4.9 170 423 7645 Table 6 - Aquifer and flow properties Equation 9 is used to compute the vertical dispersivity ( v ? ) and equations 10 to 12 for calculating the infiltration rate ( f I ). Then substituting these properties into equation 7 yields the mixing zone depth ( gw ? ). These values are used in equation 6 to yield the lateral dilution factor (LDF). Then the dilution factor is computed using equation 2, the LDF and the soil and chemical properties. Table 7 shows the dilution factor calculation for the 5 potential sources of contamination. 58 ID f I v ? gw ? LDF DF cm/year meters meters 891459 3.890 0.177 3.352 5.88E+02 1.10E-04 918210 3.890 0.177 3.350 1.17E+03 5.55E-05 918980 3.890 0.177 3.363 2.10E+02 3.08E-04 938894 3.890 0.177 3.351 8.39E+02 7.71E-05 946212 3.890 0.177 3.351 8.39E+02 7.71E-05 Table 7 - Dilution Factor (DF) calculation results The second phase is the calculation of the attenuation factor. The first step in the attenuation factor computation is to calculate the groundwater seepage velocity from the time of travel and the flow distance. The retardation factor is then computed using equation 15, the K d of the contaminant and the soil properties. For organic compounds the K d is derived from K oc and foc as shown in equation 28. Then the retarded velocity of the contaminant can be estimated with equation 14 and the retardation factor. To complete the inputs needed for the AF calculation the longitudinal vertical and transverse dispersivities are calculated with equations 16-18. The width of the source (W) is set equal to the sources width from the DF calculation and its depth (D) is equal to the depth of the mixing zone ( gw ? ) from the DF calculation. Substituting these properties into equation 13, together with the degradation rate and the flow distance yields the Attenuation Factor (AF). Table 8 shows the results of the attenuation factor calculation. 59 ID GW L x ? y ? z ? w V Ri V coc W D AF meters meters meters meters meter/day meter/day meters meters 891459 205 20.5 6.8 1.0 0.878 59.09 0.015 31.62 3.35 1.69E-01 918210 205 20.5 6.8 1.0 1.724 59.09 0.029 31.62 3.35 1.43E-01 918980 170 17.0 5.6 0.9 0.402 59.09 0.007 31.62 3.36 2.40E-01 938894 170 17.0 5.6 0.9 1.465 59.09 0.025 31.62 3.35 1.87E-01 946212 170 17.0 5.6 0.9 0.402 59.09 0.025 31.62 3.35 1.87E-01 Table 8 - Results of the Attenuation Factor (AF) calculation The final Dilution Attenuation Factor (DAF) is calculated by multiplying the dilution and attenuation factors as shown in equation 19. A conservative concentration estimated to reach the well is calculated using the saturation limit as the source term and the DAF. The saturation limit formula is given in equation 20 and is based on soil and chemical properties. The concentration estimated to reach the well is calculated using equation 21. Table 9 shows the computation of the DAF and the concentration reaching the well. ID DF AF DAF C sat C well mg/liter mg/liter 891459 1.10E-04 1.69E-01 1.86E-05 3.09E+04 5.75E-01 918210 5.55E-05 1.43E-01 7.91E-06 3.09E+04 2.45E-01 918980 3.08E-04 2.40E-01 7.38E-05 3.09E+04 2.28E+00 938894 7.71E-05 1.87E-01 1.44E-05 3.09E+04 4.45E-01 946212 7.71E-05 1.87E-01 1.44E-05 3.09E+04 4.45E-01 Average concentration 7.98E-01 Table 9 - Results of the DAF and concentration calculations 60 The overall susceptibility estimated for the well is an aggregation of all the concentrations calculated for the individual sources of contamination. In the Texas source water assessment program the average concentration is used to estimate the susceptibility of the well. The average concentration is compared to water quality thresholds, determined by TCEQ, to evaluate the wells susceptibility. For example an average concentration smaller than the threshold will be considered as low, between the threshold and half the water quality standard will be medium and concentrations above half the water quality standard will be high. The water quality threshold used for benzene in the Texas source water assessment program is 1E-4 mg/l and the water quality standard is 5.0E-03 mg/l. Comparing the average concentration shown in table 9 with the water quality thresholds, the susceptibility of the well to Benzene contamination is determined as high (the estimated concentration is larger then the water quality standard). 61 4. RESULTS The dilution attenuation component results in a set of conservative concentrations computed for each potential source of contamination in the contributing zone of the assessed well. The concentrations can be aggregated using various methods to yield one concentration expected at the well. These are used to determine the susceptibility of the well to contaminants. The higher the predicted concentration, the more susceptible the well is to contamination. A variety of physical properties influence the susceptibility of wells to contamination. These include the presence of sources of contamination in the vicinity of the well as well as the properties of the aquifer in which the well is screened. To try and understand the relationships between the aquifer properties, contaminant sources and the susceptibility of the well, spatial trends were examined and compared with water quality monitoring information. The following sections give examples of methods and processes that can be used to help determine the susceptibility of water supply wells. 4.1 COMPARISON OF THE DILUTION ATTENUATION RESULTS WITH WATER QUALITY DATA The dilution attenuation results were compared with a water quality database to study the accuracy of the model in capturing water quality problems. As described above, the Tier 2 model yields a conservative estimation of pollutant concentrations reaching a well from each source of contamination. The 62 aggregation of these results gives an estimate of the susceptibility of the well to contamination. These results are compared to water quality samples taken at public water supplies. TCEQ compiled a water quality database (the ?Master Chemical list?) that shows water quality monitoring results at public water supplies. This database gives information at a specific entry point of the water supply system, where the samples were taken. The database includes about one million records of monitoring data at over 10,000 entry points for 159 contaminants. These monitoring points may receive water from a number of wells, thus it is difficult to make conclusions on the origin of the contaminant measured at the monitoring point. The following image illustrates this difficulty. Figure 13 - Wells contributing to a monitoring point on the water supply network Another water quality database used in the comparison is the ?Finished Water? database, compiled by TCEQ from the ?Master Chemical List? and used in the source water assessment program. In this database water quality measurements from the ?Master chemical list? were compared to a threshold, determined by TCEQ, and all observations that exceeded the threshold were Monitoring point Well 63 included in the dataset. Due to the difficulty in relating the detection to a specific well, all wells that contribute to that monitoring point were treated as contaminated and were included in the database. The result of this process is a list of wells in which specific contaminants have been detected. The ?Finished Water? database includes over 85,000 wells related with 124 contaminants at over 5,500 public water supplies. Three groups of contaminants are selected for the comparison, BTEX compounds (Benzene, Toluene, Ethylbenzene and Xylenes), PCE (Tetrachloroethylene) and PCE daughter products (TCE, 1,1-DCE, 1,2-DCE, Vinyl Chloride) and MTBE (Methyl-T-Butyl Ether). These contaminants were selected because they are generally related to point sources of contamination. The dilution attenuation component models only point sources of contamination. To minimize the influence of non point sources, these groups of contaminants that are likely to arise from point sources of contamination, were selected for the comparison. Water quality measurements from the ?Master chemical list? database were compared with a series of thresholds ranging from the detection limit to water quality standards. Each water quality sample is compared to the threshold and assigned a yes/no value for exceeding or not exceeding the threshold. The positive results of the comparisons were then aggregated by the public water supply, resulting in a list of water supplies with at least one detection that exceeds the threshold. The following table presents the thresholds and water quality standards used for the comparison. 64 Contaminant CD * Group Threshold = Detection limit Water quality standard mg/l Microgram/l mg/l Microgram/l BENZENE 56 BTEX 0.0001 0.1 0.005 5.0 ETHYLBENZENE 125 BTEX 0.0001 0.1 0.700 700.0 TOLUENE 211 BTEX 0.0001 0.1 1.000 1000.0 XYLENES (TOTAL) 226 BTEX 0.0001 0.1 10.000 10000.0 METHYL-T-BUTYL ETHER 159 MTBE 0.0001 0.1 0.244 244.4 1,1-DICHLOROETHANE 5 TCE 0.0001 0.1 2.444 2444.2 1,2-DICHLOROETHANE 12 TCE 0.0001 0.1 0.005 5.0 TETRACHLOROETHYLENE 208 TCE 0.0001 0.1 0.005 5.0 TRICHLOROETHYLENE 219 TCE 0.0001 0.1 0.005 5.0 VINYL CHLORIDE 225 TCE 0.0001 0.1 0.002 2.0 * CD is a unique identifier used in the Texas Source Water Assessment Program Table 10 - List of contaminants used in the comparison, their threshold and water quality standard The results from the aggregation of water quality detections were then compared to the estimated concentrations computed using the Tier 2 model. The following table shows the results obtained from this comparison when the threshold was varied between the detection limit and the water quality standard. 65 Threshold Water supplies with detections above the threshold Supplies with detections above the threshold and sources of contamination identified % of supplies with detections that have contaminant sources Microgram/l Detection limit 0.1 1547 505 32% Detection limit ? 10 1 1053 348 33% Detection limit ? 100 10 154 60 38% Water quality standard Contaminant dependent 39 13 33% Table 11 - comparison of the number of water supplies with detections and the number of water supplies with identified sources of contamination The above table shows that only up to 38% of the public water supplies that had water quality detections of BTEX, PCE compounds or MTBE actually had sources of contamination identified within their capture zone. The implications of this result is that if one tried to predict where water quality problems might occur by looking at the contamination sources, only up to 38% of the occurrences would be predicted. This estimation only looks at the occurrences of contaminant sources within the capture zone of the well without using the reduction of concentration modeled by the dilution attenuation component. Thus, 66 the estimation is less conservative if the dilution attenuation component is used the accuracy of the predictions will be lower. 4.2 EXTRACTING SPATIAL TRENDS FROM THE RESULTS Spatial trends of susceptibility can be observed by aggregating and interpolating the results computed for each well in the dilution attenuation component. The susceptibility of a water supply can be correlated with a variety of attributes, which can roughly be categorized into two categories. The first is the presence of contamination sources, which determine what potential contaminants are in the area of the water supply and where these contaminants are introduced into the environment. The second category includes the aquifer properties, which determines the likelihood that the contaminants introduced to the environment will reach the wells and at what magnitude. The following sections show examples of spatial trends that can be used to study susceptibility patterns. These trends include distributions of contaminant sources and wells and the characteristics of the aquifers assessed. 4.2.1 ANALYSING SOURCES OF CONTAMINATION USING SPATIAL FUNCTIONS The density function in ArcGIS spatial analysis was used to extract the density of potential sources of contamination for each chemical group. Mapping density highlights the areas of higher concentration of features. It is especially useful in areas with many features where it is hard to identify trends by simple 67 mapping of the features (Mitchell, 1999). The following maps illustrate the advantage of density maps vs. simple mapping of features. Figure 14 - Potential sources of contamination for BTEX compounds From the above map it is apparent that spatial trends are hard to extract in areas where many features are located. In order to better understand the spatial trends a density map of the features is created. Using the map below one can understand the relative spatial trends and distribution of BTEX sources. These types of maps can be used to estimate the potential for contamination from the different contaminants based on the occurrence of sources in the water supplies vicinity. Houston Dallas San Antonio 68 Figure 15 - Density (number of sources per square km) of potential sources of contamination for BTEX compounds The density map shows a correlation between the urban areas and the number of potential sources of contamination for BTEX compounds. A similar correlation can be noticed with TCE and its degradation products (referred to as the TCE group of compounds) where high densities of sources are correlated with large metropolitan areas such as Dallas, Houston and San Antonio. Figure 16 shows this correlation. Houston Dallas San Antonio 69 Figure 16 - Density of potential sources of contamination for TCE group compounds (sources per square km) 4.2.2 CORRELATION BETWEEN DENSITY OF CONTAMINANT SOURCES, WELL DISTRIBUTION AND WATER QUALITY DETECTIONS Using the density maps to describe the source of pollutants into the environment one can try to find a correlation between the presence of contaminants in high density areas of contaminant sources and water quality observations. The following map shows this relationship between the water quality detections and the density of the contaminant sources. Houston Dallas San Antonio 70 Figure 17 - BTEX measurements above the detection limit (0.1 microgram/l) Figure 17 shows the BTEX detections that exceeded a threshold of 0.1 microgram per liter on top of the density of BTEX sources of contamination. The detections are based on the ?Finished Water? water quality database described in section 4.1. The development of the density map is shown in Figures 15 and 16. The map in Figure 17 shows that the majority of detections are in areas of high density of contaminant sources. Still there are large areas that show high density of contamination sources but have few water quality detections. This can be explained by the following map, which displays the water quality detections together with the spatial distribution of wells. Houston Dallas San Antonio 71 Figure 18 shows BTEX detections from the ?Finished Water? database over the density of water supply wells. It is clear from the map that a majority of detections are found in areas with high densities of wells. The absence of detections in some areas with high densities of contaminant sources observed in figure 17 can be attributed to the absence of wells in those areas. Generally, where there are no wells there is no need for monitoring, thus contaminants will not be detected. Figure 18 - BTEX detections compared with well density 72 Figure 19 shows a similar case for TCE and its degradation products, where the water quality detections fall within dense areas of wells. These maps demonstrate the combination of factors that influence water quality. Characteristics such as the density of wells may contribute to water quality deterioration when combined with contaminant source occurrences. Another alternative is that where large numbers of wells exist, more detections will be observed based on the larger sampling space that increases the probability to observe detections. Figure 19 - TCE group detections compared with well density 73 When analyzing these maps one must use caution because the construction of the ?Finished Water? database may create trends in the data that are not realistic. In the database, all wells contributing to a sampling point with one or more detections are considered contaminated. This may result in multiple detections where only one detection actually exists. In order to insure the trends seen in the above maps are realistic the ?Finished Water? database was aggregated by the public water supply and only one well from that water supply was assumed contaminated. The following maps show the results of the aggregation by water supply for the BTEX and TCE groups of contaminants. Figure 20 - BTEX detections aggregated by water supply compared with density of wells 74 Figure 20 shows that the aggregation by water supply resulted in a similar distribution of water quality detections as when all wells in the water supply were treated as contaminated. The following map illustrates the same result for TCE compounds. Figure 21 - TCE group detections aggregated by water supply compared with well density The similar distribution of water quality detections in both cases suggests the use of the ?Finished Water? database in its original form is reasonable for the contaminants assessed. Although the distribution of the detections is similar a closer look at the magnitude of detections, or the density of detections in a certain area, may be significantly different between the aggregation methods. A single 75 detection measured at a monitoring point related to many wells may result in high density of detections due to the assignment of the contaminant to all the wells. The following maps show the densities of BTEX detections for both methods of aggregation. Figure 22 - Density of BTEX detections in the ?Finished Water? database 76 Figure 23 - Density of BTEX detections aggregated by water supply The above maps demonstrate the differences between the methods of aggregation. Although the spatial distribution is similar in both cases the density of detections varies considerably when different aggregations are used. This can be explained by the assignment of contaminants to a number of wells contributing to a monitoring point in the ?Finished Water? database. When aggregating by water supply only one detection is considered. The following example illustrates a case where the densities computed are considerably different depending on the aggregation method. The first map shows an area of high density when the ?Finished Water? database is used and the second map shows the same area when the results are aggregated by water supply. 77 Figure 24 - High density of BTEX detections in the ?Finished Water? database 78 Figure 25 - Density of BTEX detections when aggregated by PWS The comparison of figures 24 and 25 shows a large variation between the aggregation methods. A closer look at the detection points created by the different methods may explain the difference in densities shown in the above maps. The following map shows detections of BTEX using both methods. The bright red points are wells treated as contaminated in the ?Finished Water? database while the darker point is the well selected to represent the water supply in the aggregated dataset. This example explains the differences in the density maps between the two methods. Although the spatial distribution of the detections is the same, aggregating the detections by water supply will result in lower densities in some cases. 79 Figure 26 - BTEX detections for both methods of aggregation 4.2.3 SPATIAL DISTRIBUTION OF THE DILUTION ATTENUATION RESULTS The dilution attenuation component results in a conservative estimation of the concentrations reaching a well from a specific contamination source. A number of aggregation schemes can be used to combine the individual influences of the sources of contamination into one overall concentration for the well. The source water assessment program uses the average concentration in the process of determining susceptibility. Therefore the average concentration is used to aggregate the individual results from the dilution attenuation component and compare these results with the water quality detections. 80 The results of the dilution attenuation calculation were classified into three categories: high, medium and low. These groupings were established by comparing the average concentration computed for the well with the water quality thresholds and standards described in table 10. Concentrations lower than the threshold were considered as low, between the threshold and half the water quality standard were considered medium and the concentrations exceeding half the water quality standard were assigned to the high category. Figures 27 and 28 show the wells for which high concentrations of BTEX and TCE group compounds were computed. Figure 27 - High BTEX concentrations computed in the dilution attenuation component 81 Figure 28 - TCE (and TCE products) High concentrations computed in the dilution attenuation component Results of the dilution attenuation component were converted into density maps and the water quality detections were compared with these density maps to assess the correlation between the high concentration category of the dilution attenuation results and the water quality detections. The following maps show the relationship between the water quality detections and the dilution attenuation component results for BTEX and TCE group compounds. 82 Figure 29 - BTEX detections in the ?Finished Water? database overlying a density map of high concentrations from the dilution attenuation component The above map shows the relationship between the density of high concentrations, from the dilution attenuation component, and the detections of BTEX in the ?Finished Water? database. This correlation is especially strong in the denser areas while in the less dense areas the relationship is weaker. Figure 30 presents the same concept for the TCE group compounds (TCE and TCE degradation products), the map also shows correlation in the denser 83 areas, where more high concentrations were predicted with the dilution attenuation model. Figure 30 - TCE group detections in the ?Finished Water? overlying a density map of high concentrations from the dilution attenuation component In both of the above maps there are areas with elevated densities of high concentrations from the dilution attenuation model but few detections shown in the water quality database. The dilution attenuation model is conservative and the concentrations estimated with the model should yield higher and more frequent concentrations then the ones actually monitored. This may result in areas where 84 the model predicts high concentrations but the water quality measurements don?t show elevated concentrations. The following figures show the density of the dilution attenuation component high predictions for BTEX and TCE compounds, compared with the density of detections in the ?Finished Water database?. The figures on the left show the detections density and the right figures present the densities of the predicted high concentrations. Figure 31 - Densities of monitored (left) and predicted (right) high concentrations for BTEX compounds 85 Figure 32 - Densities of monitored (left) and predicted (right) high concentrations for TCE group compounds Figures 31 and 32 demonstrate the variations between the predicted results and the monitored results. Although some areas with high density of monitoring detections can be predicted with the model, many still are not predicted accurately. This highlights the difficulty in modeling areas of high susceptibility on such a large scale, with variations in aquifer and soil properties. 86 4.2.4 INFLUENCE OF AQUIFER PROPERTIES The influence of aquifer properties on the susceptibility of water supplies is examined through two case studies. The Houston and Dallas metropolitan areas were selected as sample study areas. Both areas have high densities of contaminant sources and wells (see figures 15, 16 and 18) but the aquifers from which water is derived have considerably varying properties. Houston area wells are mostly screened in the coastal lowlands aquifer system, especially in the Chicot and Evangeline aquifers. The wells in the Dallas area discharge from the Trinity aquifer system in particular from the Faluxy and Travis Peak formations. Figure 33 - Trinity and Coastal lowlands aquifer systems Houston Dallas 87 The lithology of the Coastal lowland aquifer system, also known as the ?Gulf Coast aquifer? is generally sand, silt and clay. The Chicot and Evangeline aquifers are hydrogeologic designations for subdivisions of the upper mostly sandy part of the system (USGS, 1996). The Chicot aquifer is the top part of the formation and is unconfined in the Houston area. The Evangeline aquifer underlies the Chicot aquifer and is a confined aquifer. These formations are shown in the following diagram from the USGS ground water atlas (USGS, 1996). Figure 34 - Correlation chart of the coastal lowlands aquifer system (USGS, 1996) Formations of interest 88 The Trinity aquifer in its east central and northeastern areas consists of the Twin Mountains, Glen Rose and Paluxy formations (USGS, 1996). The Walnut formation confines the aquifer in these areas. The arrangement of these formations is illustrated in the following diagram together with the formations of the Edwards aquifer. Figure 35 - Correlation chart of the Edwards - Trinity aquifer system (USGS, 1996) The above correlation diagrams illustrate the difference between the aquifer systems. Wells in the Houston area are screened mostly in the upper sandy Formations of interest 89 formations. These aquifers should be more susceptible to contamination because of the short distance between the surface and the aquifer and the lack of a thick confining unit. Wells in the Dallas area are mostly screened in deeper formations (Faluxy and Travis Peak), which are overlaid by a confining unit (Walnut formation). The increased distance between the surface and the presence of a confining formation should result in less susceptibility to contamination. The influence of the varying aquifer systems is analyzed by comparing the water quality detections in both areas. A density map of BTEX and TCE group contaminants detections was produced to compare the number of detections per area in both study cases. A closer look at the study area reveals differences in the patterns of detections. Figure 36 - Density of BTEX detections in the Dallas and Houston areas Figure 36 shows the difference in the density of BTEX detections from the ?Finished Water? database. The map shows that the Houston area has higher Dallas Area Houston Area 90 densities of BTEX detections than the Dallas area. This difference may be related to the different aquifer systems, where the unconfined upper aquifers (Chicot and Evangeline), in the Houston area, exhibit more detections per square kilometer than the deeper and confined aquifer (Faluxy and Travis Peak), in the Dallas area. Although this relationship is reasonable, this variability can also be related to variations in the sources of contamination densities and the spatial distribution of the wells. A similar analysis was performed for TCE and its degradation products (referred to as TCE group), and the results are shown in Figures 37 - 39. Figure 37 - Density of detections of TCE group contaminants in the Dallas area from the ?Finished Water? database Dallas Area 91 Figure 38 - Density of detections of TCE group contaminants in the Houston area from the ?Finished Water? database The density of TCE detections near the Dallas area is higher than the one in the Houston area. This result contradicts the assumption that wells in the Houston area are more susceptible to contamination than in the Dallas area. This contradiction may be related to the small number of detections in the ?Finished Water? dataset. Due to the small number of detections, the spatial patterns are hard to distinguish and high density spots may be created from superficial patterns in the database. These unrealistic local patterns may occur when a number of Houston Area 92 wells are treated as contaminated because they are related to one monitoring point. To eliminate these trends the detections were aggregated by water supply. Figure 39 - Density of detections of TCE group contaminants from the aggregated ?Finished Water? database Figure 39 shows that when aggregating the TCE results by water supply the spatial trends are considerably different than the trends observed without the aggregation. When the aggregated dataset is used to compute the density of Houston Area Dallas Area 93 detections the Houston area has higher densities than the Dallas area. This distribution is more reasonable when considering the aquifer systems. The above results demonstrate the importance of the aggregation methods on the spatial trends of detections. These trends can be analyzed to determine more susceptible areas and to find relationships between the aquifers characteristics and the susceptibility of wells to contamination. 94 5. CONCLUSIONS Determining the susceptibility of water supplies to contamination is a complex problem, which involves identification of sources of contamination and assessing the effects these sources might have on water quality. To understand the relationship between the sources of contamination and the water supply wells one must model the transport of contaminants from the point of introduction to the environment through the subsurface to the assessed well. The tier 2 model applied in the Texas source water assessment program gives a physical relationship between point sources of contamination and water supply wells. The model describes the dilution and attenuation of contaminants in the groundwater transport using well-established leaching and transport formulations. The focus of this study is to establish a model that provides a numerical link between potential sources of contamination and water supply wells. The model includes the major natural processes taking place in the environment during contaminants migration in groundwater systems. These processes include dilution, sorption, dispersion and degradation. This numerical connection enables the calculation of expected contaminant concentrations and these can be used to better estimate the susceptibility of water supplies to contamination. 95 The model proposed is executable on a large scale, due to extensive dataset construction under the Texas source water assessment program by the USGS and TCEQ and the capabilities introduced through GIS software. The detailed information developed in the program enables the use of a screening model (tier 2), which requires site specific properties of aquifers and soils, on a regional scale. Another important aspect of the study is the construction of the chemical database containing physical-chemical properties and degradation rates, necessary to model the variety of contaminants and their behavior in the environment. The chemical database includes conservative estimates of chemical properties that effect the concentration reduction during the transport in groundwater systems. These parameters have a large impact on the models results and in some cases dominate the contaminants interaction within the environment. The variability in these parameters is large, thus making it difficult to select one representative value to model the contaminant. This is particularly true for metals for which interactions are dependent on a variety of factors such as pH, water composition and precipitation of solids. Future studies should address these issues once the initial assessments are analyzed. More detailed representation of the chemical properties might be necessary to model certain contaminants, perhaps by using aquifer specific water compositions and pH to model the contaminants interactions within the aquifers. This is especially important in the estimation of solubility values of metals, which are used to determine the initial concentration 96 at the source of contamination. A different approach, such as using observed concentrations as the source concentration, may be more suitable. Comparison of the results from the dilution attenuation component with water quality detections did not show high correlation. Only up to 38% of the public water supplies with water quality detections had identified sources of contamination, resulting in low prediction rates of the model. It appears that many detections are present where no sources of contamination are identified, suggesting the need for better identification of contamination sources to better predict the susceptibility from point sources. The magnitude of the task undertaken has effect on the models precision. Accurately assessing more than 13,000 wells, over the entire state of Texas, with varying aquifers and soil properties is a challenging task. Although the accuracy rates of the model appear to be low, it is important to keep in mind that this component is only one of a number of methods used to determine susceptibility. These include susceptibility from non point sources, intrinsic susceptibility and contaminant occurrences. When combined together these should yield a more accurate analysis of the susceptibility. The use of spatial functions in ArcGIS shows promising prospects in susceptibility studies. The examples shown highlight the importance of the spatial patterns and how they might relate to water quality. Interpolation of properties such as the density of contaminant sources and wells can help in determining areas of higher susceptibility. The results presented are initial results from the Texas source water assessment program. USGS and TCEQ are undergoing a 97 process of executing the susceptibility assessment and verifying the results. More detailed studies of the models results and the correlation of contaminant occurrence, well density and aquifer properties are recommended to better understand these patterns and relationships. Finally, this method will hopefully contribute to the development of better susceptibility assessments by incorporating site-specific models (such as the tier 2 model) into regional assessments. The execution of these models permits a more accurate and detailed description of contaminants introduced into the environment and their transport and interaction within groundwater systems. 98 APPENDIX 1 ? LIST OF CONTAMINANTS WITH CHEMICAL PHYSICAL PROPERTIES The following table shows the final list of contaminants and their physical-chemical properties. These properties were used in the Tier 2 model to calculate the dilution attenuation factor. A full description of the database development, the assumptions and sources of information used in its construction are given in section 3.5.2. Description of the fields ? CD ? Identifier number of the contaminant used in the Texas Source Water Assessment Program ? CAS ? CAS number in the TRRP PCL table. ? CONTAMINANT ? Name of the contaminant ? TYPE ? the type of the contaminant, organic (O), inorganic (I) or metal (M). ? H ? Selected Henry?s law constant (dimensionless). ? LOG KOC ? The log of selected Koc values (cm 3 -water/g-carbon). ? SOLUBILITY - Selected solubility value (mg/l). CD CAS CONTAMINANT TYPE H LogKd LOGKOC SOLUBILITY Dg 1 630-20-6 1,1,1,2-TETRACHLOROETHANE O 9.977E-02 none 2.543 1.100E+03 1.801E-05 2 71-55-6 1,1,1-TRICHLOROETHANE O 7.116E-01 none 2.040 1.330E+03 2.204E-06 3 79-34-5 1,1,2,2-TETRACHLOROETHANE O 1.518E-02 none 1.890 2.970E+03 2.735E-05 4 79-00-5 1,1,2-TRICHLOROETHANE O 3.409E-02 none 1.502 4.420E+03 1.648E-06 5 75-34-3 1,1-DICHLOROETHANE O 2.325E-01 none 1.403 5.500E+03 3.343E-06 6 75-35-4 1,1-DICHLOROETHYLENE O 1.056E+00 none 1.743 2.400E+03 9.117E-06 7 563-58-6 1,1-DICHLOROPROPENE O 1.819E+00 none 2.143 7.488E+02 6.418E-05 8 87-61-6 1,2,3-TRICHLOROBENZENE O 3.800E-02 none 3.663 1.884E+01 4.011E-05 99 CD CAS CONTAMINANT TYPE H LogKd LOGKOC SOLUBILITY Dg 9 96-18-4 1,2,3-TRICHLOROPROPANE O 1.419E-02 none 1.883 1.900E+03 1.671E-06 10 120-82-1 1,2,4-TRICHLOROBENZENE O 5.874E-02 none 3.220 4.880E+01 3.343E-06 11 95-63-6 1,2,4-TRIMETHYLBENZENE O 1.840E-01 none 2.970 7.959E+01 2.149E-05 12 107-06-2 1,2-DICHLOROETHANE O 4.882E-02 none 1.093 8.700E+03 3.343E-06 13 78-87-5 1,2-DICHLOROPROPANE O 1.168E-01 none 1.593 2.800E+03 4.668E-07 14 122-66-7 1,2-DIPHENYLHYDRAZINE O 1.422E-07 none 2.553 1.840E+03 3.343E-06 15 108-67-8 1,3,5-TRIMETHYLBENZENE O 2.720E-01 none 3.010 5.148E+01 6.418E-05 16 541-73-1 1,3-DICHLOROBENZENE O 1.088E-01 none 2.230 1.100E+02 3.343E-06 17 142-28-9 1,3-DICHLOROPROPANE O 4.038E-02 none 1.613 2.157E+03 6.418E-05 18 542-75-6 1,3-DICHLOROPROPENE O 1.226E-01 none 1.642 1.994E+03 6.418E-05 19 594-20-7 2,2-DICHLOROPROPANE O 3.394E-01 none 2.192 1.682E+03 6.418E-05 20 1746-01-6 2,3,7,8-TCDD O 1.474E-03 none 6.413 1.103E-03 1.020E-06 21 93-76-5 2,4,5-T O 2.826E-07 none 2.474 2.780E+02 6.685E-06 22 93-72-1 2,4,5-TP O 3.748E-07 none 3.413 1.400E+02 6.418E-05 23 88-06-2 2,4,6-TRICHLOROPHENOL O 1.076E-04 none 2.117 9.820E+02 6.610E-07 24 94-75-7 2,4-D O 5.820E-09 none 2.423 8.900E+02 6.685E-06 25 120-83-2 2,4-DICHLOROPHENOL O 9.060E-05 none 1.857 4.500E+03 2.799E-05 26 51-28-5 2,4-DINITROPHENOL O 2.012E-07 none -2.000 5.800E+03 2.288E-06 27 121-14-2 2,4-DINITROTOLUENE O 2.234E-06 none 1.593 4.462E+02 3.343E-06 28 606-20-2 2,6-DINITROTOLUENE O 3.090E-05 none 1.620 3.524E+02 3.343E-06 29 95-49-8 2-CHLOROTOLUENE O 1.347E-01 none 2.610 1.540E+02 6.418E-05 30 591-78-6 2-HEXANONE O 3.381E-03 none 0.993 1.794E+04 2.777E-04 31 95-48-7 2-METHYLPHENOL O 4.964E-05 none 1.562 2.040E+04 8.596E-05 32 16655-82-6 3-HYDROXYCARBOFURAN O 2.466E-12 none 0.373 6.207E+03 6.418E-05 33 106-43-4 4-CHLOROTOLUENE O 1.335E-01 none 2.696 1.358E+02 6.418E-05 34 99-87-6 4-ISOPROPYLTOLUENE O 4.551E-01 none 3.360 2.788E+01 1.605E-04 35 108-10-1 4-METHYL-2-PENTANONE O 5.709E-03 none 0.923 1.900E+04 8.596E-05 36 83-32-9 ACENAPHTHENE O 6.443E-03 none 3.533 4.240E+00 5.899E-06 37 208-96-8 ACENAPHTHYLENE O 4.739E-03 none 3.553 3.930E+00 1.003E-05 38 34256-82-1 ACETOCHLOR O 9.225E-07 none 2.642 2.230E+02 4.011E-05 39 67-64-1 ACETONE O 1.613E-03 none -0.627 6.000E+05 8.596E-05 40 107-13-1 ACRYLONITRILE O 5.709E-03 none -0.137 7.500E+04 2.616E-05 41 15972-60-8 ALACHLOR O 3.442E-07 none 2.279 2.400E+02 4.011E-05 42 116-06-3 ALDICARB O 5.820E-08 none 0.743 6.000E+03 1.895E-06 43 1646-88-4 ALDICARB SULFONE O 1.096E-07 none -0.959 8.000E+03 6.418E-05 44 1646-87-3 ALDICARB SULFOXIDE O 4.009E-08 none -1.167 2.800E+04 6.418E-05 45 309-00-2 ALDRIN O 1.820E-03 none 4.680 7.840E-02 1.017E-06 47 14903-36-7 Aluminum Cation M 0.000E+00 4.914E-01 none 1.148E+00 0.000E+00 48 120-12-7 ANTHRACENE O 2.300E-03 none 4.064 6.905E-01 1.308E-06 100 CD CAS CONTAMINANT TYPE H LogKd LOGKOC SOLUBILITY Dg 49 64924-52-3 Antimonate M 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 50 53469-21-9 AROCLOR O 7.860E-03 none 5.903 2.770E-01 1.605E-05 51 15584-04-0 Arsenate M 0.000E+00 1.462E+00 none NoValueReported 0.000E+00 52 1332-21-4 ASBESTOS I 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 53 1912-24-9 ATRAZINE O 9.763E-08 none 2.204 2.141E+02 4.011E-05 54 16541-35-8 Barium Cation M 0.000E+00 1.613E+00 none 1.336E+04 0.000E+00 55 25057-89-0 BENTAZON O 9.019E-08 none 1.953 5.000E+02 6.418E-05 56 71-43-2 BENZENE O 2.274E-01 none 1.743 2.000E+03 1.671E-06 57 56-55-3 BENZO(A)ANTHRACENE O 1.393E-04 none 5.373 2.907E-02 8.848E-07 58 50-32-8 BENZO(A)PYRENE O 1.891E-05 none 5.740 1.038E-02 4.011E-05 59 205-99-2 BENZO(B)FLUORANTHENE O 2.718E-05 none 5.393 2.065E-02 9.864E-07 60 191-24-2 BENZO(G,H,I)PERYLENE O 1.369E-05 none 6.200 2.842E-03 9.257E-07 61 207-08-9 BENZO(K)FLUORANTHENE O 4.448E-07 none 5.723 1.079E-02 2.812E-07 62 14701-08-7 Beryllium ion M 0.000E+00 2.898E+00 none 1.190E+01 0.000E+00 63 71-52-3 BICARBONATE I 1.168E+00 -1.800E+01 none NoValueReported 0.000E+00 64 11113-50-1 Boric acid I 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 65 314-40-9 BROMACIL O 4.344E-09 none 1.723 8.150E+02 6.418E-05 66 24959-67-9 BROMIDE I 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 67 108-86-1 BROMOBENZENE O 8.382E-02 none 2.384 4.460E+02 6.418E-05 68 74-97-5 BROMOCHLOROMETHANE O 3.690E-02 none 1.021 2.042E+04 1.605E-04 69 75-27-4 BROMODICHLOROMETHANE O 8.770E-02 none 1.613 4.500E+03 6.418E-05 70 75-25-2 BROMOFORM O 2.213E-02 none 1.940 3.200E+03 3.343E-06 71 74-83-9 BROMOMETHANE O 2.581E-01 none 0.803 1.520E+04 3.167E-05 72 23184-66-9 BUTACHLOR O 2.110E-06 none 4.114 2.300E+01 6.418E-05 73 85-68-7 BUTYL BENZYL PHTHALATE O 5.213E-05 none 4.138 2.900E+00 6.685E-06 74 22537-48-0 Cadmium cation M 0.000E+00 4.314E-01 none 1.095E+04 0.000E+00 75 14102-48-8 Calcium cation M 0.000E+00 0.00 none 3.922E+03 0.000E+00 76 63-25-2 CARBARYL O 1.804E-07 none 1.973 4.162E+02 2.006E-05 77 1563-66-2 CARBOFURAN O 1.278E-07 none 1.462 7.000E+02 6.418E-05 78 75-15-0 CARBON DISULFIDE O 5.957E-01 none 1.553 2.928E+03 1.605E-04 79 56-23-5 CARBON TETRACHLORIDE O 1.142E+00 none 2.270 8.050E+02 3.343E-06 80 3812-32-6 CARBONATE I 1.179E+03 -1.800E+01 none NoValueReported 0.000E+00 81 57-74-9 CHLORDANE O 2.011E-03 none 5.080 5.600E-02 4.341E-07 82 5103-71-9 CHLORDANE (ALPHA-CHLORDANE) O 2.011E-03 none 5.833 4.640E-02 1.605E-05 83 12789-03-6 CHLORDANE (GAMMA-CHLORDANE) O 2.011E-03 none 5.833 1.299E-02 1.605E-05 84 39765-80-5 CHLORDANE (TRANS-NONACHLOR) O 1.026E-03 none 5.963 6.120E-03 1.605E-05 85 16887-00-6 CHLORIDE I 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 86 108-90-7 CHLOROBENZENE O 1.287E-01 none 2.330 5.020E+02 4.011E-06 87 75-00-3 CHLOROETHANE O 2.120E-01 none 1.041 2.000E+04 2.149E-05 88 67-66-3 CHLOROFORM O 1.518E-01 none 1.583 7.920E+03 6.685E-07 101 CD CAS CONTAMINANT TYPE H LogKd LOGKOC SOLUBILITY Dg 89 74-87-3 CHLOROMETHANE O 3.649E-01 none 0.522 2.262E+04 2.149E-05 90 17493-86-6 Chromate M 0.000E+00 1.279E+00 none 1.274E-02 0.000E+00 91 218-01-9 CHRYSENE O 5.030E-05 none 5.423 2.635E-02 6.017E-07 92 156-59-2 CIS-1,2-DICHLOROETHYLENE O 1.688E-01 none 1.462 6.410E+03 6.418E-05 93 10061-01-5 CIS-1,3-DICHLOROPROPENE O 9.150E-02 none 1.642 2.700E+03 6.418E-05 94 17493-86-6 Copper ion M 0.000E+00 7.782E-01 none 1.029E+01 0.000E+00 96 21725-46-2 CYANAZINE O 1.225E-10 none 1.692 1.838E+02 1.605E-05 97 57-12-5 CYANIDE I 3.406E-03 -1.800E+01 none NoValueReported 0.000E+00 98 75-99-0 DALAPON O 3.732E-06 none 1.292 5.020E+05 1.003E-05 99 2136-79-0 DCPA DI-ACID DEGRADATE O 8.398E-12 none 1.743 1.754E+02 4.011E-05 100 887-54-7 DCPA MONO-ACID DEGRADATE O 3.351E-09 none 2.803 1.826E+01 4.011E-05 101 72-55-9 DDE O 8.730E-04 none 5.040 6.500E-02 1.070E-07 102 103-23-1 DI-(2-ETHYLHEXYL)ADIPATE O 1.795E-05 none 5.580 7.800E-01 2.149E-05 103 117-81-7 DI-(2-ETHYLHEXYL)PHTHALATE O 1.117E-05 none 7.212 3.000E-01 3.094E-06 104 333-41-5 DIAZINON O 4.675E-06 none 2.120 4.000E+01 6.418E-05 105 53-70-3 DIBENZ(A,H)ANTHRACENE O 4.656E-07 none 6.152 3.304E-03 6.401E-07 106 124-48-1 DIBROMOCHLOROMETHANE O 3.239E-02 none 1.770 5.250E+03 6.685E-06 107 67708-83-2 DIBROMOCHLOROPROPANE O 6.081E-03 none 2.230 1.000E+03 6.418E-05 108 74-95-3 DIBROMOMETHANE O 3.401E-02 none 1.312 1.100E+04 2.149E-05 109 1918-00-9 DICAMBA O 9.019E-08 none 0.342 8.310E+03 6.418E-05 110 75-71-8 DICHLORODIFLUOROMETHANE O 1.419E+01 none 1.773 2.800E+02 3.343E-06 111 75-09-2 DICHLOROMETHANE O 9.104E-02 none 0.863 1.540E+04 2.149E-05 112 60-57-1 DIELDRIN O 1.110E-04 none 4.330 1.950E-01 5.571E-07 113 84-66-2 DIETHYL PHTHALATE O 1.871E-05 none 2.033 1.080E+03 1.074E-05 114 131-11-3 DIMETHYL PHTHALATE O 4.344E-06 none 1.500 4.190E+03 8.596E-05 115 84-74-2 DI-N-BUTYL PHTHALATE O 5.945E-05 none 4.114 1.120E+01 5.232E-05 116 88-85-7 DINOSEB O 1.886E-05 none 3.080 5.200E+01 4.892E-06 117 2764-72-9 DIQUAT O 2.690E-12 none 1.973 7.000E+01 1.605E-04 118 298-04-4 DISULFOTON O 8.936E-05 none 3.632 1.600E+01 2.865E-05 119 330-54-1 DIURON O 2.085E-08 none 2.292 1.506E+02 6.418E-05 120 145-73-3 ENDOTHALL O 1.593E-14 none 1.522 1.000E+05 2.777E-04 121 72-20-8 ENDRIN O 4.947E-05 none 3.970 2.500E-01 1.605E-05 122 759-94-4 EPTC O 6.578E-04 none 2.823 3.700E+02 6.418E-05 124 97-63-2 ETHYL METHACRYLATE O 6.651E-03 none 1.553 1.900E+04 1.605E-04 125 100-41-4 ETHYLBENZENE O 3.260E-01 none 2.310 2.286E+02 5.278E-06 126 106-93-4 ETHYLENE DIBROMIDE O 2.759E-02 none 1.573 4.320E+03 1.003E-05 128 86-73-7 FLUORENE O 2.644E-03 none 3.793 1.980E+00 1.003E-05 129 16984-48-8 FLUORIDE I 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 130 944-22-9 FONOFOS O 2.888E-04 none 3.553 1.070E+01 6.418E-05 132 1071-83-6 GLYPHOSATE O 1.688E-17 none -4.387 1.000E+06 1.605E-04 102 CD CAS CONTAMINANT TYPE H LogKd LOGKOC SOLUBILITY Dg 136 76-44-8 HEPTACHLOR O 1.216E-02 none 4.070 1.800E-01 1.605E-05 137 1024-57-3 HEPTACHLOR EPOXIDE O 3.446E-04 none 3.859 2.750E-01 1.090E-06 138 118-74-1 HEXACHLOROBENZENE O 2.224E-02 none 4.450 1.922E-01 2.880E-07 139 87-68-3 HEXACHLOROBUTADIENE O 4.261E-01 none 3.840 3.200E+00 3.343E-06 140 77-47-4 HEXACHLOROCYCLOPENTADIENE O 7.150E-01 none 3.980 1.800E+00 1.605E-05 141 15035-72-0 Sulfide I 4.087E-01 -1.800E+01 none NoValueReported 0.000E+00 142 193-39-5 INDENO[1,2,3,CD]PYRENE O 2.852E-06 none 6.312 3.751E-03 8.242E-07 143 74-88-4 IODOMETHANE O 2.176E-01 none 1.124 1.244E+04 2.149E-05 144 15438-31-0 Iron Ion M 0.000E+00 4.914E-01 none 1.747E+04 0.000E+00 145 98-82-8 ISOPROPYLBENZENE O 4.757E-01 none 3.272 7.503E+01 7.521E-05 146 845-52-3 LAMBAST O 5.171E-11 none 1.963 1.188E+02 4.011E-05 147 14701-27-0 Lead ion M 0.000E+00 7.782E-01 none 1.956E+04 0.000E+00 148 58-89-9 LINDANE O 1.409E-04 none 3.040 7.300E+00 5.010E-06 149 330-55-2 LINURON O 2.586E-07 none 2.813 7.500E+01 3.380E-06 150 106-42-3 M + P XYLENE O 2.854E-01 none 2.763 2.286E+02 1.605E-04 151 14581-92-1 Magnesium ion M 0.000E+00 0.000E+00 none 2.391E+03 0.000E+00 152 14333-14-3 Manganate M 0.000E+00 6.902E-01 none NoValueReported 0.000E+00 153 14302-87-5 Mercury ion M 0.000E+00 1.716E+00 none NoValueReported 0.000E+00 154 2032-65-7 METHIOCARB O 4.882E-08 none 2.533 1.035E+02 6.418E-05 155 16752-77-5 METHOMYL O 8.150E-10 none 0.212 5.800E+04 1.605E-04 156 72-43-5 METHOXYCHLOR O 8.398E-06 none 4.693 3.020E-01 3.297E-06 157 78-93-3 METHYL ETHYL KETONE O 1.937E-03 none -0.097 2.400E+05 8.596E-05 158 80-62-6 METHYL METHACRYLATE O 1.330E-02 none 0.993 1.600E+04 2.149E-05 159 1634-04-4 METHYL-T-BUTYL ETHER O 2.428E-02 none 0.553 4.800E+04 3.343E-06 160 51218-45-2 METOLACHLOR O 3.133E-08 none 2.513 8.640E+02 4.011E-05 161 21087-64-9 METRIBUZIN O 4.840E-09 none 1.312 1.304E+03 6.418E-05 162 2212-67-1 MOLINATE O 5.253E-05 none 1.699 9.700E+02 6.418E-05 163 108-90-7 MONOCHLOROBENZENE O none none none none none 164 108-38-3 M-XYLENE O 2.970E-01 none 2.292 2.072E+02 3.343E-06 165 91-20-3 NAPHTHALENE O 1.820E-02 none 2.913 1.421E+02 9.048E-06 166 104-51-8 N-BUTYLBENZENE O 5.570E-01 none 3.480 1.608E+01 1.605E-04 167 14701-22-5 Nickel ion M 0.000E+00 1.531E+00 none 2.395E+02 0.000E+00 168 14797-55-8 NITRATE I 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 169 none NITRATE+NITRITE I 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 170 14797-65-0 NITRITE I 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 171 98-95-3 NITROBENZENE O 8.563E-04 none 1.462 2.090E+03 3.054E-06 172 103-65-1 N-PROPYLBENZENE O 4.240E-01 none 3.030 7.073E+01 1.605E-04 173 23120-99-2 Organotins O 2.873E+00 none -3.487 1.000E+06 8.023E-05 174 95-50-1 ORTHO-1,2-DICHLOROBENZENE O 7.943E-02 none 2.840 1.500E+02 3.343E-06 103 CD CAS CONTAMINANT TYPE H LogKd LOGKOC SOLUBILITY Dg 175 23135-22-0 OXAMYL O 1.600E-11 none -0.866 2.800E+05 6.418E-05 176 95-47-6 O-XYLENE O 2.140E-01 none 2.110 2.424E+02 3.343E-06 178 106-46-7 PARA-1,4-DICHLOROBENZENE O 9.970E-02 none 2.810 9.024E+01 3.343E-06 179 53469-21-9 PCBs O 7.860E-03 none 5.724 2.770E-01 1.605E-05 180 87-86-5 PENTACHLOROPHENOL O 1.014E-06 none 2.613 1.400E+01 7.917E-07 181 14797-73-0 PERCHLORATE I 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 183 85-01-8 PHENANTHRENE O 1.750E-03 none 4.072 1.150E+00 3.008E-06 184 1918-02-1 PICLORAM O 2.205E-12 none 0.973 1.818E+03 4.011E-05 185 1610-18-0 PROMETON O 3.700E-08 none 2.603 7.500E+02 4.011E-05 186 1918-16-7 PROPACHLOR O 3.785E-06 none 1.793 7.000E+02 6.418E-05 187 139-40-2 PROPAZINE O 1.903E-07 none 2.543 9.608E+01 4.011E-05 188 106-42-3 P-XYLENE O 2.854E-01 none 2.490 2.286E+02 3.343E-06 189 129-00-0 PYRENE O 4.573E-04 none 4.580 2.249E-01 3.167E-07 190 13982-63-3 RADIUM-226 I 0.000E+00 4.771E-01 none NoValueReported 1.187E-06 191 15262-20-1 RADIUM-228 I 0.000E+00 4.771E-01 none NoValueReported 2.374E-04 192 10043-92-2 Radon I 4.395E+00 -1.800E+01 none NoValueReported 1.733E-01 193 121-82-4 RDX O 2.615E-06 none 0.483 6.062E+03 6.418E-05 194 135-98-8 S-BUTYLBENZENE O 5.070E-01 none 3.320 1.810E+01 1.605E-04 195 7782-49-2 SELENIUM M 0.000E+00 6.990E-01 none NoValueReported 0.000E+00 196 14701-21-4 Silver ion M 0.000E+00 4.314E-01 none 1.021E+04 0.000E+00 197 122-34-9 SIMAZINE O 3.897E-08 none 1.793 5.899E+02 4.011E-05 198 17341-25-2 Sodium ion M 0.000E+00 -1.800E+01 none NoValueReported 0.000E+00 200 14701-18-9 Strontium ion M 0.000E+00 0.000E+00 none 8.506E+03 1.373E-02 201 10098-97-2 STRONTIUM-90 M 0.000E+00 0.000E+00 none 8.506E+03 6.548E-05 202 100-42-5 STYRENE O 1.138E-01 none 2.562 3.437E+02 5.730E-06 203 14808-79-8 SULFATE I 0.000E+00 6.990E-01 none NoValueReported 0.000E+00 204 98-06-6 T-BUTYLBENZENE O 5.461E-01 none 3.390 2.734E+01 6.418E-05 206 5902-51-2 TERBACIL O 4.964E-09 none 1.502 8.717E+02 6.418E-05 207 13071-79-9 TERBUFOS O 9.929E-04 none 4.093 5.070E+00 6.418E-05 208 127-18-4 TETRACHLOROETHYLENE O 7.322E-01 none 2.190 2.000E+02 1.671E-06 209 109-99-9 TETRAHYDROFURAN O 2.917E-03 none 0.072 1.000E+06 1.605E-04 210 7440-28-0 THALLIUM M 0.000E+00 -1.800E+01 none NoValueReported 4.748E-04 211 108-88-3 TOLUENE O 2.747E-01 none 2.146 5.731E+02 4.298E-05 212 7440-14-4 Radium Isotop M 0.000E+00 4.771E-01 none NoValueReported 1.187E-06 215 8001-35-2 TOXAPHENE O 1.397E-04 none 4.981 7.400E-01 1.605E-05 216 156-60-5 TRANS-1,2-DICHLOROETHYLENE O 1.688E-01 none 1.700 6.300E+03 6.418E-05 217 10061-02-6 TRANS-1,3-DICHLOROPROPENE O 9.150E-02 none 1.642 2.800E+03 6.418E-05 218 122-34-9 TRIAZINES O 3.897E-08 none 1.793 5.899E+02 4.011E-05 219 79-01-6 TRICHLOROETHYLENE O 4.075E-01 none 1.970 1.280E+03 7.280E-07 220 75-69-4 TRICHLOROFLUOROMETHANE O 4.013E+00 none 2.130 1.100E+03 1.671E-06 104 CD CAS CONTAMINANT TYPE H LogKd LOGKOC SOLUBILITY Dg 221 1582-09-8 TRIFLURALIN O 2.012E-03 none 4.137 6.000E-01 1.605E-05 222 15086-10-9 TRITIUM I 0.000E+00 -1.800E+01 none NoValueReported 1.583E-04 223 none URANIUM M 0.000E+00 -3.979E-01 none NoValueReported 4.220E-13 224 108-05-4 VINYL ACETATE O 2.114E-02 none 0.342 3.025E+04 1.605E-04 225 75-01-4 VINYL CHLORIDE O 1.150E+00 none 1.040 8.800E+03 4.186E-07 226 95-47-6 XYLENES (TOTAL) O 2.140E-01 none 2.110 2.424E+02 3.343E-06 227 15176-26-8 Zinc ion M 0.000E+00 -1.000E+00 none 3.192E+02 2.713E-03 105 APPENDIX 2 - ORIGINAL AND FINAL CAS NUMBERS AND CONTAMINANTS The following table shows the original vs. final CAS numbers for the contaminants assessed in the Source Water Assessment Program. The method used for determining CAS numbers is presented in section 3.5.2.1. Description of the fields ? CD ? Identifier number of the contaminant. ? O_CAS ? Original CAS number in the TRRP PCL table. ? O_CONTAMINANT ? Original name in the TRRP PCL table ? FINAL_CAS ? The final CAS number used in the dilution attenuation component. ? FINAL_CONTAMINANT - The final contaminant name used in the dilution attenuation component. CD O_CAS O_ CONTAMINANT FINAL_CAS FINAL_CONTAMINANT 1 630-20-6 1,1,1,2-TETRACHLOROETHANE 630-20-6 1,1,1,2-TETRACHLOROETHANE 2 71-55-6 1,1,1-TRICHLOROETHANE 71-55-6 1,1,1-TRICHLOROETHANE 3 79-34-5 1,1,2,2-TETRACHLOROETHANE 79-34-5 1,1,2,2-TETRACHLOROETHANE 4 79-00-5 1,1,2-TRICHLOROETHANE 79-00-5 1,1,2-TRICHLOROETHANE 5 75-34-3 1,1-DICHLOROETHANE 75-34-3 1,1-DICHLOROETHANE 6 75-35-4 1,1-DICHLOROETHYLENE 75-35-4 1,1-DICHLOROETHYLENE 7 563-58-6 1,1-DICHLOROPROPENE 563-58-6 1,1-DICHLOROPROPENE 8 87-61-6 1,2,3-TRICHLOROBENZENE 87-61-6 1,2,3-TRICHLOROBENZENE 9 96-18-4 1,2,3-TRICHLOROPROPANE 96-18-4 1,2,3-TRICHLOROPROPANE 10 120-82-1 1,2,4-TRICHLOROBENZENE 120-82-1 1,2,4-TRICHLOROBENZENE 11 95-63-6 1,2,4-TRIMETHYLBENZENE 95-63-6 1,2,4-TRIMETHYLBENZENE 12 107-06-2 1,2-DICHLOROETHANE 107-06-2 1,2-DICHLOROETHANE 13 78-87-5 1,2-DICHLOROPROPANE 78-87-5 1,2-DICHLOROPROPANE 14 122-66-7 1,2-DIPHENYLHYDRAZINE 122-66-7 1,2-DIPHENYLHYDRAZINE 106 CD O_CAS O_ CONTAMINANT FINAL_CAS FINAL_CONTAMINANT 15 108-67-8 1,3,5-TRIMETHYLBENZENE 108-67-8 1,3,5-TRIMETHYLBENZENE 16 541-73-1 1,3-DICHLOROBENZENE 541-73-1 1,3-DICHLOROBENZENE 17 142-28-9 1,3-DICHLOROPROPANE 142-28-9 1,3-DICHLOROPROPANE 18 542-75-6 1,3-DICHLOROPROPENE 542-75-6 1,3-DICHLOROPROPENE 19 594-83-2 2,2-DICHLOROPROPANE 594-20-7 2,2-DICHLOROPROPANE 20 1746-01-6 2,3,7,8-TCDD 1746-01-6 2,3,7,8-TCDD 21 93-76-5 2,4,5-T 93-76-5 2,4,5-T 22 93-72-1 2,4,5-TP 93-72-1 2,4,5-TP 23 88-06-2 2,4,6-TRICHLOROPHENOL 88-06-2 2,4,6-TRICHLOROPHENOL 24 94-75-7 2,4-D 94-75-7 2,4-D 25 120-83-2 2,4-DICHLOROPHENOL 120-83-2 2,4-DICHLOROPHENOL 26 51-28-5 2,4-DINITROPHENOL 51-28-5 2,4-DINITROPHENOL 27 121-14-2 2,4-DINITROTOLUENE 121-14-2 2,4-DINITROTOLUENE 28 606-20-2 2,6-DINITROTOLUENE 606-20-2 2,6-DINITROTOLUENE 29 95-49-8 2-CHLOROTOLUENE 95-49-8 2-CHLOROTOLUENE 30 591-78-6 2-HEXANONE 591-78-6 2-HEXANONE 31 95-48-7 2-METHYLPHENOL 95-48-7 2-METHYLPHENOL 32 16655-82-6 3-HYDROXYCARBOFURAN 16655-82-6 3-HYDROXYCARBOFURAN 33 106-43-4 4-CHLOROTOLUENE 106-43-4 4-CHLOROTOLUENE 34 99-87-6 4-ISOPROPYLTOLUENE 99-87-6 4-ISOPROPYLTOLUENE 35 108-10-1 4-METHYL-2-PENTANONE 108-10-1 4-METHYL-2-PENTANONE 36 83-32-9 ACENAPHTHENE 83-32-9 ACENAPHTHENE 37 208-96-8 ACENAPHTHYLENE 208-96-8 ACENAPHTHYLENE 38 34256-82-1 ACETOCHLOR 34256-82-1 ACETOCHLOR 39 67-64-1 ACETONE 67-64-1 ACETONE 40 107-13-1 ACRYLONITRILE 107-13-1 ACRYLONITRILE 41 15972-60-8 ALACHLOR 15972-60-8 ALACHLOR 42 116-06-3 ALDICARB 116-06-3 ALDICARB 43 1646-88-4 ALDICARB SULFONE 1646-88-4 ALDICARB SULFONE 44 1646-87-3 ALDICARB SULFOXIDE 1646-87-3 ALDICARB SULFOXIDE 45 309-00-2 ALDRIN 309-00-2 ALDRIN 46 NONE ALKALINITY NOT EVALUATED ALKALINITY 47 7429-90-5 ALUMINUM 14903-36-7 ALUMINUM CATION 48 120-12-7 ANTHRACENE 120-12-7 ANTHRACENE 49 7440-36-0 ANTIMONY 64924-52-3 ANTIMONATE 50 NONE AROCLOR 53469-21-9 AROCLOR 51 7440-38-2 ARSENIC 15584-04-0 ARSENATE 52 1332-21-4 ASBESTOS 1332-21-4 ASBESTOS 53 1912-24-9 ATRAZINE 1912-24-9 ATRAZINE 54 7440-39-3 BARIUM 16541-35-8 BARIUM CATION 107 CD O_CAS O_ CONTAMINANT FINAL_CAS FINAL_CONTAMINANT 55 25057-89-0 BENTAZON 25057-89-0 BENTAZON 56 71-43-2 BENZENE 71-43-2 BENZENE 57 56-55-3 BENZO(A)ANTHRACENE 56-55-3 BENZO(A)ANTHRACENE 58 50-32-8 BENZO(A)PYRENE 50-32-8 BENZO(A)PYRENE 59 205-99-2 BENZO(B)FLUORANTHENE 205-99-2 BENZO(B)FLUORANTHENE 60 191-24-2 BENZO(G,H,I)PERYLENE 191-24-2 BENZO(G,H,I)PERYLENE 61 207-08-9 BENZO(K)FLUORANTHENE 207-08-9 BENZO(K)FLUORANTHENE 62 7440-41-7 BERYLLIUM 14701-08-7 BERYLLIUM ION 63 71-52-3 BICARBONATE 71-52-3 BICARBONATE 64 7440-42-8 BORON 11113-50-1 BORIC ACID 65 314-40-9 BROMACIL 314-40-9 BROMACIL 66 NONE BROMIDE 24959-67-9 BROMIDE 67 108-86-1 BROMOBENZENE 108-86-1 BROMOBENZENE 68 74-97-5 BROMOCHLOROMETHANE 74-97-5 BROMOCHLOROMETHANE 69 75-27-4 BROMODICHLOROMETHANE 75-27-4 BROMODICHLOROMETHANE 70 75-25-2 BROMOFORM 75-25-2 BROMOFORM 71 74-83-9 BROMOMETHANE 74-83-9 BROMOMETHANE 72 23184-66-9 BUTACHLOR 23184-66-9 BUTACHLOR 73 85-68-7 BUTYL BENZYL PHTHALATE 85-68-7 BUTYL BENZYL PHTHALATE 74 7440-43-9 CADMIUM 22537-48-0 CADMIUM CATION 75 7440-70-2 CALCIUM 14102-48-8 CALCIUM CATION 76 63-25-2 CARBARYL 63-25-2 CARBARYL 77 1563-66-2 CARBOFURAN 1563-66-2 CARBOFURAN 78 75-15-0 CARBON DISULFIDE 75-15-0 CARBON DISULFIDE 79 56-23-5 CARBON TETRACHLORIDE 56-23-5 CARBON TETRACHLORIDE 80 3812-32-6 CARBONATE 3812-32-6 CARBONATE 81 57-74-9 CHLORDANE 57-74-9 CHLORDANE 82 5103-71-9 CHLORDANE (ALPHA-CHLORDANE) 5103-71-9 CHLORDANE (ALPHA-CHLORDANE) 83 12789-03-6 CHLORDANE (GAMMA-CHLORDANE) 12789-03-6 CHLORDANE (GAMMA-CHLORDANE) 84 39765-80-5 CHLORDANE (TRANS-NONACHLOR) 39765-80-5 CHLORDANE (TRANS-NONACHLOR) 85 68188-88-5 CHLORIDE 16887-00-6 CHLORIDE 86 108-90-7 CHLOROBENZENE 108-90-7 CHLOROBENZENE 87 75-00-3 CHLOROETHANE 75-00-3 CHLOROETHANE 88 67-66-3 CHLOROFORM 67-66-3 CHLOROFORM 89 74-87-3 CHLOROMETHANE 74-87-3 CHLOROMETHANE 90 7440-47-3 CHROMIUM 11104-59-9 CHROMATE 91 218-01-9 CHRYSENE 218-01-9 CHRYSENE 92 156-59-2 CIS-1,2-DICHLOROETHYLENE 156-59-2 CIS-1,2-DICHLOROETHYLENE 93 10061-01-5 CIS-1,3-DICHLOROPROPENE 10061-01-5 CIS-1,3-DICHLOROPROPENE 94 7440-50-8 COPPER 17493-86-6 COPPER ION 108 CD O_CAS O_ CONTAMINANT FINAL_CAS FINAL_CONTAMINANT 95 NONE CRYPTOSPORIDIUM PARVUM NOT EVALUATED CRYPTOSPORIDIUM PARVUM 96 21725-46-2 CYANAZINE 21725-46-2 CYANAZINE 97 57-12-5 CYANIDE 57-12-5 CYANIDE 98 75-99-0 DALAPON 75-99-0 DALAPON 99 NONE DCPA DI-ACID DEGRADATE 2136-79-0 DCPA DI-ACID DEGRADATE 100 NONE DCPA MONO-ACID DEGRADATE 887-54-7 DCPA MONO-ACID DEGRADATE 101 72-55-9 DDE 72-55-9 DDE 102 103-23-1 DI-(2-ETHYLHEXYL)ADIPATE 103-23-1 DI-(2-ETHYLHEXYL)ADIPATE 103 117-81-7 DI-(2-ETHYLHEXYL)PHTHALATE 117-81-7 DI-(2-ETHYLHEXYL)PHTHALATE 104 333-41-5 DIAZINON 333-41-5 DIAZINON 105 53-70-3 DIBENZ(A,H)ANTHRACENE 53-70-3 DIBENZ(A,H)ANTHRACENE 106 124-48-1 DIBROMOCHLOROMETHANE 124-48-1 DIBROMOCHLOROMETHANE 107 67708-83-2 DIBROMOCHLOROPROPANE 67708-83-2 DIBROMOCHLOROPROPANE 108 74-95-3 DIBROMOMETHANE 74-95-3 DIBROMOMETHANE 109 1918-00-9 DICAMBA 1918-00-9 DICAMBA 110 75-71-8 DICHLORODIFLUOROMETHANE 75-71-8 DICHLORODIFLUOROMETHANE 111 75-09-2 DICHLOROMETHANE 75-09-2 DICHLOROMETHANE 112 60-57-1 DIELDRIN 60-57-1 DIELDRIN 113 84-66-2 DIETHYL PHTHALATE 84-66-2 DIETHYL PHTHALATE 114 131-11-3 DIMETHYL PHTHALATE 131-11-3 DIMETHYL PHTHALATE 115 84-74-2 DI-N-BUTYL PHTHALATE 84-74-2 DI-N-BUTYL PHTHALATE 116 88-85-7 DINOSEB 88-85-7 DINOSEB 117 2764-72-9 DIQUAT 2764-72-9 DIQUAT 118 298-04-4 DISULFOTON 298-04-4 DISULFOTON 119 330-54-1 DIURON 330-54-1 DIURON 120 145-73-3 ENDOTHALL 145-73-3 ENDOTHALL 121 72-20-8 ENDRIN 72-20-8 ENDRIN 122 759-94-4 EPTC 759-94-4 EPTC 123 NONE ESCHERICHIA COLI NOT EVALUATED ESCHERICHIA COLI 124 97-63-2 ETHYL METHACRYLATE 97-63-2 ETHYL METHACRYLATE 125 100-41-4 ETHYLBENZENE 100-41-4 ETHYLBENZENE 126 106-93-4 ETHYLENE DIBROMIDE 106-93-4 ETHYLENE DIBROMIDE 127 NONE FECAL VIRUSES NOT EVALUATED FECAL VIRUSES 128 86-73-7 FLUORENE 86-73-7 FLUORENE 129 16984-48-8 FLUORIDE 16984-48-8 FLUORIDE 130 944-22-9 FONOFOS 944-22-9 FONOFOS 131 NONE GIARDIA LAMBLIA NOT EVALUATED GIARDIA LAMBLIA 132 1071-83-6 GLYPHOSATE 1071-83-6 GLYPHOSATE 109 CD O_CAS O_ CONTAMINANT FINAL_CAS FINAL_CONTAMINANT 133 NONE GROSS ALPHA NOT EVALUATED GROSS ALPHA 134 NONE GROSS BETA NOT EVALUATED GROSS BETA 135 NONE HARDNESS NOT EVALUATED HARDNESS 136 76-44-8 HEPTACHLOR 76-44-8 HEPTACHLOR 137 1024-57-3 HEPTACHLOR EPOXIDE 1024-57-3 HEPTACHLOR EPOXIDE 138 118-74-1 HEXACHLOROBENZENE 118-74-1 HEXACHLOROBENZENE 139 87-68-3 HEXACHLOROBUTADIENE 87-68-3 HEXACHLOROBUTADIENE 140 77-47-4 HEXACHLOROCYCLOPENTADIENE 77-47-4 HEXACHLOROCYCLOPENTADIENE 141 7783-06-4 HYDROGEN SULFIDE 15035-72-0 SULFIDE 142 193-39-5 INDENO[1,2,3,CD]PYRENE 193-39-5 INDENO[1,2,3,CD]PYRENE 143 74-88-4 IODOMETHANE 74-88-4 IODOMETHANE 144 7439-89-6 IRON 15438-31-0 IRON ION 145 98-82-8 ISOPROPYLBENZENE 98-82-8 ISOPROPYLBENZENE 146 845-52-3 LAMBAST 845-52-3 LAMBAST 147 7439-92-1 LEAD 14701-27-0 LEAD ION 148 58-89-9 LINDANE 58-89-9 LINDANE 149 330-55-2 LINURON 330-55-2 LINURON 150 NONE M + P XYLENE 106-42-3 P XYLENE 151 7439-95-4 MAGNESIUM 14581-92-1 MAGNESIUM ION 152 7439-96-5 MANGANESE 14333-14-3 MANGANATE 153 7439-97-6 MERCURY 14302-87-5 MERCURY ION 154 2032-65-7 METHIOCARB 2032-65-7 METHIOCARB 155 16752-77-5 METHOMYL 16752-77-5 METHOMYL 156 72-43-5 METHOXYCHLOR 72-43-5 METHOXYCHLOR 157 78-93-3 METHYL ETHYL KETONE 78-93-3 METHYL ETHYL KETONE 158 80-62-6 METHYL METHACRYLATE 80-62-6 METHYL METHACRYLATE 159 1634-04-4 METHYL-T-BUTYL ETHER 1634-04-4 METHYL-T-BUTYL ETHER 160 51218-45-2 METOLACHLOR 51218-45-2 METOLACHLOR 161 21087-64-9 METRIBUZIN 21087-64-9 METRIBUZIN 162 2212-67-1 MOLINATE 2212-67-1 MOLINATE 163 NONE MONOCHLOROBENZENE 108-90-7 MONOCHLOROBENZENE 164 108-38-3 M-XYLENE 108-38-3 M-XYLENE 165 91-20-3 NAPHTHALENE 91-20-3 NAPHTHALENE 166 104-51-8 N-BUTYLBENZENE 104-51-8 N-BUTYLBENZENE 167 7440-02-0 NICKEL 14701-22-5 NICKEL ION 168 14797-55-8 NITRATE 14797-55-8 NITRATE 169 NONE NITRATE+NITRITE NOTEVALUATED NITRATE+NITRITE 170 14797-65-0 NITRITE 14797-65-0 NITRITE 171 98-95-3 NITROBENZENE 98-95-3 NITROBENZENE 110 CD O_CAS O_ CONTAMINANT FINAL_CAS FINAL_CONTAMINANT 172 103-65-1 N-PROPYLBENZENE 103-65-1 N-PROPYLBENZENE 173 NONE ORGANOTINS 23120-99-2 ORGANOTINS 174 95-50-1 ORTHO-1,2-DICHLOROBENZENE 95-50-1 ORTHO-1,2-DICHLOROBENZENE 175 23135-22-0 OXAMYL 23135-22-0 OXAMYL 176 95-47-6 O-XYLENE 95-47-6 O-XYLENE 177 NONE P ALKALINITY NOTEVALUATED P ALKALINITY 178 106-46-7 PARA-1,4-DICHLOROBENZENE 106-46-7 PARA-1,4-DICHLOROBENZENE 179 NONE PCBS 53469-21-9 PCBS 180 87-86-5 PENTACHLOROPHENOL 87-86-5 PENTACHLOROPHENOL 181 14797-73-0 PERCHLORATE 14797-73-0 PERCHLORATE 182 NONE PH NOTEVALUATED PH 183 85-01-8 PHENANTHRENE 85-01-8 PHENANTHRENE 184 1918-02-1 PICLORAM 1918-02-1 PICLORAM 185 1610-18-0 PROMETON 1610-18-0 PROMETON 186 1918-16-7 PROPACHLOR 1918-16-7 PROPACHLOR 187 139-40-2 PROPAZINE 139-40-2 PROPAZINE 188 106-42-3 P-XYLENE 106-42-3 P-XYLENE 189 129-00-0 PYRENE 129-00-0 PYRENE 190 15262-20-1 RADIUM-226 13982-63-3 NONE 191 13982-63-3 RADIUM-228 15262-20-1 NONE 192 10043-92-2 RADON 10043-92-2 RADON 193 121-82-4 RDX 121-82-4 RDX 194 135-98-8 S-BUTYLBENZENE 135-98-8 S-BUTYLBENZENE 195 7782-49-2 SELENIUM 7782-49-2 SELENIUM 196 7440-22-4 SILVER 14701-21-4 SILVER ION 197 122-34-9 SIMAZINE 122-34-9 SIMAZINE 198 7440-23-5 SODIUM 17341-25-2 SODIUM ION 199 NONE SPECIFIC CONDUCTANCE NOTEVALUATED SPECIFIC CONDUCTANCE 200 14158-27-1 STRONTIUM-89 14701-18-9 STRONTIUM ION 201 10098-97-2 STRONTIUM-90 10098-97-2 STRONTIUM-90 202 100-42-5 STYRENE 100-42-5 STYRENE 203 14808-79-8 SULFATE 14808-79-8 SULFATE 204 98-06-6 T-BUTYLBENZENE 98-06-6 T-BUTYLBENZENE 205 NONE TDS NOTEVALUATED TDS 206 5902-51-2 TERBACIL 5902-51-2 TERBACIL 207 13071-79-9 TERBUFOS 13071-79-9 TERBUFOS 208 127-18-4 TETRACHLOROETHYLENE 127-18-4 TETRACHLOROETHYLENE 209 109-99-9 TETRAHYDROFURAN 109-99-9 TETRAHYDROFURAN 210 7440-28-0 THALLIUM 7440-28-0 THALLIUM 211 108-88-3 TOLUENE 108-88-3 TOLUENE 111 CD O_CAS O_ CONTAMINANT FINAL_CAS FINAL_CONTAMINANT 212 NONE TOTAL ALPHA EMITTING RADIUM 7440-14-4 RADIUM ISOTOP 213 NONE TOTAL COLIFORM NOTEVALUATED TOTAL COLIFORM 214 NONE TOTAL TRIHALOMETHANES NOTEVALUATED NONE 215 8001-35-2 TOXAPHENE 8001-35-2 TOXAPHENE 216 156-60-5 TRANS-1,2-DICHLOROETHYLENE 156-60-5 TRANS-1,2-DICHLOROETHYLENE 217 10061-02-6 TRANS-1,3-DICHLOROPROPENE 10061-02-6 TRANS-1,3-DICHLOROPROPENE 218 NONE TRIAZINES 122-34-9 TRIAZINES 219 79-01-6 TRICHLOROETHYLENE 79-01-6 TRICHLOROETHYLENE 220 75-69-4 TRICHLOROFLUOROMETHANE 75-69-4 TRICHLOROFLUOROMETHANE 221 1582-09-8 TRIFLURALIN 1582-09-8 TRIFLURALIN 222 10028-17-8 TRITIUM 15086-10-9 TRITIUM 223 NONE URANIUM NONE NONE 224 108-05-4 VINYL ACETATE 108-05-4 VINYL ACETATE 225 75-01-4 VINYL CHLORIDE 75-01-4 VINYL CHLORIDE 226 NONE XYLENES (TOTAL) 95-47-6 XYLENES (TOTAL) 227 7440-66-6 ZINC 15176-26-8 ZINC ION 112 APPENDIX 3 - PARTITION COEFFICIENTS FOR ORGANIC COMPOUNDS The following table shows the partitioning coefficients selected for the contaminants. A more detailed description of the sources of information and selection methods is presented in section 3.5.2.3 Description of the fields ? CD ? Identifier number of the contaminant. ? FINAL_CAS ? The final CAS number used in the dilution attenuation component. ? FINAL_CONTAMINANT - The final contaminant name used in the dilution attenuation component. ? TYPE ? the type of the contaminant, organic, inorganic or metal. ? H ? Selected Henry?s law constant (dimensionless). ? LOG KOC ? The log of selected Koc values (cm 3 -water/g-carbon). ? SOLUBILITY - Selected solubility value (mg/l). Description of the colors The colors below relate the selected values for partition coefficients with their source of information. EPI Suite software TRRP PCL table PhysPro 113 CD FINAL_CAS FINAL_CONTAMINANT TYPE H LOG KOC SOLUBILITY 1 630-20-6 1,1,1,2-TETRACHLOROETHANE O 9.977E-02 2.543 1.100E+03 2 71-55-6 1,1,1-TRICHLOROETHANE O 7.116E-01 2.040 1.330E+03 3 79-34-5 1,1,2,2-TETRACHLOROETHANE O 1.518E-02 1.890 2.970E+03 4 79-00-5 1,1,2-TRICHLOROETHANE O 3.409E-02 1.502 4.420E+03 5 75-34-3 1,1-DICHLOROETHANE O 2.325E-01 1.403 5.500E+03 6 75-35-4 1,1-DICHLOROETHYLENE O 1.056E+00 1.743 2.400E+03 7 563-58-6 1,1-DICHLOROPROPENE O 1.819E+00 2.143 7.488E+02 8 87-61-6 1,2,3-TRICHLOROBENZENE O 3.800E-02 3.663 1.884E+01 9 96-18-4 1,2,3-TRICHLOROPROPANE O 1.419E-02 1.883 1.900E+03 10 120-82-1 1,2,4-TRICHLOROBENZENE O 5.874E-02 3.220 4.880E+01 11 95-63-6 1,2,4-TRIMETHYLBENZENE O 1.840E-01 2.970 7.959E+01 12 107-06-2 1,2-DICHLOROETHANE O 4.882E-02 1.093 8.700E+03 13 78-87-5 1,2-DICHLOROPROPANE O 1.168E-01 1.593 2.800E+03 14 122-66-7 1,2-DIPHENYLHYDRAZINE O 1.422E-07 2.553 1.840E+03 15 108-67-8 1,3,5-TRIMETHYLBENZENE O 2.720E-01 3.010 5.148E+01 16 541-73-1 1,3-DICHLOROBENZENE O 1.088E-01 2.230 1.100E+02 17 142-28-9 1,3-DICHLOROPROPANE O 4.038E-02 1.613 2.157E+03 18 542-75-6 1,3-DICHLOROPROPENE O 1.226E-01 1.642 1.994E+03 19 594-20-7 2,2-DICHLOROPROPANE O 3.394E-01 2.192 1.682E+03 20 1746-01-6 2,3,7,8-TCDD O 1.474E-03 6.413 1.103E-03 21 93-76-5 2,4,5-T O 2.826E-07 2.474 2.780E+02 22 93-72-1 2,4,5-TP O 3.748E-07 3.413 1.400E+02 23 88-06-2 2,4,6-TRICHLOROPHENOL O 1.076E-04 2.117 9.820E+02 24 94-75-7 2,4-D O 5.820E-09 2.423 8.900E+02 25 120-83-2 2,4-DICHLOROPHENOL O 9.060E-05 1.857 4.500E+03 26 51-28-5 2,4-DINITROPHENOL O 2.012E-07 -2.000 5.800E+03 27 121-14-2 2,4-DINITROTOLUENE O 2.234E-06 1.593 4.462E+02 28 606-20-2 2,6-DINITROTOLUENE O 3.090E-05 1.620 3.524E+02 29 95-49-8 2-CHLOROTOLUENE O 1.347E-01 2.610 1.540E+02 30 591-78-6 2-HEXANONE O 3.381E-03 0.993 1.794E+04 31 95-48-7 2-METHYLPHENOL O 4.964E-05 1.562 2.040E+04 32 16655-82-6 3-HYDROXYCARBOFURAN O 2.466E-12 0.373 6.207E+03 33 106-43-4 4-CHLOROTOLUENE O 1.335E-01 2.696 1.358E+02 114 CD FINAL_CAS FINAL_CONTAMINANT TYPE H LOG KOC SOLUBILITY 34 99-87-6 4-ISOPROPYLTOLUENE O 4.551E-01 3.360 2.788E+01 35 108-10-1 4-METHYL-2-PENTANONE O 5.709E-03 0.923 1.900E+04 36 83-32-9 ACENAPHTHENE O 6.443E-03 3.533 4.240E+00 37 208-96-8 ACENAPHTHYLENE O 4.739E-03 3.553 3.930E+00 38 34256-82-1 ACETOCHLOR O 9.225E-07 2.642 2.230E+02 39 67-64-1 ACETONE O 1.613E-03 -0.627 6.000E+05 40 107-13-1 ACRYLONITRILE O 5.709E-03 -0.137 7.500E+04 41 15972-60-8 ALACHLOR O 3.442E-07 2.279 2.400E+02 42 116-06-3 ALDICARB O 5.820E-08 0.743 6.000E+03 43 1646-88-4 ALDICARB SULFONE O 1.096E-07 -0.959 8.000E+03 44 1646-87-3 ALDICARB SULFOXIDE O 4.009E-08 -1.167 2.800E+04 45 309-00-2 ALDRIN O 1.820E-03 4.680 7.840E-02 48 120-12-7 ANTHRACENE O 2.300E-03 4.064 6.905E-01 50 53469-21-9 AROCLOR O 7.860E-03 5.903 2.770E-01 53 1912-24-9 ATRAZINE O 9.763E-08 2.204 2.141E+02 55 25057-89-0 BENTAZON O 9.019E-08 1.953 5.000E+02 56 71-43-2 BENZENE O 2.274E-01 1.743 2.000E+03 57 56-55-3 BENZO(A)ANTHRACENE O 1.393E-04 5.373 2.907E-02 58 50-32-8 BENZO(A)PYRENE O 1.891E-05 5.740 1.038E-02 59 205-99-2 BENZO(B)FLUORANTHENE O 2.718E-05 5.393 2.065E-02 60 191-24-2 BENZO(G,H,I)PERYLENE O 1.369E-05 6.200 2.842E-03 61 207-08-9 BENZO(K)FLUORANTHENE O 4.448E-07 5.723 1.079E-02 65 314-40-9 BROMACIL O 4.344E-09 1.723 8.150E+02 67 108-86-1 BROMOBENZENE O 8.382E-02 2.384 4.460E+02 68 74-97-5 BROMOCHLOROMETHANE O 3.690E-02 1.021 2.042E+04 69 75-27-4 BROMODICHLOROMETHANE O 8.770E-02 1.613 4.500E+03 70 75-25-2 BROMOFORM O 2.213E-02 1.940 3.200E+03 71 74-83-9 BROMOMETHANE O 2.581E-01 0.803 1.520E+04 72 23184-66-9 BUTACHLOR O 2.110E-06 4.114 2.300E+01 73 85-68-7 BUTYL BENZYL PHTHALATE O 5.213E-05 4.138 2.900E+00 76 63-25-2 CARBARYL O 1.804E-07 1.973 4.162E+02 77 1563-66-2 CARBOFURAN O 1.278E-07 1.462 7.000E+02 78 75-15-0 CARBON DISULFIDE O 5.957E-01 1.553 2.928E+03 115 CD FINAL_CAS FINAL_CONTAMINANT TYPE H LOG KOC SOLUBILITY 79 56-23-5 CARBON TETRACHLORIDE O 1.142E+00 2.270 8.050E+02 81 57-74-9 CHLORDANE O 2.011E-03 5.080 5.600E-02 82 5103-71-9 CHLORDANE (ALPHA-CHLORDANE) O 2.011E-03 5.833 4.640E-02 83 12789-03-6 CHLORDANE (GAMMA-CHLORDANE) O 2.011E-03 5.833 1.299E-02 84 39765-80-5 CHLORDANE (TRANS-NONACHLOR) O 1.026E-03 5.963 6.120E-03 86 108-90-7 CHLOROBENZENE O 1.287E-01 2.330 5.020E+02 87 75-00-3 CHLOROETHANE O 2.120E-01 1.041 2.000E+04 88 67-66-3 CHLOROFORM O 1.518E-01 1.583 7.920E+03 89 74-87-3 CHLOROMETHANE O 3.649E-01 0.522 2.262E+04 91 218-01-9 CHRYSENE O 5.030E-05 5.423 2.635E-02 92 156-59-2 CIS-1,2-DICHLOROETHYLENE O 1.688E-01 1.462 6.410E+03 93 10061-01-5 CIS-1,3-DICHLOROPROPENE O 9.150E-02 1.642 2.700E+03 96 21725-46-2 CYANAZINE O 1.225E-10 1.692 1.838E+02 98 75-99-0 DALAPON O 3.732E-06 1.292 5.020E+05 99 2136-79-0 DCPA DI-ACID DEGRADATE O 8.398E-12 1.743 1.754E+02 100 887-54-7 DCPA MONO-ACID DEGRADATE O 3.351E-09 2.803 1.826E+01 101 72-55-9 O 8.730E-04 5.040 6.500E-02 102 103-23-1 DI-(2-ETHYLHEXYL)ADIPATE O 1.795E-05 5.580 7.800E-01 103 117-81-7 DI-(2-ETHYLHEXYL)PHTHALATE O 1.117E-05 7.212 3.000E-01 104 333-41-5 DIAZINON O 4.675E-06 2.120 4.000E+01 105 53-70-3 DIBENZ(A,H)ANTHRACENE O 4.656E-07 6.152 3.304E-03 106 124-48-1 DIBROMOCHLOROMETHANE O 3.239E-02 1.770 5.250E+03 107 67708-83-2 DIBROMOCHLOROPROPANE O 6.081E-03 2.230 1.000E+03 108 74-95-3 DIBROMOMETHANE O 3.401E-02 1.312 1.100E+04 109 1918-00-9 DICAMBA O 9.019E-08 0.342 8.310E+03 110 75-71-8 DICHLORODIFLUOROMETHANE O 1.419E+01 1.773 2.800E+02 111 75-09-2 DICHLOROMETHANE O 9.104E-02 0.863 1.540E+04 112 60-57-1 DIELDRIN O 1.110E-04 4.330 1.950E-01 113 84-66-2 DIETHYL PHTHALATE O 1.871E-05 2.033 1.080E+03 114 131-11-3 DIMETHYL PHTHALATE O 4.344E-06 1.500 4.190E+03 115 84-74-2 DI-N-BUTYL PHTHALATE O 5.945E-05 4.114 1.120E+01 116 88-85-7 DINOSEB O 1.886E-05 3.080 5.200E+01 117 2764-72-9 DIQUAT O 2.690E-12 1.973 7.000E+01 116 CD FINAL_CAS FINAL_CONTAMINANT TYPE H LOG KOC SOLUBILITY 118 298-04-4 DISULFOTON O 8.936E-05 3.632 1.600E+01 119 330-54-1 DIURON O 2.085E-08 2.292 1.506E+02 120 145-73-3 ENDOTHALL O 1.593E-14 1.522 1.000E+05 121 72-20-8 ENDRIN O 4.947E-05 3.970 2.500E-01 122 759-94-4 EPTC O 6.578E-04 2.823 3.700E+02 124 97-63-2 ETHYL METHACRYLATE O 6.651E-03 1.553 1.900E+04 125 100-41-4 ETHYLBENZENE O 3.260E-01 2.310 2.286E+02 126 106-93-4 ETHYLENE DIBROMIDE O 2.759E-02 1.573 4.320E+03 128 86-73-7 FLUORENE O 2.644E-03 3.793 1.980E+00 130 944-22-9 FONOFOS O 2.888E-04 3.553 1.070E+01 132 1071-83-6 GLYPHOSATE O 1.688E-17 -4.387 1.000E+06 136 76-44-8 HEPTACHLOR O 1.216E-02 4.070 1.800E-01 137 1024-57-3 HEPTACHLOR EPOXIDE O 3.446E-04 3.859 2.750E-01 138 118-74-1 HEXACHLOROBENZENE O 2.224E-02 4.450 1.922E-01 139 87-68-3 HEXACHLOROBUTADIENE O 4.261E-01 3.840 3.200E+00 140 77-47-4 HEXACHLOROCYCLOPENTADIENE O 7.150E-01 3.980 1.800E+00 142 193-39-5 INDENO[1,2,3,CD]PYRENE O 2.852E-06 6.312 3.751E-03 143 74-88-4 IODOMETHANE O 2.176E-01 1.124 1.244E+04 145 98-82-8 ISOPROPYLBENZENE O 4.757E-01 3.272 7.503E+01 146 845-52-3 LAMBAST O 5.171E-11 1.963 1.188E+02 148 58-89-9 LINDANE O 1.409E-04 3.040 7.300E+00 149 330-55-2 LINURON O 2.586E-07 2.813 7.500E+01 150 106-42-3 M + P XYLENE O 2.854E-01 2.763 2.286E+02 154 2032-65-7 METHIOCARB O 4.882E-08 2.533 1.035E+02 155 16752-77-5 METHOMYL O 8.150E-10 0.212 5.800E+04 156 72-43-5 METHOXYCHLOR O 8.398E-06 4.693 3.020E-01 157 78-93-3 METHYL ETHYL KETONE O 1.937E-03 -0.097 2.400E+05 158 80-62-6 METHYL METHACRYLATE O 1.330E-02 0.993 1.600E+04 159 1634-04-4 METHYL-T-BUTYL ETHER O 2.428E-02 0.553 4.800E+04 160 51218-45-2 METOLACHLOR O 3.133E-08 2.513 8.640E+02 161 21087-64-9 METRIBUZIN O 4.840E-09 1.312 1.304E+03 162 2212-67-1 MOLINATE O 5.253E-05 1.699 9.700E+02 164 108-38-3 M-XYLENE O 2.970E-01 2.292 2.072E+02 165 91-20-3 NAPHTHALENE O 1.820E-02 2.913 1.421E+02 117 CD FINAL_CAS FINAL_CONTAMINANT TYPE H LOG KOC SOLUBILITY 166 104-51-8 N-BUTYLBENZENE O 5.570E-01 3.480 1.608E+01 171 98-95-3 NITROBENZENE O 8.563E-04 1.462 2.090E+03 172 103-65-1 N-PROPYLBENZENE O 4.240E-01 3.030 7.073E+01 173 23120-99-2 Organotins O 2.873E+00 -3.487 1.000E+06 174 95-50-1 ORTHO-1,2-DICHLOROBENZENE O 7.943E-02 2.840 1.500E+02 175 23135-22-0 OXAMYL O 1.600E-11 -0.866 2.800E+05 176 95-47-6 O-XYLENE O 2.140E-01 2.110 2.424E+02 178 106-46-7 PARA-1,4-DICHLOROBENZENE O 9.970E-02 2.810 9.024E+01 179 53469-21-9 PCBs O 7.860E-03 5.724 2.770E-01 180 87-86-5 PENTACHLOROPHENOL O 1.014E-06 2.613 1.400E+01 183 85-01-8 PHENANTHRENE O 1.750E-03 4.072 1.150E+00 184 1918-02-1 PICLORAM O 2.205E-12 0.973 1.818E+03 185 1610-18-0 PROMETON O 3.700E-08 2.603 7.500E+02 186 1918-16-7 PROPACHLOR O 3.785E-06 1.793 7.000E+02 187 139-40-2 PROPAZINE O 1.903E-07 2.543 9.608E+01 188 106-42-3 P-XYLENE O 2.854E-01 2.490 2.286E+02 189 129-00-0 PYRENE O 4.573E-04 4.580 2.249E-01 193 121-82-4 RDX O 2.615E-06 0.483 6.062E+03 194 135-98-8 S-BUTYLBENZENE O 5.070E-01 3.320 1.810E+01 197 122-34-9 SIMAZINE O 3.897E-08 1.793 5.899E+02 202 100-42-5 STYRENE O 1.138E-01 2.562 3.437E+02 204 98-06-6 T-BUTYLBENZENE O 5.461E-01 3.390 2.734E+01 206 5902-51-2 TERBACIL O 4.964E-09 1.502 8.717E+02 207 13071-79-9 TERBUFOS O 9.929E-04 4.093 5.070E+00 208 127-18-4 TETRACHLOROETHYLENE O 7.322E-01 2.190 2.000E+02 209 109-99-9 TETRAHYDROFURAN O 2.917E-03 0.072 1.000E+06 211 108-88-3 TOLUENE O 2.747E-01 2.146 5.731E+02 215 8001-35-2 TOXAPHENE O 1.397E-04 4.981 7.400E-01 216 156-60-5 TRANS-1,2-DICHLOROETHYLENE O 1.688E-01 1.700 6.300E+03 217 10061-02-6 TRANS-1,3-DICHLOROPROPENE O 9.150E-02 1.642 2.800E+03 218 122-34-9 TRIAZINES O 3.897E-08 1.793 5.899E+02 219 79-01-6 TRICHLOROETHYLENE O 4.075E-01 1.970 1.280E+03 220 75-69-4 TRICHLOROFLUOROMETHANE O 4.013E+00 2.130 1.100E+03 221 1582-09-8 TRIFLURALIN O 2.012E-03 4.137 6.000E-01 118 CD FINAL_CAS FINAL_CONTAMINANT TYPE H LOG KOC SOLUBILITY 224 108-05-4 VINYL ACETATE O 2.114E-02 0.342 3.025E+04 225 75-01-4 VINYL CHLORIDE O 1.150E+00 1.040 8.800E+03 226 95-47-6 XYLENES (TOTAL) O 2.140E-01 2.110 2.424E+02 119 APPENDIX 4 ? PARTITION COEFFITIONS AND DEGRADATION RATES FOR METALS AND INORGANIC COMPOUNDS The following table presents the partitioning coefficients for inorganic and metal constituents. A more detailed description of the sources of information and the selection methods is presented in section 3.5.2.3. Field description ? CD ? Identifier number of the contaminant. ? CAS? the final CAS number used in the dilution attenuation component. ? CONTAMINANT - The final contaminant name used in the dilution attenuation component. ? The log(Kd) value selected. The minimum Log(Kd) was selected from the available references. ? Degradation Rate- The degradation rate used in the assessment. (1) US.EPA, 1996. Attachment C. Chemical properties (2) U.S EPA, 2000. Attachment C - Radiological properties for SSL development, Soil Screening Guidance for Radionuclides (3) Schwarzenbach et al, 1993. Environmental organic chemistry. 120 CD CAS Contaminant TYPE H Final LOG(KD) (1) Log(Kd) From Chemical properties PH=6.8 (2) Log(Kd) from Radiological document Degradation Rate 1/day COMMENTS 227 15176-26-8 Zinc ion M 0.00E+00 -1.00 1.79 -1.00 4.710E-06 223 none none M 0.00E+00 -0.40 None -3.979E-01 7.327E-16 222 15086-10-9 TRITIUM I 0.00E+00 -18.00 None -1.800E+01 2.747E-07 212 7440-14-4 Radium Isotop M 0.00E+00 0.48 None 4.77E-01 2.061E-09 210 7440-28-0 THALLIUM M 0.00E+00 -18.00 1.85 -18.00 8.242E-07 203 14808-79-8 SULFATE I 0.00E+00 0.70 None None 0.000E+00 Kd same as Se 201 10098-97-2 STRONTIUM-90 M 0.00E+00 0.00 None 0.00E+00 1.137E-07 200 14701-18-9 Strontium ion M 0.00E+00 0.00 None 0.00E+00 2.383E-05 198 17341-25-2 Sodium ion M 0.00E+00 -18.00 None -1.80E+01 0.000E+00 196 14701-21-4 Silver ion M 0.00E+00 0.43 0.92 0.43 0.000E+00 195 7782-49-2 SELENIUM M 0.00E+00 0.70 0.70 None 0.000E+00 192 10043-92-2 Radon I 4.39E+00 -18.00 None -1.800E+01 3.008E-04 H From Schwarzenbach (3) 191 15262-20-1 RADIUM-228 I 0.00E+00 0.48 None 4.771E-01 4.121E-07 190 13982-63-3 RADIUM-226 I 0.00E+00 0.48 None 4.771E-01 2.061E-09 181 14797-73-0 PERCHLORATE I 0.00E+00 -18.00 None None 0.000E+00 170 14797-65-0 NITRITE I 0.00E+00 -18.00 None None 0.000E+00 169 none NITRATE+NITRITE I 0.00E+00 -18.00 None None 0.000E+00 168 14797-55-8 NITRATE I 0.00E+00 -18.00 None None 0.000E+00 167 14701-22-5 Nickel ion M 0.00E+00 1.53 1.81 1.53 0.000E+00 153 14302-87-5 Mercury ion M 0.00E+00 1.72 1.72 None 0.000E+00 152 14333-14-3 Manganate M 0.00E+00 0.69 None 6.90E-01 0.000E+00 151 14581-92-1 Magnesium ion M 0.00E+00 0.00 None None 0.000E+00 Kd same as Sr 147 14701-27-0 Lead ion M 0.00E+00 0.78 None 7.78E-01 0.000E+00 144 15438-31-0 Iron Ion M 0.00E+00 0.49 None 4.91E-01 0.000E+00 141 15035-72-0 Sulfide I 4.09E-01 -18.00 None None 0.000E+00 H from Schwarzenbach (3) 129 16984-48-8 FLUORIDE I 0.00E+00 -18.00 None None 0.000E+00 97 57-12-5 CYANIDE I 3.41E-03 -18.00 None None 0.000E+00 H from Schwarzenbach (3 94 17493-86-6 Copper ion M 0.00E+00 0.78 None None 0.000E+00 Kd same as lead 90 17493-86-6 Chromate M 0.00E+00 1.28 1.28E+00 None 0.000E+00 Kd value is for chromate which is a weaker adsorber 85 16887-00-6 CHLORIDE I 0.00E+00 -18.00 None None 0.000E+00 80 3812-32-6 CARBONATE I 1.18E+03 -18.00 None None 0.000E+00 75 14102-48-8 Calcium cation M 0.00E+00 0.00 None None 0.000E+00 Kd equal to Sr 121 CD CAS Contaminant TYPE H Final LOG(KD) (1) Log(Kd) From Chemical properties PH=6.8 (2) Log(Kd) from Radiological document Degradation Rate 1/day COMMENTS 74 22537-48-0 Cadmium cation M 0.00E+00 0.43 1.88 0.43 0.000E+00 66 24959-67-9 BROMIDE I 0.00E+00 -18.00 None None 0.000E+00 64 11113-50-1 Boric acid I 0.00E+00 -18.00 None None 0.000E+00 63 71-52-3 BICARBONATE I 1.17E+00 -18.00 None None 0.000E+00 62 14701-08-7 Beryllium ion M 0.00E+00 2.90 2.90 None 0.000E+00 54 16541-35-8 Barium Cation M 0.00E+00 1.61 1.61 None 0.000E+00 52 1332-21-4 ASBESTOS I 0.00E+00 -18.00 None None 0.000E+00 51 15584-04-0 Arsenate M 0.00E+00 1.46 1.46 None 0.000E+00 49 64924-52-3 Antimonate M 0.00E+00 -18.00 None -18.00 0.000E+00 47 14903-36-7 Aluminum Cation M 0.00E+00 0.49 None None 0.000E+00 Kd same as iron 122 APPENDIX 5 - DEGRADATION RATES FOR ORGANIC COMPOUNDS The following table presents the degradation rates selected for the organic constituents. A more detailed description of the sources of information and the selection methods is presented in section 3.5.2.4. Field description ? CD ? Identifier number of the contaminant. ? CAS? the final CAS number used in the dilution attenuation component. ? CONTAMINANT - The final contaminant name used in the dilution attenuation component. ? Half Life ? The estimated half-life of the contaminant in hours. ? Degradation ? The degradation rate computed from the half-life. * GW = Groundwater, SW = Surface water CD CONTAMINANT CAS HALF LIFE (Hours) Degradation rate (1/day) GW SW GW SW 1 1,1,1,2-TETRACHLOROETHANE 630-20-6 1604 1604 1.801E-05 1.801E-05 2 1,1,1-TRICHLOROETHANE 71-55-6 13104 6552 2.204E-06 4.408E-06 3 1,1,2,2-TETRACHLOROETHANE 79-34-5 1056 1440 2.735E-05 2.006E-05 4 1,1,2-TRICHLOROETHANE 79-00-5 17520 8760 1.648E-06 3.297E-06 5 1,1-DICHLOROETHANE 75-34-3 8640 3696 3.343E-06 7.814E-06 6 1,1-DICHLOROETHYLENE 75-35-4 3168 4320 9.117E-06 6.685E-06 7 1,1-DICHLOROPROPENE 563-58-6 450 900 6.418E-05 3.209E-05 8 1,2,3-TRICHLOROBENZENE 87-61-6 720 1440 4.011E-05 2.006E-05 9 1,2,3-TRICHLOROPROPANE 96-18-4 17280 8640 1.671E-06 3.343E-06 10 1,2,4-TRICHLOROBENZENE 120-82-1 8640 4320 3.343E-06 6.685E-06 11 1,2,4-TRIMETHYLBENZENE 95-63-6 1344 900 2.149E-05 3.209E-05 12 1,2-DICHLOROETHANE 107-06-2 8640 4320 3.343E-06 6.685E-06 13 1,2-DICHLOROPROPANE 78-87-5 61872 30936 4.668E-07 9.336E-07 14 1,2-DIPHENYLHYDRAZINE 122-66-7 8640 1740 3.343E-06 1.660E-05 123 CD CONTAMINANT CAS HALF LIFE (Hours) Degradation rate (1/day) 15 1,3,5-TRIMETHYLBENZENE 108-67-8 450 900 6.418E-05 3.209E-05 16 1,3-DICHLOROBENZENE 541-73-1 8640 4300 3.343E-06 6.717E-06 17 1,3-DICHLOROPROPANE 142-28-9 450 900 6.418E-05 3.209E-05 18 1,3-DICHLOROPROPENE 542-75-6 450 900 6.418E-05 3.209E-05 19 2,2-DICHLOROPROPANE 594-83-2 450 900 6.418E-05 3.209E-05 20 2,3,7,8-TCDD 1746-01-6 28320 14160 1.020E-06 2.040E-06 21 2,4,5-T 93-76-5 4320 900 6.685E-06 3.209E-05 22 2,4,5-TP 93-72-1 450 900 6.418E-05 3.209E-05 23 2,4,6-TRICHLOROPHENOL 88-06-2 43690 1440 6.610E-07 2.006E-05 24 2,4-D 94-75-7 4320 900 6.685E-06 3.209E-05 25 2,4-DICHLOROPHENOL 120-83-2 1032 900 2.799E-05 3.209E-05 26 2,4-DINITROPHENOL 51-28-5 12624 3840 2.288E-06 7.521E-06 27 2,4-DINITROTOLUENE 121-14-2 8640 900 3.343E-06 3.209E-05 28 2,6-DINITROTOLUENE 606-20-2 8640 900 3.343E-06 3.209E-05 29 2-CHLOROTOLUENE 95-49-8 450 900 6.418E-05 3.209E-05 30 2-HEXANONE 591-78-6 104 208 2.777E-04 1.389E-04 31 2-METHYLPHENOL 95-48-7 336 360 8.596E-05 8.023E-05 32 3-HYDROXYCARBOFURAN 16655-82-6 450 900 6.418E-05 3.209E-05 33 4-CHLOROTOLUENE 106-43-4 450 900 6.418E-05 3.209E-05 34 4-ISOPROPYLTOLUENE 99-87-6 180 360 1.605E-04 8.023E-05 35 4-METHYL-2-PENTANONE 108-10-1 336 360 8.596E-05 8.023E-05 36 ACENAPHTHENE 83-32-9 4896 900 5.899E-06 3.209E-05 37 ACENAPHTHYLENE 208-96-8 2880 1440 1.003E-05 2.006E-05 38 ACETOCHLOR 34256-82-1 720 1440 4.011E-05 2.006E-05 39 ACETONE 67-64-1 336 360 8.596E-05 8.023E-05 40 ACRYLONITRILE 107-13-1 1104 552 2.616E-05 5.232E-05 41 ALACHLOR 15972-60-8 720 1440 4.011E-05 2.006E-05 42 ALDICARB 116-06-3 15240 8664 1.895E-06 3.333E-06 43 ALDICARB SULFONE 1646-88-4 450 900 6.418E-05 3.209E-05 44 ALDICARB SULFOXIDE 1646-87-3 450 900 6.418E-05 3.209E-05 45 ALDRIN 309-00-2 28400 14200 1.017E-06 2.034E-06 48 ANTHRACENE 120-12-7 22080 1440 1.308E-06 2.006E-05 50 AROCLOR 53469-21-9 1800 3600 1.605E-05 8.023E-06 53 ATRAZINE 1912-24-9 720 1440 4.011E-05 2.006E-05 55 BENTAZON 25057-89-0 450 900 6.418E-05 3.209E-05 56 BENZENE 71-43-2 17280 900 1.671E-06 3.209E-05 57 BENZO(A)ANTHRACENE 56-55-3 32640 1440 8.848E-07 2.006E-05 58 BENZO(A)PYRENE 50-32-8 720 1440 4.011E-05 2.006E-05 59 BENZO(B)FLUORANTHENE 205-99-2 29280 1440 9.864E-07 2.006E-05 60 BENZO(G,H,I)PERYLENE 191-24-2 31200 15600 9.257E-07 1.851E-06 61 BENZO(K)FLUORANTHENE 207-08-9 102720 1440 2.812E-07 2.006E-05 65 BROMACIL 314-40-9 450 900 6.418E-05 3.209E-05 67 BROMOBENZENE 108-86-1 450 900 6.418E-05 3.209E-05 124 CD CONTAMINANT CAS HALF LIFE (Hours) Degradation rate (1/day) 68 BROMOCHLOROMETHANE 74-97-5 180 360 1.605E-04 8.023E-05 69 BROMODICHLOROMETHANE 75-27-4 450 900 6.418E-05 3.209E-05 70 BROMOFORM 75-25-2 8640 4320 3.343E-06 6.685E-06 71 BROMOMETHANE 74-83-9 912 672 3.167E-05 4.298E-05 72 BUTACHLOR 23184-66-9 450 900 6.418E-05 3.209E-05 73 BUTYL BENZYL PHTHALATE 85-68-7 4320 360 6.685E-06 8.023E-05 76 CARBARYL 63-25-2 1440 900 2.006E-05 3.209E-05 77 CARBOFURAN 1563-66-2 450 900 6.418E-05 3.209E-05 78 CARBON DISULFIDE 75-15-0 180 360 1.605E-04 8.023E-05 79 CARBON TETRACHLORIDE 56-23-5 8640 8640 3.343E-06 3.343E-06 81 CHLORDANE 57-74-9 66528 33264 4.341E-07 8.682E-07 82 CHLORDANE (ALPHA-CHLORDANE) 5103-71-9 1800 3600 1.605E-05 8.023E-06 83 CHLORDANE (GAMMA- CHLORDANE) 12789-03-6 1800 3600 1.605E-05 8.023E-06 84 CHLORDANE (TRANS-NONACHLOR) 39765-80-5 1800 3600 1.605E-05 8.023E-06 86 CHLOROBENZENE 108-90-7 7200 3600 4.011E-06 8.023E-06 87 CHLOROETHANE 75-00-3 1344 672 2.149E-05 4.298E-05 88 CHLOROFORM 67-66-3 43200 4320 6.685E-07 6.685E-06 89 CHLOROMETHANE 74-87-3 1344 672 2.149E-05 4.298E-05 91 CHRYSENE 218-01-9 48000 1440 6.017E-07 2.006E-05 92 CIS-1,2-DICHLOROETHYLENE 156-59-2 450 900 6.418E-05 3.209E-05 93 CIS-1,3-DICHLOROPROPENE 10061-01-5 450 900 6.418E-05 3.209E-05 96 CYANAZINE 21725-46-2 1800 3600 1.605E-05 8.023E-06 98 DALAPON 75-99-0 2880 1440 1.003E-05 2.006E-05 99 DCPA DI-ACID DEGRADATE 2136-79-0 720 1440 4.011E-05 2.006E-05 100 DCPA MONO-ACID DEGRADATE 887-54-7 720 1440 4.011E-05 2.006E-05 101 DDE 72-55-9 270000 3600 1.070E-07 8.023E-06 102 DI-(2-ETHYLHEXYL)ADIPATE 103-23-1 1344 672 2.149E-05 4.298E-05 103 DI-(2-ETHYLHEXYL)PHTHALATE 117-81-7 9336 550 3.094E-06 5.251E-05 104 DIAZINON 333-41-5 450 900 6.418E-05 3.209E-05 105 DIBENZ(A,H)ANTHRACENE 53-70-3 45120 1440 6.401E-07 2.006E-05 106 DIBROMOCHLOROMETHANE 124-48-1 4320 4320 6.685E-06 6.685E-06 107 DIBROMOCHLOROPROPANE 67708-83-2 450 900 6.418E-05 3.209E-05 108 DIBROMOMETHANE 74-95-3 1344 672 2.149E-05 4.298E-05 109 DICAMBA 1918-00-9 450 900 6.418E-05 3.209E-05 110 DICHLORODIFLUOROMETHANE 75-71-8 8640 4320 3.343E-06 6.685E-06 111 DICHLOROMETHANE 75-09-2 1344 900 2.149E-05 3.209E-05 112 DIELDRIN 60-57-1 51840 25920 5.571E-07 1.114E-06 113 DIETHYL PHTHALATE 84-66-2 2688 1344 1.074E-05 2.149E-05 114 DIMETHYL PHTHALATE 131-11-3 336 360 8.596E-05 8.023E-05 115 DI-N-BUTYL PHTHALATE 84-74-2 552 336 5.232E-05 8.596E-05 116 DINOSEB 88-85-7 5904 2952 4.892E-06 9.784E-06 117 DIQUAT 2764-72-9 180 360 1.605E-04 8.023E-05 125 CD CONTAMINANT CAS HALF LIFE (Hours) Degradation rate (1/day) 118 DISULFOTON 298-04-4 1008 900 2.865E-05 3.209E-05 119 DIURON 330-54-1 450 900 6.418E-05 3.209E-05 120 ENDOTHALL 145-73-3 104 208 2.777E-04 1.389E-04 121 ENDRIN 72-20-8 1800 3600 1.605E-05 8.023E-06 122 EPTC 759-94-4 450 900 6.418E-05 3.209E-05 124 ETHYL METHACRYLATE 97-63-2 180 360 1.605E-04 8.023E-05 125 ETHYLBENZENE 100-41-4 5472 360 5.278E-06 8.023E-05 126 ETHYLENE DIBROMIDE 106-93-4 2880 4320 1.003E-05 6.685E-06 128 FLUORENE 86-73-7 2880 1440 1.003E-05 2.006E-05 130 FONOFOS 944-22-9 450 900 6.418E-05 3.209E-05 132 GLYPHOSATE 1071-83-6 180 360 1.605E-04 8.023E-05 136 HEPTACHLOR 76-44-8 1800 3600 1.605E-05 8.023E-06 137 HEPTACHLOR EPOXIDE 1024-57-3 26496 13248 1.090E-06 2.180E-06 138 HEXACHLOROBENZENE 118-74-1 100272 50136 2.880E-07 5.761E-07 139 HEXACHLOROBUTADIENE 87-68-3 8640 4320 3.343E-06 6.685E-06 140 HEXACHLOROCYCLOPENTADIENE 77-47-4 1800 3600 1.605E-05 8.023E-06 142 INDENO[1,2,3,CD]PYRENE 193--39-5 35040 6000 8.242E-07 4.814E-06 143 IODOMETHANE 74-88-4 1344 672 2.149E-05 4.298E-05 145 ISOPROPYLBENZENE 98-82-8 384 360 7.521E-05 8.023E-05 146 LAMBAST 845-52-3 720 1440 4.011E-05 2.006E-05 148 LINDANE 58-89-9 5765 5765 5.010E-06 5.010E-06 149 LINURON 330-55-2 8544 4272 3.380E-06 6.761E-06 150 P XYLENE 106-42-3 180 360 1.605E-04 8.023E-05 154 METHIOCARB 2032-65-7 450 900 6.418E-05 3.209E-05 155 METHOMYL 16752-77-5 180 360 1.605E-04 8.023E-05 156 METHOXYCHLOR 72-43-5 8760 3600 3.297E-06 8.023E-06 157 METHYL ETHYL KETONE 78-93-3 336 360 8.596E-05 8.023E-05 158 METHYL METHACRYLATE 80-62-6 1344 672 2.149E-05 4.298E-05 159 METHYL-T-BUTYL ETHER 1634-04-4 8640 4320 3.343E-06 6.685E-06 160 METOLACHLOR 51218-45-2 720 1440 4.011E-05 2.006E-05 161 METRIBUZIN 21087-64-9 450 900 6.418E-05 3.209E-05 162 MOLINATE 2212-67-1 450 900 6.418E-05 3.209E-05 164 M-XYLENE 108-38-3 8640 672 3.343E-06 4.298E-05 165 NAPHTHALENE 91-20-3 3192 900 9.048E-06 3.209E-05 166 N-BUTYLBENZENE 104-51-8 180 360 1.605E-04 8.023E-05 171 NITROBENZENE 98-95-3 9456 4728 3.054E-06 6.109E-06 172 N-PROPYLBENZENE 103-65-1 180 360 1.605E-04 8.023E-05 174 ORTHO-1,2-DICHLOROBENZENE 95-50-1 8640 4320 3.343E-06 6.685E-06 175 OXAMYL 23135-22-0 450 900 6.418E-05 3.209E-05 176 O-XYLENE 95-47-6 8640 672 3.343E-06 4.298E-05 178 PARA-1,4-DICHLOROBENZENE 106-46-7 8640 4320 3.343E-06 6.685E-06 179 PCBs 53469-21-9 1800 3600 1.605E-05 8.023E-06 180 PENTACHLOROPHENOL 87-86-5 36480 3600 7.917E-07 8.023E-06 126 CD CONTAMINANT CAS HALF LIFE (Hours) Degradation rate (1/day) 183 PHENANTHRENE 85-01-8 9600 1440 3.008E-06 2.006E-05 184 PICLORAM 1918-02-1 720 1440 4.011E-05 2.006E-05 185 PROMETON 1610-18-0 720 1440 4.011E-05 2.006E-05 186 PROPACHLOR 1918-16-7 450 900 6.418E-05 3.209E-05 187 PROPAZINE 139-40-2 720 1440 4.011E-05 2.006E-05 188 P-XYLENE 106-42-3 8640 672 3.343E-06 4.298E-05 189 PYRENE 129-00-0 91200 1440 3.167E-07 2.006E-05 193 RDX 121-82-4 450 900 6.418E-05 3.209E-05 194 S-BUTYLBENZENE 135-98-8 180 360 1.605E-04 8.023E-05 197 SIMAZINE 122-34-9 720 1440 4.011E-05 2.006E-05 202 STYRENE 100-42-5 5040 672 5.730E-06 4.298E-05 204 T-BUTYLBENZENE 98-06-6 450 900 6.418E-05 3.209E-05 206 TERBACIL 5902-51-2 450 900 6.418E-05 3.209E-05 207 TERBUFOS 13071-79-9 450 900 6.418E-05 3.209E-05 208 TETRACHLOROETHYLENE 127-18-4 17280 8640 1.671E-06 3.343E-06 209 TETRAHYDROFURAN 109-99-9 180 360 1.605E-04 8.023E-05 211 TOLUENE 108-88-3 672 528 4.298E-05 5.470E-05 215 TOXAPHENE 8001-35-2 1800 3600 1.605E-05 8.023E-06 216 TRANS-1,2-DICHLOROETHYLENE 156-60-5 450 900 6.418E-05 3.209E-05 217 TRANS-1,3-DICHLOROPROPENE 10061-02-6 450 900 6.418E-05 3.209E-05 218 TRIAZINES 122-34-9 720 1440 4.011E-05 2.006E-05 219 TRICHLOROETHYLENE 79-01-6 39672 8640 7.280E-07 3.343E-06 220 TRICHLOROFLUOROMETHANE 75-69-4 17280 8640 1.671E-06 3.343E-06 221 TRIFLURALIN 1582-09-8 1800 3600 1.605E-05 8.023E-06 224 VINYL ACETATE 108-05-4 180 360 1.605E-04 8.023E-05 225 VINYL 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